jfa journal

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A.T. Olagunju1,2,3, J.A. Morgan2, A. Aftab4, J.R. Gatchel5, P. Chen6, A. Dols7,8, M. Sajatovic6, W.T. Regenold9


1. Department of Psychiatry and Behavioral Neurosciences, McMaster University/St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada; 2. Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia; 3. Department of Psychiatry, College of Medicine, University of Lagos, Lagos, Nigeria; 4. Department of Psychiatry, University of California, San Diego, CA, USA; 5. Division of Geriatric Psychiatry, McLean Hospital, Harvard Medical School, Boston, MA, USA; 6. Departments of Psychiatry & Neurology, Case Western Reserve University School of Medicine, University Hospitals Cleveland Medical Centre, Cleveland, OH, USA; 7. GGZ inGeest Specialized Mental Health Care, Department of Old Age Psychiatry, 1081 HJ, Amsterdam, The Netherlands; 8. Amsterdam UMC, Vrije Universiteit Amsterdam, Psychiatry, Amsterdam Public Health research institute and Neuroscience Amsterdam, 1081 HV, Amsterdam, The Netherlands; 9. Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, Division of Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
Corresponding author: Andrew T. Olagunju, Department of Psychiatry and Behavioral Neurosciences, McMaster University/St Joseph’s Healthcare Hamilton, 100 West 5th Street, Hamilton, ON, L8N 3K7 Canada, Email:olagunja@mcmaster.ca

J Frailty Aging 2020;in press
Published online December 9, 2020, http://dx.doi.org/10.14283/jfa.2020.64


Objectives: To better understand the role of nutrition in older adults (aged 50 years or older) with bipolar disorders (OABD), we conducted a systematic review of the literature and appraise existing evidence. Methods: Following PRISMA guidelines, we searched databases including Medline/PubMed, PsychINFO, EMBASE, CINAHL, Scopus, Web of Science, Cochrane Register, FDA website, and clinical trial registries through 2019 for eligible reports. The search string combined MeSH terms for bipolar disorder, nutrition and older adults. This was supplemented by snowball searching of references in relevant studies and authors were contacted to request their work where necessary. All included studies were rated with the National Institutes of Health Study Quality Assessment Tools based on study designs. Results: Of 2280 papers screened, ten studies including eight observational and two interventional studies. The topic foci of the papers examined several nutrients, (including vitamin B12, vitamin D, coenzyme Q10, homocysteine, and folate), nutritional deficiencies and biochemical correlates. The prevalence rates of deficiencies varied with specific nutrients (3.7% to 71.6% for Vitamin B12 and 34.6% for Vitamin D), and between inpatient versus outpatient populations. While nutritional interventions appeared to be associated with improvement in both affective and cognitive outcomes, the sample sizes of OABD varied and were generally small. Conclusion: While there is evidence for the benefits of nutritional interventions on affective, cognitive and overall outcome in OABD, the quality of the evidence is limited. Our findings underscore the need for high quality studies to inform evidence-based guidelines for nutritional assessment and supplemention in OABD.

Key words: Bipolar disorder, depression, geriatrics, nutrition, older adults, mania.



Recent international collaborative research indicates that suboptimal diet contributes significantly to the global burden of diseases with 11 million deaths and loss of 255 million disability-adjusted life-years attributable to dietary risk factors in 2017 (1-3). Despite the evidence linking poor nutrition to disease, nutrition and nutritional supplements are often overlooked in the assessment and treatment of patients generally, and in individuals with bipolar disorder (BD) in particular.
Older adults with bipolar disorder (OABD) may be especially prone to nutritional deficiencies due to various age-related factors that can diminish nutrient intake and absorption. Reduced dietary intake of nutrients may occur due to decreased sensory function, decreased appetite and dysphagia with advanced age (4). Causes of decreased nutrient absorption that are more common in older adults include: age related changes in the gut or atrophic gastritis; peptic ulcer disease with Helicobacter pylori infection; gastric and intestinal resections and taking medications that can interfere with nutrient absorption. Many of these interfering medications are commonly prescribed and include inhibitors of gastric acid secretion such as proton pump inhibitors and histamine-2 receptor antagonists; anti-epileptics, metformin, methotrexate, triamterene and trimethoprim (5, 6). Importantly, nutritional status, particularly vitamin levels, can influence cognitive function and affective symptoms in older adults with mood disorders, including BD (7-10).
In addition to the lack of awareness of diet and nutritional deficiencies in the care of OABD, clinicians are often unaware of the increasing use of nutritional supplements by their patients. Herbal and nutritional compounds are used widely by older adults and have been reported to be taken by nearly one in three older adults with bipolar disorder or major depression (11). Most individuals who use these supplements do not mention this to their clinicians, putting them at risk for potential drug-supplement interactions with adverse health effects.
Given the clinical importance of nutritional deficiencies and nutritional supplement use, a working group within the International Society for Bipolar Disorders (ISBD) (12) OABD taskforce undertook a systematic review with the following objectives in mind: 1) to synthesize literature evidence on nutritional deficiencies and supplements in OABD; 2) to review the quality of research evidence on nutritional deficiencies and supplements in relation to the epidemiology, pathophysiology, clinical treatment, outcome and wellbeing of OABD, and 3) to make recommendations regarding further research that will promote evidence-based assessment and management of OABD.



Eligibility criteria

We followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines in conducting the literature search in this review (13, 14). All literature for all years through 2019 was searched. The age cut-off for OABD for eligible studies in this review is 50 years and older based on the knowledge that around 50 years adults already have decreased absorption of nutrients, which is a primary age-related cause of nutritional deficiency (15, 16). Screening for nutrient deficiencies has been recommended in people aged 50 years and older in the United States and elsewhere in the world (15-18). We included all study designs including retrospective, observational, cross-sectional, and intervention trial but excluded case reports. Other inclusion criteria were availability of study full-text in the English language,19 and a clearly described representation of OABD among the study participants.

Information sources

We searched databases including Medline/PubMed, PsychINFO, EMBASE, Web of Science, Cochrane Register, the Food and Drug Administration (FDA) website, and international clinical trial registries for all years through 2019 for eligible reports. The bibliographies of included studies and relevant reviews were snow-ball searched for additional studies. Study authors were also contacted to request their work where necessary.

Search strategy

We used a search strategy addressing the following key terms in various permutations: nutrition, older adults, and bipolar disorder combined with OR and AND. Search strings included “diet* OR Diet, Food, and Nutrition OR food OR nutrition* OR “nutritional status” OR “nutritional physiological phenomenon” OR micronutrient* vitamin* OR multivitamin* OR vitamin B OR folate OR B6 OR B12 OR niacin OR vitamin C OR vitamin D OR vitamin E OR calcium OR chromium OR iron OR magnesium OR zinc OR artemisin OR caffeine OR dehydroepiandrosterone OR DHEA OR echinacea OR fish oil OR GABA OR gamma-amino butyric acid OR garlic OR ginkgo OR glucosamine OR karaela OR melatonin OR methylsulfonylmethane OR primrose oil OR probiotics OR S-adenosylmethionine OR St. Johns wort OR tryptophan OR tyrosine OR valerian AND “old age” OR aged OR elder* OR geriatric* OR geriatric psychiatry OR older OR aging OR ageing AND “bipolar disorder” OR manic depressi* OR bipolar depression. Search strings were performed in titles, abstracts, major topic/subject headings, and MeSH headings.

Study selection

Study selection, review of included studies and data extraction were conducted by Andrew T. Olagunju (ATO), Julie A. Morgan (JAM), Awais Aftab (AA), and supervised by William T. Regenold (WTR). Titles and abstracts were screened independently by at least two authors to shortlist studies for further review. The full texts of the shortlisted studies were reviewed by at least two authors independently according to the inclusion criteria. Inconsistencies about inclusion or exclusion of studies were resolved by discussion between authors or in consultation with WTR or other members of the task force to reach consensus.

Quality/bias assessment

As studies with different designs and representation of older adults were eligible, the Study Quality Assessment Tools of the National Institutes of Health (NIH) for the assessment of Observational Cohort and Cross-Sectional Studies, Case-Control, and Controlled Intervention Studies were used to assess the quality of included studies.20 We rated individual studies on a range of 12-14 items depending on their designs to produce a comprehensive overview of the risk of bias in individual studies and highlighted relevant overall quality limitations.

Data collection process

Data items collected from eligible reports included study characteristics, nutrients, main findings and implications for OABD (Table 1). In total, we screened 2280 titles and abstracts to produce a shortlist of 92 potential reports for full-text review. Of these 92 reports, 10 studies were selected for inclusion in the final review. (See Figure 1)

Table 1
Studies included in the review examining nutrition in older adults with bipolar disorder

Legend: BD- bipolar disorders, BCAA- branched-chain amino acid, C-cross-sectional, CC-case control, MTHFR- Methylene tetrahydrofolate reductase, N-total number of sample, NR-not reported, OABD- Older adults with bipolar disorders, R-retrospective chart review, RCT-randomized controlled clinical trial, YMRS- Young Mania Rating Scale

Figure 1
Flow of studies through the systematic review



Study characteristics

Ten studies were included in the review (5, 7, 8, 10, 11, 21-24, 25). The publication dates of these reports spanned three decades from 1990 to 2015. Of the ten studies, six were retrospective chart reviews, two were cross-sectional studies, and the remaining two were clinical trials. Considering the study participants, in two studies all participants were adults diagnosed with BD with a representation of OABD, (21, 22) while in the remaining studies (n=8), participants with BD represented a subset of the total sample. In general, the topic foci in the included studies addressed different aspects of nutrition and nutritional deficiencies, including biochemical, epidemiological and clinical factors, and nutritional supplements in OABD. All were able to specify the sample sizes of OABD in the included studies aged 50 years or older, ranging from 3-50. Follow-up was limited as the majority of the studies reported cross-sectional observations. (See table 1)

Biochemical, clinical and epidemiological findings on nutrients

Table 2 presents findings on specific nutrients and nutritional supplements covered in the ten studies included in this review. In total, four classes of nutrients including vitamins, minerals, and dietary herbs and nutritional supplements are covered in the studies and described below.

Table 2
Quality assessment with NIH scale-tool based on study design

1-14 are quality assessment items/criteria; CCS- case-controlled studies; CD- cannot determine; CIS- controlled intervention studies, NIH- National Institute of Health; NA- not applicable; NR-not reported; OCCSS- observational cohort and cross-sectional studies; OR-overall rating; SD-study design.



Five retrospective chart reviews (5, 7, 8, 10, 23) focused on vitamin B12 and/or folate. Importantly, several reports looked at vitamin B12 deficiency in mixed populations of older adults including patients with BD. The prevalence rates of Vitamin B12 deficiency ranged from 3.7%8 to 71.6%.10 Lower levels of vitamin B12 correlated strongly with memory loss and poor cognitive performance in psychotic depression (8). Approximately one quarter of geriatric clinic outpatients with B12 deficiency had neuropsychiatric symptoms with behavioural disturbance, memory loss and sensorimotor disorders being most common (10). Folate deficiency was reported to be 1.3% in acute geropsychiatric inpatients (7). One study concluded that the biochemically interrelated vitamins, B12 and folate, may exert both separate and concomitant influences on affect and cognition and that poorer vitamin status may contribute to certain geropsychiatric disorders that have later life onset and lack a familial predisposition (7).
Another study investigated the relationships between Vitamin B12 and folate levels as well as homocysteine (HCY) levels on brain volume changes and brain white matter disease in geriatric patients with psychiatric disorders (23). These authors found that, low serum concentrations of folate, but not of B12, were associated with magnetic resonance imaging (MRI) evidence of brain white-matter disease and smaller hippocampus and amygdala brain-volume measures. Elevated HCY, which can result from folate deficiency, was also associated with MRI evidence of brain white matter disease (23). Finally, one study found that among older adult psychiatric inpatients, B12 serum levels and percentages of probable and possible B12 deficiency did not differ significantly between cognitive disorder patients and non-cognitive disorder patients, including OABD, suggesting that it was reasonable to routinely screen older adult psychiatric inpatients for B12 deficiency whether or not cognitive disorder symptoms were present (5). One retrospective chart review study (24) and cross-sectional study(25) addressed vitamin D in OABD. Vitamin D deficiency prevalence rates ranged from 3.7 % to 34.6%. Vitamin D insufficiency (less severe and defined as levels <30 ng/mL) was reported in approximately 75% of psychiatric inpatients in two cross sectional assessments (25). However, no associations were found between vitamin d levels and a screen of global cognitive function or psychiatric diagnoses (24, 25).

Dietary, Herbs and Nutritional Supplements

In one cross sectional study of OABD and older adults with major depression (11), herbs and nutraceutical (HNC) products were ingested by 30% of individuals. About 40% incorrectly believed that HNC products were FDA-regulated at that time. The majority (64%) had not discussed the use of HNC with treating physicians and some preferred to take HNC compared to physician-prescribed psychotropic medications (14-20%). The use of HNC was more common in those with BD (44%) than those with unipolar major depression (16%).
Two open label trials (21, 22) reported significant reduction in the severity of depressive symptoms with high-dose CoEnzyme Q10 in OABD (21). These findings could support a role of therapy targeted at mitochondrial function in affective disorders (22).

Study Quality Assessment

Quality assessment of all included studies (n = 10) was rated fair (n=8) and good (n=2) with risk of bias items included in the tool. The limited representation of OABD represented a risk of bias. Notwithstanding the limitations in the quality of the studies, the reported outcomes on nutritional deficiencies and supplements in OABD were considered the best available evidence for the recommendations made. (See table 2)



Clinical implications

Most of the studies reviewed—seven of ten studies (70%) focused on three vitamins—vitamin B12, vitamin D and folate and the consequences of their deficiencies. These studies support a relationship between vitamin B12 deficiency and both affective symptoms and cognitive impairment in OABD (7, 8). They also provide evidence for associations between folate levels and hippocampus and amygdala volumes and brain MRI white matter hyper- intensities (WMHs) in older adult psychiatric patients (23). Vitamin D deficiency was reported to be common in older adult psychiatric patients. Generally, vitamin deficiencies did not differ across psychiatric disorders, indicating that screening for these deficiencies in older adults should not be limited to particular diagnostic groups such as cognitive disorders. Evidence for a therapeutic role for vitamin supplementation was limited. There is some evidence for a benefit from folate supplementation for OABD taking lithium.
Tissue levels of homocysteine can be elevated due to vitamin B12 or folate deficiencies. Four studies examined the relationships among vitamin B12, folate and homocysteine levels and their associations with psychiatric symptoms or evidence of brain disease (23). These studies support an inverse relationship between B12 and folate blood levels on one hand and homocysteine blood levels on the other in individuals with bipolar depression. They also suggest that HCY blood levels are elevated in individuals with BD and support an association between elevated levels and impairment in executive function and in the extent of brain WMHs. These studies provide evidence for measuring HCY blood levels along with vitamin B12 and folate levels in OABD.
Studies of diet and nutritional supplements revealed that supplement use is very common among OABD (11). Furthermore, supplement use was typically not discussed with health providers (11).
Clinical trials of adjunctive nutritional supplements found preliminary evidence for a benefit from Coenzyme Q10 on depressive symptoms in OABD (21, 22). The positive results in these supplement studies require replication in a larger group of OABD prior to their consideration for routine clinical use in OABD.

Study limitations

There are several study limitations of this review. The included studies were not homogenous nor entirely focused on OABD. Of the ten articles included in the review, only two reported study results exclusively from a sample or population of OABD. By expanding our inclusion criteria to capture studies with some significant number of OABD participants, we were able to report a more comprehensive review of studies. The majority of these studies were retrospective chart reviews and cross-sectional studies, while the number of RCTs was limited. Consequently, inferences on causal relationships between the nutrients studied and the pathophysiology or treatment of OABD is limited. Furthermore, none of the included studies (n=10) looked at the potential contributions of gender and the type of bipolar disorder (I and II) to their study findings despite the clinical importance of these two factors (26).



Scientific literature on nutrition and nutritional supplements in OABD exists but is limited in both quantity and quality. However, we can draw several conclusions from this review. First, because older adults are prone to nutritional deficits and suffer from several vitamin deficiencies that influence cognition and affective symptoms, there should be consideration given to better research to develop clinical guidelines for routine screening for deficiencies of vitamin B12, folate and vitamin D in OABD. Second, screening for elevated blood levels of HCY that result from vitamin deficiencies should also be considered. Notably there is need for well-designed and powered clinical trials to examine the effects of nutritional interventions including dietary questionnaires, vitamin deficiency screening, HCY blood level screening, and adjunctive nutrient supplementation in OABD.


• Nutrition is critical to physical health and general well-being of older adults with bipolar disoder (OABD), however nutritional deficiencies are common albeit varies for specific nutrients.
• While there is evidence for the benefits of nutritional interventions on affective, cognitive and overall outcome in OABD, the quality of the evidence is limited.
• Findings in this review underscore the need for high quality studies for development of evidence-based guideline for assessing and supplementing nutrition in OABD.


Acknowledgements: The authors thank the International Society for Bipolar Disorders executives, and staff for their support for this task force. Our gratitude also goes to Ms Maureen Bell of the University of Adelaide library for her support and advice.
Conflicts of interest: The authors declare no conflicts of interest to the content of the manuscript.
Disclosure information: Financial Disclosure information for Martha Sajatovic MD are stated below: Research grants within past 3 years: Otsuka, Alkermes, Janssen, International Society for Bipolar Disorders, Reuter Foundation, Woodruff Foundation, Reinberger Foundation, National Institutes of Health (NIH), Centers for Disease Control and Prevention (CDC); Consultant: Alkermes, Bracket, Otsuka, Janssen, Neurocrine, Health Analytics; Royalties: Springer Press, Johns Hopkins University Press, Oxford Press, UpToDate; CME activities: American Physician’s Institute, MCM Education, CMEology, Potomac Center for Medical Education, Global Medical Education, Creative Educational Concepts
Financial Disclosure information: Dr. Gatchel reports other relevant financial activities from Merck, grants from Alzheimer’s Association, grants from BrightFocus Foundation, outside the submitted work.



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M. El Shatanofy1, J. Chodosh1,2,3, M.A. Sevick1,2, J. Wylie-Rosett4,  L. DeLuca5, J.M. Beasley1


1. Department of Medicine, New York University School of Medicine, New York, New York, USA; 2. Department of Population Health, New York University School of Medicine, New York, New York, USA; 3. VA New York Harbor Healthcare System, New York, New York, USA; 4. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA; 5. Department of Psychology, Ferkauf Graduate School, Bronx, New York, USA.
Corresponding author: Jeannette M. Beasley, PhD MPH RD, Assistant Professor, Division of General Internal Medicine and Clinical Innovation, NYU School of Medicine, 462 First Avenue, 6th Floor CD673, New York, NY 10016, T: 646-501-4681, jeannette.beasley@nyumc.org

J Frailty Aging 2020;9(3)172-178
Published online May 11, 2020, http://dx.doi.org/10.14283/jfa.2020.25



Background: The Home Delivered Meals Program (HDMP) serves a vulnerable population of adults aged 60 and older who may benefit from technological services to improve health and social connectedness. Objective: The objectives of this study are (a) to better understand the needs of HDMP participants, and (b) to characterize the technology-readiness and the utility of delivering information via the computer. Design: We analyzed data from the 2017 NSOAAP to assess the health and functional status and demographic characteristics of HDMP participants. We also conducted a telephone survey to assess technology use and educational interests among NYC HDMP participants. Measurements: Functional measures of the national sample included comorbidities, recent hospitalizations, and ADL/IADL limitations. Participants from our local NYC sample completed a modified version of the validated Computer Proficiency Questionnaire. Technology readiness was assessed by levels of technology use, desired methods for receiving health information, and interest in learning more about virtual senior centers. Results: About one-third (32.4%) of national survey HDMP participants (n=902) reported insufficient resources to buy food and 17.1% chose between food or medications. Within the NYC HDMP participant survey sample (n=33), over half reported having access to the internet (54.5%), 48.5% used a desktop or laptop, and 30.3% used a tablet, iPad, or smartphone. Conclusion: The HDMP provides an opportunity to reach vulnerable older adults and offer additional resources that can enhance social support and improve nutrition and health outcomes. Research is warranted to compare technological readiness of HDMP participants across urban and rural areas in the United States.

Key words: Home-delivered meals program, aging, nutrition, health behaviors, technology.



The Home-Delivered Meals Program (HDMP) is a public-private partnership dedicated to reducing hunger and isolation among older adults and supports over 5,000 community-based senior nutrition programs nationwide (1). The Older Americans Act (OAA) Title III, federal legislation first passed in 1965, provides nutrition programming that includes both congregate and home-delivered meals for adults aged 60 and over. In 2018, 225 million home-delivered meals were provided to 2.4 million older adults (2).
The HDMP provides more than just daily nutrition to older adults. It is designed to improve food security, social connectedness, and health care utilization. Recipients of home-delivered meals report that nutrition programs are essential to helping them remain in their communities; however, there is still a gap to be filled (3, 4). Compared to both congregate meal recipients and the general public, home-delivered meal recipients are more likely to self-report worsening health over the past twelve months, self-rate their health as fair to poor, and have five or more medical conditions (5). Assessing HDMP participants’ health status and interest in further assistance is important to addressing the mismatch between services and outcomes. If the mismatch is exacerbated by limited access to services, then perhaps technology can help us tailor interventions with high potentials for scaling up (6).
While technology has been used in interventions to alleviate undernutrition in different age groups, older adults are often excluded from such projects because they are assumed to lack the basic technological skills (7, 8). Data suggest that there is a digital divide between older adults and the rest of the population as well as within the population of adults aged 65 and older (8), but that difference is shrinking (9). Between 2000 and 2016, internet use among a nationally representative sample of older adults rose from 14% to 67% (9). From 2013 to 2016, ownership of a smartphone also rose from 18% to 42% and use of social networking sites like Facebook or Twitter rose from 27% to 34% (9). Overall, use of the internet, smartphones, tablets, and social media among older adults have grown over the past two decades; however, the pace of growth is slower among HDMP participants (16, 17). In order to develop useful interventions for HDMP participants, it is important to understand what health behaviors older adults are interested in learning about and how they would prefer to receive this information.
There is limited knowledge on how HDMP participants would respond to combined nutritional support and technological interventions (7). This is important because undernutrition impedes healthy aging, aspects of daily living, and has been associated with increased morbidity and mortality (10-13). To improve older adults’ overall health, we need to understand barriers to proper diet quality and access to food. The purpose of this paper is to characterize the health and functional status of a national sample of HDMP participants and to characterize the technology-readiness of a local sample of NYC HDMP participants.



Cross-sectional data from the 2017 National Survey of Older Americans Act Participants (NSOAAP) (n=902) were used to characterize the health and functional status and demographic characteristics of HDMP participants. The NSOAAP is a telephone survey that has been conducted annually since 2005 (14). Its goal is to evaluate the effectiveness of home-delivered and congregate meals, transportation, case management, and other programs on aging funded by Title III of the Older Americans Act. The NSOAAP is conducted using a two-stage sampling design and sample weighting to achieve data output based on a representative sample of HDMP participants. Base weights are computed by taking the inverse of the selection probability for each sampled participant, then adjusting for non-response, trimming the extreme weights, and completing a post-stratification adjustment using available control totals.
To characterize technology use, educational interests, and preferred methods for receiving health information among older adults, we also completed a telephone survey with a random sample of NYC HDMP participants. Encore Community Services, a program that provides a range of social, recreational, and educational activities for older adults as well as preparing, packing, and delivering home-delivered meals, provided the home phone numbers and cell phone numbers for 109 HDMP participants. Five attempts between 9 am and 2 pm were made to reach each participant (Figure 1). We tried contacting each participant by house phone first and then cell phone. Not all participants provided both house and cell phone numbers. Of the 79 people who answered the phone, 41.8% (n=33) provided verbal consent to participate in the survey. Four people could not complete the survey due to language barriers and three people could not participate due to reported cognitive problems such as dementia. Four surveys were incomplete due to missing/refused responses for questions on educational interests and demographics.

Figure 1
Flow chart of Local New York City technology survey responses


To assess the impact of computer and internet access and training on the well-being of older adults with limited computer experience, we administered a modified version of the Computer Proficiency Questionnaire (CPQ). The original CPQ contains 33 questions grouped into 6 subscales: computer basics, printing, communication, internet, scheduling software, and multimedia use (15). The CPQ was shortened to prevent respondent fatigue, as many participants did not want to answer questions for more than ten minutes. We piloted the modified CPQ among 10 HDMP recipients and made edits to arrive upon a 26-item survey that focused on technology use and methods of receiving health information. Data were analyzed using SPSS (Version 25, IBM Corp., Armonk, NY).



National (US) Data

The NSOAAP sample was largely white, high school-educated women who were living alone, with nearly a third being 85 years of age and older (Table 1). A substantial number of respondents reported comorbidities; almost three-quarters reported hypertension and arthritis, half reported hyperlipidemia, about two-fifths had heart disease, and over a third reported diabetes. Health care utilization was also common, with a third reporting a hospital stay in the past year. Nonetheless, more than half of the sample described their health as good or better.
Four-fifths of participants reported at least one limitation with activities of daily living and nearly one-third reported three or more limitations (Table 2). Most commonly, participants reported difficulty walking (67.2%), followed by bathing (37.4%) and bed/chair transfer (33.7%). Furthermore, half of the participants reported three or more limitations with instrumental activities of daily living. Most commonly, participants reported difficulties going outside the home (53.3%), preparing meals (43.5%), and doing light housework (43.0%).

Table 1
Home-Delivered Meals Participant Characteristics, National and Local Level

Note. Weighted to account for the sampling design within the nationwide sample. Some participants selected more than one race, so percentages do not add up to 100%.


Over two-thirds of participants reported having enough resources to buy food (67.6%), and 14.8% skipped meals due to inadequate resources over the past month (Table 2). More than four-fifths of participants reported that the home-delivered meals helped them live independently (82.3%), feel more secure (82.2%), and feel better able to care for themselves (81.2%; see Table 2).


Table 2
Health and Functional Status of Nationwide HDMP Participants (n=903), 2017

Note. Weighted to account for the sampling design within the nationwide sample. ADL=activities of daily living; IADL=instrumental activities of daily living.


Local New York City (NYC) Data

The local NYC sample was mostly comprised of white women who were living alone, with nearly a third being 65 to 74 years old (Table 1). The mean body mass index was 26.6 ± 5.7 kg/m2 per self-reported height and weight. Most participants were classified as overweight or obese (55.5%), while 40.7% had a BMI in the normal weight range. No participants reported “excellent” self-perceived general health or self-perceived diet quality. Similar to the national sample, the most common response for self-perceived general health and self-perceived general diet was “good” (33.0% for the national sample and 37.9% for the local sample; see Table 1).

Almost half of the participants reported finding information about health on the internet (Table 3), but this rose to 88.9% among the subset of participants having access to the internet (n=18). Less than one-fifth of participants said that they use a computer for activities such as entering events into a calendar, video chatting with others via web-cam, or posting messages to social media. Nearly one-third of participants said that they use a tablet such as an iPad (30.3%; see Table 3). Similarly, about one-third of participants said that they use a smartphone (30.3%; see Table 3). Almost half of the participants indicated that they did not use any types of computers. All 16 people who reported computer use said that they use desktops or laptops.
Most participants reported that they would like to receive their health information from in-person (home or office) visits with a health professional (90.0%; see Table 3). Other desired methods for receiving health information included: telephone calls with a health professional (63.3%), email (36.7%), and videos through computer, smartphone, or iPad (36.7%). Almost three-quarters of participants reported other desired methods for receiving health information. Responses were recorded and grouped into three categories (Table 3): mail (33.3%), media (television/newspapers/newsletters) (23.3%), and peers and family members (26.7%).

Table 3
Technology Use Among Local NYC HDMP Participants,
n (%)

Note. Sample size differences are due to missing survey responses.


Over half of the participants reported educational interests in exercise, improving sleep, meeting new people, and virtual senior centers (58.1%, 54.8%, 51.6%, and 55.2%, respectively; see Table 4), but only one-quarter wanted to learn more about losing weight (25.8%; see Table 4). Others (n=14; see Table 4) wanted to learn more about medical problems such as arthritis, knee replacements, prosthetics, gastritis, irritable bowel syndrome, neuropathy, strokes, Parkinson’s, and memory problems like Alzheimer’s. Interest in diabetes was also common, with 38.7% of participants reporting that they would like to learn more about the disease (Table 4).

Table 4
Educational Interests Local NYC HDMP Participants, n (%)

Note. Sample size differences are due to missing survey responses.



This study expanded research on the demographics of HDMP participants in the United States as well as technology use and educational interests among HDMP participants in the NYC area. More than half of the nationwide participants self-rated their health as good or better and about half of the NYC participants (n=31) reported an interest in learning more about healthy eating, improving sleep, and exercise. In addition, 94% of the participants surveyed in NYC described an interest in learning more about one or more health topics. The purpose of this study was to analyze the demographics of HDMP participants and to identify preferred methods for receiving health information among a local NYC sample. Our local survey suggests that more than half of older adults are interested in learning more about technological services such as virtual senior centers, and barriers for internet access could be explored and addressed among those lacking internet access.
Many scholars have advocated for the expansion of virtual-based senior centers to help older adults age in place (16, 17). Before expanding these services, however, we need to understand how familiar older adults are with technology and how willing they are to learn about virtual senior centers. In our local sample, almost 90% of those who had access to the internet said that they found information about health online. Some participants said that they were interested in learning more about technologies but expressed concerns over the user-friendliness and affordability of certain devices. Most concerns were shared as side-notes during our telephone interviews. Other concerns, which have been reported in past research, include low self-efficacy among disabled older adults and sensory and ergonomic problems that hinder ease of use (16, 17). Among older adults with cognitive impairments, there is also a greater risk of unintentionally violating privacy rights through technology-assisted health care services (18).
Two recent studies in the Netherlands have underscored the value of using technology to help older adults at risk of malnutrition. Lindhardt and Nielsen (7) completed a quasi-experimental study to better understand the effects of combining technology and nutritional support for older adults and found that older adults at nutritional risk experienced better strength, intake, appetite, and relationships with family after receiving enriched meals for 12 weeks after discharge and using a tablet for goal setting, self-monitoring, and feedback. In a similar study, van Doorn-van Atten, de Groot, Romeaet al. (19) also found that older adults at risk of undernutrition showed improvements in nutrition after undergoing a home dietary monitoring intervention comprised of tele-monitoring and nutrition education. Taken together, these studies suggest that technology use can address more than just nutritional needs among older adults. It can leverage solutions to poor diets, health problems, and social isolation.
To rapidly scale up successful interventions and improve connectedness among older adults, we should consider how technological services could be integrated with the HDMP (20-24). Past studies have shown that, separately, home-delivered meals and technologies can help older adults age in place (3, 4, 20-24). Combined, home-delivered meal services and technologies can work synergistically to help older adults attain better overall health outcomes. The goal is to improve access to health information and the growing number of telehealth and telemental health interventions as well as to encourage participation in free chronic disease self-management programs and support groups (16, 18, 23, 25, 26).
One limitation to integrating technological services with the HDMP is the insufficient funding of the HDMP by the OAA (5, 27). The OAA covers less than a quarter (23%) of the total cost to provide meals, safety checks, and visits to over 174,000 seniors (28). Adjusted for inflation, federal funding has decreased by 19% while the population of older adults has increased by 34% over the past 20 years (28). Consequently, many programs across the United States have experienced growing waiting lists that are disproportionately comprised of widowed, less educated, older, Black, Hispanic, and Medicaid-receiving seniors (29).
We acknowledge that this study had several limitations. First, due to the low response rate of the telephone survey, it is unclear whether the local sample represents the broader community of HDMP participants across the United States. We cannot generalize our findings to rural populations across the United States, which may have lower access to the internet and therefore lower levels of computer literacy. In the future, technology use among urban and rural populations in the United States should be compared. Since rural areas tend to have fewer modes of transportation than urban areas, we predict that increasing computer use in rural areas will improve connectivity among peers and medical professionals.
Another limitation of this study was the lack of questions focused on attitudes toward computer/internet use. These questions would have enhanced our understanding of how older adults perceive the usefulness of technology for health management. In addition, responses to the national and local surveys were self-reported, and self-reported responses to health and healthcare utilization tend to be affected by recall and social desirability response bias.
Future work should incorporate weekly diaries of technology use and sample more representative groups within NYC and across the United States. This would help us target where technology could contribute the greatest health benefits (20-24). Partnering with existing social programs such as the HDMP can enhance services through technology training and supportive health interventions. Ultimately, this can help us provide the most vulnerable members of our society the care they deserve.



Data from our national sample of older adults revealed multiple comorbidities and ADL/IADL limitations such as going outside the home, but data from our NYC survey suggest that HDMP participants could benefit from technological interventions that could support nutrition, social connectedness, and healthy aging. Past studies have shown that technological interventions can improve access to health information; however, technology use among older adults, particularly HDMP participants, has been lagging (9, 30-33). Future work should compare technology use among different populations of HDMP participants in the United States and explore how additional supports, such as videoconferencing, could improve health outcomes and maintenance of positive behaviors.


Ethics approval and consent to participate: This study was approved by the Institutional Review Board at New York University Langone School of Medicine. All participants provided verbal informed consent.
Availability of data and material: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publication of this paper.
Funding: This study was funded by the New York Center for Diabetes Translational Research (P30DK111022-01).
Acknowledgements: The authors appreciate the contributions of New York City’s Department for the Aging in general, and Jose L. Sanchez from Encore Community Services in particular, for their assistance with this project.


Supplementary material1

Supplementary material2



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33. Lukasik S, Tobis S, Wieczorowska-Tobis K, Suwalska A. Could Robots Help Older People with Age-Related Nutritional Problems? Opinions of Potential Users. Int J Environ Res Public Health. 2018;15(11).

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1. Department of Geriatrics, CHU Université Catholique de Louvain, Namur, Belgium; 2. Institute of Health and Society, Université Catholique de Louvain, Brussels, Belgium; 3. Departments of Public Health and Primary Care, Katholieke Universiteit Leuven, Leuven, Belgium; 4. Clinical Pharmacy Research Group, Louvain Drug Research Institute, Université catholique de Louvain, Brussels, Belgium; 5. Scientific Support Unit, CHU Université Catholique de Louvain, Namur, Belgium; 6. Unit of Research on Cellular Biology, NARILIS-Namur Research Institute for Life Sciences, University of Namur, Belgium; 7. GIGA Research Institute, University of Liège, Liège, Belgium.
Corresponding author: Florence Potier M.D., Department of Geriatrics, Centre Hospitalier Universitaire Université Catholique de Louvain Namur, 1, rue Dr G. Therasse, 5530 Mont-Godinne, Belgium. Tel 0032/81422175. Fax: 0032/81423885. Email: florence.potier@uclouvain.be

J Frailty Aging 2018;in press
Published online April 26, 2018, http://dx.doi.org/10.14283/jfa.2018.11



Background: Evidence suggests that providing care for a disabled elderly person may have implications for the caregiver’s own health (decreased immunity, hypertension, and depression). Objective: Explore if older spousal caregivers are at greater risks of frailty compared to older people without a load of care. Design: Case-control study. Setting: Participants were assessed at home in Wallonia, Belgium. Participants: Cases: community-dwelling spousal caregivers of older patients, recruited mainly by the geriatric outpatient clinic. Controls: people living at home with an independent spouse at the functional and cognitive level matched for age, gender and comorbidities. Measurements: Mini nutritional assessment-short form (MNA-SF), short physical performance battery (SPPB), frailty phenotype (Fried), geriatric depression scale (GDS-15), clock drawing test, sleep quality, and medications. The multivariable analysis used a conditional logistic regression. Results: Among 79 caregivers, 42 were women; mean age and Charlson comorbidity index were 79.4±5.3 and 4.0±1.2, respectively. Among care-receivers (mean age 81.4±5.2), 82% had cognitive impairment. Caregiving was associated with a risk of frailty (Odd Ratio (OR) 6.66; 95% confidence interval (CI) 2.20-20.16), the consumption of antidepressants (OR 4.74; 95% CI 1.32 -17.01), shorter nights of sleep (OR 3.53; 95% CI 1.37-9.13) and more difficulties maintaining a social network (OR 5.25; 95% CI 1.68-16.40). Conclusions: Spousal caregivers were at an increased risk of being frail, having shorter nights of sleep, taking antidepressants and having difficulties maintaining their social network, compared to non-caregiver controls. Older spousal caregivers deserve the full attention of professionals to prevent functional decline and anticipate a care breakdown.

Key words: Caregiving, frailty, nutrition, depression.



In 2014, 34% of the population in 20 European countries provided care to a family member (1). Among older couples, spouses are first in line to provide care (2), and they are key elements in the home support of dependent older persons (3).
As we followed our patients in geriatric consultations, it seemed to us that spouses gave care until exhaustion. In this context, we wondered how to assess the health of older spousal caregivers.
Informal care could be associated with a reduction in physical and mental health. For instance, evidence suggests that caregivers have decreased immunity observed with a lower antibody response to vaccines (4, 5), have higher rates of hypertension and coronary diseases (6, 7) and are at greater risk for depression and anxiety (8).
In later life, these effects on health may lead to increased frailty (9). The frailty phenotype is generally defined as a decline in homeostatic reserves in multiple physiological systems, resulting in greater vulnerability (10). Frailty as a phenotype has significant public health relevance because frail older adults have a greater risk of falls, disability, hospitalization, institutionalization, and death (10, 11). These risks could anticipate a care breakdown and therefore could result in hospitalizations or nursing home admission of the care-receiver.
To date, little is known about the risk of frailty among spousal caregivers. Therefore, the purpose of this study is to explore if older spousal caregivers are at greater risk for frailty compared to older people without a load of care.



This was a cross-sectional analysis of the baseline data from a cohort study of older spousal caregivers.


Caregivers were recruited through the geriatric outpatient clinic and the memory centre of the University Hospital of Louvain in Namur and because of the efforts of general practitioners and home nurses from March 2015 until May 2016. Cases were defined as spouses of older patients with a cognitive deficit (score of more than 2/7 on the Global Deterioration Scale (12)) or functional impairment (a minimum dependence of 1 activity in daily living) who were still living at home. Controls were people living at home with an independent spouse at the functional and cognitive level. Controls were recruited with the help of general practitioners and home nurses and via senior associations. Controls were matched for age, gender, and comorbidity.
All participants had to be 70 years or older. All provided written informed consent that was approved by the CHU UCL Namur Institutional Review Board (NUB: B039201422799).

Socio-demographic data

Participants were assessed at home. The following data were collected: age, gender, educational level and income level. Furthermore, medical history was taken including any medications, smoking status, alcohol consumption, sleep duration and sleep perturbation. Socioeconomic status was constructed as suggested by Cockerham (13). A total score was calculated as the sum of education (primary school=1; unfinished secondary education=2; secondary education=3; higher education=4), income (difficult=1; easy=2) and past occupation (worker-farmer-unemployed=1; craftsman-self-employed=2; employee-officer=3, manager-liberal profession=5). Questions also addressed their social network including family, friends or neighbours on a 5-point Likert-type scale (1 = very difficult to maintain contact; 5 = very easy to maintain contact).

Medical data

Comorbidity was evaluated with the Charlson Comorbidity Index (14). The occurrence of falls, hospitalizations and the numbers of medications were recorded. Body mass index (BMI) was computed as the ratio between weight in kilograms and height in square metres. The short physical performance battery (15) assessing lower extremity function included balance (ability to stand in tandem positions), gait speed (time to walk 4 metres) and strength (time to rise from a chair and return to the seated position 5 times). Nutrition was assessed with the mini Nutritional Assessment short form (16); a risk of malnutrition was considered for total scores below 12/14. The frailty phenotype was assessed according to the definition of L. Fried (10), a pre-frail status considered for a total score of 1 or 2 out of 5 and a frail status for a total score above 2/5. The grip strength of the dominant hand was measured with the Martin vigorimeter. The highest score of the three trials was retained. Finally, cognitive status was evaluated with the clock drawing test (17).

Psychosocial data

The sense of coherence of the participants was measured with the Sense of Coherence Scale (SOC-13), a 7-point Likert-type scale (18). SOC seems to be a health-promoting resource that strengthens resilience and develops a positive subjective state of health. Depressive symptoms were screened with the Geriatric Depression Scale-15 (19); a participant was considered at risk of depression with a score above 5/15.

Additional data of caregivers

Caregivers completed two additional self-report questionnaires. The Caregiver Reaction Assessment (CRA) (20) was used to estimate the various aspects of the caregiving situation by considering positive dimensions such as self-esteem. Caregiver burden was measured using the Zarit Burden Interview (ZBI) (Zarit et al. 1980) that consists of 22 self-reported items. We also collected the following information concerning the caregiving situation: the time spent giving care or supervision, the duration of being a caregiver, the activities of daily living they perform and the informal and professional support they have.

Medical data of care-receiver

Functional impairment was evaluated with the Katz Index (21) on a 6-point scale, with lower scores indicating greater dependence. In cognitive disorder cases, the severity of dementia was rated with the Global Deterioration Scale (12), and behavioural disturbances were screened with the Neuropsychiatric Index (22, 23). All data for the care-receiver were completed by their caregivers.

Statistical analyses

The sample size (79 caregivers and 79 controls) was calculated with an expected difference between caregivers and controls in Interleukin-6 (IL-6), a pro-inflammatory biomarker. Indeed, previous observational studies have found an association between frailty and elevated levels of pro-inflammatory mediators, such as IL-6, implicating a chronic, pro-inflammatory state in the pathogenesis of frailty (24). The biological results will not be discussed in this article.
Variables were compared between caregivers and controls using the Mac Nemar test for categorical variables and the Wilcoxon signed rank test for continuous measures. Variables that were significantly different between caregivers and controls were entered into a conditional logistic regression. Then, a stepwise selection based on the Akaike’s information criterion (AIC) was performed to select the final multivariable model. The results are presented as odd ratios (OR) and 95% confidence intervals (CI). We did not consider age, gender and comorbidities because controls were matched on these variables. Data were analysed using the SPSS statistical software package (version 24; SPSS Inc., Chicago, IL, USA) and R statistical software Version 3.3.1. (R Foundation for Statistical Computing, Vienna, Austria). Statistical tests were two-tailed, and a P-value < 0.05 was considered significant.



Descriptive analysis

A total of 79 community-dwelling spousal caregivers of older patients were recruited (49% by the geriatric outpatient clinic, 11% by the memory centre, 13% by general practitioners, 9% by home nurses, and 18% through different senior associations). The median age was 79.0 years [76-84], and the sample was almost equivalent in gender (53% of women). Care-receivers’ median age was 81 years [78-85]. A large majority (82%) of the care-receivers had cognitive impairment, and 68% had cognitive impairment with behavioural disorders. Their functional status was variable with a median of 3 [1-5] notes of 6 on the Katz ADL scale.

Table 1 Caregiver and control socio-demographic and psychological variables at baseline

Table 1
Caregiver and control socio-demographic and psychological variables at baseline

a. Socioeconomic status score was calculated as the sum of education, income and past occupation; b. Risk of depression: GDS>5/15


Univariate analysis

The comparison of caregivers and controls on socio-demographic and psychological variables is shown in Table 1 and on clinical variables on Table 2. We found nine variables that were significantly different between caregivers and controls. It was more difficult for caregivers to maintain their social network (family, friends, and neighbourhood). They had lower scores in physical performance and a higher risk of malnutrition and frailty (Table 2). Specifically, a higher number of caregivers had lost more than three kilograms in three months. Thirty percent of the caregivers were at risk for depression, and the consumption of antidepressants was also higher in caregivers than in controls. Caregivers reported more perturbed sleep and shorter nights (<8 hours) than controls. Finally, caregivers showed lower scores in sense of coherence.

Table 2 Caregiver and control clinical variables at baseline

Table 2
Caregiver and control clinical variables at baseline

a. SPPB: Short Physical Performance Battery (score 0-12); b. Cognition: pathologic clock drawing test
Caregiving, frailty, nutrition, depression.

Multivariable analysis

The results of the conditional logistic regression are shown in Table 3. Our model showed that for the same age, gender and comorbidities, caregiving is associated with a risk of frailty, the consumption of antidepressants, shorter nights of sleep and more difficulties maintaining a social network.

Table 3 Conditional logistic regression comparing caregivers and controls

Table 3
Conditional logistic regression comparing caregivers and controls

a. Social network: difficult or very difficult to maintain the social network



Exploratory analysis was made to identify the factors associated with caregivers’ frailty (Table 4). We could not find a relationship between frailty and age, comorbidity, burden or self-esteem of the caregiver. Caregivers’ frailty was not associated with cognitive or functional status of the care-receiver. However, frailty was associated with the involvement of a nurse at home (OR 5.80; 95%CI 1.71-19.64). Caregivers involved in care for more than three years were at a lower risk of frailty.

Table 4 Factors associated with caregiver frailty in logistic regression

Table 4
Factors associated with caregiver frailty in logistic regression

a. Katz Index on a 6-point scale; b. Global Deterioration Scale



Our study identified that older spousal caregivers were more likely to present with frailty, perturbed sleep, difficulties maintaining a social network and use of anti-depressive drugs than people without this load of care. After the multivariable analysis, caregivers showed a six times greater risk of being frail compared with non-caregiver controls. We screened frailty with the definition of L. Fried and focused on physical indicators including muscle strength, endurance or weight loss. More precisely, caregivers were more likely to be in a pre-frail stage (one or two present criteria), which is identified as a high risk of progressing to frailty (10). The difference was especially shown in the criteria of unintentional weight loss (more than 4.5 kg within the past year) and low physical activity. It is important to note that these two criteria are also considered in the MNA score. According to MNA, 35% of the caregivers were at risk for malnutrition. No differences were observed between male and female caregivers in nutritional status, although Puranen et al. (25) found that male gender caregivers were associated with a lower nutrient intake.
We found that caregiver frailty was associated with the attendance of a nurse at home. Home nurses are, thus, potentially well placed to detect frailty of the spouses of their patients. In contrast, giving care for more than three years was associated with more caregiver robustness. This is consistent with the study of Fredman et al., which notes that it is necessary to be healthy to remain a caregiver (26).
Caregivers reported more sleep problems than controls (perturbed sleep and shorter nights). Caregivers’ sleep deficits have already been confirmed with objective sleep assessments such as polysomnography (27, 28).
Finally, older spousal caregivers were more likely to use anti-depressive drugs. Several reviews indicated a positive association between frailty and depression (29, 30). Actually, depression and frailty share presenting symptoms, such as low daily activity profiles, that could result from exhaustion and loss of interest.
The theoretical framework underlying the assertion that caregiving is associated with a reduction in physical health is based on a model of the impact of stress on health (31). Caregivers may experience distress when their resources become insufficient (information, coping, finances, lack of respite, etc.). However, in our study, positive experiences, such as self-esteem, or on the contrary, negative experiences, such as the burden, were not associated with caregiver frailty.
The risk of frailty associated with caregiving can also be partly explained by the fact that caregivers are less likely to engage in preventive health behaviours. Indeed, in the Caregiver Health Effects Study, having a spouse with an ADL impairment predicted poor preventive health behaviours on the part of the caregiver, including not finding time for exercise, inadequate rest and forgetting to take medications (32).
A potential confounding factor that could explain the differences in health outcomes between caregivers and controls could be “assortative mating”. Assortative mating means that people select spouses that have similar lifestyle factors that may influence their risk of negative health outcome (33).
A recent paper based on the Health and Retirement Study showed that dementia caregivers were significantly more likely to experience increased frailty relative to non-dementia caregivers (34). To calculate this outcome, they developed a frailty index based on available data from a survey: chronic illnesses, ADL and IADL limitations, depression, obesity and self-rated health.
To our knowledge, our study is the first assessing the health of old spousal caregivers based on clinical data collected at participants’ homes. Furthermore, the majority of the studies assessing caregivers concern female caregivers. Our sample was almost equivalent in gender.


This study has limitations that should be considered when interpreting the results. First, because these are cross-sectional data, we cannot establish causality between caregiving and negative health outcomes. Second, both groups are convenience samples, and the recruitment methods were different between the caregiving group (more recruitment from the geriatric outpatient clinic) and the control group (more recruitment from general practitioners and senior associations). Third, the control group was matched for gender, age and comorbidities with Charlson’s index, which might not be the most precise tool to assess the comorbidity of geriatric patients. Furthermore, all data were completed by the caregivers. Lastly, this study concerns a specific caregiving subtype; spousal caregivers of geriatric patients that most suffer from cognitive deficit, thus limiting the generalizability of our results.


Healthcare providers have a role to play in the prevention of caregiver frailty, e.g., to develop advice relative to nutrition and depression screening. They should also propose solutions for home-care, allowing caregivers to share a minimum of social activities and find time for exercise.
In conclusion, many caregivers of geriatric patients are spouses who are old themselves. These spousal caregivers are at an increased risk of being frail, having shorter nights of sleep, taking antidepressants and having difficulties maintaining their social network. Older spousal caregivers deserve the full attention of professionals to prevent functional decline and anticipate a care breakdown


Acknowledgments: We would like to thank Dr. Eric Mormont from the memory centre of the University Hospital of Louvain in Namur, Aide et Soins à Domicile Namur and the general practitioners for referring participants to the project.
Funding: This work was supported by the Walloon region, Fond d’innovation sociale “Germaine Tillion” convention 1318184.  The sponsors had  no role in the design and conduct of the study, in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Disclosure of interest: The authors report no conflicts of interest.



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1. Department of Aging and Geriatric Research, University of Florida, FL, USA; 2. Department of Clinical and Health Psychology, University of Florida, FL, USA.
Corresponding author: Stephen D. Anton, PhD, 2004 Mowry Rd, PO Box 100107, Gainesville, FL 32611, Phone: 352-273-7514, Fax: 352-273-5920, E-mail: santon@ufl.edu


J Frailty Aging 2018;7(2):142-146
Published online February 14, 2018, http://dx.doi.org/10.14283/jfa.2018.6



Fermented Papaya Preparation (FPP®) has shown antioxidative and anti-inflammatory effects in preclinical and clinical aging studies. However, clinical trials are needed to fully evaluate the safety of FPP® in moderate-functioning, generally healthy older adults. In this randomized (9g/day of FPP® or placebo), crossover design study, we enrolled 30 older moderate-functioning older adults (70-100 years old). The participants completed both a treatment and a placebo condition. After eight (8) weeks on each of these regimens (with a 4-week wash-out period in between), participants had their venous blood drawn for assessment of blood chemistries, metabolic outcomes and inflammatory biomarkers. Participants were asked to report any adverse events during the course of the study and complete post-treatment outcome assessments for anthropometric and metabolic outcomes. The major finding related to safety was that there were no adverse changes in blood chemistries and few adverse events in the FPP® condition, which did not differ from placebo (p>0.05). There were no serious adverse effects in either condition. Twenty-nine (29) participants (mean age 78.2±5.3 yrs) completed the study with 94% adherence to the dosing regimen. There were no significant effects of FPP® on anthropometric and metabolic outcomes. In addition, no effects on markers of inflammation were observed. Our trial demonstrates FPP® supplementation is safe and feasible in adults ages 70 years and older. Based on these findings and the positive effects FPP has demonstrated in previous trials, future trials should examine the effects of FPP® in older adults with impaired health status and/or older adults who may have insufficient anti-oxidant protection due to their genetic background.

Key words: Natural compound, inflammation, immune, stress, nutrition, lifestyle.




A large and growing body of literature supports the health benefits of whole food-based nutrition in all age groups (1). Recent research suggests that the large variety of nutrients found in fruits and vegetables (i.e., phytochemicals) act in synergistic ways to produce strong anti-oxidant and anti-proliferative effects, which are unlikely to be duplicated by pharmaceutical agents (2). Both in vitro and in vivo studies have found that phytochemicals reduce the risk for a number of disease states, such as cardiovascular disease and cancer (3).
Whole food-based nutritional products may provide an alternative means to help many individuals increase their intake of phytochemicals. Many products, however, lack scientific evidence to support purported benefits.
In contrast to the vast majority of dietary products, Fermented Papaya Preparation (FPP®), FPP® has both clinical and preclinical data supporting its safety and efficacy. For example, FPP® was recently found to enhance antioxidant protection and to decrease DNA damage in healthy older adults (4). Another human study found FPP® supplementation had beneficial effects on inflammatory markers as well as on the expression of heat shock protein 70 (5), suggesting that FPP® may have beneficial effects on age-related proinflammatory disease conditions. Additionally, FPP® reduced oxidative-inflammatory damage to the liver in patients with cirrhosis (6). In rodents, FPP® has been found to modulate oxidative stress levels in the brain (7) and potentially provide protection against oxidative injuries induced through ischemia-reperfusion (8); however, the mechanisms underlying these beneficial effects appear to be complex and may involve multiple signaling pathways. In vitro studies suggest that FPP®’s antioxidant properties and ability to modulate redox states may be related to both its hydroxyl-scavenging and iron-chelating properties (9).
Although studies to date suggest FPP® has a number of health benefits for an aging population, placebo-controlled, clinical trials are needed to fully evaluate the safety of FPP® in moderate-functioning, generally healthy older adults. We evaluated the effects of FPP® supplementation (dosage = 9 grams per day divided into three equal doses) for eight (8) weeks on metabolic and blood values in generally healthy, older adults (age = 70-100 years).  It was hypothesized that FPP® would be safe and effective in improving metabolic and physical function in moderate-functioning older adults. The purpose of this paper is to describe the effects of FPP on outcomes related to safety and tolerability in older adults.


Materials and Methods


A total of 30 older moderate-functioning men and women aged between 70 and 100 years were enrolled in this study. Participants were recruited to be sedentary (exercising less than 120 min per week), non-smoking, having body mass index (BMI) between 25 and 40 kg/m2, without cardiovascular and pulmonary comorbidities and disability.


This study was approved by the University of Florida’s Western Institutional Review Board (WIRB project# WIRB20140628). The study is registered at clinicaltrials.gov (NCT02051634). All participants provided written informed consent prior to participating in this study.

Stuy product

Fermented Papaya Preparation (FPP®), which is made by biofermentation of Carica papaya by Osato Research Institute in Japan. This product is distributed in convenient sachets. FPP® is produced under strict quality control at the factory in Japan which received ISO9001:2015 (the international quality standard) and ISO14001:2015 (the international environmental standard) certification (http://en.FPP®-japan.com/production/). Placebo sachets made also by Osato Research Institute in Japan looked the same as FPP® but contained granulated sugar.

Study design and procedure

Study design

A randomized, crossover design was used, such that all participants completed both a treatment and a placebo condition in a counter-balanced manner.  Participants were randomized into 1 of 2 conditions for 8 weeks: (1) FPP® then placebo (granulated sugar), or (2) placebo then FPP®.

Study Procedure

Participants attended six (6) visits including a group Pre-Screening Visit, one Screening Visit performed individually for each potential study participant, and four Assessment Visits (V1-V4). Participants were asked to provide a fasting blood sample that was utilized to evaluate clinical laboratory parameters at the Screening visit and all four (4) assessment visits. Eligible participants were then assigned to either the treatment (FPP) or placebo condition for eight weeks.  During both conditions, participants were instructed to take their study product (FPP® or placebo) in the form of a sachet three times per day 30-40 minutes before a meal (i.e., breakfast, lunch, and dinner). Specifically, they placed the product on their tongues and let it dissolve.  Participants were asked to complete post-treatment outcome assessments at the University of Florida’s Institute on Aging – Clinical and Translational Research Building (IOA – CTRB). Following the first post-treatment test day (V2), participants completed a six (6) week washout period and then returned to the IOA – CTRB to complete the other study arm. Participants kept empty sachets and returned them to the clinic for adherence monitoring and accountability.


Safety outcomes

Blood chemistries (complete blood count and comprehensive metabolic profile) were assessed by Quest Diagnostic Clinical Laboratories.

Adverse events

Adverse events were assessed at each follow-up visit and based on participant reports and clinical observations of symptoms throughout the study. During each visit, participants were asked to report any health-related problems or symptoms they were experiencing. A grading scale based on the latest National Cancer Institute’s criteria for adverse events was used to quantify the severity of reported adverse effects. Any reported adverse events were reviewed by study staff before the participant was allowed to continue receiving the study product.

Anthropometric and metabolic outcomes

Body weight was determined in a fasting state and following a morning void. Body mass index (BMI: kg/m2) was calculated with body weight and height measured using a stadiometer and standardized procedures.
Waist circumference was measured at the narrowest part of the torso, between the xiphoid process and the umbilicus.
Resting systolic and diastolic blood pressure was taken after participants spent 10 min seated in a quiet room, free of distractions. Blood pressure was obtained according to a standardized protocol (10) by the Study Registered Nurse. Blood pressure was taken from the brachial artery via auscultation while the participant was in a seated position. Three readings of blood pressure, spaced 1 min apart, were taken using a sphygmomanometer with appropriate cuff size.
Glucose levels were measured by Quest Diagnostic Clinical Laboratories, which is accredited by the College of American Pathologists.

Inflammatory biomarkers

Serum inflammatory biomarkers (C-reactive protein, interleukin-6 and myeloperoxidase) were assessed using enzyme-linked immunosorbent assay (ELISA). Method details were described elsewhere (11).

Statistical analyses

Each outcome in response to treatment at baseline and 8-week visit was summarized (by mean + SD) and compared using two-sample t-test. All analyses were performed using IBM SPSS version 24. Prior determination by laboratory personnel and a physician approving lab results before enrollment, analytic errors were excluded from the analysis.




Twenty-nine (29) of the 30 participants enrolled completed the study.  The study sample was comprised of older men and women (78.2±5.3 yrs.; 19 women and 10 men). Participants’ baseline characteristics were as follows: height (163.7 ±9.2 cm), weight (87.6 ±16.6 kg), BMI (32.5±4.2 kg/m2) and waist circumference (104.4 ±11.4 cm). Figure 1 shows participant flow through the study.


The mean adherence level (percentage or prescribed product taken during the two intervention periods) were as follows: placebo (94%) and FPP® (94%).

Table 1 Blood chemistries

Table 1
Blood chemistries


Safety outcomes

Blood chemistries – Blood chemistry values remained within normal ranges over time in both treatment and placebo groups, and there were no differences in changes in blood chemistry values between the two groups. Table 1 presents comparisons between the baseline and the 8-week results when participants were assigned to the FPP treatment group. Only one data point was removed from the analysis, which was determined by a measurement error (alkaline phosphatase being 4-fold upper reference value).


Table 2 Anthropometric and metabolic outcomes

Table 2
Anthropometric and metabolic outcomes

Figure 1 Consort flow diagram presenting the participant flow during the trial

Figure 1
Consort flow diagram presenting the participant flow during the trial

Adverse Events

Participants reported a small number of adverse events during the trial, including musculoskeletal (7 FPP® vs. 11 placebo events), gastro-intestinal (2 FPP® vs 3 placebo events), fatigue (2 FPP® vs 1 placebo events), however, the occurrence of adverse events was not significantly different between the FPP and placebo condition (ps >0.05). No significant adverse events were reported during either condition.

Anthropometric and metabolic outcomes

There were no significant differences between the baseline and the 8-week follow-up results within the treatment (FPP®) group (Table 2) or in comparison to the placebo group.

Inflammatory biomarkers

The results show that FPP® did not influence the levels of any inflammatory biomarker: interleukin-6, C-reactive protein or myeloperoxidase (all ps>0.05). There were also no significant differences between the FPP® and placebo groups (placebo data not shown).



The main finding of this trial was that FPP® at a dose of 9 g per day did not adversely affect blood chemistries and metabolic outcomes in moderately functioning, generally healthy older adults, and, therefore is considered safe for this population. Also noteworthy, participants reported few adverse events during both the FPP and placebo condition in the present study, and these events did not differ in frequency when participants were taking FPP versus placebo. As expected, the placebo also did not adversely affect blood chemistries or metabolic outcomes.
Somanah et al. also reported no adverse changes in blood chemistry levels or adverse events in 127 young and middle-age Mauritian neo-diabetic participants who consumed FPP® (9g/day) or placebo for a 16-week period (12).  Noteworthy, the dose used in the present study was identical to that used in the Somanah et al. study. Our study extends the findings of Somanah et al. to older adults and suggests FPP® is a safe dietary supplement for adults of all ages.
The lack of significant difference in adverse events between FPP® and placebo is in agreement with Somanah et al (12) who did not note any adverse events when using the same amount of FPP® per day. Our group and others have shown that there are few adverse events associated with FPP® supplementation which supports a safe use of this product in future studies in participants with impaired health status.
In contrast to the findings of Somanah et al., FPP® did not affect serum CRP, IL-6 or MPO concentrations in the present study. Somanah et al. reported significant reduction in CRP, uric acid and ferritin levels, which suggest lower levels of systemic inflammation and potentially oxidative stress (12). Somanah et al., however, studied diabetic individuals, which may have contributed to the difference in findings. Our older participants had no diagnosed comorbidities, and therefore had less metabolic dysregulation than the participants in the Somanah et al. study.  Taking together, the findings of our study plus Somanah suggest FPP may be able to safely provide protection against oxidative stress and inflammation.
Importantly, renal function, elevated uric acid, urea and urinary albumin and creatinine levels during insulin resistance in overweight and hypertensive subjects may be of major concern (12). Santiago et al. have shown that a short-term intake of FPP® could improve renal integrity (13).  Effects of FPP® on systemic inflammatory cytokines and uric acid levels in older adults with impaired renal function (e.g. type 2 diabetes) deserves further investigation (14).
The present study had some limitations. Considering the nature of a pilot trial, this study included a small number of participants over a short period of time (8 weeks).  However, with crossover designs, a smaller number of participants are needed for purpose of a pilot trial (15). Others have reported a 4-week supplementation period as a potential limitation to obtaining significant results (12). An important strength of the present study is that levels of adherence to the product regimen were high (>90%) (16) and promising for a future fully powered trial in older adults.
In conclusion, our trial demonstrates FPP® supplementation as safe and feasible in our population of adults 70 years and older. Based on these findings and the positive effects FPP has demonstrated in previous trials, future trials should examine the effects of FPP® in older adults with impaired health status and/or older adults who may have insufficient anti-oxidant protection due to their genetic background.


Acknowledgements: The authors would like to thank the Osato Research Institute, Japan for supporting the conduct of this study.
Author Contributions: Christiaan Leeuwenburgh, Stephen Anton, Todd Manini and Adam Woods conceived and designed the experiments; Robert Mankowski analyzed the data; Robert Mankowski drafted the manuscript, Christiaan Leeuwenburgh and Stephen Anton revised and consulted on the manuscript preparation.
Conflicts of Interest: The authors declare no conflict of interest.



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1. Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Japan; 2. The Japan Society for the Promotion of Science, 8 Ichiban, Chiyoda, Tokyo, Japan; 3. Falls and Balance Research Group, Neuroscience Research Australia, Sydney, New South Wales, Australia; 4. Faculty of Medicine, University of Tsukuba, Tsukuba, Japan; 5. Tokyo Metropolitan Institute of Gerontology, Itabashi, Tokyo, Japan; 6. National Center for Geriatrics and Gerontology, Obu, Aichi, Japan; 7. Nestlé Research Center, Bunkyo-ku, Tokyo, Japan; 8. Nestlé Research Center, Lausanne, Switzerland
Corresponding author: Yoshiro Okubo, Falls and Balance Research Group, Neuroscience Research Australia, Address: Barker Street Randwick Sydney NSW 2031 Australia, Tel.: +61 2 9399 1065, E-mail: y.okubo@neura.edu.au

J Frailty Aging 2017;in press
Published online October 4, 2017, http://dx.doi.org/10.14283/jfa.2017.38



Objective: Develop and evaluate the feasibility and validity of the Nutrition and Functionality Assessment (NFA) which identifies “target” older adults who could benefit from a personalized program following evaluation of their nutrition status and physical functionality. Design: Cross-sectional study. Setting: Community and geriatric day-care centers and university in Japan. Participants: 267 older adults aged 65-90. Measurements: The “target” individuals were screened based on gait speed (0.6-1.5 m/s). Nutrition (Mini Nutrition Assessment-short form and protein intake), strength (30s chair sit-to-stand and hand-grip strength) and endurance (6-minute walk) were assessed. Physical activity was monitored using a tri-axil accelerometer for a week. Fried frailty phenotype was also assessed. Results: Out of 267 individuals, 185 (69%) had gait speed between 0.6-1.5 m/s, corresponding to our “target” group from which, 184 (95%) completed the nutrition and physical functionality assessments with the physical activity monitoring. The NFA was completed in approximately 30 minutes. No adverse events directly due to the NFA were reported. NFA physical functionality and global scores were significantly related to frailty phenotype but nutrition score was not related to frailty phenotype. Conclusion: The study demonstrated that the NFA is a safe and feasible tool to screen target older adults and simultaneously evaluate their nutritional status and physical functionality. Validity of the NFA was partially confirmed by the significant association of the global and physical functionality scores with frailty phenotype. More studies are required to validate and maximize the applicability of the NFA in communities and institutions in Japan and elsewhere.

Key words: nutrition, physical functionality, physical activity, aged, assessment.




The disablement pathway in older age begins silently at the cellular level with, for example, reduced rate of muscle protein synthesis (1), increased rates of muscle protein breakdown and inflammation (2). Altered sensations of thirst, hunger and satiety and reduction in food intake (3) are associated with metabolic, physiological and behavioral alterations leading to decline in muscle mass, strength and mobility, and are manifested as sarcopenia, frailty, malnutrition, and functional dependence (4-6). Regular exercise can activate the physiological systems even in older age, improve the physical function and mitigate disability (7). Appropriate nutrition goes hand in hand with exercise in maintaining physical function because it facilitates post-exercise protein synthesis and inhibits protein breakdown in skeletal muscle (8).
Identifying the early onset of the disablement pathway or functional decline, even before older adults recognize it as a problem, is key for an effective health management strategy in the older population (9). A number of performance-based physical function assessments with good psychometric properties have been developed and used in research and community settings (10). Several nutritional screening tools have also been developed for use specifically in the community setting (11). However, there have been no assessment tools that could capture both the nutritional state and physical function which are the two major, modifiable risk factors of disability in older adults (12, 13). Such comprehensive nutritional and physical assessment tools would be highly beneficial, in providing guidance for a standardized and personalized, nutritional and exercise program to prevent the disability as well as sarcopenia, malnutrition and frailty that are common in older age (4-6).
Here we propose a new assessment tool called “Nutrition and Functionality Assessment (NFA)”. The concept of the NFA is to quickly identify target older individuals using gait speed and provide an easy and reliable assessment of the current nutritional status and physical functionality of the individuals. The NFA stratifies the target older individuals into 3 status levels for each test related to malnutrition, protein-intake, strength and endurance. The compilation of the results of these tests allow to calculate different scores including an NFA global score and sub-scores for nutritional state and physical functionality. These scores and status level of each test are expected to guide health care providers with vital information for a personalized nutrition and exercise program and to be used as a monitoring tool for follow-up evaluation. However, since the NFA has been designed on a theoretical basis, its feasibility and validity among community-dwelling older adults was unclear. Moreover, cut-off values used to stratify the target population needed to be determined using actual data obtained from older adults who underwent the NFA. Therefore, the purpose of this study was to develop and evaluate the feasibility and validity of the NFA among community-dwelling older adults.




Participants were recruited through local advertisements and flyers in Ibaraki and Miyagi prefectures, Japan. The study was conducted in a municipal community center, geriatric day-care service, and university in 2013. The eligibility criteria were as follows: (1) aged between 65 and 90 years, (2) able to walk with/without walking aid, and (3) free of cognitive impairment. The exclusion criteria were as follows: (1) doctors restriction on exercise, (2) lower/upper extremity surgery or fracture in the last 3 months, (3) having history of neurological disease with residual impairment, and (4) unable to understand and carry out performance tests and questionnaires correctly. The participants read and signed the informed consent approved by the institutional review board for testing.


The NFA consists of 3 steps. First, participants were screened by normal gait speed (m/s) along a 6-m course with 1-m acceleration/deceleration space at each end. The target individuals who would benefit from a personalized, nutritional and exercise program were defined as those with gait speed between 0.6-1.5m/s. We excluded those who walked slower than 0.6m/s (14, 15) who were too frail and would require intensive care or referral to a geriatrician and who walked faster than 1.5m/s (16) for being too fit to benefit from the program. Second, the participants qualified as the target individuals were assessed for nutrition (malnutrition and protein intake) and physical functionality (strength and endurance). The Mini Nutritional Assessment-Short Form (MNA-SF) (17) was used to assess risk of malnutrition. Protein intake was assessed using the brief dietary history questionnaire (BDHQ), consisting of 58-item fixed-portion-type questionnaire (18). Strength was assessed using hand-grip strength (average of 2 trials) and 30s sit-to-stand tests (19). Endurance was assessed using a 6-minute walk test using a 50-meter course (20). Third, physical activity was monitored for 1 week using a 3-axis accelerometer worn at least 12 hours a day (Active style Pro, Omron Inc., Japan).

Calculation of NFA score

For each test performed the participants were stratified into 3 status levels with level 1 being lowest and level 3 being highest. The cutoff values for the malnutrition and protein were based on previously defined classification of risk of malnutrition (1: 0-7 [malnourished], 2: 8-11 [at risk of malnutrition], 3: 12-14 [normal nutritional status]) (17) and protein-intake recommendation for older adults (1: <0.8, 2: 0.8-1.2 and 3: >1.2 g/kg [per body weight]) (21), respectively. The nutrition score was calculated as the sum of the status levels for the malnutrition and protein intake (range: 2-6). The cutoff values of the 3 status levels for the strength and endurance were based on the 33 and 66 percentiles in this sample. The strength status level was calculated as the average of the status levels in the hand-grip strength and sit-to-stand tests. The physical functionality score was calculated as the sum of the status levels for the strength and endurance (range: 2-6). The NFA global score was calculated as the sum of the nutrition and physical functionality scores (range: 4-12).

Feasibility measurements

The feasibility of the NFA was evaluated first by the proportion of the population that was able to complete the NFA, second by the time to fill in the MNA-SF and carry out the physical performance tests (6-min walk, sit-to-stand, and hand-grip strength), third by the incidence of adverse events.

Frailty phenotype

The validity of the NFA was also evaluated by comparison to the frailty phenotype which consisted of 5 frailty indicators: unintentional weight loss, weakness, exhaustion, slowness and low physical activity (4). The participants with 0, 1-2 and 3-5 positive indicators were classified as non-frail, pre-frail and frail.

Sample size estimation

Sample size was estimated based on the 6-minute walk test, to determine 2 cut-off values, 33 and 66 percentiles, to stratify older participants into 3 status levels. Previous studies on older adults reported on average 570 m with a standard deviation of 90 m (22-24). A set of simulations was performed, in order to estimate the standard error of the percentiles: with 150 participants, 33 and 66 percentiles can be estimated with a precision of +/-7%.

Statistical analyses

The cut-off values for the physical functionality tests were determined based on the 33 and 66 percentiles of the target group whose gait speed was between 0.6-1.5 m/s. The association between the nutrition, physical functionality and global scores and the frailty phenotype was examined using the Spearman’s rank correlation coefficient. P < 0.05 was considered to be statistically significant. All the analyses were performed in R statistical software, version 3.0.1.



Screening of the target individuals and baseline demographics

Out of 301 people recruited in the study, 34 were excluded based on the inclusion and exclusion criteria: <65 and >90 years (n=14), dementia (n=16), exercise restriction (n=4), surgery/fracture (n=1) and no consent (n=1). Thus, 267 older adults were enrolled in the study and screened by gait speed. Of the 267 participants, 3 women and 79 participants (29 men and 50 women) were excluded as being too frail (<0.6m/s) and too fit (>1.5m/s) based on the gait speed, respectively. The remaining 185 (69%) participants were qualified as the target individuals for the NFA (Table 1). The target individuals had an average age of 71.7 years and gait speed of 1.29 m/s.

Table 1 Characteristics of the participants classified as the target individuals

Table 1
Characteristics of the participants classified as the target individuals

Note: Values are mean ± standard deviation or n (prevalence).


Completion rate of the NFA tools

All the target individuals completed the malnutrition (MNA-SF), protein-intake (BDHQ), hand-grip strength and sit-to-stand. Two women with knee pain chose not to perform the 6-minute walk. Seven participants did not receive/wear the accelerometer properly for 12 hours each day. Subsequently, 178 participants (95%) completed the whole NFA.

Time to complete the NFA

The time to administer MNA-SF and the physical performance tests were 1.2 ± 0.5 minutes and 14.8 ± 2.1 minutes, respectively. This originally planned measure does not include times to measure body height and weight and calculate a body mass index (approximately 4 minutes) for the MNA-SF and to administer the BDHQ (approximately 10 minutes) for the protein intake. Thus, a total of 30 minutes would be required to complete the NFA.

Adverse events

A hip fracture caused by a fall in the neighbourhood was reported but was not related to the NFA. Non-serious adverse events included myalgia (n=5), arthralgia (n=2), back pain (n=2), fatigue (n=1), muscle spasms (n=1), herpes zoster (n=1) and palmar-plantar eryth (n=1). These adverse events were related to the 6-minute walk (n=5), sit-to-stand (n=2) and hand-grip strength (n=2). Two participants already had herpes zoster and palmar-plantar eryth prior to the NFA assessments.

Cut-off values and stratification

Table 2 presents cut-off values obtained for the NFA. Because of the clear gender difference in the hand-grip strength (12 kg higher in men) and the 6-minute walk (31-51 m higher in men), gender specific cut-off values were adopted for these items. In contrast, we adopted joint cut-off values for the chair sit-to-stand test because the percentile values were very similar for men and women. Figure 2 presents distributions of the calculated NFA scores. The majority of the participants were classified as having normal nutritional status but the physical functionality and global scores were evenly distributed with only 1% and 3% reaching the lowest and highest global scores, respectively.

Table 2 Cut-off values obtained for calculation of the NFA scores

Table 2
Cut-off values obtained for calculation of the NFA scores

M and F are values for male and female. MNA-SF: Mini Nutrition Assessment-Short Form; Strength status level = (hand-grip strength level + sit-to-stand level) / 2; Nutrition score (2-6) = malnutrition level (1-3) + protein intake level (1-3); Physical functionality score (2-6) = strength level (1-3) + endurance level (1-3); NFA global score (4-12) = nutrition score (2-6) + physical functionality score (2-6).

Figure 1 Flow of the Nutrition and Functionality Assessment (NFA)

Figure 1
Flow of the Nutrition and Functionality Assessment (NFA)

MNA-SF: Mini Nutrition Assessment-Short Form, BDHQ: Brief Diet Habit Questionnaire


Correlation between frailty phenotype and the NFA scores

Figure 3 illustrates scatter plots of the NFA nutrition and physical functionality scores and the Fried frailty phenotype. A significant correlation was found between the physical functionality score and the frailty phenotype (r=-0.38, p<0.05), indicating that the frailer a person is, the lower the physical functionality score is. The nutrition score was not correlated with the frailty phenotype (r=0.05, p>0.05). The global score showed significant correlation with the frailty phenotype (r=-0.35, p<0.05).

Figure 2 Histograms of the NFA nutrition, physical functionality and global scores

Figure 2
Histograms of the NFA nutrition, physical functionality and global scores



Figure 3 Scatter plots and Spearman correlation between the frailty phenotype and the NFA (1) nutrition, (2) physical functionality and (3) global scores. The big dots are actual scores (data) but the small dots are virtual data scattered around the actual scores, in order to visualize the number of participants in each category

Figure 3
Scatter plots and Spearman correlation between the frailty phenotype and the NFA (1) nutrition, (2) physical functionality and (3) global scores. The big dots are actual scores (data) but the small dots are virtual data scattered around the actual scores, in order to visualize the number of participants in each category



The study demonstrated that the NFA is a safe and feasible tool to screen target individuals and assess both nutritional status and physical functionality of older adults. Validity of the NFA was partially confirmed by the significant association of the global and physical functionality scores with frailty phenotype. However, the nutrition score was not related to frailty phenotype.


Feasibility of the NFA

The completion rate of the NFA was very high (95%). With the exception of 2 participants who chose not to perform the 6-minute walk test for their knee with pain, all other participants were able to perform all the assessment tools in the NFA, which indicates good acceptance by the participants.
Approximately 30 minutes were required to complete the whole NFA with 2 questionnaires and 5 physical performance tests. This was slightly longer than other major physical performance tests such as the Short Physical Performance Battery (3 tests, 10-15 minutes (25)), the Physical Performance Test (7 ADL items, 10-15 minutes (26). Considering the additional nutritional assessments in the NFA, the 30 minutes can be considered reasonable and feasible in most settings, especially in the community. In the institutional setting with lower functioning older adults, it is expected to take longer time to administer.
We observed some non-serious adverse events including arthralgia, back pain, fatigue and muscle spasms related to the NFA. Although most of the participants already had these symptoms before the NFA, it is important to check the physical conditions of all participants before starting. Both testers and participants should be aware of the possibility that the tests could aggravate existing symptoms and not try too hard.


Cut-off values for the NFA

The obtained percentile values are mostly congruent with our hypothetical values derived from previous studies. However, the hand-grip strength values were slightly higher than reference values from representative samples of Japanese older adults (16). The sit-to-stand values and the 6-minute walk distance in the current study were also higher than those of the US counterparts (27). Considering the long life expectancy in Japan (28) and inter-country difference in physical functionality, it may be necessary to examine the NFA values in different countries.
Nutrition status in the present study was normal in most of the participants (78%) while only 2% and 20% were malnourished or at risk of malnourishment, respectively. These results were similar to the general worldwide prevalence of risk of malnutrition (26%) (29). Our study results indicated that most of participants (69%) had protein-intakes above the recommendations for older adults to maintain lean mass (≥1.2g/kg) and only a small part (4%) had intakes below 0.8g/kg (21, 30). Overall results of the nutritional assessments indicated that the population included in our study had a good nutritional status.
There was neither floor nor ceiling effects in the NFA global score as only few participants achieved the lowest (1%) and highest (3%) scores, respectively.


Validity of the NFA against the frailty phenotype

The NFA global and physical functionality scores were confirmed to have moderate but significant association with frailty. However, the nutrition score was not related to the frailty phenotype. Weight loss, a symptom of malnutrition, was the common factor in both the MNA-SF and the frailty phenotype but its prevalence in the target population was very low (≥3kg loss: 0% and 1-3kg loss: 13%). Moreover, compared to the frailty phenotype (4), the nutrition score was consisted wider variety of nutritional aspects including appetite, weight loss, body mass index and protein-intake. The protein-intake, a dietary habit related to current and future risk of physical weakness (30), may be more meaningful in this population and useful for a preventive approach. The uniqueness of the NFA and lack of existing tools to assess both nutrition and physical functionality, made it a challenge to validate the NFA against any existing measures. However, the NFA which also aims to provide useful information for personalized nutrition and exercise program cannot be fully validated without an intervention program. A future clinical trial which uses the NFA to provide seamless screening, assessment and a personalized program to improve nutritional state and physical functionality of older adults is needed.


Although the NFA can be used in geriatric facilities, where proportion of demented people is high, its feasibility may be lower. The responsiveness of the NFA to clinically significant changes was not investigated. Further examinations are needed to explore and maximize the applicability of the NFA in communities and institutions in Japan and elsewhere.


Conflict of interest statement: This study was conducted in the University of Tsukuba with financial support by Nestlec Ltd. GVP, EO, MS and DB are employees of Nestec Ltd.
Funding: This study was financial support by Nestlec Ltd. The sponsor was involved in design of the study, analysis and interpretation of data, and preparation of the manuscript.
Acknowledgements: We express our deep gratitude for the members, personnel and volunteers in the University of Tsukuba, Town of Yamamoto and Tsuchiura Fureai Centre Nagamine who contributed in recruitment and data collection. We also thank all the participants in this study.
Ethics: The study protocol was conducted in accordance with the guidelines proposed in the Declaration of Helsinki and the current laws of the country, and was reviewed by the Research Ethics Committee of the University of Tsukuba, Japan (TAI24-51: 06/11/2012).



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1. NHMRC Centre of Research Excellence Trans-disciplinary Frailty Research To Achieve Healthy Ageing, Australia; 2. Geriatric Medicine, Dalhousie University and Nova Scotia Health Authority, Halifax, NS, Canada; 3. NHMRC Centre of Research Excellence Translating Nutritional Science To Good Health, Discipline of Medicine, School of Medicine, University of Adelaide, South Australia, Australia; 4. Faculty of Health Sciences, University of Bristol, Bristol, UK; 5. Centre for Education and Research on Ageing, University of Sydney, Concord Hospital, Concord New South Wales, Australia; 6. Alfred Health, Caulfield Hospital, Victoria, Australia; 7. John Walsh Centre for Rehabilitation Research, Sydney Medical School, University of Sydney, New South Wales, Australia; 8. Adelaide Geriatrics Training and Research with Aged Care Centre, School of Medicine, University of Adelaide and Aged & Extended Care Services, The Queen Elizabeth Hospital, Central Adelaide Local Health Network, South Australia, Australia.
Corresponding author: Olga Theou, Dalhousie University and Nova Scotia Health Authority, Suite 1313, 5955 Veterans’ Memorial Lane, Halifax, Nova Scotia B3H 2E1, Canada. Tel: 902-473-4846, e-mail: otheou@alumni.uwo.ca

J Frailty Aging 2016;in press
Published online August 3, 2016, http://dx.doi.org/10.14283/jfa.2016.108


Objective: To examine whether a testosterone and a high calorie nutritional supplement intervention can reduce frailty scores in undernourished older people using multiple frailty tools. Design: Randomized controlled trial. Setting/Participants: 53 community-dwelling, undernourished men and women aged >65 years from South Australia, Victoria and New South Wales. Intervention: Intervention group received oral testosterone undecanoate and a high calorie supplement (2108-2416 kJ/day) whereas the control group received placebo testosterone and low calorie supplement (142-191 kJ/day). Measurements: Frailty was operationalized using three frailty indices (FI-lab, FI-self-report, FI-combined) and the frailty phenotype. Results: There were no significant differences in changes in frailty scores at either 6 or 12 months follow up between the two treatment groups for all scales. Participants at the intervention group were 4.8 times more likely to improve their FI-combined score at both time points compared to the placebo group. Conclusion: A testosterone and a high calorie nutritional supplement intervention did not improve the frailty levels of under-nourished older people. Even so, when frailty was measured using a frailty index combining self-reported and lab data we found that participants who received the intervention were more likely to show persistent improvement in their frailty scores.


Key words: Frail elderly, frailty index, nutrition, testosterone, aging, older adults.


J Frailty Aging 2016;in press
Published online August 3, 2016, http://dx.doi.org/10.14283/jfa.2016.108



Given the prevalence and consequences of frailty and the projected increase in frailty disease burden, there is increasing interest in exploring effective interventions to treat and/or reverse frailty. Recent studies have provided some evidence that multidisciplinary interventions targeted at specific aspects of frailty and achieved through a personalised programme developed following comprehensive geriatric assessment potentially reduce the likelihood of frailty (1). The LIFE study for example confirmed that a physical activity intervention significantly reduced frailty scores at 12 months in older sedentary participants (2). The evidence for the role of nutrition and other anabolic modalities in frailty treatment is less clear.
A natural decrease in food intake develops with ageing, and weight and muscle mass loss ensue as the reduction in food intake exceeds energy expenditure (3). Malnutrition is considered such a significant contributor to the development of frailty that the majority of validated frailty tools include weight loss as a key criterion and some researchers even discuss nutritional frailty as a subtype of frailty (4). Perhaps those at nutritional risk or malnourished should be a target population for interventions to reduce frailty risk.
A randomised pilot study by our group showed that testosterone and a nutritional supplement reduced hospital admissions and duration of hospital stay over 12 months in undernourished older adults (5). Our follow up multi-centre, double-blind randomised controlled trial contradicted this finding (6-8). The purpose of this secondary analysis exploratory study was to examine whether a testosterone and a high calorie nutritional supplement intervention reduces frailty scores in community dwelling older people at nutritional risk.



The methodology of the primary study has been described in detail (7). To summarize, community-dwelling, at nutritional risk men and women aged >65 were recruited from South Australia, Victoria and New South Wales. Inclusion criteria were a Mini Nutritional Assessment score of 17-23.5, body mass index (BMI) of less than 22 or if they reported ≥7.5% weight loss in the 3 months prior to the study, and for those women who were prescribed estrogen or hormone replacement therapy they were required to be a stable dose for 3 months prior to the study. Participants were randomised using a stratification system within each site to either the intervention group (oral testosterone undecanoate and a high calorie supplement) or the control group (placebo testosterone and placebo low calorie supplement). Participants at the intervention group were advised to take testosterone undecanoate: 40 mg daily for women/80 mg twice a day for men, with meals. The high calorie supplement was a 180 ml drink taken twice daily, and provided 1208 -1054 kJ/180 ml depending on the flavour. In total, approximately 2108-2416 kJ were provided a day. The placebo (70.97 kJ-95.48 kJ/day) provided approximately 142-191 kJ/day depending on the flavour.
Participants were followed up at three, six and twelve months. At the three months follow up only a short version of the assessment protocol was conducted. The trial was continued during hospitalisation of any participants, as long as medical management was not affected and there were no adverse effects. Ethics approval was obtained by the Human and Research Ethics Committee of the Queen Elizabeth Hospital (South Australia), Caulfield Hospital (Victoria) and Concord Hospital (New South Wales). Written informed consent was obtained from all participants.

Frailty Measures

Frailty was operationalized using the frailty phenotype and the deficit accumulation approaches. The frailty phenotype was measured similarly to the scale proposed in the original study by Fried and colleagues using five criteria: weight loss, exhaustion, physical activity, measured grip strength, and measured walking speed (9). One modification used in this study was that instead of using the average grip strength score in dominant hand among the three trials, we used the best grip strength score in dominant hand among the three trials. Another modification was that the level of physical activity was examined using a physical activity diary for the past week, from which we calculated the energy expenditure based on the frequency and intensity of the activities performed similar to the physical activity criterion of the original scale. The cut-points used for all five criteria were exactly the same as with the original scale (9).
We also constructed three frailty indices using the data already collected; one using 80 self-reported questions (FI-self-report), another using 22 commonly done laboratory tests plus blood pressure measurements (FI-lab), and a third combining the 102 items of the other two indices (FI-combined). These frailty indices were based on the deficit accumulation approach which suggests that any symptom, sign, disease, disability, or abnormality can be included in the frailty index as long as it is associated with age and adverse health outcomes, has no more than 5% missing values, and the deficit is prevalent in at least 1% of the population (10).
For the FI-self-report we combined data from the Montreal Cognitive Assessment (7 items/domains), Geriatric Depression Scale (15 items), Independent Activities of Daily Living scale (8 items), Barthel’s Index (10 items), Short Form-36 Health Survey (36 items) and the exhaustion, physical activity, grip strength, and walking speed criteria of the frailty phenotype (4 items). Most frailty indices include self-reported items, however recent studies using data from the Canadian Study on Health and Aging (11,12), European Male Ageing Study (13), and Newcastle 85+ study (14) showed that frailty can be captured using routine laboratory data which offers a more objective and often more practical way to assess frailty within the clinical settings, although without the benefit of identifying elements for a care plan, as would be the case with a frailty index based on a comprehensive geriatric assessment. Thus, in this study we created a frailty index combining the following measures: alkaline phosphatase, aspartate aminotransferase, bicarbonate, total bilirubin, creatinine, fasting glucose, lactate dehydrogenase, mean corpuscular volume, neurotrophil count, phosphate, platelet count, potassium, protein, red cell distribution width, sodium, total calcium, urea, white cell count, pulse, systolic blood pressure, diastolic blood pressure, and pulse pressure. For all three indices, we did not include any items that were directly associated with nutrition. Each included item was mapped to a 0 to 1 interval with a value of 0 when the deficit was absent and 1 when the deficit was fully expressed (e.g. abnormal lab value). The frailty index score of participants was calculated by dividing the number of deficits present by the total number of measures considered. If participants had more than 20% of the items missing in the FI-self-report or FI-lab then their frailty level was not calculated for that index. In order for the FI-combined score to be calculated the participant should had a valid score at both the FI-self-report and FI-lab.

Statistical analysis

We first compared the frailty scores at baseline and at six and twelve months follow up for both groups (intervention and placebo) using mixed-design analysis of variance with treatment group as the between-subject factor and time as the within-subject factor. We then compared the change in scores at follow-up between the treatment groups using one way analysis of variance. Some frailty scores exhibited positive skew and so to check whether the distribution of the data affected the findings, analyses were repeated using square-root transformations of the frailty scores and no differences were found in the results. We also compared the number of people who improved their score from baseline using chi-square tests. We then evaluated whether people in the intervention group were more likely to improve their frailty level at six months, twelve months, or at both time-points using logistic regression analyses adjusted for baseline frailty level. Analyses were repeated for all frailty scales in separate regression models. Further covariates (age, sex) were explored for the regression models but no substantive changes were found to the conclusions. Analyses were conducted using SPSS (version 18, SPSS Inc.). Data are reported as mean ± standard deviation, all reported confidence intervals are within 95% and the statistical significance level was set at a P value of 0.05.



From the 770 people who were screened by phone a total of 53 commenced the study and were included in this analysis. The reasons for the low participation have been described in detail (8). At six months nine participants (three from the placebo group and six from the intervention group) were lost to follow up and at 12 months a total of 17 participants (nine from the placebo group and eight from the intervention group) were lost to follow up. Two participants from the placebo group died but based on medical notes, these people were severely frail before their death so they were included in the analysis as part of the group whose frailty level worsened from baseline. The FI-lab and FI-combined scores were not calculated for one participant at baseline, one participant at six months, and five participants at twelve months follow up due to missing data.

Table 1 Frailty scores at each assessment point for all scales (mean±SD)

Table 1
Frailty scores at each assessment point for all scales (mean±SD)

Note: No significant interaction of time with the treatment group for all frailty tools; *the participants of the intervention group had higher FI-combined scores for both baseline and follow up assessments (p=0.03); **baseline frailty phenotype score or prevalence were significantly higher than scores or prevalence at six and twelve months follow up (p<0.01). No statistically significant difference between scores or prevalence at six and twelve months (p>0.05).


Among all participants, there were no statistically significant differences in frailty scores at baseline between the intervention and control group. The mean FI-lab was 0.21±0.10 (placebo 0.20±0.11, intervention 0.21±0.10), the mean FI-self-report was 0.20±0.17 (placebo 0.21±0.17, intervention 0.19±0.17), the mean FI-combined was 0.20±0.14 (placebo 0.21±0.15, intervention 0.20±0.14), the mean frailty phenotype score was 2.4±1.6 (placebo 2.3±1.5, intervention 2.5±1.7), and the prevalence based on the frailty phenotype was 43.4% (placebo 36%, intervention 50%).

Table 2 Number of participants that frailty scores improved at follow up

Table 2
Number of participants that frailty scores improved at follow up



Among those who completed the study and had valid frailty scores for all three assessments, mixed-design ANOVA showed that there was no significant interaction of time with the treatment group for all frailty scales. For the FI-combined we found that the participants of the intervention group had higher frailty scores for all assessments (p=0.03). For the frailty phenotype we found participants were frailer at baseline than at both follow up assessments (p<0.01); however there were no difference between the six and twelve month assessments (p>0.05) (Table 1). There was no significant difference between the two treatment groups in the absolute and percentage change of frailty scores at both follow up points for all scales (p>0.05).
There was no difference between the two treatment groups in the number of participants who improved their frailty score at six or twelve months or at both time-points for all scales except the FI-combined which showed that more participants of the intervention group improved at both time-points (p=0.05) (Table 2). The logistic regression analysis also showed that the intervention group was more likely to improve the FI-combined score at both time points even after adjusting for the baseline frailty score (p=0.05) (Table 3).

Table 3 Logistic regression models examining whether participants at the intervention group were more likely to improve their frailty scores at follow up

Table 3
Logistic regression models examining whether participants at the intervention group were more likely to improve their frailty scores at follow up

*per 0.01 score increase for the three frailty indices and per 1 point increase for the frailty phenotype; Note: Bolded text indicates statistical significant odds ratios (OR)



The evidence whether nutritional or testosterone interventions can reverse frailty is very limited. We found that a testosterone and a high calorie nutritional supplement intervention did not improve frailty levels when frailty was measured with the frailty phenotype or a frailty index which included solely self-reported data or lab data. However, when we looked at the proportion of participants demonstrating improvement in frailty scores at both six and twelve months, 59% of participants in the treatment group compared to 25% of participants in the placebo group demonstrated persistent improvement when frailty was measured using a frailty index combining self-reported and lab data.
The primary study (7), which our analysis was based, included multiple outcome measures such as hospital admissions, duration of admission, and quality of life and showed that the intervention did not have any effect in these measures. This may have been due to only 7% of the screened subjects deciding to participate, resulting in a study group that was 25% of the target sample size (8). In addition, the primary study used specific domains of health as outcome measures.
In this secondary exploratory analysis we used frailty as the outcome measure. Frailty represents the overall health state of an individual, and may be a more sensitive measure for a change in health. In our study we included the two most commonly used frailty measures, the frailty phenotype and the frailty index. Both of these measures have strong predictive validity in relation to worsening health status, poor mobility, ADL disability, institutionalization, and death (9,15). Even so a systematic review (16) on frailty assessment tools concluded that the frailty index seems to be the most suitable instrument to evaluate outcome measures in frailty research because it includes more items from various domains than the frailty phenotype, and in this way covers better the multidimensionality of frailty. Also by having a continuous scoring system the frailty index can better discriminate and measure change after an intervention. We also found that the FI-combined was more sensitive to change than the FI-self-report or the FI-lab. Similarly previous studies have shown that adding more items to a frailty index can strengthen its predictive ability (17) especially when combining self-reported and clinical measures (14, 18).
Limitations of this study were the small sample size and the 17 participants who were lost to follow-up at the 12 months assessment. Even so the number of drop outs was similar between the intervention and placebo groups. Future studies need to examine how recruitment for this type of interventions can be improved and especially their feasibility for clinical applications. In addition our study focused on older Australians at-risk of under-nutrition and therefore the findings may not be generalizable to other populations. Further exploratory studies in older people at-risk of frailty should be considered also. Strengths of this study are the long follow up assessment and the use of multiple frailty measures as outcome measures.
Randomised controlled trials that include frailty as an outcome measure have investigated nutrition intervention alone or in combination with other interventions such as exercise and comprehensive geriatric assessments but never with anabolic hormone (19-21). If this is to be considered a potential treatment strategy, larger studies are essential especially given the increased cardiovascular risk that some studies have noted with testosterone (22).


Funding: This study was supported by National Health and Medical Research Council Project Grant 627178. Additionally, personnel support was made possible through Hospital Research Foundation program funding to the Health Observatory.
Clinical Trial Registration: This trial was also registered on the 4th of May 2010 with the Australia and New Zealand Clinical Trials Registry (ACTRN12610000356066).
Conflicts of interest: None



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1. Department of Psychology, Drexel University, USA; 2. Department of Healthcare Policy and Research, Weill Cornell Medical College, USA; 3. Department of Psychiatry, Weill Cornell Medical College, USA

Corresponding author: Alexandra Greenfield, M.S., Drexel University, 3141 Chestnut Street, Stratton Suite 119, Philadelphia, PA 19104, USA, Phone: 914-980-5720, apg54@drexel.edu

Alternate Corresponding Author: Jo Anne Sirey, Ph.D., Weill Cornell Medical College, 21 Bloomingdale Rd, White Plains, NY 10605, USA, Phone: 914-997-4333, Fax: 914-682-6979, jsirey@med.cornell.edu



Objectives: This study used the Evans model of public health determinants to identify factors associated with nutritional risk in older adults. Design: The Evans model domains (physical and mental well-being, social/environmental statuses, individual choice, and economic security) were measured in a sample of homebound older adults. Regularized logistic regression analysis with LASSO penalty function was used to determine the strongest domain of the Evans model. Using traditional logistic regression, individual variables across all domains were compared to identify the significant predictors. Setting: Older adults receiving home meal services were referred to the study by community program staff. Participants: Participants included 164 homebound older adults (age > 60) who endorsed at least one gateway symptom of depression. Measurements: Nutritional risk was determined using the Mini Nutritional Assessment. Domains of the Evans model were measured using the MAI Medical Condition Checklist, items from the IADL scale, the Structured Clinical Interview for DSM-IV Axis I Disorders, the Duke Social Support Index, living arrangements, marital status, the Alcohol Use Disorders Identification Test, items from the SCID Screening Module, and a self-report of perceived financial security. Results: Poor mental well-being, defined by a diagnosis of major depressive disorder, was identified as the strongest Evans model domain in the prediction of nutritional risk. When each variable was independently evaluated across domains, instrumental support (Wald’s Z=-2.24, p=0.03) and a history of drug use (Wald’s Z=-2.40, p=0.02) were significant predictors. Conclusions: The Evans model is a useful conceptual framework for understanding nutritional health, with the mental domain found to be the strongest domain predictor of nutritional risk. Among individual variables across domains, having someone to help with shopping and food preparation and a history of drug use were associated with lower nutritional risk. These analyses highlight potential targets of intervention for nutritional risk among older adults.  


Key words: Depression, nutrition, model selection, Grouped LASSO, redundancy analysis. 



Malnutrition is a substantial problem among older adults. More than one out of five older adults suffer from malnutrition and more than two out of five are at risk for malnutrition, which is commonly referred to as “nutritional risk” (1). Poor nutrition may lead to a range of negative health outcomes by causing a decline in functional status, worsening of existing medical problems, and significantly shorter survival rates (2). It is important to identify individuals at nutritional risk because interventions that target factors associated with nutritional risk may reduce the adverse effects of malnutrition and reverse its course. Nutritional interventions have also been linked to shorter length of hospitalizations (3) and lower overall costs of medical services (4).

A number of factors have been shown to contribute to the development of malnutrition in older adults. While malnutrition is not an inevitable consequence of aging, many of the physical changes associated with aging are also related to decreased food intake and appetite, declining energy, loss of taste and smell, poor oral health, and dysphagia (5). Chronic illnesses such as diabetes, arthritis, oral manifestations, and gastrointestinal disorders are strongly associated with poor nutrition due to decreases in functional ability, changes in dietary intake, and weight loss (5). In addition to these physical risk factors, being female or a racial/ethnic minority (6), having greater levels of depressive symptoms (7), living alone (6), and loneliness (8) have all been found to be independently related to poor nutritional status in older adults.

Given the complexity of nutrition-related challenges facing the older adult, it is important to apply a theoretical model that takes into account the physical, mental, social, and environmental domains that affect nutritional risk. Few studies have examined nutritional health using a multidimensional model that can explore the relative importance of the predictors. 

One study (9) used hierarchical regression analysis to test the dynamics of several factors shown to be individually important to nutritional health in a sample of community-dwelling older adults. The theoretical framework for this study was the Roy Adaptation Model (RAM), a biopsychosocial approach which posits that health outcomes are a function of an individual’s ability to adapt to changing environmental demands. In the context of nutritional health, this may refer to one’s functional status in response to multiple medical comorbidities. After adjusting for the confounding effects of the various factors, depressive symptoms, functional status, oral health, and income were identified as significant predictors of nutritional health.

The current study seeks to determine factors associated with nutritional risk using the Evans model of the determinants of population health (10) as a conceptual framework. This model’s approach is based on the World Health Organization’s definition of health as not merely the absence of disease or injury, but a state of complete physical, mental, and social well-being. The Evans model considers the major domains that contribute to illness, including (1) physical well-being, (2) social/environmental status, (3) individual choice, and (4) economic security [10]. To capture the potential impact of mental health, we expanded the “physical well-being” domain into two distinct domains: physical well-being and mental well-being. 

The Evans model has been used to guide the analyses of factors that predict hypertension (11), high cholesterol (11), and coronary heart disease (10). In a previous study that used the Evans model to assess nutritional risk in community-dwelling older adults, the overall model was not found to be a statistically significant predictor of nutritional status (12). However, nutritional status was operationalized as body mass index and weight change, which may not be valid markers of malnutrition in the older adult population due to expected changes in body composition due to aging. 

The current study measures the Evans model domains (physical well-being, mental well-being, social/environmental status, individual choice, and economic security) in a sample of homebound older adults receiving home-delivered meals. Specifically, we investigate two questions: 1) which domain of the Evan model has the strongest association with nutritional risk, and 2) which variables among all the domains of the Evans model are the strongest predictors of nutritional risk. Regularized logistic regression analysis with grouped LASSO penalty function was used to determine the strongest predictive domain (group of variables) affecting nutritional risk. Using traditional logistic regression, individual variables across all domains were compared to identify the best predictor(s). By applying these two statistical techniques to a theoretical framework for the prediction of nutritional risk, we hope to improve the ability to identify older adults at risk of developing malnutrition and its associated negative outcomes. 



Sample: This study uses cross-sectional data collected from a sample of homebound older adults recruited for Open Door, a community-based mental health intervention study (NIMH R01 MH087557) (13). Study participants were adults age 60 or older who are homebound, eligible for home delivered meals services, and endorse either depressed mood or lack of interest or pleasure, the gateway symptoms necessary for a diagnosis of depression. This sample with depressive symptoms, physical limitations and medical burden was chosen for the analysis because of their complex clinical presentation and potential vulnerability to high nutritional risk and poor outcomes. The sample also provided sufficient variability across all of the Evans model domains to allow for robust analyses. The home delivered meals program has served approximately 2.6 million frail older adults (14) who are ‘confined’ due to a condition, illness or injury that restricts the ability of the individual to leave their home without assistance. Compared to the overall U.S. population above age 60, recipients of home delivered meals are more likely to be older, Black, living alone in poor health, have greater difficulty performing everyday tasks, and be at a high nutritional risk (15, 16).  

When an eligible study participant was identified by home delivered meals staff, verbal consent was obtained for the study personnel to contact the older adult, describe the study, and assess interest. If the subject chose to participate, a counselor made an in-home visit to obtain informed consent and conduct a baseline assessment. Exclusion criteria included the presence of significant substance abuse history or psychotic disorder, active suicidal ideation requiring immediate attention, cognitive impairment (Mini-Mental State Examination (17); MMSE < 24), aphasia, inability to speak English or currently in mental health treatment (either antidepressant medication or psychotherapy). This study was approved by the Weill Cornell Medical College Institutional Review Board (Protocol 0808009247).

Of 362 older adults referred for evaluation by community staff, 106 were ineligible after a phone screening as they were already in mental health treatment or too cognitively impaired to understand study recruitment procedures. Of those who were contacted for an in-person screen, 77 were excluded from the sample. Of the 77 excluded, 44 were too cognitively impaired or medically ill, 18 were already receiving treatment for depression, 6 had an alternative psychiatric diagnosis or substance abuse history, 3 could not speak English, 1 changed his/her mind about participation in research, 1 was at high risk for suicide, 1 was not homebound, and 3 were identified in the “other” category. 164 subjects were included in this analysis. 

Outcome Variable

Nutritional risk status was assessed using the Mini Nutritional Assessment (18). This assessment is a validated tool for nutritional screening and assessment in older adults (19), and it is routinely administered throughout the state by providers receiving funding for home meal programs. The measure assesses nutritional intake, food preparation and yields a continuous score from 0 to 21 with high nutritional risk defined as a score of 6 or greater.   

Factors Associated with Nutritional Risk 

To apply the Evans model to determine the factors associated with nutritional risk, measures collected by the Open Door study were matched with the associated Evans model domains (mental well-being, physical well-being, social/environmental status, individual choice, and economic security) as described in Table 1. We consider each domain of the Evan’s model as potential predictors of nutritional risk after controlling for demographic factors and cognitive status. In the mental domain of the Evans model, poor mental well-being was defined as suffering from major depression. A research diagnosis of Major Depressive Disorder (MDD) was determined using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-1) (20) administered by trained social workers under the supervision of a clinical psychologist. Depression status was dichotomized into those individuals who met DSM-IV (SCID) criteria for MDD and those who did not. The physical domain of the model included measures of medical burden and physical capacity for food preparation. Medical burden was defined as the number of current medical conditions endorsed in the Multi-level Assessment Inventory Medical Condition Checklist (MAI) (21). Capacity for food preparation was defined as the ability to shop and prepare foods. It includes the degree to which an individual is reliant on others for food shopping and was measured using self-report questions from Instrumental Activities of Daily Living (IADL) (22). For this older adult sample, the social/environmental domain of the model combined measures of social support and living arrangements. Social support was defined using the three subscales of the Duke Social Support Index (DSSI) (23), measuring subjective support, social integration and instrumental support. The individual’s living arrangements indicated whether there was someone in the home who could support the individual’s nutritional health. Those individuals who lived alone and were unmarried or unpartnered were defined as having an environment that is less supportive of nutritional intake. The economic domain of the model was measured using the individual’s perceived current financial security. This measure captured the degree to which an individual felt with or without resources that could affect nutritional status. Subjects described their financial security as: 1) “can’t make ends meet”, 2) “have just enough to get along”, or 3) “are currently comfortable.”  The negative individual choice domain in the Evans model was defined by alcohol and drug use based on the growing number of older adults using and abusing alcohol and drugs (24). The SCID Screening Module was used to evaluate current and past street drug or prescription drug abuse. Current alcohol consumption was measured by the Alcohol Use Disorders Identification Test (AUDIT-C) (25). The AUDIT-C has been shown to be useful in the detection of hazardous drinking among older adults, who may be particularly sensitive to the toxic effects of alcohol due to impaired metabolism and potential interactions with medications (26).


Table 1 Evans Factors, Domains Assessed, and Associated Measures


Statistical Covariates

Demographic variables (summarized in Table 2) were included as covariates in the analyses to control for the effects of characteristics such as gender, age, and race. Although participants did not have cognitive impairment, cognitive status as measured by the MMSE was included as a covariate to account for the potential association between cognitive functioning and nutritional health in older adults (27).


Table 2 Demographic Characteristics and Evans Factors by Nutritional Risk Status

Notes. MMSE: Mini-Mental Status Examination; MAI Medical Condition Checklist: Multi-level Assessment Medical Condition Checklist; IADL: Instrumental Activities of Daily Living; AUDIT-C: Alcohol Use Disorder Identification Test; SCID: Structured Clinical Interview for the DSM-IV


Data Analysis Plan

Prior to testing hypotheses, analyses were conducted to determine interdependence between variables to ensure that each variable is independent and represents unique aspects of the sample. Hierarchical clustering and redundancy analysis, as described below, were performed to evaluate redundancy. Nutritional risk was defined as a binary outcome, using the cut-off score of 6 or greater on the Mini Nutritional Assessment. The goals of the statistical analyses were: 1) to identify the strongest domain among the five Evans model domain associated with nutritional risk; and 2) to investigate association of the individual variables (making up the domains) with nutritional health. 

Redundancy Analysis to Evaluate Interdependence

The goal of this analysis was to evaluate the correlations and interrelationships among the predictor variables (both demographic and the variables within the Evans model domains) and determine collinearity between these variables. To this end, we performed hierarchical clustering and redundancy analysis (28, 29). Hierarchical clustering is an algorithm that assesses similarity between independent variables. It starts out by assigning each variable to its own cluster, finds the closest pair of clusters using a linkage metric and merges them into a single cluster. The algorithm then computes the similarity between the new cluster and each of the old clusters. This process is repeated until one single cluster is generated. Spearman’s rank correlation is used as the similarity metric and complete linkage function is used as the linkage metric between clusters. A dendrogram depicting the relationships between the variables is presented where the arrangement of ‘branches’ depicts which variables or clusters of variables are similar to each other and the height of ‘branches’ indicates strength of the similarity (See Figure 1). 


Figure 1 Dendogram representing hierarchical clustering of 17 predictors with Spearman’s rank correlation as the distance metric


To determine if a variable is redundant, we perform a redundancy analysis to reveal how well each independent variable can be predicted from all other variables. Redundancy analysis considers the regression of each variable on the other variables, dropping the most predictive variables in a stepwise manner until no variable can be predicted with an R2 of at least 0.8. As a result of this analysis, a variable is considered redundant if it can be predicted from a linear combination of all other variables. Non-redundant variables are retained for the statistical model.

Domains Analysis of Nutritional Risk with Grouped LASSO

To identify the strongest domain associated with nutritional risk among the five domains of the Evans model, a regularized logistic regression was performed with a Grouped LASSO (Least Absolute Shrinkage and Selection Operator) penalty function (Yuan, 2006). A traditional logistic regression analysis allows us to find associations of individual variables with nutritional risk, but grouped LASSO analysis provides the relative importance of groups of variables combined into a single domains. For example, a domain has five variables A, B, C, D and E, one variable (say C) may be highly associated with nutritional risk, but the domain as a whole may still be weakly associated with nutritional risk. The grouped LASSO analysis is a regularized regression technique, which operates by optimizing estimates of regression coefficients with a LASSO penalty function. LASSO penalty, when used in conjunction with a regression model, is a method of variable selection to select a subset of predictors which fit the model as well as the entire set of predictors. Grouped LASSO performs selection on groups of variables instead of individual variables. The selection is performed by penalizing groups of regression coefficients (corresponding to the variables in a particular group) with the LASSO penalty function. 

The LASSO penalty function shrinks the coefficients of unimportant groups of variables to zero (effectively removing them from the regression model); all regression coefficients below a certain threshold are treated as zero and removed from the model. The degree of penalty or shrinkage is controlled by a tuning parameter (λ), which determines the threshold below which regression coefficients will be turned into zero.  We chose a series of values for λ such that the regression model corresponding to the largest value of λ only includes the intercept, demographic variables and cognitive status. The five domains of the Evans model represent five groups of variables upon which Grouped LASSO selection was performed. The demographic variables and cognitive status represented an additional group which was always retained in the model. The first group to be included in the model along this decreasing series of λ is deemed as the strongest domain among the Evans model domains. 

Variable Analysis of Nutritional Risk with Logistic Regression

To evaluate the association of the twelve specific variables (that make up the Evans model domains) with nutritional risk, a logistic regression model was performed controlling for demographic factors and cognitive status. The strongest predictor of risk was determined by the ranks of the standardized coefficients or equivalently z-statistic of logistic regression (30). Strength of prediction of a future observation is represented by the c-statistic which measures the area under the Receiver Operating Characteristics or ROC curve (plot of sensitivity vs. 1-specificity).  



The sample consisted of 164 participants who screened positive for depression as a part of routine assessment for home delivered meals eligibility. Table 2 presents the demographic and clinical characteristics of the sample. 

The redundancy analysis performed on the set of seventeen variables (Evans model variables combined with the demographic variables) shows that at R2=0.8 level there are no redundant predictors, i.e. no single variable can be predicted from the other variables with an R2 of 0.8. Instrumental social support had the highest R2=0.47 (in a model with instrumental support as a dependent variable and all variables except instrumental support as independent variables) among all variables. The dendrogram (Fig 1) shows the hierarchical clustering of independent variables using Spearman’s rank correlation as the distance metric. This shows that current living situation and marital status have highest similarity (ρ2=0.30) followed by subjective social support (DSSI) and instrumental social support (DSSI) (ρ2= 0.23). As a result of the redundancy and clustering analysis we can conclude that the interdependency and correlations among the seventeen variables of nutritional risk is negligible and the proposed association analysis does not need to take these into account.


Table 3 Logistic Regression Analysis of Nutritional Risk

Note. IADLs: Instrumental Activities of Daily Living


In the Grouped LASSO analysis, to identify the domain most associated with nutritional risk, the intercept, demographic and cognitive variables were fixed and not subjected to selection. Among the Evans model domains most associated with nutritional risk was the mental domain defined as a diagnosis of MDD after controlling for demographic variables such as race, ethnicity, age, gender and education and cognitive status measured by MMSE. The second domains selected were the physical domain (defined as medical burden and impairments in instrumental activities of daily living), individual choice (alcohol use and report of drug use) and economic (self-reported financial status) domains. The social domain was the final domain to enter the model. The relative importance of each domain is determined by when it is included in the model along the path of the shrinkage parameter (see Methods).

Using a traditional logistic regression model, all twelve variables from the Evans model and all demographic factors were included in the model with nutritional risk as the dependent variable. The strongest associated variables were a history of drug use (Wald’s Z=-2.40, p=0.02) and instrumental support (Wald’s Z=-2.24, p=0.03). They were followed by major depression (Wald’s Z=1.95, p=0.05) and medical burden (Wald’s Z=1.86, p=0.06), although not statistically significant. The c-statistic for this regression was c=0.79 indicating that the model predicted nutritional risk significantly better than chance (c=0.5). The model with all variables (Evans model variables and demographic and cognitive status variables) and a model with only demographic and cognitive status variables were compared using a likelihood ratio test and showed significant improvement (χ2(11)=1.93, p = 0.0013). This indicates the usefulness of the Evans model variables as predictors of nutritional risk. History of drug use (OR=0.17, 95% CI 0.02-0.6) and having higher instrumental social support (OR = 0.84, 95% CI 0.71- 0.97) were significantly associated with lower odds of nutritional risk.  



Using the Evans population health model as a conceptual framework, we evaluated factors associated with nutritional risk in a sample of homebound older adults with depressive symptoms, medical burden, limited mobility and isolation. We employed innovative statistical strategies to investigate which domain of the Evans model had the strongest association with nutritional risk and traditional statistical analyses to determine which variables among all the domains of the Evans model were associated with nutritional risk. Our findings indicated that the domain of mental well-being, which we defined as a diagnosis of major depressive disorder, had the strongest association with nutritional risk. Across all variables, poor instrumental support with respect to food shopping and preparation and a history of drug use were significantly associated with lower nutritional risk. These two findings capture the outcomes evaluated by two different statistical approaches. Among the Evans model domains, grouped LASSO analysis identified the mental domain as having the strongest association with nutritional risk, whereas traditional logistic regression analysis picked instrumental support (belonging to the social/environmental domain) and history of drug use (individual choice domain) as the strongest associated variables. As domains, social and individual choice domains are less important relative to the mental domain, but individual variables within these domains are highly associated with nutritional risk.

The results of this study identify mental well-being, or the presence of major depressive disorder, as the strongest among all Evans model domains in predicting nutritional risk. This supports the data from community samples and extends the finding that depression may increase homebound older adults’ vulnerability to malnutrition (7, 31). Major depression can have a pervasive impact on an individual’s life, influencing many areas of functioning. Depression has been associated with involuntary weight loss in older adults (32), as well as increased risk of disability (33), disease (34) health risk behaviors (35), and social isolation (36), all of which have been shown to be independently related to nutritional status (8, 37-39). In this sample where depression is untreated, the interrelation of nutrition and depression may be particularly evident. 

Our finding that greater instrumental support is related to lower nutritional risk is consistent with other literature suggesting the importance of social resources in supporting the nutrition of older adults (40). These older adults have others who can provide assistance for food shopping and meal preparation. Access to this specific type of social support seems to play a critical role in nutritional health outcomes for homebound older adults.

The significant relationship between prior drug use and low nutritional risk was unexpected. However, the measure used in this study captured history of drug use rather than contemporary use. It is possible that older adults who have engaged in previous risky behaviors when younger have become more attentive to self-care as they age. It is also possible that the limited variability in our sample yielded spurious findings, as only 6 study participants reported a history of drug use. Future work should assess the association between current and past substance use and nutritional behaviors to further explore this relationship.

A limitation of this study is the selection of a homebound sample with depressive symptoms who have already been identified as potentially at risk for poor nutritional health due to restricted mobility. While there was a wide range of depressive symptoms within this sample, we recognize that we have increased the possibility of finding a relationship between major depression and nutritional risk due to increased prevalence of depressive symptoms. However, we believe that this is important to explore given the findings that more than 1 out of 10 home meal recipients has clinically significant depression and an additional 1 out of 8 have mild depression (Sirey et al., 2008). We also recognize that the cross-sectional nature of the study limits our ability to determine the impact of the identified factors on the course of nutritional risk. Our exploration of the Evans model was limited to the available scales administered by the Open Door study. Some of the variables included in our analyses may have had insufficient variability to demonstrate significant relationships with nutritional risk. To improve the integrity of the analyses, we included demographic variables and cognitive status as covariates, and we revised the original Evans model by separating physical and mental well-being into distinct domains. However, the model lacked the inclusion of additional factors that may contribute to nutritional risk in older adults. For instance, a higher frequency of service use has been shown to be associated with lower nutritional risk (41). 

Despite its shortcomings, this study helps to illuminate potential future targets of intervention for nutritional risk. In particular, depression, instrumental support, drug use, and nutrition may be interdependent health concerns for older adults. The use of innovative statistical procedures allowed us to evaluate the associations between nutritional risk and both the individual variables and conceptual domains of the Evans model. It is important to better understand the relative importance of individual predictors and larger conceptual domains in the context of nutritional risk, as this will help health care providers target the most vulnerable patients for preventative measures and the promotion of positive health outcomes.  


Conflict of Interest: All authors have disclosed that there are no financial, personal or potential conflicts. 

Author Contributions: Alexandra P. Greenfield, M.S., Samprit Banerjee, Ph.D., Alyssa DePasquale, B.A., and Nathalie Weiss, B.A., Jo Anne Sirey, Ph.D.: preparation of the paper. Greenfield, Sirey. Banerjee, and DePasquale: study concept, design, and methods. Sirey and Banerjee: analysis and interpretation of data. 

Sponsor’s Role: This research is supported by a grant from the National Institute of Mental Health (R01 MH079265, PI: J Sirey; P30 MH085943, PI GS Alexopoulos). The study design and conclusions are the sole responsibility of the authors and not the sponsor. 



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Corresponding auhtor: Chemin du Raidillon, CH-1066 Epalinges, Switzerland, Email: yves.guigoz@gmail.com

J Frailty Aging 2012;1(2):52-55
Published online February 14, 2012, http://dx.doi.org/10.14283/jfa.2012.10


In this short communication, we review the relationship between frailty and malnutrition risk in the elderly. Frailty is a term used for elderly at increased risk of adverse outcomes, including disability, falls, hospitalization, need for long-term care, and mortality. The Mini Nutritional Assessment (MNA) was designed and validated in a series of studies to assess nutritional status of elderly, as integral part of the comprehensive geriatric assessment, with a 2-steps screening process; when the MNA-SF classify a person at risk, the full MNA should be completed. The MNA and MNA-SF are sensitive, specific, and accurate in identifying nutrition risk. Increased risk of malnutrition, a common condition in the elderly, is closely associated with many potential contributors of frailty. The maintenance of optimal physical and cognitive performances depends on the early screening of critical conditions to develop preventive targeted interventions; the MNA supports such preventive action.

Key words: Frailty, nutrition, mini nutritional assessment, disability, screening.


Frailty is a dynamic state affecting an individual who experiences losses in one or more domains of human functioning (physical, psychological, social). It is caused by the influence of a range of variables and increases the risk of adverse outcomes, including disability, falls, hospitalization, need for long-term care, and mortality (1). The frailty phenotype is determined by the presence of at least three of the following five components: weight loss, exhaustion, weakness, slow walking speed, and/or sedentariness (2). Weakness is often the first warning sign, whereas weight loss and exhaustion are more likely to usually characterize the onset of frailty (3). Physical impairment is a major contributor to frailty in community-dwelling older persons (4), and gait speed reduction has shown to identify subjects at increased risk of adverse outcomes (5).

The Mini Nutritional Assessment (MNA) was designed and validated in a series of studies to assess nutritional status of older persons (i.e., which geriatric patients are at risk for malnutrition?) (6-9), to become integral part of the comprehensive geriatric assessment (CGA) (10, 11). The MNA is composed of 18 items (questions) grouped in four sections, all together providing a multidimensional nutritional assessment of the older subject: anthropometric assessment (weight, height, arm and calf circumferences, and weight loss), general assessment (six questions related to lifestyle, medication, and mobility), dietary assessment (eight questions related to number of meals, food and fluid intake, and autonomy of feeding), and subjective assessment (self-perception of health and nutrition) (6, 7). Six questions, showing the strongest correlations with the results of the MNA full version, are used to constitute the MNA-Short Form (MNA-SF). When the MNA-SF classifies a person as at risk of malnutrition, the subject should undergo the complete MNA assessment (8). Recently, the MNA-SF has shown to be correlated with calf circumference (particularly useful when body mass index, BMI, is not available/doable) and to improve the detection of malnutrition (9). Moreover, it has been independently validated in different healthcare settings (12).

Frailty and Malnutrition

Interestingly, the MNA includes several aspects of frailty such as low BMI, weight loss, low food intake, strength/muscle mass (mobility and calf circumference), and neuropsychological problems (depression and cognitive function). The prevalence of malnutrition is related to the level of disability, and gradually increases from community-dwelling older patients to hospitalized and institutionalized elders (11, 13-15). It is noteworthy that older persons at risk of malnutrition are often identified as being also frail (16-18), and this risk is closely linked with the healthcare needs (19, 20). Malnourished frail subjects are at higher risk of adverse clinical outcomes, independently of the healthcare setting (21-26). Moreover, the MNA score is well correlated with cognitive decline (27-30) and depressive symptoms (31-34). Thus, the increased risk of malnutrition, a common condition in the elderly, is closely associated with many potential contributors of frailty (10, 15, 35-37).

The risk of malnutrition is associated with lower food intakes (38, 39), low weight, or weight loss (35, 40, 41). In community-dwelling older persons, it is often due to a reduction of food intake because of a loss of appetite (42, 43). Furthermore, weight loss has been indicated as a marker of cognitive decline (44, 45), which is also significantly associated with frailty (35, 46, 47).

The presence of chronic diseases in the elderly characterizes a chronic inflammatory state, which affects lean body mass, protein metabolism, and immune response. Malnutrition and the risk for malnutrition determine lower muscle mass/strength and worse functional capacity (34, 48-50).

The assessment of nutritional status by the evaluation of serum proteins is potentially misleading because protein concentrations are affected by inflammation (51-54). For example, hypoalbuminemia is associated with increased inflammatory biomarkers (often due to concurrent chronic diseases) (55, 56). Interestingly, it has been suggested that the risk of malnutrition assessed by the MNA may be detected before albumin concentrations decline, in particular through the evaluation of decreased food intakes (7, 57). Moreover, the relationships between malnutrition with immunological parameters (57-59) and inflammatory markers also underlie the complex scenario of cachexia (52, 60-62). It is possible that the link existing between chronic inflammation and malnutrition may become the specific target for interventions in the next future (63, 64).

Mini Nutritional Assessment and Comprehensive Geriatric Assessment

Under specific conditions, (the risk of) malnutrition is not an isolated problem, but part of a polymorbidity (52). This implies that MNA should be regarded as a component of the CGA in which it is well integrated (65-67). This is particularly evident in cancer patients (37, 68-70), probably because of the relevance of the frailty condition during chemotherapy, but can also be easily applied to other conditions (71-73).

The new version of the MNA-SF should be more increasingly used in the evaluation of older persons, especially among institutionalized patients. The maintenance of optimal physical and cognitive performances depends on the early screening of critical conditions to develop preventive targeted interventions. The MNA supports such preventive action making possible the early identification of subjects at risk of malnutrition before relevant weight changes occur (7, 31, 74).

In community-dwelling older persons aged 85 years and older, low comorbidity, low risk of malnutrition (assessed by the MNA), and low risk of falls were associated with successful aging (75). In the New Mexico Aging Process Study, the mean MNA score of elders in good or excellent health status was 27 compared to those frail individuals reporting a mean MNA score of 25 (74), well above the malnutrition risk score of ≤23.5. These data support the important role played by adequate nutrition at advanced age, and indicate the need of always to consider its evaluation in the clinical and research setting.

In conclusion, the standardized and global use of the MNA is in line with the acknowledgment of adequate nutrition as a crucial component of the wellbeing in geriatrics. The MNA is a well-validated and broadly used instrument. To date, the risk of malnutrition is still too poorly recognized, although widely indicated as a key factor to detect. Further research is needed to improve and optimize interventions, specifically adapted to special age-related conditions (such as Alzheimer’s disease), which are particularly difficult to explore and burdening for public health.


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1. Department of Geriatrics. National Institute of Medical Sciences and Nutrition “Salvador Zubiran”, Mexico City, Mexico; 2. Centre de Recherche INSERM, U897, Bordeaux, F-33076 France; Université Victor Segalen Bordeaux 2, Bordeaux, F-33076 France; 3. Division of Graduate Studies and Research, Faculty of Dentistry, National Autonomous University of Mexico, Mexico; 4. Department of Public Health, Faculty of Medicine, National Autonomous University of Mexico, Mexico; 5. National Institute of Geriatrics, Periférico Sur No. 2767, Col. San Jerónimo Lídice, Del. Magdalena Contreras, Mexico City, C.P. 10200, Mexico. Tel. (+52) 55 5573 8686

Corresponding Author: Roberto Carlos Castrejón-Pérez. National Institute of Geriatrics, Periférico Sur No. 2767, Col. San Jerónimo Lídice, Del. Magdalena Contreras, Mexico City, C.P. 10200, Mexico. Tel. (+52) 55 5573 8686. Email: roberto.castrejon@salud.gob.mx

J Frailty Aging 2013;2(2):68-76
Published online February 12, 2016, http://dx.doi.org/10.14283/jfa.2013.11


Background: “Frailty” has emerged as a condition associated with an increased risk of functional decline among the elderly, which may be differentiated from aging, disability, and co-morbidities. Objective: The Mexican Study of Nutritional and Psychosocial Markers of Frailty among Community-Dwelling Elderly has emerged to help answer many questions about frailty among the older adults. This report presents the design of the study and baseline data of its participants. Design: The “Coyoacan cohort” is a longitudinal observational study developed in Mexico City. Participants: A representative sample of 1,294 non-institutionalized men and women aged 70 years and older were randomly recruited to undergo a face-to-face interview and a comprehensive geriatric assessment (including clinical evaluations and blood samples) between 2008 and 2009.  Measurements: Data collected included socio-demographic and economic characteristics, medical history, oral health, drug use, cognitive function and mood, nutritional status, physical performance and functional status, physical activity, quality of life, social networks, and biological data. Frailty was defined as the presence of ≥3 of the following components: slowness, poor muscle strength, low physical activity, exhaustion and unintentional weight loss. Results: A total of 1,124 participants completed the interview. The mean age was 79.5 ± 7.1 years, and 55.9% were female. Nine hundred and forty-five subjects completed the clinical evaluation and 743 blood samples were collected. The baseline prevalence of frailty was 14.1%. Conclusions: Understanding the medical, biological, and environmental factors that contribute to the phenomenon of frailty is the goal of the current research in the field.

Key words: Frailty, Latin American, nutrition, psychological markers.



Aging populations confront health-care systems around the world with an increasing prevalence of functional decline and chronic diseases. Such a state of affairs has prompted significant efforts to untangle the relationships between aging, chronic diseases, and disability. Furthermore, disability could be considered as a public-health problem, especially in the elderly population. For the coming decades, disability is expected to considerably affect greater proportions of the elderly population, which further aggravates the problem. In this context, avoiding disability could mean an improvement in health-related outcomes, such as quality of life or mortality, and it would certainly help prevent the near collapse of health-care systems.

To prevent disability, health-care systems need to be able to identify subjects at risk for adverse health-related outcomes. In an attempt to do so, the concept of “frailty” has emerged. Nevertheless, despite a large volume of research dedicated to this issue, it is still an evolving topic. However, a consensus regarding the definition of frailty does exist. Frailty is understood in geriatrics as a state of increased vulnerability and loss of adaptability to stressful situations; the more widely known set of diagnostic criteria have been able to predict an increased risk of disability, hospitalization, institutionalization, mortality and falls (1-3).

However, if frailty must work as an intermediate state between health and disability for health-care purposes, it is necessary to rely on a clear operational definition. The phenotype described by Fried et al. a decade ago has been widely used in research (1), and it has proven to be highly predictive of adverse health related-outcomes in different populations around the world (1, 4-10). The Fried et al. phenotype relies on the presence of three or more of the following five items: unintentional weight loss, exhaustion, poor muscle strength, slowness, and self-reported low physical activity. This phenotype has its origin in the Cardiovascular Health Study (CHS) and provides us with a theoretical framework that includes causes of frailty, manifestations and outcomes. Nevertheless, frailty has also been shown to possess non-physical correlates, including psychological and social aspects, as well as comorbidity-related associations that are not taken into account in this phenotype and that have been a recurrent concern through previous work (3, 11). One could argue that Fried and colleagues’s phenotype lacks items on economic vulnerability, cognitive state and clinical entities and that the prevalence of these covariates may differ according to the population studied.

Therefore, studying “frailty” across a large spectrum of population-based studies and describing its possible relationships with medical, social, psychological, and biological markers may contribute to the better understanding of  this syndrome and prevent its adverse health-related outcomes (2).

In Latin America, information on frailty is virtually non-existent. However, two North American studies have shown an unusually high prevalence of this syndrome among Hispanic community-dwelling elderly patients (ranging from 20% to 42.6%) compared to Caucasian populations (6, 12). This finding could be explained by several mechanisms, such as those related to social and cultural differences and differences in co-morbidities.

The “Mexican Study of Nutritional and Psychosocial Markers of Frailty among Community-Dwelling Elderly” (The Coyoacan Cohort study) was designed to fill the gap regarding the study of the syndrome of frailty in the Mexican population. Its main objective was to describe the nutritional, psychosocial and medical determinants of frailty among the Mexican elderly population and to better delineate the role of frailty in the development of disability. The ultimate aim of this approach was to identify those in the elderly population who are at higher risk of adverse health-related outcomes so that they can be targeted for preventive and treatment strategies. Therefore, the aim of the present work was to present and describe the design of “The Coyoacan Cohort”.

Materials and Methods


“The Coyoacan Cohort” was an observational and longitudinal study developed and conducted by the Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán” (INCMNSZ) in Mexico City. The study was performed in collaboration with the National Institute of Public Health (INSP), the Health Ministry of Mexico City, and the National Autonomous University of Mexico. The study protocol and the informed consent format were approved by the Ethical Committees of the INCMNSZ and the INSP.

Coyoacan is one of the 16 districts in Mexico City. According to the 2005 National Population Survey, its inhabitants represented 3.6% (628,063 inhabitants) of the total population of Mexico City. This district was selected for recruitment purposes because of logistical convenience reasons.

To be eligible for recruitment, participants had to meet the following criteria: age 70 years and older; established residence in Coyoacan; not being institutionalized; and being registered at the “Food Support, Medical Care and Free Drugs Program” (FMDP), which is a government program that includes 95% of the community-dwelling elderly (≥ 70 years of age) in Mexico City.

Recruitment was drawn from a random sample procedure stratified by age and gender. The sampling frame was constituted by the FMDP database, and the sample unit was the individual (one per house). A sample of 1,294 was calculated to ensure a sample size that could estimate a prevalence of frailty of at least 14% among participants with an α= 5% and ß= 20%.

Eligible subjects were first contacted and invited to participate in the study by their community health-promoter (usually a well-known person to them) who was also responsible for introducing them to the research team and interviewers. Each participant signed an informed consent and agreed to participate. Each participant was free to refuse a specific part of the examination (e.g.: blood sampling); partial refusal did not constitute exclusion from the study. Twenty-four participants in the eligible sample from the governmental database could not be reached; among those contacted, the acceptance rate was 86.9% (37 persons refused to participate, 18 were already dead, and the rest had other causes of non-participation). A total of 1,124 persons were interviewed.

The baseline visits were completed between April 30th 2008 and May 14th 2008. A second phase was devoted to the clinical assessment and the collection of biological samples (between June 2008 and July 2009). One-year and three-year follow-up visits were performed.

Data collection

The baseline data collection was performed in two phases, a face-to-face interview (at the participant’s home) and a medical examination (at a local health center). During the first collection phase, data were obtained using a computerized standardized questionnaire with a validated program developed by the INSP. This phase was completed by 28 pollsters who were trained in standardized survey interviewing. A wide range of information was obtained during this collection phase, including self-reported data regarding socio-demographic characteristics, general health-related information, medication use, oral health (self-reported and clinically evaluated) and mental health (Table 1). To ensure the validity of this self-reported information, the global cognition status was assessed at the beginning of the interview with the Mini Mental State Examination (MMSE) (13). If the participant exhibited a very low score (MMSE < 16), the pollster tried to find a cognitively unimpaired proxy, preferably the participant’s primary caregiver or someone living in the same place. The average interview length was 90 minutes, and all 1,124 subjects completed this phase. Prior to the formal data collection, a pilot study was conducted to test this tool among a similar population group in a simulated situation.

In the second collection phase, the participants were evaluated by an interdisciplinary and standardized team that included a physician, a nutritionist, a nurse and a dentist (Table 1). Each participant underwent a comprehensive geriatric assessment that included physical performance tests, cognitive tests, nutrition, medical and dental assessment, as well as anthropometric measurements. The mean evaluation length was approximately 2.5 hours; this evaluation was completed by 945 participants (84% of the final sample).

Finally, after an overnight fast, a 20 ml blood sample was collected by trained nurses who were familiar with geriatric patients. Blood samples from the 743 subjects were collected between February 2009 and July 2009. The biological samples were processed and stored according to the recommendations of the International Society for Biological and Environmental Repositories.

Frailty assessment

Frailty was defined according to the construct previously validated in the CHS (1). The five components of the original phenotype were considered and defined as follows:
–     Unintentional weight loss was a self-reported weight loss of 5 kg or more in the last 6 months;
–     Poor endurance was a self-report of exhaustion and was assessed by two questions from the Center for Epidemiologic Studies-Depression scale (CES-D) (14) (“I felt that everything I did was an effort” and “I could not get going”). Participants were asked “How often, in the last week, did you feel this way?”, and the answer was quoted as follows: 0 (rarely or none of the time), 1 (some or a little of the time), 2 (a moderate amount of the time), or 3 (most of the time). Participants answering “2” (a moderate amount of the time) or “3” (most of the time) to either of these two questions were considered as frail for this criterion;
–     Low physical activity was defined based on The Physical Activity Scale for the Elderly questionnaire (PASE) (15). Participants who scored in the lowest quintile after adjusting for gender were categorized as frail for the low physical activity criterion;
–     Slowness was defined as a walking speed measured by the timed 4-m walking test, after adjusting for gender and height. The lowest quintile was used to identify those with a slowed gait speed. In the case of missing data in this test, participants who answered “yes” or “can’t do” to any of the following questions could also be considered as frail for this criterion: Because of a health problem, “do you have difficulty walking one block?” or “do you have difficulty with climbing several flights of stairs without resting?”
–     Poor muscle strength was identified by means of a handgrip strength measure. Those in the lowest quintile, after adjusting for gender and body mass, index were considered as frail participants for this criterion. In the case of missing data in this test, participants who answered “yes” to the question, “Because of a health problem, do you have difficulty with lifting or carrying objects weighting over 5 kg, like a heavy bag of groceries?”, were also categorized as frail for this criterion.

As proposed by Fried et al., the subjects were categorized as “frail” if they fulfilled three or more frailty criteria among the five possible. They were categorized as “pre-frail” if they fulfilled one or two criteria, and they were defined as “non-frail” if they fulfilled no criteria.

Other variables assessed

Heath-related variables. Patient pathological history was self-reported and included items on childhood diseases, fertility (among women), morbidity, complications and treatments (pharmacologic adherence, prescriptions, etc.) of several entities (Table 1), as well as an estimation of body mass index changes though life (using an eight-figure, gender-specific silhouette drawing) (16).

The presence of nonspecific symptoms was also analyzed and included several organ systems (cardiovascular, respiratory, and muscle-skeletal). Geriatric syndromes were thoroughly assessed with a focus on frailty (previously described assessment) and disability. Disability was evaluated through several scales, including Activities of Daily Living (ADL) (17) and Instrumental Activities of Daily Living (IADL) (18). Physical activity was assessed with the PASE questionnaire (15).

Information on preventive medicine (vaccination, cancer screening) and dependence to toxic substances (tobacco use, alcohol abuse) and general indexes of health, such as quality of life were also included (Table 1).

Anthropometric measurements. The standing height and weight were measured with the subjects dressed in indoor clothing without shoes using a portable stadiometer Seca™ 214 and a personal scale Seca™ 803. The height and weight were recorded, and the body mass index was calculated (Table 1). The knee height was measured at baseline using a Seca 207 while the subject was seated. The mid-upper-arm, waist, hip, calf, and head circumferences were determined with a non-stretch fiberglass measuring tape on the left side of the body while the participant was standing erect. The maximal voluntary handgrip strength of the left hand was measured (in kilograms) with participants standing using a Baseline™ Smedley spring-type hand dynamometer. All measurements were made according to standard procedures (19).

Biological data and bank of blood specimen. A 20-ml blood sample was obtained between 08:00-09:00 after an overnight fast by trained nurses. The biological specimens were processed and stored in the INCMNSZ.  The coding, sampling, processing, and storage of blood samples were according to the recommendations of the International Society for Biological and Environmental Repositories (20) (Table 1).

Table 1 Data collected at the baseline in the Coyoacan cohort study


Statistical analysis

The current report presents a descriptive analysis of the entire sample, stratified by age and gender. Variables were described using arithmetic mean and standard deviation (SD) or frequency and proportion where appropriate.


The baseline sample consists of 1,124 participants who completed the first phase questionnaire. The mean age was 79.5 (SD 7.1) ranging from 70 to 104 years, and 55.9% were female. Nine hundred and forty-five subjects completed the comprehensive geriatric assessment, and 743 blood samples were collected.

Women had fewer years of education (6.1, SD 4.9 years) than men (7.7, SD 4.9 years). The older age group had a lower education, independently of gender. Regarding marital status, most of the men had a partner and most of the women were widows at all age groups. Among the participants, 88% reported that they have had a previous work occupation, and 37% were currently receiving income from a retirement payment or fund; 58.3% self-reported a fair economic situation. Religion was considered a very important concern for 71.7% (Table 2) of the participants. Twenty percent of the participants reported experiencing some form of abuse or mistreatment.

Table 2 Socio-demographic characteristics of participants by age group and gender at baseline

For what concerns health-related data, 78% of the participants perceived their health as “good” or “fair”. Current smoking and alcohol consumptions decreased with increasing age. Hypertension was the most prevalent self-reported chronic disease, followed by hypercholesterolemia and diabetes. Nine percent of the participants had fallen at least three times in the last year, and 45.3% reported a fear of falling. Among the participants, 11% were hospitalized at least once during  the previous year and 56.1% consumed three or more medications. Influenza vaccination was more frequent than anti-pneumococcal vaccination (68.9%) (Table 3).

Table 3 Health Status of participants in the Coyoacan cohort study at baseline

MNA = Mini-Nutritional Assessment; IADL = Instrumental activities of daily living; ADL = Activities of daily living; SPPB = Short physical performance battery; PASE = Physical Activity Scale for the Elderly; SD = Standard deviation.

In our Study, 20.8% of the participants were edentulous. Of these participants, 68.4% used partial or complete removable dental prostheses and 46.7% had used dental services 12 months prior to the interview.

The mean body mass index decreased with increasing age. The same tendency was observed for calf circumference. According to the MNA, 3.4% of the participants were malnourished. The proportion of malnutrition was higher among older subjects for both genders and was more frequent in women than in men (Table 3).

Difficulty in at least one ADL and IADL was observed in 26.1% and 46.3% of the subjects, respectively. Such difficulty increased with age in both men and women. The physical performance score decreased with increasing age strata and worse punctuations were found in women than in men, as shown by the handgrip strength, the Timed Up & Go test, and the Short Physical Performance Battery. Physical activity decreased in the oldest subgroups (Table 3).

The mean MMSE score was 21.4 (SD 5.3) for all the participants, and the score decreased with increasing age. Twenty percent of participants had depressive symptoms according to the GDS, and 6.3% had an abnormal score for the anxiety items of the Hospital Anxiety and Depression (HAD) scale. The mean score on the self-esteem Rosenberg scale was 20.8 (SD 4.1). The score on the physical component of the SF-36 Health Survey questionnaire decreased with age, whereas the mental component score was almost unchanged across the different age groups (Table 4).

Table 4 Mental health and health-related quality of life at baseline of participants in the Coyoacan cohort study

MMSE = Mini-Mental State Examination; HAD = Hospital anxiety and depression scale; CES-D = Center for Epidemiologic Studies Depression Scale; GDS = Geriatric Depression Scale; SD = Standard deviation.

With de exception of weight loss, the prevalence of frailty components was higher for women than men. The prevalence of frailty was 14.1% (Table 5).

Table 5 Frailty status and its components, at baseline, stratified by sex and age of participants in the Coyoacan cohort study

*Frail: presence of three or more components; pre-frail: presence of 1 or 2 components; Non-frail: absence of frailty components.


The Coyoacan cohort is the first epidemiological longitudinal study exploring frailty in Mexico. For this reason, this study may provide important information on the prevalence of frailty in the Mexican elderly population and its potential nutritional and psychosocial determinants. In addition, this data set contains information on oral health, which has not been explored in this population and seems to be associated with the syndrome of frailty (21, 22). This holistic approach may help identify elderly people with a higher risk of adverse health-related outcomes and identify preventive intervention strategies that could be targeted.

Previous work has shown that social characteristics, such as early life conditions, the economic situation and employment over life, are significantly and consistently associated with frailty (1, 4-10). These findings suggest the prevalence of frailty, overall and within each of its components and characteristics, may vary across different cultures and populations. Unfortunately, some populations around the world have barely been studied; this is the case for Latin American countries, including Mexico. A few years ago, Alvarado et al. published a paper focusing on the social and health conditions in Latin American populations nested on the SABE project, and concluded that the prevalence of frailty was higher in Latin America compared to other developed countries (6). The authors also found that childhood hunger, poor health, poor socio-economic conditions, adult educational level,  employment type (non-white collar occupation), and current social conditions were associated with higher odds of frailty. However, these findings have not been replicated in this population. Moreover, the other components of frailty, to our knowledge, have not been studied in Latin American subjects. These include the frequency of the components and the chronology in which they appear.

As a whole, the data collected for this study allow us to explore the previously stated phenomenon from a number of perspectives, including psychological, medical, biological, social, and nutritional perspectives. Some of the data collected are particularly valuable due to the very scarce data available in the literature. For example, the Coyoacan cohort contains a variety of information regarding drug prescriptions, that have been described to affect muscle strength and physical function, which affects frailty as described by Fried et al. (23). Therefore, besides frailty issues, this study also represents a valuable source of information on more general health issues of the Mexican elderly population.

It should also be mentioned that the cohort consists of a population that is representative of the urban elderly population in Mexico, particularly of the community-dwelling population and includes a large number of subjects.

Nonetheless, this study has several limitations that need to be underlined. The first limitation is the measures used to assess frailty. Although the consequences of frailty are well agreed upon, various operational definitions are available. The data were incomplete for a minor proportion of the study population from the medical assessment, which included the objective measures of frailty. However, this limitation was overcome with the utilization of alternative definitions that have proved to be representative of frailty in several other studies, and, according to our opinion, did not put the value of the cohort at stake. The second limitation is the loss of information between the first data collection phase and the second phase completed by the medical team. However, the loss of information can be explained by the influenza epidemic that Mexico City experienced in 2009. The strict measures implemented by the Ministry of Health have probably discouraged the elderly to participate (24).

In conclusion, understanding the medical, biological, and environmental factors contributing to the phenomenon of frailty is the goal of current research in the field. Elderly persons who are frail would benefit from complex, multidisciplinary care instead of the usual care, which explains why efforts must be directed to detect this clinical state before irreversible disability or other adverse outcomes appear (44). More analyses of the Coyoacan cohort database are required to ascertain whether intervention programs targeting frail subjects may delay or even reverse disability and loss of autonomy in a population with different needs and characteristics to those of European or North American countries.

Acknowledgements: The Mexican Study of Nutritional and Psychosocial Markers of Frailty among Community-Dwelling Elderly was funded by the National Council for Science and Technology of Mexico (CONACyT) (SALUD-2006-C01- 45075).

Conflict of Interest: None of the authors declare a conflict of interest.


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Musculoskeletal Research Programme, Institute of Medical Sciences, University of Aberdeen

Corresponding Author: Dr Stuart Gray, Room 106 Health Sciences Building, Musculoskeletal Research Programme, Institute of Medical Sciences, Foresterhill, University of Aberdeen, AB25 2ZD, United Kingdom, e-mail: s.r.gray@abdn.ac.uk

J Frailty Aging 2013;2(4):211-216
Published online February 11, 2016, http://dx.doi.org/10.14283/jfa.2013.31


We are living in an “ageing society” meaning that there will be an increase in the incidence of age related health problems. One issue consistently observed in ageing is for muscle mass and strength to be reduced, a condition termed sarcopenia. The consequences of these changes are numerous and include a reduction in quality of life and an increased risk of falls. The mechanisms underlying sarcopenia remain to be elucidated but include an anabolic resistance to both nutrients and exercise and so the search for strategies to overcome this resistance is of great importance. There are several nutritional strategies purported to be useful in the treatment of sarcopenia and in recent years the n-3 PUFAs found in fish oil have been of increasing interest. This review will discuss the main nutritional interventions used in the treatment of sarcopenia with a focus on fish oils.

Key words: Aging, fish oil, exercise, nutrition, sarcopenia.



With increasing age comes an increase in the incidence of several clinical problems, such as arthritis, cardiovascular disease, hypertension and Alzheimer’s disease. One other major change that occurs in older people is the dramatic alteration in body composition, with a loss of lean mass (i.e. skeletal muscle) and an increase in fat mass. The loss in skeletal muscle (approximately 0.5-2.0% per year) was termed sarcopenia by Michael Rosenberg in 1989, (1) and occurs even in healthy active older individuals. In order to carry out numerous activities of daily living, such as stepping on to a bus or rising from a chair, one requires a degree of muscle strength and function which is often not present in sarcopenia. We therefore see substantial impairments in muscle strength and functional abilities which can reduce older adults’ quality of life and increase the risk of falls and subsequent hospitalisation (2). Reporting incidences of sarcopenia can be problematic due to issues in diagnosis (3) but an incidence of 13-24% in those aged 50-70 years and up to 50% in those over 80yrs of age are regularly quoted (4). Similar complexities are present when trying to quantify the economic cost of sarcopenia but this was estimated to be $18.5 billion in the United States in the year 2000 (5). Taken together it is clear why we need to further our understanding of the mechanisms underlying sarcopenia and any potential treatments available.

At present the precise mechanisms which underlie sarcopenia remain to be elucidated and we will briefly introduce some of these potential mechanisms, which are also summarised in Figure 1. It has been shown that approximately 5% of the variance in leg lean mass can be attributed to genetic causes (6) making this an unlikely cause of sarcopenia. Although this does not mean there are no important genes in sarcopenia it does show why the majority of research has focussed on investigating other more environmental causes.

Early research found that older muscle was characterised by denervation and loss of alpha-motoneurons occurs, which is associated with a small increase in the size of motor units, suggesting that there may be a degree of reinnervation occurring (7). Several recent studies have confirmed these findings making it likely motoneuron loss and incomplete fibre reinnervation by the remaining motoneurons has a role in the aetiology of sarcopenia. On top of these alterations in motoneurons we also see differences in the satellite cell numbers and function with age. A twofold reduction in satellite cell numbers has been found in old muscle and these cells also have a reduced proliferative capacity in response to muscle injury, leading to dysfunctional regeneration (8).

It has been known for some time that there are dramatic changes in endocrine function that occur with age and that these effects are gender dependent (9). The most marked of these changes relate to the pancreas, with a decrease in insulin production and peripheral insulin sensitivity, and the thyroid, with a reduction in plasma thyroxine and increase in thyrotropin stimulating hormone. In males a gradual change in hypothalamic-pituitary-gonadal axis function has been observed, characterised by a decrease in circulating free and total testosterone termed the “andropause” (10). As testosterone has well documented anabolic effects it is possible that this andropause may play a role in sarcopenia in males. While some studies have found beneficial effects of testosterone treatment (11), these effects are generally small and probably do not outweigh the potential side effects of treatment. When females reach the menopause there is a decrease in ovarian oestrogen production which has been associated with the decline in muscle strength, although in general the research doesn’t fully support this assertion (12).

It has been previously demonstrated that sarcopenia is characterised by a state of chronic low grade inflammation, i.e. a two-four fold elevation in circulating inflammatory cytokines. Such elevations in cytokines (i.e. IL-6 and TNF-α) have been found to correlate with functional disability and may be involved in sarcopenia through effects on pathways controlling protein metabolism (13, 14). Circulating levels of myostatin have also recently been suggested to play a role in sarcopenia as it is known to inhibit muscle growth resulting in atrophy (15). Until recently it was not possible to test circulating myostatins role in human ageing as it could not be reliably measured. Now, using a well validated assay, it has been shown that neither myostatin nor its related factors differed in sarcopenic men (compared to young and old non-sarcopenic men) (16).

As mentioned elevation in inflammatory cytokines are purported to be involved in sarcopenia through interference with protein metabolism. Whether these cytokines are the causative factor in age related changes in protein metabolism remains to be established but, regardless of cause, there are clear differences in protein metabolism (i.e. muscle protein synthesis (MPS) and muscle protein breakdown (MPB)) when young and old are compared. Several researchers have shown that under resting/fasting conditions MPS is not different between young and old, but in response to an anabolic stimuli (in this case amino acids) the increase in MPS is attenuated in older people (17). Furthermore when MPB is measured there is no difference in basal MPB with age but there is a blunted inhibition of MPB in response to insulin (18). These alterations in protein metabolism demonstrate what has been termed an “anabolic resistance”, to stimuli such as amino acids and insulin, in older muscle, with these changes having a deleterious effect on the ability of older muscle to increase or maintain its size appropriately.

Another anabolic stimuli, for muscle, is resistance exercise and it is known that such exercise can have beneficial effects on muscle mass and function even in those over the age of 90 years (19). However older people do not adapt as well as younger to resistance exercise and this anabolic resistance to exercise has been demonstrated after a single exercise session, with a reduction in MPS (20), and after more prolonged resistance exercise training, with an attenuated increase in muscle volume (as measured by MRI) and strength (21).

Nutritional Interventions in Sarcopenia

Several nutritional strategies have been proposed to have potential benefits in the treatment of sarcopenia and we will discuss two main interventions now and briefly summarise the available evidence before discussing the potential for fish oil to be efficacious in sarcopenia. There are several nutritional interventions, such as antioxidants, that we will not cover here and readers are directed to one of the many excellent reviews in this area (e.g. 22).

Figure 1 Potential mechanisms underlying, and the consequences of, sarcopenia


MPS: Muscle protein synthesis, MPB: Muscle protein breakdown.



The importance of protein in ageing is highlighted by the findings that elderly individuals, in general, consume less than the recommendations for daily protein intake (0.8 g/kg/day) (23). Evidence of this importance is seen in epidemiological studies where older individuals with the highest daily protein intake lost around 40% less lean mass compared to those with the lowest daily protein intake (24). As several researchers have shown that protein, particularly leucine, has an anabolic effect in muscle this lack of protein may contribute to sarcopenia (25; 26). However, as mentioned earlier older muscle does not respond with the same magnitude of increase in MPS as younger muscle (17, 27) but an anabolic effect, although diminished, is still observed and this has lead some researchers to suggest that older adults should consume between 1.0 and 1.5 g of protein/kg/day (22), although the long term benefits of this remain to be fully elucidated.

As resistance exercise is also known to have anabolic effects it is hypothesised that combined exercise and protein regimens may maximise protein metabolism in older adults. In young adults protein supplementation has been found to increase MPS, inhibit MPB, and result in an overall positive protein balance after exercise (e.g. 28), supporting the assertion that increases in dietary protein are able to maximise adaptations to resistance exercise. In older individuals the results are more ambiguous. In healthy older adults it has recently been demonstrated that an extra 15 g/day protein has no beneficial effects on the adaptations in muscle size and strength after 24 weeks of resistance exercise (29). However, in a separate study it was found that a total of 30 g/day protein resulted in an increase in lean mass after 24 weeks of resistance exercise training, with no such change in the placebo group, in frail individuals (30). On a more cautionary note there were no improvements in muscle function in the protein group, over those observed in the placebo group. The ultimate goal of any such intervention is to improve muscle function and so whether protein supplementation will be useful in sarcopenia remains to be established.

Vitamin D

Vitamin D is found in dietary sources such as oily fish (e.g. salmon and sardines), eggs, fortified fat spreads and breakfast cereals but the majority is produced in the skin, from cholesterol, when exposed to sufficient sunlight. Amongst the many clinical consequences of vitamin D deficiency muscle weakness is consistently observed (e.g. 31). More recent research has also shown that low serum 25(OH)D is associated with a more rapid loss of muscle mass and function (32). In recent years research investigating the role of vitamin D in muscle has increased after it was shown that Vitamin D receptors are found in skeletal muscle tissue and that their expression decreases with age (33). Several randomised control trials have now been carried out investigating whether Vitamin D supplementation can improve muscle function in older adults, which in general demonstrate a benefit of supplementation on muscle function and the risk of falls (34). When combined with exercise there are only a handful of studies investigating Vitamin D supplementation, generally finding no beneficial effects on adaptations (34).

Introduction to n-3 polyunsaturated fatty acids

Polyunsaturated fatty acids (PUFAs) are vital components in the cell membranes of all cells in the bodies. The two main fatty acids found in fish oil, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which can also be produced in the body from alpha-linolenic acid although the magnitude of conversion is extremely low (35), are known to have beneficial effects in many diseases and conditions, such as cardiovascular disease (36), atherosclerosis (37), diabetes (38) and neurological conditions such as Alzheimer’s disease and dementia (39). Many of these benefits are purported to be due to the ability of these fatty acids to modulate immune function and inflammation. The normal Western diet contains relatively high quantities of the n-6 PUFA arachidonic acid (AA) and low levels of EPA/DHA. This means that the phospholipids of inflammatory cells (i.e. monocytes, neutrophils and lymphocytes) from human blood contain 10-20% AA, 0.5-1.0% EPA and 2-4% DHA (for review see 40). Any increase in EPA/DHA consumption can alter this phospholipid composition, via an incorporation into the cell membranes, increasing EPA/DHA content with a concomitant reduction in AA content (41).
This change in cell phospholipid composition has many effects on cell function and inflammatory processes. These changes include alterations to the membranes physical properties (i.e. fluidity), cell signalling (i.e. alterations in the function of membrane bound receptors or intracellular signal transduction) and the patterns of lipid mediators released, on which we will now focus. As AA is normally the predominant fatty acid found in cell membranes it is also most frequent source for the production of eicosanoids and it is generally the case that AA derived eicosanoids, such as prostaglandin E2 will act in a pro-inflammatory manner, although this may be an oversimplification (42). With an increase in EPA content in cell membranes it is used more frequently as a substrate for eicosanoid production, resulting in the production of a different series of lipid mediators (e.g. prostaglandin E3). In general, although not always (43), these EPA derived mediators are less potent in their pro-inflammatory actions (44) and may therefore have beneficial anti-inflammatory effects. A further beneficial effect may also be derived from the metabolism of EPA and DHA to produce products called resolvins and protectins which have anti-inflammatory effects and important roles in the resolution of inflammation (45).

It can be seen, therefore, that EPA/DHA may be useful in the treatment of conditions with an inflammatory component and may therefore be useful in the treatment of benefits of intrathecal baclofen cost sarcopenia, which we know also has an inflammatory component.

Fish oils and sarcopenia

Research in 2008 in the Hertfordshire study found that in ~3000 people grip strength increased for every extra portion of oily fish that an individual consumed per week, indicating that the n-3 PUFAs found in oily fish may be an important determinant of muscle strength in older adults (46). Furthermore in early animal studies it was shown that EPA/DHA can stimulate protein anabolism, insulin-induced glucose metabolism and potentially attenuate the age related loss of lean mass (47-49). While these studies were indicative of a beneficial effect of EPA/DHA, in muscle wasting human intervention studies were needed to demonstrate this.

In early human studies of cancer cachexia (i.e., the involuntary weight loss due to depletion of both muscle mass and adipose tissue seen in cancer patients), there are some studies indicating an anabolic/protective effect in skeletal muscle (50, 51). However, in a recent systematic review on fish oil consumption and muscle loss in advanced cancer the final conclusion was that positive effects were detected only in smaller trials with poor methodology while in larger randomized controlled trials significant benefits were not observed (52). As the underlying mechanisms responsible for the loss of muscle mass in cancer and sarcopenia are quite different, studies in older adults were required to uncover whether EPA/DHA can have any beneficial effects in sarcopenia.

This was therefore recently addressed in a study by Smith et al. (53) who measured MPS and anabolic signalling pathways before and after 8 weeks of n-3 PUFA supplementation (1.86 g/d EPA and 1.50 g/d DHA) in healthy older adults. Supplementation with EPA/DHA increased MPS, in part, through activation of the p70s6k signalling pathway, findings this groups also replicated in young and middle-aged groups (54). Specifically EPA/DHA supplementation increased MPS during a hyperaminoacidaemic hyperinsulinaemic clamp but did not change the basal protein synthesis or circulating markers of inflammation. On a cautionary note this study shows an effect of EPA/DHA stimulation on an acute measure such as clamp stimulated MPS but it remains to be determined whether there will be any long term effects on muscle mass and/or function.

There has also been some research to determine whether the combination of EPA/DHA and resistance exercise can maximise protein anabolism and reduce the burden of sarcopenia. Rodacki et al. (55) investigated the effect of 90 days of resistance exercise on the neuromuscular system (muscular activation and force) in older women, with or without EPA/DHA supplementation (2 g/d) and they reported that resistance exercise undertaken with EPA/DHA supplementation enhanced the adaptations in neuromuscular function and functional capacity (chair-rising test) in older women. In this study no placebo supplements were given and so caution may be wise when interpreting these results. In support of their findings, however, within our lab we have found similar results, with the increase in strength and functional abilities after resistance exercise approximately two-fold higher in those taking EPA/DHA as opposed to placebo, with no change in circulating markers of inflammation (unpublished results). These are the first studies showing the importance of EPA/DHA supplementation in enhancing the adaptive responses to resistance exercise and may indicate the potential for fish oils to be useful in the treatment of sarcopenia.

What also remains to be established are the mechanisms through which EPA/DHA may help in the maintenance of muscle mass with age, a few of which are indicated in Figure 2. The original hypothesis in this field was that EPA/DHA would improve the maintenance of muscle mass due to their anti-inflammatory properties (42). This may not, however, be the case as, in our recent work and that of Smith et al (53), improvements in MPS and muscle function were observed without changes in circulating cytokines. Other potential mechanisms underlying the effects of EPA/DHA on muscle include improvements in insulin sensitivity. Enhanced insulin sensitivity may increase the insulin-derived inhibition of MPB and also increase the delivery of amino acids to muscle via increases in blood flow (56). A further mechanism may relate to the increase in EPA/DHA incorporated into the skeletal muscle membranes altering signal transduction pathways involved in protein metabolism. Indeed in our recent work (49), in aging rats, we found that EPA/DHA supplementation increased the activation of the signal transduction enzyme family phosphoinositide 3-kinases (PI 3-kinases) which catalyses the conversion of phosphatidylinositol (4,5)-biphosphate (PIP2) to phosphatidylinositol (3,4,5)-triphosphate (PIP3) in the inner leaflet of the plasma membrane (57). Due to the increase in EPA/DHA in the muscle cell membrane we hypothesize that there was an increase in PIP3 potency (58), which resulted in the observed increase in the downstream p70s6k, a crucial protein in the maintenance and increase of muscle mass (59). There is very little experimental evidence, at present, to support or refute these mechanisms and so further well mechanistic experiments are needed in this area.

Figure 2 Potential mechanisms underlying the beneficial effects of fish oil in sarcopenia

EPA/DHA: eicosapentaenoic acid/docosahexaenoic acid, PIP3: phosphatidylinositol (3,4,5)-triphosphate, PGE2: prostaglandin E2, AA: amino acids, MPB: Muscle protein breakdown, MPS: Muscle protein synthesis.


The potential benefits of the n-3 PUFAs found in fish oil in the treatment of sarcopenia could be of great benefit to older adults and the burden on health care systems, particularly within this “ageing society”. The review has highlighted the current data available investigating n-3 PUFAs and the loss of muscle associated with age and it appears that there may be beneficial physiological effects of n-3 PUFAs in sarcopenia, but whether these translate into clinically significant benefits and the underlying mechanisms behind any effect remain to be discovered.

Conflict of Interests: The authors have no conflict of interests to declare.   


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