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S. Pakzad1, P. Bourque1, N. Fallah2


1. École de psychologie, Université de Moncton, 18 Antonine-Maillet Avenue, Moncton, New Brunswick, Canada E1A 3E9; 2. Division of Neurology, Department of Medicine, University of British Columbia, UBC Hospital, Vancouver, British Columbia, Canada, V6T 2B5
Corresponding author: Dr. Sarah Pakzad, Ph.D. 531, Taillon, Université de Moncton, Moncton, NB E1A 3E9, Tel: (506) 858-4245; Fax: (506) 852-3125, Email: sarah.pakzad@umoncton.ca

J Frailty Aging 2021;10(1)31-37
Published online April 24, 2020, http://dx.doi.org/10.14283/jfa.2020.21



Background: Given the important association between cardiovascular disease and cognitive decline, and their significant implications on frailty status, the contribution of neurocognitive frailty measure helping with the assessment of patient outcomes is dearly needed. Objectives: The present study examines the prognostic value of the Neurocognitive Frailty Index (NFI) in the elderly with cardiovascular disease. Design: Secondary analysis of the Canadian Study of Health and Aging (CSHA) dataset was used for prediction of 5-year cognitive changes. Setting: Community and institutional sample. Participants: Canadians aged 65 and over [Mean age: 80.4 years (SD=6.9; Range of 66-100)]. Measurement: Neurocognitive Frailty Index (NFI) and Modified Mini-Mental State (3MS) scores for cognitive functioning of all subjects at follow-up and mortality rate were measured. Results: The NFI mean score was 9.63 (SD = 6.04) and ranged from 0 to 33. This study demonstrated that the NFI was significantly associated with cognitive changes for subjects with heart disease and this correlation was a stronger predictor than age. Conclusion: The clinical relevance of this study is that our result supports the prognostic utility of the NFI tool in treatment planning for those with modifiable cardiovascular disease risk factors in the development of dementia.

Key words: Frailty, cardiovascular disease, mortality, cognitive deficits.



While cardiovascular diseases (CVD) are associated to cognitive decline and dementia in the elderly (1, 2), much consideration has been given to the contribution of cardiovascular diseases to cognitive impairment (3). Today, CVD are established to strongly relate to cognitive impairment and dementia in the elderly (1). Interestingly, CVD can lead to Alzheimer’s disease (AD) and vascular dementia (1).
Coronary heart disease, hypertension, left ventricular hypertrophy, among others, are risk factors for heart failure (4). The association between CVD and cognitive impairment in older people is multifactorial and involves common risk factors such as atherosclerosis, hypertension, and diabetes mellitus (1). A large European longitudinal study (5) has shown that modifiable cardiovascular risk factors (CRFs) including diabetes, smoking, hypertension, and low physical activities are associated with lower cognitive test scores. A review (3)additionally underscored that traditional CRFs (e.g., high blood pressure, hyperglycemia, insulin resistance, diabetes mellitus, hyperlipidemia, cigarette smoking, heart failure, stroke, and atrial fibrillation) are also risk factors for AD. However, it is unclear whether those modifiable behaviors significantly partake in lowering the risk of developing cognitive impairment (6). As highlighted, under promising strategies for prevention of dementia (7), these risk factors need to be identified in vulnerable individuals so that appropriate preventative interventions can be undertaken to reduce the risk of developing cognitive impairment. Indeed, evidence shows that healthy cardiovascular lifestyles mitigate cognitive disorders through modifiable CVD risk factors (8).
Growing evidence shows that vascular/cerebrovascular pathology can accelerate the progression of preclinical AD and speed disease evolution (9). For instance, one study (10) note that those who have CVD are at a greater risk of developing mild cognitive impairment (MCI), and another review and synthesis, examining the pathophysiologic relationship between AD, cerebrovascular disease, and cardiovascular risk [9] showed that effectively controlling vascular risk factors serves to delay onset of AD and that the management of CRFs is associated with a reduced risk of dementia.
Frailty, an age-related fragility associated with multidimensional loss of energy, physical ability, cognition and general health reserves (11), has been positively associated with cognitive function in individuals with CVD (12) and is more prevalent in patients with CVD (13). To this end, it is noteworthy of underscoring the significance of the bidirectional relationship between frailty and both cognitive and functional reserve, and implication in neuropathology and brain reserve, motor signs of aging and the reversibility of cognitive frailty (14, 15). Hence, we need to underscore the importance of a frailty index as a prognostic tool to evaluate the risk of cognitive impairment and death.
Among those high-risk groups, individuals with cardiovascular health conditions are more vulnerable to the development of dementia (16). Thus, index of ‘frailty’ needs to be considered in cognitive decline as a modifying and prognostic variable since morbidity and mortality are related to CVD (17). The association between CVD and cognitive impairment have long been established using statistical approach whereas multidimensional approach is lagging behind that could confirm this association.
Recently, the Neurocognitive Frailty Index (NFI) was developed to create a more comprehensive measure of frailty in the elderly (11), and evidence supports its higher accuracy compared to other frailty outcome measures (11). Although evidence shows that the NFI provides higher accuracy to predict outcomes, the contribution of neurocognitive frailty indicator in improving patient prognostic in high-risk groups has not been established, that subsequently warrants further studies (11).
Given the link between CVD and dementia, there is an urgent need to examine these associations (18). Thus, a scientific group of experts convened by the Alzheimer’s Association, with scientific input from the National Institute of Neurological Disorders and Stroke and the National Heart, Lung and Blood Institute from the National Institutes of Health (2) to help in developing more accurate outcome measures and clinical criteria to examine the effects of controllable vascular risk factors for developing AD. Furthermore, the risk of developing MCI is higher for people who have CVD (19) or CRFs (20). This knowledge is important because this can help in examining ways to prevent or better manage the precursors of dementia. As noted earlier (11), studies to further evaluate the contribution of neurocognitive frailty indicators in improving prediction of patient outcomes are needed. Hence, this study evaluates the prognostic value of the NFI in the elderly with cardiovascular disease.


Materials and Methods

Study population

Participants’ data were drawn from the Canadian Study of Health and Aging (CSHA) (21), a study of the epidemiology of dementia in Canada. Planned in 1989, as a national longitudinal study, the CSHA followed over 10,000 elderly Canadians (from 36 communities and institutions) over a ten-year period from 1991 to 2001 and has collected a wide range of information on changing health status over time [http://www.csha.ca]. The CSHA was conducted in three waves but in this study only data from waves 1 and 2 were used: CSHA-1 (1991 to 1992) and CSHA-2 (1996 to 1997) for prediction of 5-year cognitive changes. The current analysis focused on the 997 participants who received a consensus diagnosis of no-cognitive impairment (NCI) or cognitive impairment but not dementia (CIND) on CSHA-1. Samples included those who completed neuropsychological tests at CSHA-1 and received a clinical diagnostic assessment at CSHA-1 and CSHA-2 (n=1228). The CIND category included individuals whose level of cognitive impairment was measured to be greater than the NCI group but less than the dementia group. Of the original 997 participants, 299 individuals had died in five years of follow-up, but these were included in our participant sample.


NFI was defined as a combined score of 42 physical and cognitive elements. The physical elements included 4 domains, activities of daily living, instrumental activities of daily living, general health and comorbidities. The cognitive elements include 8 domains, short-term memory, long-term memory, verbal abstract thinking, judgment, aphasia, apraxia, agnosia, and constructional difficulty, as they were available in the dataset. Global cognitive score was obtained via the 3MS (22) at baseline and at follow-up, and mortality rate was used as an outcome measure.

Coding of Neurocognitive Frailty Index

The initial NFI (11) was determined from 42 variables, here we used 41 variables, as CVD was our chief focus. The NFI variables, which include both physical and cognitive elements, are described in our earlier study (11). Thirty-three physical components were selected from the CSHA-1 dataset at baseline (1991). Binary variables were recoded, using the ‘’0’’ to indicate the absence of the deficit, and ‘’1’’ to indicate the presence of a deficit. Particularly in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) items, data converted as score “1” for “can’t do at all” or “with some help” denoting needed help, and we assigned score “0” for “without any help”. The Self-Rated Health Question, «How is your health these days? Very good, pretty good, not too good, poor, very poor» was rated between ”0” and “1”. Each lower self-rating of health was coded to represent a larger deficit «very good=0», «pretty good=0.25», «good=0.5», «poor=0.75» and «very poor=1». For variables with dichotomous response (general health), data coded into a score between “0” where no deficit is present and “1” where the deficit is present. Overall, physical component scores varied between zero “0” to thirty-three “33”.
Similarly, we recoded the cognitive component but by recognizing an ordinal scale of variables. A simple recoding was done for the eight elements: short-term memory, long-term memory, verbal abstract thinking, judgment, aphasia, apraxia, agnosia, and constructional difficulty. A score was assigned a “0” for response “None”, “1” for “Questionable”, “2” for “Mild”, “3” for “Moderate” and “4” for “Severe”. There were eight measures in this part which means theoretically that a person could have a score between zero “0” to thirty-two “32”. Each of the 41 components added together, therefore there was a total score of NFI ranging between zero “0” and sixty-five “66”. The physical and cognitive variables description and cut points for the NFI are presented in a previous article (11).

Statistical Analysis

The parametric tests were used for normally distributed variables and nonparametric tests were used for the not normally distributed. Before applying regression models univariate tests (including chi-square, t-test, ANOVA, correlation) were used for data exploration.
Categorical NFI scores were created using classification and regression trees, using the applied Chi-squared Automatic Interaction Detector (CHAID) model. This modeling accounts for the binomial distributions in response variable. Validation of the multivariate analyses was tested with a 10-fold cross-validation; CHAID identifies ‘nodes’, or persons subgroups, that are most homogeneous with regards to probability of death. These nodes were then applied to Cox regression for further investigation. In addition to bivariate tests and decision tree, two separate Cox regression models were applied to the data to estimate chance of death in 5 years of follow-up. Cox regression was used in two formats of NFI (one for NFI as a continuous and another as a categorical variable). A p-value of 0.05 or lower was considered statistically significant. Statistical analyzes were performed using SPSS (version 23) (23) and R×64 (version 3.1) (24).



This was a secondary analysis of the Canadian Study of Health and Aging (CSHA) dataset (21, 25). The NFI mean was 9.63 (SD=6.04) and ranged from 0 to 33. Distribution of NFI was slightly skewed to the right (skewness=0.95, SE=0.08; Kurtosis= 0.90, SE=0.16). At baseline, the mean age of sample was 80.4 years (SD=6.9; Range: 66-100). There was a clear positive, yet small association between age and NFI at baseline (rho=0.27, p<0.001, 2-tailed).

Table 1
Demographic characteristics and cognitive status of the sample as a function of NFI score categories

Note: 3MS: Modified Mini-Mental State; CSHA: Canadian Study of Health and Aging; NFI: Neurocognitive Frailty Index. Data showing mean (standard deviation) for all variables, and total number (percentage) for sex category. * NFI score defined as number of deficits for example “0-3” means zero to three deficits


Presented in Table 1, demographic characteristics and cognitive data were described using means and standard deviation for age, 3MS at baseline (CSHA1) and at follow-up (CSHA2), years of education, and male sex percentage, as a function of NFI scores breakdown. Here, NFI result was grouped into 4 categories based on decision trees results (Figure 1).

Figure 1
Decision trees to determine optimal stratification of NFI for predicting mortality


Tables 2 presents the results of two separate regression models predicting outcomes 3MS for participants with and without CVD. In both models, NFI is significantly related to 3MS at follow-up; however, NFI is stronger predictor in people with CVD than without. In the multiple linear regression analysis, adjusted for age, sex, education, and 3MS at baseline, the NFI was correlated to 3MS at follow-up (regression coefficient=-0.6), and age (p<0.05). Every additional deficit used to calculate the NFI was associated with an increased chance of cognitive decline. However, this association was stronger in people with CVD. NFI (regression coefficient=-0.79) was significantly associated with cognitive status at follow-up for people with CVD and this was stronger than age (p<0.05).

Table 2
Associations between NFI and Cognitive status (3MS at follow-up) using Multiple Linear regression for participants without CVD in Panel A and with CVD in Panel B

*Note: B: Beta coefficient; SE: standard Error.


Although in both groups (with and without heart problems), NFI is related to the outcome (without:R2=0.08, rho=0.32, p<0.01; with:R2=0.02, rho=0.141, p<0.05), the association between NFI and 3MS indicates different variability to predict the outcome in people with and without CVD (Table 2).
The CHAID decision tree analysis was used to determine optimal stratification groups to best identify association between NFI and mortality. Result indicated that the NFI could be grouped into four categories. Group one (category) is for the people who have an NFI score of less than 2.75 deficits and mortality rate of 7.6%. The second and third groups have a mortality rate of 21.8% and 36.3%, respectively, indicating a three to five times more mortality rate, compared to baseline. The highest risk was for people with an NFI score of more than 17.8 and mortality rate of 58%. We used this result for making 4 levels of NFI to predict mortality.
Cox regression was applied for modeling probability of mortality when NFI was considered as a continuous variable. NFI was significantly related to mortality probability with an odds ratio of 1.08. This indicates participants with a higher NFI score have a higher mortality probability. In this model, age, gender and CVD were significant (Table 3, Panel A). In the proportional hazard ratio analysis, accounting for age, gender and education as confounding variables, the value of NFI (as categorical) was more highly correlated to survival than age. Every additional deficit used to calculate the NFI was associated with an increased risk. By using results of the decision tree grouping, the NFI was significantly associated with mortality for the second group of participants in comparison to the fittest people at baseline with an NFI score between 4 and 8 deficits (HR = 2.63, 95% confidence interval (CI) 1.22-5.71; p-value = 0.014). Participants with 18+ NFI deficits showed greater risk [HR = 9.172, (95% CI 4.12-20.41)], indicating they have 9 times more chance of death compared to baseline (Table 3, Panel B). Moreover, mortality probability was greater in people with CVD (HR = 1.28) than without (Figure 2, Panel A). Although all levels of NFI were highly correlated to mortality (Figure 2, Panel B), the Kaplan-Meier curve shows increasing level of NFI, suggesting dose-response effect in relation to survival.

Table 3
Association between NFI and mortality probability using Cox proportional hazard model (Panel A). Association between NFI and mortality using Cox proportional hazard model (Panel B)

*Note: B: Beta coefficient; CI: Confidence Interval; HR: Hazard Ratio; NFI (≤3 deficits) is baseline. SE: standard Error.


Figure 2
Kaplan-Meier curves for the proportional survival of people with and without CVD (Panel A) also with various levels of NFI (Panel B). In Panel B, node 1 indicate NFI < 4 and node 2 for NFI between 4 and 8, node 3 related to NFI score between 9 and 17, and finally node 4 is related to NFI more than 18 score



Our study aimed to predict cognitive status and 5-year survival rate in participants with CVD in the Canadian Study of Health and Aging (21) dataset using a deficit accumulation approach. The NFI, as a measure of frailty, was used to examine the association between frailty and cognition as measured by 3MS at baseline and 5 years later in individuals with and without CVD.
We have shown a significant and positive association between age and NFI, indicating that neurocognitive frailty increases with age. Additionally, the magnitude of the association for the NFI was larger than age. Furthermore, higher NFI score was associated with higher probability of mortality. Mortality was 28% greater in individuals with than without CVD.
Our study results support previous findings from various scientific groups. For example, the Alzheimer’s Association that noted CVD are present in most cases with AD, without suggesting that all individuals with AD have heart problems (9). Similarly, the Cardiovascular Health Study Cognition Study (26, 27) found subjects with mild cognitive deficits have CVD. Using the results of models from this study, baseline cognition and age were significant predictors, yet a more comprehensive prediction can be achieved by including frailty measures. Additionally, our results concur with studies examining vascular risk factors involving cognitive decline. A longitudinal study (28) found the associations between smoking and long-term blood pressure with the risk of cognitive decline. However, a review (3) underlined the need for more sensitive neuropsychological measures. Our study shows that the NFI serves as a more effective prognostic tool to evaluate the risk of developing dementia. Findings from an earlier study (29)underscore the association between vascular risk factors and cognitive function at midlife, and their different impacting pathways on brain mechanisms. Although we have shown that the NFI was highly correlated to mortality, a dose-response effect in relation to survival is noted with increasing levels of NFI. Thus, the identification of CRFs using the NFI might serve to identify those at higher risk for dementia so to provide preventative interventions.
Perhaps the most important implication of our result is the integration of physical frailty and cognitive elements as a neurocognitive frailty measure such as the NFI to better understand the relationship between CVD and cognitive decline. Indeed, in line with our findings, others (12) have noted the importance of including the frailty status in the clinical process of evaluating risk factors for dementia to optimize the treatment plan. The present study provides validation-evidence for the use of NFI with elderly individuals at risk for cognitive deficit with co-existing CVD to enhance the prognostic value of cardiovascular risk factors.

Noteworthy that our study is not without limitation, and we can underscore our use of cognitive screening tool as a possible issue. In our study the cognitive status of all subjects was assessed through the Modified Mini-Mental State test; although this test offers increased validity over the MMSE, it remains a screening tool for the evaluation of cognitive impairment.
We have established an association between CVD and cognitive deficits; however, further investigation is needed to clarify the extent and types of modifiable CVD risk factors (e.g., diabetes and hypertension) and their consequence on the cognitive functions. As previously highlighted (30) to identify individuals needing specific intervention plans and to maximize outcomes based on risk profiles using the NFI future research is warranted. Since the NFI risk profile could be useful in treatment planning, and that CVD are related to cognitive deficits as noted by several colleagues (31), maintaining vessel elasticity through physical exercise could be beneficial to those at risk of developing cognitive deficits.


Conflict of Interest: None to declare
Sponsors’ role: None to declare
Disclosure: The research team would like to acknowledge the access given to the Canadian Study of Health and Aging (CSHA) dataset by Dr Kenneth Rockwood, which was much appreciated.
Ethical Standards: The authors declare that the experiments and data collected comply with the current laws of the country in which they were performed.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



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A.J. Jor’dan1,2, M.E. Jacob1,3,4,5, E. Leritz1,2, J.F. Bean1,3,5


1. New England Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA, USA; 2. Department of Psychiatry, and 3. Department of Physical Medicine & Rehabilitation, Harvard Medical School, Boston, MA, USA; 4. School of Public Health, Boston University, Boston, MA, USA; 5. Spaulding Rehabilitation Hospital, Boston, MA, USA.
Corresponding author: Azizah J. Jor’dan, PhD, Instructor of Psychiatry, New England GRECC, 150 South Huntington Ave, Boston, MA 02130, Tel: 857-364-6339  Fax: 857-364-5368, E-mail:  azizahjordan@hsl.harvard.edu

J Frailty Aging 2019;in press
Published online November 26, 2019, http://dx.doi.org/10.14283/jfa.2019.42



Background: The mobility of older adults is limited by the compounding effects of vascular health conditions, or vascular risk burden. However, little is known about the role of neuromuscular attributes among those in which vascular risk burden contributes to mobility limitations. Objective: We investigated (1) the relationship between the absence/presence of type 2 diabetes, hypertension, and/or obesity and mobility measures and neuromuscular attributes, and (2) whether the association between vascular risk burden and mobility is mediated by lower limb neuromuscular attributes. Design: Cross-sectional analysis of baseline data from 430 older adults within the Boston RISE Study. Measurements: Measures of mobility were the Short Physical Performance Battery, habitual gait speed, and functional mobility as measured by the Late Life Function Instrument. We also evaluated lower limb neuromuscular attributes, namely leg strength, leg velocity, trunk extensor muscle endurance, knee and ankle range of motion, and sensory loss. Results: Participants self-reported the presence of None (n=93), One (n=179), Two (n=114), or Three (n=44) of the following conditions: diabetes, hypertension, and obesity. Multivariable regression models indicated that those with a greater vascular risk burden had worse performance on the Short Physical Performance Battery (p=0.01), slower gait speed (p=0.0003) and lower Basic and Advanced Late Life Function Instrument scores (p<0.003). These associations were independent of multiple covariates. Vascular risk burden was also found to be negatively associated with leg strength (p=0.0002) and knee flexion range of motion (p<0.0001) and an associated non-significant trend was observed with leg velocity (p=0.06). In addition, the association between vascular risk burden and mobility outcomes were found to be partially mediated by leg strength, leg velocity, and knee flexion range of motion. Conclusions:  Among older adults with vascular risk burden and mobility problems, neuromuscular impairments in attributes such as leg strength, leg velocity, and knee range of motion may need to be treatment priorities.

Key words: Vascular risk, mobility, neuromuscular attributes, cardiovascular disease.



Mobility is important in the lives of older adults as it helps to maintain independence. The decline in mobility has been separately linked to vascular risk factors such as obesity, hyperglycemia, hyperlipidemia, and hypertension in older adults (1-2). For example, diabetes and hypertension are independently linked to slow gait speed, a metric of mobility and a vital indicator of mortality (1). An increasing number of vascular risk components (i.e., hypertension, high C-reactive protein, obesity, diabetes, and smoking) has been shown to linearly increase the likelihood of gait speed decline, and disability among older adults (2). In parallel, other studies have independently linked poor mobility outcomes to lower limb neuromuscular attributes, such as leg strength and velocity, trunk muscle endurance, and range of motion (ROM) among older adults (3). There is also evidence suggesting that Metabolic Syndrome (i.e., the presence of three or more of the following: hypertension, hyperglycemia, hyperlipidemia, and abdominal obesity), is associated with lower limb weakness among men (4). However, it is not known if the compounding of vascular risk, or vascular risk burden (VRB), affects other lower limb neuromuscular attributes.
Vascular risk factors rarely occur in isolation (5), and it is not evident how the co-existence of multiple vascular conditions, or the VRB, influences mobility in older adults. Additionally, the mechanisms by which VRB influence mobility are not clear.
Therefore, the aim of this study was to determine the link between VRB and 1) mobility as assessed by the Short Physical Performance Battery (SPPB), habitual gait speed, and the Late Life Function Instrument (LLFI); and 2) lower limb neuromuscular attributes (e.g., leg strength and leg velocity) and to determine whether the potential associations between VRB and mobility measures were mediated by lower limb neuromuscular attributes. We hypothesized that older adults with a greater VRB will have poorer mobility performance and be deficient in lower limb neuromuscular attributes. We further hypothesized that lower limb neuromuscular factors that are associated with VRB status, will mediate the association between VRB and mobility.



Study Design

This cross-sectional study used the data from the 430 community-dwelling primary care patients who participated in the Boston Rehabilitative Impairment Study of the Elderly (Boston RISE) and were examined at baseline between December 2009 and January 2012. This prospective cohort study aimed to identify modifiable neuromuscular impairments that are associated with mobility decline in older adults. The design and methodology for this study has been previously published in detail (6). Briefly, to be included, individuals had to be ≥65 years of age, able to understand and communicate in English, and have difficulty or task modification with walking half a mile or climbing one flight of stairs. The exclusion criteria were: 1) presence of a terminal disease (e.g. receiving hospice services, metastatic cancer), 2) major surgery or myocardial infarction in the last 6 months, 3) planned major surgery, 4) planned move from the area within 2 years, 5) Mini-Mental Status Exam (MMSE) score <18, or 6) major medical problems that interfered with safe and successful testing (e.g., history hip replacement with recurrent dislocation, uncontrolled hypertension, loss of lower extremity). All participants provided written informed consent as approved by the Institutional Review Board of Spaulding Rehabilitation Hospital.

Data Collection

Socio-demographic characteristics (age, sex, race, education) and disease status (type 2 diabetes and hypertension) were self-reported using a comorbidity questionnaire that consisted of 17 frequent general practice conditions for which patients were receiving treatment and/or had limitations on their activities (7). Study staff measured height and weight using standardized techniques, calculated BMI, and classified participants as obese if their BMI was 30 or higher. Depression severity was assessed using the Patient Health Questionnaire-9 (PHQ-9), and cognitive ability was assessed using the MMSE.

Mobility Measures

Mobility measures consisted of the following: 1) SPPB, a reliable and valid screening test used to characterize lower extremity function (8). The test includes measures of progressive standing balance, habitual walking speed, and chair stand ability; 2) gait speed, a sensitive measure for assessing functional status and overall health. This measure was collected during two 4-meter usual pace walking trials from the SPPB; and 3) the Basic and Advanced Lower Limb Function subdomains of the LLFI, a validated questionnaire that captures the participant’s perceived ability to do specific aspects of their daily routines (function) and is a component of the larger Late Life Function and Disability Instrument (9). Given the aim of the current study, which focused on mobility outcomes, we evaluated scores on the Basic and Advanced lower extremity function subdomains of the LLFI.

Neuromuscular Measures

Leg strength was measured by determining the 1 repetition maximum (1RM) for each leg with a Keiser pneumatic leg press machine using a previously published protocol (10). Peak leg strength was defined as the maximum value observed on either side. Peak power was defined as the highest recorded power out of five trials performed with each leg at 40% and 70% of the 1RM. Peak leg velocity was calculated by dividing peak power by the graphically displayed force at peak power recorded during the testing. Trunk extensor muscle endurance (11) was measured as the length of time in seconds (up to 150 seconds) that the participant was able to maintain their trunk in a neutral position within the sagittal plane in line with their pelvis and legs with arms crossed against the chest. Knee and ankle ROM were measured with a goniometer (12). Ankle ROM was considered to be impaired if there was inability to dorsiflex past 90° or plantar flex past 110° in either leg. Foot sensation was measured over the dorsum of the big toes using the Semmes-Weinstein monofilament test. Both impaired ankle ROM and sensory loss were dichotomized as being present or absent.

Statistical Analysis

Participants were classified into VRB groups (i.e., None, One, Two or Three condition[s]) based on the self-reported absence or presence of type 2 diabetes, hypertension, and obesity. VRB groups were characterized using means and standard deviations for continuous variables and frequency (percentage) for categorical data. The Kruskal-Wallis test was used to determine if the group demographics, PHQ-9, MMSE, mobility measures, and the neuromuscular attributes differed by VRB.
Multivariable regression analyses were used to examine the association between VRB (predictor) and 1) mobility measures (i.e., SPPB total score, gait speed, Basic and Advanced LLFI), and 2) neuromuscular attributes (leg strength, leg velocity, trunk extensor muscle endurance, knee flexion ROM, ankle ROM, and sensory loss). Separate models were developed for each outcome and were adjusted for the pre-specified confounders of age, race, sex, PHQ-9, and MMSE scores. Mediation analysis using regression was subsequently conducted to determine whether the potential associations between VRB and mobility measures were in part mediated by neuromuscular attributes. Attributes that demonstrated 1) a significant association with VRB or 2) a clinically meaningful group difference, as determined by previous literature, were introduced into mobility measure regression models. A mediator effect was defined as a reduction in the estimate by >10% when the neuromuscular attribute was included in the regression model (13, 14). Significance levels were set to p<0.05. All analyses were performed using JMP software (SAS Institute, Cary, NC).



Group demographics, mobility, and neuromuscular characteristics are listed in Table 1. Those with no vascular conditions represented 22% of the cohort, 42% had one vascular condition, 26% had two vascular conditions, and 10% had three vascular conditions. The groups were similar in sex, race, education, and MMSE scores. Gait speed, Basic and Advanced LLFI, lower leg strength, and knee flexion ROM differed by VRB group.
Regression analysis determined that VRB status was linked to performance on the SPPB (p=0.01), gait speed (p=0.0003), and lower Basic (p=0.003) and Advanced LLFI scores (p<0.0001) (Table 1), such that those with a greater VRB had worse performance on these mobility measures, after adjusting for age, race, sex, PHQ-9, and MMSE scores (Table 3, Model 1).

Table 1 Complete population demographic and clinical characteristics

Table 1
Complete population demographic and clinical characteristics

Data = Mean (SD); p < 0.05 Kruskal-Wallis; None = no vascular risk symptoms; One = one vascular risk symptom; Two = 2 vascular risk symptoms/obesity; Three = 3 vascular risk symptoms/obesity; PHQ – Patient Health Questionnaire; SPPB – Short Physical Performance Battery; LLFI – Late Life Function Instrument; ROM – range of motion; Normal mean/range:  SPPB (for average age 75 years) – women, 7.79 ± 3.22 and men, 9.03 ± 3.12; Gait speed (for range age 70 -79 years) – women, 1.13 m/s  and men, 1.26 m/s; MMSE – score of ≥24 denotes normal cognition; PHQ-9 Depression – score of 0-4 mean «none to minimal» depression.


As shown in Table 2, those with a greater VRB were shown to have higher impairment in lower leg strength (p=0.0002) and knee flexion ROM (p<0.0001), independent of age, race, sex, PHQ-9, and MMSE scores. Of note, although statistically significant, the variation specifically explained by the knee flexion model is weak. Leg velocity did not achieve statistical significance (p=0.06), however, the observed differences between groups (0.09m/s) exceeded what is defined as a clinically meaningful threshold in the literature (15) and leg velocity was therefore included in subsequent mediation analyses. Trunk extension muscle endurance, ankle ROM, or sensory loss did not differ by VRB status (Table 2).

Table 2 Separate Univariate and Multivariable Regression Models Evaluating the Association between Vascular Risk Burden (VRB) and Measures of Mobility and Neuromuscular Attributes

Table 2
Separate Univariate and Multivariable Regression Models Evaluating the Association between Vascular Risk Burden (VRB) and Measures of Mobility and Neuromuscular Attributes

Model Statisticsa – Univariate R2 and p-value for the overall regression model; Model Statisticsb – Multivariable R2 and p-value for the overall regression model; VRB – Data = Least Square Means ± SE;  (*) p < 0.05 for VRB indicator variable in the multivariable regression model; (†) clinically meaningful difference; None = no vascular risk symptoms; One = one vascular risk symptom; Two = 2 vascular risk symptoms/obesity; Three = 3 vascular risk symptoms/obesity; SPPB – Short Physical Performance Battery; LLFI – Late Life Function Instrument; ROM – range of motion; All models adjusted for age, race, sex, PHQ-9, and Mini-mental Exam Scores; c Logistic Regression


Regression analysis was used to test whether VRB status predicted mobility (i.e., SPPB, gait speed, Basic and Advanced LLFI) when significant and clinically meaningful lower limb neuromuscular attributes (i.e., leg strength, knee flexion, and leg velocity) were included as potential mediators. As shown in Table 3, when lower leg strength was included in the subsequent models (Model 2), VRB was no longer associated with SPPB (p=0.49) or Basic LLFI (p=0.07). When leg strength was added to the model, VRB coefficients were attenuated by 20-84% for SPPB, 33-73% for gait speed, 33-56% for Basic LLFI, and 41-43% for Advanced LLFI models.
When knee flexion ROM was added to the model (Model 3), VRB was no longer associated with SPPB (p=0.10). Additionally, VRB coefficients were attenuated by 26-38% for SPPB, 28-70% for gait speed, 24-40% for Basic LLFI, and 29-243% for Advanced LLFI models.
Model 4 shows results for the inclusion of leg velocity in the model. VRB was no longer associated with SPPB (p=0.05) and VRB coefficients were attenuated by 17-40% for SPPB and 20-67% for gait speed. Additionally, the coefficient for VRB category ONE was attenuated by 14% when leg velocity was added to the Advanced LLFI model (Table 3).

Table 3 Separate Multivariable Regression Models Examining Mediation of the Association between Vascular Risk Burden (VRB) and Mobility by Leg Strength, Knee Flexion ROM, or Leg Velocity

Table 3
Separate Multivariable Regression Models Examining Mediation of the Association between Vascular Risk Burden (VRB) and Mobility by Leg Strength, Knee Flexion ROM, or Leg Velocity

Model 1 adjusted for age, race, sex, PHQ-9, and MMSE Scores; Model 2 = Model 1 + leg strength; Model 3 = Model 1 + knee flexion range of motion (ROM); Model 4 = Model 1 + leg velocity; (†) = >10 % change in estimate from Model representing significant mediation.



This analysis found that VRB status was negatively associated with mobility, such that greater vascular burden is linked to greater limitation on both performance-based and patient-reported measures of mobility. Additionally, VRB status was associated with higher impairment in leg strength, leg velocity, and knee ROM. We also found that these same neuromuscular attributes partially mediated the association between VRB status and mobility measures, independent of age, race, sex, PHQ-9, and MMSE scores.
Multi-morbidity of metabolic conditions puts individuals at a higher risk for developing mobility limitations (2). Our study demonstrated that VRB, as defined by the absence or presence of type 2 diabetes, hypertension, and obesity, was associated with worse performance on the SPPB, slower gait speed, and lower scores on the Basic and Advanced LLFI. Our findings are consistent with other research addressing mobility and metabolic syndrome as well as vascular risk factors for coronary heart disease and cardiovascular disease. For example, a study of metabolic syndrome by Penninx et al observed that older adults with metabolic syndrome had a 50% higher chance of developing future mobility limitations (16).
Another important finding from this study is that VRB status was linked to certain neuromuscular attributes. Individuals with the greatest VRB (i.e., three) had lower leg strength, compared to those with less burden. Additionally, there was a weak, yet significant association between VRB and knee flexion, such that those with two or three vascular risks had a lower degree of knee flexion ROM, compared to those with no or one vascular risk. Post hoc comparison of VRB status and leg velocity determined that those with three vascular risks manifested significantly slower leg velocity, compared to those with one vascular risk. Trunk muscle endurance may also have clinical relevance (11), but this association was difficult to detect given the small size of the Three VRB group and diversity of the vascular risks within each group. However, other studies have shown independent links between type 2 diabetes, obesity, or hypertension and neuromuscular consequences (17-19). For example, studies report that regardless of neuropathy, diabetic patients as well as those who are obese have decreased lower extremity muscle strength and power measures (20-22). One longitudinal study found a 50% accelerated skeletal muscle decline in those with type 2 diabetes, compared to those without, while another study reported that obesity was linked to a four-fold higher incidence of developing knee osteoarthritis, which impairs knee ROM (20, 23). These findings are supported by our results which suggest that lower leg strength, leg velocity, and knee flexion ROM is linked to the burden of underlying vascular disturbances.
The capacity to perform independent functioning depends upon the integrity of the varied body systems that underlie performance. In the current study, leg strength, leg velocity, and knee ROM were all linked to mobility skills among primary care patients with varying VRB status. The exact linkage between vascular/metabolic conditions and the manifestation of mobility limitations is not known. However, this association may be due to the vascular damage both centrally in the brain and in the periphery caused by type 2 diabetes, hypertension, and obesity. These vascular changes can manifest as reduced arterial wall compliance, arterial thickness and stiffening, endothelial dysfunction, and impaired relaxing and contracting mechanisms which leads to peripheral vascular resistance (24-26). These impairments also support studies associating vascular risk conditions with brain atrophy and reduced blood flow in the brain (27, 28). Moreover, our findings build upon prior research focusing upon mobility problems in general and among individuals with peripheral artery disease (3, 19, 29). When taken together, vascular risk conditions cause inadequate supply of blood and oxygen levels to meet the metabolic demands associated with exercises as well as other physical activities.
Higher vascular risk burden is often associated with aging, lower cognitive functioning, higher BMI and mortality rates (30-32). Interestingly, MMSE scores were not able to differentiate VRB within this cohort. This could be due to the variation in group sample size, group differences in age, and/or the protective factor of education. However, BMI was indeed able to differentiate the VRB groups, such that those with the highest VRB were those with the highest BMI. Within the current study, the group with the highest VRB was significantly younger than those with no VRB. This is could be due to the smaller sample of Three VRB compared to the None VRB groups. Additionally, the None group consisted of a wider age range (66 – 91) compared to the Three VRB group (65 – 83). We speculate that those with no VRB are living longer and ultimately are able to be studied, while those with higher VRBs, whom are at higher risk for mortality, are not.
Our study has several limitations. Due to its cross-sectional design, we could not determine causality. This was not a population-based sample, and data collection was confined to a specific single healthcare system within the study area, thus our findings may not be generalizable to a more racially or ethnically diverse population in a different region of the country. The vascular conditions utilized were self-reported and obesity was clinically assessed, as opposed to the more specific metabolic syndrome condition of abdominal obesity. Additionally, data on hyperlipidemia was not collected in the parent study (Boston RISE). Lastly, neuropathy, specific to type 2 diabetes, was not included in the data collection; however, sensory loss was assessed as a variable of interest for all participants. The strengths of this study include extensive data collection of neuromuscular impairment status and severity in a large sample of older adult primary care patients with mobility difficulty. Outcomes measures in the current reflect a cohort that may be frail (e.g., low SPPB scores). Although we did not specifically measure frailty, our findings are likely to apply to frail older adults as well. Furthermore, all study measures were well established, valid, and reliable among older adults (6) and all of the neuromuscular attributes we studied are clinically relevant and prioritized within rehabilitative care.
In conclusion, our results suggest that greater VRB is associated with impaired neuromuscular integrity, specifically leg strength and velocity and knee ROM, which is associated with the manifestation of mobility limitation in older adults. These findings underscore the relevance of peripheral neuromuscular attributes that underlie mobility limitations among patients with VRB and, therefore, should be considered in rehabilitative treatment.

Funding: This work is supported by the National Institutes of Health (R01 grant number AG032052-03 and K24 grant number HD070966-01) and the National Center for Research Resources (grant number UL1RR025758-01). Manuscript preparation was supported by the National Institutes of Health (K99 grant number AG051766) awarded to A.J.J.

Acknowledgements: 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.
Conflict of interest:  The authors report no disclosures.
Ethical standards: The procedures followed were in accordance with the ethical standards of the Institutional Review Board and with the Helsinki Declaration of 1975.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.



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