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TOWARDS A LARGE-SCALE ASSESSMENT OF THE RELATIONSHIP BETWEEN BIOLOGICAL AND CHRONOLOGICAL AGING: THE INSPIRE MOUSE COHORT

 

Y. Santin1, S. Lopez2, I. Ader5, S. Andrieu3,4, N. Blanchard6, A. Carrière5, L. Casteilla5, B. Cousin5, N. Davezac2, P. De Souto Barreto3,4, C. Dray1, N. Fazilleau6, D. Gonzalez-Dunia6, P. Gourdy1, S. Guyonnet3,4, N. Jabrane-Ferrat6, O. Kunduzova1, F. Lezoualc’h1, R. Liblau6, L.O. Martinez1, C. Moro1, P. Payoux7, L. Pénicaud5, V. Planat-Bénard5, C. Rampon2, Y. Rolland3,4, J.-P. Schanstra1, F. Sierra9, P. Valet1, A. Varin5, N. Vergnolle8, B. Vellas3,4, J. Viña10, B.P. Guiard2, A. Parini1

 

1. Institut des Maladies Métaboliques et Cardiovasculaires, Inserm, Université Paul Sabatier, UMR 1048 – I2MC, Toulouse, France; 2. Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse, France; 3. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), Toulouse, France; 4. UPS/Inserm UMR1027, University of Toulouse III, Toulouse, France; 5. STROMALab, CNRS ERL 5311, Etablissement Français du Sang-Occitanie (EFS), National Veterinary School of Toulouse (ENVT), Inserm U1031, University Toulouse III Paul Sabatier, Toulouse, France; 6. Centre de Physiopathologie Toulouse Purpan, INSERM/CNRS/UPS UMR 1043, University of Toulouse III, Toulouse, France; 7. ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
8. IRSD, Université de Toulouse, INSERM, INRA, ENVT, UPS, U1220, CHU Purpan, CS60039, 31024, Toulouse, France; 9. Division of Aging Biology, National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, Maryland, USA; 10. Freshage Research Group-Dept. Physiology-University of Valencia, CIBERFES, INCLIVA, Valencia, Spain.
Corresponding author: Professor Angelo Parini, Institut des Maladies Métaboliques et Cardiovasculaires, Inserm/Université Paul Sabatier UMR 1048 – I2MC, 1 avenue Jean Poulhès BP 84225 31432 Toulouse Cedex 4 – France, Phone: (+33)561325601, e-mail: angelo.parini@inserm.fr

J Frailty Aging 2020;in press
Published online August 7, 2020, http://dx.doi.org/10.14283/jfa.2020.43

 


Abstract

Aging is the major risk factor for the development of chronic diseases. After decades of research focused on extending lifespan, current efforts seek primarily to promote healthy aging. Recent advances suggest that biological processes linked to aging are more reliable than chronological age to account for an individual’s functional status, i.e. frail or robust. It is becoming increasingly apparent that biological aging may be detectable as a progressive loss of resilience much earlier than the appearance of clinical signs of frailty. In this context, the INSPIRE program was built to identify the mechanisms of accelerated aging and the early biological signs predicting frailty and pathological aging. To address this issue, we designed a cohort of outbred Swiss mice (1576 male and female mice) in which we will continuously monitor spontaneous and voluntary physical activity from 6 to 24 months of age under either normal or high fat/high sucrose diet. At different age points (6, 12, 18, 24 months), multiorgan functional phenotyping will be carried out to identify early signs of organ dysfunction and generate a large biological fluids/feces/organs biobank (100,000 samples). A comprehensive correlation between functional and biological phenotypes will be assessed to determine: 1) the early signs of biological aging and their relationship with chronological age; 2) the role of dietary and exercise interventions on accelerating or decelerating the rate of biological aging; and 3) novel targets for the promotion of healthy aging. All the functional and omics data, as well as the biobank generated in the framework of the INSPIRE cohort will be available to the aging scientific community. The present article describes the scientific background and the strategies employed for the design of the INSPIRE Mouse cohort.

Key words: INSPIRE program, biological aging, mouse cohort, frailty, biomarkers.


 

Introduction

The improvement of medical care and living conditions has increased life expectancy. Although being a progress per se, the extension of life expectancy is associated with an elevated risk of all types of chronic diseases as well as the decline in intrinsic capacities (1). Research on the basic “biology of aging” aims to increase life expectancy and to improve the quality of life. In this context, geroscience has emerged as a new interdisciplinary field seeking to define the biological underpinnings of aging that lie at the crossroads of age-dependent biology, chronic disease and health (2, 3). The geroscience hypothesis postulates that, since aging plays a major role in most chronic diseases, addressing aging physiology will reduce or delay the onset of multiple age-associated defects.
Frailty is a clinical state of increased vulnerability resulting from aging-related decline in function and reserve across multiple physiological systems, that carries an increased risk for poor health outcomes including falls, incident disability, hospitalization, and mortality (4). Even though frailty is an age-associated syndrome, the idea that it is not a normal and inevitable part of aging is growing. Hence, frailty can be conceptualized as a result of accelerated biological aging (5), and elucidating its etiology is thus critical for its prevention and/or treatment. Therefore, there is a pressing need to discover markers to differentiate biological age from chronological age and to identify individuals at higher risk of developing chronic diseases, ultimately with the goal to propose pharmacological and non-pharmacological approaches targeting biological processes underlying aging.
According to this integrated view, the INSPIRE research program has been created to foster research in the field of geroscience and healthy aging. INSPIRE aims at promoting healthy aging and preventing dependency through, among other strategies, the constitution of a bio-resource platform going from animals to humans in order to provide clinical, biological and technological resources for research and development on aging (for a detailed review on the INSPIRE program, see [6]). Besides the implementation of digital medicine (ICOPE program from the WHO) and the constitution of an INSPIRE Human Translational Cohort (6, 7), the INSPIRE program will create a unique Mouse Cohort dedicated to basic research, whose setup and design will be described in the present article.

 

Overview of the INSPIRE Mouse cohort

The primary goal of the INSPIRE Mouse cohort is to foster an understanding of the close relationship between the molecular mechanisms of biological aging and the onset of clinical frailty. This approach will importantly lead to the identification of frailty biomarkers. Complementarily, the INSPIRE Mouse cohort will enable to better characterize frailty in mice by implementing already existing tools such as the “Valencia Score”, a frailty score mainly based on neuromuscular alterations (8), or the “Howlett and Rockwood frailty index” relying on a list of deficits that accumulate during aging (9). This will eventually lead to the creation of an “INSPIRE Frailty Score” suitable for mice and as close as possible to the clinical scenario in humans. Since multiple molecular pathways are involved in the aging process and can contribute to various aspects of frailty, a panel of valid biomarkers in combination with functional measures of frailty would allow both diagnosis and follow up in preclinical and clinical settings (10).
A major asset of the INSPIRE Mouse cohort is its “mirroring” of the INSPIRE Human cohort in order to facilitate the translation of results from basic science to humans (Figure 1). To further improve the extrapolation of the results to the clinic, “humanized” living conditions, i.e. high fat/high sucrose diet and sedentary lifestyle, will be studied as common risk factors of accelerated aging. A particular attention has also been paid to the selection of a mouse strain that congruently mimics human heterogeneity. Besides, as main studies on frailty have been done on either male or female mice, comparison of frailty between genders will also be a major advantage of the INSPIRE Mouse cohort. These important considerations should facilitate the crosstalk between humans and experimental models, therefore speeding up the discovery process (Figure 1).
Here, we provide detailed information on the INSPIRE Mouse cohort setup, by putting forward an innovative methodology ranging from the study design to comprehensive phenotyping. This will be done by integrating measures evaluating different dimensions of frailty including cognitive/motor capacities, cardiac function assessment, body composition, metabolic parameters, urinary incontinency and immune function as defined in humans (syndrome diagnosis). Importantly, tissue biobanking for frailty biomarkers identification is implemented.

 

Study design

The INSPIRE Mouse cohort was designed to be as close as possible to human lifestyle. As a major issue in aging studies in mice is that most are carried out in inbred strains, the INSPIRE Mouse cohort will gather a genetically heterogeneous mouse stock to better mimic human diversity. In addition, besides normal aging, physical activity/exercise we will be studied as a human-relevant paradigm of delayed aging, while obesity/overweight will be evaluated as a risk factor for accelerated aging in mice. These are well-known risk factors for frailty in humans (11–14) and, as compared to other experimental approaches, they are particularly suitable to promote cognitive (15, 16), cardiometabolic (17, 18) and immune dysfunctions (19), that are involved in progressive/long-term frailty (20, 21). These aspects are described below.

Selection of mouse strain

Animal models have been critical tools in biomedical research, and among them, the laboratory mouse is undoubtedly the most commonly used experimental non-human model. The prevalence of mouse models in biomedical research, in particular in the field of aging, is unsurprisingly considerable given that mice require relatively inexpensive care, reproduce quickly, and have a high genetic similarity to humans (22). Especially, inbred strains (like C57Bl/6J mice or BALB/c mice), transgenic and congenic mice with inbred backgrounds are most used. An inbred strain is defined as a strain that has been through at least 20 generations of sib-mating, making animals from the same inbred strain effectively genetically identical (i.e. isogenic) (23). However, such strains do not reflect genetically diverse human populations, and therefore constitute only a small part of the picture. Hence, outbred stocks (as contrary to inbred mice that are referred as strains, outbred mice are referred to as stocks) represent new research options that parallel or even exceed human genetic diversity, offering more generalizability of responses across populations. Unfortunately, unfamiliarity with outbred mice and concerns about difficulty, genetic variability and lack of reproducibility have impeded their widespread use by the research community. Nevertheless, while there is a common belief suggesting that inbred strains should present less variability of outcomes (24), presenting practical and ethical advantages, a recent review of the literature shows this to be erroneous. Indeed, several studies have shown that for a majority of readouts, inbred and outbred mice showed comparable phenotypic variations (25–27). In addition, Tuttle et al. performed a systematic review of the primary literature, and found that strain type (i.e. inbred or outbred) did not have any effect on within-strain variability regardless of trait category including anatomy, behavior, immune function, molecules and organ function (26). Therefore, except in cases where precise genotypic regulation or standardization is required, it appears that outbred stocks from heterogeneous backgrounds are more appropriate models in many biomedical research applications.
Among the available outbred stocks, the SWISS mice are commonly used. The initial stock was bred at the Centre Anti-Cancéreux Romand in Lausanne, Switzerland, in the 1920s and consisted of two male and seven female albino mice derived from a non-inbred stock. These mice have many advantages for long-term studies, as they are inexpensive, robust and commercially available. They have been used for mouse transgenesis experiments, principally due to efficient breeding and large litter sizes. Importantly, they have a large genetic diversity, which is similar to that found within and between human populations (28). In addition, SWISS mice are sensitive to high fat diets (29–33) and have been used in aging studies (34, 35), the latter being of primary importance for the INSPIRE project. Indeed, Antoch and collaborators reported that mean life expectancy of these mice was 121.1 ± 9.2 weeks for males and 109.6 ± 6.9 weeks for females, with maximum lifespan being of 150 weeks and 164 weeks respectively (35).
For the reasons stated above, the INSPIRE Mouse cohort will gather SWISS mice as a model mimicking the genetic heterogeneity of human populations. It is important to note that females are often underrepresented in animal studies, leading to a compromised understanding of female biology and resulting in poorer treatment outcomes for women. By looking primarily in males, important biological effects can be missed or misinterpreted, partly due to hormonal and genetic intrinsic differences. In addition, contrary to a common belief, recent analyses have found that variability in female performance without regard for the estrous phase is not higher than performance variability in males (36). We thus decided to include both male and female SWISS mice in the INSPIRE cohort in order to further improve the reliability and representativeness of our findings (Table 1). When planning a study that includes an advanced age group, it is important to provide extra animals to ensure sufficient statistical power as a result of early mortality. Therefore, the number of males and females in each group was statistically adjusted considering both spontaneous and high fat diet-induced mortality (35, 37) (Table 1). Finally, as the tracking of each mouse is critical to carry out an individual follow-up, microchips will be implanted in mice so they will be easily identified using a microchip reader.

Table 1
Mouse cohort organization

For the cross-sectional study, end-point analysis will be performed at 6, 12, 18 and 24 months which roughly correspond to 30, 42, 56 and 70 years in humans. Two conditions that affect human and mouse health will be studied: high fat high sucrose (HFHS) obesity and exercise. For the longitudinal study, mice will be allowed to live their natural lifespan, and mean and maximal lifespans will be then calculated. Both male (M) and female (F) SWISS mice will be included in the cohort.

 

High fat high sucrose (HFHS) diet-induced obesity as a model of accelerated aging

There is strong evidence that excessive adiposity contributes to the impairment of several parameters of frailty, notably reducing the ability of older adults to perform physical activities, impairing different forms of memory and increasing metabolic instability (38). Many obesity-related conditions including low-grade inflammation, insulin resistance, type 2 diabetes and low physical activity are risk factors for frailty. In order to study the biological and molecular changes that occur during aging, and to depict the differences between accelerated and normal aging, a model of diet-induced obesity will be used to induce accelerated aging.
At present, there is a range of commercial high-fat diets that have been demonstrated to make small rodents obese. However, some of these diets contain levels of dietary fat that are much higher than the levels that humans routinely consume. The typical American or European diet contains about 35–40% fat by energy, and a tolerable high-fat human diet might contain 50–60% of energy as fat. However, the 60% fat rodent diet often used in experimental paradigms presents a much greater distortion of the fat content of a normal rodent chow. Thus, rodent studies with a 60% fat content might not be as relevant to human physiology as those which use a 40-45% fat diet (39). Moreover, mice fed with 60% fat diet become more obese, and do so faster than the ones fed with 40-45% fat diet. Thus, while many researchers use the 60% rodent diet as a matter of economics and convenience, it is not the best option for long-term follow-up studies. It is noteworthy that fatty acid (FA) composition of the diet should also be considered besides the percentage of fat in the diet. Moreover, it has been suggested that HFD with high sugar content better mimic the human western diet (40).
For all the aforementioned reasons, we decided to use a customized high fat high sucrose (HFHS) diet containing 40 % energy from animal and vegetal fat (among which 41% saturated fatty acids, 45% monounsaturated fatty acids and 14% polyunsaturated fatty acids) and 25% by weight sucrose. This diet or its corresponding customized control diet will be given to the mice from 6 to 24 months (Table 1). Interestingly, comparable HFHS diets have been shown to promote sarcopenia, bone loss and impaired neurological function in mice (41, 42). These findings represent some of the major features observed in aging humans, suggesting that HFHS diet-fed mice represent a useful model for studying accelerated aging.

Voluntary activity through running wheel access as a model of decelerated aging

Behavioral paradigms that are commonly used to model human exercise training in mice include forced treadmill running, forced wheel running and voluntary wheel running. Mice running behavior in voluntary wheels is closer to the natural running pattern than forced exercise, as it is performed under non-stress conditions, does not require a negative stimulus, and does not interfere in the normal nocturnal-diurnal rhythmicity of the animal (43). Remarkably, laboratory mice run spontaneously when they have access to running wheels, and this behavior is also observed in feral mice when running wheels are placed in nature (44). Voluntary wheel running thus consists of a rewarding behavior and not a stereotypic behavior that can result from environmental restriction and devoid of any goal or function (45). Another advantage of voluntary wheel running is that, since no direct intervention from the experimenter is required, it can be easily used in long-term studies. Hence, voluntary activity will be assessed in the INSPIRE Mouse cohort by giving mice access to upright running wheels (Table 1). To obtain continuous recording throughout the lifespan, we will use a sophisticated method connected to an analysis software that will record detailed activity parameters, including the number and duration of each running period, as well as the number of revolutions, speed, total distance and time, and dark/light cycle activity patterns on running wheels. As mice will be identified by microchips, parameters will be obtained for each single mouse, and at the time of this writing, we are developing a “toll like” detection system to measure individual mouse voluntary activity. Of note, to avoid enrichment/steric hindrance-linked bias, wheels will be placed in all cages. Nevertheless, in the control groups for which the effect of «no physical exercise” will be assessed, running wheels will be blocked (Table 1).

Spontaneous mobility

Mobility is among the most studied and most relevant parameters affecting quality of life with strong prognostic value for disability and survival. Indeed, locomotor impairments in older adults represent a pre-clinical transitional stage towards disability (46). It is thus necessary to understand how aging-related changes in mobility in mice resemble changes in humans.
To this end, the INSPIRE program will provide a life-long measurement of aging-related locomotor activity in mice, through automated home cage monitoring. This technique enables to monitor animals over long periods of time without human intervention. The system we will use, known as Digital Ventilated Cages (DVC®), is designed to gather continuous animal activity data directly from the home cage while keeping cages into conventional Individual Ventilated Cages (IVC) racks (Supp material). It provides a reduction in animal distress thereby increasing welfare, minimization of biases and increased reproducibility of data (47, 48). Therefore, mice belonging to the INSPIRE cohort will be housed in DVC cages so locomotor activity of all mice will be continuously and automatically monitored throughout their life. This activity metric represents the overall in-cage activity generated by all mice in a cage from any electrode and is not tracking activity of individual group-housed animals. Therefore, this parameter will be complemented by the individual aforementioned measure, i.e. voluntary activity through running wheel, as well as neuromuscular function by Valencia Score and behavioral cognitive tests (see the following section).

 

Comprehensive phenotyping

In this section, we provide an overview of our methodology for the measurement of healthspan and frailty in naturally aging, diet-induced accelerated aging, and exercise-induced decelerated aging in mice. These methods cover a spectrum of highly relevant biological indicators of frailty including cognitive, neuromuscular, cardiac, metabolic and immune function as well as urinary incontinency (For precise timeline, see figure 2). The goal is to improve the currently available “Frailty Scores” with an extended “INSPIRE Frailty Score” suitable for mice, and taking into account accurate parameters to get closer to the human clinical settings (9, 49) (Figure 3).

Figure 1
Parallel between INSPIRE Mouse and Human cohorts

The animal cohort will mimic the human diversity in functional status by providing both healthy and frail animal models to investigations. Both cohorts will allow the normalization and optimization of clinical and biological parameters, and will provide common dataset with equivalent clinical (e.g., cognitive function, mobility) and biological tests. Running animal and human cohorts in parallel is expected to facilitate cross-talks between the experimental models and the clinic in order to 1) identify causal mechanisms of clinical frailty; 2) discover biomarkers associated with functional loss; and 3) develop new therapeutic strategies allowing healthy aging. Of note, the INSPIRE Research Initiative will also use Nothobranchius Furzeri (African Killifish) and pet dogs as additional cohorts to investigate aging process.

Figure 2
Representation of experimental timeline

For the cross-sectional study, the multiple tests at 6, 12, 18 and 24 months will be performed over a period of 3-4weeks. The nature of these tests is indicated below each end-point. One month before end-point analysis (5, 11, 17 and 23 months), bladder function will be assessed. At 9, 15 and 21 months, blood will be collected in a longitudinal way to mainly evaluate immune system modifications. Both mouse mobility and voluntary activity will be continuously recorded during the whole study. For the longitudinal study, mice will be allowed to live out their maximum natural lifespan. * indicates the start of the HFHS diet at 6 months. FBO: Feces, Blood, Organs.

 

Observational study: longitudinal vs cross-sectional

Both cross-sectional and longitudinal approaches are observational studies commonly used in aging research. In cross-sectional studies, data are collected as a whole to study a mouse population at a single point in time to examine the relationship between variables of interest. Conversely, in longitudinal studies, data are gathered from the same mouse repeatedly over an extended period of time.
In the case of age-related healthspan studies, data are collected at predetermined ages from multiple individuals within a population. The cross-sectional study design allows performing invasive or terminal procedures but precludes the evaluation of lifespan. In the case of the INSPIRE Mouse cohort, a major cross-sectional study will be conducted with endpoint analyses being performed in different groups of mice at the ages of 6, 12, 18 and 24 months-old (Table 1), which roughly correspond to ages from 30 to 70 years in humans. This will allow us to carry out a large number of tests to evaluate and characterize the onset of frailty in aging mice (Figure 2). Importantly, it will also enable to evaluate if some organs “age” prematurely compared to others, and to presume the role of different organ dysfunction in the onset and progression of frailty.
Conversely, longitudinal studies allow mice to live out their maximum natural lifespan, either dying naturally or being euthanized in case of major decline. Therefore, a longitudinal sub-cohort with 120 animals (60 males and 60 females) will be implemented to the INSPIRE cross-sectional study to evaluate the spontaneous mouse mortality, and to determine mean and maximal lifespans in our animal facilities (Table 1, figure 2).

Frailty evaluation by the “Valencia Score”

The development of frailty scores suitable for mice and which resemble those that are used in the clinical scenario has become an essential challenge in basic gerontological research. In pursuit of this goal, the “Valencia Score” has been recently developed to measure frailty in rodents (8). It is based on the human clinical parameters described by Linda Fried and co-workers [50], and thus facilitates the extrapolation to humans, as it relies on five robust clinical criteria including unintentional weight loss, weakness, poor endurance, slowness and low activity level, that can be easily measured in mice. According to this score, if a mouse fails three or more components out of five, it is considered as frail, if it fails one or two criteria, it is classified as prefrail, whereas if it does not fail any criteria it is considered as robust, which is equivalent to the clinical classification defined in the Fried Frailty Score. We decided to use the Valencia Score as a starting point to evaluate frailty, and the following parameters will be therefore primarily measured.

Body weight

Animals’ body weights will be recorded biweekly throughout their lifespan to have a precise follow-up of weight evolution. In order to have reliable and individual data, all the mice will be weighted. As suggested by Gomez-Cabrera and colleagues, a 5% weight loss over a one-month period will be considered positive for this frailty criterion (8), a parameter reflecting the unintentional weight loss commonly observed in frail people.
In order to avoid variability in locomotor activity and other parameters driven by differences in circadian rhythms, all testing will be done starting at the same time. Tests will be run in the order listed, from the least to the most stressful, thereby decreasing the chance that one test might affect the behavior evaluated in the subsequent paradigm.

Grip strength

The grip strength test is a simple non-invasive method designed to assess neuromuscular function through animal’s limb strength. It takes advantage of the animal’s tendency to grasp a horizontal metal bar or grid while suspended by its tail. It allows to determine the maximum force, or peak of force, developed by a mouse when the operator tries to move it away from the bar or grid. The measurement is carried out using a high-precision sensor and an electronic device, guaranteeing a perfect capture and display of the maximal force. As suggested by the “Valencia Score”, a cut-off point below which 20% of the observations may be found has to be calculated, and all the animals ranking below this 20th percentile will be considered to fulfill the frailty criterion of weakness, which is frequently measured in the clinical setting.

Motor coordination

The tightrope test is a method for evaluating neuromuscular coordination and vigor. It is positively correlated to lifespan in rodents and has been extensively validated as a behavioral marker of aging since it was first described in the seventies (51, 52). When animals are placed on a tightrope, they are able to grasp the string with the four legs and tail and move to reach a side pole. Mice are scored positive if they are unable to reach the side pole before a 60 sec time-limit or if they fall from the rope. Usually, obese and aged mice cannot lift their hind legs and, after hanging for a few seconds from the forepaws, fall on the cage bedding. In this case, mice are scored as “positive” for this frailty criterion.

Incremental treadmill test

Poor endurance and slowness are key components of the diagnosis of frailty in humans. These parameters can be evaluated in mice by measuring the running time and speed values when performing an incremental intensity test in a treadmill. For endurance, the running time values will be measured. Then, similar to the grip test, a 20th percentile will be calculated as a cut-off point. The animals that will report a running time under this “threshold” will fulfill this frailty criterion. Besides endurance, running speed will be measured as an index of “slowness”. The same aforementioned calculation will be performed to define a threshold under which mice will be considered as positive for the “slowness criterion”. Of note, very old animals are usually unable to keep even the lowest running intensities. In our study this is likely to be exacerbated in older mice fed the HFHS diet. As in clinical practice, subjects that are unable to perform any one test are categorized as positive for that criterion.
In the case of the INSPIRE Mouse cohort, the Valencia Score will be used as a primary indicator to evaluate frailty in mice. However, as this score is mainly based on neuromuscular alterations that are commonly observed in frail people, implementation of additional parameters would be of great value to better characterize frailty onset and progression. Therefore, in order to detect early signs of frailty that might not be detected by the Valencia Score, complementary measurements will be carried out on the INSPIRE Mouse cohort in order to propose an extended “INSPIRE Frailty Score”, including cognitive, cardiac, metabolic as well as other biological functions (Figure 2). These measurements are described in the following sections.

Behavioral cognitive tests

Behavioral indicators of healthspan in mice include gait/ataxia, motivated activity, cognition, and affective function (53). In the context of aging, we will primarily use the spontaneous alternation Y-maze, which assesses prefrontal cortex- and hippocampus-dependent spatial working and reference memory, reflecting changes in cognitive performance (54).
The Y-maze spontaneous alternation test is based on rodent’s innate curiosity to explore previously unvisited areas and is used to assess spatial working memory. When placed in a Y-shaped maze, a mouse will show a tendency to enter previously unexplored arms, thus showing alternation in the arm visits. The number of arm entries and the successive entry sequences in the 3 arms are recorded in order to calculate the percentage of alternation. An entry occurs when the four legs are in the arm.

Cardiac function

Cardiac dysfunction is a main issue in elderly people, and its assessment could be of great interest in the diagnosis and the better characterization of frailty. Despite the absence of underlying pathologies like hypertension or myocardial infarction which lead to heart failure with reduced ejection fraction (HFrEF), the ‘normal’ aged heart usually exhibits changes like arterial stiffening, increased myocardial stiffness, decreased diastolic myocardial relaxation, increased left ventricular (LV) mass and decreased peak contractility (55). In addition, aging and related comorbidities (obesity, hypertension, diabetes, chronic obstructive disease, anemia and chronic kidney disease) may initiate or aggravate chronic systemic inflammation that may further affect cardiac remodeling and dysfunction (56). Therefore, the majority of elderly patients exhibit heart failure but have a preserved systolic LV function, a syndrome known as heart failure with preserved ejection fraction (HFpEF). Patients with this syndrome have severe symptoms of exercise intolerance, frequent hospitalizations and increased mortality. Despite the importance of HFpEF, optimal treatments remain largely insufficient. The INSPIRE Mouse cohort thus represents a model to better understand HFpEF pathophysiology within a ‘systemic’ perspective. Of note, approximately 85% of elderly HFpEF patients are overweight or obese, and the HFpEF epidemic has largely paralleled the obesity epidemic (57). Therefore, HFHS diet-induced obesity also represents a congruent mouse model of HFpEF.
For the evaluation of cardiac function, we have selected echocardiography. In addition to traditional parameters reflecting systolic function (ejection fraction and ventricular wall thickness), particular attention will be given to the measurement of diastolic (dys)function. In particular, the evaluation of mitral inflow will be assessed, as it is very informative and plays an important role in grading diastolic dysfunction (Supp material). Of much interest, these parameters will be complemented with strain imaging to measure the regional and global deformation of the myocardium, which allows for early detection of subclinical LV dysfunction.
The combination of the aforementioned cardiac parameters will allow to better highlight HFpEF in mice and to upgrade the Valencia Frailty Score with the degree of diastolic dysfunction.

Metabolic function

During aging, there are changes in body composition, including a loss of lean body mass, bone mass, body water, and a relative increase of fat mass. The bone deteriorates in composition, structure and function, which predisposes to osteoporosis. Furthermore, the increase in fat mass is distributed more specifically in the abdominal region, which is associated with cardiovascular disease and diabetes (58). Changes in body composition often occur in the absence of weight fluctuations, being due to alterations in energy balance, with a positive balance leading to weight gain and a negative balance resulting in weight loss. These key parameters will thus be assessed in the INSPIRE Mouse cohort.

Body composition and bone analysis

Magnetic Resonance Imaging
Body composition analysis will be performed by Magnetic Resonance Imaging (MRI) which provides an accurate estimate of whole-body fat, lean, free water, and total water masses in live mice. This technology combines simplicity of use, short scan times, and the comfort of animals which do not need to be anesthetized.

X-ray micro computed tomography
Bone analysis will be done by micro-computed tomography (micro-CT), which can provide ultrahigh-resolution images with resolution of less than 10 µm. This analysis will be performed after bone collection following terminal anesthesia. This technique will evaluate key parameters of bone microarchitecture like cortical thinning, cortical porosity, thinning of the trabeculae and loss of trabecular connectivity.

Plasmatic metabolic profiling

In addition to the aforementioned parameters, key plasmatic markers will be measured in plasma collected 2h after fasting, at the time of euthanasia. The combination of biochemical and multiplex immunoassay analysis will allow us to determine a broad range of metabolic markers in mice. These markers include, but are not limited to, hepatic enzymes, lipids and lipoproteins, incretins, glycated proteins, glucose, lactic acid, glucagon, insulin, leptin, PYY, amylin, peptide C, ghrelin and others.
The consideration of metabolic function in the INSPIRE frailty score will be of great importance to correlate body and bone compositions, and plasmatic metabolic profiling with neuromuscular and cardiac alterations, which will allow a better characterization of the sequential progression of frailty.

Bladder function

Urinary incontinence is a major problem in the elderly population, especially among women (59). Affected individuals often make great efforts to deny or hide urinary incontinence, which can lead to psychosocial hindrance. Its consideration is thus important in the characterization of frailty, but unfortunately its measurement is often undervalued in aging research, in particular in animal cohorts.
We thus decided to measure urinary incontinence in the INSPIRE Mouse cohort in order to study lower urinary tract function during aging. To this end, a spontaneous void spot assay (VSA) will be performed (Supp material), so urinary spotting patterns will be used as an indirect way of measuring bladder function and outlet control (60). As urinary incontinence is usually considered as a feature of frailty in humans, its measurement in mice will improve the scoring of frailty to be closer to the clinical evaluation.

Immune function

A crucial component of aging is a set of alterations in the immune system that can manifest as a decreased ability to fight infection, diminished response to vaccination, increased incidence of cancer and constitutive low-grade inflammation (61). The latter, which has been called “inflammaging”, has drawn particular attention in the field of aging, as recent studies have provided evidence that a pool of molecules can be secreted by senescent cells, a process known as senescence-associated secretory phenotype (SASP). This SASP includes cytokines, chemokines, proteases and growth factors that can affect neighboring cells via autocrine/paracrine pathways.
Immunological markers will be assessed in the INSPIRE Mouse cohort at different time points, i.e. 9, 15 and 21 months through submandibular blood collection and 6, 12, 18 and 24 months through terminal blood collection in the posterior vena cava (Figure 2). These markers will be measured in plasma by multiplex immunoassays and include, but are not limited to, IL6, IL-1 beta, TNF alpha, IL-12, IFN gamma, IL-2, IL-10, TGF beta, IL-4, IL-13, IL-17, CCL2, CXCL9, CXCL10, CCL22, CCL17, CRP. In addition, end-point blood collection will also serve at determining the white blood cell count of mice.
Adding some key markers reflecting immune system modifications in the characterization of frailty would be of great interest, as this feature is not considered in the current evaluation of frailty in mice.

Organ collection, biobanking and multi-omics analysis

After phenotyping, mice will be sacrificed and urine, feces, blood and tissues will be collected for biobanking as appropriate. Mice will be fasted 2h before euthanasia. Urine will be collected after placing mice in metabolic cages for 12 hours, the day before euthanasia. Feces will be collected during mouse handling, just before euthanasia and directly frozen. Blood will be collected just after euthanasia from the posterior vena cava, which is recommended for terminal stage studies in order to collect a maximal volume of blood. The fluids will be prepared as appropriate (e.g. for plasma collection), aliquoted and stored at -80°C before further investigations. Concerning the tissues, as many tissues as possible will be collected. Each tissue will be then subdivided into two pieces: the first one will be included in Optimal Cutting Temperature (OCT) compound, paraformaldehyde (PFA) or glutaraldehyde as appropriate, and cut into ultrathin slices for complete anatomopathological analysis. The other piece will be flash frozen in liquid nitrogen and, shortly before analysis, tissues will be fragmented with a biopulverizer into tiny pieces the size of grains of sand or course powder. This technique was selected for different reasons: 1) it reduces the number of collector tubes; 2) it limits sampling bias during organ collection; and 3) it optimizes subsequent rapid and complete lysis using lytic solutions or mechanical homogenizers. All the samples will be stored in a Biological Resource Center dedicated to the conservation of biological resources according to strict criteria of ethics and quality.
Multi-Omics analysis will be performed on biological fluids, feces and tissues. To facilitate the transfer of the results to the Human cohort, priority will be given to the analyses in plasma, urine (in particular proteomics and metabolomics profiling) and feces (microbiota analysis). The goal of this approach is to define a set of robust and accurate biomarkers for normal, accelerated, and decelerated aging. The tissues will be then dedicated to the multi-Omics-designed identification of novel tissue-specific candidate biomarkers for frailty and accelerated/decelerated aging, and to the validation of the novel candidate targets for prevention and treatment of accelerated aging (Figure 3).

Figure 3
Graphical abstract of the proposed INSPIRE Frailty Score

The goal of the INSPIRE Mouse cohort is to propose a clinically relevant “INSPIRE Frailty Score”, combining both functional and biological parameters, which will bring important knowledge on frailty characterization, assessment and target identification.

 

Conclusions and perspectives

Belonging to the global INSPIRE platform on geroscience, the INSPIRE Mouse cohort represents a unique way to model and better characterize frailty in mice. Although excellent institutions like the Buck Institute and the National Institute on Aging also carry out comparable studies in mice dedicated to investigate biological aging, the main originality of the INSPIRE Mouse cohort relies on the focus on getting closer to the human lifestyle to define the time course and the mechanisms of frailty/accelerated aging onset. Within this line, the selection of outbred mice that better parallel human genetic diversity, is a determining parameter offering more generalizability of responses across populations. In addition, including both males and females, and mimicking “humanized” lifestyles through voluntary physical activity and HFHS-diet induced obesity further approach real human living conditions.
Through a large functional and biological phenotyping of mice, a first objective of the INSPIRE project is to define the age at which early signs of frailty arise. Indeed, frailty is considered as a clinical syndrome appearing in advanced ages (62), but this is because the definition of frailty is mainly based on clinical criteria becoming discriminating in old patients. However, it is likely that the biological mechanisms leading to frailty and accelerated aging may be induced and detectable much earlier than the actual clinical signs of frailty. The goal here is to define the early signs of premature aging and to correlate them to the normal/altered functional phenotype to 1/ define the age of frailty onset and 2/ identify the organ/system(s) primarily altered in the frailty process. To this aim, the development of a clinically relevant score for frailty in mice is essential. Within this line, the “Howlett and Rockwood frailty index” is a simple and noninvasive index, based on 31 health-related variables like alopecia, distended abdomen, hearing loss and breathing rate (9). Although this 31-item check list is based on deficit accumulation during aging, we believe that investigator bias may play a critical role in diagnosis of frailty, which may affect the comparison of results across studies. More recently, the Valencia Score has been developed to determine frailty in naturally aging mice, based on five clinical components previously reported for humans by Fried and co-workers (8, 50). Despite its undeniable interest, this approach is primarily focused on the in-depth study of aging-related neuromuscular alterations and does not evaluate other key aspects of frailty such as cognitive, cardiac or metabolic impairments. Therefore, for the INSPIRE Mouse cohort, mice will be initially labeled as ‘frail/pre-frail/robust’ based solely on the Valencia test. Then, functional phenotyping will allow us to know if other aspects of frailty that are currently undervalued (e.g. cardiac or metabolic alterations) are detectable earlier than neuromuscular defects, which could greatly refine frailty detection. Then, a cut-off will be empirically determined for each parameter in order to set a more accurate frailty score. This method will bring key information on frailty by 1/ evaluating the effect of HFHS-induced overweight and sedentarism on frailty onset and 2/ including clinically relevant criteria like cognitive, cardiac, metabolic, bladder and immune parameters in addition to the currently measured neuromuscular deficits (Figure 3). Importantly, all these parameters, which will be supplemented by the longitudinal follow up of mouse mobility and voluntary activity, closely reflect changes observed in humans and therefore better approach the human frailty criteria.
Besides phenotypic measures, molecular biomarkers will be highly valuable and complementary in the prediction of healthy/unhealthy aging. Through a better understanding of the close relationship between the molecular mechanisms of cell premature aging and the onset of frailty/accelerated aging, the INSPIRE Mouse cohort will foster the identification of a panel of robust and sensitive frailty biomarkers that have not been extensively studied so far. Multi-Omics analysis of blood, urine and feces will allow to rapidly identify such biomarkers’ profiles (that can be conceptualized as a “frailty ID”), which might inform timely pharmacological and non-pharmacological preventive strategies acting directly on aging and contributing to a healthy state even in late ages. Then, these multi-Omics approaches will be extended to tissues to eventually discover novel tissue-specific putative biomarkers and therapeutic targets of frailty/accelerated aging (Figure 3).
An important notion, tightly linked to frailty is resilience, which is defined as the capacity to respond to or recover from clinically relevant stresses (63). Therefore, resilience must be evaluated in aging studies and necessitates the development of new animal models, which would be of particular great value for testing the benefits of geroprotectors. However, modelling resilience in mice is challenging, as there is no consensus on its precise definition or on how best to measure it (64). Although some models are currently available, there is very little data related to the characterization of the multiple deficits caused, especially in aged animals. As of this writing, INSPIRE investigators (gathering physicians, pharmacists, epidemiologists, geriatricians, clinicians, molecular biologists and others interested in the process of aging) are working on the tremendous question of “resilience modelling”, aiming at reaching a consensus on the suitability of such models.
To sum up, the INSPIRE Mouse cohort will importantly lead to the precise functional characterization of frailty together with the identification of robust molecular biomarkers to predict healthy/unhealthy aging. The resulting INSPIRE Frailty Score, combining both functional and biological parameters, will thus allow to refine frailty characterization and detection in animal models (figure 3). Therefore, by belonging to the global INSPIRE platform on geroscience (6, 65) and through its interaction with the INSPIRE Human Translational cohort and the INSPIRE Icope Care Cohort (6, 7, 66), the INSPIRE Mouse cohort should speed up the discovery process in the field of aging, with the final goal to increase access to healthy aging for the current and next generations.

 

Acknowledgments: We thank Massimiliano Bardotti, Rémy Burcelin and Sarah Gandarillas for their help in the design of the cohort. The Inspire Program was supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175), the European Regional Development Fund (ERDF) (Project number: MP0022856), and the Inspire Chairs of Excellence funded by: Alzheimer Prevention in Occitania and Catalonia (APOC), EDENIS, KORIAN, Pfizer, Pierre-Fabre.
Conflict of interest: All authors of the paper “Towards a large-scale assessment of relationship between biological and chronological aging: The INSPIRE Mouse cohort” declare no conflict of interest related to this manuscript.
Permissions: 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.
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.

 

SUPPLEMENTARY MATERIAL

 

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PREDICTOR BIOMARKERS OF NONELECTIVE HOSPITAL READMISSION AND MORTALITY IN MALNOURISHED HOSPITALIZED OLDER ADULTS

 

K.M. Pencina1, S. Bhasin1, M. Luo2, G.E. Baggs2, S.L. Pereira2, G.J. Davis3, N.E. Deutz4, T.G. Travison5

 

1. Research Program in Men’s Health: Aging and Metabolism, Boston Claude D. Pepper Older Americans Independence Center, Brigham and Women’s Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA, USA; 2. Abbott Nutrition Division, Research and Development, 3300 Stelzer Road, Columbus, OH, USA; 3. Abbott Diagnostic Division, Oncology Diagnostics & Immunoassay Development R&D, 100 Abbott Park Road, Abbott Park, IL, USA; 4. Center for Translational Research in Aging and Longevity, Department of Health and Kinesiology, Texas A & M University, College Station, TX, USA; 5. Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
Corresponding author: Karol M. Pencina, PhD, Research Program in Men’s Health: Aging and Metabolism, Brigham and Women’s Hospital, 221 Longwood Avenue, Boston, MA 02115 Phone: 617 525 9049, Email: kpencina@bhw.harvard.edu

J Frailty Aging 2020;9(4)226-231
Published online March 16, 2020, http://dx.doi.org/10.14283/jfa.2020.10


 

Abstract

Background: 90-day mortality and rehospitalizations are important hospital quality metrics. Biomarkers that predict these outcomes among malnourished hospitalized patients could identify those at risk and help direct care plans. Objectives: To identify biomarkers that predict 90-day (primary) and 30-day (secondary) mortality or nonelective rehospitalization. Design and Participants: An analysis of the ability of biomarkers to predict 90- and 30-day mortality and rehospitalization among malnourished hospitalized patients. Setting: 52 blood biomarkers were measured in 193 participants in NOURISH, a randomized trial that determined the effects of a nutritional supplement on 90-day readmission and death in patients >65 years. Composite outcomes were defined as readmission or death over 90-days or 30-days. Univariate Cox Proportional Hazards models were used to select best predictors of outcomes. Markers with the strongest association were included in multivariate stepwise regression. Final model of hospital readmission or death was derived using stepwise selection. Measurements: Nutritional, inflammatory, hormonal and muscle biomarkers. Results: Mean age was 76 years, 51% were men. In univariate models, 10 biomarkers were significantly associated with 90-day outcomes and 4 biomarkers with 30-day outcomes. In multivariate stepwise selection, glutamate, hydroxyproline, tau-methylhistidine levels, and sex were associated with death and readmission within 90-days. In stepwise selection, age-adjusted model that included sex and these 3 amino-acids demonstrated moderate discriminating ability over 90-days (C-statistic 0.68 (95%CI 0.61, 0.75); age-adjusted model that included sex, hydroxyproline and Charlson Comorbidity Index was predictive of 30-day outcomes (C-statistic 0.76 (95%CI 0.68, 0.85). Conclusions: Baseline glutamate, hydroxyproline, and tau-methylhistidine levels, along with sex and age, predict risk of 90-day mortality and nonelective readmission in malnourished hospitalized older patients. This biomarker set should be further validated in prospective studies and could be useful in prognostication of malnourished hospitalized patients and guiding in-hospital care.

Key words: Biomarkers, 90-day readmission, 30-day readmission, nutritional biomarkers of mortality and readmission, mortality in hospitalized patients.


 

Introduction

Twenty percent of patients hospitalized for an acute illness are readmitted nonelectively within 90-days; 90-day nonelective rehospitalizations are associated with poor outcomes and increased healthcare costs (1-3). Recent changes in the Medicare reimbursement policies in the United States and the Provisions in the Patient Protection and Affordable Care Act now consider nonelective readmission rates as an important hospital quality metric (1, 4). Therefore, there is substantial interest in developing biomarkers that can predict 90- and 30-day mortality and nonelective readmissions (5-10).
Malnutrition is prevalent in hospitalized older adults and associated with longer length of hospital stay, increased risk for patient readmissions, increased overall mortality, and poor health outcomes, including higher rates of 90- and 30-day nonelective readmissions (11-15). There is an unmet need to identify biomarkers that predict mortality and the risk of readmission in malnourshed hospitalized elderly patients. Such biomarkers could be helpful in identifying high-risk patients upon admission so more intensive interventions could be targeted on these patients early to improve health outcomes and reduce mortality and rehospitalization rates.
Accordingly, the primary aim of this investigation was to Identify biomarkers that predict an increased risk of 90-day mortality or rehospitalization in malnourished older adults admitted to the hospital. A secondary aim was to identify markers that predict an increased risk of hospital readmission or death within 30 days post discharge.
To address these aims, we measured 52 blood biomarkers in the participants in NOURISH (Nutrition Effect On Unplanned Readmissions and Survival in Hospitalized Patients) Trial (18), a randomized trial that determined the effects of a nutritional supplement on 90-day readmission and death in malnourished hospitalized patients >65 years. The relatively large sample size of the NOURISH trial, and the availability of data on readmission and death, and availability of samples for the measurement of a large number of nutritional, inflammatory, hormonal and muscle biomarkers enabled us to test the hypothesis that one or more circulating biomarker can predict the risk of nonelective readmission or death within 90 days and 30 days after hospital discharge among malnourished hospitalized patients.

 

Methods

The NOURISH Trial

The design and primary results of the NOURISH Trial have been published (15, ClinicalTrials.gov Identifier: NCT01626742) and are briefly discussed in Supplementary materials.

The Participants

As described (15), the eligible participants were 65 years or older and had been hospitalized within the preceding 72 hours with a primary diagnosis of acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, or pneumonia. These conditions were selected because these are four of the commonest causes of hospitalization and nonelective readmission, and major contributors to 30- and 90-day mortality among hospitalized patients. Furthermore, under the Hospital Readmission Reduction Program (HRRP) these are the four nonsurgical conditions that CMS monitors and penalizes hospitals for excess readmission rates.
The nutritional status was assessed using the subjective global assessment (SGA) and those deemed to have moderate (class B) or severe malnutrition (class C) were deemed eligible (15). Main exclusion criteria included diabetes mellitus determined by medical history and HbA1c; currently active or treated cancer; and severe renal or liver dysfunction (15).
The study interventions are described in Supplementary materials. The current analyses aimed to identify baseline biomarkers that were associated with 90 and 30-day outcomes in 193 individuals in whom blood sample was available at admission for biomarker analyses.

Outcomes

The primary outcome for this secondary analysis was the 90-day composite incidence of nonelective hospital readmission or death post-discharge. The secondary outcome was the occurrence of nonelective hospital readmission or death within 30 days post-discharge.

Biomarkers

A total of 52 circulating biomarkers were measured at baseline in 193 individuals who participated in the Nourish Trial: these included amino acids and related metabolites, nutritional markers, hormones, acute phase reactants, inflammatory markers, minerals and others.  The biomarkers that were included in the analyses are listed in Table 1 and blood sample collection and measurement methods are provided in Supplementary materials.

Statistical Methods

The Analytic Sample

The current analysis included all 193 patients having baseline assessments of blood biomarkers. The biomarkers whose concentrations were below the lower limit of quantitation in ≥ 30% of subjects were excluded from statistical analyses.

Analytical Approach

Statistical methods implemented in this study are discussed in detail in Supplementary materials. The aim of this study was to identify baseline biomarkers that were associated with and predictive of the outcome: composite 90-day and 30-day nonelective hospital readmission or death post discharge using Cox Proportional Hazards model.
Fifty two baseline markers were divided into 9 clinically meaningful classes of predictors to facilitate selection of the most informative candidates and to avoid potential overfitting: demographic, nutritional, hormones, acute phase proteins, inflammation, minerals, iron related, amino acids and other. Univariate Cox Proportional Hazards models were performed on all analyzed markers to select best predictors within each class. Markers within each class, with the strongest association with outcomes based on p-value, were selected for further consideration. The final model of hospital readmission or death was derived using stepwise selection technique with entry and stay criteria of alpha 0.05. C-statistics as a metric of model discrimination was calculated to assess performance of the final model. Goodness-of-fit was evaluated quantitively using Greenwood-Nam-D’Agostino calibration test for survival models (17) (p-values larger than 0.05 indicates good calibration) and qualitatively using calibration plots.
Inclusion into the final model of treatment assignment, age and other important clinical variables related to outcome, was also considered and those models were compared to model derived from stepwise selection. To correct for over-fitting, final model performance was internally validated by computation of bootstrap estimate of optimization for C-statistic (18).

 

Results

The analyses included all 193 participants, who had measurement of biomarkers at baseline.

Baseline Characteristics of Study Participants

The participants in the analytic sample were on average 76 years old, 51% were men, and the mean body mass index was 24.7 kg/m2 (Supplementary Table 1). Overall, the participants in both treatment arms had similar baseline characteristics. Similar to parent trial, the Kaplan-Meier estimates for readmission and death outcomes did not differ between placebo and treatment groups, in our analytical cohort, with Log-rank and Tarone-Ware tests being non-significant (all p-values > 0.20) (data not shown).

Table 1 Results from stepwise selection for 90-day and 30-day outcome (N=191)

Table 1
Results from stepwise selection for 90-day and 30-day outcome (N=191)

Legend:  * Hazard Ratio estimates and 95% CIs are expressed per one interquartile range. C-statistics for 90-day outcome model selected in stepwise procedure was 0.68 (95% CI: 0.61, 0.75). C-statistic for 30-day outcome model selected in stepwise procedure was 0.76 (95% CI: 0.68, 0.84).

 

Model selection

We first performed univariate Cox proportional hazard models on all analyzed biomarkers to select the predictors within each class that were significantly associated with 90-day and 30-day outcomes. In the univariate analyses, the following 10 biomarkers were significantly associated with the 90-day nonelective hospital readmission or death at the <0.05 level: sex, Charlson Comorbidity Index score, parathyroid hormone (PTH), serum amyloid A (SAA), serum amyloid P component (SAP), ferritin, glutamate, hydroxyproline, citrulline and tau-methylhistidine (Supplementary Table 2).

Table 2 Multivariate Cox Proportional Hazards regression model with performance metrics for 90-day incidence of hospital readmission or death post discharge

Table 2
Multivariate Cox Proportional Hazards regression model with performance metrics for 90-day incidence of hospital readmission or death post discharge

Legend: Glutamate, Hydroxyproline, Tau-methylhistidine per interquartile range. * C-statistic for model with Age and Sex only. ** C-statistic for model with Age, Sex, Glutamate, Hydroxyproline, and Tau-methylhistamine. *** C-statistic for model with Age, Sex, Glutamate, Hydroxyproline, Tau-methylhistamine, and Total Charlson Comorbidity Score.† C-statistic for model with Age, Sex, Glutamate, Hydroxyproline, Tau-methylhistamine, Total Charlson Comorbidity Score and Intervention.

 

In the univariate analyses of nonelective hospital readmission or death within 30 days post discharge, only 4 biomarkers were below the 0.05 significance level and all 4 were included in the stepwise selection model: sex, Charlson Comorbidity Index score, glutamate and hydroxyproline (Supplementary Table 3).
In multivariate stepwise selection, sex and circulating levels of the amino acids glutamate, hydroxyproline and tau-methylhistidine were associated with death and nonelective hospital readmission within 90-days post-discharge (Table 1). Although not statistically significant, age was included in the final model because of its clinical import. Glutamate, hydroxyproline and tau-methylhistidine demonstrated statistically significant association with the primary outcome of 90-day death or nonelective hospital readmission with hazard ratios expressed per one interquartile range difference of 0.64 (95% CIs: 0.44, 0.94; p-value=0.021), 1.29 (95% CIs: 1.0,1.66, p-value=0.049), and 1.31 (95% CIs: 1.07, 1.59; p-value=0.007), respectively, indicating higher glutamate levels were associated with fewer events, and higher tau-methylhistidine or hydroxyproline levels associated with more events in this population (Figure 1).

Figure 1 Hazard Ratios for 90-day outcome. Predictors selected in the stepwise regression model

Figure 1
Hazard Ratios for 90-day outcome. Predictors selected in the stepwise regression model

 

For 30-day outcomes, for the reasons discussed above, the age factor was added to the final risk model. In the stepwise selection three biomarkers, sex, Charlson Comorbidity Index score and Hydroxyproline stayed in multivariate model and showed statistically significant association with 30-day nonelective rehospitalization and death post-discharge with HRs of 0.22 (95% CIs: 0.07, 0.65; p-value=0.006), 1.35 (95% CIs: 1.05, 1.72; p-value=0.017) and 1.45 (95% CIs: 1.06, 2.00; p=0.021), respectively (Table 1).

Model performance

Model discrimination. Final selected multivariate regression model for 90-day nonelective hospital readmission or death demonstrated moderate ability of biomarkers to distinguish between events and non-events (Table 2). Adding amino acid biomarkers (glutamate, hydroxyproline and tau-methylhistidine) to the model improved the discrimination in comparison with model with age and sex only (C-statistic: 0.68 (95% CI: 0.61, 0.75)). The inclusion of the Charlson Comorbidity Index score only slightly improved model performance of the multivariate model (C-statistic: 0.69 (95% CIs: 0.62, 0.76)). Inclusion of the intervention factor in the model did not affect the discrimination metrics (Table 2).
Similar analysis was done for the 30-day outcome. In the multivariate stepwise method, the age-adjusted model that included sex, Charlson Comorbidity Index score and Hydroxyproline demonstrated good discrimination with C-statistic 0.76 (95% CIs: 0.68, 0.85). Further addition of intervention factor into the model did not improve the C-statistics: 0.77 (95% CIs: 0.69, 0.85) (Table 3).

Table 3 Multivariate Cox Proportional Hazards regression model with performance metrics for 30-day incidence of hospital readmission or death post discharge

Table 3
Multivariate Cox Proportional Hazards regression model with performance metrics for 30-day incidence of hospital readmission or death post discharge

Legend: * C-statistic for model with Age and Sex only. ** C-statistic for model with Age, Sex, Charlson Comorbidity Score and Hydroxyproline. *** C-statistic for model with Age, Sex, Charlson Comorbidity Score, Hydroxyproline and intervention.

 

Model calibration. Age-adjusted models selected by the stepwise procedure for both outcomes demonstrated acceptable calibration (Supplementary Figures 1 and 2). Furthermore, goodness-of-fit metric demonstrated good calibration for the age-adjusted model with sex indicator and three amino acids for 90-day hospital readmission or death (Chi-squared=5.16, p=0.271).
Internal Validation. C-statistic metrics corrected for Harrell’s bootstrap estimate for optimization were 0.65 (95% CI: 0.58, 0.72) and 0.73 (95% CI: 0.64, 0.81) for stepwise selected age-adjusted models for the 90-day and the 30-day outcomes, respectively.

 

Discussion

In malnourished hospitalized patients, the baseline circulating levels of glutamate, hydroxyproline, and tau-methylhistidine , when considered together with sex and age were predictive of the risk of 90-day mortality and nonelective hospital readmission. These findings are clinically important because identifying hospitalized, malnourished patients at risk of death and nonelective rehospitalization could help target additional interventions and resources towards this at-risk population. Such prognostic information offered by this biomarker set could inform the care plan and its intensity for hospitalized patients. These findings should be validated in a prospective study of similar hospitalized older patients.
The circulating biomarker amino acids – hydroxyproline, tau-methylhistidine , and glutamate – that emerged from the multivariate step wise regression analyses as predictors of the primary outcome have biologic plausibility. Cachexia observed in sepsis and other catabolic states is associated with increased breakdown of myofibrillar proteins (19-21). Mechanistically, acute illnesses are associated with increased muscle protein breakdown, mediated typically by upregulation of proteins involved in protein degradation, such as polyubiquitins, Ub fusion proteins, the Ub ligases atrogin-1/MAFbx (muscle atrophy f box) and MuRF-1 (muscle-specific RING finger-1), multiple subunits of 20S proteasome and its 19S regulator, and cathepsin L (19-21). Urinary excretion of tau-methylhistidine and its circulating levels have been used as a biologic marker for skeletal muscle protein breakdown in humans and animals (22-23). Similarly, hydroxyproline is a component of collagen, and increases in serum hydroxyproline levels suggest increased collagen turnover, implicating musculoskeletal remodeling. Glutamate levels are low in sepsis and acute illness and glutamate metabolism is altered in illness wherein deamination predominates over transamination (24). Consequently, circulating glutamine levels are reduced in patients with critical illness or following major surgery, and a low plasma concentration of both glutamate (24) and glutamine (25-26) at ICU admission has been recognized as an independent risk factor for post-ICU mortality. Old age has been well recognized as a risk factor for adverse health outcomes among hospitalized patients (3, 27). The role of sex as a predictor of health outcomes in hospitalized patients is complex, less well understood, and varies with disease condition. Among middle-aged adults hospitalized with an acute myocardial infarction, women have higher unadjusted mortality rates than men; however, in general, overall mortality rates are higher among older hospitalized men than among older women (3, 27).
A number of additional biomarkers, including albumin, prealbumin, transferrin, C-reactive protein, creatinine, cystatin 3, pentraxine-3, hemoglobin, red cell distribution width, fibrinogen, and others have been evaluated individually or in small sets in patients with sepsis, heart failure, acute renal failure, cardiac shock, or in patients admitted to the intensive care unit as biomarkers of health outcomes, such as death, intubation, or readmission (5-10). Some of the traditional biomarkers of nutritional status such as albumin and prealbumin did not emerge as predictors in this analysis; this is consistent with recent evidence that these acute phase proteins do not consistently change with weight loss or illness, and their utility as indicators of malnutrition is limited (28).
This study has several strengths and some limitations. The present analyses represent a comprehensive systematic investigation of one of the largest batteries of biomarkers in malnourished hospitalized patients. In contrast to several previous studies that have focused on single or a small set of narrowly-focused biomarkers, we investigated a large number of nutritional, inflammatory, hormonal, and muscle-related circulating biomarkers. We focused on biomarkers for which analytically verified assays are commercially available to facilitate accelerated translation to clinical practice. The primary outcome – a composite 90-day mortality and nonelective rehospitalization post-discharge – is a highly impactful health outcome that has important clinical and public health implications for patient care and healthcare policy. The candidate conditions – acute myocardial infarction, heart failure, chronic obstructive pulmonary disease and pneumonia represent four of most common causes of hospitalization and the most frequent causes of nonelective hospital admission.
The study also has some limitations. The analyses were performed on a sample enrolled in a randomized clinical trial in patients in whom biomarker data were available. The patient population included in these analysis were malnourished because those patients are at high risk of 90-day mortality and readmission. Additional studies are needed in patients who are not malnourished. Some of the traditional markers of nutritional status such as albumin and prealbumin did not emerge as predictors in this analysis; this is consistent with recent evidence that these acute phase proteins do not consistently change with weight loss, illness, or nitrogen balance, and their utility as indicators of malnutrition is limited (28). Also, the temporality of the design and robustness to covariate control suggest the possibility of causal associations between biomarkers and our endpoints; additional larger studies are needed to formally investigate mediation of effects along specific pathways. The relatively small number of mortality events especially during the 30-day postdischarge period, may have limited the statistical power for the analyses of 30-day outcomes. The C-statistics for the primary model for 90-day readmission or death approached the threshold of 0.7 that is generally considered “good” discrimination (29); the ability of our model to distinguish between events and non-events was similar or better than performance reported in other studies with similar settings evaluating hospital readmission outcomes (30). The findings need confirmation in a larger prospective study.
In summary, the baseline circulating levels of the amino acids glutamate, hydroxyproline, and tau-methylhistidine, when considered together with sex and age were predictive of the risk of 90-day mortality and nonelective hospital readmission in malnourished patients who were hospitalized for acute myocardial infarction, heart failure, chronic obstructive pulmonary disease or pneumonia. Additional prospective studies are needed to validate the performance characteristics of this biomarker set in predicting 90-day mortality, nonelective readmission, and other health outcomes. If validated, such a biomarker set could be useful in prognostication of malnourished hospitalized patients, thereby serving as an additional guide to the design and intensity of in-hospital management of hospitalized patients.

 

Funding support: This investigator-initiated study was supported by funding from Abbott Nutrition to Brigham and Women’s Hospital. Dr. Shalender Bhasin reports receiving research grants from NIH, FNIH, AbbVie, Alivegen, FPT, and MIB for investigated-initiated research not related to this manuscript; these grants and contracts are managed by Brigham and Women’s Hospital. He has received consulting fees from AbbVie and OPKO. He holds equity interest in FPT, LLC. Drs. Pereira, Luo, Davis and Briggs are employees of Abbott Nutrition and they had the role in interpretation of data and review of the manuscript.
Acknowledgements: We would like to thank Jeff Nelson, PhD (Abbott Nutrition) for conducting the NOURISH study and providing consultation and support to the project.
Ethical standards: The NOURISH protocol was approved by the Copernicus Group Institutional Review Board for Human Subjects Research (Durham, NC), Western Institutional Review Board for Human Subjects Research (Olympia, WA) and the local Institutional Review Boards of the participating centers. All participants provided written informed consent and were randomized within 72 hours of hospital admission. Drs. Pencina and Travison had full access to the data and performed the analyses. Drs. Pencina, Travison and Bhasin wrote the manuscript; all other authors participated in the review and revisions of the manuscript. Drs. Pereira, Luo, Baggs, and Davis are employees of Abbott Nutrition. They provided the data from the NOURISH Trial and participated in the discussions, review of the analyses and the manuscript.
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.

 

SUPPLEMENTARY MATERIALS

 

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ICFSR TASK FORCE PERSPECTIVE ON BIOMARKERS FOR SARCOPENIA AND FRAILTY

 

L. Rodriguez-Mañas1, I. Araujo de Carvalho2, S. Bhasin3, H.A. Bischoff-Ferrari4, M. Cesari5, W. Evans6, J.M. Hare7, M. Pahor8, A. Parini9, Y. Rolland10, R.A. Fielding11, J. Walston12, B. Vellas13 and the ICFSR Task Force

 

1. Servicio de Geriatría, Hospital Universitario de Getafe, Toledo, Spain; 2. World Health Organization, Geneva, Switzerland; 3. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4. University Hospital and University of Zurich, Zurich, Switzerland; 5. Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy; 6. Duke University Medical Center, Durham NC, USA; 7. Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA; 8. University of Florida Institute on Aging, Gainesville, FL, USA; 9. Institute of Cardiovascular and Metabolic Diseases, INSERM U1048, CHU Toulouse, Toulouse France; 10. Service de Médecine Interne et Gérontologie, Clinique Gérontopôle, Hôpital La Crave, Casselardit, Toulouse, France; 11. Tufts University, Boston, MA, USA; 12. Johns Hopkins Division of Geriatric Medicine and Gerontology, Baltimore, Maryland, USA; 13. Gerontopole, INSERM U1027, Alzheimer’s Disease Research and Clinical Center, Toulouse University Hospital, Toulouse, France.
Corresponding author: L. Rodriguez Mañas, Hospital Universitario de Getafe, Spain, leocadio.rodriguez@salud.madrid.org

Task force members: Samuel Agus (Paris, France), Islene Araujo de Carvalho (Geneva, Switzerland), Mylène Aubertin Leheudre (Montréal, Canada), Karen Bandeen-Roche (Baltimore, USA), Ann Belien (Hesusden-Zolder, Belgium), Shalender Bhasin (Boston, USA), Heike Bischoff-Ferrari  (Zurich, Switzerland), Andreas Busch (Vevey, Switzerland), Ryne Carney (Washington, USA), Matteo Cesari (Milano, Italy), Caroline Couleur (Epalinges, Switzerland), Alfonso Cruz Jentoft (Madrid, Spain), Susanna Del Signore (London, United Kingdom), Carla Delannoy (Vevey, Switzerland), Waly Dioh (Paris, France), Sonya Eremenco (Tucson, USA), Bill Evans (Durham, USA), Toby Ferguson (Cambridge, USA), Jack Guralnik (Baltimore, USA), Ludo Haazen (Hesusden-Zolder, Belgium), Joshua Hare (Miami, USA), Aaron Hinken (Collegeville, USA), Darren Hwee (South San Francisco, USA), Lori Janesko (Uniontown, USA), Kala Kaspar (Vevey, Switzerland),  Francesco Landi (Roma, Italy), Valérie Legrand (Nanterre, France), Bradley Morgan (South San Francisco, USA), John Morley (St Louis, USA), John Muscedere (Kingston, Canada), David Neil (Collegeville, USA),  Marco Pahor (Gainesville, USA),  Marika Paul (Columbus, USA), Subashan  Perera (Pittsburgh, USA), Suzette Pereira (Columbus, USA), John Rathmacher (Ames, USA), Reginster Jean Yves (Liège, Belgium), Leocadio Rodriguez Mañas (Getafe (Madrid), Spain), Michelle Rossulek (Cambridge, USA), Jorge Ruiz (Miami, USA), Lisa Tarasenko (Cambridge, USA), Effie Tozzo (Cambridge, USA), Heber Varela (Miami, USA), Bruno Vellas (Toulouse, France), Jeremy Walston (Baltimore, USA), Debra Waters (Dunedin, New Zealand), Linda Woodhouse (Edmonton, Canada)

J Frailty Aging 2019;in press
Published online October 7, 2019, http://dx.doi.org/10.14283/jfa.2019.32

 


Abstract

Biomarkers of frailty and sarcopenia are essential to advance the understanding of these conditions of aging and develop new diagnostic tools and effective treatments. The International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force – a group of academic and industry scientists from around the world — met in February 2019 to discuss the current state of biomarker development for frailty and sarcopenia. The D3Cr dilution method, which assesses creatinine excretion as a biochemical measure of muscle mass, was suggested as a more accurate measure of functional muscle mass than assessment by dual energy x-ray absorptiometry (DXA). Proposed biomarkers of frailty include markers of inflammation, the hypothalamic-pituitary-adrenal (HPA) axis response to stress, altered glucose insulin dynamics, endocrine dysregulation, aging, and others, acknowledging the complex multisystem etiology that contributes to frailty. Lack of clarity regarding a regulatory pathway for biomarker development has hindered progress; however, there are currently several international efforts to develop such biomarkers as tools to improve the treatment of individuals presenting these conditions. .

Key words: Frailty, sarcopenia, biomarkers, consensus.


 

Introduction

Biomarkers have proven essential to advance understanding of the biological underpinnings of various diseases, as diagnostic tools, and in clinical trials as indicators of treatment effectiveness. For complex conditions such as sarcopenia and frailty, the multiplicity of phenotypes and pathogenic mechanisms makes the development of biomarkers particularly challenging, since biological markers associated with single aspects of the condition are only marginally associated with clinically relevant outcomes (1).
In February 2019, the International Conference on Frailty and Sarcopenia Research (ICSFR) Task Force convened a meeting to discuss the current status of biomarker development for sarcopenia and frailty. The ICFSR Task Force comprises academic and industry scientists from 13 countries in North America, Europe, Asia, and Australia/Oceania who are involved in the development of interventions to treat these disabling age-related conditions.
The term sarcopenia was coined by Rosenberg in the late ‘80s to describe age-related loss of muscle mass and was later revised to incorporate declines in muscle strength and physical function (2, 3). Assessment of muscle mass by dual energy x-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI) have provided the most widely used biomarkers for sarcopenia (4). However, CT and MRI have limitations related to the high cost and complexity of the technology, and DXA has shown poor correlation with health-related quality of life (5).
Frailty is a syndrome characterized by progressive functional decline, decreased physiological reserve and resilience and increased vulnerability to a variety of stressors (6). Multiple operational definitions of frailty have been proposed (7-9). The phenotypic criteria proposed by Fried and colleagues, which define frailty by the presence of weakness, slowness, weight loss, declining physical function, and fatigue continue to be the most widely used (10).
For both frailty and sarcopenia, identification of biomarkers depends on the definition of the condition and the goal is to develop clinically relevant markers as diagnostic tools, to assess treatment effectiveness, to understand biological etiology, and to advance prevention efforts. In 2016, an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code was established for sarcopenia, which enabled it to be recognized by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) and the National Centre for Classification in Health (NCCH) in Australia as a separately reportable condition (11). By removing barriers for diagnosing sarcopenia, the ICD-10 code will enable standardized data collection and improve the efficiency of clinical trials (12). However, the presence of multiple, largely overlapping operational definitions and the multidimensional nature of frailty make it unlikely that an ICD code for frailty will be established in the near future.
Frailty is strongly associated with muscle mass and function; thus, sarcopenia has been proposed as the biological substrate for physical frailty (13). Merging the two conditions into a single entity – Physical Frailty and Sarcopenia (PF&S) – a condition that can be diagnosed and potentially treated has also been proposed (14, 15) and a core inflammatory profile with a gender-specific signature has been identified (16).

 

Biomarkers of sarcopenia and frailty

A definition of sarcopenia should take into account the role of muscle mass in the risk of disability and age-related risk of chronic disease; thus, for sarcopenia measures of muscle mass, quality, and function have been proposed as potential biomarkers. Plasma growth and differentiation factor-15 (GDF-15) has also been associated with sarcopenia-related outcomes and increases with age but has not been evaluated as a sarcopenia biomarker (17).  Since frailty has a complex multisystem etiology, biomarkers needed to assess multiple dysregulated systems. Proposed biomarkers of frailty include inflammatory markers such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), C-reactive protein (CRP), neutrophil cell count (18), and others (Table 1).

Table 1 Possible Biomarkers of Frailty and Sarcopenia

Table 1
Possible Biomarkers of Frailty and Sarcopenia

CRP, C-reactive protein; DXA, dual X-ray absorptiometry; GDF-15, growth differentiation factor 15; IFNγ, interferon gamma; IL-6, interleukin 6; IL-8, interleukin 8; MCP-1, monocyte chemoattractant protein 1; MPO, myeloperoxidase; PDGF-BB, platelet derived growth factor BB; SIRT, silent mating-type information regulation 2 homolog 1; TNF-α, tumor necrosis factor alpha.

 

D3-Creatine (D3Cr) dilution – a biomarker for sarcopenia

Although dozens of papers measure lean mass and call it muscle mass, lean mass is not the same as muscle mass. Indeed, the Foundation of the National Institutes of Health (FNIH) Sarcopenia project concluded that low lean mass is poor predictor of functional impairment (19). The relationship between muscle mass measured by XX and fracture risk is highly significant; however appendicular lean mass (ALM) is not related to what? at all (20).
To assess the true effects of intrinsic, age-associated effects on skeletal muscle contractile function, an accurate measure of functional muscle mass undiluted by lipid, connective tissue, and fibrotic tissue is needed. Assessment of creatinine excretion provides such a measure of muscle mass (21). Creatine is irreversibly converted to creatinine and excreted in urine, where it can be measured by liquid chromatography-mass spectrometry (LCMS). Evans and colleagues developed a direct and accurate method for measuring creatine pool size by orally administering stable isotope-labelled creatine and then collecting a single fasted urine sample 48-96 hours after dosing for measurement of D3Cr (22, 23). This D3Cr dilution method uses an algorithm based on urine levels of creatine and creatinine to determine the dilution of the oral label in the whole-body creatine pool of skeletal muscle, thus providing an accurate measure of skeletal muscle mass.
In the Osteoporotic Fractures in Men (MrOS) study, a multi-site study of community dwelling men 80 years and older, the D3Cr dilution method was compared to DXA, high-resolution peripheral quantitative CT (HRpQCT), Short Physical Performance Batter (SPPB), the 400-meter walk test (400MW), and force plate for lower extremity power. Muscle mass by the D3Cr dilution method showed a moderate correlation with DXA total lean mass but no correlation with DXA ALM/ht2. It also demonstrated a strong relation between muscle mass determined by D3Cr dilution method with physical performance (SPPB, chair stands), incidence of falls, and mobility limitations (20). In assessing the relative importance of muscle versus fat in sarcopenic obesity, repeated assessment of multiple measures at 18-month intervals showed that muscle mass determined using the D3Cr dilution method correlated with grip strength and walking speed even though there was no change in total lean mass, ALM, or ALM/ht2. Muscle mass determined using the D3Cr dilution method also was shown to be a strong predictor of disability (24). These results suggest that muscle mass is a primary determinant of physical performance and adverse outcomes, and that the relative effects of higher body fatness are less important. However, results regarding the D3Cr dilution method need to be replicated in large representative cohorts.

Frailty biomarkers

Potential biological triggers of frailty in older adults may include increased inflammation and mitophagy (25); altered stress response systems mediated through the angiotensin system, the HPA axis, and the sympathetic nervous system; and decreased energy production. Chronic inflammatory markers such as IL-6, CRP, interleukin-1-receptor agonist, interleukin-18, and soluble TNF-α receptor 1 (sTNFR1), combined in an inflammation index score, appears to capture the magnitude of chronic inflammation in aging and was shown to be a better predictor of mortality compared to single measures (26). However, these markers are highly variable and non-specific and influenced by meals and time of day. Recent studies suggest that sTNFR1 is the least variable over weeks and months.
Salivary cortisol has been used as a marker of the hypothalamic-pituitary-adrenal (HPA) axis response to stress (27), and a diurnal pattern of cortisol levels (lower in the morning, higher in the evening) has been associated with frailty (28, 29). Frailty has also been associated with lower levels of serum insulin-like growth factor (30), lower levels of testosterone and high levels of estradiol (31, 32), elevated levels of silent mating-type information regulation 2 homolog 1 (SIRT1) (33), altered glucose-insulin dynamics (34), endocrine dysregulation (35), endothelial dysfunction (36), elevated clotting factors (37), mitochondrial dysfunction (38), and alterations in the metabolome (39).
Given that frailty is an aging-related syndrome, biomarkers of aging are also important and have been gaining increased attention with the emergence of the field of gerosciences (40-42). For example, possible biomarkers of frailty include a marker of nuclear membrane defects, which has been associated with aging (43), the expression of several mRNAs involved in the cell response to stress (44), and markers of mTOR activation, the adaptive immune system, and cell senescence.
Age-related changes in the adaptive and innate immune response including the chronic low-level proinflammatory state known as inflammaging, and immunosenescence, which is strongly driven by inflammaging, result in increased susceptibility to influenza and other disease and a decreased response to influenza vaccination (6, 45-47). Hare and colleagues have been developing mesenchymal stem cells (MSCs) as a treatment for many diseases of aging, including frailty. They have shown that MSCs improve immune potential by modulating T and B cell response. Moreover, their studies suggest that vaccine responsiveness may represent an ideal biomarker of aging in that it correlates with the frailty phenotype, changes with interventions that change the phenotype (such as MSC treatment), represents a biologically plausible mechanism of frailty, and provides medically meaningful information.

 

Regulatory considerations

While frailty is an acceptable concept in clinical care and for characterizing populations, it is not presently a “disease entity” recognized by and ICD-10 code. However, to adapt our health care system to an aging population, transitioning from a disease-centered to a function-centered approach will be necessary to maintain function in older adults and prevent dependency.  For these reasons, biomarkers of frailty are urgently needed. Frailty is an entity where several physiological systems are dysregulated or malfunctioning. Thus, an isolated biomarker of frailty would have limited usefulness in drug development whereas the search for panels of biomarkers seems promising.
Context of use is an important consideration for regulators. Biomarkers are useful as indicators of target engagement or for screening, diagnosis, or assessing outcomes in specifically-designated populations. Consensus from the field on what would represent an appropriate biomarker/set of biomarkers for proof of concept versus clinical trials could support efforts to achieve regulatory acceptance. However, it is necessary that the physiopathological mechanisms underlying the two conditions of interest are carefully defined and limited in order to propose unequivocal biomarkers of “disease”. Ongoing projects such as the Sarcopenia and Physical fRailty IN older people: multi-componenT Treatment strategies (SPRINTT), funded by the Innovative Medicines Initiative (IMI), are going in exactly this direction (48). Running in parallel with SPRINTT, the BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons (BIOSPHERE) study analyzed 12 candidate serum biomarkers to identify and validate a panel of PF&S biomarkers that capture the multi-factorial nature of PF&S, identify potential intervention targets, and provide potential diagnostic tools and endpoints for use in clinical trials (49). In this same regard, FRAILOMICS is evaluating the role of sets of biomarkers in the prediction of the risk of developing physical frailty, its diagnosis and its prognosis in terms of incident disability and death (50, 51).

 

Conclusions

Recognizing that the field is in the early stages of developing biomarkers for sarcopenia and frailty, the Task Force identified several research gaps and barriers that need to be addressed to expedite this process and move biomarkers from research to clinical settings.
Part of the difficulty resides in the difficulty of applying the usual standards applicable to stand-alone diseases of young and adult individuals to the more complex and heterogeneous nature of age-related conditions of advanced age. In addition, it is important to improve our understanding of measurements able to capture the conditions of interest in order to promote their optimal  translation from research into clinical practice. Practical issues such as cost effectiveness also need to be considered.
Moreover, current biomarker discovery efforts have been limited by being based on predefined hypotheses. Broader screening of potential biomarkers through omics and an integrated bioinformatics approaches could advance discovery efforts. Since frailty is a failure of many systems, panels of biomarkers will likely be required. Machine learning and information technology innovation could thus be used to develop risk scores that could be used in clinical and research settings. Other technologies, such as induced pluripotent stem cells (iPSCs), could be used to study markers of senescence and could also enable a move towards personalized medicine.

 

Acknowledgements: The authors thank Lisa J. Bain for assistance in the preparation of this manuscript.
Conflicts of interest:  The Task Force was partially funded by one educational grant, Aging In Motion, and registration fees from industrial participants (Biogen, Biophytis, Cytokinetics, Glaxosmithkline, Longeveron, Pfizer and Rejuvenate Biomed NV). These corporations placed no restrictions on this work.
L. Rodriguez Mañas, M. Cesari, M Pahor, J. Walston declare there are no conflicts. S. Bhasin reports grants from AbbVie, grants from Alivegen, grants from MIB, grants from Abbott, other from FPT, other from AbbVie, outside the submitted work. He has a patent Free testosterone determination issued. Y. Rolland reports grants from Biophytis, Novartis, outside the submitted work. R. Fielding reports grants from National Institutes of Health (National Institute on Aging),  during the conduct of the study; grants, personal fees and other from Axcella Health, other from Inside Tracker, grants and personal fees from Biophytis, grants and personal fees from Astellas, personal fees from Cytokinetics, personal fees from Amazentis, grants and personal fees from Nestle’, personal fees from Glaxo Smith Kline, outside the submitted work. B. Vellas reports grants from Nestle, Nutricia, Novartis outside the submitted work.
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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PROCEEDINGS OF THE CANADIAN FRAILTY NETWORK WORKSHOP: IDENTIFYING BIOMARKERS OF FRAILTY TO SUPPORT FRAILTY RISK ASSESSMENT, DIAGNOSIS AND PROGNOSIS. TORONTO, JANUARY 15, 2018

 

J. Muscedere1, P.M. Kim2, J. Afilalo3, C. Balion4, V.E. Baracos5, D. Bowdish6, M. Cesari7, J. D. Erusalimsky8, T. Fülöp9, G. Heckman10, S.e. Howlett11, R.G. Khadaroo12, J.L. Kirkland13, L. Rodriguez Mañas14, E. Marzetti15, G. Paré4, P. Raina16, K. Rockwood17, A. Sinclair18, C. Skappak19, C. Verschoor16, S. Walter20 for the Canadian Frailty Network

 

1. Department of Critical Care Medicine, Queen’s University; 2. Canadian Frailty Network, Kingston, ON, Canada; 3. Division of Cardiology and Centre for Clinical Epidemiology, Jewish General Hospital, McGill University; 4. Department of Pathology and Molecular Medicine, McMaster University; 5. Department of Oncology, University of Alberta; 6. Department of Pathology and Molecular Medicine, Master University; 7. Department of Clinical Sciences and Community Health, Università di Milano; and Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy; 8. Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, UK; 9 Research Center on Aging, University of Sherbrooke, Québec, Canada; 10. School of Public Health and Health Systems, Schlegel University of Waterloo Research Institute for Aging, University of Waterloo; 11. Departments of Pharmacology and Medicine (Geriatric Medicine), Faculty of Medicine, Dalhousie University; 12. Department of Surgery and Critical Care Medicine, University of Alberta; 13. Robert and Arlene Kogod Center on Aging, Mayo Clinic; 14. Department of Geriatrics, Hospital Universitario de Getafe, Madrid; 15. Fondazione Policlinico Universitario «Agostino Gemelli» IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy; 16. Department of Health Research Methods, Evaluation and Impact, McMaster University; 17. Division of Geriatric Medicine, Dalhousie University; 18. Foundation for Diabetes Research in Older People at Diabetes Frail Ltd; 19. Schwartz/Reisman Emergency Medicine Institute, Mount Sinai Hospital, University of Toronto, and the Division of Emergency Medicine, McMaster University; 20. Fundación de Investigación Biomédica Hospital Universitario de Getafe, Getafe, Spain. Dept. of Epidemiology and Biostatistics, University of California San Francisco, California, United States of America.
Corresponding author: Dr. John Muscedere, Kingston Health Sciences Centre, 76 Stuart Street, Kingston, Ontario, Canada, Email: john.muscedere@kingstonhsc.ca
J Frailty Aging 2019;in press
Published online April 30, 2019, http://dx.doi.org/10.14283/jfa.2019.12

 


Abstract

The Canadian Frailty Network (CFN), a pan-Canadian not-for-profit organization funded by the Government of Canada through the Networks of Centres of Excellence Program, is dedicated to improving the care of older Canadians living with frailty. The CFN has partnered with the Canadian Longitudinal Study on Aging (CLSA) to measure potential frailty biomarkers in biological samples (whole blood, plasma, urine) collected in over 30,000 CLSA participants.  CFN hosted a workshop in Toronto on January 15 2018, bringing together experts in the field of biomarkers, aging and frailty. The overall objectives of the workshop were to start building a consensus on potential frailty biomarker domains and identify specific frailty biomarkers to be measured in the CLSA biological samples. The workshop was structured with presentations in the morning to frame the discussions for the afternoon session, which was organized as a free-flowing discussion to benefit from the expertise of the participants. Participants and speakers were from Canada, Italy, Spain, United Kingdom and the United States.   Herein we provide pertinent background information, a summary of all the presentations with key figures and tables, and the distillation of the discussions.  In addition, moving forward, the principles CFN will use to approach frailty biomarker research and development are outlined.  Findings from the workshop are helping CFN and CLSA plan and conduct the analysis of biomarkers in the CLSA samples and which will inform a follow-up data access competition.

Key words: CFN, CLSA, frailty, frailty index, seniors, older adults, biomarkers, aging.


 

Introduction

Older adults living with frailty represent an increasing concern for health systems due to increased vulnerability to acute stressors, increased risk of functional impairment, increased healthcare utilization including emergency department visits, hospitalizations, and increased mortality (1-4). Therefore, it is critically important to identify, as early as possible, those at imminent risk of frailty and those living with frailty. In addition, there is a need to be able to assess the severity of frailty as objectively as possible for both prognostic purposes and monitoring of response to therapeutic interventions.
There are two main views of frailty; as a syndrome or as a state arising from an accumulation of deficits. Considerable progress and increased understanding of both approaches has occurred since their introduction (5). Frailty as a phenotype can be measured by Fried or Bergman phenotype criteria (1, 6). This model derives from an attempt to characterize the clinical manifestations of vulnerability outside of multimorbidity and disability. Frailty as an accumulation of deficits can be measured by the Frailty Index (FI), which is the number of physiological deficits affecting an individual divided by the total number of deficits measured (7, 8). The theoretical underpinning for the deficit model arises from the observation that deficits increase variably with age in humans and in animals and that the risk of morbidity, functional decline and death increases with an increasing number of deficits both individually and on a population basis (9, 10).
Although the concept of frailty is widely accepted, how to best detect and measure the severity of frailty remains controversial and as a consequence, there are many clinical frailty assessment instruments utilized in practice and research (11-13). Shortcomings of current instruments include the requirement for relatively large amounts of data, the use of specialized procedures, subjective assessments and the lack of responsiveness to therapeutic interventions (14-16). Frailty biomarkers have the potential to complement the clinical evaluation of frailty including aiding in its diagnosis, assessment of severity, and evaluation of prognosis (17, 18). Although clinical frailty assessment instruments may be more effective from a population-based approach, whereas frailty biomarkers, may be able to individualize diagnosis/prognosis and personalize care by determining an individual’s biological frailty profile.
The utility of biomarkers has been demonstrated in the diagnosis of some cancers and other disorders (19-21), and to assess treatment responses and disease progression in various diseases (22, 23). In addition, biomarkers offer the promise of precision medicine where a person’s care and treatment is based on one’s genetics and biology and there is a need to extend and explore this promise to the challenges posed by frailty.
A promising venue to study frailty biomarkers are longitudinal research initiatives that collect clinical data on aging in large numbers of participants coupled with biological samples. The Canadian Longitudinal Study on Aging (CLSA) (www.clsa-elcv.ca/) which studies the aging process in over 50,000 Canadians is one of the largest and most comprehensive initiatives in the world. Selected participants undergo comprehensive clinical evaluations including frailty assessments supplemented with hand grip-strength, timed up-and-go, chair rise, 4-metre walk and standing balance. Using a FI of 90 possible health deficits with 0.25 as the threshold for frailty, approximately 7% of CLSA participants are frail, increasing to approximately 11% in those over the age of 75 (24). Biological specimens are collected in a selected cohort every three years including whole blood, serum, plasma and buffy coat containing peripheral blood mononuclear cells (Table 1).  For a biomarker to be considered for inclusion in the CLSA, it needs to have at least preliminary evidence to link the biomarker to a pathophysiological frailty pathway or mechanism.
Here we report the proceedings of a symposium convened by the Canadian Frailty Network (CFN) (www.cfn-nce.ca) in collaboration with the CLSA. The overall objective of this meeting was to inform future efforts of CFN to improve the availability of frailty biomarkers and to guide further CFN funded analyses of biological samples held by the CLSA. Specific objectives of the symposium were to:
1.    Explore the current state of evidence of biomarkers for frailty.
2.    Obtain an understanding of other frailty biomarker initiatives around the world.
3.    Obtain guidance on biomarkers that could be measured in the CLSA biological samples, in addition to those already being assayed, to identify the biological, biochemical and genetic factors/markers associated with the onset and progression of frailty in order to develop predictive, prognostic and diagnostic tests to aid in the care and treatment of people living with frailty.

Table 1 Biomarkers currently being analysed in the CLSA samples

Table 1
Biomarkers currently being analysed in the CLSA samples

Workshop details

The workshop was held in Toronto, Canada on January 15th 2018. Participants for the workshop were international stakeholders, key opinion leaders and frailty and/or biomarker experts.  They were identified using one or more of the following criteria: they were leading large scale initiatives involving frailty and the measurement of biomarkers, they were investigators on CFN-funded research grants studying frailty and measuring biomarkers, they had published peer-reviewed studies on frailty and related biomarkers, were clinicians caring for older adults living with frailty and/or were in relevant decision-making roles. The twenty-two delegate attendees included basic researchers (e.g., biochemist, pharmacologist, immunologist), clinician researchers (e.g., intensivist, geriatrician, cardiologist) and health-care administrators/policy experts.

The current state of frailty biomarkers and frailty assessment

Multiple clinical frailty assessment instruments exist, each with advantages and disadvantages and it is not clear which instrument is optimal for a particular care setting (15). In addition, due to variability in their measurement characteristics, the prevalence of frailty depends on the instrument utilized (25, 26). This variability together with the subjective nature of some frailty assessments have generated increasing interest in integrating clinical assessments with objective laboratory-based biomarker tests.   The working definition for this discussion was that a biomarker is, “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.” (NIH working group) (27). Some potential uses of biomarkers conceived by the 2001 NIH working group are listed in Table 2.

Table 2 Potential utility of biomarkers

Table 2
Potential utility of biomarkers

 

Biomarkers may lead to the identification of mechanisms and pathophysiological pathways leading to or causing frailty, and the early identification of frailty which, importantly, may be reversible in early stages (28, 29). In addition, identification and modification of treatment plans in those identified as frail may ameliorate poor outcomes from healthcare interventions (30-32). Lastly biomarkers may help with selecting interventions, monitor response to treatments, and identify those for whom some interventions are unlikely to be of benefit.
Although no single biomarker has yet proven to be of sufficient diagnostic or prognostic utility to be clinically useful for frailty, there has been increasing work on integrating laboratory evaluation in the assessment of frailty (33). As an example, a FI composed of laboratory results has been developed and shown to be effective in identifying frailty in individuals living in the community and those in long-term care facilities (17). This index of biomarkers (the FI-Lab) predicts increased risk of death in community-dwelling and institutionalized individuals and it can be combined with a clinical FI to improve prediction of outcomes in older adults (17, 18, 34-36).
A recent systematic review of frailty biomarker studies in community-dwelling individuals of trials using a validated definition of frailty and comparing two or more biomarkers was recently reported (33). Biomarkers related to immune function, inflammation, endocrine function and metabolic syndrome were the most frequently reported. There have been conflicting results regarding the utility of single biomarkers for the detection and assessment of frailty. Associations with frailty have been shown for inflammatory markers (e.g., interleukin-6 (IL-6), Tumor necrosis factor-alpha (TNF-alpha), C-reactive protein (CRP)), reduced total lymphocyte count and other markers of immunocompetence, although this has not been observed with some markers that have been associated with aging such as telomere length and oxidative stress, when studied individually (37). Other systematic reviews have come to the same conclusion. The variety of biomarkers reported in recent systematic reviews are summarized in Table 3 (33, 38-40).

Table 3 Summary of biomarkers examined in prior studies

Table 3
Summary of biomarkers examined in prior studies

 

Specific Frailty Biomarker Considerations

Frailty Index (FI) approach to biomarkers

Consistent with the deficit accumulation approach, Mitnitski et al. (18) re-analysed the data in Collerton et al. (37) to create an FI composed of 40 biomarkers of cellular ageing, inflammation and haematology. Like the FI-Lab, this suggests that currently available single biomarkers have relatively low information value by themselves when compared to a combination of multiple markers and an FI approach with a panel of biomarkers may be a more promising avenue of investigation, and more closely correspond to how deficits propagate to give rise to frailty (41, 42).
The deficit accumulation approach has been validated in animal models of frailty, where both laboratory-based and clinical FI tools have been developed and behave similarly as in humans (9, 43-45). Animal studies also indicate that levels of pro-inflammatory cytokines increase in proportion to FI scores (45). Further studies suggest that mechanisms that give rise to frailty are present at the cellular/subcellular levels and scale up to impact function at the level of the organ and ultimately the organism (46, 47). The availability of animal models of frailty with biomarker and clinical criteria has led to the study of possible interventions for frailty in pre-clinical models using resveratrol, caloric restriction and an angiotensin converting enzyme inhibitor (48, 49). All this is consistent with the multifactorial nature of frailty, thus requiring the evaluation of a biological network (and not a standalone biomarker) for adequately capturing the complexity of the state.
In conclusion, measuring a larger set of biomarkers (e.g., 40) is likely to be better than measuring a smaller set. Given current evidence, inflammatory markers should be included in any frailty biomarker panel as should measures of metabolism (large panel metabolomics) and other measures related to aging processes (e.g., telomere length, oxidative stress, DNA damage and repair).  Following a standard procedure for creating a FI from clinical data, for a biomarker to be included as a deficit in a FI, the biomarker should have the following characteristics, as outlined by Searle et al. (8):
•    Abnormal biomarker levels should be associated with a negative health-related outcome.
•    The risk associated with abnormal levels of the biomarker should be higher as age increases.
•    The biomarker should be neither too rare (i.e., < 1%) nor too common (i.e., > 80% by age 80).
•    Biomarker data should be available for at least 80% of individuals.

Frailty and skeletal muscle

Muscle mass, has for the most part, been neglected in frailty assessment instruments, perhaps because there are no good practical tools to measure it.  Most current methods are crude (e.g., calipers, body mass index), complex or difficult to use or access (e.g., magnetic resonance imaging). Computerized tomography (CT) scans are of increasing promise due to their availability and growing evidence indicates that muscle mass seen on CT scans, as evidenced by psoas muscle area, correlates with outcomes including mortality (50-52). In particular, muscle mass analysis can be an important measure for frailty assessment in patients who are already undergoing CT scans for other medical reasons and can be of added prognostic value to other biochemical biomarkers/tests. Although CTs are of potential utility, further research is required before they can be recommended for routine clinical use. Bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DEXA) are also commonly used for the assessment of muscle mass but their availability are limited and their ability to guide clinical practice requires further evaluation (53, 54). Overall, skeletal muscle assessment should be tailored to the objectives desired and the availability of the diagnostic test. Also different tests may be required for different care settings (55).

Anemia and hypoalbuminemia

Anemia has been shown to increase rapidly with age and is associated with adverse geriatric outcomes such as disability, reduced physical function, cognitive dysfunction and dementia (56, 57). The presence of anemia is also associated with an increased likelihood of frailty (58). It is unknown whether this is due to reduced oxygen delivery, resulting in inactivity and fatigue, or if anemia is the result of other conditions linked to frailty, including chronic inflammation and/or malnutrition. In addition, it is unknown if the successful treatment of anemia can reverse or attenuate frailty?
Reduced serum albumin has been associated with disability and mortality (59). While hypoalbuminemia was previously thought to be a marker of malnutrition, it is now known that it is more likely due to systemic inflammation (60). The incorporation of albumin and anemia into clinical frailty criteria has been shown to be predictive of death and worsening disability in selected surgical populations (e.g., transcatheter aortic valve replacement) (www.frailtytool.com) (61, 62). Moreover, these two parameters are commonly measured clinically thereby increasing their availability for the assessment of frailty.

Other biomarkers

Other biomarkers such as vitamin D and androgen levels have also been linked to frailty.  A systematic review suggests that low levels of vitamin D are associated with an increase in the risk of frailty (63), although potential benefits of vitamin D supplementation on frailty requires additional evidence (64). There is no consensus in the literature with respect to the biological role of androgens in frailty. An earlier review concluded that more work needs to be done (65), although recent longitudinal studies show an association between testosterone and FI scores (66, 67). Evidence to date suggests that testosterone treatment may be beneficial in older people (68-70). However, further evidence is required before recommending that these biomarkers be incorporated into future frailty biomarker panels.

 

Current frailty studies and biomarker databases

Participants of this CFN sponsored workshop are leading a number of international initiatives investigating the potential utility of biomarkers for frailty assessment. These initiatives are discussed below.

Sarcopenia & Physical fRailty IN older people: multi-componenT Treatment strategies (SPRINTT); www.mysprintt.eu/en/wp

The SPRINTT project (5 years) was started in 2014 as a private/public partnership between the European Commission and the European Federation of Pharmaceutical Industries and Associations.  The overall goal of SPRINTT is to develop innovative therapeutic interventions against physical frailty and sarcopenia as a prototype geriatric indication. SPRINTT includes a definitive large phase III randomized clinical trial (RCT) (71) comparing the efficacy of a multi-component intervention in preventing mobility disability, based on long-term structured physical activity, personalised nutritional counselling/dietary intervention and an information and communication technology intervention, versus a healthy ageing lifestyle education program.  The RCT completed recruitment in November 2018 and follow-up is planned to be completed in 2019.  One of the accomplishments of SPRINTT was the operationalization of a new condition incorporating physical frailty and sarcopenia, whose methodology was endorsed by the European Medicines Agency (EMA) (72).
For each participant biological samples are collected (whole blood, serum, plasma and urine) for future analysis (Table 4).  The collection of these biospecimens has two objectives: 1) development of discriminant biomarkers that will allow for comparison of biomarker levels between physical frailty and sarcopenia (at baseline) versus physical frailty and no sarcopenia. 2) development of prognostic and predictive biomarkers for the identification of study participants at high risk for the development of mobility disability and prediction of response to the multi-component intervention.

Table 4 Candidate biomarkers being considered for SPRINTT

Table 4
Candidate biomarkers being considered for SPRINTT

FRAILOMIC; www.frailomic.org

The main goal of the FRAILOMIC initiative is to develop clinical instruments composed of clinical data, laboratory biomarkers and omics-based laboratory biomarkers. Specifically, the three objectives of FRAILOMIC are to develop clinical instruments that can: 1) improve diagnostic accuracy of frailty in day-to-day practice, 2) predict the risk of frailty and 3) assess the prognosis of frailty in terms of death, disability and other adverse outcomes.  FRAILOMIC has four main phases, with the first two phases now complete, which have identified the biomarkers listed in Table 5.  Preliminary results show that biomarkers potentially useful for the diagnosis of frailty are different from those useful for assessing frailty risk and prognosis.

Table 5 Biomarkers identified in exploratory phase. Biomarkers identified according to their relationship with function, muscle and longevity. Selection of biomarkers attempted to be as inclusive as practical

Table 5
Biomarkers identified in exploratory phase. Biomarkers identified according to their relationship with function, muscle and longevity. Selection of biomarkers attempted to be as inclusive as practical

 

Discussion

Biomarkers, definition of frailty and fit within clinical practice

Key to the selection of biomarkers for frailty assessment will be an agreed upon definition of frailty. However, coming to a consensus definition of frailty and choosing the appropriate clinical instruments have proven to be difficult.  Current frailty instruments/tools in use today, whether based on a deficit accumulation model or a phenotypic model have varied sensitivity, specificity, and positive and negative predictive values (73-76). Moreover, frailty assessment tools may perform differently in different populations and settings (11-13). Overall, it is very likely that there will be no single best reference clinical frailty assessment instrument, so to proceed with biomarker work, a single measurement paradigm will need to be selected and used consistently.
In regards to the selection of the reference paradigm, it should be connected with the underlying biology of frailty. It is generally agreed that frailty is a result of the age-associated accumulation of deficits across multiple systems and therefore the FI paradigm seems to be a reasonable starting point. The FI is consistent in different care settings, countries and species because of its basis in systems biology. In addition, the FI is multi-dimensional and allows for grading of frailty severity, which is an important determinant of outcome. One drawback to the FI is that it may be impractical as a clinical assessment tool since it requires large amounts of data, stemming from the results of a comprehensive evaluation of the individual but this is less of a disadvantage when used for research purposes as a reference standard.
Any adopted biomarker needs to pair with the clinical instrument used to better diagnose, predict risk and determine therapeutic responsiveness (77). The utility of the FI is that it can utilize readily measured clinical deficits, many of which are currently recorded and even exist in electronic health records (10). The FI can utilize readily available and routine laboratory tests, that can also be augmented with novel biomarkers. This would apply to laboratory tests such hemoglobin, albumin, estimated glomerular filtration rate, low density lipoprotein and glycosylated hemoglobin. If a combination of these perform well in a frailty assessment tool such as the FI, the utility of more complex, more difficult to measure and more expensive biomarkers would need to be demonstrated before they are adopted. Overall, to be most applicable and advance the possibility of personalized medicine for people living with frailty, it would be better to start broadly with the FI and then tailor biomarkers to the specific needs of the person.

Clinical frailty assessment instruments versus biomarker-based frailty assessment tools

Biomarkers may direct clinicians, but do not replace geriatric expertise. Any biomarker(s) chosen will need to assist clinical activities beyond what already exists, be easier to use and be more cost-effective. Biomarkers that are difficult and expensive to measure or require specialized equipment should be reserved for environments dealing with complex cases or for research purposes.  Some assessments, such as gait speed combined with grip strength, may already provide data that biomarkers promise to do (74, 75). Biomarkers will need to increase the sensitivity, specificity, negative predictive value and positive predictive value of clinical evaluations or instruments, especially for risk prediction and this will need to be demonstrated in clinical use. Also, for their adoption, it will need to be demonstrated that their utilization is associated with better outcomes.
When a clinical frailty assessment tool is used in combination with biomarkers, it is important that the biomarker(s) increase the utility of the tool as follows: 1) It allows the tool to better identify a population as truly frail than the clinical tool alone, and 2) It allows for better selection of a care plan for an individual than the clinical tool alone. To determine if a biomarker has these key characteristics, further research will be required once candidate biomarkers are identified. As an example, if a biomarker(s) identifies a nutrient deficiency, we need to know that acting on that information would improve outcomes. This will likely only be answered by doing randomized clinical trials with the ability to detect changes in patient-centered outcomes such as quality of life. Overall, the identification of frailty in a more rigorous manner, with attendant care plans, would be a major step forward.

Generally available versus novel biomarkers

Biomarkers that are only associated with a particular disease may not be of high interest when considering frailty since they are likely specific for the disease. Biomarkers more applicable to frailty are likely those that are closer to the physiology and basic biology of aging. Studies of the basic biology of aging has led to a number of new interventions targeting fundamental aging processes that have been found to impact development of age-related disorders, including many chronic diseases, geriatric syndromes and loss of resilience/loss of ability to respond to stressors in animal models (78, 79). These new interventions will need to be tested in human clinical trials. Biomarkers may be better able to characterize the populations enrolled in these trials to avoid the heterogeneity that is characteristic of frailty and aging. If successful, these interventions may be able to fundamentally change geriatric medicine, with the ultimate goal of increasing health-span and not necessarily life-span.

Need for a variety of biomarkers

An ideal biomarker would present early in the course of frailty and correlate with its severity. There are indications that different biomarkers are needed for risk stratification, diagnosis and prognosis. Overall, it is unlikely that a single biomarker can address all these objectives (80). Moreover, the utility of a diagnostic biomarker is dependent on the prevalence of the condition which in frailty are care setting dependent increasing from a small percentage of the general population to as much as 60% in hospital, and majority in long-term care (81-84). In very high prevalence environments, diagnostic frailty biomarkers may have limited utility. For prognosis, we need to be as precise as possible as to what they predict to make them useful to clinicians; risk of poor functional outcomes may be as important as mortality.

Broader versus narrower sets of biomarkers

For discovery, there may be value in adopting a biomarker approach based on metabolomic profiling, understanding that this approach might not have clinical value as yet but will help to generate a wide range of markers, which from a research perspective may have great utility. The added benefit of utilizing metabolomics on whole blood, plasma or urine is that once mass spectrometry (MS) or nuclear magnetic resonance (NMR) data are captured, these data can be reanalyzed in the future for new markers. Metabolomics has already produced results that are leading to frailty interventions, such as ketogenic compounds, for which clinical trials are about to start (85, 86). There are at least 20 proteins or peptides in mice related to frailty where preliminary human trials are being considered (87, 88). Also with newer techniques, proteins can be measured in very small quantities of sample (e.g., 10 microlitres). The caveat is that although MS and NMR are both powerful technologies with great discovery (research) potential, at this time translating their application to clinical settings is more difficult than other approaches, such as determining inflammation by measuring interleukins or acute phase CRP.
There are large studies utilizing metabolomics but no such large study exists for frailty investigation. However, this work has already commenced in the CLSA with 1,000 people having metabolomics analysis. Further metabolomics analysis within the CLSA database will be a unique opportunity to significantly add to the evidentiary base.

 

Conclusions

This workshop brought together experts in the field of biomarkers, aging and/or frailty to discuss frailty biomarkers. This discussion will inform CFN and CLSA in planning for the analysis of a select group of biomarkers in the CLSA samples and a follow-up data access competition to utilize these new biomarker data in conjunction with the wealth of clinical data collected by the CLSA.
There was considerable discussion regarding the appropriate way to think about frailty and how the frame of reference will influence the choice of biomarkers to analyze.  Considerations discussed included probabilistic medicine and population-based care versus precision medicine or individualized medicine and how these overall approaches should influence the use of biomarkers and their incorporation into practice and ultimately influence policy.  There was debate about care settings and biomarker utility across care settings. In addition, the overall objective of using biomarkers as diagnostic tools for frailty versus their use to quantify frailty risk versus their use as frailty prognostic tools were topics of high interest and discussion. By the end of the workshop, there was a clear sense that research and clinical trials need to be conducted regarding frailty biomarkers and the need for the development of biomarker tools to be used either alone or with currently existing clinical frailty assessment instruments such as the FI.
Arising from the meeting, the list of principles to guide future CFN biomarker initiatives including its partnership with the CLSA, are as follows:
1)    Biomarkers should reflect a pathophysiological pathway or mechanism that is fundamental to frailty onset, development/progression and severity. Conceptually there maybe two categories of biomarkers:
i)    Biomarkers that are linked with frailty but are not causal to the pathophysiology of frailty. These would not be actionable.
ii)    Biomarkers that are a component of the pathophysiology of frailty and have a causal role. These would be actionable such that the modulation of the biomarker could directly affect the onset or severity of frailty and/or progression of frailty.
2)    The utility of biomarkers can be classified into two different types:
i)    Biomarkers to increase the utility of (or support) existing clinical frailty measures (e.g., FI).
ii)    Biomarkers to be used independently of clinical frailty measures.
3)    Biomarkers should be able to be embedded in clinical assessments and tools, but more research on how to best achieve this is needed. Concomitant use of both a clinical frailty assessment instrument and biomarkers is likely to be the optimal method to bring about personalized frailty assessment and individualized care plans.
4)    Biomarkers chosen for a clinical tool should be evaluated on their ability to accomplish the ultimate clinical purpose. For instance, biomarkers used for diagnosis may be different from those used for risk assessment, which may differ from those used for prognosis.
5)    Different care settings are likely to require different biomarkers due to variation in prevalence of both frailty and biomarkers in different populations.
6)    Any candidate biomarker should be validated in different populations, care settings and environments.
7)    An ideal frailty biomarker would be able to measure the effectiveness of an intervention.
8)    Practical considerations related to ease of measurement (i.e., special instruments and/or expertise required) and ease of securing biological samples (e.g., tissue biopsy vs blood sample collection) should be considered when selecting frailty biomarkers.

 

Acknowledgements: The Canadian Frailty Network (CFN) is a pan-Canadian network focused on the care of older citizens living with frailty. CFN is comprised of nearly 3,500 corporate and non-profit partners, researchers, scientists, health-care professionals, citizens, students, trainees, educators, and decision-makers. CFN supports and catalyzes original research and innovations to improve the care and quality of life of Canadians living with frailty across all settings of care. The Network also trains the next generation of health-care professionals and scientists. CFN is funded by the Government of Canada through the Networks of Centres of Excellence (NCE) Program. In early 2017, in recognition of the work done in its first five years of operation, the Government of Canada announced funding for a second five-term (2017-2022).

Conflict of interest disclosures: Matteo Cesari, Emanuele Marzetti and Leocadio Rodriguez Mañas are partners of the SPRINTT consortium, which is partly funded by the European Federation of Pharmaceutical Industries and Associations (EFPIA) . Kenneth Rockwood is President and Chief Science Officer of DGI Clinical, which in the last five years has contracts with pharma and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, Otsuka) on individualized outcome measurement. In 2017 he attended an advisory board meeting with Lundbeck. He is Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in first phase (2013-2018), from Pfizer Canada and Sanofi Canada.   He receives career support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research, and research support from the Canadian Institutes of Health Research, the Nova Scotia Health Research Foundation, the Capital Health Research Fund and the Fountain Family Innovation Fund of the Nova Scotia Health Authority Foundation.

 

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BIOMARKERS OF SARCOPENIA IN CLINICAL TRIALS RECOMMENDATIONS FROM THE INTERNATIONAL WORKING GROUP ON SARCOPENIA

 

M. CESARI1, R.A. FIELDING2, M. PAHOR3, B. GOODPASTER4, M. HELLERSTEIN5, G. ABELLAN VAN KAN1, S.D. ANKER6,7, S. RUTKOVE8, J.W. VRIJBLOED9, M. ISAAC10, Y. ROLLAND1, C. M’RINI11, M. AUBERTIN-LEHEUDRE12, J.M. CEDARBAUM13, M. ZAMBONI14, C.C. SIEBER15, D. LAURENT16, W.J. EVANS17, R. ROUBENOFF18, J.E. MORLEY19, B.VELLAS1 FOR THE INTERNATIONAL WORKING GROUP ON SARCOPENIA

 

1.  Institut du Vieillissement, Gérontopôle and INSERM Unit 1027, Université de Toulouse, Toulouse, France; 2. Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA; 3. Department of Aging and Geriatric Research, Institute on Aging, University of Florida, Gainesville, FL, USA; 4. Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA, USA; 5. Department of Nutritional Sciences and Toxicology, University of California at Berkeley, San Francisco, CA, USA; 6. Department of Cardiology, Campus Virchow-Klinikum, Charité Universitätsmedizin Berlin, Germany; 7. Centre for Clinical and Basic Research, IRCCS San Raffaele, Rome, Italy; 8. Harvard Medical School, Boston, MA, USA; 9. Neurotune AG, Schlieren, Switzerland; 10. Human Medicine Special Areas, Scientific Advice Section, European Medicines Agency, London, UK; 11. Institut Mérieux, Lyon, France; 12. Départment de kinanthropologie, Université du Quebec, Montreal, Canada; 13. Clinical Research Operations, Neuroscience & Neuromuscular Disorders, Cytokinetics Inc., South San Francisco, CA, USA; 14. Department of Medicine, University of Verona, Verona, Italy; 15. Institute for Biomedicine of Aging, Friedrich-Alexander-University Erlangen-Nürnberg, Nürnberg, Germany; 16. Novartis Institutes for Biomedical Research, Basel, Switzerland; 17. Muscle Metabolism DPU, Metabolic Pathways CEDD, GlaxoSmithKline, Research Triangle Park, NC, USA; 18. Musculoskeletal Translational Medicine, Novartis Institutes for Biomedical Research, Cambridge, MA, USA; 19. University School of Medicine and GRECC, VA Medical Center, St. Louis, MO, USA.

Corresponding author: Matteo Cesari, MD, PhD, Institut du Vieillissement, Gerontopôle; Université de Toulouse. 37 Allées Jules Guesde, 31000 Toulouse, France. Phone: +33 (0)5 6114-5628; Fax: +33 (0)5 6114-5640; Email: macesari@gmail.com. Alternative address for correspondence: Roger Fielding, PhD, Jean Mayer USDA Human Nutrition Research Center on Aging; Tufts University. 711 Washington Street, 02111 Boston, MA, USA. Email: roger.fielding@tufts.edu

J Frailty Aging 2012;1(3):102-110
Published online February 16, 2012, http://dx.doi.org/10.14283/jfa.2012.17


Abstract

Sarcopenia, the age-related skeletal muscle decline, is associated with relevant clinical and socioeconomic negative outcomes in older persons. The study of this phenomenon and the development of preventive/therapeutic strategies represent public health priorities. The present document reports the results of a recent meeting of the International Working Group on Sarcopenia (a task force consisting of geriatricians and scientists from academia and industry) held on June 7-8, 2011 in Toulouse (France). The meeting was specifically focused at gaining knowledge on the currently available biomarkers (functional, biological, or imaging-related) that could be utilized in clinical trials of sarcopenia and considered the most reliable and promising to evaluate age-related modifications of skeletal muscle. Specific recommendations about the assessment of aging skeletal muscle in older people and the optimal methodological design of studies on sarcopenia were also discussed and finalized. Although the study of skeletal muscle decline is still in a very preliminary phase, the potential great benefits derived from a better understanding and treatment of this condition should encourage research on sarcopenia. However, the reasonable uncertainties (derived from exploring a novel field and the exponential acceleration of scientific progress) require the adoption of a cautious and comprehensive approach to the subject.

Key words: Biomarkers, sarcopenia, elderly, skeletal muscle, imaging, screening, follow-up, assessment, aging, consensus paper.

 

The present article is jointly published in the Journal of Frailty & Aging and in the Journal of Cachexia, Sarcopenia and Muscle


 

Introduction

One of the most recognized changes in body composition with senescence is the loss of skeletal muscle mass. This loss occurs even among physically active older persons and was originally termed «sarcopenia» for the Greek words «flesh» and «loss» (1). The age-related loss in skeletal muscle mass is associated with substantial social and economic costs and is characterized by impairments in strength, limitations in function, and ultimately physical disability and institutionalization (2-4). In consideration of the increased awareness of this syndrome and the continued rapid development of therapeutic strategies to slow or reverse sarcopenia, the International Working Group on Sarcopenia was convened to address issues related to the successful conduct of clinical trials in this area (5). This task force, consisting of geriatricians and scientists from academia and industry, met again in Toulouse, France in June of 2011, to discuss the current state of the art in the development of biomarkers to be utilized in clinical trials on sarcopenia. The purpose of this meeting was to gain an understanding of the currently available parameters that could be utilized in clinical trials of sarcopenia and to discuss future research needs in this area. Specific topics that were addressed include: review of current consensus definitions of sarcopenia, the importance of muscle performance and quality, biomarkers in other clinical states and chronic diseases, potential biomarkers for sarcopenia, applications in clinical trials, and recommendations for future studies.

Definition of sarcopenia

Since the advent of the term «sarcopenia» in 1989, there has been a dramatic increase in publications in this area and clinical interest in this condition (6). Originally described as the age-related decrease in skeletal muscle mass (7), until very recently there has been a lack of consensus on the operational definition of sarcopenia without clinically appropriate correlates for this syndrome. In the past two years, a number of academic societies have put forward operational definitions of sarcopenia (8-11). Although each consensus definition has some distinct features, there is general agreement among these groups on the definition of sarcopenia. A summary of consensus sarcopenia definitions is presented in Table 1. The characteristics of sarcopenia highlighted in these reports include: an objective measure of muscle or fat free mass using dual energy x-ray absorptiometry (DXA) or computed tomography (CT), a reliable measure of muscle strength, and/or an objective test of physical functioning. Although the sequence of events and specific recommendations differ somewhat, the general approaches proposed require that patients be identified with measured deficits in physical function for which sarcopenia may be the cause, and subsequently quantification of muscle strength and mass to definitively confirm the diagnosis.

Table 1 Summary of consensus sarcopenia definitions

Definition of biomarker

A biomarker is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”(12). Hence, biomarkers support the diagnosis, facilitate the tracking of changes over time, and help clinical and therapeutic decision-making processes. Taking this definition into account, the functional, biological, or imaging-related parameters considered in the present document will be hereby generally referred to with the term «biomarker».

There are currently numerous parameters that are potentially able to track the age-related skeletal muscle decline. Depending on the parameter chosen to define sarcopenia, different information might be obtained. Such variability depends on the specific characteristics of each parameter and the mechanisms measured by the parameter. The intrinsic (e.g., accuracy, specificity, sensitivity) and extrinsic (e.g., cost, availability, time to be performed) properties of each biomarker will largely drive its use in research trials, making it more suitable for screening, baseline evaluation, and/or definition of outcomes (Table 2).

Table 2 Possible biomarkers to be used in trials on sarcopenia

* The importance of all these biomarkers in the evaluation of sarcopenia will largely depend on the study hypotheses, the specific aims, and/or the target population. – : Not recommended for this use; + : may be of use, but severely limited; ++ : suitable for this use; +++ : recommended for this use

The use of biomarkers in a given study must be «fit for purpose». Thus, several different biomarkers may be required to support different aspects of the development of a therapeutic intervention. For example, biomarkers for detection and diagnosis may not be the same as those that ideally track disease progression. Likewise, for new therapeutic agents, a single assay may not suffice as a biomarker reflecting both target engagement and the pharmacodynamic effects of a drug.

Muscle quantity versus muscle quality

Although muscle mass can objectively define the presence of sarcopenia, several components of skeletal muscle function are not adequately captured by simply measuring mass or cross-sectional area. It is now clear that there is a certain degree of divergence between changes in muscle mass and alterations in muscle performance. The well-described decline in skeletal muscle mass in older adults is a critical determinant of age-related weakness, which is defined as a reduction in maximal voluntary joint torque or power. Yet, it is now clear that the relationship between force production capability and muscle size in older adults is less robust than it is in young people (13). Indeed, longitudinal studies have demonstrated that the age-related decline in muscle strength far exceeds the observed changes in muscle mass or size, particularly in weight-stable individuals (14, 15). Furthermore, longitudinal studies indicate that maintenance or even gain of muscle mass may not prevent weakness in older adults (15, 16). In addition, a number of age-related changes in force production capability is not readily explained by a reduction in muscle mass, including decreased specific force (force per cross sectional area) (17, 18) and slower rate of isometric force production (expressed relative to peak torque or to body weight) (19, 20). Furthermore, voluntary weight loss leads to reductions in muscle mass/size with no declines in muscle strength (21). It is also noteworthy that pharmacologic interventions that increase muscle mass/size do not necessarily improve voluntary strength. Similarly, physical activity interventions that increase muscle strength do not necessarily augment muscle size (22, 23). Noticeably, gains in muscle strength secondary to increased physical activity generally precede measurable changes in skeletal muscle mass/size.

The progressive muscle atrophy with aging is associated with a loss of overall muscle force and changes in force and power generation of the remaining muscle fibers (24). However, several additional physiological mechanisms that accompany the phenomenon of sarcopenia may directly influence muscle function and force production with advancing age. Recent evidence has shown that adipose tissue accumulation around and between muscle fibers concomitant with reductions in muscle cross-sectional area occurs with aging, and that this skeletal muscle attenuation is inversely associated with muscle performance (18, 25). Age-related changes in the nervous system may also play a substantial role in the decline in muscle power generation (26). These include loss of motor neurons and concomitant remodeling of motor units through collateral reinnervation (27), impairment of neuromuscular activation observed as decreased maximal motor unit firing rates (28-30) and uncoordinated patterns of intermuscular neural activation (31). Finally, changes in individual muscle fiber composition and intrinsic contractile properties may influence the decline in muscle force among older adults. For instance, cross-sectional observations suggest that reductions in muscle torque may be related to changes in fiber composition and, in particular, to the preferential atrophy of type II (fast-twitch) fibers with aging (32). Specific changes in the intrinsic ability of aged muscle to generate force have also been observed (33). Decreases in specific force (force normalized per cross sectional area) and unloaded shortening velocity in type I and IIA fibers have been reported in older males compared with young controls (32, 34). Conversely, recent longitudinal data have demonstrated that, despite reductions in whole muscle cross-sectional area, single muscle fiber contractile function is preserved with advancing age as existing fibers may compensate and partially correct these deficits, therefore maintaining optimal force-generating capacity (14).

Although precise and valid measures of muscle mass are important components of sarcopenia assessment, these gross measures of muscle size do not adequately account for the dynamic components (force, power, activation) of muscle function that are responsible for performing activities of daily living. Future trials on sarcopenia adopting clinically meaningful endpoints should evaluate these key biomarkers of muscle function through the use of state-of-the-art methodologies.

Quantitative assessment of sarcopenia

The bidimensional definition of sarcopenia simultaneously includes a functional parameter (i.e., muscle performance) and a quantitative index (i.e., muscle mass). Therefore, techniques aimed at capturing the objective amount of skeletal mucle mass are required. Multiple methodologies are currently available to accomplish this task (35).

DXA is the most commonly used imaging technique for several reasons. First of all, because it is commonly available in clinical and research settings, being relatively inexpensive, sufficiently precise, and well-accepted by older persons. Second, the initial operative definition of sarcopenia proposed by Baumgartner and colleagues (3) was based on appendicular lean mass measured by DXA. Later on, DXA was used to provide alternative definitions of sarcopenia based on the fat-adjusted residual method (36). Nevertheless, it cannot be ignored that the first operative definition is dated more than 10 years, and during this time several steps forward have been made in refining imaging techniques as well as understanding the sarcopenia phenomenon.

The identification of the “gold standard” for the quantitative evaluation of muscle mass in clinical trials (which is currently lacking) should be based on criteria of accuracy (i.e., the degree of conformity of a measure to a standard or a true value), precision (i.e., the degree of refinement with which an operation is performed or a measurement stated), reproducibility (i.e., the quality of being reproducible under the same operating conditions over a period of time, or by different operators), sensitivity to change (i.e., the degree of being modified by interventions), and accessibility (i.e., its usual availability in research and clinical centers).

DXA currently represents the more accessible technique for body composition assessment. It may accurately provide estimates of lean, fat, and bone tissues in the entire body or in specific regions. Moreover, it is inexpensive and quick to be performed. The radiation exposure associated with DXA is low and highly acceptable (about 1 mrem, a quantity similar to that of a 3-day background). The main limitations of this imaging approach reside in some analytical differences across manufacturers and models, and the risk of biased results due to the low differentiation between water and bone-free lean tissue.

CT accurately measures a direct physical property of the muscle (e.g., cross-sectional area and volume). It also allows the evaluation of muscle density (a parameter related to intramyocellular lipid deposits) as well as subcutaneous and intramuscular adipose tissue deposition. The radiation exposure associated with this technique is higher (i.e., about 15 mrem) than with DXA.

Magnetic resonance imaging (MRI) presents a high agreement with CT and provides similar measures. It does not involve radiation exposure, and also has the additional capacity of multiple slice acquisition, thus rendering 3D volumetric estimates. The lack of radiation exposure makes MRI the method of choice for many studies where ethics committee or national authority approval is more difficult to obtain for CT. The major limitations of this methodology reside in the higher technical complexity and costs, and in the inapplicability to subjects with older models of implanted metal devices (e.g., joint prostheses, pace-makers, etc.). Both CT and MRI may be limited in the ability to accomodate very obese individuals.

Finally, it needs to be emphasized that imaging provides information only about one of the two sarcopenia dimensions. As discussed earlier, changes in muscle function and quantity do not necessarily follow similar trajectories with aging (37). Therefore, interventions able to increase lean mass may not necessarily produce parallel gains in strength and vice versa (38). To overcome this issue and include the two components of sarcopenia in the same variable, it has been proposed to compute an index of skeletal muscle quality derived from the ratio between strength and mass (15, 39, 40).

One of the most recently developed techniques which might find larger application in the near future for the evaluation of sarcopenia is the electrical impedance myography (EIM) (41). This is a noninvasive, painless approach based on the surface application and measurement of a high-frequency, low-intensity electrical current applied to specific muscles. EIM detects changes in the conductivity and permittivity of skeletal muscle caused by alterations in muscle composition and structure. EIM is repeatable and sensitive to skeletal muscle changes in patients with amyotrophic lateral sclerosis (42). Moreover, its changes over time may also have clinical relevance as they are predictive of survival in animal models of amyotrophic lateral sclerosis (43). Finally, it is also noteworthy that the EIM phase shows a consistent inverse relationship with age (44).

An alternative method to measure skeletal muscle size is by ultrasonography. This technique has shown to be a valid (versus MRI-based measurements) and highly reliable way for assessing cross-sectional areas of large individual human muscles (45). It is particularly useful in mobility-impaired subjects who cannot easily be transported to scanners such as CT or MRI machines.

Also remarkable is the development of mass isotopomer distribution analysis based on the evaluation of protein and proteome synthesis rate obtained by heavy water labeling (46, 47). Although this technique can still be considered suitable mainly for research settings, its flexibility and the large amount of information it provides about a wide spectrum of proteins make it extremely promising.

Other techniques are also available to detect sarcopenia, but their limited validation, low accuracy, and difficult large-scale implementation discourage their use. For example, bioeletrical impedance analysis (BIA) is a popular, very simple and low-cost technique, but its results are far from being accurate. The BIA technique is based on the notion that tissues rich in water and electrolytes are less resistant to the electrical passage than adipose tissue. The BIA is therefore based on a single body resistance parameter (not a direct measure of skeletal muscle), and its results can be easily altered by fluid retention and health status in general. For these reasons, a recent consensus paper by the Society of Sarcopenia, Cachexia and Wasting Disorders has discouraged the use of BIA for the assessment of sarcopenia (9).

Definition of critical thresholds

There is still resistance to accept sarcopenia as a clinical condition despite its well-established relationship with major health-related negative events (in particular, mobility and physical disability) (8). This issue might (at least partly) be explained by the current lack of clinically relevant thresholds that distinguish normal from abnormal values of skeletal muscle mass.

Several approaches can be adopted to identify critical cut-points. A paradigmatic example potentially lending support to the operative definition of sarcopenia might be provided by the approach previously adopted to identify osteoporosis on the basis of bone mineral density. In fact, approaches that have been developed for bone and osteoporosis may serve well for skeletal muscle and sarcopenia. The clinical definition of a specific condition (which will consequently lead to the indication for treatment) might be based on:

1)    A parallel clinical diagnosis. For osteoporosis, diagnosis can be obtained by evaluating the presence of vertebral fractures or deformities at the X-ray examination. Vertebral fractures indicate decreased bone strength, regardless of bone mineral density. It is well-established that patients with vertebral fractures present an increased risk of new events, and therefore require treatment. This approach is legitimate and may well work, but may find some limitations when applied in primary prevention.

2)    A biological assessment. Given its well-established association with fracture risk, bone mineral density may represent the key parameter on which to rely to determine the presence or absence of osteoporosis. However, bone mineral density (like any other biological marker) exists as a continuous variable, does not present a clear threshold, and is parallel to gradients of risk. Although necessary to provide clinical relevance to biological markers, any categorization will lead to a loss of information and will inevitably introduce an “arbitrary” decision. For the definition of osteoporosis, the cut-off defining the disease was arbitrarily set by a committee which judged the -2.5 standard deviations at the T-score as an adequate match between risk and prevalence. One major problem with the bone definition that should not be repeated for sarcopenia is the inclusion of osteopenia. Osteopenia (defined by a bone mineral density T-score ranging between -1 and 2.5 SDs) encompasses about 50% of the female healthy population, and has led to confusion and concerns among policy-makers regarding the validity of a construct that cannot really be considered abnormal. An approach consistent with this model has also been adopted in the definition of other clinical conditions such as anemia (48).

3)    The risk of adverse clinical outcomes. The indication to treatment of a specific condition (e.g., osteoporosis) might be based on the evaluation of risk of events (i.e., fractures) resulting from the assessment of multiple factors (which may even not include bone mineral density) (49). This approach will not be exclusively based on the single evaluation of a (potentially inaccurate and/or arguable) biomarker, but on a more comprehensive screening and on cost-effectiveness analyses (e.g., treat if the 10-year risk is exceeding a critical threshold). With this rationale, the FRAX (50) and QFractureScores(51) algorithms were recently developed to guide osteoporosis treatment.

In summary, the presence of sarcopenia might be determined by 1) relying on a clinical diagnosis closely related to skeletal muscle decline (e.g., mobility disability) after exclusion of secondary causes, 2) a representative scientific committee identifying a critical threshold for a biological parameter directly representative of skeletal muscle health, and/or 3) developing a risk index to guide treatment.

Biological markers of sarcopenia

Given the syndromic nature of sarcopenia, intervention strategies aimed at preventing/treating its process might need to target multiple risk factors. In this context, several biological markers have been shown to be associated with skeletal muscle mass, strength and function, thus representing potential markers for the effect of the studied interventions. Such a list is quite long, and each biomarker identifies a specific mechanism contributing the age-related skeletal muscle decline, although they are not specific to muscle and many are likely to turn out to be only weakly associated with clinically relevant outcomes. The most common markers are inflammatory biomarkers [e.g., C-reactive protein (52, 53), interleukin-6 (52-54), and tumor necrosis factor-α (52, 54)], clinical parameters [e.g., hemoglobin (55, 56), serum albumin (57, 58), urinary creatinine (59)], hormones [e.g., dehydroepiandrosterone sulfate (60), testosterone (61), insulin-like growth factor-1 (62), and vitamin D (63-65)], products of oxidative damage [e.g., advanced glycation end-products (66), protein carbonyls (67, 68), and oxidized low-density lipoproteins (69)], or antioxidants [e.g., carotenoids (70, 71), and α-tocopherol (70)].

Other promising biomarkers have been identified in the last years and may represent useful parameters to more directly explore sarcopenia because they are closely related to skeletal muscle changes. For example, plasma concentrations of procollagen type III N-terminal peptide (P3NP) represent an interesting marker of skeletal muscle remodeling (72, 73). P3NP is a fragment released by the cleavage of procollagen type III to generate collagen III (a protein produced in soft connective tissues, skin, and muscle). Preliminary studies have also suggested an interesting role played by biomarkers specifically linked to the neuromuscular junction in evaluating skeletal muscle modifications (74, 75).

Clinical outcome measures of sarcopenia

Ultimately, the goal of clinical trials for sarcopenia treatments will require the evaluation of clinical benefit. In fact, clinical measures can also be considered as biomarkers as they reflect the impact of the pathological process of sarcopenia on the patient’s health. The assessment of measures of muscle strength (e.g., hand grip), muscle power (e.g., leg extension power), and physical performance [e.g., Short Physical Performance Battery (4) and gait speed tests] comprise important indices of the individual’s physical function. In addition, functional outcome measures will need to be developed in order to help understand the impact of any treatment-related quantitative gains in performance on the person’s daily life.

Recommendations

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Adoption of comprehensive operative definitions

The lack of a unique operative definition of sarcopenia and the numerous methodological issues could potentially hinder efforts to study sarcopenia and to develop effective treatments. Such difficulties should not hamper the process of exploring this syndrome which severely affects the health status of millions of older persons. The current ambiguities can be easily overcome by adopting flexible and comprehensive approaches in the design of studies, for example by avoiding reliance on a single parameter or technique to evaluate age-related skeletal muscle decline. The adoption of a variety of assessment approaches in combination is agreeable. Although this might lead to the risk of conflicting results (and increase the need of resources), it will serve to 1) capture different domains of the sarcopenia syndrome, 2) provide useful insights about the pathophysiological process underlying this phenomenon, and 3) facilitate the development and use of the findings in future and more definitive studies. In this context, it is noteworthy the lack of studies simultaneously testing different techniques measuring skeletal muscle (e.g., MRI, CT, DXA, etc) in relationship with clinically meaningful outcomes. Such studies might greatly help in the standardization of instruments and in the adoption of an univocal direction in the study of sarcopenia.

MRI and CT scan to be equally considered as “gold standard” imaging techniques

It is now clear that to be adequately assessed, the sarcopenia phenomenon cannot merely rely on the evaluation of the contractile part of skeletal muscle. The close relationship between lean mass and adipose tissue in determining age-related decline of skeletal muscle is evident (38, 76, 77). Therefore, techniques allowing the simultaneous evaluation of fat and muscle should be preferred. DXA, CT and MRI are the most important assessment instruments. CT and MRI should be considered the “gold standard” techniques. The balance of pros and cons for both CT and MRI does not allow a clear indication on which of the two should be preferred. Resources, instrument availability, and need of details will represent the factors guiding the investigator’s preference for one over the other. On the other hand, DXA should not be discarded, and still represents the instrument more likely to promote the “clinical relevance” of sarcopenia. For its characteristics, DXA may be an extremely interesting methodology to be used for preliminary screening. Moreover, its use in combination with either CT or MRI will help drive the research in the field towards more clinical aspects. While imaging and other biomarkers will be valuable tools for initial proof of concept studies, assessment tools for evaluating the effect of treatments on outcomes reflecting clinical benefit will be required to support eventual pivotal studies.

Adequate length of study

To evaluate the efficacy of a specific intervention on sarcopenia, it is necessary that the follow-up will be sufficiently long to allow the hypothesized modifications of biomarkers. Surely, not all biomarkers will be similarly influenced by the intervention. Such variations will depend on multiple factors, including the population characteristics, the type and strength of the tested intervention, and the sensibility of the biomarker to changes. However, six months have been generally indicated as the minimum timeframe to expect changes in imaging parameters.

Sarcopenia is a “work in progress”

The study of sarcopenia is still in its infancy, but we have clearly acknowledged the great potential benefits arising from the understanding and treatment of this condition at both person and population levels. Taking together the uncertainties of exploring a novel field with the exponential acceleration of scientific progress, it is currently difficult to provide long-lasting statements, recommendations, and guidelines. It is likely that what seems reasonable today will be confounded by several studies in the near future. For this reason, extreme caution is needed to avoid jeopardizing the future development of research in the field. It is important to consider the study of sarcopenia as a “work in progress”, always amenable to changes and redirections. After all, the first Phase II trials in this syndrome are just starting, and this is the appropriate time to raise doubts and pose questions. With time, a stronger foundation for sarcopenia research will be developed that will ultimately lead to larger scale and more definitive studies. In this context, it is critical that an ongoing dialogue be initiated and sustained amongst investigators with an interest in age-dependent decline of muscle.

Acknowledgements: Dr. Fielding’s contribution is based upon work supported by the US Department of Agriculture, under agreement No. 58-1950-7-707.

Members of the International Working Group on Sarcopenia: Gabor Abellan Van Kan, France; Sandrine Andrieu, France; Stefan D. Anker, Germany; Patricia Anthony, Switzerland; Christian Asbrand, Germany; Mylène Aubertin-Leheudre, Canada; Sebastien Barbart-Artigas, Canada; Olivier Benichou, France; Cécile Bonhomme, France; Pascale Borensztein, France; Denis Breuillé, Switzerland; Sergio Castro Henriquez, Chile; Jesse M. Cedarbaum, USA; Matteo Cesari, France; Patricia Chatelain, France; Wm. Cameron Chumlea, USA; Richard V. Clark, USA; Capucine De Meynard, France; William J. Evans, USA; Gary Fanjiang, USA; Luigi Ferrucci, USA; Roger A. Fielding, USA; Philippe Garnier, France; Sophie Gillette-Guyonnet, France; Bret Goodpaster, USA; Marie-Françoise Gros, France; Luis Miguel F. Gutierrez Robledo, Mexico; Marc Hellerstein, USA; Kelly Krohn, USA; Maria Isaac, United Kingdom; Didier Laurent, Switzerland; Menghua Luo, USA; Hélène Matheix-Fortunet, France; Inge Mohede, The Netherlands; John E. Morley, USA; Christine M’Rini, France; Ramon Navarro, France; Bruno Oesch, Switzerland; Reinhard Ommerborn, Germany; Marco Pahor, USA; Patrick Ritz, France; Yves Rolland, France; Daniel Rooks, USA; Ronnen Roubenoff, USA; Fariba Roughead, Switzerland; Seward Rutkove, USA; Cornel C. Sieber, Germany; Michèle Storrs-Malibat, France; Stephanie Studenski, USA; Yannis Tsouderos, France; Bruno Vellas, France; Sjors Verlaan, The Netherlands; Stephan Von Haehling, Germany; J. Willem Vrijbloed, Switzerland; Sander Wijers, The Netherlands; Mauro Zamboni, Italy.

Conflicts of interest: MC has received consultancy fees from Sanofi-Aventis and Pfizer; RAF is consultant with Merck, Eli Lilly, Cytokinetics, DMI, Kraft Foods, and Unilever; MH is stockholder, chairmen of scientific advisory board and consultant for KineMed, Inc.; SA is consultant with Brahms, Vifor, Professional Dietetics, PsiOxus, Takeda, receives research support from Vifor, BG Medicine, and has received fees for speaking at meetings from Brahms, Vifor; SR has equity in and receives consulting income from Convergence Medical Devices, Inc; WV is employee and shareholder of Neurotune AG; YR receives support from Lactalis, Lundbeck, Lilly, Nutricia, Servier, Cheisi, Ipsen, Novartis; JMC is employee and shareholder of Cytokinetics, Inc; MZ has received a fee from Abbot for a conference; DL and RR are employed by Novartis; WJE is employed by GlaxoSmithKline; JEM is consultant and stokeholder of Mattern Pharmaceuticals and consultant for Sanofi-Aventis; BV is consultant and member of Advisory Board with Novartis, Servier, Nestlè. MP, BG, GAVK, MI, CMR, MAL, CCS have no conflict of interest to declare.

Disclaimer: Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the Authors and do not necessarily reflect the position of the supporting organizations or agencies.

 

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THE TREVISO DEMENTIA (TREDEM) STUDY: A BIOMEDICAL, NEURORADIOLOGICAL, NEUROPSYCHOLOGICAL AND SOCIAL INVESTIGATION OF DEMENTIA IN NORTH-EASTERN ITALY

 

M. GALLUCCI1,2, E. MARIOTTI1,2, D. SARAGGI1,2, T. STECCA1,2, M.G. ODDO1,2, C. BERGAMELLI1,2, P. BOLDRINI1,2, S. MAZZUCO3, F. ONGARO3, P. MECOCCI4, F. DI PAOLA5, M. BENDINI5, G.L. FORLONI6, D. ALBANI6, P. ANTUONO7, L. CABERLOTTO8, A. ZANARDO8, M. SICULI8, G.B. GAJO9, E. DURANTE9, G. BUSCATO10

 

1. Cognitive Impairment Center, General Hospital of Treviso, Treviso, Italy; 2. Department of Rehabilitative Medicine, General Hospital of Treviso, Treviso, Italy; 3. Department of Statistics University of Padova, Padova, Italy; 4. Section of Gerontology and Geriatrics, Department of Clinical and Experimental Medicine, University of Perugia, Perugia, Italy; 5. Neuroradiology Unit, General Hospital of Treviso, Treviso, Italy; 6. Department of Neuroscience, “Mario Negri” Institute for Pharmacological Research, Milan, Italy; 7. Dementia Research Center, The Medical College of Wisconsin,  Milwaukee, USA; 8. Department of Clinical Pathology, General Hospital of Treviso, Treviso, Italy; 9. Transfusional Medicine Department, General Hospital of Treviso, Treviso, Italy; 10. Treviso Alzheimer Association, Treviso, Italy.

Corresponding author: Maurizio Gallucci, MD. Cognitive Impairment Center, General Hospital of Treviso, Piazza Ospedale, 1, I-31100 Treviso, Italy. Phone: +39 (0422) 322-024  e-mail: mgallucci@ulss.tv.it

J Frailty Aging 2012;1(1):24-31
Published online February 14, 2012, http://dx.doi.org/10.14283/jfa.2012.5


Abstract

Background: The incidence of dementia increases exponentially with age but knowledge of real disease-modifying interventions is still limited. Objectives: To describe the study design and methods of a large prospective cohort study aimed at exploring the complex underlying relationships existing among cognition, frailty, and health-related events in older persons with cognitive impairment.  Design: Prospective cohort study of a representative population of outpatients attending the Treviso Cognitive Impairment Center between 2000 and 2010. Setting: The TREVISO DEMENTIA (TREDEM) Study conducted in Treviso, Italy. Participants: 490 men and 874 women, mean age 79.1 ± 7.8 years (range 40.2–100 years). Measurements: Physiological data, biochemical parameters, clinical conditions, neuroradiological parameters (e.g., brain atrophy and cerebral vascular lesions identified by computerized tomography scans), neuropsychological assessment, and physical function markers were measured at baseline. Patients were followed-up to 10 years. Results: The final sample included in the study was predominantly composed of women and characterized by an initial physical function impairment and increased vascular risk profile. Cognitive function of the sample population showed moderate cognitive impairment (Mini Mental State Examination 20.2 ± 6.3; Clinical Dementia Rating 1.2 ± 0.7), and a prevalence of vascular dementia of 26.9%. Cortical, subcortical and hippocampus atrophy were all significantly correlated with age and cognitive function. Conclusion: Results obtained from the preliminary analyses conducted in the TREDEM study suggest that the database will support the accomplishment of important goals in understanding the nature of cognitive frailty and neurodegenerative diseases.

Key words: Alzheimer’s disease, vascular dementia, mild cognitive impairment, biomarkers, risk factors, TREDEM.


 

Introduction

Dementia, characterised by progressive loss of memory and higher cortical functions, is one of the most common diseases in the elderly, with prevalence rates ranging between 5.9–9.4% for subjects aged over 65 years in the European Union (1). It represents the fourth cause of death after cardiovascular disease, cancer and cerebrovascular diseases. Alzheimer’s disease and vascular dementia are the two main types of dementia affecting 50–60% and 20% of all demented patients, respectively.

Among the age-related diseases, dementia is one of the most devastating in terms of individual’s quality of life, loss of autonomy, social and healthcare costs, and also the main reason for institutionalization of older persons. For these reasons,  studies dedicated to identifying the clinical and subclinical mechanisms underlying dementia are specially needed and repeatedly evoked.
In the present paper, we present the methodology and preliminary results of the TREVISO DEMENTIA (TREDEM) Study, a large prospective cohort study taking place in North-Eastern Italy. Interestingly, Treviso, where the TREDEM Study is performed, is the province with the highest longevity in Italy. In fact, life expectancy at birth was 83.4 and 76.4 years in women and men, respectively (well above the national average) in 1998-2000. In the following years, life expectancy even increased reaching 84.5 (women) and 77.6 (men) years in 2003, and 85.5 (women) and 79.6 (men) years in 2006. The choice of setting a study on dementia in this area was meant to imply the extremely important relationship existing between cognitive function and aging process.

Main aims of TREDEM are:
a) to investigate the complex process of cognitive decline and frailty of older persons using an interdisciplinary approach, studying biological and clinical data, computerized tomography (CT)-imaging, neuropsychological data, as well as lifestyles and social factors.
b) to define the features associated with survival in demented subjects and in those with mild cognitive impairment (MCI);
c) to estimate healthcare costs due to cognitive disorders, in particular in terms of hospitalizations;
d) to assess level, type, and burden of caregivers’ activities, in order to optimize the planning of specific interventions in the field.

Methods

Study population

The TREDEM study is an observational cohort study conducted at the Cognitive Impairment Centre of the Treviso General Hospital (Treviso, Italy). All the 1,364 recruited participants were outpatients attending the Center between the years 2000 and 2010. Patients with psychosis and depression diagnoses were excluded. The local Ethic Committee approved the entire study protocol. All participants, or caregivers when needed, provided written informed consent.

All participants underwent a multidimensional assessment consisting of sociodemographic and clinical (cognitive, behavioral, neurological, functional, physical) evaluation. Clinical history was obtained from the patient’s family and/or caregivers with special focus on symptomatic manifestations of the cognitive disorder (i.e., memory, language and executive functions; behavioral disturbances; hallucinations; other psychiatric symptoms), thus allowing a better diagnostic differentiation of dementia subsyndromes. All patients underwent standard laboratory and instrumental workup for dementia (including assessment of thyroid-stimulating hormone, vitamin B12, folate, and homocysteine concentrations, as well as brain CT scan).

Data collection

The following data were obtained at the baseline clinic visit: age, gender, marital status, number of living sons, menopause age (in women), social support, (past) occupation, education, alcohol intake, smoking habit, urinary and fecal continence, sleep quality, nutritional status, weight, height, body mass index (BMI), blood pressure, visual and hearing function, familial history of dementia or depression. A fasting blood sample was collected during the visit; routine hematological and clinical chemistry tests were performed at the Clinical Chemistry Laboratory of the Treviso Hospital using standard laboratory methods. Hematologic parameters were assessed with an ADVIA 2120 Hematologic System (Siemens Healthcare Diagnostics, Deerfield, IL, USA). Main biochemical markers were determined by commercial methods (Roche Diagnostics GmbH, D-68298 Mannheim Germany) on a Modular Analyzer. Erythrocyte sedimentation rate (ESR) was performed with automatic analyzer Test 1 TH (Alifax S.p.A, Polverara, Italy). High sensitivity C-reactive protein (hs-CRP) serum concentrations were measured using a Behring Nephelometer Analyzer II (Siemens Healthcare Diagnostics, Deerfield, IL, USA). Folic acid and vitamin B12 concentrations were measured in serum by chemiluminescence method with an Immulite 2000 (Siemens Healthcare Diagnostics, Deerfield, IL, USA). Total serum homocysteine (Hcy) concentration was measured by High Performance Liquid Chromatography (HPLC) after reduction of disulphide bonds with dithiothreitol and deproteinisation with sulphosalicyclic acid using Bio-Rad HPLC Hcy Assay kit (Herculaes, CA, USA). Glomerular filtration rate (GFR) was estimated using the Cockcroft-Gault formula (2).

Clinical assessment, chronic diseases, and diagnosis

The clinical evaluation of each patient included assessment of personal and familial clinical history as well as physical, neurological, geriatric, and neuropsychological examinations. The presence of clinical conditions was adjudicated on the basis of medical records and evaluation of laboratory tests. In particular, history and risk factors for cardiovascular disease, cerebrovascular disease, and cancer were carefully and specifically explored.

The Cumulative Illness Rating Scale (CIRS) (3) was used to evaluate the presence and severity of comorbidities in recruited patients. Diagnosis of dementia was made on the basis of the Diagnostic and Statistical Manual of Mental Disorders (4). The criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) (5) and of the National Institute of Neurological Disorders and Stroke and Association Internationale pour la Recherché et l’Enseignement en Neurosciences (NINDS-AIREN) (6) were used to identify the diagnoses of Alzheimer’s disease and vascular dementia, respectively. Consensus criteria for the diagnoses of dementia with Lewy Bodies (7), of frontotemporal dementia (8), and of mild cognitive impairment (9) were adopted.

Physical disability was measured using the Activities of Daily Living (ADL) (10) and the Instrumental ADL (IADL) scales (11, 12). The Multidimensional Prognostic Index (13) was also assessed to estimate the risk of 1-year mortality. The Comprehensive Geriatric Assessment (CGA) conducted in each TREDEM participants also included the evaluation of the Short Portable Mental Status Questionnaire, the Mini Nutritional Assessment, the Exton Smith Scale, the evaluation of current medication use, and assessment of the individual’s social support network.

Structural neuroimaging

CT scans were acquired with the volumetric scanner EMOTION 6 Siemens. Section orientation was parallel to the orbitomeatal plane. Sections on the same plane (time of 2 s, 120 kV, 130 mA, section thickness of 5 mm, no intersection gap) covered the remaining brain from the inferior aspect of the cerebellum to the vertex of the cranium.

Temporal lobe atrophy was assessed by a linear measure taken from CT films. CT images were acquired in a standardized fashion, with thin slices (2 mm) on the temporal lobe plane that is -20° caudal to the orbitomeatal line (14). Thin sections (time of 2 s, 120 kV, 160 mA, section thickness of 2 mm, no intersection gap) were taken along the breadth of the temporal lobe from the floor of the middle cranial fossa to the inferior aspect of the orbit.

The measurement of temporal atrophy (width of the temporal horn, WTH) was centred in the anterior part of the temporal horn, in the area of the hippocampus head. Since the largest proportion of hippocampus atrophy typical of Alzheimer’s disease occurs in the region of the hippocampus head (15), WTH was defined as an enlargement of the temporal horn owing to the shrinkage of the hippocampus. The widths of the right and left temporal horns were measured separately (Figure 1). To capture the asymmetry of the Alzheimer’s disease process, the side indicating more severe atrophy was recorded.

Interuncal distance was measured at the level of the suprasellar cistern where the distance between the unci of the temporal lobes was maximal (16). The transverse width of the intracranial area was used as a proxy for head size. The transverse width of the intracranial area was measured on the scan at the level of the suprasellar cistern. The transverse width was taken as the maximum distance between the inner aspects of the cranial bone.

All data were analyzed from 8 to 10 images in the temporal lobe region, and from 16 to 18 images rostral to the temporal region. No contrast medium was used. All the measurements were taken and recorded by two independent raters blinded to clinical information.

Brain atrophy. Severity (low, moderate, or severe) of atrophy in (frontal, parietal, temporal, and occipital) cortical and (periinsular, basal, vault) subcortical regions as well as lateral ventricular enlargement were recorded. In the presence of atrophy signs, possible asymmetry between right and left sides was ascertained.

Leukoaraiosis. The degree of diffuse hypodensity of the white matter was separately quantified in 3 regions: 1) frontal (anterior to the central sulcus, starting from the most cranial slice including a complete and definite image of the lateral ventricle); 2) parietal (anterior to the parieto-occipital sulcus and posterior to the central sulcus, from the most caudal to the most cranial slice where it was visible); and 3) occipital (from the most caudal slice on which the occipital horn of the lateral ventricle was imaged, until the most cranial slice where the parieto-occipital sulcus was visible). A score of 0 indicated “no hypodensity”, a score of 1 indicated “questionable hypodensity” (might be regarded as “normal for age”), a score of 2 indicated “definite hypodensity” but confined to the periventricular area or not reaching the cortex, and a score of 3 “marked hypodensity” reaching the cortex or extending into the white matter of the gyral digitations. The total score ranged from 0 to 18 (17, 18).

Lacunes. Lacunes were defined as well-defined areas of marked and homogeneous hypodensity with a well-defined and regular contour, measuring generally from 2 to 10 mm of diameter. Their presence and number was separately assessed in 6 regions: frontal, temporal (posterolaterally to the Sylvian fissure, starting from the most caudal slice on which it was present), parietal, and occipital lobes, basal ganglia, and thalamus.

Larger infarctions. Presence and number of larger infarction signs were separately assessed in the frontal, temporal, parietal, and occipital lobes at cortical and subcortical level.

Hemorrhage. Presence and number of (past) hemorrhagic lesions were separately assessed in the frontal, temporal, parietal and occipital lobes at cortical and subcortical level.

Hierarchical Vascular Rating Scale. The Hierarchical Vascular Rating Scale (HVRS), a CT-based weighted rating scale, was used to measure cortical and subcortical ischemic vascular disease in TREDEM participants (19). This scale was developed upon the assumption that different lesions may be differently associated to clinical phenotypes (19). HVRS considers leukoaraiosis, cortical and subcortical lesions in 7 regions: frontal, temporal (posterolaterally to the Sylvian fissure, starting from the most caudal slice on which it was present), parietal, and occipital lobes, basal ganglia, internal capsule, and cerebellum. The score ranges from 0 (absent vascular lesions) to 6 (highest level of vascular lesions). HVRS is a valid and accurate instrument to detect a) the mildest clinical levels of cortical and subcortical ischemic vascular disease; b) within the mild range, different degrees of clinical severity, and c) different degrees of cortical and subcortical ischemic vascular disease in patients with mixed (degenerative and vascular) cognitive impairment.

Cancers. Presence, type, and number of cancer lesions were separately assessed in the frontal, temporal, parietal and occipital lobes at cortical and subcortical level.

Neuropsychological Assessment

A trained psychologist performed a complete neuropsychological assessment of participants by administrating the following tests: Mini Mental State Examination (20), Digit Span (21), Rey Auditory-Verbal Learning Test (22), Short story recall, Attentive Matrices, Frontal Assessment Battery (23), Trail Making Test (24), Phonologic word fluency (25), Categorical word fluency, Token Test (26), Constructional praxis (as copy of drawings and copy of drawings with landmarks) (25), Clinical Dementia Rating Scale (27, 28), Geriatric Depression Scale (29), and Hamilton Depression Rating Scale (30).

The Neuropsychiatric Inventory (NPI) (31) was used to evaluate non cognitive symptoms. Caregiver’s distress due to the neuropsychological symptoms of the patient was evaluated by the Neuropsychiatric Inventory Distress (NPI-D) scale (32).

A questionnaire on social, quality of life, and health aspects of caregivers was also submitted to a random subset of 158 proxies. Demographic and social characteristics of these caregivers were recorded as well as quality of life measured. Burden was also quantified by the number of hours of assistance in a day.

Main longitudinal outcomes

Data about hospitalizations for all the TREDEM participants were recorded, including number of admissions, hospitalization length (days), clinical diagnosis and diagnosis-related group at each hospital discharge. Participants’ survival was monitored throughout the follow-up by referring to the Treviso hospitals data, local nursing homes medical records, and participants’ proxies.

Statistical analysis

Participants were characterized by a variety of clinical and surveyed variables. Statistical analyses were conducted using the software package JMP7 (SAS Institute Inc., Cary, NC, USA). Means and standard deviations (SD), or percentages are presented. Chi square or t-tests were used as appropriate. Statistical significance was set at a p level of 0.05.

Results

The TREDEM study has already produced several reports during the constitution of its final database. In fact, previous papers have already been published reporting findings obtained from preliminary in itinere analyses. In this section, we summarize the preliminary findings that the TREDEM study has been able to provide during these last years.

In an initial sample of 89 demented patients (33), we demonstrated the high accuracy of the HVRS in the identification of patients with vascular dementia. In the same paper, we also documented that the width of the temporal horns of lateral ventricles is correlated with diagnosis of Alzheimer’s disease and may represent an important criterion to consider in the differential diagnosis.

Hyperhomocysteinemia is an important risk factor for atherosclerosis and it has been also suggested as a diagnostic marker for Alzheimer’s disease. In 2004 (34), we measured homocysteinemia in subjects with Alzheimer’s disease, with vascular dementia, and in healthy controls. Participants of the TREDEM cohort (constituting the dementia groups) presented significantly higher concentrations of Hcy than controls, even after adjustment for potential confounders (i.e., age, albumin, serum folate and vitamin B12). Consistent findings were also reported in other reports from our group (35, 36) suggesting Hcy as an important biological risk factor in the mechanisms underlying cerebral atrophy and cognitive decline.

Another preliminary contribution of the TREDEM database was published in 2006 (37) and was focused on presenting the Self-Organizing Map in Alzheimer’s disease and vascular dementia patients. Self-Organizing Map is one of the most important architectures of neural networks generating a graphical representation of the typologies associated with the input data defined in spaces with high dimensionality. In our study (37), we demonstrated that Self-Organizing Maps, useful in data clustering (38), correctly distinguish different types of dementia and are correlated with a wide spectrum of subclinical and biological patterns.

Main characteristics of the final TREDEM study cohort are presented in Table 1. The sample is composed of 490 men and 874 women (mean age 79.1 ± 7.8 years, range 40.2–100 years). A group of 55 subjects attending the Centre did not comply with the criteria for dementia or MCI and their cognitive profile was normal: we called them «Normal». Overall, TREDEM participants had a mild degree of cognitive impairment (MMSE 20.2 ± 6.3 and CDR 1.2 ± 0.7). Also due to the inclusion/exclusion criteria of the study, depressive symptoms were not particularly relevant (GDS 5.1 ± 3.2). Widowhood affected nearly half of partecipants (45.1%), mostly women (85%). Education was 6.3 years, probably due to the older age of the study population. Mean body mass index was 25.5 (SD ± 5.0) Kg/m2 indicating a relatively overweight of the sample, contrary to what previously found in other studies on demented patients only (39). In fact, mild overweight is probably influenced by the presence of MCI (BMI 26.5±4) and normal (BMI 25.6±3). Mean systolic blood pressure was 155.5 (SD ± 21.2) mmHg, suggesting a limited control of hypertensive disease and possibly a common mechanism at the basis of the cognitive impairment in TREDEM. Supporting the higher cardiovascular risk profile of the sample, the study participants also exhibited high baseline concentrations of Hcy (20.1 nmol/ml). The sample presented a moderate degree of disability (ADL 4.3 ± 1.8 and IADL 3.6 ± 2.9).

Table 1 The main data of Treviso Dementia (TREDEM) Study

The prevalence of Alzheimer’s disease in the study sample was 30.7%. However, after exclusion of 338 MCI and 55 normal subjects, the prevalence of Alzheimer’s disease may rise up to 43%, more consistent with the prevalence generally reported in literature (40). On the other hand, the prevalence of vascular dementia (26.9%) was moderately higher than that usually reported (41). Although vascular lesions were often present, their severity was only moderate (mean HVRS 1.58 ± 1.44).

In our sample, 84.1% of participants showed cortical atrophy and 65.2% subcortical atrophy. Hippocampus atrophy (measured as the average amplitude of the temporal horn) is  pathological (cm 0.67 on the right side and 0.66 on the left side) (42), and significantly correlated with age and inversely with MMSE (both p values <0.001).

Figure 1 The measurement of temporal atrophy (width of the temporal horn, WTH) on the temporal lobe plane. Temporal lobe atrophy was assessed by a linear measure taken on the temporal lobe plane that is -20° caudal to the orbitomeatal line. The measurement of temporal was centred in the anterior part of the temporal horn. WTH was defined as an enlargement of the temporal horn owing to the shrinkage of the hippocampus (kindly provided by G. Frisoni).

 

Discussion

Since dementia is strongly correlated to the aging process, we chose to conduct the study in the Treviso area, the province characterized by the greatest longevity in Italy and where, at the same time, about 8,000 cognitively impaired older persons currently live. The TREDEM study presents several strengths worth to be mentioned. The sample is highly representative being our center a reference point for the regional community. Data on a variety of domains (including a wide set of neuroradiological and neuropsychological parameters) were collected so to allow a clear description of cases and dementia subsyndromes. Moreover, in the very next future, the TREDEM study is going to expand its neuroradiological imaging by including functional magnetic resonance data and genotyping of the participants.

The large database we constituted is indeed unique especially because composed of an older, cognitively impaired, and frail sample population. Such characteristics are often at the basis for the exclusion of subjects from clinical research, given the huge number of difficulties and issues related to the recruitment of this type of patients. Furthermore, this work is particularly promising for the study of frailty because focused on evaluating a specific phenotype of frailty in the elderly, which is the “cognitive frailty”. In fact, although frailty is usually defined within the physical function perimeter, cognition may well play a major role in determining the higher susceptibility to stressors, a feature typical of the frailty syndrome. In this context, it is also noteworthy the high prevalence of women, widows, and poorly educated participants, consistent with the overall frail (in the broadest sense) status of the TREDEM population.

The TREDEM study is focused on cognitive disorders and can be considered complementary to another experience of epidemiological and clinical research we recently conducted in Treviso: the Treviso Longeva (TRELONG) Study (43). TRELONG is a prospective cohort database focused on studying longevity and successful aging in older persons. The importance of cognitive function as a crucial determinant of successful aging (44) led us to conceive, design, and conduct TREDEM. Our previous TRELONG experience was also important because it provided us with the infrastructures and the required expertise to develop a large-scale study such as TREDEM. The greatest challenge faced in the conduction of TREDEM was the retrieval of the longitudinal data of participants (mainly because all of them were outpatients living at various locations in the province of Treviso). However, thanks to previous TRELONG experience, we developed the required expertise in coordinating clinical research in older persons and established the most useful collaborations with several public health and institutional services providing us the access to crucial information and support.

The TREDEM study is specially designed to study the possible relationships existing between a wide spectrum of preclinical and clinical parameters in the heterogeneous dementia condition. The preliminary analyses performed on partial data from the first patients enrolled in the TREDEM study have already produced extremely promising findings. The completion of the study with the recruitment of an overall sample population of 1,364 patients is likely to provide the basis for further interesting investigations in the field of cognitive function and dementia-related frailty.  We’ll also compare the characteristics of the three identified subpopulations (dementia, MCI and normal) to discover the most significant discriminating parameters.

Acknowledgements: We are grateful to all people who kindly agreed to participate in this study and to Giuliana Santamaria for editing the manuscript.

 

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