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Apr 1, 2015 - Longevity steadily increased over the past several decades, owing to changes in economy ...... Biodemography of human ageing. Nature 464 ...

Experimental Gerontology 76 (2016) 25–32

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Experimental Gerontology journal homepage: www.elsevier.com/locate/expgero

Review

Muscle function and fat content in relation to sarcopenia, obesity and frailty of old age — An overview Assaf Buch a,b,⁎, Eli Carmeli a,c, Lital Keinan Boker c, Yonit Marcus a,b, Gabi Shefer a,b, Ofer Kis a, Yitshal Berner b,d, Naftali Stern a,b a

Institute of Endocrinology, Metabolism and Hypertension, Tel Aviv Sourasky Medical Center, Tel-Aviv, Israel The Sackler Faculty of Medicine, Tel-Aviv University, Israel School of Public Health, Haifa University, Haifa, Israel d Meir Medical Center, Kfar Saba, Israel b c

a r t i c l e

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Article history: Received 30 July 2015 Received in revised form 14 November 2015 Accepted 14 January 2016 Available online 16 January 2016 Section Editor: T.E. Johnson Keywords: Fat mass Frailty Muscle function Muscle mass Obese frail Sarcopenia Sarcopenic obese

a b s t r a c t Background and aim: In western countries, the proportion of people over age 60 is increasing faster than any other group. This is linked to higher rates of obesity. Older age, co-morbidities and obesity are all associated with frailty syndrome. In the core of both frailty and sarcopenia there are dysfunction and deterioration of the muscle and the fat tissues. This overview interlinks the phenotypes presented in older adults such as sarcopenia and frailty—alone and with relation to obesity, muscle function and fat tissue accumulation. Recent findings: Observational studies have well described the loss of muscle mass and strength through the years of adult life, both components of frailty and sarcopenia. They have shown that these changes are associated with dysmetabolism and functional deterioration, independent of common explanatory variables. In the metabolic mechanism core of this link, insulin resistance and higher ectopic fat accumulation may play a role. Basic experiments have partially validated this hypothesis. Whether there is a synergistic effect of obesity and frailty phenotype on morbidity risk is still questionable and currently under investigation; however, few cohort studies have shown that the frail–obese or sarcopenic–obese group have higher probability for metabolic complications. Summary: Muscle mass loss and fat accumulation in the muscle in the elderly, with or without the presence of obesity, may explain some of the functional and metabolic defects shown in the frail, sarcopenic population. © 2016 Elsevier Inc. All rights reserved.

Contents 1. 2. 3. 4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Muscle mass and strength loss at the core of sarcopenia and dynapenia . . . The frailty syndrome — a more complex expression of muscle dysfunction Integration of the obese phenotype and muscle function . . . . . . . . . 4.1. General and basic science . . . . . . . . . . . . . . . . . . . . 4.2. Risk for metabolic derangement . . . . . . . . . . . . . . . . . 4.3. Risk for functional impairment . . . . . . . . . . . . . . . . . . 5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflicts of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction ⁎ Corresponding author at: The Institute of Endocrinology Metabolism and Hypertension, Tel Aviv-Sourasky Medical Center, 6 Weizmann St., Tel-Aviv 64239, Israel. E-mail address: [email protected] (A. Buch).

http://dx.doi.org/10.1016/j.exger.2016.01.008 0531-5565/© 2016 Elsevier Inc. All rights reserved.

Longevity steadily increased over the past several decades, owing to changes in economy, society and medical care. The growing number of

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centenaries since 1950 well reflects the improvements in life expectancy (Vaupel, 2010). In western countries, the proportion of people over age 60 is increasing faster than any other group (WHO, n.d.). Japan, for instance, demonstrates a rapid aging of the population with a parallel shrinking of the country's total population. The elderly segment (≥ 65 years) reached 25% in 2013 with a prediction to reach 40% in 2060. This trend is accompanied by a decrease in the population size from a peak of 128.08 million in 2008 to an expected 80 million in 2060 due to lower birth rate (Arai et al., 2015). However, the longer life and the postponement of death are inevitably linked to increased occurrence of disease and functional deterioration. Thus the promotion of “healthy aging” as opposed to “sick aging”, is an obvious emerging target in the pursuit of well-being and successful aging (Rodrigues et al., 2012). The Healthy Life Expectancy (HALE) (computed as the number of years reached in good health) was assessed in 187 countries. In 2010, global HALE at birth was 58.3 years for male and 61.8 years for female. The increase in HALE over the years is slower than the increase in life expectancy, stressing the need to delay the appearance and minimize the health impact of the overall effect of non-fatal disease on the aging population (Salomon et al., 2012). As the extension of life expectancy exceeds that of HALE, a rising economic challenge is emerging, with about one third of the health expenditure in industrialized countries now consumed by the care of people over 70 years (Lally and Crome, 2007). Another global public health challenge is attributed to the obesity pandemic. Obesity rates are increasing in general and also in older adults, in both genders, in developed and undeveloped countries (Finucane et al., 2011). Based on data on obesity in adults (N 20 years) from 199 regions between the years 1980 and 2008, it has been shown that global mean BMI had increased at an annualized rate of 0.4 kg/m2/ decade for men and 0.5 kg/m2/decade for women (Finucane et al., 2011). In addition to a higher body mass, aging is accompanied by significant alterations in body composition, with declining lean body mass (LBM) and increases in fat mass (FM) (Landi et al., 2014). These changes, occurring alone or accompanied by obesity, are related to poor physical performance and mobility and are associated with an increased risk of dysmetabolic characteristics and morbidity (Chung et al., 2013; Hardy et al., 2013; Lim et al., 2010; Blaum et al., 2005; Villareal et al., 2004). Interaction of obese phenotype and lower muscle mass (MM), quality and strength have been the focus of research investigation in both epidemiology and basic science perspective (Villareal et al., 2004; Roubenoff, 2004; Kim et al., 2005; Addison et al., 2014; Marcus et al., 2012). Scientific literature describing obesity and frailty syndrome is mounting, however, only recently the combination of both phenotypes was has been highlighted. It is our intention to present in a logic manner the frailty and sarcopenia conditions both alone and together with obesity, with an emphasis on link between muscle and fat tissue function which is at the core of these conditions. This overview presents both epidemiologic data and mechanistic information. It is not a systematic review, because no specific quantitative question was proposed, but rather an attempt to integrate the available information in a logic format. 2. Muscle mass and strength loss at the core of sarcopenia and dynapenia It has been found that total MM, as well as muscle size, peak at about the age of 24 years. Similar to strength, MM is well maintained throughout the fifth decade as only a modest (10%) decrease in muscle size occurs between 24 and 50 years of age. Between 50 and 80 years of age an additional diminution of 30% occurs, with an annual 1% decrease in muscle cross sectional area beyond the fifth decade of life (Springer, 2015). This cumulative age-related change may eventually lead to sarcopenia, i.e., a state of significant functional and quantitative muscle loss (Rosenberg, 2011). By this simple definition, 100% of older adults are sarcopenic, however, variations in muscle loss exist, which lead to

the need of differentiating normal muscle loss from an unhealthy loss with respect to function. Taking into account the high variation among individuals of muscle loss, time of its occurrence, and the variety of measuring approaches in published studies, estimation of prevalence rates of sarcopenia is a challenging task (Janssen, 2011). Within the existing literature, the prevalence of sarcopenia in 60- to 70-years old is in the order of 5% to 13%, increasing up to 50% of the population aged 80 years or older (Janssen, 2011) and is likely further aggravated by metabolic and other chronic diseases. An example of this effect is evident from the Korean Sarcopenic Obesity Study that included 810 subjects (414 patients with diabetes and 396 control subjects). The participants were examined by dual-energy X-ray absorptiometry (DEXA). The prevalence of sarcopenia was clearly higher among the older diabetic subjects (≥60 years) in both genders compared to non-diabetics (19% vs. 5.1%, P = .005, in men and 27% vs. 14%, P = .013, in women). Interestingly, higher prevalence of sarcopenia in middle age (40–59 years) was seen only in diabetic women (16.7% vs. 4.1% in non-diabetics, P = .002) (Kim et al., 2010). A standardized approach has been suggested to compare the MM of elderly to a normal, young adult population: MM levels that are two standard deviations (SD) or more under the mean are considered as low MM (CruzJentoft et al., 2010). This approach replicates, in essence though not numerically, the classification of osteopenia/osteoporosis in humans. Additional terms have been used in this context, such as primary sarcopenia, which is age-related, and secondary sarcopenia, which is due to immobility–inactivity, disease or malnutrition, and not necessarily associated with aging (Janssen, 2011). Furthermore, consensus opinions on operative definitions of sarcopenia have generally relied on 3 measures: low MM, low strength, and low performance. Guided by these principles, sarcopenia can be subclassified into an escalating set of stages: presarcopenia, sarcopenia, and severe sarcopenia (Cruz-Jentoft et al., 2010) (Fig. 1). A cross-sectional survey using data from the Third National Health and Nutrition Examination Survey (NHANES III) classified the subjects into two categories of sarcopenia using a reference of young adult values (within 1–2 SD below the mean = class I, 2 SD or more below the mean = class II). The prevalence in older (≥ 60 years) women compared to older men of class I (59% vs 45%, respectively) and of class II (10% vs 7%, respectively) sarcopenia was greater (P b .001). The likelihood of functional impairment and disability was approximately two times greater in the older men and three times greater in the older women with class II sarcopenia than in the older men and women with a normal skeletal muscle mass index (SMI, computed as skeletal muscle mass / body mass × 100) (Janssen et al., 2002). In the Cardiovascular Health Study (prospective cohort) 5036 men and women (≥65 years) were followed for 8 years. The baseline analysis revealed that 70.7% of the men and 41.9% of the women had moderate sarcopenia, whereas 17.1% of the men and 10.7% of the women had severe sarcopenia. Overall, the incidence of disability, as assessed by scoring the participants' difficulty to perform six daily tasks, was 27% greater in those with severe sarcopenia compared to subjects with normal MM (95% CI 1.07–1.50), independent of morbidity, adiposity status, as well as other explanatory factors for sarcopenia. This difference was gender-related and fully accounted for by the larger effect in women [Odds Ratio (OR) 1.37 (95% CI 1.10–1.72)], with no significant effect in men (Janssen, 2006). Data on the temporal association between loss of MM and functionality were extracted from the Health Aging and Body Composition (ABC) cohort of 3075 men and women. After 2.5 years of follow-up, mobility limitations were observed in 22.3% of the men and 31.8% of the women. MM (at mid-thigh area) proved as a predictive factor for these limitations, as persons in the lowest quartile of muscle mass in this body area were 2.25 (95% CI, 1.54–3.29 for men) and 1.70 (95% CI, 1.25–2.31 for women) times more likely to develop mobility limitations. Further adjustments only slightly attenuated this effect (Visser et al., 2005). After 3 years of follow-up, the loss of leg LBM was also independently associated with strength decline in both men and women (r =

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Fig. 1. Consensus on sarcopenia definition based on muscle mass, strength and functionality (Cruz-Jentoft et al., 2010).

0.171 and r = 0.176 respectively; P b 0.05 for both) (Goodpaster et al., 2006). As opposed to functionality, the association of sarcopenia with metabolic morbidity and total mortality is controversial (Janssen, 2011). It is the interaction of obesity and lower muscle performance which is more related to these outcomes and will be discussed in the next session. Notably, and somewhat counter-intuitively, muscle strength and MM do not necessary correlate or predict each other (Clark and Manini, 2008). As mentioned, in the health ABC cohort MM and strength correlated, however, annualized rates of isokinetic knee extensor strength loss (up to 4.1% in men, and up to 3.0% in women) were about three times greater than the rates of loss of leg lean mass as assessed by DEXA (∼1% per year). The principle of reversibility was not demonstrated as well: gain of LBM was not accompanied by strength maintenance or gain (Goodpaster et al., 2006). In another study, one hundred and twenty subjects (46–78 years of age) were followed for a mean of 9.7 ± 1.1 years. The age-associated changes in MM (estimated indirectly by multiplying the mass of creatinine (gr) excreted in 24 h by 18.5) explained b5% of the variance in the change in strength (isokinetic knee extensor strength) (Hughes et al., 2001). The consensus definition of sarcopenia is therefore justifiably comprised of both MM and strength (Cruz-Jentoft et al., 2010), suggesting their possible independent role in sarcopenia. However, methodological limitations complicate the interaction of muscle mass and strength in this setting. Based on the representative NHANES database, MM (assessed by height-adjusted appendicular skeleton muscle mass (aASM) using DEXA) and strength (assessed by isokinetic quadriceps strength using a dynamometer) were positively correlated, independent of age and gender (r = .365, P b .001). The relationship was attenuated by comorbidities such as obesity (Chen et al., 2013), thus indirectly supporting the concept that sarcopenic state in obese subjects may differ from the sarcopenic state in non-obese subjects. Notwithstanding the broader definition of sarcopenia, a clear tendency presently exists to focus on the significance of muscle strength rather than MM. Thus, an additional term was suggested — dynapenia, which is referred to the age-related loss of strength (Clark and Manini, 2008). As mentioned, annualized decline in muscle strength may vary between 3%–4%, as shown in the health ABC cohort, and 0.8%–2%, as shown elsewhere in the literature (Goodpaster et al., 2006). Low muscle strength is well known to place older adults at an increased risk of mobility limitations and mortality. Analysis of 20 studies examining the relative risk (RR) for mortality of low strength and poor

physical performance/disability yielded an unweighted RR of 2.2. In one review, ninety percent of studies showed significant association between strength and poor physical performance, while only 35% showed an association with MM (Clark and Manini, 2012). After 3 years in the health ABC cohort, only muscle attenuation (muscle fat) and muscle strength independently predicted mobility limitations (P b .05) (Visser et al., 2005). After six years of follow-up in this study, low quadriceps strength was associated with higher risk for mortality, independently of muscle mass and size, activity level, body composition and other variables. Adjusted Hazard Ratio (HR) between quadriceps strength (per SD of 38 Nm) and mortality was 1.36 (95% CI, 1.12–1.65) for men and 1.56 (95% CI, 1.05–2.30) for women. Muscle size, determined by either computed tomography (CT) or DEXA regional LBM, was not strongly related to mortality (Newman et al., 2006). Due to the association between loss of muscle strength with other metabolic morbidities it is plausible that strength loss might be a better predictor of physical function, morbidity and mortality than the loss of MM (Janssen, 2011). The pathogenesis of the sarcopenic state is multifactorial. Alongside with external factors such as nutritional status and lower mobility, there are internal factors such as endocrine function, variation in the rate of apoptosis, micro-injuries and variability in recovery and mitochondrial dysfunction. Several potential biomarkers were suggested to cluster in relation to skeletal muscle function and were proposed to correlate with weakness, inflammation, growth and contractile ability; however, not all were investigated in sarcopenic people, thus their validity for that state is mostly unknown (Kalinkovich and Livshits, 2015). 3. The frailty syndrome — a more complex expression of muscle dysfunction No single best definition of frailty exists and its presence is often subtle or asymptomatic (Walston et al., 2006). In addition to declining MM and strength, the frailty syndrome can present as decrease in endurance, balance or walking performance, as well as low activity (Fried et al., 2001). This complicates the categorization of the frailty syndrome into clinically obvious and discrete sub-entities which are traditionally in demand for the purpose of specific, well-defined practice and treatment modalities. Indeed, the diversity of the clinical presentation comprises a challenge for the consensus diagnostic tools now utilized to identify the frailty syndrome (Walston et al., 2006; Lang et al., 2009; Ferrucci et al., 2004). Potential definitions of frailty abound, defining

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frailty as synonymous with disability, comorbidity, or advanced old age. Increasingly, geriatricians define frailty as a biologic syndrome of decreased reserve and resistance to stressors, resulting from a cumulative decline across multiple physiologic systems, culminating in vulnerability to adverse outcomes (Fried et al., 2001). This “risk concept” distinguishes frailty from disability. Frailty is a progressive process with a latent phase. It may thus be categorized into 3 stages: pre frail, frail and the phase of complications. Progression in the frailty ladder reflects decline in homeostasis mechanisms resulting in a steeper loss of performance. Further, such progression is accelerated relative to normal aging (Lang et al., 2009). According to one hypothesis, frailty evolves when an impairment threshold of one or more systems has been surpassed, which triggers a cascade of dysregulation in multiple systems. Such “snowball” disruption may influence an array of clinical domains, as well as comorbid conditions and disability (Walston et al., 2006). The ‘frailty cycle’ may be triggered by the separate or joint effects of lack of physical exercise, inadequate nutrition, unhealthy environment, injuries, disease, age and/or obesity related hormonal alterations and inflammation and polypharmacy (recreational, social and pharmaceutical). These interconnected factors, on the background of aging, can lead to chronic undernutrition causing loss of bone and skeletal MM, which can be further amplified by the synergistic influence of hormonal changes, proinflammatory responses and/or skeletal, joint or neural deterioration. Eventually, sarcopenia and loss of strength (dynapenia) may ensue, resulting in decreased functionality with lower performance as assessed, for example, by walking speed. Lower activity reflects declining abilities and motivation (Lang et al., 2009; Ferrucci et al., 2004) (Fig. 2). The multiple facets of decline are correlated, connected and often overlap, making it harder to pinpoint a single, clear threshold or cutoff for diagnosis, and to allow the distinction of frailty from other medical conditions. This complexity comprises an obvious obstacle for attempts to describe the pathophysiology flow of the frailty syndrome and to validate a cause and effect theories. Further information on mechanisms of these conditions (sarcopenia, dynapenia and frailty), with frailty at the center, has been extensively reviewed elsewhere (Clark and Manini, 2008; Walston et al., 2006; Lang et al., 2009, 2010). The prospective Cardiovascular Health Study estimated both incidence and prevalence rates of frailty among 5317 men and women (≥65 years) of different ethnic origins. Frailty was defined by the presence of 3 or more of the following symptoms: (A) unintentional weight loss in the course of 1 year; (B) self-reported exhaustion; (c) weakness in the dominant hand; (D) slow walking speed and (E) low physical activity. The overall prevalence rate (n = 5317) was 6.9%, which was modified by gender and age stratum [higher in women than men (7.3% vs. 4.9%) and with advancing age]. African American minorities had higher rates of frailty in both genders (14.4% for women and 7.4% for men). Frailty was also associated with lower education and income, poorer health, and the presence of comorbid chronic diseases and disability. Adjusted for several variables, frailty comprised a risk factor for death over 7 years [HR = 1.63 (95% CI 1.27–2.08)]. It was also an independent predictive factor for hospitalization, worsening disability and mobility (Fried et al., 2001). In another study of middle-age to older community dwellers from 10 European countries where similar criteria were used to detect frailty (weight loss, exhaustion, weakness, slowness and low activity), the overall prevalence of frailty was 17% [range 5.8% (Switzerland)–27% (Spain)]. Despite standardized diagnostic tools, unexpectedly large differences in the rate of frailty existed among the surveyed countries. This could be attributed to cultural differences in interpretation of questions as well as to real across-country differences (SantosEggimann et al., 2009). Geographic and cultural effects on frailty rates have been observed not only within the USA and Europe, but also in Latin America. A survey of 7334 older adults (≥ 60 years) living in five large Latin American and Caribbean cities, found considerably high frailty rates both in males (21%–35%) and females (30% to 48%) (Alvarado et al., 2008).

4. Integration of the obese phenotype and muscle function Implications of the synergism of muscle function and features and obesity have been widely studied by various methods. Here we briefly summarize the interaction between the muscle and the fat tissues and refer to data on the obese–frail and sarcopenic phenotype. 4.1. General and basic science Fat and MM are interconnected via many pathways. Loss of MM induces a 2%–3% decline in basal metabolic rate per decade after the age of 20 years, and a 4% decline per decade after the age of 50 years. This may lead to an increased risk of weight gain, with lower physical activity performed in the background. Obesity induces subclinical inflammation, which may contribute to the development of sarcopenia (Kim et al., 2014). In the Korean Sarcopenic Obesity Study, a prospective study of 379 Korean men and women (mean age 51.9 ± 14.6 years) followed for 27.6 ± 2.8 months, visceral fat at baseline was negatively associated with changes in appendicular lean soft tissue (ALST) during follow-up (r = − 0.20, P b 0.001). Adjustment for age and sex did not affect the strength of the association (r = − 0.17, P = 0.001). However, there was no relationship between baseline ALST and changes in visceral fat (r = − 0.08, P = 0.135). There were no significant changes in total body weight (Kim et al., 2014). There is evidence that intramuscular adipose tissue (IMAT) may not be an inert fat depot: in young (~20 years) healthy individuals subjected to 30 days of leg disuse by suspension, IMAT increased (15%–20%) and exceeded the loss of lean calf and thigh muscles (Manini et al., 2007). This suggests that IMAT does not just “fill” the space left by lean mass loss but is independently regulated and may negatively affect the muscle tissue. Even in the absence of obesity, aging alone is linked to (i) decreased MM (sarcopenia), strength and power (dynapenia) (Clark and Manini, 2008; Hughes et al., 2001; Kim et al., 2014; Manini et al., 2007) and (ii) increased abdominal FM and/or redistribution of fat depots to harmful ectopic sites such as skeletal muscles and liver (Kim et al., 2005; Janssen, 2011; Lang et al., 2010; Kallman et al., 1990). In elderly obese or type 2 diabetic people, elevated levels of IMAT (ectopic muscle fat) are associated with both insulin resistance (Goodpaster et al., 2003) and lesser muscle strength (Springer, 2015; Rossi et al., 2011; Goodpaster et al., 2003). The interconnection between muscle and FM via ectopic fat (with involvement of the liver) has been previously explored. The most apparent cause of ectopic lipid deposition in skeletal muscle (and the liver) is a level of energy intake that exceeds the level of energy expenditure, resulting in spillover of energy storage from adipose tissue to the liver and skeletal muscle (Shulman, 2014). Such cascade is indirectly supported by the presence in obese individuals of enlarged, lipidoverloaded adipocytes which are presumably unable to accumulate additional fat. However, this “spill over” hypothesis cannot explain why some non-obese patients suffering from metabolic disorders could also be accompanied by ectopic lipid accumulation. In the extreme and uncommon phenotype of atrophic subcutaneous fat and insulin resistance, i.e., lipodystrophy syndromes, specific mutations underlie the anomalous shift of fat from the “natural” to ectopic deposition in the liver and/or in skeletal muscle, but the mechanisms responsible for preferred ectopic fat distribution in the common non-obese, often thin subject, remain mostly obscure (Liu et al., 2014). Limited ability to store subcutaneous fat leads to ectopic fat deposition (including marked hepatic steatosis), hypertriglyceridemia and profound insulin resistance in the muscle and the liver (Petersen et al., 2002). Transgenic mice with targeted overexpression of lipoprotein lipase in the liver or the muscle have liver and muscle specific fat accumulation and insulin resistance (Ferreira et al., 2001; Kim et al., 2001). Taken together, these studies suggest that ectopic accumulation of intracellular lipid

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Fig. 2. Frailty syndrome — determinants, associated factors and sarcopenia in the core (adopted from Lang PO et al. (Lang et al., 2009)). This figure shows schematically the frailty syndrome as composed of different factors. The loss of muscle (sarcopenia) is associated with insulin resistance and results in loss of functionality and strength, yielding a lower physical activity. Abbreviations: RMR, Resting Metabolic Rate; VO2, oxygen consumption.

can lead to insulin resistance in muscle and the liver even in the absence of peripheral and visceral adiposity (Shulman, 2014). Insulin resistance, abetted by background inflammatory responses, promotes lipolysis, such that the released fatty acid flux from adipose tissue to the periphery, muscle and liver, where they “colonize” abnormally (Shulman, 2014). Aging is another potential contributor to the deposition of ectopic fat and its consequences. It is associated with the accumulation of lipids outside the predominantly subcutaneous fat compartment of younger age, into muscle, liver, heart and bone, in which their increased presence likely contributes to organ dysfunction. Aging per se is also associated with a progressive loss of subcutaneous fat, affecting the periphery first, migrating centrally later, accumulation of visceral fat, and ectopic fat deposition (in muscle, liver, bone marrow, and elsewhere) (Cartwright et al., 2007) (Fig. 3). Time-dependent, often yet ill-defined acquired decrements/damage in mitochondrial function, metabolism and biogenesis with aging or inherited defects with delayed expression (such as in subjects with insulin resistance with a family history of type 2 diabetes) may also play a role. Decreased ability to oxidize fat facilitates intramyocellular lipid accumulation and may thus aggravate muscle insulin resistance (Petersen et al., 2003, 2004; Befroy et al., 2007). Both aging and inflammation may influence adipose tissue

differentiation (Goodpaster et al., 2008; Shulman, 2014). Inflammation decreases the lipid storage capacity of adipose tissue by inhibiting preadipocyte differentiation and increasing lipolysis. It may inhibit some critical transcription factors and enzymes, including peroxisome proliferator-activated receptor gamma (PPARγ), cytosine–cytosine– adenosine–adenosine–thymidine (CCAAT) enhancer binding protein (C/EBPα), sterol regulatory element-binding protein 1 (SREBP-1), fatty acid synthase (FAS), acetyl-CoA carboxylase enzymes (ACC), and stearyl coenzyme A desaturase 1 (SCD-1), all playing a role in differentiation of mesenchymal stem cells, as well as of preadipocytes, into mature adipocytes (Liu et al., 2014). Interleukin-6 (IL-6) and TNF-α were both associated with decreased expression of PPARγ2 and C/EBPα, which prevented the normal development of preadipocytes to fully differentiated adipose cells. This resulted in lower lipid storage and, hence, higher probability of lipid deposition in non-adipose tissues (Gustafson and Smith, 2006). TNF-α-knockout mice, fed with high fat diet for 2 weeks, showed a two-fold higher adipose fat pad mass than control mice, as well as lower hepatic steatosis and adipose inflammatory markers (Salles et al., 2012). Altogether these data support the importance of fat storage capacity of adipose tissue, influenced by inflammation, to prevent abnormal lipid deposition in non-adipose tissues (Liu

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Fig. 3. Fat distribution in human body — demonstration (adapted from Cartwright MJ et al. (Cartwright et al., 2007)). This figure shows schematically the changes in muscle and fat mass morphology over time and the changes in the subtypes of fat. Yellow cells represent subcutaneous fat, reddish cells represent visceral fat, and yellow cells within muscle represent ectopic fat lesions. As can be seen, muscle mass is decreasing through the years, whereas fat mass (visceral and subcutaneous) increases in size until midlife. Elderly age is associated with decrease in subcutaneous fat (parallel to the loss of lean body mass) and the emergence of ectopic fat in muscle (but also in other organs). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

et al., 2014; Gustafson and Smith, 2006; Salles et al., 2012). Evidence is also mounting that the capacity of preadipocytes for replication, differentiation, and resistance to apoptosis declines with aging (Cartwright et al., 2007). Muscle-derived cytokines (myokines) secreted by contracting skeletal myocytes in response to enhanced muscle contraction have been shown to exert endocrine-like actions such as through crosstalk between muscle and other tissues. In bone, myokines stimulate bone growth and repair where reduced myostatin leads to an increase in bone density and strength (Brotto and Johnson, 2014). In Adipose tissue irisin secretion stimulated the formation of brite (brown + white) adipocytes and increased the activity of brown adipose tissue (BAT) which is known to have an increased ability to oxidize lipids and produce heat (Gamas et al., 2015). In the liver, muscle derived IL-6 contributes to the regulation of glucose plasma levels either by induction of its release from the liver or by cytokine effects on insulin sensitivity (El-Kadre and Tinoco, 2013) and myonectin decreases plasma-free fatty acids levels through the stimulation of their uptake in adipose tissue and liver (Gamas et al., 2015). On the other hand, muscle dysfunction might result from an imbalance between positive regulators of muscle growth such as bone morphogenetic proteins (BMPs), brain-derived neurotrophic factor (BDNF), follistatin (FST) and irisin, and negative muscle regulators including TGF, myostatin, activins A and B, and growth and differentiation factor-15 (GDF-15) (Kalinkovich and Livshits, 2015). 4.2. Risk for metabolic derangement In a community-based elderly (≥ 65 years) cohort in Korea, the sarcopenic–obese phenotype was evaluated in 287 men and 278 women and its prevalence ranged between 16.7%–35.1% for men and 5.7%–48.1% for women. The calculated range was affected by the definition used to classify sarcopenia in the obese state: appendicular skeletal muscle (by DEXA) has been divided by either height square or body weight, with the first resulting in lower rate of sarcopenia. In this cohort, higher prevalence of the metabolic syndrome was seen in the sarcopenic obese group compared to the obese (non-sarcopenic) or sarcopenic (nonobese) groups [OR = 8.28; (95% CI 4.45–15.4), OR = 5.51 (95% CI 2.81– 10.8) and OR = 2.64 (95% CI 1.08–6.44) respectively], adjusted to age, sex, smoking status, alcohol consumption and physical activity level (Lim et al., 2010). A representative population-based survey, conducted by the Korean Ministry of Health, examined the relationship between body composition and cardiometabolic risk factors in 2943 elderly (≥60 years). Subjects were stratified to four phenotypes: sarcopenic obese, sarcopenic non-obese, non-sarcopenic obese, non-sarcopenic non-obese (sarcopenia was defined by 1 SD below the standard ALST adjusted for total weight). Results showed that subjects in the sarcopenic

obese group were more likely to have insulin resistance, cardio vascular disease (CVD) risk factors and the metabolic syndrome compared to the other groups. For instance, in men, the metabolic syndrome was present in 60.9% of the sarcopenic–obese, but only 29.2% in sarcopenic– non-obese subjects; 48.6% in obese non-sarcopenic but just 11.6% in the non-sarcopenic non-obese phenotype, with similar trends seen in women. This suggests that in the induction of the metabolic syndrome, there is an additive effect of sarcopenia and obesity (Chung et al., 2013). In the Cardiovascular Health Study 3366 communitydwelling older (≥65 years) men and women were followed for 8 years. Sarcopenia and obesity alone did not increase the risk for CVD, coronary heart disease, stroke or congestive heart failure. However, the combined sarcopenic–obese state, defined according to muscle strength but not muscle mass, was modestly, and independent of some explanatory variables, associated with increased CVD risk [HR 1.23 (95% CI 0.99–1.54)], and congestive heart failure [HR 1.42 (95% CI 1.05–1.91)]. Further adjustment to other CVD risk factors attenuated this association (Stephen and Janssen, 2009). 4.3. Risk for functional impairment Rolland et al. (Rolland et al., 2009) examined in elderly women the synergistic effect of obesity and sarcopenia on functional measures such as performing physical daily activities. Whereas sarcopenia alone was not linked to self-reported difficulties, the co-existence of both obesity and sarcopenic obesity were associated with reduced self-perceived performance. However, in studies in which objective, rather selfperceived physical capacity was assessed, physical performance capacity was not worse in the obese–sarcopenic than in the obese nonsarcopenic phenotype (Bouchard et al., 2009). In a cohort of 674 men and women (61.4 ± 7 years) followed for 5 years, a small increase in the risk of falls was observed in the dynapenic obese phenotype, but not in the non-dynapenic–non obese and the sarcopenic (low MM only) obese groups (Scott et al., 2014). It is plausible that the elderly are uniquely susceptible to specific adverse effects of obesity due to the need to carry a greater weight with lesser MM and strength. Indeed, there is a ‘U’ shaped correlation between body weight and frailty so that both underweight (BMI b 18.5 kg/m2) and obese subjects are more likely to present a frail phenotype (Hubbard et al., 2010). Both elderly obese and frail non-obese groups were significantly different than a reference group of non-obese non frail subjects, having lower aerobic capacity, lower strength abilities, reduced physical performance and, all over, lower functionality (Villareal et al., 2004). Obesity, per-se, is associated with anatomical and functional changes in the aging brain (Muller et al., 2007). Smaller brain volume, reduced cerebral white matter integrity and atrophy in the frontal lobes, anterior cingulated cortex, hippocampus, and thalamus were observed in older

A. Buch et al. / Experimental Gerontology 76 (2016) 25–32

obese compared to those with normal BMI subjects (Raji et al., 2010; Walther et al., 2010). Whereas midlife obesity is associated with an increased risk for dementia (Whitmer et al., 2005a,b), the risk for dementia in elderly obese subjects is still under debate (Salles et al., 2012; Brotto and Johnson, 2014). Yet, insulin-signaling facilitates cognitive tasks, whereas insulin resistance in the elderly is associated with reduced cognitive efficiency and flexibility (Abbatecola et al., 2004). In older subjects (≥70 years), sarcopenia and obesity, either independently or concurrently, are reportedly associated with lower cognitive function compared to the non-sarcopenic, non-obese state (Levine and Crimmins, 2012). Determinants of frailty and sarcopenia, such as poor muscle quality, fat distribution and insulin resistance, are also associated with decreased cognitive functioning (Gustafson et al., 2004a,b; Wolf et al., 2007; Sturman et al., 2008). The emerging link between muscle and brain is presently poorly understood. One potential mechanism may involve brain-derivedneurotrophic-peptide (BDNP), a member of the neurotrophin family which can exert positive effects on neuronal growth, differentiation and plasticity and regulates synaptic transmission in the CNS. It is also involved in the maintenance of myoblasts and muscle fibers as well as in the regulation of motoneuron survival, presynaptic release of neurotransmitters, and the maintenance of the postsynaptic region in skeletal myofibers (Kalinkovich and Livshits, 2015). However, while BDNP can be expressed in brain and muscle, how this peptide may underlie parallel effects in these organs remains conjectural at the present time. 5. Summary Sarcopenia and frailty are dynamic and highly prevalent states associated with aging and aging-related conditions. As life expectancy is increasing, sarcopenia and frailty become increasingly important. Their effect on different metabolic, such as cardio-metabolic morbidity, and functional outcomes, such as daily activities and cognitive abilities, has been referred to in the scientific literature. Mounting data suggest that these effects may be modified by the co-presence of obesity in the background. The interaction between obesity and lower MM and strength, has been rather consistently associated with higher prevalence of metabolic impairments. Their relation to functional properties, such as physical ability may be more complex. Multiple mechanisms such as hormonal dysregulation (e.g., insulin resistance, decline in sex hormones), promotion of lipolysis and inflammatory pathways, migration of fat into the muscle, have been identified in obesity and could play a contributory role. Growing efforts are now directed into finding and classifying sensitive biomarkers for sarcopenia, such as myokines, which could also be mechanistically informative. Awareness of these conditions, prevention and treatment actions may lower the gap between HALE and life expectancy and improve the quality of life in the elderly population. Funding This work was supported by the Sagol Foundation for the Metabolic Syndrome Research Center 3-4. Conflicts of interest There are no conflicts of interest. Acknowledgment This work was performed in partial fulfillment of the requirements for a Ph.D. degree by Assaf Buch at the Sackler Faculty of Medicine, Tel Aviv University, Israel. We thank Sandra Levi for illustrating the figures.

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