The Paradox of Overnutrition in Aging and Cognition

Journal Articles

Roger A. Fielding

Goodpaster BH, Park SW, Harris TB, et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci. 2006;61(10):1059-64.

Reid KF, Fielding RA. Skeletal muscle power: a critical determinant of physical functioning in older adults. Exerc Sport Sci Rev. 2012;40(1):4-12.

Clark DJ, Fielding RA. Neuromuscular contributions to age-related weakness. J Gerontol A Biol Sci Med Sci. 2012;67(1):41-7.

John Gunstad

Gunstad J, Lhotsky A, Wendell CR, et al. Longitudinal examination of obesity and cognitive function: results from the Baltimore longitudinal study of aging. Neuroepidemiology. 2010;34(4):222-9.

Siervo M, Arnold R, Wells JC, et al. Intentional weight loss in overweight and obese individuals and cognitive function: a systematic review and meta-analysis. Obes Res. 2011;12(11):968-83.

Spitznagel MB, Garcia S, Miller LA, et al. Cognitive function predicts weight loss after bariatric surgery. Surg Obes Relat Dis. 2011. [Epub ahead of print]

Deborah R. Gustafson

Gustafson D, Rothenberg E, Blennow K, et al. An 18-year follow-up of overweight and risk of Alzheimer disease. Arch Intern Med. 2003;163(13):1524-8.

Gustafson DR, Karlsson C, Skoog I, et al. Mid-life adiposity factors relate to blood-brain barrier integrity in late life. J Intern Med. 2007;262(6):643-50.

Gustafson DR, Bäckman K, Waern M, et al. Adiposity indicators and dementia over 32 years in Sweden. Neurology. 2009;3(19):1559-66.

Gustafson DR, Bäckman K, Joas E, et al. 37 years of body mass index and dementia: observations from the prospective population study of women in Gothenburg, Sweden. J Alzheimers Dis. 2012;28(1):163-71.

Gustafson DR. Adiposity and cognitive decline: underlying mechanisms. J Alzheimers Dis. 2012;30 Suppl 2:S97-112.

Steven B. Heymsfield

Baumgartner RN, Waybe SJ, Waters DL, et al. Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res. 2004;12(12):1995-2004.

Roberts SB, Rosenberg I. Nutrition and aging: changes in the regulation of energy metabolism with aging. Physiol Rev. 2006;86(2):651-67.

Zamboni M, Mazzali G, Fantin F, et al. Sarcopenic obesity: a new category of obesity in the elderly. Nutr Metab Cardiovasc Dis. 2008;18(5):388-95.

Suetta C, Hvid LG, Justesen L, et al. Effects of aging on human skeletal muscle after immobilization and retraining. J Appl Physiol. 2009;107(4):1172-80.

Lenore J. Launer

Peila R, Rodriguez BL, Launer LJ, et al. Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: The Honolulu-Asia Aging Study. Diabetes. 2002;51(4):1256-62.

Akomolafe A, Beiser A, Meigs JB, et al. Diabetes mellitus and risk of developing Alzheimer disease: results from the Framingham Study. Arch Neurol. 2006;63(11):1551-5.

Majnous AG 3rd, Baker R, Koopman RJ, et al. Impact of the population at risk of diabetes on projections of diabetes burden in the United States: an epidemic on the way. Diabetalogia. 2007;50(5):934-40.

Saczynski JS, Jónsdóttir MK, Garcia ME, et al. Cognitive impairment: an increasingly important complication of type 2 diabetes: the age, gene/environment susceptibility—Reykjavik study. Am J Epidemiol. 2008;168(10):1132-9.

Qiu C, Cotch MF, Sigurdsson S,et al. Cerebral microbleeds, retinopathy, and dementia: the AGES-Reykjavik Study. Neurology. 2010;75(24):2221-8.

Josef Penninger

Pospisilik JA, Knauf C, Joza N, et al. Targeted deletion of AIF decreases mitochondrial oxidative phosphorylation and protects from obesity and diabetes. Cell. 2007;131(3):476-91.

Pospisilik JA, Schramek D, Schnidar H, et al. Drosophila genome-wide obesity screen reveals hedgehog as a determinant of brown versus white adipose cell fate. Cell. 2007;40(1):148-60.

Elling U, Taubenschmid J, Wirnsberger G, et al. Forward and reverse genetics through derivation of haploid mouse embryonic stem cells. Cell Stem Cell. 2011;9(6):563-74.

Teperino R, Amann S, Bayer M, et al. Hedgehog partial agonism drives Warburg-like metabolism in muscle and brown fat. Cell. 2012;151(2):414-26.

David IW Phillips

Hales CN, Barker DJ, Clark PM, et al. Fetal and infant growth and impaired glucose tolerance at age 64. BMJ. 1991;303(6809):1019-22.

Yajnik CS, Fall CH, Coyaji KJ, et al. Neonatal anthropometry: the thin-fat Indian baby. The Pune Maternal Nutrition Study. Int J Obes Relat Metab Discord. 2003;27(2):173-80.

Bhargava SK, Sachdev HS, Fall CH, et al. Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. N Eng J Med. 2004;350(9):865-75.

Lillycrop KA, Phillips ES, Jackson AA, et al. Dietary protein restriction of pregnant rats induces and folic acid supplementation prevents epigenetic modification of hepatic gene expression in the offspring. J Nutr. 2005;135(6):1382-6.

Nikos Scarmeas

Scarmeas N, Stern Y, Tang MX, et al. Mediterranean diet and risk for Alzheimer's disease. Ann Neurol. 2006;59(6):912-21.

Scarmeas N, Luchsinger JA, Schupf N, et al. Physical activity, diet, and risk of Alzheimer disease. JAMA. 2009;302(6):627-37.

Gu Y, Nieves JW, Stern Y, et al. Food combination and Alzheimer disease risk: a protective diet. Arch Neurol. 2010;67(6):699-706.

Tangney CC, Aggarwal NT, Li H, et al. Vitamin B12, cognition, and brain MRI measures: a cross-sectional examination. Neurology. 2011;77(13):1276-82.

Morris MC, Tangney CC. A potential design flaw of randomized trials of vitamin supplements. JAMA. 2011;305(13):1348-9.

Gu Y, Schupf N, Cosentino SA, et al. Nutrient intake and plasma β-amyloid. Neurology. 2012;78(23):1832-40.

Yaakov Stern

Stern Y. What is cognitive reserve? Theory and research application of the reserve concept. J Int Neuropsychol Soc. 2002;8(3):448-60.

Bennett DA, Wilson RS, Schneider JA, et al. Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology. 2003;60(12):1909-15.

Valenzuela MJ, Sachdev P. Brain reserve and dementia: a systematic review. Psychol Med. 2006;36(4):441-54.

Scarneas N, Luchsinger JA, Schupf N, et al. Physical activity, diet, and risk of Alzheimer disease. JAMA. 2009;302(6):627-37.

Valenzuela MJ, Matthews FE, Brayne C, et al. Multiple biological pathways link cognitive lifestyle to protection from dementia. Biol Psychiatry. 2012;71(9):783-91.

Steffener J, Stern Y. Exploring the neural basis of cognitive reserve in aging. Biochim Biophys Acta. 2012;1822(3):467-73.

Reports

Alzheimer's Association. Alzheimer's disease facts and figures. Alzheimer's and Dementia: The Journal of the Alzheimer's Association. March 2012; 8:131-168.

Centers for Disease Control and Prevention. Overweight and obesity: adult obesity facts. 2012.

Presented by

According to the Alzheimer's Association, approximately 5.4 million Americans and 36 million people worldwide suffer from Alzheimer's disease (AD); 50% of Americans over the age of 80 have AD or another form of dementia; and the prevalence of AD in the U.S. is projected to triple by 2050. Obesity is also growing: more than a third of American adults are obese, according to the Centers for Disease Control and Prevention.

Diabetes, which is correlated with obesity, is already known to lead to cognitive decline by causing neuronal and vascular damage. Now, epidemiologists have begun to link overnutrition to age-related brain pathologies—specifically, cognitive decline, dementia, and AD—even in the absence of diabetes. Numerous mechanisms may underlie this link; obesity is a risk factor for vascular disease, for example, which can cause dementia.

Potential mechanisms linking overweight and obesity to cognitive impairment and dementia. Obesity increases the risk of vascular and blood vessel events and influences hormones, inflammatory agents, and other signaling molecules. (Image courtesy of Deborah R. Gustafson)

The conference aimed to pin down the interaction between obesity and dementia. Numerous studies show that obesity in midlife increases AD risk, but the picture in older adults is more complex: if obesity occurs shortly before dementia, it may mitigate dementia. Epidemiological data can suggest a biological basis for this link, but incorporating other data like brain imaging and blood markers will help to elucidate it more clearly.

If overnutrition is indeed a risk for cognitive decline, it is at least in theory a modifiable one. Considering that no effective treatments exist for AD, the search for modifiable risks and for ways to mitigate these risks is essential. Environmental factors may serve as points of intervention; research indicates that mental stimulation, physical exercise, and perhaps diet can lower the risk of cognitive decline. The question is how to apply this research to clinical populations. Studies show that weight gain in midlife significantly increases the risk of AD; could weight loss—perhaps through drastic means such as bariatric surgery—similarly lower it?

Speakers:
Josef Penninger, IMBA-Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Austria
David IW Phillips, University of Southampton, UK

Highlights

• The hedgehog pathway might provide druggable targets for insulin-independent diabetes therapies.
• The diabetes epidemic in the developing world is driven by poor prenatal nutrition and low birth weight, which have lifelong effects.

Systems biology: identifying genes and pathways

Josef Penninger from the IMBA-Institute of Molecular Biotechnology of the Austrian Academy of Sciences opened the conference by describing his work using fly and mouse models to identify genetic mechanisms underlying obesity. His group identified 453 candidate genes that affect adipose regulation, screening more than 8000 Drosophila genes for regulators of triglyceride levels using a technique called RNA interference which blocks the expression of target genes.

In a follow-up screen, the group knocked out obesity genes in neuron, muscle, liver, and fat cells in Drosophila. The hedgehog signaling pathway, a key regulator of cancer and stem cells, stood out in fat cells as a regulator of obesity, so the researchers created a mutant mouse model with amped-up hedgehog signaling to study its effects. Mice were extremely skinny and carried very little white adipose tissue, "bad" fat. However, they had normal amounts of brown adipose tissue, "good" fat, so-called because it burns fat molecules and helps to regulate glucose metabolism. The hedgehog pathway is thus a previously undiscovered master regulator of fat lineage, Penninger said, and could be targeted to develop insulin-independent medicines to control diabetes.

Penninger's group is also developing mouse embryonic stem cells containing half the normal number of chromosomes (one set), which provide much greater precision for studying specific genes or gene networks. This technique is faster than working with in vivo models, as it allows researchers to create distinct mutations in millions of cells per day and test these cells to examine the function of mutated genes. Investigating adipocyte differentiation, genes identified earlier in the fly study were mutated to identify functional effects in the cell. Penninger noted that he plans to create a bank of thousands of mutated stem-cell clones that will be accessible to scientists worldwide.

A global view: precursors and trends in metabolic disease

Taking a more policy-oriented approach, David IW Phillips from the University of Southampton argued that in order to understand some ailments we experience at the end of life, it is necessary to examine the beginning of life. In areas of the developing world where people maintain traditional lifestyles, diabetes is largely absent. But westernization results in higher diabetes rates than are found in western countries, particularly in areas that are initially poorest.

One possible explanation is that people from historically food-scarce cultures carry "thrifty genes," which gear the metabolism toward efficient fat storage during times of food abundance. Phillips described an alternative explanation, termed phenotypic plasticity, which posits that adverse conditions in early development reprogram the fetus and predispose individuals to type 2 diabetes. Poor prenatal nutrition may result in low birth weight, accompanied by a suite of lifelong physiological changes. Low-birth-weight babies have altered body composition (less muscle and more fat), abnormal vascular function, and alterations in the structure and function of the liver, kidney, and pancreas. In adulthood, they tend to develop symptoms associated with type 2 diabetes, including obesity (specifically belly fat), hypertension, and insulin resistance.

More than two decades ago, researchers found low birth weight to be predictive of developing diabetes in adulthood—particularly in adults who are overweight. Animal studies examining the hypothalamic–pituitary–adrenal axis, a neuroendocrine pathway that controls the body's reaction to stress, hint at a possible mechanism: when there is a high level of maternal glucocorticoid, stress hormones cross the placenta and directly affect the fetal brain, leading to some of the same physiological effects observed in nutritionally-deprived infants. In pregnant rats, undernourishment was found to act as a stressor, causing epigenetic changes in glucocorticoid receptors in offspring (preventable by supplementing the maternal diet with folate). In studies of 9–10 year-olds, Phillips's group found that children born smaller experience a more dramatic cortisol response to stress. Such metabolic differences in low-birth-weight babies suggest that they are physiologically adapted for adversity; in an environment of overnutrition this could lead to diabetes, Phillips said.

In India, which has a high proportion of low-birth-weight babies, cultural and economic trends lead to chronically poor nutrition in women. Babies weigh an average of 2.6kg–3.0 kg, compared to 3.3kg–3.5 kg for Caucasian babies born in the UK. Their so-called "thin-fat" body composition persists into adulthood and predisposes them to diabetes. Phillips posited a cycle in which an undernourished child becomes an undernourished mother, who produces a low-birth-weight infant. Combined with urbanization, and its dietary and lifestyle changes, these conditions lead to a significant rise in diabetes.

A schematic representing environmental and physiological factors driving the diabetes epidemic in the developing world. (Image courtesy of David IW Phillips)

Efforts to curb the diabetes epidemic in the developing world must therefore focus on improving maternal nutrition, even before conception, rather than attempting to modify adult risk factors or even risk factors in infants or young children, Phillips said. Several interventions are being tried, such as an ongoing study in India which is testing maternal folate supplementation.

Speakers:
Yaakov Stern, Columbia University College of Physicians and Surgeons
Lenore J. Launer, National Institute on Aging, National Institutes of Health

Highlights

• Cognitive reserve—flexibility in cognitive networks—may protect against dementia, enabling the brain to adapt to pathology.
• Diabetes may spur cognitive damage through a combination of vascular and neurological damage.

The epidemiology of cognitive reserve

One unanswered question in AD and dementia research is: Why do people who have similar brain pathologies experience different levels of cognitive decline? Yaakov Stern from Columbia University College of Physicians and Surgeons described a variable he terms "reserve," which may explain this mismatch. Stern described two types of reserve: brain reserve connotes a physiological surplus—of neurons or synapses, for example—that allows the brain to sustain more pathology before its effects are felt; cognitive reserve, the focus of Stern's lab, instead connotes flexibility in cognitive networks that enables the brain to adapt to pathology.

Stern noted that although brain reserve and cognitive reserve are not accepted by all researchers, epidemiological work supports the idea. Lifestyle variables such as higher education, occupational attainment, pre-morbid IQ, and frequency and type of leisure activities, all correlate with a later onset of clinical manifestations of AD; some studies also correlate higher literacy with slower cognitive decline in normal aging. Imaging and autopsy studies show that of two people matched for cognitive function, a person with more education, for example, is expected to have more extensive AD pathology than a person with less education, suggesting that the brain can withstand physiological degradation and that this resilience is the result of cognitive reserve.

A graphical representation of cognitive reserve. Individuals with high reserve accumulate more AD pathology before clinical symptoms begin to manifest. (Image courtesy of Yaakov Stern)

Stern's group is now applying advanced imaging techniques to explore the neurophysiological basis of cognitive reserve. In a recent study, researchers identified two brain networks used in a letter recognition task. One network appeared in both young and old adults; the other, in older adults only. Older adults who used the secondary network exclusively had particularly high levels of brain atrophy in the first network, and their performance in the recognition task was significantly worse than that of subjects who used both networks. But cognitive reserve, Stern said, seemed to provide an advantage: among older adults who used the second network, those with a higher pre-morbid IQ outperformed others. Researchers are now working to determine which activities or lifestyle factors might mitigate cognitive decline.

Diabetes, cognition, and dementia

Lenore J. Launer from the National Institute on Aging discussed the hypothesis that diabetes, which often correlates with obesity, is a key link between high body mass index and cognitive disorders. Diabetes tends to manifest about 10 to 15 years earlier than dementia, and numerous studies link long-term diabetes to cognitive decline. Genetic factors, inflammation, changes in insulin regulation, and neuronal and vascular damage associated with both diabetes and high BMI may link these conditions to brain pathology.

In two epidemiological studies, Launer and colleagues reported that when diabetes is combined with an increased genetic risk for AD (presence of the ApoE4 allele), the risk of developing AD is four times higher than baseline, significantly higher than the sum of either risk factor alone. Amyloid deposits were increased in the brain and blood vessels in diabetics who carried the genetic marker. Long-term diabetes tends to correlate with reduced cortical grey matter volume, and the study found that microvascular lesions, which are prevalent in diabetes, affected cognitive function. This suggests that structural changes in the brain—a combination of vascular and neuronal damage—may underlie cognitive decline.

In addition to these primary features of diabetes, co-occurring conditions such as high blood pressure, high cholesterol, and high body mass index may also contribute to cognitive deficits, Launer noted. Indeed, high blood pressure in midlife has been found to confer an increased risk of AD 25 years later.

Speakers:
Deborah R. Gustafson, SUNY Downstate Medical Center; University of Gothenburg, Sweden
Nikos Scarmeas, University of Athens, Greece; Columbia University
John Gunstad, Kent State University
 
Panel Moderator:
John G. Kral, SUNY Downstate Medical Center

Highlights

• Obesity in midlife raises the risk of dementia, but in older age it may be protective. This could be because earlier obesity is a vascular risk, while later it becomes a marker for aging.
• Determining the cognitive effects of nutritional interventions is challenging: objective measures must be developed.
• Bariatric surgery can improve cognition, suggesting that extensive weight loss might delay the onset of dementia.

The effects of obesity on dementia

Only in the last decade have researchers become aware that overweight and obesity can increase the risk for neurodegenerative disorders and dementia. Deborah R. Gustafson from SUNY Downstate Medical Center and the University of Gothenburg described her work to elucidate the connection.

In 2003, Gustafson and colleagues published the first report of this link, based on data from an epidemiological study in women born in Sweden in 1901–1902, which found that women with higher BMIs in their 70s were at increased risk for AD in their 80s. This result raised skepticism among researchers because people with dementia generally lose weight, and therefore tend to have a lower BMI. In a second epidemiological study which surveyed data from Swedish women who had been prospectively followed for 45 years, a higher waist-to-hip ratio in middle age approximately doubled the risk of dementia in older age. Subsequent studies have confirmed the link between BMI in middle age and dementia, and some have replicated the earlier findings on higher BMI later in life.

Studies of later life, however, have been mixed. Paradoxically, Gustafson said, studies on the association between declining BMI and dementia report that in the years just before dementia sets in, an increase in BMI may be protective against dementia. Among patients with mild cognitive impairment and mild AD, Gustafson found that dementia progressed more slowly in those who were overweight. Therefore, weight change might not only affect the course of dementia but also serve as a clinical predictor of progression, she suggested.

Possible mechanisms by which hormones from adipose tissue might affect the brain. Leptin and adiponectin directly interact with the arcuate nucleus in the hypothalamus, causing the release of peptides that modulate food intake and metabolism. (Image courtesy of Deborah R. Gustafson)

Gustafson hypothesized that BMI in midlife may act as a vascular risk for dementia, in the same way that it is a risk for cardiovascular disease and diabetes, but become a marker for neurodegenerative events associated with general aging later in life. Studies report that obesity might contribute to brain pathologies such as brain atrophy, white matter lesions, and a breakdown of the blood–brain barrier. The biological mechanisms underlying this relationship are unclear, but Gustafson suggested that hormones released by adipose tissue might directly affect neurons in a region of the hypothalamus that regulates food intake, energy balance, and other homeostatic processes.

Measuring nutrient effects: limitations, complexities, and interpretations

Previous studies relate cognitive performance and risk for Alzheimer’s disease and other dementias with a series of micronutrients, macronutrients, foods, and dietary patterns. This literature is quite extensive; however, it is also conflicting since noted associations are often not replicated or confirmed and associations of opposing directionality are often reported. Nikos Scarmeas from the University of Athens and Columbia University explained the limitations and complexities of this research.

The uncertainty of previous studies could lead to an interpretation that there exists no biological association between nutrition and cognition; nevertheless, before dismissing such an association, Scarmeas recommended interpreting results in light of methodological limitations.

Nutritional supplementation may have biological efficacy only in those with relative deficiencies, but subjects participating in nutrition studies are often at normal levels for many nutrients, leaving no room for diet-related physiological improvement. Observational epidemiological approaches are subject to confounding and the possibility for reverse causality, while randomized clinical trials pose multiple practical limitations, including cost, the need for long-term intervention, and difficulty implementing behavioral changes (such as dietary change).

It is also difficult to measure cognitive outcomes and nutrient exposure: dementia diagnoses are subjective and hampered by a lack of reliable biomarkers and variable neuropsychological evaluations; and food questionnaires probe for answers that require complex considerations and are limited in the number of questions that can be asked. Nutrient biomarker tests are costly and can only assess some nutritional elements, but our food is composed of thousands of chemical substances with potentially important cognitive effects. This can lead to a reductionistic consideration of a limited number of nutritional elements, ignoring confounding and interaction and failing to summarize dietary habits adequately. A more holistic approach that looks at dietary patterns can partially remedy these problems and provide significant public health information, but it cannot elucidate the exact biological causality between diet and cognition.

Attempts to address some of these issues include consideration of baseline levels of nutrients; measurements of nutrient biomarkers with an examination of their relation to neurodegeneration-related (and other) biomarkers; estimation of dietary patterns; and use of brain imaging biomarkers. This work has emerged relatively recently, but Scarmeas expressed hope that it will increase in the near future and enhance our currently poor understanding of the relationship between nutrition and cognition.

Improving cognition through weight loss

John Gunstad from Kent State University began by noting that approximately 50% of AD cases are related to modifiable risk factors like obesity, smoking, and high blood pressure. Gunstad's work examines whether weight loss in midlife can improve cognitive function. A handful of studies have found a small but positive effect of moderate weight loss on cognition; a larger, randomized study now underway should provide more definitive data.

Most weight-loss efforts are only moderately successful, especially among severely obese people. But if slow, moderate weight loss can improve brain function, then fast, extensive weight loss might have a more dramatic effect. Following this reasoning, Gunstad examined the effects of Roux-en-Y gastric bypass (bariatric surgery), which can result in a 50%–75% loss of excess weight and is known to dramatically improve insulin resistance, cardiovascular function, and other factors that tie obesity to AD.

Before surgery, 40% of the test group had noticeable cognitive deficits; memory function and other cognitive features showed improvements just 12 weeks later. A year later, when participants' weight had fallen into a healthy range, the entire group displayed normal memory. Interestingly, when after 36 months a subset of participants regained some of the weight they had lost, their cognitive scores grew correspondingly worse.

Gunstad noted some caveats. It is not known whether a person with a lower BMI would also experience benefits from weight loss, or how long a person would need to be obese before cognitive deficits emerge. Nonetheless, these findings raise hope that modifying weight could delay the onset of AD.

Panel discussion

In a panel discussion moderated by John G. Kral of SUNY Downstate Medical Center, the speakers suggested future directions for research. Several discussed ways in which epidemiological research could be improved to better track the relationship between obesity and cognitive decline. John Gunstad began by advocating for more frequent and thorough data collection with the help of smart phones or other technological devices that could capture parameters such as cognitive function, blood pressure, and diabetes control on a daily, weekly, or monthly basis. Yaakov Stern noted that several cohorts of individuals who have been tracked since birth are now reaching 60 years old or older and data from these cohorts could be used to search for associations between childhood factors and cognitive status (as well as related health features) in aging. Nikos Scarmeas pointed out that epidemiological studies should focus on linking biological measures such as blood biomarkers to subgroups of individuals with specific gene polymorphisms, rather than on seeking correlations with gross clinical outcomes as they have in the past.

Researchers recommended large clinical trials of potential interventions. Stern pointed out that exercise in older people has consistently shown to have positive effects on age-related decline. He advocated for a very large-scale randomized trial to look for meaningful reversals in behavior with interventions that have shown hints of efficacy, such as a Mediterranean diet. Those trials will be difficult to interpret, cautioned Lenore Launer: age-related conditions take many years to develop, and an intervention that does not show efficacy in a 6-month or even a 4-year trial could still be valid.

Deborah Gustafson and others noted that although many of the underlying causes of poor outcomes in aging are already well known, such as overeating and lack of exercise, this knowledge has not been enough to drive behavioral change. Researchers should consider utilizing marketing techniques used in industry to publicize new products to promote health policy, Scarmeas said. He also suggested studying countries such as Japan, where obesity is not a rising health concern, to understand the cultural factors driving weight gain in the U.S. and other regions.

Speakers:
Steven B. Heymsfield, Pennington Biomedical Research Center
Roger A. Fielding, Tufts University

Highlights

• The combination of sarcopenia and obesity is a silent syndrome with especially poor outcomes.
• Muscle power is a stronger indicator of muscle function than muscle strength or muscle mass.

Muscle make-up over the lifespan

The focus shifted next to age-related changes in muscle mass and function, underlying mechanisms, and possible interventions. Steven B. Heymsfield from Pennington Biomedical Research Center began by describing compositional changes that characterize sarcopenia, an age-related decline in muscle mass, which include an infiltration of fat into muscle tissue and an increasing prevalence of low-density muscle.

A host of factors besides age, such as race, genetics, height, and weight, determine muscle mass. People lose muscle mass at different rates, likely due to differences in physical activity and nutrition, but the pathophysiology of sarcopenia is poorly understood, Heymsfield said. Contributing factors include neurological and muscular processes, hormonal changes, inflammatation, and anabolic processes. Sarcopenia also represents a significant component of frailty, a larger and more disabling feature of aging.

Heymsfield discussed the link between adipose tissue and muscle mass, noting that body-fat composition increases with age, even when overall body weight remains stable or declines. Because sarcopenia causes muscle loss, it often masks obesity, making the co-occurrence of these conditions nearly invisible, said Heymsfield. Yet the combination of sarcopenia and obesity has a synergistic effect that is particularly problematic: increased abdominal fat increases inflammatory mediators and promotes insulin resistance, while a decline in muscle mass promotes reduced physical activity, compounding the effects of inflammatory and metabolic changes. Heymsfield likened sarcopenic obesity to an elderly version of the "thin-fat" syndrome described in Indian babies in the previous talk. Studies show that individuals with sarcopenic obesity experience higher levels of mortality and disability. In one report, elderly people were 2 to 3 times more likely to report a drop in abilities required for independent living, compared to either sarcopenia or obesity alone. Heymsfield concluded that much work remains to be done to understand molecular mechanisms and develop treatments for sarcopenia.

Roger A. Fielding from Tufts University described his lab's approach to measuring potential interventions—exercise-based, nutritional, and pharmaceutical—that could promote muscle function in older adults, delving specifically into his work on the distinction between muscle mass and muscle strength.

Muscle strength is related to muscle mass: differences in mass explain about 70% of the variance in strength among 70–85 year-olds. But the two measures also diverge: interventions that increase one do not necessarily increase the other, and changes in muscle mass do not correlate well with changes in strength as people age. This discrepancy prompted Fielding's group to develop a test to measure muscle power. While muscle strength is simply the highest force with which a muscle contraction can be generated, muscle power also includes the speed with which this force can be generated. Their data suggest that loss of power is a stronger indicator of a decline in muscle function than loss of strength.

Muscle is subject to age-related changes such as infiltration by fat (orange) and replacement of Type II (fast twitch) fibers with Type I (slow twitch) fibers. (Image courtesy of Roger A. Fielding)

Using a simple performance test, Fielding's lab compared differences in muscle power between healthy middle-aged adults (average age 47 years), healthy older adults (average age 74 years), and mobility-limited older adults (average age 77 years). Leg power was similar in the two healthy groups, but was much reduced in the mobility-limited group—a difference the researchers traced to deficits in the ability of motor neurons to rapidly activate muscle fibers. A follow-up study three years later revealed that an age-related decline in performance in healthy older adults could similarly be explained by decreased neuromuscular activation.

Leg power has been used by other researchers to test interventions for age-related musculoskeletal decline, such as testosterone and potassium bicarbonate, as well as novel drugs for cancer-related muscle wasting. Fielding suggested that the functional limitations researchers observe in the lab may serve as biomarkers for disabilities people experience outside the lab.