Age-related deposition of fat in skeletal muscle is associated with functional limitations. Skeletal muscle fat may be present in people with preserved muscle mass or accompanied by muscle wasting. However, it is not clear if the association between muscle fat deposition and physical performance is moderated by muscle mass. To determine whether the association between midthigh intermuscular fat and physical performance is moderated by muscle area. We performed a cross-sectional analysis of the Health, Aging, and, Body Composition (ABC) study data collected in 2002–2003 (n = 1897, women: 52.2%). Midthigh muscle cross-sectional area (by computed tomography) and physical performance measures were compared across quartiles of intermuscular fat absolute area. Moderation analysis was performed to determine the conditional effect of intermuscular fat on physical performance as a function of muscle area. Conditional effects were evaluated at three levels of muscle area (mean and ± 1 standard deviation [SD]; 213.2 ± 53.2 cm²). Simple slope analysis showed that the negative association between intermuscular fat area (cm²) and leg strength (N·m) was of greater magnitude (beta coefficient [b], 95% confidence interval [CI] = −0.288 [−0.427, −0.148]) in participants with greater muscle area (ie, 1 SD above the mean) compared to those with lower muscle area (ie, at mean [b = −0.12 {−0.248, 0.008}] or 1 SD below the mean [b = 0.048 {−0.122, 0.217}]). Similarly, the negative association of intermuscular fat with 400-m walk speed (m/s) and chair stand (seconds) was greater in those with higher muscle areas (p < .001) compared to those with lower muscle areas. The association between higher intermuscular fat area and impaired physical function in aging is moderated by muscle area.

Little is known about the association of specific nutrients, especially proteins, on the age-related gut dysbiosis. To determine the associations between the quantity and sources (vegetable and animal) of dietary protein intake and gut microbiome composition in community-dwelling older men. We performed a cross-sectional analysis on 775 older men from the Osteoporotic Fractures in Men (MrOS) Study (age 84.2 ± 4.0 years) with available dietary information and stool samples at visit 4 (2014–16). Protein intake was estimated from a brief food frequency questionnaire and adjusted to total energy intake. The gut microbiome composition was determined by 16S (v4) sequencing (processed by DADA2 and SILVA). 11,534 amplicon sequence variants (ASVs) were identified and assigned to 21 phyla with dominance of Firmicutes (45%) and Bacteroidetes (43%). We performed α-diversity, β-diversity, and taxa abundance (by ANCOM-BC) to determine the associations between protein intake and gut microbiome. Median protein intake was 0.7 g/(kg body weight · d). Participants with higher energy-adjusted protein intakes had higher Shannon and Chao1 α-diversity indices (P < 0.05). For β-diversity analysis, participants with higher protein intakes had a different center in weighted and unweighted UniFrac PCoA vs. those with lower intake (P < 0.05), adjusted for age, race, education, clinical center, batch number, fiber and energy intake, weight, height, and medications. Similarly, higher protein consumptions from either animal or vegetable sources were associated with higher gut microbiome diversity. Several genus-level ASVs, including Christensenellaceae, Veillonella, Haemophilus, and Klebsiella were more abundant in participants with higher protein intakes, whereas Clostridiales bacterium DTU089 and Desulfovibrio were more abundant in participants with lower protein intake (Bonferroni corrected P < 0.05). We observed significant associations between protein intake and gut microbiome diversity in community-living older men. Further studies are needed to elucidate the mediation role of gut microbiome on the relationship between protein intake and health outcomes in older adults.

Emerging studies highlight chrononutrition's impact on body composition through circadian clock entrainment, but its effect on older adults' muscle health remains largely overlooked. To determine the associations between chrononutrition behaviors and muscle health in older adults. Dietary data from 828 older adults (76 ± 5 years) recorded food/beverage amounts and their clock time over the past 24 h. Studied chrononutrition behaviors included: (1) The clock time of the first and last food/beverage intake; (2) Eating window (the time elapsed between the first and last intake); and (3) Eating frequency (Number of self‐identified eating events logged with changed meal occasion and clock time). Muscle mass (D₃‐creatine), leg muscle volume (MRI), grip strength (hand‐held dynamometer), and leg power (Keiser) were used as outcomes. We used linear regression to assess the relationships between chrononutrition and muscle health, adjusting for age, sex, race, marital status, education, study site, self‐reported health, energy, protein, fiber intake, weight, height, and moderate‐to‐vigorous physical activity. Average eating window was 11 ± 2 h/day; first and last intake times were at 8:22 and 19:22, respectively. After multivariable adjustment, a longer eating window and a later last intake time were associated with greater muscle mass (β ± SE: 0.18 ± 0.09; 0.27 ± 0.11, respectively, p < 0.05). The longer eating window was also marginally associated with higher leg power (p = 0.058). An earlier intake time was associated with higher grip strength (−0.38 ± 0.15; p = 0.012). Chrononutrition behaviors, including longer eating window, later last intake time, and earlier first intake time were associated with better muscle mass and function in older adults.

Context: Specific plasma amino acid (AA) profiles including elevated postabsorptive branched-chain amino acids (BCAAs) have been associated with insulin resistance (IR), mostly estimated by homeostatic model assessment. This study assessed the associations of postabsorptive AAs with IR directly measured by insulin-mediated glucose disposal and determined the quantitative value of AAs and conventional IR predictors. Design: Fifty-one healthy, 31 overweight or obese (Ow/Ob), and 52 men and women with type 2 diabetes (T2D) were studied retrospectively. The main outcome measures were the glucose disposal (M/I) index (using 3-[³H]-glucose) during a hyperinsulinemic–euglycemic clamp and whole-body protein turnover (using 1-[¹³C]-leucine). Results: Compared with healthy participants, M/I was lower in Ow/Ob participants and lowest in those with T2D. BCAAs, glutamate, and lysine were higher in the Ow/Ob and T2D groups than in healthy participants; glycine and threonine were lower. Most AAs were higher in men. Principal component analysis identified component 1 (C1: BCAAs, methionine) and C3 (glycine, threonine, serine). Glutamate, C1, ornithine, lysine, methionine, and tyrosine correlated negatively with M/I; C3 and glycine correlated positively. Waist circumference and sex strongly influenced AA–IR relationships; only glutamate correlated after these factors were controlled for. From regression analysis, waist circumference, fasting glucose, insulin, and free fatty acids (FFAs) negatively predicted 64% of the M/I variance; glutamate added 2% more. In nondiabetic participants, IR was predicted by waist circumference, insulin, and FFAs, without contribution from AAs. Conclusion: Several postabsorptive AAs correlated with IR but added limited predictive value to conventional markers because levels were determined largely by abdominal adiposity. Data suggest a sex-specific regulation of AA metabolism by excess adiposity, particularly the BCAAs, warranting investigation.

Body composition assessment by computed tomography (CT) predicts health outcomes in diverse populations. However, its performance in predicting mortality has not been directly compared to dual-energy X-ray absorptiometry (DXA). Additionally, the association between different body compartments and mortality, acknowledging the compositional nature of the human body, is not well studied. Compositional data analysis, which is applied to multivariate proportion-type data set, may help to account for the interrelationships of body compartments by constructing log ratios of components. Here, we determined the associations of baseline CT-based measures of mid-thigh cross-sectional areas versus DXA measures of body composition with all-cause mortality in the Health ABC cohort, using both traditional (individual body compartments) and compositional data analysis (using ratios of body compartments) approaches. The Health ABC study assessed body composition in 2911 older adults in 1996–1997. We investigated the individual and ratios of (by compositional analysis) body compartments assessed by DXA (lean, fat, and bone masses) and CT (muscle, subcutaneous fat area, intermuscular fat, and bone) on mortality, using Cox proportional hazard models. Lower baseline muscle area by CT (hazard ratio [HR]_men = 0.56; 95% confidence interval [95% CI]: 0.48–0.67, HR_women = 0.60; 95% CI: 0.48–0.74) and fat mass by DXA (HR_men = 0.48; 95% CI: 0.24–0.95) were predictors of mortality in traditional Cox regression analysis. Consistently, compositional data analysis revealed that lower muscle area versus IMF, muscle area versus bone area, and lower fat mass versus lean mass were associated with higher mortality in both sexes. Both CT measure of muscle area and DXA fat mass (either individually or relative to other body compartments) were strong predictors of mortality in both sexes in a community research setting.