Impact of Early Growth on Postprandial Responses in Later Life

Peer reviewe

Body composition as a predictor of physical performance in older age : A ten- year follow-up of the Helsinki Birth Cohort Study

Background: This study assessed how different measures of body composition predict physical performance ten years later among older adults. Methods: The participants were 1076 men and women aged 57 to 70 years. Body mass index (BMI), waist circumference, and body composition (bioelectrical impedance analysis) were measured at baseline and physical performance (Senior Fitness Test) ten years later. Linear regression analyses were adjusted for age, education, smoking, duration of the follow-up and physical activity. Results: Greater BMI, waist circumference, fat mass, and percent body fat were associated with poorer physical performance in both sexes (standardized regression coefficient [beta] from -0.32 to -0.40, p <0.001). Lean mass to BMI ratio was positively associated with later physical performance (beta = 0.31 in men, beta = 0.30 in women, p <0.001). Fat-free mass index (lean mass/height(2)) in both sexes and lean mass in women were negatively associated with later physical performance. Lean mass residual after accounting for the effect of height and fat mass was not associated with physical performance. Conclusions: Among older adults, higher measures of adiposity predicted poorer physical performance ten years later whereas lean mass was associated with physical performance in a counterintuitive manner. The results can be used when appraising usefulness of body composition indicators for definition of sarcopenic obesity.Peer reviewe

Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits

OBJECTIVE Common circadian-related gene variants associate with increased risk for metabolic alterations including type 2 diabetes. However, little is known about whether diet and sleep could modify associations between circadian-related variants (CLOCK-rs1801260, CRY2-rs11605924, MTNR1B-rs1387153, MTNR1B-rs10830963, NR1D1-rs2314339) and cardiometabolic traits (fasting glucose [FG], HOMA-insulin resistance, BMI, waist circumference, and HDL-cholesterol) to facilitate personalized recommendations. RESEARCH DESIGN AND METHODS We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations and interactions between dietary intake/sleep duration and selected variants on cardiometabolic traits from 15 cohort studies including up to 28,190 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. RESULTS We observed significant associations between relative macronutrient intakes and glycemic traits and short sleep duration (<7 h) and higher FG and replicated known MTNR1B associations with glycemic traits. No interactions were evident after accounting for multiple comparisons. However, we observed nominally significant interactions (all P < 0.01) between carbohydrate intake and MTNR1B-rs1387153 for FG with a 0.003 mmol/L higher FG with each additional 1% carbohydrate intake in the presence of the T allele, between sleep duration and CRY2-rs11605924 for HDL-cholesterol with a 0.010 mmol/L higher HDL-cholesterol with each additional hour of sleep in the presence of the A allele, and between long sleep duration (≥9 h) and MTNR1B-rs1387153 for BMI with a 0.60 kg/m2 higher BMI with long sleep duration in the presence of the T allele relative to normal sleep duration (≥7 to <9 h). CONCLUSIONS Our results suggest that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian-related genetic variants. Until further mechanistic examination of the nominally significant interactions is conducted, recommendations applicable to the general population regarding diet—specifically higher carbohydrate and lower fat composition—and normal sleep duration should continue to be emphasized among individuals with the investigated circadian-related gene variants

Genome-wide association meta-analysis of fish and EPA plus DHA consumption in 17 US and European cohorts

Background Regular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences. Objective To identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption. Design We conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts. Results Heritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (Freq(A) = 0.015) was associated with 0.029 servings/day (similar to 1 serving/month) lower fish consumption (P = 1.96x10(-8)). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10(-7)). Post-hoc calculations demonstrated 95% statistical power to detect a genetic variant associated with effect size of 0.05% for fish and 0.08% for EPA+DHA. Conclusions These novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.Peer reviewe

Gene × dietary pattern interactions in obesity: Analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GR

Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist–hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006–0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance

Postprandial triglycerides responses.

<p>Mean (± SEM) responses of plasma triglycerides for subjects with slow growth during infancy (open circles, n = 12) and for the control group (filled circles, n = 12) to A) a REC-meal and B) a FF-meal.</p

Glucose, insulin, triglycerides and free fatty acids response curve after test meals.¹

<p>SGI-group, slow growth during infancy; CON-group, normal growth during infancy; REC-meal, a meal followed the macronutrient composition of the dietary guidelines; FF-meal, a fast-food meal; IAUC, incremental area under curve; IAOC, incremental area over curve.</p>1<p>Results are mean ± SD; n = 12; Glucose and insulin IAUCs were log-transformed prior statistical tests.</p>2<p>Differences between groups was tested by an independent sample t-test.</p>3<p>n = 11.</p

The foodstuffs and the energy nutrient content of the test meals.

<p>REC-meal, a meal followed the macronutrient composition of the dietary guidelines; FF-meal, a fast-food meal.</p>1<p>Whole-grain rye bread, REAL-ruisleipä; Fazer Ltd, Helsinki, Finland.</p>2<p>BigMac hamburger; McDonalds Ltd, Helsinki, Finland.</p>3<p>Keiju margarine 70%; Raisio Ltd, Raisio, Finland.</p>4<p>McDonalds Ltd, Helsinki, Finland.</p>5<p>Aamu cheese 5%; Arla Ltd, Söderkulla, Finland.</p>6<p>Orange juice, Vip Appelsiini täysmehu; Vip-Juicemaker Ltd, Kuopio, Finland.</p>7<p>Barley porridge prepared with low-fat (1.5% fat) milk.</p>8<p>Raspberry jam, Menu Vadelmahillo; Roberts Ltd, Turku, Finland.</p>9<p>Dumle cocoa powder; Fazer Ltd, Helsinki, Finland.</p