Strong influence of dietary intake and physical activity on body fatness in elderly Japanese men: age-associated loss of polygenic resistance against obesity

The impact of early sports participation on body fatness in adulthood is not mediated by current physical activity

OBJECTIVE

The aim was to analyze the relationship between early sports participation (ESP) and body fatness (BF) in adults, as well as to identify whether this possible relationship is directly influenced by the current physical activity (PA) level.

METHODS

This cross-sectional study combined baseline data of two cohort. The BF estimated by DXA. The ESP, the subjects reported the engagement in sports during childhood (7-10 years) and adolescence (11-17 years) through two yes/no questions and current PA (described as steps) was device-measured using pedometers. Were identified as potential covariates and therefore adjusted the multivariate models: age, ethnicity, alcohol consumption, smoking, and sleep quality. Statistical analysis consisted of the chi-square test, analysis of variance/covariance, and structural equation modeling (software BioEstat version 5.0; p-value < .05).

RESULTS

Adults engaged in ESP had lower BF; among women, the variance in BF explained by ESP was 25.5%; among men, it was 9.2%. Sports participation in early life (r = -.436 [95% CI: -0.527 to -0.346]) and current PA (r = -.431 [95% CI: -0.522 to -0.340]) were inversely related to BF, as well as positively related to each other (r = .328 [95% CI: 0.226 to 0.430]). In the mediation model, current PA partially mediated (18.5%) the impact of ESP on BF, while current PA and ESP remained relevant determinants of BF.

CONCLUSION

Early sports participation and current PA have a significant impact on BF in adulthood, which is of similar magnitude and independent of each other.

Effects of gene-lifestyle interactions on obesity based on a multi-locus risk score: A cross-sectional analysis

BACKGROUND

The relationship between lifestyle and obesity is a major focus of research. Personalized nutrition, which utilizes evidence from nutrigenomics, such as gene-environment interactions, has been attracting attention in recent years. However, evidence for gene-environment interactions that can inform treatment strategies is lacking, despite some reported interactions involving dietary intake or physical activity. Utilizing gene-lifestyle interactions in practice could aid in optimizing interventions according to genetic risk.

METHODS

This study aimed to elucidate the effects of gene-lifestyle interactions on body mass index (BMI). Cross-sectional data from the Japan Multi-Institutional Collaborative Cohort Study were used. Interactions between a multi-locus genetic risk score (GRS), calculated from 76 ancestry-specific single nucleotide polymorphisms, and nutritional intake or physical activity were assessed using a linear mixed-effect model.

RESULTS

The mean (standard deviation) BMI and GRS for all participants (n = 12,918) were 22.9 (3.0) kg/m2 and -0.07 (0.16), respectively. The correlation between GRS and BMI was r(12,916) = 0.13 (95% confidence interval [CI] 0.11-0.15, P < 0.001). An interaction between GRS and saturated fatty acid intake was observed (β = -0.11, 95% CI -0.21 to -0.02). An interaction between GRS and n-3 polyunsaturated fatty acids was also observed in the females with normal-weight subgroup (β = -0.12, 95% CI -0.22 to -0.03).

CONCLUSION

Our results provide evidence of an interaction effect between GRS and nutritional intake and physical activity. This gene-lifestyle interaction provides a basis for developing prevention or treatment interventions for obesity according to individual genetic predisposition.

Multiscale Risk Factors of Cardiovascular Disease: CLSA Analysis of Genetic and Psychosocial Factors

Background: Cardiovascular disease (CVD) is a complex disease resulting from multiscale risk factors including genetics, age, and psychosocial factors (PSFs) such as depression and social isolation. However, previous research has lacked in operationalizing multiscale risk factors to determine individual and interactive associations over the life course. Therefore, this study aimed to evaluate individual and interactive associations of multiscale risk factors for CVD outcomes including genetics and PSFs at middle and older-aged stages of the life course. Methods: Baseline data from the Canadian Longitudinal Study on Aging (CLSA; n = 9,892 with genome-wide genotyping data) was used for this investigation. A 39 single nucleotide polymorphism polygenic risk score (PRS) for CVD was constructed. PSFs consisted of: (1) Depressive symptoms categorized into: "none" (Group 1, reference), "current" (Group 2), "clinical depression with no current symptoms" (Group 3), and "potential, recurrent depression" (Group 4); and (2) Social isolation index as a binary variable comprised of marital status, living arrangements, retirement status, contacts, and social participation. Heart-related disorders (HRD: myocardial infarction, angina and heart disease) was the primary outcome of interest and peripheral/vascular-related disorders (PVRD: stroke, peripheral vascular disease and hypertension) was the secondary outcome. Multivariable logistic regression models adjusted for socio-demographic factors were conducted stratified by age group (middle-aged: 45-69 years, older-aged: ≥70 years). Results: PRS was associated with HRD among middle- and older-aged participants [OR (95% confidence interval)] [1.06 (1.03-1.08), 1.06 (1.03-1.08), respectively]. Most depressive symptoms groups compared to the reference associated with HRD and PVRD, but only Group 4 associated with PVRD among older-aged [1.69 (1.08-2.64)]. Social isolation was associated with only PVRD among middle-aged [1.84 (1.04-3.26)]; however, socially isolated CLSA participants were underrepresented in the genotyped cohort (1.2%). No significant PRS^*PSFs interactions were observed. Conclusions: Genetics and PSFs are independently associated with CVD. Varying observations across age groups underscores the need to advance research on multiscale risk factors operating both at a given point in time and over the life course. Future cohort studies may benefit from use of mobile assessment units to enable better reach to socially isolated participants for collection of biospecimens.

Visceral fat area is a strong predictor of leukocyte cell-derived chemotaxin 2, a potential biomarker of dyslipidemia

BACKGROUND

Leukocyte cell-derived chemotaxin 2 (LECT2) is a hepatokine linking obesity to skeletal muscle insulin resistance. Although previous studies reported that obesity was associated with high levels of circulating LECT2 in human, the associations of detailed body fat distribution with LECT2 levels have not been examined. Furthermore, although animal study suggested that exercise decreased circulating LECT2 levels, it remains unknown whether physical fitness is associated with LECT2 levels in human. We therefore examined the relationship of plasma LECT2 levels with various adiposity indices and cardiorespiratory fitness (CRF) in middle-aged and elderly Japanese men. Furthermore, we examined the relationship of LECT2 levels with the presence of metabolic syndrome, hypertension, insulin resistance and dyslipidemia to determine the clinical significance of measuring circulating LECT2.

MATERIALS AND METHODS

This was a cross-sectional study of 143 Japanese men (age: 30-79 years). Participants' plasma LECT2 levels were measured by an enzyme-linked immunosorbent assay. To assess their abdominal fat distributions, visceral fat area (VFA) and subcutaneous fat area (SFA) were measured using magnetic resonance imaging. CRF was assessed by measuring peak oxygen uptake ([Formula: see text]).

RESULTS

All adiposity indices measured in this study were positively correlated with plasma LECT2 levels, while [Formula: see text] was negatively correlated with LECT2 levels after adjustment for age. The correlations, except for VFA were no longer significant with further adjustment for VFA. Stepwise multiple linear regression analysis revealed that VFA was the strongest predictor of plasma LECT2 levels. Plasma LECT2 levels differed based on the presence of metabolic syndrome and dyslipidemia, but not hypertension and insulin resistance. Logistic regression analyses revealed that plasma LECT2 levels were significantly associated with dyslipidemia independently of VFA; VFA was not significantly associated with dyslipidemia after adjustment for LECT2.

CONCLUSION

VFA was the strongest predictor of plasma LECT2 that is a potential biomarker linking visceral obesity to dyslipidemia.