Abstract:The aim of this study was to determine whether an association exists between children's and parental dietary patterns (DP), and whether the number of shared meals or soft drink availability during meals strengthens this association. In 2013/2014 the I.Family study cross-sectionally assessed the dietary intakes of families from eight European countries using 24-h dietary recalls. Usual energy and food intakes from six-to 16-year-old children and their parents were estimated based on the NCI Method. A total of 1662 child-mother and 789 child-father dyads were included; DP were derived using cluster analysis. We investigated the association between children's and parental DP and whether the number of shared meals or soft drink availability moderated this association using mixed effects logistic regression models.

Our data demonstrated adverse effects of moderate fructose consumption for 12 weeks on multiple cardiometabolic risk factors in particular on liver fat content despite only relative low increases in weight and waist circumference. Our study also indicates that there are remarkable individual differences in susceptibility to visceral adiposity/liver fat after real-world daily consumption of fructose-sweetened beverages over 12 weeks.

Objective. To investigate how obesity, insulin resistance and low-grade inflammation link to circulating metabolites, and whether the connections are due to genetic or environmental factors.Subjects and methods. Circulating serum metabolites were determined by proton NMR spectroscopy. Data from 1368 (531 monozygotic (MZ) and 837 dizygotic (DZ)) twins were used for bivariate twin modeling to derive the genetic (r g ) and environmental (r e ) correlations between waist circumference (WC) and serum metabolites. Detailed examination of the associations between fat distribution (DEXA) and metabolic health (HOMA-IR, CRP) was performed among 286 twins including 33 BMI-discordant MZ pairs. Results. Fat, especially in the abdominal area (i.e. WC, android fat % and android to gynoid fat ratio), together with HOMA-IR and CRP correlated significantly with an atherogenic lipoprotein profile, higher levels of branched-chain (BCAA) and aromatic amino acids, higher levels of glycoprotein, and a more saturated fatty acid profile. In contrast, a higher proportion of gynoid to total fat associated with a favorable metabolite profile. There was a significant genetic overlap between WC and several metabolites, most strongly with phenylalanine (r g = 0.40), glycoprotein (r g = 0.37), serum triglycerides (r g = 0.36), BCAAs (r g = 0.30-0.40), HDL Keywords:Twin study Bivariate twin model 2 0 1 6 ) 1 1 Conclusions. A wide range of unfavorable alterations in the serum metabolome was associated with abdominal obesity, insulin resistance and low-grade inflammation. Twin modeling and obesity-discordant twin analysis suggest that these associations are partly explained by shared genes but also reflect mechanisms independent of genetic liability. Genetic correlation Serum metabolites Obesity measures M E T A B O L I S M C L I N I C A L A N D E X P E R I M E N T A L 6 5 (

Our results suggest that, in physically active individuals, the genetic variation in weight is reduced, which possibly suggests that physical activity is able to modify the action of the genes responsible for predisposition to obesity, whereas the protein content of the diet has no appreciable effect.

The purpose of this study was to investigate the relationship of fat mass (FM) and lean mass (LM) with bone mineral density (BMD) independent of genetic effects. We also assessed the extent to which genetic and environmental influences explain the associations between these phenotypes. Body composition and BMD were measured using dual-energy X-ray absorptiometry in 57 monozygotic and 92 same-sex dizygotic twin pairs, aged 23 to 31 years, chosen to represent a wide range of intrapair differences in body mass index (BMI; 0 to 15.2 kg/m2). Heritability estimates were adjusted for height and gender. In multiple linear regression analysis, intrapair differences in both FM and LM were independently associated with intrapair differences in BMD at most skeletal sites after adjustment for gender and differences in height. Within monozygotic and dizygotic pairs, LM was a significantly stronger predictor of whole-body BMD than FM (p < .01). Additive genetic factors explained 87% [95% confidence interval (CI) 80%–91%), 81% (95% CI 70%–88%), and 61% (95% CI 41%–75%) of the variation in whole-body BMD, LM, and FM, respectively. Additive genetic factors also accounted for 69% to 88% of the covariance between LM and BMD and for 42% to 72% of the covariance between FM and BMD depending on the skeletal site. The genetic correlation between LM and whole-body BMD (rg = 0.46, 95% CI 0.32–0.58) was greater than that of FM and whole-body BMD (rg = 0.25, 95% CI 0.05–0.42). In conclusion, our data indicate that peak BMD is influenced by acquired body weight as well as genetic factors. In young adulthood, LM and BMD may have more genes in common than do FM and BMD. © 2011 American Society for Bone and Mineral Research.

This review offers an update on research conducted with FinnTwin12 (FT12), the youngest of the three Finnish Twin Cohorts. FT12 was designed as a two-stage study. In the first stage, we conducted multiwave questionnaire research enrolling all eligible twins born in Finland during 1983–1987 along with their biological parents. In stage 2, we intensively studied a subset of these twins with in-school assessments at age 12 and semistructured poly-diagnostic interviews at age 14. At baseline, parents of intensively studied twins were administered the adult version of the interview. Laboratory studies with repeat interviews, neuropsychological tests, and collection of DNA were made of intensively studied twins during follow-up in early adulthood. The basic aim of the FT12 study design was to obtain information on individual, familial and school/neighborhood risks for substance use/abuse prior to the onset of regular tobacco and alcohol use and then track trajectories of use and abuse and their consequences into adulthood. But the longitudinal assessments were not narrowly limited to this basic aim, and with multiwave, multirater assessments from ages 11 to 12, the study has created a richly informative data set for analyses of gene–environment interactions of both candidate genes and genomewide measures with measured risk-relevant environments. Because 25 years have elapsed since the start of the study, we are planning a fifth-wave follow-up assessment.

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Information on familial resemblance is important for the design of effective family-based interventions. We aimed to quantify familial correlations and estimate the proportion of variation attributable to genetic and shared environmental effects (i.e., familiality) for dietary intake variables and determine whether they vary by generation, sex, dietary quality, or by the age of the children. The study sample consisted of 1435 families (1007 mothers, 438 fathers, 1035 daughters, and 1080 sons) from the multi-center I.Family study. Dietary intake was assessed in parents and their 2–19 years old children using repeated 24-h dietary recalls, from which the usual energy and food intakes were estimated with the U.S. National Cancer Institute Method. Food items were categorized as healthy or unhealthy based on their sugar, fat, and fiber content. Interclass and intraclass correlations were calculated for relative pairs. Familiality was estimated using variance component methods. Parent–offspring (r = 0.11–0.33), sibling (r = 0.21–0.43), and spouse (r = 0.15–0.33) correlations were modest. Parent–offspring correlations were stronger for the intake of healthy (r = 0.33) than unhealthy (r = 0.10) foods. Familiality estimates were 61% (95% CI: 54–68%) for the intake of fruit and vegetables and the sum of healthy foods and only 30% (95% CI: 23–38%) for the sum of unhealthy foods. Familial factors explained a larger proportion of the variance in healthy food intake (71%; 95% CI: 62–81%) in younger children below the age of 11 than in older children equal or above the age of 11 (48%; 95% CI: 38–58%). Factors shared by family members such as genetics and/or the shared home environment play a stronger role in shaping children’s intake of healthy foods than unhealthy foods. This suggests that family-based interventions are likely to have greater effects when targeting healthy food choices and families with younger children, and that other sorts of intervention are needed to address the intake of unhealthy foods by children.