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Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two US populations.

ABSTRACT: Combining multiple genetic variants related to obesity into a genetic risk score (GRS) might improve identification of individuals at risk of developing obesity. Moreover, characterizing gene-diet interactions is a research challenge to establish dietary recommendations to individuals with higher predisposition to obesity. Our objective was to analyze the association between an obesity GRS and body mass index (BMI) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) population, focusing on gene-diet interactions with total fat and saturated fatty acid (SFA) intake, and to replicate findings in the Multi-Ethnic Study of Atherosclerosis (MESA) population. Cross-sectional analyses included 783 white US participants from GOLDN and 2,035 from MESA. Dietary intakes were estimated with validated food frequency questionnaires. Height and weight were measured. A weighted GRS was calculated on the basis of 63 obesity-associated variants. Multiple linear regression models adjusted by potential confounders were used to examine gene-diet interactions between dietary intake (total fat and SFA) and the obesity GRS in determining BMI. Significant interactions were found between total fat intake and the obesity GRS using these variables as continuous for BMI (P for interaction=0.010, 0.046, and 0.002 in GOLDN, MESA, and meta-analysis, respectively). These association terms were stronger when assessing interactions between SFA intake and GRS for BMI (P for interaction=0.005, 0.018, and <0.001 in GOLDN, MESA, and meta-analysis, respectively). SFA intake interacts with an obesity GRS in modulating BMI in two US populations. Although determining the causal direction requires further investigation, these findings suggest that potential dietary recommendations to reduce BMI effectively in populations with high obesity GRS would be to reduce total fat intake mainly by limiting SFAs.

SUBMITTER: Casas-Agustench P

PROVIDER: S-EPMC4214897 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two US populations.

Casas-Agustench Patricia P Arnett Donna K DK Smith Caren E CE Lai Chao-Qiang CQ Parnell Laurence D LD Borecki Ingrid B IB Frazier-Wood Alexis C AC Allison Matthew M Chen Yii-Der Ida YD Taylor Kent D KD Rich Stephen S SS Rotter Jerome I JI Lee Yu-Chi YC Ordovás José M JM

Journal of the Academy of Nutrition and Dietetics 20140501 12

Combining multiple genetic variants related to obesity into a genetic risk score (GRS) might improve identification of individuals at risk of developing obesity. Moreover, characterizing gene-diet interactions is a research challenge to establish dietary recommendations to individuals with higher predisposition to obesity. Our objective was to analyze the association between an obesity GRS and body mass index (BMI) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) population, focu ...[more]

PMID: 24794412

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Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Project description:ImportanceBody mass index (BMI) is used to diagnose obesity in adolescents worldwide, despite evidence that weight does not scale with height squared in adolescents. To account for this, health care providers diagnose obesity using BMI percentiles for each age (BMI z scores), but this does not ensure that BMI is accurate in adolescents.ObjectiveTo compare the accuracy of BMI vs other body fat indices of the form body mass divided by heightn in estimating body fat levels in adolescents.Design, setting, and participantsCross-sectional data from the 1999 to 2006 US National Health and Nutrition Examination Survey were analyzed between September 2015 and December 2016.Main outcomes and measuresDual-energy x-ray absorptiometry and anthropometric data were used to determine changes in body fat levels, body proportions, and the scaling relationships among body mass, height, and percent body fat. To assess the merits of each adiposity index, 3 criteria were used: stability with age, accuracy in estimating percent body fat, and accuracy in classifying adolescents as overweight vs normal weight.ResultsParticipants included 2285 non-Hispanic white participants aged 8 to 29 years. Percent body fat varied with both age and height during adolescence, invalidating the standard weight-to-height regression as the way of finding the optimal body fat index. Because the correct regression model (percent body fat is proportional to mass divided by heightn) suggested that percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-ponderal mass index (TMI; mass divided by height cubed) as an alternative to BMI z scores. For ages 8 to 17 years, TMI yielded greater stability with age and estimated percent body fat better than BMI (R2?=?0.64 vs 0.38 in boys and R2?=?0.72 vs 0.66 in girls). Moreover, TMI misclassified adolescents as overweight vs normal weight less often than BMI z scores (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 19.4%; 95% CI, 17.8%-20.0%; P?<?.001) and performed equally as well as updated BMI percentiles derived from the same data set (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 8.0%; 95% CI, 6.9%-9.1%; P?=?.62).Conclusions and relevanceThe tri-ponderal mass index estimates body fat levels more accurately than BMI in non-Hispanic white adolescents aged 8 to 17 years. Moreover, TMI diagnoses adolescents as overweight more accurately than BMI z scores and equally as well as updated BMI percentiles but is much simpler to use than either because it does not involve complicated percentiles. Taken together, it is worth considering replacing BMI z scores with TMI to estimate body fat levels in adolescents.

Project description:Background/objectiveThe evidence that maternal non-nutritive sweetener (NNS) intake during pregnancy increases childhood obesity risk is conflicting. A potential reason for this is that all prior studies examined childhood body mass index (BMI) at only one timepoint and at different ages. We examined the extent to which NNS intake during pregnancy is associated with offspring BMI z-score and body fat longitudinally from birth to 18 years.SubjectsA total of 1683 children from Project Viva, a prospective pre-birth cohort, were recruited from 1999 to 2002 in Massachusetts.MethodsWe assessed maternal NNS intake in the first and second trimesters of pregnancy using a semiquantitative food frequency questionnaire. Our outcomes were offspring BMI z-score, (at birth, infancy (median 6.3 months), early childhood (3.2 years), mid-childhood (7.7 years), and early adolescence (12.9 years)), sum of skinfolds (SS), fat mass index (FMI) measured by dual x-ray absorptiometry, and BMI z-score trajectory from birth to 18 years. We adjusted models for maternal pre-pregnancy BMI, age, race/ethnicity, education, parity, pre-pregnancy physical activity, smoking, and paternal BMI and education.ResultsA total of 70% of mothers were white and pre-pregnancy BMI was 24.6 ± 5.2 kg/m2. The highest quartile of NNS intake (Q4: 0.98 ± 0.91 servings/day) was associated with higher BMI z-score in infancy (β 0.20 units; 95% CI 0.02, 0.38), early childhood (0.21; 0.05, 0.37), mid-childhood (0.21; 0.02, 0.40), and early adolescence (0.14; -0.07, 0.35) compared with Q1 intake (Q1: 0.00 ± 0.00 servings/day). Q4 was also associated with higher SS in early childhood (1.17 mm; 0.47, 1.88), mid-childhood (2.33 mm; 0.80, 3.87), and early adolescence (2.27 mm; -0.06, 4.60) and higher FMI in mid-childhood (0.26 kg/m2; -0.07, 0.59). Associations of maternal NNS intake with offspring BMI z-score became stronger with increasing age from 3 to 18 years (Pinteraction < 0.0001).ConclusionsMaternal NNS intake during pregnancy is associated with increased childhood BMI z-score and body fat from birth to teenage years. This is relevant given the escalating obesity epidemic, and popularity of NNS.

Project description:Nutrigenetics studies the role of genetic variation on interactions between diet and health, aiming to provide more personalized dietary advice. However, replication has been low. Our aim was to study interaction among a functional APOA2 polymorphism, food intake, and body mass index (BMI) in independent populations to replicate findings and to increase their evidence level.Cross-sectional, follow-up (20 years), and case-control analyses were undertaken in 3 independent populations. We analyzed gene-diet interactions between the APOA2 -265T>C polymorphism and saturated fat intake on BMI and obesity in 3462 individuals from 3 populations in the United States: the Framingham Offspring Study (1454 whites), the Genetics of Lipid Lowering Drugs and Diet Network Study (1078 whites), and Boston-Puerto Rican Centers on Population Health and Health Disparities Study (930 Hispanics of Caribbean origin).Prevalence of the CC genotype in study participants ranged from 10.5% to 16.2%. We identified statistically significant interactions between the APOA2 -265T>C and saturated fat regarding BMI in all 3 populations. Thus, the magnitude of the difference in BMI between the individuals with the CC and TT+TC genotypes differed by saturated fat. A mean increase in BMI of 6.2% (range, 4.3%-7.9%; P = .01) was observed between genotypes with high- (> or =22 g/d) but not with low- saturated fat intake in all studies. Likewise, the CC genotype was significantly associated with higher obesity prevalence in all populations only in the high-saturated fat stratum. Meta-analysis estimations of obesity for individuals with the CC genotype compared with the TT+TC genotype were an odds ratio of 1.84 (95% confidence interval, 1.38-2.47; P < .001) in the high-saturated fat stratum, but no association was detected in the low-saturated fat stratum (odds ratio, 0.81; 95% confidence interval, 0.59-1.11; P = .18).For the first time to our knowledge, a gene-diet interaction influencing BMI and obesity has been strongly and consistently replicated in 3 independent populations.

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Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two US populations.

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Saturated fat intake modulates the association between an obesity genetic risk score and body mass index in two US populations.

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