Shared genetic contributions of fruit and vegetable consumption with BMI in families 20 y after sharing a household

The American Journal of Clinical Nutrition, Oct 2011

Background: Obesity has a strong genetic basis, but the identification of genetic variants has not resulted in improved clinical care. However, phenotypes that influence weight, such as diet, may have shared underpinnings with obesity. Interestingly, diet also has a genetic basis. Thus, we hypothesized that the genetic underpinnings of diet may partially overlap with the genetics of obesity.

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Shared genetic contributions of fruit and vegetable consumption with BMI in families 20 y after sharing a household

Shared genetic contributions of fruit and vegetable consumption with BMI in families 20 y after sharing a household1-3 Lisa J Martin Seung-Yeon Lee Sarah C Couch John Morrison Jessica G Woo Background: Obesity has a strong genetic basis, but the identification of genetic variants has not resulted in improved clinical care. However, phenotypes that influence weight, such as diet, may have shared underpinnings with obesity. Interestingly, diet also has a genetic basis. Thus, we hypothesized that the genetic underpinnings of diet may partially overlap with the genetics of obesity. Objective: Our objective was to determine whether dietary intake and BMI share heritable components in adulthood. Design: We used a cross-sectional cohort of parents and adult offspring (n = 1410) from the Princeton Follow-up Study. Participants completed Block food-frequency questionnaires 15-27 y after sharing a household. Heritability of dietary intakes was estimated by using variance components analysis. Bivariate genetic analyses were used to estimate the shared effects between BMI and heritable dietary intakes. Results: Fruit, vegetable, and protein consumption exhibited moderate heritability [(mean 6 SE) 0.26 6 0.06, 0.32 6 0.06, and 0.216 0.06, respectively; P , 0.001], but other dietary intakes were modest (h2 , 0.2). Only fruit and vegetable consumption exhibited genetic correlations with BMI (qg = 20.28 6 0.13 and 20.30 6 0.13, respectively; P , 0.05). Phenotypic correlations with BMI were not significant. Conclusions: We showed that fruit, vegetable, and protein intakes are moderately heritable and that fruit and vegetable consumption shares underlying genetic effects with BMI in adulthood, which suggests that individuals genetically predisposed to low fruit and vegetable consumption may be predisposed to higher BMI. Thus, obese individuals who have low fruit and vegetable consumption may require targeted interventions that go beyond low-calorie, plant-based programs for weight management. Am J Clin Nutr 2011;94:1138-43. INTRODUCTION Obesity is a major public health problem that affects ;34% of US adults ( 1 ), resulting in substantial morbidity and mortality ( 2 ). Obesity is a multifactorial disease with a strong genetic basis ( 3 ). Whereas many genetic variants have been consistently associated with obesity, these variants account for only a small portion of the variation; thus, they have not resulted in improved clinical care ( 4 ). However, the identification of shared underpinnings with phenotypes that influence body weight could inform future research, leading to an increased understanding of the underlying genetic architecture and ultimately to improved clinical care. Epidemiologic studies have shown that diet amount and quality influence obesity risk. Indeed, poor nutrition is viewed as a primary cause of the obesity epidemic ( 5 ), and dietary modification is a frequently used lever to counteract obesity ( 6 ). Unfortunately, efforts to change dietary intake at the population level have met with minimal success ( 7 ). The limited success in changing dietary behaviors is not surprising because dietary behavior is a consequence of decisions based on behavioral predispositions; intra- and interpersonal factors; and social, environmental, and economic determinants ( 8, 9 ). These behavioral predispositions, in turn, may be partially biologically determined by genetic factors. For example, taste preference is a strong biological determinant of food choice and dietary behavior that has been shown to have an underlying genetic basis ( 10?15 ). Given that both obesity and determinants of diet have a genetic basis, there may be common genetic factors underlying dietary intake and BMI (in kg/m2) ( 16 ). Indeed, several variants in genes (FTO and MC4R) consistently associated with BMI are also associated with nutritional intake ( 17, 18 ). However, no studies have estimated directly whether intake of certain nutrients or food groups, such as fruit and vegetables, share a common genetic basis with BMI. Because of these relations, we hypothesized that part of the genetic underpinning of diet may overlap with the genetics of obesity. Thus, our objective was to determine whether we could detect evidence of shared genetic effects between energy, macronutrient, and fruit and vegetable consumption and BMI. To accomplish this objective, we used data from a cohort of parents and their adult children who had completed food-frequency questionnaires. This cohort was optimal for examination of the 1 From the Divisions of Human Genetics (LJM) and Biostatistics and Epidemiology (LJM and JGW), and the Heart Institute (JM and JGW), Cincinnati Children?s Hospital Medical Center, Cincinnati, OH; and the Departments of Nutritional Sciences (S-YL and SCC) and Pediatrics (LJM, JM, and JGW), University of Cincinnati College of Medicine, Cincinnati, OH. 2 Supported in part by National Heart, Lung, and Blood Institute Lipid Research Clinics Contract NO1-HV-2-2914-L, Research Grants RO1 HL162394 and GM28356, and Resource Grant RR03635; and American Heart Association Grant 9750129N. 3 Address correspondence to LJ Martin, Division of Human Genetics, MLC 4006, Cincinnati Children?s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229. E-mail: . Received March 8, 2011. Accepted for publication July 13, 2011. First published online August 10, 2011; doi: 10.3945/ajcn.111.015461. genetic effects of dietary intake because shared household effects should have been minimized, given the duration of time since the parents and children had lived together (15?27 y). We identified dietary patterns that showed the strong heritability, and tested for evidence of their shared genetic effects with BMI. SUBJECTS AND METHODS Princeton Follow-up Study cohort This study used data from the Princeton Follow-up Study (PFS), a follow-up study of the National Heart, Lung, and Blood Institute Lipid Research Clinic Prevalence Study conducted from 1973 to 1978 ( 19 ). In 2000?2004, 25 y after the original study, the PFS recontacted the participants and their families who had been included originally in the Cincinnati site of the Lipid Research Clinic ( 20 ). Self-reported races were either white or black (Table 1). Detailed dietary and anthropomorphic measures were collected from PFS participants. The institutional review boards of Cincinnati Children?s Hospital Medical Center and the University of Cincinnati approved this study, and all subjects provided written informed consent. Dietary assessment Dietary information was collected with the use of the 1998 Block food-frequency questionnaire, a validated tool to estimate usual dietary patterns over the past year ( 21, 22 ), which contains 110 food items including 13 fruit and 18 vegetable items. The food list for this questionnaire was developed based on the NHANES III dietary recall data, and the nutrient database was developed based on the USDA Nutrient Database for Standard Reference ( 23, 24 ). Individual portion size is asked for each food, and pictures are provided to enhance accuracy of quantification. Total calorie and macronutrient intakes and the servings of fruit and vegetables consumed were calculated through Block Dietary Data Systems and Berkeley Nutrition Services. Macronutrient intakes and fruit and vegetable consumption were compared with goals from the Healthy People 2010 guidelines ( 25 ). Anthropometric measurement Height and weight were measured with subjects in light indoor clothing with shoes removed, and standard protocols were used ( 26 ). Two measurements of height and weight were made, with a third measurement if the first 2 measurements differed by more than a set amount. The means of the 2 closest PFS measurements were used in the analyses. BMI was used to characterize body habitus. Statistical analysis Descriptive statistics were calculated for all study variables with the use of SAS software, version 9.2 (SAS Institute). Data were examined for normality, and the dietary data were ln transformed to improve distributional qualities. Observations .4 SDs from the mean were removed before analysis. Descriptive statistics are reported as means 6 SDs for normally distributed data, as median (interquartile range) for nonnormally distributed data, or as frequencies for discrete data. Significant demographic differences between parents and offspring were tested with Student?s t tests, Wilcoxon rank sum, and chi-square tests, as appropriate. We performed Spearman correlations between our dietary data and BMI. Heritability To explore the heritability of calorie, macronutrient intakes, fruit and vegetable consumption, and BMI, we applied a variance components model. This model is similar to a mixed model with fixed and random effects. In our variance components model, the fixed effects are the covariates age, sex, and race to account for known variation by these factors. The random effects are defined by partitioning the covariance between relative pairs into additive genetic and error covariance. Thus the covariance between relative pairs is defined by X ? 2Urg2 ? Ire2 ?1? where rg2 is the genetic variance due to residual additive genetic factors, and r2e is the error variance not explained by the additive genetic component. The additive genetic variance is multiplied by Full cohort (n = 1410) Parents (n = 425) the structuring matrix for familial patterning (2U), which follows the Mendelian expectation that parents and offspring, as well as sibling pairs, have one-half of their genes in common. The error variance is multiplied by the identity matrix (I), which permits individual specific effects. To solve for the variance terms, we used maximum-likelihood?based variance decomposition implemented in SOLAR (Sequential Oligogenic Linkage Analysis Routines; Southwest Foundation for Biomedical Research), which simultaneously considers all possible relative pairs ( 27 ). Heritability is a measure of familial patterning consistent with additive genetic inheritance and is estimated as h2 = r2G/r2P. Significance of the h2 estimates is assessed by likelihood ratio tests. The maximum likelihood for the general model in which all parameters are estimated is compared with that for restricted models in which the value of the parameter to be tested is held at zero. Twice the difference in the natural log likelihoods of the 2 models compared is distributed asymptotically, approximately as a 1/2:1/2 mixture of v12 and a point mass at zero ( 28 ). Here, values of P 0.05 after Bonferroni correction for multiple comparisons were considered to be significant. Bivariate analysis In the multivariate model, the phenotype covariance between 2 traits is decomposed to include the genetic correlation between traits due to additive genetic effects and the correlation between traits due to shared (unmeasured) environmental effects, Xab ? 2Uqgrgargb ? qereareb ?2? where a and b are the 2 traits of interest, qg is the additive genetic correlation between the 2 traits, and qe is the correlation between unmeasured environmental effects. The genetic correlation estimates the proportion of genes shared between the 2 traits. If a = b, then qg = 1 and the covariance of a pair of relatives simplifies to Equation 1. This approach has been implemented in SOLAR ( 29 ). The phenotypic correlations after the familial correlation was accounted for are derived from Equation 3: qp ? qg qffihffiffi2ffiffiqffiffiffi2ffiffi a hb ? qe qffi1ffiffiffiffiffiffiffiffihffiffi2ffiffiqffiffiffiffiffiffiffiffiffiffiffi2ffiffi 2 a 1 2 hb ?3? where h2i is the heritability for trait i. Thus, the phenotypic correlation is influenced by both the genetic and environmental correlations where each is weighted by the proportion of variation due to genes (genetic) and environmental factors (nongenetic), respectively. In this report, we conducted bivariate analysis of BMI with all dietary traits with significant heritability estimates, with P 0.05 considered significant (because of the small number of variables considered for bivariate analyses). Derived phenotypic correlations could not be tested, because SEs cannot be estimated. RESULTS Population characteristics and dietary intake A total of 1410 participants, including 850 parent?offspring pairs and 776 sibling pairs, entered the analysis (Table 1). The cohort was largely white (74.3%), and 44.2% male, with an average (mean 6 SD) age of 47.6 6 13.5 y (range: 28.3?90.5). The average BMI was 29.0 6 6.5. The daily servings of fruit and vegetables were low: only 21% of individuals met the Healthy People 2010 behavior indicator of consuming 2 servings of fruit/d, 36.7% met the Healthy People 2010 indicator of 3 servings of vegetables/d, and 13.5% met both behavior indicators. Only 17% obtained ,30% of calories from fat. Parents had significantly lower consumption per day of total calories and percentage of total calories (and grams) from carbohydrate, protein, fat, and saturated fat than did their offspring. In addition, parents had more servings per day of fruit and vegetables and greater BMI than their offspring. Familial patterning of dietary intakes and BMI Fruit and vegetable servings per day and percentage of calories (and grams) from protein, as well as BMI, exhibited moderate yet significant heritability estimates (Table 2). However, total calories and percentage of total calories and grams from carbohydrate, fat, and saturated fat intakes exhibited more modest heritability. With the use of Spearman phenotypic correlations (uncorrected for family structure), we observed no dietary intake measure that was correlated with BMI (Table 3). With the use of bivariate genetic analysis, we were able to estimate the proportion of genetic variance shared because of underlying genes (qg) between dietary intakes and BMI for those factors that exhibited significant heritability (Table 3). This analysis was also used to estimate the residual environmental correlation (qe) and to derive phenotypic correlations that accounted for familial structure (qp). Both fruit and vegetable consumption exhibited significant negative qg with BMI, whereas protein intake (grams) trended toward a positive qg with BMI. The negative genetic correlation between fruit and vegetable consumption and BMI is interpreted in a manner similar to that of a standard correlation (ie, when negative, one trait is increasing and the other is decreasing). Thus, our results indicate that a proportion of genes associated with increases in fruit and vegetable consumption and lower protein intake are the same as those that contribute to lower BMI. Because this may be a function of total caloric intake, we also examined percentage calories from protein, and had similar findings (data not shown). Vegetable and protein consumption showed evidence of significant positive qe. 1 Values are presented as estimates 6 SEs; P values in parentheses. Analyses of qg and qe were performed with the use of variance components analysis in Sequential Oligogenic Linkage Analysis Routines (SOLAR; Southwest Foundation for Biomedical Research) and included age, sex, and race as covariates. Analyses of qpu were performed with the use of Spearman rank correlation. 2 qpf is derived from qg and qe and thus does not include an SE. However, because of the opposite influences of qg and qe, phenotypic correlation coefficients between either fruit or vegetable consumption and BMI with adjustment for family structure were estimated at very near zero. Only protein (qp = 0.22) exhibited a substantial positive phenotypic correlation with BMI; estimates of phenotypic correlation coefficients between calorie and other macronutrient intakes with BMI were near zero. DISCUSSION Using adult nuclear families, we showed that fruit, vegetable, and protein intakes, estimated with the use of a food-frequency questionnaire, are moderately heritable and share underlying genetic effects with BMI. By contrast, intakes of total calories, carbohydrate, fat, and saturated fat showed no evidence of additive genetic effects. In addition, we observed that BMI was negatively genetically correlated with fruit and vegetable intake, and positively genetically correlated with protein intake, despite a lack of phenotypic correlation. Taken together, these results suggest that there is an underlying genetic basis to fruit, vegetable, and protein intake, and that this may help define a subtype of obesity. This is one of few studies to examine the heritability of diet in middle-aged adults and their parents. We showed fruit, vegetable, and protein intake to be moderately heritable. With respect to protein intake, these findings are consistent with twin studies, which show heritability of protein preference ( 30 ), meat consumption ( 12, 14, 31 ), and proportion of calories from protein ( 32 ). Whereas heritability exhibited a broad range (0.11?0.78), protein consumption exhibited robust heritability evidence because these studies used different diet assessment methods. Our findings for vegetable intake heritability were similarly consistent with twin studies (h2 range: 0.14?0.37) ( 12, 14, 30, 31 ). Furthermore, we report that fruit intake was heritable, which is consistent with studies on fruit preference (h2 = 0.51) (30) and juice intake ( 14, 31 ). Heritability of fruit and vegetable intake may be explained by taste preferences. Sensitivity to bitter taste, as shown in cruciferous vegetables, exhibits large interindividual variation that ranges from extreme sensitivity to no sensitivity ( 33, 34 ). This sensitivity has been attributed, in part, to the ability to taste 6-n-propylthiouracil. Indeed, 6-n-propylthiouracil nontasters have a higher vegetable intake than do tasters ( 35, 36 ). 6-nPropylthiouracil sensitivity is controlled, in part, by a variation in TAS2R38 ( 10, 11, 37 ). However, the effect of genetic variation in TAS2R38 on vegetable intake is not clear ( 10, 38 ). Undoubtedly, additional factors determine food intake, including availability and exposure ( 9, 39 ). Total calorie, carbohydrate, fat, and saturated fat intake did not exhibit significant evidence of heritability and phenotypic correlation. Several twin studies report macronutrient intake heritability ( 14, 31 ). However, twin studies are prone to inflation of the genetic component for diet studies (40), which has been attributed to a higher degree of shared environment for monozygotic twins compared with other relatives ( 41 ). Previous family studies have also suggested strong phenotypic correlations between relative pairs, but a modest effect of genetics for total intake, fat, and carbohydrate ( 32, 42, 43 ). Because these studies examined families who lived together, these similarities may have been driven by a shared family environment. Because we examined parents and their adult offspring who lived separately, the absence of a shared environment may explain this inconsistency. To our knowledge, our study is the first to report a negative genetic correlation between fruit and vegetable consumption and BMI, which suggests that there may be a common underlying genetic basis to fruit and vegetable consumption and adiposity. There is evidence in the literature of an underlying genetic relation between diet and BMI. Specifically, genetic variants associated with BMI are also associated with nutritional intake, including genetic variants in MC4R (energy intake) and FTO (loss of eating control) ( 17, 18 ). Although we have not examined specific genetic variants, our results suggest a novel paradigm: by sharing genetic underpinnings, dietary intake and BMI may be affected in parallel by genetic susceptibility, rather than high BMI simply resulting from suboptimal dietary intake or vice versa. Importantly, the moderate genetic correlation (;30%) between BMI and fruit/vegetable intake suggests that not all individuals who are obese have a genetic predisposition toward lower fruit and vegetable intake; rather, individuals may have different underlying causes of obesity. These results are potentially clinically significant because the underlying cause of obesity may influence treatment effectiveness. For individuals genetically predisposed to avoid fruit and vegetables, interventions to increase fruit and vegetable consumption are likely to fail. Thus, for these individuals, other intervention strategies (such as reductions in total calories or increased physical activity) may have improved success. Through the removal of intervention strategies with a low likelihood of success, treatments for obesity can be individualized for maximum success. Importantly, in this study we showed significant genetic correlations not mirrored by the phenotypic correlations. Indeed, we observed that whereas neither fruit nor vegetable consumption had substantial phenotypic correlations, they both exhibited significant genetic correlations. Similar to our findings, the results of a twin study showed near-zero phenotypic correlations between BMI and preferences for fatty foods, but showed shared genetic contributions ( 16 ). This may seem counterintuitive because intervention studies have shown that individuals with higher fruit and vegetable intake are less likely to gain weight ( 44 ). However, without directed intervention, the longitudinal relation between fruit and vegetable intake and weight change is modest ( 44, 45 ). This cross-sectional study may be further limited in the detection of phenotypic correlations, because both fruit and vegetable intake and BMI are the result of influences across these individuals? lifespan. Whereas the phenotypic correlations may be difficult to detect, the underlying genetics remain constant; thus, genetic correlations between traits should be more constant and provide a unique source of information. In addition, because the phenotypic correlation is a function of both the genetic and environmental correlations, when the genetic and environmental correlations differ in signs (eg, positive compared with negative), a near-zero phenotypic correlation can result, even when significant genetic correlations exist. Taken together, our results and others? suggest that phenotypic and genetic correlations may provide different information, and reliance on phenotypic correlations may lead to underrecognition of shared genetic effects. To our knowledge, few studies have estimated dietary intake heritability among adults. However, this study was unable to explore the variability due to shared, compared with unshared, environmental factors. Because adults in this cohort were unlikely to share a food environment, our assumption is that the majority of the error variance is individual specific. However, it is unknown how long a shared family environment may shape an individual?s food intake ( 32, 43, 46 ). Studies to more completely examine family resemblance of dietary intake, which incorporate measurement of both heritability and family environment, are necessary. In conclusion, the present study shows significant heritability of fruit, vegetable, and protein intake in parents and their adult offspring ;20 y after they shared a common household. This cohort also provides evidence for a set of pleiotropic genes with effects on dietary intakes of fruit, vegetables, and protein, as well as BMI in adulthood. Identification of these genes and a definition of the role of family environment in shaping food choices may enable more specific interventions to reduce obesity. We thank the PFS participants. The authors? responsibilities were as follows?JM: designed and collected the original data; LJM, S-YL, SCC, JM, and JGW: participated in the development of the research question; LJM: performed statistical analyses; S-YL and SCC: provided critical insight into the analysis of the dietary data. LJM, S-YL, and JGW: drafted the manuscript; and LJM, S-YL, SCC, JM, and JGW: revised the manuscript. The authors had no conflicts of interest. 1. 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Martin, Lisa J, Lee, Seung-Yeon, Couch, Sarah C, Morrison, John, Woo, Jessica G. Shared genetic contributions of fruit and vegetable consumption with BMI in families 20 y after sharing a household, The American Journal of Clinical Nutrition, 2011, 1138-1143, DOI: 10.3945/ajcn.111.015461