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
Sarah C Couch
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.
Obesity is a major public health problem that affects ;34% of
US adults (
), resulting in substantial morbidity and mortality
). Obesity is a multifactorial disease with a strong genetic
). 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 (
). 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
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 (
), and dietary
modification is a frequently used lever to counteract obesity (
Unfortunately, efforts to change dietary intake at the population
level have met with minimal success (
). 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 (
). 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 (
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) (
). Indeed, several variants in genes
(FTO and MC4R) consistently associated with BMI are also
associated with nutritional intake (
). 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,
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 (
). 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
). 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 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 (
), 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
). 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
Height and weight were measured with subjects in light indoor
clothing with shoes removed, and standard protocols were used
). 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
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.
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
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
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 (
values of P 0.05 after Bonferroni correction for multiple
comparisons were considered to be significant.
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
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 (
). The phenotypic correlations after the
familial correlation was accounted for are derived from
qp ? qg
a hb ? qe
2 a 1 2 hb
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
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.
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
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
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
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 (
12, 14, 31
), and proportion of calories from protein
). 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 (
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 (
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 (
6-nPropylthiouracil sensitivity is controlled, in part, by a variation in
10, 11, 37
). However, the effect of genetic variation in
TAS2R38 on vegetable intake is not clear (
additional factors determine food intake, including availability
and exposure (
Total calorie, carbohydrate, fat, and saturated fat intake did not
exhibit significant evidence of heritability and phenotypic
correlation. Several twin studies report macronutrient intake
). 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 (
). 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
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) (
). 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 (
). This may seem counterintuitive
because intervention studies have shown that individuals with
higher fruit and vegetable intake are less likely to gain weight
). However, without directed intervention, the longitudinal
relation between fruit and vegetable intake and weight change is
). 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
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.
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