Abstract
Background and aims
Lipoprotein lipase (LPL) is a candidate gene for obesity based on its role in triglyceride hydrolysis and the partitioning of fatty acids towards storage or oxidation. Whether dietary fatty acids modify LPL associated obesity risk is unknown.
Methods and results
We examined five single nucleotide polymorphisms (SNPs) (rs320, rs2083637, rs17411031, rs13702, rs2197089) for potential interaction with dietary fatty acids for obesity traits in 1171 participants (333 men and 838 women, aged 45–75 y) of the Boston Puerto Rican Health Study (BPRHS). In women, SNP rs320 interacted with dietary polyunsaturated fatty acids (PUFA) for body mass index (BMI) (P = 0.002) and waist circumference (WC) (P = 0.001) respectively. Higher intake of PUFA was associated with lower BMI and WC in homozygotes of the major allele (TT) (P = 0.01 and 0.005) but not in minor allele carriers (TG and GG). These interactions were replicated in an independent population, African American women of the Atherosclerosis Risk in Communities (ARIC) study (n = 1334).
Conclusion
Dietary PUFA modulated the association of LPL rs320 with obesity traits in two independent populations. These interactions may be relevant to the dietary management of obesity, particularly in women.
Keywords: Gene-diet interaction, Lipoprotein lipase, Polyunsaturated fatty acids, Obesity
Introduction
Obesity in the US has reached an overall prevalence of nearly 34% [1], with greater prevalence in some ethnic minorities [2], which might be related to differences in genetic background and behavioral factors [3–5]. The investigation of genetic variants for obesity in conjunction with behavioral factors, especially diet, may benefit development of more specific strategies to ameliorate susceptibility to weight gain.
Lipoprotein lipase (LPL) is a candidate gene for obesity, based on its encoded function to absorb fatty acids across tissues [6,7]. LPL contributes to fat storage in adipocytes [8], regulation of thermogenesis in skeletal muscle [9]. However, in spite of LPL’s demonstrated role in obesity, relevant association studies with LPL single nucleotide polymorphism (SNP) have inconsistent findings and show sex-specific differences [10,11].
One hypothesis that may account for the inconsistency is that unexamined factors may modulate LPL-associated obesity risk. Dietary fat type (e.g., saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA)) have been evaluated for obesity risk independently of genotype for decades [4]. However, it remains to be explored whether intakes of different fatty acids alter obesity-related traits in the context of LPL genotype.
Therefore, we aimed to determine whether dietary fatty acids interact with LPL variants for obesity traits in a population of multiple ancestries, stratified by sex. We also aimed to replicate our findings in an independent population.
Methods
Study populations
Discovery population: The Boston Puerto Rican Health Study (BPRHS)
In the BPRHS, there were 1171 participants of Puerto Rican origin, aged 45–75 years, living in the Greater Boston, MA metropolitan area, after excluding those with missing data and implausible energy intake, defined as <2512 kJ (600 kcal) per day or >20093 kJ (4800 kcal) per day. Details for the study have been described previously [12]. Fasting blood were collected for biochemical and genetic analyses. Anthropometric methods were consistent with techniques used by the National Health and Nutrition Examination Surveys. The study protocol was approved by the Institutional Review Board at Tufts Medical Center and Tufts University Health Sciences Campus. Informed consent was received by all participants or their representatives.
Replication population: Atherosclerosis Risk in Communities Study (ARIC)
Participants of replication study in ARIC included 2186 African American (AA) and 8689 European American (EA), considering the multiple ancestry nature of the BPRHS. ARIC was a multi-center study with participants aged 44–66 years from Forsyth County, North Carolina; Jackson, Mississippi; suburban areas of Minneapolis, Minnesota; and Washington County, Maryland [13]. Individuals with implausible energy intakes, defined as in the BPRHS, were excluded from analysis. Body weight was measured using a calibrated scale with subjects in scrub suits without shoes and height was measured using a ruler. Waist circumference (WC) at the umbilicus was measured using a tape measure. Fasting blood was collected from an antecubital vein into a vacuum tube with ethylenediamine tetraacetic acid. Triglycerides and high-density lipoprotein (HDL) were assayed using enzymatic methods and dextran-magnesium precipitation respectively [14]. This study was approved by the Institutional Review Board at each field center, and the University of North Carolina at Chapel Hill. Informed consent was received by all participants or their representatives.
SNP selection, genotyping and linkage disequilibrium (LD) analysis
Four lipids related SNPs (rs2083637, rs17411031, rs13702, and rs2197089) [15–17], and rs320 (common name as HindIII ) with inconsistent associations with obesity [10,11] were selected as discovery panel tested in BPRHS. Genotyping in BPRHS was performed using the ABI TaqMan SNP genotyping system 7900HT (Applied Biosystems, Foster City, CA). Hardy–Weinberg equilibrium (HWE) was evaluated by Chi-square tests. LD and haplotype was analyzed by HaploView4.2 [18] according to 1000 Genomes Project. Genotypes of replication SNP rs327 in ARIC was imputed by MACH (v1.0.16) [19] with HapMap r22 reference populations, Utah residents with Northern and Western European Ancestry (CEU) and Yoruba in Ibadan, Nigeria (YRI) based on the genome-wide SNP data obtained by the Affymetrix 6.0 chip (Affymetrix, Santa Clara, CA).
Dietary assessment
The BPRHS used a semi-quantitative food frequency questionnaire (FFQ) [20]. The ARIC study used a modified 66-item interviewer-administered FFQ [21]. Dietary fatty acids intake was expressed as a percentage of total energy intake.
Population ancestry admixture
The population admixture of participants in the BPRHS was estimated with reference to three ancestral populations including Native American (15%), Southern European (57%), and West African (27%), and the major principal component estimated by EIGENSTRAT was adjusted in the analysis [22]. The first 10 principal components, estimated using Eigensoft, represent admixture for ARIC EAs. Percentage of European ancestry for ARIC AAs was estimated based on the reference population of CEU using 1350 ancestry informative markers by ANCESTRYMAP [23].
Statistical analysis
Analysis of covariance and general linear models were applied to test genetic associations and interactions between SNPs and different types of dietary fat intake (SFA, MUFA and PUFA) to modulate body mass index (BMI) and WC, assuming an additive genetic model and adjusting age, admixture, smoking, drinking, total fat intake (interaction test), total energy intake, antilipemic medication, diabetes status, hormone replacement therapy (inwomen), physical activity, and education. Dietary fat intake was analyzed as both continuous and categorical variables, dichotomized by the population median, for validation. Log transformation was performed when necessary. Data were analyzed using SAS 9.2 (SAS Institute, Inc. Cary, NC). A two-tailed P-value of <0.02 was considered statistically significant, adjusting for three multiple tests introduced by three SNPs reaching HWE. Successful replication was considered when the analysis with the same additive model and covariates resulted in the significant interactions in the same direction across different populations. Inverse-variance weighted fixed-effects meta-analyses were conducted using METAL (University of Michigan; www.sph.umich.edu/csg/abecasis/metal/) to summarize the interactions across populations.
Bioinformatics analysis
Sequences surrounding the candidate SNP with significant interactions were screened via NUBIScan [24], a software evaluated peroxisome proliferator-activated receptors response elements (PPREs), for which fatty acids (especially PUFAs) are the primary ligand.
Results
Population characteristics
Demographic data, anthropometrics, lipids, nutrient intakes, disease status and medication use for both the BPRHS and ARIC are presented by sex (Table 1). Women in the BPRHS and in ARIC AAs were characterized for obesity status, using the cutoff of BMI ≥30 kg/m2 for obesity and cutoff of WC ≥ 88 cm in women for abdominal obesity. Dietary PUFA contributed 8.5% of energy intake in the BPRHS and 5% in ARIC, and it was similar for men and women within each cohort.
Table 1.
Participant characteristics in the BPRHS and ARIC, by sex.a
| Discovery |
Replication |
|||||
|---|---|---|---|---|---|---|
| BPRHS |
ARIC African Amercian |
ARIC European American |
||||
| Men (n = 333) |
Women (n = 838) |
Men (n = 852) |
Women (n = 1334) |
Men (n = 4170) |
Women (n = 4519) |
|
| General characteristics | ||||||
| Age, year | 57 (8) | 58 (7) | 53.92 (6) | 53.2 (6) | 54.66 (6) | 53.9 (6) |
| Current smoker, n | 111 (33%) | 172 (21%) | 310 (36%) | 335 (25%) | 1009 (24%) | 1142 (25%) |
| Current drinker, n | 158 (47%) | 290 (35%) | 423(50%) | 280 (21%) | 2961 (71%) | 2818 (62%) |
| Anthropometric measures | ||||||
| BMI, kg/m2 | 29.6 (5.1) | 32.9 (7.0) | 28 (4.8) | 30.7 (6.6) | 27.4 (4.0) | 26.5 (5.4) |
| WC, cm | 102 (15) | 102 (16) | 98 (13) | 100 (16) | 100 (10) | 93 (15) |
| Lipids | ||||||
| Triglycerides, mmol/l | 1.66 (1.77) | 1.55 (1.63) | 1.4 (1.25) | 1.24 (0.74) | 1.66 (1.13) | 1.45 (0.93) |
| Total cholesterol, mmol/l | 4.47 (1.14) | 4.87 (1.05) | 5.47 (1.11) | 5.62 (1.16) | 5.45 (0.99) | 5.64 (1.1) |
| HDL cholesterol, mmol/l | 1.03 (0.31) | 1.21 (0.31) | 1.29 (0.42) | 1.5 (0.44) | 1.11 (0.32) | 1.49 (0.44) |
| LDL cholesterol, mmol/l | 2.57 (0.91) | 2.86 (0.89) | 3.57 (1.06) | 3.57 (1.13) | 3.61 (0.91) | 3.49 (1.02) |
| Dietary Intake | ||||||
| Total energy, kcal/d | 2483 (883) | 2044 (875) | 1732 (657) | 1514 (569) | 1799 (652) | 1515 (522) |
| Carbohydrate, % total energy/d | 48.9 (7.5) | 51.5 (7.7) | 48.5 (9.1) | 50.5 (9.6) | 47.3 (9.1) | 49.5 (9.3) |
| Protein, % total energy/d | 16.8 (2.9) | 17.2 (3.5) | 17.2 (3.9) | 18.7 (4.5) | 17.1 (3.7) | 18.5 (4.2) |
| Total fat, % total energy/d | 32.9 (5.7) | 31.7 (5.5) | 32.0 (6.3) | 32.1 (6.5) | 33.6 (6.8) | 32.8 (6.8) |
| SFA, % total energy/d | 10.1 (2.5) | 9.6 (2.3) | 11.4 (2.7) | 11.4 (2.7) | 12.4 (3.0) | 12.1 (3.1) |
| MUFA, % total energy/d | 11.4 (2.1) | 11.0 (2.1) | 12.6 (2.8) | 12.5 (2.9) | 13.0 (3.0) | 12.4 (3.0) |
| PUFA, % total energy/d | 8.6 (2.0) | 8.5 (2.0) | 4.7 (1.2) | 4.9 (1.3) | 5.1 (1.5) | 5.1 (1.5) |
| Cholesterol, mg/d | 377 (191) | 283 (162) | 312 (162) | 250 (124) | 269 (139) | 223 (104) |
| Disease and medication | ||||||
| Have diabetes, n | 137 (41%) | 331 (40%) | 156 (18%) | 256 (19%) | 405 (10%) | 334 (7%) |
| Take antilipemic medication, n | 133 (40%) | 343 (41%) | 6 (1%) | 7 (1%) | 84 (2%) | 87 (2%) |
| Take anti-depressants, n | 84 (25%) | 323 (39%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Take hormone replacement therapy in women, n |
0 (0%) | 22 (3%) | 0 (0%) | 211(16%) | 0 (0%) | 984 (22%) |
Data are means (standard deviation) or n (%).
Discovery in BPRHS
Minor allele frequencies (MAF) were greater than 0.25 (Table 2). Rs2083637 and rs17411031 deviated from HWE (P = 0.03 and P = 0.02) and thus were excluded in the following analysis.
Table 2.
Minor allele frequency (MAF) of LPL SNPs in the BPRHS.a
| Men | Women | P b | ||
|---|---|---|---|---|
| rs320 | MAF | 0.29 | 0.27 | 0.08 |
| TT | 154 | 418 | ||
| TG | 135 | 332 | ||
| GG | 22 | 50 | ||
| rs2083637 | MAF | 0.28 | 0.24 | 0.03 |
| AA | 166 | 454 | ||
| AG | 131 | 334 | ||
| GG | 25 | 32 | ||
| rs17411031 | MAF | 0.28 | 0.24 | 0.02 |
| CC | 162 | 446 | ||
| CG | 130 | 332 | ||
| GG | 24 | 31 | ||
| rs13702 | MAF | 0.36 | 0.35 | 0.7 |
| TT | 125 | 334 | ||
| TC | 142 | 370 | ||
| CC | 41 | 93 | ||
| rs2197089 | MAF | 0.39 | 0.41 | 0.25 |
| AA | 123 | 286 | ||
| AG | 149 | 390 | ||
| GG | 50 | 140 |
Data are n.
P: P value for HWE testing.
Rs320, rs13702, and rs2197089 were not associated with BMI and WC (Supplementary Table 1). Both categorical PUFA (Supplementary Table 2) and continuous PUFA (Fig. 1) showed significant interaction with rs320 for BMI (P = 0.02 and P = 0.002, respectively) and WC (P = 0.02 and P = 0.001, respectively). In homozygotes of the major allele (TT), higher PUFA intake associated with lower BMI (P = 0.01) and smaller WC (P = 0.005) (Fig. 1). However, in carriers of minor allele (TG and GG), there were no associations (P > 0.1) (Fig. 1). Significance and interaction pattern were similar for N6- and N3-PUFA (Supplementary Table 2). Results remained the same after excluding women (n = 22) using hormone replacement (Data not shown). No significant interactions were observed in men (Fig. 1 and Supplementary Table 2), for SFA or MUFA, or with rs13702 or rs2197089 (data not shown). Data reported below apply to the single SNP rs320.
Figure 1. Discovery of continuous interaction between rs320 and dietary PUFA intake for obesity traits in the BPRHS, by sex.
Predicted BMI (A for women, B for men) and WC (C for women, D for men) by rs320 genotype were plotted against continuous dietary PUFA adjusted for age, admixture, smoking, drinking, total energy intake, total fat intake, diabetes status, antilipemic medication, hormone replacement therapy in women, physical activity, and education. P values indicate the statistical significance of the adjusted interaction term and adjusted regression coefficients in the regression line corresponding to three genotype groups of rs320 (TT, TG, and GG).
Replication in ARIC
Rs327 was selected for replication in ARIC because of its close distance to rs320 (459 bp), the high imputation quality (R2 = 1), and its strong LD with rs320 in CEU, African ancestry in the Southwest USA (ASW), and Mexican ancestry in Los Angeles (MXL) populations in the 1000 Genomes Project (R2 > 0.8) (Fig. 2).
Figure 2. Replication and meta-analysis of continuous interaction between rs320/rs327 and dietary PUFA intake for obesity traits by sex.
LD correlation (100 × R2 value in diamond), haplotypes and corresponding frequencies of rs320 and replication SNP rs327 in 1000 Genomes CEU (A), ASW (B) and MXL (C) are presented. Forest plots of this continuous interactions modulating BMI (left) and WC (right) in women (D) and men (E) are presented. The estimates represent the difference in the magnitude of the PUFA association (per +1-unit intake of PUFA) with BMI or WC per copy of the T allele of rs320/rs327, adjusting for age, admixture, smoking, drinking, total energy intake, total fat intake, diabetes status, antilipemic medication, hormone replacement therapy in women, physical activity, and education. Filled square and horizontal line represent the estimate and 95% CI for each population, and the filled diamond represent the summary estimate and its 95% CI.
Interaction with continuous PUFA in AA women was significant for WC (P = 0.01) and approached significance for BMI (P = 0.07). Interactions were not significant in AA men or EAs (Fig. 2), and similar results were with categorical PUFA (Supplementary Fig. 1).
In a meta-analysis of the women from all three populations, rs320/rs327 showed significant interaction with continuous PUFA intake for both BMI (P = 0.009) and WC (P = 0.003) (Fig. 2). However, interaction P values were even lower when the meta-analysis included only two women population, BPRHS and ARIC AAs (P = 0.001 for BMI and P = 4 × 10−5 for WC). The betas for the interaction term were consistently negative in three populations. There were no interactions in men (Fig. 2). Analysis with categorical PUFA exhibited similar results (Supplementary Fig. 1).
Bioinformatics analysis
There was a PPRE approximately 14 kb downstream of rs320 (Fig. 3).
Figure 3. Genomic structure of LPL locus.
The gene is transcribed as indicated by the large horizontal arrow. Exons are the larger rectangular black boxes.
Discussion
In the current study, we identified and replicated significant interactions between dietary PUFA intake and LPL rs320 for obesity traits in women. In the homozygosity for the major allele (TT), increase in PUFA intake associated with lower BMI and WC. Our observed gene–diet interaction may have improved our ability to detect dietary effects that are apparent only under certain genotype.
Genetic modulation of dietary associations has been suggested by the studies for plasma lipids [25,26]. TT group showed greater improvement in plasma lipids with calorie restriction intervention [25]. Our group also found that TT genotype had a greater postprandial lipidemia response following an oral fat load [26]. These may suggest that T-carriers are more responsive to diet than G-carriers, which is consistent with our findings for PUFA intake. Considering the existence of the PPRE near rs320, we hypothesize that the apparently greater responsiveness of T allele to PUFA may involve the higher binding affinity to the transcription factor of this allele compared to G allele [27]. The anti-obesity effects of PUFA found are consistent with previous studies [28], which may be related to decreased adipocyte LPL activity [29], reduced triglyceride storage and less fat deposition.
Our sex-specific finding is consistent with previous study [7], and may be due to the sex-specific LPL response. LPL expression in muscle was 160% higher in women than men [6]. Our finding of null genetic association with obesity is consistent with a US study [11] not a French one [10], which may further support our hypothesis of gene–diet interaction due to potential differences in dietary pattern.
Limitations include the cross-sectional design and the absence of adipose LPL expression measurement. The variable ancestral admixtures and high prevalence of obesity in both minority populations (BPRHS and ARIC AAs) restricted our study from drawing conclusions about the relative contributions of genetic background, behavioral and socio-economic factors to the observed interactions. As our results are preliminary, additional studies are desirable.
In summary, we detected a significant interaction between LPL rs320 and dietary PUFA for obesity-related outcomes in women, and these findings were strengthened by replication in a second, independent population. Our observations may be relevant to the nutritional management of obesity in women.
Supplementary Material
Acknowledgments
J.M.O. and C.E.S. contributed to experimental design, results interpretation, manuscript writing and editing. Y.M. contributed to genotyping, data analysis, results interpretation, and manuscript writing. K.L.T. contributed to sample collection of BPRHS, results interpretation, manuscript editing. K.E.N, Y.S., K.L.Y., and A.E.J. contributed to sample collection and data analysis in ARIC, and manuscript editing. Y.C.L. and T.H. contributed to assistance with genotyping in BPRHS, results interpretation and manuscript editing. C.Q.L., L.D.P., and K.R. contributed to results interpretation and manuscript editing. This study was supported by the National Institutes of Health (NIH), National Institute on Aging, grant number P01AG023394 and NIH/National Heart, Lung and Blood Institute, grant number HL54776 and P50 HL105185, and NIH/National Institute of Diabetes and Digestive and Kidney Diseases, grant number DK075030, and contracts 53-K06-5-10 and 58-1950-9-001 from the US Department of Agriculture Research Service, and NIH/Eunice Kennedy Shriver National Institute of Child Health and Human Development, grant number R01 HD057194, and NIH/National Institute of Diabetes And Digestive And Kidney Diseases, grant number R01 DK075681. Caren Smith is supported by K08 HL112845-01. The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268 201100007C, HHSN268201100008C, HHSN268201 100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Dr. North is supported by NIDDK R01 DK089256. The authors thank the staff and participants of the ARIC study for their important contributions. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The USDA is an equal opportunity provider and employer.
Abbreviations
- LPL
lipoprotein lipase
- SNP
single nucleotide polymorphism
- SFA
saturated fatty acids
- MUFA
monounsaturated fatty acids
- PUFA
polyunsaturated fatty acids
- BPRHS
Boston Puerto Rican Health Study
- ARIC
Atherosclerosis Risk in Communities Study
- AA
African American
- EA
European American
- WC
waist circumference
- HDL
high density lipoprotein
- LD
linkage disequilibrium
- HWE
Hardy–Weinberg Equilibrium
- CEU
Utah residents with Northern and Western European Ancestry
- YRI
Yoruba in Ibadan, Nigeria
- FFQ
food frequency questionnaire
- BMI
body mass index
- PPRE
peroxisome proliferator-activated receptors response element
- MAF
minor allele frequency
- ASW
African ancestry in Southwest USA
- MXL
Mexican ancestry in Los Angeles
- LDL
low density lipoprotein
- UTR
untranslated region
- TSS
transcription start site
Footnotes
Conflicts of interest The authors have no competing interests.
Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.numecd.2014.07.003.
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