Genetic polymorphisms in carnitine palmitoyltransferase 1A gene are associated with variation in body composition and fasting lipid traits in Yup'ik Eskimos[S]

      Variants of carnitine palmitoyltransferase 1A (CPT1A), a key hepatic lipid oxidation enzyme, may influence how fatty acid oxidation contributes to obesity and metabolic outcomes. CPT1A is regulated by diet, suggesting interactions between gene variants and diet may influence outcomes. The objective of this study was to test the association of CPT1A variants with body composition and lipids, mediated by consumption of polyunsaturated fatty acids (PUFA). Obesity phenotypes and fasting lipids were measured in a cross-sectional sample of Yup'ik Eskimo individuals (n = 1141) from the Center of Alaska Native Health Research (CANHR) study. Twenty-eight tagging CPT1A SNPs were evaluated with outcomes of interest in regression models accounting for family structure. Several CPT1A polymorphisms were associated with HDL-cholesterol and obesity phenotypes. The P479L (rs80356779) variant was associated with all obesity-related traits and fasting HDL-cholesterol. Interestingly, the association of P479L with HDL-cholesterol was still significant after correcting for body mass index (BMI), percentage body fat (PBF), or waist circumference (WC). Our findings are consistent with the hypothesis that the L479 allele of the CPT1A P479L variant confers a selective advantage that is both cardioprotective (through increased HDL-cholesterol) and associated with reduced adiposity.
      Obesity is associated with a series of metabolic conditions clinically referred to as metabolic syndrome, which includes hypertension, dyslipidemia, hyperglycemia, and the development of type 2 diabetes (T2D). Approximately sixty percent of obese individuals have metabolic complications (
      • Park Y-W.
      • Zhu S.
      • Palaniappan L.
      • Heshka S.
      • Carnethon M.R.
      • Heymsfielow S.B.
      The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994.
      ); however, “healthy obese” individuals have been identified with excessive accumulation of body fat that does not translate to dyslipidemia and insulin resistance (
      • Blüher M.
      The distinction of metabolically “healthy” from “unhealthy” obese individuals.
      ,
      • Wildman R.P.
      Healthy obesity.
      ). For example, some Eskimo/Inuit people indigenous to Alaska are obese, but they have historically demonstrated low prevalence of insulin resistance, metabolic syndrome, and T2D (
      • Ebbesson S.O.E.
      • Schraer C.D.
      • Risica P.M.
      • Adler A.I.
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      • Mayer A.M.
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      • Yeh J.
      • Go O.T.
      • Robbins D.C.
      Diabetes and impaired glucose tolerance in three Alaskan Eskimo populations. The Alaska-Siberia Project.
      ,
      • Schraer C.D.
      • Risica P.M.
      • Ebbesson S.O.E.
      • Go O.T.
      • Howard B.V.
      • Mayer A.M.
      Low fasting insulin levels in Eskimos compared to American Indians: are Eskimos less insulin resistant?.
      ,
      • Schraer C.D.
      • Ebbesson S.O.E.
      • Adler A.I.
      • Cohen J.S.
      • Boyko E.J.
      • Nobmann E.D.
      Glucose tolerance and insulin-resistance syndrome among St. Lawrence Island Eskimos.
      ,
      • Murphy N.J.
      • Schraer C.D.
      • Bulkow L.R.
      • Boyko E.J.
      • Lanier A.P.
      Diabetes mellitus in Alaskan Yup'ik Eskimos and Athabascan Indians after 25 yr.
      ). Specifically, Yup'ik Eskimo peoples living in Southwest Alaska have obesity prevalence comparable to the general US population, yet the prevalence of metabolic syndrome (
      • Boyer B.B.
      • Mohatt G.V.
      • Plaetke R.
      • Herron J.
      • Stanhope K.L.
      • Stephensen C.
      • Havel P.J.
      Metabolic syndrome in Yup'ik Eskimos: the Center for Alaska Native Health Research (CANHR) Study.
      ) and T2D (
      • Mohatt G.V.
      • Plaetke R.
      • Klejka J.
      • Luick B.
      • Lardon C.
      • Bersamin A.
      • Hopkins S.
      • Dondanville M.
      • Herron J.
      • Boyer B.B.
      The Center for Alaska Native Health Research Study: a community-based participatory research study of obesity and chronic disease-related protective and risk factors.
      ) is significantly less than that observed in the general US population (
      • Cowie C.C.
      • Rust K.F.
      • Ford E.S.
      • Eberhardt M.S.
      • Byrd-Holt D.D.
      • Li C.
      • Williams D.E.
      • Gregg E.W.
      • Bainbridge K.E.
      • Saydah S.H.
      • et al.
      Full accounting of diabetes and pre-diabetes in the US population in 1988–1994 and 2005–2006.
      ,
      • Ford E.S.
      Prevalence of the metabolic syndrome in US populations.
      ). Although the mechanisms that allow Yup'ik Eskimo people to carry excess body fat without developing features of metabolic syndrome and T2D are unknown, dietary and genetic factors are likely to be relevant (
      • Makhoul Z.
      • Kristal A.R.
      • Gulati R.
      • Luick B.
      • Bersamin A.
      • Boyer B.B.
      • Mohatt G.V.
      Associations of very high intakes of eicosapentaenoic and docosahexaenoic acids with biomarkers of chronic disease risk among Yup'ik Eskimos.
      ,
      • Makhoul Z.
      • Kristal A.R.
      • Gulati R.
      • Luick B.
      • Bersamin A.
      • O'Brien D.
      • Hopkins S.E.
      • Stephensen C.B.
      • Stanhope K.L.
      • Havel P.J.
      • et al.
      Associations of obesity with triglycerides and C-reactive protein are attenuated in adults with high red blood cell eicosapentaenoic and docosahexaenoic acids.
      ). Because weight loss as a treatment for obesity-related comorbidities is difficult to achieve and maintain (
      • Tsai A.G.
      • Wadden T.A.
      Systematic review: an evaluation of major commercial weight loss programs in the United States.
      ,
      • Foster G.D.
      • Wadden T.A.
      • Vogt R.A.
      • Brewer G.
      What is a reasonable weight loss? Patients’ expectations and evaluations of obesity treatment outcomes.
      ,
      • Curioni C.C.
      • Lourenço P.M.
      Long-term weight loss after diet and exercise: a systematic review.
      ,
      • Dansinger M.L.
      • Tatsioni A.
      • Wong J.B.
      • Chung M.
      • Balk E.M.
      Meta-analysis: the effect of dietary counseling for weight loss.
      ), understanding the underlying mechanisms that protect this population from features of metabolic syndrome despite their adiposity would have implications for treatment of obesity without the necessity of weight loss.
      It has been proposed that the “healthy obesity” observed in Yup'ik Eskimo individuals is in part related to exposure to a diet rich in n-3 polyunsaturated fatty acids (n-3 PUFA) (
      • Bersamin A.
      • Zidenberg-Cherr S.
      • Stern J.S.
      • Luick B.R.
      Nutrient intakes are associated with adherence to a traditional diet among Yup'ik Eskimos living in remote Alaska Native communities: the CANHR Study.
      ,
      • Schumacher C.
      • Davidson M.
      • Ehrsam G.
      Cardiovascular disease among Alaska Natives: a review of the literature.
      ). n-3 PUFAs consumed by Yup'ik Eskimo people are principally composed of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), and their PUFA intake is 20 times greater than the current mean intake of the general US population (4.1 ± 0.5 g/day versus 0.05 g/day in men; 2.8 ± 0.3 g/day versus 0.09 g/day in women) (
      • Bersamin A.
      • Luick B.R.
      • King I.B.
      • Stern J.S.
      • Zidenberg-Cherr S.
      Westernizing diets influence fat intake, red blood cell fatty acid composition, and health in remote Alaskan Native communities in the Center for Alaska Native Health Study.
      ,
      • Johnson J.S.
      • Nobmann E.D.
      • Asay E.
      • Lanier A.P.
      Dietary intake of Alaska Native people in two regions and implications for health: the Alaska Native Dietary and Subsistence Food Assessment Project.
      ). Cross-sectional studies in a Yup'ik Eskimo population offer a unique opportunity to examine the association of elevated n-3 PUFA exposure with body composition, fasting lipids, and lipoprotein levels. Studies in both animals and humans have demonstrated that EPA and DHA impact body composition and circulating fasting lipid levels by modulating gene expression to favor increased fatty acid oxidation and reduction of fat deposition (
      • Buckley J.D.
      • Howe P.R.C.
      Long-chain omega-3 polyunsaturated fatty acids may be beneficial for reducing obesity--a review.
      ). Evidence that elevated n-3 PUFA consumption has a direct influence on “healthy” obesity remains inconclusive (
      • Makhoul Z.
      • Kristal A.R.
      • Gulati R.
      • Luick B.
      • Bersamin A.
      • Boyer B.B.
      • Mohatt G.V.
      Associations of very high intakes of eicosapentaenoic and docosahexaenoic acids with biomarkers of chronic disease risk among Yup'ik Eskimos.
      ,
      • Makhoul Z.
      • Kristal A.R.
      • Gulati R.
      • Luick B.
      • Bersamin A.
      • O'Brien D.
      • Hopkins S.E.
      • Stephensen C.B.
      • Stanhope K.L.
      • Havel P.J.
      • et al.
      Associations of obesity with triglycerides and C-reactive protein are attenuated in adults with high red blood cell eicosapentaenoic and docosahexaenoic acids.
      ) and warrants experimental designs that evaluate gene-diet interactions that may mediate this effect in populations with elevated daily dietary intake of n-3 PUFA.
      Mitochondrial carnitine palmitoyltransferase 1 (CPT1), a member of the carnitine palmitoyltransferase family, is a gene that controls fatty acid oxidation in skeletal, adipose, and liver tissue (
      • McGarry J.D.
      • Brown N.F.
      The mitochondrial carnitine palmitoyltransferase system. From concept to molecular analysis.
      ). Fatty acid oxidation is often impaired in the obese condition (
      • Ravussin E.
      • Smith S.R.
      Increased fat intake, impaired fat oxidation, and failure of fat cell proliferation result in ectopic fat storage, insulin resistance, and type 2 diabetes mellitus.
      ,
      • Zurlo F.
      • Lillioja S.
      • Esposito-Del Puente A.
      • Nyomba B.L.
      • Raz I.
      • Saad M.F.
      • Swinburn B.A.
      • Knowler W.C.
      • Bogardus C.
      • Ravussin E.
      Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24-h RQ.
      ), which may contribute to hepatic steatosis, hepatic insulin resistance, and impaired hepatic lipid handling (
      • Reddy J.K.
      • Rao M.S.
      Lipid metabolism and liver inflammation. II. Fatty liver disease and fatty acid oxidation.
      ). CPT1 as a major control point for fatty acid oxidation may, therefore, be a key player in “healthy obesity,” especially if certain single nucleotide polymorphisms (SNP) are resistant to impaired fatty acid oxidation, which often accompanies obesity. Interestingly, as n-3 PUFA increases mitochondrial fatty acid oxidation by stimulating the activity of CPT1 (
      • Madsen L.
      • Rustan A.C.
      • Vaagenes H.
      • Berge K.
      • Dyrøy E.
      • Berge R.K.
      Eicosapentaenoic and docosahexaenoic acid affect mitochondrial and peroxisomal fatty acid oxidation in relation to substrate preference.
      ), the interaction between n-3 PUFAs and SNPs in CPT1 may improve lipid profiles.
      Mammalian tissues express three CPT1 isoforms: CPT1A (liver), CPT1B (muscle), and CPT1C (brain), which are encoded on separate genes (
      • Esser V.
      • Britton C.H.
      • Weis B.C.
      • Foster D.W.
      • McGarry J.D.
      Cloning, sequencing, and expression of a cDNA encoding rat liver carnitine palmitoyltransferase I. Direct evidence that a single polypeptide is involved in inhibitor interaction and catalytic function.
      ,
      • Yamazaki N.
      • Shinohara Y.
      • Shima A.
      • Terada H.
      High expression of a novel carnitine palmitoyltransferase I like protein in rat brown adipose tissue and heart: isolation and characterization of its cDNA clone.
      ,
      • Price N.T.
      • van der Leij F.R.
      • Jackson V.N.
      • Corstorphine C.G.
      • Thomson R.
      • Sorensen A.
      • Zammit V.A.
      A novel brain-expressed protein related to carnitine palmitoyltransferase I.
      ). In the presence of L-carnitine, CPT1 facilitates the transfer of long-chain fatty acids (LCFA) across the mitochondrial membrane for β-oxidation (
      • McGarry J.D.
      Travels with carnitine palmitoyltransferase I: from liver to germ cell with stops in between.
      ). Mitochondrial β-oxidation of dietary and endogenous LCFA is tightly regulated through allosteric inhibition of CPT1 by malonyl-CoA, an intermediate in fatty acid synthesis (
      • Swanson S.T.
      • Foster D.W.
      • McGarry J.D.
      • Brown N.F.
      Roles of the N- and C-terminal domains of carnitine palmitoyltransferase I isoforms in malonyl-CoA sensitivity of the enzymes: insights from expression of chimaeric proteins and mutation of conserved histidine residues.
      ). In liver cells, the partnership between malonyl-CoA and CPT1A has been shown to be a key regulatory point that modulates the oxidation of dietary and endogenous LCFA (
      • Akkaoui M.
      • Cohen I.
      • Esnous C.
      • Lenoir V.
      • Sournac M.
      • Girard J.
      • Prip-Buus C.
      Modulation of the hepatic malonyl-CoA-carnitine palmitoyltransferase 1A partnership creates a metabolic switch allowing oxidation of de novo fatty acids.
      ). Although CPT1A is a candidate gene for obesity (
      • Saunders C.L.
      • Chiodini B.D.
      • Sham P.
      • Lewis C.M.
      • Abkevich V.
      • Adeyemo A.A.
      • de Andrade M.
      • Arya R.
      • Berenson G.S.
      • Blangero J.
      • et al.
      Meta-analysis of genome-wide linkage studies in BMI and obesity.
      ) and CPT1A SNPs are associated with elevated fasting HDL-cholesterol levels (
      • Rajakumar C.
      • Ban M.R.
      • Cao H.
      • Young T.K.
      • Bjerregaard P.
      • Hegele R.A.
      Carnitine palmitoyltransferase IA polymorphism P479L is common in Greenland Inuit and is associated with elevated plasma apolipoprotein A-I.
      ), it is unknown whether the interaction between n-3 PUFA intake and CPT1A SNPs influence changes in body composition and fasting lipids.
      In this study, we tested the hypothesis that SNPs within or near the CPT1A gene are associated with body composition and fasting lipid phenotypes in a large cross-sectional cohort of Yup'ik Eskimo peoples, a population whose daily dietary intake involves a 30-fold range of exposure of n-3 PUFA, and we examined whether these associations were modified by n-3 PUFA intake.

      METHODS

       Subjects and study design

      The Center for Alaska Native Health Research (CANHR) studies genetic, behavioral, and dietary risk factors underlying obesity and their relationship to diabetes and cardiovascular disease among Yup'ik Eskimo peoples (
      • Mohatt G.V.
      • Plaetke R.
      • Klejka J.
      • Luick B.
      • Lardon C.
      • Bersamin A.
      • Hopkins S.
      • Dondanville M.
      • Herron J.
      • Boyer B.B.
      The Center for Alaska Native Health Research Study: a community-based participatory research study of obesity and chronic disease-related protective and risk factors.
      ). A community-based participatory research framework guides all CANHR investigations; participant ascertainment is open to all members of the community meeting a specified age minimum. Recruitment of Yup'ik Eskimo participants was initiated in 2003 and continues in 11 Southwest Alaska communities. All residents 14 years of age and older are invited to participate, and the resulting distribution of age in our study sample reflects the age distribution among eligible participants according to 2000 US census data. Participants sign informed-consent documents before entering the study using protocols that were approved by the University of Alaska Institutional Review Board, the National and Alaska Area Indian Health Service Institutional Review Boards, and the Yukon-Kuskokwim Health Corporation Human Studies Committee. The analyses in this report were performed on 1,141 nonpregnant Yup'ik Eskimo participants with ages that ranged between 14 and 94 years at the time of enrollment.

       Anthropometric and biochemical measurements

      Anthropometric measurements were obtained by trained staff using protocols from the NHANES III Anthropometric Procedures Manual (
      • Lohman T.G.
      • Roche A.F.
      Anthropometric Standardization Reference Manual.
      ) as previously described (
      • Boyer B.B.
      • Mohatt G.V.
      • Plaetke R.
      • Herron J.
      • Stanhope K.L.
      • Stephensen C.
      • Havel P.J.
      Metabolic syndrome in Yup'ik Eskimos: the Center for Alaska Native Health Research (CANHR) Study.
      ). These measurements included height, weight, and four circumferences (waist, hip, triceps, and thigh). Percentage body fat was measured by electrical bioimpedance using a Tanita TBF-300A body composition analyzer (Tanita Corp., Arlington Heights, IL). Blood samples were collected from participants after an overnight fast, and lipoprotein measures, including total cholesterol, HDL-cholesterol, LDL-cholesterol, VLDL-cholesterol, apolipoprotein A-I, and plasma triglycerides levels, were assayed as previously described by Boyer et al. (
      • Boyer B.B.
      • Mohatt G.V.
      • Plaetke R.
      • Herron J.
      • Stanhope K.L.
      • Stephensen C.
      • Havel P.J.
      Metabolic syndrome in Yup'ik Eskimos: the Center for Alaska Native Health Research (CANHR) Study.
      ).

       Biomarker for marine n-3 PUFA intake: analysis of RBC nitrogen stable isotope ratio

      n-3 PUFA intake was assessed in Yup'ik Eskimo individuals using the nitrogen stable isotope ratio (δ15N) of red blood cells (RBC) as previously described (
      • O'Brien D.M.
      • Kristal A.R.
      • Jeannet M.A.
      • Wilkinson M.J.
      • Bersamin A.
      • Luick B.
      Red blood cell delta15N: a novel biomarker of dietary eicosapentaenoic acid and docosahexaenoic acid intake.
      ). RBC aliquots were autoclaved for 20 min at 121°C to destroy blood-borne pathogens, and samples were weighed into 3.5 × 3.75 mm tin capsules and freeze dried to a final mass of 0.2–0.4 mg. Samples were analyzed at the Alaska Stable Isotope Facility by continuous-flow isotope ratio mass spectrometry, using a Costech ECS4010 Elemental Analyzer (Costech Analytical Technologies, Valencia, CA) interfaced to a Finnigan Delta Plus XP isotope ratio mass spectrometer via the Conflo III interface (Thermo-Finnigan Inc., Breman, Germany). Isotope ratios were analyzed relative to IAEA-certified reference materials calibrated to atmospheric nitrogen, for which 15N/14N = 0.0036765. By convention and for ease of interpretation, isotope ratios are presented as delta values in “permil” relative to atmospheric nitrogen: δ15N = [(15N/14Nsample15N/14Nstandard)/(15N/14Nstandard)] ∙ 1000‰. We concurrently prepared and ran multiple laboratory standards (peptone, δ15N = 7.00) to assess analytical accuracy and precision; these were analyzed after every eighth sample and gave values of δ15N = 7.01 ± 0.24‰ (mean ± SD). The range of isotopic variation in our dataset (9‰) was very large relative to analytical precision (0.2‰). We modeled the effects of n-3 PUFA intake as a categorical variable, and they were included in the association analysis. This categorical variable is hereafter referred to as δ15N.

       SNP selection and genotyping

      A comprehensive list of DNA variants were selected for genotyping within and near (5 kb upstream and 5 kb downstream) the CPT1A gene collected from HapMap data, release 3, National Center for Biotechnology Information (NCBI) B36, dbSNP 126 (
      • International HapMap Consortium
      The International HapMap Project.
      ). Given that no publically available genotypic information exists on Yup’ik Eskimo people, we referenced the Caucasian (CEU) and Han Chinese (CHB) populations in HapMap using the Seattle SNPs database (http://pga.mbt.washington.edu/) to identify potential genetic variants that may be common in our study population. A set of 27 maximally informative tagging SNPs (tSNP) were selected to represent common linkage disequilibrium clusters with the LDselect algorithm as implemented in the MultiPop-TagSelect program, using thresholds of r2= 0.80 and minor allele frequency (MAF) >1% (
      • Carlson C.S.
      • Eberle M.A.
      • Rieder M.J.
      • Yi Q.
      • Kruglyak L.
      • Nickerson D.A.
      Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium.
      ,
      • Howie B.N.
      • Carlson C.S.
      • Rieder M.J.
      • Nickerson D.A.
      Efficient selection of tagging single-nucleotide polymorphisms in multiple populations.
      ). We chose to relax our MAF criteria to include SNPs with MAF > 0.01 to genotype tagging SNPs in the CPT1A gene that may be common (MAF ≥ 0.05) in Yup’ik Eskimos despite being rare (MAF < 0.05) in CEU and CHB populations. We also included the nonsynonymous P479L (rs80356779) CPT1A SNP for genotyping based on previous associations with elevated plasma HDL-cholesterol and apolipoprotein A-I levels in the Greenland Inuit (
      • Rajakumar C.
      • Ban M.R.
      • Cao H.
      • Young T.K.
      • Bjerregaard P.
      • Hegele R.A.
      Carnitine palmitoyltransferase IA polymorphism P479L is common in Greenland Inuit and is associated with elevated plasma apolipoprotein A-I.
      ). Genotyping of the 28 SNPs, including P479L, was carried out by allele-specific primer extension of multiplex amplified products and detection using matrix-assisted laser desorption ionization time-of-flight spectrometry on a Sequenom iPLEX platform at the Broad Institute (
      • Tang K.
      • Fu D-J.
      • Julien D.
      • Braun A.
      • Cantor C.R.
      • Köster H.
      Chip-based genotyping by mass spectrometry.
      ). Linkage disequilibrium (LD) among SNPs was based on pairwise haplotype frequencies calculated using the hapfreq command in the FBAT program (
      • Horvath S.
      • Xu X.
      • Lake S.L.
      • Silverman E.K.
      • Weiss S.T.
      • Laird N.M.
      Family-based tests for associating haplotypes with general phenotype data: application to asthma genetics.
      ).

       Quality control of phenotypic and genotypic data

      Simple linear models were fit to each of the outcome variables using all of the covariates (age, sex, community membership) included in the association models, and the distributions of the residuals were examined for normality with the R statistical programming language (v2.10.1, R Development Core, 2009). Box-Cox transformations were applied to traits whose residuals did not follow a normal distribution (
      • Box G.E.P.
      • Cox D.R.
      An analysis of transformations.
      ). Family data was extracted from a Progeny database (Progeny Software LLC, South Bend, IN) and merged into a single extended pedigree using PedMerge (
      • Plaetke R.
      • Balbi F.
      PedMerge: merging pedigrees to facilitate family-based genetic statistical analyses.
      ). Genotypic data were tested for Mendelian inconsistencies using PEDCHECK (
      • O'Connell J.R.
      • Weeks D.E.
      PedCheck: a program for identification of genotype incompatibilities in linkage analysis.
      ). In this sample, Illumina IV linkage panel (Illumina, Inc., San Diego, CA) genotypes were available from an ongoing linkage study and were used to construct principal components of ancestry (PCA) using the PCA program in the EIGENSTRAT analysis package (
      • Price A.L.
      • Patterson N.J.
      • Plenge R.M.
      • Weinblatt M.E.
      • Shadick N.A.
      • Reich D.
      Principal components analysis corrects for stratification in genome-wide association studies.
      ). The second PCA discriminated the individuals in the study into two groups that correspond to the proximity of the community to the coast. On the basis of this observation, we defined a dichotomous community group variable. We assessed Hardy-Weinberg equilibrium (HWE) using PLINK (v1.07) (
      • Purcell S.
      • Neale B.M.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.R.
      • Bender D.
      • Maller J.B.
      • Sklar P.
      • de Bakker P.I.W.
      • Daly M.J.
      • et al.
      PLINK: a tool set for whole-genome association and population-based linkage analyses.
      ) and determined allele frequencies for each SNP using the FREQ module in the program Statistical Analysis for Genetic Epidemiology (S.A.G.E., 2009). The present study restricted analysis to only include SNPs with MAF ≥ 5% that did not deviate from HWE after Bonferroni correction (P < 0.002).

       Association analysis

      Each SNP was tested for association with obesity-related phenotypes using the program ASSOC (
      • George V.T.
      • Elston R.C.
      Testing the association between polymorphic markers and quantitative traits in pedigrees.
      ) in the Statistical Analysis for Genetic Epidemiology (S.A.G.E. 2009) software package, which can incorporate complex pedigree data, covariates, and interactions into association analysis. We included both demographic (age, community, and sex) and environmental covariates (δ15N) in the ASSOC analysis. Likelihood ratio statistics were calculated to compare three nested models and test the null hypothesis of no association between CPT1A SNPs and obesity traits after including demographic and environmental covariates. Effect sizes (β) are presented as the change in transformed phenotypes according to minor allele that was determined in a linear model adjusted for demographic and environmental covariates.
      Model 1 included baseline covariates (age, sex, community membership, and δ15N quartiles); Model 2 included baseline covariates and SNP to test for an additive genetic effect of SNP (defined as the number of minor alleles); and Model 3 included baseline covariates, the additive genetic effect of SNP, and interactions between the additive genetic effect and δ15N quartiles. Note that Model 3 is the only model to test directly gene-diet interaction under the null hypothesis. We treated each phenotype tested as representing a separate family of null hypotheses and corrected for the number of tests within each family (
      • Grove W.M.
      • Andreasen N.C.
      Simultaneous tests of many hypotheses in exploratory research.
      ). Multiple-test correction to control the familywise error rate was calculated according to the number of nonredundant SNPs with MAF ≥ 0.05 that were tested for association and interaction. Given the correlation among neighboring genetic markers, the effective number of nonredundant SNPs in this study was estimated using spectral decomposition of LD matrices (
      • Nyholt D.R.
      A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other.
      ,
      • Li J.
      • Ji L.
      Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix.
      ).

      RESULTS

       Characteristics of Yuprsquoik Eskimo participants

      General clinical characteristics and descriptive statistics on Yup’ik Eskimo men and women are presented in Table 1. Yup’ik women in this study had a mean age of 37.6 (± 17.3) years, and men reported a mean age of 35.9 (±17.3) years. Women had significantly greater body mass index (BMI), percentage body fat (PBF), hip circumference (HC), fasting total cholesterol, HDL-cholesterol, and ApoA1 levels compared with men (P < 0.05). According to the standard cutoff points for overweight (BMI = 25–29.9 kg/m2) and obese (BMI ≥ 30 kg/m2), 28.6% of women and 30.7% of men were overweight, whereas 37.0% of women and 7.9% of the men were classified as obese.
      TABLE 1Descriptive statistics of obesity-related traits in Yup'ik Eskimos
      WomenMenP
      Variables
       No. of participants601539
       Age (yr)37.6 ± 17.335.9 ± 17.40.1113
       Height (cm)156.1 ± 6.2167.7 ± 7.0<0.0001
       Weight (kg)69.8 ± 16.773.0 ± 15.60.0003
      Obesity measures
       BMI (kg/m)28.7 ± 6.825.9 ± 4.8<0.001
       Percentage body fat (%)35.1 ± 8.921.1 ± 8.0<0.0001
       Waist circumference (cm)90.4 ± 15.989.2 ± 14.00.2102
       Hip circumference (cm)104.1 ± 12.896.7 ± 8.3<0.0001
       Thigh circumference (cm)51.1 ± 5.650.2 ± 5.40.0071
      Lipid measures
       Cholesterol (mg/dl)216.4 ± 44.6208.9 ± 48.00.0088
       HDL (mg/dl)64.8 ± 18.556.2 ± 15.4<0.0001
       Apolipoprotein A-I (mg/dl)170.8 ± 26.7159.5 ± 26.8<0.0001
       LDL (mg/dl)134.9 ± 36.5135.9 ± 40.20.5500
       VLDL (mg/dl)16.9 ± 8.817.3 ± 10.60.4760
       Triglyceride (mg/dl)83.6 ± 42.884.8 ± 52.50.5967
      Values are mean ± SD. Differences by gender are derived using Student t-test.

       Distribution of delta15N in study population

      In 1,138 Yup'ik Eskimo participants, n-3 PUFA intake was assessed using RBC δ15N as a biomarker of EPA and DHA intake. Summary statistics grouped by gender and δ15N quartiles are reported in Table 2. The mean δ15N value was 9.0‰ with a range of 6.2–15.2‰. This range was large relative to analytical precision (0.2‰) and was 3.75 times greater than the RBC (clot) δ15N values previously reported for a random sample of US residents (
      • Kraft R.A.
      • Jahren A.H.
      • Saudek C.D.
      Clinical-scale investigation of stable isotopes in human blood: delta13C and delta15N from 406 patients at the Johns Hopkins Medical Institutions.
      ). According to the linear relationship between RBC δ15N and RBC EPA reported elsewhere for this population (
      • O'Brien D.M.
      • Kristal A.R.
      • Jeannet M.A.
      • Wilkinson M.J.
      • Bersamin A.
      • Luick B.
      Red blood cell delta15N: a novel biomarker of dietary eicosapentaenoic acid and docosahexaenoic acid intake.
      ), the corresponding mean EPA (% RBC fatty acids) was 2.66% with a range of ∼0–9.1%. Measurement of δ15N by gender yielded means of 9.1‰ for females and 8.8‰ for males. The mean RBC δ15N values by quartile were 7.3‰, 8.2‰, 9.1‰, and 11.0‰ in quartiles 1–4, respectively. These values correspond to EPA (% RBC fatty acids) quartile means of: 0.9%, 1.8%, 2.8%, and 4.7% (
      • O'Brien D.M.
      • Kristal A.R.
      • Jeannet M.A.
      • Wilkinson M.J.
      • Bersamin A.
      • Luick B.
      Red blood cell delta15N: a novel biomarker of dietary eicosapentaenoic acid and docosahexaenoic acid intake.
      ). The standard deviation of δ15N in this sample did not differ by gender (1.5‰ for both females and males).
      TABLE 2.Distribution of the RBC nitrogen stable isotope ratio (δ15N), a concentration biomarker for long chain n-3 polyunsaturated fatty acid (n-3 PUFA) intake in Yup'ik Eskimos
      SexQuartiles of δ15N
      The relationship between δ15N and EPA follows the linear model: EPA (%RBC fatty acid) = 1.04 ·δ15N – 6.7‰, as previously described for this population (37).
      TotalWomenMenQ1Q2Q3Q4
      No. of participants1138598540272278290298
      Mean ± SD (‰)9.0 ± 1.59.1 ± 1.58.8 ± 1.57.3 ± 0.38.2 ± 0.29.1 ± 0.311.0 ± 1.0
      Maximum15.215.213.57.88.69.815.2
      Minimum6.26.36.26.27.88.69.8
      Range (‰)9.08.97.31.60.811.25.4
      Isotope ratios are presented as delta values in “permil” relative to atmospheric nitrogen: δ15N = [(15N/14Nsample15N/14Nstandard)/(15N/14Nstandard)] · 1000‰.
      a The relationship between δ15N and EPA follows the linear model: EPA (%RBC fatty acid) = 1.04 ·δ15N – 6.7‰, as previously described for this population (
      • O'Brien D.M.
      • Kristal A.R.
      • Jeannet M.A.
      • Wilkinson M.J.
      • Bersamin A.
      • Luick B.
      Red blood cell delta15N: a novel biomarker of dietary eicosapentaenoic acid and docosahexaenoic acid intake.
      ).

       Genetic variation in the CPT1A gene

      DNA was available in 1,141 Yup’ik Eskimo participants, and the mean number of individuals successfully genotyped was 1,078 (range of 986–1,137, depending on the SNP). Twenty-eight CPT1A SNPs were genotyped with a mean success rate of 94.7% (range 76.1–99.7%). In this sample, 4 SNPs were monomorphic, 12 SNPs had MAF < 0.05 and MAF > 0.01, and 12 SNPs had MAF ≥ 0.05. Genotyping results for SNPs with MAF ≥ 0.05 are presented in Table 3. The rs2924697 SNP (MAF = 0.28) was the only polymorphism with MAF ≥ 0.05 that deviated significantly from Hardy-Weinberg proportions and was excluded from the analysis. The nonsynonymous P479L SNP was common in our sample, and the major L479 allele had a frequency of 0.74. We selected the 11 CPT1A SNPs with MAF ≥ 0.05 that did not deviate from HWE proportions for genetic analysis (Table 3). The spectral decomposition of LD matrix (
      • Nyholt D.R.
      A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other.
      ) estimated that 8 of the 11 markers with MAF ≥ 0.05 were nonredundant genetic markers, and we corrected our analysis for eight tests, setting the per-test α level to <0.0063 (two-tailed).
      TABLE 3.SNPs genotyped within and near the CPT1A gene with MAF ≥ 0.05
      SNP
      Seattle SNPs Genome Variation Server on March 2008 (dbSNP build 126) Version 5.01.
      Allele
      Major/ Minor allele.
      MAF
      MAF computed using FREQ module in S.A.G.E.
      GenotypeIndividuals Genotyped
      Number of individuals genotyped for each CPT1A SNP.
      HWE P
      Computed using PLINK.
      AAABBB
      rs2278908C/T0.06240102410660.1004
      rs2278907A/G0.09954107411370.0270
      rs3019598G/A0.05237101110500.0664
      P479L (rs80356779)A/G
      The P479L SNP is an A→G missense mutation at nucleotide position c.1436 of CPT1A, which results in the substitution of a conserved proline (P479) for a leucine (L479) at position 479 in the CPT1A polypeptide.
      0.264427275910750.1080
      rs2305508C/T0.4715850740710720.7469
      rs4930248T/C0.10108897410720.0369
      rs3794020C/T0.3410344746110111
      rs2924697G/C0.2815334529860
      rs11228372G/A0.14715890610711
      rs11228373G/C0.202719291311320.0077
      rs3019594C/T0.212618586110720.0120
      rs597316G/C0.05142102610690.4096
      AA, homozygous recessive for minor allele; AB, heterozygous; BB, homozygous dominant allele.
      a Seattle SNPs Genome Variation Server on March 2008 (dbSNP build 126) Version 5.01.
      b Major/ Minor allele.
      c MAF computed using FREQ module in S.A.G.E.
      d Number of individuals genotyped for each CPT1A SNP.
      e Computed using PLINK.
      f The P479L SNP is an A→G missense mutation at nucleotide position c.1436 of CPT1A, which results in the substitution of a conserved proline (P479) for a leucine (L479) at position 479 in the CPT1A polypeptide.

       Association between fasting lipid parameters and CPT1A SNPs

      The results of association analysis between fasting lipid traits and CPT1A SNPs with MAF ≥ 0.05 are summarized in Table 4. HDL-cholesterol was significantly associated with seven SNPs: rs2278908 (P = 0.0007, β=−2.3, SE = 0.7), rs3019598 (P = 0.0014, β=−2.2, SE = 0.7), P479L (P = 0.0001, β=−1.0, SE = 0.3), rs11228372 (P = 0.0013, β=−1.2, SE = 0.4), rs11228373 (P < 0.0001, β=−1.3, SE = 0.3), rs3019594 (P < 0.0001, β=−1.4, SE = 0.3), and rs597316 (P = 0.0014, β=−2.2, SE = 0.7). The rs11228373 and rs3019594 SNPs were also significantly associated with ApoA1 (P = 0.0014, β=−1.1, SE = 0.4 and P = 0.0008, β=−1.2, SE = 0.4, respectively) and total cholesterol (P = 0.0063, β=−0.7, SE = 0.2 and P = 0.0031, β=−0.7, SE = 0.2, respectively) (Table 4). Note that rs11228373 and rs3019594 are in moderately strong LD (r2= 0.75). The P479L variant was also associated with HDL-cholesterol (P = 0.0001) and was not in strong LD with either the rs11228373 (r2= 0.58) or rs3019594 (r2= 0.61) SNP (supplementary Table I). Our model predicted that individuals homozygous for the common allele (L479) of P479L had elevated fasting HDL-cholesterol levels compared with individuals homozygous for the P479L minor allele (P479). After adjusting Model 2 for BMI, the CPT1A SNPs (rs2278908, rs3019598, P479L, rs11228373, rs3019594, and rs597316) associated with fasting total cholesterol, HDL-cholesterol, ApoA1 were still significant (supplementary Table II).
      TABLE 4Association of CPT1A SNPs with fasting lipid phenotypes
      Lipid Measures
      SNPCholHDLApoA1LDLVLDLTG
      rs22789080.08420.00070.05010.91240.94490.8209
      (β=−0.9, SE = 0.5)(β=2.3, SE = 0.7)(β=−1.5, SE = 0.8)(β=−0.1, SE = 0.7)(β=−0.1, SE = 0.8)(β= 0.2, SE = 0.8)
      rs22789070.14780.03350.48930.73160.45510.7217
      (β=−0.6, SE = 0.4)(β=−1.0, SE = 0.5)(β=−0.4, SE = 0.6)(β=−0.2, SE = 0.5)(β=−0.4, SE = 0.6)(β=−0.2, SE = 0.5)
      rs30195980.13570.00140.23820.95880.90730.7069
      (β=−0.8, SE = 0.6)(β=2.2, SE = 0.7)(β=−1.0, SE = 0.8)(β= 0.0, SE = 0.7)(β= 0.1, SE = 0.8)(β= 0.3, SE = 0.8)
      P479L (rs80356779)0.08340.00010.00770.66290.63550.1407
      (β=−0.4, SE = 0.2)(β=1.0, SE = 0.3)(β=−0.8, SE = 0.3)(β=−0.1, SE = 0.3)(β= 0.1, SE = 0.3)(β= 0.4, SE = 0.3)
      rs23055080.03210.60520.05200.02500.70300.2284
      (β=−0.4, SE = 0.2)(β= 0.1, SE = 0.2)(β= 0.5, SE = 0.2)(β=−0.5, SE = 0.2)(β=−0.1, SE = 0.2)(β=−0.3, SE = 0.2)
      rs49302480.09120.28340.92450.44130.87350.9755
      (β=−0.6, SE = 0.4)(β=−0.5, SE = 0.4)(β= 0.1, SE = 0.5)(β=−0.3, SE = 0.4)(β=−0.1, SE = 0.5)(β= 0.0, SE = 0.5)
      rs37940200.55390.22610.01320.22440.75300.6833
      (β=−0.1, SE = 0.2)(β= 0.3, SE = 0.2)(β= 0.7, SE = 0.3)(β=−0.3, SE = 0.2)(β= 0.1, SE = 0.3)(β=−0.1, SE = 0.3)
      rs112283720.01390.00130.01500.29820.69860.6246
      (β=−0.7, SE = 0.3)(β=1.2, SE = 0.4)(β=−1.1, SE = 0.4)(β=−0.4, SE = 0.4)(β=−0.2, SE = 0.4)(β= 0.2, SE = 0.4)
      rs112283730.0063<0.00010.00140.30950.81990.3369
      (β=0.7, SE = 0.2)(β=1.3, SE = 0.3)(β=1.1, SE = 0.4)(β=−0.3, SE = 0.3)(β=−0.1, SE = 0.4)(β= 0.3, SE = 0.3)
      rs30195940.0031<0.00010.00080.23770.65160.1691
      (β=0.7, SE = 0.2)(β=1.4, SE = 0.3)(β=1.2, SE = 0.4)(β=−0.4, SE = 0.3)(β= 0.2, SE = 0.4)(β= 0.5, SE = 0.3)
      rs5973160.27120.00140.04490.82130.25000.0417
      (β=−0.6, SE = 0.6)(β=2.2, SE = 0.7)(β=−1.6, SE = 0.8)(β=−0.2, SE = 0.7)(β= 0.9, SE = 0.8)(β= 1.6, SE = 0.8)
      Association of CPT1A SNPs in a linear regression model adjusted for age, sex, community membership, and n-3 PUFA intake. Estimates of effect size (β) are reported using transformed phenotypes. Results are significant at P < 0.0063 (highlighted in bold). Multiple-test correction for eight tests for a phenotype was estimated using the spectral decomposition of LD matrix (
      • Nyholt D.R.
      A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other.
      ).
      ApoA1, apolipoprotein A-I; Chol, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; VLDL very low density lipoprotein.

       Association between CPT1A SNPs and obesity phenotypes

      The results of association analysis between obesity traits and CPT1A SNPs with MAF ≥ 0.05 are summarized in Table 5. Thigh circumference (ThC) was associated with seven SNPs: rs2278908 (P = 0.0024, β= 2.1, SE = 0.7), rs2278907 (P = 0.0002, β= 1.9, SE = 0.5), P479L (P < 0.0001, β= 1.2, SE = 0.3), rs4930248 (P = 0.0042, β= 1.3, SE = 0.5), rs11228372 (P = 0.0011, β= 1.3, SE = 0.4), rs11228373 (P = 0.0006, β= 1.1, SE = 0.3), and rs3019594 (P = 0.0001, β= 1.3, SE = 0.3). Hip circumference was associated with five SNPs: rs2278907 (P = 0.0057, β= 1.2, SE = 0.4), P479L (P < 0.0001, β= 0.9, SE = 0.2), rs11228372 (P = 0.0034, β= 0.9, SE = 0.3), rs11228373 (P = 0.0063, β= 0.7, SE = 0.3), and rs3019594 (P = 0.0005, β= 0.9, SE = 0.3).
      TABLE 5Association of CPT1A SNPs with obesity phenotypes
      Obesity Measures
      SNPBMIPBFHCThCWC
      rs22789080.51370.40060.01790.00240.5103
      (β= 0.4, SE = 0.6)(β= 0.4, SE = 0.5)(β= 1.4, SE = 0.6)(β= 2.1, SE = 0.7)(β= 0.5, SE = 0.8)
      rs22789070.37780.43390.00570.00020.6956
      (β= 0.4, SE = 0.42)(β= 0.3, SE = 0.4)(β= 1.2, SE = 0.4)(β= 1.9, SE = 0.5)(β= 0.2, SE = 0.5)
      rs30195980.76910.69300.05780.00890.9006
      (β= 0.2, SE = 0.6)(β= 0.2,SE = 0.5)(β= 1.1, SE = 0.6)(β= 1.9, SE = 0.7)(β= 0.1, SE = 0.8)
      P479L (rs80356779)0.00210.0007<0.0001<0.00010.0006
      (β= 0.7, SE = 0.2)(β= 0.7, SE = 0.2)(β= 0.9, SE = 0.2)(β= 1.2, SE = 0.3)(β= 1.0, SE = 0.3)
      rs23055080.71080.63260.76040.17010.4912
      (β=−0.1, SE = 0.2)(β=−0.1, SE = 0.2)(β= 0.1, SE = 0.2)(β= 0.3, SE = 0.2)(β=−0.2, SE = 0.2)
      rs49302480.23690.18740.01910.00420.1503
      (β= 0.4, SE = 0.4)(β= 0.4, SE = 0.3)(β= 0.9, SE = 0.4)(β= 1.3, SE = 0.5)(β= 0.7, SE = 0.5)
      rs37940200.43860.22760.38970.87630.1390
      (β=−0.2, SE = 0.2)(β=−0.2, SE = 0.2)(β=−0.2, SE = 0.2)(β= 0.0, SE = 0.2)(β=−0.4, SE = 0.3)
      rs112283720.02770.02440.00340.00110.0294
      (β= 0.7, SE = 0.3)(β= 0.6, SE = 0.3)(β= 0.9, SE = 0.3)(β= 1.3, SE = 0.4)(β= 0.9, SE = 0.4)
      rs112283730.16050.25170.00630.00060.1095
      (β= 0.4, SE = 0.3)(β= 0.3, SE = 0.2)(β= 0.7, SE = 0.3)(β= 1.1, SE = 0.3)(β= 0.5, SE = 0.3)
      rs30195940.02940.03120.0005<0.00010.0154
      (β= 0.6, SE = 0.3)(β= 0.5, SE = 0.2)(β= 0.9, SE = 0.3)(β= 1.3, SE = 0.3)(β= 0.8, SE = 0.3)
      rs5973160.33420.60790.07310.01610.2518
      (β= 0.6, SE = 0.6)(β= 0.3, SE = 0.5)(β= 1.1, SE = 0.6)(β= 1.8, SE = 0.7)(β= 0.9, SE = 0.8)
      Association of CPT1A SNPs in a linear regression model adjusted for age, sex, community membership, and n-3 PUFA intake. Estimates of effect size (β) are reported using transformed phenotypes. Results are significant at P < 0.0063 (highlighted in bold). Multiple-test correction for eight tests for a phenotype was estimated using the spectral decomposition of LD matrix (
      • Nyholt D.R.
      A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other.
      ).
      The P479L SNP and rs3019594 (r2= 0.61 between these SNPs) were SNPs most significantly associated with both thigh circumference and hip circumference (P < 0.0001 and P = 0.0034, respectively). The P479L SNP was the only SNP associated with all obesity measures, which included BMI (P = 0.0021, β= 0.7, SE = 0.2), percentage body fat (P = 0.0007, β= 0.0007, SE = 0.2), hip circumference (P < 0.0001, β= 0.9, SE = 0.2), thigh circumference (P < 0.0001, β= 1.2, SE = 0.3), and waist circumference (WC; P = 0.0006, β= 1.0, SE = 0.3). Individuals homozygous for the common P479L allele (L479) had a lower percentage body fat, smaller BMI, and reduced thigh, hip, and waist circumferences compared with P479 homozygotes (Table 6).
      TABLE 6Obesity-related trait distribution within P479L (rs80356779) genotypes in Yup'ik Eskimos people
      Obesity MeasuresL479/L479L479/P479P479/P479P
       BMI (kg/m)26.2 (24.4–28.1)28.0 (25.3–31.3)31.1 (26.2–37.8)0.0021
       Percentage body fat (%)27.8 (24.9–30.7)30.0 (25.8–34.3)33.7 (26.8–41.0)0.0007
       Waist circumference (cm)87.1 (82.7–91.9)91.9 (85.2–99.8)100.4 (88.1–116.4)0.0006
       Hip circumference (cm)98.2 (95.1–101.7)101.6 (96.8–107.3)106.6 (98.0–118.2)<0.0001
       Thigh circumference (cm)49.8 (48.0–51.7)53.4 (50.6–56.4)57.9 (52.9–63.5)<0.0001
      Lipid MeasuresL479/L479L479/P479P479/P479P
       Cholesterol (mg/dl)211.0 (197.4–225.6)199.6 (181.6–219.8)193.9 (166.5–266.8)0.0834
       HDL (mg/dl)58.3 (53.3–64.1)53.3 (47.2–60.6)49.6 (41.1–60.7)0.0001
       Apolipoprotein A-I (mg/dl)164.2 (155.2–173.9)156.1 (144.2–169.1)150.1 (132.4–170.5)0.0077
       LDL (mg/dl)134.6 (122.4–147.8)128.1 (111.6–146.4)125.0 (99.7–155.1)0.6629
       VLDL (mg/dl)15.1 (12.9–18.0)15.8 (12.5–20.4)17.2 (11.8–26.9)0.6355
       Triglyceride (mg/dl)71.7 (61.7–84.2)75.4 (60.9–95.5)83.0 (58.6–125.1)0.1407
      Values are reported as predicted mean (95% CI) obtained from ASSOC output. Association of the P479L (L479>P479) minor allele in the linear regression model adjusted for age, sex, community membership, and n-3 PUFA intake. Results are significant at P < 0.0063 (highlighted in bold). Multiple-test correction for eight tests for a phenotype was estimated using the spectral decomposition of LD matrix (
      • Nyholt D.R.
      A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other.
      ).

      DISCUSSION

      CPT1A has been implicated as candidate obesity gene in a meta-analysis of whole-genome linkage studies (
      • Saunders C.L.
      • Chiodini B.D.
      • Sham P.
      • Lewis C.M.
      • Abkevich V.
      • Adeyemo A.A.
      • de Andrade M.
      • Arya R.
      • Berenson G.S.
      • Blangero J.
      • et al.
      Meta-analysis of genome-wide linkage studies in BMI and obesity.
      ); however, the contribution of CPT1A polymorphisms to variation in the metabolic consequences of obesity and obesity phenotypes remains unclear. Our results demonstrate that CPT1A polymorphisms are associated with obesity and fasting lipid phenotypes in this Yup'ik Eskimo study population and may influence the “healthy obesity” phenotype. Specifically, the P479L SNP was associated with all measures of body composition (BMI, PBF, HC, ThC, and WC) and fasting HDL-cholesterol levels. We found that individuals homozygous for the major L479 allele of the P479L variant had reduced body fat and central adiposity relative to individuals homozygous for the minor P479 allele. These data indicate that individuals carrying both copies of the L479 allele of the nonsynonomous P479L variant in CPT1A have reduced adiposity and elevated HDL-cholesterol, even after controlling for BMI. Interestingly, when we investigated whether the P479L association with HDL was mediated by other obesity phenotypes, we found the L479 allele was still significantly associated with HDL-cholesterol after correction for either PBF or WC (data not shown). We hypothesize that the L479 allele may contribute to “healthy obesity” observed in Yup'ik Eskimo people by modulating hepatic lipid oxidation.
      Three studies have previously investigated the influence of CPT1A polymorphisms on obesity and lipid phenotypes in humans (
      • Rajakumar C.
      • Ban M.R.
      • Cao H.
      • Young T.K.
      • Bjerregaard P.
      • Hegele R.A.
      Carnitine palmitoyltransferase IA polymorphism P479L is common in Greenland Inuit and is associated with elevated plasma apolipoprotein A-I.
      ,
      • Hirota Y.
      • Ohara T.
      • Zenibayashi M.
      • Kuno S.
      • Fukuyama K.
      • Teranishi T.
      • Kouyama K.
      • Miyake K.
      • Maeda E.
      • Kasuga M.
      Lack of association of CPT1A polymorphisms or haplotypes on hepatic lipid content or insulin resistance in Japanese individuals with type 2 diabetes mellitus.
      ,
      • Robitaille J.
      • Houde A.
      • Lemieux S.
      • Pérusse L.
      • Gaudet D.
      • Vohl M-C.
      Variants within the muscle and liver isoforms of the carnitine palmitoyltransferase I (CPT1) gene interact with fat intake to modulate indices of obesity in French-Canadians.
      ). Hirota and colleagues (
      • Robitaille J.
      • Houde A.
      • Lemieux S.
      • Pérusse L.
      • Gaudet D.
      • Vohl M-C.
      Variants within the muscle and liver isoforms of the carnitine palmitoyltransferase I (CPT1) gene interact with fat intake to modulate indices of obesity in French-Canadians.
      ) found no association between CPT1A polymorphisms and obesity or fasting lipid phenotypes in Japanese individuals with T2D. In a cross-sectional cohort of French-Canadians, Robitaille et al. reported an association between the nonsynonymous A275T (rs17610395) SNP with BMI (P = 0.05) and waist circumference (P = 0.008) only after accounting for dietary fat intake (
      • Hirota Y.
      • Ohara T.
      • Zenibayashi M.
      • Kuno S.
      • Fukuyama K.
      • Teranishi T.
      • Kouyama K.
      • Miyake K.
      • Maeda E.
      • Kasuga M.
      Lack of association of CPT1A polymorphisms or haplotypes on hepatic lipid content or insulin resistance in Japanese individuals with type 2 diabetes mellitus.
      ). Finally, in Greenland Inuit, Rajakumar et al. showed the L479 allele in the nonsynonymous P479L variant was associated with elevated fasting HDL-cholesterol and ApoA1 levels (
      • Rajakumar C.
      • Ban M.R.
      • Cao H.
      • Young T.K.
      • Bjerregaard P.
      • Hegele R.A.
      Carnitine palmitoyltransferase IA polymorphism P479L is common in Greenland Inuit and is associated with elevated plasma apolipoprotein A-I.
      ).
      The present study found an association between SNPs (rs2278908, rs3019598, and rs597316) investigated by Hirota et al. with fasting HDL-cholesterol and replicated the P479L association with fasting HDL-cholesterol and ApoA1 levels reported by Rajakumar et al. We have shown that these SNPs were still significantly associated with HDL-cholesterol and ApoA1 after controlling for BMI, PBF, or WC. Furthermore, we used a log likelihood ratio test to determine whether the P479L SNP association with HDL-cholesterol and ApoA1 was independent of the rs11228373 and rs3019594 SNPs. We found that both rs11228373 and rs3019594 SNPs were still significant predictors for HDL-cholesterol and ApoA1 levels, even when P479L was already in the model. Given that the rs11228373 and rs3019594 SNPs were in moderately strong linkage disequilibrium with P479L as measured by r2 (r2= 0.58 and 0.61, respectively), we cannot rule out the possibility that the apparent association with P479L is not due to a true association with either rs11228373 or rs3019594 or both.
      We extend the findings of Rajakumar et al. to show the L479 allele of P479L is also associated with reduced adiposity in an independent Eskimo/Inuit population. Interestingly, our analysis did not replicate the A275T (rs17610395) association with BMI and WC reported by Robitaille et al. because this SNP was not included in the analysis due to a low MAF (MAF = 0.02). Factors that may account for differences in results reported in the present study may include, but are not limited to, differences in statistical analysis, small sample size, and population stratification (
      • Redden D.T.
      • Allison D.B.
      Nonreplication in genetic association studies of obesity and diabetes research.
      ). Our study, however, benefited from a sample size large enough to detect significant SNP associations, and we used a statistical approach that accounts for family structure while allowing for covariates.
      Our Yup'ik Eskimo study population was ideally suited to investigate the contribution of n-3 PUFA and genetic factors to “healthy obesity” due to the 30-fold range of EPA and DHA consumption (
      • Bersamin A.
      • Luick B.R.
      • King I.B.
      • Stern J.S.
      • Zidenberg-Cherr S.
      Westernizing diets influence fat intake, red blood cell fatty acid composition, and health in remote Alaskan Native communities in the Center for Alaska Native Health Study.
      ), which can be precisely estimated in large samples using nitrogen stable isotope ratios from red blood cell samples (
      • O'Brien D.M.
      • Kristal A.R.
      • Jeannet M.A.
      • Wilkinson M.J.
      • Bersamin A.
      • Luick B.
      Red blood cell delta15N: a novel biomarker of dietary eicosapentaenoic acid and docosahexaenoic acid intake.
      ). When we examined whether the interaction between n-3 PUFA intake and CPT1A SNPs modifies the association with “healthy obesity” phenotypes (supplementary Table III), we did not find significant gene-diet interactions that modified the association with body composition. Interestingly, interactions between n-3 PUFA intake and rs3794020 and rs2305508 were associated with HDL-cholesterol and ApoA1 levels, whereby n-3 PUFA intake enhanced the positive association of rs3794020 and rs2305508 minor alleles on these traits. Although n-3 PUFA interactions with genetic factors have received considerable attention, our results should be interpreted with caution given the sample size and nominal significance.
      We hypothesize that the observed association between the P479L variant on body composition and fasting lipid phenotypes in the presence of n-3 PUFA intake may, in part, explain the presence of a “healthy obese” phenotype among Yup'ik Eskimos. In humans, low rates of endogenous lipid oxidation are associated with obesity (
      • Zurlo F.
      • Lillioja S.
      • Esposito-Del Puente A.
      • Nyomba B.L.
      • Raz I.
      • Saad M.F.
      • Swinburn B.A.
      • Knowler W.C.
      • Bogardus C.
      • Ravussin E.
      Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24-h RQ.
      ), and mechanisms that alter an individual's metabolic profile in favor of fatty acid oxidation have been suggested to reduce the accumulation of body fat (
      • Buckley J.D.
      • Howe P.R.C.
      Anti-obesity effects of long-chain omega-3 polyunsaturated fatty acids.
      ). Consumption of n-3 PUFA increases hepatic fatty acid β-oxidation, primarily through activity of CPT1A (
      • Ide T.
      • Kobayashi H.
      • Ashakumary L.
      • Rouyer I.A.
      • Takahashi Y.
      • Aoyama T.
      • Hashimoto T.
      • Mizugaki M.
      Comparative effects of perilla and fish oils on the activity and gene expression of fatty acid oxidation enzymes in rat liver.
      ,
      • Ide T.
      • Murata M.
      • Sugano M.
      Stimulation of the activities of hepatic fatty acid oxidation enzymes by dietary fat rich in alpha-linolenic acid in rats.
      ). Functional studies in fibroblast cells have demonstrated that the L479 allele of P479L variant in CPT1A results in a CPT1A enzyme with diminished catalytic activity compared with control cells (
      • Brown N.F.
      • Mullur R.S.
      • Subramanian I.
      • Esser V.
      • Bennett M.J.
      • Saudubray J-M.
      • Feigenbaum A.S.
      • Kobari J.A.
      • Macleod P.M.
      • McGarry J.D.
      • et al.
      Molecular characterization of L-CPT I deficiency in six patients: insights into function of the native enzyme.
      ). However, expression of the L479 allele in fibroblasts was also shown to abolish the ability of malonyl-CoA to inhibit CPT1A (
      • Brown N.F.
      • Mullur R.S.
      • Subramanian I.
      • Esser V.
      • Bennett M.J.
      • Saudubray J-M.
      • Feigenbaum A.S.
      • Kobari J.A.
      • Macleod P.M.
      • McGarry J.D.
      • et al.
      Molecular characterization of L-CPT I deficiency in six patients: insights into function of the native enzyme.
      ). Interestingly, these data are consistent with a study in rats demonstrating that malonyl-CoA-insensitive CPT1A was more effective than overexpression of wild-type CPT1A at oxidizing lipid substrates (
      • Akkaoui M.
      • Cohen I.
      • Esnous C.
      • Lenoir V.
      • Sournac M.
      • Girard J.
      • Prip-Buus C.
      Modulation of the hepatic malonyl-CoA-carnitine palmitoyltransferase 1A partnership creates a metabolic switch allowing oxidation of de novo fatty acids.
      ). Therefore, in the presence of n-3 PUFAs, there may be a net increase in the basal activity of CPT1A among individuals carrying the L479 allele, and fatty acids normally packaged in the liver as VLDL will instead be oxidized in the hepatocyte (
      • Greenberg C.R.
      • Dilling L.A.
      • Thompson G.R.
      • Seargeant L.E.
      • Haworth J.C.
      • Phillips S.
      • Chan A.
      • Vallance H.D.
      • Waters P.J.
      • Sinclair G.
      • et al.
      The paradox of the carnitine palmitoyltransferase type Ia P479L variant in Canadian Aboriginal populations.
      ). Taken together, we hypothesize that the combined effects of n-3 PUFA intake and the high frequency of the P479L variant in Eskimo/Inuit populations may influence “healthy obesity” phenotypes primarily through reduced hepatic VLDL formation and subsequent reductions of plasma triglycerides and VLDL. This model is consistent with our observations that obese Yup'ik Eskimo people with high intake of n-3 PUFAs have low triglyceride levels, reduced c-reactive protein levels (
      • Makhoul Z.
      • Kristal A.R.
      • Gulati R.
      • Luick B.
      • Bersamin A.
      • O'Brien D.
      • Hopkins S.E.
      • Stephensen C.B.
      • Stanhope K.L.
      • Havel P.J.
      • et al.
      Associations of obesity with triglycerides and C-reactive protein are attenuated in adults with high red blood cell eicosapentaenoic and docosahexaenoic acids.
      ), and high circulating HDL-cholesterol levels (
      • Makhoul Z.
      • Kristal A.R.
      • Gulati R.
      • Luick B.
      • Bersamin A.
      • Boyer B.B.
      • Mohatt G.V.
      Associations of very high intakes of eicosapentaenoic and docosahexaenoic acids with biomarkers of chronic disease risk among Yup'ik Eskimos.
      ), suggesting that n-3 PUFAs may protect from chronic disease in the presence of obesity.
      CPT1A deficiency has been associated with risk for hypoketotic hypoglycemia, hepatic encephalopathy, and sudden infant death syndrome (
      • Bennett M.J.
      • Narayan S.B.
      • Santani A.B.
      Carnitine palmitoyltransferase 1A deficiency.
      ,
      • Brivet M.
      • Boutron A.
      • Slama A.
      • Costa C.
      • Thuillier L.
      • Demaugre F.
      • Rabier D.
      • Saudubray J.M.
      • Bonnefont J-P.
      Defects in activation and transport of fatty acids.
      ,
      • Bougnères P.F.
      • Saudubray J.M.
      • Marsac C.
      • Bernard O.
      • Odièvre M.
      • Girard J.
      Fasting hypoglycemia resulting from hepatic carnitine palmitoyl transferase deficiency.
      ,
      • Prasad C.
      • Johnson J.P.
      • Bonnefont J-P.
      • Dilling L.A.
      • Innes A.M.
      • Haworth J.C.
      • Beischel L.
      • Thuillier L.
      • Prip-Buus C.
      • Singal R.
      • et al.
      Hepatic carnitine palmitoyl transferase 1 (CPT1 A) deficiency in North American Hutterites (Canadian and American): evidence for a founder effect and results of a pilot study on a DNA-based newborn screening program.
      ), as well as muscle cramps, vomiting, and occasional loss of consciousness (
      • Brown N.F.
      • Mullur R.S.
      • Subramanian I.
      • Esser V.
      • Bennett M.J.
      • Saudubray J-M.
      • Feigenbaum A.S.
      • Kobari J.A.
      • Macleod P.M.
      • McGarry J.D.
      • et al.
      Molecular characterization of L-CPT I deficiency in six patients: insights into function of the native enzyme.
      ,
      • Prasad C.
      • Johnson J.P.
      • Bonnefont J-P.
      • Dilling L.A.
      • Innes A.M.
      • Haworth J.C.
      • Beischel L.
      • Thuillier L.
      • Prip-Buus C.
      • Singal R.
      • et al.
      Hepatic carnitine palmitoyl transferase 1 (CPT1 A) deficiency in North American Hutterites (Canadian and American): evidence for a founder effect and results of a pilot study on a DNA-based newborn screening program.
      ). Nevertheless, the high frequency of the L479 allele in Inuit and Yup’ik Eskimo people suggested to us and several others that it may confer a selective advantage (
      • Rajakumar C.
      • Ban M.R.
      • Cao H.
      • Young T.K.
      • Bjerregaard P.
      • Hegele R.A.
      Carnitine palmitoyltransferase IA polymorphism P479L is common in Greenland Inuit and is associated with elevated plasma apolipoprotein A-I.
      ,
      • Greenberg C.R.
      • Dilling L.A.
      • Thompson G.R.
      • Seargeant L.E.
      • Haworth J.C.
      • Phillips S.
      • Chan A.
      • Vallance H.D.
      • Waters P.J.
      • Sinclair G.
      • et al.
      The paradox of the carnitine palmitoyltransferase type Ia P479L variant in Canadian Aboriginal populations.
      ,
      • Gessner B.D.
      • Gillingham M.B.
      • Johnson M.A.
      • Richards C.S.
      • Lambert W.E.
      • Sesser D.
      • Rien L.C.
      • Hermerath C.A.
      • Skeels M.R.
      • Birch S.
      • et al.
      Prevalence and distribution of the c.1436C→T sequence variant of carnitine palmitoyltransferase 1A among Alaska Native infants.
      ,
      • Collins S.A.
      • Sinclair G.
      • McIntosh S.
      • Bamforth F.
      • Thompson R.
      • Sobol I.
      • Osborne G.
      • Corriveau A.
      • Santos M.
      • Hanley B.
      • et al.
      Carnitine palmitoyltransferase 1A (CPT1A) P479L prevalence in live newborns in Yukon, Northwest Territories, and Nunavut.
      ). We hypothesized that genetic variants in CPT1A may be associated with obesity because of the central role of the CPT1A enzyme in fatty acid oxidation. Our results and those of Rajakumar and colleagues (
      • Rajakumar C.
      • Ban M.R.
      • Cao H.
      • Young T.K.
      • Bjerregaard P.
      • Hegele R.A.
      Carnitine palmitoyltransferase IA polymorphism P479L is common in Greenland Inuit and is associated with elevated plasma apolipoprotein A-I.
      ) are consistent with a cardioprotective role of the L479 allele of P479L through its association with elevated HDL-cholesterol levels. In this study, we have also shown that genetic variants of CPT1A are associated with reduced adiposity, and we have replicated the association of elevated fasting HDL-cholesterol and ApoA1 levels with carriers of the L479 allele in this Yup’ik Eskimo study population. Furthermore, we found that CPT1A SNPs associated with HDL-cholesterol and ApoA1 levels were independent of obesity as measured by BMI, PBF, and WC. The P479L variant was not in strong LD (r2 > 0.8) with any other CPT1A polymorphisms associated with body composition and fasting lipid parameters, suggesting that the P479L may have a causal role in “healthy obesity.” Although we cannot exclude the possibility that other variants are in strong LD with the P479L, our data suggest that the P479L variant in CPT1A increases hepatic fatty acid oxidation and may contribute to “healthy obesity” observed in this Yup’ik Eskimo study population. Functional genomic studies of the CPT1A variant and its modulation by n-3 PUFA intake, in addition to further investigation of the CPT1A gene in epidemiological studies among Arctic populations with variable n-3 PUFA intake, will be required to validate the larger public health impact of these results. This study lays the foundation for future population-specific dietary recommendations based on gene-diet interactions.

      Acknowledgments

      The authors thank the community field research assistants who helped with the study recruitment and data collection. The CANHR team expresses sincere appreciation to all study participants and their communities for welcoming and teaching them so much about the Yup'ik way of life.

      Supplementary Material

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