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Research Article

Genetic Modulation of Lipid Profiles following Lifestyle Modification or Metformin Treatment: The Diabetes Prevention Program

  • Toni I. Pollin equal contributor,

    equal contributor Contributed equally to this work with: Toni I. Pollin, Tamara Isakova, Paul W. Franks

    Affiliation: Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, and Program in Genetics and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America

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  • Tamara Isakova equal contributor,

    equal contributor Contributed equally to this work with: Toni I. Pollin, Tamara Isakova, Paul W. Franks

    Affiliation: Division of Nephrology and Hypertension, Department of Medicine, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States of America

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  • Kathleen A. Jablonski,

    Affiliation: The Biostatistics Center, George Washington University, Rockville, Maryland, United States of America

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  • Paul I. W. de Bakker,

    Affiliations: Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America, Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands, Department of Epidemiology, University Medical Center Utrecht, Utrecht, The Netherlands

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  • Andrew Taylor,

    Affiliations: Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America, Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America

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  • Jarred McAteer,

    Affiliations: Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America, Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America

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  • Qing Pan,

    Affiliation: The Biostatistics Center, George Washington University, Rockville, Maryland, United States of America

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  • Edward S. Horton,

    Affiliations: Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America

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  • Linda M. Delahanty,

    Affiliations: Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America, Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Diabetes Clinic, Massachusetts General Hospital, Boston, Massachusetts, United States of America

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  • David Altshuler,

    Affiliations: Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America, Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America

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  • Alan R. Shuldiner,

    Affiliations: Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, and Program in Genetics and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, Maryland, United States of America

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  • Ronald B. Goldberg,

    Affiliations: Lipid Disorders Clinic, Division of Endocrinology, Diabetes, and Metabolism, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States of America, The Diabetes Research Institute, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States of America

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  • Jose C. Florez,

    Affiliations: Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America, Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America, Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America

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  • Paul W. Franks, for the Diabetes Prevention Program Research Group equal contributor mail,

    equal contributor Contributed equally to this work with: Toni I. Pollin, Tamara Isakova, Paul W. Franks

    paul.franks@med.lu.se (PWF); dppmail@biostat.bsc.gwu.edu (DPPRG)

    Affiliations: Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden, Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America

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  • George A. Bray

    Membership of the Diabetes Prevention Program Research Group is provided in the Acknowledgments.

    X
  • Published: August 30, 2012
  • DOI: 10.1371/journal.pgen.1002895

Abstract

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, Pinteraction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; Pinteraction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, Pinteraction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; Pinteraction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

Author Summary

The study included 2,993 participants from the Diabetes Prevention Program, a randomized clinical trial of intensive lifestyle intervention, metformin treatment, and placebo control. We examined associations between 32 gene variants that have been reproducibly associated with dyslipidemia and concentrations of lipids and NMR lipoprotein particle sizes and numbers. We also examined whether genetic background influences a person's response to cardioprotective interventions on lipid levels. Our analysis, which focused on determining whether common genetic variants impact the effects of cardioprotective interventions on lipid and lipoprotein particle size, shows that in persons with a high genetic risk score the benefit of intensive lifestyle intervention on LDL and small LDL particle levels is substantially diminished; this information may be informative for the targeted prevention of dyslipidemia, as it suggests that genetics might help identify persons in whom lifestyle intervention is likely to be an effective treatment for elevated lipids and lipoproteins. The NMR subfraction analyses provide novel insight into the biology of dyslipidemia by illustrating how numerous genetic variants that have previously been associated with lipid levels also modulate NMR lipoprotein particle sizes and number. This information may be informative for the targeted prevention of cardiovascular disease.

Introduction

Dyslipidemia is a strong risk factor for atherosclerotic heart disease [1][3], has a well-defined genetic basis [4], and is modifiable through therapeutic lifestyle changes and weight-loss interventions [5], [6]. Individuals at risk for diabetes are also at high risk of cardiovascular disease [7], and individualized lifestyle intervention programs, like the one incorporated into the Diabetes Prevention Program (DPP), have a salutary effect on dyslipidemia and cardiovascular disease risk in this population. However, the cost of widespread implementation of such interventions has been highlighted as a major limitation [8] and not all benefit equally from such interventions. Identifying persons most likely to benefit from intensive lifestyle modification could provide justification for targeting this subpopulation first, making the clinical translation of findings from studies such as the DPP more feasible.

Selection of persons whose dyslipidemia is likely to respond well to lifestyle interventions or pharmacotherapy could help target resources and optimize prevention strategies. To do so requires knowledge of the underlying risk factors for the trait and knowledge of how personal characteristics interact with exercise, diet, and weight loss. Although the heritability of polygenic dyslipidemia [9][11] and its sequelae [12] have been elucidated, little is known of how lifestyle interventions modify the effects of these loci, singly or in combination, on lipid profiles. Thus, learning how a person's genetic background modulates his or her response to therapeutic lifestyle changes and weight-loss interventions might help optimize the targeting of interventions designed to mitigate cardiovascular and metabolic disease risk.

The purpose of this study was to examine whether loci reliably associated with polygenic dyslipidemia modified the response to cardio-protective interventions in the DPP, a randomized clinical trial of intensive lifestyle modification, metformin treatment, or placebo with standard care. We hypothesized i) that the baseline lipid profiles of DPP participants would be associated with gene variants known to associate with polygenic dyslipidemia and ii) that improvement in lipidemia following treatment would depend on these same genetic variants. We also used NMR spectroscopy to characterize the associations of these previously reported loci with lipoprotein subfractions.

Results

Table 1 shows participant characteristics stratified by DPP treatment arm. The effects of the DPP interventions on 1 yr changes in weight [14], insulin secretion [13], beta-cell function [13], and lipid traits [29] are reported in detail elsewhere.

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Table 1. Baseline Characteristics of the Study Population by Treatment Group [Quantitative Traits Are Shown as Median (Interquartile Range)].

doi:10.1371/journal.pgen.1002895.t001

Individual SNP Replication

Thirty-two SNPs previously associated with triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C) and/or high-density lipoprotein-cholesterol (HDL-C) levels were considered [10]. Thirty-one of these were successfully genotyped in the DPP, and two SNPs in CETP, serving as HapMap proxies (r2≥0.90) for rs173539, including rs247616, were subsequently successfully genotyped, with rs247616 retained as the replacement for rs173539. Twenty-three of these 32 non-redundant SNPs replicated with their respective traits in a directionally consistent manner (P≤0.05), including 8/11 for TG, 9/14 for HDL-C and 8/11 for LDL-C. Two of the SNPs, rs12678919 and rs964184, replicated for both HDL-C and TG (Table S1).

Association of Individual SNPs with All Four Lipid Traits and Ten Lipoprotein Traits

Additionally, we evaluated the associations of the 32 lipid loci with baseline lipids and nuclear magnetic resonance (NMR)-derived lipoprotein traits (Large HDL particles, Small HDL particles, Total HDL particles, HDL size, LDL size, Total LDL particles, Small LDL particles, Total VLDL particles, Large VLDL particles, VLDL size). Of all analyses of baseline traits, roughly one third of the tests were nominally significant associations, and 35 associations were significant after correcting for all 448 hypothesis tests; these involved 12 SNPs and 13 traits (Table S2). Interestingly, SNP rs10401969 did not replicate for LDL-C (C vs. T: β±SEM = −0.1±1.6 mg/dl, additive P = 0.94), but was associated with decreased large VLDL (mean 5.43, 4.26, 4.07 nmol/L for TT, TC, CC genotypes respectively, additive P = 4×10−5) and smaller VLDL size (53.18, 50.94, 49.55 nm, P = 2×10−6). SNP rs7679 did not quite reach nominal significance for decreased HDL-C (C vs. T: β ±SEM = −0.016±0.009 ln mg/dl, additive P = 0.07), but was very strongly associated with increased small HDL particle number (17.93, 20.14 and 21.89 µmol/L for TT, CT and CC genotypes respectively; additive P = 4×10−18) and consequently total HDL particle number (34.07, 35.10 and 36.78 µmol/L, P = 2×10−5).

Association of Genetic Risk Score (GRS) with Baseline Lipid and Lipoprotein Traits

A lipid GRS was calculated for each individual by first replacing missing genotypes with ethnicity-specific imputed means and then adding up the number of risk alleles possessed for each of the 32 independent SNPs. Of the 32 SNPs evaluated, 11 were originally associated in the meta-analysis with LDL cholesterol, 10 with HDL cholesterol only, seven with triglycerides only, and four with both HDL cholesterol and triglycerides. A risk allele was defined as one associated with increased TG or LDL-C or decreased HDL in the original meta-analysis [10]. After adjustment for age, sex, ethnicity, and BMI, the GRS was significantly associated with all baseline traits evaluated except total HDL particles (P = 0.26, Table 2). The following are P-values for the effects of the GRS, as a quantitative covariate, and geometric means for the upper and lower ethnicity-specific GRS quartiles for each trait. A higher GRS was associated with elevated baseline levels of: total cholesterol (P = ×410−11, 206 vs. 195 mg/dl), LDL-C (P = ×910−8;, 129 vs. 121 mg/dl arithmetic means), TG (P = ×410−19, 160 vs. 127 mg/dl), total VLDL particles (P = ×610−14, 67 vs. 53 nmol/L), large VLDL particles (P = ×110−14, 6.57 vs. 4.21 nmol/L), total LDL particles (P = ×210−10, 1412 vs. 1262 nmol/L), small LDL particles (P = ×210−11, 743 vs. 543 nmol/L), small HDL particles (P = µ0.0005, 19.17 vs. 18.10 mol/L), and VLDL particle size (P = ×110−5, 53.86 vs. 51.84 nm). A higher GRS was also associated with lower baseline levels of: HDL-C (P = ×110−15, 43 vs. 47 mg/dl), LDL particle size (P = ×110−19, 0.256 vs. 0.269 nm), large HDL particles (P = ×210−8µ, 2.98 vs. 3.68 mol/L), and HDL particle size (P = 0.0003, 8.82 vs. 8.90 nm). All of these results are consistent with a greater number of risk alleles increasing the atherogenicity of the lipoprotein profile.

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Table 2. Association of 32-SNP GRS with Baseline Lipid and Lipoprotein Traits (n2,843).

doi:10.1371/journal.pgen.1002895.t002

GRS×Intervention Interactions of Baseline-Adjusted One-Year Traits

Two traits showed evidence of GRS×lifestyle interaction: LDL-C (P = 0.02) and small LDL particles (P = 0.01, Table 3; Figure 1; Figure S1a–S1f). For these two traits, there was a residual detrimental impact of GRS in the lifestyle (i.e., the GRS was associated with higher levels at one year even after adjusting for baseline levels) but not the metformin or placebo group, suggesting that the lifestyle intervention was less effective at lipid-lowering in those with a higher genetic burden. A unit (allele) GRS increase was associated with higher residual LDL-C levels in the lifestyle group (β+±0.087, SEE0.022 mg/dl, P = ×810−5) but not in the metformin (β−±0.03, SEE0.22 mg/dl, P = 0.90) or placebo (β+±0.20, SEE0.22 mg/dl, P = 0.35) groups (Figure 1). Similarly, the GRS was associated with higher residual ln-small LDL particles in the lifestyle group (β+±0.030, SEE0.0.012 ln nmol/L, P = 0.01), but not in the metformin (β−±0.013, SEE0.008 ln nm/L, P = 0.12) or placebo (β−±0.002, SEE0.008, P = 0.74) groups (Figure 1×). There were no metforminGRS interactions significant at the P = ×0.05 level. In addition to the three traits discussed, several traits showed residual detrimental effects of the GRS in one or more strata (total cholesterol, TG, LDL size, total VLDL particles, large HDL particles, and HDL size) without any statistical evidence of treatmentGRS interaction (Table 3).

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Figure 1. LDL-C levels.

LDL-C levels at baseline and 1 year (A) and small LDL particle levels at baseline and 1 year (B) stratified by treatment group and lipid GRS. Each column shows ethnicity-adjusted arithmetic (for LDL-C) or geometric (for small LDL particles) means (with upper 95 confidence), stratified above and below (less than or equal to) the ethnic-specific median GRS value. Ethnic-specific median GRS values are 34 alleles for Caucasian, African American and American Indian ethnicities and 35 for Hispanic and Asian/Pacific Islander ethnicities.

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Table 3. Association of 32-SNP GRS with Baseline-Adjusted One-Year Lipid and Lipoprotein Traits (n2,686).

doi:10.1371/journal.pgen.1002895.t003

Discussion

In the present study, the majority of previously associated SNPs replicated for baseline lipid traits, and there was a statistically significant relationship between the GRS and the vast majority of standard lipid traits and NMR lipoprotein subfractions. Importantly, in several cases the evidence for association with NMR subfractions was much stronger than for the original standard lipid trait, which may be owing to the relative proximity of the subfractions to the genetic loci. For example, SNP rs7679, originally associated with total HDLC levels in previous GWASs, in the DPP was not significantly associated with HDL-C (P = 0.07) but was strongly associated with small HDL particle levels (P = ×410−18). This SNP is near PLTP, encoding phospholipid transfer protein, a molecule directly influencing HDL particle size [14]. Such findings extend our understanding of lipid biology and suggest that, compared to standard lipid levels, measurements of lipoprotein subfractions may provide a more effective way of capturing genetically influenced risk. This is particularly important, as recent studies have shown that HDL-C is a heterogeneous trait, which in the context of clinical use may benefit from sub-stratification by genotype [15].

We also observed that within the lifestyle group but not the placebo or metformin groups, the GRS was associated with higher LDL-C and small LDL particle levels after one year of intervention. These findings suggest that the genetic burden on these traits cannot be completely overcome by lifestyle modification. However, even those with the greatest genetic burden benefit to a limited extent from lifestyle intervention in terms of LDL-C reduction (Figure 1A), although the effect on LDL particle size reduction is almost completely ablated (Figure 1B'). Even a true residual effect of lifestyle on LDL-C in people with the highest GRS does not negate the clinical relevance of our findings in terms of potential to facilitate tailored treatment decisions. Seeing less of an effect of lifestyle in a particular patient subgroup indicates that these persons may benefit from more frequent surveillance, more intense lifestyle interventions, or aggressive pharmaceutical interventions to supplement lifestyle interventions. Conversely, knowing that lifestyle intervention is likely to be adequate in persons with the lowest genetic burden may maximize the patients diet adherence and potentially reduce the costs and side effects associated with prescribing lipid-lowering medications unnecessarily. The availability of information on genetic background may also facilitate patient-provider dialogue, owing to improved diagnostic accuracy. This is similar to the strategy used to control cholesterol levels in patients with a monogenic disorder such as familial hypercholesterolemia due to a severe loss of function mutation in LDLR, where lifestyle intervention combined with pharmacotherapy is needed to bring LDL-C levels within an acceptable range (Third Report of the NCEP-ATP III on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults).

×It is also important to bear in mind that the interaction effects may be underestimated in our paper. This is because the majority of SNPs included in the GRS are likely to be imperfect proxies for unobserved functional variants, resulting in some degree of genotype misclassification. Moreover, all 32 SNPs were included within the GRS, even though not all SNPs convey statistically significant effects in the DPP and do not individually modify the effects of the interventions. A parsimonious GRS including only those SNPs that are statistically significant in the DPP would likely be overfitted to our data, resulting in biased conclusions about the strength and magnitude of genetreatment interactions.

Dyslipidemia is a long-established risk factor for CVD [1][3]. Thus, the primary and secondary prevention of atherosclerotic CVD often involves intervening on lipid levels [16]. Lifestyle interventions [17] and metformin treatment [18]; that result in weight loss have the potential to improve lipid profiles nevertheless, as long recognized [19], changes in lipid profiles following interventions vary greatly from one person to the next. Some of the variability in response to interventions may be because genotypes modulate the effects of preventive interventions on lipid homeostasis and CVD risk [20].

Of the many known dyslipidemia-predisposing loci discovered so far [10]×, only a handful have been the focus of studies testing hypotheses of genetreatment interactions [21][28], and most of these studies are small (N<150), non-randomized trials of dietary intervention. Although some of these studies have focused on genomic regions that are confirmed to harbor dyslipidemia-predisposing loci, such as APOB, CETP, LIPC and LPL [21], [22], [24][28], no exhaustive studies testing whether GWAS-discovered loci [10], [29], [30] modify response to treatments have been previously reported.

The GRS used in this study attenuated the impact of the DPP lifestyle intervention on LDL levels and small LDL particle number. This suggests that a genetic predisposition to high LDL levels and more small LDL particles is difficult to overcome through lifestyle intervention alone. These data also unmask the effects of an underlying genetic defect of LDL levels and small LDL particles found in individuals with a high genetic burden, which becomes visible when adiposity and blood TG content are reduced through lifestyle intervention. This information may justify the combination of lifestyle and lipid lowering drug treatment from the outset in these individuals, rather than the usual approach of stepping from lifestyle to drug therapy when the former fails.

;The DPP lifestyle intervention prioritized weight loss, daily fat gram intake and physical activity goals over intake of saturated fat, cholesterol, viscous fiber and plant stanols/sterols this may have influenced the nature of the changes in the lipid profile. When compared to the metformin and placebo groups, the lifestyle intervention group reported improved physical activity levels and reductions in calorie intake, resulting in significantly greater weight losses [31], each of which has major influences on TG levels. The lifestyle intervention group reported significantly greater reductions in percent calories from total fat and saturated fat than the metformin and placebo groups [31]. However, they did not, on average, achieve the National Cholesterol Education Program target for saturated fat intake and did not focus on the other therapeutic lifestyle changes, such as the additional dietary changes mentioned above, that often have the largest effects on LDL concentrations. The ethnic diversity of the DPP cohort facilitates the generalizability of results, but may also lead to confounding by population stratification in genetic analyses. However, Sensitivity analyses in the European White sub-cohort of the DPP yielded comparable effect estimates to the results obtained in the entire DPP genetics cohort (results for baseline traits shown in Table S3), supporting the conclusion that confounding by population stratification is unlikely to explain our findings.

×Interestingly, no significant interaction was observed between the GRS and other biochemical components of the lipid profile in the present study. It is important to bear in mind, however, that despite being the largest clinical trial of its kind, the DPP is only moderately powered to detect genetreatment interactions [32];× it is likely, therefore, that genetreatment interactions that are small in magnitude will have been overlooked here. Moreover, during the course of writing this paper, many smaller impact lipid loci have been discovered [9], [11]. Thus, it is possible that with a larger sample size and the inclusion of some or all of these additional loci, we may have discovered interaction effects on other lipid traits.

In summary, we have shown that common genetic loci that influence polygenic dyslipidemia also modify the effects of clinical interventions designed to mitigate cardiovascular and metabolic risk. This report is the first comprehensive effort to examine validated lipid loci within the context of a large randomized clinical trial. The findings of this study may facilitate the implementation of complex trait genetics into the clinical setting.

Methods

Participants

The DPP was a multi-center randomized controlled trial that examined the effects of metformin or intensive lifestyle modification on the incidence of type 2 diabetes [33], [34]~~%%~– = . Briefly, overweight persons with elevated but non-diabetic fasting and post-challenge glucose levels were randomized to receive placebo, metformin (850 mg twice daily) or a program of intensive lifestyle modification. The lifestyle intervention was designed to achieve 150 min/wk of physical activity and 7 weight loss via focus on daily fat gram goals. Fat gram goals were based on initial weight and 25 of calories from fat using a calorie level estimate to produce a weight loss of 0.51 kg/wk. The principal endpoint was the development of diabetes by confirmed semi-annual fasting plasma glucose or annual oral glucose tolerance testing (OGTT). Other phenotypes, such as changes in weight, waist circumference, lipids, insulin and glucose, were also ascertained. Written, informed consent was obtained from each participant, and each of the 27 DPP centers obtained institutional review board approval prior to initiation of the study protocol. A total of 2,993 participants in the placebo, lifestyle and metformin groups had DNA available and provided consent for genetic analysis. Individuals taking lipid lowering medications at baseline (n145) were excluded from all analyses.

Measurements

≥[]All participants fasted for 12 hrs the night before blood was drawn from an antecubital vein. Standard blood lipid measurements (triglyceride TG, total cholesterol, HDL-C, calculated LDL-C) were performed at the DPP central biochemistry laboratory. TG and total cholesterol levels were measured using enzymatic methods standardized to the Centers for Disease Control and Prevention reference methods [35]. HDL fractions for cholesterol analysis were obtained by the treatment of whole plasma with dextran sulfate Mg+2 [36]. LDL cholesterol was calculated by the Friedewald equation [37]>β. In participants with TGs4.5 mmol/l, the lipoprotein fractions were separated using preparative ultracentrifugation of plasma by quantification [38]. Lipoprotein subclass particle concentrations and average VLDL, LDL, and HDL particle diameters were measured by NMR spectroscopy at LipoScience, Inc (Raleigh, NC) with modification of existing methods [39].

Genotyping

Thirty-two SNPs previously associated with lipid concentrations in GWAS meta-analyses [10] were selected. DNA was extracted from peripheral blood leukocytes using standard methods. Genotyping was performed by allele-specific primer extension of multiplex amplified products and detection using matrix-assisted laser desorption ionization time-of-flight mass spectrometry on a Sequenom iPLEX platform [40]%%. The mean genotyping success rate was 96.7. The minimum call rate was 94.0. All SNPs were in Hardy-Weinberg equilibrium within each self-reported ethnic group.

Statistical Analysis

The SAS software v9.2 (SAS, Carey, NC) was used for analyses. Baseline total cholesterol, HDL-C, TG and all lipoprotein sub-fraction levels were natural log transformed for non-normality, and LDL-C was evaluated directly. For replicating the previously reported associations of SNPs with baseline traits and evaluating the association of the individual SNPs with NMR lipoprotein particle sizes and numbers, measurements were compared across genotypic groups by ANCOVA (general model, 2 df F test for three possible genotypes), and evidence for an additive effect of genotype was also evaluated using the measured genotype approach, in which each genotype was assigned a value of 0, 1 or 2 according to the number of minor alleles. Analyses of baseline traits were adjusted for age, sex, self-reported ethnicity (to minimize confounding due to potential differences in both allele frequency and lipid traits across ethnicities) and BMI. For the individual SNP analyses, the Bonferroni-corrected P-value for significance was set at P<× = ; = 0.0001 to account for multiple comparisons (32 SNPs14 traits448 tests 0.05/4480.0001).

A genetic risk score (GRS) was calculated from the 32 SNPs using the direction of association from the initial association seen in the published meta-analysis [10]; for each SNP, an allele was designated as a risk allele if it was associated with higher TG or LDL-C and/or lower HDL-C. In order to be able to incorporate all individuals in the analysis, including those missing genotypes at one or more loci, a simple imputation procedure within each self-reported ethnic group was implemented (in order to account for allele frequency differences across ethnicities) prior to score calculation. First, after coding the genotype as the number of minor alleles (0, 1 or 2), an ethnicity-specific mean genotype was calculated and rounded to the nearest whole number. Missing genotypes were replaced by the appropriate rounded mean genotype [41]%××. We calculated a GRS for each individual by adding up the number of risk alleles for each of the 32 tested SNPs, where a risk allele was defined as one associated with increased TG or LDL-C or decreased HDL. The GRS was then included as a quantitative independent variable in a multiple regression model for each baseline lipid/lipoprotein trait to test for association, adjusted for age, sex, self-reported ethnicity, and BMI. GRS quartiles were constructed separately within each self-reported ethnicity prior to calculating quartile-specific arithmetic means or geometric means and 95 confidence intervals. To test for interaction of the risk score with treatment, a multiple regression model was constructed with the 1 year value as the outcome variable and including GRS, lifestyle and metformin treatment and GRSlifestyle and GRSmetformin terms, along with adjustments for the corresponding baseline trait, baseline age, sex and self-reported ethnicity.

Sample Size and Power

A priori power calculations are an important study-planning tool, providing relevant information on likely effect sizes and variances is accessible. It is possible to obtain a broad understanding of the power constraints of our study by extrapolating results from other experimental settings (as described in detail in [42]), but specific a priori× power calculations could not be performed for the current study because reliable effect estimates and variances for tests of genetreatment interactions for the index genotypes and phenotypes were unavailable in the published literature at the time this study was planned. Post-hoc' power calculations were not performed, as these are well known to cause bias when interpreting a studys results [43][46]. However, confidence intervals are included in the figures, which give insight into the precision of the GRS effect estimates and hence the power to detect those estimates in the DPP cohort.

Supporting Information

Figure S1.

a: Box plots for Small LDL particles measured at baseline in the placebo arm of the Diabetes Prevention Program stratified by level of the genetic risk score. b: Box plots for Small LDL particles measured at baseline in the metformin arm of the Diabetes Prevention Program stratified by level of the genetic risk score. c: Box plots for Small LDL particles measured at baseline in the lifestyle arm of the Diabetes Prevention Program stratified by level of the genetic risk score. d: Box plots for Small LDL particles measured at 1 yr follow-up in the placebo arm of the Diabetes Prevention Program stratified by level of the genetic risk score. e: Box plots for Small LDL particles measured at 1 yr follow-up in the metformin arm of the Diabetes Prevention Program stratified by level of the genetic risk score. f: Box plots for Small LDL particles measured at 1 yr follow-up in the lifestyle arm of the Diabetes Prevention Program stratified by level of the genetic risk score.

doi:10.1371/journal.pgen.1002895.s001

(PPTX)

Table S1.

≤Details of individual SNP replication results (n2,843). The table compares results for each of the SNP loci in published meta-analysis and elsewhere with those reported here in the DPP. Analyses and means adjusted for age, sex, ethnicity and BMI.

doi:10.1371/journal.pgen.1002895.s002

(DOCX)

Table S2.

<Associations of individual SNPs with lipid or lipoprotein traits significant at the Bonferroni-significant p-value of 0.0001 for additive model (see Table 1 for units). Shown are geometric means for all traits except LDL-C, for which arithmetic means are shown. All analyses and means adjusted for age, sex, ethnicity and BMI.

doi:10.1371/journal.pgen.1002895.s003

(DOCX)

Table S3.

= Associations of individual SNPs with lipid or lipoprotein traits measured at baseline in White participants from the DPP (n1,565). Analyses were performed to determine whether population stratification owing to the mutliethnic nature of the DPP is likely to confound the associations reported in the main analyses. The comparability of the results in White DPP participants with the main analyses indicates that confoudning by population stratification is unlikely to underly the main results reported here. Analyses and means adjusted for age, sex, and BMI.

doi:10.1371/journal.pgen.1002895.s004

(DOCX)

Acknowledgments

The Investigators gratefully acknowledge the commitment and dedication of the participants of the DPP.

Contributors

List of all DPP staff:

Pennington Biomedical Research Center (Baton Rouge, LA)

George A. Bray, MD*

Iris W. Culbert, BSN, RN, CCRC**

Catherine M. Champagne, PhD, RD

Barbara Eberhardt, RD, LDN

Frank Greenway, MD

Fonda G. Guillory, LPN

April A. Herbert, RD

Michael L. Jeffirs, LPN

Betty M. Kennedy, MPA

Jennifer C. Lovejoy, PhD

Laura H. Morris, BS

Lee E. Melancon, BA, BS

Donna Ryan, MD

Deborah A. Sanford, LPN

Kenneth G. Smith, BS, MT

Lisa L. Smith, BS

Julia A. St.Amant, RTR

Richard T. Tulley, PhD

Paula C. Vicknair, MS, RD

Donald Williamson, PhD

Jeffery J. Zachwieja, PhD

University of Chicago (Chicago, IL)

Kenneth S. Polonsky, MD*

Janet Tobian, MD, PhD*

David Ehrmann, MD*

Margaret J. Matulik, RN, BSN**

Bart Clark, MD

Kirsten Czech, MS

Catherine DeSandre, BA

Ruthanne Hilbrich, RD

Wylie McNabb, EdD

Ann R. Semenske, MS, RD

Jefferson Medical College (Philadelphia, PA)

Jose F. Caro, MD*

Pamela G. Watson, RN, ScD*

Barry J. Goldstein, MD, PhD*

Kellie A. Smith, RN, MSN**

Jewel Mendoza, RN, BSN**

Renee Liberoni, MPH

Constance Pepe, MS, RD

John Spandorfer, MD

University of Miami (Miami, FL)

Richard P. Donahue, PhD*

Ronald B. Goldberg, MD*

Ronald Prineas, MD, PhD*

Patricia Rowe, MPA**

Jeanette Calles, MSEd

Paul Cassanova-Romero, MD

Hermes J. Florez, MD

Anna Giannella, RD, MS

Lascelles Kirby, MS

Carmen Larreal

Valerie McLymont, RN

Jadell Mendez

Juliet Ojito, RN

Arlette Perry, PhD

Patrice Saab, PhD

The University of Texas Health Science Center (San Antonio, TX)

Steven M. Haffner, MD, MPH*

Maria G. Montez, RN, MSHP, CDE**

Carlos Lorenzo, MD, PhD

Arlene Martinez, RN, BSN, CDE

University of Colorado (Denver, CO)

Richard F. Hamman, MD, DrPH*

Patricia V. Nash, MS**

Lisa Testaverde, MS**

Denise R. Anderson, RN, BSN

Larry B. Ballonoff, MD

Alexis Bouffard, MA,

B. Ned Calonge, MD, MPH

Lynne Delve

Martha Farago, RN

James O. Hill, PhD

Shelley R. Hoyer, BS

Bonnie T. Jortberg, MS, RD, CDE

Dione Lenz, RN, BSN

Marsha Miller, MS, RD

David W. Price, MD

Judith G. Regensteiner, PhD

Helen Seagle, MS, RD

Carissa M. Smith, BS

Sheila C. Steinke, MS

Brent VanDorsten, PhD

Joslin Diabetes Center (Boston, MA)

Edward S. Horton, MD*

Kathleen E. Lawton, RN**

Ronald A. Arky, MD

Marybeth Bryant

Jacqueline P. Burke, BSN

Enrique Caballero, MD

Karen M. Callaphan, BA

Om P. Ganda, MD

Therese Franklin

Sharon D. Jackson, MS, RD, CDE

Alan M. Jacobsen, MD

Lyn M. Kula, RD

Margaret Kocal, RN, CDE

Maureen A. Malloy, BS

Maryanne Nicosia, MS, RD

Cathryn F. Oldmixon, RN

Jocelyn Pan, BS, MPH

Marizel Quitingon

Stacy Rubtchinsky, BS

Ellen W. Seely, MD

Dana Schweizer, BSN

Donald Simonson, MD

Fannie Smith, MD

Caren G. Solomon, MD, MPH

James Warram, MD

VA Puget Sound Health Care System and University of Washington (Seattle, WA)

Steven E. Kahn, MB, ChB*

Brenda K. Montgomery, RN, BSN, CDE**

Wilfred Fujimoto, MD

Robert H. Knopp, MD

Edward W. Lipkin, MD

Michelle Marr, BA

Dace Trence, MD

University of Tennessee (Memphis, TN)

Abbas E. Kitabchi, PhD, MD, FACP*

Mary E. Murphy, RN, MS, CDE, MBA**

William B. Applegate, MD, MPH

Michael Bryer-Ash, MD

Sandra L. Frieson, RN

Raed Imseis, MD

Helen Lambeth, RN, BSN

Lynne C. Lichtermann, RN, BSN

Hooman Oktaei, MD

Lily M.K. Rutledge, RN, BSN

Amy R. Sherman, RD, LD

Clara M. Smith, RD, MHP, LDN

Judith E. Soberman, MD

Beverly Williams-Cleaves, MD

Northwestern University's Feinberg School of Medicine (Chicago, IL)

Boyd E. Metzger, MD*

Mariana K. Johnson, MS, RN**

Catherine Behrends

Michelle Cook, MS

Marian Fitzgibbon, PhD

Mimi M. Giles, MS, RD

Deloris Heard, MA

Cheryl K.H. Johnson, MS, RN

Diane Larsen, BS

Anne Lowe, BS

Megan Lyman, BS

David McPherson, MD

Mark E. Molitch, MD

Thomas Pitts, MD

Renee Reinhart, RN, MS

Susan Roston, RN, RD

Pamela A. Schinleber, RN, MS

Massachusetts General Hospital (Boston, MA)

David M. Nathan, MD*

Charles McKitrick, BSN**

Heather Turgeon, BSN**

Kathy Abbott

Ellen Anderson, MS, RD

Laurie Bissett, MS, RD

Enrico Cagliero, MD

Jose C. Florez, MD, PhD+

Linda Delahanty, MS, RD

Valerie Goldman, MS, RD

Alexandra Poulos

University of California-San Diego (San Diego, CA)

Jerrold M. Olefsky, MD*

Mary Lou Carrion-Petersen, RN, BSN**

Elizabeth Barrett-Connor, MD

Steven V. Edelman, MD

Robert R. Henry, MD

Javiva Horne, RD

Simona Szerdi Janesch, BA

Diana Leos, RN, BSN

Sundar Mudaliar, MD

William Polonsky, PhD

Jean Smith, RN

Karen Vejvoda, RN, BSN, CDE, CCRC

St. Luke's-Roosevelt Hospital (New York, NY)

F. Xavier Pi-Sunyer, MD*

Jane E. Lee, MS**

David B. Allison, PhD

Nancy J. Aronoff, MS, RD

Jill P. Crandall, MD

Sandra T. Foo, MD

Carmen Pal, MD

Kathy Parkes, RN

Mary Beth Pena, RN

Ellen S. Rooney, BA

Gretchen E.H. Van Wye, MA

Kristine A. Viscovich, ANP

Indiana University (Indianapolis, IN)

David G. Marrero, PhD*

Melvin J. Prince, MD*

Susie M. Kelly, RN, CDE**

Yolanda F. Dotson, BS

Edwin S. Fineberg, MD

John C. Guare, PhD

Angela M. Hadden

James M. Ignaut, MA

Marcia L. Jackson

Marion S. Kirkman, MD

Kieren J. Mather, MD

Beverly D. Porter, MSN

Paris J. Roach, MD

Nancy D. Rowland, BS, MS

Madelyn L. Wheeler, RD

Medstar Research Institute (Washington, DC)

Robert E. Ratner, MD*

Gretchen Youssef, RD, CDE**

Sue Shapiro, RN, BSN, CCRC**

Catherine Bavido-Arrage, MS, RD, LD

Geraldine Boggs, MSN, RN

Marjorie Bronsord, MS, RD, CDE

Ernestine Brown

Wayman W. Cheatham, MD

Susan Cola

Cindy Evans

Peggy Gibbs

Tracy Kellum, MS, RD, CDE

Claresa Levatan, MD

Asha K. Nair, BS

Maureen Passaro, MD

Gabriel Uwaifo, MD

University of Southern California/UCLA Research Center (Alhambra, CA)

Mohammed F. Saad, MD*

Maria Budget**

Sujata Jinagouda, MD**

Khan Akbar, MD

Claudia Conzues

Perpetua Magpuri

Kathy Ngo

Amer Rassam, MD

Debra Waters

Kathy Xapthalamous

Washington University (St. Louis, MO)

Julio V. Santiago, MD (deceased)*

Samuel Dagogo-Jack, MD, MSc, FRCP, FACP*

Neil H. White, MD, CDE*

Samia Das, MS, MBA, RD, LD**

Ana Santiago, RD**

Angela Brown, MD

Edwin Fisher, PhD

Emma Hurt, RN

Tracy Jones, RN

Michelle Kerr, RD

Lucy Ryder, RN

Cormarie Wernimont, MS, RD

Johns Hopkins School of Medicine (Baltimore, MD)

Christopher D. Saudek, MD*

Vanessa Bradley, BA**

Emily Sullivan, MEd, RN**

Tracy Whittington, BS**

Caroline Abbas

Frederick L. Brancati, MD, MHS

Jeanne M. Clark, MD

Jeanne B. Charleston, RN, MSN

Janice Freel

Katherine Horak, RD

Dawn Jiggetts

Deloris Johnson

Hope Joseph

Kimberly Loman

Henry Mosley

Richard R. Rubin, PhD

Alafia Samuels, MD

Kerry J. Stewart, EdD

Paula Williamson

University of New Mexico (Albuquerque, NM)

David S. Schade, MD*

Karwyn S. Adams, RN, MSN**

Carolyn Johannes, RN, CDE**

Leslie F. Atler, PhD

Patrick J. Boyle, MD

Mark R. Burge, MD

Janene L. Canady, RN, CDE

Lisa Chai, RN

Ysela Gonzales, RN, MSN

Doris A. Hernandez-McGinnis

Patricia Katz, LPN

Carolyn King

Amer Rassam, MD

Sofya Rubinchik, MD

Willette Senter, RD

Debra Waters, PhD

Albert Einstein College of Medicine (Bronx, NY)

Harry Shamoon, MD*

Janet O. Brown, RN, MPH, MSN**

Elsie Adorno, BS

Liane Cox, MS, RD

Jill Crandall, MD

Helena Duffy, MS, C-ANP

Samuel Engel, MD

Allison Friedler, BS

Crystal J. Howard-Century, MA

Stacey Kloiber, RN

Nadege Longchamp, LPN

Helen Martinez, RN, MSN, FNP-C

Dorothy Pompi, BA

Jonathan Scheindlin, MD

Elissa Violino, RD, MS

Elizabeth Walker, RN, DNSc, CDE

Judith Wylie-Rosett, EdD, RD

Elise Zimmerman, RD, MS

Joel Zonszein, MD

University of Pittsburgh (Pittsburgh, PA)

Trevor Orchard, MD*

Rena R. Wing, PhD*

Gaye Koenning, MS, RD**

M. Kaye Kramer, BSN, MPH**

Susan Barr, BS

Miriam Boraz

Lisa Clifford, BS

Rebecca Culyba, BS

Marlene Frazier

Ryan Gilligan, BS

Susan Harrier, MLT

Louann Harris, RN

Susan Jeffries, RN, MSN

Andrea Kriska, PhD

Qurashia Manjoo, MD

Monica Mullen, MHP, RD

Alicia Noel, BS

Amy Otto, PhD

Linda Semler, MS, RD

Cheryl F. Smith, PhD

Marie Smith, RN, BSN

Elizabeth Venditti, PhD

Valarie Weinzierl, BS

Katherine V. Williams, MD, MPH

Tara Wilson, BA

University of Hawaii (Honolulu, HI)

Richard F. Arakaki, MD*

Renee W. Latimer, BSN, MPH**

Narleen K. Baker-Ladao, BS

Ralph Beddow, MD

Lorna Dias, AA

Jillian Inouye, RN, PhD

Marjorie K. Mau, MD

Kathy Mikami, BS, RD

Pharis Mohideen, MD

Sharon K. Odom, RD, MPH

Raynette U. Perry, AA

;Southwest American Indian Centers (Phoenix, AZ Shiprock, ;NM Zuni, NM)

William C. Knowler, MD, DrPH+*

Norman Cooeyate**

Mary A. Hoskin, RD, MS**

Carol A. Percy, RN, MS**

Kelly J. Acton, MD, MPH

Vickie L. Andre, RN, FNP

Rosalyn Barber

Shandiin Begay, MPH

Peter H. Bennett, MB, FRCP

Mary Beth Benson, RN, BSN

Evelyn C. Bird, RD, MPH

Brenda A. Broussard, RD, MPH, MBA, CDE

Marcella Chavez, RN, AS

Tara Dacawyma

Matthew S. Doughty, MD

Roberta Duncan, RD

Cyndy Edgerton, RD

Jacqueline M. Ghahate

Justin Glass, MD

Martia Glass, MD

Dorothy Gohdes, MD

Wendy Grant, MD

Robert L. Hanson, MD, MPH

Ellie Horse

Louise E. Ingraham, MS, RD, LN

Merry Jackson

Priscilla Jay

Roylen S. Kaskalla

David Kessler, MD

Kathleen M. Kobus, RNC-ANP

Jonathan Krakoff, MD

Catherine Manus, LPN

Sara Michaels, MD

Tina Morgan

Yolanda Nashboo (deceased)

Julie A. Nelson, RD

Steven Poirier, MD

Evette Polczynski, MD

Mike Reidy, MD

Jeanine Roumain, MD, MPH

Debra Rowse, MD

Sandra Sangster

Janet Sewenemewa

Darryl Tonemah, PhD

Charlton Wilson, MD

Michelle Yazzie

George Washington University Biostatistics Center (DPP Coordinating Center Rockville, MD)

Raymond Bain, PhD*

Sarah Fowler, PhD*

Tina Brenneman**

Solome Abebe

Julie Bamdad, MS

Jackie Callaghan

Sharon L. Edelstein, ScM

Yuping Gao

Kristina L. Grimes

Nisha Grover

Lori Haffner, MS

Steve Jones

Tara L. Jones

Richard Katz, MD

John M. Lachin, ScD

Pamela Mucik

Robert Orlosky

James Rochon, PhD

Alla Sapozhnikova

Hanna Sherif, MS

Charlotte Stimpson

Marinella Temprosa, MS

Fredricka Walker-Murray

Central Biochemistry Laboratory (Seattle, WA)

Santica Marcovina, PhD, ScD*

Greg Strylewicz, PhD**

F. Alan Aldrich

Carotid Ultrasound

Dan O'Leary, MD*

CT Scan Reading Center

Elizabeth Stamm, MD*

Epidemiological Cardiology Research Center- Epicare (Win ston-Salem, NC)

Pentti Rautaharju, MD, PhD*

Ronald J. Prineas, MD, PhD*/*

Teresa Alexander

Charles Campbell, MS

Sharon Hall

Yabing Li, MD

Margaret Mills

Nancy Pemberton, MS

Farida Rautaharju, PhD

Zhuming Zhang, MD

Nutrition Coding Center (Columbia, SC)

Elizabeth Mayer-Davis, PhD*

Robert R. Moran, PhD**

Quality of Well-Being Center (La Jolla, CA)

Ted Ganiats, MD*

Kristin David, MHP*

Andrew J. Sarkin, PhD*

NIH/NIDDK (Bethesda, MD)

R. Eastman, MD

Judith Fradkin, MD

Sanford Garfield, PhD

&Centers for Disease Control Prevention (Atlanta, GA)

Edward Gregg, PhD

Ping Zhang, PhD

University of Michigan (Ann Arbor, MI)

William Herman, MD, MPH

Genetics Working Group

Jose C. Florez, MD, PhD1, 2

David Altshuler, MD, PhD1, 2

Paul I.W. de Bakker, PhD2

Paul W. Franks, PhD, MPhil, MS6, 7

Robert L. Hanson, MD, MPH3

Kathleen Jablonski, PhD5

William C. Knowler, MD, DrPH3

Jarred B. McAteer, AB1, 2

Toni I. Pollin, PhD4

Alan R. Shuldiner, MD4

1 Massachusetts General Hospital, Boston, Massachusetts, United States of America,

2 Broad Institute, Cambridge, Massachusetts, United States of America,

3 National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States of America,

4 University of Maryland, College Park, Maryland, United States of America,

5 Coordinating Center, Millersville, Maryland, United States of America,

6 Lund University, Lund, Sweden,

7 Harvard School of Public Health, Boston, Massachusetts, United States of America

Author Contributions

Conceived and designed the experiments: TIP TI JCF PWF. Performed the experiments: ESH LMD ARS RBG. Analyzed the data: TIP KAJ. Contributed reagents/materials/analysis tools: ESH LMD ARS RBG KAJ AT JM QP DA PIWdB. Wrote the paper: TIP TI PWF. Critiqued and approved the manuscript: TIP TI KAJ PIWdB AT JM QP ESH LMD DA ARS RBG JCF PWF.

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