A quarter of the world’s population is estimated to meet the criteria for metabolic syndrome (MetS), a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type 2 diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with MetS components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations of fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference was used, which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on MetS to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more MetS components, indicating that MetS is a complex, heterogeneous disorder. Associated loci harbor genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the MetS factor GWAS predicts 5.9% of the variance in MetS. These results provide mechanistic insights into the genetics of MetS and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple MetS components.

Metabolic syndrome (MetS) is defined by the International Diabetes Federation and American Heart Association/National Heart, Lung, and Blood Institute as the presence of three out of five symptoms, including elevated fasting glucose (FG), low HDL cholesterol (HDL-C), high diastolic or systolic blood pressure (SBP), elevated triglycerides (TG), and increased waist circumference (WC) (1). MetS confers increased risk of coronary artery disease and type 2 diabetes (2,3) and is related to elevated risk for cholelithiasis and mental disorders, amongst others (46). The prevalence of MetS has increased rapidly among U.S. adults, from 25.3% in 1988–1994 to 38.3% in 2017–2018 (79). MetS is caused by a combination of environmental factors, such as a sedentary lifestyle and poor diet, and genetic factors (10), which are not yet completely understood. A single causative etiology to MetS has not been established; both obesity and insulin resistance have been thought to be at the core of MetS (11). Excess adipose tissue (especially abdominal) is known to release products that directly influence hypertension, hyperglycemia, and dyslipidemia (11). On the other hand, insulin-resistant muscle tissue can contribute to the development of MetS components as well (11). Twin heritability estimates of the individual MetS components range between 0.33 (TG) and 0.40 (HDL-C) (12). More recently, the largest genome-wide association study (GWAS) of MetS, by Lind (13), identified a number of genetic loci associated with MetS risk. This GWAS was run on 291,107 individuals with self-reported British descent and European ethnicity. MetS status was based on similar criteria as described above.

While MetS is often studied based on phenotypic clustering, in this study we focus on genetic clustering of its components, by using structural equation modeling (SEM). SEM is a multivariate statistical analysis technique that combines factor analysis and a structural (path) model (Supplementary Material). Next to its potential to explore latent constructs and their relationships with measured variables, an advantage of SEM is its ability to examine many (complex) relationships simultaneously while accounting for measurement error (14). Combined with genetics, SEM can model the shared genetic architectures across phenotypes into one or more latent factors and identify variants with effects on this shared dimension (15).

It is well accepted and established that MetS, a combination of several risk factors, carries a greater risk for adverse clinical outcomes than a single risk factor (16,17). Furthermore, it is known that when one of the MetS components is present within an individual, e.g., increased WC, the chance that another is present as well is highly elevated. As MetS is not a disease that can be directly measured but, rather, is estimated based on phenotypic clustering of its components, this study exploits SEM to investigate whether genetics associated with the individual components show clustering as well. The following questions are asked: (1) To what extent are the genetic/biological elements for individual MetS components shared? (2) Can we leverage this genetic overlap to detect additional genetic loci associated with MetS? And (3) can we investigate biological features underlying MetS? To answer these questions, we applied genomic SEM (15) on previously published GWAS summary statistics to scrutinize the genetic factor structure underlying the MetS components. Having established one common factor using genomic SEM, we run a GWAS on this common MetS factor, identify functional features of associated single nucleotide polymorphisms (SNPs), and investigate overlap between the common MetS factor and the individual components. By addressing these questions, we aim to provide insight into the genetic architecture underlying MetS.

This study was performed according to a preregistered analysis plan (https://osf.io/kwq27/).

Input Summary Statistics

To maximize sample size, we selected GWAS reflecting a continuous spectrum of the MetS components FG, HDL-C, SBP, TG, and WC. Genomic SEM requires that GWAS be run with genome-wide arrays and so including no individuals genotyped on a targeted chip such as MetaboChip (15). We selected GWAS run on Europeans only. Furthermore, we choose only GWAS that were not corrected for BMI. Correcting for BMI is often performed, as metabolic traits are highly genetically correlated, but for our study this would not result in reflection of the underlying genetic structure properly because this would alter the correlation between WC and the other MetS components. Selected GWAS used in our main analyses are outlined in Supplementary Material, and a full description of methods can be found in the individual manuscripts.

Cross Trait Genetic Correlations

Observed scale SNP heritabilities of the MetS components and genetic correlations between the components and other traits and diseases were estimated with LD Score regression (LD SCore [LDSC] software]) (18) (Supplementary Material). Observed scale SNP heritability was used instead of the customary liability scale heritability for binary phenotypes, for comparative purposes but also because the latter requires sample prevalence of a phenotype, which we could not estimate for the MetS factor. Observed scale SNP heritability assumes an underlying continuous liability, which is likely to be the case in MetS.

Exploratory and Confirmatory Factor Analysis

We used genomic SEM (15) to model multivariate genetic associations among MetS components based on genetic correlations and SNP heritabilities derived from GWAS summary statistics. Genomic SEM is not biased by sample overlap. In the first stage, the genetic covariance matrix, derived from LDSC, and sampling matrix for the five MetS components were estimated in genomic SEM. Quality control for this step consisted of removing SNPs with a minor allele frequency (MAF) <1% (when available), INFO score <0.9 (when available), SNPs from the MHC region, and SNPs not present in HapMap 3. MAF and INFO scores were not always available in the individual GWAS summary statistics, but filtering SNPs to HapMap 3 should ensure a set of relatively common SNPs of good quality.

First, an exploratory factor analysis (EFA) was performed to investigate how many factors were needed to describe the observed genetic covariance matrix between the five MetS components. For this EFA, promax rotation was used in the R factanal package (19). A scree plot was generated with the R nFactors package (20). As a one-factor model was suggested, a confirmatory factor analysis was run within genomic SEM to establish how well this one-factor model fitted the data. Model fit, used to evaluate the extent to which the model-implied covariance matrix approximates the empirical, observed covariance matrix, is considered good with comparative fix index values >0.95 (21). Furthermore, standardized root mean square values <0.10 are considered acceptable fit and <0.05 good fit (15).

Common Factor GWAS and Functional Annotation

Using the univariate genetic summary statistics for each of the components as indicators of the MetS genetic common factor, we used genomic SEM to run a GWAS on the MetS factor. Following genomic SEM guidelines, quality control for this step involved restricting to SNPs with an INFO score >0.6 (when available) and to SNPs present in the 1000 Genomes phase 3 European reference data set with a MAF of >1% in the reference panel (22). The qqman package in R was used to generate a Manhattan plot (23). Summary statistics and code are publicly available via https://ctg.cncr.nl/software/summary_statistics. Summary statistics obtained from the common factor MetS GWAS and those from the individual risk factor GWAS were submitted to FUMA v1.3.7 (24) for exploration of functional consequences of all candidate SNPs, with use of default parameters. Additionally, MAGMA, version 1.08 (25), was used in FUMA to perform gene-based, gene-set, and gene-property analyses (Supplementary Material). To further specify expression of identified genes, we used FUMA to run cell-type analyses with expression data from mouse and human brain single-cell RNA–sequencing analyses (26).

Polygenic Prediction

We used common factor MetS and MetS components summary statistics to predict variance explained of FG, HDL-C, SBP, TG, and WC measurements as well as MetS diagnosis in an external data set, from the Framingham Heart Study (FHS), using polygenic risk scoring. Quality control is described in Supplementary Material. Because FHS subjects were included in the GWAS of FG, HDL-C, and TG that were used in this study, we used Erase Sample Overlap and Relatedness (EraSOR) (27) to correct for sample overlap (14% for FG and 7% for HDL-C and TG) (Supplementary Material). We then calculated polygenic risk scores (PRS), which consist of the sum of GWAS alleles weighted by their effect sizes, to predict MetS diagnosis and MetS component measurements in FHS, with PRSice-2 (28). We used summary statistics from the MetS components (adjusted with EraSOR for FG, HDL-C, and TG because of sample overlap with FHS), the EraSOR-adjusted MetS factor, and Lind GWAS, with default parameters. Sex, age2, and the first 20 principal components were added as covariates. Finally, we investigated the ability to predict MetS diagnosis with a logistic regression model containing the MetS factor PRS and covariates and a model with all the MetS components PRS and covariates. We calculated Nagelkerke R2 and compared the performance of the two models with a likelihood ratio test using the rms package in R (20,29).

Drug Gene Set Analysis

For identification of drugs associated with MetS that may be candidates for treatment, genetically informed drug repurposing was performed with use of DRUg Gene SEt Analysis (DRUGSEA) (30), a software tool for performing drug–gene set analysis. DRUGSEA tests drug-phenotype associations using competitive gene set analysis in MAGMA (25) (Supplementary Material).

Data and Resource Availability

The data set generated and analyzed during the current study are available from the CTG Lab repository (https://ctg.cncr.nl/software/summary_statistics/). The resources used during the current study are available under the following: summary statistics, FG https://magicinvestigators.org/downloads/, HDL-C and TG https://csg.sph.umich.edu/willer/public/lipids2010/, SBP https://atlas.ctglab.nl/traitDB/3380, WC https://atlas.ctglab.nl/traitDB/3185, coronavirus disease 2019 (COVID-19) https://www.covid19hg.org/results/, coronary artery disease and cholelithiasis https://www.leelabsg.org/resources/, type 2 diabetes https://diagram-consortium.org/downloads.html, MetS GWAS by Lind https://www.ebi.ac.uk/gwas/efotraits/EFO_0000195, schizophrenia https://walters.psycm.cf.ac.uk/, other psychiatric traits https://www.med.unc.edu/pgc/download-results/, smoking initiation and alcoholic drinks per week https://conservancy.umn.edu/handle/11299/201564, insomnia https://ctg.cncr.nl/software/summary_statistics, age at first birth, number of children, and educational attainment https://www.thessgac.com/, and height https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files#GWAS_Anthropometric_2014_Height_Summary_Statistics. Software includes LD Score regression, https://github.com/bulik/ldsc; genomic SEM, https://github.com/genomicsem/genomicsem; linkage disequilibrium information 1000 Genomes EUR phase 3 and HapMap 3 SNP list, https://utexas.app.box.com/s/vkd36n197m8klbaio3yzoxsee6sxo11v; FUMA, https://fuma.ctglab.nl/; MAGMA, https://ctg.cncr.nl/software/magma; EraSOR, https://choishingwan.gitlab.io/EraSOR/; and PRSice-2, https://choishingwan.github.io/PRS-Tutorial/prsice/.

Genetic Correlations Between MetS Components

Genetic correlation is a quantitative parameter that describes the genetic relationship between two traits or between two GWAS on the same trait. Genetic correlations between GWAS summary statistics of the MetS components (Table 1 and Supplementary Table 1), calculated with LD Score regression (LDSC) (18), are negative for HDL-C, as expected, as decreased HDL-C is a MetS component (11), and positive for all others. The highest correlation is between HDL-C and TG (rG = −0.60 ± 0.07) (Fig. 1A and Supplementary Tables 2 and 3). SBP has relatively the lowest correlation with other components, confirming conjectures that blood pressure is less “metabolic” than other components (11). Despite the varying magnitude of genetic correlation between MetS components, all except one correlation are significant (Fig. 1A and Supplementary Table 3), suggesting that a shared genetic structure underlies MetS.

Figure 1

Genetic correlations between MetS components, confirmatory factor analysis, and common factor GWAS of MetS. A: Genetic correlations between the MetS components, calculated with LDSC. *Genetic correlation between two components is significant at the multiple testing threshold (P < 0.005 [0.05 of 10 correlations]). B: Path diagram of the standardized common factor model estimated with genomic SEM. SEs are shown within parentheses. C: Manhattan plot of the MetS factor GWAS. For each chromosome with a lead SNP with a P value <1e−20, the protein coding gene that is in closest proximity to the strongest SNP is shown (one per chromosome). The red line indicates whether a SNP is genome-wide significant, shown here as the negative log10-transformed P value on the y-axis. The y-axis is limited to −log10(P) = 100 to improve visualization (maximum −log10(P) = 151.29 for the locus near FTO). The subscript G is used to indicate genetic variables. U reflects the variance in the MetS components not explained by the common factor.

Figure 1

Genetic correlations between MetS components, confirmatory factor analysis, and common factor GWAS of MetS. A: Genetic correlations between the MetS components, calculated with LDSC. *Genetic correlation between two components is significant at the multiple testing threshold (P < 0.005 [0.05 of 10 correlations]). B: Path diagram of the standardized common factor model estimated with genomic SEM. SEs are shown within parentheses. C: Manhattan plot of the MetS factor GWAS. For each chromosome with a lead SNP with a P value <1e−20, the protein coding gene that is in closest proximity to the strongest SNP is shown (one per chromosome). The red line indicates whether a SNP is genome-wide significant, shown here as the negative log10-transformed P value on the y-axis. The y-axis is limited to −log10(P) = 100 to improve visualization (maximum −log10(P) = 151.29 for the locus near FTO). The subscript G is used to indicate genetic variables. U reflects the variance in the MetS components not explained by the common factor.

Close modal
Table 1

Selected summary statistics of MetS components

TraitNGenomic risk lociSNP heritability, h2 (SE)Reference
FG 46,186 13 0.09 (0.02) Dupuis et al. (2010) (54
HDL-C 99,000 50 0.11 (0.02) Teslovich et al. (2010) (55
SBP 361,402 255 0.13 (0.01) Watanabe et al. (2019) (52
TG 96,598 31 0.15 (0.03) Teslovich et al. (2010) (55
WC 385,932 367 0.20 (0.01) Watanabe et al. (2019) (52
TraitNGenomic risk lociSNP heritability, h2 (SE)Reference
FG 46,186 13 0.09 (0.02) Dupuis et al. (2010) (54
HDL-C 99,000 50 0.11 (0.02) Teslovich et al. (2010) (55
SBP 361,402 255 0.13 (0.01) Watanabe et al. (2019) (52
TG 96,598 31 0.15 (0.03) Teslovich et al. (2010) (55
WC 385,932 367 0.20 (0.01) Watanabe et al. (2019) (52

The number of significant genomic risk loci was determined in FUMA (24). SNP heritabilities, all on the observed scale, were estimated using LD Score regression (18).

Factor Analyses

For investigation of how many latent factors should be constructed, an EFA was performed. Results from this analysis suggest retaining one factor (Supplementary Fig. 4A), which corresponds well with reported phenotypic factor models of MetS (31). A follow-up confirmatory factor analysis shows adequate fit to the data [χ2(5) = 28.42, P = 3.01e−5, comparative fix index = 0.95, standardized root mean square = 0.058]. This model (hereafter referred to as “the MetS factor”) was selected as our final factor model. Factor loadings are relatively high (0.45–0.70) for HDL-C, TG, WC, and FG and lower but still significant for SBP (0.22 (P = 2.69e−21)) (Fig. 1B and Supplementary Table 4).

Common Factor GWAS

We then used the MetS factor, summarizing the genetic variance shared between the five MetS components, to identify SNPs associated with MetS. We employed genomic SEM, which can be used even in the case of uneven sample sizes or sample overlap (15), to conduct a common factor GWAS on the MetS factor; i.e., we estimated individual SNP effects, using an effective population size of 461,920 (as estimated with genomic SEM). We identify 6,718 genome-wide significant (P < 5e−8) SNPs (Fig. 1C) tagging 318 independent (r2 < 0.1) lead SNPs located in 235 genomic risk loci (Supplementary Table 5). The estimated LDSC linkage disequilibrium intercept indicates no evidence of bias due to uncorrected population stratification (intercept 0.97 [SE 0.02]) (Supplementary Fig. 4C).

Total observed scale SNP heritability (h2) for our MetS factor is 0.14 (SE 0.01), compared with 0.09 (0.005) observed scale h2 from the largest GWAS on MetS to date by Lind (13). This previous GWAS, which was based on phenotypic assessment (the presence of three or more of five components) of binarized MetS, yielded 61 loci (13), of which we replicated 35 (Supplementary Table 6).

Functional Annotation

Because SNPs affect phenotypes through influencing gene expression or protein structure, we mapped SNP-level results to genes in four ways. FUMA (24) was used to annotate individual significant SNPs to genes through 1) positional mapping, coupling SNPs to 845 genes by genomic location; 2) cis– and trans–expression quantitative trait loci mapping, linking SNPs to 2,186 genes of which the expression may be influenced; and 3) chromatin interaction mapping, linking SNPs to 2,636 genes with which they have three-dimensional DNA-DNA interactions (see research design and methods). Overall, FUMA implicates 3,693 mapped genes (Supplementary Table 7). Additionally, MAGMA (25) was used to perform a gene-based association study. SNPs were mapped to 518 significantly associated genes (P < 2.82e−6 [0.05/17,706 genes]) (Supplementary Fig. 4B and Supplementary Table 8). A total of 328 genes were mapped with all four methods (Supplementary Table 9). Of those, ABCA1 was especially notable because it was mapped by four separate loci on four different chromosomes. ABCA1 aids HDL-C in transporting excess cholesterol and has a function in TG metabolism and blood glucose homeostasis (32,33).

As genes do not function in isolation, we ran a competitive gene set analysis in MAGMA, which tests whether the mean association of genes within a gene set with the genetic MetS factor is stronger than that of genes not represented in that gene set. Fifteen gene sets are significantly enriched at P < 3.23e−6 (0.05/15,481 gene sets) (Fig. 2A and Supplementary Table 10). Six of these are involved in lipoprotein particle remodeling. Because there is considerable overlap between these gene sets, we used a series of conditional gene set analyses to better characterize these associations (see research design and methods and Supplementary Table 10). This indicates that there were five independently associated gene sets: TG-rich lipoprotein particle remodeling, DNA binding, DNA repair after ultraviolet radiation, and negative and positive regulation of the biosynthetic process (Supplementary Table 10). Most results are in line with findings from earlier MetS (13,34,35).

Figure 2

Gene-set, tissue, and cell-type enrichment analyses. Bonferroni-corrected significant analyses (horizontal line, the number of tests differs per analysis) are depicted in blue. For each analysis, the top 20 results are shown. y-axes show the −log10-transformed P values of association. A: MAGMA gene set analysis using 15,481 gene sets from MSigDB v6.2. B: Gene expression profiles of MetS factor–associated genes obtained from MAGMA gene property analysis using 54 tissues from the Genotype-Tissue Expression (GTEx) database, version 8. C and D: Single-cell gene expression profiles of associated genes obtained from MAGMA gene property analysis using RNA-sequencing data from all mouse tissue sets (C) (n = 805) and all human brain tissue sets (D) (n = 255) available in FUMA (Supplementary Material). GW, gestational week; UV, ultraviolet.

Figure 2

Gene-set, tissue, and cell-type enrichment analyses. Bonferroni-corrected significant analyses (horizontal line, the number of tests differs per analysis) are depicted in blue. For each analysis, the top 20 results are shown. y-axes show the −log10-transformed P values of association. A: MAGMA gene set analysis using 15,481 gene sets from MSigDB v6.2. B: Gene expression profiles of MetS factor–associated genes obtained from MAGMA gene property analysis using 54 tissues from the Genotype-Tissue Expression (GTEx) database, version 8. C and D: Single-cell gene expression profiles of associated genes obtained from MAGMA gene property analysis using RNA-sequencing data from all mouse tissue sets (C) (n = 805) and all human brain tissue sets (D) (n = 255) available in FUMA (Supplementary Material). GW, gestational week; UV, ultraviolet.

Close modal

Next, we examined which tissues and cell types were enriched for genes associated with the MetS factor. Linking MAGMA gene-based P values to tissue-specific gene expression, we observe strong enrichment (significant at P < 9.26e−4 for 54 tissues) of genes expressed in the cerebellum (P = 7.72e−10 and P = 4.99e−10 for the cerebellum and cerebellar hemisphere, respectively), as well as the (frontal) cortex (Brodmann area 9, P = 2.38e−4, and cortex, P = 2.81e−4) and the pituitary (P = 4.04e−4) (Fig. 2B and Supplementary Table 11).

The significance of brain tissue was confirmed with FUMA cell-type analyses in mice, which show enrichment (significant at P < 6.21e−05 for 805 cell types) in brain neurons (P = 2.04e−5), embryonic mesenchyme neurons (neuropeptide Y high) (P = 2.13e−5) and oligodendrocyte precursor cells (P = 4.72e−5), besides enteroendocrine cells from the large intestine (P = 1.90e−7), which produce gut hormones that coordinate appetite, food absorption, digestion, and insulin secretion (36) (Fig. 2C and Supplementary Table 12). All significant cell types remain significant after within–data set conditional cell-type analysis with forward selection (with repeatedly retaining the cell type with the lowest marginal P value for each pair of significantly associated cell types).

Focusing on human brain cells, we find strong associations (significant at P < 1.96e−4 for 255 cell types) with GABAergic cells (midbrain GABA cells, P = 1.21e−12; midbrain neuroblast GABA cells, P = 3.72e−12; GABAergic neurons in the prefrontal cortex at gestational week 26, P = 1.25e−10; GABAergic neurons in the prefrontal cortex at gestational week 23, P = 1.70e−8; hippocampal GABAergic2 interneurons, P = 1.03e−5; and GABAergic neurons in the prefrontal cortex at gestational week 16, P = 3.64e−5), cells belonging to the dopamine system (midbrain dopaminergic 1 cells, P = 1.28e−6, and pyramidal neurons from the hippocampal Cornu Ammonis 1 region, P = 1.19e−4), and neuroblasts (midbrain mediolateral neuroblast 5 cells, P = 1.16e−6) (Fig. 2D and Supplementary Table 13). After within-dataset conditional cell type analysis, associations for midbrain GABA cells, GABAergic neurons in the prefrontal cortex at gestational week 26, hippocampal GABAergic2 interneurons, and pyramidal neurons from the hippocampal Cornu Ammonis 1 region remained significant.

Overlap Between MetS Components and MetS Factor

To examine what the MetS components share and what makes them unique, we investigated overlapping loci, genes, gene sets, tissues, and cell types among the MetS components and the MetS factor. Of the 235 loci associated with MetS factor, 27 do not overlap with any loci for MetS components, 155 overlap with loci associated with one MetS component, and 53 overlap with loci associated with two or more MetS components (Supplementary Table 14). One MetS locus is significant in four of the five components. Whereas to conclude that these overlapping loci represent the same signal would require functional follow-up studies, this overlap does suggest that some genetic signal of MetS components is shared.

From further scrutinizing overlap among MetS components in loci, mapped genes from the MAGMA gene-based analysis, gene sets, tissues, and cell types, we can conclude there is little pairwise overlap (Fig. 3 and Supplementary Tables 14–19). Overall, both our MetS factor and the Lind GWAS show the largest overlap with WC (except for gene sets and for mouse cell types). Because WC both had the highest factor loading on the MetS factor and had the highest SNP heritability (Table 1), we aimed to investigate to what extent WC is driving our MetS results. We therefore repeated the MetS factor GWAS without WC. Results are described in Supplementary Material. The results suggest that, even though there is significant genetic correlation with the MetS factor when we leave out WC, the functional features differ, and WC therefore seems to be a key determinant of the genetics associated with the MetS factor. However, we cannot rule out that the large overlap of the MetS factor GWAS with the WC GWAS is determined by the power of the latter.

Figure 3

Overlap between MetS factor GWAS results with components and the MetS GWAS by Lind. Overlapping genomic risk loci (A), significant genes from the MAGMA gene-based analysis (B), gene sets (C), enriched tissues (D), mouse cell types (E), and human brain cell types (F). MetS components are within black lines. The cell color reflects the percentage shared of phenotype 1 results on the y-axis, with phenotype 2 on the x-axis. MetS Lind, phenotypic MetS GWAS by Lind.

Figure 3

Overlap between MetS factor GWAS results with components and the MetS GWAS by Lind. Overlapping genomic risk loci (A), significant genes from the MAGMA gene-based analysis (B), gene sets (C), enriched tissues (D), mouse cell types (E), and human brain cell types (F). MetS components are within black lines. The cell color reflects the percentage shared of phenotype 1 results on the y-axis, with phenotype 2 on the x-axis. MetS Lind, phenotypic MetS GWAS by Lind.

Close modal

Genetic Correlations With External Traits

In investigation of whether the MetS factor showed genetic overlap with other traits, we used LDSC to estimate genetic correlations of our MetS factor GWAS with various external traits. The genetic correlation between the MetS factor GWAS and Lind GWAS is 0.92 (P = 0) (Fig. 4 and Supplementary Table 26). The MetS factor shows significant (at P = 1.92e−3 [0.05/26 traits]) positive genetic correlations with known phenotypically associated diseases and traits: type 2 diabetes (rG = 0.69, P = 3.39e−239), cholelithiasis (rG = 0.52, P = 2.34e−31), coronary artery disease (rG = 0.48, P = 1.58e−75), childhood BMI (rG = 0.47, P = 2.03e−38), and very severe respiratory confirmed COVID-19 (rG = 0.26, P = 6.30e−7). Less expected significant positive genetic correlations are with attention-deficit/hyperactivity disorder (rG = 0.34, P = 2.85e−35), smoking initiation (rG = 0.24, P = 1.71e−44), insomnia (rG = 0.21, P = 1.14e−17), number of children born (rG = 0.17, P = 1.56e−8), major depressive disorder (rG = 0.16, P = 7.54e−11), cannabis use disorder (rG = 0.16, P = 5.47e−7), and height (rG = 0.10, P = 9.08e−9). Significant negative genetic correlations were found with age at first birth (rG = −0.40, P = 5.01e−82), educational attainment (rG = −0.37, P = 5.97e−123), anorexia nervosa (rG = −0.22, P = 2.40e−15), and schizophrenia (rG = −0.09, P = 7.97e−07).

Figure 4

Genetic correlations between the MetS factor and other complex diseases and traits. Error bars represent 95% CIs. Asterisks reflect significance at the multiple testing–corrected P value threshold (0.05/26 = 1.92e−3): 10e−30 < P < 1.92e−3 (*), 10e−60 < P < 10e−30 (**), 10e−90 < P < 10e−60 (***), and P < 10e−90 (****). ADHD, attention-deficit/hyperactivity disorder.

Figure 4

Genetic correlations between the MetS factor and other complex diseases and traits. Error bars represent 95% CIs. Asterisks reflect significance at the multiple testing–corrected P value threshold (0.05/26 = 1.92e−3): 10e−30 < P < 1.92e−3 (*), 10e−60 < P < 10e−30 (**), 10e−90 < P < 10e−60 (***), and P < 10e−90 (****). ADHD, attention-deficit/hyperactivity disorder.

Close modal

Polygenic Prediction

To investigate predictive power of the MetS factor GWAS, we calculated PRS with PRSice-2 (28) using summary statistics of the MetS factor, the MetS components used in this study, and the Lind GWAS (13). We then investigated what proportion of phenotypic variance of FG, HDL-C, SBP, TG, WC, and MetS (research design and methods) they explained in unrelated second and third generation participants of the FHS (n = 2,095). Because the FG, HDL-C, and TG GWAS contained FHS participants, we corrected those summary statistics for sample overlap with EraSOR (27) and reran our MetS factor GWAS with those adjusted summary statistics. Variance explained in MetS is 0.059 with MetS factor GWAS summary statistics, which is higher than all other summary statistics (Fig. 5 and Supplementary Table 27). Furthermore, variance explained by MetS PRS plus covariates is 0.21 (Nagelkerke R2), whereas variance explained by all MetS component PRS plus covariates is 0.19, showing that the MetS PRS predictive abilities are better than the sum of its parts (P = 0.0058) (Supplementary Table 28).

Figure 5

Variance explained by MetS factor GWAS summary statistics, the MetS components GWAS summary statistics used to define MetS factor, and the largest phenotypic MetS GWAS, by Lind. R2 indicates the proportion of variance explained by the PRS, independent of covariates. MetS Lind, phenotypic MetS GWAS by Lind.

Figure 5

Variance explained by MetS factor GWAS summary statistics, the MetS components GWAS summary statistics used to define MetS factor, and the largest phenotypic MetS GWAS, by Lind. R2 indicates the proportion of variance explained by the PRS, independent of covariates. MetS Lind, phenotypic MetS GWAS by Lind.

Close modal

Drug Repurposing

To investigate whether any clinically used drugs are candidates to treat MetS, we ran a drug-repurposing analysis with DRUGSEA (30). After competitive and conditional gene-set analyses, one drug is significantly associated with MetS factor, fenofibrate (P = 7.84e−06) (Supplementary Table 29). Fenofibrate activates the peroxisome proliferator–activated receptor α, which triggers lipoprotein lipase leading to lipolysis and improved HDL-C and TG levels (37). Additionally, several drug categories were enriched for drugs with high MAGMA Z statistics from the drug gene set analysis run in MAGMA (Supplementary Material).

Leveraging the power advantage expected in a genomic SEM approach, we studied the genetics of MetS, which was defined by a combination of FG, HDL-C, SBP, TG, and WC. We show that the genetic and biological elements associated with MetS components are most often unique (see Supplementary Note for a disquisition about whether MetS can be seen as a [homogeneous] syndrome). Their modest but significant genetic correlation was modeled into a common MetS factor, and a GWAS run on that factor yielded 235 associated genome-wide significant loci and provided new biological insights in MetS etiology.

First, our MetS factor GWAS results point toward involvement of the brain, especially the cerebellum. Many food intake–regulating hormones cross the blood-brain barrier and act as signaling factors in the central nervous system, controlling appetite and adipose tissue lipolysis, for example. Tissue enrichment analysis of genes found in the Lind GWAS also pointed uniquely toward involvement of the cerebellum (Supplementary Table 17), confirming our findings. In a recent study by Low et al. (38), the cerebellum showed differences in neural activity in individuals with Prader-Willi syndrome, a syndrome characterized by obesity and lack of satiation. They later showed that activating neurons within the mouse cerebellum led to a pronounced reduction of food intake. They suggested that this was guided by an increase in striatal dopamine levels and a reduction of the phasic dopamine response following food consumption, which is likely to reduce meal size by attenuating the reward value of food. Zooming in on human brain cells, we find a strong signal for dopaminergic cells as well as GABAergic cells. Targeting GABAergic pathways might be a promising therapeutic strategy for improving MetS and associated diseases; GABA knockdown in mice improved insulin sensitivity, decreased food intake, and induced weight loss (39).

Second, we observed positive genetic correlations with diseases known to be phenotypically associated with MetS, such as coronary artery disease and type 2 diabetes. Furthermore, MetS factor showed genetic correlations with neuropsychiatric diseases such as attention-deficit/hyperactivity disorder, smoking initiation, insomnia, major depressive disorder, cannabis use disorder, and schizophrenia. This is consistent with earlier reports that revealed clinical (40) or genetic (41,42) overlap between MetS components and neuropsychiatric disease and is backed by the neuroregulatory cell involvement and the enrichment of antidepressant drugs in the drug repurposing analysis.

Third, our MetS factor GWAS showed the highest overlap, both genetically and in follow-up results, e.g., the enrichment of brain tissues (43), with WC. Furthermore, the most significant loci from the MetS factor GWAS are near FTO and MC4R, two genes strongly implicated by obesity GWAS (44). This corresponds with other studies that point toward abdominal obesity, which exacerbates other cardiometabolic risk factors, as a major driving force and therapeutic target of MetS (11,45). The Lind GWAS also showed relatively the largest overlap with WC, albeit less pronounced than our MetS factor. However, our MetS factor GWAS is affected by the large sample size and high SNP heritability (h2 = 0.20) of the WC GWAS, and WC’s relatively high genetic factor loading on the MetS factor, which increases power to detect WC-related SNP effects on the MetS factor (15). Larger sample size and higher SNP heritability also affect the power to detect enriched gene sets, tissues, and cell types and might have been insufficient for FG and TG, for example.

Finally, results of drug repurposing analyses suggest fenofibrate as a potential therapeutic agent. Early trials show a moderate beneficial effect of fenofibrate on cardiovascular events (46), but recent reports indicated a significant reduction of major cardiovascular events in individuals with MetS with fenofibrate used as an add-on to statin (adjusted hazard ratio 0.74 [95% CI 0.58–0.93], P = 0.01) (47). Furthermore, it has been suggested that fenofibrate might have a greater beneficial effect in individuals with multiple MetS features (48). Studies in rodents show that fenofibrate targets multiple MetS components, including TGs, HDL-C, insulin resistance, and obesity (4951). However, it is unlikely that one drug would prove to be a panacea for all individuals with MetS, given the heterogeneity of the syndrome.

It is important to consider this work in the light of its main limitations. As we did not have access to an independent, well-powered sample to replicate our MetS factor GWAS findings, our results should be interpreted with caution. The GWAS summary statistics of SBP that we used did not correct for antihypertensive medication use. However, the well-powered GWAS on SBP from the International Consortium for Blood Pressure (ICBP) cohort all corrected for BMI, which would lead to an incorrect genetic structure of the proposed SEM model. We therefore opted for the uncorrected SBP GWAS by Watanabe et al. (52). Also, mapped SNPs and genes might not be causative. Fine mapping and further functional experiments could point toward causative genes and guide therapeutic strategies, such as is exemplified for metabolic traits by Akbari et al. (53), for example. Furthermore, our results depend on the availability of functional data sets in FUMA. Finally, the samples used in our study are, for technical reasons, restricted to individuals of European ancestry and might not generalize to the general population.

In conclusion, to tackle the complex observed phenotypic and genetic covariance of the components that make up MetS, we have used an SEM approach that leverages genetic correlations of individual components to delineate the genetic architecture of MetS (10). We show shared and unique enrichment in specific biological pathways among MetS components, tissues, and cell types. Our findings give starting points for further functional follow-up experiments and provide valuable information for potential therapeutic targets.

This article contains supplementary material online at https://doi.org/10.2337/figshare.20497188.

Acknowledgments. The Framingham Heart Study data were obtained from the database of Genotypes and Phenotypes (dbGaP) (accession no. phs000007). The authors thank all study participants, researchers, and staff (including Meta-Analyses of Glucose and Insulin-related traits Consortium [MAGIC] investigators, the Early Growth Genetics [EGG] Consortium, the COVID-19 host genetics initiative, the DIAbetes Genetics Replication And Meta-analysis [DIAGRAM] consortium, the Psychiatric Genomics Consortium [PGC], the Genetic Investigation of ANthropometric Traits [GIANT] Consortium, CTGlab, and the Social Science Genetic Association Consortium [SSGAC]) who contributed to publicly available summary statistics and the developers of the tools that enabled this study.

Funding. C.d.L. was funded by F. Hoffman-La Roche AG. M.N. is supported by a ZonMw Vici grant, 2020 (09150182010020).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. E.S.v.W., I.E.J., J.E.S., C.d.L., M.N., and D.P. contributed to study conceptualization. E.S.v.W. performed data curation. Investigations were performed by E.S.v.W. and N.Y.B. Software was used by E.S.v.W. and N.Y.B. Visualization was performed by E.S.v.W. E.S.v.W. wrote the original draft of the manuscript. E.S.v.W., N.Y.B., J.E.S., C.d.L., M.N., S.v.d.S., and D.P. reviewed and edited the manuscript. E.S.v.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the 8th Joint Dutch/UK Clinical Genetics Societies and Cancer Genetics Groups meeting.

1.
Alberti
KGMM
,
Eckel
RH
,
Grundy
SM
, et al.;
International Diabetes Federation Task Force on Epidemiology and Prevention
;
Hational Heart, Lung, and Blood Institute
;
American Heart Association
;
World Heart Federation
;
International Atherosclerosis Society
;
International Association for the Study of Obesity
.
Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity
.
Circulation
2009
;
120
:
1640
1645
2.
Wilson
PWF
,
D’Agostino
RB
,
Parise
H
,
Sullivan
L
,
Meigs
JB
.
Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus
.
Circulation
2005
;
112
:
3066
3072
3.
Lakka
HM
,
Laaksonen
DE
,
Lakka
TA
, et al
.
The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men
.
JAMA
2002
;
288
:
2709
2716
4.
Papanastasiou
E
.
The prevalence and mechanisms of metabolic syndrome in schizophrenia: a review
.
Ther Adv Psychopharmacol
2013
;
3
:
33
51
5.
Vancampfort
D
,
Vansteelandt
K
,
Correll
CU
, et al
.
Metabolic syndrome and metabolic abnormalities in bipolar disorder: a meta-analysis of prevalence rates and moderators
.
Am J Psychiatry
2013
;
170
:
265
274
6.
Grundy
SM
.
Cholesterol gallstones: a fellow traveler with metabolic syndrome?
Am J Clin Nutr
2004
;
80
:
1
2
7.
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults
.
Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III)
.
JAMA
2001
;
285
:
2486
2497
8.
Moore
JX
,
Chaudhary
N
,
Akinyemiju
T
.
Metabolic syndrome prevalence by race/ethnicity and sex in the United States, National Health and Nutrition Examination Survey, 1988-2012
.
Prev Chronic Dis
2017
;
14
:
E24
9.
Liang
XP
,
Or
CY
,
Tsoi
MF
,
Cheung
CL
,
Cheung
BMY
.
Prevalence of metabolic syndrome in the United States National Health and Nutrition Examination Survey (nhanes) 2011–2018
.
Eur Heart J
2021
;
42
(
Suppl. 1
):
ehab724.2420
10.
Lusis
AJ
,
Attie
AD
,
Reue
K
.
Metabolic syndrome: from epidemiology to systems biology
.
Nat Rev Genet
2008
;
9
:
819
830
11.
Grundy
SM
,
Brewer
HB
Jr
,
Cleeman
JI
,
Smith
SC
Jr
;
American Heart Association
;
National Heart, Lung, and Blood Institute
.
Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition
.
Circulation
2004
;
109
:
433
438
12.
van Dongen
J
,
Willemsen
G
,
Chen
W-M
,
de Geus
EJC
,
Boomsma
DI
.
Heritability of metabolic syndrome traits in a large population-based sample
.
J Lipid Res
2013
;
54
:
2914
2923
13.
Lind
L
.
Genome-wide association study of the metabolic syndrome in UK Biobank
.
Metab Syndr Relat Disord
2019
;
17
:
505
511
14.
Beran
TN
,
Violato
C
.
Structural equation modeling in medical research: a primer
.
BMC Res Notes
2010
;
3
:
267
15.
Grotzinger
AD
,
Rhemtulla
M
,
de Vlaming
R
, et al
.
Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits
.
Nat Hum Behav
2019
;
3
:
513
525
16.
Malik
S
,
Wong
ND
,
Franklin
SS
, et al
.
Impact of the metabolic syndrome on mortality from coronary heart disease, cardiovascular disease, and all causes in United States adults
.
Circulation
2004
;
110
:
1245
1250
17.
Girman
CJ
,
Rhodes
T
,
Mercuri
M
, et al.;
4S Group and the AFCAPS/TexCAPS Research Group
.
The metabolic syndrome and risk of major coronary events in the Scandinavian Simvastatin Survival Study (4S) and the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS)
.
Am J Cardiol
2004
;
93
:
136
141
18.
Bulik-Sullivan
BK
,
Loh
PR
,
Finucane
HK
, et al.;
Schizophrenia Working Group of the Psychiatric Genomics Consortium
.
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
.
Nat Genet
2015
;
47
:
291
295
19.
Hartmann
K
,
Krois
J
,
Waske
B
.
R factanal package, 2018
.
20.
R Core Team
.
R: A Language and Environment for Statistical Computing
.
Vienna, Austria
,
R Foundation for Statistical Computing
,
2019
21.
Kaplan D
.
Structural Equation Modeling: Foundations and Extensions
.
Thousand Oaks, CA
,
SAGE Publications
,
2008
22.
Auton
A
,
Brooks
LD
,
Durbin
RM
, et al.;
1000 Genomes Project Consortium
.
A global reference for human genetic variation
.
Nature
2015
;
526
:
68
74
23.
Turner
,
S. D.
qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots
.
2018
.
Accessed 24 November 2021. Available from https://doi.org/10.21105/joss.00731
24.
Watanabe
K
,
Taskesen
E
,
van Bochoven
A
,
Posthuma
D
.
Functional mapping and annotation of genetic associations with FUMA
.
Nat Commun
2017
;
8
:
1826
25.
de Leeuw
CA
,
Mooij
JM
,
Heskes
T
,
Posthuma
D
.
MAGMA: generalized gene-set analysis of GWAS data
.
PLOS Comput Biol
2015
;
11
:
e1004219
26.
Watanabe
K
,
Umićević Mirkov
M
,
de Leeuw
CA
,
van den Heuvel
MP
,
Posthuma
D
.
Genetic mapping of cell type specificity for complex traits
.
Nat Commun
2019
;
10
:
3222
27.
Choi
SW
,
Shin
T
,
Mak
H
,
Hoggart
CJ
,
O’reilly
PF
.
EraSOR: erase sample overlap in polygenic score analyses
.
13 December 2021 [preprint]. bioRxiv:2021.12.10.472164 (2021)
.
28.
Choi
SW
,
O’Reilly
PF
.
PRSice-2: Polygenic Risk Score software for biobank-scale data
.
Gigascience
2019
;
8
:
giz082
29.
Harrell
FE
Jr
.
rms: Regression Modeling Strategies
.
Accessed 30 June 2022. Available from https://cran.r-project.org/web/packages/rms/index.html
30.
Bell
N
,
Uffelmann
E
,
Posthuma
D
.
Using DRUg Gene SEt Analysis (DRUGSEA) to identify drug repurposing candidates for psychiatric and non-psychiatric phenotypes
.
9 September 2022 [preprint]. medRxiv: 2022.09.06.22279660v1
31.
Pladevall
M
,
Singal
B
,
Williams
LK
, et al
.
A single factor underlies the metabolic syndrome: a confirmatory factor analysis
.
Diabetes Care
2006
;
29
:
113
122
32.
Babashamsi
MM
,
Koukhaloo
SZ
,
Halalkhor
S
,
Salimi
A
,
Babashamsi
M
.
ABCA1 and metabolic syndrome; a review of the ABCA1 role in HDL-VLDL production, insulin-glucose homeostasis, inflammation and obesity
.
Diabetes Metab Syndr
2019
;
13
:
1529
1534
33.
Liu
Y
,
Tang
C
.
Regulation of ABCA1 functions by signaling pathways
.
Biochim Biophys Acta
2012
;
1821
:
522
529
34.
Kraja
AT
,
Vaidya
D
,
Pankow
JS
, et al
.
A bivariate genome-wide approach to metabolic syndrome: STAMPEED consortium
.
Diabetes
2011
;
60
:
1329
1339
35.
Shim
U
,
Kim
H-N
,
Sung
Y-A
,
Kim
H-L
.
Pathway analysis of metabolic syndrome using a genome-wide association study of Korea Associated Resource (KARE) cohorts
.
Genomics Inform
2014
;
12
:
195
202
36.
Gribble
FM
,
Reimann
F
.
Function and mechanisms of enteroendocrine cells and gut hormones in metabolism
.
Nat Rev Endocrinol
2019
;
15
:
226
237
37.
Duez
H
,
Lefebvre
B
,
Poulain
P
, et al
.
Regulation of human apoA-I by gemfibrozil and fenofibrate through selective peroxisome proliferator-activated receptor α modulation
.
Arterioscler Thromb Vasc Biol
2005
;
25
:
585
591
38.
Low
AYT
,
Goldstein
N
,
Gaunt
JR
, et al
.
Reverse-translational identification of a cerebellar satiation network
.
Nature
2021
;
600
:
269
273
39.
Geisler
CE
,
Ghimire
S
,
Bruggink
SM
, et al
.
A critical role of hepatic GABA in the metabolic dysfunction and hyperphagia of obesity
.
Cell Rep
2021
;
35
:
109301
40.
Nousen
EK
,
Franco
JG
,
Sullivan
EL
.
Unraveling the mechanisms responsible for the comorbidity between metabolic syndrome and mental health disorders
.
Neuroendocrinology
2013
;
98
:
254
266
41.
Hübel
C
,
Gaspar
HA
,
Coleman
JRI
, et al.;
ADHD Working Group of the Psychiatric Genomics Consortium
;
Meta-Analyses of Glucose and Insulin-related traits consortium (MAGIC)
;
Autism Working Group of the Psychiatric Genomics Consortium
;
Bipolar Disorder Working Group of the Psychiatric Genomics Consortium
;
Eating Disorders Working Group of the Psychiatric Genomics Consortium
;
Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium
;
OCD & Tourette Syndrome Working Group of the Psychiatric Genomics Consortium
;
PTSD Working Group of the Psychiatric Genomics Consortium
;
Schizophrenia Working Group of the Psychiatric Genomics Consortium
;
Sex Differences Cross Disorder Working Group of the Psychiatric Genomics Consortium
;
Substance Use Disorders Working Group of the Psychiatric Genomics Consortium
;
German Borderline Genomics Consortium
;
International Headache Genetics Consortium
.
Genetic correlations of psychiatric traits with body composition and glycemic traits are sex- and age-dependent
.
Nat Commun
2019
;
10
:
5765
42.
Demontis
D
,
Walters
RK
,
Martin
J
, et al.;
ADHD Working Group of the Psychiatric Genomics Consortium (PGC)
;
Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium
;
23andMe Research Team
.
Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder
.
Nat Genet
2019
;
51
:
63
75
43.
Locke
AE
,
Kahali
B
,
Berndt
SI
, et al.;
LifeLines Cohort Study
;
ADIPOGen Consortium
;
AGEN-BMI Working Group
;
CARDIOGRAMplusC4D Consortium
;
CKDGen Consortium
;
GLGC
;
ICBP
;
MAGIC Investigators
;
MuTHER Consortium
;
MIGen Consortium
;
PAGE Consortium
;
ReproGen Consortium
;
GENIE Consortium
;
International Endogene Consortium
.
Genetic studies of body mass index yield new insights for obesity biology
.
Nature
2015
;
518
:
197
206
44.
van der Klaauw
AA
,
Farooqi
IS
.
The hunger genes: pathways to obesity
.
Cell
2015
;
161
:
119
132
45.
Després
JP
.
Abdominal obesity: the most prevalent cause of the metabolic syndrome and related cardiometabolic risk
.
Eur Heart J
2006
;
8
(
Suppl.
):
B4
B12
46.
Jakob
T
,
Nordmann
AJ
,
Schandelmaier
S
,
Ferreira-González
I
,
Briel
M
.
Fibrates for primary prevention of cardiovascular disease events
.
Cochrane Database Syst Rev
2016
;
11
:
CD009753
47.
Kim
NH
,
Han
KH
,
Choi
J
,
Lee
J
,
Kim
SG
.
Use of fenofibrate on cardiovascular outcomes in statin users with metabolic syndrome: propensity matched cohort study
.
BMJ
2019
;
366
:
l5125
48.
Scott
R
,
O’Brien
R
,
Fulcher
G
, et al.;
Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) Study Investigators
.
Effects of fenofibrate treatment on cardiovascular disease risk in 9,795 individuals with type 2 diabetes and various components of the metabolic syndrome: the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study
.
Diabetes Care
2009
;
32
:
493
498
49.
Shin
Y
,
Lee
M
,
Lee
D
,
Jang
J
,
Shin
SS
,
Yoon
M
.
Fenofibrate regulates visceral obesity and nonalcoholic steatohepatitis in obese female ovariectomized C57BL/6J mice
.
Int J Mol Sci
2021
;
22
:
3675
50.
Jeong
S
,
Yoon
M
.
Fenofibrate inhibits adipocyte hypertrophy and insulin resistance by activating adipose PPARα in high fat diet-induced obese mice
.
Exp Mol Med
2009
;
41
:
397
405
51.
Yoon
M
,
Jeong
S
,
Nicol
CJ
, et al
.
Fenofibrate regulates obesity and lipid metabolism with sexual dimorphism
.
Exp Mol Med
2002
;
34
:
481
488
52.
Watanabe
K
,
Stringer
S
,
Frei
O
, et al
.
A global overview of pleiotropy and genetic architecture in complex traits
.
Nat Genet
2019
;
51
:
1339
1348
53.
Akbari
P
,
Gilani
A
,
Sosina
O
, et al.;
Regeneron Genetics Center
;
DiscovEHR Collaboration
.
Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity
.
Science
2021
;
373
:
eabf8683
54.
Dupuis
J
,
Langenberg
C
,
Prokopenko
I
, et al.;
DIAGRAM Consortium
;
GIANT Consortium
;
Global BPgen Consortium
;
Anders Hamsten on behalf of Procardis Consortium
;
MAGIC investigators
.
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
.
Nat Genet
2010
;
42
:
105
116
55.
Teslovich
TM
,
Musunuru
K
,
Smith
AV
, et al
.
Biological, clinical and population relevance of 95 loci for blood lipids
.
Nature
2010
;
466
:
707
713
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.