Multiomic Mendelian Randomization Study Investigating the Impact of PCSK9 and HMGCR Inhibition on Type 2 Diabetes Across Five Populations

Type 2 diabetes (T2D) affects ∼410 million people worldwide (1), with prevalence differing significantly by region and race and ethnicity (2). For example, in the U.S., T2D prevalence in the Hispanic (HISP) population is nearly double that in the European (EUR) population (2). These differences highlight the need for a deeper understanding of T2D risk factors, molecular mechanisms, and treatment strategies across diverse populations (3).

T2D often coexists with cardiovascular disease (CVD), the leading cause of death in patients with T2D (4). Preventing CVD through lipid-lowering therapies, such as statins and proprotein convertase subtilisin/kexin 9 (PCSK9) inhibitors, is crucial in T2D management (5). Although statins have been linked to a modestly increased T2D risk in recent meta-analyses (6–8), the relationship between PCSK9 inhibition and T2D is less clear. Randomized controlled trial (RCT) data failed to find evidence of an adverse impact on T2D risk (9), and long-term efficacy and safety data are currently not available. Early drug target Mendelian randomization (MR) studies suggested that PCSK9 variants lower LDL cholesterol (LDL-C) but increase T2D risk (7), although more recent MR studies have not confirmed this (7). Most studies have focused on the EUR population, leaving the long-term effects of statin and PCSK9 inhibition on T2D in non-EUR populations unknown.

To address long-term safety concerns in non-EUR populations, we used summary-level genome-wide association study (GWAS) data from East Asian (EAS), South Asian (SAS), African (AFR), HISP, and EUR populations, performing drug target MR analyses to assess the impact of LDL-C lowering via PCSK9 variants on T2D risk and glycemic traits. We compared these estimates with those for LDL-C lowering by the statin target HMG-CoA reductase (HMGCR) (8). A multiomic approach was applied, using PCSK9 instruments based on LDL-C levels (10), circulating PCSK9 protein levels (10), and liver PCSK9 expression data (11) to model the effects of anti-PCSK9 monoclonal antibodies and inclisiran (12). Given preclinical findings suggesting pancreatic PCSK9 may influence β-cell LDLR expression and insulin secretion (13), we also analyzed the glycemic impact of pancreatic PCSK9 expression. These results will enhance our understanding of the long-term safety of PCSK9 and HMGCR inhibition in diverse populations and their efficacy for CVD management in the context of T2D, addressing health disparities caused by the underrepresentation of non-EUR populations in studies (14).

Figure 1 provides an overview of the study, which follows the STROBE guideline (STROBE checklist).

This study used only preexisting deidentified publicly accessible summary-level GWAS data. Details regarding sex, age, and other demographic groupings for these data sets are provided in the respective GWAS publications referenced in the manuscript (with links provided in Supplementary Table 1). Sex was not considered a factor in the statistical analysis. Furthermore, all contributing GWAS studies received institutional review board approval and informed consent from participants, in line with the Declaration of Helsinki.

For our exposure LDL-C data, we used GWASs of LDL-C levels from the 2021 Global Lipid Genetics Consortium (GLGC) LDL-C population-specific meta-analyses of AFR (n ≤ 94,623), EAS (n ≤ 82,587), SAS (n ≤ 40,472), HISP (n ≤ 46,039), and EUR populations (n ≤ 1,320,016) (15). For T2D in EAS, SAS, and EUR populations, we used the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) population-specific meta-analyses (EAS n = 433,540; SAS n = 49,492; EUR n = 898,132) (16). HISP and AFR data were not available in the DIAMANTE cohorts, and we used HISP T2D data (n = 10,106) from the Population Architecture using Genomics and Epidemiology study (17). Because a recent MR study evaluated the impact of lipids and lipid-lowering drug targets on T2D in the AFR population using results from the Million Veteran Program cohort (18), we used another independent AFR GWAS of T2D (N = 4,347) (19). We used GWAS data from the Meta-Analyses of Glucose and Insulin-Related Traits (MAGIC) Consortium (20) to evaluate the glycemic impact of lipid-lowering targets on HbA_1c, fasting glucose, fasting insulin, and 2-h glucose levels across five populations (2-h glucose data were unavailable for the SAS population). We also assessed the impact on insulin-stimulated glucose uptake in the EUR cohort using GWASs of the modified Stumvoll insulin sensitivity index (ISI) and insulin fold change (IFC) (21).

To construct the PCSK9 and HMGCR instruments, we selected genetic variants within 100 kb on either side of gene boundaries that were associated with LDL-C levels (genome-wide significance P < 5 × 10⁻⁸) to proxy the primary physiologic response to pharmacologic inhibition of these targets (9). We clumped the PCSK9 and HMGCR variants at linkage disequilibrium (LD) r² ≤ 0.2 using a 250-kb window and the respective population-specific 1000 Genomes Project (1000G) reference panels (22). That is, for each population-specific analysis, we applied the corresponding reference panels from the 1000G: the EAS panel for EAS analyses, the SAS panel for SAS analyses, the AFR panel for AFR analyses, the HISP panel for HISP analyses, and the EUR panel for EUR analyses. For PCSK9, we also created instruments composed only of functional variants (the gain-of-function R46L [rs505151] and the loss-of-function E670G [rs11591147] (23)). Both E670G and R46L data were available for analysis in SAS, AFR, HISP, and EUR populations, whereas only R46L data were available in the EAS population because E670G information was not present in the EAS LDL-C GWAS data. Detailed information for each drug target instrument is shown in Supplementary Tables 2 and 3.

To further explore the impact of PCSK9 inhibition on T2D and glycemic traits, we conducted drug target MR analyses using expression quantitative trait loci (eQTL) from GTEx (version 8) and protein QTL (pQTL) from deCODE (N = 35,559) (10). We created tissue-specific PCSK9 expression instruments using liver eQTL (n = 178) and pancreatic eQTLs (n = 243) (11). cis-PCSK9 variants were selected and clumped using the same criteria as for LDL-C data-derived PCSK9 instruments. PCSK9 protein levels were measured in normalized units (10), and eQTL data were in transcripts per million (11). Because pQTL and eQTL data for non-EUR cohorts were unavailable, these analyses were limited to EUR T2D data and glycemic markers.

Change in LDL-C levels is the primary biomarker measured to assess the physiologic response to PCSK9 inhibition and statin therapy (24,25), and dyslipidemia has also been associated with T2D risk (20). Therefore, we investigated the relationships of circulating LDL-C and both T2D and glycemic traits in the five populations using polygenic LDL-C instruments. For the polygenic LDL-C instruments, we identified variants associated in respective population-specific 2021 GLGC GWASs of LDL-C levels at conventional genome-wide significance (P < 5 × 10⁻⁸) located throughout the genome. We clumped the variants at LD r² ≤ 0.001 (10,000-kb window) using the appropriate 1000G reference panel (22). Information for polygenic LDL-C instruments is provided in Supplementary Table 2.

The assumptions of MR also apply to drug target MR. These assumptions (Supplementary Fig. 1) include 1) relevance (MR instruments must be associated with the exposure) 2) exchangeability (MR instruments should not influence outcomes through pathways other than the exposure of interest), and 3) exclusion restriction (instruments should not affect the outcome via an exposure-independent mechanism or influence another trait that could affect the outcome [no horizontal pleiotropy]) (26). To assess the relevance assumption, we calculated F statistics and R² for each population-specific variant, retaining only strong instruments (F statistic >10) (26). Complementary MR methods and alternative instruments were used to assess adherence to the exchangeability and exclusion restriction assumptions (26). For single-variant instruments, we used the Wald ratio. For instruments with two or more variants, we applied inverse-variance weighting (IVW) MR, MR Egger, and maximum likelihood methods, incorporating correlation matrices from the 1000G to account for LD between variants (27). These methods assessed the relationship of PCSK9 and HMGCR with outcomes and checked for potential MR assumption violations (26,27). Consistency across methods indicates unbiased estimates (26). For polygenic LDL-C and PCSK9 instruments (R46L and E670G variants), IVW was the main method for multi–single nucleotide polymorphism (SNP) instruments, with Wald ratio for single-SNP instruments. Complementary methods (MR Egger, weighted median, and weighted mode) were used as sensitivity analyses to ensure robustness and evaluate MR assumption violations, such as horizontal pleiotropy (26,27). We also conducted the MR Egger intercept test, Cochran Q test for heterogeneity, and MR Steiger test to check the causal direction (27). The MR LASSO method was used to remove outliers in polygenic LDL-C analyses.

The MAGIC GWAS glycemic trait data were adjusted for BMI, which can introduce bias into the SNP associations and MR effect estimates (28). Multivariable MR (MVMR), which estimates the direct effect of multiple exposures on an outcome, can reduce this bias by incorporating the heritable covariate (BMI) used in the GWAS (29). To assess the robustness of glycemic trait analyses, we performed MVMR incorporating BMI genetics into the PCSK9, HMGCR, and polygenic LDL-C models using GWAS BMI data available for all populations (30–33). MVMR instruments were constructed using the same instrumentation strategies as outlined for the drug target and single-variable MR methods described above. We could not construct an MVMR instrument for PCSK9 in the SAS population to confirm the HbA_1c finding because the PCSK9 SAS instrument contained only a single SNP, making it impossible to perform these analyses.

We report MR 95% CIs as odds ratios (ORs) for T2D risk and effect estimates for continuous glycemic traits, aligning estimates with the physiologic effects of PCSK9 inhibitors and statins by converting MR estimates to an SD lowering in LDL-C. Although we advise caution in relying solely on P values (34), we used a Bonferroni-corrected threshold of 0.005 (0.05/5 outcomes × two targets) to define strong evidence for a genetics-based relationship. Findings with P values between 0.005 and 0.05 were considered weak evidence. For MVMR sensitivity analyses, we used P = 0.05 and assessed consistency through overlapping CIs across MR methods.

For drug target estimates with P < 0.05, we performed colocalization analyses to assess shared causal variants between exposure and outcome in the PCSK9 or HMGCR locus, evaluating the exclusion restriction MR assumption (35). The primary method involved testing for single or multiple causal variants using the coloc.abf function in the coloc R package. This function estimates posterior probabilities for five configurations of association between two traits, assuming one causal variant per trait: 1) H0 (no association for either trait), 2) H1 (association for trait 1), 3) H2 (association for trait 2), 4) H3 (both traits are associated with different variants) and 5) H4 (both traits share a causal variant) (35). We included variants within ±100 kb of the loci and used default coloc priors (p₁, p₂ = 1 × 10⁻⁴ and p₁₂ = 1 × 10⁻⁵). Low H3 and H4 probabilities with high H1 indicated underpowered analyses, possibly because of weak genetic signals (35). An alternative H4 was calculated by dividing H4 by the sum of H3 and H4, as used in other drug target MR studies (35). If H3 >0.6 suggested multiple causal variants, we used SuSiE regression colocalization to confirm multiple shared variants (36). Colocalization evidence was defined by H4 or alternate H4 >0.60.

This study was conducted using publicly available data and codes that are accessible in public databases and open for public access. Links to GWAS sources and the lipid-lowering drug targets and polygenic lipid instruments are available in Supplementary Table 1. All analyses were completed with existing software packages. This study uses the TwoSampleMR (https://mrcieu.github.io/TwoSampleMR/), MendelianRandomization (https://cran.r-project.org/web/packages/MendelianRandomization/index.html) (37,38), and coloc R (https://github.com/chr1swallace/coloc) packages, with R software (version 4.0.3). BioRender.com was used to assist with figure generation. Inquiries can be directed to the corresponding author.

F statistics for PCSK9 and HMGCR drug target instruments in each population were strong (Supplementary Table 2). The average F statistics for LDL-C lowering ranged from 40.13 (SAS) to 279 (EUR) for PCSK9 variants and from 55.3 (AFR) to 241.3 (EUR) for HMGCR variants. Alternate PCSK9 instruments (Supplementary Tables 3 and 4) were similarly robust, suggesting minimal weak instrument bias (39) (Supplementary Table 5). PCSK9 instruments explained an average of 1.5% of LDL-C variance (3.9% in EAS and 0.5% in SAS), whereas HMGCR instruments explained 0.36% on average (0.12% in AFR and 0.62% in EAS). Polygenic LDL-C instruments were also strong (Supplementary Table 2).

Estimates of genetically proxied PCSK9 inhibition and T2D risk in SAS, EAS, HISP, and EUR populations included the null (Fig. 2 and Supplementary Table 6), aligning with results using functional variants (R46L and E670G in SAS, HISP, and EUR and R46L in EAS) (Supplementary Table 7). In the AFR population, there was weak evidence that LDL-C lowering via PCSK9 variants increased T2D risk (OR 1.53; 95% CI 1.058–2.22; P = 0.024), but this was inconsistent across instruments. In contrast, strong evidence in EAS and EUR populations indicated HMGCR inhibition increased T2D risk, with SAS showing weak evidence (OR 1.698; 95% CI 1.051–2.743; P = 0.031) (Fig. 2). HMGCR estimates were generally consistent across MR methods, with no evidence of pleiotropy from MR Egger intercept estimates. Regarding colocalization of the T2D results with P values <0.05, we observed evidence of a shared causal variant between LDL-C and T2D in the EAS, EUR, and SAS populations (Supplementary Table 8). The PCSK9 T2D finding in the AFR population did not colocalize (H4 0.033). However, we observed evidence of a single shared causal variant between LDL-C and T2D in HMGCR for both EAS and SAS populations (EAS H4 0.682; SAS H4 0.813), whereas SuSiE colocalization confirmed evidence of shared multiple causal variants between LDL-C and T2D in the HMGCR locus in the EUR population (SuSiE H4 0.76) (Supplementary Table 8).

IVW and Wald ratio results from the primary drug target MR analyses on LDL-C lowering via PCSK9 and HMGCR loci and glycemic markers are presented in Fig. 3. Although no PCSK9 estimates surpassed the multiple comparisons threshold, we found weak evidence for reduced HbA_1c in the SAS population and increased fasting insulin in the EAS population (Fig. 3 and Supplementary Table 6), with the latter being robust and directionally consistent in MVMR adjusting for BMI (Supplementary Tables 9–13). The PCSK9 HbA_1c finding in the SAS population was supported by colocalization (H4 0.835) (Supplementary Table 8), whereas the EAS population showed some evidence for colocalization (H4 0.537), although below the study threshold. Estimates using PCSK9 functional variants (R46L and E670G) spanned the null (Supplementary Table 7).

For HMGCR, we found strong evidence that genetically proxied inhibition increased HbA_1c in the HISP population (β = 0.167; 95% CI 0.059–0.275; P = 0.002), supported by colocalization (H4 0.813) (Supplementary Table 8) and robust after BMI adjustment (Supplementary Table 12), with weaker evidence in the EUR population (β = 0.040; 0.001–0.0979; P = 0.043), although not robust in MVMR (Supplementary Table 13). No colocalization was observed for other glycemic traits in the HMGCR locus with MR estimate P values <0.05. Estimates for insulin resistance measures in the EUR population indicated weak evidence that a 1-SD reduction in LDL-C via PCSK9 variants increased IFC (β = 0.14; 95% CI 0.03–0.34; P = 0.009) but not ISI (β = −0.07; −0.16 to 0.012; P = 0.092), whereas HMGCR variants showed weak evidence for reduced ISI (β = −0.16; −0.28 to −0.03; P = 0.013) (Table 14). These MR estimates were consistent with results using PCSK9 functional variants across populations (Supplementary Tables 3 and 7).

Given the mechanisms of approved PCSK9 inhibitors (anti-PCSK9 antibodies lowering circulating PCSK9 protein and inclisiran inhibiting hepatic PCSK9 expression) (10,40), we investigated the impact of genetically lowered circulating, hepatic, and pancreatic PCSK9 on T2D and glycemic markers. Results aligned with LDL-C lowering via PCSK9 variants (Figs. 4 and 5 and Supplementary Table 15). We found weak evidence linking genetically lowered circulating PCSK9 protein with reduced HbA_1c and 2-h glucose, but no associations with other glycemic markers, and the HbA_1c finding was not replicated with hepatic/pancreatic PCSK9 instruments. In IFC and ISI analyses, PCSK9 pQTL instruments showed weak evidence of increased IFC (β = 0.04; 95% CI 0.009–0.078; P = 0.013) (Supplementary Table 15).

Full results of the polygenic lipid instrument are presented in Supplementary Table 10. Before interpretation, we aligned each polygenic LDL-C estimate to correspond to the expected physiologic response to pharmacologic lipid-lowering therapy (i.e., a change in T2D risk or glycemic marker level per unit SD lowering in LDL-C). In line with the results observed using HMGCR variants, we observed strong evidence that lower LDL-C levels increased risk of T2D in SAS (OR 1.34; 95% CI 1.166–1.529; P = 2.81 × 10⁻⁵) and EUR populations (1.056; 1.013–1.101; P = 0.001). Polygenic LDL-C estimates in the other populations included the null.

We used drug target MR to compare the relationships of genetic LDL-C lowering via PCSK9 and HMGCR inhibition with T2D and glycemic markers using data from five populations. We found a neutral safety profile for PCSK9 inhibition on T2D in SAS, EAS, HISP, and EUR populations, adding to the growing body of genetics-based literature finding generally safe adverse effect profiles of long-term PCSK9 lowering (41–44). Sensitivity analyses using functional PCSK9 variant R46L, PCSK9 protein levels, and both hepatic and pancreatic PCSK9 gene expression similarly yielded null results. Our assessment of the genetic PCSK9 T2D relationships across cohorts representing five populations using complementary MR methods, sensitivity analyses based upon functional PCSK9 variants, and analyses using multiomic PCSK9 instruments, along with other lines of evidence failing to find an adverse increase in T2D risk by PCSK9 inhibition (18), further strengthens our inference of the neutral adverse effect profile and should be reassuring for any concerns regarding T2D diabetes risk from pharmacologic PCSK9 inhibition. Importantly, although global T2D prevalence is high, it varies widely across geographic regions and by race and ethnicity (2), and epidemiologic data suggest that certain populations may have higher or lower risk of developing T2D (2). Nevertheless, despite the need for more diversity in all clinical trials and genetics-based studies (14), apart from a recent study investigating the relationships of lipids, lipid-lowering targets, and T2D risk among African Americans (18), the existing PCSK9 T2D literature is based primarily on analysis of individuals in the EUR population, highlighting the need for population-specific work to inform our understanding of PCSK9 inhibition and the risk of T2D. Finally, our polygenic LDL-C findings aligned with previous observational and genetics-based work finding that lower levels of circulating LDL-C are linked with higher T2D risk (Supplementary Discussion).

We found weak evidence (P < 0.05) indicating PCSK9 inhibition was associated with increased T2D risk in the AFR population; however, the result was not robust to our sensitivity analyses using functional PCSK9 variants as instruments. In addition, we did not find a relationship between HMGCR and T2D risk in the AFR data. Our AFR T2D results were based on cohorts from continental Africa, and neither the PCSK9 nor the HMGCR finding aligned with the recent MR study by Soremekun et al. (18) assessing the impact of LDL-C lowering by PCSK9 and HMGCR inhibition on T2D risk among African Americans in the Million Veteran Program. Soremekun et al. (18) found that HMGCR inhibition increased T2D risk, but PCKS9 inhibition did not. These discrepancies may also reflect the impact of genetic admixture, the mixing of different ancestral populations (45), which can influence genetic associations by introducing variability in allele frequencies and genetic backgrounds across populations (45). In the context of genetic studies, admixture can lead to confounding effects, where associations identified in one population may not hold true in another because of differences in genetic architecture (45). This variability can obscure or inflate the true effects of genetic variants on traits such as T2D risk. For example, in populations with a high degree of admixture, such as the African American population (46), the presence of alleles from different ancestral backgrounds may alter the expression and impact of genes targeted by therapies such as HMGCR inhibitors or PCSK9 inhibitors. Therefore, the observed genetic associations in a more genetically homogenous population may differ when studied in an admixed population, leading to discrepancies in the findings. Moreover, Africans possess more genetic and linguistic diversity, with >3,000 indigenous languages, largely shaped by geography. However, >90% of these ethnolinguistic groups lack genetic data (14). Focusing on the African diaspora overlooks Africa’s genetic diversity, perpetuating imbalances and health disparities (14), underscoring the importance of future investigation and replication with these populations.

For HMGCR inhibition, our results replicate and extend reports of increased T2D risk from RCTs evaluating statin use and MR analyses using variants in the HMGCR region as proxies for long-term HMGCR inhibition (6–8), by also finding adverse relationships between HMGCR and T2D risk in EAS (having strong genetic evidence with drug target MR estimates surpassing correction for multiple comparisons and also demonstrating evidence of colocalization) and SAS populations (weak evidence with a less precise MR estimate, but evidence of colocalization). They also extend recent population genetic work in diverse populations that applied a clustering-based method to a GWAS of T2D along with other cardiometabolic diseases and glycemic markers to develop genetic signatures underlying subtypes of T2D using GWAS data from EUR and non-EUR cohorts (47). We did not find corresponding evidence for adverse effects of HMGCR inhibition on glycemic traits in either the EAS or SAS population (in fact, HMGCR inhibition reduced fasting glucose in the EAS population, and this result was robust in MVMR to correct bias for the BMI adjustment in the fasting glucose GWAS data, suggesting some potential glycemic benefits); it is possible that the adverse impact on T2D may be via potential pathways that have been previously reported (e.g., statin-related weight gain) (48).

Because the heterogenous HMGCR findings suggested adverse relationships in three of five study populations, suggesting potential population specificity and biologic mechanisms, it is possible that the observed differences in the estimates reflect differing allele frequencies of the HMGCR variants across the populations; however, the R² values for HMGCR instruments were generally comparable across populations, and the variants used for population-specific instruments were largely distinct, likely capturing the genetic architecture of the HMGCR locus specific to each population. Furthermore, MR studies may be biased by population differences, which include differences in allele frequencies, between the exposure and outcome data (49). Although we matched populations between the exposure and outcome pairs, we cannot eliminate the possibility that there is remaining population stratification present between the GLGC LDL-C, DIAMANTE T2D, and MAGIC glycemic trait cohorts that may influence HMGCR T2D and other findings in our study. Therefore, future studies are necessary with additional data sources to replicate and confirm the suggested population-level differences in lifelong HMGCR inhibition. We emphasize, as previous studies have, that the modest increase in T2D risk with statin therapy does not outweigh its significant cardiovascular benefits (7). Although the drug target MR design cannot explore mechanisms, it has been suggested that statins may increase T2D risk by impairing pancreatic insulin secretion and reducing insulin sensitivity in adipose tissue (50). The genetic risk of long-term HMGCR inhibition may not match that of shorter statin therapy. Exploratory analyses showed no link between HMGCR inhibition and fasting insulin or postprandial insulin resistance (21), indicating the T2D risk is not due to increased insulin resistance. In contrast, PCSK9 inhibition was associated with reduced insulin resistance, because lower PCSK9 was linked to increased IFC. These findings were consistent across several PCSK9 instruments, suggesting a potential beneficial effect of PCSK9 inhibition on insulin secretion, warranting further study.

There are study limitations. First, although participants from five populations across 34 countries and four continents improve our understanding of lipid-lowering therapies and T2D risk, the results may not apply to unrepresented populations or geographically distinct groups within included populations. We were unable to evaluate country-specific heterogeneity or ethnolinguistic differences within populations. Causal inference requires triangulating study designs (51), and although increased diversity in genetic studies is important, long-term RCTs across diverse populations are needed to further understand lipid-lowering therapy and T2D relationships. Some population-based estimates showed heterogeneity, possibly reflecting differences in health care access and glycemic control. For example, in the HISP cohort, HMGCR inhibition was linked with increased HbA_1c, whereas PCSK9 inhibition was not, potentially because of health care challenges in this population (52). Recent work found that HISP patients who preferred Spanish had poorer HbA_1c control than those who preferred English (53), highlighting the need for programs to address organizational barriers to glycemic control in the HISP population (53). Other limitations of the drug target MR framework include the inability to assess off-target effects of PCSK9 and HMGCR inhibition beyond their lipid-lowering mechanisms. Future non-EUR MR studies will be essential as more data becomes available. Although cis-instruments reduce MR assumption violations compared with polygenic MR, confounding or pleiotropy bias cannot be fully ruled out (26). The Supplementary Discussion lists study strengths.

In conclusion, our study finds no adverse impact of genetically proxied LDL-C lowering by PCSK9 variants on T2D or glycemic markers in EAS, SAS, HISP, and EUR cohorts. We observed an adverse relationship in the AFR population, but this was not robust in sensitivity analyses and conflicts with recent findings from African American data using U.S.-based data, highlighting the need for further research. For HMGCR, we confirm a slight increase in T2D risk, although this is likely outweighed by the cardiovascular benefits of statin therapy. These findings should guide clinicians and patients concerned about T2D risk when considering lipid-lowering therapies.

Acknowledgments. The authors thank the contributing GWASs and the participants and investigators in those studies, without whom this effort would not be possible. The graphical abstract, Fig. 1, and the drug target icons in the forest plots were made using BioRender.com.

Funding. This work was supported by National Institutes of Health intramural funding (ZIA-AA000242 to F.W.L) and the Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism.

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

Author Contributions. D.B.R. analyzed the data and wrote the first manuscript draft. D.B.R. and F.W.L. designed the study. J.W., J.J., P.P., C.C., G.D.S., D.R., and F.W.L. provided critical revision of the manuscript for important intellectual content. D.B.R. 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 9th Meeting of the Study Group on Genetics of Diabetes, Exeter, U.K., 17–19 April 2024.