Multiomic Mendelian Randomization Study Investigating the Impact of PCSK9 and HMGCR Inhibition on Type 2 Diabetes Across Five Populations
Open accessAbstract
The prevalence of type 2 diabetes (T2D) varies among populations of different races/ethnicities. The influence of genetically proxied LDL cholesterol lowering through proprotein convertase subtilisin/kexin 9 (PCSK9) and HMG-CoA reductase (HMGCR) on T2D in non-European populations is not well established. A drug target Mendelian randomization approach was used to assess the effects of PCSK9 and HMGCR inhibition on T2D risk and glycemic traits in five populations: East Asian (EAS), South Asian (SAS), Hispanic (HISP), African (AFR), and Europe (EUR). Our study did not find relationships between genetically proxied PCSK9 inhibition and T2D risk in the EAS (odds ratio [OR] 1.02; 95% CI 0.95–1.10), SAS (1.05; 0.97–1.14), HISP (1.03; 0.94–1.12), or EUR population (1.04; 0.98–1.11). However, in the AFR population, primary analyses suggested an increased risk of T2D resulting from PCSK9 inhibition (OR 1.53; 95% CI 1.058–2.22; P = 0.024), although this was not supported in sensitivity analyses. Genetically proxied HMGCR inhibition was associated with an increased risk of T2D in SAS (OR 1.44; 95% CI 1.30–1.61; P = 9.8 × 10−12), EAS (1.36; 1.22–1.51; P = 4.2 × 10−10), and EUR populations (1.52; 1.21–1.90; P = 3.3 × 10−4). These results were consistent across various sensitivity analyses, including colocalization, indicating a robust finding. The findings indicate a neutral impact of long-term PCSK9 inhibition on T2D and glycemic markers in most non-EUR populations, with a potential increased risk in AFR cohorts. By contrast, HMGCR inhibition increased the risk of T2D in SAS, EAS, and EUR cohorts, underscoring the need to consider diversity in genetic research on metabolic diseases. Article Highlights:
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The discordance between the meta-analysis and Mendelian randomization studies on diabetes risk most likely arises because genetic studies reflect lifetime exposure compared to short-term pharmacological interventions.
“Furthermore, we found no significant association between PCSK9 inhibition and diabetes (RR 1.04; 95% CI 0.77 to 1.41; I2 = 0%), despite the weak signal reported in some Mendelian randomization studies[30,31], with the discordance most likely arising from the fact that genetic studies reflect lifetime exposure as compared to short-term pharmacological interventions.”
From Oral PCSK9 inhibitors for elevated LDL-C: A systematic review and meta-analysis of randomized trials (2026) · cites this paper as [31]
The Introduction (page unavailable) notes that trials found no adverse effect of PCSK9 inhibition on diabetes risk while early MR studies suggested an increase, though more recent MR studies did not confirm it. The Discussion (page unavailable) distinguishes genetic effects of long-term HMGCR inhibition from shorter statin therapy, but does not say that exposure duration is the most likely explanation for the PCSK9 findings. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 52 · Cited by 10
Research works printed but not linked: 42
- [2]Barroso I. The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits. Diabetologia 2021; 64: 2653– 2664 34595549 PMC8563561
- [3]Cusi K, Ocampo GL. Unmet needs in Hispanic/Latino patients with type 2 diabetes mellitus. Am J Med 2011; 124(Suppl.10): S2– S9 21939795
- [4]Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet 2017; 389: 2239– 2251 28190580
- [5]Elnaem MH, Mohamed MHN, Huri HZ, Azarisman SM, Elkalmi RM. Statin therapy prescribing for patients with type 2 diabetes mellitus: a review of current evidence and challenges. J Pharm Bioallied Sci 2017; 9: 80– 87 28717329 PMC5508420
- [6]Preiss D, Seshasai SRK, Welsh P,. Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis. JAMA 2011; 305: 2556– 2564 21693744
- [9]Sabatine MS, Leiter LA, Wiviott SD,. Cardiovascular safety and efficacy of the PCSK9 inhibitor evolocumab in patients with and without diabetes and the effect of evolocumab on glycaemia and risk of new-onset diabetes: a prespecified analysis of the FOURIER randomised controlled trial. Lancet Diabetes Endocrinol 2017; 5: 941– 950 28927706
- [11]Aguet F, Anand S, Ardlie KG,.; GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020; 369: 1318– 1330 32913098 PMC7737656
- [14]Fatumo S, Chikowore T, Choudhury A, Ayub M, Martin AR, Kuchenbaecker K. A roadmap to increase diversity in genomic studies. Nat Med 2022; 28: 243– 250 35145307 PMC7614889
- [15]Graham SE, Clarke SL, Wu K-HH,.; Global Lipids Genetics Consortium. The power of genetic diversity in genome-wide association studies of lipids. Nature 2021; 600: 675– 679 34887591 PMC8730582
- [16]Mahajan A, Spracklen CN, Zhang W,.; eMERGE Consortium. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 2022; 54: 560– 572 35551307 PMC9179018
- [17]Wojcik GL, Graff M, Nishimura KK,. Genetic analyses of diverse populations improves discovery for complex traits. Nature 2019; 570: 514– 518 31217584 PMC6785182
- [18]Soremekun O, Karhunen V, He Y,. Lipid traits and type 2 diabetes risk in African ancestry individuals: a Mendelian randomization study. eBioMedicine 2022; 78: 103953 35325778 PMC8941323
- [19]Chen J, Sun M, Adeyemo A,. Genome-wide association study of type 2 diabetes in Africa. Diabetologia 2019; 62: 1204– 1211 31049640 PMC6560001
- [20]Mooradian AD. Dyslipidemia in type 2 diabetes mellitus. Nat Clin Pract Endocrinol Metab 2009; 5: 150– 159 19229235
- [21]Williamson A, Norris DM, Yin X,.; Meta-Analysis of Glucose and Insulin-Related Traits Consortium (MAGIC). Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake. Nat Genet 2023; 55: 973– 983 37291194 PMC7614755
- [22]Auton A, Brooks LD, Durbin RM,.; 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 2015; 526: 68– 74 26432245 PMC4750478
- [23]Qiu C, Zeng P, Li X,. What is the impact of PCSK9 rs505151 and rs11591147 polymorphisms on serum lipids level and cardiovascular risk: a meta-analysis. Lipids Health Dis 2017; 16: 111 28606094 PMC5469167
- [24]Sabatine MS. PCSK9 inhibitors: clinical evidence and implementation. Nat Rev Cardiol 2019; 16: 155– 165 30420622
- [25]Mihaylova B, Emberson J, Blackwell L,.; Cholesterol Treatment Trialists’ (CTT) collaborators. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 2012; 380: 581– 590 22607822 PMC3437972
- [27]Burgess S, Mason AM, Grant AJ,. Using genetic association data to guide drug discovery and development: review of methods and applications. Am J Hum Genet 2023; 110: 195– 214 36736292 PMC9943784
- [28]Hartwig FP, Tilling K, Davey Smith G, Lawlor DA, Borges MC. Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations. Int J Epidemiol 2021; 50: 1639– 1650 33619569 PMC8580279
- [29]Sanderson E. Multivariable Mendelian randomization and mediation. Cold Spring Harb Perspect Med 2021; 11: a038984 32341063 PMC7849347
- [30]Chen C-Y, Chen T-T, Feng Y-CA,. Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits. Cell Genom 2023; 3: 100436 38116116 PMC10726425
- [32]Fernández-Rhodes L, Graff M, Buchanan VL,. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits: The Hispanic/Latino Anthropometry Consortium. HGG Adv 2022; 3: 100099 35399580 PMC8990175
- [33]Pulit SL, Stoneman C, Morris AP,.; GIANT Consortium. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 2019; 28: 166– 174 30239722 PMC6298238
- [34]Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature 2019; 567: 305– 307 30894741
- [35]Zuber V, Grinberg NF, Gill D,. Combining evidence from Mendelian randomization and colocalization: review and comparison of approaches. Am J Hum Genet 2022; 109: 767– 782 35452592 PMC7612737
- [36]Wallace C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet 2021; 17: e1009440 34587156 PMC8504726
- [37]Hemani G, Zheng J, Elsworth B,. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018; 7: e34408 29846171 PMC5976434
- [38]Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol 2017; 46: 1734– 1739 28398548 PMC5510723
- [39]Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 2017; 13: e1007081 29149188 PMC5711033
- [40]Lonsdale J, Thomas J, Salvatore M,. The Genotype-Tissue Expression (GTEx) project. Nat Genet 2013; 45: 580– 585 23715323 PMC4010069
- [41]Rosoff DB, Bell AS, Wagner J,. Assessing the Impact of PCSK9 and HMGCR inhibition on liver function: drug-target Mendelian randomization analyses in four ancestries. Cell Mol Gastroenterol Hepatol 2024; 17: 29– 40 37703945 PMC10665960
- [43]Bell AS, Wagner J, Rosoff DB, Lohoff FW. Proprotein convertase subtilisin/kexin type 9 (PCSK9) in the central nervous system. Neurosci Biobehav Rev 2023; 149: 105155 37019248
- [45]Korunes KL, Goldberg A. Human genetic admixture. PLoS Genet 2021; 17: e1009374 33705374 PMC7951803
- [46]Zakharia F, Basu A, Absher D,. Characterizing the admixed African ancestry of African Americans. Genome Biol 2009; 10: R141 20025784 PMC2812948
- [47]Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016; 40: 304– 314 27061298 PMC4849733
- [48]Redberg RF. Statins and weight gain. JAMA Intern Med 2014; 174: 1046– 1046 24763439
- [49]Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008; 27: 1133– 1163 17886233
- [50]Yu Q, Chen Y, Xu C-B. Statins and new-onset diabetes mellitus: LDL receptor may provide a key link. Front Pharmacol 2017; 8: 372 28659805 PMC5468445
- [52]Fortmann AL, Gallo LC, Philis-Tsimikas A. Glycemic control among Latinos with type 2 diabetes: the role of social-environmental support resources. Health Psychol 2011; 30: 251– 258 21553968 PMC3384508
- [53]Aceves B, Ezekiel-Herrera D, Marino M,. Disparities in HbA1c testing between aging US Latino and non-Latino white primary care patients. Prev Med Rep 2022; 26: 101739 35295668 PMC8918837
- Authors
- Rosoff, D. B., Wagner, J., Jung, J., Pacher, P., Christodoulides, C., Davey Smith, G., Ray, D., & Lohoff, F. W.
- Affiliations
- National Institutes of Health (US)
- 4Laboratory of Cardiovascular Physiology and Tissue Injury, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD
- University of Oxford (GB)
- 3Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
- John Radcliffe Hospital (GB)
- Corresponding author
- Lohoff, Falk W.
- ORCID
- Venue
- Diabetes
- Publisher
- American Diabetes Association
- Published
- 2024-10-17
- Volume
- 74
- Issue
- 1
- Pages
- 120-130
- ISSN
- 0012-1797 (ISSN-L)
- 1939-327X
- PMC
- PMC11664021
- OpenAlex
- W4403510026
- Cited by
- 10
- References
- 52
- Citations by year
- 2026: 5 · 2025: 5
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Diabetes (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- Clinical Center (ZIA-AA000242)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- type 2 diabetes, PCSK9 inhibition, HMGCR inhibition, statins, LDL cholesterol lowering, genetic proxies, drug-target Mendelian randomization, glycemic traits, ethnic differences, African populations, South Asian populations, East Asian populations, Hispanic population, colocalization, triglycerides, HDL cholesterol
- MeSH terms
- Proprotein Convertase 9, Diabetes Mellitus, Type 2, Europe, Female, Humans, Hydroxymethylglutaryl CoA Reductases, Cholesterol, LDL, Male, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Polymorphism, Single Nucleotide, Mendelian Randomization Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review