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Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis

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Abstract

Purpose

The prevalence of diabetes is increasing worldwide. The associations between the lipid profile and glycated hemoglobin (HbA1c), fasting glucose, and diabetes remain unclear, so we aimed to perform a cohort study and a two-sample Mendelian randomization (MR) study to investigate the causality between blood lipid profile and HbA1c, fasting glucose, and diabetes.

Methods

A total of 25,171 participants from the Taiwan Biobank were enrolled. We applied a cohort study and an MR study to assess the association between blood lipid profile and HbA1c, fasting glucose, and diabetes. The summary statistics were obtained from the Asian Genetic Epidemiology Network (AGEN), and the estimates between the instrumental variables (IVs) and outcomes were calculated using the inverse-variance weighted (IVW) method. A series of sensitivity analyses were performed.

Results

In the cohort study, high-density lipoprotein cholesterol (HDL-C) was negatively associated with HbA1c, fasting glucose, and diabetes, while the causal associations between HDL-C and HbA1c (βIVW = − 0.098, p = 0.003) and diabetes (βIVW = − 0.594, p < 0.001) were also observed. Furthermore, there was no pleiotropy effect in this study using the MR-Egger intercept test and MR-PRESSO global test.

Conclusions

Our results support the hypothesis that a genetically determined increase in HDL-C is causally related to a reduction in HbA1c and a lower risk of diabetes.

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Data availability

Summary data of blood lipid profiles GWAS were used in our study is available at the AGEN consortium website (https://blog.nus.edu.sg/agen/summary-statistics/lipids/).

Abbreviations

MR:

Mendelian randomization

IVW:

Inverse-variance weighted

HbA1c:

Glycated hemoglobin

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

RCTs:

Randomized controlled trials

IVs:

Instrumental variables

SNPs:

Single nucleotide polymorphisms

WHR:

Waist-hip ratio

BMI:

Body mass index

PRESSO:

Pleiotropy residual sum and outliers

MBE:

Mode-based estimate

References

  1. International Diabetes Federation diabetes atlas Tenth edition. 2021

  2. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N et al (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract 157:107843

    Article  PubMed  Google Scholar 

  3. Diagnosis and classification of diabetes mellitus (2013) Diabetes Care 36(Suppl 1):S67-74

    Google Scholar 

  4. Siqueira ISL, Alves Guimarães R, Mamed SN, Santos TAP, Rocha SD, Pagotto V et al (2020) Prevalence and risk factors for self-report diabetes mellitus: a population-based study. Int J Environ Res Public Health 17:6497

    Article  PubMed  PubMed Central  Google Scholar 

  5. Malta DC, Bernal RTI, Iser BPM, Szwarcwald CL, Duncan BB, Schmidt MI (2017) Factors associated with self-reported diabetes according to the 2013 National Health Survey. Rev Saude Publ 51:12s

    Article  Google Scholar 

  6. Bertoldi AD, Kanavos P, França GV, Carraro A, Tejada CA, Hallal PC et al (2013) Epidemiology, management, complications and costs associated with type 2 diabetes in Brazil: a comprehensive literature review. Global Health 9:62

    Article  PubMed  PubMed Central  Google Scholar 

  7. Zheng Y, Ley SH, Hu FB (2018) Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol 14:88–98

    Article  PubMed  Google Scholar 

  8. Gudjinu HY, Sarfo B (2017) Risk factors for type 2 diabetes mellitus among out-patients in Ho, the Volta regional capital of Ghana: a case–control study. BMC Res Notes 10:324

    Article  PubMed  PubMed Central  Google Scholar 

  9. Temneanu OR, Trandafir LM, Purcarea MR (2016) Type 2 diabetes mellitus in children and adolescents: a relatively new clinical problem within pediatric practice. J Med Life 9:235–239

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Khan HA, Sobki SH, Khan SA (2007) Association between glycaemic control and serum lipids profile in type 2 diabetic patients: HbA1c predicts dyslipidaemia. Clin Exp Med 7:24–29

    Article  CAS  PubMed  Google Scholar 

  11. Laverdy OG, Hueb WA, Sprandel MC, Kalil-Filho R, Maranhão RC (2015) Effects of glycemic control upon serum lipids and lipid transfers to HDL in patients with type 2 diabetes mellitus: novel findings in unesterified cholesterol status. Exp Clin Endocrinol Diabetes 123:232–239

    Article  CAS  PubMed  Google Scholar 

  12. Wang S, Ji X, Zhang Z, Xue F (2020) Relationship between lipid profiles and glycemic control among patients with type 2 diabetes in Qingdao, China. Int J Environ Res Public Health 17:5317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Drew BG, Duffy SJ, Formosa MF, Natoli AK, Henstridge DC, Penfold SA et al (2009) High-density lipoprotein modulates glucose metabolism in patients with type 2 diabetes mellitus. Circulation 119:2103–2111

    Article  CAS  PubMed  Google Scholar 

  14. Zhu XW, Deng FY, Lei SF (2015) Meta-analysis of Atherogenic Index of Plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Prim Care Diabetes 9:60–67

    Article  PubMed  Google Scholar 

  15. Davis PJ, Liu M, Sherman S, Natarajan S, Alemi F, Jensen A et al (2018) HbA1c, lipid profiles and risk of incident type 2 Diabetes in United States Veterans. PLoS ONE 13:e0203484

    Article  PubMed  PubMed Central  Google Scholar 

  16. Liu J, van Klinken JB, Semiz S, van Dijk KW, Verhoeven A, Hankemeier T et al (2017) A Mendelian randomization study of metabolite profiles, fasting glucose, and type 2 diabetes. Diabetes 66:2915–2926

    Article  CAS  PubMed  Google Scholar 

  17. Agarwal T, Lyngdoh T, Dudbridge F, Chandak GR, Kinra S, Prabhakaran D et al (2020) Causal relationships between lipid and glycemic levels in an Indian population: a bidirectional Mendelian randomization approach. PLoS ONE 15:e0228269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM et al (2017) PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study. Lancet Diabetes Endocrinol 5:97–105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fall T, Xie W, Poon W, Yaghootkar H, Mägi R, Knowles JW et al (2015) Using genetic variants to assess the relationship between circulating lipids and type 2 diabetes. Diabetes 64:2676–2684

    Article  CAS  PubMed  Google Scholar 

  20. White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW et al (2016) Association of lipid fractions with risks for coronary artery disease and diabetes. JAMA Cardiol 1:692–699

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R et al (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 9:224

    Article  PubMed  PubMed Central  Google Scholar 

  22. Yuan S, Larsson SC (2020) An atlas on risk factors for type 2 diabetes: a wide-angled Mendelian randomisation study. Diabetologia 63:2359–2371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Soremekun O, Karhunen V, He Y, Rajasundaram S, Liu B, Gkatzionis A et al (2022) Lipid traits and type 2 diabetes risk in African ancestry individuals: a Mendelian Randomization study. EBioMedicine 78:103953

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Haase CL, Tybjærg-Hansen A, Nordestgaard BG, Frikke-Schmidt R (2015) HDL cholesterol and risk of type 2 diabetes: a mendelian randomization study. Diabetes 64:3328–3333

    Article  CAS  PubMed  Google Scholar 

  25. Marott SC, Nordestgaard BG, Tybjærg-Hansen A, Benn M (2016) Components of the Metabolic syndrome and risk of type 2 diabetes. J Clin Endocrinol Metab 101:3212–3221

    Article  CAS  PubMed  Google Scholar 

  26. Pan W, Sun W, Yang S, Zhuang H, Jiang H, Ju H et al (2020) LDL-C plays a causal role on T2DM: a Mendelian randomization analysis. Aging (Albany NY) 12:2584–2594

    Article  CAS  PubMed  Google Scholar 

  27. Lee K, Lim CY (2019) Mendelian randomization analysis in observational epidemiology. J Lipid Atheroscler 8:67–77

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Smith GD, Ebrahim S (2003) “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32:1–22

    Article  PubMed  Google Scholar 

  29. Chen CH, Yang JH, Chiang CWK, Hsiung CN, Wu PE, Chang LC et al (2016) Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Hum Mol Genet 25:5321–5331

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Lin WY, Liu YL, Yang AC, Tsai SJ, Kuo PH (2020) Active cigarette smoking is associated with an exacerbation of genetic susceptibility to diabetes. Diabetes 69:2819–2829

    Article  CAS  PubMed  Google Scholar 

  31. Diagnosis and classification of diabetes mellitus (2006) Diabetes Care 29(Suppl 1):S43–S48

    Google Scholar 

  32. Gillett MJ (2009) International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care 32(7):1327–1334

    Article  Google Scholar 

  33. Daniel L. Hartl AGC (2007) Principles of population genetics. 4th edn

  34. Hellwege JN, Keaton JM, Giri A, Gao X, Velez Edwards DR, Edwards TL (2017) Population stratification in genetic association studies. Curr Protoc Hum Genet. 95:1.22.1-3.3

    PubMed  Google Scholar 

  35. Zhang F, Zhang L, Deng HW (2009) A PCA-based method for ancestral informative markers selection in structured populations. Sci China C Life Sci 52:972–976

    Article  PubMed  Google Scholar 

  36. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  37. Hartwig FP, Davies NM, Hemani G, Davey SG (2016) Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique. Int J Epidemiol 45:1717–1726

    Article  PubMed  Google Scholar 

  38. Network AGE. 2017.

  39. Nowak C, Ärnlöv J (2018) A Mendelian randomization study of the effects of blood lipids on breast cancer risk. Nat Commun 9:3957

    Article  PubMed  PubMed Central  Google Scholar 

  40. Beeghly-Fadiel A, Khankari NK, Delahanty RJ, Shu XO, Lu Y, Schmidt MK et al (2020) A Mendelian randomization analysis of circulating lipid traits and breast cancer risk. Int J Epidemiol 49:1117–1131

    Article  PubMed  Google Scholar 

  41. Luo M, Sun M, Wang T, Zhang S, Song X, Liu X et al (2023) Gut microbiota and type 1 diabetes: a two-sample bidirectional Mendelian randomization study. Front Cell Infect Microbiol 13:1163898

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zhang LP, Zhang XX (2022) Relationship between lipids and sleep apnea: Mendelian randomization analysis. World J Clin Cases 10:11403–11410

    Article  PubMed  PubMed Central  Google Scholar 

  43. Anderson CA, Pettersson FH, Clarke GM, Cardon LR, Morris AP, Zondervan KT (2010) Data quality control in genetic case–control association studies. Nat Protoc 5:1564–1573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Burgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40:755–764

    Article  PubMed  Google Scholar 

  45. Pierce BL, Ahsan H, Vanderweele TJ (2011) Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40:740–752

    Article  PubMed  Google Scholar 

  46. Fortier I, Raina P, Van den Heuvel ER, Griffith LE, Craig C, Saliba M et al (2017) Maelstrom research guidelines for rigorous retrospective data harmonization. Int J Epidemiol 46:103–105

    PubMed  Google Scholar 

  47. Jung S (2013) Structural equation modeling with small sample sizes using two-stage ridge least-squares estimation. Behav Res Methods 45:75–81

    Article  PubMed  Google Scholar 

  48. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG (2015) Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30:543–552

    Article  PubMed  PubMed Central  Google Scholar 

  49. Burgess S, Davies NM, Thompson SG (2016) Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol 40:597–608

    Article  PubMed  PubMed Central  Google Scholar 

  50. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM et al (2019) Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 4:186

    Article  PubMed  Google Scholar 

  51. Burgess S, Dudbridge F, Thompson SG (2016) Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med 35:1880–1906

    Article  PubMed  Google Scholar 

  52. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665

    Article  PubMed  PubMed Central  Google Scholar 

  53. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512–525

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hartwig FP, Davey Smith G, Bowden J (2017) Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46:1985–1998

    Article  PubMed  PubMed Central  Google Scholar 

  55. Burgess S, Foley CN, Allara E, Staley JR, Howson JMM (2020) A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun 11:376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Huang D, Lin S, He J, Wang Q, Zhan Y (2022) Association between COVID-19 and telomere length: a bidirectional Mendelian randomization study. J Med Virol 94:5345–5353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7

  58. Dragioti E, Gerdle B, Larsson B (2019) Longitudinal associations between anatomical regions of pain and work conditions: a study from The SwePain Cohort. Int J Environ Res Public Health 16:2167

    Article  PubMed  PubMed Central  Google Scholar 

  59. Westerlund H, Kivimäki M, Singh-Manoux A, Melchior M, Ferrie JE, Pentti J et al (2009) Self-rated health before and after retirement in France (GAZEL): a cohort study. Lancet 374:1889–1896

    Article  PubMed  Google Scholar 

  60. Diagnosis and classification of diabetes mellitus (2014) Diabetes Care 37(Suppl 1):S81-90

    Google Scholar 

  61. Cai X, Zhang Y, Li M, Wu JH, Mai L, Li J et al (2020) Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis. BMJ 370:m2297

    Article  PubMed  PubMed Central  Google Scholar 

  62. Cai X, Liu X, Sun L, He Y, Zheng S, Zhang Y et al (2021) Prediabetes and the risk of heart failure: a meta-analysis. Diabetes Obes Metab 23:1746–1753

    Article  PubMed  Google Scholar 

  63. Gordon SM, Hofmann S, Askew DS, Davidson WS (2011) High density lipoprotein: it’s not just about lipid transport anymore. Trends Endocrinol Metab 22:9–15

    Article  CAS  PubMed  Google Scholar 

  64. Rahmoun MN, Ghembaza CE, El-Amine GM (2019) Lipid profile in type 2 patients with diabetes from Tlemcen: A Western Algerian population. Diabetes Metab Syndr 13:1347–1351

    Article  PubMed  Google Scholar 

  65. von Eckardstein A, Sibler RA (2011) Possible contributions of lipoproteins and cholesterol to the pathogenesis of diabetes mellitus type 2. Curr Opin Lipidol 22:26–32

    Article  Google Scholar 

  66. Fazio S, Linton MF (2013) Killing two birds with one stone, maybe: CETP inhibition increases both high-density lipoprotein levels and insulin secretion. Circ Res 113:94–96

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Drew BG, Rye KA, Duffy SJ, Barter P, Kingwell BA (2012) The emerging role of HDL in glucose metabolism. Nat Rev Endocrinol 8:237–245

    Article  CAS  PubMed  Google Scholar 

  68. Chapman MJ, Le Goff W, Guerin M, Kontush A (2010) Cholesteryl ester transfer protein: at the heart of the action of lipid-modulating therapy with statins, fibrates, niacin, and cholesteryl ester transfer protein inhibitors. Eur Heart J 31:149–164

    Article  CAS  PubMed  Google Scholar 

  69. Association AD (2020) 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2021. Diabetes Care 44:S15–S33

    Article  Google Scholar 

  70. Sacks DB (2011) A1C versus glucose testing: a comparison. Diabetes Care 34:518–523

    Article  PubMed  PubMed Central  Google Scholar 

  71. Little RR, Sacks DB (2009) HbA1c: how do we measure it and what does it mean? Curr Opin Endocrinol Diabetes Obes 16:113–118

    Article  PubMed  Google Scholar 

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Funding

This work was supported by grants from the Ministry of Science and Technology, Taiwan [MOST 107-2314-B-037-086-MY3 and MOST 110-2314-B-037-048 MY3]. The funding agencies had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

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Contributions

YCL and TNW contributed to the conception and design. TNW contributed to the acquisition of data. YCL and HPT contributed to statistical analysis and interpretation. YCL and TNW drafted the manuscript for important intellectual content. TNW 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.

Corresponding author

Correspondence to T.-N. Wang.

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The authors have declared that they have no conflict of interest.

Ethical approval

Ethical approval for the study was granted by the Institutional Review Board of Kaohsiung Medical University and Chung-Ho Memorial Hospital and the Ethics and Governance Committee (EGC) of the Taiwan Biobank.

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Written informed consent of participation was obtained from all participants when joining TWB and the personal information of each participant was fully encrypted for protection.

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Lin, YC., Tu, HP. & Wang, TN. Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis. J Endocrinol Invest 47, 913–925 (2024). https://doi.org/10.1007/s40618-023-02209-x

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