GLM7代谢指数各成分与脑卒中因果关系的 孟德尔随机化研究进展
Progress in Mendelian Randomization Studies on the Causal Association of GLM7 Index Components with Stroke
摘要: 脑卒中是全球范围内致残和致死的主要原因之一,糖脂代谢异常是其重要危险因素。传统观察性研究易受混杂偏倚和反向因果干扰,孟德尔随机化(MR)利用遗传变异作为工具变量,可提供更可靠的因果推断。GLM7指数是新近提出的一个综合代谢–衰老指标,整合了年龄、BMI、空腹血糖、胰岛素、甘油三酯、LDL-C和HDL-C。由于年龄不具备遗传基础,本综述聚焦于GLM7中的六个可遗传代谢成分,系统梳理其与脑卒中的MR研究证据。通过检索PubMed、Web of Science、中国知网和万方数据库,纳入符合条件的原始MR研究。结果发现:LDL-C (尤其是小密亚型)与缺血性卒中(特别是大动脉亚型)的因果关系证据最为充分(Grade 1);BMI和甘油三酯升高增加缺血性卒中风险(Grade 2);HDL-C对缺血性卒中具有保护作用但对脑出血风险增加(Grade 2/3);血糖(以HbA1c为代表)和胰岛素抵抗与卒中风险正相关,但空腹血糖和空腹胰岛素的直接证据尚不一致(Grade 3)。各成分对大动脉、小血管和心源性卒中的效应存在显著异质性。本文还探讨了GLM7复合指数进行MR分析的方法学框架,包括遗传工具构建、水平多效性校正及与观察性研究的差异。未来需开展跨种族验证、多变量MR及GLM7全基因组关联研究,以进一步明确该指数的因果效应。
Abstract: Stroke is one of the leading causes of disability and mortality worldwide, and abnormalities in glycolipid metabolism are important risk factors. Traditional observational studies are susceptible to confounding bias and reverse causation, while Mendelian randomization (MR) utilizes genetic variants as instrumental variables, providing more reliable causal inference. The GLM7 index is a recently proposed composite metabolic-aging indicator integrating age, BMI, fasting glucose, insulin, triglycerides, LDL-C, and HDL-C. Since age lacks a genetic basis, this review focuses on the six heritable metabolic components of GLM7 and systematically summarizes MR evidence on their associations with stroke. Eligible original MR studies were identified through searches in PubMed, Web of Science, CNKI, and Wanfang databases. Results showed that LDL-C (especially small dense subtype) has the most robust causal association with ischemic stroke (particularly large artery stroke) (Grade 1). Elevated BMI and triglycerides increase the risk of ischemic stroke (Grade 2). HDL-C shows a protective effect against ischemic stroke but increases the risk of intracerebral hemorrhage (Grade 2/3). Glycemic traits (represented by HbA1c) and insulin resistance are positively associated with stroke risk, while direct evidence for fasting glucose and fasting insulin remains inconsistent (Grade 3). Significant heterogeneity exists in the effects of different components on large artery, small vessel, and cardioembolic stroke. This review also discusses methodological frameworks for conducting MR analysis on the composite GLM7 index, including genetic instrument construction, correction for horizontal pleiotropy, and expected differences from observational studies. Future studies should focus on cross-ethnic validation, multivariable MR, and genome-wide association studies of GLM7 to further clarify the causal effects of this index.
文章引用:王一婷, 曹琦, 师强. GLM7代谢指数各成分与脑卒中因果关系的 孟德尔随机化研究进展[J]. 临床医学进展, 2026, 16(5): 3483-3491. https://doi.org/10.12677/acm.2026.1652171

参考文献

[1] Wang, Z., Chen, S., Feng, X., Chen, X., Evans, P.C., Strijdom, H., et al. (2025) GLM7—A Novel Composite Glycolipid Index Derived from Routine Health Indicators for Enhanced Diagnosis and Prediction of Multimorbidity. Advanced Science, 12, e10552. [Google Scholar] [CrossRef
[2] Li, G., Zhang, H. and Jiang, J. (2024) Genetic Associations of Childhood and Adult BMI on Chronic Heart Failure and Ischemic Stroke: A Mendelian Randomization. IJC Heart & Vasculature, 52, Article 101425. [Google Scholar] [CrossRef] [PubMed]
[3] Daghlas, I. and Gill, D. (2024) Mechanisms of Hypercoagulability Driving Stroke Risk in Obesity: A Mendelian Randomization Study. Neurology, 103, e209431. [Google Scholar] [CrossRef] [PubMed]
[4] Yang, R., Zhang, T. and Han, F. (2024) Disentangling the Genetic Overlap between Ischemic Stroke and Obesity. Diabetology & Metabolic Syndrome, 16, Article No. 102. [Google Scholar] [CrossRef] [PubMed]
[5] Wang, X., Chen, H., Chang, Z., Zhang, J. and Xie, D. (2024) Genetic Causal Role of Body Mass Index in Multiple Neurological Diseases. Scientific Reports, 14, Article No. 7256. [Google Scholar] [CrossRef] [PubMed]
[6] Marini, S., Merino, J., Montgomery, B.E., Malik, R., Sudlow, C.L., Dichgans, M., et al. (2020) Mendelian Randomization Study of Obesity and Cerebrovascular Disease. Annals of Neurology, 87, 516-524. [Google Scholar] [CrossRef] [PubMed]
[7] Georgakis, M.K., Harshfield, E.L., Malik, R., Franceschini, N., Langenberg, C., Wareham, N.J., et al. (2021) Diabetes Mellitus, Glycemic Traits, and Cerebrovascular Disease: A Mendelian Randomization Study. Neurology, 96, e1732-e1742. [Google Scholar] [CrossRef] [PubMed]
[8] Yuan, S., Mason, A.M., Burgess, S. and Larsson, S.C. (2022) Differentiating Associations of Glycemic Traits with Atherosclerotic and Thrombotic Outcomes: Mendelian Randomization Investigation. Diabetes, 71, 2222-2232. [Google Scholar] [CrossRef] [PubMed]
[9] Lee, S.H., Kimm, H., Lee, B., Nam, C.M., Kim, S.Y., Lee, S., et al. (2024) Causal Effect of Fasting Serum Glucose on Atherosclerotic Cardiovascular Disease: A Multivariable Mendelian Randomization. Epidemiology and Health, 46, e2024096. [Google Scholar] [CrossRef] [PubMed]
[10] Zhu, Y., Li, M., Wang, H., Yang, F., Pang, X., Du, R., et al. (2023) Genetically Proxied Antidiabetic Drugs Targets and Stroke Risk. Journal of Translational Medicine, 21, Article No. 681. [Google Scholar] [CrossRef] [PubMed]
[11] de Ruiter, S.C., Tschiderer, L., Grobbee, D.E., Ruigrok, Y.M., Willeit, P., den Ruijter, H.M., et al. (2025) Diabetes, Glycaemic Traits and Cardiovascular Disease in Females and Males: Observational and Mendelian Randomisation Analyses in the UK Biobank. Diabetes, Obesity and Metabolism, 27, 3789-3799. [Google Scholar] [CrossRef] [PubMed]
[12] Bai, W., Zhou, G., Jiang, H., Li, X. and Shao, J. (2025) Shared Genetic Architecture between Stroke and Blood Lipids: A Large-Scale Genome-Wide Cross-Trait Analysis. Human Genomics, 19, Article No. 75. [Google Scholar] [CrossRef] [PubMed]
[13] Georgakis, M.K., Malik, R., Anderson, C.D., Parhofer, K.G., Hopewell, J.C. and Dichgans, M. (2020) Genetic Determinants of Blood Lipids and Cerebral Small Vessel Disease: Role of High-Density Lipoprotein Cholesterol. Brain, 143, 597-610. [Google Scholar] [CrossRef] [PubMed]
[14] Qin, H., Yang, F., Zhao, H., Zhao, J., Lin, S., Shang, Y., et al. (2023) Associations of Lipids and Lipid-Lowering Drugs with Risk of Stroke: A Mendelian Randomization Study. Frontiers in Neurology, 14, Article ID: 1185986. [Google Scholar] [CrossRef] [PubMed]
[15] Zhao, Y., Zhuang, Z., Li, Y., Xiao, W., Song, Z., Huang, N., et al. (2024) Elevated Blood Remnant Cholesterol and Triglycerides Are Causally Related to the Risks of Cardiometabolic Multimorbidity. Nature Communications, 15, Article ID: 2451. [Google Scholar] [CrossRef] [PubMed]
[16] Yu, X., Shen, G., Zhang, Y., Cui, C., Zha, Y., Li, P., et al. (2024) Genetically Predicted Small Dense Low-Density Lipoprotein Cholesterol and Ischemic Stroke Subtype: Multivariable Mendelian Randomization Study. Frontiers in Endocrinology, 15, Article ID: 1404234. [Google Scholar] [CrossRef] [PubMed]
[17] Wang, R., Huang, S., Li, H., Yang, Y., Chen, S. and Yu, J. (2022) Genetic Determinants of Circulating Metabolites and the Risk of Stroke and Its Subtypes. European Journal of Neurology, 29, 3711-3719. [Google Scholar] [CrossRef] [PubMed]
[18] Cai, H., Cai, B., Liu, Z., Wu, W., Chen, D., Fang, L., et al. (2020) Genetic Correlations and Causal Inferences in Ischemic Stroke. Journal of Neurology, 267, 1980-1990. [Google Scholar] [CrossRef] [PubMed]