Causal associations between HbA1c and multiple diseases unveiled through a Mendelian randomization phenome-wide association study in East Asian populations

Most analyses of hemoglobin A1c (HbA1c) and multiple common diseases have focused on European populations, thus there is a need for Mendelian randomization phenome-wide association study (MR-PheWAS) in East Asian populations. We used MR-PheWAS to investigate the potential causal associations between HbA1c and 159 types of diseases in the Biobank Japan dataset, employing the inverse variance weighted as the primary statistical approach, supplemented by MR-Egger and weighted median analyses. Additionally, multiple sensitivity analyses were conducted to assess heterogeneity and pleiotropy. High HbA1c levels are associated with an increased risk of type 1 diabetes (odds ratio [OR] = 4.07; 95% confidence interval [CI]: 2.34~7.07), type 2 diabetes (OR = 4.76; 95% CI: 3.01~7.55), cataract (OR = 1.33; 95% CI: 1.18~1.51), diabetic nephropathy (OR = 5.70; 95% CI: 2.24~14.46), and peripheral arterial disease (OR = 1.62; 95% CI: 1.29~2.04). Conversely, elevated HbA1c levels are associated with a reduced risk of asthma (OR = 0.76; 95% CI: 0.67~0.86), breast cancer (OR = 0.75; 95% CI: 0.65~0.87), and cerebral aneurysm (OR = 0.71; 95% CI: 0.57~0.88). The results of the causal association between HbA1c and numerous diseases in East Asian populations provides insights for the region's specialized glycemic control and disease prevention programs, as well as new preventive and treatment options.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2025-03-14
DOI
https://doi.org/10.1097/md.0000000000041861
Citations
16
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
29.08
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Causal associations between HbA1c and multiple diseases unveiled through a Mendelian randomization phenome-wide association study in East Asian populations

Li Han, Meihua Bao, Binsheng He, Rumeng Chen et al.
16 citations
Medicine
Genetic Associations and Epidemiology
29.08
article

Causal associations between HbA1c and multiple diseases unveiled through a Mendelian randomization phenome-wide association study in East Asian populations

Li Han, Meihua Bao, Binsheng He, Rumeng Chen, Sen Li, Zhu Wu, Yining Ding, Shuling Xu, Zhiwei Zheng
article en
16 citations

Abstract

Most analyses of hemoglobin A1c (HbA1c) and multiple common diseases have focused on European populations, thus there is a need for Mendelian randomization phenome-wide association study (MR-PheWAS) in East Asian populations. We used MR-PheWAS to investigate the potential causal associations between HbA1c and 159 types of diseases in the Biobank Japan dataset, employing the inverse variance weighted as the primary statistical approach, supplemented by MR-Egger and weighted median analyses. Additionally, multiple sensitivity analyses were conducted to assess heterogeneity and pleiotropy. High HbA1c levels are associated with an increased risk of type 1 diabetes (odds ratio [OR] = 4.07; 95% confidence interval [CI]: 2.34~7.07), type 2 diabetes (OR = 4.76; 95% CI: 3.01~7.55), cataract (OR = 1.33; 95% CI: 1.18~1.51), diabetic nephropathy (OR = 5.70; 95% CI: 2.24~14.46), and peripheral arterial disease (OR = 1.62; 95% CI: 1.29~2.04). Conversely, elevated HbA1c levels are associated with a reduced risk of asthma (OR = 0.76; 95% CI: 0.67~0.86), breast cancer (OR = 0.75; 95% CI: 0.65~0.87), and cerebral aneurysm (OR = 0.71; 95% CI: 0.57~0.88). The results of the causal association between HbA1c and numerous diseases in East Asian populations provides insights for the region's specialized glycemic control and disease prevention programs, as well as new preventive and treatment options.

MedicineVol. 104(11)
Changsha Medical University (CN), Beijing University of Chinese Medicine (CN), S.P.E.C.I.E.S. (US)
Good health and well-being
Openalex Percentile: Top 0%
Genetic Associations and Epidemiology
29.08
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.