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Wide Metabonomics and Lipidomics of Type 2 Diabetes Mellitus Risk in Chinese Adult Twins

29 Pages Posted: 2 Nov 2023

See all articles by Ninghao Huang

Ninghao Huang

Peking University - Department of Epidemiology and Biostatistics

Xuanming Hong

Peking University - Department of Epidemiology and Biostatistics

Weihua Cao

Peking University - Department of Epidemiology and Biostatistics

Jun Lv

Peking University - Department of Epidemiology and Biostatistics

Canqing Yu

Peking University Health Science Center - School of Public Health

Dianjianyi Sun

Peking University - Department of Epidemiology and Biostatistics

Chunxiao Liao

Peking University - Department of Epidemiology and Biostatistics

Yuanjie Pang

Peking University - Department of Epidemiology and Biostatistics

Runhua Hu

Peking University - Department of Epidemiology and Biostatistics

Zengchang Pang

Qingdao Center for Disease Control and Prevention

Min Yu

Zhejiang Provincial Center for Disease Control and Prevention - Department of Non-Communicable Disease Control and Prevention

Hua Wang

Chinese Center for Disease Control and Prevention (China CDC) - Jiangsu Provincial Center for Disease Control and Prevention

Xianping Wu

Sichuan Center for Disease Control and Prevention

Yu Liu

Heilongjiang Provincial Center for Disease Control and Prevention

Wenjing Gao

Peking University - Department of Epidemiology and Biostatistics

Tao Huang

Peking University - Department of Epidemiology and Biostatistics

Liming Li

Peking University - Department of Epidemiology and Biostatistics

More...

Abstract

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder with a growing global prevalence. Lipidomic and metabolomic analyses have been used to investigate the relationship between circulating metabolites and T2DM characteristics. However, few studies have explored this relationship specifically in twin populations.

Methods: The data came from China's National Twin Registry (CNTR). 420 participants (210 twin pairs) were included. A mixed-effects model was applied to explore the relationship between individual metabolite/lipid levels and T2DM-related characteristics, such as homeostasis model assessment of T2DM, fasting insulin, fasting glucose, HbA1c, and insulin resistance (HOMA-IR). To compare metabolite/lipid levels in twins with inconsistent T2DM (one twin had T2DM while the other did not), the empirical Bayesian paired T-test was used. Lastly, bivariate structural equation models (SEM) were employed to investigate the genetic/environmental correlation between metabolite/lipid levels and T2DM. To ensure the statistical reliability of the results, all P values were corrected using the False Discovery Rate (FDR) method.

Results: Mixed linear model results showed that a higher level of But-2-enoic (β=0.18, 95% confident interval [95% CI]: 0.14 to 0.22, adjusted P-value [Padj] = 0.002), a type of unsaturated fatty acid, And phosphatidylethanolamines (PE), including PE(39:6) (β=0.12, 95% CI: 0.09 to 0.15, Padj = 0.007), PE(40:7) (β=0.09, 95% CI: 0.07 to 0.11, Padj = 0.008), were found to be significantly associated with T2DM. In the empirical Bayesian paired T-test, a total of 71 metabolite levels associated with T2DM were identified, among which triglyceride (TG) (e.g., TG(53:3), TG(55:3), TG(55:5), etc.), L-Leucine (β=0.06, 95% CI: 0.01 to 0.12, Padj = 0.02), Deoxycholic acid 3-sulfate (β=0.23, 95% CI: 0.05 to 0.42, Padj = 0.02), PE(38:2) (β=0.08, 95% CI: 0.01 to 0.07, Padj = 0.04), and sphingomyelins [SM(d44:6)] (β=0.04, 95% CI: 0.01 to 0.06, Padj = 0.004) were found to be significantly elevated in affected twins as compared to their unaffected co-twins. The bivariate SEM analysis revealed that genetic correlations of 1 and 0.53 for D-Glucose and But-2-enoic acid, -0.04 and -0.37 for L-Lactic acid and phosphatidylcholines [PC(32:1p)], and 0.19 and 0.12 for PE(39:6) and PE(40:7). The environmental factors also contributed to the associations between T2DM and metabolite/lipid levels, with point estimates of 0.27 and 0.29 for D-Glucose and But-2-enoic acid, 0.4 and -0.14 for GM3(d41:1) and L-Lactic acid, and -0.33, -0.25, -0.44, and −0.18 for PC.

Conclusion: In summary, our findings reveal regional aggregation of metabolite/lipid levels in T2DM, which highlights a potential pathway for future research. Furthermore, we demonstrate that PC and PE levels are partially subject to genetic control, while the association between metabolite/lipid levels and T2DM is partly explained by shared genes and environmental influences. The study sheds light on the complex relationship between metabolite/lipid levels and T2DM and provides a foundation for further research in this area.

Funding: The CNTR wase supported from the National Natural Science Foundation of China (82173499, 82073633, 81973126, 81573223), the Special Fund for Health scientific research in public welfare (201502006, 201002007), and the Peking University Outstanding Discipline Construction Project of Epidemiology and Biostatistics.

Declaration of Interest: The authors declare no conflict of interest.

Ethical Approval: This study was approved by the Biomedical Ethics Committee at Peking University, Beijing, China (IRB00001052-13022, IRB00001052-14021, IRB00001052-22032). All enrolled subjects provided written informed consent.

Keywords: Twin study, Type 2 diabetes, Lipids, Metabolites

Suggested Citation

Huang, Ninghao and Hong, Xuanming and Cao, Weihua and Lv, Jun and Yu, Canqing and Sun, Dianjianyi and Liao, Chunxiao and Pang, Yuanjie and Hu, Runhua and Pang, Zengchang and Yu, Min and Wang, Hua and Wu, Xianping and Liu, Yu and Gao, Wenjing and Huang, Tao and Li, Liming, Wide Metabonomics and Lipidomics of Type 2 Diabetes Mellitus Risk in Chinese Adult Twins. Available at SSRN: https://ssrn.com/abstract=4618842 or http://dx.doi.org/10.2139/ssrn.4618842

Ninghao Huang

Peking University - Department of Epidemiology and Biostatistics ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Xuanming Hong

Peking University - Department of Epidemiology and Biostatistics ( email )

Weihua Cao

Peking University - Department of Epidemiology and Biostatistics ( email )

Jun Lv

Peking University - Department of Epidemiology and Biostatistics ( email )

Canqing Yu

Peking University Health Science Center - School of Public Health ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China

Dianjianyi Sun

Peking University - Department of Epidemiology and Biostatistics ( email )

Chunxiao Liao

Peking University - Department of Epidemiology and Biostatistics ( email )

Yuanjie Pang

Peking University - Department of Epidemiology and Biostatistics ( email )

Runhua Hu

Peking University - Department of Epidemiology and Biostatistics ( email )

Zengchang Pang

Qingdao Center for Disease Control and Prevention ( email )

Min Yu

Zhejiang Provincial Center for Disease Control and Prevention - Department of Non-Communicable Disease Control and Prevention ( email )

Hangzhou
China

Hua Wang

Chinese Center for Disease Control and Prevention (China CDC) - Jiangsu Provincial Center for Disease Control and Prevention ( email )

Nanjing
China

Xianping Wu

Sichuan Center for Disease Control and Prevention ( email )

Chengdu, 610041
China

Yu Liu

Heilongjiang Provincial Center for Disease Control and Prevention ( email )

Harbin
China

Wenjing Gao

Peking University - Department of Epidemiology and Biostatistics ( email )

Tao Huang (Contact Author)

Peking University - Department of Epidemiology and Biostatistics ( email )

No. 38 Xueyuan Road
Haidian District
Beijing, Beijing 100871
China
86-10-82801528 (Phone)

Liming Li

Peking University - Department of Epidemiology and Biostatistics ( email )