Cluster analysis based on glycometabolism-related factors for males and females with normal fasting plasma glucose and the risk of type 2 diabetes mellitus

MA Rui; CHEN Yichao; XIE Hankun; LIU Yu; FAN Yao; TANG Wei; SHEN Chong

doi:10.16462/j.cnki.zhjbkz.2023.12.009

Volume 27 Issue 12

Dec. 2023

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MA Rui, CHEN Yichao, XIE Hankun, LIU Yu, FAN Yao, TANG Wei, SHEN Chong. Cluster analysis based on glycometabolism-related factors for males and females with normal fasting plasma glucose and the risk of type 2 diabetes mellitus[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(12): 1413-1420. doi: 10.16462/j.cnki.zhjbkz.2023.12.009

Citation:

MA Rui, CHEN Yichao, XIE Hankun, LIU Yu, FAN Yao, TANG Wei, SHEN Chong. Cluster analysis based on glycometabolism-related factors for males and females with normal fasting plasma glucose and the risk of type 2 diabetes mellitus[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(12): 1413-1420. doi: 10.16462/j.cnki.zhjbkz.2023.12.009

Citation:

MA Rui, CHEN Yichao, XIE Hankun, LIU Yu, FAN Yao, TANG Wei, SHEN Chong. Cluster analysis based on glycometabolism-related factors for males and females with normal fasting plasma glucose and the risk of type 2 diabetes mellitus[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(12): 1413-1420. doi: 10.16462/j.cnki.zhjbkz.2023.12.009

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Cluster analysis based on glycometabolism-related factors for males and females with normal fasting plasma glucose and the risk of type 2 diabetes mellitus

doi: 10.16462/j.cnki.zhjbkz.2023.12.009

MA Rui¹,
CHEN Yichao¹,
XIE Hankun¹,
LIU Yu²,
FAN Yao³,
TANG Wei⁴,
SHEN Chong^{1, 3
,
,}

1.
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
2.
Center for Disease Control and Prevention of Jurong City, Zhenjiang 212499, China
3.
Development of Clinical Epidemiology, Geriatric Hospital Affiliated to Nanjing Medical University, Nanjing 210024, China
4.
Department of Endocrinology, Geriatric Hospital of Nanjing Medical University, Nanjing 210024, China

Funds:

National Key Research and Development Program of China 2018YFC2000703

National Natural Science Foundation of China 82173611

More Information

Corresponding author: SHEN Chong, E-mail: sc@njmu.edu.cn
Received Date: 2023-04-07
Rev Recd Date: 2023-09-21
Publish Date: 2023-12-10

Abstract

Abstract

Objective We did cluster analysis in male and female populations with normal fasting plasma glucose based on factors related to glycemic metabolism, and evaluated the association between cluster allocation and the risk of type 2 diabetes mellitus (T2DM). Methods The study population consisted of baseline participants with fasting plasma glucose < 6.1 mmol/L from a prospective cohort in Jurong City.Hierarchical K-means clustering was conducted separately for 2 857 males and 4 398 females.Cluster variables included age, fasting plasma glucose, the homeostasis model assessment 2 estimates of β-cell function and insulin resistance, triglyceride, upper neck circumference, waist-to-height ratio, and physical activity index.The Kaplan-Meier curve for cumulative hazard was used to estimate the incidence of T2DM in male and female clusters.Cox proportional hazard regression model was applied to assess the association between cluster allocation and T2DM risk. Results The male and female populations were classified into 5 distinctive clusters, respectively, with various phenotypic characteristics and different incidence of T2DM.Male cluster 1 had the lowest body mass index (BMI) but the highest age, and cluster 2 had the highest level of physical activity.Male cluster 3 had the highest BMI and the lowest level of physical activity.Cluster 4 was characterized by high insulin resistance and compensatory β-cell secretion, while cluster 5 exhibited a phenotype of young age and severe dyslipidemia.Female cluster 1, 2, and 4 were similar to those of males.Female cluster 3 had the lowest age, while cluster 5 presented features of the lowest physical activity, obesity, and dyslipidemia.Compared with male cluster 1, the risk of T2DM in cluster 3 and 4 increased by 1.28 times and 0.87 times respectively (all P < 0.05).After adjusting for covariates, the HR (95% CI) were 1.651(0.927-2.939) and 1.516(0.779-2.950) with no statistical significance (allP>0.05).Compared to female cluster 1, cluster 3 had a lower risk of developing T2DM, with an adjusted HR (95% CI) of 0.380(0.205-0.704).Cluster 5 had a higher risk of T2DM than cluster 1(P < 0.05), but the association was not significant after adjusting for covariates (HR=1.172, 95% CI: 0.717-1.916). Conclusions Our findings indicate that heterogeneity of physiological metabolism exists among individuals with normal fasting plasma glucose before the diagnosis of T2DM.The clustering phenotypic characteristics also present certain gender differences.Cluster analysis for non-diabetic populations based on glycometabolism-related factors could aid in the improvement of early detection and screening for high-risk subgroups, as well as precise intervention to effectively reduce the risk of diabetes and complications.
- Fasting plasma glucosee,
- Type 2 diabetes mellitus,
- Cluster analysis,
- Glucose metabolism

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References(16)

References

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