不同性别FPG正常人群的糖代谢相关因素聚类分析及2型糖尿病发病风险评估

马睿; 陈逸超; 谢涵坤; 刘宇; 樊垚; 唐伟; 沈冲

doi:10.16462/j.cnki.zhjbkz.2023.12.009

不同性别FPG正常人群的糖代谢相关因素聚类分析及2型糖尿病发病风险评估

doi: 10.16462/j.cnki.zhjbkz.2023.12.009

马睿¹,
陈逸超¹,
谢涵坤¹,
刘宇²,
樊垚³,
唐伟⁴,
沈冲^{1, 3, ,}

1.
南京医科大学公共卫生学院流行病学系，南京 211166
2.
句容市疾病预防控制中心，镇江 212499
3.
南京医科大学附属老年医院临床流行病学研究室，南京 210024
4.
南京医科大学附属老年医院内分泌科，南京 210024

基金项目:

国家重点研发计划 2018YFC2000703

国家自然科学基金 82173611

详细信息

通讯作者:
沈冲，E-mail: sc@njmu.edu.cn

中图分类号: R181.3;R587.1
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- 被引次数: 0
出版历程
- 收稿日期: 2023-04-07
- 修回日期: 2023-09-21
- 刊出日期: 2023-12-10

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 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

摘要

摘要: 目的基于糖代谢相关因素, 对空腹血糖(fasting plasma glucose, FPG)正常的男性和女性分别进行聚类分析, 并评价不同聚类组与2型糖尿病(type 2 diabetes mellitus, T2DM)发病风险的关系。方法选取句容市队列研究基线FPG < 6.1 mmol/L的研究对象, 分别对2 857名男性和4 398名女性进行层次K-means (hierarchical K-means, HK-means)聚类分析, 聚类变量包括年龄、FPG、稳态模型评估胰岛素分泌指数与抵抗指数、三酰甘油、上颈围、腰围身高比及体力活动指数。采用Kaplan-Meier曲线和Cox比例风险回归模型分析评估聚类组的T2DM绝对和相对发病风险。结果男女人群分别被分为具有不同表型特征及T2DM发病风险的5个聚类组(cluster)。男性cluster 1体质指数(body mass index, BMI)水平最低和年龄最高, cluster 2体力活动指数最高, cluster 3 BMI水平最高且体力活动指数最低, cluster 4表现出高胰岛素抵抗且胰岛β细胞代偿分泌的特征, cluster 5年龄最低但血脂异常。女性cluster 1、2和4与男性特征相似, cluster 3年龄最低, cluster 5体力活动指数最低且肥胖伴随血脂异常。与男性cluster 1相比, cluster 3和4的T2DM发病风险增加了1.28和0.87倍(均P < 0.05);调整协变量后, HR (95% CI)分别为1.651(0.927~2.939)与1.516(0.779~2.950), 差异均无统计学意义(均P>0.05)。相比于女性cluster 1, cluster 3 T2DM发病风险较低, 调整后HR (95% CI)为0.380(0.205~0.704);Cluster 5 T2DM的发病风险较高(P < 0.05), 调整协变量后差异无统计学意义(HR=1.172, 95% CI: 0.717~1.916)。结论 FPG正常的人群在未诊断为糖尿病时即存在生理代谢异质性, 其聚类表型特征呈现出一定的男女性别差异。基于糖代谢相关因素在非糖尿病人群中进行聚类, 有助于提高高危亚组的早期筛查和防控干预。
- 空腹血糖 /
- 2型糖尿病 /
- 聚类分析 /
- 糖代谢
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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图 1 男性人群和女性人群的层次聚类树状图

Figure 1. Hierarchical cluster dendrogram of male and female participants

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图 2 男女性人群各聚类组2型糖尿病的发病情况

Figure 2. Incidence of type 2 diabetes in male and female populations by cluster

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图 3 男女性人群聚类分组与2型糖尿病发病风险的关联

Cluster 1为参照组；模型1：未调整协变量；模型2：调整体质指数、吸烟、饮酒及糖尿病家族史。

Figure 3. Associations between clusters and incidence of type 2 diabetes in male and female populations

Cluster 1 was the reference group. Model 1: unadjusted covariates. Model 2: adjusted for body mass index, smoking, drinking, and the family history of diabetes.

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表 1 研究对象男性和女性的基线特征比较

Table 1. Baseline characteristics of study participants

特征Characteristic	男性 ^① Male ^①	女性 ^① Female ^①	H值value	P值value
人数Population number	2 857 (39.38)	4 398 (60.62)
年龄/岁Age/year	62.25 (52.75, 69.17)	58.25 (49.42, 66.17)	180.10	< 0.001
BMI/(kg·m^-2)	24.05 (21.79, 26.29)	24.55 (22.42, 26.84)	53.46	< 0.001
FPG/(mmol·L^-1)	5.38 (5.03, 5.70)	5.39 (5.05, 5.68)	0.77	0.380
胰岛素/(pmol·L^-1) Insulin /(pmol·L^-1)	95.10 (67.14, 136.32)	102.66 (71.71, 148.68)	34.28	< 0.001
HOMA2-β	125.80 (95.90, 165.90)	130.80 (101.00, 175.00)	19.54	< 0.001
HOMA2-IR	1.77 (1.26, 2.53)	1.92 (1.35, 2.74)	35.02	< 0.001
TG/(mmol·L^-1)	1.14 (0.83, 1.65)	1.26 (0.94, 1.75)	57.58	< 0.001
TC/(mmol·L^-1)	4.91 (4.39, 5.52)	5.02 (4.46, 5.63)	26.97	< 0.001
HDL-C/(mmol·L^-1)	1.49 (1.23, 1.80)	1.52 (1.28, 1.77)	2.35	0.126
LDL-C/(mmol·L^-1)	2.73 (2.26, 3.22)	2.82 (2.34, 3.34)	25.31	< 0.001
腰围/cm Waist circumference/cm	82.00 (75.00, 88.50)	80.00 (73.50, 86.00)	85.00	< 0.001
臀围/cm Hip circumference/cm	92.70 (88.10, 97.00)	93.00 (89.00, 98.00)	16.18	< 0.001
腰臀比Waist-to-hip ratio	0.89 (0.84, 0.92)	0.85 (0.81, 0.89)	402.55	< 0.001
腰围身高比Waist-to-height ratio	0.50 (0.46, 0.54)	0.52 (0.48, 0.56)	178.97	< 0.001
上颈围/cm Upper neck circumference/cm	36.00 (34.00, 38.00)	32.50 (31.00, 34.00)	2 336.53	< 0.001
下颈围/cm Lower neck circumference/cm	36.00 (34.20, 38.00)	33.00 (31.00, 34.40)	2 157.78	< 0.001
PAI/[MET-(h·d^-1)]	10.17 (3.00, 32.00)	10.67 (4.00, 22.00)	0.46	0.498
吸烟Smoking	1 577 (55.20)	68 (1.55)	2 842.45	< 0.001
饮酒Drinking	1 556 (54.46)	454 (10.33)	1 683.52	< 0.001
糖尿病家族史Family history of diabetes	324 (11.34)	507 (11.53)	0.06	0.836
高血压Hypertension	1 812 (63.42)	2 186 (49.70)	131.74	< 0.001
注：1. BMI, 体质指数; HOMA2-β, 稳态模型评估胰岛素分泌指数; HOMA2-IR, 稳态模型评估胰岛素抵抗指数; TG, 三酰甘油; TC, 总胆固醇; HDL-C, 高密度脂蛋白胆固醇; LDL-C, 低密度脂蛋白胆固醇; PAI, 体力活动指数; MET, 代谢当量。 2. 高血压定义为SBP≥140 mmHg和(或)DBP≥90 mmHg，或自我报告有经医生诊断的高血压病史且目前正在服用降压药物。 ①以[人数(占比/%)]或[M(P₂₅, P₇₅)]表示。 Notes: 1. BMI, body mass index; HOMA2-β, homeostasis model assessment 2 estimates of β-cell function; HOMA2-IR, homeostasis model assessment 2 estimates of insulin resistance; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PAI, physical activity index; MET, metabolic equivalent. 2. Hypertension was defined as average SBP ≥ 140 mmHg or DBP ≥ 90 mmHg or a self-reported diagnosis of hypertension, or currently taking antihypertensive medications. ① [Number of people (proportion/%)] or [M(P₂₅, P₇₅)].

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表 2 男性人群聚类分组后的聚类特征

Table 2. Cluster characteristics of the male population

变量Variable	Cluster 1 ^①	Cluster 2 ^①	Cluster 3 ^①	Cluster 4 ^①	Cluster 5 ^①	P值 value
人数Population number	1 091 (38.19)	332 (11.62)	1 014 (35.49)	362 (12.67)	58 (2.03)
年龄/岁Age/year	66.25 (59.33, 73.71)	60.62 (51.48, 65.35)	60.92 (51.25, 67.50)	58.88 (48.27, 66.63)	51.34 (45.94, 61.23)	< 0.001
BMI/(kg·m^-2)	21.78 (20.31, 23.34)	23.74 (22.11, 26.11)	26.16 (24.56, 27.69)	24.56 (22.38, 26.66)	26.09 (24.40, 27.39)	< 0.001
FPG/(mmol·L^-1)	5.37 (5.03, 5.69)	5.33 (4.97, 5.64)	5.47 (5.16, 5.77)	5.12 (4.64, 5.51)	5.34 (5.01, 5.74)	< 0.001
胰岛素/(pmol·L^-1) Insulin /(pmol·L^-1)	79.44 (57.27, 106.17)	83.85 (58.47, 113.70)	98.88 (74.30, 128.15)	217.47 (175.70, 275.76)	94.35 (68.47, 127.76)	< 0.001
HOMA2-β	110.90 (86.85, 140.25)	118.45 (89.75, 148.52)	125.95 (98.60, 153.48)	239.90 (212.67, 286.70)	124.60 (102.72, 149.60)	< 0.001
HOMA2-IR	1.50 (1.08, 2.01)	1.56 (1.12, 2.11)	1.86 (1.40, 2.40)	3.94 (3.17, 4.93)	1.77 (1.29, 2.37)	< 0.001
TG/(mmol·L^-1)	0.91 (0.74, 1.18)	1.05 (0.78, 1.58)	1.41 (1.03, 2.01)	1.34 (0.97, 1.97)	7.11 (6.14, 9.79)	< 0.001
TC/(mmol·L^-1)	4.81 (4.30, 5.40)	4.90 (4.41, 5.50)	4.95 (4.44, 5.52)	4.94 (4.37, 5.57)	5.98 (5.38, 6.80)	< 0.001
HDL-C/(mmol·L^-1)	1.72 (1.44, 2.04)	1.59 (1.31, 2.00)	1.32 (1.13, 1.58)	1.33 (1.14, 1.69)	0.92 (0.78, 1.06)	< 0.001
LDL-C/(mmol·L^-1)	2.60 (2.15, 3.09)	2.62 (2.18, 3.14)	2.85 (2.38, 3.33)	2.78 (2.30, 3.35)	3.29 (2.95, 3.75)	< 0.001
腰围/cm Waist circumference/cm	75.00 (70.00, 79.00)	82.15 (77.00, 87.58)	88.20 (85.00, 93.00)	83.00 (77.23, 89.00)	87.10 (81.25, 93.65)	< 0.001
臀围/cm Hip circumference/cm	88.40 (85.00, 92.00)	93.00 (89.00, 97.00)	97.00 (94.00, 100.00)	93.00 (89.12, 98.00)	95.00 (91.25, 99.88)	< 0.001
腰臀比Waist-to-hip ratio	0.84 (0.81, 0.88)	0.89 (0.85, 0.92)	0.92 (0.89, 0.95)	0.89 (0.86, 0.93)	0.92 (0.88, 0.95)	< 0.001
腰围身高比Waist-to-height ratio	0.46 (0.43, 0.48)	0.50 (0.47, 0.53)	0.53 (0.51, 0.56)	0.51 (0.46, 0.54)	0.52 (0.50, 0.56)	< 0.001
上颈围/cm Upper neck circumference/cm	34.00 (33.00, 35.00)	36.00 (35.00, 37.52)	38.00 (37.00, 39.00)	36.85 (35.00, 38.00)	38.00 (35.25, 39.38)	< 0.001
下颈围/cm Lower neck circumference/cm	34.20 (33.00, 35.95)	36.00 (35.00, 37.00)	38.00 (36.50, 39.00)	36.50 (35.00, 38.00)	38.00 (35.85, 39.75)	< 0.001
PAI/[MET-(h·d^-1)]	8.89 (2.00, 24.00)	72.00 (58.67, 90.00)	8.00 (2.00, 19.25)	8.00 (2.67, 24.50)	11.34 (2.00, 33.83)	< 0.001
吸烟Smoking	594 (54.45)	214 (64.46)	528 (52.07)	201 (55.52)	40 (68.97)	< 0.001
饮酒Drinking	521 (47.75)	222 (66.87)	566 (55.82)	200 (55.25)	47 (81.03)	< 0.001
糖尿病家族史Family history of diabetes	97 (8.89)	31 (9.34)	140 (13.81)	47 (12.98)	9 (15.52)	< 0.050
高血压Hypertension	640 (58.66)	199 (59.94)	720 (71.01)	212 (58.56)	41 (70.69)	< 0.001
注：BMI, 体质指数；HOMA2-β, 稳态模型评估胰岛素分泌指数；HOMA2-IR, 稳态模型评估胰岛素抵抗指数；TG, 三酰甘油；TC, 总胆固醇；HDL-C, 高密度脂蛋白胆固醇；LDL-C, 低密度脂蛋白胆固醇；PAI, 体力活动指数；MET, 代谢当量。 ①以[人数(占比/%)]或[M(P₂₅, P₇₅)]表示。 Notes: BMI, body mass index; HOMA2-β, homeostasis model assessment 2 estimates of β-cell function; HOMA2-IR, homeostasis model assessment 2 estimates of insulin resistance; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PAI, physical activity index; MET, metabolic equivalent. ① [Number of people (proportion/%)] or [M(P₂₅, P₇₅)].

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表 3 女性人群聚类分组后的聚类特征

Table 3. Cluster characteristics of the female population

变量Variable	Cluster 1 ^①	Cluster 2 ^①	Cluster 3 ^①	Cluster 4 ^①	Cluster 5 ^①	P值 value
人数Population number	1220 (27.74)	359 (8.16)	1156 (26.28)	557 (12.66)	1106 (25.15)
年龄/岁Age/year	63.42 (58.65, 69.33)	58.42 (51.92, 63.88)	47.04 (41.92, 51.10)	58.08 (49.58, 66.42)	62.92 (53.75, 69.06)	< 0.001
BMI/(kg·m^-2)	23.07 (21.25, 24.78)	24.67 (22.84, 26.37)	23.34 (21.64, 25.25)	25.16 (23.30, 27.53)	27.27 (25.63, 29.41)	< 0.001
FPG/(mmol·L^-1)	5.57 (5.31, 5.82)	5.37 (4.98, 5.63)	5.15 (4.83, 5.43)	5.18 (4.79, 5.52)	5.52 (5.22, 5.77)	< 0.001
胰岛素/(pmol·L^-1) Insulin /(pmol·L^-1)	82.11 (58.97, 111.39)	95.22 (69.06, 132.93)	91.83 (65.81, 123.15)	234.36 (195.42, 283.08)	114.57 (84.92, 149.43)	< 0.001
HOMA2-β	105.30 (83.50, 129.80)	127.20 (99.75, 163.10)	131.25 (104.45, 167.15)	247.40 (219.70, 285.90)	134.70 (109.62, 164.85)	< 0.001
HOMA2-IR	1.57 (1.13, 2.11)	1.79 (1.27, 2.48)	1.70 (1.22, 2.28)	4.24 (3.48, 5.05)	2.16 (1.61, 2.78)	< 0.001
TG/(mmol·L^-1)	1.19 (0.93, 1.57)	1.17 (0.90, 1.57)	1.04 (0.79, 1.40)	1.45 (1.04, 1.95)	1.63 (1.23, 2.32)	< 0.001
TC/(mmol·L^-1)	5.17 (4.63, 5.73)	5.01 (4.53, 5.60)	4.74 (4.18, 5.37)	5.01 (4.50, 5.66)	5.15 (4.61, 5.79)	< 0.001
HDL-C/(mmol·L^-1)	1.62 (1.38, 1.87)	1.58 (1.35, 1.80)	1.57 (1.34, 1.80)	1.43 (1.23, 1.69)	1.38 (1.16, 1.62)	< 0.001
LDL-C/(mmol·L^-1)	2.91 (2.44, 3.41)	2.81 (2.37, 3.33)	2.62 (2.16, 3.08)	2.84 (2.36, 3.39)	2.93 (2.45, 3.45)	< 0.001
腰围/cm Waist circumference/cm	77.00 (72.00, 81.00)	80.00 (75.00, 85.00)	74.00 (70.00, 79.00)	81.00 (76.00, 86.40)	89.00 (85.00, 94.00)	< 0.001
臀围/cm Hip circumference/cm	90.50 (87.00, 94.60)	93.00 (90.00, 97.00)	90.50 (87.00, 95.00)	94.60 (90.10, 98.20)	99.00 (95.00, 103.00)	< 0.001
腰臀比Waist-to-hip ratio	0.84 (0.81, 0.88)	0.86 (0.82, 0.89)	0.82 (0.79, 0.85)	0.86 (0.82, 0.90)	0.90 (0.87, 0.93)	< 0.001
腰围身高比Waist-to-height ratio	0.50 (0.47, 0.53)	0.52 (0.49, 0.55)	0.48 (0.44, 0.51)	0.52 (0.49, 0.56)	0.58 (0.55, 0.61)	< 0.001
上颈围/cm Upper neck circumference/cm	31.00 (30.00, 32.00)	32.00 (31.00, 34.00)	32.00 (31.00, 33.00)	33.00 (31.50, 34.00)	35.00 (34.00, 36.00)	< 0.001
下颈围/cm Lower neck circumference/cm	31.45 (30.00, 32.73)	32.10 (31.00, 34.00)	32.30 (31.00, 34.00)	33.00 (31.80, 34.50)	34.75 (33.23, 36.00)	< 0.001
PAI/[MET-(h·d^-1)]	10.00 (4.00, 18.00)	56.00 (46.00, 72.00)	9.33 (4.00, 16.00)	12.00 (4.00, 21.33)	8.00 (4.00, 16.00)	< 0.001
吸烟Smoking	28 (2.30)	5 (1.39)	5 (0.43)	8 (1.44)	22 (1.99)	< 0.001
饮酒Drinking	118 (9.67)	47 (13.09)	98 (8.49)	58 (10.41)	133 (12.03)	0.025
糖尿病家族史Family history of diabetes	131 (10.74)	36 (10.03)	179 (15.48)	67 (12.03)	94 (8.50)	< 0.001
高血压Hypertension	669 (54.84)	179 (49.86)	263 (22.75)	278 (49.91)	797 (72.06)	< 0.001
注：BMI, 体质指数；HOMA2-β, 稳态模型评估胰岛素分泌指数；HOMA2-IR, 稳态模型评估胰岛素抵抗指数；TG, 三酰甘油；TC, 总胆固醇；HDL-C, 高密度脂蛋白胆固醇；LDL-C, 低密度脂蛋白胆固醇；PAI, 体力活动指数；MET, 代谢当量。 ①以[人数(占比/%)]或[M(P₂₅, P₇₅)]表示。 Notes: BMI, body mass index; HOMA2-β, homeostasis model assessment 2 estimates of β-cell function; HOMA2-IR, homeostasis model assessment 2 estimates of insulin resistance; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PAI, physical activity index; MET, metabolic equivalent. ① [Number of people (proportion/%)] or [M(P₂₅, P₇₅)].

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参考文献(16)

[1]	Li YZ, Teng D, Shi XG, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American diabetes association: national cross sectional study[J]. BMJ, 2020, 369: m997. DOI: 10.1136/bmj.m997.
[2]	刘敏. 我国糖尿病地区分布及其疾病负担研究[D]. 北京: 中国疾病预防控制中心, 2019. Liu M. Geographical distribution and burden of diabetes in China[D]. Beijing: Chinese Center for Disease Control and Prevention, 2019.
[3]	Tinajero MG, Malik VS. An update on the epidemiology of type 2 diabetes: a global perspective[J]. Endocrinol Metab Clin North Am, 2021, 50(3): 337-355. DOI: 10.1016/j.ecl.2021.05.013.
[4]	Jia WP, Weng JP, Zhu DL, et al. Standards of medical care for type 2 diabetes in China 2019[J]. Diabetes Metab Res Rev, 2019, 35(6): e3158. DOI: 10.1002/dmrr.3158.
[5]	Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables[J]. Lancet Diabetes Endocrinol, 2018, 6(5): 361-369. DOI: 10.1016/S2213-8587(18)30051-2.
[6]	Sarría-Santamera A, Orazumbekova B, Maulenkul T, et al. The identification of diabetes mellitus subtypes applying cluster analysis techniques: a systematic review[J]. Int J Environ Res Public Health, 2020, 17(24): 9523. DOI: 10.3390/ijerph17249523.
[7]	Bauman A, Bull F, Chey T, et al. The international prevalence study on physical activity: results from 20 countries[J]. Int J Behav Nutr Phys Act, 2009, 6(1): 1-11. DOI: 10.1186/1479-5868-6-21.
[8]	Ainsworth BE, Haskell WL, Leon AS, et al. Compendium of physical activities: classification of energy costs of human physical activities[J]. Med Sci Sports Exerc, 1993, 25(1): 71-80. DOI: 10.1249/00005768-199301000-00011.
[9]	中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版)[J]. 中华内分泌代谢杂志, 2021, 41(5): 482-548. DOI: 10.3760/cma.j.cn121383-20210825-08063. Chinese Diabetes Society. Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition)[J]. Int J Endocrinol Metab, 2021, 41(5): 482-548. DOI: 10.3760/cma.j.cn121383-20210825-08063.
[10]	Chen B, Tai PC, Harrison R, et al. Novel hybrid hierarchical-K-means clustering method (H-K-means) for microarray analysis[C]. 2005 IEEE computational systems bioinformatics conference- workshops (CSBW'05). August 8-12, 2005, Stanford, CA, USA. IEEE, 2005: 105-108. DOI: 10.1109/CSBW.2005.98.
[11]	Ma RCW. Epidemiology of diabetes and diabetic complications in China[J]. Diabetologia, 2018, 61(6): 1249-1260. DOI: 10.1007/s00125-018-4557-7.
[12]	Liu YC, Li ZM, Xiong H, et al. Understanding of internal clustering validation measures[C]//2010 IEEE International Conference on Data Mining. December 13-17, 2010, Sydney, NSW, Australia. IEEE, 2011: 911-916. DOI: 10.1109/ICDM.2010.35.
[13]	Bellary S, Kyrou I, Brown JE, et al. Type 2 diabetes mellitus in older adults: clinical considerations and management[J]. Nat Rev Endocrinol, 2021, 17(9): 534-548. DOI: 10.1038/s41574-021-00512-2.
[14]	Wagner R, Heni M, Tabák AG, et al. Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes[J]. Nat Med, 2021, 27(1): 49-57. DOI: 10.1038/s41591-020-1116-9.
[15]	Zhang XX, Liu J, Shao S, et al. Sex differences in the prevalence of and risk factors for abnormal glucose regulation in adults aged 50 years or older with normal fasting plasma glucose levels[J]. Front Endocrinol (Lausanne), 2020, 11: 531796. DOI: 10.3389/fendo.2020.531796.
[16]	Ryan JC, Barnes M, Cox DN. Identifying modifiable factors that could arrest progression to type 2 diabetes: a cluster analysis of Australian adults[J]. Prev Med, 2021, 153: 106796. DOI: 10.1016/j.ypmed.2021.106796.