碳水化合物摄入量与糖尿病前期患病的关联分析

李婷; 高倩; 司鑫雨; 王彤

doi:10.16462/j.cnki.zhjbkz.2025.06.012

碳水化合物摄入量与糖尿病前期患病的关联分析

doi: 10.16462/j.cnki.zhjbkz.2025.06.012

李婷^{1, 2},
高倩²,
司鑫雨²,
王彤^{1, 2, 3, ,}

1.
山西医科大学医学科学院，太原 030001
2.
山西医科大学公共卫生学院卫生统计学教研室，太原 030001
3.
煤炭环境致病与防治教育部重点实验室，太原 030001

基金项目:

国家自然科学基金 82073674

国家自然科学基金 82373692

国家自然科学基金 82204163

山西省基础研究计划资助项目 202203021212382

详细信息

通讯作者:
王彤，E-mail: tongwang@sxmu.edu.cn

中图分类号: R181.3
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出版历程
- 收稿日期: 2024-09-09
- 修回日期: 2025-02-20
- 网络出版日期: 2025-07-07
- 刊出日期: 2025-06-10

Association between carbohydrate intake and prevalence of prediabetes

LI Ting^{1, 2},
GAO Qian²,
SI Xinyu²,
WANG Tong^{1, 2, 3
, ,}

1.
Academy of Medical Sciences, Shanxi Medical University, Taiyuan 030001, China
2.
Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
3.
Ministry of Education Key Laboratory of Coal Environmental Pathogenicity and Prevention, Taiyuan 030001, China

Funds:

National Natural Science Foundation of China 82073674

National Natural Science Foundation of China 82373692

National Natural Science Foundation of China 82204163

Fundamental Research Program of Shanxi Province 202203021212382

More Information

Corresponding author: WANG Tong, E-mail: tongwang@sxmu.edu.cn

摘要

摘要: 目的探讨每日碳水化合物摄入量与糖尿病前期患病的关系，为糖尿病前期形成提供证据。方法基于美国健康与营养调查2007―2016年数据，纳入与本研究相关且关键指标无缺失的14 299名研究对象。采用非参数协变量均衡广义倾向性评分方法均衡已测得的混杂因素，通过限制性立方样条探究二者关系。结果研究人群中位年龄为43岁，糖尿病前期患病率为39.28%。在调整混杂因素后，碳水化合物摄入量与糖尿病前期患病呈非线性关系(P_非线性 < 0.001)。与每日摄入量为243.21 g相比，随着碳水化合物摄入量的增加，糖尿病前期的患病风险逐渐升高(OR值逐渐上升)。在高、中和低水平活动人群中，与每日碳水化合物摄入量分别为350.66 g、335.80 g、152.22 g相比，随着碳水化合物摄入量的增加，糖尿病前期的患病风险逐渐升高(OR值逐渐上升)。结论碳水化合物摄入量与糖尿病前期患病呈非线性关系，每日摄入超过约240 g的碳水化合物可能是糖尿病前期患病的危险因素。且身体活动水平下降，增加糖尿病前期患病风险的碳水化合物摄入量也会降低。
- 糖尿病前期 /
- 碳水化合物摄入量 /
- 非参数协变量均衡广义倾向性评分法 /
- 限制性立方样条
Abstract: Objective This study aims to investigate the relationship between daily carbohydrate intake and the prevalence of prediabetes, and to provide direct evidence for the formation of prediabetes. Methods Based on the data from the National Health and Ntrition Examination Survey from 2007 to 2016, 14 299 research subjects related to this study with no missing key indicators were included. The non-parametric covariate balancing generalized propensity score was used to balance the measured confounding factors, and the restricted cubic spline was adopted to explore the relationship between them. Results The median age of the study population was 43 years old, and the prevalence of prediabetes was 39.28%. After adjusting for confounding factors, there was a nonlinear relationship between carbohydrate intake and prediabetes (P_nonlinear < 0.001). Compared with the daily intake of 243.21 g, the risk of prediabetes increased with the increase of carbohydrate intake (OR value gradually increases). In the high, medium and low levels of activity, the risk of prediabetes was gradually increased with the increase of carbohydrate intake (OR value gradually increases) compared with the daily carbohydrate intake of 350.66 g, 335.80 g and 152.22 g, respectively. Conclusions There is a nonlinear relationship between carbohydrate intake and prediabetes. A daily carbohydrate intake of more than approximately 240 g may be a risk factor for prediabetes. Moreover, the carbohydrate intake associated with elevated prediabetes risk is lower in individuals with reduced physical activity.
- Prediabetes /
- Carbohydrate intake /
- Non-parametric covariate balancing generalized propensity score /
- Restricted cubic spline

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图 1 NHANES数据集研究对象筛选流程图

NHANES：美国健康与营养调查。

Figure 1. Flow chart of subject screening for NHANES dataset

NHANES: National Health and Nutrition Examination Survey.

下载: 全尺寸图片幻灯片

图 2 在不同模型下碳水化合物摄入量与糖尿病前期之间的关系

A：模型1，未调整混杂因素; B：模型2，调整了人口学特征; C：模型3，调整了人口学特征和生活方式; D：模型4，调整了人口学特征、生活方式和体检数据。

Figure 2. Association between carbohydrate intake and prediabetes under different models

A: model 1, unadjusted confounders; B: model 2, adjusted demographic characteristics; C: model 3, adjusted demographic characteristics and lifestyle; D: model 4, adjusted demographic characteristics, lifestyle and physical examination data.

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图 3 不同模型的均衡性表现

模型1：未调整混杂因素; 模型2：调整了人口学特征; 模型3：调整了人口学特征和生活方式; 模型4：调整了人口学特征、生活方式和体检数据。

Figure 3. Performance of the balance of the different models

Model 1: unadjusted confounders; Model 2: adjusted demographic characteristics; Model 3: adjusted demographic characteristics and lifestyle; Model 4: adjusted demographic characteristics, lifestyle and physical examination data.

下载: 全尺寸图片幻灯片

图 4 均衡混杂因素方法的敏感性分析

A和B分别表示使用npCBGPS和logistic方法调整了人口学特征、生活方式和体检数据。

Figure 4. Sensitivity analysis of the balanced confounders methods

A and B represent adjustments for demographic characteristics, lifestyle, and physical examination data using npCBGPS and logistic methods, respectively.

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图 5 不同身体活动情况的敏感性分析

A、B、C表示分别在低、中、高身体活动水平人群中使用npCBGPS调整了人口学特征、生活方式和体检数据。

Figure 5. Sensitivity analysis of different physical activity status

A, B, C represent adjustments for demographic characteristics, lifestyle, and physical examination data using npCBGPS in populations with low, medium, and high levels of physical activity, respectively.

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表 1 研究对象基线特征

Table 1. Baseline characteristics of the subjects

变量 Variable	非糖尿病前期 Non-prediabetic state^①(n=8 682)	糖尿病前期 Prediabetic state^① (n=5 617)	检验统计量 Statistic of test	P值 value
年龄组/岁Age groups/years			1 349.90	< 0.001
18~ < 45	5 690(65.5)	1 979(35.2)
45~ < 60	1 708(19.7)	1 658(29.5)
≥60	1 284(14.8)	1 980(35.3)
性别Gender			50.83	< 0.001
男Male	4 228(48.7)	3 079(54.8)
女Female	4 454(51.3)	2 538(45.2)
婚姻状况Marital status			63.00	< 0.001
离婚/丧偶/分居/未婚Divorced/widowed/separated/never married	3 887(44.8)	2 137(38.0)
已婚/同居Married/live with partners	4 795(55.2)	3 480(62.0)
受教育程度Education			80.18	< 0.001
高中及以下High school or lower	3 429(39.5)	2 645(47.1)
大学及以上University or higher	5 253(60.5)	2 972(52.9)
种族Race			69.89	< 0.001
墨西哥裔美国人Mexican American	1 206(13.9)	825(14.7)
其他西班牙裔Other Hispanics	852(9.8)	567(10.1)
非西班牙裔白人Non-Hispanic whites	4 168(48.0)	2 394(42.6)
非西班牙裔黑人Non-Hispanic blacks	1 497(17.2)	1 251(22.3)
其他种族(包括多种族) Other races (including multi-racial)	959(11.1)	580(10.3)
家庭收入贫困比Ratio of family income to poverty			9.74	0.008
低Low (< 1)	1 860(21.4)	1 116(19.9)
中Medium (1~4)	4 335(49.9)	2 950(52.5)
高High (>4)	2 487(28.7)	1 551(27.6)
吸烟Smoking			78.18	< 0.001
一生中吸烟 < 100支Lifetime smoking < 100 cigarettes	5 379(62.0)	3 061(54.5)
一生中吸烟≥100支Lifetime smoking ≥100 cigarettes	3 303(38.0)	2 556(45.5)
饮酒Drinking			152.41	< 0.001
近1年未喝过酒Haven't drunk in the past year	1 130(13.0)	1 168(20.8)
近1年喝过酒Have drunk in the past year	7 552(87.0)	4 449(79.2)
蛋白质Protein /(g·d^-1)	77.48(58.81, 101.25)	76.96(58.22, 100.36)	24 784 322	0.096
脂肪Fat/(g·d^-1)	73.11(52.76, 99.10)	72.81(52.76, 99.10)	24 542 764	0.509
BMI/(kg·m^-2)	25.68(22.63, 29.41)	27.96(24.64, 31.93)	18 612 707	< 0.001
碳水化合物Carbohydrate/(g·d^-1)	244.95(185.00, 315.56)	239.68(183.01, 311.12)	25 019 315	0.008
身体活动Physical activity/(MET·h·w^-1)			20.01	< 0.001
< 1	17(0.2)	27(0.5)
1~48	4 870(56.1)	3 299(58.7)
>48	3 795(43.7)	2 291(40.8)
注：①以中位数和四分位数间距M(P₂₅, P₇₅)对定量变量进行统计描述，以频数(构成比)对定性变量进行统计描述。 Note: ① The median and interquartile range M(P₂₅, P₇₅) were used to describe the quantitative variables, and the frequency and constituent ratio n(%) were used to describe the qualitative variable.

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