中老年人群中血清维生素与生物衰老速度的关联性

张倩; 马爽; 殷金珠; 王彤; 高倩

doi:10.16462/j.cnki.zhjbkz.2025.06.016

中老年人群中血清维生素与生物衰老速度的关联性

doi: 10.16462/j.cnki.zhjbkz.2025.06.016

张倩¹,
马爽²,
殷金珠³,
王彤¹,
高倩^1, ,

1.
山西医科大学公共卫生学院卫生统计学教研室，煤炭环境致病与防治教育部重点实验室，太原 030001
2.
山西医科大学医学科学院，太原 030001
3.
国药同煤总医院中心实验室，山西省神经疾病防治研究委级重点实验室，大同 037003

基金项目:

国家自然科学基金 82204163

国家自然科学基金 82373692

山西省基础研究计划资助项目 202203021212382

山西省神经疾病防治研究委级重点实验室开放课题 TMSYSKF2023004

详细信息

通讯作者:
高倩，E-mail: gaoqian@sxmu.edu.cn

中图分类号: R181
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出版历程
- 收稿日期: 2025-01-06
- 修回日期: 2025-04-10
- 网络出版日期: 2025-07-07
- 刊出日期: 2025-06-10

Association between serum vitamins and biological aging in middle-aged and elderly people

1.
Department of Health Statistics, School of Public Health, Shanxi Medical University, Key Laboratory of Coal Environmental Pathogenicity and Prevention, Ministry of Education, Taiyuan 030001, China
2.
School of Medical Sciences, Shanxi Medical University, Taiyuan 030001, China
3.
Department of Central Laboratory, Sinopharm Tongmei General Hospital, Shanxi Health Commission Key Laboratory of Nervous System Disease Prevention and Treatment, Datong 037003, China

Funds:

National Natural Science Foundation of China 82204163

National Natural Science Foundation of China 82373692

Fundamental Research Program of Shanxi Province 202203021212382

Open Project of Shanxi Health Commission Key Laboratory of Nervous System Disease Prevention and Treatment TMSYSKF2023004

More Information

Corresponding author: GAO Qian, E-mail: gaoqian@sxmu.edu.cn

摘要

摘要: 目的探讨中老年人血清维生素与生物衰老速度之间的关联。方法基于2017―2018年美国国家健康与营养调查(National Health and Nutrition Examination Survey, NHANES)数据，纳入2 172名>45岁的研究对象。收集包括人口统计学资料、生活习惯和基础疾病史在内的协变量。利用多重线性回归分析模型，评估中老年人血清中的维生素C(vitamin C, VC)、维生素D₃(vitamin D₃, VD₃)、维生素A(vitamin A, VA)和维生素E(vitamin E, VE)与生物衰老速度的关联性。采用分位数g-计算(quantile g-computation, QGCOMP)、加权分位数和(weighted quantile sum, WQS)2种回归方法，分析不同血清维生素对生物衰老速度的影响及其联合效应。结果研究对象平均年龄为(62.69±10.29)岁，男性与女性间血清维生素水平的差异均有统计学意义：VD₃(Z=-2.376, P=0.017)、VC(Z=-7.230, P < 0.001)、VA(Z=6.418, P < 0.001)、VE(Z=-7.324, P < 0.001)。多重线性回归分析表明，VC与生物衰老速度呈负相关(β=-0.032, 95% CI: -0.041~-0.023, P < 0.001)。WQS与QGCOMP分析显示，血清维生素联合作用减缓衰老速度(β=-0.459, 95% CI: -0.585~-0.333, P < 0.001; β=-0.381, 95% CI: -0.538~-0.223, P < 0.001)，VC在延缓衰老中起主要作用(权重：0.936, 0.885)。结论血清维生素是生物衰老的保护性因素，VC在减缓衰老速度的联合效应中作用最显著。
- 维生素 /
- 生物衰老 /
- 联合效应
Abstract: Objective To investigate the association between serum levels of vitamins and the rate of biological aging in middle-aged and older adults. Methods Based on the 2017-2018 National Health and Nutrition Examination Survey (NHANES) data, a total of 2 172 study participants aged 45 years and above were enrolled. Demographic information, lifestyle habits and underlying disease history were collected. Multiple linear regression was used to assess the association between serum vitamins C (VC), vitamins D₃ (VD₃), vitamins A (VA) and vitamins E (VE) and the rate of biological aging in middle-aged and older adults. Quantile g-computation (QGCOMP) and weighted quantile sum (WQS) regression methods were applied to analyse the effects of different serum vitamins on the rate of biological ageing and their joint effects. Results The average age of the study population was (62.69 ± 10.29) years, and serum vitamin levels between male and female showed significant differences: VD₃ (Z=-2.376, P=0.017), VC (Z=-7.230, P < 0.001), VA (Z=6.418, P < 0.001), and VE (Z=-7.324, P < 0.001). Multiple linear regression analyses showed that VC was negatively associated with the rate of biological aging (β=-0.032, 95% CI: -0.041-0.023, P < 0.001). WQS and QGCOMP analyses showed a joint effect of serum vitamins slowing down the rate of ageing (β=-0.459, 95% CI: -0.585-0.333, P < 0.001; β=-0.381, 95% CI: -0.538-0.223, P < 0.001), with VC playing the major role (weights: 0.936, 0.885). Conclusions Serum vitamins are protective factors in biological aging. VC plays the most significant role in the combined effect of slowing the rate of aging.
- Vitamins /
- Biological ageing /
- Combined effects

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图 1 NHANES数据筛选流程

NHANES：国家健康与营养调查; VD₃：维生素D₃; VC：维生素C; VA：维生素A; VE：维生素E。

Figure 1. NHANES data screening process

NHANES: National Health and Nutrition Examination Survey; VD₃: vitamin D₃; VC: vitamin C; VA: vitamin A; VE: vitamin E.

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图 2 血清维生素的相关性热图

VD₃：维生素D₃; VC：维生素C; VA：维生素A; VE：维生素E; a：P＜0.05。

Figure 2. Heat map of correlation between serum vitamins

VD₃: vitamin D₃; VC: vitamin C; VA: vitamin A; VE: vitamin E; a: P < 0.05.

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图 3 血清维生素与生物衰老速度关联的森林图结果

FDR：经FDR(错误发现率)校正后的P值; VD₃：维生素D₃; VC：维生素C; VA：维生素A; VE：维生素E。

Figure 3. Forest plot results of the association between serum vitamins and biological aging

FDR: P-value corrected for false discovery rate; VD₃: vitamin D₃; VC: vitamin C; VA: vitamin A; VE: vitamin E.

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表 1 不同特征人群衰老速度比较结果

Table 1. Comparative results of the speed of ageing in people with different characteristics

变量Variable	人数 Number of people	构成比 Proportion/%	生物衰老速度^① Rate of biological ageing	H/Z值 value	P值 value
性别Gender				6.961	< 0.001
男Male	1 149	52.90	-3.41 (-6.70, 1.07)
女Female	1 023	47.10	-4.92 (-9.10, -0.56)
文化程度Education level				10.700	0.005
基础教育Elementary education	408	18.78	-3.89 (-7.57, 0.64)
中等教育Secondary education	547	25.18	-3.51 (-6.78, 0.74)
高等教育Higher education	1 217	56.03	-4.47 (-8.04, 0.04)
种族Race				136.000	< 0.001
墨西哥裔美国人Mexican American	245	11.28	-5.46 (-9.29, -1.76)
其他西班牙裔Other Hispanics	198	9.12	-5.41 (-7.78, -1.65)
非西班牙裔白人Non-Hispanic whites	912	41.99	-3.41 (-6.82, 0.50)
非西班牙裔黑人Non-Hispanic blacks	503	23.16	-2.06 (-6.25, 2.70)
其他种族Other races	314	14.46	-7.16 (-10.44, -2.31)
婚姻状况Marital status				24.200	< 0.001
有伴侣Have a mate	1 302	59.94	-4.48 (-8.04, -0.15)
曾经有伴侣Had a mate	713	32.83	-3.53 (-7.10, 0.90)
从未有伴侣Never had a mate	157	7.23	-2.07 (-6.34, 3.41)
贫困指数Poverty index				72.300	< 0.001
< 1.3	527	24.26	-2.48 (-6.50, 2.19)
1.3~3.5	930	42.82	-3.61 (-7.46, 0.89)
>3.5	715	32.92	-5.46 (-8.58, -1.92)
BMI/(kg·m^-2)				177.000	< 0.001
< 25	475	21.87	-6.37 (-10.53, -1.79)
25~30	745	34.30	-5.16 (-8.16, -1.41)
>30	952	43.83	-2.03 (-5.73, 2.25)
可替宁Cotinine/(ng·mL^-1)				119.000	< 0.001
< 0.011	843	38.81	-5.37 (-8.67, -1.46)
0.011~＜0.033	360	16.57	-4.61 (-8.20, -0.87)
0.033~16.300	500	23.02	-3.72 (-7.22, 1.16)
>16.300	469	21.59	-1.42 (-5.35, 2.88)
饮酒Drinking				30.900	< 0.001
从不饮酒Never drinking	660	30.39	-3.14 (-6.84, 2.16)
轻度饮酒Light drinking	572	26.34	-4.68 (-8.67, -0.30)
重度饮酒Heavy drinking	940	43.28	-4.35 (-7.73, -0.57)
高血压Hypertension				-5.596	< 0.001
否No	1 033	47.56	-5.14 (-8.83, -1.44)
是Yes	1 139	52.44	-2.70 (-6.73, 1.88)
糖尿病Diabetes mellitus				-7.130	< 0.001
否No	1 536	70.72	-4.68 (-8.01, -0.67)
是Yes	636	29.28	-2.19 (-6.58, 2.57)
心血管病Cardiovascular disease				-9.246	< 0.001
否No	1 750	80.57	-4.64 (-8.13, -0.50)
是Yes	422	19.43	-1.29 (-5.57, 3.85)
注：①对于非正态分布的连续变量，表示为M(P₂₅, P₇₅)。 Note: ① For non-normally distributed continuous variables, denoted M(P₂₅, P₇₅).

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表 2 血清维生素与生物衰老速度的联合效应估计结果

Table 2. Results of the estimation of the combined effect of serum vitamins and biological ageing

方法 Methods	总人群Total			男性Male			女性Female
方法 Methods	β值value (95% CI)	t值 value	P值 value	β值value (95% CI)	t值 value	P值 value	β值value (95% CI)	t值 value	P值 value
QGCOMP
模型1 Model 1	-0.526(-0.684~-0.368)	-6.525	< 0.001	-0.390(-0.626~-0.154)	-3.243	0.001	-0.634(-0.851~-0.417)	-5.723	< 0.001
模型2 Model 2	-0.331(-0.491~-0.171)	-4.048	< 0.001	-0.179(-0.421~0.064)	-1.446	0.150	-0.450(-0.666~-0.233)	-4.071	< 0.001
模型3 Model 3	-0.381(-0.538~-0.223)	-4.731	< 0.001	-0.226(-0.463~0.011)	-1.866	0.060	-0.495(-0.712~-0.277)	-4.459	< 0.001
WQS
模型1 Model 1	-0.592(-0.720~-0.463)	-9.013	< 0.001	-0.527(-0.701~-0.353)	-5.944	< 0.001	-0.615(-0.819~-0.412)	-5.924	< 0.001
模型2 Model 2	-0.458(-0.586~-0.330)	-7.007	< 0.001	-0.396(-0.564~-0.227)	-4.605	< 0.001	-0.429(-0.635~-0.223)	-0.196	< 0.001
模型3 Model 3	-0.459(-0.585~-0.333)	-7.118	< 0.001	-0.408(-0.574~-0.243)	-4.845	< 0.001	-0.447(-0.660~-0.233)	-4.101	< 0.001
注：QGCOMP，分位数g-计算; WQS，加权分位数和。 Note: QGCOMP, quantile g-computation; WQS, weighted quantile sum.

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表 3 血清维生素与生物衰老速度的联合效应的敏感性分析

Table 3. Results of sensitivity analysis of the combined effect of serum vitamins and biological ageing

方法 Methods	总人群Total			男性Male			女性Female
方法 Methods	β值value (95% CI)	t值 value	P值 value	β值value (95% CI)	t值 value	P值 value	β值value (95% CI)	t值 value	P值 value
QGCOMP
模型1 Model 1	-0.550(-0.703~-0.397)	-7.157	< 0.001	-0.363(-0.581~-0.146)	-3.196	0.042	-0.670(-0.875~-0.466)	-6.951	< 0.001
模型2 Model 2	-0.350(-0.505~-0.196)	-4.501	< 0.001	-0.168(-0.398~0.006)	-1.434	0.152	-0.466(-0.673~-0.230)	-4.600	< 0.001
模型3 Model 3	-0.402(-0.551~-0.254)	-5.296	< 0.001	-0.218(-0.438~0.001)	-1.943	0.052	-0.516(-0.721~-0.312)	-5.180	< 0.001
WQS
模型1 Model 1	-0.601(-0.745~-0.458)	-8.222	< 0.001	-0.579(-0.761~-0.397)	-6.234	< 0.001	-0.778(-0.998~-0.559)	-6.949	< 0.001
模型2 Model 2	-0.443(-0.581~-0.306)	-6.313	< 0.001	-0.444(-0.624~-0.264)	-4.840	< 0.001	-0.535(-0.752~-0.318)	-4.827	< 0.001
模型3 Model 3	-0.424(-0.569~-0.279)	-5.741	< 0.001	-0.433(-0.613~-0.254)	-4.727	< 0.001	-0.527(-0.744~-0.311)	-4.783	< 0.001
注：QGCOMP，分位数g-计算; WQS，加权分位数和。 Note: QGCOMP, quantile g-computation; WQS, weighted quantile sum.

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