Abstract
目的
探究健康生活方式与心血管生物衰老的关联及不同生活方式对心血管生物衰老的相对贡献度。
方法
基于英国生物银行（UK Biobank, UKB）基线调查中临床生化指标与人体测量数据，采用Klemera Doubal算法（Klemera-Doubal method, KDM）构建心血管生物年龄（cardiovascular biological age, CBA）并计算CBA加速。首先，运用多重线性回归模型估计健康生活方式与CBA加速的关联。进一步采用QGC（quantile g-computation）分析不同健康生活方式对CBA加速的相对贡献度，并分别对男性和女性人群进行分析。同时，按年龄、性别、体重指数、种族及心血管疾病家族史进行分层分析，以探究人群异质性。
结果
共纳入251478名研究对象。总生活方式得分和7个生活方式因素与CBA加速均呈负相关(总生活方式得分：偏回归系数=－0.75，95%可信区间: －0.77～－0.73)。在不同生活方式因素的相对贡献中，饮酒和膳食占比较高（分别为25.8%和25.7%），但存在性别差异：男性以饮酒为主（29.5%），膳食次之（23.0%），女性则以膳食为主（34.5%），饮酒占比较低（5.5%）。分层分析提示性别、体重指数、种族可能是潜在的效应修饰因子。
结论
生活方式作为一种可干预行为可以延缓心血管生物衰老速度，且饮酒和膳食可能是较为有效的干预手段。
Keywords: 心血管生物年龄, 生物年龄, 健康生活方式, 心血管生物衰老
Abstract
Objective
To investigate the association between healthy lifestyle factors and cardiovascular biological aging, as well as the relative contributions of different lifestyle factors.
Methods
Based on the clinical biochemical data and anthropometric data from the baseline survey of the UK Biobank (UKB), the Klemera-Doubal method (KDM) was used to establish cardiovascular biological age (CBA), and CBA acceleration was calculated accordingly. Multiple linear regression models were used to estimate the associations between healthy lifestyle factors and CBA acceleration. Then, the Quantile g-computation (QGC) was applied to evaluate the relative contributions of different lifestyle factors to CBA acceleration, with further analyses conducted separately for male and female populations. Additionally, stratified analyses were performed based on age, sex, body mass index (BMI), racial background, and family history of cardiovascular diseases to examine population heterogeneity.
Results
A total of 251478 participants were included in the study. Both the overall healthy lifestyle score and each of the 7 lifestyle factors were negatively associated with CBA acceleration (overall lifestyle score: β = －0.75, 95% CI: －0.77 to －0.73). Regarding the relative contributions of different lifestyle factors, alcohol consumption and diet accounted for the highest proportions (25.8% and 25.7%, respectively). However, there were differences by sex—alcohol consumption contributed the most in men (29.5%), followed by diet (23.0%), while in women, diet contributed the most (34.5%) and alcohol consumption accounted for a relatively low proportion (5.5%). Stratified analyses suggested that sex, BMI, and race might be potential effect modifiers.
Conclusion
Lifestyle factors, as modifiable behaviors, can slow the rate of cardiovascular biological aging. Among these factors, alcohol consumption and diet may represent effective targets for intervention.
Keywords: Cardiovascular biological age, Biological age, Healthy lifestyle, Cardiovascular biological aging
心血管疾病（cardiovascular disease, CVD）是全球重要的公共卫生问题，衰老在其发生和发展过程中起着重要作用 [ 1- 2] 。生物年龄（biological age, BA）作为综合多种衰老相关生物标志物的指标，反映机体整体衰老程度并能预测年龄相关疾病 [ 3] 。目前，DNA甲基化、蛋白质组学、脆弱指数和临床复合生物标志物等已被用于量化BA [ 4- 5] 。其中，临床复合生物标志物作为评价健康和疾病状态的敏感指标，易获取、成本低、适用于大样本人群 [ 6] ，已被用于构建器官系统的生物年龄 [ 7] 。心血管生物年龄（cardiovascular biological age, CBA）以年龄为标尺，基于临床复合生物标志物量化心血管功能的生物衰老程度，被认为是CVD相关死亡率的优越预测因子，可改善心血管事件的风险预测 [ 2, 8- 9] 。
生物衰老可以通过早期干预加以改变 [ 10- 11] 。健康的生活方式作为最简单的干预方法，有助于改善由线粒体功能障碍、胰岛素信号受损、内皮稳态失衡和氧化应激失衡引起的早期生物衰老 [ 10, 12- 13] 。目前该领域的研究多聚焦于单一生活方式因素对衰老的影响 [ 14- 16] ，或仅关注全身水平的生物年龄评估 [ 17] 。然而不同器官系统衰老程度存在异质性 [ 2] 。关于生活方式与基于生物年龄构建的心血管生物衰老之间的关联尚缺乏系统探讨。明确何种生活方式干预能够最有效地延缓心血管系统的生物衰老，对于实现CBA调控的最佳效益具有关键意义。
本研究基于英国生物银行基线数据，采用Klemera Doubal（KDM）算法构建的CBA量化评估心血管功能的生物衰老程度，进而探讨健康生活方式与心血管生物衰老的关联，并运用QGC （quantile g-computation）方法探究不同生活方式因素对CBA的相对贡献度。
1. 资料与方法
1.1. 研究对象
英国生物银行（UK Biobank, UKB）作为一项前瞻性队列研究，包含丰富的生活方式、生物标志物在内的表型资源，来自50多万名37～73岁的参与者，基线调查数据收集于2006年至2010年。所有参与者在基线时都进行了一系列评估，包括电子问卷、人体测量、全面体检和生化指标测试。并在数据收集前签署知情同意书。由于UKB数据的开放获取，以及构建CBA时依赖于临床生物标志物，为了确保统计效力与样本代表性，本研究采用其基线数据（项目编号：117185）。
本研究排除了缺乏构建CBA生物标志物（69656人）、缺少健康生活方式数据（174964人）以及缺少协变量（6259人），共纳入251478名研究对象。
1.2. 暴露测量：健康生活方式
本研究基于7项健康行为构建了健康生活方式评分（ 表1），包括健康膳食、从不吸烟、适度饮酒、规律的体力活动、较少久坐行为、充足的睡眠和适当的社会关系。健康膳食指每日摄入水果、蔬菜、鱼类、瘦肉（包括红肉与加工肉类）、全谷物和精制谷物等七类食物中至少四种 [ 18] 。从不吸烟即无吸烟史；适度饮酒定义为不饮酒，或女性每天饮酒0～14 g，男性每天饮酒0～28 g [ 18] 。根据世界卫生组织关于身体活动和久坐行为建议的指南 [ 19] ，规律的体力活动为每周至少150 min的中度活动或每周75 min的剧烈活动（或同等的组合）；每日久坐时间少于4 h为较少久坐行为；充足的睡眠指每日睡眠7～9 h [ 20] ；社会关系健康定义为社会隔离程度最低或中等（得分0或1）。每符合一项标准计1分，总分范围为0～7分。根据得分将生活方式划分为3个等级：积极（5～7分），一般（2～4分）和不良（0～1分） [ 21] 。
表 1. The detailed information of lifestyle assessment.
健康生活方式的定义
| Lifestyle factor | Source | Definition |
| Alcohol consumption | The Dietary Guidelines for Americans (DGA) [ 18, 22] . | Moderate alcohol consumption was defined as:
For women: > 0 and ≤ 14 g/day, For men: > 0 and ≤ 28 g/day. No drinking or moderate alcohol consumption is scored as 1. |
| Diet | The Dietary Guidelines for Americans (DGA) [ 18, 22] . | A healthy dietary pattern is defined as including at least four out of seven foods.
A healthy dietary pattern is scored as 1. |
| Physical activity | Recommendations from World Health Organization guidelines on
physical activity [ 19] . |
Meeting the criteria for healthy physical activity is scored as 1. |
| Sleep | NHS, American Academy of Sleep Medicine (AASM) and Sleep
Research Society (SRS) recommendation [ 20] . |
Meeting the standard healthy sleep is defined as 1. |
| Smoking | Many UK national guidelines suggest quitting smoking, such as
NHS and the National Institute for Health and Care Excellence [ 22] . |
Having never smoked is scored as 1. |
| Sedentary behavior | Recommendations from World Health Organization guidelines on
sedentary behavior [ 3, 19] . |
Being sedentary for less than 4 hours is scored as 1. |
| Social connection | Social connection is assessed according to the social isolation index,
which is calculated based on the sum of the following 3 indices: the number of people living in the household, frequency of friend/ family visits, and participation in leisure/social activity [ 23] . |
The least and moderately isolated are defined as frequent social
connection, which is scored as 1. |
1.3. 结局测量：CBA与CBA加速
为系统评估心血管功能的生物衰老程度，参考既往研究 [ 2] ，纳入了肥胖、血脂异常、高血压与高血糖等典型心血管危险因素，并结合HAMCZYK等 [ 6] 提出的血管衰老相关生物标志物（包括C-反应蛋白与胰岛素样生长因子1），共筛选22项候选生物标志物。本研究采用KDM算法量化CBA，该算法基于参考人群样本，通过将各项生物标志物对实足年龄进行回归分析，从 ｍ个回归模型中整合信息，得出个体生物年龄的最优估计值。KDM已在人群中得到很好的验证，并且在预测死亡率以及心血管疾病发病风险方面表现良好 [ 3, 24] 。对所有候选生物标志物及人体测量指标进行了数据预处理。若指标呈非正态分布，则采用Box-Cox变换以近似满足正态性假设。首先排除基线缺失率高于30%的2项生物标志物；随后保留与实足年龄显著相关的指标（相关系数| r|>0.1且 P<0.05），共筛选出9项；进一步结合生物学知识分析指标间相关性，剔除反映同一衰老维度的冗余标志物：在高度相关的腰围与腰臀比中保留腰臀比，在血糖相关指标中保留糖化血红蛋白。最终纳入7项生物标志物：体脂率（BFP）、C-反应蛋白（CRP）、糖化血红蛋白（HbA1c）、胰岛素样生长因子1（IGF-1）、甘油三酯（TG）、收缩压（SBP）和腰臀比（WHR）。男性和女性衰老过程存在差异，本研究按性别分层并分别计算BA。CBA加速定义为个体CBA与其实足年龄的差值，以消除年龄对结果的影响。该指标反映个体心血管系统相对于参考人群的衰老偏离程度：正值表示生理年龄大于实际年龄，负值则表示更为年轻。将CBA加速划分为：|CBA加速|≤1、CBA加速<－1、CBA加速>1，以|CBA加速|≤1作为参照组 [ 25] 。
1.4. 统计学方法
连续变量以均值±标准差表示，分类变量以频数（百分比）描述，用于概括研究对象的基线特征。采用多重线性回归分析健康生活方式与心血管生物年龄（CBA）加速之间的关联。协变量选择基于既往文献 [ 7, 22] ，模型1未经调整；模型2调整年龄和性别；模型3进一步调整种族、教育程度、体重指数（BMI）、社会经济地位（Townsend剥夺指数）和心血管疾病家族史。采用分位数g-计算（QGC）方法评估不同生活方式因素对CBA加速的相对贡献度 [ 26] 。该方法首先将各组分暴露指标的原始数据根据需要转换为分位数化编码后的数据，而后根据各暴露指标分类化后的数据进行建模分析，直接构建线性模型或广义线性模型，并采用g-computation的方法和思想进行效应估计，用以评估不同暴露之间的相对贡献度。本文用于评估不同生活方式因素对CBA加速的相对贡献度，并分男性和女性人群进行分析。为探索人群特征对不同生活方式与CBA加速之间关系的潜在影响，按年龄、性别、BMI、种族、是否有心血管疾病家族史进行分层分析。采用R软件（4.3.1版）进行数据处理和统计分析。
2. 结果
2.1. 人口特征
表2总结了参与者的基线特征。在251478名参与者中，男性为125168（49.8%），白人为241266（95.9%），平均年龄为（56.83±8.10）岁。总体生活方式为积极的有158846人（63.2%），中等90491人（36.0%），不良2141人（0.9%）。CBA加速<－1者有125399人，参考组|CBA加速| ≤1者有24684人， CBA加速>1者有101395人。相比于参考组，CBA加速<－1者生活方式得分更高，健康的膳食、从不吸烟、适度饮酒、规律的体育活动、少坐行为和健康的睡眠比例更高。而CBA加速>1者生活方式总分更低，不良的生活方式占比增多。
表 2. Baseline characteristics of the study participants.
研究对象的基线特征
| Characteristic | Overall
( n = 251478) |
CBA acceleration < －1
( n = 125399) |
|CBA acceleration |≤ 1
( n = 24684) |
CBA acceleration > 1
( n = 101395) |
P |
| * The socioeconomic status is measured by Townsend deprivation index. | |||||
| Age/yr., mean (SD) | 56.83 (8.10) | 56.65 (8.22) | 57.10 (8.10) | 56.98 (7.94) | < 0.001 |
| Sex/case (%) | < 0.001 | ||||
| Male | 125168 (49.8) | 62117 (49.5) | 10223 (41.4) | 52828 (52.1) | |
| Female | 126310 (50.2) | 63282 (50.5) | 14461 (58.6) | 48567 (47.9) | |
| Education/case (%) | 0.522 | ||||
| High | 95566 (38.0) | 47480 (37.9) | 9381 (38.0) | 38705 (38.2) | |
| Intermediate | 95862 (38.1) | 48063 (38.3) | 9379 (38.0) | 38420 (37.9) | |
| Low | 16646 (6.6) | 8291 (6.6) | 1654 (6.7) | 6701 (6.6) | |
| Other | 43404 (17.3) | 21565 (17.2) | 4270 (17.3) | 17569 (17.3) | |
| BMI/(kg/m 2), mean (SD) | 27.02 (4.43) | 24.89 (3.09) | 26.81 (3.43) | 29.70 (4.61) | < 0.001 |
| Socioeconomic status * (mean [SD]) | －1.55 (2.92) | －1.74 (2.83) | －1.66 (2.85) | －1.29 (3.04) | < 0.001 |
| Race/case (%) | < 0.001 | ||||
| White | 241266 (95.9) | 121588 (97.0) | 23778 (96.3) | 95900 (94.6) | |
| Other | 10212 (4.1) | 3811 (3.0) | 906 (3.7) | 5495 (5.4) | |
| Family history of CVD/case (%) | 0.56 (0.50) | 0.54 (0.50) | 0.57 (0.50) | 0.58 (0.49) | < 0.001 |
| Total lifestyle score (mean [SD]) | 4.87 (1.33) | 5.09 (1.26) | 4.91 (1.30) | 4.59 (1.37) | < 0.001 |
| Lifestyle class/case (%) | < 0.001 | ||||
| Favorable | 158846 (63.2) | 87633 (69.9) | 15789 (64.0) | 55424 (54.7) | |
| Intermediate | 90491 (36.0) | 37198 (29.7) | 8763 (35.5) | 44530 (43.9) | |
| Unfavorable | 2141 (0.9) | 568 (0.5) | 132 (0.5) | 1441 (1.4) | |
| Lifestyle component | |||||
| Physical activity/case (%) | < 0.001 | ||||
| Unfavorable | 58540 (23.3) | 24985 (19.9) | 5499 (22.3) | 28056 (27.7) | |
| Healthy | 192938 (76.7) | 100414 (80.1) | 19185 (77.7) | 73339 (72.3) | |
| Alcohol consumption/case (%) | < 0.001 | ||||
| Unfavorable | 80748 (32.1) | 36955 (29.5) | 8080 (32.7) | 35713 (35.2) | |
| Healthy | 170730 (67.9) | 88444 (70.5) | 16604 (67.3) | 65682 (64.8) | |
| Social connection/case (%) | < 0.001 | ||||
| Unfavorable | 30374 (12.1) | 14499 (11.6) | 2743 (11.1) | 13132 (13.0) | |
| Healthy | 221104 (87.9) | 110900 (88.4) | 21941 (88.9) | 88263 (87.0) | |
| Never smoke/case (%) | < 0.001 | ||||
| Unfavorable | 115830 (46.1) | 53222 (42.4) | 11165 (45.2) | 51443 (50.7) | |
| Healthy | 135648 (53.9) | 72177 (57.6) | 13519 (54.8) | 49952 (49.3) | |
| Sleep/case (%) | < 0.001 | ||||
| Unfavorable | 62753 (25.0) | 28688 (22.9) | 6038 (24.5) | 28027 (27.6) | |
| Healthy | 188725 (75.0) | 96711 (77.1) | 18646 (75.5) | 73368 (72.4) | |
| Sedentary/case (%) | < 0.001 | ||||
| Unfavorable | 76581 (30.5) | 31935 (25.5) | 7412 (30.0) | 37234 (36.7) | |
| Healthy | 174897 (69.5) | 93464 (74.5) | 17272 (70.0) | 64161 (63.3) | |
| Diet/case (%) | < 0.001 | ||||
| Unfavorable | 109587 (43.6) | 48616 (38.8) | 10558 (42.8) | 50413 (49.7) | |
| Healthy | 141891 (56.4) | 76783 (61.2) | 14126 (57.2) | 50982 (50.3) | |
2.2. 生活方式与CBA加速的关系
如 表3，总体生活方式得分和7个生活方式因素与CBA加速均呈负相关。总体生活方式得分的回归系数为 β=－0.75（95% CI：－0.77，－0.73）。在3个调整模型中，系数变化趋势基本一致。3个模型系数的总体趋势保持一致。将生活方式分为积极、中等、不良3个类别，与不良生活方式相比，积极生活方式的CBA加速负关联最强（ β=－4.69，95% CI：－4.98，－4.39）；中等次之（ β=－3.03，95% CI：－3.33，－2.73）。
表 3. Association between various healthy lifestyles and CBA acceleration.
各种健康生活方式与CBA加速之间的关联
| Lifestyle factors / categories | Model 1 | Model 2 | Model 3 | |||||
| β (95% CI) | P | β (95% CI) | P | β (95% CI) | P | |||
| Model 1 is unadjusted. Model 2 is adjusted for age and sex. Model 3 is further adjusted for race, education, BMI, socioeconomic status (i.e., Townsend deprivation index), smoking status, frequency of alcohol consumption, total physical activity, and family history of CVD. | ||||||||
| Seven lifestyle factors | ||||||||
| Healthy diet | －1.68 (－1.75, －1.61) | < 0.001 | －1.77 (－1.84, －1.70) | < 0.001 | －1.22 (－1.27, －1.16) | < 0.001 | ||
| Never smoking | －1.17 (－1.24, －1.11) | < 0.001 | －1.21 (－1.28, －1.14) | < 0.001 | －0.83 (－0.89, －0.78) | < 0.001 | ||
| Moderate alcohol consumption | －1.07 (－1.14, －1.00) | < 0.001 | －1.06 (－1.14, －0.99) | < 0.001 | －1.22 (－1.28, －1.16) | < 0.001 | ||
| Regular physical activity | －1.77 (－1.85, －1.69) | < 0.001 | －1.76 (－1.84, －1.68) | < 0.001 | －0.58 (－0.65, －0.52) | < 0.001 | ||
| Less sedentary behavior | －2.10 (－2.17, －2.03) | < 0.001 | －2.13 (－2.20, －2.06) | < 0.001 | －0.47 (－0.53, －0.41) | < 0.001 | ||
| Healthy sleep | －1.05 (－1.13, －0.97) | < 0.001 | －1.05 (－1.13, －0.97) | < 0.001 | －0.21 (－0.27, －0.14) | < 0.001 | ||
| Appropriate social connection | －0.17 (－0.27, －0.07) | < 0.01 | －0.20 (－0.30, －0.10) | < 0.001 | －0.21 (－0.29, －0.12) | < 0.001 | ||
| Total lifestyle score | －1.38 (－1.40, －1.35) | < 0.001 | －1.40 (－1.43, －1.38) | < 0.001 | －0.75 (－0.77, －0.73) | < 0.001 | ||
| Lifestyle class | ||||||||
| Unfavorable | Ref | Ref | Ref | |||||
| Intermediate | －4.14 (－4.51, －3.77) | < 0.001 | －4.18 (－4.54, －3.81) | < 0.001 | －3.03 (－3.33, －2.73) | < 0.001 | ||
| Favorable | －7.23 (－7.60, －6.87) | < 0.001 | －7.30 (－7.67, －6.94) | < 0.001 | －4.69 (－4.98, －4.39) | < 0.001 | ||
2.3. 分层分析
为探寻健康生活方式得分与CBA加速的关联在不同人群中是否存在异质性，本研究进一步进行了分层分析。如 图1的结果所示，健康生活方式对CBA加速的关联在男性中比女性中更强。在BMI≥25 kg/m 2者（ β=－1.13，95%CI：－1.16，－1.10）比BMI<25 kg/m 2（ β=－0.77，95%CI：－0.80，－0.73）中更强。提示年龄、性别、BMI、种族、家族史可能是潜在的效应修饰因子。
图 1.
Stratified analysis results of healthy lifestyles and CBA acceleration based on population characteristics
健康生活方式和CBA加速基于人群特征的分层分析结果
Adjustment is made for age, sex, BMI, race, education, social deprivation index, and family history of cardiovascular disease (stratification factors were not adjusted for within their own strata).
2.4. 不同生活方式对CBA加速的相对贡献度
在调整潜在混杂因素后，7种生活方式对CBA加速的相对贡献度从高到低依次是饮酒（25.8%）、膳食（25.7%）、吸烟（17.6%）、体力活动（12.3%）、社会联系（4.4%）以及睡眠（4.3%）；在男性中饮酒的贡献度最高，膳食次之，而在女性中膳食因素的贡献占比最高，饮酒的贡献则相对较低（ 图2）。
图 2.
The relative contribution of different lifestyles to CBA acceleration
不同生活方式对CBA加速的相对贡献度
The data are adjusted for age, sex, BMI, race, education, Townsend deprivation index, and family history of cardiovascular disease (the data in B and C are not adjusted for sex). Figure A shows the results of the main analyses, and Figures B and C show the relative contribution of different lifestyles to CBA acceleration in the male and female populations, respectively.
3. 讨论
本研究旨在探讨健康生活方式与CBA加速之间的关联，并系统分析不同生活方式因素的相对贡献度。研究结果显示，健康生活方式与CBA加速均呈负相关。在各项生活方式因素中，饮酒与膳食的贡献度最为突出，但存在明显的性别差异：男性以饮酒为主，女性则以膳食为主，饮酒占比偏低。分层分析进一步提示，健康生活方式与CBA加速的关联可能受年龄、性别、BMI、种族以及心血管疾病家族史影响。CBA作为反映心血管功能生物衰老的指标，从生物年龄的维度探讨了生活方式对心血管生物衰老的影响。
本研究表明健康生活方式可以延缓心血管生物衰老速度。心血管衰老的关键因素是与年龄相关的动脉功能障碍，其特征是动脉僵硬增加和动脉内皮功能受损 [ 11] 。主动脉僵硬是衡量功能性血管衰老的指标，健康生活方式与降低主动脉僵硬有关 [ 27] 。具体而言，影响动脉硬度的主要血流动力学因素是血压水平，血压通常受吸烟、饮酒和膳食模式的影响 [ 28] 。体力活动也是动脉僵硬的重要决定因素。一项荟萃分析的结果表明，高强度阻力运动与动脉僵硬升高有关 [ 29] 。此外，健康的生活方式可以促进分子信号转导，对于维持细胞的正常生理功能至关重要。健康生活方式通过新陈代谢、钙信号传导、免疫反应和基因功能的变化影响细胞的功能，促进细胞和分子信号传导，减轻心脏细胞衰老的分子效应，进而保持年轻的细胞表型 [ 11] 。
研究结果表明适度饮酒对心血管衰老之间的负相关贡献最大。既往研究表明适度饮酒可以延长寿命，降低患心血管疾病的风险 [ 30- 31] 。一氧化氮在心血管稳态中起着关键作用 [ 32] 。乙醇作为酒精饮品的主要生物活性成分，当以较低的酒精水平饮用时，通过诱导内皮细胞NO合成酶，导致一氧化氮（NO）的生物利用度水平升高 [ 33] 。另外，健康膳食对心血管生物衰老的相对贡献度仅次于适度饮酒，但两者占比相差不大。越来越多的证据表明，膳食可能会随着年龄的增长而有力地调节动脉功能。膳食延缓心血管生物衰老可能源于其在改善营养感知网络失衡方面的关键作用，从而阻碍衰老和心血管疾病的进展 [ 34] 。此外，热量限制和禁食可能是通过调节关键信号通路〔如哺乳动物雷帕霉素靶蛋白（mTOR）、腺苷单磷酸活化蛋白激酶（AMPK）和sirtuin通路〕来改善健康和延长寿命最有效、可行和安全的干预措施 [ 35] 。膳食模式强调地中海膳食富含单不饱和脂肪酸和抗氧化剂的成分，主要通过降脂作用、氧化应激缓解、炎症抑制和通过特定氨基酸限制抑制营养敏感途径来增强内皮功能和减少心血管事件 [ 36- 37] 。而且膳食干预可能是降低心血管发病率最具成本效益、可行和成本最低的方法 [ 38] 。此外，吸烟、久坐行为、缺乏身体活动和睡眠不足等不良生活方式，可促进 CVD 的发生并加速衰老 [ 34] 。久坐行为抑制了抗衰老途径中与肌肉生长和 SIRT 相关的代谢信号通路的合成。睡眠质量不佳，包括睡眠时间短、入睡时间长和睡眠效率低下，与端粒长度纵向缩短的速度较快有关 [ 39] 。
本研究结果强调了生活方式干预对心血管健康的积极影响，揭示了生活方式对心血管生物衰老的综合影响和不同因素的相对重要性。研究发现，不同生活方式因素对心血管生物衰老的贡献度存在性别差异。既往研究结果显示男性与女性的生物衰老速度不同，男性的衰老速度相对较快 [ 40] 。这一发现提示在制定干预策略时应充分考虑个体的性别特点，制定针对性的策略，满足不同性别群体的需求以提高干预效果。此外，研究结果为预防心血管疾病提供了全新的思路。通过积极鼓励健康的生活方式，如适量饮酒和合理膳食，可以有效地降低心血管生物衰老的速度，从而预防心血管疾病的发生。
本研究的优势在于探讨了不同健康生活方式与基于反映心血管功能生物衰老的复合生物标志物构建的CBA加速之间的关联关系，以及不同生活方式因素的相对贡献度，对心血管生物衰老的干预提供了独特见解。然而，研究还存在一些局限性。第一，生活方式的报告依赖于参与者自我报告的数据，可能会存在信息偏倚。第二，由于数据的可及性，无法涵盖与心血管衰老相关的所有临床复合生物标志物。第三，UKB为西方人群，相应结果还需要国内大规模数据库进行验证。最后，尽管本研究已经尽可能控制潜在可观测混杂因素，但仍可能存在未测量混杂。CBA作为反映心血管功能生物衰老的指标，为评估生活方式对心血管健康影响提供了新的视角。未来的研究应进一步探讨其他生活方式因素（例如，睡眠质量和心理健康）对CBA的影响，并在更大样本的人群中验证这些结果，以期为心血管健康的预防与干预提供更为全面的证据支持。
综上，本研究表明各种健康生活方式因素与CBA加速之间均呈负相关，年龄、性别、BMI、种族、家族史可能是潜在的效应修饰因子。健康生活方式干预，特别是健康膳食和适度饮酒，在延缓CBA加速方面表现出显著效果。为心血管衰老的干预提供了宝贵见解，并对心血管衰老的早期预防和公共卫生政策指导具有重要意义。
* * *
作者贡献声明 蔡佳洁负责论文构思、数据审编、正式分析、调查研究、研究方法、可视化、初稿写作和审读与编辑写作，张宁负责数据审编和软件，向毅和张红梅负责验证和审读与编辑写作，肖雄负责论文构思、经费获取、研究项目管理、提供资源和监督指导。所有作者已经同意将文章提交给本刊，且对将要发表的版本进行最终定稿，并同意对工作的所有方面负责。
Author Contribution CAI Jiajie is responsible for conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing--original draft, and writing--review and editing. ZHANG Ning is responsible for data curation and software. XIANG Yi and ZHANG Hongmei are responsible for validation and writing--review and editing. XIAO Xiong is responsible for conceptualization, funding acquisition, project administration, resources, and supervision. All authors consented to the submission of the article to the Journal. All authors approved the final version to be published and agreed to take responsibility for all aspects of the work.
利益冲突 所有作者均声明不存在利益冲突
Declaration of Conflicting Interests All authors declare no competing interests.
Funding Statement
国家自然科学基金面上项目（No. 82273740）和四川省自然科学基金（No. 2024NSFSC0552）资助
Contributor Information
佳洁 蔡 (Jiajie CAI), Email: cjj98120416@163.com.
雄 肖 (Xiong XIAO), Email: xiaoxiong.scu@scu.edu.cn.
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