Weight Status and Metabolic Characteristics on the Longitudinal Trajectory of Systolic Blood Pressure: a Cohort Study

doi:10.12114/j.issn.1007-9572.2025.0222

Chinese General Practice ›› 2026, Vol. 29 ›› Issue (15): 2006-2013.DOI: 10.12114/j.issn.1007-9572.2025.0222

• Original Research • Previous Articles Next Articles

Weight Status and Metabolic Characteristics on the Longitudinal Trajectory of Systolic Blood Pressure: a Cohort Study

1. School of Health Management, Xinjiang Medical University, Urumqi 830017, China
2. The Cardiovascular Disease Center of Xinjiang Medical University, Urumqi 830054, China
3. School of Public Health, Xinjiang Medical University, Urumqi 830000, China
4. Department of Cardiology, Xinjiang Uygur Autonomous Region People's Hospital/Xinjiang Key Laboratory of Cardiovascular Homeostasis and Regeneration Research, Urumqi 830001, China

Received:2025-06-25 Revised:2025-12-08 Published:2026-05-20 Online:2026-04-14
Contact: YANG Yining

体重状态及其代谢特征对收缩压纵向轨迹的影响：一项队列研究

1．830017　新疆维吾尔自治区乌鲁木齐市，新疆医科大学健康管理学院
2．830054　新疆维吾尔自治区乌鲁木齐市，新疆医科大学心血管病中心
3．830000　新疆维吾尔自治区乌鲁木齐市，新疆医科大学公共卫生学院
4．830001　新疆维吾尔自治区乌鲁木齐市，新疆维吾尔自治区人民医院心内科　新疆心脏血管稳态与再生医学研究重点实验室

通讯作者: 杨毅宁
作者简介:
作者贡献：
邱欣雨负责研究设计、数据整理，完成论文初稿撰写；赵倩协助研究设计与方案制订、完成部分数据分析，并对论文内容提出修改建议；加木勒·买买提依明、爱克丹·艾尔肯负责数据收集并参与数据整理；陈玉斐、韩聪聪参与统计分析与讨论部分撰写；李晓梅负责研究指导、协助方法与讨论部分内容完善；杨毅宁负责全面指导研究实施与论文写作，监督文章的整体质量，审定最终版本。
基金资助:
新疆维吾尔自治区重点研发计划项目(2022B03022-1); 新疆医科大学青年科技拔尖人才项目(XYD2024Q06)

Abstract

Abstract:

Background

Weight significantly impacts blood pressure, yet the link between their combined metabolic status and systolic blood pressure (SBP) trajectories remains unclear.

Objective

The impact of different body weight statuses and metabolic characteristics on the trajectory changes of SBP was investigated.

Methods

A total of 2 051 subjects who participated in health check-ups in Urumqi City, Xinjiang Uygur Autonomous Region from July 2019 to September 2021 were selected as study subjects, with baseline information collected. The dynamic changes of SBP and new-onset hypertension status were collected every 12 months at the Health Management Center of the First Affiliated Hospital of Xinjiang Medical University. Group-based trajectory modeling (GBTM) was used to determine the optimal number of trajectories for SBP among the subjects, grouping them based on SBP change trajectories. Restricted cubic spline (RCS) analysis was conducted to examine the potential dose-response relationship between BMI and SBP trajectories under different weight statuses and metabolic features. Multivariate Logistic regression analysis was employed to explore the impact of weight status and its metabolic characteristics on SBP change trajectories.

Results

The average age of the subjects was (43.4±8.8) years old, including 883 males (43.1%) and 1 168 females. According to weight status and metabolic characteristics, participants were categorized into metabolically healthy normal weight (MHNW, n=915, 44.6%), metabolically healthy overweight/obese (MHO, n=608, 29.6%), metabolically unhealthy overweight/obese (MUO, n=379, 18.5%), and metabolically unhealthy normal weight (MUNW, n=149, 7.3%). During follow-up, a total of 362 (17.6%) new cases of hypertension were identified. GBTM categorized the subjects into three groups based on SBP trajectories: low-level group (n=870), mid-level group (n=1 067), and high-level group (n=114). There were statistically significant differences in gender, age, education below high school level, smoking and drinking proportions, BMI, SBP, diastolic blood pressure (DBP), fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol among the three groups (P<0.05). Within the low-, mid-, and high-level SBP trajectory groups, there were statistically significant differences in gender distribution across the four metabolic phenotypes (P<0.05). RCS results showed that BMI was linearly positively correlated with SBP trajectory risk in MHNW, MUNW, and MUO phenotype populations (P_non-linear>0.05, P<0.001), while in the MHO phenotype population, this correlation was nonlinear (P_non-linear<0.05, P<0.001). Multivariate Logistic regression analysis revealed that compared with MHNW, MHO (OR=2.29, 95%CI=1.84-2.87), MUNW (OR=5.32, 95%CI=3.62-7.83), and MUO (OR=5.20, 95%CI=3.90-6.95) were independent risk factors for increased SBP trajectory risk levels (P<0.05), and as weight status and metabolic characteristic types changed, so did the SBP trajectory risk level increase (P_trend<0.001). Further stratified analysis by gender showed that MHO, MUNW, and MUO remained independent risk factors for SBP trajectory risk levels in both genders (P<0.05).

Conclusion

The SBP trajectories of the subjects can be divided into three groups: low-level, mid-level, and high-level. MUNW, MHO, and MUO may influence the long-term trend of SBP, suggesting that personalized blood pressure management strategies should be developed targeting different body weight statuses and metabolic characteristics.

Key words: Hypertension, Systolic blood pressure trajectories, Body weight status, Metabolism, Influencing factors, Prospective cohort study

摘要：

背景

体重严重影响血压，但其联合代谢状态与收缩压（SBP）轨迹之间的关系尚不明确。

目的

探讨不同体重状态及其代谢特征对SBP纵向轨迹变化的影响。

方法

选取2019年7月—2021年9月在新疆维吾尔自治区乌鲁木齐市社区招募的2 051例健康体检者为研究对象，收集基线信息。研究对象每12个月于新疆医科大学第一附属医院健康管理中心收集SBP动态变化情况及高血压新发状态。采用组基-轨迹模型（GBTM）确定研究对象SBP最优轨迹数，对SBP变化轨迹进行分组。采用限制性立方样条曲线（RCS）分析不同体重状态及代谢特征者BMI与SBP轨迹的潜在的剂量-反应关系。采用多因素Logistic回归分析探讨体重状态及代谢特征对SBP变化轨迹的影响。

结果

研究对象平均年龄（43.4±8.8）岁，男883例（43.1%），女1 168例。根据体重状态及代谢特征分组，代谢健康体重正常（MHNW）人群915例（44.6%）、代谢健康超重/肥胖（MHO）人群608例（29.6%）、代谢异常超重/肥胖（MUO）人群379例（18.5%）、代谢异常体重正常（MUNW）人群149例（7.3%），随访期间共新发高血压362例（17.6%）。GBTM将研究对象按照SBP轨迹分为低水平组（SBP呈现为低水平状态，n=870）、中水平组（SBP呈现中水平稳定趋势，n=1 067）、高水平组（SBP呈现上升到高水平后下降趋势，n=114）。低水平组、中水平组、高水平组研究对象性别、年龄、高中以下、吸烟、饮酒比例、BMI、SBP、舒张压（DBP）、空腹血糖、总胆固醇、甘油三酯、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇比较，差异有统计学意义（P<0.05）。在低水平、中水平、高水平SBP轨迹组内，4种代谢表型的性别分布差异有统计学意义（P<0.05）。RCS结果显示，在MHNW、MUNW和MUO表型人群中，BMI与SBP轨迹风险呈线性正相关（P_非线性>0.05，P<0.001），在MHO表型人群中，BMI与SBP轨迹风险呈非线性相关（P_非线性<0.05，P<0.001）。多因素Logistic回归分析结果显示，与MHNW相比，MHO（OR=2.29，95%CI=1.84~2.87）、MUNW（OR=5.32，95%CI=3.62~7.83）、MUO（OR=5.20，95%CI=3.90~6.95）均为SBP轨迹风险等级增加的独立危险因素（P<0.05）；且随着体重状态及其代谢特征分型变化，SBP轨迹风险等级增加（P_趋势<0.001）。进一步行性别分层分析，在不同性别人群中MHO、MUNW及MUO仍是SBP轨迹风险等级的独立危险因素（P<0.05）。

结论

研究对象SBP轨迹可分为3组，即低水平组、中水平组和高水平组。MUNW、MHO、MUO可能影响SBP的长期变化趋势，提示应针对不同体重状态及代谢特征制订个性化的血压管理策略。

关键词: 高血压, 收缩压轨迹, 体重状态, 代谢, 影响因素, 前瞻性队列研究

QIU Xinyu,ZHAO Qian,CHEN Yufei, et al. Weight Status and Metabolic Characteristics on the Longitudinal Trajectory of Systolic Blood Pressure: a Cohort Study[J]. Chinese General Practice, 2026, 29(15): 2006-2013. DOI: 10.12114/j.issn.1007-9572.2025.0222.
邱欣雨,赵倩,陈玉斐等. 体重状态及其代谢特征对收缩压纵向轨迹的影响：一项队列研究[J]. 中国全科医学, 2026, 29(15): 2006-2013. DOI: 10.12114/j.issn.1007-9572.2025.0222.

Figures/Tables 8

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分组数量	BIC值	AIC值	AvePP值	群体中个体的比例（%）
2	52 350	52 283	0.98/0.78	96.39/3.61
3	52 345	52 249	0.83/0.81/0.79	52.42/52.02/5.56
4	52 370	52 246	0.81/0.52/0.63/0.74	48.51/6.74/40.96/3.80
5	52 394	52 242	0.84/0.57/0.62/0.72/0.84	56.36/2.10/3.40/7.65/0.49

分组数量	BIC值	AIC值	AvePP值	群体中个体的比例（%）
2	52 350	52 283	0.98/0.78	96.39/3.61
3	52 345	52 249	0.83/0.81/0.79	52.42/52.02/5.56
4	52 370	52 246	0.81/0.52/0.63/0.74	48.51/6.74/40.96/3.80
5	52 394	52 242	0.84/0.57/0.62/0.72/0.84	56.36/2.10/3.40/7.65/0.49

组别	例数	性别（男/女）	年龄（±s，岁）	高中以下[例（%）]	已婚[例（%）]	吸烟[例（%）]	饮酒[例（%）]	糖尿病[例（%）]	膳食多样性得分（±s，分）	睡眠较好[例（%）]
低水平组	870	217/653	41.1±7.7	39（4.5）	791（90.9）	101（11.6）	505（5.7）	17（2.0）	7.0±1.2	636（73.1）
中水平组	1 067	603/464	44.6±8.8	107（10.0）	996（93.3）	263（24.6）	723（67.8）	25（2.3）	7.0±1.2	823（77.1）
高水平组	114	63/51	50.5±9.5	16（14.0）	109（95.6）	31（27.2）	77（67.5）	6（5.3）	7.0±1.1	87（76.3）
χ²（F）值		202.17	84.52^a	25.51	5.78	57.23	20.34	4.83	1.17^a	4.25
P值		<0.001	<0.001	<0.001	0.056	<0.001	<0.001	0.089	0.309	0.120
组别	体力活动[例（%）]			BMI（±s，kg/m²）	SBP（±s，mmHg）	DBP（±s，mmHg）	FBG（±s，mmol/L）	TC（±s，mmol/L）	TG（±s，mmol/L）	HDL-C（±s，mmol/L）	LDL-C（±s，mmol/L）
组别	低水平	中水平	高水平	BMI（±s，kg/m²）	SBP（±s，mmHg）	DBP（±s，mmHg）	FBG（±s，mmol/L）	TC（±s，mmol/L）	TG（±s，mmol/L）	HDL-C（±s，mmol/L）	LDL-C（±s，mmol/L）
低水平组	221（25.4）	575（66.1）	74（8.5）	22.9±3.1	107±8	68±7	4.9±1.1	4.7±0.9	1.2±1.1	1.3±0.3	3.0±0.7
中水平组	240（22.5）	719（67.4）	108（10.1）	24.9±3.3	124±8	77±7	5.1±1.1	5.0±1.0	1.7±1.4	1.2±0.3	3.3±0.7
高水平组	29（25.4）	69（60.5）	16（14.0）	26.0±4.0	129±7	81±6	5.4±1.1	5.1±1.0	2.2±2.3	1.2±0.3	3.4±0.7
χ²（F）值	6.28			119.88^a	1 272.21^a	497.23^a	14.81^a	34.61^a	50.96^a	29.06^a	42.76^a
P值	0.180			<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

组别	例数	性别（男/女）	年龄（±s，岁）	高中以下[例（%）]	已婚[例（%）]	吸烟[例（%）]	饮酒[例（%）]	糖尿病[例（%）]	膳食多样性得分（±s，分）	睡眠较好[例（%）]
低水平组	870	217/653	41.1±7.7	39（4.5）	791（90.9）	101（11.6）	505（5.7）	17（2.0）	7.0±1.2	636（73.1）
中水平组	1 067	603/464	44.6±8.8	107（10.0）	996（93.3）	263（24.6）	723（67.8）	25（2.3）	7.0±1.2	823（77.1）
高水平组	114	63/51	50.5±9.5	16（14.0）	109（95.6）	31（27.2）	77（67.5）	6（5.3）	7.0±1.1	87（76.3）
χ²（F）值		202.17	84.52^a	25.51	5.78	57.23	20.34	4.83	1.17^a	4.25
P值		<0.001	<0.001	<0.001	0.056	<0.001	<0.001	0.089	0.309	0.120
组别	体力活动[例（%）]			BMI（±s，kg/m²）	SBP（±s，mmHg）	DBP（±s，mmHg）	FBG（±s，mmol/L）	TC（±s，mmol/L）	TG（±s，mmol/L）	HDL-C（±s，mmol/L）	LDL-C（±s，mmol/L）
组别	低水平	中水平	高水平	BMI（±s，kg/m²）	SBP（±s，mmHg）	DBP（±s，mmHg）	FBG（±s，mmol/L）	TC（±s，mmol/L）	TG（±s，mmol/L）	HDL-C（±s，mmol/L）	LDL-C（±s，mmol/L）
低水平组	221（25.4）	575（66.1）	74（8.5）	22.9±3.1	107±8	68±7	4.9±1.1	4.7±0.9	1.2±1.1	1.3±0.3	3.0±0.7
中水平组	240（22.5）	719（67.4）	108（10.1）	24.9±3.3	124±8	77±7	5.1±1.1	5.0±1.0	1.7±1.4	1.2±0.3	3.3±0.7
高水平组	29（25.4）	69（60.5）	16（14.0）	26.0±4.0	129±7	81±6	5.4±1.1	5.1±1.0	2.2±2.3	1.2±0.3	3.4±0.7
χ²（F）值	6.28			119.88^a	1 272.21^a	497.23^a	14.81^a	34.61^a	50.96^a	29.06^a	42.76^a
P值	0.180			<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001

表型	例数	男	女
MHNW	566	90（15.9）	476（84.1）
MHO	208	87（41.8）	121（58.2）
MUNW	36	7（19.4）	29（80.6）
MUO	60	33（55.0）	27（45.0）
χ²值		85.93
P值		<0.001

Weight Status and Metabolic Characteristics on the Longitudinal Trajectory of Systolic Blood Pressure: a Cohort Study

体重状态及其代谢特征对收缩压纵向轨迹的影响：一项队列研究

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References 31

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表型	例数	男	女
MHNW	333	117（35.1）	216（64.9）
MHO	372	244（65.6）	128（34.4）
MUNW	91	40（44.0）	51（56.0）
MUO	271	202（74.5）	69（25.5）
χ²值		116.07
P值		<0.001

表型	例数	男	女
MHNW	16	6（37.5）	10（62.5）
MHO	28	19（67.9）	9（32.1）
MUNW	22	8（36.4）	14（63.6）
MUO	48	30（62.5）	18（37.5）
χ²值		8.03
P值		0.045

变量	总人群		男		女
变量	OR（95%CI）	P值	OR（95%CI）	P值	OR（95%CI）	P值
MHO	2.29（1.84~2.87）	<0.001	2.20（1.55~3.14）	<0.001	2.35（1.74~3.16）	<0.001
MUNW	5.32（3.62~7.83）	<0.001	6.69（3.34~13.46）	<0.001	4.40（2.78~6.96）	<0.001
MUO	5.20（3.90~6.95）	<0.001	5.21（3.44~7.92）	<0.001	5.60（3.62~8.65）	<0.001
P_趋势值	<0.001		<0.001		<0.001

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