1.华北理工大学附属医院:,急诊科,河北唐山
5.华北理工大学附属医院:,重症医学科,河北唐山
2.华北理工大学研究生院,河北唐山
3.暨南大学第一附属医院重症医学科,广东广州
4.山西医科大学研究生院,山西太原
6.开滦总医院心血管内科,河北唐山
WU Shouling,E-mail:drwusl@163.com
SUN Lixia,E-mail:13323151999@189.cn
Received:21 October 2025,
Revised:2026-01-05,
Accepted:05 January 2026,
Published:30 January 2026
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曹柳柳,李曼,武朝辉,等.残余胆固醇的变异性与心脏传导阻滞风险的U型关联[J].陆军军医大学学报,2026,48(02):216-228.DOI:10.16016/j.2097-0927.202510061.
CAO L L,LI M,WU Z H,et al.U-shaped association between variability of remnant cholesterol and risk of cardiac conduction block[J].J Army Med Univ,2026,48(02):216-228.DOI:10.16016/j.2097-0927.202510061.
曹柳柳,李曼,武朝辉,等.残余胆固醇的变异性与心脏传导阻滞风险的U型关联[J].陆军军医大学学报,2026,48(02):216-228.DOI:10.16016/j.2097-0927.202510061. DOI:
CAO L L,LI M,WU Z H,et al.U-shaped association between variability of remnant cholesterol and risk of cardiac conduction block[J].J Army Med Univ,2026,48(02):216-228.DOI:10.16016/j.2097-0927.202510061. DOI:
目的
2
本研究基于开滦队列,探讨长期随访期间残余胆固醇(remnant cholesterol,RC)的变异性与心脏传导阻滞(cardiac conduction block,CCB)的关联。
方法
2
采用回顾性队列研究,以连续参加2006、2008、2010年度体检的开滦研究人群作为研究队列,根据纳入和排除标准,最终入选39 299例观察对象,计算随访期间RC的变异性,RC的变异性通过标准差(standard deviation,SD)、平均真实变异性(average real variability,ARV)、变异系数(coefficient of variation,CV)、独立于均值的变异(variation independent of mean,VIM)测量。以RC的变异性四分位进行分组,将观察对象分成4组,每2年进行1次随访,以发生CCB、死亡或随访结束(2019年12月31日)为随访终点,采用Kaplan-Meier法计算不同分组发生终点事件的累积发病率,并用Log-Rank检验比较各组终点事件累积发生率的差异。用Cox比例风险模型及限制性立方样条曲线(restricted cubic spline, RCS)分析RC的变异性与CCB及其亚型的关联。
结果
2
39 299例观察对象在平均随访7.43年后,发生CCB者共916人。以CCB为结局事件,校正混杂因素后,以RC-SD四分位分组,与RC-SD最低发病密度组(Q2)相比,Q1组发生CCB的
HR
值为1.344(95%
CI
: 1.110~1.626,
P
=0.002),Q3、Q4组发生CCB的
HR
值分别1.068(95%
CI
: 0.878~1.299,
P
=0.509)、1.311(95%
CI
: 1.089~1.
579,
P
=0.004)。以RC-ARV、RC-CV、RC-VIM四分位分组均得出类似的结论。RCS曲线表明,RC-SD与CCB之间呈U型关联(
P
overall
<
0.05,
P
nonlinearity
<
0.05)。敏感性分析、分层分析进一步验证了此结论。
结论
2
RC的变异性与CCB呈U型关联,该研究为预防CCB的发生提供了新的方向。对策 我国血脂管理策略应从追求“低水平稳定”,向兼顾“指标长期平稳性” 转型升级。即将 “维持血脂在适宜区间的生理性波动” 这一新理念,系统整合至国家心脑血管疾病防治规划、基层公共卫生服务项目及全民健康教育体系,以此推动慢性病管理迈向更加精准、科学的新阶段。
Objective
2
To investigates the association between the variability of remnant cholesterol (RC) during long-term follow-up and cardiac conduction block (CCB) based on the Kailuan cohort.
Methods
2
A retrospective study was conducted on the research cohort in the Kailuan study population who consecutively participated in the physical examinations of 2006
2008
and 2010. Based on the inclusion and exclusion criteria
39 299 subjects were ultimately enrolled in the study. The variability of RC during the follow-up period was calculated
with the variability assessed through standard deviation (SD)
average real variability (ARV)
coefficient of variation (CV)
and variability independent of the mean (VIM). The participants were divided into 4 groups based on the interquartile range of RC variability
and followed up every 2 years. The endpoints were defined as the occurrence of CCB
death
or the termination of follow-up (December 31
2019). Kaplan-Meier test and Log-Rank test were carried out respectively calculate and compare the cumulative incidence rate of endpoint events across groups. Cox proportional hazards model and restricted cubic spline (RCS) analysis were performed to analyze the association between the variability of RC and CCB and its subtypes.
Results
2
After an average follow-up of 7.43 years in 39 299 participants
916 patients developed CCB. After adjusting for confounding factors
using CCB as the outcome event
and were dividing the participan
ts into groups based on the RC-SD quartiles
the hazard ratios (
HR
) for CCB occurrence were 1.344 in the Q1 group (95%
CI
: 1.110~1.626
P
=0.002)
1.068 (95%
CI
: 0.878 to 1.299
P
=0.509) in the Q3 group and 1.311 (95%
CI
: 1.089~1.579
P
=0.004) in the Q4 group
when compared to the group of lowest incidence density (the Q2 group). Similar results were obtained for the quartiles of RC-ARV
RC-CV
and RC-VIN grouping. RCS analysis revealed a U-shaped association between RC-SD and CCB (
P
overall
<
0.05
P
nonlinearity
<
0.05). Sensitivity and stratified analyses further validated this finding.
Conclusion
2
There exists a U-shaped association between the variability of RC and the risk of CCB. This finding provides a new direction for preventing the occurrence of CCB. Countermeasures China’s blood lipid management strategy should undergo a paradigm shift from pursuing “low-level stability” toward integrating “long-term stability of biomarker levels” This entails systematically incorporating the new concept of “maintaining physiological fluctuations of blood lipids within an optimal range” into the national prevention and treatment plans for cardiovascular and cerebrovascular diseases
primary public health service programs
and the public health education system. Such a transition will propel chronic disease management into a more precise and scientific new stage.
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