IRCCS MultiMedica, Via Gaudenzio Fantoli, 16/15, Milan 20138, Italy.
CORESEARCH-Center for Outcomes Research and Clinical Epidemiology, Corso Umberto I, 103, 65122, Pescara, Italy.
Eur J Prev Cardiol. 2023 Jun 1;30(8):719-727. doi: 10.1093/eurjpc/zwad072.
Cardiovascular risk factor control fluctuates, tends to change over time, and is potentially impacted by multifactorial interactions. Currently, the presence of risk factors, rather than their variability or interplay with one another, is taken into account to define the population at risk. The association between variability of risk factors and cardiovascular morbidity and mortality risk among patients with Type 2 diabetes mellitus (T2DM) remains debatable.
Using registry-derived data, we identified 29 471 people with T2DM, without cardiovascular disease (CVD) at baseline, and with at least five measurements of risk factors. Variability for each variable was expressed as quartiles of the standard deviation during 3 years (exposure). The incidence of myocardial infarction, stroke, and all-cause mortality was assessed during 4.80 (2.40-6.70) years following the exposure phase. The association between the measures of variability and the risk of developing the outcome was investigated through multivariable Cox proportional-hazards regression analysis with stepwise variable selection. Then, the recursive partitioning and amalgamation (RECPAM) algorithm was used to explore the interaction among the variability of risk factors associated with the outcome. An association between the variability of HbA1c, body weight, systolic blood pressure, and total cholesterol with the outcome considered was found. Among the six classes of risk identified by RECPAM, patients with a high variability of both body weight and blood pressure had the highest risk [Class 6, hazard ratio (HR) = 1.81; 95% confidence interval (CI) 1.61-2.05] compared with patients with low variability of both body weight and total cholesterol (Class 1, reference), despite a progressive reduction in the mean level of risk factors during successive visits. Individuals with high weight variability but low-moderate systolic blood pressure variability (Class 5, HR = 1.57; 95% CI 1.28-1.68), patients with moderate/high weight variability associated with high/very high HbA1c variability (Class 4, HR = 1.33; 95% CI 1.20-1.49), subjects with moderate/high weight variability and with low/moderate HbA1c variability (Class 3, HR = 1.12; 95% CI 1.00-1.25), as well as those with low weight variability associated with high/very high total cholesterol variability (Class 2, HR = 1.14; 95% CI 1.00-1.30) also showed a significant increase in the risk of an event.
Combined high variability of two risk factors, particularly body weight and blood pressure, is associated with cardiovascular risk among patients with T2DM. These findings highlight the importance of continuous balancing of multiple risk factors.
心血管风险因素的控制波动不定，往往随时间而变化，并且可能受到多因素相互作用的影响。目前，考虑到风险因素的存在，而不是其变异性或相互作用，来定义高危人群。2 型糖尿病（T2DM）患者的风险因素变异性与心血管发病率和死亡率之间的关联仍存在争议。
我们使用基于登记的资料，确定了 29471 名无心血管疾病（CVD）且基线时至少有 5 次风险因素测量值的 T2DM 患者。在 3 年期间（暴露期），每个变量的变异性均表示为标准差的四分位数。在暴露阶段后 4.80（2.40-6.70）年评估心肌梗死、卒中和全因死亡率。通过逐步变量选择的多变量 Cox 比例风险回归分析，评估了变异性测量值与发展结局之间的关联。然后，使用递归分区和合并（RECPAM）算法来探索与结局相关的风险因素变异性之间的相互作用。发现糖化血红蛋白、体重、收缩压和总胆固醇的变异性与结局之间存在关联。在 RECPAM 确定的六个风险类别中，体重和血压变异性均高的患者（类别 6，风险比[HR] = 1.81；95%置信区间[CI] 1.61-2.05）与体重和总胆固醇变异性均低的患者（类别 1，参考）相比风险最高，尽管在连续就诊期间，风险因素的平均水平逐渐降低。体重变异性高但收缩压变异性中低（类别 5，HR = 1.57；95%CI 1.28-1.68）、体重中高变异性与 HbA1c 中高/极高变异性相关（类别 4，HR = 1.33；95%CI 1.20-1.49）、体重中高变异性且 HbA1c 中低/中变异性（类别 3，HR = 1.12；95%CI 1.00-1.25）以及体重变异性低但总胆固醇变异性高/极高（类别 2，HR = 1.14；95%CI 1.00-1.30）的患者，其发生事件的风险也显著增加。
两种风险因素（特别是体重和血压）的高联合变异性与 T2DM 患者的心血管风险相关。这些发现强调了持续平衡多种风险因素的重要性。
