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Methodological challenges in studies of the role of blood lipids variability in the incidence of cardiovascular disease
Lipids in Health and Disease ( IF 4.5 ) Pub Date : 2021-05-19 , DOI: 10.1186/s12944-021-01477-x
Leonelo E Bautista 1 , Oscar L Rueda-Ochoa 2
Affiliation  

It has long been known that high levels of total and LDL-cholesterol, and low levels of HDL increase the risk of developing atherosclerotic cardiovascular diseases [1]. Recent studies suggest that high intra-individual variability in blood lipids may increase the risk of major cardiovascular events (MACE: non-fatal myocardial infarction, stroke, or cardiovascular death), independently of individual average levels [2]. Here we discuss some methodological challenges in studies of the causal effect of blood lipids variability (BLV) on the risk of MACE, and suggest possible solutions.

Studies of the BLV-MACE association have shown that high intra-individual blood lipids variability (BLV) is independently associated with MACE in different types of patients [3,4,5,6,7], as well as in the general population [8, 9].Visit to visit BLV has been measured by conducting serial measurements of blood lipids and calculating their measure-to-measure variability as the standard deviation (SD), the coefficient of variation (CV), the average successive variability (ASV = ∑(Xi – Xi + 1)/n) or the variance Independent of the mean (VIM = (k SD/ \( {\overline{x}}^p \)), where k = Mp, and p is the regression coefficient of log (SD) over the log(\( \overline{X}\Big) \) [9, 10]. These estimates of individual BLV are then included in regression models to estimate the between-subject effect of BLV. Measures such as SD and CV are strongly related to the mean of visit values, while VIM is independent of the mean [2].

An accurate assessment of individual BLV requires multiple measurements at given intervals of time. Unfortunately, there is considerable uncertainty about how many measurements are needed and how long the intervals should be in order to get an assessment of BLV that is accurate and also etiologically relevant. Researchers should not assume that because data are available, BLV based on that data is accurate or biologically and clinically relevant. Average lipid levels, at a given point in time, reflects accumulated exposure up to that time, and is unequivocally related to the risk of MACE [1]. In contrast, it is uncertain whether BLV measured over a period of weeks or months accurately reflects a cumulative exposure, particularly in patients receiving cholesterol-lowering drugs. This uncertainty is due in part to our limited knowledge on BLV over time. In a meta-analysis conducted by Smith et al. [11] the coefficient of variation (CV = standard deviation/mean) for HDL were 5.5, 5.8, and 7.8% when measurements were conducted daily, weekly, and monthly, respectively. Corresponding values for LDL were 6.1, 6.2, and 10.8%. Similar findings have been reported in more recent studies [12, 13]. These studies seem to indicate BLV is low, at least when measured in a relative scale. On the other hand, studies on age-related BLV indicate that LDL cholesterol levels increase with age and reach a plateau in men between the age of 50 and 60 years, and in women between the age of 60 and 70 years [14], while HDL cholesterol levels diminish or have minimal changes with age [15, 16]. Taken together, these studies suggest BLV measured in intervals of even years may be small and of little clinical significance, after accounting for age-related changes. Therefore, well-designed studies describing the accuracy of BLV patterns are highly desirable. Knowledge generated in those studies would inform the design and interpretation of future studies, identify high-risk individuals, and determine what measurement intervals and indicators of BLV are clinically relevant.

Evidence of mechanisms explaining a BLV-MACE association is limited. One proposed hypothesis is that high BLV promotes the development and progression of atherosclerotic plaques. Clark et al. showed that BLV was associated with progression of coronary atherosclerotic plaques, measured as the proportion of the arterial wall occupied by the plaque, in patients included in trials assessing the effect of intensive lipid lowering with statins [17]. However, the BLV effects on plaque development were weak, were not present in individuals who achieved treatment target, and were not independent of average levels of blood lipids. On the other hand, in patients receiving cholesterol-lowering drugs, the BLV-MACE association may also be just a consequence of poor treatment adherence [18].

The BLV-MACE association observed in several studies, though statistically significant, has been consistently weak. The average increases in risks of MACE associated with LDL-variability ranged between 10 and 34% in studies in cardiovascular patients [3,4,5,6], and between 6 and 11% in the general population [8, 9]. Associations from studies in treated patients should be cautiously interpreted. Interventions to reduce the risk of MACE in these patients are guided by their levels of cardiovascular risk factors. If those interventions are more frequent or intense in patients with high levels of blood lipids, the risk of MACE in patients with high BLV may be underestimated, since BLV increases with average blood lipid levels. This implies that data on co-interventions must be collected at the times blood lipids are measured, to control for their confounding effect. On the other hand, the effect of BLV from studies in the general population are too weak (maximum hazard ratio of 1.11) and could be explained by a confounding factor that increases the proportion of individuals with high BLV and the risk of MACE by just 46% [19].

It is also uncertain whether BLV is a modifiable therapeutic target, beyond the modification of average levels, and how this may be achieved. Moreover, before implementation of BLV measurement in the clinical context it is necessary to ascertain whether different measures of BLV improve predictability of MACE to the same degree, and which may be easier to use in patient care. Implementation is also hampered by the fact that BLV measures, such as SD, CV, and VIM are study-population specific and, therefore, their values cannot be applied to other populations. Moreover, the BLV-MACE association observed in several studies, though statistically significant, may not improve the performance of models excluding BLV or predictions from a cardiovascular risk score [20].

On the other hand, in most studies, BLV has been evaluated through short-time intervals and taken as the baseline exposure. This implies, without due justification, that BLV is a time-fixed variable that does not change or changes little over the follow-up period. Longitudinal follow-up studies, with repeated evaluations of BLV, time-dependent risk factors, and MACE or surrogates of MACE, such as carotid artery intima-media thickness and plaque stability, could be used to test this assumption and elucidate both the stability of BLV and its within-individual effects. In addition, Cox proportional hazard model has been used to assess the BLV-MACE association in most studies [3, 5,6,7,8,9, 21]. Although appropriate for the analysis of survival data, Cox regression does not take advantage of the correlation across the repeated measurements of blood lipids or the observed trajectory in average lipid levels. Moreover, Cox regression only considers between group variability, leaving out the estimation of within-individual and total effects of BLV. Other analytical approaches, such as multilevel/mixed linear regression models [22], joint modelling [23, 24], and g methods [25, 26], could be helpful to address the limitations of Cox regression. In particular, these methods could be useful to estimate the potential impact of pharmacological interventions to reduce BLV using data from observational studies.

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Affiliations

  1. Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA

    Leonelo E. Bautista

  2. Department of Basic Sciences, Director Cardiovascular Research Group, Universidad Industrial de Santander, Bucaramanga, Colombia

    Oscar L. Rueda-Ochoa

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Bautista, L.E., Rueda-Ochoa, O.L. Methodological challenges in studies of the role of blood lipids variability in the incidence of cardiovascular disease. Lipids Health Dis 20, 51 (2021). https://doi.org/10.1186/s12944-021-01477-x

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中文翻译:

研究血脂变异在心血管疾病发病率中的作用的方法学挑战

众所周知,高水平的总胆固醇和低密度脂蛋白胆固醇以及低水平的高密度脂蛋白会增加患动脉粥样硬化性心血管疾病的风险 [1]。最近的研究表明,血脂的高个体内变异可能会增加主要心血管事件(MACE:非致命性心肌梗死、中风或心血管死亡)的风险,与个体平均水平无关 [2]。在这里,我们讨论血脂变异性 (BLV) 对 MACE 风险的因果影响研究中的一些方法学挑战,并提出可能的解决方案。

BLV-MACE 协会的研究表明,在不同类型的患者 [3,4,5,6,7] 以及一般人群中,高个体内血脂变异性 (BLV) 与 MACE 独立相关 [3,4,5,6,7] 8, 9]. 通过连续测量血脂并计算其测量间变异性,如标准偏差 (SD)、变异系数 (CV)、平均连续变异性 (ASV) 来测量 BLV = ∑( X i – X i + 1 )/n) 或与均值无关的方差 (VIM = ( k SD/ \( {\overline{x}}^p \) ),其中k = M p,并且p是 log (SD) 在 log(\( \overline{X}\Big) \) [9, 10]。然后将这些对个体 BLV 的估计包括在回归模型中,以估计 BLV 的受试者间效应。SD 和 CV 等度量与访问值的均值密切相关,而 VIM 与均值无关 [2]。

对单个 BLV 的准确评估需要在给定的时间间隔内进行多次测量。不幸的是,需要多少次测量以及间隔应该多长时间才能获得准确且与病因相关的 BLV 评估存在相当大的不确定性。研究人员不应假设因为数据可用,基于该数据的 BLV 是准确的或具有生物学和临床相关性。特定时间点的平均血脂水平反映了截至该时间点的累积暴露量,并且与 MACE 风险明确相关 [1]。相比之下,不确定在数周或数月内测量的 BLV 是否准确反映了累积暴露,尤其是在接受降胆固醇药物的患者中。这种不确定性部分是由于我们对 BLV 的了解有限。在 Smith 等人进行的荟萃分析中。[11] 每天、每周和每月进行测量时,HDL 的变异系数(CV = 标准偏差/平均值)分别为 5.5、5.8 和 7.8%。LDL 的相应值为 6.1、6.2 和 10.8%。最近的研究报告了类似的发现 [12, 13]。这些研究似乎表明 BLV 较低,至少在相对尺度上测量时如此。另一方面,对年龄相关 BLV 的研究表明,LDL 胆固醇水平随着年龄的增长而增加,并在 50 至 60 岁之间的男性和 60 至 70 岁之间的女性中达到稳定水平 [14],而HDL 胆固醇水平随着年龄的增长而降低或变化很小 [15, 16]。综合起来,这些研究表明,在考虑了与年龄相关的变化后,以偶数年为间隔测量的 BLV 可能很小,临床意义也很小。因此,非常需要描述 BLV 模式准确性的精心设计的研究。这些研究中产生的知识将为未来研究的设计和解释提供信息,识别高风险个体,并确定 BLV 的测量间隔和指标与临床相关。

解释 BLV-MACE 关联机制的证据有限。一种提出的假设是高 BLV 促进动脉粥样硬化斑块的发展和进展。克拉克等人。表明 BLV 与冠状动脉粥样硬化斑块的进展相关,以斑块占据动脉壁的比例衡量,在评估他汀类药物强化降脂效果的试验中的患者中 [17]。然而,BLV 对斑块发展的影响较弱,在达到治疗目标的个体中不存在,并且与平均血脂水平无关。另一方面,在接受降胆固醇药物的患者中,BLV-MACE 关联也可能只是治疗依从性差的结果 [18]。

在几项研究中观察到的 BLV-MACE 关联虽然具有统计学意义,但一直很弱。在心血管患者的研究中,与 LDL 变异相关的 MACE 风险的平均增加范围为 10% 至 34% [3,4,5,6],而在一般人群中为 6% 至 11% [8, 9]。应谨慎解释治疗患者研究中的关联。降低这些患者 MACE 风险的干预措施以他们的心血管危险因素水平为指导。如果这些干预措施对高血脂患者更频繁或更强烈,则可能会低估高 BLV 患者发生 MACE 的风险,因为 BLV 会随着平均血脂水平而增加。这意味着必须在测量血脂时收集有关联合干预的数据,以控制它们的混杂效应。另一方面,BLV 在一般人群中的研究效果太弱(最大风险比为 1.11),可以用一个混杂因素来解释,即高 BLV 的个体比例和 MACE 风险仅增加了 46 % [19]。

也不确定 BLV 是否是一个可修改的治疗目标,超出了平均水平的修改,以及如何实现这一点。此外,在临床环境中实施 BLV 测量之前,有必要确定不同的 BLV 测量是否在相同程度上提高了 MACE 的可预测性,并且在患者护理中可能更容易使用。由于 BLV 措施(如 SD、CV 和 VIM)是特定于研究人群的,因此它们的值不能应用于其他人群,这也阻碍了实施。此外,在几项研究中观察到的 BLV-MACE 关联,虽然具有统计学意义,但可能不会提高排除 BLV 或心血管风险评分预测的模型的性能 [20]。

另一方面,在大多数研究中,BLV 已通过短时间间隔进行评估并作为基线暴露。这意味着,在没有正当理由的情况下,BLV 是一个时间固定的变量,在后续期间不会发生变化或变化很小。纵向随访研究,重复评估 BLV、时间依赖性危险因素和 MACE 或 MACE 的替代品,如颈动脉内中膜厚度和斑块稳定性,可用于测试这一假设并阐明两者的稳定性BLV 及其个体内效应。此外,Cox 比例风险模型已在大多数研究中用于评估 BLV-MACE 关联 [3, 5,6,7,8,9, 21]。虽然适用于分析生存数据,Cox 回归没有利用重复测量血脂或观察到的平均血脂水平轨迹之间的相关性。此外,Cox 回归仅考虑组间变异性,而忽略了对 BLV 的个体内和总效应的估计。其他分析方法,例如多级/混合线性回归模型 [22]、联合建模 [23、24] 和 g 方法 [25、26],可能有助于解决 Cox 回归的局限性。特别是,这些方法可用于使用观察性研究的数据估计药物干预对降低 BLV 的潜在影响。其他分析方法,例如多级/混合线性回归模型 [22]、联合建模 [23、24] 和 g 方法 [25、26],可能有助于解决 Cox 回归的局限性。特别是,这些方法可用于使用观察性研究的数据估计药物干预对降低 BLV 的潜在影响。其他分析方法,例如多级/混合线性回归模型 [22]、联合建模 [23、24] 和 g 方法 [25、26],可能有助于解决 Cox 回归的局限性。特别是,这些方法可用于使用观察性研究的数据估计药物干预对降低 BLV 的潜在影响。

不适用。

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隶属关系

  1. 美国威斯康星大学麦迪逊分校医学与公共卫生学院人口健康科学系

    莱昂内洛·E·包蒂斯塔

  2. 哥伦比亚布卡拉曼加桑坦德工业大学基础科学系心血管研究组主任

    Oscar L. Rueda-Ochoa

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Bautista, LE, Rueda-Ochoa, OL 血脂变异在心血管疾病发病率中的作用研究中的方法学挑战。Lipids Health Dis 20, 51 (2021)。https://doi.org/10.1186/s12944-021-01477-x

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  • DOI : https://doi.org/10.1186/s12944-021-01477-x

更新日期:2021-05-19
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