Visit-to-visit variability of metabolic parameters and progression of atherosclerosis in computed tomography: follow up of an asymptomatic cohort

Introduction

Traditional cardiovascular disease (CVD) risk factors, such as obesity, blood pressure (BP), fasting blood glucose level, and blood cholesterol levels, reflect the variability of risk factors between individuals. Recent studies have shown that the variability in these risk factors within individuals also contributes to CVD risk and related deaths (1-3). Variability in traditional risk factors within an individual, a measure of the instability of the risk factors over time, can occur for several environmental reasons, such as lifestyle changes, starting medications, or incomplete adherence to treatment. However, intra-individual variability itself may act as a novel risk factor. BP variability increased the risk of CVD in different body mass index (BMI) groups and affects prognosis even in patients undergoing percutaneous coronary interventions (4,5).

Since previous studies on variability were primarily epidemiological observational studies, little is known about the intermediate process by which risk factor variability affects the development of CVD and mortality. Greater variability in atherogenic lipoprotein levels is related to the progression of coronary atherosclerosis when intravascular ultrasound is serially performed in patients with coronary artery disease (6). There have been no studies on the association between the variability of metabolic risk factors and the progression of atherosclerosis using coronary computed tomography angiography (CCTA). We attempted to elucidate the mechanism by which risk factor variability increases CVD-related mortality by hypothesizing that this variability contributes to coronary artery stenosis. This study aimed to examine whether intra-individual variability of traditional risk factors affects the progression of stenosis or plaque in coronary arteries on subsequent CCTA in an asymptomatic Korean health examination cohort. We present this article in accordance with the STROBE reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-75/rc).

Methods

Data source and study population

This study has a retrospective cohort design. We reviewed the medical records of 764 individuals who underwent CCTA without specific symptoms for regular health examinations twice or more with an interval of more than one year between 2010–2020 at the Haeundae Paik Hospital in South Korea. Participants were excluded if they had already undergone coronary stenting (n=7), had missing values for personal history (n=3), were foreigners or Koreans residing abroad (n=73), or had fewer than three laboratory examinations during the study period (n=294). Ultimately, 387 individuals were included in the study. Figure 1 shows the inclusion and exclusion criteria used to select the study subjects.

Figure 1 Inclusion and exclusion criteria used to select the study subjects. CCTA, coronary computed tomography angiography.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Haeundae Paik Hospital Institutional Review Board (No. HPIRB 2021-07-021). The requirement of informed consent was waived due to the retrospective nature of the analyses.

Definitions of measurements and variability

Participants answered a self-reported questionnaire on their lifestyle variables (smoking status, alcohol consumption, and physical activity), past medical history, and socioeconomic variables (marital status and educational attainment) (Appendix 1). Smoking status was divided into non-smokers, past smokers, and current smokers. Problem drinking of alcohol was defined according to age and sex as follows; ≥14 cups per week for males under the age of 65, ≥7 cups per week for males over the age of 65 and females under the age of 65, ≥3 cups per week for females over the age of 65. Regular exercise was defined as moderate intensity exercise or walking performed at least five times per week or vigorous intensity exercise performed at least three times per week. Information on the diagnosis and medication for hypertension, diabetes mellitus, dyslipidaemia, stroke, and ischaemic heart disease was obtained using a questionnaire. We defined a medical history of each disease as being diagnosed by a doctor and taking medications in the questionnaire. Family history of these diseases was also recorded. Educational attainment and menopause in women were also confirmed using a questionnaire.

BMI was calculated as the weight in kilograms divided by the square of height in metres. Waist circumference (WC) was measured using flexible tape at the narrowest point between the uppermost lateral border of the iliac crest and the lowest border of the rib cage at the end of normal expiration. BP was measured in the upper arm by trained staff after the participants had been seated for more than five min. Plasma glucose, high-sensitivity C-reactive protein (hs-CRP), triglycerides, high-density lipoprotein (HDL)-cholesterol, and low-density lipoprotein (LDL)-cholesterol were measured using blood drawn after an 8–12 hour overnight fast.

Visit-to-visit variability was evaluated using at least three measurements of BMI, WC, systolic and diastolic BP, plasma glucose, total cholesterol, triglyceride, HDL-cholesterol, and LDL-cholesterol values during the follow-up period. The average real variability (ARV) was calculated as the average of the absolute differences between consecutive measurements. The following formula was used to calculate the ARV, where N denotes the number of variable measurements.

ARV=1N−1∑k=1N−1|Valuek+1−Valuek|[1]

Study outcome

All CCTA examinations were performed using a 320-slice multidetector CT scanner (Aquilion One, Toshiba, Japan). Patients with a heart rate >65 beats per minute received oral and intravenous metoprolol premedication if needed. We used prospective electrocardiogram (ECG)-gated CCTA with a single-breath-hold technique to minimise radiation exposure. Images were reconstructed with a slice thickness of 0.5 mm and reconstruction slice interval of 0.5 mm.

CCTA images were evaluated using axial, coronal, sagittal, cross-sectional, and curved multiplanar reformation images. One radiologist reviewed all CCTA images and was blinded to the patients’ clinical information. Coronary atherosclerotic lesions were quantified for the degree of luminal diameter stenosis by visual estimation and graded using the Coronary Artery Disease Reporting and Data System (CAD-RADS™) as follows: no plaque or stenosis (0%), minimal stenosis or plaque with no stenosis (1–24%), mild stenosis (25–49%), moderate stenosis (50–69%), severe stenosis (70–99%), and occlusion (100%) (7). Progression of coronary artery atherosclerosis was defined as worsening of the degree of stenosis graded with CAD-RADS™ in any coronary artery on the final CCTA compared with the stenosis seen on CCTA at baseline.

Statistical analysis

Data are presented as mean ± standard deviation for continuous variables or count (%) for categorical variables. We conducted the Wilcoxon rank sum test after the normality test and the chi-square test or Fisher’s exact test, as appropriate. We stratified the study population into four groups according to the ARV values of the metabolic parameters. The incidence rate of coronary artery atherosclerosis progression was measured as the number of events during the follow-up period divided by 1,000 person-years. The hazard ratios (HR) and 95% confidence intervals (CI) for coronary artery atherosclerosis progression were analysed using multiple Cox proportional hazard models. Model 1 was adjusted for age, sex, smoking, alcohol consumption, exercise, and educational status. Model 2 was adjusted for baseline BMI, WC, systolic and diastolic BPs, plasma glucose, total cholesterol, triglyceride, HDL-cholesterol, and LDL-cholesterol. Model 3 was further adjusted for a history of hypertension, diabetes, dyslipidaemia, ischaemic heart disease, and stroke. Statistical significance was set at a two-sided test P<0.05. We performed a sensitivity analysis after excluding participants who were taking medications for metabolic diseases at baseline, because adherence to medication can affect the variability of risk factors. All statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA).

Results

Baseline characteristics of participants

Of the 387 participants, 126 (32.56%) showed progression of coronary artery stenosis during a mean follow-up period of 3.91 (range, 1–9) years. During the follow-up period, four patients underwent coronary stent intervention. Nine patients demonstrated coronary atherosclerosis regression, exhibiting an improvement in the degree of stenosis as graded by CAD-RADS™ compared to the baseline measurements (for example, moderate to mild or severe to mild). A representative case of progression is illustrated in Figure 2. Table 1 shows the baseline characteristics of the progression and non-progression groups. Participants with stenosis progression were more likely to be male (88.89% vs. 77.39%; P=0.007) and current smokers (38.10% vs. 26.44%; P=0.02) than those in the no-progression group. The prevalence of diabetes was higher in patients in the progression group (11.90% vs. 5.36%; P=0.02). Family members with diabetes were more common in the progression group (32.54% vs. 21.07%; P=0.01). At baseline, there were more participants in the progression group with 50% or more stenosis of the left anterior descending artery (7.94% vs. 1.92%; P=0.004) and right coronary artery (3.97% vs. 0.38%; P=0.008).

Figure 2 A case of normal coronary artery that progressed to significant stenosis after 6 years. Stretched MPR CT angiographic (A) and corresponding axial (B) CT images of a 38-year-old male reveal normal RCA with no atherosclerotic disease or stenosis at baseline (CAD-RADS category 0). After 6 years, stretched MPR CT angiographic (C) and corresponding axial CT (D) images show a focal plaque (arrow) with small calcification at the proximal RCA that is causing severe luminal stenosis (CAD-RADS category 4, 70–99%). No other lesion was identified. He did not have previous history of hypertension, diabetes, dyslipidemia, stroke or ischemic heart disease, and has smoked for 10 years. MPR CT, multi-planar reformatted computed tomography; RCA, right coronary artery; CAD-RADS, Coronary Artery Disease Reporting and Data System.

Variability of metabolic parameters and risk of coronary artery stenosis progression

Table 2 shows the minimum and maximum values of multiple examinations for various metabolic risk factors of the participants. The ARV values of the risk factors are also presented. The minimum values of all metabolic risk factors showed no significant differences between the two groups, except for WC. However, the progression group had significantly higher maximum BMI (24.88±2.68 vs. 25.60±2.84 kg/m²; P=0.02), WC (85.43±7.68 vs. 88.37±7.77 cm; P<0.001), systolic BP (123.52±13.11 vs. 128.96±13.30 mmHg; P<0.001) and diastolic BP (75.22±8.93 vs. 78.98±9.73 mmHg; P<0.001), plasma glucose (101.80±14.44 vs. 109.58±23.12 mg/dL; P<0.001), triglyceride (159.01±96.79 vs. 189.48±118.58 mg/dL; P=0.002), and hs-CRP levels (0.23±0.38 vs. 0.29±0.44 mg/dL; P=0.02). The ARVs of plasma glucose (7.54±7.47 vs. 9.87±11.21 mg/dL; P=0.007), triglyceride (46.38±55.04 vs. 60.91±85.62 mg/dL; P=0.001), LDL-cholesterol (24.75±23.44 vs. 28.50±21.21 mg/dL; P=0.014), and hs-CRP (0.12±0.23 vs. 0.16±0.35 mg/dL; P=0.02) were significantly higher in the progression group.

As shown in Table 3, the risk of progression to stenosis increased with the ARV quartile of blood lipid variables. Compared to the lowest variability for LDL-cholesterol, individuals with higher variability showed an increased risk of stenosis progression after adjusting for covariates: HR 1.07 (95% CI: 0.61–1.87) for Q2, HR 2.23 (95% CI: 1.33–3.73) for Q3, HR 1.56 (95% CI: 0.91–2.66) for Q4 (P for trend =0.005). Triglycerides also showed a significant linear trend (P for trend =0.04), and Q4 had a greater risk of stenosis progression (HR, 2.09; 95% CI: 1.24–3.52). Notably, the risk of stenosis progression was significantly reduced as the ARV of HDL-cholesterol increased: HR 0.92 (95% CI: 0.57–1.48) for Q2, HR 0.56 (95% CI: 0.35–0.89) for Q3, HR 0.47 (95% CI: 0.27–0.81) for Q4 (P for trend =0.01).

Sensitivity analysis

Sensitivity analyses were performed after excluding participants taking medications for hypertension, diabetes mellitus, dyslipidaemia, stroke, and ischaemic heart disease at baseline (Table S1). Among the 300 individuals without medication at baseline, 92 (30.7%) showed progression of coronary artery stenosis. After adjusting for covariates, the risk of stenosis progression in individuals with higher LDL-cholesterol variability was still higher than that in Q1 (HR, 2.38; 95% CI: 1.28–4.40 for Q3; P for trend =0.007). Triglycerides also showed a significant linear trend (P for trend =0.03), and Q4 had a greater risk of stenosis progression (HR, 2.70; 95% CI: 1.38–5.28). The ARV of HDL-cholesterol showed an inverse relationship with stenosis progression: HR 0.56 (95% CI: 0.33–0.96) for Q3, HR 0.40 (95% CI: 0.20–0.79) for Q4 (P for trend =0.007).

Discussion

In this retrospective cohort study, we found that high variability in LDL-cholesterol and triglyceride levels within same individuals was associated with an increased risk of coronary artery stenosis progression during a mean 4-year follow-up period. HDL cholesterol variability is inversely associated with stenosis progression. The variability in BMI, WC, plasma glucose, and BP did not show any significant association with the outcome, even after adjusting for covariables. Several randomised controlled trials for patients taking lipid-lowering medications revealed that visit-to-visit variability in LDL cholesterol is a significant predictor of mortality and morbidity (1,8,9). The strength of our study is that it demonstrates that CAD stenosis is an intermediate link between intra-individual variability of traditional risk factors and increased mortality. Our study provides real-world evidence that high variability in LDL-cholesterol and triglyceride levels increases the risk of progression of coronary artery stenosis based on CCTA follow up.

Our study has some limitations. First, it had a retrospective cohort design and included only participants who had two or more CCTA scans from a health examination cohort in a single centre, making selection bias unavoidable. Of the subjects who had two or more CCTAs to confirm coronary atherosclerosis progression, 38% (294/764) were excluded because they had less than three laboratory studies. Second, we did not adjust for the medication compliance of the participants which may influence the variability of risk factors. Patients with worse lipid profiles are most likely to have greater rates of CAD progression but also higher chances of being on medications; therefore, they are also more likely to have greater variation in lipid levels. However, considerable inter-individual variation exists in response to statin therapy, even with good compliance to statin therapy, and hypo-responders showed greater atheroma progression (10). Our sensitivity analysis, excluding those taking medications, also revealed that CAD stenosis progression was associated with variability in LDL cholesterol and triglyceride levels. Third, we identified the progression of coronary artery atherosclerosis according to the degree of stenosis, and CAD burden using high-risk plaque features or coronary artery calcium (CAC) score was not evaluated. It has been known that major adverse cardiac events were found to be linked to high-risk plaque including positive remodeling, low-attenuation plaque, and spotty calcification (11). However, baseline atheroma volume, and not the presence of high-risk plaque features, was the most important predictor of lesions developing into obstructive lesions in a multicentre longitudinal study evaluating serial CCTA (12). Most patients with detectable high-risk plaques on CCTA remained without acute cardiac ischaemic events or cardiac death in several cohort studies (13). The updated CAD-RADS classification follows an established framework of stenosis and plaque burden for patients with stable chest pain (14). Therefore, the degree of stenosis is an important and practical marker for evaluating and predicting the risk of progression of coronary artery atherosclerosis in clinical practice, especially when institutional protocols do not include CAC score to assess total coronary plaque burden.

The precise mechanism that can explain the association between blood lipid variability and the risk of cardiovascular events warrants further investigation. One possible explanation is that greater variability in LDL-cholesterol levels hinders lipid efflux from atheroma and finally leads to plaque vulnerability and progression at the vascular wall (1,6,15). Genetic variants may contribute the link of lipid variability and coronary artery stenosis. In a recent study of statin-naive Koreans, some single nucleotide polymorphisms (SNPs) related to LDL-cholesterol variability or HDL-cholesterol variability were found to be associated with advanced coronary artery stenosis (16). It is also unknown whether the same mechanism causes higher variability of LDL-cholesterol, HDL-cholesterol, and triglycerides, indicating that variability of one lipid measurement did not correlate well with the variability of the others in the Treating to New Targets (TNT) trial (8).

In comparison to LDL-cholesterol variability, the effect of HDL-cholesterol variability on coronary artery stenosis progression has not been studied. Our results suggest that greater HDL cholesterol variability may protect against stenosis progression. This contradicts the findings from the TNT trial, which showed that higher variability for HDL cholesterol was associated with an increased risk of coronary events (8). Although plasma HDL-cholesterol concentrations correlated negatively with atherosclerotic CVD risk, an increase in plasma HDL-cholesterol concentrations with pharmacological intervention did not reduce the risk of coronary heart disease events or related death (17). Moreover, recently, a U-shaped relationship has been found between plasma HDL cholesterol and all-cause mortality (18,19). Further research is needed to determine whether HDL variability is related to HDL functioning as an antioxidant and an acceptor of macrophage cholesterol efflux (20,21).

Meanwhile, the variability in BMI, WC, plasma glucose, and BP was not associated with coronary artery stenosis progression in our study. Previous studies in the general Korean population who did not have diabetes, hypertension, and dyslipidaemia showed that high variability in BMI, systolic BP, plasma glucose, and total cholesterol were independent predictors of all-cause mortality, myocardial infarction, stroke, heart failure, and atrial fibrillation (2,3,22). As the number of metabolic risk factors with higher variability increased, mortality and cardiovascular morbidity tended to increase further, but the correlation of variability between these risk factors was not high in the previous studies (3,23). Our results suggest that lipid variability may be more strongly associated with the progression of coronary artery stenosis than other risk factors. The variability of multiple cardiovascular risk factors eventually increases mortality and morbidity, but there appears to be no common mechanism at work among the factors, and various pathways are intricately linked; for example, body weight fluctuations are associated with diabetes, which increases the risk of CVD morbidity (24). In a recent study, BP variability influenced the morphology and composition of coronary plaques by inflammation and haemodynamics (25). On the one hand, it has been argued that BP variability is an early marker of epiphenomenon of frailty in older adults (26). Future research is needed on the mechanisms and interrelationships of the variability of each risk factor on coronary artery atherosclerosis progression.

Conclusions

High intra-individual variability in LDL-cholesterol and triglyceride levels were independent predictors of coronary artery stenosis progression on CCTA follow up in an asymptomatic health examination cohort. These results highlight the importance of maintaining stable blood lipid profiles to prevent the progression of coronary artery stenosis in the general clinical setting. The mechanisms underlying these associations should be investigated in future studies.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-75/rc

Data Sharing Statement: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-75/dss

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-75/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-75/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Haeundae Paik Hospital Institutional Review Board (No. HPIRB 2021-07-021). The requirement of informed consent was waived due to the retrospective nature of the analyses.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bangalore S, Breazna A, DeMicco DA, et al. Visit-to-visit low-density lipoprotein cholesterol variability and risk of cardiovascular outcomes: insights from the TNT trial. J Am Coll Cardiol 2015;65:1539-48. [Crossref] [PubMed]
2. Kwon S, Lee SR, Choi EK, et al. Visit-to-visit variability of metabolic parameters and risk of heart failure: A nationwide population-based study. Int J Cardiol 2019;293:153-8. [Crossref] [PubMed]
3. Kim MK, Han K, Park YM, et al. Associations of Variability in Blood Pressure, Glucose and Cholesterol Concentrations, and Body Mass Index With Mortality and Cardiovascular Outcomes in the General Population. Circulation 2018;138:2627-37. [Crossref] [PubMed]
4. Chen H, Chen Y, Wu W, et al. Effect of visit-to-visit blood pressure variability on cardiovascular events in populations with different body mass indexes: a prospective cohort study. BMJ Open 2020;10:e035836. [Crossref] [PubMed]
5. Seo SM, Chung WB, Choi IJ, et al. Visit-to-visit variability of systolic blood pressure predicts all-cause mortality in patients received percutaneous coronary intervention with drug-eluting stents. Heart Vessels 2018;33:489-97. [Crossref] [PubMed]
6. Clark D 3rd, Nicholls SJ, St John J, et al. Visit-to-visit cholesterol variability correlates with coronary atheroma progression and clinical outcomes. Eur Heart J 2018;39:2551-8. [Crossref] [PubMed]
7. Cury RC, Abbara S, Achenbach S, et al. CAD-RADS(TM) Coronary Artery Disease - Reporting and Data System. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr 2016;10:269-81. [Crossref] [PubMed]
8. Waters DD, Bangalore S, Fayyad R, et al. Visit-to-visit variability of lipid measurements as predictors of cardiovascular events. J Clin Lipidol 2018;12:356-66. [Crossref] [PubMed]
9. Sheng CS, Miao Y, Ding L, et al. Prognostic significance of visit-to-visit variability, and maximum and minimum LDL cholesterol in diabetes mellitus. Lipids Health Dis 2022;21:19. [Crossref] [PubMed]
10. Kataoka Y, St John J, Wolski K, et al. Atheroma progression in hyporesponders to statin therapy. Arterioscler Thromb Vasc Biol 2015;35:990-5. [Crossref] [PubMed]
11. Ferencik M, Mayrhofer T, Bittner DO, et al. Use of High-Risk Coronary Atherosclerotic Plaque Detection for Risk Stratification of Patients With Stable Chest Pain: A Secondary Analysis of the PROMISE Randomized Clinical Trial. JAMA Cardiol 2018;3:144-52. [Crossref] [PubMed]
12. Lee SE, Sung JM, Andreini D, et al. Differences in Progression to Obstructive Lesions per High-Risk Plaque Features and Plaque Volumes With CCTA. JACC Cardiovasc Imaging 2020;13:1409-17. [Crossref] [PubMed]
13. Achenbach S. Imaging the Vulnerable Plaque on Coronary CTA. JACC Cardiovasc Imaging 2020;13:1418-21. [Crossref] [PubMed]
14. Cury RC, Leipsic J, Abbara S, et al. CAD-RADS™ 2.0 - 2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI). J Cardiovasc Comput Tomogr 2022;16:536-57. [Crossref] [PubMed]
15. Rohatgi A, Khera A, Berry JD, et al. HDL cholesterol efflux capacity and incident cardiovascular events. N Engl J Med 2014;371:2383-93. [Crossref] [PubMed]
16. Park JB, Shin E, Lee JE, et al. Genetic Determinants of Visit-to-Visit Lipid Variability: Genome-Wide Association Study in Statin-Naïve Korean Population. Front Cardiovasc Med 2022;9:811657. Erratum in: Front Cardiovasc Med 2022;9:869777. [Crossref] [PubMed]
17. Briel M, Ferreira-Gonzalez I, You JJ, et al. Association between change in high density lipoprotein cholesterol and cardiovascular disease morbidity and mortality: systematic review and meta-regression analysis. BMJ 2009;338:b92. [Crossref] [PubMed]
18. Madsen CM, Varbo A, Nordestgaard BG. Extreme high high-density lipoprotein cholesterol is paradoxically associated with high mortality in men and women: two prospective cohort studies. Eur Heart J 2017;38:2478-86. [Crossref] [PubMed]
19. Oh IH, Hur JK, Ryoo JH, et al. Very high high-density lipoprotein cholesterol is associated with increased all-cause mortality in South Koreans. Atherosclerosis 2019;283:43-51. [Crossref] [PubMed]
20. Pownall HJ, Rosales C, Gillard BK, et al. High-density lipoproteins, reverse cholesterol transport and atherogenesis. Nat Rev Cardiol 2021;18:712-23. [Crossref] [PubMed]
21. Soria-Florido MT, Schröder H, Grau M, et al. High density lipoprotein functionality and cardiovascular events and mortality: A systematic review and meta-analysis. Atherosclerosis 2020;302:36-42. [Crossref] [PubMed]
22. Lee SR, Choi EK, Han KD, et al. Effect of the variability of blood pressure, glucose level, total cholesterol level, and body mass index on the risk of atrial fibrillation in a healthy population. Heart Rhythm 2020;17:12-9. [Crossref] [PubMed]
23. Bangalore S, Fayyad R, Messerli FH, et al. Relation of Variability of Low-Density Lipoprotein Cholesterol and Blood Pressure to Events in Patients With Previous Myocardial Infarction from the IDEAL Trial. Am J Cardiol 2017;119:379-87. [Crossref] [PubMed]
24. Yeboah P, Hsu FC, Bertoni AG, et al. Body Mass Index, Change in Weight, Body Weight Variability and Outcomes in Type 2 Diabetes Mellitus (from the ACCORD Trial). Am J Cardiol 2019;123:576-81. [Crossref] [PubMed]
25. Liu Y, Luo X, Jia H, et al. The Effect of Blood Pressure Variability on Coronary Atherosclerosis Plaques. Front Cardiovasc Med 2022;9:803810. [Crossref] [PubMed]
26. Rouch L, De Souto Barreto P, Hanon O, et al. Visit-to-Visit Blood Pressure Variability and Incident Frailty in Older Adults. J Gerontol A Biol Sci Med Sci 2021;76:1369-75. [Crossref] [PubMed]