Visit-to-visit lipid variability: Clinical significance, effects of lipid-lowering treatment, and (pharmaco) genetics

Long-term visit-to-visit variability in low-density lipoprotein cholesterol is associated with poor cardiovascular and kidney outcomes in patients with primary nephrotic syndrome

PURPOSE

It is unclear whether long-term variability in low-density lipoprotein cholesterol (LDL-C) is associated with clinical outcomes in patients with nephrotic syndrome (NS).

METHODS

A large cohort of 1100 patients with primary NS underwent treatment and regular follow-up. Long-term variability in LDL-C was assessed by calculating its weighted standard deviation (w-SD). The primary endpoints of this study were the occurrence of arteriosclerotic cardiovascular disease (ASCVD) or kidney dysfunction. Factors associated with the w-SD of LDL-C were evaluated by linear regression. Associations of the w-SD of LDL-C with clinical outcomes were evaluated by Cox proportional hazards regression.

RESULTS

Over a median follow-up of 44.8 (interquartile range, 26.8, 70.1) months, 198 patients developed ASCVD (45.9 cases per 1,000 patient-years), and 84 patients developed kidney dysfunction (17.6 cases per 1,000 patient-years). The incidence rates of the primary outcomes increased across the quartiles of the w-SD of LDL-C (log-rank, P < 0.001). Multivariate Cox regression analysis showed that higher LDL-C variability was associated with an increased risk of ASCVD [hazard ratio (HR), 2.236; 95% confidence interval (CI), 1.684-2.969, P < 0.001] and an increased risk of kidney dysfunction (HR, 3.047; 95% CI 2.240-4.144, P < 0.001). The results were similar after adjusting the w-SD of LDL-C by its related parameters (baseline and mean LDL-C as well as mean total cholesterol), although the mean LDL-C was also an independent risk factor for ASCVD and kidney dysfunction.

CONCLUSION

Long-term variability in LDL-C was independently associated with the risk of ASCVD and kidney dysfunction in NS patients.

WiSER: Robust and scalable estimation and inference of within-subject variances from intensive longitudinal data

The availability of vast amounts of longitudinal data from electronic health records (EHRs) and personal wearable devices opens the door to numerous new research questions. In many studies, individual variability of a longitudinal outcome is as important as the mean. Blood pressure fluctuations, glycemic variations, and mood swings are prime examples where it is critical to identify factors that affect the within-individual variability. We propose a scalable method, within-subject variance estimator by robust regression (WiSER), for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. It is robust against the misspecification of the conditional distribution of responses or the distribution of random effects. It shows similar performance as the correctly specified likelihood methods but is 103 ∼ 105 times faster. The estimation algorithm scales linearly in the total number of observations, making it applicable to massive longitudinal data sets. The effectiveness of WiSER is evaluated in extensive simulation studies. Its broad applicability is illustrated using the accelerometry data from the Women's Health Study and a clinical trial for longitudinal diabetes care.

Association between visit-to-visit lipid variability and incident cancer: a population-based cohort study

Dyslipidaemia is associated with increased cancer risk. However, the prognostic value of visit-to-visit lipid variability (VVLV) is unexplored in this regard. To investigate the associations between VVLV and the risk of incident cancer, we conducted a retrospective cohort study on adult patients attending a family medicine clinic in Hong Kong during 2000-2003, excluding those with <3 tests for low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, and total cholesterol (TC) each, those with prior cancer diagnosis, and those with <1 year of follow-up. Visit-to-visit LDL-C, HDL-C, TC, and triglycerides variabilities were measured by the coefficient of variation (CV). Patients were followed up until 31^st December 2019 for the primary outcome of incident cancer. Altogether, 69,186 patients were included (26,679 males (38.6%); mean age 60±13 years; mean follow-up 16±3 years); 7958 patients (11.5%) had incident cancer. Higher variability of LDL-C, HDL-C, TC, and TG was associated with higher risk of incident cancer. Patients in the third tercile of the CV of LDL-C (adjusted hazard ratio (aHR) against first tercile 1.06 [1.00, 1.12], p=0.049), HDL-C (aHR 1.37 [1.29, 1.44], p<0.001), TC (aHR 1.10 [1.04, 1.17], p=0.001), and TG (aHR 1.11 [1.06, 1.18], p<0.001) had the highest risks of incident cancer. Among these, only HDL-C variability remained associated with the risk of incident cancer in users of statins/fibrates. To conclude, higher VVLV was associated with significantly higher long-term risks of incident cancer. VVLV may be a clinically useful tool for cancer risk stratification.

GWAS of longitudinal trajectories at biobank scale

Biobanks linked to massive, longitudinal electronic health record (EHR) data make numerous new genetic research questions feasible. One among these is the study of biomarker trajectories. For example, high blood pressure measurements over visits strongly predict stroke onset, and consistently high fasting glucose and Hb1Ac levels define diabetes. Recent research reveals that not only the mean level of biomarker trajectories but also their fluctuations, or within-subject (WS) variability, are risk factors for many diseases. Glycemic variation, for instance, is recently considered an important clinical metric in diabetes management. It is crucial to identify the genetic factors that shift the mean or alter the WS variability of a biomarker trajectory. Compared to traditional cross-sectional studies, trajectory analysis utilizes more data points and captures a complete picture of the impact of time-varying factors, including medication history and lifestyle. Currently, there are no efficient tools for genome-wide association studies (GWASs) of biomarker trajectories at the biobank scale, even for just mean effects. We propose TrajGWAS, a linear mixed effect model-based method for testing genetic effects that shift the mean or alter the WS variability of a biomarker trajectory. It is scalable to biobank data with 100,000 to 1,000,000 individuals and many longitudinal measurements and robust to distributional assumptions. Simulation studies corroborate that TrajGWAS controls the type I error rate and is powerful. Analysis of eleven biomarkers measured longitudinally and extracted from UK Biobank primary care data for more than 150,000 participants with 1,800,000 observations reveals loci that significantly alter the mean or WS variability.

Elevation of Hemoglobin A1c Increases the Atherosclerotic Plaque Vulnerability and the Visit-to-Visit Variability of Lipid Profiles in Patients Who Underwent Elective Percutaneous Coronary Intervention

Background

Increased plaque vulnerability and higher lipid variability are causes of adverse cardiovascular events. Despite a close association between glucose and lipid metabolisms, the influence of elevated glycated hemoglobin A1c (HbA1c) on plaque vulnerability and lipid variability remains unclear.

Methods

Among subjects undergoing percutaneous coronary intervention (PCI) from 2009 through 2019, 366 patients received intravascular optical coherence tomography (OCT) assessment and 4,445 patients underwent the scheduled follow-ups within 1 year after PCI. Vulnerability features of culprit vessels were analyzed by OCT examination, including the assessment of lipid, macrophage, calcium, and minimal fibrous cap thickness (FCT). Visit-to-visit lipid variability was determined by different definitions including standard deviation (SD), coefficient of variation (CV), and variability independent of the mean (VIM). Multivariable linear regression analysis was used to verify the influence of HbA1c on plaque vulnerability features and lipid variability. Exploratory analyses were also performed in non-diabetic patients.

Results

Among enrolled subjects, the pre-procedure HbA1c was 5.90 ± 1.31%, and the average follow-up HbA1c was 5.98 ± 1.16%. By OCT assessment, multivariable linear regression analyses demonstrated that patients with elevated HbA1c had a thinner minimal FCT (β = −6.985, P = 0.048), greater lipid index (LI) (β = 226.299, P = 0.005), and higher macrophage index (β = 54.526, P = 0.045). Even in non-diabetic patients, elevated HbA1c also linearly decreased minimal FCT (β = −14.011, P = 0.036), increased LI (β = 290.048, P = 0.041) and macrophage index (β = 120.029, P = 0.048). Subsequently, scheduled follow-ups were performed during 1-year following PCI. Multivariable linear regression analyses proved that elevated average follow-up HbA1c levels increased the VIM of lipid profiles, including low-density lipoprotein cholesterol (β = 2.594, P < 0.001), high-density lipoprotein cholesterol (β = 0.461, P = 0.044), non-high-density lipoprotein cholesterol (β = 1.473, P < 0.001), total cholesterol (β = 0.947, P < 0.001), and triglyceride (β = 4.217, P < 0.001). The result was consistent in non-diabetic patients and was verified when SD and CV were used to estimate variability.

Conclusion

In patients undergoing elective PCI, elevated HbA1c increases the atherosclerotic plaque vulnerability and the visit-to-visit variability of lipid profiles, which is consistent in non-diabetic patients.

Genetic Determinants of Visit-to-Visit Lipid Variability: Genome-Wide Association Study in Statin-Naïve Korean Population

Background and Aim

There is a growing evidence that fluctuation in lipid profiles is important in cardiovascular outcomes. We aimed to identify single nucleotide polymorphism (SNP) variants associated with low-density lipoprotein-cholesterol (LDL-C) and high-density lipoprotein-cholesterol (HDL-C) variability in statin-naïve Korean subjects and evaluate their associations with coronary atherosclerosis.

Methods

In statin-naïve subjects from Gene-Environment of Interaction and phenotype cohort, we performed genome-wide association studies of lipid variability; the discovery (first) and replication (second) sets included 4,287 and 1,086 subjects, respectively. Coronary artery calcium (CAC) score and degree of coronary artery stenosis were used as outcome measures. Cholesterol variability was determined by standard deviation and average successive variability, and significant coronary atherosclerosis was defined as CAC score ≥400 or coronary stenosis ≥70%.

Results

Mean HDL-C and LDL-C level were 54 ± 12 and 123 ± 30 mg/dL in the first set and 53 ± 12 and 126 ± 29 mg/dL in the second set. APOA5 rs662799 and APOA5 rs2266788 were associated with LDL-C variability and PXDNL rs80056520, ALDH2 rs671, HECTD4 rs2074356, and CETP rs2303790 were SNPs associated for HDL-C variability. APOA5 rs662799 passed Bonferroni correction with p-value of 1.789 × 10⁻⁹. Among the SNPs associated with cholesterol variability, rs80056520 and rs2266788 variants were associated with CACS ≥400 and coronary stenosis ≥70% and rs662799 variant was associated with coronary stenosis ≥70%.

Conclusion

Two SNPs associated with LDL-C variability (APOA5 rs662799 and rs2266788) and one SNP associated with HDL-C variability (PXDNL rs80056520) were significantly associated with advanced coronary artery stenosis. Combining GWAS results with imaging parameters, our study may provide a deeper understanding of underlying pathogenic basis of the link between lipid variability and coronary atherosclerosis.

The association of blood lipid parameters variability with ischemic stroke in hypertensive patients

BACKGROUND AND AIMS

The relationship between lipid variability and stroke among patients with hypertension were inconclusive. We aimed to investigate the association of lipid variability with ischemic stroke in hypertensive patients.

METHODS AND RESULTS

This retrospective cohort study included 4995 individuals with hypertension between 2013 and 2015, and recorded their status of ischemic stroke until the end of 2018. The variability in total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured using the standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM) and average absolute difference between successive values (ASV). Multivariate Cox proportional hazards models with hazard ratios (HRs) and 95% confidence interval (CI) were performed. There were 110 cases of ischemic stroke during a median follow up of 4.2 years. The multivariable adjusted HRs and 95% CIs comparing the highest versus the lowest quartiles of SD of TC, LDL-C, HDL-C and TG were 4.429 (95% CI: 2.292, 8.560), 2.140 (95% CI: 1.264, 3.621), 1.368 (95% CI: 0.793, 2.359) and 1.421 (95% CI: 0.800, 2.525), respectively. High variability in TC and LDL-C were associated with a higher risk for ischemic stroke. Similarly, the results were consistent when calculating variability of TC and LDL-C using CV, ASV and VIM, and in various subgroup analyses.

CONCLUSION

Higher variability of TC and LDL-C associated with the risk of ischemic stroke among hypertensive patients. These findings suggest reducing variability of lipid parameters may decrease adverse outcomes.

Lipid Level, Lipid Variability, and Risk of Multiple Myeloma: A Nationwide Population-Based Study of 3,527,776 Subjects

Simple Summary

There is preclinical evidence that abnormalities in lipid metabolism promote cancer development, and a few studies show the association between lipid levels and multiple myeloma (MM). However, to our knowledge, the role of lipid variability as a risk factor for MM has not been evaluated. We investigated whether lipid level and its variability are associated with the development of MM at a population level. Lower baseline lipid levels of total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol and triglycerides, and high variability in high-density lipoprotein cholesterol were all associated with increased risk of developing MM. These findings support the role of lipid metabolism in MM risk.

Abstract

(1) Background: There is evidence that abnormality in lipid metabolism promotes cancer development. This study investigated whether lipid level and its variability are associated with the development of MM at a population level. (2) Methods: A retrospective cohort study included a total of 3,527,776 subjects aged 40 and above who participated in ≥3 health examinations within the previous five years, including the index year (2012–2013). Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) were measured, and visit-to-visit lipid variability were calculated by variability independent of the mean (VIM) method. The study population was followed from the health examination date in the index year until the diagnosis of MM, death, or the last follow-up date (31 December 2017). (3) Results: During a median (5–95%) 5.1 years of follow-up, 969 subjects developed MM. A lower risk of MM was observed with higher quartiles of baseline lipid levels compared to the lowest quartile group (Q4 vs. Q1: adjusted hazard ratios (aHRs) 0.51, 95% confidence interval (CI) (0.42–0.61) for TC; 0.50 (0.41–0.61) for HDL-C; 0.65 (0.54–0.77) for LDL-C; and 0.72 (0.60–0.87) for TG in model (3). Among all lipid measures, only variability in HDL-C was associated with risk of MM: aHRs (95% CI) were 1.12 (0.91–1.38), 1.19 (0.97–1.46), and 1.34 (1.09–1.65) in the Q2, Q3, and Q4, respectively, compared to the Q1 of VIM of HDL-C. (4) Conclusions: This study shows that patients with lower lipid levels and high HDL-C variability are at increased risk of developing MM.

Effects of Cardiovascular Risk Factor Variability on Health Outcomes

Innumerable studies have suggested "the lower, the better" for cardiovascular risk factors, such as body weight, lipid profile, blood pressure, and blood glucose, in terms of health outcomes. However, excessively low levels of these parameters cause health problems, as seen in cachexia, hypoglycemia, and hypotension. Body weight fluctuation is related to mortality, diabetes, obesity, cardiovascular disease, and cancer, although contradictory findings have been reported. High lipid variability is associated with increased mortality and elevated risks of cardiovascular disease, diabetes, end-stage renal disease, and dementia. High blood pressure variability is associated with increased mortality, myocardial infarction, hospitalization, and dementia, which may be caused by hypotension. Furthermore, high glucose variability, which can be measured by continuous glucose monitoring systems or self-monitoring of blood glucose levels, is associated with increased mortality, microvascular and macrovascular complications of diabetes, and hypoglycemic events, leading to hospitalization. Variability in metabolic parameters could be affected by medications, such as statins, antihypertensives, and hypoglycemic agents, and changes in lifestyle patterns. However, other mechanisms modify the relationships between biological variability and various health outcomes. In this study, we review recent evidence regarding the role of variability in metabolic parameters and discuss the clinical implications of these findings.

Biomarker variability and cardiovascular disease residual risk

PURPOSE OF REVIEW

A number of cohorts and clinical trials have reported observing associations between intraindividual variation of biomarkers and manifestations of cardiovascular disease (CVD).

RECENT FINDINGS

Intraindividual (or 'visit-to-visit') variability of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), apolipoprotein B, high-density lipoprotein cholesterol (HDL-C), non-HDL-C, and triglyceride have all been found to associate with CVD outcomes, independent of their mean absolute levels, independent of each other, and independent of other traditional risk factors. These findings have been confirmed recently in large cohort studies in different populations, and in post-hoc analyses of clinical trial data. Lipoprotein variability has been associated with myocardial infarction, other arterial disease including cerebrovascular, and with cardiovascular and overall mortality. The association of higher variability of LDL-C with atheroma progression has also been assessed directly using intravascular ultrasound and carotid intima-media thickness. The lipoprotein variability of an individual contributes to their residual risk of CVD, although the mechanism remains unclear.

SUMMARY

There is compelling evidence that lipoprotein variability contributes to residual risk; however, a more standardized approach is required before the risk attributable to variability can be assessed effectively.

Intra-individual variability of total cholesterol is associated with cardiovascular disease mortality: A cohort study

BACKGROUND AND AIMS

The relationship between serum total cholesterol (TC) and mortality remains inconsistent. Additionally, intra-individual variability of cholesterol has been of increasing interest as a new indicator for health outcomes. We aimed to examine the association between TC and its variability and risk of mortality.

METHODS AND RESULTS

We performed a retrospective cohort study with 122,645 individuals aged over 40 years in Ningbo, China. The intra-individual variability was calculated using four metrics including standard deviation, coefficient variation, variation independent of mean and average successive variability. Hazard ratios and 95% confidence intervals were estimated for the associations of baseline and variability in TC with risk of mortality by Cox proportional hazards regression models. During 591,585.3 person-years of follow-up, 4563 deaths (including 1365 from cardiovascular disease, 788 from stroke and 1514 from cancer) occurred. A U-shaped association was observed for baseline TC level and risk of total, cardiovascular and cancer mortality, with lowest mortality at 5.46 mmol/L, 5.04 mmol/L and 5.51 mmol/L, respectively. As compared with subjects with TC variability in the lowest quartile, individuals in the highest quartile had 21% higher risk of all-cause mortality (HR = 1.21, 95% CI: 1.05 to 1.40), and 41% higher risk of CVD mortality (HR = 1.41, 95%CI: 1.10 to 1.81).

CONCLUSION

Both too low and too high baseline TC level were associated with higher risk of total, cardiovascular disease and cancer mortality. Variability of TC could be a risk factor of total and CVD mortality, independent of mean TC level. Future studies are needed to confirm these findings.