Performance of Methods to Estimate Low-Density Lipoprotein Cholesterol in Women With and Without HIV Infection

The Impact of Regular Screening and Lifestyle Modification on Cardiovascular Disease Risk Factors in South African Women Living With HIV

BACKGROUND

The Integration of cardiovascular disease SCreening and prevention in the HIV MAnagement plan for women of reproductive age study set out to determine the effectiveness of screening and lifestyle modification in modifying cardiovascular disease (CVD) risk factors in women living with HIV (WLHIV).

METHODS

In this prospective, quasiexperimental, intervention study, WLHIV aged 18-<50 years were enrolled from 2 clinics (intervention [I-arm]) and (control arms [C-arm]) in Umlazi, South Africa, between November 2018 and May 2019. Women in the I-arm received lifestyle modification advice on diet, physical activity, alcohol use, and smoking cessation and underwent annual screening for CVD risk. The CVD risk factors were assessed through standardized questionnaires and clinical and laboratory procedures at baseline and at end of 3 years of follow-up. Prevalence of metabolic syndrome and other CVD indices were compared between arms at end-of-study (EOS).

RESULTS

Total of 269 WLHIV (149 I-arm and 120 C-arm) with a mean ± SD age of 36 ± 1 years were included in the EOS analyses after 32 ± 2 months of follow-up. The metabolic syndrome prevalence at EOS was 16.8% (25/149) in the I-arm and 24% (24/120) in the C-arm (risk ratio 0.9; 95% CI: 0.5 to 1.1; P 0.86). Proportion of women with fasting blood glucose >5.6 mmol/L in the I-arm and C-arm were 2.7% (4/149) and 13.3% (16/120) respectively (risk ratio 0.2; 95% CI: 0.069 to 0.646; P < 0.01). High-density lipoprotein improved with the intervention arm from baseline to EOS (95% CI: -0.157 to -0.034; P < 0.05).

CONCLUSIONS

Although there was no significant difference in the prevalence of metabolic syndrome between study arms, we observed decreased blood glucose levels in the I-arm compared with the C-arm and improved high-density lipoprotein within the I-arm, following lifestyle modification and regular screening for CVD risk factors in WLHIV.

How should low-density lipoprotein cholesterol be calculated in 2022?

PURPOSE OF REVIEW

The reference method for low-density lipoprotein-cholesterol (LDL-C) quantitation is β-quantification, a technically demanding method that is not convenient for routine use. Indirect calculation methods to estimate LDL-C, including the Friedewald equation, have been used since 1972. This calculation has several recognized limitations, especially inaccurate results for triglycerides (TG) >4.5 mmol/l (>400 mg/dl). In view of this, several other equations were developed across the world in different datasets.The purpose of this review was to analyze the best method to calculate LDL-C in clinical practice by reviewing studies that compared equations with measured LDL-C.

RECENT FINDINGS

We identified 45 studies that compared these formulae. The Martin/Hopkins equation uses an adjustable factor for TG:very low-density lipoprotein-cholesterol ratios, validated in a large dataset and demonstrated to provide more accurate LDL-C calculation, especially when LDL <1.81 mmol/l (<70 mg/dl) and with elevated TG. However, it is not in widespread international use because of the need for further validation and the use of the adjustable factor. The Sampson equation was developed for patients with TG up to 9 mmol/l (800 mg/dl) and was based on β-quantification and performs well on high TG, postprandial and low LDL-C samples similar to direct LDL-C.

SUMMARY

The choice of equation should take into the level of triglycerides. Further validation of different equations is required in different populations.

Machine Learning Estimation of Low-Density Lipoprotein Cholesterol in Women With and Without HIV

INTRODUCTION

Low-density lipoprotein cholesterol (LDL-C) is typically estimated from total cholesterol, high-density lipoprotein cholesterol, and triglycerides. The Friedewald, Martin-Hopkins, and National Institutes of Health equations are widely used but may estimate LDL-C inaccurately in certain patient populations, such as those with HIV. We sought to investigate the utility of machine learning for LDL-C estimation in a large cohort of women with and without HIV.

METHODS

We identified 7397 direct LDL-C measurements (5219 from HIV-infected individuals, 2127 from uninfected controls, and 51 from seroconvertors) from 2414 participants (age 39.4 ± 9.3 years) in the Women's Interagency HIV Study and estimated LDL-C using the Friedewald, Martin-Hopkins, and National Institutes of Health equations. We also optimized 5 machine learning methods [linear regression, random forest, gradient boosting, support vector machine (SVM), and neural network] using 80% of the data (training set). We compared the performance of each method using root mean square error, mean absolute error, and coefficient of determination (R2) in the holdout (20%) set.

RESULTS

SVM outperformed all 3 existing equations and other machine learning methods, achieving the lowest root mean square error and mean absolute error, and the highest R2 (11.79 and 7.98 mg/dL, 0.87, respectively, compared with those obtained using the Friedewald equation: 12.45 and 9.14 mg/dL, 0.87). SVM performance remained superior in subgroups with and without HIV, with nonfasting measurements, in LDL <70 mg/dL and triglycerides > 400 mg/dL.

CONCLUSIONS

In this proof-of-concept study, SVM is a robust method that predicts directly measured LDL-C more accurately than clinically used methods in women with and without HIV. Further studies should explore the utility in broader populations.