Adipose Tissue Inflammation and Cardiovascular Disease: An Update.

PURPOSE OF REVIEW: The obesity epidemic is on the rise, and while it is well known that obesity is associated with an increase in cardiovascular risk factors such as type 2 diabetes mellitus, hypertension, and obstructive sleep apnea, recent data has highlighted that the degree and type of fat distribution may play a bigger role in the pathogenesis of cardiovascular disease (CVD) than body mass index (BMI) alone. We aim to review updated data on adipose tissue inflammation and distribution and CVD. RECENT FINDINGS: We review the pathophysiology of inflammation secondary to adipose tissue, the association of obesity-related adipokines and CVD, and the differences and significance of brown versus white adipose tissue. We delve into the clinical manifestations of obesity-related inflammation in CVD. We discuss the available data on heterogeneity of adipose tissue-related inflammation with a focus on subcutaneous versus visceral adipose tissue, the differential pathophysiology, and clinical CVD manifestations of adipose tissue across sex, race, and ethnicity. Finally, we present the available data on lifestyle modification, medical, and surgical therapeutics on reduction of obesity-related inflammation. Obesity leads to a state of chronic inflammation which significantly increases the risk for CVD. More research is needed to develop non-invasive VAT quantification indices such as risk calculators which include variables such as sex, age, race, ethnicity, and VAT concentration, along with other well-known CVD risk factors in order to comprehensively determine risk of CVD in obese patients. Finally, pre-clinical biomarkers such as pro-inflammatory adipokines should be validated to estimate risk of CVD in obese patients.

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.

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

BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is estimated from total cholesterol, high-density lipoprotein cholesterol and triglycerides using predefined equations which assume fixed or varying relationships between these parameters and may underestimate or overestimate LDL-C. Data on the performance of these equations in persons with HIV are limited. We sought to investigate the utility of the 3 most widely used methods (Friedewald, Hopkins, and the recently proposed NIH equation) to predict LDL-C in persons with HIV. METHODS: We identified 7397 direct LDL-C (5219 HIV, 2127 uninfected controls, 51 seroconvertors) measurements in the Women's Interagency HIV Study, and used the 3 equations (Friedewald, Hopkins, and NIH) to calculate LDL-C. We compared the performance of the 3 equations using root mean square error and coefficient of determination (R2). RESULTS: Overall, the Friedewald equation had the best performance characteristics, outperforming Hopkins and NIH methods with lower root mean square error and higher R2 at lower triglyceride levels. However, this association did not hold true at higher triglyceride levels (quartiles 3 and 4), whereas the Hopkins equation had better performance characteristics in quartile 3, none of the 3 equations were optimal in quartile 4. After adjusting for fasting status and triglycerides levels, HIV+ had larger mean difference compared with directly measured LDL using all 3 methods. CONCLUSIONS: All 3 methods have lower accuracy in HIV+ vs HIV- women, even after adjusting for triglyceride levels and fasting status. Further research should focus on identifying methods to estimate LDL-C in HIV.