Personalizing cholesterol treatment recommendations for primary cardiovascular disease prevention.

Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. Optimal statin therapy decisions rely on shared decision making and may be uncertain for a given patient. In areas of clinical uncertainty, personalized approaches based on real-world data may help inform treatment decisions. We sought to develop a personalized statin recommendation approach for primary ASCVD prevention based on historical real-world outcomes in similar patients. Our retrospective cohort included adults from a large Northern California electronic health record (EHR) aged 40-79 years with no prior cardiovascular disease or statin use. The cohort was split into training and test sets. Weighted-K-nearest-neighbor (wKNN) regression models were used to identify historical EHR patients similar to a candidate patient. We modeled four statin decisions for each patient: none, low-intensity, moderate-intensity, and high-intensity. For each candidate patient, the algorithm recommended the statin decision that was associated with the greatest percentage reduction in LDL-C after 1 year in similar patients. The overall cohort consisted of 50,576 patients (age 54.6 ± 9.8 years) with 55% female, 48% non-Hispanic White, 32% Asian, and 7.4% Hispanic patients. Among 8383 test-set patients, 52%, 44%, and 4% were recommended high-, moderate-, and low-intensity statins, respectively, for a maximum predicted average 1-yr LDL-C reduction of 16.9%, 20.4%, and 14.9%, in each group, respectively. Overall, using aggregate EHR data, a personalized statin recommendation approach identified the statin intensity associated with the greatest LDL-C reduction in historical patients similar to a candidate patient. Recommendations included low- or moderate-intensity statins for maximum LDL-C lowering in nearly half the test set, which is discordant with their expected guideline-based efficacy. A data-driven personalized statin recommendation approach may inform shared decision making in areas of uncertainty, and highlight unexpected efficacy-effectiveness gaps.

Leptin Decreases Energy Expenditure Despite Increased Thyroid Hormone in Patients With Lipodystrophy.

CONTEXT: Leptin is an adipokine that signals energy sufficiency. In rodents, leptin deficiency decreases energy expenditure (EE), which is corrected following leptin replacement. In humans, data are mixed regarding leptin-mediated effects on EE. OBJECTIVE: To determine the effects of metreleptin on EE in patients with lipodystrophy. DESIGN, SETTING, AND PATIENTS: Nonrandomized crossover study of 25 patients with lipodystrophy (National Institutes of Health, 2013-2018). INTERVENTION: The initiation cohort consisted of 17 patients without prior exposure to metreleptin, studied before and after 14 days of metreleptin. The withdrawal cohort consisted of 8 previously metreleptin-treated patients, studied before and after 14 days of metreleptin withdrawal. MAIN OUTCOMES: 24-h total energy expenditure (TEE), resting energy expenditure (REE), autonomic nervous system activity [heart rate variability (HrV)], plasma-free triiodothyronine (T3), free thyroxine (T4), epinephrine, norepinephrine, and dopamine. RESULTS: In the initiation cohort, TEE and REE decreased by 5.0% (121 ±â€…152 kcal/day; P = 0.006) and 5.9% (120 ±â€…175 kcal/day; P = 0.02). Free T3 increased by 19.4% (40 ±â€…49 pg/dL; P = 0.01). No changes in catecholamines or HrV were observed. In the withdrawal cohort, free T3 decreased by 8.0% (P = 0.04), free T4 decreased by 11.9% (P = 0.002), and norepinephrine decreased by 34.2% (P = 0.03), but no changes in EE, epinephrine, dopamine, or HrV were observed. CONCLUSIONS: Metreleptin initiation decreased EE in patients with lipodystrophy, but no changes were observed after metreleptin withdrawal. Thyroid hormone was higher on metreleptin in both initiation and withdrawal cohorts. Decreased EE after metreleptin in lipodystrophy may result from reductions in energy-requiring metabolic processes that counteract increases in EE via adipose tissue-specific neuroendocrine and adrenergic signaling.

County-Level Factors Associated With Cardiovascular Mortality by Race/Ethnicity.

Background Persistent racial/ethnic disparities in cardiovascular disease (CVD) mortality are partially explained by healthcare access and socioeconomic, demographic, and behavioral factors. Little is known about the association between race/ethnicity-specific CVD mortality and county-level factors. Methods and Results Using 2017 county-level data, we studied the association between race/ethnicity-specific CVD age-adjusted mortality rate (AAMR) and county-level factors (demographics, census region, socioeconomics, CVD risk factors, and healthcare access). Univariate and multivariable linear regressions were used to estimate the association between these factors; R2 values were used to assess the factors that accounted for the greatest variation in CVD AAMR by race/ethnicity (non-Hispanic White, non-Hispanic Black, and Hispanic/Latinx individuals). There were 659 740 CVD deaths among non-Hispanic White individuals in 2698 counties; 100 475 deaths among non-Hispanic Black individuals in 717 counties; and 49 493 deaths among Hispanic/Latinx individuals across 267 counties. Non-Hispanic Black individuals had the highest mean CVD AAMR (320.04 deaths per 100 000 individuals), whereas Hispanic/Latinx individuals had the lowest (168.42 deaths per 100 000 individuals). The highest CVD AAMRs across all racial/ethnic groups were observed in the South. In unadjusted analyses, the greatest variation (R2) in CVD AAMR was explained by physical inactivity for non-Hispanic White individuals (32.3%), median household income for non-Hispanic Black individuals (24.7%), and population size for Hispanic/Latinx individuals (28.4%). In multivariable regressions using county-level factor categories, the greatest variation in CVD AAMR was explained by CVD risk factors for non-Hispanic White individuals (35.3%), socioeconomic factors for non-Hispanic Black (25.8%), and demographic factors for Hispanic/Latinx individuals (34.9%). Conclusions The associations between race/ethnicity-specific age-adjusted CVD mortality and county-level factors differ significantly. Interventions to reduce disparities may benefit from being designed accordingly.

The Hispanic paradox in the prevalence of obesity at the county-level.

OBJECTIVE: The percentage of Hispanics in a county has a negative association with prevalence of obesity. Because Hispanic individuals are unevenly distributed in the United States, this study examined whether this protective association persists when stratifying counties into quartiles based on the size of the Hispanic population and after adjusting for county-level demographic, socioeconomic, healthcare, and environmental factors. METHODS: Data were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings. Counties were categorized into quartiles based on their percentage of Hispanics, 0%-5% (n = 1794), 5%-20% (n = 962), 20%-50% (n = 283), and >50% (n = 99). For each quartile, univariate and multivariate regression models were used to evaluate the association between prevalence of obesity and demographic, socioeconomic, healthcare, and environmental factors. RESULTS: Counties with the top quartile of Hispanic individuals had the lowest prevalence of obesity compared to counties at the bottom quartile (28.4 ± 3.6% vs. 32.7 ± 4.0%). There was a negative association between county-level percentage of Hispanics and prevalence of obesity in unadjusted analyses that persisted after adjusting for all county-level factors. CONCLUSIONS: Counties with a higher percentage of Hispanics have lower levels of obesity, even after controlling for demographic, socioeconomic, healthcare, and environmental factors. More research is needed to elucidate why having more Hispanics in a county may be protective against county-level obesity.

Multimethod, multidataset analysis reveals paradoxical relationships between sociodemographic factors, Hispanic ethnicity and diabetes.

INTRODUCTION: Population-level and individual-level analyses have strengths and limitations as do 'blackbox' machine learning (ML) and traditional, interpretable models. Diabetes mellitus (DM) is a leading cause of morbidity and mortality with complex sociodemographic dynamics that have not been analyzed in a way that leverages population-level and individual-level data as well as traditional epidemiological and ML models. We analyzed complementary individual-level and county-level datasets with both regression and ML methods to study the association between sociodemographic factors and DM. RESEARCH DESIGN AND METHODS: County-level DM prevalence, demographics, and socioeconomic status (SES) factors were extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data. Analogous individual-level data were extracted from 2007 to 2016 National Health and Nutrition Examination Survey studies and corrected for oversampling with survey weights. We used multivariate linear (logistic) regression and ML regression (classification) models for county (individual) data. Regression and ML models were compared using measures of explained variation (area under the receiver operating characteristic curve (AUC) and R2). RESULTS: Among the 3138 counties assessed, the mean DM prevalence was 11.4% (range: 3.0%-21.1%). Among the 12 824 individuals assessed, 1688 met DM criteria (13.2% unweighted; 10.2% weighted). Age, gender, race/ethnicity, income, and education were associated with DM at the county and individual levels. Higher county Hispanic ethnic density was negatively associated with county DM prevalence, while Hispanic ethnicity was positively associated with individual DM. ML outperformed regression in both datasets (mean R2 of 0.679 vs 0.610, respectively (p<0.001) for county-level data; mean AUC of 0.737 vs 0.727 (p<0.0427) for individual-level data). CONCLUSIONS: Hispanic individuals are at higher risk of DM, while counties with larger Hispanic populations have lower DM prevalence. Analyses of population-level and individual-level data with multiple methods may afford more confidence in results and identify areas for further study.

Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models.

Importance: Obesity is a leading cause of high health care expenditures, disability, and premature mortality. Previous studies have documented geographic disparities in obesity prevalence. Objective: To identify county-level factors associated with obesity using traditional epidemiologic and machine learning methods. Design, Setting, and Participants: Cross-sectional study using linear regression models and machine learning models to evaluate the associations between county-level obesity and county-level demographic, socioeconomic, health care, and environmental factors from summarized statistical data extracted from the 2018 Robert Wood Johnson Foundation County Health Rankings and merged with US Census data from each of 3138 US counties. The explanatory power of the linear multivariate regression and the top performing machine learning model were compared using mean R2 measured in 30-fold cross validation. Exposures: County-level demographic factors (population; rural status; census region; and race/ethnicity, sex, and age composition), socioeconomic factors (median income, unemployment rate, and percentage of population with some college education), health care factors (rate of uninsured adults and primary care physicians), and environmental factors (access to healthy foods and access to exercise opportunities). Main Outcomes and Measures: County-level obesity prevalence in 2018, its association with each county-level factor, and the percentage of variation in county-level obesity prevalence explained by linear multivariate and gradient boosting machine regression measured with R2. Results: Among the 3138 counties studied, the mean (range) obesity prevalence was 31.5% (12.8%-47.8%). In multivariate regressions, demographic factors explained 44.9% of variation in obesity prevalence; socioeconomic factors, 33.0%; environmental factors, 15.5%; and health care factors, 9.1%. The county-level factors with the strongest association with obesity were census region, median household income, and percentage of population with some college education. R2 values of univariate regressions of obesity prevalence were 0.238 for census region, 0.218 for median household income, and 0.160 for percentage of population with some college education. Multivariate linear regression and gradient boosting machine regression (the best-performing machine learning model) of obesity prevalence using all county-level demographic, socioeconomic, health care, and environmental factors had R2 values of 0.58 and 0.66, respectively (P < .001). Conclusions and Relevance: Obesity prevalence varies significantly between counties. County-level demographic, socioeconomic, health care, and environmental factors explain the majority of variation in county-level obesity prevalence. Using machine learning models may explain significantly more of the variation in obesity prevalence..

The Impact of Ethnicity on Metabolic Outcomes After Bariatric Surgery.

BACKGROUND: Previous studies have demonstrated that ethnic minority patients experience significant metabolic improvements after bariatric surgery but less so than non-Hispanic whites. Previous research has primarily investigated differences between non-Hispanic white and black patients. Thus, there remains a need to assess differences in diabetic outcomes among other ethnic groups, including Hispanic and Asian patient populations. MATERIALS AND METHODS: A retrospective analysis including 650 patients with type II diabetes mellitus (T2DM), who underwent either laparoscopic Roux-en-Y gastric bypass or laparoscopic sleeve gastrectomy (LSG) procedures, was conducted to understand ethnic disparities in diabetic metabolic outcomes, including weight loss, serum concentrations of glucose, fasting insulin, and hemoglobin A1c (HbA1c). Data were from a single academic institution in northern California. Ethnicity data were self reported. T2DM was defined as having one or more of the following criteria: a fasting glucose concentration >125 mg/dL, HbA1c >6.5%, or taking one or more diabetic oral medications. Diabetes resolution was defined as having a fasting glucose <125 mg/dL, a HbA1c <6.5%, and discontinuation of diabetic oral medications. RESULTS: Within-group comparisons in all ethnic groups showed significant reductions in body mass index, body weight, fasting insulin, fasting glucose, and HbA1c by 6 mo, but Asian patients did not experience further improvement in body mass index or diabetic outcomes at the 12-mo visit. Black patients did not experience additional reductions in fasting insulin or glucose between the 6- and 12-mo visit and their HbA1c significantly increased. Nevertheless, the majority of patients had diabetes remission by the 12-mo postoperative visit (98%, 97%, 98%, and 92% in Non-Hispanic, Hispanic, black, and Asian, respectively). CONCLUSIONS: The results of this study demonstrate that bariatric surgery serves as an effective treatment for normalizing glucose metabolism among patients with T2DM. However, this study suggests that additional interventions that support black and Asian patients with achieving similar metabolic outcomes as non-Hispanic white and Hispanic patients warrant further consideration.

Metreleptin-mediated improvements in insulin sensitivity are independent of food intake in humans with lipodystrophy.

BACKGROUND: Recombinant leptin (metreleptin) ameliorates hyperphagia and metabolic abnormalities in leptin-deficient humans with lipodystrophy. We aimed to determine whether metreleptin improves glucose and lipid metabolism in humans when food intake is held constant. METHODS: Patients with lipodystrophy were hospitalized for 19 days, with food intake held constant by a controlled diet in an inpatient metabolic ward. In a nonrandomized, crossover design, patients previously treated with metreleptin (n = 8) were continued on metreleptin for 5 days and then taken off metreleptin for the next 14 days (withdrawal cohort). This order was reversed in metreleptin-naive patients (n = 14), who were reevaluated after 6 months of metreleptin treatment on an ad libitum diet (initiation cohort). Outcome measurements included insulin sensitivity by hyperinsulinemic-euglycemic clamp, fasting glucose and triglyceride levels, lipolysis measured using isotopic tracers, and liver fat by magnetic resonance spectroscopy. RESULTS: With food intake constant, peripheral insulin sensitivity decreased by 41% after stopping metreleptin for 14 days (withdrawal cohort) and increased by 32% after treatment with metreleptin for 14 days (initiation cohort). In the initiation cohort only, metreleptin decreased fasting glucose by 11% and triglycerides by 41% and increased hepatic insulin sensitivity. Liver fat decreased from 21.8% to 18.7%. In the initiation cohort, changes in lipolysis were not independent of food intake, but after 6 months of metreleptin treatment on an ad libitum diet, lipolysis decreased by 30% (palmitate turnover) to 35% (glycerol turnover). CONCLUSION: Using lipodystrophy as a human model of leptin deficiency and replacement, we show that metreleptin improves insulin sensitivity and decreases hepatic and circulating triglycerides and that these improvements are independent of its effects on food intake. TRIAL REGISTRATION: ClinicalTrials.gov NCT01778556FUNDING. This research was supported by the intramural research program of the NIDDK.