Genetic Testing and Risk Scores: Impact on Familial Hypercholesterolemia

Familial Hypercholesterolemia (FH) is an inherited lipid disorder affecting 1 in 220 individuals resulting in highly elevated low-density lipoprotein levels and risk of premature coronary disease. Pathogenic variants causing FH typically involve the LDL receptor (LDLR), apolipoprotein B-100 (APOB), and proprotein convertase subtulisin/kexin type 9 genes (PCSK9) and if identified convey a risk of early onset coronary artery disease (ASCVD) of 3- to 10-fold vs. the general population depending on the severity of the mutation. Identification of monogenic FH within a family has implications for family-based testing (cascade screening), risk stratification, and potentially management, and it has now been recommended that such testing be offered to all potential FH patients. Recently, robust genome wide association studies (GWAS) have led to the recognition that the accumulation of common, small effect alleles affecting many LDL-c raising genes can result in a clinical phenotype largely indistinguishable from monogenic FH (i.e., a risk of early onset ASCVD of ~3-fold) in those at the extreme tail of the distribution for these alleles (i.e., the top 8% of the population for a polygenic risk score). The incorporation of these genetic risk scores into clinical practice for non-FH patients may improve risk stratification but is not yet widely performed due to a less robust evidence base for utility. Here, we review the current status of FH genetic testing, potential future applications as well as challenges and pitfalls

Lectin-Dependent Enhancement of Ebola Virus Infection via Soluble and Transmembrane C-type Lectin Receptors

Mannose-binding lectin (MBL) is a key soluble effector of the innate immune system that recognizes pathogen-specific surface glycans. Surprisingly, low-producing MBL genetic variants that may predispose children and immunocompromised individuals to infectious diseases are more common than would be expected in human populations. Since certain immune defense molecules, such as immunoglobulins, can be exploited by invasive pathogens, we hypothesized that MBL might also enhance infections in some circumstances. Consequently, the low and intermediate MBL levels commonly found in human populations might be the result of balancing selection. Using model infection systems with pseudotyped and authentic glycosylated viruses, we demonstrated that MBL indeed enhances infection of Ebola, Hendra, Nipah and West Nile viruses in low complement conditions. Mechanistic studies with Ebola virus (EBOV) glycoprotein pseudotyped lentiviruses confirmed that MBL binds to N-linked glycan epitopes on viral surfaces in a specific manner via the MBL carbohydrate recognition domain, which is necessary for enhanced infection. MBL mediates lipid-raft-dependent macropinocytosis of EBOV via a pathway that appears to require less actin or early endosomal processing compared with the filovirus canonical endocytic pathway. Using a validated RNA interference screen, we identified C1QBP (gC1qR) as a candidate surface receptor that mediates MBL-dependent enhancement of EBOV infection. We also identified dectin-2 (CLEC6A) as a potentially novel candidate attachment factor for EBOV. Our findings support the concept of an innate immune haplotype that represents critical interactions between MBL and complement component C4 genes and that may modify susceptibility or resistance to certain glycosylated pathogens. Therefore, higher levels of native or exogenous MBL could be deleterious in the setting of relative hypocomplementemia which can occur genetically or because of immunodepletion during active infections. Our findings confirm our hypothesis that the pressure of infectious diseases may have contributed in part to evolutionary selection of MBL mutant haplotypes

The leaky pipeline of diverse race and ethnicity representation in academic science and technology training in the United States, 2003-2019.

IntroductionDiverse race and ethnicity representation remains lacking in science and technology (S&T) careers in the United States (US). Due to systematic barriers across S&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S&T training in the US.MethodsWe analyzed US S&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S&T transition points: bachelor to doctorate degrees (2003-2019) and doctorate degrees to postdoctoral positions (2010-2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression.ResultsFor 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95% confidence interval [CI] 0.81-0.92; RR 0.85, 95% CI 0.81-0.89; and RR 0.82, 95% CI 0.77-0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95% CI 0.66-0.78; Asian men RR 0.73, 95% CI 0.70-0.77)]. We observed that Black men (RR 0.60, 95% CI 0.51-0.69) and Black women (RR 0.56, 95% CI 0.49-0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02).ConclusionWe quantified diverse race and ethnicity representation in contemporary US S&T training and found that Black men and women experienced the most consistent loss in representation across the S&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities

The leaky pipeline of diverse race and ethnicity representation in academic science and technology training in the United States, 2003–2019

Introduction Diverse race and ethnicity representation remains lacking in science and technology (S&T) careers in the United States (US). Due to systematic barriers across S&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S&T training in the US. Methods We analyzed US S&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S&T transition points: bachelor to doctorate degrees (2003–2019) and doctorate degrees to postdoctoral positions (2010–2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression. Results For 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95% confidence interval [CI] 0.81–0.92; RR 0.85, 95% CI 0.81–0.89; and RR 0.82, 95% CI 0.77–0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95% CI 0.66–0.78; Asian men RR 0.73, 95% CI 0.70–0.77)]. We observed that Black men (RR 0.60, 95% CI 0.51–0.69) and Black women (RR 0.56, 95% CI 0.49–0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02). Conclusion We quantified diverse race and ethnicity representation in contemporary US S&T training and found that Black men and women experienced the most consistent loss in representation across the S&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities

Race and ethnicity representation in the US science and technology pipeline, 2003–2019.

Panel A–Race and ethnicity representation among US women in bachelor, doctorate, postdoctoral positions in 2019 versus US Census estimates; Panel B–Race and ethnicity representation among US men in bachelor, doctorate, postdoctoral positions in 2019, versus US Census estimates; Panel C–Ratios of doctorate to bachelor representation by race and ethnicity for US women, 2003–2019, all p-trend > 0.05; Panel D–Ratios of doctorate to bachelor representation by race and ethnicity for US men, 2003–2019, NHW men p-trend = 0.004 (ratio change per year 0.004 [95% CI 0.002,0.007]), all other p-trend > 0.05; Panel E—Ratios of postdoctoral to doctorate representation by race and ethnicity for US women, 2010–2019, NHW women p-trend = 0.007 (ratio change per year 0.006 [95% CI 0.002, 0.01]), all p-trend > 0.05; Black women p-trend = 0.02 (ratio change per year -0.01 [95% CI -0.02, -0.002]), all other p-trend > 0.05; Panel F–Ratios of postdoctoral to doctorate representation by race and ethnicity for US men, 2010–2019, all p-trend > 0.05. For panels A-B, US Census estimates refer to population-level estimates from the 2019 American Community Survey, all sexes combined. For panels C-F, ratios are obtained by dividing the representation of the race and ethnicity group in the subsequent stage by representation in the previous stage. A ratio less than 1 suggests loss of representation. Native Hawaiian and Pacific Islanders not visualized to due to incomplete data availability. AI/AN groups are not visualized due to representation <0.5%. Abbreviations: CI, confidence interval; NHW, Non-Hispanic White.</p

Metabolic Markers to Predict Incident Diabetes Mellitus in Statin-Treated Patients (from the Treating to New Targets and the Stroke Prevention by Aggressive Reduction in Cholesterol Levels Trials).

The goal of this analysis was to evaluate the ability of insulin resistance, identified by the presence of prediabetes mellitus (PreDM) combined with either an elevated triglyceride (TG&nbsp;&gt;1.7&nbsp;mmol/l) or body mass index (BMI ≥27.0&nbsp;kg/m2), to identify increased risk of statin-associated type 2 diabetes mellitus (T2DM). Consequently, a retrospective analysis of data from subjects without diabetes in the Treating to New Targets and the Stroke Prevention by Aggressive Reduction in Cholesterol Levels randomized controlled trials was performed, subdividing participants into 4 experimental groups: (1) normal fasting glucose (NFG) and TG ≤1.7&nbsp;mmol/l (42%); (2) NFG and TG &gt;1.7&nbsp;mmol/l (22%); (3) PreDM and TG ≤1.7&nbsp;mmol/l (20%); and (4) PreDM and TG &gt;1.7&nbsp;mmol/l (15%). Comparable groupings were created substituting BMI values (kg/m2 &lt;27.0 and ≥27.0) for TG concentrations. Patients received atorvastatin or placebo for a median duration of 4.9&nbsp;years. Incident T2DM, defined by developing at least 2 fasting plasma glucose (FPG) concentrations ≥126&nbsp;mg/dl, an increase in FPG ≥37&nbsp;mg/dl, or a clinical diagnosis of T2DM, was observed in 8.2% of the total population. T2DM event rates (statin or placebo) varied from a low of 2.8%/3.2% (NFG and TG ≤1.7&nbsp;mmol/l) to a high of 22.8%/7.6% (PreDM and TG &gt;1.7&nbsp;mmol/l) with intermediate values for only an elevated TG &gt;1.7&nbsp;mmol/l (5.2%/4.3%) or only PreDM (12.8%/7.6%). Comparable differences were observed when BMI values were substituted for TG concentrations. In conclusion, these data suggest that (1) the diabetogenic impact of statin treatment is relatively modest in general; (2) the diabetogenic impact is accentuated relatively dramatically as FPG and TG concentrations and BMI increase; and (3) PreDM, TG concentrations, and BMI identify people at highest risk of statin-associated T2DM

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records

Background Statins are guideline‐recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high‐risk population. Identifying reasons for statin nonuse, which are typically described in unstructured electronic health record data, can inform targeted system interventions to improve statin use. We aimed to leverage a deep learning approach to identify reasons for statin nonuse in patients with diabetes. Methods and Results Adults with diabetes and no statin prescriptions were identified from a multiethnic, multisite Northern California electronic health record cohort from 2014 to 2020. We used a benchmark deep learning natural language processing approach (Clinical Bidirectional Encoder Representations from Transformers) to identify statin nonuse and reasons for statin nonuse from unstructured electronic health record data. Performance was evaluated against expert clinician review from manual annotation of clinical notes and compared with other natural language processing approaches. Of 33 461 patients with diabetes (mean age 59±15 years, 49% women, 36% White patients, 24% Asian patients, and 15% Hispanic patients), 47% (15 580) had no statin prescriptions. From unstructured data, Clinical Bidirectional Encoder Representations from Transformers accurately identified statin nonuse (area under receiver operating characteristic curve [AUC] 0.99 [0.98–1.0]) and key patient (eg, side effects/contraindications), clinician (eg, guideline‐discordant practice), and system reasons (eg, clinical inertia) for statin nonuse (AUC 0.90 [0.86–0.93]) and outperformed other natural language processing approaches. Reasons for nonuse varied by clinical and demographic characteristics, including race and ethnicity. Conclusions A deep learning algorithm identified statin nonuse and actionable reasons for statin nonuse in patients with diabetes. Findings may enable targeted interventions to improve guideline‐directed statin use and be scaled to other evidence‐based therapies

Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease

Sarraju, Coquet et al. utilise a natural language processing approach to identify reasons for statin nonuse in the health records of patients with atherosclerotic cardiovascular disease (ASCVD). Their approach identifies patient-level and clinician-level reasons for statin nonuse, with differences by ASCVD type and patient race/ethnicity

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