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Introduction: The COVID-19 pandemic disrupted clinical research. CLEAR Outcomes investigated the effect of bempedoic acid (BA) versus placebo in 13 970 patients with statin intolerance and high cardiovascular (CV) risk. BA reduced the risk of the primary endpoint (composite of CV death, nonfatal myocardial infarction, nonfatal stroke, or coronary revascularization) by 13%. CLEAR Outcomes began before and continued for 2.7 years after the start of the pandemic., Methods: The impact of the COVID-19 pandemic on patient disposition, adverse events, and major adverse CV events (MACE) in CLEAR Outcomes was assessed., Results: Rates of severe infection, hospitalization, or first MACE associated with a positive COVID-19 test were low and balanced between treatment groups. Rates of all-cause death, non-CV death, and undetermined death increased in the pandemic period compared with the pre-pandemic period, while rates of CV death with a known etiology remained stable. A sensitivity analysis excluding undetermined deaths occurring after the onset of the pandemic from the CV death designation yielded hazard ratios of 0.84 (95% CI, 0.76-0.93) for the primary endpoint and 0.94 (95% CI, 0.76-1.16) for the secondary endpoint of CV death, compared with 0.87 (95% CI, 0.79-0.96) and 1.04 (95% CI, 0.88-1.24), respectively, in the original analysis., Conclusion: The CLEAR Outcomes trial continued uninterrupted throughout the COVID-19 pandemic. Certain trial endpoints may have been impacted by the pandemic. Specifically, the classification of undetermined deaths as CV deaths may have attenuated the effect of BA on key efficacy endpoints., (© 2024 The Author(s). Clinical Cardiology published by Wiley Periodicals, LLC.)

Background: The prevalence of obesity has been increasing worldwide, with substantial implications for public health. Obesity is independently associated with cardiovascular morbidity and mortality and is estimated to cost the health system over $200 billion dollars annually. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as a practice-changing therapy for weight loss and cardiovascular risk reduction independent of diabetes., Methods: We used large language models to augment our previously reported artificial intelligence-enabled topic modeling pipeline to analyze over 390,000 unique GLP-1 RA-related Reddit discussions., Results: We find high interest around GLP-1 RAs, with a total of 168 topics and 33 groups focused on the GLP-1 RA experience with weight loss, comparison of side effects between differing GLP-1 RAs and alternate therapies, issues with GLP-1 RA access and supply, and the positive psychological benefits of GLP-1 RAs and associated weight loss. Notably, public sentiment in these discussions was mostly neutral-to-positive., Conclusions: These findings have important implications for monitoring new side effects not captured in randomized control trials and understanding the public health challenge of drug shortages., (© 2024. The Author(s).)

Atherosclerotic plaque results from a complex interplay between lipid deposition, inflammatory changes, cell migration and arterial wall injury. Over the past two decades, clinical trials utilizing invasive arterial imaging modalities, such as intravascular ultrasonography, have shown that reducing levels of atherogenic lipoproteins, mainly serum LDL-cholesterol (LDL-C), to very low levels can safely reduce overall atherosclerotic plaque burden and favourably modify plaque composition. Classically, this outcome has been achieved with intensive statin therapy. Since 2016, newer and potent lipid-lowering strategies, such as proprotein convertase subtilisin-kexin type 9 inhibition, have shown incremental effects on plaque regression and risk of clinical events. Despite maximal reduction in plasma LDL-C levels, considerable residual cardiovascular risk remains in some patients. Therefore, there is a need to study therapeutic approaches that address residual risk beyond LDL-C reduction to promote plaque stabilization or regression. Contemporary imaging modalities, such as coronary computed tomography angiography, enable non-invasive assessment of the overall atherosclerotic plaque burden as well as of certain local plaque characteristics. This technology could allow further study of plaque stabilization and regression using novel therapeutic approaches. Non-invasive plaque assessment might also offer the potential to guide personalized management strategies if validated for this purpose., (© 2024. Springer Nature Limited.)

Background: Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease (ASCVD)., Objectives: The authors assessed differences in Lp(a) testing and levels by disaggregated race, ethnicity, and ASCVD risk., Methods: This was a retrospective cohort study of patients from a large California health care system from 2010 to 2021. Eligible individuals were ≥18 years old, with ≥2 primary care visits, and complete race and ethnicity data who underwent Lp(a) testing. Race and ethnicity were self-reported and categorized as follows: non-Hispanic (NH) White, NH-Black, Hispanic (Mexican, Puerto Rican, other), NH-Asian (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other). Logistic regression models tested associations between elevated Lp(a) (≥50 mg/dL) and race, ethnicity, and ASCVD risk., Results: 13,689 (0.9%) individuals underwent Lp(a) testing with a mean age of 54.6 ± 13.8 years, 49% female, 28.8% NH Asian. Over one-third of those tested had Lp(a) levels ≥50 mg/dL, ranging from 30.7% of Mexican patients to 62.6% of NH-Black patients. The ASCVD risk of those tested varied by race: 73.6% of Asian Indian individuals had <5% 10-year risk, whereas 27.2% of NH-Black had established ASCVD. Lp(a) prevalence ≥50 mg/dL increased across the ASCVD risk spectrum. After adjustment, Hispanic (OR: 0.76 [95% CI: 0.66-0.88]) and Asian (OR: 0.88 [95% CI: 0.81-0.96]) had lower odds of Lp(a) ≥50 mg/dL, whereas Black individuals had higher odds (OR: 2.46 [95% CI: 1.97-3.07])., Conclusions: Lp(a) testing is performed infrequently. Of those tested, Lp(a) levels were frequently elevated and differed significantly across disaggregated race and ethnicity groups. The prevalence of elevated Lp(a) increased with increasing ASCVD risk, with significant variation by race and ethnicity., Competing Interests: Dr Rodriguez has received grants from the 10.13039/100000002National Institutes of Health/10.13039/100000050National Heart, Lung, and Blood Institute (1K01HL144607; R01HL168188), the American Heart Association/Harold Amos Faculty Development Program, and the 10.13039/100000862Doris Duke Charitable Foundation (#2022051) during the conduct of the study. Dr Palaniappan has received grants from the 10.13039/100000002National Institutes of Health/10.13039/100000050National Heart, Lung, and Blood Institute (K24 HL150476). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Dr Rodriguez has consulting relationships with Healthpals, Novartis, NovoNordisk (CEC), Esperion Therapeutics, Movano Health, Kento Health, Inclusive Health, Edwards, and Arrowhead Pharmaceuticals outside the submitted work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts., (©Vishala Mishra, Ashish Sarraju, Neil M Kalwani, Joseph P Dexter. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.04.2024.)

Objective: There remain disparities by race and ethnicity in atherosclerotic cardiovascular disease (ASCVD). Statins reduce low-density lipoprotein cholesterol (LDL-c) and improve ASCVD outcomes. ASCVD treatment patterns across disaggregated race and ethnicity groups are incompletely understood. We aimed to evaluate statin use and LDL-c control for ASCVD by race and ethnicity., Methods: From an electronic health record (EHR)-based cohort from a multisite Northern California health system, we included adults with an ASCVD diagnosis from 2010 to 2021 and at least 2 primary care visits, stratified by race and ethnicity (Non-Hispanic White [NHW], Non-Hispanic Black [Black], Hispanic, and Asian). Hispanic (Mexican, Puerto Rican, Other) and Asian (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other) groups were disaggregated. Primary outcomes were 1-year post-ASCVD statin use (prescription) and LDL-c control (at least one value <70 mg/dL). Adjusted odds ratios (ORs) were estimated using logistic regression., Results: Of 133,158 patients, there were 89,944 NHW, 6,294 Black, 12,478 (9.4 %) Hispanic and 13,179 (9.9 %) Asian patients. At 1 year after incident ASCVD, there was suboptimal statin use (any statins <60 %, high-intensity <25 %) and LDL-c control (<30 %) across groups, with lowest proportions in Black patients for statin use (46.7 %, any statin) and LDL-c control (10.7 %, OR 0.89 (0.81-0.97), referent NHW). Disaggregation of Asian and Hispanic groups unmasked within-group heterogeneity., Conclusions: In patients with incident ASCVD, we describe suboptimal and heterogenous 1-year post-ASCVD guideline-directed statin use and 1-year post-ASCVD LDL-c control across disaggregated race and ethnicity groups. Findings may improve understanding of ASCVD treatment disparities and guide implementation., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Fatima Rodriguez reports financial support was provided by National Heart Lung and Blood Institute. Latha Palaniappan reports financial support was provided by National Heart Lung and Blood Institute. Fatima Rodriguez reports financial support was provided by the American Heart Association/Harold Amos Medical Faculty Development Program. Fatima Rodriguez reports financial support was provided by Doris Duke Charitable Foundation. Fatima Rodriguez reports a relationship with HealthPals, Inc. that includes: consulting or advisory and equity or stocks. Fatima Rodriguez reports a relationship with Novartis that includes: consulting or advisory. Fatima Rodriguez reports a relationship with Novo Nordisk Inc that includes: consulting or advisory. Fatima Rodriguez reports a relationship with Esperion Therapeutics Inc that includes: consulting or advisory. Fatima Rodriguez reports a relationship with Movano Health that includes: consulting or advisory. Fatima Rodriguez reports a relationship with Kento Health that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. FR reports consulting relationships with Healthpals, Novartis, Novo Nordisk (CEC), Esperion, Movano Health, and Kento Health outside the submitted work. The remaining authors report no relevant disclosures or competing interests., (© 2024 The Authors. Published by Elsevier B.V.)

Background: High blood pressure affects approximately 116 million adults in the United States. It is the leading risk factor for death and disability across the world. Unfortunately, over the past decade, hypertension control rates have decreased across the United States. Prediction models and clinical studies have shown that reducing clinician inertia alone is sufficient to reach the target of ≥80% blood pressure control. Digital health tools containing evidence-based algorithms that are able to reduce clinician inertia are a good fit for turning the tide in blood pressure control, but careful consideration should be taken in the design process to integrate digital health interventions into the clinical workflow., Methods: We describe the development of a provider-facing hypertension management platform. We enumerate key steps of the development process, including needs finding, clinical workflow analysis, treatment algorithm creation, platform design and electronic health record integration. We interviewed and surveyed 5 Stanford clinicians from primary care, cardiology, and their clinical care team members (including nurses, advanced practice providers, medical assistants) to identify needs and break down the steps of clinician workflow analysis. The application design and development stage were aided by a team of approximately 15 specialists in the fields of primary care, hypertension, bioinformatics, and software development., Conclusions: Digital monitoring holds immense potential for revolutionizing chronic disease management. Our team developed a hypertension management platform at an academic medical center to address some of the top barriers to adoption and achieving clinical outcomes. The frameworks and processes described in this article may be used for the development of a diverse range of digital health tools in the cardiovascular space.

Background: There are established sex-specific differences in heart failure with reduced ejection fraction (HFrEF) outcomes. Randomized clinical trials (RCTs) based on cardiovascular outcome benefits, typically either reduced cardiovascular mortality or hospitalization for heart failure (HHF), influence current guidelines for therapy., Objectives: The authors evaluate the representation of women in HFrEF RCTs that observed reduced all-cause or cardiovascular mortality or HHF., Methods: We queried Cumulative Index to Nursing and Allied Health Literature, Excerpta Medica dataBASE, Medical Literature Analysis and Retrieval System Online, and PubMed for HFrEF RCTs that reported a statistically significant benefit of intervention resulting in improved mortality or HHF published from 1980 to 2021. We estimated representation using the participation-to-prevalence ratio (PPR). A PPR of 0.8 to 1.2 was considered representative., Results: The final analysis included 33 RCTs. Women represented only 23.2% of all enrolled participants (n = 24,366/104,972), ranging from 11.4% to 40.1% per trial. Overall PPR was 0.58, with per-trial PPR estimates ranging from 0.29 to 1.00. Only 5 trials (15.2%) had a PPR of women representative of the disease population. Representation did not change significantly over time. The proportion of women in North American trials was significantly greater than trials conducted in Europe ( P = 0.03). The proportion of women was greater in industry trials compared to government-funded trials ( P = 0.05)., Conclusions: Women are underrepresented in HFrEF RCTs that have demonstrated mortality or HHF benefits and influence current guidelines. Representation is key to further delineation of sex-specific differences in major trial results. Sustained efforts are warranted to ensure equitable and appropriate inclusion of women in HFrEF trials., Competing Interests: All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Objective: Statins are the cornerstone for the prevention and treatment of cardiovascular disease. Patients often consult online patient education materials (OPEMs) to inform medical decision-making. We therefore aimed to assess the readability and reliability of OPEMs related to statins., Methods: A total of 17 statin-related terms were queried using an online search engine to identify the top 20 search results for each statin-related term. Each OPEM was then grouped into the following categories based on 2 independent reviewers: government OPEMs (national, state, or local government agencies); healthcare/nonprofit OPEMs (major health systems and nonprofit organizations with a specific cardiovascular health focus); industry/commercial OPEMs (pharmaceutical manufacturers and online pharmacies); lay press OPEMs (healthcare-oriented news organizations); and dictionary/encyclopedia OPEMs. Grade-level readability for each OPEM was calculated using 5 standard readability metrics and compared with AMA-recommended readability recommendations. Reliability of each OPEM was evaluated using the JAMA benchmark criteria for online health information and certification from Health on the Net (HONCode)., Results: A total of 340 websites were identified across the 17 statin search terms. There were 211 statin OPEMs after excluding non-OPEM results; 172 OPEMs had unique content. Statin OPEM readability exceeded the recommended 6th grade AMA reading level (average reading grade level of 10.9). The average JAMA benchmark criteria score was 2.13 (on a scale of 0-4, with higher scores indicating higher reliability), and only 60% of statin OPEMs were HONCode-certified. There was an inverse association between readability and reliability. The most readable results were from industry and commercial sources, while the most reliable sites were from lay press sources., Conclusions: Statin OPEMs are written at an overall averaging reading grade level of 10.9. There was an inverse association between readability and reliability. Lack of accessible, high-quality online health information may contribute to statin nonadherence., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Fatima Rodriguez reports consulting fees from Novartis, NovoNordisk (CEC), Esperion, and HealthPals. Outside the submitted work. The remaining authors report no relevant disclosures or competing interests., (© 2023 The Authors. Published by Elsevier B.V.)

Competing Interests: Disclosures Dr Rodriguez reports consulting relationships with HealthPals, Novartis, Amgen, NovoNordisk (CEC), and AstraZeneca. The remaining authors have nothing to disclose.

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Sarraju et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity., Methods: Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets., Results: There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p =0.003)., Conclusions: Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests., (© 2023 The Authors. Published by Elsevier B.V.)

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.

Importance: Despite compelling evidence that statins are safe, are generally well tolerated, and reduce cardiovascular events, statins are underused even in patients with the highest risk. Social media may provide contemporary insights into public perceptions about statins., Objective: To characterize and classify public perceptions about statins that were gleaned from more than a decade of statin-related discussions on Reddit, a widely used social media platform., Design, Setting, and Participants: This qualitative study analyzed all statin-related discussions on the social media platform that were dated between January 1, 2009, and July 12, 2022. Statin- and cholesterol-focused communities, were identified to create a list of statin-related discussions. An artificial intelligence (AI) pipeline was developed to cluster these discussions into specific topics and overarching thematic groups. The pipeline consisted of a semisupervised natural language processing model (BERT [Bidirectional Encoder Representations from Transformers]), a dimensionality reduction technique, and a clustering algorithm. The sentiment for each discussion was labeled as positive, neutral, or negative using a pretrained BERT model., Exposures: Statin-related posts and comments containing the terms statin and cholesterol., Main Outcomes and Measures: Statin-related topics and thematic groups., Results: A total of 10 233 unique statin-related discussions (961 posts and 9272 comments) from 5188 unique authors were identified. The number of statin-related discussions increased by a mean (SD) of 32.9% (41.1%) per year. A total of 100 discussion topics were identified and were classified into 6 overarching thematic groups: (1) ketogenic diets, diabetes, supplements, and statins; (2) statin adverse effects; (3) statin hesitancy; (4) clinical trial appraisals; (5) pharmaceutical industry bias and statins; and (6) red yeast rice and statins. The sentiment analysis revealed that most discussions had a neutral (66.6%) or negative (30.8%) sentiment., Conclusions and Relevance: Results of this study demonstrated the potential of an AI approach to analyze large, contemporary, publicly available social media data and generate insights into public perceptions about statins. This information may help guide strategies for addressing barriers to statin use and adherence.

Background: People with type 2 diabetes mellitus (T2DM) have elevated cardiovascular (CV) risk, including for hospitalization for heart failure (HHF). Canagliflozin reduced CV and kidney events in patients with T2DM and high CV risk or nephropathy in the CANVAS (CANagliflozin cardioVascular Assessment Study) Program and the CREDENCE (Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation) trial., Objectives: The aim of this study was to assess the effects of canagliflozin on CV outcomes according to baseline estimated glomerular filtration rate (eGFR) and urine albumin:creatinine ratio (UACR) in pooled patient-level data from the CANVAS Program and CREDENCE trial., Methods: Canagliflozin effects on CV death or HHF were assessed by baseline eGFR (<45, 45-60, and >60 mL/min/1.73 m 2 ) and UACR (<30, 30-300, and >300 mg/g). HRs and 95% CIs were estimated by using Cox regression models overall and according to subgroups., Results: A total of 14,543 participants from the CANVAS Program (N = 10,142) and the CREDENCE (N = 4,401) trial were included, with a mean age of 63 years, 35% female, 75% White, 13.2% with baseline eGFR <45 mL/min/1.73 m 2 , and 31.9% with UACR >300 mg/g. Rates of CV death or HHF increased as eGFR declined and/or UACR increased. Canagliflozin significantly reduced CV death or HHF compared with placebo (19.4 vs 28.0 events per 1,000 patient-years; HR: 0.70; 95% CI: 0.62-0.79), with consistent results across eGFR and UACR categories (all P interaction >0.40)., Conclusions: Risk of CV death or HHF was higher in those with lower baseline eGFR and/or higher UACR. Canagliflozin consistently reduced CV death or HHF in participants with T2DM and high CV risk or nephropathy regardless of baseline renal function or level of albuminuria. (Canagliflozin Cardiovascular Assessment Study [CANVAS], NCT01032629; A Study of the Effects of Canagliflozin [JNJ-24831754] on Renal Endpoints in Adult Participants With Type 2 Diabetes Mellitus [CANVAS-R], NCT01989754; and Evaluation of the Effects of Canagliflozin on Renal and Cardiovascular Outcomes in Participants With Diabetic Nephropathy [CREDENCE], NCT02065791)., Competing Interests: Funding Support and Author Disclosures The CANVAS Program and CREDENCE trial were sponsored by Janssen Research & Development, LLC, and were conducted collaboratively by the sponsor, an academic-led Steering Committee, and an Academic Research Organization, George Clinical. Analyses were performed by Janssen and independently confirmed by George Clinical statisticians. Technical editorial assistance was provided by Elizabeth Meucci, PhD, of MedErgy, and was funded by Janssen Global Services, LLC. Dr Bakris has received research funding paid to the University of Chicago for serving as principal investigator on national clinical trials for Bayer, Janssen, and Novo Nordisk; has served as a consultant for Merck, Relypsa, Novo Nordisk, and AstraZeneca; has served on a steering committee for Vascular Dynamics; has served as Editor of the American Journal of Nephrology and Nephrology; has served as Editor-in-Chief of UpToDate, and Nephrology and Hypertension Section Editor of UpToDate; and has served as Associate Editor of Diabetes Care, Hypertension Research, and Nephrology, Dialysis, and Transplantation. Drs Blais, Damaraju, and Gogate are employees of Janssen Scientific Affairs, LLC. Dr Cannon has received research grants from Amgen, Better Therapeutics, Boehringer Ingelheim, Bristol Myers Squibb, Daiichi-Sankyo, Janssen, Merck, Novo Nordisk, and Pfizer; has received consulting fees from Aegerion, Alnylam, Amarin, Amgen, Applied Therapeutics, Ascendia, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Janssen, Lexicon, Merck, Pfizer, Rhoshan, and Sanofi; and serves on Data and Safety Monitoring Boards for the Veterans Administration, Applied Therapeutics, and Novo Nordisk. Dr Cherney has received honoraria from Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, AbbVie, Janssen, Bayer, Prometic, Bristol Myers Squibb, and Novo Nordisk; and has received operational funding for clinical trials from Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, and Novo Nordisk. Dr Figtree has received research support from a National Health and Medical Research Council (Australia) Practitioner Fellowship, and compensation from Janssen for serving on the Adjudication Panel of the CANVAS Program; has received consulting income from Amgen, Boehringer Ingelheim, and Sanofi; has received grant support from Abbott Diagnostics, Bayer, Janssen, Novartis, Pfizer, Merck, and Roche Diagnostics; and serves as national study lead for CSL. Dr Greene has received consulting fees from Janssen, Durect, and Pfizer; and has received research support from AstraZeneca and Boehringer Ingelheim. Dr Heerspink has served as a consultant for AbbVie, Astellas, AstraZeneca, Boehringer Ingelheim, Fresenius, Gilead, Janssen, Merck, and Mitsubishi Tanabe; and has received grant support from AbbVie, AstraZeneca, Boehringer Ingelheim, and Janssen. Dr Januzzi is supported by the Hutter Family Professorship; is a Trustee of the American College of Cardiology; has received grant support from Novartis Pharmaceuticals, Applied Therapeutics, and Abbott Diagnostics; has received consulting income from Abbott, Janssen, Novartis, Pfizer, Merck, and Roche Diagnostics; and participates in clinical endpoint committees/data safety monitoring boards for Abbott, AbbVie, Amgen, Bayer, CVRx, Janssen, Siemens, and Takeda. Dr Jardine is responsible for research projects that have received funding from CSL, Dimerix, Eli Lilly, and MSD; and has received advisory, steering committee, and/or speaker fees from AstraZeneca, Bayer, Boehringer Ingelheim, Chinook, CSL, Janssen, Merck, MSD, Roche, and Vifor (all consultancy or honoraria are paid to her institution). Dr Kosiborod has received research grants from AstraZeneca and Boehringer Ingelheim; has served as a consultant or on advisory boards for Amgen, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Esperion Therapeutics, Janssen, Merck (Diabetes and Cardiovascular), Novo Nordisk, Sanofi, and Vifor Pharma; has received other research support from AstraZeneca; and has received honoraria from AstraZeneca, Boehringer Ingelheim, and Novo Nordisk. Dr Levin serves as a scientific advisor to Boehringer Ingelheim, AstraZeneca, and the National Institute of Diabetes and Digestive and Kidney Diseases; is on the data safety and monitoring board for the National Institute of Diabetes and Digestive and Kidney Diseases, Kidney Precision Medicine, University of Washington Kidney Research Institute Scientific Advisory Committee; is funded by the Canadian Institute of Health Research and Kidney Foundation of Canada; and has received fees for time as CREDENCE National Coordinator from Janssen, directed to her academic team. Dr Lingvay has received institutional research payments from Novo Nordisk, Sanofi, Novartis, Pfizer, Merck, Mylan, and GI Dynamics; and has received personal fees for consultancy from AstraZeneca, Eli Lilly, TARGETPharma, Novo Nordisk, Boehringer Ingelheim, Janssen, MannKind, Valeritas, Intarcia, Intercept, Zealand Pharma, Bayer; and has received consultancy fees from Sanofi. Dr Mahaffey has received research grants or contracts from the American Heart Association, Apple, Inc, Bayer, California Institute Regenerative Medicine, Eidos, Ferring, Gilead, Google (Verily), Idorsia, Johnson & Johnson, Luitpold, PAC-12, Precordior, and Sanifit; has provided consulting or other services for Amgen, Applied Therapeutics, AstraZeneca, Bayer, CSL Behring, Elsevier, FibroGen, Inova, Johnson & Johnson, Lexicon, MyoKardia, Novartis, Novo Nordisk, Otsuka, PhaseBio, Portola, Quidel, Sanofi, and Theravance; and has equity in Precordior. Dr Neal has received institutional payments for research projects from Janssen; and has received fees for consultancy from Janssen, Merck Sharp & Dohme, and Mitsubishi Tanabe. Dr Perkovic has received fees for advisory boards, steering committee roles, or scientific presentations from AbbVie, Astellas, AstraZeneca, Bayer, Baxter, Bristol Myers Squibb, Boehringer Ingelheim, Dimerix, Durect, Eli Lilly, Gilead, GlaxoSmithKline, Janssen, Merck, Mitsubishi Tanabe, Mundipharma, Novartis, Novo Nordisk, Pfizer, PharmaLink, Relypsa, Retrophin, Sanofi, Servier, Vifor, and Tricida. Dr Weir serves as a scientific advisor to Akebia, AstraZeneca, Janssen, Boehringer Ingelheim, Vifor, Merck, Novo Nordisk, and Bayer; and receives grant support from the National Institute of Diabetes and Digestive and Kidney Diseases and the National Heart, Lung, and Blood Institute. Dr Sarraju has reported that he has no relationships relevant to the contents of this paper to disclose., (Copyright © 2022. Published by Elsevier Inc.)

Purpose of the Review: Despite marked progress in cardiovascular disease management in the last several decades, there remain significant, persistent disparities in cardiovascular health in historically marginalized racial and ethnic groups. Here, we outline current state of health disparities in cardiovascular disease, discuss the interplay between social determinants of health, structural racism, and cardiovascular outcomes, and highlight strategies to address these issues., Recent Findings: Across the continuum of atherosclerotic cardiovascular disease (ASCVD) prevention, there remain significant disparities in outcomes including morbidity and mortality by race, ethnicity, and socioeconomic status (SES). These disparities begin early in childhood (primordial prevention) and continue with a higher prevalence of cardiovascular risk factors (primary prevention), and in the uptake of evidence-based therapies (secondary prevention). These disparities are driven by social determinants of health and structural racism that disproportionately disadvantage historically marginalized populations. Structural racism and social determinants of health contribute to significant disparities in cardiovascular morbidity and mortality., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: There remains uncertainty regarding optimal primary atherosclerotic cardiovascular disease (ASCVD) prevention practices for older adults., Objective: To assess statin treatment patterns and incident ASCVD among older patients for primary prevention across the spectrum of ASCVD risk., Design: Retrospective cohort study of participants without ASCVD aged 65-79 years. Patients were stratified by age (65-69, 70-75, > 75 years) and 10-year ASCVD risk category (low/borderline, intermediate, high) based on the Pooled Cohort Equations. Multivariable logistic regressions were used to identify predictors of moderate- or high-intensity statin prescriptions. Cox proportional models were used to estimate hazard ratios (HRs) for incident ASCVD., Participants: Patients aged 65-79 years without ASCVD from a Northern California health system., Main Measures: Statin prescriptions and incident ASCVD events., Key Results: There were 54,066 patients, with 10,288 (19%) aged > 75 years and 57% women. Compared with younger groups, adults > 75 years were less likely to be prescribed moderate- or high-intensity statin prescriptions across ASCVD risk groups (all p < 0.001); this persisted after multivariable adjustment including for ASCVD risk (odds ratio [OR] 0.80, 95% confidence interval [CI] 0.74-0.86). Adults > 75 years were more likely to experience incident ASCVD (HR 1.42, 95% CI 1.23-1.63). Women (OR 0.85, 95% CI 0.81-0.89) and underweight older adults (OR 0.45, 95% CI 0.33-0.61) were also less likely to receive moderate- or high-intensity statins., Conclusions: Among older adults aged 65-79 years without prior ASCVD, those > 75 years of age were less likely to receive moderate- or high-intensity statins regardless of ASCVD risk compared with their younger counterparts, while experiencing more incident ASCVD. Efforts are warranted to study the reasons for age-based differences in statin use in older adults, particularly those at highest ASCVD risk., (© 2021. Society of General Internal Medicine.)

Background: Statins conclusively decrease mortality in atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide, and are strongly recommended by guidelines. However, real-world statin utilization and persistence are low, resulting in excess mortality. Identifying reasons for statin nonuse at scale across health systems is crucial to developing targeted interventions to improve statin use., Methods: We developed and validated deep learning-based natural language processing (NLP) approaches (Clinical Bidirectional Encoder Representations from Transformers [BERT]) to classify statin nonuse and reasons for statin nonuse using unstructured electronic health records (EHRs) from a diverse healthcare system., Results: We present data from a cohort of 56,530 ASCVD patients, among whom 21,508 (38%) lack guideline-directed statin prescriptions and statins listed as allergies in structured EHR portions. Of these 21,508 patients without prescriptions, only 3,929 (18%) have any discussion of statin use or nonuse in EHR documentation. The NLP classifiers identify statin nonuse with an area under the curve (AUC) of 0.94 (95% CI 0.93-0.96) and reasons for nonuse with a weighted-average AUC of 0.88 (95% CI 0.86-0.91) when evaluated against manual expert chart review in a held-out test set. Clinical BERT identifies key patient-level reasons (side-effects, patient preference) and clinician-level reasons (guideline-discordant practices) for statin nonuse, including differences by type of ASCVD and patient race/ethnicity., Conclusions: Our deep learning NLP classifiers can identify crucial gaps in statin nonuse and reasons for nonuse in high-risk populations to support education, clinical decision support, and potential pathways for health systems to address ASCVD treatment gaps., Competing Interests: Competing interestsF. Rodriguez has received consulting fees from Novartis, NovoNordisk, and HealthPals. T. Hernandez-Boussard has received consulting fees from Verantos and Institute for Systems Biology. The remaining authors have no competing interests to declare., (© The Author(s) 2022.)

Hispanic populations generally experience more adverse socioeconomic conditions yet demonstrate lower mortality compared with Non-Hispanic White (NHW) populations in the US. This finding of a mortality advantage is well-described as the "Hispanic paradox." The Coronavirus Disease 2019 (COVID-19) pandemic has disproportionately affected Hispanic populations. To quantify these effects, we evaluated US national and county-level trends in Hispanic versus NHW mortality from 2011 through 2020. We found that a previously steady Hispanic mortality advantage significantly decreased in 2020, potentially driven by COVID-19-attributable Hispanic mortality. Nearly 16% of US counties experienced a reversal of their pre-pandemic Hispanic mortality advantage such that their Hispanic mortality exceeded NHW mortality in 2020. An additional 50% experienced a decrease in a pre-pandemic Hispanic mortality advantage. Our work provides a quantitative understanding of the disproportionate burden of the pandemic on Hispanic health and the Hispanic paradox and provides a renewed impetus to tackle the factors driving these concerning disparities., (© 2022. The Author(s).)

The Coronavirus Disease 2019 (COVID-19) pandemic curtailed clinical trial activity. Decentralized clinical trials (DCTs) can expand trial access and reduce exposure risk but their feasibility remains uncertain. We evaluated DCT feasibility for atrial fibrillation (AF) patients on oral anticoagulation (OAC). DeTAP (Decentralized Trial in Afib Patients, NCT04471623) was a 6-month, single-arm, 100% virtual study of 100 AF patients on OAC aged >55 years, recruited traditionally and through social media. Participants enrolled and participated virtually using a mobile application and remote blood pressure (BP) and six-lead electrocardiogram (ECG) sensors. Four engagement-based primary endpoints included changes in pre- versus end-of-study OAC adherence (OACA), and % completion of televisits, surveys, and ECG and BP measurements. Secondary endpoints included survey-based nuisance bleeding and patient feedback. 100 subjects (mean age 70 years, 44% women, 90% White) were recruited in 28 days (traditional: 6 pts; social media: 94 pts in 12 days with >300 waitlisted). Study engagement was high: 91% televisits, 85% surveys, and 99% ECG and 99% BP measurement completion. OACA was unchanged at 6 months (baseline: 97 ± 9%, 6 months: 96 ± 15%, p = 0.39). In patients with low baseline OACA (<90%), there was significant 6-month improvement (85 ± 16% to 96 ± 6%, p < 0.01). 86% of respondents (69/80) expressed willingness to continue in a longer trial. The DeTAP study demonstrated rapid recruitment, high engagement, and physiologic reporting via the integration of digital technologies and dedicated study coordination. These findings may inform DCT designs for future cardiovascular trials., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

Introduction: Female authors are underrepresented in cardiology journals, although prior work suggested improvement in reducing disparities over time. Early in the recent COVID-19 pandemic, female authorship continued to lag that of their male counterparts despite a surge in publications. The cumulative impact of the COVID-19 pandemic on authorship gender disparities remains unclear. We aimed to characterize gender disparities in COVID-19-related cardiology publications across the duration of the ongoing pandemic., Methods: We retrospectively analyzed COVID-19-related research articles published in the top 20 impact factor cardiology journals between March and June 2021. Gender representation data were extracted for any author, first authors, and senior authors., Results: We found that 841 articles were related to COVID-19, with a total of 5586 authors and an average of 42 articles per journal. Less than a third (29.9%) of the total authors from publications were women. Women represented a smaller proportion of first authors (21.3%) and senior authors (16.4%)., Conclusions: Female authorship has continued to lag male authorship for the duration of the ongoing COVID-19 pandemic. The pandemic may have impeded progress in reducing gender disparities in academic cardiology publications. The low proportions of first and senior female authors may reflect the impact of the pandemic on women in cardiology in leadership domains., (© 2021. The Author(s).)

Purpose of Review: The cardiovascular benefits of sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide 1 receptor agonists (GLP-1 RA) have increased the focus of type 2 diabetes mellitus (T2DM) care on comprehensive cardiovascular risk reduction. Herein, we review the results of the cardiovascular outcomes trials of SGLT2i and GLP-1 RA, discuss the concepts of relative vs. absolute risk reduction in the context of these trials, and highlight the importance of individualized risk assessment when applying trial results to clinical practice., Recent Findings: To enable personalized treatment approaches, multiple clinical risk scores have been developed to assess risk of atherosclerotic cardiovascular disease (ASCVD) outcomes and hospitalization for heart failure (HHF) in patients with T2DM. In addition, circulating biomarkers of myocardial injury (cardiac troponin) and hemodynamic stress (natriuretic peptides) have been shown to further refine risk prediction of these clinically important cardiovascular complications. When making decisions about whether to initiate SGLT2i and GLP-1 RA, clinicians should consider the anticipated relative and absolute treatment benefits from these antihyperglycemic therapies. Clinicians can use available clinical and biomarker-based risk tools when counseling patients about their individual cardiovascular risk profiles and when estimating absolute treatment benefits from SGLT2i and GLP-1 RA., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline ( n  = 262,923), stratified by PCE-eligible ( n  = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L 2 penalty and L 1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825-0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755-0.794). Among patients aged 40-79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759-0.808) and after (AUC 0.790, 95% CI: 0.765-0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)

Purpose of Review: Results from cardiovascular (CV) outcome trials have revealed important insights into the CV safety and efficacy of glucose-lowering agents, including dipeptidyl peptidase-4 inhibitors (DPP-4i) and glucagon-like peptide-1 receptor agonists (GLP-1RA)., Recent Findings: Among patients with T2DM, DPP-4i have no significant effect on risk of major adverse CV events (MACE: CV death, myocardial infarction, or stroke) with mixed results regarding risk for heart failure (HF). While sitagliptin and linagliptin have neutral effects on HF risk, saxagliptin significantly increases the risk of HF. The CV safety of the GLP-1RA class of medications has been clearly demonstrated, and select agents, such as liraglutide, semaglutide, albiglutide, and dulaglutide, reduce the risk of MACE in patients with T2DM and established CV disease. CV outcome trials have demonstrated CV safety but not incremental efficacy for DPP-4i in most cases. Select GLP-1RA have proven efficacy for MACE and should be considered by cardiologists for CV risk mitigation in the care of patients with T2DM and established CV disease.

Familial hypercholesterolemia (FH) is an underdiagnosed dominant genetic condition affecting approximately 0.4% of the population and has up to a 20-fold increased risk of coronary artery disease if untreated. Simple screening strategies have false positive rates greater than 95%. As part of the FH Foundation's FIND FH initiative, we developed a classifier to identify potential FH patients using electronic health record (EHR) data at Stanford Health Care. We trained a random forest classifier using data from known patients ( n  = 197) and matched non-cases ( n  = 6590). Our classifier obtained a positive predictive value (PPV) of 0.88 and sensitivity of 0.75 on a held-out test-set. We evaluated the accuracy of the classifier's predictions by chart review of 100 patients at risk of FH not included in the original dataset. The classifier correctly flagged 84% of patients at the highest probability threshold, with decreasing performance as the threshold lowers. In external validation on 466 FH patients (236 with genetically proven FH) and 5000 matched non-cases from the Geisinger Healthcare System our FH classifier achieved a PPV of 0.85. Our EHR-derived FH classifier is effective in finding candidate patients for further FH screening. Such machine learning guided strategies can lead to effective identification of the highest risk patients for enhanced management strategies., Competing Interests: Competing interestsS.M. is an employee of Atomo LLC. M.M. has received personal fees from InVitae and Merck, and grant funding from Regeneron, outside the submitted work. K.M. is the founder of Atomo LLC. The remaining authors declare no competing interests. The FH Foundation (EIN 45-4597425) is a 501(c)3 public charity and research advocacy organization receiving funding from a diverse set of program sponsors including Amgen, Inc.

Background: Dietary patterns are related to mortality in selected populations with comorbidities. We studied whether dietary patterns are associated with long-term survival in a middle-aged, healthy population., Methods: In this observational cohort study at the Cooper Clinic preventive medicine center (Dallas, Tex), a volunteer sample of 11,376 men and women with no history of myocardial infarction or stroke completed a baseline dietary assessment between 1987 and 1999 and were observed for an average of 18 years. Proportional hazard regressions, including a tree-augmented model, were used to assess the association of the Dietary Approaches to Stop Hypertension (DASH) dietary pattern, Mediterranean dietary pattern, and individual dietary components with mortality. The primary outcome was all-cause mortality. The secondary outcome was cardiovascular mortality., Results: Mean baseline age was 47 years. Each quintile increase in the DASH diet score was associated with a 6% lower adjusted risk for all-cause mortality (P < .02). The Mediterranean diet was not independently associated with all-cause or cardiovascular mortality. Solid fats and added sugars were the most predictive of mortality. Individuals who consumed >34% of their daily calories as solid fats had the highest risk for all-cause mortality., Conclusions: The DASH dietary pattern was associated with significantly lower all-cause mortality over approximately 2 decades of follow-up in a middle-aged, generally healthy population. Added solid fat and added sugar intake were the most predictive of all-cause mortality. These results suggest that promotion of a healthy dietary pattern should begin in middle age, before the development of comorbid risk factors., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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 >1.7 mmol/l) or body mass index (BMI ≥27.0 kg/m 2 ), 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 mmol/l (42%); (2) NFG and TG >1.7 mmol/l (22%); (3) PreDM and TG ≤1.7 mmol/l (20%); and (4) PreDM and TG >1.7 mmol/l (15%). Comparable groupings were created substituting BMI values (kg/m 2 <27.0 and ≥27.0) for TG concentrations. Patients received atorvastatin or placebo for a median duration of 4.9 years. Incident T2DM, defined by developing at least 2 fasting plasma glucose (FPG) concentrations ≥126 mg/dl, an increase in FPG ≥37 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 mmol/l) to a high of 22.8%/7.6% (PreDM and TG >1.7 mmol/l) with intermediate values for only an elevated TG >1.7 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., (Copyright © 2016 Elsevier Inc. All rights reserved.)