tbuckley/cabot-search huggingface dataset:
Abstract 04: Intersectional Inequities in Obesity Among Multiracial Adults by Urbanicity: A Random Effects MAIHDA Analysis
Authors: Rebecca E Jones-Antwi, Jason R Carter, M. Renee Umstattd Meyer
Journal: Circulation (2024)

Based on 2020 Census Bureau data, the percentage of people who reported multiple races changed more than all of the race alone groups, increasing from 2.9% of the population (9 million people) in 2010 to 10.2% of the population (33.8 million) in 2020. Yet, the majority of national health surveys aggregate all individuals who identify with multiple races into one heterogeneous group. We hypothesize this aggregation masks important differences in terms of health. Beyond the aggregation, there are multiple dimensions of race/ethnicity including racial self-identification (the race(s) one identifies with), racial category (the census category one selects), Hispanic ethnicity, and others that do not ideally overlap among multiracial adults. For multiracial adults, it is unknown how these multiple contexts of race and ethnicity intersect with one another. We evaluated the intersection of racial self-identification, racial category, and Hispanic ethnicity on body mass index (BMI) among multiracial adults as a second-level social identity strata. We then examined if these intersectional inequities in BMI were differently patterned in urban, suburban, and rural areas. We used multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) among multiracial adults in the US (n=42,632) with a three-year (2010-2012) pooled sample of the Behavioral Risk Factor Surveillance System (BRFSS). We observed intersectional effects on BMI inequities. Most of the effects of social identity on BMI were explained by the combination of racial self-identification, racial category, and Hispanic ethnicity among multiracial adults (85.3%). Those living in a rural area had considerably higher BMIs than those living in an urban or suburban area. This is especially true for individuals with racial self-identification of White-Black, White-Native Hawaiian/Pacific Islander (NHPI), Black-American Indian/Alaska Native (AIAN), Asian-NHPI, and White-Black-AIAN and those who chose the racial category of NHPI. For example, individuals who self-identified as White-Black had an average BMI 4.5 kg/m 2 (p&lt;0.01) higher in rural areas compared to White-Blacks living in urban areas. The magnitude of this BMI gap across urban-rural environments varied considerably across social identity strata ranging from 35.3 kg/m 2 among Non-Hispanic White-Black-AIAN with no preference for one race to 22.5 kg/m 2 among Non-Hispanic White-Asian with no preference for one race. This study highlights aspects of the social construction of race and patterning across the urban-rural divide in evaluation of BMI/obesity. This study brings to light inequities within people who identify as multiracial and highlights sub-populations to focus future policy and intervention research on.
Abstract 21: Examining the Impact of Food as Medicine on Heart Health
Authors: Lisa G Rosas, Steven Chen, Lan Xiao, Mike Baiocchi, Wei-ting Chen, Benjamin O Emmert-Aronson, Elliot Ng, Josselyn Perez, Erica Martinez, Eric Melendez, Ariana Thompson-Lastad, Elizabeth Markle, June Tester
Journal: Circulation (2024)

Introduction: The recent White House Conference on Hunger, Nutrition, and Health in October 2022 made it a national priority to address food and nutrition insecurity and reduce inequities in nutrition-related chronic conditions such as heart diease. ‘Food as Medicine’ is increasing implemented to address the dual challenge of food/nutrition insecurity and chronic disease. Food as Medicine refers to integration of food-based nutrition interventions into healthcare to prevent and treat disease. However, there is limited rigorous evidence of the impact of Food as Medicine on heart health. Methods: We examined the effectiveness of Recipe4Health, a Food as Medicine program, for improving food insecurity and indicators of heart health among patients who accessed care in Federally Qualified Health Centers (FQHCs). Recipe4Health was implemented in 4 FQHCs and included a ‘Food Farmacy’ that provided 16 weekly deliveries of produce with or without a ‘Behavioral Pharmacy’ which was 16 weekly group medical visits. We examined pre/post changes over 4 months in outcomes assessed by surveys (e.g., produce intake, physical activity, food insecurity). We also compared outcomes (e.g., lipids, Body Mass Index [BMI], blood pressure [BP], HbA1c) in the Electronic Health Record (EHR) between Recipe4Health patients and controls over 12 months. Control patients were identified using propensity score matching with patients from similar clinics that had not implemented Recipe4Health. We used linear mixed effects models with repeated measures, adjusting for baseline of each outcome. Results: Study participants (n=5,286) were middle-aged (mean age 51.2 SD [13.9]) primarily women (68%), and from diverse racial/ethnic backgrounds with 51% Latinx, 21% Black, and 8% Asian American/Pacific Islander. Based on pre/post surveys, Recipe4Health participants significantly increased their produce consumption (0.41 servings [0.11, 0.72], p=007) and physical activity (41.98 minutes/week [22.33, 61.63], p&lt;.001). Recipe4Health participants also experienced statistically significant improvements in food insecurity with 59% reporting food insecurity at baseline and 48% reporting food security at follow-up (p&lt;.001). Based on EHR data, Recipe4Health participants significantly decreased their non-HDL cholesterol (-17.1 [-26.9, -7.2], p&lt;.001) and HbA1c (-0.37%, 95% CI [-0.65, -0.08]; p=0.01) compared to propensity score-matched controls at 12 months. There were no notable differences between participants and controls with respect to BMI or BP. Conclusion: This analysis from participants in the R4H program adds important data to the literature regarding Food as Medicine. Recipe4Health, a Food as Medicine program, was effective for improving some indicators of heart health including produce consumption, physical activity, non-HDL cholesterol, and HbA1c.
Abstract 39: Circulating Plasma Proteomics of Carotid Intima-Media Thickness in the UK Biobank Cohort
Authors: Ming-Li Chen, Pik Fang Kho, Rodrigo Guarischi-Sousa, Jiayan Zhou, Fahim Abbasi, Daniel Panyard, Kathleen Watson, Themistocles L Assimes
Journal: Circulation (2024)

Introduction: Ultrasound carotid intima-media thickness (cIMT) is a well-established tool for improved cardiovascular risk stratification. The relative importance of traditional risk factors, genetic susceptibility, and plasma proteomics in predicting cIMT has not been previously explored among asymptomatic individuals. Hypothesis: We hypothesized that conventional atherosclerosis risk factors, polygenic scores (PRSs), and levels of plasma proteins each provide incremental value in predicting cIMT. Plasma proteomic profiles associated with cIMT may provide insight on the biological underpinnings of early atherogenesis in carotid arteries. Methods: We examined 6136 UK Biobank participants with 1461 proteins profiled in their plasma at their baseline visit using the proximity extension assay who subsequently returned to a second study visit to undergo a cIMT measure. With cIMT as outcome, we implemented linear regression models, stepwise Akaike information criterion (AIC)-based models, and the least absolute shrinkage and selection operator (LASSO) approach to screen for and select baseline demographic, genetic, physical, medical, and proteomic determinants of cIMT. We evaluated predictive performance of selected predictors by calculating proportion of variance explained (R 2 ) of cIMT. Lastly, we performed gene set enrichment analyses with significantly associated proteins. Results: The mean follow-up time between baseline and cIMT measurements was 9 years. A LASSO model incorporating 97 proteins, in addition to age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index, reached the highest R 2 value in the testing dataset (R 2 = 0.237, 95% C.I. 0.193, 0.281). After forcing minimal non-modifiable covariates such as age and sex, a LASSO model trained by proteins alone was almost as predictive as models trained by both covariates and proteins (R 2 = 0.218, 95% C.I. 0.173, 0.262). Non-proteomic covariates of robust predictive value included age at cIMT measurement as a proxy for follow up time (univariate screening R 2 = 19.4%), baseline age (R 2 = 18.8%), blood pressure and anthropometry measurements. Fat-free mass index in trunk was the strongest predictor to cIMT among adiposity measurements (R 2 = 3.6%). CHGB (Secretogranin-1), CST6 (Cystatin-M), and PRELP (Prolargin) were proteins consistently selected across all regression models. Gene set enrichment analysis found overrepresented pathways in hemostasis (MGLL), angiogenesis (IGFBP7), vesicle-mediated transport (VTA1), and glycosaminoglycan metabolisms (HAPLN1 and VCAN). Conclusions: Plasma protein profiles provide incremental value in predicting a cIMT measurement on average nearly a decade later. Our findings suggested the roles of vasculogenic signaling and extracellular matrix proteins in remodeling.
Abstract 38: Prediction of Coronary Artery Disease Subtypes With Polygenic Risk Scores
Authors: Lathan Liou, Judit Garcia Gonzalez, Hei Man Wu, Zhe Wang, Shing Wan Choi, Clive Hoggart, Amy R Kontorovich, Jason Kovacic, Paul OReilly
Journal: Circulation (2024)

Background: Coronary Artery Disease (CAD) is a complex heterogeneous disease with multiple known etiological mechanisms. Consequently, CAD could be considered to have multiple subtypes, reflecting alternative driving etiologies that may warrant distinct therapeutic approaches. Motivated by this, we developed and optimized statistical models to predict CAD subtypes informed by genetic and clinical risk factors. Methods: We developed three models, each using logistic regression: (a) clinical risk factors only, (b) clinical risk factors plus genome-wide polygenic risk scores (PRS), and (c) clinical risk factors and pathway-based PRSs. We defined 5 different CAD subtypes on the basis of the literature: (1) stable vs. unstable, (2) occlusive vs non-occlusive, (3) high vs normal LDL, (4) high vs normal Lp(a), and (5) high vs normal ASCVD score. Clinical risk factors included: ASCVD score, ApoA, ApoB, LDL, HDL, triglycerides, and C-reactive protein. We calculated 4,402 pathway PRSs obtained from six genomic pathway databases (e.g. KEGG). We optimized the models using 80% of the samples as training, and then performed model evaluation (Bonferroni-corrected P-values) in an unseen 20% of the samples as validation. Results: Among 20,935 UK Biobank participants with CAD (mean age 63 years, 58% female), addition of pathway-based PRSs improved prediction of high LDL (P=1e-19) and high Lp(a) subtypes (P=1.8e-78). Using a lasso dimension reduction approach, the pathways most associated with the high Lp(a) subtype of CAD were: Fibronectin Binding (P=6xe-260), Apolipoprotein Binding (P=6.2e-252) and Serine-Type Endopeptidase Activity (P=1.7e-203). Conclusions: We demonstrate that prediction models distinguish CAD subtypes and that a novel pathway PRS approach can increase predictive power and provide insights into the genetic underpinnings of CAD subtypes. Pathway PRSs hold promise for the targeted therapeutic management for subgroups of CAD patients.
Abstract 47: Five-Year Change in Biological Aging: Insights From the Age Gene-Environment Susceptibility-Reykjavik Study
Authors: Nigus Asefa, Zhiguang Li, Valborg Guðmundsdóttir, Vilmundur Gudnason, Lenore Launer
Journal: Circulation (2024)

Introduction: Epigenetic clocks help to establish a pace of biological aging. Many studies employ biologic aging scores but typically assume consistent aging pace as individuals age. We examined demographic differences in biologic aging over time in an older population. Methods: DNA methylation data are from the Age Gene/Environment Susceptibility-Reykjavik Study (AGES-RS) cohort (n=2,081 participants; 57.6% female; baseline mean [SD] age 75.5 [4.8]). The participants were examined in 2002-2006 (AGES-1) and after approximately 5-year follow-up (AGES-2). We calculated the AGES-1 and AGES-2 Epigenetic Age Acceleration (EAA) separately using the DunedinPACE clock. EAA &gt;1 implies fast biological aging, EAA &lt;1 implies slow aging, and EAA~1 suggests similar biological and chronological aging pace. Individuals were categorized as having slow (&lt;1 SD of the mean), stable (within one SD), or fast (&gt;1 SD) EAA. We investigated age (&lt;76 vs. ≥76yrs) and sex differences in biological aging, as well as changes in biological aging pace. Results: Mean EAA (SD) at AGES-1 was 1.10 (0.11) and 1.13 (0.12) at AGES-2, with a strong correlation (r=0.83). While mean (SD) chronological age was comparable between women (75.4, [4.9]) and men (75.7 [4.7]) in AGES-1, women had lower EAA (1.07 [0.11]) than men (1.12 [0.10]; p &lt; 2.2e-16). At AGES-1, people &lt;76yrs had EAA 1.09 (0.11), while those aged ≥76yrs had 1.11 (0.11), p = 0.007. At AGES-1, 68.7% were stable, 15.1% slow, and 16.1% fast agers. At AGES-2, 74.6% maintained their pace, with 25.4% (n=528) changing categories. Of the 528, almost one-quarter (23.7%) went from fast to slow biological aging. Stratifying by sex, among the 314 (26.2%) women who switched categories, most (76.4%) transitioned from slow to fast, while 23.6% changed from fast to slow. Among males, 214 (24.2%) experienced EAA shifts, with 23.8% from fast to slow. When considering age groups, 297 individuals (24.3%) aged &lt; 76yrs had shifts compared to 231 (26.8%) aged ≥76yrs. Notably, the shift from slow to fast was 75.8% among those &lt; 76yrs compared to 77.1% in those ≥76yrs. Conclusion: In this older population cohort (&gt;67 years), approximately a quarter (25.4%) displayed shifts in biological aging due to methylation signals. More women had EAA shifts, but men often showed a greater biological aging. Older individuals (≥76yrs) also shifted from slow to fast aging. These findings highlight the dynamic nature of biological aging and its susceptibility to environmental influences. Further research is needed to investigate what factors are associated with these changes over time and whether the changes have clinical significance.
Abstract 50: Factors Associated With Obesity Pharmacotherapy Prescription Among Ambulatory Patients With Excess Weight
Authors: Meron Haile, Bige Ozkan, Jung-Im Shin, Michelle Ogunwole, Josef Coresh, Kimberly A Gudzune, Chiadi E Ndumele
Journal: Circulation (2024)

Background: Semaglutide, liraglutide (GLP1-RA), and tirzepatide (GLP1/GIP-RA) are potent anti-obesity pharmacotherapies resulting in marked weight loss and improved cardiometabolic outcomes. Limited insurance coverage is a well-understood barrier to these new therapies; thus, we sought to characterize additional clinical and sociodemographic factors associated with the prescription of these medications. Hypothesis: Likelihood of prescription of novel anti-obesity medications will be lower for individuals within racial and ethnic minority and lower SES subgroups. Methods: We identified adults who have obesity (BMI ≥30), existing insurance coverage for anti-obesity medications, and received outpatient care at Johns Hopkins from January 2023 to September 2023. We used multivariable logistic regression to evaluate factors associated with prescription of anti-obesity pharmacotherapies. Results: We included 18,164 patients (mean age 51 years, 64% female, 54% White, 35% Black, 5% Asian, mean BMI 36 kg/m 2 ). About 3% of eligible adults were prescribed anti-obesity medications. The likelihood of prescription was lower among Black adults (OR: 0.76, 95%CI [0.61-0.96]), males (OR: 0.54 [0.42-0.68]), and adults younger and older than 40-50 years of age; and higher for those with higher BMI (per 1-unit higher BMI, OR: 1.06 [1.05-1.07]). Living in a neighborhood with a higher area deprivation index or lower income was not independently associated with the likelihood of prescription. Clinically, those with diabetes (OR: 3.52 [2.78-4.44]) and hypertension (OR: 1.36 [1.08-1.71]) were more likely to be prescribed novel obesity pharmacotherapies. Conclusion: Among those with existing insurance coverage, the likelihood of obesity pharmacotherapy prescription differs by sociodemographic factors and cardiometabolic comorbidities. Understanding the mechanisms of these differences may have key implications for obesity management in diverse populations, and ultimately for cardiometabolic health equity.
Abstract 62: Association of Cardiovascular Health Trajectories Through Young Adulthood With Later-Life Incident Cardiovascular Disease and Mortality: The Coronary Artery Risk Development in Young Adults (CARDIA) Study
Authors: James W Guo, Hongyan Ning, Norrina B Allen, Amanda M Perak, James M Walker, Kelley P Gabriel, Donald M Lloyd-Jones
Journal: Circulation (2024)

Introduction: Associations of mid-life cardiovascular health (CVH) and incident cardiovascular disease (CVD) are well-studied. Less is known about patterns of young adulthood (YA) CVH, including its changes, and associations with later-life CVD outcomes. Methods: CVH was defined by Life’s Essential 8 (LE8) score. First, we used latent class modeling (SAS Proc Traj) to determine trajectories of continuous CVH scores (0-100) for CARDIA participants with ≥3 CVH measurements in YA (from year [Y]0, mean age 25, to Y20, mean age 45). Second, we examined direction and magnitude of change in CVH status (low, moderate, or high) between 2 CVH measurements (at Y0 and Y20), comparing participants with increased, decreased, or stable CVH status. Outcomes included incident CVD (MI, heart failure, stroke, CVD death) after Y20. Cox proportional hazards regression was used to estimate HRs for associations of YA CVH patterns with incident CVD. Results: There were 4,257 participants in YA (44% female, 48% Black) included. In the trajectory analysis, we identified four distinct CVH trajectory groups ( Figure 1A ): compared to the Persistently-High CVH group, the Moderate-Low-Declining and Moderate-Declining CVH trajectory groups had substantially higher hazards for incident CVD ( Figure 1B ). In the CVH status change analysis (N=2,884; Figure 1B ), compared to Stable High CVH in YA, Stable Low CVH had a 19-fold higher risk for incident CVD, and both increasing and decreasing CVH status in YA had greater risk for incident CVD as well. Conclusions: Trajectories of declining moderate and low CVH in YA had substantially higher risk for later-life incident CVD events. Those with decreasing CVH and those who started with lower CVH and improved it through YA, were also at higher risk. These data suggest that early life CVH and maintaining high CVH through YA are both important for prevention of later-life CVD.
Abstract 08: Blood Pressure Percentile Charts Better Identify High Risk Young Adults: The Coronary Artery Risk Development in Young Adults (CARDIA) Study
Authors: Norrina B Allen, Abigail Gauen, John T Wilkins, So Mi Cho, Bonita E Falkner, Nancy R Cook, Robert J Glynn, Donald M Lloyd-Jones, Bernard Rosner
Journal: Circulation (2024)

Introduction: Current guidelines set a static threshold for diagnosis of hypertension based on absolute blood pressure (BP) levels for all ages. However, BP generally increases with age and relative levels tend to track within individuals from young adulthood on. We aimed to determine whether BP percentiles , compared to the current AHA/ACC clinical thresholds for hypertension, are better able to identify young adults at higher risk for future CVD events. Methods: Among 5,115 CARDIA participants (ppts; mean age 25 yrs; 54% female; 51% Black), we used baseline BP level and over 35 years of follow-up for fatal and non-fatal CVD events. We calculated normative BP percentiles for adults aged 18 and older using NHANES by estimating underlying BP among treated individuals combined with observed BP for untreated individuals (Figure). Using these BP Percentile Charts, we calculated the baseline BP percentile for each ppt. Adjusted survival analyses examined the association between BP percentile (in deciles) and incident CVD events over 30 years. C statistics for models using BP percentiles vs those using current guideline-recommended thresholds were compared to determine relative accuracy. Results: At baseline, mean S/DBP was 112/70 mmHg and &lt;1% were on antihypertensive medications. Adjusted HRs for BP percentile decile demonstrated a dose-response effect with increasing risk associated with higher deciles of SBP. Compared to the 5 th decile young adults with SBPs in the highest decile 90-100% (SBP ≥127 for men and ≥120 for women) had a HR of 1.76 (95% CI 1.10-2.83). C statistics for models using BP percentiles were higher than for models using guideline-recommended thresholds (0.724 vs 0.713). No significant association was seen with DBP deciles. Conclusion: BP percentiles are better able to predict future CVD risk in young adults as compared to current age-invariant thresholds. These methods may help to risk stratify young adults and more accurately identify higher risk individuals for early prevention efforts.
Abstract 60: Cost-Effectiveness of Intensive Lipid-Lowering in Young Adulthood
Authors: Ciaran N Kohli-Lynch, Brandon K Bellows, Andrew E Moran, Yiyi Zhang, John T Wilkins, Norrina B Allen, Donald M Lloyd-Jones
Journal: Circulation (2024)

Introduction: Cumulative exposure to elevated low-density lipoprotein cholesterol (LDL-C) in young adulthood (ages 18-39) increases later life atherosclerotic cardiovascular disease risk. Statins are cost-effective in young adults with LDL-C ≥130 mg/dL. However, current guidelines only recommend statins for LDL-C ≥190 mg/dL. Some young adults may not achieve treatment goals with statin monotherapy and additional lipid-lowering therapy may be cost-effective, especially given the advent of generic pricing for ezetimibe and reduced pricing for PCSK9 inhibitors (PCSK9-Is). Objective: We aimed to estimate the cost-effectiveness of statins with supplemental ezetimibe and supplemental PCSK9-Is in US young adults with LDL-C ≥130 mg/dL. Methods: The CVD Policy Model, an established computer simulation model, calculated the lifetime costs and quality-adjusted life years (QALYs) of lipid-lowering therapy. Analyses were stratified by pretreatment LDL-C and sex and the model accounted for cumulative LDL-C exposure in young adulthood. Treatment costs and effects were derived from published literature and publicly available data. Results were presented as incremental cost-effectiveness ratios (ICERs) and categorized as highly cost-effective (&lt;$50,000/QALY), intermediately cost-effective ($50,000-$150,000/QALY), and not cost-effective (&gt;$150,000/QALY). Results: Compared with no treatment, statin plus ezetimibe therapy was highly cost-effective for young adult men with LDL-C ≥160 mg/dL (ICER: $38,400/QALY; Table) and young adult women with LDL-C ≥190 mg/dL (ICER: $26,600/QALY). Statin plus PCSK9-I therapy substantially increased QALYs but was not cost-effective in any subgroup. To be highly cost-effective for young adults with LDL-C ≥190 mg/dL, PCSK9-I prices would need to be reduced by 61%. Conclusion: With generic pricing, statin plus ezetimibe therapy is highly cost-effective for young adults with elevated LDL-C. Large price reductions are required for statins plus PCSK9-Is to be cost-effective.