Importance:Higher coffee intake has been associated with lower risk of type 2 diabetes (T2D), but the underlying biological pathways remain incompletely understood. Objective:To examine associations of coffee intake with insulin sensitivity, adiposity, and T2D risk, and assess whether coffee intake modifies associations between pathway-specific genetic susceptibility and incident T2D. Design Setting and Participants:Cross-sectional analyses among 806 participants without T2D in the VITamin D and OmegA-3 TriaL (VITAL) clinical sub-cohort, who underwent repeated dietary assessment, clinical phenotyping, and dual-energy X-ray absorptiometry imaging at baseline and year-2. Prospective analyses among 333,053 UK Biobank participants without T2D at baseline who had dietary and genetic data and were followed for a median of 13.3 years. Exposures:Coffee intake assessed by food frequency questionnaires. In UK Biobank, 12 pathway-specific polygenic scores (pPS) representing distinct T2D pathophysiological mechanisms were evaluated. Main Outcomes and Measures:The primary outcomes, in VITAL, were HbA1c, oral glucose tolerance test-derived measures of glucose response and insulin sensitivity, β-cell function, and overall, truncal, and visceral adiposity; in UK Biobank, was incident T2D. Results:In VITAL, higher coffee intake was associated with higher insulin sensitivity (standardized β per cup/day, 0.046; P = .004) and lower visceral adipose tissue mass (β, -0.047; P = .006), after adjusting for demographic, lifestyle, and clinical factors, including body mass index. In UK Biobank, higher coffee intake was associated with lower T2D incidence (hazard ratio per cup/day, 0.96; 95% CI, 0.95-0.97), lower triglyceride-to-HDL cholesterol ratio (β,-0.01; P = 2.51 × 10^-19), and lower visceral adipose tissue mass (β, -0.01; P = 4.28 × 10^-9). Associations of 3 pPS related to insulin resistance and fat distribution with incident T2D were attenuated among participants consuming higher amount of coffee than among non-consumers (P for interaction < .0043). Conclusions and Relevance:Higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower risk of T2D. Together with the attenuation of associations between pathway-specific genetic susceptibility and T2D risk among higher coffee consumers, these findings suggest that insulin resistance and visceral adiposity-related pathways may contribute to the association between coffee intake and T2D risk. Key Points:Question: Is coffee intake associated with specific insulin sensitivity and adiposity markers, and type 2 diabetes risk, and does it modify associations between pathway-specific genetic susceptibility and type 2 diabetes?Findings: In analyses repeated dietary, clinical, and imaging phenotyping in 806 VITAL participants and prospective data from 333,053 UK Biobank participants, higher coffee intake was associated with greater insulin sensitivity, lower visceral adiposity, and lower type 2 diabetes risk. Higher coffee intake also attenuated associations of three pathway-specific polygenic scores related to insulin resistance and fat distribution with type 2 diabetes risk.Meaning: These findings suggest that pathways related to insulin sensitivity and visceral adiposity may contribute to the associations between coffee intake and lower type 2 diabetes risk.

INTRODUCTION/OBJECTIVE:Both fasting and postprandial hypertriglyceridemia are associated with atherosclerotic cardiovascular disease (ASCVD). The Hellenic Postprandial Lipemia Study (HPLS, NCT02163044) is the largest prospective cohort trial assessing the effects of statin therapy on postprandial lipemia. METHODS:Individuals at high or very high risk for ASCVD were evaluated, and their characteristics were recorded at baseline (Visit 1). At Visit 2 (2-4 weeks after Visit 1) and Visit 3 (3-4 months after Visit 2), serum triglyceride (TG) levels were measured after a 12-hour fast (fTG) as well as 4 hours after the ingestion of a commercially available oral fat tolerance test meal (pTG). After Visit 2, all individuals were treated with a statin. RESULTS AND DISCUSSION:Among 900 participants, 699 completed all 3 visits, and of these, 209 (29.9%) had an abnormal pTG response. The mean (standard deviation, SD) total- and low-density lipoprotein cholesterol concentrations were 225 (50) and 148 (46) mg/dL at Visit 1, 231 (42) and 156 (40) mg/dL at Visit 2, and 171 (28) and 101 (27) mg/dL at Visit 3. At Visit 2, the mean fTG level was 127 (45) mg/dL and pTG was 188 (73) mg/dL with a mean difference of 58 mg/dL (P<0.001). At Visit 3, the mean fTG concentration was 110 (40) mg/dL, while pTG was 140 (54) mg/dL (mean difference: 29 mg/dL; P<0.001). Fasting glucose levels had no impact on pTG response in statin-treated individuals with abnormal postprandial lipemia. CONCLUSION:Nearly 30% of individuals at high-/very high-risk for ASCVD had postprandial hypertriglyceridemia. Statin treatment normalized abnormal postprandial lipemia in 75.6% of participants, and decreased pTG concentration even in those with normal fTG levels.

This cross-sectional study examines the proportion of US adults whose low-density lipoprotein cholesterol (LDL-C) levels exceed 2026 guidelines across risk categories and among individuals with atherosclerotic cardiovascular disease.

Context Risk of cardiometabolic disease increases in women transitioning to postmenopause, during which estradiol declines universally. Most of these women experience fragmentation of sleep because of nocturnal hot flashes, without a reduction in total sleep time. Objective We examined the independent impact of estradiol suppression, sleep, and their combination on cardiometabolic outcomes categorized as satiety and hunger, lipid profile, cardiac vital signs, and glucoregulation. Design Participants completed 5-night inpatient studies under eucaloric conditions, once during mid-follicular phase/estrogenized and again under estrogen-suppressed conditions, using the same experimental protocol both times. For all participants, sleep was unfragmented the first 2 nights and then experimentally fragmented without reducing total sleep time the next 3 nights. Setting Inpatient intensive physiological monitoring research facility. Participants Thirty-eight healthy premenopausal women. Intervention(s) Clinical experimental induced menopause model including GnRH agonist-induced hypoestrogenism and sleep fragmentation. Main Outcome Measure(s) Leptin and satiety. Results Estradiol suppression significantly decreased leptin and increased lipid profiles (false discovery rate [FDR]-adjusted P ≤ .05). Sleep fragmentation significantly increased heart rate (FDR-adjusted P = .002) and trended to increase fasting glucose (FDR-adjusted P = .08). Estradiol suppression and sleep fragmentation worsened individual cardiometabolic outcomes by (median, interquartile range) 4.0% (1.5%, 6.3%) from normalized baseline values. Sleep fragmentation worsened a composite cardiometabolic index derived from individual clinical cardiometabolic measures by an additional 103% over estradiol suppression alone. Conclusion Independent of aging, there are significant adverse changes in cardiometabolic health induced by core components of the transition to postmenopause, including novel effects of sleep fragmentation, a modifiable target.

AIM:The "2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia" retires and replaces the "2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol." METHODS:A comprehensive literature search was conducted from October 2024 to December 2024 to identify clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE:The focus of this clinical practice guideline is to address the evaluation, management, and monitoring of individuals with dyslipidemias, including high blood cholesterol, hypertriglyceridemia, and elevated lipoprotein(a).

AIM The "2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia" retires and replaces the "2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol." METHODS A comprehensive literature search was conducted from October 2024 to December 2024 to identify clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE The focus of this clinical practice guideline is to address the evaluation, management, and monitoring of individuals with dyslipidemias, including high blood cholesterol, hypertriglyceridemia, and elevated lipoprotein(a).

BACKGROUND:Visceral adipose tissue (VAT), fat located within the abdominal cavity, is strongly associated with systemic inflammation and poor cardiometabolic health. OBJECTIVES:This study investigates the cross-sectional relationships between multiple cardiometabolic traits, including VAT, and proteomic-based inflammatory signatures. METHODS:Body adiposity distribution quantified using dual-energy X-ray absorptiometry (DXA), cardiometabolic traits, and plasma proteomics inflammation panel (Olink Explore 384) were measured in a discovery cohort from the Vitamin D and Omega-3 Trial (VITAL; n = 525) and a replication cohort from the Cocoa Supplement and Multivitamin Outcomes Study (COSMOS; n = 371). We derived inflammatory proteomic markers of VAT, systolic blood pressure (SBP), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), fasting glucose, and insulin resistance (Homeostasis Model Assessment of Insulin Resistance; HOMA-IR). Inflammatory proteomic markers were identified via linear regression at a false discovery rate (FDR) < 0.05. RESULTS:VAT showed the strongest inflammatory proteomic profile, with 221 associated proteins (62% of those tested) and effect estimates of up to β = 0.59 (SE = 0.03). Proteins associated with the cardiometabolic traits showed directions of association largely consistent with those observed for VAT, except for HDL-C, which showed inverse associations. After adjusting the regression models for VAT, most protein associations with glucose and SBP (>97%), HOMA-IR (70%), TG (62%), and HDL-C (56%) were no longer statistically significant suggesting that VAT accounts for a substantial portion of the shared variance in these associations. To further characterize these shared molecular associations, we examined the Bayesian network architecture of 86 proteomic markers common to all cardiometabolic traits. Some of the VAT-attenuated protein signatures with high centrality were TGFB1, PDLIM7, COLEC12, and LAIR1. CONCLUSIONS:These hub-like proteins may reflect shared inflammatory pathways linking VAT with cardiometabolic traits and provide hypotheses for future mechanistic and therapeutic investigation.

Dyslipidemia remains a common and treatable risk factor for atherosclerotic cardiovascular disease (ASCVD) in the United States (US) and worldwide. In 2026, the American College of Cardiology (ACC), the American Heart Association (AHA), and other US societies released a new guideline on the management of dyslipidemia. This review summarizes 10 key takeaways in the primary prevention setting from the 2026 multisociety guideline on the evaluation and management of dyslipidemia. Key takeaways from the new guideline include early evaluation for dyslipidemia and potential genetic dyslipidemias starting in childhood, with subsequent screening every 5 years after age 19. Adult screening for dyslipidemia with Lp(a) at least once in lifetime and selective use of ApoB testing is also recommended. After obtaining lipid measurements, risk assessment is performed using the PREVENT-ASCVD score to calculate 10-year (and 30-year in adults aged 30–59) ASCVD risk. Considering demographic, clinical and laboratory data as well as coronary calcium scoring in addition to the PREVENT-ASCVD risk score allows for shared decision-making regarding initiation of lipid lowering therapy (LLT), primarily with statins. The new guideline also reintroduces treatment goals for LDL-C, non-HDL-C, and apo B in select cases based on risk category for the primary prevention population. The 2026 ACC/AHA multisociety dyslipidemia guideline incorporates evolving data on dyslipidemia evaluation and management to optimize ASCVD risk. This review describes 10 key highlights from the guideline for the evaluation and management of dyslipidemia in the primary prevention setting.

AIM The “2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Dyslipidemia” retires and replaces the “2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol.” METHODS A comprehensive literature search was conducted from October 2024 to December 2024 to identify clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE The focus of this clinical practice guideline is to address the evaluation, management, and monitoring of individuals with dyslipidemias, including high blood cholesterol, hypertriglyceridemia, and elevated lipoprotein(a).

Abstract Obesity is an established risk factor for at least 13 types of cancers, yet the role of obesity-related cardiometabolic risk factors and biomarkers in mediating this relationship remains unclear. We classified 37,540 Women’s Health Study participants as obese (body mass index [BMI]≥30 kg/m2) or non-obese and with or without any cardiometabolic risk factors (type 2 diabetes, high cholesterol, and hypertension), with status updated annually via questionnaire. Baseline blood biomarkers were measured in 27,210 women. We used Cox proportional hazards regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for risk of obesity-related cancers, adjusting for age, reproductive history, hormone therapy use, lifestyles, and cancer site-specific factors. The reference group in all models is women without obesity and without risk factors. Baseline mean age was 54.6 years, 19% of women had obesity, and ≥1 cardiometabolic risk factor was present in 65% of women who had obesity and 42% of those without obesity. We confirmed 4,895 incident obesity-related cancers over a median follow-up of 22 years. The obesity-related cancer risk was elevated among women with obesity without risk factors (HR=1.28; 95% CI=1.06,1.54) and with ≥1 risk factor (HR=1.42; 95% CI=1.29,1.56). Strongest association for obesity with risk factors was observed for endometrial cancer (585 cases; HR=3.58; 95% CI=2.70,4.76), followed by kidney (139 cases; HR=1.97; 95% CI=1.10,5.53), colorectal (626 cases; HR=1.69; 95% CI=1.27,2.25) and postmenopausal breast cancer (2,765 cases; HR=1.22; 95% CI=1.08,1.39); no association was observed for ovarian cancer (280 cases; HR=0.90; 95% CI=0.58,1.38). Obesity without risk factors was associated with an elevated risk of pancreatic (139 cases; HR=3.93; 95% CI=1.65,9.39) and endometrial cancer (HR=2.20; 95% CI=1.37,3.54). Higher obesity-related cancer risk was observed among women with obesity and elevated inflammation (C-reactive protein, soluble intercellular adhesion molecule-1, fibrinogen, and GlycA), glycemia (hemoglobin A1c), and dyslipidemia (ApoB/A1 ratio, cholesterol, high-density lipoprotein, low-density lipoprotein, and lipoprotein(a)) (HRs 1.15-1.38). Obesity with normal levels of inflammation, glycemia, and kidney function (creatinine and homocysteine) biomarkers was also related to a higher obesity-related cancer risk (HRs 1.08-1.38). In conclusion, obesity with and without cardiometabolic risk factors is positively associated with risk of several obesity-related cancers. Poor cardiometabolic health, glycemic and lipid metabolism, and inflammation may mediate obesity’s association with cancer, although excess cancer risk persists among metabolically healthier women with obesity. Citation Format: Cong Wang, Howard D. Sesso, Paulette D. Chandler, Aditi Hazra, Samia Mora, I-Min Lee, Julie E. Buring, JoAnn E. Manson, Deirdre K. Tobias. Obesity with and without cardiometabolic risk factors, biomarkers, and incident obesity-related cancer: The Women’s Health Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB210.

Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases. We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous latent trajectory, using heart-rate-aware neural ordinary differential equation (ODE) dynamics and a graph-based mesh autoencoder to reconstruct anatomically consistent 3D+t ventricular motion. A covariate-conditioned prior defines the expected end-diastolic latent state, and a Cox proportional hazards model tests whether deviations from this prior predict incident heart failure. We studied 72,386 UK Biobank participants without baseline cardiovascular disease, including 367 incident heart failure events. In a held-out evaluation subset, adding the latent score to refitted pooled cohort equations improved the stratified C-index from 0.704 to 0.785, compared with 0.764 for seven established cardiac markers. Compared with non-graph and non-ODE approaches, the proposed model gave the best trade-off between reconstruction fidelity, generative realism, and downstream prognostic performance. These results suggest that continuous full-cycle modeling of ventricular motion provides informative cardiac phenotypes beyond conventional CMR summaries, while external validation in more representative patient cohorts is required before clinical risk-prediction use.

Background and Aims : Interventions in preventive cardiology traditionally focus on four standard modifiable cardiovascular risk factors (SMuRFs): hypertension, dyslipidaemia, diabetes mellitus, and smoking. Yet, a substantial proportion of incident cardiovascular events accrues for individuals with none of these factors, particularly among women for whom cardiovascular disease remains under-detected and under-treated. The utility of the inflammatory biomarker high-sensitivity C-reactive protein (hsCRP) was evaluated to detect cardiovascular risk in SMuRF-less women participating in the prospective NIH-funded Women's Health Study. Methods : High-sensitivity C-reactive protein was measured at baseline among 12 530 initially healthy American women with no standard modifiable risk factors who were followed over 30 years for first major adverse cardiovascular events (myocardial infarction, coronary revascularization, ischaemic stroke, or cardiovascular death). Hazard ratios (HRs) and 95% confidence intervals (95% CI) for incident coronary heart disease (CHD), ischaemic stroke, and total cardiovascular events were calculated across quintiles of hsCRP, along with 30-year cumulative incidence curves. Hazard ratios were also computed according to common clinical thresholds of hsCRP, according to standard deviation change in hsCRP, and as a continuous variable in penalized spline regression models. Results : During 30-year follow-up, 973 first major cardiovascular events accrued. Median baseline hsCRP was significantly higher among SMuRF-less women who subsequently suffered a cardiovascular event when compared with those who did not (median hsCRP 2.22 vs 1.50 mg/L, P < .0001). In age-adjusted analyses, the HRs for the primary endpoint of incident CHD from lowest (referent) to highest levels of hsCRP at study entry were 1.0 (referent), 1.24, 1.41, 1.57, and 2.23 (P-trend < .0001) such that CHD risk over 30 years increased 21% for each increasing quintile of hsCRP (age-adjusted HR 1.21, 95% CI 1.13-1.29, P < .0001). Corresponding HRs for the top vs bottom quintile of hsCRP were 1.69 (95% CI 1.16-2.47) for ischaemic stroke and 1.74 (95% CI 1.42-2.14) for total cardiovascular disease events. Using common clinical hsCRP thresholds, SMuRF-less women with hsCRP > 3 mg/L had a 77% higher risk of CHD events, a 39% higher risk of ischaemic stroke events, and a 52% higher risk of total cardiovascular disease events when compared with those with hsCRP < 1 mg/L. Spline analyses demonstrated linear association with risk across the spectrum of hsCRP values. Hazards were moderately attenuated after additional adjustment for body mass index and estimated glomerular filtration rate [covariate-adjusted CHD HR 1.86 (95% CI 1.35-2.58, P = .0002) for comparison of the top vs bottom quintile of hsCRP and 1.52 (95% CI 1.20-1.92, P = .0006) for comparison of those with hsCRP > 3 mg/L to those < 1 mg/L]. Conclusions : Over a 30-year horizon, cardiovascular events commonly occur among 'SMuRF-less but inflamed' women who are otherwise missed by current screening algorithms, a clinically important observation given recent trial data demonstrating that statin therapy reduces risk by 38% among such individuals.

Background:Immunoglobulin G (IgG) plays a critical role in immune defense yet our understanding of its role in cardiovascular disease (CVD) is evolving. Observational studies have correlated statin use with changes in IgG N-glycan structures. However, statin effects on IgG N-glycan changes have not been tested in randomized controlled trials, and their direct association with CVD remains unclear. Methods:IgG N-glycans were measured at baseline and after one year of randomized high-intensity statin interventions in 2 sub-studies of randomized trials: JUPITER (Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin; NCT00239681; primary prevention; discovery, n = 239 participants); and TNT (Treating to New Targets; NCT00327691; secondary prevention; validation, n = 711). Using linear regression adjusted for baseline levels of IgG N-glycans and clinical risk factors (e.g., age, sex) as well as the occurrence of CVD during the year of follow-up, we investigated the one-year randomized effects of high-intensity rosuvastatin v. placebo on IgG N-glycans in JUPITER. Significant statin-IgG N-glycan associations were then validated in TNT with one-year randomized effects of high- v. low-intensity atorvastatin intervention. We examined the architecture of IgG N-glycan connectivity at baseline using a data-driven Bayesian network and compared it with the architecture after one year of randomized statin intervention. We then investigated whether the changes in IgG N-glycans triggered by statins were associated with incident CVD events. Results:We identified 5 IgG N-glycans (corresponding to core fucosylated, monosialylated, and disialylated IgG N-glycans) in JUPITER whose levels decreased significantly with statin versus placebo (false discovery rate < 0.05), with an approximate 11.3-25.9% reduction in the individual IgG N-glycan levels. Four out of the five IgG N-glycans altered by statin were validated in TNT. Furthermore, monosialylation and core fucosylation (glycan peaks, GP 16 and 18) were inversely associated with CVD in JUPITER (OR = 0.87 and 0.73 per standard deviation increase, 95% CI: (0.57, 0.98) and (0.55, 0.96) respectively), and validated in TNT. Despite the effect of statin therapy on certain IgG N-glycans, the overall architecture of the IgG N-glycan network remained unchanged after one year of statin intervention. Conclusion:High-intensity statin interventions decreased several specific IgG N-glycan levels without changing the overall architecture of IgG N-glycan connectivity. Two IgG N-glycans that were decreased by statins were inversely associated with CVD outcomes, suggesting that statins have effects on monosialylated and core fucosylated IgG N-glycans, which may affect their cardioprotective properties. These findings highlight a potential immunomodulatory role of statins through IgG N-glycan alterations that should be further investigated in relation to CVD.