Background: The Cardiovascular-Kidney Metabolic (CKM) Syndrome Stages are associated with heightened risk of cardiovascular diseases (CVD). However, the individual and additive impact of CKM Stage 3 (subclinical CVD) criteria on risk of incident clinical CVD is unclear. Methods: We included participants in the Dallas Heart Study, a population-sampled cohort from Dallas County, who attended study Visit 1 (2000-2002) and were categorized as Stage 2 or 3 CKM based on the presence of CVD risk factors or abnormal subclinical CVD biomarkers, respectively. CKM Stage 3 criteria included coronary artery calcium by cardiac CT (CAC) > 100 HU, abnormal left ventricular ejection fraction (< 50%) or mass (> 89 g/m 2 in women&> 112 g/m 2 in men) by cardiac MRI (CMR), elevated NT-proBNP (≥125 pg/mL), elevated high sensitive-troponin T or I (≥14 ng/L and ≥10 ng/L women; ≥22 ng/L and ≥12 ng/L in men, respectively) despite normal kidney function (eGFR> 90 mL/min/1.73 m 2 ), and high CVD risk based on very high risk by KDIGO HeatMap or PREVENT score > 20%. Participants were followed for incident coronary heart disease (CHD), heart failure (HF), stroke, or death through December 31, 2018. Multivariable Cox proportional hazard models adjusting for age, sex, and race/ethnicity were used to assess the association of Stage CKM 3 criteria with incident CVD using CKM Stage 2 as reference. Results: The study included 1,995 participants (1,568 CKM Stage 2, 427 CKM Stage 3) with a mean age of 45±10 years, 54% were female, and 50% reported Black race. Over a median follow-up of 17 years, 405 (20%) had incident CHD, HF, stroke, or died. Compared to Stage 2 participants, those who met two or three CKM Stage 3 criterion demonstrated higher risk of incident CVD compared to those who only met one Stage 3 criterion (Hazard Ratios 3.5 [95% CI 2.3-5.3] and 3.0 [95% CI 1.6-5.7] vs. 2.0 [95% CI 1.6-2.5], respectively; Figure 1A ). Among those with only 1 Stage 3 criterion (n=364), the most common was CAC (46%), followed by NT-proBNP (24%), troponin (15%), CMR (11%), and PREVENT score (5%). Those with elevated troponin alone were not at higher risk compared to Stage 2 (HR 1.3, 0.7-2.5, p=0.44), while the others were ( Figure 1B ). Conclusion: Within the CKM Syndrome framework, a greater number of Stage 3 criteria met is associated with greater risk of incident CVD compared to Stage 2. The prognostic value of isolated elevation of Tn to define Stage 3 requires further study.

Background: Natriruetic peptide (NP) screening is recommended for HF prevention in diabetes mellitus (DM), with NT-proBNP >125 pg/mL considered abnormal. However, evidence supporting this threshold is derived primarily from shorter-term studies. Lifetime risk (LTR) estimates for HF across NT-proBNP concentrations in DM is unknown. Methods: Participant-level data from 6 prospective cohorts (ARIC, MESA, CHS, FOS, FHS Generation 3, CRIC) and the Look AHEAD trial control group were pooled excluding participants with prevalent HF or ASCVD at baseline. Participants were stratified according to well-established NP thresholds ( Figure) . LTR of HF was determined at different index ages using the pratical incidence estimator macro with modified Kaplan-Meier methods that uses age as a time scale and mortality as a competing risk. A positive control group of adults with DM and prevalent CVD was analyzed separately. Results: Among 5,986 participants (age 61 y, 45% male, 25% Black), 1,134 developed HF over 79,347 person-years (14.3 per 1,000 person-years). At index age 55 y, the LTR of HF through 90 years was 34.4%. Lifetime risk of HF increased progressively across increasing NT-proBNP categories at each index age ( Figure 1A and 1B ). At index age 55 y, the LTR estimate for the NT-proBNP >125 pg/mL category (48.7%) was comparable to the positive control group with prevalent CVD (n = 1,687; 51.7%). LTR of HF was higher in Black (vs. non-Black) adults at every category with detectable NT-proBNP and was comparable to prevalent CVD at lower thresholds (50-125 pg/ml) ( Figure 1B ). LTR of HF among adults with low short-term HF risk (based on WATCH-DM or 10-y HF PREVENT risk scores) and NT-proBNP >125 pg/mL was high and comparable with the prevalent CVD group. Adding NT-proBNP to 30-year PREVENT HF risk score substantially improved LTR prediction performance (AUROC for 30-year risk: 0.569 to 0.685, p<0.001). Conclusions: In adults with DM, higher NT-proBNP concentrations identify higher LTR of HF, with the highest risk noted among those with concentrations >125 pg/ml supporting the current guideline recommended screening thresholds to discriminate lifetime risk. Lifetime risk of HF associated with NT-proBNP concentrations vary by race with disporporationtely high risk observed among Black adults.

Therapeutic Area ASCVD/CVD Risk Assessment Background The predicting risk of cardiovascular disease events (PREVENT) calculator was recently (2023) developed as an updated cardiovascular disease risk calculator from the prior Pooled Cohort Equations (PCE) calculator. Few studies are available comparing the accuracy and implications on risk categorization of using the PREVENT vs. PCE calculator. Methods Participants from the Dallas Heart Study first phase (DHS1) aged 40 to 65 without known cardiovascular disease at baseline and with complete follow up data for atherosclerotic cardiovascular disease (ASCVD) events (fatal or non-fatal myocardial infarction or stroke) were included. Discrimination was assessed using the Harrell C-statistic. Calibration was assessed evaluating observed vs. predicted 10-year ASCVD risk across risk deciles using the Nam-D'Agostino χ2 test. Categorical net reclassification was performed by cross-tabulating risk estimates from PREVENT and PCE in those with and without ASCVD events. The predicted risk categories based on clinically relevant treatment thresholds were: <5%, <7.5% and ³7.5% 10-year ASCVD risk. Replication was performed in the Dallas Heart Study second phase (DHS2) cohort which was slightly more contemporary (enrollment 2009 vs. 2001). Results The DHS1 cohort comprised 1346 individuals, mean age 49.6 (±6.6) years, 42% male, 48% Black individuals. Applying the PREVENT and PCE calculators resulted in similar c-statistics (0.7291 vs. 0.7253). Both calculator risk estimates diverged from the observed risk deciles (p<0.0001 each) with PREVENT generally underestimating risk and PCE overestimating risk, particularly in the middle and higher deciles (Fig 1,2). Among the 170 individuals who experienced an ASCVD event, PREVENT incorrectly down-classified ASCVD risk compared with PCE in 70 (41%), and only up-classified risk in 3 (Fig 3). Among the 1176 who did not experience an event, PREVENT appropriately down-classified risk in 324 (28%), and up-classified risk in 6 (Fig 4). When evaluating the DHS2 cohort (n=1742, mean age 52 years), similar results were found. Conclusions In a large, multiethnic, population-based cohort, the PREVENT calculator had comparable discrimination of ASCVD events as the PCE. Both miscalibrated observed risk, with PREVENT underestimating and PCE overestimating risk. The lower estimates by PREVENT could result in fewer individuals recommended preventive therapies, reducing therapy burden but also potentially increasing ASCVD events.

Introduction: The recent AHA presidential advisory on Cardiovascular-Kidney-Metabolic Syndrome (CKM) proposed a novel staging scheme, but limited data exist regarding CKM stage prevalence in the community. Prior population-based studies have lacked subclinical imaging measures, and have not reported variability by age, gender, and race/ethnicity. Methods: We estimated the population prevalence of CKM stages in Dallas County, from among 2,817 participants in the population-sampled Dallas Heart Study who attended study Visit 1 (2000-2002). Participants underwent protocol measurement of body composition, lipids, fasting blood sugar, serum creatinine, NT-proBNP, hs-cTnT, urinary albumin and creatinine, coronary artery calcium by cardiac CT (CAC), and cardiac function and mass by cardiac MRI. These were used to operationalize the following CKM stages: 0 – no CKM risk factors; 1 – excess or dysfunctional adiposity (body mass index, waist circumference, and fasting blood glucose); 2 – metabolic risk factors (hypertriglyceridemia, hypertension, diabetes, metabolic syndrome) and chronic kidney disease; 3 – subclinical cardiovascular diseases (CAC, LV hypertrophy or dysfunction by cardiac MRI, elevated cardiac biomarkers (NT-proBNP or hs-cTnT), high AHA-PREVENT or KDIGO scores); 4 – prevalent cardiovascular diseases (coronary heart disease, heart failure, atrial fibrillation, stroke). We used sampling weights to estimate the prevalence of CKM stages in Dallas County in 2000-2002 overall and by age category (30-44, 45-59, 60-65 years), gender, and race/ethnicity. Results: Among the 2,817 participants with a mean age of 44±10 years, the sample weighted demographics were 50% women, 52% non-Hispanic White, 20% non-Hispanic Black, and 26% Hispanic race/ethnicity. Among Dallas County adults, only 10% were CKM Stage 0 (no risk factors). The weighted prevalence of CKM Stages 1 through 4 was 16%, 46%, 23%, and 5%, respectively ( Figure A) . CKM stage prevalence was similar between men and women, while CKM Stage 4 was more frequent among older individuals and among non-Hispanic Black compared with non-Hispanic White and Hispanic individuals ( Figure B ). Conclusion: The public health burden of CKM is substantial. Ninety percent of Dallas County residents in 2000-2002 had some form of CKM syndrome, nearly half demonstrated metabolic dysfunction (Stage 2), and nearly one-fourth had subclinical cardiovascular disease (Stage 3).

BACKGROUND:Approximately one-half of patients with cardiogenic shock (CS) require invasive mechanical ventilation (IMV). Much of the data regarding IMV management is extrapolated from other populations, and little is known regarding management and outcomes of patients with CS who require IMV. OBJECTIVES:This study aims to provide data on IMV management in a CS-specific cohort. METHODS:Retrospective study of 104 patients treated for CS requiring IMV at an academic safety net hospital from 2017 to 2023. Indications for IMV, ventilator settings, and medications were obtained. Outcomes included in-hospital mortality, survival to extubation, and reintubation. RESULTS:Reasons for intubation included ongoing cardiac arrest (37%) and hypoxic respiratory failure (32%). Most were on low-level ventilator support 24 hours after intubation (median fraction of inspired oxygen 40% [IQR: 30%-50%], positive end-expiratory pressure 5 cm H2O [IQR: 5-8]). Spontaneous breathing trials were delayed in 78%, primarily due to hemodynamic instability (82%). Nonpalliative extubation occurred in 62% after a median of 4.8 days (IQR: 2.3-8.0). Among patients who received temporary mechanical circulatory support (tMCS) (49%) and survived, tMCS was removed before extubation in 98%. Reintubation occurred in 14% within 48 hours, and in-hospital mortality was 41%. CONCLUSIONS:In this cohort, patients were frequently on minimal ventilator support within 24 hours of intubation, yet spontaneous breathing trials and extubation were delayed due to hemodynamic instability. Rates of failed extubation were comparable to other forms of critical illness. Further research is necessary to determine optimal approaches to ventilator liberation in patients with CS, particularly when hemodynamic derangements or tMCS persist in patients who are otherwise candidates for extubation.

Background Most data linking chronic stress with cardiovascular disease (CVD) risk factors and outcomes have focused on single‐domain stress measurements. We evaluated the association between a novel composite measure of chronic perceived stress and CVD risk factors and outcomes in a diverse population. Methods and Results Individual chronic stress subcomponents (generalized stress, psychosocial, financial, and neighborhood stress) were standardized and integrated to create a novel composite stress score (CSS). Participants from the DHS (Dallas Heart Study) phase 2 (2007–2009) visit without prevalent CVD who completed chronic stress questionnaires were included (n=2685). Associations between CSS and demographics, cardiac risk factors, and health behaviors were assessed in multivariable analyses. Cox proportional hazards models adjusting for traditional risk factors were used to determine associations of the CSS with adjudicated atherosclerotic CVD and global CVD (atherosclerotic CVD, heart failure, and atrial fibrillation) outcomes. CSS was higher among participants who were younger, women, and Black or Hispanic individuals, with lower income and educational attainment (P<0.0001 for each). In multivariable regression models adjusting for age, sex, race and ethnicity, income and education, higher CSS associated with hypertension, smoking, higher body mass index, hemoglobin A1C, high‐sensitivity C‐reactive protein, and sedentary time (P<0.01 for each). Over a median follow‐up of 12.4 years, higher CSS associated with atherosclerotic CVD (adjusted hazard ratio [HR]. 1.22 per SD [95% CI, 1.01–1.47]) and global CVD (adjusted HR, 1.20 [95% CI, 1.03–1.40]). No interactions were seen between CSS, demographic factors, and outcomes. Conclusions Composite measures of chronic stress are higher in vulnerable populations and may help identify individuals at risk for CVD who may benefit from enhanced prevention strategies.

Therapeutic Area ASCVD /CVD Risk Reduction Background Hypercholesterolemia affects 86 million US adults, but only 54% are on cholesterol-lowering medications. Social media platforms like TikTok with over 1 billion users worldwide, offer both educational opportunities and risks of misinformation. This study evaluates the quality, accuracy, and health impact of cholesterol-related videos on social media through a TikTok analysis. Methods We searched #highcholesterol and #cholesterol on a new TikTok account on August 11, 2024. Of the 14,200 and 58,000 videos identified from each search term respectively, we evaluated the top 150 videos in each. Of the 300 total videos, 200 met inclusion criteria and were analyzed. Video analysis included engagement metrics, content, PEMAT-AV understandability and actionability scores, quality (GQS, mDISCERN), accuracy, and harm-benefit scores. Video content creators were classified as cardiologists, non-cardiologist physicians, other healthcare professionals, and lay creators. Evaluations were standardized with a grading rubric and a cardiologist-led training. Each video was graded for quality, accuracy, and harm-benefit by 2 independent reviewers. Videos with discrepant scores were graded by an expert cardiologist. Results The 200 videos had a total of 85,355,400 views, 3,264,000 likes, and 1,009,200 shares. 12% were made by cardiologists, 11.5% by other physicians, and 32.0% by other healthcare professionals. Diet (54%) and pathophysiology (59%) were the most discussed topics; exercise (4%) was the least discussed. 53% were advisory, 37% educational, and 18% promotional. Mean GQS, mDISCERN, PEVAT understandability, and PEVAT actionability (65% ± 39%) scores by content creator type were significantly different (Figure 1, p < 0.05), with higher scores noted in physician creators. Of the videos, 41% were deemed inaccurate and 36% were deemed potentially harmful. Videos by cardiologists and other physicians had significantly higher accuracy and health benefit scores than those by non-physicians (Figure 2). Conclusions The quality of content related to cholesterol on TikTok is low. Cholesterol misinformation is prevalent on TikTok. Videos by non-physician creators have lower quality, inaccuracies, and potential for harm. As video sharing social media platforms gain traction, clinician awareness of inaccuracies in patient-accessed cholesterol information is vital and strategies for credible content creation are necessary to combat this issue.

Introduction: The Friedewald equation (F-LDL-C) is the most widely used estimate of LDL-cholesterol (LDL-C), but it can be inaccurate at high TG and low LDL-C. The Martin-Hopkins (MH-LDL-C) and the Sampson (S-LDL-C) equations more accurately estimate LDL-C. Individuals with discordant LDL-C estimates may be undertreated for their ASCVD risk, depending on the equation used. The association of discordance in LDL-C estimates with ASCVD risk is not well established. Hypothesis: Individuals with greater discordance in LDL-C estimates are at higher risk for incident ASCVD Methods: We estimated F-LDL-C, MH-LDL-C, and S-LDL-C in 6636 patients (mean 61.6 years, 47% male) with TG < 400 mg/dL in the Multi-Ethnic Study of Atherosclerosis. We divided the cohort into quintiles (Q1-Q5) of LDL-C discordance, measured by the absolute difference of LDL-C values between equations (MH-LDL-C minus F-LDL-C, S-LDL-C minus F-LDL-C, MH-LDL-C minus S-LDL-C). We examined the association of characteristics (sex, age, race, BMI, diabetes, hypertension, tobacco use) with quintile of discordance using the Jonckheere-Terpstra test. We used multivariable adjusted Cox regression models to assess the hazard associated with quintiles of discordance for ASCVD events (MI, stroke, CV death, or revascularizations). Results: Greater LDL-C discordance (when MH-LDL-C and S-LDL-C were higher than F-LDL-C) was significantly associated with male sex, higher BMI, diabetes, hypertension, and tobacco use in unadjusted models. There were 1,275 ASCVD events over a median follow up of 18.4 years. The distribution of intra-quintile LDL-C values grew exponentially in Q5 across all groups, with the MH – F cohort demonstrating the greatest LDL-C range (absolute difference of 31.2 mg/dL). In fully adjusted Cox models, those in Q4 (HR 1.23, 95% CI 1.03-1.48) and Q5 (HR 1.28, 95% CI 1.06-1.55) of LDL-C discordance (where MH-LDL-C was higher than F-LDL-C) had a higher hazard for ASCVD events. Greater discordance between S-LDL-C and F-LDL-C trended towards increased ASCVD risk, but the results were not statistically significant. Conclusions: Greater LDL-C discordance where MH-LDL-C was higher than F-LDL-C is independently associated with greater ASCVD, after adjustments for variables associated with higher discordance. Our findings favor using newer equations to estimate LDL-C and suggest that this high-risk group is at risk for being undertreated if targeting the widely used F-LDL-C estimates.

Introduction: Chronic neighborhood stressors contribute to disparate CVD outcomes, with neighborhood socioeconomic deprivation (NSD) linked to inflammation. Separately, relationships have been seen with CVD and specific monocyte phenotypes. However, the connection between neighborhood exposures and monocyte subsets is less clear. Thus, we examined NSD with monocyte phenotypes, hypothesizing that chronic NSD cross-sectionally associates with monocyte subsets. Methods: This study utilized data from the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based prospective cohort of adults aged 45-84 years (N=6814). NSD was scored from principal factor analyses using U.S. Census data (2000), with higher values indicating higher deprivation. Monocyte phenotypes were measured from cryopreserved peripheral blood mononuclear cells by flow cytometry at MESA Exam 1 (2000-02). Subsets were characterized as classical monocytes (CMs, CD14++CD16-), intermediate monocytes (IMs, CD14+CD16+), and non-classical monocytes (NCMs, CD14+CD16++). Linear regression models were used to examine associations between NSD and monocyte phenotypes, adjusting for individual-level covariates. Results: Of the MESA cohort, participants with monocyte phenotypes (n=1527) were included in analyses (age 62.9±10.5 years, 50.5% male, 37.4% White, 28.6% Black, 20.6% Hispanic). Higher NSD was associated with lower CMs but higher IMs and NCMs (Table). When gender-stratified, relationships remained significant for CMs but not for IMs. In men, higher NSD was associated with higher NCMs but not when adjusted for covariates. Conclusion: Neighborhood deprivation as a marker of chronic stress was associated with shifts in monocyte subsets in a partially sex-dependent manner, with differential relationships with CMs, IMs, and NCMs. With IMs and NCMs associated with accelerated CVD, these findings may help illuminate the role of monocytes in how neighborhood exposures lead to CVD. Future analyses will examine interactions with race/ethnicity and inflammatory biomarkers.

Objective: Familial Hypercholesterolemia (FH) is underdiagnosed and undertreated. Several electronic health record (EHR) algorithms have been developed to improve identification of patients with FH. The approach to improving downstream processes of care and implementation of appropriate treatment after identification of these individuals is unclear. Methods: Individuals at UT Southwestern Medical Center with an LDL-C >= 190mg/dL (n = 8368) ever recorded in the EHR were included in an FH registry. As part of a QI program, random individuals from the registry deemed to possibly have FH were contacted via (1) MyChart message, (2) phone call, (3) letter, and/or (4) InBasket message to their PCP to notify them of the potential FH diagnosis, higher risk of ASCVD events, and offering referral to an FH specialist. Participants were contacted 1-4 times by one of these modalities. Chart extraction of contacted patients was performed to determine the type and frequency of contact and downstream visits and interventions. The composite primary outcome of the study included changes to lipid-lowering medications, family screening for FH, and new chart diagnosis of FH. Results: A total of 242 patients from the FH registry were reviewed of which 108 (mean age 55, 69 % women, highest mean LDL-C 267 +/- 47 mg/dL) met the inclusion criteria. A total of 180 patient contact attempts were made (mean 1.7 per patient) with most being by MyChart (48 %) and telephone (41 %). Of those contacted, 35 % had a follow-up visit with a PCP and/or a lipid specialist, and 22 % saw any composite change. Patients whose PCP was contacted were more likely to have adjustments made to their lipid lowering medication(s) (p = 0.016), be diagnosed with FH (p = 0.025), and have a follow-up visit (p = 0.033). A greater number of contacts (2.17 vs 1.52, p < 0.001) was also associated with any composite change in outcome. Conclusions: Approximately 1 in 5 individuals in a large healthcare system who were contacted for a recorded LDL-C > 190 mg/dL had a meaningful improvement in the management of severe hypercholesterolemia and diagnosis of FH. Various process factors were associated with a greater change in clinical care. These data highlight the importance of systematic evaluation to enhance interventions to improve the care of individuals with possible FH.

Introduction: While previous studies have quantified the community prevalence of Cardiovascular-Kidney-Metabolic (CKM) Stages, limited data exist regarding the expected progression of CKM Stages in mid-life. Methods: Among participants in the population-sampled Dallas Heart Study (DHS) longitudinal cohort, we estimated the prevalence of CKM Stages at Visit 1 (DHS1; 2000-2002) and Visit 2 (DHS2, 2007–2009). Protocol measurements of body composition, lipids, fasting blood sugar, serum creatinine, NT-proBNP, hs-cTnT, urinary albumin and creatinine, coronary artery calcium by cardiac CT (CAC), and cardiac function and mass by cardiac MRI were conducted at both visits. To account for Visit 2 non-attendance, we performed additional sensitivity analysis using inverse probability of attrition weights (IPAW) with the following DHS1 variables as predictors of DHS2 attendance: age, sex, race, obesity, income, education level, eGFR, ejection fraction, cardiovascular risk score, and history of heart failure, coronary heart disease, or stroke. Results: Among 2,991 participants at DHS1, 2030 also attended DHS2 and had an age of 44±10 years at DHS1 and 52±10 years at DHS2, 58% were female, and 50% reported non-Hispanic Black race/ethnicity. Over the median 6.8 (IQR 6.3-7.3) years between DHS1 and DHS2, the prevalence of CKM Stage 1 decreased from 14.7 to 10.8%; while the prevalence of CKM Stage 4 increased from 6% to 13%. Overall, 32% had progression in CKM Stage ( Figure) . Among the 280 (14%) participants who improved their CKM Stage, 206 (74%) derived from Stage 3 at DHS1, primarily meeting these criteria due to elevated troponin. Similar findings were observed in analyses incorporating IPAW to account for DHS2 non-attendance. Conclusion: In a community-based cohort, CKM Stages progressed in nearly one-third over 7 years in mid-life. The prevalence of advanced CKM Stages (i.e., Stage 3 and 4) increased from 28% to 34%. The role and criteria of cardiac biomarkers in defining Stage 3 CKM warrant further study.

Background: The recent AHA presidential advisory on Cardiovascular-Kidney-Metabolic Syndrome (CKM) proposed a novel staging scheme, but limited data exist regarding the associations of CKM Stages with incident cardiovascular events and mortality. Methods: We included participants in the Dallas Heart Study, a population-sampled cohort from Dallas County, who attended study Visit 1 (2000-2002) and underwent protocol measurement of body composition, lipids, fasting blood sugar, serum creatinine, NT-proBNP, hs-cTnT, urinary albumin and creatinine, coronary artery calcium by cardiac CT (CAC), and cardiac function and mass by cardiac MRI. CKM Stages were defined as per AHA CKM definitions: Stage 0 – no CKM risk factors; 1 – excess or dysfunctional adiposity; 2 – metabolic risk factors and/or chronic kidney disease; 3 – subclinical cardiovascular diseases [CAC, left ventricular hypertrophy or dysfunction by cardiac MRI, elevated cardiac biomarkers (NT-proBNP or hs-cTnT), high AHA-PREVENT or KDIGO scores]; 4 – prevalent cardiovascular diseases [coronary heart disease (CHD), heart failure (HF), atrial fibrillation, stroke]. Participants were followed for fatal and non-fatal clinical outcomes, including CHD, HF, and stroke through December 31 th 2018. Multivariable Cox proportional hazard models were used to assess the relationship of the CKM Stage with incident events compared to absent CKM or Stage 1, adjusting for age, sex, and race. Results: Among the 2,991 participants (age 44±10 years, 56% female, 50% reported non-Hispanic Black race), CKM stage prevalence was 9% Stage 0, 14% Stage 1, 47% Stage 2, 22% Stage 3, and 7% Stage 4. Over a median follow-up of 16.9 (IQR 16.4 -17.6) years, 19% died or developed CHD, HF, or stroke. No significant differences in risk were observed between Stage 0 and Stage 1 for composite and individual outcomes. Compared to those with Stage 0 or 1 CKM, a graded association was observed between greater CKM Stage 2-4 and heightened risk of composite CHD, HF, stroke, or death [HR 1.8 (95% CI 1.3-2.5), 3.5 (2.5-4.8), 5.4 (3.8-7.8), respectively] in adjusted model. Similar trends were observed for each component of the composite ( Figure) . Conclusion: Over 17 years of follow-up, individuals with CKM Stage 1 did not experience worse outcomes than those free of CKM Stage. CKM Stages 2, 3, and 4 were associated with a stepwise higher risk of all cause mortality and incident CHD, HF, and stroke compared to those with Stage 0 or 1 CKM.