Cardiovascular health metrics defined by Life's Essential 8 scores and subsequent macrovascular and microvascular complications in individuals with type 2 diabetes: A prospective cohort study.

AIM: To investigate the association between cardiovascular health metrics defined by Life's Essential 8 (LE8) scores and vascular complications among individuals with type 2 diabetes (T2D). MATERIALS AND METHODS: This prospective study included 11 033 participants with T2D, all devoid of macrovascular diseases (including cardiovascular and peripheral artery disease) and microvascular complications (e.g. diabetic retinopathy, neuropathy and nephropathy) at baseline from the UK Biobank. The LE8 score comprised eight metrics: smoking, body mass index, physical activity, non-high-density lipoprotein cholesterol, blood pressure, glycated haemoglobin, diet and sleep duration. Cox proportional hazards models were established to assess the associations of LE8 scores with incident macrovascular and microvascular complications. RESULTS: During a median follow-up of 12.1 years, we identified 1975 cases of incident macrovascular diseases and 1797 cases of incident microvascular complications. After adjusting for potential confounders, each 10-point increase in the LE8 score was associated with an 18% lower risk of macrovascular diseases and a 15% lower risk of microvascular complications. Comparing individuals in the highest and lowest quartiles of LE8 scores revealed hazard ratios of 0.55 (95% confidence interval 0.47-0.62) for incident macrovascular diseases, and 0.61 (95% confidence interval 0.53-0.70) for incident microvascular complications. This association remained robust across a series of sensitivity analyses and nearly all subgroups. CONCLUSION: Higher LE8 scores were associated with a lower risk of incident macrovascular and microvascular complications among individuals with T2D. These findings underscore the significance of adopting fundamental strategies to maintain optimal cardiovascular health and curtail the risk of developing diabetic vascular complications.

Comprehensive Multiple Risk Factor Control in Type 2 Diabetes to Mitigate Heart Failure Risk: Insights From a Prospective Cohort Study.

OBJECTIVE: The impact of comprehensive risk factor control on heart failure (HF) risk and HF-free survival time in individuals with type 2 diabetes (T2D) was evaluated in this study. RESEARCH DESIGN AND METHODS: This prospective study included 11,949 individuals diagnosed with T2D, matched with 47,796 non-T2D control study participants from the UK Biobank cohort. The degree of comprehensive risk factor control was assessed on the basis of the major cardiovascular risk factors, including blood pressure, BMI, LDL cholesterol, hemoglobin A1c, renal function, smoking, diet, and physical activity. Cox proportional hazards models were used to measure the associations between the degree of risk factor control and HF risk. Irwin's restricted mean was used to evaluate HF-free survival time. RESULTS: During a median follow-up of 12.3 years, 702 individuals (5.87%) with T2D and 1,402 matched control participants (2.93%) developed HF. Each additional risk factor controlled was associated with an average 19% lower risk of HF. Optimal control of at least six risk factors was associated with a 67% lower HF risk (hazard ratio [HR] 0.33; 95% CI 0.20, 0.54). BMI was the primary attributable risk factor for HF. Notably, the excess risk of HF associated with T2D could be attenuated to levels comparable to those of non-T2D control participants when individuals had a high degree of risk factor control (HR 0.66; 95% CI 0.40, 1.07), and they exhibited a longer HF-free survival time. CONCLUSIONS: Comprehensive management of risk factors is inversely associated with HF risk, and optimal risk factor control may prolong HF-free survival time among individuals with T2D.

Sleep Patterns and Traditional Cardiovascular Health Metrics: Joint Impact on Major Adverse Cardiovascular Events in a Prospective Cohort Study.

BACKGROUND: This study examines the association between traditional cardiovascular health (CVH) metrics and major adverse cardiovascular events (MACE) incidence in individuals with diverse sleep patterns. METHODS AND RESULTS: We analyzed data from 208 621 participants initially free of cardiovascular disease (CVD) in the UK Biobank study. Sleep patterns were assessed using scores for chronotype, duration, insomnia, snoring, and daytime dozing. Traditional CVH scores were derived from the Life's Simple 7 metrics. Cox proportional hazards multivariate regression assessed associations between distinct combinations of CVH and sleep scores and MACE, including nonfatal myocardial infarction, nonfatal stroke, and CVD mortality. Over a mean follow-up of 12.73 years, 9253 participants experienced incident MACE. Individuals with both a healthy sleep pattern and ideal CVH levels had the lowest MACE risk compared with those with a poor sleep pattern and poor CVH levels (hazard ratio, 0.306 [95% CI, 0.257-0.365]; P<0.001). Elevated CVH scores were associated with a reduced risk of MACE across different sleep patterns. Similar trends were observed for individual MACE components, heart failure, and all-cause mortality. These findings remained robust in sensitivity analyses and across various subgroups. CONCLUSIONS: In individuals without known CVD, maintaining a favorable sleep pattern and achieving optimal CVH levels, as measured by traditional metrics, were associated with the lowest MACE risk. Enhanced CVH significantly reduced CVD risk, even in individuals with a poor sleep pattern. These results emphasize the importance of considering multiple dimensions of sleep health alongside CVH to mitigate CVD risk. REGISTRATION: URL: https://www.ukbiobank.ac.uk; Unique identifier: 91090.

Variability in Lipid Profiles During Young Adulthood and the Risk of Coronary Artery Calcium Incidence in Midlife: Insights From the CARDIA Study.

BACKGROUND: Intraindividual variability in lipid profiles is recognized as a potential predictor of cardiovascular events. However, the influence of early adulthood lipid profile variability along with mean lipid levels on future coronary artery calcium (CAC) incidence remains unclear. METHODS: A total of 2395 participants (41.6% men; mean±SD age, 40.2±3.6 years) with initial CAC =0 from the CARDIA study (Coronary Artery Risk Development in Young Adults) were included. Serial lipid measurements were obtained to calculate mean levels and variability of total cholesterol, low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and triglycerides. CAC incidence was defined as CAC >0 at follow-up. RESULTS: During a mean follow-up of 9.0 years, 534 individuals (22.3%) exhibited CAC incidence. Higher mean levels of total cholesterol, LDL-C, and non-HDL-C were associated with a greater risk of future CAC incidence. Similarly, 1-SD increment of lipid variability, as assessed by variability independent of the mean, was associated with an increased risk of CAC incidence (LDL-C: hazard ratio, 1.139 [95% CI, 1.048-1.238]; P=0.002; non-HDL-C: hazard ratio, 1.102 [95% CI, 1.014-1.198]; P=0.022; and triglycerides: hazard ratio, 1.480 [95% CI, 1.384-1.582]; P<0.001). Combination analyses demonstrated that participants with both high lipid levels and high variability in lipid profiles (LDL-C and non-HDL-C) faced the greatest risk of CAC incidence. Specifically, elevated variability of LDL-C was associated with an additional risk of CAC incidence even in low mean levels of LDL-C (hazard ratio, 1.396 [95% CI, 1.106-1.763]; P=0.005). These findings remained robust across a series of sensitivity and subgroup analyses. CONCLUSIONS: Elevated variability in LDL-C and non-HDL-C during young adulthood was associated with an increased risk of CAC incidence in midlife, especially among those with high mean levels of atherogenic lipoproteins. These findings highlight the importance of maintaining consistently low levels of atherogenic lipids throughout early adulthood to reduce subclinical atherosclerosis in midlife. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005130.