Global Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality.

BACKGROUND: Five modifiable risk factors are associated with cardiovascular disease and death from any cause. Studies using individual-level data to evaluate the regional and sex-specific prevalence of the risk factors and their effect on these outcomes are lacking. METHODS: We pooled and harmonized individual-level data from 112 cohort studies conducted in 34 countries and 8 geographic regions participating in the Global Cardiovascular Risk Consortium. We examined associations between the risk factors (body-mass index, systolic blood pressure, non-high-density lipoprotein cholesterol, current smoking, and diabetes) and incident cardiovascular disease and death from any cause using Cox regression analyses, stratified according to geographic region, age, and sex. Population-attributable fractions were estimated for the 10-year incidence of cardiovascular disease and 10-year all-cause mortality. RESULTS: Among 1,518,028 participants (54.1% of whom were women) with a median age of 54.4 years, regional variations in the prevalence of the five modifiable risk factors were noted. Incident cardiovascular disease occurred in 80,596 participants during a median follow-up of 7.3 years (maximum, 47.3), and 177,369 participants died during a median follow-up of 8.7 years (maximum, 47.6). For all five risk factors combined, the aggregate global population-attributable fraction of the 10-year incidence of cardiovascular disease was 57.2% (95% confidence interval [CI], 52.4 to 62.1) among women and 52.6% (95% CI, 49.0 to 56.1) among men, and the corresponding values for 10-year all-cause mortality were 22.2% (95% CI, 16.8 to 27.5) and 19.1% (95% CI, 14.6 to 23.6). CONCLUSIONS: Harmonized individual-level data from a global cohort showed that 57.2% and 52.6% of cases of incident cardiovascular disease among women and men, respectively, and 22.2% and 19.1% of deaths from any cause among women and men, respectively, may be attributable to five modifiable risk factors. (Funded by the German Center for Cardiovascular Research (DZHK); ClinicalTrials.gov number, NCT05466825.).

Gender differences in midlife to later-life cumulative burden and variability of obesity measures and risk of all-cause and cause-specific mortality.

BACKGROUND/OBJECTIVES: Previous studies have reported the gender-specific association between general and central obesity measures, using snapshot assessments, and mortality events. This study seeks to further explore this link by examining how the longitudinal cumulative burden and variability of obesity measures from midlife to later-life impact mortality events in the Atherosclerosis Risk in Communities (ARIC) study population, specifically in relation to gender differences. SUBJECTS/METHODS: Using data from the ARIC study, a total of 7615 (4360 women) participants free of cardiovascular disease, cancer, and early mortality events were included in the data analysis. Longitudinal cumulative burden (estimated by the area under the curve (AUC) using a quadratic mixed-effects method) and variability (calculated according to average successive variability (ASV)) were considered as exposures, separately and all together. Cox proportional hazard regression models were used to estimate multivariable-adjusted standardized hazard ratios. RESULTS: The mean age was 62.4 and the median follow-up was 16.9 years. In men, AUCs of waist-related obesity measures, and also ASVs of all obesity measures were associated with increased all-cause mortality risk. In women, waist circumference and waist-to-height ratio AUCs were associated with increased all-cause mortality risk. Regarding cardiovascular mortality, all adiposity measures ASVs in both genders and waist-related obesity measures AUCs in men were associated with increased risk. Significant gender differences were found for the associations between cumulative and variability of waist-to-hip ratio for all-cause mortality and all adiposity measures ASVs for cardiovascular mortality risk with higher impact among men. CONCLUSIONS: Cumulative burden and variability in general and central obesity measures were associated with higher all-cause and cardiovascular mortalities among men. In women, general obesity measures variability, as well as cumulative and variability of central adiposity measure, increased all-cause mortality risk.

Weight change and risk of incident type 2 diabetes: short, medium and long-term follow-up in tehran lipid and glucose study.

BACKGROUND: Despite the high burden of obesity and Type 2 diabetes (T2DM) in the Middle East/West Asia region, the effect of weight change on the development of T2DM is poorly addressed. Therefore, we aimed to assess the impact of 3-year body weight change on incident of T2DM over 3-, 6-, and 9-year periods among Iranian adults. METHODS: A total of 6930 participants (men = 2567) aged ≥ 20 years free of T2DM or cancer at baseline were included. Weight measurements were taken at baseline (2002-2005) and approximately 3 years later. Participants were categorized based on their weight change ratio into ≥ 5% loss, stable (± 5%), and ≥ 5% gain. Generalized estimating equations (GEE), adjusted with age, sex, education levels, baseline measurements of fasting plasma glucose, weight, waist circumference, triglycerides to high-density lipoprotein cholesterol ratio, family history of diabetes, current smoker, hypertension, and prevalent cardiovascular disease were applied to estimate the Odds ratios (ORs) and 95% confidence intervals (CIs) of weight change categories for incident T2DM, considering stable weight as a reference. RESULTS: During median follow-ups of 3-, 6-, and 9-year, 295, 505, and 748 cases of T2DM occurred, respectively. Weight gain of ≥ 5%, as compared to stable weight group (± 5%), was associated with increased T2DM risk, with ORs of 1.58 (95% CI 1.16-2.14), 1.76 (1.41-2.20), and 1.70 (1.40-2.05) for the 3-, 6-, and 9-year follow-ups, respectively, in multivariable analysis; corresponding values for weight loss ≥ 5% were 0.48 (0.29-0.80), 0.57 (0.40-0.81), and 0.51 (0.38-0.68), respectively. This association persisted even after adjusting for attained weight. Subgroup analysis showed consistent associations across age, gender, and body mass index categories. CONCLUSION: Weight gain and loss of ≥ 5% were associated with increased and decreased risks of incident T2DM, respectively, regardless of attained weight. This association was consistent over various follow-up durations among the Iranian population as recommended by guidelines.

Optimizing cardiovascular disease mortality prediction: a super learner approach in the tehran lipid and glucose study.

BACKGROUND & AIM: Cardiovascular disease (CVD) is the most important cause of death in the world and has a potential impact on health care costs, this study aimed to evaluate the performance of machine learning survival models and determine the optimum model for predicting CVD-related mortality. METHOD: In this study, the research population was all participants in Tehran Lipid and Glucose Study (TLGS) aged over 30 years. We used the Gradient Boosting model (GBM), Support Vector Machine (SVM), Super Learner (SL), and Cox proportional hazard (Cox-PH) models to predict the CVD-related mortality using 26 features. The dataset was randomly divided into training (80%) and testing (20%). To evaluate the performance of the methods, we used the Brier Score (BS), Prediction Error (PE), Concordance Index (C-index), and time-dependent Area Under the Curve (TD-AUC) criteria. Four different clinical models were also performed to improve the performance of the methods. RESULTS: Out of 9258 participants with a mean age of (SD; range) 43.74 (15.51; 20-91), 56.60% were female. The CVD death proportion was 2.5% (228 participants). The death proportion was significantly higher in men (67.98% M, 32.02% F). Based on predefined selection criteria, the SL method has the best performance in predicting CVD-related mortality (TD-AUC > 93.50%). Among the machine learning (ML) methods, The SVM has the worst performance (TD-AUC = 90.13%). According to the relative effect, age, fasting blood sugar, systolic blood pressure, smoking, taking aspirin, diastolic blood pressure, Type 2 diabetes mellitus, hip circumference, body mss index (BMI), and triglyceride were identified as the most influential variables in predicting CVD-related mortality. CONCLUSION: According to the results of our study, compared to the Cox-PH model, Machine Learning models showed promising and sometimes better performance in predicting CVD-related mortality. This finding is based on the analysis of a large and diverse urban population from Tehran, Iran.

Trajectory patterns of metabolic syndrome severity score and risk of type 2 diabetes.

BACKGROUND: The available evidence indicates that the severity of metabolic syndrome tends to worsen progressively over time. We assessed the trajectory of age and sex-specific continuous MetS severity score (cMetS-S) and its association with the development of diabetes during an 18-year follow-up. METHODS: In a prospective population-based Tehran Lipid and Glucose Study, 3931 eligible participants free of diabetes, aged 20-60 years, were followed at three-year intervals. We examined the trajectories of cMetS-S over nine years using latent growth mixture modeling (LGMM) and subsequent risks of incident diabetes eight years later. The prospective association of identified trajectories with diabetes was examined using the Cox proportional hazard model adjusting for age, sex, education, and family history of diabetes, physical activity, obesity (BMI ≥ 30 kg/m2), antihypertensive and lipid-lowering medication, and baseline fasting plasma glucose in a stepwise manner. RESULTS: Among 3931 participants, three cMetS-S trajectory groups of low (24.1%), medium (46.8%), and high (29.1%) were identified during the exposure period. Participants in the medium and high cMetS-S trajectory classes had HRs of 2.44 (95% CI: 1.56-3.81) and 6.81 (95% CI: 4.07-10.01) for future diabetes in fully adjusted models, respectively. Normoglycemic individuals within the high cMetS-S class had an over seven-fold increased risk of diabetes (HR: 7.12; 95% CI: 6.05-12.52). CONCLUSION: Although most adults exhibit an unhealthy metabolic score, its severity usually remains stable throughout adulthood over ten years of follow-up. The severity score of metabolic syndrome has the potential to be utilized as a comprehensive and easily measurable indicator of cardiometabolic dysfunction. It can be employed in clinical settings to detect and track individuals at a heightened risk of developing T2DM, even if their glucose levels are normal.

The Association of Cardiovascular Diseases Risk Scores and Osteosarcopenia Among Older Adult Populations: The Results of Bushehr Elderly Health (BEH) Program.

Considering the association of cardiovascular disease (CVD) with both osteoporosis and sarcopenia, this study aimed to explore the association between a newly developed CVD risk score and osteosarcopenia in the elderly population. Participants in the second phase of the Bushehr Elderly Health (BEH) program were included. Osteosarcopenia was defined as having both osteopenia/osteoporosis and sarcopenia. The 10-year CVD risk score was estimated using the WHO lab-based model. The participants were considered as high-risk if the CVD risk was ≥ 20%. The estimated risks were compared in individuals with and without osteosarcopenia. The association of CVD risk and osteosarcopenia was investigated using a logistic regression model, adjusted by potential confounders. In all, 2392 participants (1161 men) with a mean age of 69.3 (± 6.3) years were studied and 532 [242 (45.5%) men] individuals were diagnosed with osteosarcopenia. The median (IQR) CVD risks were 0.340 (0.214) and 0.229 (0.128) in men with and without osteosarcopenia, respectively (P < 0.001); In women, the corresponding values were 0.260 (0.147) and 0.207 (0.128), respectively (P < 0.001). Adjusted by confounders, CVD risk ≥ 20% in women, increased the odds of osteosarcopenia by 72%. Body mass index showed an inverse association with osteosarcopenia in both men (0.81, 95%CI: 0.78-0.85) and women (0.66, 95%CI: 0.62-0.70). Considering the area under the ROC curve, the models showed a discriminative ability of 82% in men and 89% in women. This study displayed a significant association between WHO CVD risk score and osteosarcopenia. Due to the difficult diagnosis of osteosarcopenia, the high association of cardiovascular risk score with this disease can help identify high-risk individuals and refer them for further diagnostic procedures. Considering the high prevalence of osteosarcopenia and its complications in the older population, comprehensive strategies are needed to find high-risk populations.

External validation of the American prediction model for incident type 2 diabetes in the Iranian population.

BACKGROUND: The primary aim of the present study was to validate the REasons for Geographic and Racial Differences in Stroke (REGARDS) model for incident Type 2 diabetes (T2DM) in Iran. METHODS: Present study was a prospective cohort study on 1835 population aged ≥ 45 years from Tehran lipids and glucose study (TLGS).The predictors of REGARDS model based on Bayesian hierarchical techniques included age, sex, race, body mass index, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and fasting plasma glucose. For external validation, the area under the curve (AUC), sensitivity, specificity, Youden's index, and positive and negative predictive values (PPV and NPV) were assessed. RESULTS: During the 10-year follow-up 15.3% experienced T2DM. The model showed acceptable discrimination (AUC (95%CI): 0.79 (0.76-0.82)), and good calibration. Based on the highest Youden's index the suggested cut-point for the REGARDS probability would be ≥ 13% which yielded a sensitivity of 77.2%, specificity 66.8%, NPV 94.2%, and PPV 29.6%. CONCLUSIONS: Our findings do support that the REGARDS model is a valid tool for incident T2DM in the Iranian population. Moreover, the probability value higher than the 13% cut-off point is stated to be significant for identifying those with incident T2DM.

A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis.

BACKGROUND: Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data. METHOD: Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC). RESULTS: The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches. CONCLUSION: Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

Cohort profile update: Tehran cardiometabolic genetic study.

The Tehran cardiometabolic genetic study (TCGS) is a large population-based cohort study that conducts periodic follow-ups. TCGS has created a comprehensive database comprising 20,367 participants born between 1911 and 2015 selected from four main ongoing studies in a family-based longitudinal framework. The study's primary goal is to identify the potential targets for prevention and intervention for non-communicable diseases that may develop in mid-life and late life. TCGS cohort focuses on cardiovascular, endocrine, metabolic abnormalities, cancers, and some inherited diseases. Since 2017, the TCGS cohort has augmented by encoding all health-related complications, including hospitalization outcomes and self-reports according to ICD11 coding, and verifying consanguineous marriage using genetic markers. This research provides an update on the rationale and design of the study, summarizes its findings, and outlines the objectives for precision medicine.

Are HOMA-IR and HOMA-B good predictors for diabetes and pre-diabetes subtypes?

BACKGROUND: To investigate the association between the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) and Homeostasis Model Assessment of Beta-cell function (HOMA-B) with the incidence of diabetes and pre-diabetes subtypes. METHODS: A total of 3101 normoglycemic people aged 20-70 years were included in the 6-year follow-up study. Multinomial logistic regression was used to calculate the incidence possibility of isolated Impaired Fasting Glucose (iIFG), isolated Impaired Glucose Tolerance (iIGT), Combined impaired fasting glucose & impaired glucose tolerance (CGI), and Diabetes Mellitus (DM) per standard deviation (SD) increment in HOMA-IR and HOMA-B in the crude and multivariable model. RESULTS: In the multivariate model, an increase in one SD change in HOMA-IR was associated with a 43, 42, 75, and 92% increased risk of iIFG, iIGT, CGI, and DM, respectively. There was a positive correlation between the increase in HOMA-B and the incidence of iIGT; however, after adjusting the results for metabolic syndrome components, it was inversely correlated with the incidence of iIFG [Odds Ratio = 0.86(0.75-0.99)]. CONCLUSIONS: HOMA-IR is positively correlated with diabetes and pre-diabetes subtypes' incidence, and HOMA-B is inversely correlated with the incidence of iIFG but positively correlated with iIGT incidence. However, none of these alone is a good criterion for predicting diabetes and pre-diabetes.