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.

Factors associated with smoking intensity among adult smokers: findings from the longitudinal cohort of the Tehran lipid and glucose study.

BACKGROUND: Smoking is a significant public health problem, and there is a scarcity of documents regarding its severity, particularly in developing countries. This study aimed to determine factors related to the number of cigarettes consumed daily by adult smokers in Tehran. METHODS: This study was conducted within the framework of the longitudinal study of Tehran Lipid and Glucose Study (TLGS). The study included 786 adult smokers living during four consecutive follow-ups from 2005 to 2016. The intensity of smoking was measured by the number of cigarettes consumed daily by adult smokers. Data analysis was done longitudinally and based on the mixed effects zero-inflated discrete Weibull (ZIDW) regression model. RESULTS: The mean age of the individuals was 40.35 ± 12.68 years, and 643 (81.8%) of them were men. Also, 52.7% of individuals were daily smokers, 15.6% were occasional smokers, and 31.7% were non-smokers who became smokers during the study. Variables of age 1.005 (95%CI: 1.001-1.008), gender of male 1.196 (95%CI: 1.051-1.39), and marital status (divorced/widowed vs. single) 1.168 (95%CI: 1.015-1.39) were positively associated with smoking intensity. Education level (master and higher vs. illiterate) 0.675 (95%CI: 0.492-0.926)), employment status (student vs. unemployed) 0.683 (95%CI: 0.522-0.917), (housewife vs. unemployed) 0.742 (95%CI: 0.606-0.895), (Unemployed with income vs. unemployed) 0.804 (95%CI: 0.697, 0.923), implementation of smoking prohibition regulations (yes vs. no) 0.88 (95%CI: 0.843-0.932), and history of cardiovascular disease in male relatives (yes vs. no) 0.85 (95%CI: 0.771-0.951) were associated with lower smoking intensity. CONCLUSION: We showed that demographic factors are associated with the intensity of smoking among adults and should be considered in policymakers' intervention programs to reduce smoking and quit smoking.

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population.

BACKGROUND: The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis. METHODS: In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005-2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014-2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set. RESULTS: Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome. CONCLUSIONS: Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Patterns of change in obesity indices and other cardiometabolic risk factors before the diagnosis of type 2 diabetes: two decades follow-up of the Tehran lipid and glucose study.

BACKGROUND: Identifying patterns of variation in obesity indices and other cardiometabolic risk factors before the diagnosis of type 2 diabetes could provide insight into the critical period when drastic changes occurred and facilitate targeted interventions for the prevention of diabetes. Therefore, this study sought to explore patterns of change in obesity indices and other cardiometabolic risk factors before diabetes diagnosis. METHODS: We investigated 6305 participants (43.7% men) aged 20-65 from the Tehran Lipid and Glucose Study (TLGS) who were free of diabetes at baseline. First, we jointly estimated developmental multi-trajectories of obesity indices using multivariate latent class growth mixed model, and then patterns of cardiometabolic risk factors within the identified multi-trajectories were assessed using mixed-effects models. RESULTS: Three patterns of change in obesity indices were identified. Most participants belonged to the "progressing" group (83.4%; n = 742), with a slight but steadily rising in obesity indices until diagnosis in both men and women. All multi-trajectory groups showed similar exponential increases in fasting and 2-h plasma glucose concentrations 6 years before diagnosis and linear increases in blood pressure and total and LDL cholesterol throughout follow-up. Patterns of triglyceride and HDL cholesterol accompanied each group's patterns of change in obesity indices. CONCLUSION: Three patterns of the joint progression of obesity indices before diabetes diagnosis were accompanied by similar blood glucose patterns and other cardiometabolic risk factors. These findings suggest the impact of the increasing trend of obesity indices and other metabolic factors on the incidence of diabetes and emphasize the importance of assessing the metabolic risk factors at each visit.

Changes in Fasting plasma glucose status and risk of mortality events in individuals without diabetes over two decades of Follow-up: a pooled cohort analysis.

BACKGROUND: We aimed to assess the gender-specific impact of 3-year changes in fasting plasma glucose (FPG) status on the risk of all-cause, cardiovascular (CV), and cancer mortality in individuals without type 2 diabetes (T2DM) during an 18-year follow-up. METHODS: The study population included 14,378 participants aged 30-60 years (8272 women) from three population-based cohort studies, including Atherosclerosis Risk in Communities, Multi-Ethnic Study of Atherosclerosis, and Tehran Lipid and Glucose Study. Subjects were classified into six categories based on the approximately three-year changes in FPG status: (1) normal FPG (NFG) to NFG (reference category); (2) NFG to impaired fasting glucose (IFG) (i.e., 126 > FPG ≥ 100 mg/dl); (3) NFG to T2DM; (4) IFG to NFG; (5) IFG to IFG; (6) IFG to T2DM. Multivariable stratified Cox regression, adjusting for age, body mass index (BMI), BMI-Change, smoking status, hypertension, and hypercholesterolemia was used to estimate hazard ratios (HRs (95% CI)) for all-cause and cause-specific mortality events. Women-to-men ratios of HRs (RHRs) for each category were also estimated. RESULTS: During follow-up, 2,362 all-cause mortality events were recorded. Among women, all categories of FPG change, excluding IFG-NFG (HR, 95%CI 1.24 (0.98-1.57), p = 0.07), were associated with a higher risk of all-cause mortality compared to the NFG-NFG category. Moreover, women in IFG-T2DM group were at increased risk for CV mortality (2.21 (1.42-3.44)). We also found that women in NFG-IFG (1.52 (1.20-1.91)), NFG-T2DM (2.90 (1.52-5.51)), and IFG-IFG (1.30 (1.02-1.66)) categories had a higher risk for cancer mortality. However, among men, a higher risk of all-cause mortality was found for only two groups of NFG-T2DM (1.78 (1.15-2.74)) and IFG-T2DM (1.34 (1.04-1.72)). Women with IFG-IFG had a 24% higher risk for all-cause mortality events than their men counterparts (RHR; 1.24 (1.01-1.54)). After further adjustment for physical activity, results were in line with the main findings, excluding T2DM up to six years after the measurement period and early mortality events. CONCLUSION: In women, the IFG status, whether as incident, persistent, or converted to T2DM, had a higher risk for mortality events; however, among men, only conversion to T2DM conferred an excess risk of all-cause mortality.

Improvement of glycemic indices by a hypocaloric legume-based DASH diet in adults with type 2 diabetes: a randomized controlled trial.

PURPOSE: The current study aimed to investigate the effects of legumes inclusion in the hypocaloric dietary approaches to stop hypertension (DASH) diet on fasting plasma glucose (FPG) and cardiometabolic risk factors in overweight and obese patients with type 2 diabetes over 16 weeks. Also, the modulatory effects of rs7903146 variant in the transcription factor 7 like 2 (TCF7L2) gene that is associated with the risk of diabetes, were assessed on these cardiometabolic risk factors. METHODS: This study was a randomized controlled trial. Three-hundred participants, aged 30-65 years, whose TCF7L2 rs7903146 genotype was determined, were studied. The participants were randomly assigned to receive either the hypocaloric DASH diet or a hypocaloric legume-based DASH diet. The primary outcome was the difference in FPG change from baseline until the 16-week follow-up between the two dietary interventions. The secondary outcomes were differences in insulin resistance and lipid profile changes between the dietary intervention diets. RESULTS: A reduction in FPG, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), triglyceride, total cholesterol, and low-density lipoprotein cholesterol (LDL-C) was observed at week 16 in both hypocaloric dietary interventions. Compared to the DASH diet, the legume-based DASH diet decreased the FPG and HOMA-IR. There is no interaction between rs7903146 and intervention diets on glycemic parameters. CONCLUSION: The DASH diet, enrich in legumes, could improve the glycemic parameters in participants with type 2 diabetes, regardless of having rs7903146 risk or non-risk allele. REGISTRATION NUMBER OF CLINICAL TRIAL: Iranian Registry of Clinical Trials (IRCT) (code: IRCT20090203001640N17).

Type 2 diabetes remission after Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), and one anastomosis gastric bypass (OAGB): results of the longitudinal assessment of bariatric surgery study.

BACKGROUND: Several studies on various bariatric surgeries involving patients with type 2 diabetes mellitus (T2DM) showed an overall rate of remission of hyperglycemia. However, there is little known about predictive factors on remission after different types of surgeries. The aim of this study was to identify the T2DM remission rate and to determine the effects of preoperative factors characteristics of remission of type 2 diabetes in Iran. METHODS: We conducted a retrospective analysis of 1351 patients with T2DM operated by three different types of surgeries (Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), and One Anastomosis Gastric Bypass (OAGB)). Diabetes remission was defined according to the American Diabetes Association (ADA) criteria. Binary logistic regression analyses were employed. RESULTS: A total of 1351 patients, 675 patients (50.0%) undergoing OAGB, 475 (35.2%) RYGB, and 201 (14.9%) SG. 80.6%, 84.2% of OAGB, 81.7%, 82.6% of RYGB, and 77.1%, 81.5% of SG participants were in T2DM remission after 1 and 3 years, respectively. 1- and 3-year remission were associated with preoperative age, duration of T2DM, FBS and HbA1c, BMI, insulin therapy, and a family history of obesity (p < 0.05). CONCLUSION: The remission of T2DM after RYGB, SG, and OAGB surgery is dependent on various preoperative factors. Patients with younger age, shorter duration of T2DM, lower preoperative HbA1c and FBS, higher BMI, who were not on insulin therapy, and not having a family history of obesity were the best candidates to achieve a prolonged diabetes remission.

Association of cumulative excess weight and waist circumference exposure with transition from metabolically healthy obesity to metabolically unhealthy.

BACKGROUND AND AIMS: The association between obesity severity and duration with the transition from metabolically healthy obese/overweight (MHO) phenotype to metabolically unhealthy obese (MUO) phenotype is not well understood. METHODS AND RESULTS: This study includes the Tehran Lipid and Glucose Study participants who were initially classed as MHO. Cumulative excess weight (CEW) and cumulative excess waist circumference (CEWC) scores, which represent the accumulation of body mass index and waist circumference deviations from expected values over time (kg/m2 ∗ y and cm ∗ y, respectively), were calculated until the transition from MHO to MUO or the end of follow-up. The sex-stratified association of CEW and CWEC with the transition from MHO to MUO was investigated by time-dependent Cox models, adjusting for confounders. Out of 2525 participants, 1732 (68.5%) were women. During 15 years of follow-up, 1886 (74.6%) participants transitioned from MHO to MUO. A significant association was found between CEW and CEWC quartiles with the development of MUO among women participants (fully adjusted hazard ratios in the fourth quartile of CEW and CEWC [95% (CI)]:1.65 [1.37-1.98] and [95% CI]: 1.83 [1.53-2.19]). There was no significant association between CEW and CEWC with the MHO transition to MUO among men participants. CONCLUSION: Over 15 years of follow-up in TLGS, general and central obesity accumulation was associated with the increased transition from MHO to MUO among women participants. More research with a larger sample size is needed to confirm and explain why the results are different for men and women.

The trend of 10-year cardiovascular risk among diabetic and non-diabetic participants in Tehran Lipid and glucose study: 1999-2018.

BACKGROUND: Assessing the risk of cardiovascular disease (CVD) is crucial in preventive cardiology. We aimed to determine the trend of CVD risk among individuals with and without diabetes during two decades of follow-up in a Middle Eastern cohort. METHODS: We studied 8,450 individuals (55.5% women) aged 40-75 years who participated in the Tehran Lipid and Glucose Study (TLGS). Diabetes status and CVD risk factors were evaluated in six examinations from 1999 to 2018. The individual 10-year CVD risk score was calculated using the ACC/AHA recommended risk equation. We used generalized estimating equation models (GEE) to assess the time trends of CVD risk factors and CVD risk scores in diabetic and non-diabetic groups separately. RESULTS: The age-adjusted ACC/AHA risk score significantly decreased in non-diabetic women and men (from 3.2% to 1.6% in women and 6.8% to 5.0% in men; p for trend < 0.001). Whereas the risk significantly decreased among diabetics men (from 13.8% to 11.5%), it increased somehow among diabetics women (from 5.3% to 5.5%). Furthermore, in both sexes, diabetic individuals compared to non-diabetic ones had better control on their systolic blood pressure, total cholesterol, and fasting plasma glucose during the last two decades. CONCLUSIONS: The CVD risk and most CVD risk factors improved in individuals with and without diabetes in the past two decades; however, they have not reached the targets yet. So, more stringent lifestyle modifications and treatment strategies are needed, especially for primary prevention in the general population.

Diabetes mellitus risk prediction in the presence of class imbalance using flexible machine learning methods.

BACKGROUND: Early detection and prediction of type two diabetes mellitus incidence by baseline measurements could reduce associated complications in the future. The low incidence rate of diabetes in comparison with non-diabetes makes accurate prediction of minority diabetes class more challenging. METHODS: Deep neural network (DNN), extremely gradient boosting (XGBoost), and random forest (RF) performance is compared in predicting minority diabetes class in Tehran Lipid and Glucose Study (TLGS) cohort data. The impact of changing threshold, cost-sensitive learning, over and under-sampling strategies as solutions to class imbalance have been compared in improving algorithms performance. RESULTS: DNN with the highest accuracy in predicting diabetes, 54.8%, outperformed XGBoost and RF in terms of AUROC, g-mean, and f1-measure in original imbalanced data. Changing threshold based on the maximum of f1-measure improved performance in g-mean, and f1-measure in three algorithms. Repeated edited nearest neighbors (RENN) under-sampling in DNN and cost-sensitive learning in tree-based algorithms were the best solutions to tackle the imbalance issue. RENN increased ROC and Precision-Recall AUCs, g-mean and f1-measure from 0.857, 0.603, 0.713, 0.575 to 0.862, 0.608, 0.773, 0.583, respectively in DNN. Weighing improved g-mean and f1-measure from 0.667, 0.554 to 0.776, 0.588 in XGBoost, and from 0.659, 0.543 to 0.775, 0.566 in RF, respectively. Also, ROC and Precision-Recall AUCs in RF increased from 0.840, 0.578 to 0.846, 0.591, respectively. CONCLUSION: G-mean experienced the most increase by all imbalance solutions. Weighing and changing threshold as efficient strategies, in comparison with resampling methods are faster solutions to handle class imbalance. Among sampling strategies, under-sampling methods had better performance than others.

Clinical features, risk factors and a prediction model for in-hospital mortality among diabetic patients infected with COVID-19: data from a referral centre in Iran.

OBJECTIVES: The aim of this study was to identify risk factors of in-hospital mortality among diabetic patients infected with COVID-19. STUDY DESIGN: This is a retrospective cohort study. METHODS: Using logistic regression analysis, the independent association of potential prognostic factors and COVID-19 in-hospital mortality was investigated in three models. Model 1 included demographic data and patient history; model 2 consisted of model 1, plus vital signs and pulse oximetry measurements at hospital admission; and model 3 included model 2, plus laboratory test results at hospital admission. The odds ratios (ORs) and 95% confidence intervals (95% CIs) were reported for each predictor in the different models. Moreover, to examine the discriminatory powers of the models, a corrected area under the receiver-operating characteristic curve (AUC) was calculated. RESULTS: Among 560 patients with diabetes (men = 291) who were hospitalised for COVID-19, the mean age of the study population was 61.8 (standard deviation [SD] 13.4) years. During a median length of hospitalisation of 6 days, 165 deaths (men = 93) were recorded. In model 1, age and a history of cognitive impairment were associated with higher mortality; however, taking statins, oral antidiabetic drugs and beta-blockers was associated with a lower risk of mortality (AUC = 0.76). In model 2, adding the data for respiratory rate (OR 1.07 [95% CI 1.00-1.14]) and oxygen saturation (OR 0.95 [95% CI 0.92-0.98]) slightly increased the AUC to 0.80. In model 3, the data for platelet count (OR 0.99 [95% CI 0.99-1.00]), lactate dehydrogenase (OR 1.002 [95% CI 1.001-1.003]), potassium (OR 2.02 [95% CI 1.33-3.08]) and fasting plasma glucose (OR 1.04 [95% CI 1.02-1.07]) significantly improved the discriminatory power of the model to AUC 0.86 (95% CI 0.83-0.90). CONCLUSIONS: Among patients with type 2 diabetes, a combination of past medical and drug history and pulse oximetry data, with four non-expensive laboratory measures, was significantly associated with in-hospital COVID-19 mortality.