Tehran Lipid and Glucose Study: A National Legacy

Maternal androgen excess increases the risk of metabolic syndrome in female offspring in their later life: A long-term population-based follow-up study

PURPOSE

Hyperandrogenic intrauterine environment may lead to the development of metabolic disorders in offspring in their later life. In this study, we aimed to determine the impact of maternal hyperandrogenism (MHA) on metabolic syndrome (MetS) risk in female offspring in their later life.

METHODS

In this cohort study conducted in Tehran, Iran, female offspring with MHA (n = 323) and without MHA (controls) (n = 1125) were selected. Both groups of female offspring were followed from the baseline to the date of the incidence of events, censoring, or end of the study period, whichever came first. We used age-scaled unadjusted and adjusted Cox regression models to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between MHA and MetS in female offspring. The software package STATA was used for statistical analysis, and the significance level was set at P < 0.05.

RESULTS

We observed a higher risk of MetS (unadjusted HR (95% CI), 1.36 (1.05-1.77)), (P = 0.02) and (adjusted HR (95% CI), 1.34 (1.00-1.80)), (P = 0.05, borderline)), in female offspring with MHA, compared to controls. The results were adjusted for the potential confounders including body mass index (BMI) at baseline, net changes of BMI, physical activity, education status, and birth weight.

CONCLUSION

Our results suggest that MHA increases the risk of developing MetS in female offspring in their later life. Screening of these female offspring for MetS may be recommended.

Body mass index trajectories from childhood concerning emotional states in young adulthood: Tehran Lipid and Glucose Study

This study aimed to identify body mass index (BMI) trajectories from childhood and their relationships with depression, anxiety, and stress symptoms in young adulthood. A total of 687 children aged 4-18 years were recruited from the Tehran Lipid and Glucose Study. Throughout 18 years of follow-up, BMI was measured every 3 years for a maximum of 6 data points. Participants completed the depression, anxiety, and stress scale in their young adulthood (aged 22-36). The group-based trajectory modelling was applied to identify BMI patterns. The logistic regression model was used to assess the association between BMI trajectories and depression, anxiety, and stress symptoms in adulthood. Two BMI trajectories were identified from childhood to young adulthood: healthy weight (HW = 69.6%) and persistent increasing overweight/obesity (PIO = 30.4%). After adjusting for potential confounders, compared with the HW group, men in the PIO group were more likely to experience higher stress levels (OR: 1.62, 95% CI: 0.99-2.63; p = 0.05). No significant association was observed between the PIO trajectory and depression and anxiety among both sexes and stress symptoms in females. Our results highlight that developing overweight and obesity from childhood may be related to increased stress in males.

Ideal cardiovascular health status and risk of cardiovascular disease and all-cause mortality: over a decade of follow-up in the Tehran lipid and glucose study

Objective

To quantify the association between ideal cardiovascular health (CVH) metrics and incident cardiovascular disease (CVD) including different subtypes [coronary heart disease (CHD), stroke, and sudden death], and all-cause mortality in an Iranian population.

Methods

The study population included 6,388 participants (2,726 men) aged 48.0 ± 12.4 years free of CVD at baseline. We utilized the American Heart Association’s 2020 impact target criteria of ideal, intermediate, and poor CVH. The multivariate Cox proportional Hazard model, adjusted for age, sex, educational level, marital status, and family history of CVD, was applied to estimate the hazard ratio (HR) of outcomes per one additional metric of ideal CVH metrics. Furthermore, the risk was also calculated for ideal and intermediate categories considering poor category as a reference.

Results

During the median follow-up of 11.26 years, 692 CVD, 589 CHD, 130 stroke, 111 sudden death, and 519 all-cause mortality events were reported. All of the individual ideal CVH metrics were independent predictors except intermediate physical activity level for CVD, BMI &lt; 25 kg/m2, and intermediate physical activity for all-cause mortality. Each additional metrics of ideal CVH decreased the risk by 31 (0.69, 0.65–0.73) for CVD, 32 (0.68, 0.64–0.73) for CHD, 31 (0.69, 0.60–0.80) for stroke, 25 (0.75, 0.64–0.88) for sudden death, and 13% (0.87, 0.81–0.93) for all-cause mortality events. Moreover, intermediate and ideal categories of CVH metrics were associated with lower risk for different CVD outcomes, i.e., 44 (0.56, 0.48–0.65) and 76% (0.24, 0.17–0.35) for CVD; 43 (0.57, 0.47–0.67) and 75% (0.25, 0.16–0.37) for CHD, 58 (0.42, 0.29–0.61) and 86% (0.14, 0.04–0.44) for stroke; 56 (0.44, 0.29–0.66) and 55% (0.45, 0.21–0.99) for sudden death; and 25 (0.75, 0.62–0.90) and 46% (0.54, 0.37–0.80) for all-cause mortality events, respectively. We also assessed the impact of changes in ideal CVH status from phase III to phase IV (2008–2011) on CVD events among 5,666 participants. Accordingly, compared to those remaining in the poor category, all of the changes in ideal CVH categories showed a lower risk for CVD events.

Conclusion

Among the Iranian population, meeting higher ideal CVH metrics is associated with a lower risk of different CVD events and mortality outcomes.

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

BACKGROUND

Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors.

METHODS

This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance.

RESULTS

50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS.

CONCLUSION

Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.

Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach

BACKGROUND

Markov system dynamic (MSD) model has rarely been used in medical studies. The aim of this study was to evaluate the performance of MSD model in prediction of metabolic syndrome (MetS) natural history.

METHODS

Data gathered by Tehran Lipid & Glucose Study (TLGS) over a 16-year period from a cohort of 12,882 people was used to conduct the analyses. First, transition probabilities (TPs) between 12 components of MetS by Markov as well as control and failure rates of relevant interventions were calculated. Then, the risk of developing each component by 2036 was predicted once by a Markov model and then by a MSD model. Finally, the two models were validated and compared to assess their performance and advantages by using mean differences, mean SE of matrices, fit of the graphs, and Kolmogorov-Smirnov two-sample test as well as R2 index as model fitting index.

RESULTS

Both Markov and MSD models were shown to be adequate for prediction of MetS trends. But the MSD model predictions were closer to the real trends when comparing the output graphs. The MSD model was also, comparatively speaking, more successful in the assessment of mean differences (less overestimation) and SE of the general matrix. Moreover, the Kolmogorov-Smirnov two-sample showed that the MSD model produced equal distributions of real and predicted samples (p = 0.808 for MSD model and p = 0.023 for Markov model). Finally, R2 for the MSD model was higher than Markov model (73% for the Markov model and 85% for the MSD model).

CONCLUSION

The MSD model showed a more realistic natural history than the Markov model which highlights the importance of paying attention to this method in therapeutic and preventive procedures.

The dynamics of metabolic syndrome development from its isolated components among Iranian adults: findings from 17 years of the Tehran lipid and glucose study (TLGS)

Background

Evaluating the process of changes in the Metabolic Syndrome (MetS) components over time is one of the ways to study of the MetS natural history. This study aimed to determine the trend of changes in the progression of MetS from its isolated components.

Methods

This longitudinal study was performed on four follow-up periods of the Tehran Lipid and Glucose Study (TLGS) between 1999 and 2015. The research population consisted of 3905 adults over the age of 18 years. MetS was diagnosed based on the Joint Interim Statement (JIS). The considered components were abdominal obesity, hypertension, hyperglycemia, and dyslipidemia.

Results

The highest incidence of MetS from its components was related to hypertension in the short term (3.6-year intervals). In the long run, however, the highest increase in the MetS incidence occurred due to abdominal obesity. Overall, the incidence of MetS increased due to obesity and dyslipidemia, but decreased due to the other factors. Nonetheless, the trend of MetS incidence from all components increased in total. The most common components were dyslipidemia with a decreasing trend and obesity with an increasing trend during the study.

Conclusion

The results indicated that obesity and hypertension components played a more important role in the further development of MetS compared to other components in the Iranian adult population. This necessitates careful and serious attention in preventive and control planning.