Effect of family history of diabetes and obesity status on lifetime risk of type 2 diabetes in the Iranian population

Cardiovascular Risk Factors in Socioeconomically Disadvantaged Populations in a Suburb of the Largest City in Western Romania

Background and Objectives: Cardiovascular disease (CVD) remains a major public health issue worldwide, disproportionately affecting socioeconomically disadvantaged populations due to the social determinants of health (SDOHs). In Western Romania, these populations are particularly vulnerable to CVD. This study aims to investigate the prevalence and impact of cardiovascular risk factors (CVRFs) among socioeconomically disadvantaged individuals in Western Romania and identify the primary CVRFs contributing to the high incidence of CVD within this population. Materials and Methods: A retrospective observational design was employed, utilizing data from the medical records of 1433 eligible individuals. The inclusion criteria were based on Eurostat's EU-SILC benchmarks, focusing on severe material deprivation, at-risk-of-poverty rates, and low work intensity. Data on demographics, familial and personal medical history, smoking status, blood pressure, glucose, cholesterol, triglycerides, and HbA1c levels were collected. Results: Of the 1433 subjects, 34.75% were male, with a median age of 52 years. Significant conditions included diabetes (7.39%), coronary disease (3.83%), arterial hypertension (35.58%), and dyslipidemia (21.28%). Median ages were higher for those with diabetes (65 vs. 51 years, p < 0.0001), coronary disease (64 vs. 51 years, p < 0.0001), arterial hypertension (65 vs. 43 years, p < 0.0001), and dyslipidemia (66 vs. 47 years, p < 0.0001). BMI (Body Mass Index) classifications showed 33.77% were overweight, 21.21% obese, and 15.07% morbidly obese. Smokers were younger than non-smokers (48 vs. 54 years, p < 0.0001). Conclusions: The findings highlight the significant prevalence of CVRFs among socioeconomically disadvantaged populations in Western Romania. Socioeconomically disadvantaged populations exhibit a significantly higher prevalence of cardiovascular risk factors such as diabetes, impaired glucose regulation, hypertension, and dyslipidemia compared to their before known status.

The prognostic significance of insulin resistance in COVID-19: a review

Objectives

Emerging publications indicate that diabetes predisposes patients with COVID-19 to more severe complications, which is partly attributed to inflammatory condition. In the current review, we reviewed recent published literature to provide evidence on the role of insulin resistance (IR) in diabetes, the association between diabetes and COVID-19 severity and mortality, the impact of COVID-19 infection on incident new-onset diabetes, mechanisms responsible for IR in COVID-19 patients, and the predictive value of different surrogates of IR in COVID-19.

Method

The literature search performs to find out studies that have assessed the association between IR surrogates and morbidity and mortality in patients with COVID-19.

Results

We showed that there is a bulk of evidence in support of the fact that diabetes is a potent risk factor for enhanced morbidity and mortality in COVID-19 patients. COVID-19 patients with diabetes are more prone to remarkable dysglycemia compared to those without diabetes, which is associated with an unfavourable prognosis. Furthermore, SARS-COV2 can make patients predispose to IR and diabetes via activating ISR, affecting RAAS signaling pathway, provoking inflammation, and changing the expression of PPARɣ and SREBP-1. Additionally, higher IR is associated with increased morbidity and mortality in COVID-19 patients and different surrogates of IR can be utilized as a prognostic biomarker for COVID-19 patients.

Conclusion

Different surrogates of IR can be utilized as predictors of COVID-19 complications and death.

Tracking correlations and predictors of plasma glucose in young adulthood: A comprehensive analysis from adolescence to young adulthood in TLGS study

AIMS

We investigated the tracking correlations between fasting plasma glucose (FPG) in adolescence with both FPG and 2-hour post-load glucose (2 h-PG) in adulthood, and identified the predictors of FPG and 2 h-PG in young adulthood using traditional risk factors during adolescence and adulthood.

METHODS

We included 2188 participants (1033 male) from the Tehran lipid and glucose study within the age ranges 11-18 and 19-40 years during 1999-2018. The area under the curve (AUC) was computed using the growth curve models, and predictors were identified by the linear regression model.

RESULTS

The partial correlation between AUCs of FPG in adolescence and adulthood was 0.37 (P < 0.001). The correlation between AUCs of FPG in adolescence and 2 h-PG in adulthood was 0.17 (P < 0.001). The AUC of FPG was a significant positive predictor for both FPG and 2 h-PG in young adulthood. Other predictors of adult FPG included sex, as well as BMI and the ratio of triglycerides to HDL-cholesterol during both adolescence and adulthood.

CONCLUSIONS

Tracking correlation was observed for FPG, suggesting that monitoring and managing risk factors in adolescence may have implications for future glucose metabolism in young adulthood.

Clinical features of early-onset type 2 diabetes and its association with triglyceride glucose-body mass index: a cross-sectional study

Objective

The incidence of early-onset type 2 diabetes (T2D) has increased significantly, with insulin resistance (IR) and obesity being the main drivers of its onset. This study aims to investigate the clinical characteristics of early-onset T2D and its association with triglyceride glucose body mass index (TyG-BMI), an emerging surrogate of IR.

Methods

A total of 1000 adults newly diagnosed with T2D were enrolled and divided into early-onset T2D (18~40 years, N=500) and late-onset T2D groups (≥40 years, N=500). Independent t and chi-squared tests were used to compare the characteristics of the two groups, and logistic regression analysis, trend tests, restricted cubic spline curves (RCSs), and receiver operating characteristic (ROC) curves were used to identify the relationship between TyG-BMI and early-onset T2D.

Results

Patients with early-onset T2D were more likely to have a higher body mass index (BMI), hemoglobin A1C (HbA1c), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA), triglyceride glucose index (TyG), and TyG-BMI (P < 0.05). A higher TyG-BMI was associated with an increased risk of early-onset T2D (P < 0.001). The RCSs showed a nonlinear relationship between TyG-BMI and early-onset T2D, and the slope of the curve increased with an increase in TyG-BMI (P for nonlinearity < 0.001). In the subgroup analysis, additive interactions between TyG-BMI and the risk of early-onset T2D were observed for sex, family history of diabetes, BMI, fatty liver, and hypertension (P < 0.001). ROC curve showed that the area under the curve of TyG-BMI was 0.6781, which was larger than its main components (TyG, BMI, FPG, TG). The best cutoff value was 254.865, the sensitivity was 74.6%, and the specificity was 53.6%.

Conclusion

Patients with early-onset T2D are characterized by severe IR, metabolic disorders, and being overweight/obese and an increase in TyG-BMI is independently associated with an increased risk of early-onset T2D.

Relative importance of triglyceride glucose index combined with body mass index in predicting recovery from prediabetic state to normal fasting glucose: a cohort analysis based on a Chinese physical examination population

BACKGROUND

Prediabetes is a high-risk state for diabetes, and numerous studies have shown that the body mass index (BMI) and triglyceride-glucose (TyG) index play significant roles in risk prediction for blood glucose metabolism. This study aims to evaluate the relative importance of BMI combination with TyG index (TyG-BMI) in predicting the recovery from prediabetic status to normal blood glucose levels.

METHODS

A total of 25,397 prediabetic subjects recruited from 32 regions across China. Normal fasting glucose (NFG), prediabetes, and diabetes were defined referring to the American Diabetes Association (ADA) criteria. After normalizing the independent variables, the impact of TyG-BMI on the recovery or progression of prediabetes was analyzed through the Cox regression models. Receiver Operating Characteristic (ROC) curve analysis was utilized to visualize and compare the predictive value of TyG-BMI and its constituent components in prediabetes recovery/progression.

RESULTS

During the average observation period of 2.96 years, 10,305 individuals (40.58%) remained in the prediabetic state, 11,278 individuals (44.41%) recovered to NFG, and 3,814 individuals (15.02%) progressed to diabetes. The results of multivariate Cox regression analysis demonstrated that TyG-BMI was negatively associated with recovery from prediabetes to NFG and positively associated with progression from prediabetes to diabetes. Further ROC analysis revealed that TyG-BMI had higher impact and predictive value in predicting prediabetes recovering to NFG or progressing to diabetes in comparison to the TyG index and BMI. Specifically, the TyG-BMI threshold for predicting prediabetes recovery was 214.68, while the threshold for predicting prediabetes progression was 220.27. Additionally, there were significant differences in the relationship of TyG-BMI with prediabetes recovering to NFG or progressing to diabetes within age subgroups. In summary, TyG-BMI is more suitable for assessing prediabetes recovery or progression in younger populations (< 45 years old).

CONCLUSIONS

This study, for the first time, has revealed the significant impact and predictive value of the TyG index in combination with BMI on the recovery from prediabetic status to normal blood glucose levels. From the perspective of prediabetes intervention, maintaining TyG-BMI within the threshold of 214.68 holds crucial significance.

Factors affecting Hemoglobin A1c in the longitudinal study of the Iranian population using mixed quantile regression

Diabetes, a major non-communicable disease, presents challenges for healthcare systems worldwide. Traditional regression models focus on mean effects, but factors can impact the entire distribution of responses over time. Linear mixed quantile regression models (LQMMs) address this issue. A study involving 2791 diabetic patients in Iran explored the relationship between Hemoglobin A1c (HbA1c) levels and factors such as age, sex, body mass index (BMI), disease duration, cholesterol, triglycerides, ischemic heart disease, and treatments (insulin, oral anti-diabetic drugs, and combination). LQMM analysis examined the association between HbA1c and the explanatory variables. Associations between cholesterol, triglycerides, ischemic heart disease (IHD), insulin, oral anti-diabetic drugs (OADs), a combination of OADs and insulin, and HbA1c levels exhibited varying degrees of correlation across all quantiles (p < 0.05), demonstrating a positive effect. While BMI did not display significant effects in the lower quantiles (p > 0.05), it was found to be significant in the higher quantiles (p < 0.05). The impact of disease duration differed between the low and high quantiles (specifically at the quantiles of 5, 50, and 75; p < 0.05). Age was discovered to have an association with HbA1c in the higher quantiles (specifically at the quantiles of 50, 75, and 95; p < 0.05). The findings reveal important associations and shed light on how these relationships may vary across different quantiles and over time. These insights can serve as guidance for devising effective strategies to manage and monitor HbA1c levels.