Novel anthropometric indices for predicting type 2 diabetes mellitus.

BACKGROUND: This study aimed to compare anthropometric indices to predict type 2 diabetes mellitus (T2DM) among first-degree relatives of diabetic patients in the Iranian community. METHODS: In this study, information on 3483 first-degree relatives (FDRs) of diabetic patients was extracted from the database of the Endocrinology and Metabolism Research Center of Isfahan University of Medical Sciences. Overall, 2082 FDRs were included in the analyses. A logistic regression model was used to evaluate the association between anthropometric indices and the odds of having diabetes. Furthermore, a receiver operating characteristic (ROC) curve was applied to estimate the optimal cutoff point based on the sensitivity and specificity of each index. In addition, the indices were compared based on the area under the curve (AUC). RESULTS: The overall prevalence of diabetes was 15.3%. The optimal cutoff points for anthropometric measures among men were 25.09 for body mass index (BMI) (AUC = 0.573), 0.52 for waist-to-height ratio (WHtR) (AUC = 0.648), 0.91 for waist-to-hip ratio (WHR) (AUC = 0.654), 0.08 for a body shape index (ABSI) (AUC = 0.599), 3.92 for body roundness index (BRI) (AUC = 0.648), 27.27 for body adiposity index (BAI) (AUC = 0.590), and 8 for visceral adiposity index (VAI) (AUC = 0.596). The optimal cutoff points for anthropometric indices were 28.75 for BMI (AUC = 0.610), 0.55 for the WHtR (AUC = 0.685), 0.80 for the WHR (AUC = 0.687), 0.07 for the ABSI (AUC = 0.669), 4.34 for the BRI (AUC = 0.685), 39.95 for the BAI (AUC = 0.583), and 6.15 for the VAI (AUC = 0.658). The WHR, WHTR, and BRI were revealed to have fair AUC values and were relatively greater than the other indices for both men and women. Furthermore, in women, the ABSI and VAI also had fair AUCs. However, BMI and the BAI had the lowest AUC values among the indices in both sexes. CONCLUSION: The WHtR, BRI, VAI, and WHR outperformed other anthropometric indices in predicting T2DM in first-degree relatives (FDRs) of diabetic patients. However, further investigations in different populations may need to be implemented to justify their widespread adoption in clinical practice.

Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.

INTRODUCTION: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. MATERIAL AND METHODS: A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). RESULTS: Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65-0.75) and LGBM (AUC = 0.70, 95% CI 0.65-0.75) models achieved the highest predictive AUC values for CRC patient survival. CONCLUSIONS: This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes.

Prevalence and Motives of Social Media Use among the Iranian Population.

Background: Around the world, people are using social media (SM) for different purposes following a wide range of patterns. There is a paucity of studies addressing the issue in the Eastern Mediterranean region. In this population-based study, the frequency and patterns of SM use in Iran were investigated. Materials and Methods: To explore the prevalence and motives of SM use, a sample of 1800 Iranian people aged 10-65 years old (53.5% female) were surveyed. Social media addiction (SMA) was assessed using the Bergen Social Media Addiction Scale. Results: The results revealed that 88.5% (n = 1593) of the participants were SM users, and the average time spent by them in SM was 4.0 ± 3.9 hours. The most common motivations for SM use were communication with others (48.9%), receiving news (40.7%), and surfing the net (40.6%). Besides, burning eyes (31.0%), headache (26.8%), and sleep disturbance (25.1%) were the most common health problems experienced by SM users. The SMA prevalence was 23.1% (95% CI: 21.2, 25.1) (males: 23.8%; females: 22.5%), with a higher rate (26.0%) among adolescents and young people. Conclusion: SM use and SMA appear to be real health challenges in Iran, particularly among youth. Consequently, to decrease the negative impacts of excessive SM use, exploring the motives behind SM use and designing population-based interventions are recommended.

A Machine Learning Approach to Assess Differential Item Functioning of the KINDL Quality of Life Questionnaire Across Children with and Without ADHD.

This study aimed to investigate differential item functioning (DIF) of the child and parent reports of the KINDL measure across children with and without Attention-deficit/hyperactivity disorder (ADHD). The sample included 122 children with ADHD and 1086 healthy peers, alongside 127 and 1061 of their parents, respectively. The generalized partial credit model with lasso penalization, as a machine learning method, was used to assess DIF of the KINDL across the two groups. The findings showed that three out of 24 items of the child reports and seven out of 24 items of the parent reports of the KINDL exhibited DIF between children with and without ADHD. Accordingly, Iranian children with and without ADHD along with their parents perceive almost all items in the KINDL similarly. Hence, the observed difference in quality of life scores between children with and without ADHD is a real difference and not a reflection of measurement bias.

Therapeutic interventions for COVID-19: a living overview of reviews.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, but safe and effective treatment options remain unavailable. Numerous systematic reviews of varying qualities have tried to summarize the evidence on the available therapeutic interventions for COVID-19. This overview of reviews aims to provide a succinct summary of the findings of systematic reviews on different pharmacological and non-pharmacological therapeutic interventions for COVID-19. METHODS: We searched PubMed, Embase, Google Scholar, Cochrane Database of Systematic Reviews, and WHO database of publications on COVID-19 from 1 December 2019 through to 11 June 2020 for peer-reviewed systematic review studies that reported on potential pharmacological or non-pharmacological therapies for COVID-19. Quality assessment was completed using A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2) measure. RESULTS: Out of 816 non-duplicate studies, 45 were included in the overview. Antiviral and antibiotic agents, corticosteroids, and anti-malarial agents were the most common drug classes used to treat COVID-19; however, there was no direct or strong evidence to support their efficacy. Oxygen therapy and ventilatory support was the most common non-pharmacological supportive care. The quality of most of the included reviews was rated as low or critically low. CONCLUSION: This overview of reviews demonstrates that although some therapeutic interventions may be beneficial to specific subgroups of COVID-19 patients, the available data are insufficient to strongly recommend any particular treatment option to be used at a population level. Future systematic reviews on COVID-19 treatments should adhere to the recommended systematic review methodologies and ensure that promptness and comprehensiveness are balanced.The reviews of this paper are available via the supplemental material section.

Epidemiological characteristics of COVID-19: a systematic review and meta-analysis.

Our understanding of the Coronavirus disease 2019 (COVID-19) continues to evolve and there are many unknowns about its epidemiology. This study aims to synthesise case fatality rate (CFR) among confirmed COVID-19 patients, incubation period and time from onset of COVID-19 symptoms to first medical visit, intensive care unit (ICU) admission, recovery, and death. We searched MEDLINE, Embase, Google Scholar, and bibliographies of relevant articles from 01 December 2019 to 11 March 2020 without any language restrictions. Quantitative studies that recruited people with confirmed COVID-19 diagnosis were included. Two independent reviewers extracted the data. Out of 1675 non-duplicate studies, 43 were included in the meta-analysis. The pooled mean incubation period was 5.68 (99% confidence interval [CI]: 4.78, 6.59) days. The pooled mean number of days from the onset of COVID-19 symptoms to first clinical visit was 4.92 (95% CI: 3.95, 5.90), ICU admission was 9.84 (95% CI: 8.78, 10.90), recovery was 18.55 (95% CI: 13.69, 23.41), and death was 15.93 (95% CI: 13.07, 18.79). Pooled CFR among confirmed COVID-19 patients was 0.02 (95% CI: 0.02, 0.03). We found that the incubation period and lag between the onset of symptoms and first clinical visit for COVID-19 are longer than other respiratory viral infections including Middle East respiratory syndrome and severe acute respiratory syndrome; however, the current policy of 14 days of mandatory quarantine for everyone potentially exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might be too conservative. Longer quarantine periods might be more justified for extreme cases.