Search published articles


Showing 104 results for Hi
Investigating the Mediating Role of Nutrition in the Relationship Between Socio-Economic Status and Body Mass Index in 6-13 Years Students: A Structural Analysis
Elahe Faghihifar, Marjan Ajami, Sareh Shakerian,
Volume 19, Issue 4 (3-2024)
Abstract
Background and Objectives: Childhood obesity has become a global challenge today. Many studies have shown the relationship between obesity and socioeconomic factors. Therefore, this study aimed to evaluate socio-economic inequalities with nutritional patterns and obesity in children.
Methods: This study was conducted using the structural analysis methodology on 80 children from 6 to 13 years old, selected randomly from those referred to the health assessment centers of Sonqor and Kolyai, Iran. The body mass index was calculated using the standard method. Nutritional patterns were measured using the 24-hour food recall questionnaire, and socioeconomic status was assessed using related standard questionnaires. The data were analyzed using SPSS 24 and AMOS 24 software.
Results: The results showed that 28.75% of the subjects were obese or overweight. The structural analysis showed that the socioeconomic variable directly affected the nutritional pattern and body mass (-0.43) with an impact coefficient of as much as 0.65. The nutritional pattern variable affected BMI with an impact factor (-0.74). The bootstrap test results indicated that the significant effect of socio-economic status on BMI is mediated by nutritional pattern (-0.48). Prediction values show two economic-social and nutritional pattern variables predicted 0.16 and 0.29 BMI changes, respectively.
Conclusion: The findings of this study showed the effect of socioeconomic status on nutritional patterns and body mass index in the research community. Today, The issue of increasing body mass in the world and our country is one of the most important social challenges. Considering the heterogeneous effects of socioeconomic status on nutritional patterns and body mass index, it is necessary to formulate and implement preventive policies according to the conditions of the communities to achieve effective results.

Predicting the Occurrence of Preterm Birth and Determining its Risk Factors Individually Using an Interpretable Machine Learning Model
Ramin Farrokhi, Samaneh Hosseinzadeh, Abbas Habibelahi, Akbar Biglarian,
Volume 20, Issue 1 (6-2024)
Abstract
Background and Objectives: Identifying pregnant women who are at risk of premature birth and determining its risk factors is essential because it affects their health. This study aimed to use an interpretable machine-learning model to predict premature birth.
Methods: In this study, data from 149,350 births in Tehran in 2019 were utilized from the Iranian Mothers and Babies Network (IMaN) dataset. Various factors related to the mother and the fetus, such as the mother's demographic variables and health status, medical history, pregnancy conditions, childbirth, and associated risks, were considered. The machine learning models, including multilayer neural networks, random forest, and XGBoost, were employed to predict the occurrence of preterm birth after data preprocessing. The models were evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve. The Python programming language version 3.10.0 was applied to analyze the data.
Results: About 8.67% of births were premature. The XGBoost algorithm achieved the highest prediction accuracy (90%). According to the model output, multiple births, which account for 46% of pregnant women's births, had the highest importance score. Delivery risk factors had a score of 41%, and other variables, including neurological and mental illness, preeclampsia, and cardiovascular disease, were subsequently ranked in order of importance for this particular individual.
Conclusion: Using an interpretable machine learning method could predict the occurrence of premature birth. Based on risk factors, the interpretable machine learning method can provide personalized preventive recommendations for every pregnant woman, aiming to reduce the risk of preterm birth.

Utilizing Machine Learning to Predict Antimicrobial Resistance in Bacteria
Monireh Rahimkhani, Maryam Gilani,
Volume 20, Issue 1 (6-2024)
Abstract
Antibiotic resistance has increased significantly in recent years. On the other hand, machine learning (ML) algorithms are increasingly used in medical research and healthcare and are gradually improving clinical performance.
Using ML to fight antimicrobial resistance (AMR) is one of the most critical areas of interest among the various applications of these new methods. The rise of antibiotic resistance and managing multidrug-resistant infections that are difficult to treat are important challenges.
Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance and thus support clinicians in selecting the appropriate treatment. Machine learning and artificial intelligence (AI) in predicting antimicrobial resistance are among today's sciences. Therefore, an antimicrobial stewardship program (ASP) should be implemented to optimize antibiotic prescribing and limit AMR.

The Association between the Consumption of Dairy Products and the Risk of Cardiovascular Disease: Tehran Lipid and Glucose Study
Zahra Gaeini, Sevda Alvirdizadeh, Parvin Mirmiran, Fereidoun Azizi,
Volume 20, Issue 2 (9-2024)
Abstract
Background and Objectives: The association between the consumption of dairy products and the risk of cardiovascular disease (CVD) is not well-known yet. Here, we aimed to determine the potential effects of total intake and subtypes of dairy products on the development of CVD in an Iranian adult population.
Methods: Among adult participants of the third phase of the Tehran Lipid and Glucose Study (TLGS), after excluding those with incomplete dietary, biochemical and anthropometric data, and those who had CVD events at baseline, 2635 adults were selected and followed up till the sixth phase of the TLGS. Baseline dietary intakes were evaluated using a validated food frequency questionnaire with 168 items. There was no significant difference between the baseline characteristics of participants who did not complete the FFQ and those of the total population in the third phase of the TLGS. Finally, the risk of CVD events after adjusting for potential confounding variables was evaluated across the tertile categories of dairy products using the Cox proportional hazard regression models.
Results: During a 10.6-year follow-up, the incidence rate of CVD was 6.5%. After adjusting for confounding factors, there was no significant association between CVD risk and total dairy, low-fat and high-fat dairy, fermented and non-fermented dairy products, high- and low-fat milk, high- and low-fat yogurt, cheese, and cream cheese, as well as ice cream.
Conclusion: According to numerous evidence in previous studies that revealed there is no association between the consumption of dairy products, and CVD risk, independent of high-fat or low-fat dairy products. Hence, it is vital to reconsider dietary recommendations on lowering the intake of high-fat dairy products for the prevention of CVD.

Page 6 from 6     

© 2025 , Tehran University of Medical Sciences, CC BY-NC 4.0

Designed & Developed by : Yektaweb