Multiple pregnancies and the risk of diabetes mellitus in postmenopausal women

The relationship between pregnancy count and duration of breast-feeding with metabolic syndrome (Fasa Persian cohort study)

BACKGROUND

Changes that occur during pregnancy and after that during breastfeeding induce some symptoms similar to metabolic syndrome (MetS) risk factors. This study aims to determine the relationship between pregnancy, as well as the duration of breastfeeding and MetS controlling the effect of other risk factors like hypertension, glucose intolerance, triglyceride, central obesity, and reduction of high-density lipoprotein in women of Fasa Persian Cohort Study.

MATERIALS AND METHODS

In this cross-sectional study, 5015 women aged 35-70 years were investigated in the Sheshdeh region from 2016 to 2021, and the information related to the disease symptoms was collected through questionnaires, examinations, and laboratory tests. MetS was calculated based on two guidelines according to adult treatment panel III (ATP III) and international diabetes federation (IDF) methods. For reporting the data, the odds ratio with its 95% confidence interval was used. In order to eliminate the effect of confounders, logistic regression was used.

RESULTS

Prevalence of MetS showed a descending trend in women with up to two pregnancies and it reached 22.6% and 22.4% using ATPIII and IDF methods respectively, while with an increase in the number of pregnancies of more than two, MetS prevalence was ascending. The prevalence of MetS did not have any specific trend across various breastfeeding duration groups. Multivariate analysis approved that the odds ratio of developing MetS in comparison with women who had two pregnancies was significantly increasing trend when the pregnancy counts increased.

CONCLUSION

The chance of developing MetS based on both IDF and ATP III methods after adjustment for confounding effects would grow with an increase in the number of pregnancies to more than two and breast-feeding of more than seven years. It is recommended that women with more than two pregnancies or the long duration of breast-feeding women undergo a specialized examination to investigate and control MetS problems so that future diseases could be prevented.

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

Metabolic syndrome risk unexpectedly predicted by traditional food consumption: shifting food systems and health among the Awajún of the Peruvian Amazon

BACKGROUND

Dietary changes, especially declines in traditional food diversity and increases in the consumption of processed foods, have previously been shown to increase the risk of developing cardiovascular diseases.

AIM

We evaluated the prevalence and risk factors for metabolic syndrome in four Awajún communities in the Peruvian Amazon.

SUBJECTS AND METHODS

A total of 222 participants answered a socioeconomic questionnaire, a 24-h food recall, and completed a physical examination for metabolic syndrome diagnosis. A Poisson regression with robust variance was used in the statistical analysis of risk factors for metabolic syndrome.

RESULTS

We found an overall prevalence of 24% for metabolic syndrome. Being female, increased age and body mass index were significant risk factors for metabolic syndrome. Surprisingly, risk more than doubled with the ratio of traditional foods. However, this study revealed that there are fewer traditional foods being consumed in this study compared to prior studies among the Awajún.

CONCLUSION

We suggest that the unexpected relationship between traditional food consumption and metabolic syndrome may be due to substantial reductions in the diversity of traditional foods. Currently available traditional foods are primarily high in carbohydrates and are supplemented with cheap, non-perishable, and carbohydrate heavy market food items.