Obesity and visceral fat: Indicators for anemia among household women visiting a health camp on world obesity day

Hiremath, R.N.; Kumar, Mukesh; Huchchannavar, Raghavendra; Ghodke, Sandhya

doi:10.1016/j.cegh.2023.101255

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can pose a major public health challenge, especially for children and adults [4]. According to studies, global projections of adult obesity rates in 2010, 2025, and 2030 indicate an increase in obesity levels depending on the individual's degree of obesity [5], [6]. In addition, research on childhood obesity confirms a significant increase, which is now considered a global epidemic [7].…”

Section: Introductionmentioning

confidence: 99%

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

View full text Add to dashboard Cite

Obesity has become a widespread problem that affects not only physical well-being but also mental health. To address this problem and provide solutions, Machine Learning (ML) technology tools are being applied. Studies are currently being developed to improve the prediction of obesity. This study aimed to predict obesity levels in nutritional patients by analyzing their physical and dietary habits using the Decision Tree (DT) model. For the development of this work, we chose to use the CRISP-DM framework to follow the development in an organized way, thus achieving a better understanding of the data and describing, evaluating, and analyzing the results. The results of this work yielded metrics with significant values for predicting obesity: so much so that the accuracy rate was 92.89%, the sensitivity rate was 94% and the F1 score was 93%. Likewise, accuracy metrics above 88% were obtained for each level of obesity, demonstrating the effectiveness of the DT model in predicting this type of task. Finally, the results demonstrate that the DT model is effective in predicting obesity, with significant results that motivate further research to continue improving accuracy in this type of task.

show abstract

Section: Introductionmentioning

confidence: 99%

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Iparraguirre-Villanueva,

Mirano-Portilla,

Gamarra-Mendoza

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

Adipokine Modulation in Endometrial Hyperplasia by Polyunsaturated Fatty Acids

Supriya,

Kiran,

Krishnamurthy

2024

Journal of Pharmacology and Pharmacotherapeutics

View full text Add to dashboard Cite

Background Obesity is associated with a higher prevalence of endometrial hyperplasia, thereby increasing the risk of endometrial and ovarian cancers. The precise mechanisms linking obesity to endometrial hyperplasia remain unclear, but dysregulation of adipose tissue homeostasis is known to play a significant role. Hypertrophied adipocytes in obese individuals secrete various bioactive substances, including cytokines, growth factors, hormones, and metabolites. Additionally, hyperplastic adipocytes exhibit enhanced aromatase activity, leading to increased estrogen synthesis, which further promotes the development of endometrial hyperplasia. Purpose The purpose of this study is to explore the anti-inflammatory and anti-proliferative activities of the poly unsaturated fatty acids. Methodology An extensive literature survey has been performed to identify the role of adipokines and elevated endogenous estrogen levels in activating cell survival signaling pathways, such as PI3K/Akt/mTOR, MEK/ERK1, and JAK–STAT in endometrial cells and their possible role in Endometrial Hyperplasia. Further, the possible beneficial anti-inflammatory and anti-proliferative effects of polyunsaturated fatty acids (PUFAs) such as eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and arachidonic acid (AA) were explored. Results Numerous studies suggest the beneficial role of dietary fats, such as EPA, DHA, and AA in modulating the growth of endometrium in obesity-induced endometrial hyperplasia. PUFAs can activate adenosine monophosphate-activated protein kinase (AMPK), which inhibits gluconeogenesis and lipogenesis. It also phosphorylates acetyl-CoA, leading to a decrease in malonyl-CoA, which inhibits mitochondrial CPT1. Additionally, AMPK activation promotes β-oxidation, and PPAR-γ mechanisms by down regulating the NF-kB pathway involved in endometrial hyperplasia. Conclusion This review sheds light on the potential of PUFAs in mitigating estrogen synthesis, adipokine secretion, and endogenous aromatase activity in obesity induced endometrial hyperplasia. Furthermore, it critically evaluates the role and mechanisms of PUFAs in attenuating obesity-associated endometrial hyperplasia and reducing the risk of ovarian cancer.

show abstract

Obesity and visceral fat: Indicators for anemia among household women visiting a health camp on world obesity day

Cited by 2 publications

References 16 publications

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Predicting Obesity in Nutritional Patients using Decision Tree Modeling

Adipokine Modulation in Endometrial Hyperplasia by Polyunsaturated Fatty Acids

Contact Info

Product

Resources

About