Evaluation of risk factors in the development of type 2 diabetes in a Mexican population

Martínez-Venegas, M.; Valdez-Guerrero, A.S.; Quintana-Pérez, Julio César; Rubio-Guerra, Alberto Francisco; Valle-Mondragón, Leonardo Del; Rodríguez-Bazan, J.L.; Tamay‐Cach, Feliciano; Arellano-Mendoza, Mónica Griselda

doi:10.1016/j.diabres.2019.107784

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…The results indicated that the average BMI of subjects at high-risk was 5.4 units higher than that of subjects at LR. In this sense, Marti ́nez-Venegas, Valdez-Guerrero, Quintana-Peŕez, Rubio-Guerra, Del Valle-Mondragon, Rodri ́guez-Bazan, Tamay-Cach, and Arellano-Mendoza 23 established that the risk of T2DM doubles with a BMI of 30− 33 kg/m 2 , increases 5-fold between 34 and 36 kg/m 2 , and increases 10-fold at 40 kg/m 2 . Likewise, the percentage of body fat also showed a statistically significant difference among groups.…”

Section: Resultsmentioning

confidence: 98%

Feeding Habits, Nutritional Condition, Fecal Intestinal Metabolites, and Risk of Developing Diabetes Mellitus II in the Mexican Population: A Cross-Sectional Study

García-Martínez

Alarcón-Domínguez

Velasco-González

et al. 2023

ACS Food Sci. Technol.

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Early identification of type 2 diabetes mellitus (T2DM) risk factors prevents its consequences. Fecal volatile organic compounds (FVOCs) can serve as disease biomarkers. The objective was to compare nutritional condition, feeding habits, and FVOCs into low-risk and high-risk (HR) T2DM developed in the Mexican population. A cross-sectional study was conducted in healthy adult volunteers between 18 and 59 years of age from Tepic, Nayarit, Mexico. The FVOCs p-cresol and acetic, propionic, butyric, and valeric acids were evaluated. The values of body mass index (BMI), body fat, visceral fat index, metabolic age, and basal metabolic rate were higher in the HR group. In addition, high total calorie intakes and high macronutrient calorie intakes were found in the HR group. Both groups showed the same values for the evaluated FVOCs. Through principal component analysis, it was found that when BMI, metabolic age, weight, and visceral fat index increase, adults will be more likely to be at high risk for developing T2DM.

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Section: Resultsmentioning

confidence: 98%

Feeding Habits, Nutritional Condition, Fecal Intestinal Metabolites, and Risk of Developing Diabetes Mellitus II in the Mexican Population: A Cross-Sectional Study

García-Martínez

Alarcón-Domínguez

Velasco-González

et al. 2023

ACS Food Sci. Technol.

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“…DBT can also cause mood disorders and dementia; all associated morbidities lead to a decreased life quality and premature death. It has been estimated that by 2045, globally, the number of people with DBT will reach 629 million, which is equivalent to 9.9% of the total adult population of the world 6 .…”

Section: Diabetes In Mexicomentioning

confidence: 99%

Untitled

2022

IJPSR

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In the last decades, diabetes has become one of the most prevalent health concerns worldwide. Diabetes is a metabolic disease that causes hyperglycemia and is associated with altered insulin production (type 1 diabetes) or with insulin-resistance (type 2 diabetes). The present work aims to point out the relevance of glipizide as an alternative treatment for type 2 diabetes and discuss the different delivery systems developed for this drug. With this in mind, we performed a nonsystematic search in different databases like PubMed, Google Scholar, Science Direct and the FDA's and some other web pages, using key words as search criterion. Because glipizide is a drug with a short elimination half-life, a dosage of two to three tablets per day is needed, depending on the therapeutic requirements of each patient. The as aforementioned has led different research groups to develop new delivery systems for this drug; among them are nanoparticles, nanosuspensions, microspheres, self-emulsifying systems, matrix tablets, osmotic tablets, and gastroretentive systems. The developed systems have been aimed at reducing the dosage frequency of glipizide, expecting to improve therapeutic adherence.INTRODUCTION: Diabetes (DBT) is a metabolic disease that causes hyperglycemia and is associated with altered insulin production (type 1 diabetes, DBT1) or with insulin resistance (type 2 diabetes, DBT2). Progression of the disease can induce malfunctioning of organs like the heart, kidneys and eyes 1 . DBT is one of the main causes of morbidity and mortality worldwide that affects ca. 463 million adults, of whom 79% live in low-or medium-income countries.

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“…Diabetes is a prevalent metabolic disease that reduces life expectancy. The prevalence of diabetes is projected to increase from 4% in 1995 to 4.5% in 2035, and by 2045, the number of people suffering from diabetes is estimated to reach 629 million worldwide, according to the World Health Organization (WHO) [1,2]. Type 2 diabetes mellitus (T2DM), which accounts for more than 90% of diabetes cases, is one of the four priority noncommunicable diseases identified by the WHO [3].…”

Section: Introductionmentioning

confidence: 99%

Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism

Wang¹,

Gong²,

Zhu³

et al. 2020

IJCIS

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Type 2 diabetes mellitus (T2DM) has been identified as one of the most challenging chronic diseases to manage. In recent years, the incidence of T2DM has increased, which has seriously endangered people's health and life quality. Glycosylated hemoglobin (HbA1c) is the gold standard clinical indicator of the progression of T2DM. An accurate prediction of HbA1c levels not only helps medical workers improve the accuracy of clinical decision-making but also helps patients to better understand the clinical progression of T2DM and conduct self-management to achieve the goal of controlling the progression of T2DM. Therefore, we introduced the long short-term memory (LSTM) neural network to predict patients' HbA1c levels using time sequential data from electronic medical records (EMRs). We added the self-attention mechanism based on the traditional LSTM to capture the long-term interdependence of feature elements and which ensure that the memory was more profound and effective, and used the gradient search technology to minimize the mean square error of the predicted value of the network and the real value. LSTM with the self-attention mechanism performed better than the traditional deep learning sequence prediction method. Our research provides a good reference for the application of deep learning in the field of medical health management.

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Evaluation of risk factors in the development of type 2 diabetes in a Mexican population

Cited by 7 publications

References 18 publications

Feeding Habits, Nutritional Condition, Fecal Intestinal Metabolites, and Risk of Developing Diabetes Mellitus II in the Mexican Population: A Cross-Sectional Study

Feeding Habits, Nutritional Condition, Fecal Intestinal Metabolites, and Risk of Developing Diabetes Mellitus II in the Mexican Population: A Cross-Sectional Study

Untitled

Sequential Prediction of Glycosylated Hemoglobin Based on Long Short-Term Memory with Self-Attention Mechanism

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