Objective: To evaluate the prevalence of prediabetes (PD), undiagnosed type 2 diabetes (T2D), metabolic syndrome (MetS), and insulin resistance (IR) as well as related risk factors in Mexican population from Guanajuato, Mexico.
Materials And Methods: We performed a cross-sectional study in the Mexican population from León, Guanajuato, Mexico. A clinical and metabolic evaluation was performed with an oral glucose test (OGTT); PD, undiagnosed T2D, MetS, and IR were identified according to international guidelines.
Results: Of the 1 470 participants included, 32.9% had PD, 8.4% undiagnosed T2D, 48.1% MetS, and 55.7% IR. Main risk factors associated with T2D and PD were central obesity, overweight, acanthosis nigricans, family history of T2D and age.
Conclusions: The prevalence of glucose abnormalities, MetS, and IR are high in the Mexican population, and this is related to the high frequency of multiple risk factors in our population.
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http://dx.doi.org/10.21149/15414 | DOI Listing |
Commun Biol
March 2025
Instituto Nacional de Medicina Genómica (INMEGEN), México City, México.
The prevalence of type 2 diabetes (T2D) has increased significantly over the past three decades, with an estimated 30-40% of cases remaining undiagnosed. Brown and beige adipose tissues are known for their remarkable catabolic capacity, and their ability to diminish blood glucose plasma concentration. Beige adipose tissue can be differentiated from adipose-derived stem cells or through transdifferentiation from white adipocytes.
View Article and Find Full Text PDFDiabetes Metab Syndr
February 2025
Madras Diabetes Research Foundation [ICMR- Collaborating Centre of Excellence (ICMR-CCoE)], India; Dr. Mohan's Diabetes Specialties Centre (IDF Centre of Excellence in Diabetes Care), Chennai, India.
Aim: Rising prevalence of Type 2 Diabetes (T2D) among young Asians has emerged as a public health crisis that threatens the long-term health, economic stability, and productivity of nations across Asia (1). Early-onset T2D poses unique challenges, including higher rates of undiagnosed cases, more aggressive disease progression, an increased risk of chronic complications and higher mortality (2). Hyperglycemia during the reproductive age especially among the female population can potentially have transgenerational impact through epigenetic changes.
View Article and Find Full Text PDFBMJ Public Health
June 2024
Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Doha, Qatar.
Introduction: Pre-diabetes stands as a prominent, independent risk factor for the onset of type 2 diabetes (T2D), with 5%-10% of individuals with pre-diabetes progressing to T2D annually. The effectiveness of rigorous lifestyle interventions in averting the transition from pre-diabetes to T2D has been substantiated across multiple investigations and populations. Consequently, the clinical imperative of early pre-diabetes detection becomes unequivocal.
View Article and Find Full Text PDFSalud Publica Mex
April 2024
Departamento de Endocrinología Pediátrica, Hospital Regional de Alta Especialidad del Bajío. León, Guanajuato, Mexico..
Objective: To evaluate the prevalence of prediabetes (PD), undiagnosed type 2 diabetes (T2D), metabolic syndrome (MetS), and insulin resistance (IR) as well as related risk factors in Mexican population from Guanajuato, Mexico.
Materials And Methods: We performed a cross-sectional study in the Mexican population from León, Guanajuato, Mexico. A clinical and metabolic evaluation was performed with an oral glucose test (OGTT); PD, undiagnosed T2D, MetS, and IR were identified according to international guidelines.
Comput Biol Med
March 2025
Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16149, Gyeonggi-do, Republic of Korea. Electronic address:
Early diagnosis and timely treatment of diabetes are critical for effective disease management and the prevention of complications. Undiagnosed diabetes can lead to an increased risk of several health issues. Although numerous machine learning (ML) models have been designed to detect diabetes, many exhibit unsatisfactory performance, are not publicly available, and lack validation on external datasets.
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