This work presents a mathematical model of homeostasis dynamics in healthy individuals, focusing on the generation of conductive data on glucose homeostasis throughout the day under dietary and physical activity factors. Two case studies on glucose dynamics for populations under conditions of physical activity and sedentary lifestyle were developed. For this purpose, two types of virtual populations were generated, the first population was developed according to the data of a total of 89 physical persons between 20 and 75 years old and the second was developed using the Monte Carlo approach, obtaining a total of 200 virtual patients. In both populations, each participant was classified as an active or sedentary person depending on the physical activity performed. The results obtained demonstrate the capacity of virtual populations in the generation of in-silico approximations similar to those obtained from in-vivo studies. Obtaining information that is only achievable through specific in-vivo experiments. Being a tool that generates information for the approach of alternatives in the prevention of the development of type 2 Diabetes.

This work presents an analysis of the effect on glycemic variation caused by modifying the macronutrient intake sequence in a person without a diagnosis of diabetes. In this work, three types of nutritional studies were developed: (1) glucose variation under conditions of daily intake (food mixture); (2) glucose variation under conditions of daily intake modifying the macronutrient intake sequence; (3) glucose variation after a modification in the diet and macronutrient intake sequence. The focus of this research is to obtain preliminary results on the effectiveness of a nutritional intervention based on the modification of the sequence of macronutrient intake in a healthy person during 14-day periods. The results obtained corroborate the positive effect on the glucose of consuming vegetables, fiber, or proteins before carbohydrates, decreasing the peaks in the postprandial glucose curves (vegetables: 113–117 mg/dL; proteins: 107–112 mg/dL; carbohydrates: 115–125 mg/dL) and reducing the average levels of blood glucose concentrations (vegetables: 87–95 mg/dL; proteins: 82–99 mg/dL; carbohydrates: 90–98 mg/dL). The present work demonstrates the preliminary potential of the sequence in the macronutrient intake for the generation of alternatives of prevention and solution of chronic degenerative diseases, improving the management of glucose in the organism and permeating in the reduction of weight and the state of health of the individuals.

Background: The World Health Organization (WHO) launched the Acceleration Plan to STOP Obesity, highlighting the urgent need for timely implementation of proven interventions. Fiscal policies, including taxes on sugar-sweetened beverages (SSB) and non-essential energy-dense foods (NEDF), are among the most cost-effective strategies to reduce obesity rates. Delays in adopting or strengthening these measures can undermine their impact, and the consequences of postponing such policies remain unmeasured. We aimed to estimate the expected impact of delaying doubling the SSB and NEDF tax in Mexico. Methods and findings: We simulated a closed cohort of Mexican adults aged 20 years and over from 2021 to 2040. The simulated sample corresponded to the combination of the 2020−22 Health and Nutrition Surveys, which contained anthropometric and demographic information representative at a national level. We projected annual average Body Mass Index (BMI), obesity prevalence, deaths averted, and years lived without obesity (YLWO) under four scenarios: status quo and doubling the current tax on SSB and NEDF in 2025, 2030, and 2035. BMI was projected from 2021 to 2040 using Hall’s microsimulation weight change model, and a Mexican projection of total energy intake. To simulate deaths, we estimated the probability of all-cause mortality by BMI category from the National Population Council projections of the Mexican population by age and year. By 2040, doubling the taxes in 2025 resulted in an obesity prevalence of 41.6% (95% Uncertainty Interval [40.2,43.1]) in contrast to the status quo scenario (44.5%, 95% Uncertainty Interval [43.2,45.8]), and 170,600 deaths averted (95% Uncertainty Interval [130,900, 210,200]) and 25,031,900 (95% Uncertainty Interval [19,048,500, 31,015,300]) YLWO gained. A delayed intervention in 2035 resulted in an obesity prevalence of 41.7% (95% Uncertainty Interval [40.4,43.1]), 38,900 deaths averted (95% Uncertainty Interval [29,600, 48,200]), and 4,473,700 (95% Uncertainty Interval [3,378,900, 5,568,500]) YLWO gained. Our results apply only to individuals aged 20 years or older in 2021, excluding cohorts reaching age 20 between 2022 and 2040. Conclusions: Our results emphasize the urgency of advancing WHO’s Acceleration Plan to STOP Obesity. Postponing evidence-based interventions is estimated to exacerbate the burden of obesity, mortality, and suffering.

In this paper, the main objective is to estimate the percentage of glycosylated hemoglobin through an easily accessible computational platform to estimate the risk of generating type 2 diabetes mellitus in the Mexican population. The estimation of the computational tool is developed through an artificial neural network model, which was trained and validated according to a population sample of 1120 Mexican people between 18 and 59 years old. The model inputs were gender, age, body mass index, waist circumference, weekly food consumption, family history, and whether the person suffers from any chronic degenerative disease other than T2DM. We used the percentage of glycosylated hemoglobin as output, estimated according to a dynamic glucose model. The estimation results present a coefficient of determination of 99%, demonstrating an acceptable performance of the neural network model. The developed platform is an aid tool for health personnel, which seeks to generate a first approximation to the glycemic status of those communities with a high marginalization index for generating disease prevention strategies.

Background: Glucose homeostasis is a crucial physiological process, and its disruption is closely linked to the onset of Type 2 Diabetes Mellitus (T2DM), a major global health issue. Objective: This study presents a novel mathematical model to describe glucose dynamics in both healthy individuals and those with prediabetic risk factors. Methods: We analyzed 311 days of continuous glucose monitoring data from 43 participants (14 healthy and 29 at risk, aged 25–55), using a Dual Extended Kalman Filter to estimate parameters and unmeasurable variables, while accounting for parametric variability. We applied the Levenberg–Marquardt algorithm to minimize estimation error. Results: Based on average parameter values and standardized inputs, 311 simulations were conducted, showing strong agreement with experimental data (r = 0.98, p < 0.01). Conclusions: The model provides an accurate representation of glucose regulation and serves as a valuable in-silico tool for advancing preventive strategies against T2DM, marking one of the first models specifically tailored to individuals with prediabetes.