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Background/Objectives: The external environment constantly influences human health through many factors, including air quality, access to green spaces, exposure to pollutants, and climate change. Contamination poses a substantial threat to human well-being; conversely, environmental factors also positively impact health. The purpose of this study is to provide a comprehensive review of the complex relationship between various environmental factors and human health. While individual studies have explored specific aspects, a broader integrative understanding is lacking. Methods: Through databases (PubMed, Cochrane, Copernicus), 4888 papers were identified, with 166 selected for detailed analysis. Results: We summarized recent research, identifying multiple associations between environmental factors such as air pollution, climate change, solar radiation, and meteorological conditions and their impact on various health outcomes, including respiratory, cardiovascular, metabolic and gastrointestinal, renal and urogenital, neurological and psychological health, infectious and skin diseases, and major cancers. We use chord diagrams to illustrate these links. We also show the interaction between different environmental factors. Findings begin with exploring the direct impact of environmental factors on human health; then, the interplay and combined effects of environmental factors, elucidating their (often indirect) interaction and collective contribution to human health; and finally, the implications of climate change on human health. Conclusions: Researchers and policymakers need to consider that individuals are exposed to multiple pollutants simultaneously, the "multipollutant exposure phenomenon". It is important to study and regulate environmental factors by considering the combined impact of various pollutants rather than looking at each pollutant separately. We emphasize actionable recommendations and solutions. [ABSTRACT FROM AUTHOR]

This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management. [ABSTRACT FROM AUTHOR]

This paper presents an individualized multiple linear regression model based on compositional data where we predict the mean and coefficient of variation of blood glucose in individuals with type 1 diabetes for the long-term (2 and 4 h). From these predictions, we estimate the minimum and maximum glucose values to provide future glycemic status. The proposed methodology has been validated using a dataset of 226 real adult patients with type 1 diabetes (Replace BG (NCT02258373)). The obtained results show a median balanced accuracy and sensitivity of over 90% and 80%, respectively. A information system has been implemented and validated to update patients on their glycemic status and associated risks for the next few hours. [ABSTRACT FROM AUTHOR]

People with type 1 diabetes are required to adhere to their treatment rigorously to ensure maximum benefits. Diabetes tracking tools have played an important role in this regard. Type 1 diabetes monitoring has evolved and matured with the advent of blood glucose monitor sensors, insulin pens, and insulin pump automation. However, carbohydrate monitoring has seen little progress despite carbohydrates representing a major potential disruption. Relying on the modeling of carbohydrate intake using the rate of exogenous glucose appearance, we first present a methodology capable of identifying the type of carbohydrates ingested by classifying them into fast and non-fast carbohydrates. Second, we test the ability of the methodology to identify the correct synchrony between the actual mealtime and the time labeled as such in diabetes records. A deep neural network is trained with processed input data that consist of different values to estimate the parameters in a series of experiments in which, firstly, we vary the response of ingested carbohydrates for subsequent identification and, secondly, we shift the learned carbohydrate absorption curves in time to estimate when the meals were administered to virtual patients. This study validates that the identification of different carbohydrate classes in the meal records of people with type 1 diabetes could become a valuable source of information, as it demonstrates the potential to identify inaccuracies in the recorded meal records of these patients, suggesting the potential abilities of the next generation of type 1 diabetes management tools. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D).Research Design and Methods: A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise.Results: Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates.Conclusions: The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake.Register Clinicaltrials.gov: NCT03577158. [ABSTRACT FROM AUTHOR]