Gilberto Gonzalez-Arroyo

Background: Artificial intelligence (AI) models applied to diabetes mellitus research have grown in recent years, particularly in the field of medical imaging. However little work has been done exploring real-world data (RWD) sources such as electronic health records (EHR) mostly due to the lack of reliable public diabetes databases. However, with more than 500 million patients affected worldwide, complications of this condition have catastrophic consequences. In this manuscript we aim to first extract, clean and transform a novel diabetes research database, DiabetIA, and secondly train machine learning (ML) models to predict diabetic complications. Methods: In this study, we used observational retrospective data from the Mexican Institute for Social Security (IMSS) extracting and de-identifying EHR data for almost 2 million patients seen at primary care facilities. After applying eligibility criteria for this study, we constructed a diabetes complications database. Next, we trained naïve Bayesian models with various subsets of variables, including an expert-selected model. Results: The DiabetIA database is composed of 136,674 patients (414,770 records and 447 variables), with 33,314 presenting diabetes (24.3%). The most frequent diabetic complications were diabetic foot with 2,537 patients, nephropathy with 1,914 patients, retinopathy with 1,829 patients, and neuropathy with 786 patients. These complications were accurately predicted by the Gaussian naïve Bayessian models with an average area under the curve AUC of 0.86. Our expert-selected model, achieved an average AUC of 0.84 with 21 curated variables. Conclusion: Our study offers the largest longitudinal research database from EHR data in Latin America for research. The DiabetIA database provides a useful resource to estimate the burden of diabetic complications on healthcare systems. Machine learning models can provide accurate estimations of the total cases presented in medical units. For patients and their clinicians, it is imperative to have a way to calculate this risk and start clinical interventions to slow down or prevent the complications of this condition. Brief description: The study centers on establishing the DiabetIA database, a substantial repository encompassing de-identified electronic health records from 136,674 patients sourced from primary care facilities within the Mexican Institute for Social Security (IMSS). Our efforts involved curating, cleansing, and transforming this extensive dataset, and then employing machine learning models to predict diabetic complications with high accuracy.

Introduction: A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension. Methods: We collected genotyped data from a novel DTC platform where participants upload their genotype data files, and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm. Results: We collected data on N=4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D; and N=4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC=0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers. Discussion: DTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly. Conclusions: The genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

Introduction: The advent of the SARS-CoV-2 virus posed formidable challenges on a global scale. In the year 2020, existing treatments were not tailored specifically to combat this novel virus, and the absence of a developed vaccine added to the complexity. Clinical guidelines underwent rapid evolution during the initial months of the pandemic, leaving uncertainty about the efficacy of various drug combinations in treating the disease. This study delves into an analysis of outcomes during the early stages of the pandemic within the Mexican Institute of Social Security (IMSS), the largest healthcare system in Mexico. Material and Methods: In this retrospective observational study, we examined the medical records of 130,216 COVID-19 patients treated in two Mexican states throughout the year 2020. We conducted a competing risk analysis, considering death and recovery as potential outcomes. This was further complemented by a Cox-regression and Kaplan-Meier analysis. To enhance predictive insights, machine learning models were constructed to forecast outcomes at 10, 20, and 30 days. Results: Our analysis revealed a heightened prevalence of comorbidities, including obesity, diabetes, and heart disease, aligning with Mexico’s established epidemiological profile. Mortality patterns indicated occurrences approximately 15-20 days from the onset of symptoms. Notably, patients undergoing treatment with cephalosporin in conjunction with neuraminidase inhibitors (NAIs) exhibited the poorest survival rates, whereas those receiving adamantane, fluoroquinolone, or penicillin demonstrated the most favorable survival outcomes. Conclusions: The identified associations caution against the utilization of specific treatment combinations, providing crucial insights for refining the country’s clinical guidelines and optimizing patient care strategies.