Knowledge about the immune responses against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, particularly regarding the function of eosinophils, has been steadily emerging recently. There exists controversy regarding the implications of eosinophils in the coronavirus disease 2019 (COVID-19)’s pathology. We report a retrospective cohort study including the comparison of leukocyte counts in COVID-19 patients, considering the outcomes of recovery (n = 59) and death (n = 60). Among the different types of leukocytes, the eosinophil counts were those that showed the greatest difference between recovered and deceased patients. Eosinopenia (eosinophil count < 0.01 × 10⁹/L) was more frequently observed in deceased than recovered patients (p = 0.0012). The eosinophil counts more rapidly increased and showed a greater proportion over the course of the disease in the recovered than deceased patients. Furthermore, the estimated survival rate was greater in patients without eosinopenia than in patients with eosinopenia (p = 0.0070) during hospitalization. Importantly, recovered but not deceased patients showed high negative correlations of the eosinophils with the neutrophil-to-lymphocyte ratio (NLR) and neutrophil counts at Day 9 of the onset of clinical symptoms (p ≤ 0.0220). Our analysis suggests that eosinopenia may be associated with unfavorable disease outcomes and that the eosinophils have a beneficial function in COVID-19 patients, probably contributing by controlling the exacerbated inflammation induced by neutrophils.

Introduction: The intricate relationship between obesity and chronic kidney disease (CKD) progression underscores a significant public health challenge. Obesity is strongly linked to the onset of several health conditions, including arterial hypertension (AHTN), metabolic syndrome, diabetes, dyslipidemia, and hyperuricemia. Understanding the connection between CKD and obesity is crucial for addressing their complex interplay in public health strategies. Objective: This research aimed to determine the prevalence of CKD in a population with high obesity rates and evaluate the associated metabolic risk factors. Material and Methods: In this cross-sectional study conducted from January 2017 to December 2019 we included 3,901 participants of both sexes aged ≥20 years who were selected from primary healthcare medical units of the Mexican Social Security Institute (IMSS) in Michoacan, Mexico. We measured the participants’ weight, height, systolic and diastolic blood pressure, glucose, creatinine, total cholesterol, triglycerides, HDL-c, LDL-c, and uric acid. We estimated the glomerular filtration rate using the Collaborative Chronic Kidney Disease Epidemiology (CKD-EPI) equation. Results: Among the population studied, 50.6% were women and 49.4% were men, with a mean age of 49 years (range: 23–90). The prevalence of CKD was 21.9%. Factors significantly associated with an increased risk of CKD included age ≥60 years (OR = 11.70, 95% CI [9.83–15.93]), overweight (OR = 4.19, 95% CI [2.88–6.11]), obesity (OR = 13.31, 95% CI [11.12–15.93]), abdominal obesity (OR = 9.25, 95% CI [7.13–11.99]), AHTN (OR = 20.63, 95% CI [17.02–25.02]), impaired fasting glucose (IFG) (OR = 2.73, 95% CI [2.31–3.23]), type 2 diabetes (T2D) (OR = 14.30, 95% CI [11.14–18.37]), total cholesterol (TC) ≥200 mg/dL (OR = 6.04, 95% CI [5.11–7.14]), triglycerides (TG) ≥150 mg/dL (OR = 5.63, 95% CI 4.76-6.66), HDL-c <40 mg/dL (OR = 4.458, 95% CI [3.74–5.31]), LDL-c ≥130 mg/dL (OR = 6.06, 95% CI [5.12–7.18]), and serum uric acid levels ≥6 mg/dL in women and ≥7 mg/dL in men (OR = 8.18, 95% CI [6.92–9.68]), (p < 0.0001). These factors independently contribute to the development of CKD. Conclusions: This study underscores the intricate relationship between obesity and CKD, revealing a high prevalence of CKD. Obesity, including overweight, abdominal obesity, AHTN, IFG, T2D, dyslipidemia, and hyperuricemia emerged as significant metabolic risk factors for CKD. Early identification of these risk factors is crucial for effective intervention strategies. Public health policies should integrate both pharmacological and non-pharmacological approaches to address obesity-related conditions and prevent kidney damage directly.

Chronic Kidney Disease (CKD) is a serious public health problem. Hyperglycemia stimulates the production of reactive oxygen species that cause oxidative damage to proteins. AOPPs constitute a group of oxidized dityrosine-containing proteins that are generated during periods of oxidative stress. They have proved to be a valuable early marker of oxidative tissue damage and active mediators of inflammation associated with the uremic state. To analyze if advanced oxidative protein products (AOPPs) have diagnostic accuracy for identifying chronic kidney disease (CKD) in the adult population. We conducted a diagnostic test validation study in 302 adults ≥20 years old, of both sexes, with and without T2D. After obtaining informed consent, a comprehensive clinical history, anthropometric measurements (weight, BMI) and blood pressure were recorded. Glucose, cholesterol, triglyceride, HDL-c, LDL-c and AOPPs were determinates. Glomerular filtration rate (GFR) was calculated using Cockcroft–Gault (C–G) corrected by body surface area (BSA, mL/min/1.73 m²), CKD-EPI and MDRD equations to identify five stages of CKD. This study follows the Standards for Reporting Diagnostic Accuracy Studies (STARD). The median value of AOPPs was 198.32 µmol/L (minimum-maximum value: 113.48–522.42 µmol/L). The group with patients diagnosed with T2D exhibited higher concentrations (median: 487.39 µmol/L) compared to the non-diabetic group (median: 158.50 µmol/L, p = 0.0001). The selected cut-off point was ≥200 µmol/L using the closest to the median value of AOPPs with sensitivity and specificity as follows: C–G: sensitivity 96.58%; specificity 80%; likelihood ratio: 4.83; CKD-EPI: sensitivity 95.76%; specificity 79.89%; likelihood ratio: 4.76; MDRD: sensitivity 86.55%; specificity: 73.22%; likelihood ratio: 3.23. A difference was observed between AOPPs and chronic kidney disease stage. This study provides evidence that AOPPs ≥ 200 µmol/L have diagnostic accuracy in identifying stage 4–5 CKD by C–G, MDRD and CKD-EPI equations in adults with and without T2D.

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.

Organizations that issue guidance on breast cancer recommend the use of immunohistochemistry (IHC) for providing appropriate and precise care. However, little focus has been directed to the identification of maximum allowable turnaround times for IHC, which is necessary given the diversity of hospital settings in the world. Much less effort has been committed to the development of digital tools that allow hospital administrators to monitor service utilization histories of their patients. In this retrospective cohort study, we reviewed electronic and paper medical records of all suspected breast cancer patients treated at one secondary-care hospital of the Mexican Institute of Social Security (IMSS), located in western Mexico. We then followed three years of medical history of those patients with IHC testing. In 2014, there were 402 breast cancer patients, of which 30 (7.4% of total) were tested for some IHC biomarker (ER, PR, HER2). The subtyping allowed doctors to adjust (56.7%) or confirm (43.3%) the initial therapeutic regimen. The average turnaround time was 56 days. Opportune IHC testing was found to be beneficial when it was available before or during the first rounds of chemotherapy. The use of data mining tools applied to health record data revealed that there is an association between timely immunohistochemistry and improved outcomes in breast cancer patients. Based on this finding, inclusion of turnaround time in clinical guidelines is recommended. As much of the health data in the country becomes digitized, our visualization tools allow a digital dashboard of the hospital service utilization histories.