Sex differences in the influence of type 2 diabetes (T2D)-related genes, parental history of T2D, and obesity on T2D development: a case-control study.

BACKGROUND: This study investigated the effect of sex and age at type 2 diabetes (T2D) diagnosis on the influence of T2D-related genes, parental history of T2D, and obesity on T2D development. METHODS: In this case-control study, 1012 T2D cases and 1008 healthy subjects were selected from the Diabetes in Mexico Study database. Participants were stratified by sex and age at T2D diagnosis (early, ≤ 45 years; late, ≥ 46 years). Sixty-nine T2D-associated single nucleotide polymorphisms were explored and the percentage contribution (R2) of T2D-related genes, parental history of T2D, and obesity (body mass index [BMI] and waist-hip ratio [WHR]) on T2D development was calculated using univariate and multivariate logistic regression models. RESULTS: T2D-related genes influenced T2D development most in males who were diagnosed early (R2 = 23.5%; females, R2 = 13.5%; males and females diagnosed late, R2 = 11.9% and R2 = 7.3%, respectively). With an early diagnosis, insulin production-related genes were more influential in males (76.0% of R2) while peripheral insulin resistance-associated genes were more influential in females (52.3% of R2). With a late diagnosis, insulin production-related genes from chromosome region 11p15.5 notably influenced males while peripheral insulin resistance and genes associated with inflammation and other processes notably influenced females. Influence of parental history was higher among those diagnosed early (males, 19.9%; females, 17.5%) versus late (males, 6.4%; females, 5,3%). Unilateral maternal T2D history was more influential than paternal T2D history. BMI influenced T2D development for all, while WHR exclusively influenced males. CONCLUSIONS: The influence of T2D-related genes, maternal T2D history, and fat distribution on T2D development was greater in males than females.

The prevalence of diabetes worldwide is slightly higher in men than in women, particularly in those aged 50 or younger (16.5% for men versus 13.5% for women). This suggests that hormonal differences could be critical in early development of Type 2 diabetes. Some known factors previously associated with T2D, such as genes, parental history of diabetes and obesity, could have a differential influence between both sexes for the development of T2D. We compared these factors between 1008 healthy individual and 1012 TD2 patients. In this comparison, we calculated the percentage of variability of the disease explained by each factor. As expected, the most noticeable differences between men and women were observed in T2D diagnoses before age 46. Genes had a greater effect in men than in women (23.5% vs. 13.5%). While genes involved in insulin production have a greater influence on men, genes involved in peripheric insulin resistance have a greater influence on women. The overall parental history of T2D influences similarly in males (19.9%) and females (17.5%), however, the unilateral genetic influence of the mother was much greater in males than in females. The influence of global and abdominal obesity played a greater role in men than in women. In T2D diagnoses after age of 45, the influence of genes and parental history of diabetes decreases markedly, and the relative influence of global obesity augments. However, while genes linked to insulin resistance and inflammation predominate in females, genes linked to insulin secretion predominate in males.

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women.

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms.

INTRODUCTION: Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables. RESEARCH DESIGN AND METHODS: Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort. RESULTS: Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively. CONCLUSIONS: We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

Novel clinical and immunological features associated with persistent post-acute sequelae of COVID-19 after six months of follow-up: a pilot study.

BACKGROUND: Currently, there is scant information regarding the features associated to the persistence of post-COVID-19 syndrome, which is the main aim of the present study. METHODS: A cohort study of 102 COVID-19 patients was conducted. The post-COVID-19 symptoms were assessed by a standardised questionnaire. Lymphocyte immunophenotyping was performed by flow cytometry and chemokines/cytokines, neutrophil extracellular traps, the tripartite motif 63, anti-cellular, and anti-SARS-CoV-2 IgG antibodies were addressed in serum. The primary outcome was the persistence of post-COVID-19 syndrome after six months follow-up. RESULTS: Thirteen patients (12.7%) developed the primary outcome and had a more frequent history of post-COVID-19 syndrome 3 months after infection onset (p = .044), increased levels of IL-1α (p = .011) and IP-10 (p = .037) and increased CD57 expression in CD8+ T cells (p = .003). There was a trend towards higher levels of IFN-γ (p = .051), IL-1ß (p = .062) and IL-6 (p = .087). The history of post COVID-19 in the previous 3 months, obesity, baseline serum MIP-1α and IP-10, and CD57 expression in CD8+ T cells were independently associated with the persistence of post-COVID-19 syndrome. CONCLUSION: Our data suggest an important relationship between a pro-inflammatory state mediated through metabolic pathways related to obesity and increased cellular senescence as a key element in the persistence of post-COVID-19 syndrome at six months of follow-up.

Diagnostic Accuracy of Capillary Blood Glucometer Testing for Gestational Diabetes.

Purpose: Few pregnant women in low-resource settings are screened for gestational diabetes mellitus (GDM) using the gold standard oral glucose tolerance test (OGTT). This study compared capillary blood glucose testing with 2-h plasma glucose measurements obtained using the 75-g OGTT to screen for GDM at primary healthcare clinics in Mexico. Patients and Methods: Pregnant women who participated in a previous prospective multicenter longitudinal cohort study and who had not been previously diagnosed with diabetes were included. Participants were evaluated using the plasmatic 2-h 75-g OGTT with simultaneous capillary blood glucose measurements using a glucometer. The study endpoint was the comparability of the glucometer results to the gold standard OGTT when collected simultaneously. Sensitivity, specificity, and area under the curve of the glucose measurements obtained for capillary blood compared with venous plasma (gold standard) were calculated to determine diagnostic accuracy. Results: The study included 947 pregnant women who had simultaneous glucose measurements available (blood capillary [glucometer] and venous blood OGTT). Overall, capillary blood glucose testing was very sensitive (89.47%); the specificity was 66.58% and the area under the curve (95% confidence interval) was 0.78 (0.74-0.81). The sensitivity, specificity and area under the curve of each capillary measurement were: 89.47%, 66.58% and 0.78 (0.74-0.82) for the fasting measurement, 91.53%, 93.24% and 0.92 (0.88-0.96) for the one-hour measurement, and 89.80%, 93.32%, 0.91 (0.87-0.95) for the second-hour measurement, respectively. No adverse events were reported. Conclusion: Capillary OGTT is a valid alternative to the gold standard OGTT for screening of GDM in low-resource situations or in situations where there are other limitations to performing the OGTT as part of primary healthcare services.

MIDO COVID: A digital public health strategy designed to tackle chronic disease and the COVID-19 pandemic in Mexico.

Screening, prevention, and management of non-communicable diseases (NCDs, including obesity, hypertension, and type 2 diabetes) is the core function of Integrated Measurement for Early Detection (MIDO), a digital strategy developed by the Carlos Slim Foundation in Mexico. An extension of this strategy, MIDO COVID, was developed to address the need for an integrated plan in primary health care during the COVID-19 pandemic. MIDO COVID facilitates planning, surveillance, testing, and clinical management of SARS-CoV-2 infections and the major NCDs and their pre-disease states, to streamline the continuum of care. MIDO COVID screening was applied in 1063 Carso Group workplaces in 190 municipalities of the 32 Mexican states. Staff were trained to screen healthy workers for NCDs using a questionnaire, anthropomorphic measurements, and blood work; healthy individuals returning to work also received a SARS-CoV-2 antibody test. Between June 26 and December 31, 2020, 58,277 asymptomatic individuals underwent screening. The prevalence of obesity, hypertension, and type 2 diabetes was 32.1%, 25.7%, and 9.7% respectively. Only 2.2%, 8.8%, and 4.5% of individuals, respectively, were previously aware of their condition. Pre-obesity was identified in 38.6%, pre-hypertension in 17.4%, and prediabetes in 7.5% of the population. Risk of SARS-CoV-2 infection was highest for individuals with multiple NCDs. Many Mexicans are unaware of their health status and potentially increased risk of COVID-19 and serious complications. As a universal strategy implemented regardless of social factors, MIDO COVID promotes equity in access to health care prevention and early stage detection of NCDs; the information gained may help inform decisionmakers regarding prioritising vulnerable populations for immunisation.

Clinical and immunological features associated to the development of a sustained immune humoral response in COVID-19 patients: Results from a cohort study.

Background: Until now, most of the research addressing long-term humoral responses in coronavirus disease 2019 (COVID-19) had only evaluated the serum titers of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgGs, without the assessment of the baseline antiviral clinical and immune profile, which is the aim of this study and may be the key factor leading to a broad and sustained antibody response. Methods: We included 103 patients with COVID-19. When the patients sought medical attention (baseline), a blood sample was drawn to perform immunophenotype of lymphocytes by flow cytometry. The patients were assessed 15 days after baseline and then every month until the third month, followed by a last visit 6 months after recruitment. We evaluated the anti-SARS-COV-2 IgG at all time points, and the serum levels of cytokines, chemokines, anti-cellular (AC) antibodies and neutrophil extracellular traps were also assessed during the follow-up. The primary outcome of the study was the presence of a sustained immune humoral response, defined as an anti-SARS-CoV-2 IgG titer >4.99 arbitrary units/mL in at least two consecutive measures. We used generalized lineal models to assess the features associated with this outcome and to assess the effect of the changes in the cytokines and chemokines throughout time on the development of a sustained humoral immune response. Results: At baseline the features associated to a sustained immune humoral response were the diagnosis of critical disease, absolute number of lymphocytes, serum IP-10, IL-4, IL-2, regulatory T cells, CD8+ T cells, and positive AC antibodies. Critical illness and the positivity of AC antibodies were associated with a sustained humoral immune response after 3 months, whilst critical illness and serum IL-13 were the explanatory variables after 6 months. Conclusion: A sustained immune humoral response is strongly related to critical COVID-19, which is characterized by the presence of AC antibodies, quantitative abnormalities in the T cell compartment, and the serum cytokines and chemokines during acute infection and throughout time.

Symptom cluster analysis of long COVID-19 in patients discharged from the Temporary COVID-19 Hospital in Mexico City.

INTRODUCTION: Several reports have emerged describing the long-term consequences of COVID-19 and its effects on multiple systems. METHODS: As further research is needed, we conducted a longitudinal observational study to report the prevalence and associated risk factors of the long-term health consequences of COVID-19 by symptom clusters in patients discharged from the Temporary COVID-19 Hospital (TCH) in Mexico City. Self-reported clinical symptom data were collected via telephone calls over 90 days post-discharge. Among 4670 patients, we identified 45 symptoms across eight symptom clusters (neurological; mood disorders; systemic; respiratory; musculoskeletal; ear, nose, and throat; dermatological; and gastrointestinal). RESULTS: We observed that the neurological, dermatological, and mood disorder symptom clusters persisted in >30% of patients at 90 days post-discharge. Although most symptoms decreased in frequency between day 30 and 90, alopecia and the dermatological symptom cluster significantly increased (p < 0.00001). Women were more prone than men to develop long-term symptoms, and invasive mechanical ventilation also increased the frequency of symptoms at 30 days post-discharge. CONCLUSION: Overall, we observed that symptoms often persisted regardless of disease severity. We hope these findings will help promote public health strategies that ensure equity in the access to solutions focused on the long-term consequences of COVID-19.

Rapid establishment of a dedicated COVID-19 hospital in Mexico city during a public health crisis.

Healthcare systems worldwide have adapted and reorganized during the coronavirus disease 2019 (COVID-19) pandemic. Here, we provide a framework based on a public-private partnership that funded, developed, and operated a temporary COVID-19 hospital in Mexico City. We describe the creation of a collaborative network of primary healthcare triage centers and hospitals distributed throughout the city in recognition of demographic and geographic patterns that correlate with COVID-19 infections, including marginalized and impoverished areas of Mexico City. Additionally, we also report the hospital's cumulative outcomes over the 14 months of operation and show that it is feasible to transform a large public venue into a specialized hospital that incorporates a digital platform with robust clinical protocols to provide positive clinical outcomes.

During Mexico's response to the COVID-19 pandemic, the Carlos Slim Foundation (CSF), with a group of local foundations, academic institutions, and the Government of Mexico City, established a synergistic public­private partnership with the purpose of funding, designing, developing, and operating a dedicated COVID-19 hospital. This was achieved in 17 days by rapidly transforming into a hospital the largest convention center in Latin America, which is located in the heart of Mexico City. An ex professo network of eight dedicated respiratory triage community centers in coordination with other 40 federal and state primary health care clinics and hospitals was also established to streamline patient referral, thereby mitigating the impact of the COVID-19 pandemic in Mexico City's metropolitan area. We provide a framework for designing, funding, and executing the operations of a dedicated hospital in response to the COVID-19 pandemic that, from its conception, execution, operation, and closure, involved an exemplary coordination between public-private partnerships during a public health crisis. Referral, admission, treatment, clinical monitoring, discharge, and household follow-up were facilitated by the COVID360 digital health platform. The successful development and implementation of this multi-faceted digital platform allowed a lean patient-centered process, the management of clinical and administrative data, training of healthcare professionals, and the dissemination of accurate health information for data-driven decision making. This rapidly implemented temporary hospital dedicated to the comprehensive care of patients with COVID-19 was critical in coping with the increasing number of cases in Mexico City while achieving outstanding clinical outcomes.