Abstract Background The SARS-CoV-2 outbreak poses a challenge to health care systems due to its high complication rates in patients with cardiometabolic diseases. Here, we identify risk factors and propose a clinical score to predict COVID-19 lethality, including specific factors for diabetes and obesity, and its role in improving risk prediction. Methods We obtained data of confirmed and negative COVID-19 cases and their demographic and health characteristics from the General Directorate of Epidemiology of the Mexican Ministry of Health. We investigated specific risk factors associated to COVID-19 positivity and mortality and explored the impact of diabetes and obesity on modifying COVID-19-related lethality. Finally, we built a clinical score to predict COVID-19 lethality. Results Among the 177 133 subjects at the time of writing this report (May 18, 2020), we observed 51 633 subjects with SARS-CoV-2 and 5,332 deaths. Risk factors for lethality in COVID-19 include early-onset diabetes, obesity, chronic obstructive pulmonary disease, advanced age, hypertension, immunosuppression, and chronic kidney disease (CKD); we observed that obesity mediates 49.5% of the effect of diabetes on COVID-19 lethality. Early-onset diabetes conferred an increased risk of hospitalization and obesity conferred an increased risk for intensive care unit admission and intubation. Our predictive score for COVID-19 lethality included age ≥ 65 years, diabetes, early-onset diabetes, obesity, age < 40 years, CKD, hypertension, and immunosuppression and significantly discriminates lethal from non-lethal COVID-19 cases (C-statistic = 0.823). Conclusions Here, we propose a mechanistic approach to evaluate the risk for complications and lethality attributable to COVID-19, considering the effect of obesity and diabetes in Mexico. Our score offers a clinical tool for quick determination of high-risk susceptibility patients in a first-contact scenario.

WORDS)BACKGROUND: The SARS-CoV-2 outbreak poses challenge to healthcare systems due to high complication rates in patients with cardiometabolic diseases. Here, we identify risk factors and propose a clinical score to predict COVID-19 lethality, including specific factors for diabetes and obesity and its role in improving risk prediction. METHODS:We obtained data of confirmed and negative COVID-19 cases and their demographic and health characteristics from the General Directorate of Epidemiology of Mexican Ministry of Health. We investigated specific risk factors associated to COVID-19 positivity and mortality and explored the impact of diabetes and obesity on modifying COVID-19 related lethality. Finally, we built a clinical score to predict COVID-19 lethality. RESULTS:Among 177,133 subjects at May 18 th , 2020, we observed 51,633 subjects with SARS-CoV-2 and 5,332 deaths. Risk factors for lethality in COVID-19 include early-onset diabetes, obesity, COPD, advanced age, hypertension, immunosuppression, and CKD; we observed that obesity mediates 49.5% of the effect of diabetes on COVID-19 lethality. Earlyonset diabetes conferred an increased risk of hospitalization and obesity conferred an increased risk for ICU admission and intubation. Our predictive score for COVID-19 lethality included age ≥ 65 years, diabetes, early-onset diabetes, obesity, age <40 years, CKD, hypertension, and immunosuppression and significantly discriminates lethal from non-lethal COVID-19 cases (c-statistic=0.823). RESULTS:Here, we propose a mechanistic approach to evaluate risk for complications and lethality attributable to COVID-19 considering the effect of obesity and diabetes in Mexico.Our score offers a clinical tool for quick determination of high-risk susceptibility patients in a first contact scenario.

BACKGROUND COVID-19 has had a disproportionate impact on older adults. Mexico's population is younger, yet COVID-19’s impact on older adults is comparable to countries with older population structures. Here, we aim to identify health and structural determinants that increase susceptibility to COVID-19 in older Mexican adults beyond chronological aging. METHODS We analyzed confirmed COVID-19 cases in older adults using data from the General Directorate of Epidemiology of Mexican Ministry of Health. We modeled risk factors for increased COVID-19 severity and mortality, using mixed models to incorporate multilevel data concerning healthcare access and marginalization. We also evaluated structural factors and comorbidity profiles compared to chronological age for COVID-19 mortality risk prediction. RESULTS We analyzed 20,804 confirmed SARS-CoV-2 cases in adults aged ≥60 years. Male sex, smoking, diabetes, and obesity were associated with pneumonia, hospitalization and ICU admission in older adults, CKD and COPD were associated with hospitalization. High social lag indexes and access to private care were predictors of COVID-19 severity and mortality. Age was not a predictor of COVID-19 severity in individuals without comorbidities and combination of structural factors and comorbidities were better predictors of COVID-19 lethality and severity compared to chronological age alone. COVID-19 baseline lethality hazards were heterogeneously distributed across Mexican municipalities, particularly when comparing urban and rural areas. CONCLUSIONS Structural factors and comorbidity explain excess risk for COVID-19 severity and mortality over chronological age in older Mexican adults. Clinical decision-making related to COVID-19 should focus away from chronological aging onto more a comprehensive geriatric care approach.

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IntroductionDiabetes and hyperglycemia are risk factors for critical COVID-19 outcomes; however, the impact of pre-diabetes and previously unidentified cases of diabetes remains undefined. Here, we profiled hospitalized patients with undiagnosed type 2 diabetes and pre-diabetes to evaluate its impact on adverse COVID-19 outcomes. We also explored the role of de novo and intrahospital hyperglycemia in mediating critical COVID-19 outcomes.Research design and methodsProspective cohort of 317 hospitalized COVID-19 cases from a Mexico City reference center. Type 2 diabetes was defined as previous diagnosis or treatment with diabetes medication, undiagnosed diabetes and pre-diabetes using glycosylated hemoglobin (HbA1c) American Diabetes Association (ADA) criteria and de novo or intrahospital hyperglycemia as fasting plasma glucose (FPG) ≥140 mg/dL. Logistic and Cox proportional regression models were used to model risk for COVID-19 outcomes.ResultsOverall, 159 cases (50.2%) had type 2 diabetes and 125 had pre-diabetes (39.4%), while 31.4% of patients with type 2 diabetes were previously undiagnosed. Among 20.0% of pre-diabetes cases and 6.1% of normal-range HbA1c had de novo hyperglycemia. FPG was the better predictor for critical COVID-19 compared with HbA1c. Undiagnosed type 2 diabetes (OR: 5.76, 95% CI 1.46 to 27.11) and pre-diabetes (OR: 4.15, 95% CI 1.29 to 16.75) conferred increased risk of severe COVID-19. De novo/intrahospital hyperglycemia predicted critical COVID-19 outcomes independent of diabetes status.ConclusionsUndiagnosed type 2 diabetes, pre-diabetes and de novo hyperglycemia are risk factors for critical COVID-19. HbA1c must be measured early to adequately assess individual risk considering the large rates of undiagnosed type 2 diabetes in Mexico.

Background: Type 2 diabetes represents an increasing health burden world-wide and its prevalence in particularly higher in elderly population. Consistent epidemiological evidence suggests an increased risk of dementia associated to type 2 diabetes; the mechanisms underlying these associations, however, remain unclear. Objective: The study aims to review epidemiological, clinical and pre-clinical data that weigh on pathophysiological links, mechanisms of disease and associations between type 2 diabetes and dementia to identify areas of opportunity for future research. Methods: We searched the following electronic bibliographic databases: PUBMED, EMBASE, SCIELO, MEDLINE and OVID for clinical, translational and epidemiological research literature that summarize diabetes-related risk factors for dementia, metabolic and neurological changes associated to T2D, evidence of therapeutic approaches in type 2 diabetes and its pathophysiological implications for dementia. Results: Type 2 diabetes mellitus increases risk for all-cause dementia, vascular dementia and Alzheimer’s disease. The most evaluated mechanisms linking both disorders in pre-clinical studies include an increase in neuronal insulin resistance, impaired insulin signaling, pro-inflammatory state, mitochondrial dysfunction and vascular damage which increase deposition of β-amyloid, tau proteins and GSK3β, leading to an earlier onset of dementia in individuals with impairment in the glucose metabolism. Neuroimaging and neuropathology evidence linking cerebrovascular lesions, neurodegeneration and particularly small-vessel disease in the onset of dementia is consistent with the increased risk of incident dementia in type 2 diabetes, but consistent evidence of AD-related pathology is scarce. Epidemiological data shows increased risk of dementia related to hypoglycemic episodes, glycemic control, metabolic syndrome, insulin resistance and genetic predisposition, but the evidence is not consistent and statistical analysis might be affected by inconsistent covariate controlling. Therapeutic approaches for T2D have shown inconsistent result in relation to dementia prevention and delay of cognitive decline; lifestyle intervention, particularly physical activity, is a promising alternative to ameliorate the impact of disability and frailty on T2D-related dementia. Conclusion: Vascular disease, inflammation and impaired brain insulin signaling might occur in T2D and contribute to dementia risk. Evidence from epidemiological studies has not consistently reported associations that could integrate a unified mechanism of disease in humans. Evaluation of the effect of antidiabetic medications and non-pharmacological interventions in dementia prevention in type 2 diabetes is promising but has thus far offered inconsistent results.

IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.

Hypertension is associated with insulin resistance (IR), metabolic syndrome (MS), and arterial stiffness. Non–insulin‐based IR indexes were developed as tools for metabolic screening. Here, we aimed to evaluate the novel non–insulin‐based Metabolic Score for IR (METS‐IR) index for the prediction of incident hypertension and arterial stiffness evaluated using pulse wave velocity (PWV) analysis, compared with other non–insulin‐based IR indexes. We evaluated two populations, a cross‐sectional evaluation of high‐risk individuals (n = 305) with a wide range of metabolic comorbidities and dyslipidemia in whom PWV measurement was performed and a 3‐year prospective cohort of normotensive individuals (N = 6850). We observed a positive correlation between METS‐IR and PWV in the cross‐sectional cohort, which was higher compared with other non–insulin‐based fasting IR indexes; furthermore, PWV values >75th percentile were associated with the upper tercile of METS‐IR values. In the prospective cohort, we observed an increased risk for incident hypertension for the upper METS‐IR tercile (METS‐IR ≥ 46.42; HR: 1.81, 95% CI: 1.41‐2.34), adjusted for known cardiovascular risk factors, and observed that METS‐IR had greater increases in the predictive capacity for hypertension along with SBP and the Framingham Hypertension Risk Prediction Model compared with other non–insulin‐based IR indexes. Therefore, METS‐IR is a novel non–insulin‐based IR index which correlates with arterial stiffness and is a predictor of incident hypertension, complementary to previously validated risk prediction models.