METS-IR is a novel score to evaluate cardiometabolic risk in healthy and at-risk subjects and a promising tool for screening of insulin sensitivity.

Certain obese individuals have adiponectin levels similar to those found in normal BMI subjects; this is associated with the metabolically healthy obese phenotype.

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ImportanceFamilial hypercholesterolemia (FH) is an underdiagnosed and undertreated genetic disorder that leads to premature morbidity and mortality due to atherosclerotic cardiovascular disease. Familial hypercholesterolemia affects 1 in 200 to 250 people around the world of every race and ethnicity. The lack of general awareness of FH among the public and medical community has resulted in only 10% of the FH population being diagnosed and adequately treated. The World Health Organization recognized FH as a public health priority in 1998 during a consultation meeting in Geneva, Switzerland. The World Health Organization report highlighted 11 recommendations to address FH worldwide, from diagnosis and treatment to family screening and education. Research since the 1998 report has increased understanding and awareness of FH, particularly in specialty areas, such as cardiology and lipidology. However, in the past 20 years, there has been little progress in implementing the 11 recommendations to prevent premature atherosclerotic cardiovascular disease in an entire generation of families with FH.ObservationsIn 2018, the Familial Hypercholesterolemia Foundation and the World Heart Federation convened the international FH community to update the 11 recommendations. Two meetings were held: one at the 2018 FH Foundation Global Summit and the other during the 2018 World Congress of Cardiology and Cardiovascular Health. Each meeting served as a platform for the FH community to examine the original recommendations, assess the gaps, and provide commentary on the revised recommendations. The Global Call to Action on Familial Hypercholesterolemia thus represents individuals with FH, advocacy leaders, scientific experts, policy makers, and the original authors of the 1998 World Health Organization report. Attendees from 40 countries brought perspectives on FH from low-, middle-, and high-income regions. Tables listing country-specific government support for FH care, existing country-specific and international FH scientific statements and guidelines, country-specific and international FH registries, and known FH advocacy organizations around the world were created.Conclusions and RelevanceBy adopting the 9 updated public policy recommendations created for this document, covering awareness; advocacy; screening, testing, and diagnosis; treatment; family-based care; registries; research; and cost and value, individual countries have the opportunity to prevent atherosclerotic heart disease in their citizens carrying a gene associated with FH and, likely, all those with severe hypercholesterolemia as well.

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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.

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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.