AnthropoAge, a novel approach to integrate body composition into the estimation of biological age.

Aging is believed to occur across multiple domains, one of which is body composition; however, attempts to integrate it into biological age (BA) have been limited. Here, we consider the sex-dependent role of anthropometry for the prediction of 10-year all-cause mortality using data from 18,794 NHANES participants to generate and validate a new BA metric. Our data-driven approach pointed to sex-specific contributors for BA estimation: WHtR, arm and thigh circumferences for men; weight, WHtR, thigh circumference, subscapular and triceps skinfolds for women. We used these measurements to generate AnthropoAge, which predicted all-cause mortality (AUROC 0.876, 95%CI 0.864-0.887) and cause-specific mortality independently of ethnicity, sex, and comorbidities; AnthropoAge was a better predictor than PhenoAge for cerebrovascular, Alzheimer, and COPD mortality. A metric of age acceleration was also derived and used to assess sexual dimorphisms linked to accelerated aging, where women had an increase in overall body mass plus an important subcutaneous to visceral fat redistribution, and men displayed a marked decrease in fat and muscle mass. Finally, we showed that consideration of multiple BA metrics may identify unique aging trajectories with increased mortality (HR for multidomain acceleration 2.43, 95%CI 2.25-2.62) and comorbidity profiles. A simplified version of AnthropoAge (S-AnthropoAge) was generated using only BMI and WHtR, all results were preserved using this metric. In conclusion, AnthropoAge is a useful proxy of BA that captures cause-specific mortality and sex dimorphisms in body composition, and it could be used for future multidomain assessments of aging to better characterize the heterogeneity of this phenomenon.

Association of estimated glucose disposal rate and chronic diabetic complications in patients with type 1 diabetes.

INTRODUCTION: The role of insulin resistance in diabetic chronic complications among individuals with type 1 diabetes (T1D) has not been clearly defined. The aim of this study was to examine the performance of insulin resistance, evaluated using the estimated glucose disposal rate (eGDR) for the identification of metabolic syndrome (MS) and diabetic chronic complications. METHODS: Cross-sectional study in a tertiary care centre. We included patients of 18 years and older, with at least 6 months of T1D duration. Anthropometric, clinical and biochemical data were collected. RESULTS: Seventy patients, 41 (58.6%) women, with a median age of 36.6 years (range 18-65). Mean age of onset and duration of diabetes was 13.5 ± 6.5 and 23.6 ± 12.2 years, respectively. Twenty-one (30%) patients met the metabolic syndrome (MS) criteria. Patients with MS had lower eGDR compared to patients without (5.17 [3.10-8.65] vs. 8.86 [6.82-9.85] mg/kg/min, respectively, p = .003). Median eGDR in patients with nephropathy, retinopathy and neuropathy compared with those without was 6.75 (4.60-8.20) versus 9.53 (8.57-10.3); p < .001, 6.45 (4.60-7.09) versus 9.50 (8.60-10.14); p < .001, 5.56 (4.51-6.81) versus 9.49 [8.19-10.26] mg/kg/min; p < .001, respectively. The eGDR showed an area under the curve of 0.909, 0.879, 0.897 and 0.836 for the discrimination of MS, retinopathy, neuropathy and nephropathy, respectively. CONCLUSIONS: Patients with T1D diabetic complications have higher insulin resistance. The eGDR discriminates patients with chronic diabetic complications and MS. While more ethnic-specific studies are required, this study suggests the possibility to incorporate eGDR into routine diabetes care.

Adaptive Metabolic and Inflammatory Responses Identified Using Accelerated Aging Metrics Are Linked to Adverse Outcomes in Severe SARS-CoV-2 Infection.

BACKGROUND: Chronological age (CA) is a predictor of adverse coronavirus disease 2019 (COVID-19) outcomes; however, CA alone does not capture individual responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we evaluated the influence of aging metrics PhenoAge and PhenoAgeAccel to predict adverse COVID-19 outcomes. Furthermore, we sought to model adaptive metabolic and inflammatory responses to severe SARS-CoV-2 infection using individual PhenoAge components. METHOD: In this retrospective cohort study, we assessed cases admitted to a COVID-19 reference center in Mexico City. PhenoAge and PhenoAgeAccel were estimated using laboratory values at admission. Cox proportional hazards models were fitted to estimate risk for COVID-19 lethality and adverse outcomes (intensive care unit admission, intubation, or death). To explore reproducible patterns which model adaptive responses to SARS-CoV-2 infection, we used k-means clustering using PhenoAge components. RESULTS: We included 1068 subjects of whom 222 presented critical illness and 218 died. PhenoAge was a better predictor of adverse outcomes and lethality compared to CA and SpO2 and its predictive capacity was sustained for all age groups. Patients with responses associated to PhenoAgeAccel >0 had higher risk of death and critical illness compared to those with lower values (log-rank p < .001). Using unsupervised clustering, we identified 4 adaptive responses to SARS-CoV-2 infection: (i) inflammaging associated with CA, (ii) metabolic dysfunction associated with cardiometabolic comorbidities, (iii) unfavorable hematological response, and (iv) response associated with favorable outcomes. CONCLUSIONS: Adaptive responses related to accelerated aging metrics are linked to adverse COVID-19 outcomes and have unique and distinguishable features. PhenoAge is a better predictor of adverse outcomes compared to CA.

Impact of undiagnosed type 2 diabetes and pre-diabetes on severity and mortality for SARS-CoV-2 infection.

INTRODUCTION: Diabetes 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 METHODS: Prospective 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. RESULTS: Overall, 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. CONCLUSIONS: Undiagnosed 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.