Background The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). Methods We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Results Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). Conclusion This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

OBJECTIVE To report the prevalence of depression, eating disorder symptoms, and impaired health-related quality of life (HRQOL) and examine their longitudinal associations with glycemia and diabetes complications in young adults with youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS Participants recruited over a 4-year period were enrolled at 15 clinical diabetes centers in the follow-up observational Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY2) study. From 2014–2020, prevalence of symptoms of depression, eating disorders, and HRQOL by sex, race/ethnicity, and baseline family income were assessed annually. Longitudinal relationships between assessments of glycemia and complications with psychiatric symptoms and HRQOL were evaluated in adjusted models. RESULTS Participants (n = 514) were 21.7 ± 2.5 years old with a diabetes duration of 8.6 ± 1.5 years in year 1 of TODAY 2 (2014). Symptoms of depression and impaired HRQOL were common and increased significantly over 6 years (14.0% to 19.2%, P = 0.003; and 13.1% to 16.7%, P = 0.009, respectively). Depression and impaired HRQOL were more common in women and those with lower baseline family income but did not differ by race/ethnicity. Rates of binge eating were stable over time; self-reported purging increased. Over time, symptoms of depression were associated with higher HbA1c, hypertension, and retinopathy progression; impaired HRQOL was associated with higher BMI, systolic blood pressure, hypertension, and retinopathy progression; and symptoms of eating disorders were associated with higher BMI. CONCLUSIONS Significant psychiatric symptoms and impaired HRQOL are common among emerging adults with youth-onset type 2 diabetes and are positively associated with glycemia, hypertension, and retinopathy progression in this group that is at ongoing risk for medical morbidity.

No abstract

OBJECTIVE To describe the longitudinal effects of sex, race-ethnicity, and metabolic factors on the risk of developing diabetic kidney disease (DKD) in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) cohort. RESEARCH DESIGN AND METHODS Urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) by serum creatinine and cystatin C were assessed annually for up to 15 years after study entry. Markers of DKD included micro- and macroalbuminuria (UACR ≥30 mg/g and ≥300 mg/g, respectively), hyperfiltration (eGFR ≥135 mL/min/1.73 m2), and rapid eGFR annual decline (>3 mL/min/1.73 m2 and/or ≥3.3%). The relationships between risk factors and DKD were evaluated longitudinally using time-to-event models. RESULTS Data were available on 677 participants, average age at baseline 14 years, with a mean ± SD follow-up of 10.2 ± 4.5 years. Each 1% increment in HbA1c conferred higher risk of microalbuminuria (hazard ratio 1.24 [95% CI 1.18, 1.30]), macroalbuminuria (1.22, [1.11, 1.34]), hyperfiltration (1.11, [1.05, 1.17]), and rapid eGFR decline (1.12, [1.04, 1.20]). Higher systolic blood pressure and baseline serum uric acid, and lower indices of β-cell function (C-peptide index and oral disposition index [oDI]), increased the risk of microalbuminuria, while higher triglycerides increased risk of micro- and macroalbuminuria. Lower oDI levels, female sex, and Hispanic ethnicity were associated with higher risk of hyperfiltration. CONCLUSIONS Elevated HbA1c was a shared risk factor among all phenotypes of DKD in this longitudinal cohort of adolescents and young adults with youth-onset type 2 diabetes. Other risk factors included elevated blood pressure, triglycerides, serum uric acid, and β-cell dysfunction.