Faculty Bibliography

OBJECTIVES/UNASSIGNED: = 932) and adjusted for age at diagnosis, sex, race/ethnicity, and diabetes duration. An interaction analysis assessed differential time trends by type. RESULTS/UNASSIGNED:-scores, WC, and CRP. CONCLUSIONS/UNASSIGNED:-score, CRP, and kidney function. Further research is needed to better understand these trends and their implications for long-term CVD risk.

INTRODUCTION/UNASSIGNED:There is growing interest in using electronic health records (EHRs) for chronic disease surveillance. However, these data are convenience samples of in-care individuals, which are not representative of target populations for public health surveillance, generally defined, for the relevant period, as resident populations within city, state, or other jurisdictions. We focus on using EHR data for estimation of diabetes prevalence among young adults in New York City, as rising diabetes burden in younger ages call for better surveillance capacity. METHODS/UNASSIGNED:This article applies common nonprobability sampling methods, including raking, post-stratification, and multilevel regression with post-stratification, to real and simulated data for the cross-sectional estimation of diabetes prevalence among those aged 18-44 years. Within real data analyses, we externally validate city- and neighborhood-level EHR-based estimates to gold-standard estimates from a local health survey. Within data simulations, we probe the extent to which residual biases remain when selection into the EHR sample is non-ignorable. RESULTS/UNASSIGNED:Within the real data analyses, these methods reduced the impact of selection biases in the citywide prevalence estimate compared to gold standard. Residual biases remained at the neighborhood-level, where prevalence tended to be overestimated, especially in neighborhoods where a higher proportion of residents were captured in the sample. Simulation results demonstrated these methods may be sufficient, except when selection into the EHR is non-ignorable, depending on unmeasured factors or on diabetes status. CONCLUSIONS/UNASSIGNED:While EHRs offer potential to innovate on chronic disease surveillance, care is needed when estimating prevalence for small geographies or when selection is non-ignorable.

MOTIVATION:statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. RESULTS:We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. AVAILABILITY AND IMPLEMENTATION:REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

STUDY OBJECTIVES/OBJECTIVE:Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). METHODS:We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. RESULTS:Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. CONCLUSIONS:Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.

AIMS/HYPOTHESIS:The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. METHODS:We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. RESULTS:). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. CONCLUSIONS/INTERPRETATION:Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. DATA AVAILABILITY:Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Youth-onset type 2 diabetes (T2D) is a growing public health concern. Its genetic basis and relationship to other forms of diabetes are largely unknown. To gain insight into the genetic architecture and biology of youth-onset T2D, we analyzed exome sequences of 3,005 youth-onset T2D cases and 9,777 ancestry matched adult controls. We identified (a) monogenic diabetes variants in 2.1% of individuals; (b) two exome-wide significant (P < 4.3×10^-7) common coding variant associations (in WFS1 and SLC30A8); (c) three exome-wide significant (P < 2.5×10^-6) rare variant gene-level associations (HNF1A, MC4R, ATX2NL); and (d) rare variant association enrichments within 25 gene sets broadly related to obesity, monogenic diabetes, and β-cell function. Many association signals were shared between youth-onset and adult-onset T2D but had larger effects for youth-onset T2D risk (1.18-fold increase for common variants and 2.86-fold increase for rare variants). Both common and rare variant associations contributed more to youth-onset T2D liability variance than they did to adult-onset T2D, but the relative increase was larger for rare variant associations (5.0-fold) than for common variant associations (3.4-fold). Youth-onset T2D cases showed phenotypic differences depending on whether their genetic risk was driven by common variants (primarily related to insulin resistance) or rare variants (primarily related to β-cell dysfunction). These data paint a picture of youth-onset T2D as a disease genetically similar to both monogenic diabetes and adult-onset T2D, in which genetic heterogeneity might be used to sub-classify patients for different treatment strategies.

IMPORTANCE:Treatment challenges exist for younger adults with type 1 (T1D) and type 2 diabetes (T2D). Health care coverage, access to, and use of diabetes care are not well delineated in these high-risk populations. OBJECTIVE:To compare patterns of health care coverage, access to, and use of diabetes care and determine their associations with glycemia among younger adults with T1D and with T2D. DESIGN, SETTING, AND PARTICIPANTS:This cohort study analyzed data from a survey that was jointly developed by 2 large, national cohort studies: the SEARCH for Diabetes in Youth (SEARCH) study, an observational study of individuals with youth-onset T1D or T2D, and the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study, a randomized clinical trial (2004-2011) followed by an observational study (2012-2020). The interviewer-directed survey was administered during in-person study visits in both studies between 2017 and 2019. Data analyses were performed between May 2021 and October 2022. MAIN OUTCOMES AND MEASURES:Survey questions addressed health care coverage, usual sources of diabetes care, and frequency of care use. Glycated hemoglobin (HbA1c) levels were assayed in a central laboratory. Patterns of health care factors and HbA1c levels were compared by diabetes type. RESULTS:The analysis included 1371 participants (mean [range] age, 25 [18-36] years; 824 females [60.1%]), of whom 661 had T1D and 250 had T2D from the SEARCH study and 460 had T2D from the TODAY study. Participants had a mean (SD) diabetes duration of 11.8 (2.8) years. More participants with T1D than T2D in both the SEARCH and TODAY studies reported health care coverage (94.7%, 81.6%, and 86.7%), access to diabetes care (94.7%, 78.1%, and 73.4%), and use of diabetes care (88.1%, 80.5%, and 73.6%). Not having health care coverage was associated with significantly higher mean (SE) HbA1c levels in participants with T1D in the SEARCH study (no coverage, 10.8% [0.5%]; public, 9.4% [0.2%]; private, 8.7% [0.1%]; P < .001) and participants with T2D from the TODAY study (no coverage, 9.9% [0.3%]; public, 8.7% [0.2%]; private, 8.7% [0.2%]; P = .004). Medicaid expansion vs without expansion was associated with more health care coverage (participants with T1D: 95.8% vs 90.2%; participants with T2D in SEARCH: 86.1% vs 73.9%; participants with T2D in TODAY: 93.6% vs 74.2%) and lower HbA1c levels (participants with T1D: 9.2% vs 9.7%; participants with T2D in SEARCH: 8.4% vs 9.3%; participants with T2D in TODAY: 8.7% vs 9.3%). The T1D group incurred higher median (IQR) monthly out-of-pocket expenses than the T2D group ($74.50 [$10.00-$309.00] vs $10.00 [$0-$74.50]). CONCLUSIONS AND RELEVANCE:Results of this study suggested that lack of health care coverage and of an established source of diabetes care were associated with significantly higher HbA1c levels for participants with T1D, but inconsistent results were found for participants with T2D. Increased access to diabetes care (eg, through Medicaid expansion) may be associated with improved health outcomes, but additional strategies are needed, particularly for individuals with T2D.

BACKGROUND:The incidence of diabetes is increasing in children and young people. We aimed to describe the incidence of type 1 and type 2 diabetes in children and young people aged younger than 20 years over a 17-year period. METHODS:The SEARCH for Diabetes in Youth study identified children and young people aged 0-19 years with a physician diagnosis of type 1 or type 2 diabetes at five centres in the USA between 2002 and 2018. Eligible participants included non-military and non-institutionalised individuals who resided in one of the study areas at the time of diagnosis. The number of children and young people at risk of diabetes was obtained from the census or health plan member counts. Generalised autoregressive moving average models were used to examine trends, and data are presented as incidence of type 1 diabetes per 100 000 children and young people younger than 20 years and incidence of type 2 diabetes per 100 000 children and young people aged between 10 years and younger than 20 years across categories of age, sex, race or ethnicity, geographical region, and month or season of diagnosis. FINDINGS/RESULTS:We identified 18 169 children and young people aged 0-19 years with type 1 diabetes in 85 million person-years and 5293 children and young people aged 10-19 years with type 2 diabetes in 44 million person-years. In 2017-18, the annual incidence of type 1 diabetes was 22·2 per 100 000 and that of type 2 diabetes was 17·9 per 100 000. The model for trend captured both a linear effect and a moving-average effect, with a significant increasing (annual) linear effect for both type 1 diabetes (2·02% [95% CI 1·54-2·49]) and type 2 diabetes (5·31% [4·46-6·17]). Children and young people from racial and ethnic minority groups such as non-Hispanic Black and Hispanic children and young people had greater increases in incidence for both types of diabetes. Peak age at diagnosis was 10 years (95% CI 8-11) for type 1 diabetes and 16 years (16-17) for type 2 diabetes. Season was significant for type 1 diabetes (p=0·0062) and type 2 diabetes (p=0·0006), with a January peak in diagnoses of type 1 diabetes and an August peak in diagnoses of type 2 diabetes. INTERPRETATION/CONCLUSIONS:The increasing incidence of type 1 and type 2 diabetes in children and young people in the USA will result in an expanding population of young adults at risk of developing early complications of diabetes whose health-care needs will exceed those of their peers. Findings regarding age and season of diagnosis will inform focused prevention efforts. FUNDING/BACKGROUND:US Centers for Disease Control and Prevention and US National Institutes of Health.

BACKGROUND:Incidence is one of the most important epidemiologic indices in surveillance. However, determining incidence is complex and requires time-consuming cohort studies or registries with date of diagnosis. Estimating incidence from prevalence using mathematical relationships may facilitate surveillance efforts. The aim of this study was to examine whether a partial differential equation (PDE) can be used to estimate diabetes incidence from prevalence in youth. METHODS:We used age-, sex-, and race/ethnicity-specific estimates of prevalence in 2001 and 2009 as reported in the SEARCH for Diabetes in Youth study. Using these data, a PDE was applied to estimate the average incidence rates of type 1 and type 2 diabetes for the period between 2001 and 2009. Estimates were compared to annual incidence rates observed in SEARCH. Precision of the estimates was evaluated using 95% bootstrap confidence intervals. RESULTS:Despite the long period between prevalence measures, the estimated average incidence rates mirror the average of the observed annual incidence rates. Absolute values of the age-standardized sex- and type-specific mean relative errors are below 8%. CONCLUSIONS:Incidence of diabetes can be accurately estimated from prevalence. Since only cross-sectional prevalence data is required, employing this methodology in future studies may result in considerable cost savings.