Decline in use of high-risk agents for tight glucose control among older adults with diabetes in New York City: 2017-2022.

BACKGROUND: This study aimed to examine the prevalence of inappropriate tight glycemic control in older adults with type 2 diabetes and other chronic conditions in New York City, and to identify factors associated with this practice. METHODS: We conducted a retrospective cohort study using the INSIGHT Clinical Research Network. The study population included 11,728 and 15,196 older adults in New York City (age ≥ 75 years) with a diagnosis of type 2 diabetes, and at least one other chronic medical condition, in 2017 and 2022, respectively. The main outcome of interest was inappropriate tight glycemic control, defined as HbA1c <7.0% (<53 mmol/mol) with prescription of at least one high-risk agent (insulin or insulin secretagogue). RESULTS: The proportion of older adults with inappropriate tight glycemic control decreased by nearly 19% over a five-year period (19.4% in 2017 to 15.8% in 2022). There was a significant decrease in insulin (27.8% in 2017; 24.3% in 2022) and sulfonylurea (29.4% in 2017; 21.7% in 2022) medication prescription, and increase in use of GLP-1 agonists (1.8% in 2017; 11.4% in 2022) and SGLT-2 inhibitors (5.8% in 2017; 25.1% in 2022), among the total population. Factors associated with inappropriate tight glycemic control in 2022 included history of heart failure (adjusted odds ratio [aOR] 1.38), chronic kidney disease ([aOR] 1.93), colorectal cancer ([aOR] 1.38), acute myocardial infarction ([aOR] 1.28), "other" ([aOR] 0.72) or "unknown" ([aOR] 0.72) race, and a point increase in BMI ([aOR] 0.98). CONCLUSIONS: We found an encouraging trend toward less use of high-risk medication strategies for older adults with type 2 diabetes and multiple chronic conditions. However, one in six patients in 2022 still had inappropriate tight glycemic control, indicating a need for continued efforts to optimize diabetes management in this population.

Comparative effectiveness of an individualized model of hemodialysis vs conventional hemodialysis: a study protocol for a multicenter randomized controlled trial (the TwoPlus trial).

BACKGROUND: Most patients starting chronic in-center hemodialysis (HD) receive conventional hemodialysis (CHD) with three sessions per week targeting specific biochemical clearance. Observational studies suggest that patients with residual kidney function can safely be treated with incremental prescriptions of HD, starting with less frequent sessions and later adjusting to thrice-weekly HD. This trial aims to show objectively that clinically matched incremental HD (CMIHD) is non-inferior to CHD in eligible patients. METHODS: An unblinded, parallel-group, randomized controlled trial will be conducted across diverse healthcare systems and dialysis organizations in the USA. Adult patients initiating chronic hemodialysis (HD) at participating centers will be screened. Eligibility criteria include receipt of fewer than 18 treatments of HD and residual kidney function defined as kidney urea clearance ≥3.5 mL/min/1.73 m2 and urine output ≥500 mL/24 h. The 1:1 randomization, stratified by site and dialysis vascular access type, assigns patients to either CMIHD (intervention group) or CHD (control group). The CMIHD group will be treated with twice-weekly HD and adjuvant pharmacologic therapy (i.e., oral loop diuretics, sodium bicarbonate, and potassium binders). The CHD group will receive thrice-weekly HD according to usual care. Throughout the study, patients undergo timed urine collection and fill out questionnaires. CMIHD will progress to thrice-weekly HD based on clinical manifestations or changes in residual kidney function. Caregivers of enrolled patients are invited to complete semi-annual questionnaires. The primary outcome is a composite of patients' all-cause death, hospitalizations, or emergency department visits at 2 years. Secondary outcomes include patient- and caregiver-reported outcomes. We aim to enroll 350 patients, which provides ≥85% power to detect an incidence rate ratio (IRR) of 0.9 between CMIHD and CHD with an IRR non-inferiority of 1.20 (α = 0.025, one-tailed test, 20% dropout rate, average of 2.06 years of HD per patient participant), and 150 caregiver participants (of enrolled patients). DISCUSSION: Our proposal challenges the status quo of HD care delivery. Our overarching hypothesis posits that CMIHD is non-inferior to CHD. If successful, the results will positively impact one of the highest-burdened patient populations and their caregivers. TRIAL REGISTRATION: Clinicaltrials.gov NCT05828823. Registered on 25 April 2023.

The Association Between Type of Insurance Plan, Out-of-Pocket Cost, and Adherence to Antihypertensive Medications in Medicare Supplement Insurance Enrollees.

BACKGROUND: Medicare supplement insurance, or Medigap, covers 21% of Medicare beneficiaries. Despite offsetting some out-of-pocket (OOP) expenses, remaining OOP costs may pose a barrier to medication adherence. This study aims to evaluate how OOP costs and insurance plan types influence medication adherence among beneficiaries covered by Medicare supplement plans. METHODS: We conducted a retrospective analysis of the Merative MarketScan Medicare Supplement Database (2017-2019) in Medigap enrollees (≥65 years) with hypertension. The proportion of days covered (PDC) was a continuous measure of medication adherence and was also dichotomized (PDC ≥0.8) to quantify adequate adherence. Beta-binomial and logistic regression models were used to estimate associations between these outcomes and insurance plan type and log-transformed OOP costs, adjusting for patient characteristics. RESULTS: Among 27,407 patients with hypertension, the average PDC was 0.68 ±â€…0.31; 47.5% achieved adequate adherence. A mean $1 higher in 30-day OOP costs were associated with a 0.06 (95% confidence intervals [CIs]: -0.09 to -0.03) lower probability of adequate adherence, or a 5% (95% CI: 4%-7%) decrease in PDC. Compared with comprehensive plan enrollees, the odds of adequate adherence were lower among those with point-of-service plans (odds ratio [OR]: 0.69, 95% CI: 0.62-0.77), but higher among those with preferred provider organization (PPO) plans (OR: 1.08, 95% CI: 1.01-1.15). Moreover, the association between OOP costs and PDC was significantly greater for PPO enrollees. CONCLUSIONS: While Medicare supplement insurance alleviates some OOP costs, different insurance plans and remaining OOP costs influence medication adherence. Reducing patient cost-sharing may improve medication adherence.

Long COVID incidence in adults and children between 2020 and 2023: a real-world data study from the RECOVER Initiative.

Estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) incidence, also known as Long COVID, have varied across studies and changed over time. We estimated PASC incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). Overall, 7% of children and 8.5%-26.4% of adults developed PASC, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 4% in children and ranged from 4-7% among adults, representing a lower-bound incidence estimation based on two control groups - contemporary COVID-19 negative and historical patients (2019). Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating Long COVID remains a public health priority. Examining temporal patterns and risk factors of PASC incidence informs our understanding of etiology and can improve prevention and management.

Avoidance of care: how health-care affordability influenced COVID-19 disease severity and outcomes.

In this study we examined the association between payor type, a proxy for health-care affordability, and presenting COVID-19 disease severity among 2108 polymerase chain reaction-positive nonelderly patients admitted to an acute-care hospital between March 1 and June 30, 2020. The adjacent-category logit model was used to fit pairwise odds of individuals' having (1) an asymptomatic-to-mild modified sequential organ failure assessment (mSOFA) score (0-3) versus a moderate-to-severe mSOFA score (4-7) and (2) a moderate-to-severe mSOFA score (4-7) versus a critical mSOFA score (>7). Despite representing the smallest population, Medicare recipients experienced the highest in-hospital death rate (19%), a rate twice that of the privately insured. The uninsured had the highest rate of critical mSOFA score on admission and had twice the odds of presenting with a critical illness when compared with the privately insured (odds ratio = 2.08, P =.03). Because payor type was statistically related to the most severe presentations of COVID-19, we question whether policy changes affecting health-care affordability might have prevented deaths and rationing of scarce resources, such as intensive care unit beds and ventilators.

Learning competing risks across multiple hospitals: one-shot distributed algorithms.

OBJECTIVES: To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents. MATERIALS AND METHODS: Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data. RESULTS: The meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data. DISCUSSION: Our proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions. CONCLUSION: Our study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework.

Genetic architecture and biology of youth-onset type 2 diabetes.

The prevalence of youth-onset type 2 diabetes (T2D) and childhood obesity has been rising steadily1, producing a growing public health concern1 that disproportionately affects minority groups2. The genetic basis of youth-onset T2D and its relationship to other forms of diabetes are unclear3. Here we report a detailed genetic characterization of youth-onset T2D by analysing exome sequences and common variant associations for 3,005 individuals with youth-onset T2D and 9,777 adult control participants matched for ancestry, including both males and females. We identify monogenic diabetes variants in 2.4% of individuals and three exome-wide significant (P < 2.6 × 10-6) gene-level associations (HNF1A, MC4R, ATXN2L). Furthermore, we report rare variant association enrichments within 25 gene sets related to obesity, monogenic diabetes and ß-cell function. Many youth-onset T2D associations are shared with adult-onset T2D, but genetic risk factors of all frequencies-and rare variants in particular-are enriched within youth-onset T2D cases (5.0-fold increase in the rare variant and 3.4-fold increase in common variant genetic liability relative to adult-onset cases). The clinical presentation of participants with youth-onset T2D is influenced in part by the frequency of genetic risk factors within each individual. These findings portray youth-onset T2D as a heterogeneous disease situated on a spectrum between monogenic diabetes and adult-onset T2D.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network.

INTRODUCTION: Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved. METHODS AND ANALYSIS: The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0-17 years only (component A), three centres conduct surveillance in young adults aged 18-44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression. ETHICS AND DISSEMINATION: The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA.

Clinical Prediction Models Combining Routine Clinical Measures Have High Accuracy in Identifying Youth-Onset Type 2 Diabetes Defined by Maintained Endogenous Insulin Secretion: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We studied 2,966 youth with diabetes in the prospective SEARCH for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.75 ng/mL) after >3 years' (median 74 months) diabetes duration. Models included clinical measures at the baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL cholesterol), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS). RESULTS: Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with C-peptide ≥0.75 ng/mL (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under the receiver operating characteristic curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope 0.995-0.999). Models retained high performance for predicting retained C-peptide in older youth with obesity (AUCROC 0.88-0.96) and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92). CONCLUSIONS: Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with T2D.

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

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.

Racial Disparities in Hospitalization Rates During Long-Term Follow-Up After Deceased-Donor Kidney Transplantation.

OBJECTIVE: To compare hospitalization rates between African American (AA) and European American (EA) deceased-donor (DD) kidney transplant (KT) recipients during over a10-year period. METHOD: Data from the Scientific Registry of Transplant Recipients and social determinants of health (SDoH), measured by the Social Deprivation Index, were used. Hospitalization rates were estimated for kidney recipients from AA and EA DDs who had one kidney transplanted into an AA and one into an EA, leading to four donor/recipient pairs (DRPs): AA/AA, AA/EA, EA/AA, and EA/EA. Poisson-Gamma models were fitted to assess post-transplant hospitalizations. RESULT: Unadjusted hospitalization rates (95% confidence interval) were higher among all DRP involving AA, 131.1 (122.5, 140.3), 134.8 (126.3, 143.8), and 102.4 (98.9, 106.0) for AA/AA, AA/EA, and EA/AA, respectively, compared to 97.1 (93.7, 100.6) per 1000 post-transplant person-years for EA/EA pairs. Multivariable analysis showed u-shaped relationships across SDoH levels within each DRP, but findings varied depending on recipients' race, i.e., AA recipients in areas with the worst SDoH had higher hospitalization rates. However, EA recipients in areas with the best SDoH had higher hospitalization rates than their counterparts. CONCLUSIONS: Relationship between healthcare utilization and SDoH depends on DRP, with higher hospitalization rates among AA recipients living in areas with the worst SDoH and among EA recipients in areas with the best SDoH profiles. SDoH plays an important role in driving disparities in hospitalizations after kidney transplantation.

Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study.

Objective: With the high prevalence of pediatric obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D). Methods: We studied 2,966 youth with diabetes in the prospective SEARCH study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting c-peptide ≥250 pmol/L (≥0.75ng/ml) after >3 years (median 74 months) of diabetes duration. Models included clinical measures at baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL-C), with and without islet autoantibodies (GADA, IA-2A) and a Type 1 Diabetes Genetic Risk Score (T1DGRS). Results: Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with c-peptide ≥0.75 ng/ml (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under receiver operator curve [AUCROC] 0.95-0.98). In internal validation, optimism was very low, with excellent calibration (slope=0.995-0.999). Models retained high performance for predicting retained c-peptide in older youth with obesity (AUCROC 0.88-0.96), and in subgroups defined by self-reported race/ethnicity (AUCROC 0.88-0.97), autoantibody status (AUCROC 0.87-0.96), and clinically diagnosed diabetes types (AUCROC 0.81-0.92). Conclusion: Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with type 2 diabetes.

Initiation of Antihypertensive Medication from Midlife on Incident Dementia: The Health and Retirement Study.

BACKGROUND: Hypertension has been identified as a risk factor of dementia, but most randomized trials did not show efficacy in reducing the risk of dementia. Midlife hypertension may be a target for intervention, but it is infeasible to conduct a trial initiating antihypertensive medication from midlife till dementia occurs late life. OBJECTIVE: We aimed to emulate a target trial to estimate the effectiveness of initiating antihypertensive medication from midlife on reducing incident dementia using observational data. METHODS: The Health and Retirement Study from 1996 to 2018 was used to emulate a target trial among non-institutional dementia-free subjects aged 45 to 65 years. Dementia status was determined using algorithm based on cognitive tests. Individuals were assigned to initiating antihypertensive medication or not, based on the self-reported use of antihypertensive medication at baseline in 1996. Observational analog of intention-to-treat and per-protocol effects were conducted. Pooled logistic regression models with inverse-probability of treatment and censoring weighting using logistic regression models were applied, and risk ratios (RRs) were calculated, with 200 bootstrapping conducted for the 95% confidence intervals (CIs). RESULTS: A total of 2,375 subjects were included in the analysis. After 22 years of follow-up, initiating antihypertensive medication reduced incident dementia by 22% (RR = 0.78, 95% CI: 0.63, 0.99). No significant reduction of incident dementia was observed with sustained use of antihypertensive medication. CONCLUSION: Initiating antihypertensive medication from midlife may be beneficial for reducing incident dementia in late life. Future studies are warranted to estimate the effectiveness using large samples with improved clinical measurements.

Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes.

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 ß-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 ß-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.

Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative.

STUDY OBJECTIVES: 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.

Diabetes Care Barriers, Use, and Health Outcomes in Younger Adults With Type 1 and Type 2 Diabetes.

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.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

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: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). 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).

Trends in incidence of youth-onset type 1 and type 2 diabetes in the USA, 2002-18: results from the population-based SEARCH for Diabetes in Youth study.

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: 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: 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: US Centers for Disease Control and Prevention and US National Institutes of Health.

Estimating incidence of type 1 and type 2 diabetes using prevalence data: the SEARCH for Diabetes in Youth study.

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.