Glucagon-Like Peptide-1 Receptor Agonists and Risk for Suicidal Ideation and Behaviors in U.S. Older Adults With Type 2 Diabetes : A Target Trial Emulation Study.

BACKGROUND: A major concern has recently emerged about a potential link between glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and increased risk for suicidal ideation and behaviors based on International Classification of Diseases codes. OBJECTIVE: To investigate the association between GLP-1 RAs, compared with sodium-glucose cotransporter-2 inhibitors (SGLT2is) or dipeptidyl peptidase-4 inhibitors (DPP4is), and risk for suicidal ideation and behaviors in older adults with type 2 diabetes (T2D). DESIGN: Two target trial emulation studies comparing propensity score (PS)-matched cohorts for GLP-1 RAs versus SGLT2is and GLP-1 RAs versus DPP4is. SETTING: U.S. national Medicare administrative data from January 2017 to December 2020. PATIENTS: Older adults (≥66 years) with T2D; no record of suicidal ideation or behaviors; and a first prescription for a GLP-1 RA, SGLT2i, or DPP4i. MEASUREMENTS: The primary end point was a composite of suicidal ideation and behaviors. New GLP-1 RA users were matched 1:1 on PS to new users of an SGLT2i or DPP4i in each pairwise comparison. A Cox proportional hazards regression was used to estimate the hazard ratio (HR) and 95% CIs within matched groups. RESULTS: This study included 21 807 pairs of patients treated with a GLP-1 RA versus an SGLT2i and 21 402 pairs of patients treated with a GLP-1 RA versus a DPP4i. The HR of suicidal ideation and behaviors associated with GLP-1 RAs relative to SGLT2is was 1.07 (95% CI, 0.80 to 1.45; rate difference, 0.16 [CI, -0.53 to 0.86] per 1000 person-years); the HR relative to DPP4is was 0.94 (CI, 0.71 to 1.24; rate difference, -0.18 [CI, -0.92 to 0.57] per 1000 person-years). LIMITATIONS: Low event rate; imprecise estimates; unmeasured confounders, such as body mass index; and potential misclassification of outcomes. CONCLUSION: Among Medicare beneficiaries with T2D, this study found no clear increased risk for suicidal ideation and behaviors with GLP-1 RAs, although estimates were imprecise and a modest adverse risk could not be ruled out. PRIMARY FUNDING SOURCE: American Foundation for Pharmaceutical Education, Pharmaceutical Research and Manufacturers of America Foundation, National Institute on Aging, and National Institute of Diabetes and Digestive and Kidney Diseases.

Heterogeneous treatment effects of metformin on risk of dementia in patients with type 2 diabetes: A longitudinal observational study.

INTRODUCTION: Little is known about the heterogeneous treatment effects of metformin on dementia risk in people with type 2 diabetes (T2D). METHODS: Participants (≥ 50 years) with T2D and normal cognition at baseline were identified from the National Alzheimer's Coordinating Center database (2005-2021). We applied a doubly robust learning approach to estimate risk differences (RD) with a 95% confidence interval (CI) for dementia risk between metformin use and no use in the overall population and subgroups identified through a decision tree model. RESULTS: Among 1393 participants, 104 developed dementia over a 4-year median follow-up. Metformin was significantly associated with a lower risk of dementia in the overall population (RD, -3.2%; 95% CI, -6.2% to -0.2%). We identified four subgroups with varied risks for dementia, defined by neuropsychiatric disorders, non-steroidal anti-inflammatory drugs, and antidepressant use. DISCUSSION: Metformin use was significantly associated with a lower risk of dementia in individuals with T2D, with significant variability among subgroups.

Heterogeneous treatment effects of sodium-glucose cotransporter 2 inhibitors on risk of dementia in people with type 2 diabetes: A population-based cohort study.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain. METHODS: Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016-2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all-cause dementia. RESULTS: Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all-cause dementia over a 3.2-year follow-up, yielding a lower risk for SGLT2 inhibitors (RD, -2.5%; 95% CI, -3.0% to -2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from -4.3% (-5.5 to -3.2) to -0.9% (-1.9 to 0.2). DISCUSSION: Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all-cause dementia, but the association varied among different subgroups. HIGHLIGHTS: New users of sodium-glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all-cause dementia as compared to those of sulfonylureas. The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease. A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas.

Telehealth Implementation Response to COVID-19 in the OneFlorida+ Clinical Research Network: Perspectives of Clinicians and Health Systems Leaders.

Introduction: The COVID-19 pandemic forced health systems worldwide to make rapid adjustments to patient care. Nationwide stay-at-home mandates and public health concerns increased demand for telehealth to maintain patients' continuity of care. These circumstances permitted observation of telehealth implementation in real-world settings at a large scale. This study aimed to understand clinician and health system leader (HSL) experiences in expanding, implementing, and sustaining telehealth during COVID-19 in the OneFlorida+ clinical research network. Methods: We conducted semistructured videoconference interviews with 5 primary care providers, 7 specialist providers, and 12 HSLs across 7 OneFlorida+ health systems and settings. Interviews were audiorecorded, transcribed, and summarized using deductive team-based template coding. We then used matrix analysis to organize the qualitative data and identify inductive themes. Results: Rapid telehealth implementation occurred even among sites with low readiness, facilitated by responsive planning, shifts in resource allocation, and training. Common hurdles in routine telehealth use, including technical and reimbursement issues, were also barriers to telehealth implementation. Acceptability of telehealth was influenced by benefits such as the providers' ability to view a patient's home environment and the availability of tools to enhance patient education. Lower acceptability stemmed from the inability to conduct physical examinations during the shutdown. Conclusions: This study identified a broad range of barriers, facilitators, and strategies for implementing telehealth within large clinical research networks. The findings can contribute to optimizing the effectiveness of telehealth implementation in similar settings, and point toward promising directions for telehealth provider training to improve acceptability and promote sustainability.

Performance of a computable phenotype for identification of patients with diabetes within PCORnet: The Patient-Centered Clinical Research Network.

PURPOSE: PCORnet, the National Patient-Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. METHODS: We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012-2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD-9/ICD-10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ≥6.5%, or (CP3) an antidiabetic prescription and HbA1c ≥6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t0 . Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident-T2DM CP using electronic health record (EHR) review as reference. RESULTS: The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% (n = 1572; CI:95.1-97.0) and was consistently high across sites. The PPV for the incident-T2DM CP was 5.8% (CI:4.5-7.5). CONCLUSIONS: The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.

Validation of an algorithm for identifying type 1 diabetes in adults based on electronic health record data.

PURPOSE: Algorithms using information from electronic health records to identify adults with type 1 diabetes have not been well studied. Such algorithms would have applications in pharmacoepidemiology, drug safety research, clinical trials, surveillance, and quality improvement. Our main objectives were to determine the positive predictive value for identifying type 1 diabetes in adults using a published algorithm (developed by Klompas et al) and to compare it to a simple requirement that the majority of diabetes diagnosis codes be type 1. METHODS: We applied the Klompas algorithm and the diagnosis code criterion to a cohort of 66 690 adult Kaiser Permanente Colorado members with diabetes. We reviewed 220 charts of those identified as having type 1 diabetes and calculated positive predictive values. RESULTS: The Klompas algorithm identified 3286 (4.9% of 66 690) adults with diabetes as having type 1 diabetes. Based on chart reviews, the overall positive predictive value was 94.5%. The requirement that the majority of diabetes diagnosis codes be type 1 identified 3000 (4.5%) as having type 1 diabetes and had a positive predictive value of 96.4%. However, the algorithm criterion involving dispensing of urine acetone test strips performed poorly, with a positive predictive value of 20.0%. CONCLUSIONS: Data from electronic health records can be used to accurately identify adults with type 1 diabetes. When identifying adults with type 1 diabetes, we recommend either a modified version of the Klompas algorithm without the urine acetone test strips criterion or the requirement that the majority of diabetes diagnosis codes be type 1 codes.

Acceptability of HPV self-collection: A qualitative study of Black women living with type II diabetes and social vulnerability.

Introduction: Human papillomavirus (HPV) causes 99.7% of cervical cancer cases. Cervical cancer is preventable through early detection via HPV testing. However, the number of women screened for cervical cancer has not increased in the last several years. Lower screening rates among women living in high poverty and social vulnerability areas, Black women, and women with chronic co-morbidities (e.g., type 2 diabetes (T2D)) are associated with their higher cervical cancer mortality rates. When screened, Black women are more likely to be diagnosed at later stages and die from cervical cancer. HPV self-collection decreases barriers to cervical cancer screening and can help lessen disparities among underserved women. This study aimed to examine the acceptability of HPV self-collection among Black women with T2D living in socially vulnerable communities. Methods: Qualitative semi-structured interviews were conducted with 29 Black women with T2D living in communities with high social vulnerability. The Health Belief Model informed the development of the interview guide to gather data on the acceptability of HPV self-collection. Results: Three main themes aligned with the Health Belief Model were identified: (1) HPV self-collection provides a comfortable alternative to in-clinic HPV testing (perceived benefits); (2) HPV self-collection would result in awareness of current HPV status (health motivation); and (3) Women were concerned about collecting their sample accurately (perceived barriers). Discussion/Conclusion: Black women with T2D living in communities with high social vulnerability identified multiple benefits of cervical cancer screening through HPV self-collection. Women are concerned about their ability to collect these samples correctly. Our findings call for future studies focusing on increasing self-efficacy and skills to collect HPV samples among Black women with chronic conditions like T2D who reside in underserved communities with high social vulnerability.

Geographic variation and racial disparities in adoption of newer glucose-lowering drugs with cardiovascular benefits among US Medicare beneficiaries with type 2 diabetes.

BACKGROUND: Prior studies have shown disparities in the uptake of cardioprotective newer glucose-lowering drugs (GLDs), including sodium-glucose cotranwsporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a). This study aimed to characterize geographic variation in the initiation of newer GLDs and the geographic variation in the disparities in initiating these medications. METHODS: Using 2017-2018 claims data from a 15% random nationwide sample of Medicare Part D beneficiaries, we identified individuals diagnosed with type 2 diabetes (T2D), who had ≥1 GLD prescriptions, and did not use SGLT2i or GLP1a in the year prior to the index date,1/1/2018. Patients were followed up for a year. The cohort was spatiotemporally linked to Dartmouth hospital-referral regions (HRRs), with each patient assigned to 1 of 306 HRRs. We performed multivariable Poisson regression to estimate adjusted initiation rates, and multivariable logistic regression to assess racial disparities in each HRR. RESULTS: Among 795,469 individuals with T2D included in the analyses, the mean (SD) age was 73 (10) y, 53.3% were women, 12.2% were non-Hispanic Black, and 7.2% initiated a newer GLD in the follow-up year. In the adjusted model including clinical factors, compared to non-Hispanic White patients, non-Hispanic Black (initiation rate ratio, IRR [95% CI]: 0.66 [0.64-0.68]), American Indian/Alaska Native (0.74 [0.66-0.82]), Hispanic (0.85 [0.82-0.87]), and Asian/Pacific islander (0.94 [0.89-0.98]) patients were less likely to initiate newer GLDs. Significant geographic variation was observed across HRRs, with an initiation rate spanning 2.7%-13.6%. CONCLUSIONS: This study uncovered substantial geographic variation and the racial disparities in initiating newer GLDs.

Adaptability of High Dimensional Propensity Score Procedure in the Transition from ICD-9 to ICD-10 in the US Healthcare System.

Background: High-Dimensional Propensity Score procedure (HDPS) is a data-driven approach to assist control for confounding in pharmacoepidemiologic research. The transition to the International Classification of Disease (ICD-9/10) in the US health system may pose uncertainty in applying the HDPS procedure. Methods: We assembled a base cohort of patients in MarketScan® Commercial Claims Database who had newly initiated celecoxib or traditional NSAIDs to compare gastrointestinal bleeding risk. We then created bootstrapped hypothetical cohorts from the base cohort with predefined patient selection patterns from the ICD eras. Three strategies for HDPS deployment were tested: 1) split the cohort by ICD era, deploy HDPS twice, and pool the relative risks (pooled RR), 2) consider codes from each ICD era as a separate data dimension and deploy HDPS in the entire cohort (data dimensions) and 3) map ICD codes from both eras to Clinical Classifications Software (CCS) concepts before deploying HDPS in the entire cohort (CCS mapping). We calculated percent bias and root-mean-squared error to compare the strategies. Results: A similar bias reduction was observed in cohorts where patient selection pattern from each ICD era was comparable between the exposure groups. In the presence of considerable disparity in patient selection, we observed a bimodal distribution of propensity scores in the data dimensions strategy, indicating instrument-like covariates. Moreover, the CCS mapping strategy resulted in at least 30% less bias than pooled RR and data dimensions strategies (RMSE: 0.14, 0.19, 0.21, respectively) in this scenario. Conclusion: Mapping ICD codes to a stable terminology like CCS serves as a helpful strategy to reduce residual bias when deploying HDPS in pharmacoepidemiologic studies spanning both ICD eras.

Variation by race/ethnicity in the utilization and weight loss following metabolic bariatric surgery.

BACKGROUND: Understanding the disparities in utilization and weight loss outcomes of metabolic and bariatric surgery (MBS) by demographics will inform strategies targeting potential treatment gaps and enhance overall clinical obesity treatment. OBJECTIVE: To identify factors associated with utilization and longitudinal weight loss after MBS. SETTING: OneFlorida Clinical Research Consortium Database. METHODS: We performed a retrospective study using data from the OneFlorida Clinical Research Consortium between 2012 and 2018. We used logistic regression with intersectional effects to identify factors associated with utilization of MBS. Mixed-effect models were used to estimate longitudinal percentage total weight loss among those who underwent MBS with up to 18 months of follow-up. RESULTS: Among 429,821 patients eligible for MBS, 8290 (1.9%) underwent MBS between 2012 and 2018. Intersectional analysis revealed that non-Hispanic Black patients experienced an inferior utilization of MBS compared with non-Hispanic White and Hispanic counterparts, defined by the interaction between race/ethnicity and demographic factors, including male sex, older age, and insurance coverage. In the longitudinal weight loss assessment, 4016 patients (48.3% Roux-en-Y gastric bypass, 51.7% sleeve gastrectomy) were included. We found that non-Hispanic Black patients experienced significantly less weight loss than non-Hispanic White and Hispanic counterparts. Other factors associated with less weight loss over time included undergoing sleeve gastectomy, male sex, lower preoperative body mass index, and having type 2 diabetes at the time of surgery. CONCLUSIONS: Our findings will help to design new strategies focusing on the intersection of race/ethnicity and sociodemographic factors to improve access and effectiveness of MBS.

Newer glucose-lowering drugs and risk of dementia: A systematic review and meta-analysis of observational studies.

BACKGROUND: Preclinical studies have suggested potential beneficial effects of newer glucose-lowering drugs (GLDs) including dipeptidyl peptidase (DPP)-4 inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), and sodium glucose co-transporter-2 (SGLT2) inhibitors, in protecting humans against cognitive decline and dementia. However, population studies aiming to demonstrate such cognitive benefits from newer GLDs have produced mixed findings. This meta-analysis aimed to evaluate the association between newer GLDs and risk of dementia in adults with type 2 diabetes (T2D). METHODS: Electronic databases were searched up to March 11, 2022 to include observational studies that examined the association between DPP-4 inhibitors, GLP-1RAs, and SGLT2 inhibitors and risk of dementia (including all-cause dementia, Alzheimer's disease [AD], and vascular dementia [VD]) in people with T2D. We conducted a random-effects meta-analysis to calculate the relative risk (RR) with 95% confidence interval (CI) for each class of newer GLD. RESULTS: Ten studies (from nine articles) involving 819,511 individuals with T2D were included. Three studies found that SGLT2 inhibitor users had a lower risk of all-cause dementia than non-SGLT2 inhibitor users (RR, 0.62; 95% CI, 0.39-0.97). Five studies found that users versus nonusers of GLP-1RAs were associated with a significant reduction in the risk of all-cause dementia (RR, 0.72; 95% CI, 0.54-0.97). However, a meta-analysis for AD and VD was unavailable for SGLT2 inhibitors and GLP-1RAs because only one study was included for each drug. In seven studies, users vs. nonusers of DPP-4 inhibitors were significantly associated with a decreased risk of all-cause dementia (RR, 0.84; 95% CI, 0.74-0.94) and VD (RR, 0.59; 95% CI, 0.47-0.75) but not AD (RR, 0.82; 95% CI, 0.63-1.08). CONCLUSION: Newer GLDs were associated with a decreased risk of all-cause dementia in people with T2D. Because of the observational nature and significant heterogeneity between studies, the results should be interpreted with caution. Further research is warranted to confirm our findings.

Association of Medication Adherence With HbA1c Control Among American Indian Adults With Type 2 Diabetes Using Tribal Health Services.

OBJECTIVE: To examine HbA1c levels and adherence to oral glucose-lowering medications and their association with future HbA1c levels among American Indian adults with type 2 diabetes (T2D) receiving medications at no cost from a tribal health care system. RESEARCH DESIGN AND METHODS: Tribal citizens with T2D who used Choctaw Nation Health Services Authority (CNHSA) and Pharmacies and had HbA1c data during 2017-2018 were included in this study. Medication adherence (proportion of days covered [PDC] ≥0.80) was calculated using 2017 CNHSA electronic health record data. RESULTS: Of the 74,000 tribal citizens living on tribal lands, 4,560 were eligible; 32% had HbA1c at or below target (≤7%), 36% were above target (>7 to ≤9%), and 32% were uncontrolled (>9%) in 2017. The percentage of patients with PDC ≥0.80 was 66% for those using biguanides, 72% for sulfonylureas, 75% for dipeptidyl peptidase 4 inhibitors, and 83% for sodium-glucose cotransporter 2 inhibitors. The proportion of patients with HbA1c at or below target increased slightly from 32% in 2017 to 42% in 2018. Higher average PDC in 2017 was associated with lower HbA1c levels in 2018 (ß = -1.143; P < 0.001). CONCLUSIONS: Medication adherence was higher than that found in previous studies using self-report methods in American Indian populations, although a smaller proportion of patients had HbA1c at or below target relative to U.S. adults with T2D. Medication adherence was associated with improved HbA1c levels for most oral glucose-lowering medication classes. Future studies of American Indians should use both longitudinal prescription data from both electronic health records and pharmacy refills.

Meta-analysis of Association between Newer Glucose-Lowering Drugs and Risk of Parkinson's Disease.

Background: The association between newer classes of glucose-lowering drugs (GLDs) and the risk of Parkinson's disease (PD) remains unclear. Objective: The aim was to examine the effect of newer GLDs on the risk of PD through a meta-analysis of randomized outcome trials. Methods: The methods included randomized placebo-controlled outcome trials that reported PD events associated with three newer classes of GLDs (ie, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose co-transporter-2 inhibitors) in participants with or without type 2 diabetes. The pooled odds ratio (OR) and 95% confidence interval (CI) were estimated using Peto's method. Results: The study included 24 trials involving 33 PD cases among 185,305 participants during a median follow-up of 2.2 years. Newer GLDs were significantly associated with a lower PD risk (OR: 0.50; 95% CI: 0.25-0.98) than placebo. Conclusion: Newer GLDs may possibly be associated with a decreased risk of PD; however, larger datasets are required to confirm or refute this notion.

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes.

Background: Racial and ethnic minority groups and individuals facing social disadvantages, which often stem from their social determinants of health (SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its complications. It is crucial to implement effective social risk management strategies at the point of care. Objective: To develop an electronic health records (EHR)-based machine learning (ML) analytical pipeline to address unmet social needs associated with hospitalization risk in patients with T2D. Methods: We identified real-world patients with T2D from the EHR data from University of Florida (UF) Health Integrated Data Repository (IDR), incorporating both contextual SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing instability). The 2015-2020 data were used for training and validation and 2021-2022 data for independent testing. We developed a machine learning analytic pipeline, namely individualized polysocial risk score (iPsRS), to identify high social risk associated with hospitalizations in T2D patients, along with explainable AI (XAI) and fairness optimization. Results: The study cohort included 10,192 real-world patients with T2D, with a mean age of 59 years and 58% female. Of the cohort, 50% were non-Hispanic White, 39% were non-Hispanic Black, 6% were Hispanic, and 5% were other races/ethnicities. Our iPsRS, including both contextual and individual-level SDoH as input factors, achieved a C statistic of 0.72 in predicting 1-year hospitalization after fairness optimization across racial and ethnic groups. The iPsRS showed excellent utility for capturing individuals at high hospitalization risk because of SDoH, that is, the actual 1-year hospitalization rate in the top 5% of iPsRS was 28.1%, ~13 times as high as the bottom decile (2.2% for 1-year hospitalization rate). Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients who have increased social risk leading to hospitalization in real word patients with T2D.

The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records.

OBJECTIVE: Having sufficient population coverage from the electronic health records (EHRs)-connected health system is essential for building a comprehensive EHR-based diabetes surveillance system. This study aimed to establish an EHR-based type 1 diabetes (T1D) surveillance system for children and adolescents across racial and ethnic groups by identifying the minimum population coverage from EHR-connected health systems to accurately estimate T1D prevalence. MATERIALS AND METHODS: We conducted a retrospective, cross-sectional analysis involving children and adolescents <20 years old identified from the OneFlorida+ Clinical Research Network (2018-2020). T1D cases were identified using a previously validated computable phenotyping algorithm. The T1D prevalence for each ZIP Code Tabulation Area (ZCTA, 5 digits), defined as the number of T1D cases divided by the total number of residents in the corresponding ZCTA, was calculated. Population coverage for each ZCTA was measured using observed health system penetration rates (HSPR), which was calculated as the ratio of residents in the corresponding ZTCA and captured by OneFlorida+ to the overall population in the same ZCTA reported by the Census. We used a recursive partitioning algorithm to identify the minimum required observed HSPR to estimate T1D prevalence and compare our estimate with the reported T1D prevalence from the SEARCH study. RESULTS: Observed HSPRs of 55%, 55%, and 60% were identified as the minimum thresholds for the non-Hispanic White, non-Hispanic Black, and Hispanic populations. The estimated T1D prevalence for non-Hispanic White and non-Hispanic Black were 2.87 and 2.29 per 1000 youth, which are comparable to the reference study's estimation. The estimated prevalence of T1D for Hispanics (2.76 per 1000 youth) was higher than the reference study's estimation (1.48-1.64 per 1000 youth). The standardized T1D prevalence in the overall Florida population was 2.81 per 1000 youth in 2019. CONCLUSION: Our study provides a method to estimate T1D prevalence in children and adolescents using EHRs and reports the estimated HSPRs and prevalence of T1D for different race and ethnicity groups to facilitate EHR-based diabetes surveillance.

Noninvasive Diagnosis of Nonalcoholic Steatohepatitis and Advanced Liver Fibrosis Using Machine Learning Methods: Comparative Study With Existing Quantitative Risk Scores.

BACKGROUND: Nonalcoholic steatohepatitis (NASH), advanced fibrosis, and subsequent cirrhosis and hepatocellular carcinoma are becoming the most common etiology for liver failure and liver transplantation; however, they can only be diagnosed at these potentially reversible stages with a liver biopsy, which is associated with various complications and high expenses. Knowing the difference between the more benign isolated steatosis and the more severe NASH and cirrhosis informs the physician regarding the need for more aggressive management. OBJECTIVE: We intend to explore the feasibility of using machine learning methods for noninvasive diagnosis of NASH and advanced liver fibrosis and compare machine learning methods with existing quantitative risk scores. METHODS: We conducted a retrospective analysis of clinical data from a cohort of 492 patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD), NASH, or advanced fibrosis. We systematically compared 5 widely used machine learning algorithms for the prediction of NAFLD, NASH, and fibrosis using 2 variable encoding strategies. Then, we compared the machine learning methods with 3 existing quantitative scores and identified the important features for prediction using the SHapley Additive exPlanations method. RESULTS: The best machine learning method, gradient boosting (GB), achieved the best area under the curve scores of 0.9043, 0.8166, and 0.8360 for NAFLD, NASH, and advanced fibrosis, respectively. GB also outperformed 3 existing risk scores for fibrosis. Among the variables, alanine aminotransferase (ALT), triglyceride (TG), and BMI were the important risk factors for the prediction of NAFLD, whereas aspartate transaminase (AST), ALT, and TG were the important variables for the prediction of NASH, and AST, hyperglycemia (A1c), and high-density lipoprotein were the important variables for predicting advanced fibrosis. CONCLUSIONS: It is feasible to use machine learning methods for predicting NAFLD, NASH, and advanced fibrosis using routine clinical data, which potentially can be used to better identify patients who still need liver biopsy. Additionally, understanding the relative importance and differences in predictors could lead to improved understanding of the disease process as well as support for identifying novel treatment options.

Medication Adherence and Cardiometabolic Control Indicators Among American Indian Adults Receiving Tribal Health Services: Protocol for a Longitudinal Electronic Health Records Study.

BACKGROUND: American Indian adults have the highest prevalence of type 2 diabetes (T2D) in any racial or ethnic group and experience high rates of comorbidities. Uncontrolled cardiometabolic risk factors-insulin resistance, resulting in impaired glucose tolerance, dyslipidemia, and hypertension-increase the risk of mortality. Mortality is significantly reduced by glucose- and lipid-lowering and antihypertensive medication adherence. Medication adherence is low among American Indian adults living in non-Indian Health Service health care settings. Virtually nothing is known about the nature and extent of medication adherence among reservation-dwelling American Indian adults who primarily receive their medications without cost from Indian Health Service or tribal facilities. Electronic health records (EHRs) offer a rich but underused data source regarding medication adherence and its potential to predict cardiometabolic control indicators (C-MCIs). With the support of the Choctaw Nation of Oklahoma (CNO), we address this oversight by using EHR data generated by this large, state-of-the-art tribal health care system to investigate C-MCIs. OBJECTIVE: Our specific aims are to determine, using 2018 EHR data, the bivariate relationships between medication adherence and C-MCIs, demographics, and comorbidities and each C-MCI and demographics and comorbidities; develop machine learning models for predicting future C-MCIs from the previous year's medication adherence, demographics, comorbidities, and common laboratory tests; and identify facilitators of and barriers to medication adherence within the context of social determinants of health (SDOH), EHR-derived medication adherence, and C-MCIs. METHODS: Drawing on the tribe's EHR (2018-2021) data for CNO patients with T2D, we will characterize the relationships among medication adherence (to glucose- and lipid-lowering and antihypertensive drugs) and C-MCIs (hemoglobin A1c ≤7%, low-density lipoprotein cholesterol <100 mg/dL, and systolic blood pressure <130 mm Hg); patient demographics (eg, age, sex, SDOH, and residence location); and comorbidities (eg, BMI ≥30, cardiovascular disease, and chronic kidney disease). We will also characterize the association of each C-MCI with demographics and comorbidities. Prescription and pharmacy refill data will be used to calculate the proportion of days covered with medications, a typical measure of medication adherence. Using machine learning techniques, we will develop prediction models for future (2019-2021) C-MCIs based on medication adherence, patient demographics, comorbidities, and common laboratory tests (eg, lipid panel) from the previous year. Finally, key informant interviews (N=90) will explore facilitators of and barriers to medication adherence within the context of local SDOH. RESULTS: Funding was obtained in early 2022. The University of Florida and CNO approved the institutional review board protocols and executed the data use agreements. Data extraction is in process. We expect to obtain results from aims 1 and 2 in 2024. CONCLUSIONS: Our findings will yield insights into improving medication adherence and C-MCIs among American Indian adults, consistent with CNO's State of the Nation's Health Report 2017 goal of reducing T2D and its complications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39193.

Comparing cardiovascular benefits between GLP-1 receptor agonists and SGLT2 inhibitors as an add-on to metformin among patients with type 2 diabetes: A retrospective cohort study.

AIMS: This study aimed to compare cardiovascular benefits associated with the use of GLP-1RA versus SGLT2i as add-on therapies to metformin among adults with type 2 diabetes (T2D) with and without a history of cardiovascular complications, using real-world data. METHODS: Using data from the IBM® MarketScan® Commercial Claims Databases, metformin users above 18years with T2D who initiated GLP-1RA or SGLT2i were identified. The study endpoints include MI, stroke, CHF, and a cardiovascular composite of these three outcomes. Cox proportional hazard regression models were used to compare the risks of cardiovascular endpoints while controlling for demographics and clinical characteristics. RESULTS: We identified 13,006 adults with T2D who initiated a GLP-1RA or SGLT2i as an add-on therapy to metformin and followed for a maximum of 5years. No difference in the endpoints was observed between users of two drugs who did not have established cardiovascular disease at baseline. However, significantly lower CHF risks (HR: 0.47, 95% CI: 0.28-0.79) and cardiovascular composite (HR: 0.67, 95% CI: 0.47-0.97) were observed in SGLT2i users compared with GLP-1RA users, among individuals with established cardiovascular diseases. CONCLUSIONS: Results suggest greater cardioprotective benefit from SGLT2i compared to GLP-1RA when used for secondary prevention among adults with T2D.

Dietary supplements and alternative therapies for obesity: A Perspective from The Obesity Society's Clinical Committee.

In this Perspective Statement from The Obesity Society, the Clinical Committee discusses the use of weight loss supplements in the United States and the lack of regulatory oversight and rigorous testing of their efficacy and safety. A number of products and services claiming to promote weight loss are directly marketed to individuals with obesity and those wanting to lose weight. These products are not regulated as "drugs" by the Federal Drug Administration but, rather, are treated as dietary supplements if ingredients are "generally regarded as safe," requiring little or no testing to show efficacy or safety. Health care providers should be aware of the lack of evidence and deficiencies in regulatory oversight of dietary supplements marketed for weight loss. Regulatory authorities should protect consumers by ensuring accurate and safe marketing claims and preventing promotion of unproven and potentially unsafe products and claims.

COVID-19 vaccines are effective in people with obesity: A position statement from The Obesity Society.

The position statement is issued by The Obesity Society in response to published literature, as well as inquiries made to the Society by patients, providers, Society members, policy makers, and others regarding the efficacy of vaccines in persons with obesity against SARS-CoV-2, the virus that causes COVID-19. The Obesity Society has critically evaluated data from published peer-reviewed literature and briefing documents from Emergency Use Authorization applications submitted by Pfizer-BioNTech, Moderna, and Johnson & Johnson. We conclude that these vaccines are highly efficacious, and their efficacy is not significantly different in people with and without obesity, based on scientific evidence available at the time of publication. The Obesity Society believes there is no definitive way to determine which of these three COVID-19 vaccines is "best" for any weight subpopulation (because of differences in the trial design and outcome measures in the phase 3 trials, elapsed time between doses, and regional differences in the presence of SARS-CoV-2 variants [e.g., South Africa B.1.351 in Johnson & Johnson trial]). All three trials have demonstrated high efficacy against COVID-19-associated hospitalization and death. Therefore, The Obesity Society encourages adults with obesity ≥18 years (≥16 years for Pfizer-BioNTech) to undergo vaccination with any one of the currently available vaccines authorized for emergency use by the US Food and Drug Administration as soon as they are able.