Faculty Bibliography
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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/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.
PMID: 36868256
ISSN: 2213-8595
CID: 5448582
Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged <20 Years Through 2060: The SEARCH for Diabetes in Youth Study
OBJECTIVE:To project the prevalence and number of youths with diabetes and trends in racial and ethnic disparities in the U.S. through 2060. RESEARCH DESIGN AND METHODS/METHODS:Based on a mathematical model and data from the SEARCH for Diabetes in Youth study for calendar years 2002-2017, we projected the future prevalence of type 1 and type 2 diabetes among youth aged <20 years while considering different scenarios of future trends in incidence. RESULTS:The number of youths with diabetes will increase from 213,000 (95% CI 209,000; 218,000) (type 1 diabetes 185,000, type 2 diabetes 28,000) in 2017 to 239,000 (95% CI 209,000; 282,000) (type 1 diabetes 191,000, type 2 diabetes 48,000) in 2060 if the incidence remains constant as observed in 2017. Corresponding relative increases were 3% (95% CI -9%; 21%) for type 1 diabetes and 69% (95% CI 43%; 109%) for type 2 diabetes. Assuming that increasing trends in incidence observed between 2002 and 2017 continue, the projected number of youths with diabetes will be 526,000 (95% CI 335,000; 893,000) (type 1 diabetes 306,000, type 2 diabetes 220,000). Corresponding relative increases would be 65% (95% CI 12%; 158%) for type 1 diabetes and 673% (95% CI 362%; 1,341%) for type 2 diabetes. In both scenarios, substantial widening of racial and ethnic disparities in type 2 diabetes prevalence are expected, with the highest prevalence among non-Hispanic Black youth. CONCLUSIONS:The number of youths with diabetes in the U.S. is likely to substantially increase in future decades, which emphasizes the need for prevention to attenuate this trend.
PMCID:9887625
PMID: 36580405
ISSN: 1935-5548
CID: 5426252
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.
PMCID:9930314
PMID: 36788497
ISSN: 1471-2288
CID: 5427142
A Longitudinal View of Disparities in Insulin Pump Use Among Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study
PMID: 36475821
ISSN: 1557-8593
CID: 5383072
Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States
Background Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in-hospital mortality in patients hospitalized for PAD based on a national database. Methods and Results Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD-related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD-10-PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in-hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital-related factors. In-hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision-making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in-hospital mortality. Conclusions This study demonstrates the feasibility of ML in predicting in-hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high-risk patients for poor outcomes and guiding personalized intervention.
PMID: 36216437
ISSN: 2047-9980
CID: 5351942
Coronavirus Disease 2019 and the Injured Patient: A Multicenter Review
INTRODUCTION/BACKGROUND:Coronavirus disease 2019 (COVID-19) has been shown to affect outcomes among surgical patients. We hypothesized that COVID-19 would be linked to higher mortality and longer length of stay of trauma patients regardless of the injury severity score (ISS). METHODS:We performed a retrospective analysis of trauma registries from two level 1 trauma centers (suburban and urban) from March 1, 2019, to June 30, 2019, and March 1, 2020, to June 30, 2020, comparing baseline characteristics and cumulative adverse events. Data collected included ISS, demographics, and comorbidities. The primary outcome was time from hospitalization to in-hospital death. Outcomes during the height of the first New York COVID-19 wave were also compared with the same time frame in the prior year. Kaplan-Meier method with log-rank test and Cox proportional hazard models were used to compare outcomes. RESULTS:There were 1180 trauma patients admitted during the study period from March 2020 to June 2020. Of these, 596 were never tested for COVID-19 and were excluded from the analysis. A total of 148 COVID+ patients and 436 COVID- patients composed the 2020 cohort for analysis. Compared with the 2019 cohort, the 2020 cohort was older with more associated comorbidities, more adverse events, but lower ISS. Higher rates of historical hypertension, diabetes, neurologic events, and coagulopathy were found among COVID+ patients compared with COVID- patients. D-dimer and ferritin were unreliable indicators of COVID-19 severity; however, C-reactive protein levels were higher in COVID+ relative to COVID- patients. Patients who were COVID+ had a lower median ISS compared with COVID- patients, and COVID+ patients had higher rates of mortality and longer length of stay. CONCLUSIONS:COVID+ trauma patients admitted to our two level 1 trauma centers had increased morbidity and mortality compared with admitted COVID- trauma patients despite age and lower ISS. C-reactive protein may play a role in monitoring COVID-19 activity in trauma patients. A better understanding of the physiological impact of COVID-19 on injured patients warrants further investigation.
PMCID:9263818
PMID: 36084394
ISSN: 1095-8673
CID: 5337332
Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals
Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.
PMCID:9192715
PMID: 35697829
ISSN: 2399-3642
CID: 5290962
Global advances in health & medicine : improving healthcare outcomes worldwide. 2022:11.DOI: 10.1177/2164957X221100405
A Randomized Preference Trial Comparing Cognitive-Behavioral Therapy and Yoga for the Treatment of Late-Life Worry: Examination of Impact on Depression, Generalized Anxiety, Fatigue, Pain, Social Participation, and Physical Function
Background/UNASSIGNED:Depression, generalized anxiety, fatigue, diminished physical function, reduced social participation, and pain are common for many older adults and negatively impact quality of life. The purpose of the overall trial was to compare the effects of cognitive-behavioral therapy (CBT) and yoga on late-life worry, anxiety, and sleep; and examine preference and selection effects on these outcomes. Objective/UNASSIGNED:The present analyses compared effects of the 2 interventions on additional outcomes (depressive symptoms, generalized anxiety symptoms, fatigue, pain interference/intensity, physical function, social participation); and examined whether there are preference and selection effects for these treatments. Methods/UNASSIGNED:A randomized preference trial of CBT and yoga was conducted in adults ≥60 years who scored ≥26 on the Penn State Worry Questionnaire-Abbreviated (PSWQ-A), recruited from outpatient medical clinics, mailings, and advertisements. Cognitive-behavioral therapy consisted of 10 weekly telephone sessions. Yoga consisted of 20 bi-weekly group yoga classes. Participants were randomized to(1): a randomized controlled trial (RCT) of CBT or yoga (n = 250); or (2) a preference trial in which they selected their treatment (CBT or yoga; n = 250). Outcomes were measured at baseline and post-intervention. Results/UNASSIGNED:< .01]. Depressive symptoms, generalized anxiety, and fatigue showed clinically meaningful within-group changes in both groups. There were no changes in or difference between physical function or social participation for either group. No preference or selection effects were found. Conclusion/UNASSIGNED:Both CBT and yoga may be useful for older adults for improving psychological symptoms and fatigue. Cognitive-behavioral therapy may offer even greater benefit than yoga for decreasing pain.
PMCID:9118438
PMID: 35601466
ISSN: 2164-957x
CID: 5283742
Clinical journal of the American Society of Nephrology : CJASN. 2022:17(7):1017-1025.DOI: 10.2215/CJN.16481221
Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis
BACKGROUND AND OBJECTIVES/OBJECTIVE:Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS/METHODS:) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve. RESULTS:=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59). CONCLUSION/CONCLUSIONS:A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period. PODCAST/UNASSIGNED:This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.
PMID: 35667835
ISSN: 1555-905x
CID: 5248242
Long-Term Effects of Cognitive-Behavioral Therapy and Yoga for Worried Older Adults
OBJECTIVES/OBJECTIVE:Cognitive-behavioral therapy (CBT) and yoga decrease worry and anxiety. There are no long-term data comparing CBT and yoga for worry, anxiety, and sleep in older adults. The impact of preference and selection on these outcomes is unknown. In this secondary data analysis, we compared long-term effects of CBT by telephone and yoga on worry, anxiety, sleep, depressive symptoms, fatigue, physical function, social participation, and pain; and examined preference and selection effects. DESIGN/METHODS:In this randomized preference trial, participants (N = 500) were randomized to a: 1) randomized controlled trial (RCT) of CBT or yoga (n = 250); or 2) preference trial (selected CBT or yoga; n = 250). Outcomes were measured at baseline and Week 37. SETTING/METHODS:Community. PARTICIPANTS/METHODS:Community-dwelling older adults (age 60+ years). INTERVENTIONS/METHODS:CBT (by telephone) and yoga (in-person group classes). MEASUREMENTS/METHODS: CONCLUSIONS:CBT and yoga both demonstrated maintained improvements from baseline on multiple outcomes six months after intervention completion in a large sample of older adults. TRIAL REGISTRATION/BACKGROUND:www. CLINICALTRIALS/RESULTS:gov Identifier NCT02968238.
PMID: 35260292
ISSN: 1545-7214
CID: 5220922
