Stratifying risk for onset of type 1 diabetes using islet autoantibody trajectory clustering.

AIMS/HYPOTHESIS: Islet autoantibodies can be detected prior to the onset of type 1 diabetes and are important tools for aetiologic studies, prevention trials and disease screening. Current risk stratification models rely on the positivity status of islet autoantibodies alone, but additional autoantibody characteristics may be important for understanding disease onset. This work aimed to determine if a data-driven model incorporating characteristics of islet autoantibody development, including timing, type and titre, could stratify risk for type 1 diabetes onset. METHODS: Data on autoantibodies against GAD (GADA), tyrosine phosphatase islet antigen-2 (IA-2A) and insulin (IAA) were obtained for 1,415 children enrolled in The Environmental Determinants of Diabetes in the Young study with at least one positive autoantibody measurement from years 1 to 12 of life. Unsupervised machine learning algorithms were trained to identify clusters of autoantibody development based on islet autoantibody timing, type and titre. Risk for type 1 diabetes across each identified cluster was evaluated using time-to-event analysis. RESULTS: We identified 2-4 clusters in each year cohort that differed by autoantibody timing, titre and type. During the first 3 years of life, risk for type 1 diabetes onset was driven by membership in clusters with high titres of all three autoantibodies (1-year risk: 20.87-56.25%, 5-year risk: 67.73-69.19%). Type 1 diabetes risk transitioned to type-specific titres during ages 4 to 8, as clusters with high titres of IA-2A (1-year risk: 20.88-28.93%, 5-year risk: 62.73-78.78%) showed faster progression to diabetes compared with high titres of GADA (1-year risk: 4.38-6.11%, 5-year risk: 25.06-31.44%). The importance of high GADA titres decreased during ages 9 to 12, with clusters containing high titres of IA-2A alone (1-year risk: 14.82-30.93%) or both GADA and IA-2A (1-year risk: 8.27-25.00%) demonstrating increased risk. CONCLUSIONS/INTERPRETATION: This unsupervised machine learning approach provides a novel tool for stratifying risk for type 1 diabetes onset using multiple autoantibody characteristics. These findings suggest that age-dependent changes in IA-2A titres modulate risk for type 1 diabetes onset across 12 years of life. Overall, this work supports incorporation of islet autoantibody timing, type and titre in risk stratification models for aetiologic studies, prevention trials and disease screening.

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology.

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals.

Infections are implicated in the etiology of type 1 diabetes mellitus (T1DM); however, conflicting epidemiologic evidence makes designing effective strategies for presymptomatic screening and disease prevention difficult. Considering the temporality and combination in which infections occur may provide valuable insights into understanding T1DM etiology but is rarely studied due to limited longitudinal datasets and insufficient analytical techniques. The objective of this work was to demonstrate a computational approach to classify the temporality and combination of infections in presymptomatic T1DM. We present a sequential data mining pipeline that leverages routinely collected infectious disease data from a prospective cohort study, the Environmental Determinants of Diabetes in the Young (TEDDY) study, to extract, interpret, and compare infection sequences. We then utilize this pipeline to assess risk for developing presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Overall, we identified 229 significant sequential rules that increased the risk for developing presymptomatic biomarkers of islet autoimmunity or clinical onset of T1DM. Multiple significant sequential rules involving varicella increased the risk for all presymptomatic biomarker-specific outcomes, while a single significant sequential rule involving parasites significantly increased risk for T1DM. Significant sequential rules involving respiratory illnesses were differentially represented among the presymptomatic biomarkers of islet autoimmunity and clinical onset of T1DM. Risk for T1DM was significantly increased by a single episode of sixth disease at 12 months, representing the only single-event sequence that increased disease risk. Together, these findings provide the first insights into the timing and combination of infections in T1DM etiology, which may ultimately lead to personalized disease screening and prevention strategies. The sequential data mining pipeline developed in this work demonstrates how temporal data mining can be used to address clinically meaningful questions. This method can be adapted to other presymptomatic factors and clinical conditions.

Satellite Epidemic of Covid-19 Associated Mucormycosis in India: A Multi-Site Observational Study.

BACKGROUND: Sudden upsurge in cases of COVID-19 Associated Mucormycosis (CAM) following the second wave of the COVID-19 pandemic was recorded in India. This study describes the clinical characteristics, management and outcomes of CAM cases, and factors associated with mortality. METHODS: Microbiologically confirmed CAM cases were enrolled from April 2021 to September 2021 from ten diverse geographical locations in India. Data were collected using a structured questionnaire and entered into a web portal designed specifically for this investigation. Bivariate analyses and logistic regression were conducted using R version 4.0.2. RESULTS: A total of 336 CAM patients were enrolled; the majority were male (n = 232, 69.1%), literate (n = 261, 77.7%), and employed (n = 224, 66.7%). The commonest presenting symptoms in our cohort of patients were oro-facial and ophthalmological in nature. The median (Interquartile Range; IQR) interval between COVID diagnosis and admission due to mucormycosis was 31 (18, 47) days, whereas the median duration of symptoms of CAM before hospitalization was 10 (5, 20) days. All CAM cases received antifungal treatment, and debridement (either surgical or endoscopic or both) was carried out in the majority of them (326, 97.02%). Twenty-three (6.9%) of the enrolled CAM cases expired. The odds of death in CAM patients increased with an increase in HbA1c level (aOR: 1.34, 95%CI: 1.05, 1.72) following adjustment for age, gender, education and employment status. CONCLUSION: A longer vigil of around 4-6 weeks post-COVID-19 diagnosis is suggested for earlier diagnosis of CAM. Better glycemic control may avert mortality in admitted CAM cases.

A centennial review of discoveries and advances in diabetes: Children and youth.

Diabetes is an increasingly common chronic metabolic disorder in children worldwide. The discovery of insulin in 1921 resulted in unprecedented advancements that improved the lives of children and youth with diabetes. The purpose of this article is to review the history of diabetes in children and youth over the last century and its implications for future developments in the field. We identified 68 relevant events between 1921 and 2021 through literature review and survey of pediatric endocrinologists. Basic research milestones led to the discovery of insulin and other regulatory hormones, established the normal physiology of carbohydrate metabolism and pathophysiology of diabetes, and provided insight into strategies for diabetes prevention. While landmark clinical studies were initially focused on adult diabetes populations, later studies assessed etiologic factors in birth cohort studies, evaluated technology use among children with diabetes, and investigated pharmacologic management of youth type 2 diabetes. Technological innovations culminated in the introduction of continuous glucose monitoring that enabled semi-automated insulin delivery systems. Finally, professional organizations collaborated with patient groups to advocate for the needs of children with diabetes and their families. Together, these advances transformed type 1 diabetes from a terminal illness to a manageable disease with near-normal life expectancy and increased our knowledge of type 2 diabetes and other forms of diabetes in the pediatric population. However, disparities in access to insulin, diabetes technology, education, and care support remain and disproportionately impact minority youth and communities with less resources. The overarching goal of diabetes management remains promoting a high quality of life and improving glycemic management without undermining the psychological health of children and youth living with diabetes.

A case control investigation of COVID-19 associated mucormycosis in India.

BACKGROUND: Increased occurrence of mucormycosis during the second wave of COVID-19 pandemic in early 2021 in India prompted us to undertake a multi-site case-control investigation. The objectives were to examine the monthly trend of COVID-19 Associated Mucormycosis (CAM) cases among in-patients and to identify factors associated with development of CAM. METHODS: Eleven study sites were involved across India; archived records since 1st January 2021 till 30th September 2021 were used for trend analysis. The cases and controls were enrolled during 15th June 2021 to 30th September 2021. Data were collected using a semi-structured questionnaire. Among 1211 enrolled participants, 336 were CAM cases and 875 were COVID-19 positive non-mucormycosis controls. RESULTS: CAM-case admissions reached their peak in May 2021 like a satellite epidemic after a month of in-patient admission peak recorded due to COVID-19. The odds of developing CAM increased with the history of working in a dusty environment (adjusted odds ratio; aOR 3.24, 95% CI 1.34, 7.82), diabetes mellitus (aOR: 31.83, 95% CI 13.96, 72.63), longer duration of hospital stay (aOR: 1.06, 95% CI 1.02, 1.11) and use of methylprednisolone (aOR: 2.71, 95% CI 1.37, 5.37) following adjustment for age, gender, occupation, education, type of houses used for living, requirement of ventilatory support and route of steroid administration. Higher proportion of CAM cases required supplemental oxygen compared to the controls; use of non-rebreather mask (NRBM) was associated as a protective factor against mucormycosis compared to face masks (aOR: 0.18, 95% CI 0.08, 0.41). Genomic sequencing of archived respiratory samples revealed similar occurrences of Delta and Delta derivates of SARS-CoV-2 infection in both cases and controls. CONCLUSIONS: Appropriate management of hyperglycemia, judicious use of steroids and use of NRBM during oxygen supplementation among COVID-19 patients have the potential to reduce the risk of occurrence of mucormycosis. Avoiding exposure to dusty environment would add to such prevention efforts.

Assessing feasibility of an adolescent relationship abuse prevention program for girls.

INTRODUCTION: Sexual violence and relationship abuse are prevalent among adolescents and programs promoting gender equity, reproductive justice, and healthy relationships are key strategies for prevention. While such "gender transformative" approaches appear promising for boys, they have not been evaluated among girls. This study assessed the feasibility of this community-based program, called Sisterhood 2.0, among girls in socially disadvantaged urban neighborhoods in Pittsburgh, Pennsylvania. METHODS: This quasi-experimental trial examined feasibility of Sisterhood 2.0 (n = 246), delivered through 8 weekly sessions, assessed through attendance, retention and satisfaction. Participants completed surveys at baseline and end of program assessing other relevant measures. Generalized linear mixed models estimated changes from baseline to follow up comparing intervention to control participants. RESULTS: Eleven neighborhoods were assigned to Sisterhood 2.0 (n = 5 neighborhoods) or job-readiness training (n = 6 neighborhoods). Girls were between the ages of 13 and 19, 8-10th graders (59%), and self-identified as Black (69%). Participants most often attended because they thought the program would be interesting (74%) and returned because of the women teaching the program (71%). Girls reported experiences with physical adolescent relationship abuse (ARA) (30% in both arms), emotional ARA (66% intervention; 56% control), or sexual ARA (11% intervention; 12% control). Physical ARA perpetration was high in both arms (intervention: 47%; control: 46%). Significant intervention effects were observed in recognition of abuse (ß = 0.41, 95% confidence interval 0.03-0.78). No other significant intervention effects were observed. CONCLUSIONS: Community-based gender-transformative programming for girls is feasible and may be a promising approach for addressing interpersonal violence and promoting sexual health.

Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science.

The research-grade Autism Diagnostic Observational Schedule (ADOS) is a broadly used instrument that informs and steers much of the science of autism. Despite its broad use, little is known about the empirical variability inherently present in the scores of the ADOS scale or their appropriateness to define change and its rate, to repeatedly use this test to characterize neurodevelopmental trajectories. Here we examine the empirical distributions of research-grade ADOS scores from 1324 records in a cross-section of the population comprising participants with autism between five and 65 years of age. We find that these empirical distributions violate the theoretical requirements of normality and homogeneous variance, essential for independence between bias and sensitivity. Further, we assess a subset of 52 typical controls versus those with autism and find a lack of proper elements to characterize neurodevelopmental trajectories in a coping nervous system changing at nonuniform, nonlinear rates. Repeating the assessments over four visits in a subset of the participants with autism for whom verbal criteria retained the same appropriate ADOS modules over the time span of the four visits reveals that switching the clinician changes the cutoff scores and consequently influences the diagnosis, despite maintaining fidelity in the same test's modules, room conditions, and tasks' fluidity per visit. Given the changes in probability distribution shape and dispersion of these ADOS scores, the lack of appropriate metric spaces to define similarity measures to characterize change and the impact that these elements have on sensitivity-bias codependencies and on longitudinal tracking of autism, we invite a discussion on readjusting the use of this test for scientific purposes.

Aging with Autism Departs Greatly from Typical Aging.

Autism has been largely portrayed as a psychiatric and childhood disorder. However, autism is a lifelong neurological condition that evolves over time through highly heterogeneous trajectories. These trends have not been studied in relation to normative aging trajectories, so we know very little about aging with autism. One aspect that seems to develop differently is the sense of movement, inclusive of sensory kinesthetic-reafference emerging from continuously sensed self-generated motions. These include involuntary micro-motions eluding observation, yet routinely obtainable in fMRI studies to rid images of motor artifacts. Open-access repositories offer thousands of imaging records, covering 5-65 years of age for both neurotypical and autistic individuals to ascertain the trajectories of involuntary motions. Here we introduce new computational techniques that automatically stratify different age groups in autism according to probability distance in different representational spaces. Further, we show that autistic cross-sectional population trajectories in probability space fundamentally differ from those of neurotypical controls and that after 40 years of age, there is an inflection point in autism, signaling a monotonically increasing difference away from age-matched normative involuntary motion signatures. Our work offers new age-appropriate stochastic analyses amenable to redefine basic research and provide dynamic diagnoses as the person's nervous systems age.

Exponential Rise of Fungal Infection Reported in Rajkot Civil Hospital in Covid Pandemic Era: A Retrospective Study.

Mucormycosis is a rare but serious angio-invasive infection caused by a group of fungi called mucormycetes. Mucormycosis is an aggressive, life-threatening infection requiring prompt diagnosis and early treatment. Wide spread use of steroid and higher antibiotics may cause immune irregulation in post covid patients. A hallmark of mucormycosis infection is the presence of extensive angioinvasion with resultant vessel thrombosis and tissue necrosis. We reported exponential rising cases of fungal infection in covid pandemic era. Here we published epidemiological data of 773 fungal infected cases operated in ENT department of PDU Medical college, Rajkot in 2021. We have documented patients demographic data with comorbidity, paranasal sinuses with orbital, palatal and cerebral involvement, evaluation method, surgical and post surgical management protocol which we followed in our institute to treat all cases. We got promising result in terms of survival and less morbidity. Early presentation, less comorbidity, proper evaluation and immediate debridement with systemic antifungal coverage for adequate duration proved to be mainstay treatment of fungal infection in covid pandemic era.

Ansel Mucormycosis Staging and Outcome Evaluation Score: Our Experience in Soaring Stage of Mucormycosis.

To propose Mucormycosis staging and Outcome evaluation score. (i) To provide method of conveying clinical experience to others without ambiguity. (ii) To facilitate an estimation of prognosis. (iii) To provide useful information for treatment decision. Retrospective observational study. Tertiary care center, Rajkot. 556 confirmed operated case of mucormycosis. It was a single center observational study of 556 confirmed cases of mucormycosis. In suspected cases of fungal infection, clinical symptoms were noted along with covid history and comorbid condition. Clinical findings were noted after nasal endoscopy. Rest neurological examination was done to rule out CNS involvement. Representative sample from nasal mucosa sent for microbiological examination. MRI PNS with Brain and Orbit was advised. After surgery, specimen was sent for histopathological confirmation. We reported most common age group was 51-60 years. 52% cases presented early with only nasal involvement and 1.8% cases with late cerebral involvement presentation. From recorded all above findings we have described this diseases progression in 4 components limited to nasal, orbital, palate and/or skull base, cerebral involvement. It is bases on anatomical progression on clinical and radiological findings. Considering all four components, staging system is designed that includes stage I to stage Vb. Outcome evaluation score designed to consider factors like patient's age, comorbidity, stage of disease while presentation, IV antifungal coverage and patient's psychological condition. Our clinical and radiological diagnostic staging and outcome evaluation score may helpful for others for early and better management of mucormycosis.

Post Covid-19 Acute Invasive Fungal Rhinosinusitis (AIFR): A Study of Histopathological Findings After FESS in Radiologically Diagnosed AIFR.

COVID-19 pandemic has led to a concerning surge of post-COVID-19 AIFR. Mucormycosis (BLACK fungus) is a rare but severe and life-threatening fungal infection occurred by mucormycetes, a family of moulds. More than 49,000 cases of AIFR were reported in three months in India. It primarily affects diabetics and spreads from the nasal cavity and paranasal sinuses (PNS). It also involves eye, palate, or brain. It is diagnosed clinically followed by radiological and pathological findings. We aimed to compare and analyse the pre-operative imaging with postoperative histopathological findings. The study was conducted in ENT department of tertiary care hospital, Rajkot. 200 patients were randomly selected who were presented to ENT OPD with clinically suspected Post COVID-19 AIFR. All patients underwent detailed ENT examination and radiological modality like MRI PNS, Brain, and Orbit. After proper pre-op evaluation, all patients underwent Functional Endoscopic Sinus Surgery (FESS). MRI findings were confirmed with that of histopathological findings done on KOH mount. All the patients were showing AIFR on MRI findings whereas 49% of patients had mucormycosis on Histopathology. Various other fungal infections like aspergillosis (7%), candidiasis (1.5%) were also found on HPE. 9% of patients showed combined infection with mucor and aspergillus species. Rest of the patients showed non-fungal rhinosinusitis. Inflow of the epidemic, plenty of patients were shown invasive fungal sinusitis in MRI patterns whereas many of them were HPE negative. Thus this study was done to know the efficacy of radiological features with pathological diagnosis. We have considered both procedures standard in our study.

Environmental exposures in machine learning and data mining approaches to diabetes etiology: A scoping review.

BACKGROUND: Environmental exposures are implicated in diabetes etiology, but are poorly understood due to disease heterogeneity, complexity of exposures, and analytical challenges. Machine learning and data mining are artificial intelligence methods that can address these limitations. Despite their increasing adoption in etiology and prediction of diabetes research, the types of methods and exposures analyzed have not been thoroughly reviewed. OBJECTIVE: We aimed to review articles that implemented machine learning and data mining methods to understand environmental exposures in diabetes etiology and disease prediction. METHODS: We queried PubMed and Scopus databases for machine learning and data mining studies that used environmental exposures to understand diabetes etiology on September 19th, 2022. Exposures were classified into specific external, general external, or internal exposures. We reviewed machine learning and data mining methods and characterized the scope of environmental exposures studied in the etiology of general diabetes, type 1 diabetes, type 2 diabetes, and other types of diabetes. RESULTS: We identified 44 articles for inclusion. Specific external exposures were the most common exposures studied, and supervised models were the most common methods used. Well-established specific external exposures of low physical activity, high cholesterol, and high triglycerides were predictive of general diabetes, type 2 diabetes, and prediabetes, while novel metabolic and gut microbiome biomarkers were implicated in type 1 diabetes. DISCUSSION: The use of machine learning and data mining methods to elucidate environmental triggers of diabetes was largely limited to well-established risk factors identified using easily explainable and interpretable models. Future studies should seek to leverage machine learning and data mining to explore the temporality and co-occurrence of multiple exposures and further evaluate the role of general external and internal exposures in diabetes etiology.

Detecting hypoglycemia-induced electrocardiogram changes in a rodent model of type 1 diabetes using shape-based clustering.

Sudden death related to hypoglycemia is thought to be due to cardiac arrhythmias. A clearer understanding of the cardiac changes associated with hypoglycemia is needed to reduce mortality. The objective of this work was to identify distinct patterns of electrocardiogram heartbeat changes that correlated with glycemic level, diabetes status, and mortality using a rodent model. Electrocardiogram and glucose measurements were collected from 54 diabetic and 37 non-diabetic rats undergoing insulin-induced hypoglycemic clamps. Shape-based unsupervised clustering was performed to identify distinct clusters of electrocardiogram heartbeats, and clustering performance was assessed using internal evaluation metrics. Clusters were evaluated by experimental conditions of diabetes status, glycemic level, and death status. Overall, shape-based unsupervised clustering identified 10 clusters of ECG heartbeats across multiple internal evaluation metrics. Several clusters demonstrating normal ECG morphology were specific to hypoglycemia conditions (Clusters 3, 5, and 8), non-diabetic rats (Cluster 4), or were generalized among all experimental conditions (Cluster 1). In contrast, clusters demonstrating QT prolongation alone or a combination of QT, PR, and QRS prolongation were specific to severe hypoglycemia experimental conditions and were stratified heartbeats by non-diabetic (Clusters 2 and 6) or diabetic status (Clusters 9 and 10). One cluster demonstrated an arrthymogenic waveform with premature ventricular contractions and was specific to heartbeats from severe hypoglycemia conditions (Cluster 7). Overall, this study provides the first data-driven characterization of ECG heartbeats in a rodent model of diabetes during hypoglycemia.

Decreased access to therapeutic services for children with disabilities during COVID-19 stay-at-home orders in Western Pennsylvania.

PURPOSE: Assess the effects of stay-at-home orders on access to services utilized by families of children with disabilities (CWD). METHODS: Cross-sectional weekly surveys were fielded over four weeks, during which western Pennsylvania was under stay-at-home orders. Respondents were divided into families of CWD (N = 233) or without CWD (N = 1582). Survey questions included measures of socio-economic status, and families of CWD answered questions regarding access to services pre and post-initiation of stay-at-home orders. Differences between families with and without CWD were analyzed using chi-square tests. RESULTS: Among families of CWD that had used services previously, 76.6% of survey respondents stated that they had decreased access, with the greatest percentage experiencing loss among those previously utilizing early intervention (75.5%), outpatient therapies (69.1%), or school-based therapies (80.7%). Compared to families without CWD, families of CWD were more likely to report lower pre-COVID-19 annual incomes (p < 0.001), job or income loss related to COVID-19 (p < 0.001), and higher levels of perceived stress (p < 0.001). CONCLUSION: CWD experienced loss of services during stay-at-home orders implemented as COVID-19 mitigation measures. Due to decreased access to needed services, CWD may be at risk of medical complications and loss of developmental progress.

Temporal Bone Trauma Management: A Study of 100 Cases.

To address the management of complications after temporal bone fractures and the outcomes. A prospective clinical study of 100 patients from the Department of Trauma (Surgery + E.N.T.), P.D.U. Medical College, Rajkot between the time period of 2017-2019. Among 100 patients, 79 were males and 21 were females. The most affected age group was 16-45 years (72). The longitudinal fracture (90) is the most common type of fracture, in which non-petrous type is the most prevalent (88) as low impact injuries are more common. The most common presentations of temporal bone fracture are ear bleed (59) and decreased hearing (59), mostly over the side of trauma. The most common clinical finding is hearing impairment (59), followed by haemotympanum (20) and facial palsy (15), more common over the side of trauma. Facial palsy had been easily managed conservatively by steroids and physiotherapy in most of the cases. 12 out of 15 patients had good recovery i.e. upto grade I and II by conservative management, 3 had undergone facial nerve decompression, following which 1 had recovered completely, i.e. grade 1; 1 upto grade II while 1 did not show any improvement. Other complications included giddiness (18), trigeminal neuralgia (1) and abducens nerve palsy (1). The temporal bone is more prone to injury and complications following trauma like hearing impairment, cerebrospinal fluid leak and facial palsy resolve either spontaneously or with conservative management. Surgeries must be undertaken only if adequate conservative treatment fails and after proper investigations.

Pharmacologic Weight Management in the Era of Adolescent Obesity.

CONTEXT: Pediatric obesity is a serious health problem in the United States. While lifestyle modification therapy with dietary changes and increased physical activity are integral for the prevention and treatment of mild to moderate obesity in youth, only a modest effect on sustained weight reduction is observed in children and young adults with severe obesity. This underscores the need for additional evidence-based interventions for children and adolescents with severe obesity, including pharmacotherapy, before considering invasive procedures such as bariatric surgery. EVIDENCE ACQUISITION: This publication focuses on recent advances in pharmacotherapy of obesity with an emphasis on medications approved for common and rarer monogenic forms of pediatric obesity. EVIDENCE SYNTHESIS: We review medications currently available in the United States, both those approved for weight reduction in children and "off-label" medications that have a broad safety margin. CONCLUSION: It is intended that this review will provide guidance for practicing clinicians and will encourage future exploration for successful pharmacotherapy and other interventions for obesity in youth.

Erratum to: Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

[This corrects the article DOI: 10.1093/jamiaopen/ooab063.].

Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering.

OBJECTIVE: Hyperglycemia has emerged as an important clinical manifestation of coronavirus disease 2019 (COVID-19) in diabetic and nondiabetic patients. Whether these glycemic changes are specific to a subgroup of patients and persist following COVID-19 resolution remains to be elucidated. This work aimed to characterize longitudinal random blood glucose in a large cohort of nondiabetic patients diagnosed with COVID-19. MATERIALS AND METHODS: De-identified electronic medical records of 7502 patients diagnosed with COVID-19 without prior diagnosis of diabetes between January 1, 2020, and November 18, 2020, were accessed through the TriNetX Research Network. Glucose measurements, diagnostic codes, medication codes, laboratory values, vital signs, and demographics were extracted before, during, and after COVID-19 diagnosis. Unsupervised time-series clustering algorithms were trained to identify distinct clusters of glucose trajectories. Cluster associations were tested for demographic variables, COVID-19 severity, glucose-altering medications, glucose values, and new-onset diabetes diagnoses. RESULTS: Time-series clustering identified a low-complexity model with 3 clusters and a high-complexity model with 19 clusters as the best-performing models. In both models, cluster membership differed significantly by death status, COVID-19 severity, and glucose levels. Clusters membership in the 19 cluster model also differed significantly by age, sex, and new-onset diabetes mellitus. DISCUSSION AND CONCLUSION: This work identified distinct longitudinal blood glucose changes associated with subclinical glucose dysfunction in the low-complexity model and increased new-onset diabetes incidence in the high-complexity model. Together, these findings highlight the utility of data-driven techniques to elucidate longitudinal glycemic dysfunction in patients with COVID-19 and provide clinical evidence for further evaluation of the role of COVID-19 in diabetes pathogenesis.

Rapid-Cycle Community Assessment of Health-Related Social Needs of Children and Families During Coronavirus Disease 2019.

OBJECTIVE: To identify unmet health and social resource needs during a county-wide coronavirus disease 19 (COVID-19) stay-at-home order and phased re-opening in Western Pennsylvania. METHODS: With public health, social service, and community partners connected through an ongoing academic-community collaborative, we developed and fielded a weekly repeated cross-sectional electronic survey assessing usage of and unmet need for health and social service resources. Using 10 weeks of surveys (April 3-June 11, 2020) by Allegheny County residents, we examined variation in responses by week and by sociodemographic characteristics using chi-square tests. We shared written reports weekly and discussed emerging trends with community partners. RESULTS: Participants ranged from 229 to 1001 per week. Unmet need for at least 1 health or health-related social need resource varied by week, ranging from 55% (95% confidence interval [CI] 50%-59%) of participants in week 2 to 43% (95% CI 37%-49%) of participants in week 9 (P = .006). Increased use of at least 1 resource ranged from 53% (95% CI 47%-58%) of participants in week 3 to 36% (95% CI 31%-42%) in week 9 (P < .001). Unmet need for food and financial assistance peaked early during the stay-at-home order, while unmet need for mental health care rose later. Unmet need for food assistance varied significantly by race and ethnicity and by household prepandemic income. CONCLUSIONS: Over half of families with children reported unmet health or social service needs during the first month of a county-wide COVID-19 stay-at-home order. Unmet needs varied with race, ethnicity, and income and with duration of the stay-at-home order.