Description of postoperative complications and bacterial contamination of wound soaker catheters used to administer postoperative local analgesia after mastectomy in 11 dogs: case series.

Mastectomy is a common and painful procedure in dogs. Wound soaker catheters (WSC) are frequently used to reduce postoperative pain, including pain after mastectomy. The objectives of this case series were to describe the use of WSC for owner administration of postoperative local analgesia in dogs with mammary tumors treated surgically, to identify complications associated with WSC and to determine the frequency of bacterial colonization of the catheters. Twelve WSC were placed in 11 dogs during mastectomy surgery, left in place for three days, protected by a dressing and successfully managed by owners at home. No postoperative antibiotics were administered. No complications were identified in any cases. No bacterial growth was identified on bacteriological analysis of the twelve WSC. These results suggest that the use of WSC is a safe alternative for postoperative analgesia administration following mastectomy in dogs. Future studies comparing dogs with or without WSC with a larger number of dogs are needed to further evaluate efficacy and complications.

Early Metabolic Endpoints Identify Persistent Treatment Efficacy in Recent-Onset Type 1 Diabetes Immunotherapy Trials.

OBJECTIVE: Mixed-meal tolerance test-stimulated area under the curve (AUC) C-peptide at 12-24 months represents the primary end point for nearly all intervention trials seeking to preserve ß-cell function in recent-onset type 1 diabetes. We hypothesized that participant benefit might be detected earlier and predict outcomes at 12 months posttherapy. Such findings would support shorter trials to establish initial efficacy. RESEARCH DESIGN AND METHODS: We examined data from six Type 1 Diabetes TrialNet immunotherapy randomized controlled trials in a post hoc analysis and included additional stimulated metabolic indices beyond C-peptide AUC. We partitioned the analysis into successful and unsuccessful trials and analyzed the data both in the aggregate as well as individually for each trial. RESULTS: Among trials meeting their primary end point, we identified a treatment effect at 3 and 6 months when using C-peptide AUC (P = 0.030 and P < 0.001, respectively) as a dynamic measure (i.e., change from baseline). Importantly, no such difference was seen in the unsuccessful trials. The use of C-peptide AUC as a 6-month dynamic measure not only detected treatment efficacy but also suggested long-term C-peptide preservation (R2 for 12-month C-peptide AUC adjusted for age and baseline value was 0.80, P < 0.001), and this finding supported the concept of smaller trial sizes down to 54 participants. CONCLUSIONS: Early dynamic measures can identify a treatment effect among successful immune therapies in type 1 diabetes trials with good long-term prediction and practical sample size over a 6-month period. While external validation of these findings is required, strong rationale and data exist in support of shortening early-phase clinical trials.

Impact of catheterized ductal closure on renal and cerebral oximetry in premature neonates.

Percutaneous catheter-based closure is increasingly utilized in premature newborns. While near-infrared spectroscopy (NIRS) has been examined for assessment of interventional closure in surgical ligation, its application in percutaneous transcatheter closure remains unexplored. This study aims to assess cerebral and renal hemodynamic changes using NIRS during percutaneous closure compared to surgical closure in preterm infants. A prospective observational study enrolled preterm infants born at 32 weeks of gestation or less and diagnosed with hsPDA between January 2020 and December 2022. These infants received either surgical or catheter-based closure of the PDA. Cerebral and renal oxygen saturation was monitored using the INVOS 5100 device from 12 h before the intervention until 24 h after. Linear mixed-effects models were used to analyze time-dependent variables. Twenty-two patients were enrolled, with catheter-based closure performed in 16 cases and conventional surgery in 6 cases. Following ductal closure, a significant increase in renal and cerebral oximetry was observed alongside a decrease in renal and cerebral tissue oxygen extraction. These changes were particularly pronounced in the renal territory. No differences were detected between catheterization and surgical closure.   Conclusion: An improvement in cerebral and renal oximetry following hsPDA closure was observed. However, we did not identify differences in this pattern based on the type of interventional procedure for PDA, whether surgery or catheterization. What is Known: • The presence of a significant ductus is common in premature patients. Studies have shown that it affects cerebral and renal hemodynamics negatively, leading to decreased oximetry values in these areas. It has been reported that closure of the ductus, either pharmacologically or surgically, results in improved oximetry values. What is New: • This study assess the impact of percutaneous closure of ductus, revealing increased oximetry values in cerebral and renal territories without significant differences compared to surgical ligation. Notably, renal oximetry values showed a greater increase, underscoring the importance of multi-location monitoring.

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review.

BACKGROUND: Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS: We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS: Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS: Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.

Islet autoantibodies are markers found in the blood when insulin-producing cells in the pancreas become damaged and can be used to predict future development of type 1 diabetes. We evaluated published literature to determine whether characteristics of islet antibodies (type, levels, numbers) could improve prediction and help understand differences in how individuals with type 1 diabetes respond to treatments. We found existing evidence shows that islet autoantibody type and number are most useful to predict disease progression before diagnosis. In addition, the age when islet autoantibodies first appear strongly influences rate of progression. These findings provide important information for patients and care providers on how islet autoantibodies can be used to understand future type 1 diabetes development and to identify individuals who have the potential to benefit from intervention or prevention therapy.

Inaccurate diagnosis of diabetes type in youth: prevalence, characteristics, and implications.

Classifying diabetes at diagnosis is crucial for disease management but increasingly difficult due to overlaps in characteristics between the commonly encountered diabetes types. We evaluated the prevalence and characteristics of youth with diabetes type that was unknown at diagnosis or was revised over time. We studied 2073 youth with new-onset diabetes (median age [IQR] = 11.4 [6.2] years; 50% male; 75% White, 21% Black, 4% other race; overall, 37% Hispanic) and compared youth with unknown versus known diabetes type, per pediatric endocrinologist diagnosis. In a longitudinal subcohort of patients with data for ≥ 3 years post-diabetes diagnosis (n = 1019), we compared youth with steady versus reclassified diabetes type. In the entire cohort, after adjustment for confounders, diabetes type was unknown in 62 youth (3%), associated with older age, negative IA-2 autoantibody, lower C-peptide, and no diabetic ketoacidosis (all, p < 0.05). In the longitudinal subcohort, diabetes type was reclassified in 35 youth (3.4%); this was not statistically associated with any single characteristic. In sum, among racially/ethnically diverse youth with diabetes, 6.4% had inaccurate diabetes classification at diagnosis. Further research is warranted to improve accurate diagnosis of pediatric diabetes type.

Should atelectasis be considered a pulmonary complication and indicator of poor prognosis in cystic fibrosis?

OBJECTIVE: This study examined whether bronchoscopy leads to clinicoradiological improvement in cystic fibrosis (CF) and the predictive factors. The study also investigated whether pulmonary atelectasis is a poor prognostic factor in CF. METHODS: This multicenter, case-control, observational, retrospective study included two groups of patients with CF: a case group (patients with persistent atelectasis who were followed-up at least for 2 years) and a control group (patients without atelectasis matched 1:1 by sex and age [±3 years]). We recorded demographic data, lung function test results, pulmonary complications, comorbidities, treatments (including bronchoscopies, surgery and transplantation), and deaths. RESULTS: Each group included 55 patients (case group: 20 men, mean age 25.4 ± 10.4 years; control group: 20 men, mean age 26.1 ± 11.4 years). Bronchoscopy did not lead to clinicoradiological improvement. Allergic bronchopulmonary aspergillosis (ABPA) was more frequent in the case group. Patients in the case group more frequently used inhaled steroids, their pre-atelectasis lung function was statistically worse, and they had more exacerbations during follow-up. CONCLUSION: Moderate-to-severe pulmonary disease and ABPA can favor atelectasis. Pulmonary atelectasis can be a poor prognostic factor in CF because it increases exacerbations. Despite our results, we recommend enhancing treatment, including bronchoscopy, to prevent persistent atelectasis.

Pancreatic adenocarcinoma treated with surgical resection, toceranib phosphate and firocoxib in a dog: a case report.

Exocrine pancreatic carcinomas are rarely reported in dogs. A ductal pancreatic adenocarcinoma in a 10-year-old intact beagle is described in this report. The diagnosis was made based on clinical signs, imaging (abdominal ultrasound and CT scan) and histopathology. Treatment consisted of partial right lobe pancreatectomy followed by adjuvant therapy with toceranib phosphate (Palladia®) and firocoxib (Previcox®) for six months. The treatment was well tolerated, and the survival time was 445 days. To our knowledge, this is the longest survival reported in the literature for a dog diagnosed with exocrine pancreatic adenocarcinoma. The results described here may contribute to provide a better understanding about this neoplasia and potential treatment options.

Structure and dynamics of the cyanobacterial regulator SipA.

The small, 78-residue long, regulator SipA interacts with the non-bleaching sensor histidine kinase (NblS). We have solved the solution structure of SipA on the basis of 990 nuclear Overhauser effect- (NOE-) derived distance constraints. The average pairwise root-mean-square deviation (RMSD) for the twenty best structures for the backbone residues, obtained by CYANA, was 1.35 ± 0.21 Å, and 1.90 ± 0.16 Å when all heavy atoms were considered (the target function of CYANA was 0.540 ± 0.08). The structure is that of a ß-II class protein, basically formed by a five-stranded ß-sheet composed of antiparallel strands following the arrangement: Gly6-Leu11 (ß-strand 1), which packs against Leu66-Val69 (ß-strand 5) on one side, and against Gly36-Thr42 (ß-strand 2) on the other side; Trp50-Phe54 (ß-strand 3); and Gly57-Leu60 (ß-strand 4). The protein is highly mobile, as shown by measurements of R1, R2, NOE and ηxy relaxation parameters, with an average order parameter () of 0.70; this mobility encompasses movements in different time scales. We hypothesize that this high flexibility allows the interaction with other proteins (among them NblS), and it explains the large conformational stability of SipA.

Comparisons of Metabolic Measures to Predict T1D vs. Detect a Preventive Treatment Effect in High-Risk Individuals.

CONTEXT: Metabolic measures are frequently used to predict T1D and to understand effects of disease-modifying therapies. OBJECTIVE: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D. DESIGN: Six-month changes in metabolic endpoints were assessed for: 1) detecting treatment effects by comparing placebo and treatment arms from the randomized controlled teplizumab prevention trial and 2) predicting T1D in the TrialNet Pathway to Prevention natural history study. SETTING: Multicenter clinical trial network. INTERVENTION: 14-day intravenous teplizumab infusion. MAIN OUTCOME MEASURES: T-values from t tests for detecting a treatment effect were compared to Chi-square values from proportional hazards regression for predicting T1D for each metabolic measure. PATIENTS OR OTHER PARTICIPANTS: Participants in the teplizumab prevention trial and participants in the Pathway to Prevention study selected with the same inclusion criteria used for the teplizumab trial were studied. RESULTS: Six-month changes in glucose-based endpoints predicted diabetes better than C-peptide-based endpoints, yet the latter were better at detecting a teplizumab effect. Combined measures of glucose and C-peptide were more balanced than measures of glucose alone or C-peptide alone for predicting diabetes and detecting a teplizumab effect. CONCLUSIONS: The capacity of a metabolic endpoint to detect a treatment effect does not necessarily correspond to its accuracy for predicting T1D. However, combined glucose and C-peptide endpoints appear to be effective for both predicting diabetes and detecting a response to immunotherapy. These findings suggest that combined glucose and C-peptide endpoints should be incorporated into the design of future T1D prevention trials.

The impact of the COVID-19 pandemic on people living with HIV: a cross-sectional study in Caracas, Venezuela.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted multiple health services, including human immunodeficiency virus (HIV) testing, care, and treatment services, jeopardizing the achievement of the Joint United Nations Programme on HIV/AIDS 90-90-90 global target. While there are limited studies assessing the impact of the COVID-19 pandemic on people living with HIV (PLHIV) in Latin America, there are none, to our knowledge, in Venezuela. This study aims to assess the impact of the COVID-19 pandemic among PLHIV seen at the outpatient clinic of a reference hospital in Venezuela. METHODS: We conducted a cross-sectional study among PLHIV aged 18 years and over seen at the Infectious Diseases Department of the University Hospital of Caracas, Venezuela between March 2021 and February 2022. RESULTS: A total of 238 PLHIV were included in the study. The median age was 43 (IQR 31-55) years, and the majority were male (68.9%). Most patients (88.2%, n = 210) came for routine check-ups, while 28 (11.3%) were newly diagnosed. The majority of patients (96.1%) were on antiretroviral therapy (ART), but only 67.8% had a viral load test, with almost all (95.6%) being undetectable. Among those who attended regular appointments, 11.9% reported missing at least one medical consultation, and 3.3% reported an interruption in their ART refill. More than half of the patients (55.5%) had received at least one dose of the COVID-19 vaccine, while the rest expressed hesitancy to get vaccinated. Most patients with COVID-19 vaccine hesitancy were male (65.1%), younger than 44 years (57.5%), employed (47.2%), and had been diagnosed with HIV for less than one year (33%). However, no statistically significant differences were found between vaccinated patients and those with COVID-19 vaccine hesitancy. Older age was a risk factor for missing consultations, while not having an alcoholic habit was identified as a protective factor against missing consultations. CONCLUSION: This study found that the COVID-19 pandemic had a limited impact on adherence to medical consultations and interruptions in ART among PLHIV seen at the University Hospital of Caracas, Venezuela.

Nightmare experiences and perceived ethnic discrimination amongst female university students in the United Arab Emirates: a cross-sectional study.

Perceived ethnic discrimination is known to be associated with anxiety and depression, and in turn, anxiety and depression are known to be associated with nightmare frequency and distress. This elicits a question: is perceived ethnic discrimination associated with nightmare frequency and distress? In this study, 179 female university students from the United Arab Emirates were assessed to answer that question. Results showed that while anxiety and depression were related to nightmare experiences, perceived ethnic discrimination was a stronger predictor of nightmare experiences. We posit two explanations for this finding: one based on psychoanalytical insights, and the other based on the Disposition-Stress model with neurobiological correlates. No significant differences were found across ethnicity when it comes to nightmare experiences or perceived ethnic discrimination. This is an encouraging sign of optimal societal integration in the United Arab Emirates.

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

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

Impact on cerebral hemodynamics of the use of volume guarantee combined with high frequency oscillatory ventilation in a neonatal animal respiratory distress model.

High-frequency oscillatory ventilation (HFOV) is an alternative to conventional mechanical ventilation (CMV). Recently, the use of volume guarantee (VG) combined with HFOV has been suggested as a safe strategy capable of reducing the damage induced by ventilation in immature lungs. However, the possible impact of this new ventilation technique on cerebral hemodynamics is unknown. To evaluate the cerebral hemodynamics effect of HFOV combined with VG in an experimental animal model of neonatal respiratory distress syndrome (RDS) due to surfactant deficiency compared with HFOV and CMV+VG (control group). Eighteen newborn piglets were randomized, before and after the induction of RDS by bronchoalveolar lavage, into 3 mechanical ventilation groups: CMV, HFOV and HFOV with VG. Changes in cerebral oxygen transport and consumption and cerebral blood flow were analyzed by non-invasive regional cerebral oxygen saturation (CrSO2), jugular venous saturation (SjO2), the calculated cerebral oxygen extraction fraction (COEF), the calculated cerebral fractional tissue oxygen extraction (cFTOE) and direct measurement of carotid artery flow. To analyze the temporal evolution of these variables, a mixed-effects linear regression model was constructed. After randomization, the following statistically significant results were found in every group: a drop in carotid artery flow: at a rate of -1.7 mL/kg/min (95% CI: -2.5 to -0.81; p < 0.001), CrSO2: at a rate of -6.2% (95% CI: -7.9 to -4.4; p < 0.001) and SjO2: at a rate of -20% (95% CI: -26 to -15; p < 0.001), accompanied by an increase in COEF: at a rate of 20% (95% CI: 15 to 26; p < 0.001) and cFTOE: at a rate of 0.07 (95% CI: 0.05 to 0.08; p < 0.001) in all groups. No statistically significant differences were found between the HFOV groups. CONCLUSION: No differences were observed at cerebral hemodynamic between respiratory assistance in HFOV with and without VG, being the latter ventilatory strategy equally safe. WHAT IS KNOWN: • Preterm have a situation of fragility of cerebral perfusion wich means that any mechanical ventilation strategy can have a significant influence. High-frequency oscillatory ventilation (HFOV) is an alternative to conventional mechanical ventilation (CMV). Recently, the use of volume guarantee (VG) combined with HFOV has been suggested as a safe strategy capable of reducing the damage induced by ventilation in immature lungs. Several studies have compared CMV and HFOV and their effects at hemodynamic level. It is known that the use of high mean airway pressure in HFOV can cause an increase in pulmonary vascular resistance with a decrease in thoracic venous return. WHAT IS NEW: • The possible impact of VAFO + VG on cerebral hemodynamics is unknown. Due the lack of studies and the existing controversy, we have carried out this research project in an experimental animal model with the aim of evaluating the cerebral hemodynamic repercussion of the use of VG in HFOV compared to the classic strategy without VG.

Diabetes Study of Children of Diverse Ethnicity and Race: Study design.

AIMS: Determining diabetes type in children has become increasingly difficult due to an overlap in typical characteristics between type 1 diabetes (T1D) and type 2 diabetes (T2D). The Diabetes Study in Children of Diverse Ethnicity and Race (DISCOVER) programme is a National Institutes of Health (NIH)-supported multicenter, prospective, observational study that enrols children and adolescents with non-secondary diabetes. The primary aim of the study was to develop improved models to differentiate between T1D and T2D in diverse youth. MATERIALS AND METHODS: The proposed models will evaluate the utility of three existing T1D genetic risk scores in combination with data on islet autoantibodies and other parameters typically available at the time of diabetes onset. Low non-fasting serum C-peptide (<0.6 nmol/L) between 3 and 10 years after diabetes diagnosis will be considered a biomarker for T1D as it reflects the loss of insulin secretion ability. Participating centres are enrolling youth (<19 years old) either with established diabetes (duration 3-10 years) for a cross-sectional evaluation or with recent onset diabetes (duration 3 weeks-15 months) for the longitudinal observation with annual visits for 3 years. Cross-sectional data will be used to develop models. Longitudinal data will be used to externally validate the best-fitting model. RESULTS: The results are expected to improve the ability to classify diabetes type in a large and growing subset of children who have an unclear form of diabetes at diagnosis. CONCLUSIONS: Accurate and timely classification of diabetes type will help establish the correct clinical management early in the course of the disease.

Understanding diabetes heterogeneity: key steps towards precision medicine in diabetes.

Diabetes is a highly heterogeneous condition; yet, it is diagnosed by measuring a single blood-borne metabolite, glucose, irrespective of aetiology. Although pragmatically helpful, disease classification can become complex and limit advances in research and medical care. Here, we describe diabetes heterogeneity, highlighting recent approaches that could facilitate management by integrating three disease models across all forms of diabetes, namely, the palette model, the threshold model and the gradient model. Once diabetes has developed, further worsening of established diabetes and the subsequent emergence of diabetes complications are kept in check by multiple processes designed to prevent or circumvent metabolic dysfunction. The impact of any given disease risk factor will vary from person-to-person depending on their background, diabetes-related propensity, and environmental exposures. Defining the consequent heterogeneity within diabetes through precision medicine, both in terms of diabetes risk and risk of complications, could improve health outcomes today and shine a light on avenues for novel therapy in the future.

Energy coupling and stoichiometry of Zn2+/H+ antiport by the prokaryotic cation diffusion facilitator YiiP.

YiiP from Shewanella oneidensis is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.

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

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

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

Background: Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk. Methods: We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation. Findings: The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics. Interpretation: Demographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.