Browsing by Author "Redondo, Maria J."
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Item Baseline leptin predicts response to metformin in adolescents with type 1 diabetes and increased body mass index(Wiley, 2023) Ismail, Heba M.; Barua, Souptik; Wang, Johnny; Sabharwal, Ashutosh; Libman, Ingrid; Bacha, Fida; Nadeau, Kristen J.; Tosur, Mustafa; Redondo, Maria J.; Pediatrics, School of MedicineItem Comparisons of Metabolic Measures to Predict T1D vs Detect a Preventive Treatment Effect in High-Risk Individuals(Oxford University Press, 2024) Sims, Emily K.; Cuthbertson, David; Jacobsen, Laura; Ismail, Heba M.; Nathan, Brandon M.; Herold, Kevan C.; Redondo, Maria J.; Sosenko, Jay; Pediatrics, School of MedicineContext: Metabolic measures are frequently used to predict type 1 diabetes (T1D) and to understand effects of disease-modifying therapies. Objective: Compare metabolic endpoints for their ability to detect preventive treatment effects and predict T1D. Methods: 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, a multicenter clinical trial investigating 14-day intravenous teplizumab infusion and (2) predicting T1D in the TrialNet Pathway to Prevention natural history study. For each metabolic measure, t-Values from t tests for detecting a treatment effect were compared with chi-square values from proportional hazards regression for predicting T1D. 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. Conclusion: 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.Item Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review(Springer Nature, 2023-10-05) Felton, Jamie L.; Griffin, Kurt J.; Oram, Richard A.; Speake, Cate; Long, S. Alice; Onengut-Gumuscu, Suna; Rich, Stephen S.; Monaco, Gabriela S. F.; Evans-Molina, Carmella; DiMeglio, Linda A.; Ismail, Heba M.; Steck, Andrea K.; Dabelea, Dana; Johnson, Randi K.; Urazbayeva, Marzhan; Gitelman, Stephen; Wentworth, John M.; Redondo, Maria J.; Sims, Emily K.; Pediatrics, School of MedicineBackground: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. Methods: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. Results: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. Conclusions: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design.Item Early and late C-peptide responses during oral glucose tolerance testing are oppositely predictive of type 1 diabetes in autoantibody-positive individuals(Wiley, 2020-01-31) Ismail, Heba M.; Becker, Dorothy J.; Libman, Ingrid; Herold, Kevan C.; Redondo, Maria J.; Atkinson, Mark A.; Cleves, Mario A.; Palmer, Jerry; Sosenko, Jay; Pediatrics, School of MedicineWe examined whether the timing of the C-peptide response during an oral glucose tolerance test (OGTT) in relatives of patients with type 1 diabetes (T1D) is predictive of disease onset. We examined baseline 2-h OGTTs from 670 relatives participating in the Diabetes Prevention Trial-Type 1 (age: 13.8 ± 9.6 years; body mass index z score: 0.3 ± 1.1; 56% male) using univariate regression models. T1D risk increased with lower early C-peptide responses (30–0 min) (χ2 = 28.8, P < 0.001), and higher late C-peptide responses (120–60 min) (χ2 = 23.3, P < 0.001). When both responses were included in a proportional hazards model, they remained independently and oppositely associated with T1D, with a stronger overall association for the combined model than either response alone (χ2 = 41.1; P < 0.001). Using receiver operating characteristic curve analysis, the combined early and late C-peptide response was more accurately predictive of T1D than area under the curve C-peptide (P = 0.005). Our findings demonstrate that lower early and higher late C-peptide responses serve as indicators of increased T1D risk.Item The Effect of Age on the Progression and Severity of Type 1 Diabetes: Potential Effects on Disease Mechanisms(Springer, 2018-11) Leete, Pia; Mallone, Roberto; Richardson, Sarah J.; Sosenko, Jay M.; Redondo, Maria J.; Evans-Molina, Carmella; Medicine, School of MedicinePurpose of Review To explore the impact of age on type 1 diabetes (T1D) pathogenesis. Recent Findings Children progress more rapidly from autoantibody positivity to T1D and have lower C-peptide levels compared to adults. In histological analysis of post-mortem pancreata, younger age of diagnosis is associated with reduced numbers of insulin containing islets and a hyper-immune CD20hi infiltrate. Moreover compared to adults, children exhibit decreased immune regulatory function and increased engagement and trafficking of autoreactive CD8+ T cells, and age-related differences in β cell vulnerability may also contribute to the more aggressive immune phenotype observed in children. To account for some of these differences, HLA and non-HLA genetic loci that influence multiple disease characteristics, including age of onset, are being increasingly characterized. Summary The exception of T1D as an autoimmune disease more prevalent in children than adults results from a combination of immune, metabolic, and genetic factors. Age-related differences in T1D pathology have important implications for better tailoring of immunotherapies.Item The Evolution of Hemoglobin A1c Targets for Youth With Type 1 Diabetes: Rationale and Supporting Evidence(American Diabetes Association, 2021) Redondo, Maria J.; Libman, Ingrid; Maahs, David M.; Lyons, Sarah K.; Saraco, Mindy; Reusch, Jane; Rodriguez, Henry; DiMeglio, Linda A.; Pediatrics, School of MedicineThe American Diabetes Association 2020 Standards of Medical Care in Diabetes (Standards of Care) recommends a hemoglobin A1c (A1C) of <7% (53 mmol/mol) for many children with type 1 diabetes (T1D), with an emphasis on target personalization. A higher A1C target of <7.5% may be more suitable for youth who cannot articulate symptoms of hypoglycemia or have hypoglycemia unawareness and for those who do not have access to analog insulins or advanced diabetes technologies or who cannot monitor blood glucose regularly. Even less stringent A1C targets (e.g., <8%) may be warranted for children with a history of severe hypoglycemia, severe morbidities, or short life expectancy. During the "honeymoon" period and in situations where lower mean glycemia is achievable without excessive hypoglycemia or reduced quality of life, an A1C <6.5% may be safe and effective. Here, we provide a historical perspective of A1C targets in pediatrics and highlight evidence demonstrating detrimental effects of hyperglycemia in children and adolescents, including increased likelihood of brain structure and neurocognitive abnormalities, microvascular and macrovascular complications, long-term effects, and increased mortality. We also review data supporting a decrease over time in overall severe hypoglycemia risk for youth with T1D, partly associated with the use of newer insulins and devices, and weakened association between lower A1C and severe hypoglycemia risk. We present common barriers to achieving glycemic targets in pediatric diabetes and discuss some strategies to address them. We aim to raise awareness within the community on Standards of Care updates that impact this crucial goal in pediatric diabetes management.Item IA-2A positivity increases risk of progression within and across established stages of type 1 diabetes(Springer, 2025) Sims, Emily K.; Cuthbertson, David; Ferrat, Lauric A.; Bosi, Emanuele; Evans‑Molina, Carmella; DiMeglio, Linda A.; Nathan, Brandon M.; Ismail, Heba M.; Jacobsen, Laura M.; Redondo, Maria J.; Oram, Richard A.; Sosenko, Jay M.; Pediatrics, School of MedicineAims/hypothesis: Accurate understanding of type 1 diabetes risk is critical for optimisation of counselling, monitoring and interventions, yet even within established staging classifications, individual time to clinical disease varies. Previous work has associated IA-2A positivity with increased type 1 diabetes progression but a comprehensive assessment of the impact of screening for IA-2A positivity across the natural history of autoantibody positivity has not been performed. We asked whether IA-2A would consistently be associated with higher risk of progression within and across established stages of type 1 diabetes in a large natural history study. Methods: Genetic, autoantibody and metabolic data from adult and paediatric autoantibody-negative (n=192) and autoantibody-positive (n=4577) relatives of individuals with type 1 diabetes followed longitudinally in the Type 1 Diabetes TrialNet Pathway to Prevention Study were analysed. Cox regression was used to compare cumulative incidences of clinical diabetes by autoantibody profiles and disease stages. Results: Compared with IA-2A- individuals, IA-2A+ individuals had higher genetic risk scores and clinical progression risk within single-autoantibody-positive (5.3-fold increased 5 year risk), stage 1 (2.2-fold increased 5 year risk) and stage 2 (1.3-fold increased 5 year risk) type 1 diabetes categories. Individuals with single-autoantibody positivity for IA-2A showed increased metabolic dysfunction and diabetes progression compared with people who were autoantibody negative, those positive for another single autoantibody, and IA-2A- stage 1 individuals. Individuals at highest risk within the single-IA-2A+ category included children (HR 14.2 [95% CI 1.9, 103.1], p=0.009), individuals with IA-2A titres above the median (HR 3.5 [95% CI 1.9, 6.6], p<0.001), individuals with high genetic risk scores (HR 1.4 [95% CI 1.2,1.6], p<0.001) and individuals with HLA DR4-positive status (HR 3.7 [95% CI 1.6, 8.3], p=0.002). When considering all autoantibody-positive individuals, progression risk was similar for euglycaemic IA-2A+ individuals and dysglycaemic IA-2A- individuals. Conclusions/interpretation: IA-2A positivity is consistently associated with increased progression risk throughout the natural history of type 1 diabetes development. Individuals with single-autoantibody positivity for IA-2A have a greater risk of disease progression than those who meet stage 1 criteria but who are IA-2A-. Approaches to incorporate IA-2A+ status into monitoring strategies for autoantibody-positive individuals should be considered.Item Index60 as an additional diagnostic criterion for type 1 diabetes(Springer, 2021) Redondo, Maria J.; Nathan, Brandon M.; Jacobsen, Laura M.; Sims, Emily; Bocchino, Laura E.; Pugliese, Alberto; Schatz, Desmond A.; Atkinson, Mark A.; Skyler, Jay; Palmer, Jerry; Geyer, Susan; Sosenko, Jay M.; Type 1 diabetes TrialNet Study Group; Pediatrics, School of MedicineAims/hypothesis: We aimed to compare characteristics of individuals identified in the peri-diagnostic range by Index60 (composite glucose and C-peptide measure) ≥2.00, 2 h OGTT glucose ≥11.1 mmol/l, or both. Methods: We studied autoantibody-positive participants in the Type 1 Diabetes TrialNet Pathway to Prevention study who, at their baseline OGTT, had 2 h blood glucose ≥11.1 mmol/l and/or Index60 ≥2.00 (n = 354, median age = 11.2 years, age range = 1.7-46.6; 49% male, 83% non-Hispanic White). Type 1 diabetes-relevant characteristics (e.g., age, C-peptide, autoantibodies, BMI) were compared among three mutually exclusive groups: 2 h glucose ≥11.1 mmol/l and Index60 <2.00 [Glu(+), n = 76], 2 h glucose <11.1 mmol/l and Index60 ≥2.00 [Ind(+), n = 113], or both 2 h glucose ≥11.1 mmol/l and Index60 ≥2.00 [Glu(+)/Ind(+), n = 165]. Results: Participants in Glu(+), vs those in Ind(+) or Glu(+)/Ind(+), were older (mean ages = 22.9, 11.8 and 14.7 years, respectively), had higher early (30-0 min) C-peptide response (1.0, 0.50 and 0.43 nmol/l), higher AUC C-peptide (2.33, 1.13 and 1.10 nmol/l), higher percentage of overweight/obesity (58%, 16% and 30%) (all comparisons, p < 0.0001), and a lower percentage of multiple autoantibody positivity (72%, 92% and 93%) (p < 0.001). OGTT-stimulated C-peptide and glucose patterns of Glu(+) differed appreciably from Ind(+) and Glu(+)/Ind(+). Progression to diabetes occurred in 61% (46/76) of Glu(+) and 63% (71/113) of Ind(+). Even though Index60 ≥2.00 was not a Pathway to Prevention diagnostic criterion, Ind(+) had a 4 year cumulative diabetes incidence of 95% (95% CI 86%, 98%). Conclusions/interpretation: Participants in the Ind(+) group had more typical characteristics of type 1 diabetes than participants in the Glu(+) did and were as likely to be diagnosed. However, unlike Glu(+) participants, Ind(+) participants were not identified at the baseline OGTT.Item Index60 Identifies Individuals at Appreciable Risk for Stage 3 Among an Autoantibody-Positive Population With Normal 2-Hour Glucose Levels: Implications for Current Staging Criteria of Type 1 Diabetes(American Diabetes Association, 2022) Nathan, Brandon M.; Redondo, Maria J.; Ismail, Heba; Jacobsen, Laura; Sims, Emily K.; Palmer, Jerry; Skyler, Jay; Bocchino, Laura; Geyer, Susan; Sosenko, Jay M.; Pediatrics, School of MedicineObjective: We assessed whether Index60, a composite measure of fasting C-peptide, 60-min C-peptide, and 60-min glucose, could improve the metabolic staging of type 1 diabetes for progression to clinical disease (stage 3) among autoantibody-positive (Ab+) individuals with normal 2-h glucose values (<140 mg/dL). Research design and methods: We analyzed 3,058 Type 1 Diabetes TrialNet Pathway to Prevention participants with 2-h glucose <140 mg/dL and Index60 <1.00 values from baseline oral glucose tolerance tests. Characteristics associated with type 1 diabetes (younger age, greater Ab+, higher HLA DR3-DQ2/DR4-DQ8 prevalence, and lower C-peptide) were compared among four mutually exclusive groups: top 2-h glucose quartile only (HI-2HGLU), top Index60 quartile only (HI-IND60), both top quartiles (HI-BOTH), and neither top quartile (LO-BOTH). Additionally, within the 2-h glucose distribution of <140 mg/dL and separately within the Index60 <1.00 distribution, comparisons were made between those above or below the medians. Results: HI-IND60 and HI-BOTH were younger, with greater frequency of more than two Ab+, and lower C-peptide levels, than either HI-2HGLU or LO-BOTH (all P < 0.001). The cumulative incidence for stage 3 was greater for HI-IND60 and HI-BOTH than for either HI-2HGLU or LO-BOTH (all P < 0.001). Those with Index60 values above the median were younger and had higher frequency of two or more Ab+ (P < 0.001) and DR3-DQ2/DR4-DQ8 prevalence (P < 0.001) and lower area under the curve (AUC) C-peptide levels (P < 0.001) than those below. Those above the 2-h glucose median had higher AUC C-peptide levels (P < 0.001), but otherwise did not differ from those below. Conclusions: Index60 identifies individuals with characteristics of type 1 diabetes at appreciable risk for progression who would otherwise be missed by 2-h glucose staging criteria.Item The Influence of Type 2 Diabetes–Associated Factors on Type 1 Diabetes(American Diabetes Association, 2019-08-01) Redondo, Maria J.; Evans-Molina, Carmella; Steck, Andrea K.; Atkinson, Mark A.; Sosenko, Jay; Pediatrics, School of MedicineCurrent efforts to prevent progression from islet autoimmunity to type 1 diabetes largely focus on immunomodulatory approaches. However, emerging data suggest that the development of diabetes in islet autoantibody–positive individuals may also involve factors such as obesity and genetic variants associated with type 2 diabetes, and the influence of these factors increases with age at diagnosis. Although these factors have been linked with metabolic outcomes, particularly through their impact on β-cell function and insulin sensitivity, growing evidence suggests that they might also interact with the immune system to amplify the autoimmune response. The presence of factors shared by both forms of diabetes contributes to disease heterogeneity and thus has important implications. Characteristics that are typically considered to be nonimmune should be incorporated into predictive algorithms that seek to identify at-risk individuals and into the designs of trials for disease prevention. The heterogeneity of diabetes also poses a challenge in diagnostic classification. Finally, after clinically diagnosing type 1 diabetes, addressing nonimmune elements may help to prevent further deterioration of β-cell function and thus improve clinical outcomes. This Perspectives in Care article highlights the role of type 2 diabetes–associated genetic factors (e.g., gene variants at transcription factor 7-like 2 [TCF7L2]) and obesity (via insulin resistance, inflammation, β-cell stress, or all three) in the pathogenesis of type 1 diabetes and their impacts on age at diagnosis. Recognizing that type 1 diabetes might result from the sum of effects from islet autoimmunity and type 2 diabetes–associated factors, their interactions, or both affects disease prediction, prevention, diagnosis, and treatment.