Browsing by Author "Ismail, Heba M."
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Item 4033 Evaluating the Effect of Prebiotics on the Gut Microbiome Profile and Beta-cell Function in Newly-Diagnosed Type 1 Diabetes(Cambridge University Press, 2020-07-29) Ismail, Heba M.; Evans-Molina, Carmella; DiMeglio, Linda; Pediatrics, School of MedicineOBJECTIVES/GOALS: Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β-cells. Emerging data suggest that differences in intestinal microbiota might be critically involved both in autoimmunity and in glucose homeostasis. The prebiotic high amylose maize starch (HAMS) alters the gut microbiome profile and metabolites positively by increasing production of beneficial short chain fatty acids (SCFAs) that have significant anti-inflammatory effects. HAMS also improves glycemia, insulin sensitivity and secretion in healthy non-diabetic adults. Further, an acetylated and butyrylated form of HAMS (HAMS-AB) that increases beneficial SCFA production, namely acetate and butyrate, has been safe and effective in disease prevention in mouse T1D models. The objective of the proposed study is to assess the effect of administering a prebiotic, such as HAMS-AB, on the gut microbiome profile, SCFA production, glycemia and β-cell function in humans with T1D. METHODS/STUDY POPULATION: We hypothesize that administration of HAMS-AB will (i) improve the gut microbiome profile in humans with T1D, (ii) increase SCFA production, and (iii) improve β-cell health, β-cell function and overall glycemia. We propose a pilot randomized controlled cross-over trial of HAMS-AB in 12 youth with newly-diagnosed T1D. We will use state-of-the-art markers to profile the gut microbiome (using 16S rRNA sequencing), measure stool SCFA levels (using gas chromatography), asses β-cell stress/death (by measuring proinsulin to C-peptide ratios) and glycemia (assessed by continuous glucose monitoring and HbA1c measurements). RESULTS/ANTICIPATED RESULTS: We expect that the use of HAMS-AB in newly diagnosed youth with type 1 diabetes will alter the gut microbiome profile (thus increasing the number of fermenters and SCFA levels), β-cell function and glycemia in humans with T1D. DISCUSSION/SIGNIFICANCE OF IMPACT: Given the unknown long-term effects of immune-modulatory therapy on those at risk for or those diagnosed with T1D, the use of a prebiotic such as HAMS-AB offers a simple, safe, yet inexpensive and tolerated dietary alternative approach to mitigating disease.Item 57437 Effects of Prebiotics on the Gut Microbiome Profile, Beta-cell Function and Immune Markers in Newly-Diagnosed Type 1 Diabetes(Cambridge University Press, 2021) Ismail, Heba M.; Evans-Molina, Carmella; DiMeglio, Linda A.; Pediatrics, School of MedicineABSTRACT IMPACT: The proposed research study will provide critical pilot data on the effect of using the prebiotic (HAMS-AB) on the gut microbiome profile, Beta-cell function and immune markers in humans with T1D. OBJECTIVES/GOALS: The overall objective of this study is to assess how the prebiotic high amylose maize starch that has been acetylated and butyrylated (HAMS-AB) impacts the gut microbiome profile, short chain fatty acid (SCFA) production, glycemia, Beta-cell function/health and immune responses in newly diagnosed youth with type 1 diabetes (T1D). METHODS/STUDY POPULATION: We are performing a pilot randomized cross-over trial. We plan to recruit 12 newly-diagnosed T1D youth with residual Beta-cell function between 12-16 years of age. We will profile the gut microbiome using metagenomics, measure stool SCFA levels using mass spectrometry, assess glycemia using continuous glucose monitoring, assess insulin production using mixed meal tolerance testing, assess Beta-cell stress using proinsulin/C-peptide levels, and test immune responses by examining cytokine levels and frequency, phenotype and function of T cell markers in peripheral blood. RESULTS/ANTICIPATED RESULTS: Thus far, we have enrolled 3 participants, 1 has completed the study. Baseline assessments indicate that we have technical feasibility of performing the above studies and measurements. Recruitment and enrollment are ongoing. We hypothesize that the use of HAMS-AB in newly diagnosed youth with T1D will (i) improve the gut microbiome profile, (ii) increase SCFA production, (iii) improve overall glycemia and Beta-cell function and (iv) modulate the immune system and mitigate autoimmunity. DISCUSSION/SIGNIFICANCE OF FINDINGS: Given the failure to develop a cure for T1D despite multiple completed intervention studies and the unknown long-term effects of immune-modulatory therapy on those at risk for or those diagnosed with T1D, prebiotics such as HAMS-AB may offer a simple, safe, yet inexpensive and tolerated dietary alternative approach to mitigating disease.Item Associations of HbA1c with the Timing of C‐peptide Responses during the Oral Glucose Tolerance Test at the Diagnosis of Type 1 Diabetes(Wiley, 2019) Ismail, Heba M.; Evans-Molina, Carmella; DiMeglio, Linda A.; Becker, Dorothy J.; Libman, Ingrid; Sims, Emily K.; Boulware, David; Herold, Kevan C.; Rafkin, Lisa; Skyler, Jay; Cleves, Mario A.; Palmer, Jerry; Sosenko, Jay; Pediatrics, School of MedicineBackground In new onset type 1 diabetes (T1D), overall C‐peptide measures such as area under the curve (AUC) C‐peptide and peak C‐peptide are useful for estimating the extent of β‐cell dysfunction, and for assessing responses to intervention therapy. However, measures of the timing of C‐peptide responsiveness could have additional value. Objectives We assessed the contribution of the timing of C‐peptide responsiveness during oral glucose tolerance tests (OGTTs) to HbA1c variation at T1D diagnosis. Methods We analyzed data from 85 individuals <18 years with OGTTs and HbA1c measurements at diagnosis. Overall [AUC and peak C‐peptide] and timing measures [30‐0 minute C‐peptide (early); 60 to 120 minute C‐peptide sum‐30 minutes (late); 120/30 C‐peptide; time to peak C‐peptide] were utilized. Results At diagnosis, the mean (±SD) age was 11.2±3.3 years, BMI‐z was 0.4±1.1, 51.0% were male and the HbA1c was 43.54±8.46 mmol/mol (6.1±0.8%). HbA1c correlated inversely with the AUC C‐peptide (p<0.001), peak C‐peptide (p<0.001), early and late C‐peptide responses (p<0.001 each), and 120/30 C‐peptide (p<0.001). Those with a peak C‐peptide occurring at ≤60 minutes had higher HbA1c values than those with peaks later (p=0.003). HbA1c variance was better explained with timing measures added to regression models (R2=11.6% with AUC C‐peptide alone; R2=20.0% with 120/30 C‐peptide added; R2=13.7% with peak C‐peptide alone, R2=20.4% with timing of the peak added). Similar associations were seen between the 2‐hr glucose and the C‐peptide measures. Conclusions These findings show that the addition of timing measures of C‐peptide responsiveness better explains HbA1c variation at diagnosis than standard measures alone.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 Changes in immunofluorescence staining during islet regeneration in a cystic fibrosis-related diabetes (CFRD) ferret model(Taylor & Francis, 2024) Mohammed, Sawash M.; Bone, Robert N.; Aquino, Jacqueline Del Carmen; Mirmira, Raghavendra G.; Evans-Molina, Carmella; Ismail, Heba M.; Anatomy, Cell Biology and Physiology, School of MedicineBackground: Knockout (KO) ferrets with the cystic fibrosis transmembrane conductance regulator (CFTR) exhibit distinct phases of dysglycemia and pancreatic remodeling prior to cystic fibrosis-related diabetes (CFRD) development. Following normoglycemia during the first month of life (Phase l), hyperglycemia occurs during the subsequent 2 months (Phase Il) with decreased islet mass, followed by a period of near normoglycemia (Phase Ill) in which the islets regenerate. We aimed to characterize islet hormone expression patterns across these Phases. Methods: Immunofluorescence staining per islet area was performed to characterize islet hormone expression patterns in age matched CFTR KO and wild type (WT) ferrets, focusing on the first three phases. Results: In Phase I, insulin staining intensity was higher in CF (p < 0.01) than WT but decreased in Phase III (p < 0.0001). Glucagon was lower in CF during Phases I and increased in Phase III, while proinsulin decreased (p < 0.0001) Phases II and III. CF sections showed lower proinsulin-to-insulin ratio in Phase I (p < 0.01) and in Phase III (p < 0.05) compared to WT. Conversely, glucagon-to-insulin ratio was lower in CF in Phase I (p < 0.0001) but increased in Phase III (p < 0.0001). Mender's coefficient overlap showed higher overlap of insulin over proinsulin in CF sections in Phase II (p < 0.001) and Phase III (p < 0.0001) compared to WT. Mender's coefficient rate was higher in CF sections during Phase II (p < 0.001). Conclusion: CF ferret islets revealed significant immunofluorescent staining changes compared to WT during various phases of disease, providing insights into CRFD pathophysiology.Item 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 Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes(Springer, 2024-09) Phillip, Moshe; Achenbach, Peter; Addala, Ananta; Albanese-O'Neill, Anastasia; Battelino, Tadej; Bell, Kirstine J.; Besser, Rachel E. J.; Bonifacio, Ezio; Colhoun, Helen M.; Couper, Jennifer J.; Craig, Maria E.; Danne, Thomas; de Beaufort, Carine; Dovc, Klemen; Driscoll, Kimberly A.; Dutta, Sanjoy; Ebekozien, Osagie; Elding Larsson, Helena; Feiten, Daniel J.; Frohnert, Brigitte I.; Gabbay, Robert A.; Gallagher, Mary P.; Greenbaum, Carla J.; Griffin, Kurt J.; Hagopian, William; Haller, Michael J.; Hendrieckx, Christel; Hendriks, Emile; Holt, Richard I. G.; Hughes, Lucille; Ismail, Heba M.; Jacobsen, Laura M.; Johnson, Suzanne B.; Kolb, Leslie E.; Kordonouri, Olga; Lange, Karin; Lash, Robert W.; Lernmark, Åke; Libman, Ingrid; Lundgren, Markus; Maahs, David M.; Marcovecchio, M. Loredana; Mathieu, Chantal; Miller, Kellee M.; O'Donnell, Holly K.; Oron, Tal; Patil, Shivajirao P.; Pop-Busui, Rodica; Rewers, Marian J.; Rich, Stephen S.; Schatz, Desmond A.; Schulman-Rosenbaum, Rifka; Simmons, Kimber M.; Sims, Emily K.; Skyler, Jay S.; Smith, Laura B.; Speake, Cate; Steck, Andrea K.; Thomas, Nicholas P. B.; Tonyushkina, Ksenia N.; Veijola, Riitta; Wentworth, John M.; Wherrett, Diane K.; Wood, Jamie R.; Ziegler, Anette-Gabriele; DiMeglio, Linda A.; Pediatrics, School of MedicineGiven the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.Item Correction to: Consensus guidance for monitoring individuals with islet autoantibody‑positive pre‑stage 3 type 1 diabetes(Springer, 2024) Phillip, Moshe; Achenbach, Peter; Addala, Ananta; Albanese-O'Neill, Anastasia; Battelino, Tadej; Bell, Kirstine J.; Besser, Rachel E. J.; Bonifacio, Ezio; Colhoun, Helen M.; Couper, Jennifer J.; Craig, Maria E.; Danne, Thomas; de Beaufort, Carine; Dovc, Klemen; Driscoll, Kimberly A.; Dutta, Sanjoy; Ebekozien, Osagie; Elding Larsson, Helena; Feiten, Daniel J.; Frohnert, Brigitte I.; Gabbay, Robert A.; Gallagher, Mary P.; Greenbaum, Carla J.; Griffin, Kurt J.; Hagopian, William; Haller, Michael J.; Hendrieckx, Christel; Hendriks, Emile; Holt, Richard I. G.; Hughes, Lucille; Ismail, Heba M.; Jacobsen, Laura M.; Johnson, Suzanne B.; Kolb, Leslie E.; Kordonouri, Olga; Lange, Karin; Lash, Robert W.; Lernmark, Åke; Libman, Ingrid; Lundgren, Markus; Maahs, David M.; Marcovecchio, M. Loredana; Mathieu, Chantal; Miller, Kellee M.; O'Donnell, Holly K.; Oron, Tal; Patil, Shivajirao P.; Pop-Busui, Rodica; Rewers, Marian J.; Rich, Stephen S.; Schatz, Desmond A.; Schulman-Rosenbaum, Rifka; Simmons, Kimber M.; Sims, Emily K.; Skyler, Jay S.; Smith, Laura B.; Speake, Cate; Steck, Andrea K.; Thomas, Nicholas P. B.; Tonyushkina, Ksenia N.; Veijola, Riitta; Wentworth, John M.; Wherrett, Diane K.; Wood, Jamie R.; Ziegler, Anette-Gabriele; DiMeglio, Linda A.; Pediatrics, School of MedicineItem COVID-19 Pandemic Effects on Caregivers of Youth With Type 1 Diabetes: Stress and Self-Efficacy(American Diabetes Association, 2022-03-11) Ismail, Heba M.; Hand, Breanne L.; DiMeglio, Linda A.; Oyetoro, Rebecca; Soni, Priya Y.; Adams, Janey; Westen, Sarah; Driscoll, Kimberly A.; Albanese-O’Neill, Anastasia; Pediatrics, School of MedicineBackground Little is known about the coronavirus disease 2019 (COVID-19) pandemic’s psychological effects on caregivers of children with type 1 diabetes. Objective This study aimed to investigate the experience of caregivers of youth with type 1 diabetes during the COVID-19 pandemic. Methods A 49-item questionnaire using a 5-point Likert scale and open-response questions was distributed via e-mail and type 1 diabetes–related social media platforms from 4 May to 22 June 2020. Quantitative data were analyzed using SPSS v.25 statistical software. Descriptive statistics were used. Relationships were compared using Pearson correlation. Qualitative data were coded and categorized. Results A total of 272 caregivers participated (mean ± SD respondent age 42.1 ± 7.8 years; 94.5% females; 81.3% with college degree or higher; 52.6% with annual income >$99,000; 80.1% with private insurance). The mean ± SD age of caregivers’ children with type 1 diabetes was 11.0 ± 4.1 years, and their mean ± SD diabetes duration was 4.2 ± 3.5 years. Participants reported being diagnosed with or knowing someone with COVID-19 (24.6%), increased stress (71.9%), job loss (10.3%), and financial difficulty (26.8%) as a result of the pandemic. General self-efficacy scores were high (mean ± SD 16.2 ± 2.6, range 8–20) and significantly correlated with COVID-19–related self-efficacy (mean ± SD 12.6 ± 2.1; R = 0.394, P <0.001) and type 1 diabetes self-efficacy during COVID-19 (mean ± SD 17.1 ± 2.5; R = 0.421, P <0.001). Conclusion Despite reporting high overall self-efficacy, caregivers of children with type 1 diabetes reported greater overall stress and challenges during the pandemic. Health care providers should be prepared to provide families with specific social and mental health support.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.