Longitudinal Phenotypes of Type 1 Diabetes in Youth Based on Weight and Glycemia and Their Association With Complications

Kahkoska, Anna R.; Nguyen, Crystal; Adair, Linda A; Aiello, Allison E.; Burger, Kyle S.; Buse, John B.; Dabelea, Dana; Dolan, Lawrence M.; Malik, Faisal; Mottl, Amy K.; Pihoker, Catherine; Reboussin, Beth A.; Sauder, Katherine A.; Kosorok, Michael R.; Mayer‐Davis, Elizabeth J.

doi:10.1210/jc.2019-00734

Cited by 12 publications

(6 citation statements)

References 49 publications

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“…This is an unsupervised nonparametric technique that simultaneously partitions user trajectories from both the social and therapy dimensions into distinct cluster groups. This technique uses a hill-climbing expectation-maximization algorithm, alternating through various initialization methods until convergence is reached [45,47].…”

Section: Identifying User Profilesmentioning

confidence: 99%

Characterizing Use of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: Cluster Analysis

O’Sullivan¹,

Schmaal²,

D’Alfonso³

et al. 2022

JMIR Ment Health

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Background Multicomponent digital interventions offer the potential for tailored and flexible interventions that aim to address high attrition rates and increase engagement, an area of concern in digital mental health. However, increased flexibility in use makes it difficult to determine which components lead to improved treatment outcomes. Objective This study aims to identify user profiles on Horyzons, an 18-month digital relapse prevention intervention for first-episode psychosis that incorporates therapeutic content and social networking, along with clinical, vocational, and peer support, and to examine the predictive value of these user profiles for treatment outcomes. A secondary objective is to compare each user profile with young people receiving treatment as usual (TAU). Methods Participants comprised 82 young people (aged 16-27 years) with access to Horyzons and 84 receiving TAU, recovering from first-episode psychosis. In addition, 6-month use data from the therapy and social networking components of Horyzons were used as features for K-means clustering for joint trajectories to identify user profiles. Social functioning, psychotic symptoms, depression, and anxiety were assessed at baseline and 6-month follow-up. General linear mixed models were used to examine the predictive value of user profiles for treatment outcomes and between each user profile with TAU. Results A total of 3 user profiles were identified based on the following system use metrics: low use, maintained use of social components, and maintained use of both therapy and social components. The maintained therapy and social group showed improvements in social functioning (F2,51=3.58; P=.04), negative symptoms (F2,51=4.45; P=.02), and overall psychiatric symptom severity (F2,50=3.23; P=.048) compared with the other user profiles. This group also showed improvements in social functioning (F1,62=4.68; P=.03), negative symptoms (F1,62=14.61; P<.001), and overall psychiatric symptom severity (F1,63=5.66; P=.02) compared with the TAU group. Conversely, the maintained social group showed increases in anxiety compared with the TAU group (F1,57=7.65; P=.008). No differences were found between the low use group and the TAU group on treatment outcomes. Conclusions Continued engagement with both therapy and social components might be key in achieving long-term recovery. Maintained social use and low use outcomes were broadly comparable with TAU, emphasizing the importance of maintaining engagement for improved treatment outcomes. Although the social network may be a key ingredient to increase sustained engagement, as users engaged with this more consistently, it should be leveraged as a tool to engage young people with therapeutic content to bring about social and clinical benefits.

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Section: Identifying User Profilesmentioning

confidence: 99%

Characterizing Use of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: Cluster Analysis

O’Sullivan¹,

Schmaal²,

D’Alfonso³

et al. 2022

JMIR Ment Health

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“…Higher residual beta cell function confers lower risk of microalbuminuria and retinopathy [12]. Recently, the SEARCH for Diabetes in Youth study showed different patterns of subclinical macrovascular complications according to phenotypes of different combinations of weight and glucose levels in blood [13]. A number of studies have shown influence on complications by candidate genes such as the VEGF gene for retinopathy, the ADIPOQ gene for coronary artery disease and the ELMO1 gene for nephropathy, although replication remains controversial (reviewed in [14]).…”

Section: Heterogeneity Of Diabetes Within Types and Clinical Consequementioning

confidence: 99%

The clinical consequences of heterogeneity within and between different diabetes types

et al. 2020

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Advances in molecular methods and the ability to share large population-based datasets are uncovering heterogeneity within diabetes types, and some commonalities between types. Within type 1 diabetes, endotypes have been discovered based on demographic (e.g. age at diagnosis, race/ethnicity), genetic, immunological, histopathological, metabolic and/or clinical course characteristics, with implications for disease prediction, prevention, diagnosis and treatment. In type 2 diabetes, the relative contributions of insulin resistance and beta cell dysfunction are heterogeneous and relate to demographics, genetics and clinical characteristics, with substantial interaction from environmental exposures. Investigators have proposed approaches that vary from simple to complex in combining these data to identify type 2 diabetes clusters relevant to prognosis and treatment. Advances in pharmacogenetics and pharmacodynamics are also improving treatment. Monogenic diabetes is a prime example of how understanding heterogeneity within diabetes types can lead to precision medicine, since phenotype and treatment are affected by which gene is mutated. Heterogeneity also blurs the classic distinctions between diabetes types, and has led to the definition of additional categories, such as latent autoimmune diabetes in adults, type 1.5 diabetes and ketosis-prone diabetes. Furthermore, monogenic diabetes shares many features with type 1 and type 2 diabetes, which make diagnosis difficult. These challenges to the current classification framework in adult and paediatric diabetes require new approaches. The 'palette model' and the 'threshold hypothesis' can be combined to help explain the heterogeneity within and between diabetes types. Leveraging such approaches for therapeutic benefit will be an important next step for precision medicine in diabetes.

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“…Indeed, overweight and obese youth with type 1 diabetes are more likely to have coexisting hypertension, abnormal lipids, and elevated alanine aminotransferase compared to healthy weight peers ( 110 , 111 ). In the SEARCH registry, youth with obesity and persistently elevated hemoglobin A1c over time had the highest risk for adverse cardiovascular markers and microvascular complications compared to those with either elevated hemoglobin A1c and normal weight or obesity with fairly-well controlled A1c ( 112 ). In clamp studies, our group found that obese youth with type 1 diabetes were more insulin resistant compared to non-obese peers with type 1 diabetes.…”

Section: Added Risk For Micro- and Macrovascular Complicationsmentioning

confidence: 99%

Nutrition and Obesity in the Pathogenesis of Youth-Onset Type 1 Diabetes and Its Complications

2021

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Since the 1980s, there has been a dramatic rise in the prevalence of overweight and obesity in pediatric populations, in large part driven by sedentary lifestyles and changing dietary patterns with more processed foods. In parallel with the rise in pediatric obesity in the general population, the prevalence of overweight and obesity has increased among children and adolescents with type 1 diabetes. Adiposity has been implicated in a variety of mechanisms both potentiating the risk for type 1 diabetes as well as exacerbating long-term complications, particularly cardiovascular disease. Treatment options targeting the unique needs of obese pediatric patients, both before and after diagnosis of type 1 diabetes, are limited. In this review, we discuss the history of the epidemiology of the obesity epidemic in the context of pediatric type 1 diabetes, highlight the possible role of obesity in type 1 diabetes pathogenesis and review the concept of “double diabetes”. The impact of obesity at and after diagnosis will be discussed, including noted differences in clinical and biochemical markers, lipid abnormalities, and long-term cardiovascular complications. Finally, we will review the existing literature on pharmacologic and nutritional interventions as potential treatment strategies for youth with coexisting type 1 diabetes and obesity.

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Longitudinal Phenotypes of Type 1 Diabetes in Youth Based on Weight and Glycemia and Their Association With Complications

Cited by 12 publications

References 49 publications

Characterizing Use of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: Cluster Analysis

Characterizing Use of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: Cluster Analysis

The clinical consequences of heterogeneity within and between different diabetes types

Nutrition and Obesity in the Pathogenesis of Youth-Onset Type 1 Diabetes and Its Complications

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