Double Diabetes: A Mixture of Type 1 and Type 2 Diabetes in Youth

2009 ◽  
pp. 151-166 ◽  
Author(s):  
Paolo Pozzilli ◽  
Chiara Guglielmi
Keyword(s):  
Type 2 Diabetes ◽  
Double Diabetes ◽  
Diabetes In Youth

279-OR: Cognition in Young Adults with Youth-Onset Type 1 and Type 2 Diabetes: The SEARCH for Diabetes in Youth Study

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 279-OR
Author(s):  
ALLISON SHAPIRO ◽  
DANA DABELEA ◽  
JEANETTE M. STAFFORD ◽  
RALPH DAGOSTINO ◽  
CATHERINE PIHOKER ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Young Adults ◽  
Onset Type ◽  
Youth Study ◽  
Diabetes In Youth

scholarly journals Type 2 Diabetes in Youth

2020 ◽  
Vol 7 ◽  
pp. 2333794X2098134
Author(s):  
Goutham Rao ◽  
Elizabeth T. Jensen
Keyword(s):  
Type 2 Diabetes ◽  
The United States ◽  
Second Line Therapy ◽  
Educational Value ◽  
Line Therapy ◽  
Potential Impact ◽  
Diabetes In Youth ◽  
New Onset

The incidence of type 2 diabetes in children and adolescents in the United States rose at an annual rate of 4.8% between 2002-2003 and 2014-2015. Type 2 diabetes progresses more aggressively to complications than type 1 diabetes. For example, in one large epidemiological study, proliferative retinopathy affected 5.6% and 9.1% of children with type 1 and type 2 diabetes, respectively. Screening begins at age 10 or at onset of puberty, and is recommended among children with a BMI% ≥85 with risk factors such as a family history and belonging to a high risk racial or ethnic or racial group. HbA1C% is preferred for screening as it does not require fasting. As distinguishing between type 1 and type 2 diabetes is not straightforward, all children with new onset disease should undergo autoantibody testing. Results of lifestyle interventions for control of type 2 diabetes have been disappointing, but are still recommended for their educational value and the potential impact upon some participants. There is limited evidence for the benefit of newer mediations. Liraglutide, a GLP-1 agonist, however, has been shown to significantly reduce HbA1C% in one study and is now approved for children. Liraglutide should be considered as second line therapy.

scholarly journals Detection of Diabetes Status and Type in Youth using Electronic Health Records (EHRs): The SEARCH for Diabetes in Youth Study

2020 ◽  
Author(s):  
Brian J. Wells ◽  
Kristin M. Lenoir ◽  
Lynne E. Wagenknecht ◽  
Elizabeth J. Mayer-Davis ◽  
Jean M. Lawrence ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Multinomial Regression ◽  
Rule Based ◽  
Diabetes Status ◽  
Icd 10 ◽  
Youth Study ◽  
Diabetes In Youth

<u>Objective:</u> Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification. <p><u> </u></p> <p><u>Research Design and Methods:</u> Youth (< 20 years) with potential evidence of diabetes (N=8,682) were identified from EHRs at three children’s hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule based algorithm with targeted chart reviews where the algorithm performed poorly.</p> <p> </p> <p><u>Results:</u> The sample included 5308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs 0.936). Type 1 diabetes was classified well by both methods: sensitivity (<i>Se</i>) (>0.95), specificity (<i>Sp</i>) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combining the rule-based method with chart reviews (n=695, 7.9%) of persons predicted to have non type 1 diabetes resulted in perfect PPV for the cases reviewed, while increasing overall accuracy (0.983). The sensitivity, specificity, and PPV for type 2 diabetes using the combined method were >=0.91. </p> <p> </p> <p><u>Conclusions</u>: An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth. </p> <br>

scholarly journals Mortality in youth-onset type 1 and type 2 diabetes: The SEARCH for Diabetes in Youth study

2018 ◽  
Vol 32 (6) ◽  
pp. 545-549 ◽  
Author(s):  
Kristi Reynolds ◽  
Sharon H. Saydah ◽  
Scott Isom ◽  
Jasmin Divers ◽  
Jean M. Lawrence ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Onset Type ◽  
Youth Study ◽  
Diabetes In Youth

scholarly journals The Association of Arsenic Exposure and Metabolism With Type 1 and Type 2 Diabetes in Youth: The SEARCH Case-Control Study

Diabetes Care ◽  
2016 ◽  
Vol 40 (1) ◽  
pp. 46-53 ◽  
Author(s):  
Maria Grau-Pérez ◽  
Chin-Chi Kuo ◽  
Miranda Spratlen ◽  
Kristina A. Thayer ◽  
Michelle A. Mendez ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Case Control Study ◽  
Arsenic Exposure ◽  
Case Control ◽  
Control Study ◽  
Diabetes In Youth

scholarly journals The Many Faces of Diabetes in American Youth: Type 1 and Type 2 Diabetes in Five Race and Ethnic Populations: The SEARCH for Diabetes in Youth Study

Diabetes Care ◽  
2009 ◽  
Vol 32 (Supplement_2) ◽  
pp. S99-S101 ◽  
Author(s):  
E. J. Mayer-Davis ◽  
R. A. Bell ◽  
D. Dabelea ◽  
R. D'Agostino ◽  
G. Imperatore ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Ethnic Populations ◽  
Youth Study ◽  
The Many ◽  
Diabetes In Youth

scholarly journals Trends in Glycemic among Youth with Diabetes: The SEARCH for Diabetes in Youth Study

2022 ◽  
Author(s):  
Faisal S. Malik ◽  
Katherine A. Sauder ◽  
Scott Isom ◽  
Beth A. Reboussin ◽  
Dana Dabelea ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Household Income ◽  
Age Groups ◽  
Race Ethnicity ◽  
Youth Study ◽  
Diabetes In Youth

<b>OBJECTIVES: </b>To describe temporal trends and correlates of glycemic control in youth and young adults (YYA) with youth-onset diabetes. <p><b>RESEARCH DESIGN AND METHODS: </b>The study included 6,492 participants with type 1 or type 2 diabetes from the SEARCH for Diabetes in Youth study. Participant visit data were categorized into time periods 2002-2007, 2008-2013 and 2014-2019, diabetes durations of 1-4, 5-9, and 10+ years, and age groups 1-9, 10-14, 15-19, 20-24, 25+ years. Participants contributed one randomly selected data point to each duration and age group per time period. Multivariable regression models were used to test differences in hemoglobin A<sub>1c</sub> (HbA<sub>1c</sub>) over time by diabetes type. Models were adjusted for site, age, sex, race/ethnicity, household income, health insurance status, insulin regimen and diabetes duration, overall and stratified for each duration and age group.</p> <p><b>RESULTS: </b>Adjusted mean HbA<sub>1c</sub> for the 2014-2019 cohort of YYA with type 1 diabetes was 8.8%±0.04%. YYA with type 1 diabetes in the 10-14, 15-19, and 20-24 age groups from the 2014-2019 cohort had worse glycemic control than the 2002-2007 cohort. Race/ethnicity, household income and treatment regimen predicted differences in glycemic control in 2014-2019 type 1 diabetes participants. Adjusted mean HbA1c was 8.6%±0.12% for 2014-2019 YYA with type 2 diabetes. Participants age 25+ with type 2 diabetes had worse glycemic control relative to the 2008-2013 cohort. Only treatment regimen was associated with differences in glycemic control in type 2 diabetes participants.</p> <p><b>CONCLUSIONS: </b>Despite advances in diabetes technologies, medications, and dissemination of more aggressive glycemic targets, many current YYA are less likely to achieve desired glycemic control relative to earlier cohorts.</p> <br>

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