scholarly journals Cognitive Function in Adolescents and Young Adults With Youth-Onset Type 1 Versus Type 2 Diabetes: The SEARCH for Diabetes in Youth Study

2021 ◽  
Author(s):  
Allison L. B. Shapiro ◽  
Dana Dabelea ◽  
Jeanette M. Stafford ◽  
Ralph D’Agostino Jr ◽  
Catherine Pihoker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Depressive Symptoms ◽  
Receptive Language ◽  
Diabetes Type ◽  
Height Ratio ◽  
Cognitive Health ◽  
Diabetes In Youth ◽  
Waist Height Ratio

<u>Objective:</u> Poor cognition has been observed in children and adolescents with youth-onset type 1 (T1D) and type 2 diabetes (T2D) compared to non-diabetic controls. Differences in cognition between youth-onset T1D and T2D, however, are not known. Thus, using data from SEARCH for Diabetes in Youth, a multicenter, observational cohort study, we tested the association between diabetes type and cognitive function in adolescents and young adults with T1D (n=1,095) and T2D (n=285). <u>Research Design and Methods:</u> Cognition was assessed via the National Institutes of Health Toolbox Cognition Battery and age-corrected composite Fluid Cognition scores used as the primary outcome. Confounder-adjusted linear regression models were run. Model 1 included diabetes type and clinical site. Model 2 additionally included sex, race/ethnicity, waist-height ratio, diabetes duration, depressive symptoms, glycemic control, any hypoglycemic episode in the past year, parental education, and household income. Model 3 additionally included Picture Vocabulary score, a measure of receptive language and crystallized cognition. <u>Results:</u> Having T2D was significantly associated with lower fluid cognitive scores before adjustment for confounders (Model 1; p <0.001). This association was attenuated to non-significance with the addition of <i>a priori</i> confounders (Model 2; p= 0.06) and Picture Vocabulary scores (Model 3; p = 0.49). Receptive language, waist-height ratio, and depressive symptoms remained significant in the final model (p<0.01 for all, respectively). <u>Conclusions:</u> These data suggest that while youth with T2D have worse fluid cognition than youth with T1D, these differences are accounted for by differences in crystallized cognition (receptive language), central adiposity, and mental health. These potentially modifiable factors are also independently associated with fluid cognitive health, regardless of diabetes type. Future studies of cognitive health in people with youth-onset diabetes should focus on investigating these significant factors.

scholarly journals Cognitive Function in Adolescents and Young Adults With Youth-Onset Type 1 Versus Type 2 Diabetes: The SEARCH for Diabetes in Youth Study

2021 ◽  
Author(s):  
Allison L. B. Shapiro ◽  
Dana Dabelea ◽  
Jeanette M. Stafford ◽  
Ralph D’Agostino Jr ◽  
Catherine Pihoker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Depressive Symptoms ◽  
Receptive Language ◽  
Diabetes Type ◽  
Height Ratio ◽  
Cognitive Health ◽  
Diabetes In Youth ◽  
Waist Height Ratio

<u>Objective:</u> Poor cognition has been observed in children and adolescents with youth-onset type 1 (T1D) and type 2 diabetes (T2D) compared to non-diabetic controls. Differences in cognition between youth-onset T1D and T2D, however, are not known. Thus, using data from SEARCH for Diabetes in Youth, a multicenter, observational cohort study, we tested the association between diabetes type and cognitive function in adolescents and young adults with T1D (n=1,095) and T2D (n=285). <u>Research Design and Methods:</u> Cognition was assessed via the National Institutes of Health Toolbox Cognition Battery and age-corrected composite Fluid Cognition scores used as the primary outcome. Confounder-adjusted linear regression models were run. Model 1 included diabetes type and clinical site. Model 2 additionally included sex, race/ethnicity, waist-height ratio, diabetes duration, depressive symptoms, glycemic control, any hypoglycemic episode in the past year, parental education, and household income. Model 3 additionally included Picture Vocabulary score, a measure of receptive language and crystallized cognition. <u>Results:</u> Having T2D was significantly associated with lower fluid cognitive scores before adjustment for confounders (Model 1; p <0.001). This association was attenuated to non-significance with the addition of <i>a priori</i> confounders (Model 2; p= 0.06) and Picture Vocabulary scores (Model 3; p = 0.49). Receptive language, waist-height ratio, and depressive symptoms remained significant in the final model (p<0.01 for all, respectively). <u>Conclusions:</u> These data suggest that while youth with T2D have worse fluid cognition than youth with T1D, these differences are accounted for by differences in crystallized cognition (receptive language), central adiposity, and mental health. These potentially modifiable factors are also independently associated with fluid cognitive health, regardless of diabetes type. Future studies of cognitive health in people with youth-onset diabetes should focus on investigating these significant factors.

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 Validity of ICD-10-CM codes for determination of diabetes type for persons with youth-onset type 1 and type 2 diabetes

2019 ◽  
Vol 7 (1) ◽  
pp. e000547 ◽  
Author(s):  
Gloria C Chi ◽  
Xia Li ◽  
Sara Y Tartof ◽  
Jeff M Slezak ◽  
Corinna Koebnick ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Predictive Value ◽  
Gold Standard ◽  
Characteristic Curve ◽  
Diabetes Type ◽  
Predictive Values ◽  
Icd 10

ObjectiveDiagnosis codes might be used for diabetes surveillance if they accurately distinguish diabetes type. We assessed the validity ofInternational Classification of Disease, 10th Revision, Clinical Modification(ICD-10-CM) codes to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) among health plan members with youth-onset (diagnosis age <20 years) diabetes.Research design and methods. Diabetes case identification and abstraction of diabetes type was done as part of the SEARCH for Diabetes in Youth Study. The gold standard for diabetes type is the physician-assigned diabetes type documented in patients’ medical records. Using all healthcare encounters with ICD-10-CM codes for diabetes, we summarized codes within each encounter and determined diabetes type using percent of encounters classified as T2DM. We chose 50% as the threshold from a receiver operating characteristic curve because this threshold yielded the largest Youden’s index. Persons with ≥50% T2DM-coded encounters were classified as having T2DM. Otherwise, persons were classified as having T1DM. We calculated sensitivity, specificity, positive and negative predictive values, and accuracy overall and by demographic characteristics.ResultsAccording to the gold standard, 1911 persons had T1DM and 652 persons had T2DM (mean age (SD): 19.1 (6.5) years). We obtained 90.6% (95% CI 88.4% to 92.9%) sensitivity, 96.3% (95% CI 95.4% to 97.1%) specificity, 89.3% (95% CI 86.9% to 91.6%) positive predictive value, 96.8% (95% CI 96.0% to 97.6%) negative predictive value, and 94.8% (95% CI 94.0% to 95.7%) accuracy for discriminating T2DM from T1DM.ConclusionsICD-10-CM codes can accurately classify diabetes type for persons with youth-onset diabetes, showing promise for rapid, cost-efficient diabetes surveillance.

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