24-h energy expenditure in people with type 1 diabetes: impact on equations for clinical estimation of energy expenditure.

BACKGROUND/OBJECTIVES: Type 1 diabetes (T1D) is associated with an increase in resting metabolic rate (RMR), but the impact of T1D on other components of 24-h energy expenditure (24-h EE) is not known. Also, there is a lack of equations to estimate 24-h EE in patients with T1D. The aims of this analysis were to compare 24-h EE and its components in young adults with T1D and healthy controls across the spectrum of body mass index (BMI) and derive T1D-specific equations from clinical variables. SUBJECTS/METHODS: Thirty-three young adults with T1D diagnosed ≥1 year prior and 33 healthy controls matched for sex, age and BMI were included in this analysis. We measured 24-h EE inside a whole room indirect calorimeter (WRIC) and body composition with dual x-ray absorptiometry. RESULTS: Participants with T1D had significantly higher 24-h EE than healthy controls (T1D = 2047 ± 23 kcal/day vs control= 1908 ± 23 kcal/day; P < 0.01). We derived equations to estimate 24-h EE with both body composition (fat free mass + fat mass) and anthropometric (weight + height) models, which provided high coefficients of determination (R2 = 0.912 for both). A clinical model that did not incorporate spontaneous physical activity yielded high coefficients of determination as well (R2 = 0.897 and R2 = 0.880 for body composition and anthropometric models, respectively). CONCLUSION: These results confirm that young adults with established T1D have increased 24-h EE relative to controls without T1D. The derived equations from clinically available variables can assist clinicians with energy prescriptions for weight management in patients with T1D.

Diabetes Stigma and Clinical Outcomes in Adolescents and Young Adults: The SEARCH for Diabetes in Youth Study.

OBJECTIVE: To examine the association between diabetes stigma and HbA1c, treatment plan and acute and chronic complications in adolescents and young adults (AYAs) with type 1 or type 2 diabetes. RESEARCH DESIGN AND METHODS: The SEARCH for Diabetes in Youth study is a multicenter cohort study that collected questionnaire, laboratory, and physical examination data about AYAs with diabetes diagnosed in childhood. A five-question survey assessed frequency of perceived diabetes-related stigma, generating a total diabetes stigma score. We used multivariable linear modeling, stratified by diabetes type, to examine the association of diabetes stigma with clinical factors, adjusting for sociodemographic characteristics, clinic site, diabetes duration, health insurance, treatment plan, and HbA1c. RESULTS: Of 1,608 respondents, 78% had type 1 diabetes, 56% were female, and 48% were non-Hispanic White. The mean (SD) age at study visit was 21.7 (5.1) years (range, 10-24.9). The mean (SD) HbA1c was 9.2% (2.3%; 77 mmol/mol [2.0 mmol/mol]). Higher diabetes stigma scores were associated with female sex and higher HbA1c (P < 0.01) for all participants. No significant association between diabetes stigma score and technology use was observed. In participants with type 2 diabetes, higher diabetes stigma scores were associated with insulin use (P = 0.04). Independent of HbA1c, higher diabetes stigma scores were associated with some acute complications for AYAs with type 1 diabetes and some chronic complications for AYAs with type 1 or type 2 diabetes. CONCLUSIONS: Diabetes stigma in AYAs is associated with worse diabetes outcomes and is important to address when providing comprehensive diabetes care.

A longitudinal assessment of diabetes autoantibodies in the SEARCH for diabetes in youth study.

To assess changes in diabetes autoantibodies (DAs) over time in children and young adults with diabetes and determine whether observed changes were associated with demographic characteristics, clinical parameters and diabetes complications. Participants had DAs measured at baseline (10.3 ± 7.1 months after diabetes diagnosis) and at 12, 24 months and ≥5 years after the baseline measurement. At the ≥5-year follow-up, the presence of diabetes complications was assessed. We examined the associations between change in number of positive DAs and changes in individual DA status with the participants' characteristics and clinical parameters over time. Out of 4179 participants, 62% had longitudinal DA data and 51% had complications and longitudinal DA data. In participants with ≥1 baseline positive DA (n = 1699), 83.4% remained positive after 7.3 ± 2.3 years duration of diabetes. Decrease in number of positive DAs was associated with longer diabetes duration (p = 0.003 for 1 baseline positive DA; p < 0.001 for 2 baseline positive DAs) and younger age at diagnosis (p < 0.001 for 2 baseline positive DAs). No associations were found between change in number of positive DAs in participants with ≥1 baseline positive DA (n = 1391) and HbA1c, insulin dose, acute, or chronic complications after 7.7 ± 1.9 years duration of diabetes. DA status likely remains stable in the first 7 years after diabetes diagnosis. Younger age at diabetes diagnosis and longer duration were associated with less persistence of DAs. Measuring DAs after initial presentation may aid in diabetes classification but not likely in predicting the clinical course.

Assessment of a Precision Medicine Analysis of a Behavioral Counseling Strategy to Improve Adherence to Diabetes Self-management Among Youth: A Post Hoc Analysis of the FLEX Trial.

Importance: The Flexible Lifestyles Empowering Change (FLEX) trial, an 18-month randomized clinical trial testing an adaptive behavioral intervention in adolescents with type 1 diabetes, showed no overall treatment effect for its primary outcome, change in hemoglobin A1c (HbA1c) percentage of total hemoglobin, but demonstrated benefit for quality of life (QoL) as a prespecified secondary outcome. Objective: To apply a novel statistical method for post hoc analysis that derives an individualized treatment rule (ITR) to identify FLEX participants who may benefit from intervention based on changes in HbA1c percentage (primary outcome), QoL, and body mass index z score (BMIz) (secondary outcomes) during 18 months. Design, Setting, and Participants: This multisite clinical trial enrolled 258 adolescents aged 13 to 16 years with type 1 diabetes for 1 or more years, who had literacy in English, HbA1c percentage of total hemoglobin from 8.0% to 13.0%, a participating caregiver, and no other serious medical conditions. From January 5, 2014, to April 4, 2016, 258 adolescents were recruited. The post hoc analysis excluded adolescents missing outcome measures at 18 months (2 participants [0.8%]) or continuous glucose monitoring data at baseline (40 participants [15.5%]). Data were analyzed from April to December 2018. Interventions: The FLEX intervention included a behavioral counseling strategy that integrated motivational interviewing and problem-solving skills training to increase adherence to diabetes self-management. The control condition entailed usual diabetes care. Main Outcomes and Measures: Subgroups of FLEX participants were derived from an ITR estimating which participants would benefit from intervention, which would benefit from control conditions, and which would be indifferent. Multiple imputation by chained equations and reinforcement learning trees were used to estimate the ITR. Subgroups based on ITR pertaining to changes during 18 months in 3 univariate outcomes (ie, HbA1c percentage, QoL, and BMIz) and a composite outcome were compared by baseline demographic, clinical, and psychosocial characteristics. Results: Data from 216 adolescents in the FLEX trial were reanalyzed (166 [76.9%] non-Hispanic white; 108 teenaged girls [50.0%]; mean [SD] age, 14.9 [1.1] years; mean [SD] diabetes duration, 6.3 [3.7] years). For the univariate outcomes, a large proportion of FLEX participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization, and were assigned to the muted group (HbA1c: 105 participants [48.6%]; QoL: 63 participants [29.2%]; BMIz: 136 participants [63.0%]). Regarding the BMIz univariate outcome, mean baseline BMIz of participants assigned to the muted group was lower than that of those assigned to the intervention and control groups (muted vs intervention: mean difference, 0.48; 95% CI, 0.21 to 0.75; P = .002; muted vs control: mean difference, 0.86; 95% CI, 0.61 to 1.11; P < .001); this group also had a higher proportion of individuals with underweight or normal weight using weight status cutoffs (95 [69.9%] in muted group vs 24 [54.6%] in intervention group and 11 [30.6%] in control group; χ24 = 24.67; P < .001). The approach identified subgroups estimated to benefit based on HbA1c percentage (54 participants [25.0%]), QoL (89 participants [41.2%]), and BMIz (44 participants [20.4%]). Regarding the HbA1c percentage outcome, participants expected to benefit from the intervention did not have significantly higher baseline HbA1c percentages than those expected to benefit from usual care (9.4% vs 9.2%; difference, 0.2%; 95% CI, -0.16% to 0.56%; P = .44). However, participants in the muted group had higher mean HbA1c percentages at baseline than those assigned to the intervention or control groups (muted vs intervention: 9.9% vs 9.4%; difference, 0.5%; 95% CI, 0.13% to 0.89%; P = .02; muted vs control; 9.9% vs 9.2%; difference, 0.7%; 95% CI, 0.34% to 1.08%; P = .001). No significant differences were found between subgroups estimated to benefit in terms of the composite outcome from the FLEX intervention (91 participants [42.1%]) vs usual care (125 participants [57.9%]). Conclusions and Relevance: The precision medicine approach represents a conceptually and analytically novel approach to post hoc subgroup identification. More work is needed to understand markers of positive response to the FLEX intervention. Trial Registration: ClinicalTrial.gov identifier: NCT01286350.

Health care access and glycemic control in youth and young adults with type 1 and type 2 diabetes in South Carolina.

Affordability and geographic accessibility are key health care access characteristics. We used data from 481 youth and young adults (YYA) with diabetes (389 type 1, 92 type 2) to understand the association between health care access and glycemic control as measured by HbA1c values. In multivariate models, YYA with state or federal health insurance had HbA1c percentage values 0.68 higher (P = 0.0025) than the privately insured, and those without insurance 1.34 higher (P < 0.0001). Not having a routine diabetes care provider was associated with a 0.51 higher HbA1c (P = 0.048) compared to having specialist care, but HbA1c did not differ significantly (P = 0.069) between primary vs specialty care. Distance to utilized provider was not associated with HbA1c among YYA with a provider (P = 0.11). These findings underscore the central role of health insurance and indicate a need to better understand the root causes of poorer glycemic control in YYA with state/federal insurance.

Association of Race and Ethnicity With Glycemic Control and Hemoglobin A1c Levels in Youth With Type 1 Diabetes.

IMPORTANCE: Health disparities in the clinical presentation and outcomes among youth with type 1 diabetes exist. Long-term glycemic control patterns in racially/ethnically diverse youth are not well described. OBJECTIVES: To model common trajectories of hemoglobin A1c (HbA1c) among youth with type 1 diabetes and test how trajectory group membership varies by race/ethnicity. DESIGN SETTING AND PARTICIPANTS: Longitudinal cohort study conducted in 5 US locations. The analysis included data from 1313 youths (aged <20 years) newly diagnosed in 2002 through 2005 with type 1 diabetes in the SEARCH for Diabetes in Youth study (mean [SD] age at diabetes onset, 8.9 [4.2] years) who had 3 or more HbA1c study measures during 6.1 to 13.3 years of follow-up. Data were analyzed in 2017. EXPOSURES: Self-reported race/ethnicity. MAIN OUTCOMES AND MEASURES: Hemoglobin A1c trajectories identified through group-based trajectory modeling over a mean (SD) of 9.0 (1.4) years of diabetes duration. Multinomial models studied the association of race/ethnicity with HbA1c trajectory group membership, adjusting for demographic characteristics, clinical factors, and socioeconomic position. RESULTS: The final study sample of 1313 patients was 49.3% female (647 patients) with mean (SD) age 9.7 (4.3) years and mean (SD) disease duration of 9.2 (6.3) months at baseline. The racial/ethnic composition was 77.0% non-Hispanic white (1011 patients), 10.7% Hispanic (140 patients), 9.8% non-Hispanic black (128 patients), and 2.6% other race/ethnicity (34 patients). Three HbA1c trajectories were identified: group 1, low baseline and mild increases (50.7% [666 patients]); group 2, moderate baseline and moderate increases (41.7% [548 patients]); and group 3, moderate baseline and major increases (7.5% [99 patients]). Group 3 was composed of 47.5% nonwhite youths (47 patients). Non-Hispanic black youth had 7.98 higher unadjusted odds (95% CI, 4.42-14.38) than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group; the association remained significant after full adjustment (adjusted odds ratio of non-Hispanic black race in group 3 vs group 1, 4.54; 95% CI, 2.08-9.89). Hispanic youth had 3.29 higher unadjusted odds (95% CI, 1.78-6.08) than non-Hispanic white youth of being in the highest HbA1c trajectory group relative to the lowest HbA1c trajectory group; the association remained significant after adjustment (adjusted odds ratio of Hispanic ethnicity in group 3 vs group 1, 2.24; 95% CI, 1.02-4.92). In stratified analyses, the adjusted odds of nonwhite membership in the highest HbA1c trajectory remained significant among male patients and youth diagnosed at age 9 years or younger, but not female patients and youth who were older than 9 years when they were diagnosed (P for interaction = .04 [sex] and .02 [age at diagnosis]). CONCLUSIONS AND RELEVANCE: There are racial/ethnic differences in long-term glycemic control among youth with type 1 diabetes, particularly among nonwhite male patients and nonwhite youth diagnosed earlier in life.

Understanding antagonism and synergism: A qualitative assessment of weight management in youth with Type 1 diabetes mellitus.

AIMS: No current clinical guidelines focus on weight management in youth with type 1 diabetes mellitus (T1DM). Our aim was to characterize the patient-perceived experience and barriers to weight management in youth with T1DM. METHODS: Participants were recruited from the University of North Carolina (n = 16, 56% female, 60% White, 50% insulin pump users, mean age 14.8 years, mean HbA1c 8.5% (69 mmol/mol)) and the University of Colorado (n = 18, 50% female, 80% white, 53% pump users, mean age 15.3 years, mean HbA1c 9.3% (78 mmol/mol)). Focus groups were stratified by sex and weight status (BMI cutoff = 25). Discussions were guided by a standardized set of questions, audio-taped, transcribed, and analyzed thematically using inductive qualitative methods. RESULTS: Youth with T1DM expressed four interrelated themes of antagonism between type 1 diabetes and weight management: dysregulated appetite, disruption of blood glucose levels associated with changing diet/exercise, hypoglycemia as a barrier to weight loss, and the overwhelming nature of dual management of weight and glycemic control, and two interrelated themes of synergism: improvement in shared, underlying heath behaviors and exercise as a tool for weight and glycemic control. Variation in emphasis of specific thematic elements was greatest across sex. Youth identified five major components of a weight management program for T1DM: intensified glucose management, healthy diet with known carbohydrate content, exercise, individualization and flexibility, and psychosocial and peer support. CONCLUSIONS: There is critical need for personalized, T1DM-specific weight recommendations to overcome disease-specific barriers to weight management in the context of T1DM.

Prevalence of and disparities in barriers to care experienced by youth with type 1 diabetes.

OBJECTIVE: To describe the prevalence of access and process barriers to health care and to examine their relationship to sociodemographic and disease factors in a large and diverse cohort of US youth with type 1 diabetes. STUDY DESIGN: A cross-sectional analysis of 780 youth who participated in the SEARCH for Diabetes in Youth Study and were diagnosed with type 1 diabetes in 2002-2005. Experience of barriers to care was collected from parent report on questionnaires. Analyses included multivariate regression models to predict the presence of specific barriers to care. RESULTS: Overall, 81.7% of participants reported at least one barrier; the 3 most common were costs (47.5%), communication (43.0%), and getting needed information (48.4%). Problems with access to care, not having a regular provider, and receiving contextual care (care that takes into account personal and family context) were associated with poorer glycated hemoglobin levels. Adjusted multivariate models indicated that barriers related to access (regular provider, cost) were most likely for youth with low family income and those without public health insurance. Barriers associated with the processes of quality care (contextual care, communication) were more likely for Hispanic youth and those whose parents had less education. CONCLUSIONS: This study indicates that a large proportion of youth with type 1 diabetes experience substantial barriers to care. Barriers to access and those associated with processes of quality care differed by sociodemographic characteristics. Future investigators should expand knowledge of the systemic processes that lead to disparate outcomes for some youth with diabetes and assess potential solutions.

Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.

We explored the utility of different algorithms for diabetes case identification by using electronic health records. Inpatient and outpatient diagnosis codes, as well as data on laboratory results and dispensing of antidiabetic medications were extracted from electronic health records of Kaiser Permanente Southern California members who were less than 20 years of age in 2009. Diabetes cases were ascertained by using the SEARCH for Diabetes in Youth Study protocol and comprised the "gold standard." Sensitivity, specificity, positive and negative predictive values, accuracy, and the area under the receiver operating characteristic curve (AUC) were compared in 1,000 bootstrapped samples. Based on data from 792,992 youth, of whom 1,568 had diabetes (77.2%, type 1 diabetes; 22.2%, type 2 diabetes; 0.6%, other), case identification accuracy was highest in 75% of bootstrapped samples for those who had 1 or more outpatient diabetes diagnoses or 1 or more insulin prescriptions (sensitivity, 95.9%; positive predictive value, 95.5%; AUC, 97.9%) and in 25% of samples for those who had 2 or more outpatient diabetes diagnoses and 1 or more antidiabetic medications (sensitivity, 92.4%; positive predictive value, 98.4%; AUC, 96.2%). Having 1 or more outpatient type 1 diabetes diagnoses (International Classification of Diseases, Ninth Revision, Clinical Modification, code 250.x1 or 250.x3) had the highest accuracy (94.4%) and AUC (94.1%) for type 1 diabetes; the absence of type 1 diabetes diagnosis had the highest accuracy (93.8%) and AUC (93.6%) for identifying type 2 diabetes. Information in the electronic health records from managed health care organizations provides an efficient and cost-effective source of data for childhood diabetes surveillance.

Diabetes self-management education patterns in a US population-based cohort of youth with type 1 diabetes.

PURPOSE: The purpose of this study is to describe (1) the receipt of diabetes self-management education (DSME) in a large, diverse cohort of US youth with type 1 diabetes (T1DM), (2) the segregation of self-reported DSME variables into domains, and (3) the demographic and clinical characteristics of youth who receive DSME. METHODS: Data are from the US population-based cohort SEARCH for Diabetes in Youth. A cross-sectional analysis was employed using data from 1273 youth <20 years of age at the time of diagnosis of T1DM. Clusters of 19 self-reported DSME variables were derived using factor analysis, and their associations with demographic and clinical characteristics were evaluated using polytomous logistic regression. RESULTS: Nearly all participants reported receiving DSME content consistent with "survival skills" (eg, target blood glucose and what to do for low or high blood glucose), yet gaps in continuing education were identified (eg, fewer than half of the participants reported receiving specific medical nutrition therapy recommendations). Five DSME clusters were explored: receipt of specific MNT recommendations, receipt of diabetes information resources, receipt of clinic visit information, receipt of specific diabetes information, and met with educator or nutritionist. Factor scores were significantly associated with demographic and clinical characteristics, including race/ethnicity, socioeconomic status, and diabetes self-management practices. CONCLUSIONS: Health care providers should work together to address reported gaps in DSME to improve patient care.

Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study.

BACKGROUND: European ecologic studies suggest higher socioeconomic status is associated with higher incidence of type 1 diabetes. Using data from a case-control study of diabetes among racially/ethnically diverse youth in the United States (U.S.), we aimed to evaluate the independent impact of neighborhood characteristics on type 1 diabetes risk. Data were available for 507 youth with type 1 diabetes and 208 healthy controls aged 10-22 years recruited in South Carolina and Colorado in 2003-2006. Home addresses were used to identify Census tracts of residence. Neighborhood-level variables were obtained from 2000 U.S. Census. Multivariate generalized linear mixed models were applied. RESULTS: Controlling for individual risk factors (age, gender, race/ethnicity, infant feeding, birth weight, maternal age, number of household residents, parental education, income, state), higher neighborhood household income (p = 0.005), proportion of population in managerial jobs (p = 0.02), with at least high school education (p = 0.005), working outside the county (p = 0.04) and vehicle ownership (p = 0.03) were each independently associated with increased odds of type 1 diabetes. Conversely, higher percent minority population (p = 0.0003), income from social security (p = 0.002), proportion of crowded households (0.0497) and poverty (p = 0.008) were associated with a decreased odds. CONCLUSIONS: Our study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk. Further research is needed to understand mechanisms underlying the influence of neighborhood context.

A collaborative approach to the recruitment and retention of minority patients with diabetes in rural community health centers.

CONTEXT: Recruiting and retaining minorities from rural, community health centers is a challenge. Collaboration between the researchers and health center personnel and activities to enhance trust may improve results. PURPOSE: To describe recruitment and retention strategies and report results of a 12-month clinical trial of a telemedicine-based diabetes self-management intervention, conducted within a rural community health center. METHODS: Multi-level, multi-staged recruitment process including collaborative planning, data extraction, medical record review, telephone screen, 2 in-person enrollment visits and randomization. Target sample was adults >or=35 years of age with type 2 diabetes, glycated hemoglobin (GHb)>7% with no significant comorbidities to prevent safe participation. Follow-up visits occurred at 6 and 12 months post-randomization. FINDINGS: Of those eligible from medical record review, 65% were African-American(AA)/other and female. Approximately 33% of those successfully contacted by telephone were randomized (n=165), yielding a predominately AA/other (73.9%) and female (74.5%) sample. Among those eligible at the Telephone Screen, a greater percentage of Non-Hispanic Whites (NHW) refused participation than AA/other (54.2% vs 45.8%), while a greater percentage of women refused compared to men (64.4% vs 35.6%). Significant baseline differences were found by ethnicity for education, insurance, transportation and diastolic blood pressure; by gender for income, transportation, weight, and home monitoring of blood glucose. Overall 6 and 12 month retention rates were 90.9% and 82.4%, respectively, with a greater percentage of AA/Other and female participants retained. CONCLUSIONS: Our collaborative approach was successful in recruiting and retaining ethnically diverse study participants who reside in a rural underserved area of South Carolina. Differences in baseline characteristics and retention by ethnicity and gender were found. Successful translational research must allow for a collaborative approach addressing factors at the level of the community health center, key personnel, and patients in an effort to build trust for the purpose of advancing the science of translating research to practice.