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BACKGROUND: Screening for islet autoantibodies in children and adolescents identifies individuals who will later develop type 1 diabetes, allowing patient and family education to prevent diabetic ketoacidosis at onset and to enable consideration of preventive therapies. We aimed to assess whether islet autoantibody screening is effective for predicting type 1 diabetes in adolescents aged 10−18 years with an increased risk of developing type 1 diabetes. METHODS: Data were harmonised from prospective studies from Finland (the Diabetes Prediction and Prevention study), Germany (the BABYDIAB study), and the USA (Diabetes Autoimmunity Study in the Young and the Diabetes Evaluation in Washington study). Autoantibodies against insulin, glutamic acid decarboxylase, and insulinoma-associated protein 2 were measured at each follow-up visit. Children who were lost to follow-up or diagnosed with type 1 diabetes before 10 years of age were excluded. Inverse probability censoring weighting was used to include data from remaining participants. Sensitivity and the positive predictive value of these autoantibodies, tested at one or two ages, to predict type 1 diabetes by the age of 18 years were the main outcomes. FINDINGS: Of 20 303 children with an increased type 1 diabetes risk, 8682 were included for the analysis with inverse probability censoring weighting. 1890 were followed up to 18 years of age or developed type 1 diabetes between the ages of 10 years and 18 years, and their median follow-up was 18·3 years (IQR 14·5–20·3). 442 (23·4%) of 1890 adolescents were positive for at least one islet autoantibody, and 262 (13·9%) developed type 1 diabetes. Time from seroconversion to diabetes diagnosis increased by 0·64 years (95% CI 0·34–0·95) for each 1-year increment of diagnosis age (Pearson’s correlation coefficient 0·88, 95% CI 0·50–0·97, p=0·0020). The median interval between the last prediagnostic sample and diagnosis was 0·3 years (IQR 0·1–1·3) in the 227 participants who were autoantibody positive and 6·8 years (1·6–9·9) for the 35 who were autoantibody negative. Single screening at the age of 10 years was 90% (95% CI 86–95) sensitive, with a positive predictive value of 66% (60–72) for clinical diabetes. Screening at two ages (10 years and 14 years) increased sensitivity to 93% (95% CI 89–97) but lowered the positive predictive value to 55% (49–60). INTERPRETATION: Screening of adolescents at risk for type 1 diabetes only once at 10 years of age for islet autoantibodies was highly effective to detect type 1 diabetes by the age of 18 years, which in turn could enable prevention of diabetic ketoacidosis and participation in secondary prevention trials. FUNDING: JDRF International.

Objective To explore posttraumatic growth (PTG) in pediatric patients who have undergone solid organ transplant (SOT) and their caregivers, and to examine potential correlates of PTG. Method Youth and young adults with a history of SOT (heart, kidney, liver) at least 1 month prior to participation and caregivers completed measures of PTG, demographic, and medical factors. In total, 59 youth (M = 12.68 years, SD = 1.91), 21 young adults (M = 19.37, SD = 0.82), and 95 caregivers (M = 37.95 years, SD = 9.37) participated. Results Overall, 67% of youth, 76% of young adults, and 89% of caregivers reported PTG within the medium to very high range. Appreciation of Life was the highest PTG subscale across all groups. Youth and caregiver PTG scores were significantly positively correlated. Religious affiliation and religious coping were positively associated with PTG for caregivers, and the relationship yielded large effect sizes for young adults. Caregivers of children with kidney transplants endorsed lower PTG than other organ types and caregivers of children who had an acute medical condition endorsed greater PTG than caregivers of children who had chronic illness. Conclusion Findings suggest the pediatric SOT experience can yield positive changes such as a greater appreciation of life. Although small sample sizes may have led to reduced power for detecting significant findings for some analyses, results suggest religious, medical, and parent–child relationship factors are likely related to PTG in pediatric SOT and warrant future investigation.

Alan Kunz-Lomelin,1 Michael Killian,2 Brittany H Eghaneyan,3,* Katherine Sanchez1,* 1School of Social Work, University of Texas at Arlington, Arlington, TX, USA; 2College of Social Work, Florida State University, Tallahassee, FL, USA; 3Department of Social Work, California State University, Fullerton, CA, USA*These authors contributed equally to this workCorrespondence: Alan Kunz-Lomelin, 3825 Hunters Trail, Carrollton, TX, 75007, USA, Tel +1 (972) 358-0575, Email alan.kunzlomelin@mavs.uta.eduPurpose: We report on antidepressant (AD) adherence among Hispanics seeking mental health services in a community primary care clinic in Texas as an ancillary outcome from a National Institutes of Health-funded study that collected data on Hispanic AD adherence over a period of two years (February 2016–February 2018). Adherence to AD medications was measured throughout the year-long trial and compared across various demographic characteristics. Since Hispanic individuals often experience stigma and cultural barriers related to AD treatment, we sought to understand what factors may increase the likelihood of non-adherence in this population.Patients and Methods: This study focused on 69 patients who were prescribed AD medications while receiving treatment through an integrated health care model. Adherence was measured with the Patient Adherence Questionnaire, a validated 2-item questionnaire that asks patients about their medication use (missed medications or dosage changes) over the past week. We looked at patient adherence at two key time points (4-weeks and 13-weeks) and utilized logistic regression to identify factors that may increase or decrease the likelihood of adherence in Hispanic patients at a community primary care clinic.Results: Non-adherence to AD medication was 49.3% at 4-weeks and 57% at 13-weeks post-treatment initiation. Logistic regression analyses revealed that age was the only significant predictor of AD non-adherence. As age increased, the likelihood of adherence increased by 12.2% at 13-weeks post-treatment initiation and by 11.1% at 4-weeks post-treatment initiation.Conclusion: The likelihood of Hispanic patients to adhere to AD therapy increased with the age of the patient. Since primary care is the most likely place to be prescribed an AD for Hispanic patients, further research to better understand adherence is essential. Integrated health care interventions designed to help identify, reduce, or eliminate barriers to adherence and improve cultural understanding may help address issues of non-adherence in primary care settings.Keywords: medication, primary care, mental health, antidepressant adherence, social work

Objective: Biomarkers predicting risk of type 1 diabetes (Stage 3) among children with islet autoantibodies are greatly needed to prevent DKA and facilitate prevention therapies. Research Design and Methods: Children in the prospective TEDDY study (n=707) with confirmed diabetes-associated autoantibodies (GADA, IA-2A and/or IAA) and ≥2 HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic (ROC) analyses revealed the prognostic utility for risk of Stage 3 on a relative HbA1c increase from the baseline visit or an OGTT 2-hr plasma glucose (2-hPG). This HbA1c approach was then validated in the TrialNet Pathway to Prevention study (n=1190). Results: A 10% relative HbA1c increase from baseline best marked increased risk of Stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at baseline test, genetic sex, maximum number of autoantibodies and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c >10% increase and these additional factors revealed increased risk of Stage 3 in TEDDY (HR=12.74, 95%CI 8.7-18.6, p>10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY AUC=0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC=0.82). Conclusion: An increase of >10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of Stage 3 in youth with genetic risk and diabetes-associated autoantibodies.

Aims/hypothesis Diabetes diagnosed at Methods We studied 166 infants diagnosed with type 1 diabetes at n = 164) or children with type 1 diabetes diagnosed at 6–24 months of age (n = 152). We assessed the type 1 diabetes genetic risk score (T1D-GRS), islet autoantibodies, C-peptide and clinical features. Results We found an excess of infants with high T1D-GRS: 38% (63/166) had a T1D-GRS >95th centile of healthy individuals, whereas 5% (8/166) would be expected if all were monogenic (p p = 0.2), and had markedly reduced C-peptide levels (median z score −0.89), which were lowest in those diagnosed with type 1 diabetes at z score −1.98). Conclusions/interpretation We provide strong evidence that type 1 diabetes can present before the age of 6 months based on individuals with this extremely early-onset diabetes subtype having the classic features of childhood type 1 diabetes: high genetic risk, autoimmunity and rapid beta cell loss. The early-onset association with reduced birthweight raises the possibility that for some individuals there was reduced insulin secretion in utero. Comprehensive genetic testing for all neonatal diabetes genes remains essential for all individuals diagnosed with diabetes at

OBJECTIVE Biomarkers predicting risk of type 1 diabetes (stage 3) among children with islet autoantibodies are greatly needed to prevent diabetic ketoacidosis and facilitate prevention therapies. RESEARCH DESIGN AND METHODS Children in the prospective The Environmental Determinants of Diabetes in the Young (TEDDY) study (n = 707) with confirmed diabetes-associated autoantibodies (GAD antibody, IA-2A, and/or insulin autoantibody) and two or more HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic curve analyses revealed the prognostic utility for risk of stage 3 on a relative HbA1c increase from the baseline visit or an oral glucose tolerance test (OGTT) 2-h plasma glucose (2-hPG). This HbA1c approach was then validated in the Type 1 Diabetes TrialNet Pathway to Prevention Study (TrialNet) (n = 1,190). RESULTS A 10% relative HbA1c increase from baseline best marked the increased risk of stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at the baseline test, genetic sex, maximum number of autoantibodies, and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c ≥10% increase and these additional factors revealed increased risk of stage 3 in TEDDY (hazard ratio [HR] 12.74, 95% CI 8.7–18.6, P < 0.0001) and TrialNet (HR 5.09, 95% CI 3.3–7.9, P < 0.0001). Furthermore, the composite model using HbA1c ≥10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY area under the curve [AUC] 0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC 0.82). CONCLUSIONS An increase of ≥10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of stage 3 in youth with genetic risk and diabetes-associated autoantibodies.

IA sensitively predict future T1D, but onset often occurs many years after seroconversion. Early diagnosis results in less DKA and milder initial disease, especially in young children. Using the international TEDDY cohort, we asked if quarterly HbA1c testing could predict T1D among pre-pubertal subjects with ≥1 persistent IA (GADA, IA2A, IAA). Mean(SD) age at seroconversion and T1D onset was 43(29) and 73(22) months respectively. Of 8,504 HLA high risk children, 456 had persistent confirmed IA, had undergone at least 3 quarterly HbA1c tests in the prior 12 months, and were not on diabetes drugs or diets. Of these, 104 progressed to T1D and 352 did not. Subjects were split into training (292 total, 62 progressors) and test (164 total, 40 progressors) datasets with similar characteristics. The optimal maximum HbA1c cutpoint within 1 year pre-onset to predict T1D, by ROC analysis, was similar in training and test datasets (p=0.66). For both, HbA1c ≥5.6% gave the best Youden index. Other factors significant for T1D risk in the training dataset included age at HbA1c test, month of HbA1c test, continent and IA2A titer, but not gender, family history, HLA type, seroconversion age, IAA titer nor T1D-related SNP genotypes. Adjusted for significant factors, the optimal HbA1c cutpoint of ≥5.6% for quarterly testing was 91% sensitive, 91% specific, 73% PPV and 98% NPV to predict T1D within 1 year in children with HLA and IA risk. The final predictive model fit both training and test data similarly (p=0.28). Median(SD) time from first HbA1c ≥5.6% to diagnosis was 8.6(4.5) months, enabling monitoring and education for earlier treatment. Quarterly HbA1c surveillance is cost-effective and clinically accessible. A hierarchical plan for pediatric T1D prediction is proposed comprising initial genetic screening, then IA surveillance of those at genetic risk, then HbA1c surveillance of those with IA, and finally close glycemic surveillance of those with HbA1c ≥5.6% to ascertain metabolic onset. Disclosure M. Killian: None. K. Vehik: None. R.R. Little: None. H. Elding Larsson: None. M.J. Haller: None. M. Rewers: None. J. She: None. J. Toppari: None. B. Akolkar: None. J. Krischer: None. W. Hagopian: None.

M. Knip on TEDDY Study Grp -työryhmän jäsen. Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

Saliva offers a relatively noninvasive method for measuring analytes such as cortisol, holding particular promise for use in pediatric populations on a large scale if a rigorous collection protocol is feasible in diverse settings. The Environmental Determinants of Diabetes in the Young study protocol, conducted in centers in the United States, Sweden, Finland, and Germany, used salivary collection to assess cortisol level as a physiologic marker of stress. Saliva was collected using Sorbettes from subjects at 3.5, 4.5, and 5.5 years of age. Parents collected a morning sample, and staff collected pre- and post-blood draw samples. Feasibility was assessed based on protocol completion, adherence with instructions, factors affecting adherence, and sufficiency of saliva sample for cortisol determination. Collection of saliva samples in a diverse pediatric population is feasible. Establishing non-invasive and acceptable methods for collecting physiological parameters of stress will allow better exploration of determinants of health in this important population.

BACKGROUND & AIMS: It is not clear how intake of gluten during infancy affects subsequent risk of celiac disease. We investigated whether gluten intake before 2 years of age increases risk for celiac disease in genetically susceptible children. METHODS: We performed a case-control study of 436 pairs of children, generated from a database of 2525 children with genetic susceptibility to celiac disease in Sweden, matched for sex, birth year, and HLA genotype from September 2004 and February 2010. Children were screened annually for celiac disease using an assay for tissue transglutaminase autoantibodies (tTGA). Intestinal biopsies were collected from children who tested positive for tTGA to confirm the presence of celiac disease. Gluten intake was calculated from 3-day food records collected when the children were 9, 12, 18 and 24 months old. RESULTS: Breastfeeding duration (median 32 weeks) and age at first introduction to gluten (median 22 weeks) did not differ between cases and tTGA-negative children (controls). At the visit prior to tTGA seroconversion, cases reported a larger intake of gluten (median 4.9 g/day) than controls (median 3.9 g/day) (odds ratio [OR], 1.28; 95% confidence interval [CI], 1.13-1.46; P=.0002). More cases consumed amounts of gluten in the upper 3rd tertile (i.e. >5.0 g/day) before they tested positive for tTGA seroconversion than controls (OR, 2.65; 95% CI, 1.70-4.13; P

The objective of this project is to improve the efficiency of truck drivelines through reduction of friction and parasitic losses in transmission and drive axles. Known efficiencies for these products exceeded 97 percent, so the task was not trivial. The project relied on a working relationship between modeling and hardware testing. Modeling was to shorten the development cycle by guiding the selection of materials, processes and strategies. Bench top and fixture tests were to validate the models. Modeling was performed at a world class, high academic level, but in the end, modeling did not impact the hardware development as much as intended. Insights leading to the most significant accomplishments came from bench top and fixture tests and full scale dynamometer tests. A key development in the project was the formulation of the implementation strategy. Five technical elements with potential to minimize friction and parasitic losses were identified. These elements included churning, lubrication, surface roughness, coatings and textures. An interesting fact is that both Caterpillar and Eaton independently converged on the same set of technical elements in formulating their implementation strategies. Exploiting technical elements of the implementation strategy had a positive impact on transmission and drive axle efficiencies. During one dynamometermore » test of an Eaton Best Tech 1 transmission, all three gear ranges tested: Under drive, direct drive and over drive, showed efficiencies greater than 99 percent. Technology boosts to efficiency for transmissions reached 1 percent, while efficiency improvements to drive axle pushed 2 percent. These advancements seem small, but the accomplishment is large considering that these products normally run at greater than 97 percent efficiency. Barriers and risks to implementing these technology elements are clear. Schemes using a low fill sump and spray tubes endanger the gears and bearings by lubricant starvation. Gear coatings have exhibited durability issues, stripping away under conditions less demanding than 750,000 miles in service on the road. Failed coatings compound the problem by contaminating the lubricant with hard particles. Under the most severe conditions, super finished surfaces may polish further, reaching a surface roughness unable to support the critical oil film thickness. Low viscosity and low friction lubricants may not protect the gears and bearings adequately leading to excessive pitting, wear and noise. Additives in low friction oils may not stay in solution or suspended thus settling to the bottom and unavailable when they are needed most. Technical barriers and risks can be overcome through engineering, but two barriers remain formidable: (1) cost of the technology and (2) convincing fleet owners that the technology provides a tangible benefit. Dry sumps lower lubricant operating temperatures so the removal of heat exchangers and hoses and reduced demand on engine cooling systems justify their use. The benefits of surface texturing are varied and remain unproven. Lubricant costs seem manageable, but the cost of super finishing and gear coating are high. These are issues of scale and processing technology. Going across the board with gear super finishing and coating will reduce costs. Pushing the envelope to applications with higher torque and higher power density should drive the adoption of these technologies. Fleet owners are an educated and seasoned lot. Only technology measureable in dollars returned is used on truck fleets. To convince fleet owners of the benefit of these technologies, new precision in measuring fuel efficiency must be introduced. Legislation for a minimum standard in truck miles per gallon would also enable the use of these technologies. Improving the efficiency of truck transmissions and axle will make a noticeable impact on the fuel consumption by heavy vehicles in the United States. However, the greatest benefit will come when all the individual efficiency technologies like hybrid power, aerodynamic fairings, auxiliary power units, super single aluminum wheels, single wide tires and reduced cross section radiators are combined synergistically in a single vehicle, the high efficiency truck.« less