Quantifying the utility of islet autoantibody levels in the prediction of type 1 diabetes in children.

AIMS/HYPOTHESIS: The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. METHODS: Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. RESULTS: A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. CONCLUSIONS/INTERPRETATION: Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.

Neutralizing Ljungan virus antibodies in children with newly diagnosed type 1 diabetes.

Ljungan virus (LV), a Parechovirus of the Picornavirus family, first isolated from a bank vole at the Ljungan river in Sweden, has been implicated in the risk for autoimmune type 1 diabetes. An assay for neutralizing Ljungan virus antibodies (NLVA) was developed using the original 87-012 LV isolate. The goal was to determine NLVA titres in incident 0-18 years old newly diagnosed type 1 diabetes patients (n=67) and school children controls (n=292) from Jämtland county in Sweden. NLVA were found in 41 of 67 (61 %) patients compared to 127 of 292 (44 %) controls (P=0.009). In the type 1 diabetes patients, NLVA titres were associated with autoantibodies to glutamic acid decarboxylase (GADA) (P=0.023), but not to autoantibodies against insulin (IAA) or islet antigen-2 (IA-2A). The NLVA assay should prove useful for further investigations to determine levels of LV antibodies in patients and future studies to determine a possible role of LV in autoimmune type 1 diabetes.

Serum ghrelin and esophageal and gastric cancer in two cohorts in China.

Ghrelin is a hormone produced in the oxyntic glands of the stomach. Previous work by our group has suggested that serum ghrelin concentrations are inversely associated with gastric and esophageal cancer risk. We measured ghrelin concentrations in the Linxian General Population Nutrition Intervention Trial (NIT), and the Shanghai Women's Health Study (SWHS). In NIT, we analyzed serum samples from 298 esophageal squamous cell carcinoma (ESCC) cases, 518 gastric cardia adenocarcinoma (GCA) cases, 258 gastric noncardia adenocarcinoma (GNCA) cases and 770 subcohort controls (case-cohort). In SWHS, we measured ghrelin in plasma samples from 249 GNCA cases and 498 matched controls (nested case-control). Ghrelin was measured using radioimmunoassay. In NIT and SWHS, low ghrelin concentrations were associated with an increased risk of developing GNCA and GCA. The hazard ratio (HR Q1:Q4 ) for GNCA in NIT was 1.35 (95% CI: 0.89-2.05; p-trend = 0.02); the odds ratio in SWHS was 1.66 (95% CI: 1.02-2.70; p-trend = 0.06). Low ghrelin was associated with a twofold increase of GCA (HR Q1:Q4 = 2.00, 95% CI: 1.45-2.77; p-trend<0.001). In contrast, a lower risk of ESCC (NIT ESCC HR Q1:Q4 = 0.65, 95% CI: 0.45-0.92; p-trend = 0.02) was found in NIT. Low baseline ghrelin concentrations were associated with an increased risk for GNCA and GCA in the NIT and the SWHS. In contrast, low ghrelin concentrations at baseline were associated with a reduced risk of developing ESCC in the NIT. Ghrelin may be an early marker of future cancer risk for developing upper gastrointestinal cancer in regions of high incidence.

Evaluating longitudinal markers under two-phase study designs.

Little attention has been given to the design of efficient studies to evaluate longitudinal biomarkers. Measuring longitudinal markers on an entire cohort is cost prohibitive and, especially for rare outcomes such as cancer, may be infeasible. Thus, methods for evaluation of longitudinal biomarkers using efficient and cost-effective study designs are needed. Case cohort (CCH) and nested case-control (NCC) studies allow investigators to evaluate biomarkers rigorously and at reduced cost, with only a small loss in precision. In this article, we develop estimators of several measures to evaluate the accuracy and discrimination of predicted risk under CCH and NCC study designs. We use double inverse probability weighting (DIPW) to account for censoring and sampling bias in estimation and inference procedures. We study the asymptotic properties of the proposed estimators. To facilitate inference using two-phase longitudinal data, we develop valid resampling-based variance estimation procedures under CCH and NCC. We evaluate the performance of our estimators under CCH and NCC using simulation studies and illustrate them on a NCC study within the hepatitis C antiviral long-term treatment against cirrhosis (HALT-C) clinical trial. Our estimators and inference procedures perform well under CCH and NCC, provided that the sample size at the time of prediction (effective sample size) is reasonable. These methods are widely applicable, efficient, and cost-effective and can be easily adapted to other study designs used to evaluate prediction rules in a longitudinal setting.

Characterization of plasma lipidomics in adolescent subjects with increased risk for type 1 diabetes in the DiPiS cohort.

INTRODUCTION: Type 1 diabetes (T1D) is caused by the destruction of pancreatic islet beta cells resulting in total loss of insulin production. Recent studies have suggested that the destruction may be interrelated to plasma lipids. OBJECTIVES: Specific lipids have previously been shown to be decreased in children who develop T1D before four years of age. Disturbances of plasma lipids prior to clinical diagnosis of diabetes, if true, may provide a novel way to improve prediction, and monitor disease progression. METHODS: A lipidomic approach was utilized to analyze plasma from 67 healthy adolescent subjects (10-15 years of age) with or without islet autoantibodies but all with increased genetic risk for T1D. The study subjects were enrolled at birth in the Diabetes Prediction in Skåne (DiPiS) study and after 10-15 years of follow-up we performed the present cross-sectional analysis. HLA-DRB345, -DRB1, -DQA1, -DQB1, -DPA1 and -DPB1 genotypes were determined using next generation sequencing. Lipidomic profiles were determined using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Lipidomics data were analyzed according to genotype. RESULTS: Variation in levels of several specific phospholipid species were related to level of autoimmunity but not development of T1D. Five glycosylated ceramides were increased in insulin autoantibody (IAA) positive adolescent subjects compared to adolescent subjects without this autoantibody. Additionally, HLA genotypes seemed to influence levels of long chain triacylglycerol (TG). CONCLUSION: Lipidomic profiling of adolescent subjects in high risk of T1D may improve sub-phenotyping in this high risk population.

Parental anxiety after 5 years of participation in a longitudinal study of children at high risk of type 1 diabetes.

AIM: Parents of children participating in screening studies may experience increased levels of anxiety. The aim of this study was to assess parental anxiety levels after 5 years of participation in the Diabetes Prediction in Skåne study. Associations between parental anxiety about their child developing type 1 diabetes and clinical, demographic, and immunological factors were analyzed. METHOD: Mothers and fathers of participating 5-year-old children answered a questionnaire regarding parental anxiety associated with their child's increased risk of type 1 diabetes. Anxiety levels were assessed using the State Anxiety Inventory scale. Data were analyzed using logistic and multinomial regression. RESULTS: Parents of 2088 5-year-old children participated. Both parents answered the questionnaire for 91.2% (n = 1904) of children. In 67.1% of families, neither parent reported being anxious that their child had an increased risk of developing type 1 diabetes. Anxiety was higher in mothers of children positive for autoantibodies (OR 2.21 95% CI 1.41, 3.48, P < .001) and those perceiving their child had a higher risk for type 1 diabetes (2.01; 1.29, 3.13, P = .002). Frequency of worry was associated with parental anxiety (mothers 5.33; 3.48, 8.17, P < .001, fathers 5.27; 3.51, 7.92, P < .001). Having a family member with type 1 diabetes and having lower education level were also associated with increased anxiety. CONCLUSIONS: Diabetes in the family, the child's autoantibody status, education level, frequency of worry and risk perception where associated with higher parental anxiety. These findings add to our understanding of the impact of screening for type 1 diabetes in children on parental anxiety.

Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol.

BACKGROUND: Diabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim). METHODS: iBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes. DISCUSSION: iBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT03716401 ).

Using standard microbiome reference groups to simplify beta-diversity analyses and facilitate independent validation.

Motivation: Comparisons of microbiome communities across populations are often based on pairwise distance measures (beta-diversity). Standard analyses (principal coordinate plots, permutation tests, kernel methods) require access to primary data if another investigator wants to add or compare independent data. We propose using standard reference measurements to simplify microbiome beta-diversity analyses, to make them more transparent, and to facilitate independent validation and comparisons across studies. Results: Using stool and nasal reference sets from the Human Microbiome Project (HMP), we computed mean distances (actually Bray-Curtis or Pearson correlation dissimilarities) to each reference set for each new sample. Thus, each new sample has two mean distances that can be plotted and analyzed with classical statistical methods. To test the approach, we studied independent (not reference) HMP subjects. Simple Hotelling tests demonstrated statistically significant differences in mean distances to reference sets between all pairs of body sites (stool, skin, nasal, saliva and vagina) at the phylum, class, order, family and genus levels. Using the distance to a single reference set was usually sufficient, but using both reference sets always worked well. The use of reference sets simplifies standard analyses of beta-diversity and facilitates the independent validation and combining of such data because others can compute distances to the same reference sets. Moreover, standard statistical methods for survival analysis, logistic regression and other procedures can be applied to vectors of mean distances to reference sets, thereby greatly expanding the potential uses of beta-diversity information. More work is needed to identify the best reference sets for particular applications. Availability and implementation: https://github.com/NCI-biostats/microbiome-fixed-reference. Supplementary information: Supplementary data are available at Bioinformatics online.

Inference for case-control studies with incident and prevalent cases.

We propose and study a fully efficient method to estimate associations of an exposure with disease incidence when both, incident cases and prevalent cases, i.e., individuals who were diagnosed with the disease at some prior time point and are alive at the time of sampling, are included in a case-control study. We extend the exponential tilting model for the relationship between exposure and case status to accommodate two case groups, and correct for the survival bias in the prevalent cases through a tilting term that depends on the parametric distribution of the backward time, i.e., the time from disease diagnosis to study enrollment. We construct an empirical likelihood that also incorporates the observed backward times for prevalent cases, obtain efficient estimates of odds ratio parameters that relate exposure to disease incidence and propose a likelihood ratio test for model parameters that has a standard chi-squared distribution. We quantify the changes in efficiency of association parameters when incident cases are supplemented with, or replaced by, prevalent cases in simulations. We illustrate our methods by estimating associations of single nucleotide polymorphisms (SNPs) with breast cancer incidence in a sample of controls, incident and prevalent cases from the U.S. Radiologic Technologists Health Study.

A cross-sectional study of asymptomatic Plasmodium falciparum infection burden and risk factors in general population children in 12 villages in northern Uganda.

BACKGROUND: Plasmodium falciparum malaria is an important cause of morbidity in northern Uganda. This study was undertaken to assess village-, household-, and individual-level risk factors of asymptomatic falciparum malaria in children in 12 villages in northern Uganda. METHODS: Between 10/2011 and 02/2014, 1006 apparently healthy children under 16 years old were enrolled in 12 villages using a stratified, multi-stage, cluster survey design and assessed for P. falciparum malaria infection using the rapid diagnostic test (RDT) and thick film microscopy (TFM), and structured interviewer-administered questionnaires. Associations between weighted P. falciparum malaria prevalence (pfPR), based on RDT, and covariates were estimated as odds ratios and 95% confidence intervals (ORs, 95% CIs) using logistic models accounting for the survey design. RESULTS: Among 942 (93.5%) children successfully tested, pfPR was 52.4% by RDT and 32.7% by TFM. Overall pfPR was lower in villages where indoor residual insecticide spray (IRS) was, versus not, implemented (18.4% versus 75.2%, P < 0.0001). However, pfPR was heterogeneous both within IRS (10.6-34.8%) and non-IRS villages (63.6-86.2%). Elevated pfPR was associated with having a sibling who was RDT positive (OR 5.39, 95% CI 2.94-9.90, P = 0.0006) and reporting a fever at enrollment (aOR 4.80, 95% CI 1.94-11.9, P = 0.0094). Decreased pfPR was associated with living in an IRS village (adjusted OR 0.06, 95% CI 0.04-0.07, P < 0.0001), in a household with one (aOR 0.48, 95% CI 0.30-0.76) or more than one child below 5 years (aOR 0.23, 95% CI 0.12-0.44, Ptrend = 0.014), and reporting keeping a goat inside or near the house (aOR 0.42, 95% CI 0.29-0.62, P = 0.0021). CONCLUSIONS: The results show high but heterogeneous pfPR in villages in northern Uganda, confirm significantly decreased pfPR associated with IRS implementation, and suggest significant associations with some household characteristics. Further research is needed to elucidate the factors influencing malaria heterogeneity in villages in Uganda.

On longitudinal prediction with time-to-event outcome: Comparison of modeling options.

Long-term follow-up is common in many medical investigations where the interest lies in predicting patients' risks for a future adverse outcome using repeatedly measured predictors over time. A key quantity is the likelihood of developing an adverse outcome among individuals who survived up to time s given their covariate information up to time s. Simple, yet reliable, methodology for updating the predicted risk of disease progression using longitudinal markers remains elusive. Two main approaches have been considered in the literature. One approach, based on joint modeling (JM) of failure time and longitudinal covariate process (Tsiatis and Davidian, 2004), derives such longitudinal predictive probability from the joint probability of a longitudinal marker and an event at a given time. A second approach, the partly conditional (PC) modeling (Zheng and Heagerty, 2005), directly models the predictive probability conditional on survival up to a landmark time and information accrued by that time. In this article, we propose new PC models for longitudinal prediction that are more flexible than joint modeling and improve the prediction accuracy over existing PC models. We provide procedures for making inference regarding future risk for an individual with longitudinal measures up to a given time. In addition, we conduct simulations to evaluate both JM and PC approaches in order to provide practical guidance on modeling choices. We use standard measures of predictive accuracy adapted to our setting to explore the predictiveness of the two approaches. We illustrate the performance of the two approaches on a dataset from the End Stage Renal Disease Study (ESRDS).

Age and geographic patterns of Plasmodium falciparum malaria infection in a representative sample of children living in Burkitt lymphoma-endemic areas of northern Uganda.

BACKGROUND: Falciparum malaria is an important risk factor for African Burkitt lymphoma (BL), but few studies have evaluated malaria patterns in healthy BL-age children in populations where both diseases are endemic. To obtain accurate current data, patterns of asymptomatic malaria were investigated in northern Uganda, where BL is endemic. METHODS: Between 2011 and 2015, 1150 apparently healthy children under 15 years old were sampled from 100 villages in northern Uganda using a stratified, multi-stage, cluster survey design. Falciparum malaria prevalence (pfPR) was assessed by questionnaire, rapid diagnostic test (RDT) and thick film microscopy (TFM). Weighted pfPR and unadjusted and adjusted associations of prevalence with covariates were calculated using logistic models and survey methods. RESULTS: Based on 1143 children successfully tested, weighted pfPR was 54.8% by RDT and 43.4% by TFM. RDT sensitivity and specificity were 97.5 and 77.8%, respectively, as compared to TFM, because RDT detect malaria antigens, which persist in peripheral blood after clinical malaria, thus results based on RDT are reported. Weighted pfPR increased from 40% in children aged under 2 years to 61.8% in children aged 6-8 years (odds ratio 2.42, 95% confidence interval (CI) 1.26-4.65), then fell slightly to 49% in those aged 12-15 years. Geometric mean parasite density was 1805.5 parasites/µL (95% CI 1344.6-2424.3) among TFM-positive participants, and it was higher in children aged <5 years at 5092.9/µL (95% CI 2892.7-8966.8) and lower in those aged ≥10 years at 983.8/µL (95% CI 472.7-2047.4; P = 0.001). Weighted pfPR was lower in children residing in sub-regions employing indoor residual spraying (IRS) than in those residing in non-IRS sub-regions (32.8 versus 65.7%; OR 0.26, 95% CI 0.14, 0.46). However, pfPR varied both within IRS (3.2-55.3%) and non-IRS sub-regions (29.8-75.8%; Pheterogeneity <0.001). pfPR was inversely correlated with a child's mother's income (P = 0.011) and positively correlated with being enrolled in the wet season (P = 0.076), but sex was irrelevant. CONCLUSIONS: The study observed high but geographically and demographically heterogenous patterns of asymptomatic malaria prevalence among children living in northern Uganda. These results provide important baseline data that will enable precise evaluation of associations between malaria and BL.

Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study.

BACKGROUND: The capacity of electronic health record (EHR) data to guide targeted surveillance in chronic kidney disease (CKD) is unclear. We sought to leverage EHR data for predicting risk of progressing from CKD to end-stage renal disease (ESRD) to help inform surveillance of CKD among vulnerable patients from the healthcare safety-net. METHODS: We conducted a retrospective cohort study of adults (n = 28,779) with CKD who received care within 2 regional safety-net health systems during 1996-2009 in the Western United States. The primary outcomes were progression to ESRD and death as ascertained by linkage with United States Renal Data System and Social Security Administration Death Master files, respectively, through September 29, 2011. We evaluated the performance of 3 models which included demographic, comorbidity and laboratory data to predict progression of CKD to ESRD in conditions commonly targeted for disease management (hypertension, diabetes, chronic viral diseases and severe CKD) using traditional discriminatory criteria (AUC) and recent criteria intended to guide population health management strategies. RESULTS: Overall, 1730 persons progressed to end-stage renal disease and 7628 died during median follow-up of 6.6 years. Performance of risk models incorporating common EHR variables was highest in hypertension, intermediate in diabetes and chronic viral diseases, and lowest in severe CKD. Surveillance of persons who were in the highest quintile of ESRD risk yielded 83-94 %, 74-95 %, and 75-82 % of cases who progressed to ESRD among patients with hypertension, diabetes and chronic viral diseases, respectively. Similar surveillance yielded 42-71 % of ESRD cases among those with severe CKD. Discrimination in all conditions was universally high (AUC ≥0.80) when evaluated using traditional criteria. CONCLUSIONS: Recently proposed discriminatory criteria account for varying risk distribution and when applied to common clinical conditions may help to inform surveillance of CKD in diverse populations.

Evaluating risk of ESRD in the urban poor.

The capacity of risk prediction to guide management of CKD in underserved health settings is unknown. We conducted a retrospective cohort study of 28,779 adults with nondialysis-requiring CKD who received health care in two large safety net health systems during 1996-2009 and were followed for ESRD through September of 2011. We developed and evaluated the performance of ESRD risk prediction models using recently proposed criteria designed to inform population health approaches to disease management: proportion of cases followed and proportion that needs to be followed. Overall, 1730 persons progressed to ESRD during follow-up (median follow-up=6.6 years). ESRD risk for time frames up to 5 years was highly concentrated among relatively few individuals. A predictive model using five common variables (age, sex, race, eGFR, and dipstick proteinuria) performed similarly to more complex models incorporating extensive sociodemographic and clinical data. Using this model, 80% of individuals who eventually developed ESRD were among the 5% of cohort members at the highest estimated risk for ESRD at 1 year. Similarly, a program that followed 8% and 13% of individuals at the highest ESRD risk would have included 80% of those who eventually progressed to ESRD at 3 and 5 years, respectively. In this underserved health setting, a simple five-variable model accurately predicts most cases of ESRD that develop within 5 years. Applying risk prediction using a population health approach may improve CKD surveillance and management of vulnerable groups by directing resources to a small subpopulation at highest risk for progressing to ESRD.

Circulating fibrosis biomarkers and risk of atrial fibrillation: The Cardiovascular Health Study (CHS).

BACKGROUND: Cardiac fibrosis is thought to play a central role in the pathogenesis of atrial fibrillation (AF). Retrospective studies have suggested that circulating fibrosis biomarkers are associated with AF, but prospective studies are limited. METHODS: We measured circulating levels of 2 fibrosis biomarkers, procollagen type III, N-terminal propeptide (PIIINP) and transforming growth factor ß1 among participants of the CHS, a population-based study of older Americans. We used Cox proportional hazards and competing risks models to examine adjusted risk of incident AF over a median follow-up of 8.8 years. RESULTS: Levels of PIIINP were assessed in 2,935 participants, of whom 767 developed AF. Compared with the median PIIINP level (4.45 µg/L), adjusted hazard ratios (95% CIs) were 0.85 (0.72-1.00) at the 10th percentile, 0.93 (0.88-0.99) at the 25th percentile, 1.04 (0.95-1.04) at the 75th percentile, and 1.07 (0.90-1.26) at the 90th. Transforming growth factor ß1 levels, assessed in 1,538 participants with 408 cases of incident AF, were not associated with AF risk. CONCLUSION: In older adults, PIIINP levels were associated with risk of incident AF in a complex manner, with an association that appeared to be positive up to median levels but with little relationship beyond that. Further studies are required to confirm and possibly delineate the mechanism for this relationship.

Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays.

BACKGROUND: Two or more autoantibodies against either insulin (IAA), glutamic acid decarboxylase (GADA), islet antigen-2 (IA-2A) or zinc transporter 8 (ZnT8A) denote stage 1 (normoglycemia) or stage 2 (dysglycemia) type 1 diabetes prior to stage 3 type 1 diabetes. Automated multiplex Antibody Detection by Agglutination-PCR (ADAP) assays in two laboratories were compared to single plex radiobinding assays (RBA) to define threshold levels for diagnostic specificity and sensitivity. METHODS: IAA, GADA, IA-2A and ZnT8A were analysed in 1504 (54% females) population based controls (PBC), 456 (55% females) doctor's office controls (DOC) and 535 (41% females) blood donor controls (BDC) as well as in 2300 (48% females) patients newly diagnosed (1-10 years of age) with stage 3 type 1 diabetes. The thresholds for autoantibody positivity were computed in 100 10-fold cross-validations to separate patients from controls either by maximizing the χ2-statistics (chisq) or using the 98th percentile of specificity (Spec98). Mean and 95% CI for threshold, sensitivity and specificity are presented. FINDINGS: The ADAP ROC curves of the four autoantibodies showed comparable AUC in the two ADAP laboratories and were higher than RBA. Detection of two or more autoantibodies using chisq showed 0.97 (0.95, 0.99) sensitivity and 0.94 (0.91, 0.97) specificity in ADAP compared to 0.90 (0.88, 0.95) sensitivity and 0.97 (0.94, 0.98) specificity in RBA. Using Spec98, ADAP showed 0.92 (0.89, 0.95) sensitivity and 0.99 (0.98, 1.00) specificity compared to 0.89 (0.77, 0.86) sensitivity and 1.00 (0.99, 1.00) specificity in the RBA. The diagnostic sensitivity and specificity were higher in PBC compared to DOC and BDC. INTERPRETATION: ADAP was comparable in two laboratories, both comparable to or better than RBA, to define threshold levels for two or more autoantibodies to stage type 1 diabetes. FUNDING: Supported by The Leona M. and Harry B. Helmsley Charitable Trust (grant number 2009-04078), the Swedish Foundation for Strategic Research (Dnr IRC15-0067) and the Swedish Research Council, Strategic Research Area (Dnr 2009-1039). AL was supported by the DiaUnion collaborative study, co-financed by EU Interreg ÖKS, Capital Region of Denmark, Region Skåne and the Novo Nordisk Foundation.

Possible Relationship between the HLA-DRA1 Intron Haplotype of Three Single-Nucleotide Polymorphisms in Intron 1 of the HLA-DRA1 Gene and Autoantibodies in Children at Increased Genetic Risk for Autoimmune Type 1 Diabetes.

Recently, a haplotype of three single-nucleotide polymorphisms (tri-SNP) in intron 1 of the HLA-DRA1 gene was found to be strongly associated with type 1 diabetes risk in HLA-DR3/3 individuals. The tri-SNP reportedly function as "expression quantitative trait loci," modulating HLA-DR and -DQ expression. The aim was to investigate HLA-DRA1 tri-SNPs in relation to extended HLA class II haplotypes and human peripheral blood cell HLA-DQ cell-surface median fluorescence intensity (MFI), the first-appearing islet autoantibody, and autoimmunity burden. A total of 67 healthy subjects (10-15 y) at increased HLA risk for type 1 diabetes and with (n = 54) or without (n = 13) islet autoantibodies were followed longitudinally in the Diabetes Prediction in Skåne study. Among four tri-SNPs, AGG (n = 67), GCA (n = 47), ACG (n = 11), and ACA (n = 9), HLA-DQ cell-surface MFI on CD4+ T cells was lower in AGG than GCA (p = 0.030) subjects. Cumulative autoimmunity burden was associated with reduced HLA-DQ cell-surface MFI in AGG compared with GCA in CD16+ cells (p = 0.0013), CD4+ T cells (p = 0.0018), and CD8+ T cells (p = 0.016). The results suggest that HLA-DRA1 tri-SNPs may be related to HLA-DQ cell-surface expression and autoimmunity burden.

Long-Term GAD-alum Treatment Effect on Different T-Cell Subpopulations in Healthy Children Positive for Multiple Beta Cell Autoantibodies.

Objective: The objective of this study was to explore whether recombinant GAD65 conjugated hydroxide (GAD-alum) treatment affected peripheral blood T-cell subpopulations in healthy children with multiple beta cell autoantibodies. Method: The Diabetes Prevention-Immune Tolerance 2 (DiAPREV-IT 2) clinical trial enrolled 26 children between 4 and 13 years of age, positive for glutamic acid decarboxylase autoantibody (GADA) and at least one other autoantibody (insulin, insulinoma antigen-2, or zinc transporter 8 autoantibody (IAA, IA-2A, or ZnT8A)) at baseline. The children were randomized to two doses of subcutaneously administered GAD-alum treatment or placebo, 30 days apart. Complete blood count (CBC) and immunophenotyping of T-cell subpopulations by flow cytometry were performed regularly during the 24 months of follow-up posttreatment. Cross-sectional analyses were performed comparing lymphocyte and T-cell subpopulations between GAD-alum and placebo-treated subjects. Results: GAD-alum-treated children had lower levels of lymphocytes (109 cells/L) (p = 0.006), T-cells (103 cells/µL) (p = 0.008), T-helper cells (103 cells/µL) (p = 0.014), and cytotoxic T-cells (103 cells/µL) (p = 0.023) compared to the placebo-treated children 18 months from first GAD-alum injection. This difference remained 24 months after the first treatment for lymphocytes (p = 0.027), T-cells (p = 0.022), T-helper cells (p = 0.048), and cytotoxic T-cells (p = 0.018). Conclusion: Our findings suggest that levels of total T-cells and T-cell subpopulations declined 18 and 24 months after GAD-alum treatment in healthy children with multiple beta-cell autoantibodies including GADA.

Multiplex agglutination-PCR (ADAP) autoantibody assays compared to radiobinding autoantibodies in type 1 diabetes and celiac disease.

Multiplex Antibody-Detection by Agglutination-PCR (ADAP) assay was compared to singleplex standard radiobinding assays (RBA) to detect autoantibodies against insulin (IAA), GAD65 (GADA), islet antigen-2 (IA-2A), ZnT8 (ZnT8A) and tissue transglutaminase (TGA). Serum samples from 273 (114F/158M), 15-73 years of age healthy controls and 227 (109F/118M) newly diagnosed type 1 diabetes children, 1-11 years of age, were analyzed in both assay systems.The original WHO standard 97/550 and in-house reference standards for RBA were compared to ADAP. The ADAP and RBA generated parallel reference standards in all assays except TGA. Lower detection limits were observed in the ADAP assay for GADA,IAA and ZnT8A, markedly for TGA, but not for IA-2A. The Receiver Operating Characteristics (ROC) curve AUC analyses for pairwise comparison of ADAP with RBA showed no difference for GADA (n.s.), ADAP greater AUC for IAA (p = 0.005), RBA greater AUC for IA-2A (p = 0.0004) and ZnT8A (p < 0.0001) while ADAP TGA had a greater AUC compared to both RBA TGA-IgG (p < 0.0001) and TGA-IgA (p < 0.0001). These data suggest that the ADAP and RBA assays are comparable with equal performance for GADA, better ADAP performance for IAA while the RBA showed better performance in both IA-2A and ZnT8A associated with greater heterogeneity in autoantibody levels. The simultaneous analysis of 5 different autoantibodies by ADAP in sample volume reduced to only 4 µL and at an increased lower detection limit in all assays except IA-2A makes the ADAP automated autoantibody assay a distinct advantage for high throughput screening.