Background Anti-TNF drugs are effective treatments for the management of Crohn's disease but treatment failure is common. We aimed to identify clinical and pharmacokinetic factors that predict primary non-response at week 14 after starting treatment, non-remission at week 54, and adverse events leading to drug withdrawal.Methods The personalised anti-TNF therapy in Crohn's disease study (PANTS) is a prospective observational UK-wide study. We enrolled anti-TNF-naive patients (aged ≥6 years) with active luminal Crohn's disease at the time of first exposure to infliximab or adalimumab between March 7, 2013, and July 15, 2016. Patients were evaluated for 12 months or until drug withdrawal. Demographic data, smoking status, age at diagnosis, disease duration, location, and behaviour, previous medical and drug history, and previous Crohn's disease-related surgeries were recorded at baseline. At every visit, disease activity score, weight, therapy, and adverse events were recorded; drug and total anti-drug antibody concentrations were also measured. Treatment failure endpoints were primary non-response at week 14 ,non-remission at week 54, and adverse events leading to drug withdrawal. We used regression analyses to identify which factors were associated with treatment failure. Findings We enrolled 955 patients treated with infliximab (753 with originator; 202 with biosimilar) and 655 treated with adalimumab. Primary non-response occurred in 295 (23•8%, 95% CI 21•4-26•2) of 1241 patients who were assessable at week 14. Non-remission at week 54 occurred in 764 (63•1%, 60•3-65•8) of 1211 patients who were assessable, and adverse events curtailed treatment in 126 (7•8%, 6•6-9•2) of 1610 patients. In multivariable analysis, the only factor independently associated with primary non-response was low drug concentration at week 14 (infliximab: odds ratio 0•35 [95% CI 0•20-0•62], p=0•00038; adalimumab: 0•13 [0•06-0•28], p<0•0001); the optimal week 14 drug concentrations associated with remission at both week 14 and week 54 were 7 mg/L for infliximab and 12 mg/L for adalimumab. Continuing standard dosing regimens after primary non-response was rarely helpful; only 14 (12•4% [95% CI 6•9-19•9]) of 113 patients entered remission by week 54. Similarly, week 14 drug concentration was also independently associated with non-remission at week 54 (0•29 [0•16-0•52] for infliximab; 0•03 [0•01-0•12] for adalimumab; p<0•0001 for both). The proportion of patients who developed anti-drug antibodies (immunogenicity) was 62•8% (95% CI 59•0-66•3) for infliximab and 28•5% (24•0-32•7) for adalimumab. For both drugs, suboptimal week 14 drug concentrations predicted immunogenicity, and the development of anti-drug antibodies predicted subsequent low drug concentrations. Combination immuno-modulator (thiopurine or methotrexate) therapy mitigated the risk of developing anti-drug antibodies (hazard ratio 0•39 [95% CI 0•32-0•46] for infliximab; 0•44 [0•31-0•64] for adalimumab; p<0•0001 for both). For infliximab, multivariable analysis of immunododulator ...

Monogenic causes of autoimmunity give key insights to the complex regulation of the immune system. We report a new monogenic cause of autoimmunity resulting from de novo germline activating STAT3 mutations in 5 individuals with a spectrum of early-onset autoimmune disease including type 1 diabetes. These findings emphasise the critical role of STAT3 in autoimmune disease and contrast with the germline inactivating STAT3 mutations that result in Hyper IgE syndrome.

OBJECTIVEWith rising obesity, it is becoming increasingly difficult to distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D) in young adults. There has been substantial recent progress in identifying the contribution of common genetic variants to T1D and T2D. We aimed to determine whether a score generated from common genetic variants could be used to discriminate between T1D and T2D and also to predict severe insulin deficiency in young adults with diabetes. RESEARCH DESIGN AND METHODSWe developed genetic risk scores (GRSs) from published T1D-and T2D-associated variants. We first tested whether the scores could distinguish clinically defined T1D and T2D from the Wellcome Trust Case Control Consortium (WTCCC) (n = 3,887). We then assessed whether the T1D GRS correctly classified young adults (diagnosed at 20-40 years of age, the age-group with the most diagnostic difficulty in clinical practice; n = 223) who progressed to severe insulin deficiency <3 years from diagnosis. RESULTSIn the WTCCC, the T1D GRS, based on 30 T1D-associated risk variants, was highly discriminative of T1D and T2D (area under the curve [AUC] 0.88 [95% CI 0.87-0.89]; P < 0.0001), and the T2D GRS added little discrimination (AUC 0.89). A T1D GRS >0.280 (>50th centile in those with T1D) is indicative of T1D (50% sensitivity, 95% specificity). A low T1D GRS (<0.234, <5th centile T1D) is indicative of T2D (53% sensitivity, 95% specificity). Most discriminative ability was obtained from just nine single nucleotide polymorphisms (AUC 0.87). In young adults with diabetes, T1D GRS alone predicted progression to insulin deficiency ; P < 0.0001). T1D GRS, autoantibody status, and clinical features were independent and additive predictors of severe insulin deficiency (combined AUC 0.96 [95% CI 0.94-0.99]; P < 0.0001). CONCLUSIONSA T1D GRS can accurately identify young adults with diabetes who will require insulin treatment. This will be an important addition to correctly classifying individuals with diabetes when clinical features and autoimmune markers are equivocal.

Aims/hypothesisDiagnosing MODY is difficult. To date, selection for molecular genetic testing for MODY has used discrete cut-offs of limited clinical characteristics with varying sensitivity and specificity. We aimed to use multiple, weighted, clinical criteria to determine an individual’s probability of having MODY, as a crucial tool for rational genetic testing.MethodsWe developed prediction models using logistic regression on data from 1,191 patients with MODY (n = 594), type 1 diabetes (n = 278) and type 2 diabetes (n = 319). Model performance was assessed by receiver operating characteristic (ROC) curves, cross-validation and validation in a further 350 patients.ResultsThe models defined an overall probability of MODY using a weighted combination of the most discriminative characteristics. For MODY, compared with type 1 diabetes, these were: lower HbA1c, parent with diabetes, female sex and older age at diagnosis. MODY was discriminated from type 2 diabetes by: lower BMI, younger age at diagnosis, female sex, lower HbA1c, parent with diabetes, and not being treated with oral hypoglycaemic agents or insulin. Both models showed excellent discrimination (c-statistic = 0.95 and 0.98, respectively), low rates of cross-validated misclassification (9.2% and 5.3%), and good performance on the external test dataset (c-statistic = 0.95 and 0.94). Using the optimal cut-offs, the probability models improved the sensitivity (91% vs 72%) and specificity (94% vs 91%) for identifying MODY compared with standard criteria of diagnosis <25 years and an affected parent. The models are now available online at www.diabetesgenes.org.Conclusions/interpretationWe have developed clinical prediction models that calculate an individual’s probability of having MODY. This allows an improved and more rational approach to determine who should have molecular genetic testing.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-011-2418-8) contains peer-reviewed but unedited supplementary material, which is available to authorised users.