Clinical profiles of post-load glucose subgroups and their association with glycaemic traits over time: An IMI-DIRECT study.

AIM: To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration. METHODS: We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months. RESULTS: At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (ß = 0.36, 95% CI 0.13-0.58), Subgroup 3 (ß = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (ß = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months. CONCLUSIONS: Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Tralokinumab plus topical corticosteroids for the treatment of moderate-to-severe atopic dermatitis: results from the double-blind, randomized, multicentre, placebo-controlled phase III ECZTRA 3 trial.

BACKGROUND: Tralokinumab is a fully human monoclonal antibody that specifically neutralizes interleukin-13, a key driver of atopic dermatitis (AD). OBJECTIVES: To evaluate the efficacy and safety of tralokinumab in combination with topical corticosteroids (TCS) in patients with moderate-to-severe AD who were candidates for systemic therapy. METHODS: This was a double-blind, placebo plus TCS controlled phase III trial. Patients were randomized 2 : 1 to subcutaneous tralokinumab 300 mg or placebo every 2 weeks (Q2W) with TCS as needed over 16 weeks. Patients who achieved an Investigator's Global Assessment (IGA) score of 0/1 and/or 75% improvement in Eczema Area and Severity Index (EASI 75) at week 16 with tralokinumab were rerandomized 1 : 1 to tralokinumab Q2W or every 4 weeks (Q4W), with TCS as needed, for another 16 weeks. RESULTS: At week 16, more patients treated with tralokinumab than with placebo achieved IGA 0/1: 38·9% vs. 26·2% [difference (95% confidence interval): 12·4% (2·9-21·9); P = 0·015] and EASI 75: 56·0% vs. 35·7% [20·2% (9·8-30·6); P < 0·001]. Of the patients who were tralokinumab responders at week 16, 89·6% and 92·5% of those treated with tralokinumab Q2W and 77·6% and 90·8% treated with tralokinumab Q4W maintained an IGA 0/1 and EASI 75 response at week 32, respectively. Among patients who did not achieve IGA 0/1 and EASI 75 with tralokinumab Q2W at 16 weeks, 30·5% and 55·8% achieved these endpoints, respectively, at week 32. The overall incidence of adverse events was similar across treatment groups. CONCLUSIONS: Tralokinumab 300 mg in combination with TCS as needed was effective and well tolerated in patients with moderate-to-severe AD.

Relative risks and effective number of meioses: a unified approach for general genetic models and phenotypes.

Many common diseases are known to have genetic components, but since they are non-Mendelian, i.e. a large number of genetic factors affect the phenotype, these components are difficult to localize. These traits are often called complex and analysis of siblings is a valuable tool for mapping them. It has been shown that the power of the affected relative pairs method to detect linkage of a disease susceptibility locus depends on the locus contribution to increased risk of relatives compared with population prevalence (Risch, 1990a,b). In this paper we generalize calculation of relative risk to arbitrary phenotypes and genetic models, but also show that the relative risk can be split into the relative risk at the main locus and the relative risk due to interaction between the main locus and loci at other chromosomes. We demonstrate how the main locus contribution to the relative risk is related to probabilities of allele sharing identical by descent at the main locus, as well as power to detect linkage. To this end we use the effective number of meioses, introduced by Hössjer (2005a) as a convenient tool. Relative risks and effective number of meioses are computed for several genetic models with binary or quantitative phenotypes, with or without polygenic effects.