Patients with moderate-to-severe Crohn's disease that was resistant to TNF antagonists had an increased rate of response to induction with ustekinumab, as compared with placebo. Patients with an initial response to ustekinumab had significantly increased rates of response and remission with ustekinumab as maintenance therapy. (Funded by Janssen Research and Development; CERTIFI ClinicalTrials.gov number, NCT00771667.).

In an analysis of data from Phase 3 studies of patients with moderate to severe CD, we found serum concentrations of ustekinumab to be proportional to dose and associate with treatment efficacy. Concentrations of ustekinumab did not seem to be affected by cotreatment with immunomodulators. Clinicaltrials.gov no. NCT01369329 (UNITI 1), NCT01369342 (UNITI 2), and NCT01369355 (IM-UNITI).

BACKGROUND & AIMS: Among immunosuppressive-and biologic-naïve patients with moderately-to-severely active Crohn's disease (CD), a higher proportion of those treated with the combination of infliximab and azathioprine achieved corticosteroid-free remission at week 26 (CSFR26) than those given infliximab monotherapy; patients given the combination therapy also had higher serum concentrations of infliximab. Enhanced benefit of combination therapy may occur through synergistic modes of action or the influence of azathioprine on infliximab pharmacokinetics. METHODS: We analyzed data from 206 patients from whom week 30 serum samples were available: 97 received infliximab monotherapy (5 mg/kg, n [ 97) and 109 received combination therapy (2.5 mg/kg/day; n [ 109). Proportions of patients achieving CSFR26 and mucosal healing (absence of ulcers) at week 26 were calculated for each quartile of serum concentrations of infliximab, and exposure-response relationships were compared. RESULTS: Within quartiles of serum concentrations of infliximab, CSFR26 did not differ significantly between patients who received combination therapy vs monotherapy. However, among patients in the lowest quartile of serum concentration of infliximab, twice as many patients who received infliximab monotherapy achieved CSFR26 vs combination therapy. Anti-drug antibodies were detected only in the lowest quartile of serum concentrations of infliximab-in 35.9% of patients given monotherapy and 8.3% of patients given combination therapy. CONCLUSION: Among patients with CD and similar serum concentrations of infliximab, combination therapy with azathioprine was not significantly more effective than infliximab monotherapy. Combination therapy with azathioprine appears to improve efficacy by increasing pharmacokinetic features of infliximab. ClinicalTrials.gov, NCT00094458.

To identify specific environmental, viral, and genetic risk factors for hepatocellular carcinoma (HCC) and the interaction of such factors, we are conducting a prospective study in a high-incidence area of China. Questionnaires were completed and biosamples collected by 60,984 men ages 30-64 years, at study entry. Within 2.5 years, 183 deaths from HCC had occurred. Each HCC case was matched with 5 controls and compared for items on the questionnaire. In addition to chronic hepatitis B virus (HBV) infection, the significant risk factors were: occupation (peasant), corn consumption (in the 1970s), family history of HCC, and history of an episode of acute hepatitis as an adult. HBV, consumption of aflatoxins, a genetic factor, and possibly a second hepatitis virus infection contribute to the risk of HCC.

Background and objectiveClinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy.Subjects and MethodsIn a multi-center study (n=2495), we used unsupervised machine learning to cluster patients with obesity from Shanghai Tenth People’s hospital (n=882, main cohort) based on three clinical variables (AUCs of glucose and of insulin during OGTT, and uric acid). Verification of the clustering was performed in three independent cohorts from external hospitals in China (n = 130, 137, and 289, respectively). Statistics of a healthy normal-weight cohort (n=1057) were measured as controls.ResultsMachine learning revealed four stable metabolic different obese clusters on each cohort. Metabolic healthy obesity (MHO, 44% patients) was characterized by a relatively healthy-metabolic status with lowest incidents of comorbidities. Hypermetabolic obesity-hyperuricemia (HMO-U, 33% patients) was characterized by extremely high uric acid and a large increased incidence of hyperuricemia (adjusted odds ratio [AOR] 73.67 to MHO, 95%CI 35.46-153.06). Hypermetabolic obesity-hyperinsulinemia (HMO-I, 8% patients) was distinguished by overcompensated insulin secretion and a large increased incidence of polycystic ovary syndrome (AOR 14.44 to MHO, 95%CI 1.75-118.99). Hypometabolic obesity (LMO, 15% patients) was characterized by extremely high glucose, decompensated insulin secretion, and the worst glucolipid metabolism (diabetes: AOR 105.85 to MHO, 95%CI 42.00-266.74; metabolic syndrome: AOR 13.50 to MHO, 95%CI 7.34-24.83). The assignment of patients in the verification cohorts to the main model showed a mean accuracy of 0.941 in all clusters.ConclusionMachine learning automatically identified four subtypes of obesity in terms of clinical characteristics on four independent patient cohorts. This proof-of-concept study provided evidence that precise diagnosis of obesity is feasible to potentially guide therapeutic planning and decisions for different subtypes of obesity.Clinical Trial Registrationwww.ClinicalTrials.gov, NCT04282837.