Insulin resistance in patients with cancer: a systematic review and meta-analysis.

BACKGROUND: Insulin resistance is a critical cause of metabolic dysfunctions. Metabolic dysfunction is common in patients with cancer and is associated with higher cancer recurrence rates and reduced overall survival. Yet, insulin resistance is rarely considered in the clinic and thus it is uncertain how frequently this condition occurs in patients with cancer. METHODS: To address this knowledge gap, we performed a systematic review and a meta-analysis guided by the Preferred Items for Systematic Review and Meta-Analyses (PRISMA) statement. We included studies assessing insulin resistance in patients with various cancer diagnoses, using the gold-standard hyperinsulinemic-euglycemic clamp method. Studies eligible for inclusion were as follows: (1) included cancer patients older than 18 years of age; (2) included an age-matched control group consisting of individuals without cancer or other types of neoplasms; (3) measured insulin sensitivity using the hyperinsulinemic-euglycemic clamp method. We searched the databases MEDLINE, Embase, and Cochrane Central Register of Controlled Trials for articles published from database inception through March 2023 with no language restriction, supplemented by backward and forward citation searching. Bias was assessed using funnel plot. FINDINGS: Fifteen studies satisfied the criteria. The mean insulin-stimulated rate of glucose disposal (Rd) was 7.5 mg/kg/min in control subjects (n = 154), and 4.7 mg/kg/min in patients with a cancer diagnosis (n = 187). Thus, the Rd mean difference was -2.61 mg/kg/min [95% confidence interval, -3.04; -2.19], p<.01). Heterogeneity among the included studies was insignificant (p=.24). INTERPRETATION: These findings suggest that patients with a cancer diagnosis are markedly insulin resistant. As metabolic dysfunction in patients with cancer associates with increased recurrence and reduced overall survival, future studies should address if ameliorating insulin resistance in this population can improve these outcomes thereby improving patient care.Key pointsMetabolic dysfunction increases cancer recurrence rates and reduces survival for patients with cancer.Insulin resistance is a critical cause of metabolic dysfunctions.To date, no comprehensive compilation of research investigating insulin resistance in cancer patients has been produced.In this meta-analysis, we found that patients with various cancers were markedly insulin-resistant.

Dynamic prediction of cumulative incidence functions by direct binomial regression.

In recent years there have been a series of advances in the field of dynamic prediction. Among those is the development of methods for dynamic prediction of the cumulative incidence function in a competing risk setting. These models enable the predictions to be updated as time progresses and more information becomes available, for example when a patient comes back for a follow-up visit after completing a year of treatment, the risk of death, and adverse events may have changed since treatment initiation. One approach to model the cumulative incidence function in competing risks is by direct binomial regression, where right censoring of the event times is handled by inverse probability of censoring weights. We extend the approach by combining it with landmarking to enable dynamic prediction of the cumulative incidence function. The proposed models are very flexible, as they allow the covariates to have complex time-varying effects, and we illustrate how to investigate possible time-varying structures using Wald tests. The models are fitted using generalized estimating equations. The method is applied to bone marrow transplant data and the performance is investigated in a simulation study.