Effects of pioglitazone on metabolic abnormalities,psychopathology, and cognitive function in schizophrenic patients treated with antipsychotic medication: A randomized double-blind study

BackgroundSchizophrenic patients treated with antipsychotic drugs (AP) have an increased frequency of glucose–lipid metabolic abnormalities and diabetes. Pioglitazone has been shown to be effective in the treatment of glucose and lipid abnormalities in diabetes and decreasing longer-term conversion of impaired glucose tolerance to frank diabetes. Some studies also suggest possible pro-cognitive and antidepressant effects of pioglitazone. We studied the effects of pioglitazone on potential metabolic, symptomatic and cognitive benefits in schizophrenic patients treated with AP.Methods54 schizophrenic patients with at least both a)impaired glucose and b) triglycerides ≥ 120 mg/dL and/or low HDL levels, participated in a double-blind placebo controlled study of 3 month treatment with Pioglitazone (30–45 mg/day) or matched placebo, at 5 sites (4 U.S., 1 China). Fasting glucose and lipid parameters, and psychopathology (PANSS scale) were assessed monthly, and patients had a glucose tolerance test and cognitive testing (RBANS and CPT) at baseline and at the end of study. Statistical analysis used mixed model repeated measures analysis, supplemented by completer and LOCF analysis.ResultsIn the total sample there was an overall effect (P's < .05 to < .01) of pioglitazone on preventing deterioration in fasting glucose and improving HDL and PANSS depression scores; in the pioglitazone group comparison of baseline vs 3 month values also showed significant (P < .05) decreases in fasting insulin, 2 h glucose in GTT and insulin resistance (HOMA-IR). However there were marked differences between the responses of patients in the U.S. sites vs the China site. In the U.S. sample, patients treated with pioglitazone, when compared to placebo treated patents, had significantly lower fasting glucose (F = 3.99, P = 0.02), improved insulin sensitivity (lower H0MA-IR, F = 6.24, P = .002), lower triglycerides (F = 2.68, P = .06) and increased HDL (F = 6.50, P = .001). By the end of the study 52% of the pioglitazone treated patients compared to 15% of the placebo patients had fasting glucose in the normal range (Fisher's exact test P = .02). Pioglitazone also significantly improved PANSS depression factor scores (F = 2.82, P = 0.05). It did not improve cognitive performance on the RBANS or CPT tasks. Pioglitazone did not increase weight or produce any other significant side-effects. In the small mainland China site sample, pioglitazone treatment, as compared to placebo, did not show greater improvement in metabolic parameters or psychopathology ratings.ConclusionsIn the sample of patients from the U.S., pioglitazone was an efficacious and safe treatment for glucose and lipid metabolic abnormalities in schizophrenic patients treated with AP, and it may also have beneficial effects on depressive symptoms. It may be particularly useful in patients whose weight gain effects from antipsychotics have plateaued and where weight loss is not the primary goal. The risk vs. benefits of longer term treatment with pioglitazone has to be carefully evaluated for individual patients.     

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.     

COVID-19 reinfection or relapse: an intriguing dilemma

Clinical Rheumatology -