The aim of the present study was to assess the relationship between serum concentrations of several persistent organic pollutants and insulin resistance markers in a cohort of women with a history of ...gestational diabetes mellitus. ∑POPs was computed as the sum of individual serum POP concentrations. No statistically significant associations were found between levels of any POP and fasting glucose. However, polychlorinated biphenyl (PCB) congeners 138 and 180 were positively associated with 2-h glucose levels and PCB 180 also with fasting immunoreactive insulin (IRI). We also found a positive association of p,p′- dichlorodiphenyldichloroethylene (p,p′- DDE), PCBs (138, 153, and 180), hexachlorobenzene, and ∑POPs with 2-h IRI. Serum concentrations of PCBs (138, 153, and 180), hexachlorobenzene, and ∑POPs were also positively associated with homeostasis model assessment (HOMA2-IR) levels. Moreover, p,p′- DDE, PCBs (138, 153 and 180), hexachlorobenzene, and ∑POPs were negatively associated with Insulin Sensitivity Index (ISI-gly) levels. No significant association was found between glycated hemoglobin and the concentrations of any POP. The removal of women under blood glucose lowering treatment from the models strengthened most of the associations previously found for the whole population. Our findings suggest that exposure to certain POPs is a modifiable risk factor contributing to insulin resistance.
•We studied the relationship between persistent pollutants and insulin resistance.•PCBs, HCB, and ∑POPs were positively associated with HOMA2-IR levels.•p,p′-DDE, PCBs, HCB, and ∑POPs were negatively associated with ISI-gly levels.•The removal of women under diabetes treatment strengthened most associations.•Exposure to persistent pollutants might be a risk factor for insulin resistance.
Detecting patients at high relapse risk after the first episode of psychosis (HRR-FEP) could help the clinician adjust the preventive treatment. To develop a tool to detect patients at HRR using ...their baseline clinical and structural MRI, we followed 227 patients with FEP for 18-24 months and applied MRIPredict. We previously optimized the MRI-based machine-learning parameters (combining unmodulated and modulated gray and white matter and using voxel-based ensemble) in two independent datasets. Patients estimated to be at HRR-FEP showed a substantially increased risk of relapse (hazard ratio = 4.58, P < 0.05). Accuracy was poorer when we only used clinical or MRI data. We thus show the potential of combining clinical and MRI data to detect which individuals are more likely to relapse, who may benefit from increased frequency of visits, and which are unlikely, who may be currently receiving unnecessary prophylactic treatments. We also provide an updated version of the MRIPredict software.
