Few studies so far have examined the impact of nutritional status on the survival of children with cancer, with the majority of them focusing on hematological malignancies. We summarized published evidence reporting the association of nutritional status at diagnosis with overall survival (OS), event-free survival (EFS), relapse, and treatment-related toxicity (TRT) in children with cancer. Published studies on children with leukemia, lymphoma, and other solid tumors have shown that both under-nourished and over-nourished children at cancer diagnosis had worse OS and EFS. Particularly, the risk of death and relapse increased by 30–50% among children with leukemia with increased body mass index at diagnosis. Likewise, the risk of TRT was higher among malnourished children with osteosarcoma and Ewing sarcoma. Nutritional status seems to play a crucial role in clinical outcomes of children with cancer, thus providing a significant modifiable prognostic tool in childhood cancer management. Future studies with adequate power and longitudinal design are needed to further evaluate the association of nutritional status with childhood cancer outcomes using a more standardized definition to measure nutritional status in this population. The use of new technologies is expected to shed further light on this understudied area and give room to person-targeted intervention strategies.

Background To examine the association between optical coherence tomography angiography (OCTA) retinal measurements and Parkinson’s disease (PD). Methods We searched MEDLINE and EMBASE from inception up to November 5th, 2021 for studies examining the differences between OCTA retinal measurements in PD patients and healthy controls. We used the Hartung–Knapp–Sidik–Jonkman random-effects method to combine study-specific standardized mean differences (SMD) in pooled effect estimates and a meta-analytic extension of the E-value metric to quantify the confounding bias capable of nullifying the pooled estimates. Results Nine eligible studies for our systematic review were identified through our search strategy. The pooled SMD between the retinal vessel density of PD patients and healthy participants in the whole superficial vascular plexus (SVP), foveal SVP, parafoveal SVP and foveal avascular zone (FAZ) was −0.68 (95% CI: −1.18 to −0.17, p value = 0.02, n = 7 studies), −0.14 (95% CI: −0.88 to 0.59, p value = 0.62, n = 5 studies), −0.59 (95% CI: −1.41 to 0.23, p value = 0.12, n = 5 studies) and −0.20 (95% CI: −0.79 to 0.38, p value = 0.39, n = 5 studies), respectively. An unmeasured confounder would need to be associated with a 3.01-fold, 1.54-fold, 2.81-fold and 1.70-fold increase in the risk of PD and OCTA retinal measurements, in order for the pooled SMD estimate of vessel density in whole SVP, parafoveal SVP and FAZ, respectively, to be nullified. Conclusions Our results provide evidence on an inverse association between whole SVP vessel density and PD.

Numerous predictive models for the risk of type 2 diabetes mellitus (T2DM) exist, but a minority of them has implemented nutrition data so far, even though the significant effect of nutrition on the pathogenesis, prevention and management of T2DM has been established. Thus, in the present study, we aimed to build a predictive model for the risk of T2DM that incorporates nutrition data and calculates its predictive performance. We analysed cross-sectional data from 1591 individuals from the population-based Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013–14) and used a bootstrap enhanced elastic net penalised multivariate regression method in order to build our predictive model and select among 193 food intake variables. After selecting the significant predictor variables, we built a logistic regression model with these variables as predictors and T2DM status as the outcome. The values of area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of our predictive model were calculated. Eleven out of the 193 food intake variables were selected for inclusion in our model, which yielded a value of area under the ROC curve of 0⋅79 and a maximum PPV, NPV and accuracy of 0⋅37, 0⋅98 and 0⋅91, respectively. The present results suggest that nutrition data should be implemented in predictive models to predict the risk of T2DM, since they improve their performance and they are easy to assess.

Geographic atrophy (GA) is a progressive retinal disease associated with late-stage age-related macular degeneration (AMD), a significant cause of visual impairment in senior adults. GA lesion segmentation is important for disease monitoring in clinical trials and routine ophthalmic practice; however, its manual delineation is time-consuming, laborious, and subject to inter-grader variability. The use of artificial intelligence (AI) is rapidly expanding within the medical field and could potentially improve accuracy while reducing the workload by facilitating this task. This systematic review evaluates the performance of AI algorithms for GA segmentation and highlights their key limitations from the literature. Five databases and two registries were searched from inception until 23 March 2024, following the PRISMA methodology. Twenty-four studies met the prespecified eligibility criteria, and fifteen were included in this meta-analysis. The pooled Dice similarity coefficient (DSC) was 0.91 (95% CI 0.88–0.95), signifying a high agreement between the reference standards and model predictions. The risk of bias and reporting quality were assessed using QUADAS-2 and CLAIM tools. This review provides a comprehensive evaluation of AI applications for GA segmentation and identifies areas for improvement. The findings support the potential of AI to enhance clinical workflows and highlight pathways for improved future models that could bridge the gap between research settings and real-world clinical practice.

Background: The association of central corneal thickness (CCT) with primary open-angle glaucoma (POAG) remains uncertain. Although several observational studies assessing this relationship have reported an inverse association between CCT and POAG, this could be the result of collider bias. In this study, we leveraged human genetic data to assess through Mendelian randomisation (MR) the effect of CCT on POAG risk and whether this effect is mediated by intraocular pressure (IOP) changes.Methods: We used 24 single-nucleotide polymorphisms (SNPs) associated with CCT (p value<5×10⁻⁸) from a genome-wide association study (GWAS) (N=17 803) provided by the International Glaucoma Genetics Consortium and 53 SNPs associated with IOP (p value<5×10⁻⁸) from a GWAS of the UK Biobank (UKBB) (N=97 653). We related these instruments to POAG using a GWAS meta-analysis of 8283 POAG cases and 753 827 controls from UKBB and FinnGen.Results: MR analysis suggested a positive association between CCT and POAG (OR of POAG per 50 µm increase in CCT: 1.38; 95% CI: 1.18 to 1.61; p value<0.01). MR mediation analysis showed that 28.4% of the total effect of CCT on POAG risk was mediated through changes in IOP. The primary results were consistent with estimates of pleiotropy-robust MR methods.Conclusion: Contrary to most observational studies, our results showed that a higher CCT is associated with an increased risk of POAG.