Genetic association studies, testing the relationship between genetic variants and disease status, are useful tools for identifying genes that grant susceptibility to complex disorders. In such studies, an inadequate sample size may provide unreliable results: a small sample is unable to accurately describe the population, whereas a large sample makes the study expensive and complex to run. However, in genetic association studies, the sample size calculation is often overlooked or inadequately assessed for the small number of parameters included. In light of this, herein we list and discuss the role of the statistical and genetic parameters to be considered in the sample size calculation, show examples reporting incorrect estimation and, by using a genetic software program, we provide a practical approach for the assessment of the adequate sample size in a hypothetical study aimed at analyzing a gene–disease association.

The use of bone biopsy for histomorphometric analysis is a quantitative histological examination aimed at obtaining quantitative information on bone remodeling, structure and microarchitecture. The labeling with tetracycline before the procedure also allows for a dynamic analysis of the osteoblastic activity and mineralization process. In the nephrological setting, bone biopsy is indicated to confirm the diagnosis of subclinical or focal osteomalacia and to characterize the different forms of renal osteodystrophy (ROD). Even if bone biopsy is the gold standard for the diagnosis and specific classification of ROD, the use of this approach is very limited. The main reasons for this are the lack of widespread expertise in performing or interpreting bone biopsy results and the cost, invasiveness and potential pain associated with the procedure. In this regard, the sedation, in addition to local anesthesia routinely applied in Italian protocol, significantly reduces pain and ameliorates the pain perception of patients. Concerning the lack of widespread expertise, in Italy a Hub/Spokes model is proposed to standardize the analyses, optimizing the approach to CKD patients and reducing the costs of the procedure. In addition, new tools offer the possibility to evaluate the osteogenic potential or the ability to form bone under normal and pathological conditions, analyzing mesenchymal stem cells and their ability to differentiate in the osteogenic lineage. In the same way, circulating microRNAs are suggested as a tool for exploring osteogenic potential. The combination of different diagnostic approaches and the optimization of the bioptic procedure represent a concrete solution to spread the use of bone biopsy and optimize CKD patient management.

Objectives: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. Methods: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the “AmsterdamUMC database”) admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients. Results: The deep learning model had an area under the ROC curve (AUC) of 0,907 (± 0,007SE) with a sensitivity and specificity of 80% and 89%, respectively, for identifying oliguric AKI episodes. Logistic regression models had an AUC of 0,877 (± 0,005SE) with a sensitivity and specificity of 80% and 81%, respectively. These results were comparable to those obtained in the two US populations upon which the algorithms were previously developed and trained. Conclusion: External validation on the European sample confirmed the accuracy of the algorithms, previously investigated in the US population. The models show high accuracy in both the European and the American databases even though the two cohorts differ in a range of demographic and clinical characteristics, further underlining the validity and the generalizability of the two analytical approaches. Graphical

In CKD and in the elderly, Vascular Calcifications (VC) are associated to cardiovascular events and bone fractures. VC scores at the abdominal aorta (AA) from lateral spine radiographs are widely applied (the 0–24 semiquantitative discrete visual score (SV) being the most used). We hypothesised that a novel continuum score based on quantitative computer-assisted tracking of calcifications (QC score) can improve the precision of the SV score. This study tested the repeatability and reproducibility of QC score and SV score. In forty-four patients with VC from an earlier study, five experts from four specialties evaluated the data twice using a dedicated software. Test–retest was performed on eight subjects. QC results were reported in a 0–24 scale to readily compare with SV. The QC score showed higher intra-operator repeatability: the 95% CI of Bland–Altman differences was almost halved in QC; intra-operator R² improved from 0.67 for SV to 0.79 for QC. Inter-observer repeatability was higher for QC score in the first (Intraclass Correlation Coefficient 0.78 vs. 0.64), but not in the second evaluation (0.84 vs. 0.82), indicating a possible heavier learning artefact for SV. The Minimum Detectable Difference (MDD) was smaller for QC (2.98 vs. 4 for SV, in the 0–24 range). Both scores were insensitive to test–retest procedure. Notably, QC and SV scores were discordant: SV showed generally higher values, and an increasing trend of differences with VC severity. In summary, the new QC score improved the precision of lateral spine radiograph scores in estimating VC. We reported for the first time an estimate of MDD in VC assessment that was 25% lower for the new QC score with respect to the usual SV score. An ongoing study will determine whether this lower MDD may reduce follow-up times to check for VC progression.

Although the gut microbiota is known to affect body weight, its relationship with overweight/obesity is unclear. Our aim was to characterize microbiota composition in a cohort from the southernmost area of Italy. We investigated whether an altered gut microbiota could play an etiological role in the pathogenesis of overweight/obesity. A total of 163 healthy adults were enrolled. Microbiome analysis was performed via 16S rRNA gene sequencing. We found significant phylum variations between overweight (N = 88) and normal-weight (N = 75) subjects. Bacteroidetes and Proteobacteria were higher in overweight participants (p = 0.004; p = 0.03), and Firmicutes and Verrucomicrobia were lower (p = 0.02; p = 0.008) compared to normal-weight participants. Additionally, Akkermansia and Bifidobacterium (genus level) were significantly lower in the overweight group, as well as Akkermansia muciniphila at the species level. The Firmicutes/Bacteroidetes ratio (F/B ratio), an index of dysbiosis, was found to be inversely associated with BMI in linear and logistic regression models (p = 0.001; p = 0.005). The association remained statistically significant after adjustment for potential confounders. This cross-sectional study contributes to defining the gut microbiota composition in an adult population living in southern Italy. It confirms the relationship between overweight susceptibility and the dysbiosis status, highlighting the possible etiological role of the F/B ratio in disease susceptibility.