Journal of Nephrology最新文献

Dengue is the most prevalent arthropod-transmitted infection worldwide. Its clinical presentation ranges from subclinical illness to multi-organ failure. Acute kidney injury (AKI) is one of its complications, having a number of different pathogeneses. The patient herein described presented with thrombotic microangiopathy (TMA) and rhabdomyolysis, a combination never previously reported in the literature. He was diagnosed with dengue at a primary care hospital, after which he was referred to us with fever and oliguria. His blood workup and kidney biopsy revealed a picture of combined TMA and rhabdomyolysis-induced AKI. He developed sepsis after his first session of plasmapheresis, that had to be discontinued and he was further managed with dialysis and supportive care. The patient showed remarkable recovery, regaining kidney function after one month.

Diabetic kidney disease (DKD) is a significant complication of type 2 diabetes, posing a global health risk. Detecting and predicting diabetic kidney disease at an early stage is crucial for timely interventions and improved patient outcomes. Artificial intelligence (AI) has demonstrated promise in healthcare, and several tools have recently been developed that utilize Machine Learning with clinical data to detect and predict DKD. This review aims to explore the current landscape of AI and machine learning applications in DKD, specifically examining existing literature on risk scores and machine learning approaches for predicting DKD development. A literature search was conducted using Medline (PubMed), Google Scholar, and Scopus databases until July 2023. Relevant keywords were used to extract studies that described the role of AI in DKD. The review revealed that AI and machine learning have been successfully used to predict DKD progression, outperforming traditional risk score models. Artificial intelligence-driven research for DKD extends beyond prediction models, offering opportunities for integrating genetic and epigenetic data, advancing understanding of the disease's molecular basis, personalizing treatment strategies, and fostering the development of novel drugs. However, challenges remain, including the requirement for large datasets and the lack of standardization in AI-driven tools for DKD. Artificial intelligence and machine learning have the potential to revolutionize the management and care of DKD patients, surpassing the limitations of traditional methods reliant on existing knowledge. Future research should address the challenges associated with AI and machine learning in DKD and focus on developing AI-driven tools for clinical practice.

Background: Low muscle mass quantity and quality (myosteatosis) can be evaluated by computed tomography (CT) by measuring skeletal muscle area and muscular attenuation, respectively, at the third lumbar vertebra. We aimed to define cut-off points of skeletal muscle area and muscular attenuation to predict mortality in non-dialysis chronic kidney disease (CKD) patients.

Methods: We conducted a retrospective study including non-dialysis CKD patients over two years, who underwent an opportunistic computed tomography within a two year period, and for whom creatinine was measured within 90 days of CT. Skeletal muscle area was normalized for stature to calculate the skeletal muscle index. Area under the receiver operating characteristic (AuROC) curve and Youden's index were used, to identify the cut-point, separately according to sex.

Results: One hundred sixty-seven patients (50.9% male, mean age of 68.3 ± 16.4 years) were included, most with CKD stages 3 and 4. During a median follow-up of 4.9 (4.2) years, 39 (23.4%) patients died. Muscular attenuation showed a better ability to predict mortality (AuROC curve 0.739 [95% CI 0.623-0.855] in women and 0.744 in men [95% CI 0.618-0.869]) than skeletal muscle index (AuROC curve 0.491 [95% CI 0.332-0.651] in women and 0.711 [95% CI 0.571-0.850] in men). For muscular attenuation, the best cut-off values to predict mortality were 27.56 Hounsfield units in women and 24.58 Hounsfield units in men. For skeletal muscle index, the best cut-off values were 38.47 cm²/m² in women and 47.81 cm²/m² in men. In univariable Cox-regression both low muscle mass and myosteatosis were associated with increased mortality. In multivariable Cox-regression models only myosteatosis maintained a significant association with mortality (Hazard Ratio 2.651 (95% CI 1.232-5.703, p = 0.013)).

Conclusions: We found sex-specific cut-off values for muscle parameters using CT analysis in non-dialysis CKD patients that were associated with mortality. In this population, myosteatosis may be more closely associated with mortality than muscle quantity.

Background: Mortality and cardiovascular (CV) risk prediction in individuals with end-stage kidney disease (ESKD) on chronic hemodialysis (HD) remains challenging due to the multitude of implicated factors. In a multicenter ESKD-HD cohort, we tested the prognostic yield of the assessment of circulating Humanin, a small mitochondrial-derived peptide involved in CV protection, on CV events and mortality.

Methods: We conducted a prospective, observational, pilot study on 94 prevalent HD patients. The prognostic capacity of circulating Humanin levels was tested on a primary composite (all-cause mortality + non-fatal CV events) and a secondary exploratory endpoint (all-cause mortality alone).

Results: Baseline Humanin level was comparable in patients reaching the primary or secondary endpoint as compared to others (p = 0.69 and 0.76, respectively). Unadjusted followed by multivariable Cox regression analyses adjusted for age, left ventricular mass index (LVMi), E/e', pulse pressure and diabetes mellitus indicated a non-linear relationship between Humanin levels and the composite outcome with the highest Hazard Ratio (HR) associated with very low (< 450.7 pg/mL; HR ranging from 4.25 to 2.49) and very high (> 759.5 pg/mL; HR ranging from 5.84 to 4.50) Humanin values. Restricted cubic splines fitting univariate and multivariate Cox regression analyses visually confirmed a curvilinear trend with an increasing risk observed for lower and higher Humanin values around the median, respectively. A similar, u-shaped association was also evidenced with the secondary endpoint.

Conclusions: Altered Humanin levels may impart prognostic information in ESKD-HD patients at risk of death or CV events. Future investigations are needed to confirm whether Humanin measurement could improve CV and mortality risk prediction beyond traditional risk models.