Impact of body mass index on adverse kidney events in diabetes mellitus patients: A systematic-review and meta-analysis.

BACKGROUND: The incidence of chronic kidney disease among patients with diabetes mellitus (DM) remains a global concern. Long-term obesity is known to possibly influence the development of type 2 diabetes mellitus. However, no previous meta-analysis has assessed the effects of body mass index (BMI) on adverse kidney events in patients with DM. AIM: To determine the impact of BMI on adverse kidney events in patients with DM. METHODS: A systematic literature search was performed on the PubMed, ISI Web of Science, Scopus, Ovid, Google Scholar, EMBASE, and BMJ databases. We included trials with the following characteristics: (1) Type of study: Prospective, retrospective, randomized, and non-randomized in design; (2) participants: Restricted to patients with DM aged ≥ 18 years; (3) intervention: No intervention; and (4) kidney adverse events: Onset of diabetic kidney disease [estimated glomerular filtration rate (eGFR) of < 60 mL/min/1.73 m2 and/or microalbuminuria value of ≥ 30 mg/g Cr], serum creatinine increase of more than double the baseline or end-stage renal disease (eGFR < 15 mL/min/1.73 m2 or dialysis), or death. RESULTS: Overall, 11 studies involving 801 patients with DM were included. High BMI (≥ 25 kg/m2) was significantly associated with higher blood pressure (BP) [systolic BP by 0.20, 95% confidence interval (CI): 0.15-0.25, P < 0.00001; diastolic BP by 0.21 mmHg, 95%CI: 0.04-0.37, P = 0.010], serum albumin, triglycerides [standard mean difference (SMD) = 0.35, 95%CI: 0.29-0.41, P < 0.00001], low-density lipoprotein (SMD = 0.12, 95%CI: 0.04-0.20, P = 0.030), and lower high-density lipoprotein (SMD = -0.36, 95%CI: -0.51 to -0.21, P < 0.00001) in patients with DM compared with those with low BMIs (< 25 kg/m2). Our analysis showed that high BMI was associated with a higher risk ratio of adverse kidney events than low BMI (RR: 1.22, 95%CI: 1.01-1.43, P = 0.036). CONCLUSION: The present analysis suggested that high BMI was a risk factor for adverse kidney events in patients with DM.

Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis.

BACKGROUND: An appropriate prediction model for adverse prognosis before peritoneal dialysis (PD) is lacking. Thus, we retrospectively analysed patients who underwent PD to construct a predictive model for adverse prognoses using machine learning (ML). METHODS: A retrospective analysis was conducted on 873 patients who underwent PD from August 2007 to December 2020. A total of 824 patients who met the inclusion criteria were included in the analysis. Five commonly used ML algorithms were used for the initial model training. By using the area under the curve (AUC) and accuracy (ACC), we ranked the indicators with the highest impact and displayed them using the values of Shapley additive explanation (SHAP) version 0.41.0. The top 20 indicators were selected to build a compact model that is conducive to clinical application. All model-building steps were implemented in Python 3.8.3. RESULTS: At the end of follow-up, 353 patients withdrew from PD (converted to haemodialysis or died), and 471 patients continued receiving PD. In the complete model, the categorical boosting classifier (CatBoost) model exhibited the strongest performance (AUC = 0.80, 95% confidence interval [CI] = 0.76-0.83; ACC: 0.78, 95% CI = 0.72-0.83) and was selected for subsequent analysis. We reconstructed a compression model by extracting 20 key features ranked by the SHAP values, and the CatBoost model still showed the strongest performance (AUC = 0.79, ACC = 0.74). CONCLUSIONS: The CatBoost model, which was built using the intelligent analysis technology of ML, demonstrated the best predictive performance. Therefore, our developed prediction model has potential value in patient screening before PD and hierarchical management after PD.

Incidence and risk factors of cognitive dysfunction in hemodialysis patients: A systematic review and meta-analysis.

BACKGROUND: The study aims to explore the incidence and risk factors of cognitive dysfunction in hemodialysis patients. METHODS: PubMed, Embase, Cochrane Library, and Web of Science databases were searched for clinical studies on the association between hemodialysis and cognitive dysfunction from the database's inception to 1 December 2022. Two researchers independently completed data extraction and risk of bias assessments for the included studies. All statistical analyses were performed using STATA15.0 software. RESULTS: Ten studies were included in this meta-analysis, with a total of 5535 hemodialysis patients, that is, 2033 patients with cognitive dysfunction and 3502 patients with normal cognitive function. The Newcastle-Ottawa Scale scores of the included studies were greater than 5. Meta-analysis results suggested that the incidence of cognitive dysfunction in hemodialysis patients was (effect size = 51%, 95% confidence interval [CI] [0.33, 0.69]), and hemodialysis patients with cognitive dysfunction were often older than those with normal cognition (standard mean difference [SMD] = 0.49, 95% CI [0.31, 0.68]). Female gender was a risk factor for cognitive dysfunction in hemodialysis patients (relative risk [RR] = 1.21, 95% CI [1.04, 1.41]); diabetes (RR = 1.33, 95% CI [1.04, 1.71]) and stroke (RR = 1.66, 95% CI [1.08, 2.55]) increased the incidence of cognitive dysfunction in hemodialysis patients. CONCLUSIONS: The most important risk factors for cognitive dysfunction associated with hemodialysis might be female gender, old age, diabetes, and stroke. Close attention should be paid to such patients for early prevention.

Relationship between Dialysate Bicarbonate Concentration and All-Cause Mortality in Hemodialysis Patients.

INTRODUCTION: The optimal dialysate bicarbonate concentration (DBIC) for hemodialysis (HD) remains controversial. Herein, we analyzed the effect of dialysate bicarbonate levels on mortality in HD patients. METHODS: Patients undergoing maintenance HD were recruited from the HD unit of the Daping Hospital. Patients were categorized into quartiles according to their DBIC level (quartile 1: <31.25 mmol/L, n = 77; quartile 2: 31.25-32.31 mmol/L, n = 76; quartile 3: 32.31-33.6 mmol/L; n = 81; quartile 4: ≥33.6 mmol/L, n = 79). Demographic and clinical data were collected. Survival curves were estimated using the Kaplan-Meier method. A Cox proportional hazards regression model was used to estimate the association between DBIC and all-cause mortality. RESULTS: We included 313 patients undergoing maintenance HD with a mean DBIC of 32.16 ± 1.59 mmol/L (range, 27.20-34.72 mmol/L). The patients in quartile 4 were more likely to have higher pre- and post-HD serum bicarbonate concentrations than those in other quartiles. The mortality rate was lowest in quartile 2 (10.53%). The survival time was significantly lower in the quartile 4 group than in the other quartiles (p = 0. 008, log-rank test). After full adjustment, the hazard ratio (per 3 mmol/L higher DBIC) for all-cause mortality was 4.29 (95% confidence interval, 2.11-8.47) in all patients, whereas no significant association was observed between DBIC and initial hospitalization. CONCLUSIONS: Our data indicate that DBIC is positively associated with all-cause mortality. A DBIC concentration of 31-32 mmol/L may benefit patient outcomes. This study provides an evidence-based medical basis for optimal dialysis prescription in the future.

Real-time in situ calibration of an optically trapped probing system.

We present real-time in situ calibration of an optically trapped probing system. In the probing system, a micro/nanobead is stably trapped around the minimum of the field potential to serve as the measurement probe, whereas the random thermal force tends to destabilize it and causes Brownian motion around the equilibrium. The weighted recursive least-squares algorithm is applied to recursively update the system's parameters, such as the state transition coefficient, and to estimate specific system response and the unknown variance of the Gaussian white noise in real time according to the probe's motion. The real-time recursive algorithm was first applied to real-time calibration of measurement sensitivity and trapping stiffness for the case that the local temperature and the damping coefficient of the probe are known. It was then applied to estimate the probe's local temperature in real time. Two experiments were designed to illustrate the applicability of the real-time calibration method. The experimental results show that the recursive algorithm is able to real-time calibrate the trapping stiffness of the probing system and the measurement sensitivity of the back-focal-plane interferometry employed for position measurement. The experimental results also show that the method can estimate the probe's local temperature in real time.

Three-axis rapid steering of optically propelled micro/nanoparticles.

This paper presents the design and implementation of a three-axis steering system, wherein a micro/nanoparticle is optically trapped and propelled to serve as a measurement probe. The actuators in the system consist of a deformable mirror enabling axial steering and a two-axis acousto-optic deflector for lateral steering. The actuation range is designed and calibrated to be over 20 microm along the two lateral axes and over 10 microm along the axial direction. The actuation bandwidth of the two lateral axes is over 50 kHz and the associated resolution is 0.016 nm (1sigma). The axial resolution is 0.16 nm, while the bandwidth is enhanced to over 3 kHz by model cancellation method. The performance of the three-axis steering system is illustrated by three sets of experiments. First, active Brownian motion control of the trapped probe is utilized to enhance trapping stability. Second, a large range three-dimensional (3D) steering of a 1.87 microm probe, contouring a complex 3D trajectory in a 6 x 6 x 4 microm3 volume, is demonstrated. Third, a closed-loop steering is implemented to achieve improved precision.