Development of chronic kidney disease influenced by serum urate and body mass index based on young-to-middle-aged Japanese men: a propensity score-matched cohort study

Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

Associations of dietary inflammatory index with low estimated glomerular filtration rate, albuminuria and chronic kidney disease in U.S adults: Results from the NHANES 2011-2018

BACKGROUND AND AIMS

Chronic Kidney Disease (CKD) is characterized by a high inflammation status with ever-increasing prevalence, and defined as low estimated glomerular filtration rate (eGFR) or albuminuria. Both low eGFR and albuminuria can have independent effects on the body. The dietary inflammatory index (DII) is a validated tool used to assess the inflammatory potential of the diet. We aim to explore not only the association between DII and CKD, but also the associations of DII with low eGFR and albuminuria, respectively. In addition, their associations in different subgroups remain to be explored.

METHODS AND RESULTS

18,070 participants from the 2011-2018 NHANES with complete data of dietary intake and laboratory data were involved in our study. The data of 24-hour dietary recall interview was used to calculate DII, CKD could be reflected by laboratory data of creatinine and albumin. Then weighted multivariate logistic regression models and subgroup analyses were performed. The prevalence of low eGFR, albuminuria and CKD were 6.8%, 9.8% and 14.5%, respectively. A positive association between DII and low eGFR was observed (OR=1.12, 95%CI: 1.05-1.21), Q2, Q3 and Q4 are positively associated with a significant 39%, 65% and 71% increased risk of low eGFR compared with Q1 (P for trend<0.05). DII was also associated with CKD (OR=1.06, 95%CI: 1.01-1.11).

CONCLUSION

Significant positive associations of DII with CKD and low eGFR were observed. But we didn't find such association between DII and albuminuria.

Potential link between high FIB-4 score and chronic kidney disease in metabolically healthy men

Although the association between non-alcoholic fatty liver disease and chronic kidney disease (CKD) has been well known, it is unclear whether Fibrosis-4 (FIB-4) score is a predictor of CKD development. We performed this retrospective cohort study, with a longitudinal analysis of 5-year follow-up data from Japanese annual health check-ups. Participants with CKD (estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m² and/or proteinuria) and a habit of alcohol consumption were excluded. The cut-off FIB-4 score was 1.30, indicating increased risk of liver fibrosis. Overall, 5353 participants (men only) were analyzed without exclusion criteria. After propensity score matching, high FIB-4 score (≥ 1.30) was not an independent risk factor for incident CKD (odds ratio [OR] 1.57; 95% confidence interval [CI] 0.97-2.56). However, high FIB-4 score was a significant risk factor for CKD in non-obese (OR 1.92; 95% CI 1.09-3.40), non-hypertensive (OR 2.15; 95% CI 1.16-3.95), or non-smoking (OR 1.88; 95% CI 1.09-3.23) participants. In these participants, FIB-4 score was strongly associated with eGFR decline in the multiple linear regression analysis (β = - 2.8950, P = 0.011). Therefore, a high FIB-4 score may be significantly associated with CKD incidence after 5 years in metabolically healthy participants.

Lifestyle-Related Risk Factors for the Incidence and Progression of Chronic Kidney Disease in the Healthy Young and Middle-Aged Population

The prevalence of chronic kidney disease (CKD) increased by 88% from 1990 to 2016. Age of onset of lifestyle-related diseases (such as hypertension, diabetes mellitus, obesity, dyslipidemia, and hyperuricemia), which are risk factors for incident CKD, is lower now compared with the past. Thus, we aimed to evaluate the risk factors for the incidence and progression of CKD in the young and middle-aged population. There are differences in the risk for CKD among the young, middle-aged, and elderly populations. We aimed to assess obesity (which is basic component of metabolic syndrome), waist circumference, and abdominal adiposity, which are predictive factors of CKD in the younger population. Furthermore, we described the management and clinical evidence of hypertension, diabetes mellitus, dyslipidemia, and hyperuricemia for young and middle-aged patients, along with diet management and nutrients associated with kidney function. Kidney function in the young and middle-aged population is mostly normal, and they are considered a low-risk group for incident CKD. Thus, we expect this review to be useful in reducing the prevalence of CKD.

Hyperuricemia Is Associated With the Risk of Atrial Fibrillation Independent of Sex: A Dose-Response Meta-Analysis

Background:

Conflicting findings of the association between serum uric acid (SUA) and atrial fibrillation (AF) have been reported in both men and women. The sex-specific associations between SUA and the risk of AF are unclear, although hyperuricemia is independently associated with the risk of AF. We performed this meta-analysis to assess the sex-specific effect of SUA on the risk of AF.

Methods

The PubMed, EMBASE, and Cochrane Library databases were searched up to October 3, 2021, for studies that reported sex-specific associations of SUA levels with AF. Linear relationships were assessed by the generalized least squares trend estimation. This study was registered with PROSPERO (42020193013).

Results

Ten eligible studies with 814,804 participants (415,779 men and 399,025 women) were identified. In the category analysis, high SUA was associated with an increased risk of AF in both men (OR: 1.42; 95% CI, 1.18–1.71, I² = 34%) and women (OR: 2.02; 95% CI, 1.29–3.16, I² = 70%). In the dose-response analysis, for each 60 μmol/L (1 mg/dL) increase in the SUA level, the risk of AF increased by 15% (OR: 1.15; 95% CI, 1.07–1.25, I² = 74%) in men and 35% (OR: 1.35; 95% CI, 1.18–1.53, I² = 73%) in women. There was a borderline difference in the impact of SUA on the risk of AF between men and women (P for interaction = 0.05). A significant linear relationship between SUA and the risk of AF was observed in men (P for non-linearity = 0.91) and women (P for non-linearity = 0.92).

Conclusions

This study suggested that there was a significant linear relationship between SUA and the risk of AF among men and women, with a higher risk estimate for women. Additional trials are required to assess the effect of reduced SUA therapy on AF incidence.

Systematic Review Registration

https:www.crd.york.ac.uk/PROSPERO/, identifier: CRD 42020193013.