scholarly journals The effect of trajectory of serum uric acid on patients and renal outcomes in patients with stage-3 chronic kidney disease

2020 ◽  
Author(s):  
Shangfeng Tsai(Former Corresponding Author) ◽  
Cheng-Hsu Chen ◽  
Ming-Ju Wu ◽  
Chia-Lin Lee(New Corresponding Author)
Keyword(s):  
Chronic Kidney Disease ◽  
Uric Acid ◽  
Kidney Disease ◽  
Risk Analysis ◽  
Statistical Significance ◽  
Competing Risk ◽  
Competing Risk Analysis ◽  
Coronary Arterial Disease ◽  
Bonferroni Adjustment

Abstract Background Uric acid (UA) is associated with renal and patient survivals but the causal association in nature remains unclear. Also, no finding is yet available regarding longitudinal UA control (trajectory). Methods We enrolled 808 subjects diagnosed with stage 3 chronic kidney disease from 2007 to 2017. We plotted the mean UA over a period of 6 months with a minimum of three samples of UA was required. From the sampled points, we generated for each patient an interpolated line by joining mean values of the UA levels over time. And from the lines from all patients, we classified them into three groups of trajectories (low, medium and high) through group-based trajectory modeling, and then we further separated into either a treatment or no-treatment subgroups. Due to multiple comparisons, we performed post hoc analysis by Bonferroni adjustment. Using the univariate competing-risks regression, we calculated the competing risk analysis with subdistribution hazard ratio of possible confounders. Results All of the 6 trajectories appeared as gradually falling functions with time without any of the curves crossed over one another. For all-cause mortality risk, none of the variables (including age, gender, coronary arterial disease, cerebrovascular disease, diabetes mellitus, renin-angiotensin-aldosterone system inhibitors, trajectories of UA, and treatment of UA) was statistically significant. All 6 trajectories appeared as steady curve without crossovers among them over the entire period of follow-up. Patients with DM were statistically more likely to undergo dialysis. There was only a trend that the on-treatment trajectories, compared to their no-treatment trajectories, had lower risks for dialysis. There was no effect of UA control on patients’ survival. Conclusions Initial treatment of UA is utterly important for UA control. However, the long-term effects on patients and renal survivals maybe minor without statistical significance. Keyword: uric acid, patient survival, renal survival, long-term effect, trajectory, competing risk analysis

scholarly journals Competing-Risk Analysis of Death and End Stage Kidney Disease by Hyperkalaemia Status in Non-Dialysis Chronic Kidney Disease Patients Receiving Stable Nephrology Care

2018 ◽  
Vol 7 (12) ◽  
pp. 499 ◽  
Author(s):  
Michele Provenzano ◽  
Roberto Minutolo ◽  
Paolo Chiodini ◽  
Vincenzo Bellizzi ◽  
Felice Nappi ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Risk Analysis ◽  
Continuous Variable ◽  
Competing Risk ◽  
Competing Risk Analysis ◽  
End Stage Kidney Disease ◽  
Nephrology Care ◽  
End Stage ◽  
New Onset

Hyperkalaemia burden in non-dialysis chronic kidney disease (CKD) under nephrology care is undefined. We prospectively followed 2443 patients with two visits (referral and control with 12-month interval) in 46 nephrology clinics. Patients were stratified in four categories of hyperkalaemia (serum potassium, sK ≥ 5.0 mEq/L) by sK at visit 1 and 2: Absent (no-no), Resolving (yes-no), New Onset (no-yes), Persistent (yes-yes). We assessed competing risks of end stage kidney disease (ESKD) and death after visit 2. Age was 65 ± 15 years, eGFR 35 ± 17 mL/min/1.73 m2, proteinuria 0.40 (0.14–1.21) g/24 h. In the two visits sK was 4.8 ± 0.6 and levels ≥6 mEq/L were observed in 4%. Hyperkalaemia was absent in 46%, resolving 17%, new onset 15% and persistent 22%. Renin-angiotensin-system inhibitors (RASI) were prescribed in 79% patients. During 3.6-year follow-up, 567 patients reached ESKD and 349 died. Multivariable competing risk analysis (sub-hazard ratio-sHR, 95% Confidence Interval-CI) evidenced that new onset (sHR 1.34, 95% CI 1.05–1.72) and persistent (sHR 1.27, 95% CI 1.02–1.58) hyperkalaemia predicted higher ESKD risk versus absent, independently from main determinants of outcome including eGFR change. Conversely, no effect on mortality was observed. Results were confirmed by testing sK as continuous variable. Therefore, in CKD under nephrology care, mild-to-moderate hyperkalaemia status is common (37%) and predicts per se higher ESKD risk but not mortality.

scholarly journals P0669HIGH ESTIMATED PHENOTYPIC AGE ASSOCIATES WITH WORSE CLINICAL OUTCOME IN CHRONIC KIDNEY DISEASE PATIENTS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Hokuto Morohoshi ◽  
Ken Iseri ◽  
Lu Dai ◽  
Thomas Ebert ◽  
Anna Witasp ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Risk Analysis ◽  
Nutritional Status ◽  
Mortality Risk ◽  
Competing Risk ◽  
Chronological Age ◽  
Competing Risk Analysis ◽  
Inflammatory Status ◽  
All Cause Mortality

Abstract Background and Aims Ageing represents the greatest risk factor contributing to increased morbidity and mortality in most chronic diseases; it encompasses numerous biological changes resulting in declining physiological function and increasing burden of disease. Whether new biomarkers of ageing and risk scores for predicting physiological outcomes, including mortality, are applicable and more accurate than chronological age in patients with chronic kidney disease (CKD) is not clear. So far, the DNA methylation (DNAm) PhenoAge biomarker of ageing (Levine et al. Aging 2018) has not been tested in CKD. While we had no access to DNAm data, we applied the phenotypic age estimate proposed by Levine et al., which was included in their calculations of DNAm PhenoAge, and tested the relationship between estimated phenotypic age (ePhenoAge) and chronological age, respectively, with all-cause mortality in patients with CKD. Method In a cohort of 333 CKD patients (stage 1, n=78; stage 3-4, n=64; and stage 5, n=191) with median age 56 years, 43% females, 24% diabetes (DM), 25% cardiovascular disease (CVD), and 22% malnourished, we estimated age by ePhenoAge, using a formula with calculations based on nine biomarkers and chronological age, and compared this age index with chronological age. Framingham risk score, body composition, nutritional status, handgrip strength, and various biochemical markers (white blood cells, mean cell volume, hemoglobin, albumin, creatinine, glucose, calcium, alkaline phosphatase, intact-parathyroid hormone, triglyceride, cholesterol, HDL-cholesterol, high-sensitivity C-reactive protein (hsCRP), and interleukin (IL)-6) were recorded. During a median follow-up period of 52 months, 65 patients died, and 111 patients underwent renal transplantation. We used spline curve to illustrate sub-distribution hazard risk (sHR) for all-cause mortality versus increasing ePhenoAge and chronological age respectively as obtained by the Fine and Gray competing risk analysis. Results In univariate analyses, IL-6 (rho=0.49, p<0.001; n=268) and hsCRP (rho=0.37, p<0.001; n=333) were significantly correlated with ePhenoAge. The ePhenoAge remained significantly associated with hsCRP (p=0.02) when adjusted for sex, DM, CVD, nutritional status and CKD stages. The spline curves showing sHR for all-cause mortality derived from multivariate competing risk analysis and adjusted for sex, presence of DM and CVD, hsCRP, nutritional status and CKD stages, showed increased mortality risk with higher chronological age (sHR: 1.08, p<0.001). In contrast, the association of mortality with higher ePhenoAge (sHR: 1.04, p=0.06) was of borderline statistical significance. Conclusion All-cause mortality risk was associated with increasing chronological age in competing risk analysis with adjustments of confounders. A similar trend was observed for ePhenoAge, a finding which to a large extent may be explained by the inflammatory status of the study subjects. However, contrary to expectations, ePhenoAge was not as powerful as chronological age in predicting mortality, underlining that our knowledge about factors influencing phenotypic age in CKD patients is still limited. This should motivate further study of the potential role of other estimates of biological age in CKD.

scholarly journals MP55-12 LONG-TERM ASSESSMENT OF MORTALITY PATTERNS AFTER SURGICAL TREATMENT FOR NON-METASTATIC KIDNEY CANCER: A COMPETING RISK ANALYSIS

2017 ◽  
Vol 197 (4S) ◽  
Author(s):  
Alessandro Larcher ◽  
Alessandro Nini ◽  
Fabio Muttin ◽  
Francesco Trevisani ◽  
Francesco Ripa ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Risk Analysis ◽  
Kidney Cancer ◽  
Competing Risk ◽  
Competing Risk Analysis

Serum uric acid levels and long-term outcomes in chronic kidney disease

2013 ◽  
Vol 29 (4) ◽  
pp. 504-512 ◽  
Author(s):  
Tokiko Miyaoka ◽  
Toshio Mochizuki ◽  
Takashi Takei ◽  
Ken Tsuchiya ◽  
Kosaku Nitta
Keyword(s):  
Chronic Kidney Disease ◽  
Uric Acid ◽  
Kidney Disease ◽  
Serum Uric Acid ◽  
Long Term Outcomes

scholarly journals MP13-18 LOW SERUM URIC ACID PREDICTS LONG-TERM CHRONIC KIDNEY DISEASE RISK FOLLOWING INITIAL UROLITHIASIS EVENT

2018 ◽  
Vol 199 (4S) ◽  
Author(s):  
Andrew Ostrowski ◽  
Emily Gansert ◽  
Colleen Thomas ◽  
Joseph Cernigliaro ◽  
William Haley ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Uric Acid ◽  
Kidney Disease ◽  
Serum Uric Acid ◽  
Disease Risk ◽  
Chronic Kidney Disease Risk ◽  
Low Serum

151: Uric Acid and Long-Term Outcomes in Chronic Kidney Disease

2008 ◽  
Vol 51 (4) ◽  
pp. B65
Author(s):  
Magdalena Madero ◽  
Mark Sarnak ◽  
Tom Greene ◽  
Allan Collins ◽  
Gerald Beck ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Uric Acid ◽  
Kidney Disease ◽  
Long Term Outcomes

Long-term assessment of mortality patterns after surgical treatment for non-metastatic kidney cancer: A competing risk analysis

2017 ◽  
Vol 16 (3) ◽  
pp. e1106-e1108
Author(s):  
A. Larcher ◽  
F. Muttin ◽  
A. Nini ◽  
F. Trevisani ◽  
F. Ripa ◽  
...  
Keyword(s):  
Surgical Treatment ◽  
Risk Analysis ◽  
Kidney Cancer ◽  
Competing Risk ◽  
Competing Risk Analysis

Serum Uric Acid and Coronary Arterial Disease in Predialysis Chronic Kidney Disease Patients

2021 ◽  
pp. 263246362110307
Author(s):  
Vaia D. Raikou ◽  
Giannis Vlaseros ◽  
Despina Kyriaki ◽  
Sotiris Gavriil
Keyword(s):  
Blood Pressure ◽  
Chronic Kidney Disease ◽  
Uric Acid ◽  
Kidney Disease ◽  
Serum Uric Acid ◽  
Arterial Disease ◽  
Coronary Arterial Disease ◽  
Lower Egfr ◽  
High Uric Acid ◽  
Low Serum

Background–Aim: Uric acid is considered a marker of high cardiovascular risk. We investigated the association between serum uric acid and coronary arterial disease (CAD) in pre-dialysis chronic kidney disease (CKD) patients. Methods: We enrolled 110 subjects on mean age 69.02 ± 14.3 years old. The participants were categorized for both estimated glomerular filtration rate (eGFR) and albuminuria according to criteria 2012 of the Kidney Disease Improving Global Outcomes. Estimated pulse wave velocity (ePWV) was calculated using an equation including the age and mean blood pressure. The CAD prevalence rate was recorded. Results: The patients were divided in two groups according to uric acid cutoff point value related to high ePWV. The patients with higher uric acid were older and they had significantly higher systolic blood pressure, pulse pressure, and parathyroid hormone, but significantly lower eGFR and 1,25(OH)2 Vit D3 levels. The association between high uric acid and both high ePWV and CAD occurrence was found to be significant ( x2 = 6.7, P = .008 and x2 = 4.1, P = .03, respectively), although the relationship with albuminuria was found to be nonsignificant. In a built multifactorial model, the low serum uric acid rather than the high was found to be an independent predictor for CAD demonstration entering traditional and specific confounders. Conclusion: The low serum uric acid levels were proved to be a significant predictor for CAD accounting potential covariates, even though the high uric acid per se was found to be connected with cardiovascular disease characteristics including arterial stiffness in predialysis CKD patients.

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