Dataset Information

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction.

ABSTRACT: Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The 'omics' techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with respect to future cardiovascular events, and to identify CKD patients who are likely to respond to a treatment. Following such a strategy, a reliable risk of future events for a particular patient may be calculated and consequently the patient would also benefit from the best available treatment based on their risk profile. Moreover, a further step forward can be represented by the aggregation of multiple omics information by combining different techniques and/or different biological samples. This has already been shown to yield additional information by revealing with more accuracy the exact individual pathway of disease.

SUBMITTER: Provenzano M

PROVIDER: S-EPMC8745343 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction.

Provenzano Michele M Serra Raffaele R Garofalo Carlo C Michael Ashour A Crugliano Giuseppina G Battaglia Yuri Y Ielapi Nicola N Bracale Umberto Marcello UM Faga Teresa T Capitoli Giulia G Galimberti Stefania S Andreucci Michele M

International journal of molecular sciences 20211229 1

Chronic kidney disease (CKD) patients are characterized by a high residual risk for cardiovascular (CV) events and CKD progression. This has prompted the implementation of new prognostic and predictive biomarkers with the aim of mitigating this risk. The 'omics' techniques, namely genomics, proteomics, metabolomics, and transcriptomics, are excellent candidates to provide a better understanding of pathophysiologic mechanisms of disease in CKD, to improve risk stratification of patients with resp ...[more]

PMID: 35008760

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Project description:BackgroundLipid management in clinic is critical to the prevention and treatment of Chronic kidney disease (CKD), while the manifestations of lipid indicators vary in types and have flexible association with CKD prognosis.PurposeExplore the associations between the widely used indicators of lipid metabolism and their distribution in clinic and CKD prognosis; provide a reference for lipid management and inform treatment decisions for patients with non-dialysis CKD stage 3-5.MethodsThis is a retrospective cohort study utilizing the Self-Management Program for Patients with Chronic Kidney Disease Cohort (SMP-CKD) database of 794 individuals with CKD stages 3-5. It covers demographic data, clinical diagnosis and medical history collection, laboratory results, circulating lipid profiles and lipid distribution assessments. Primary endpoint was defined as a composite outcome(the initiation of chronic dialysis or renal transplantation, sustained decline of 40% or more in estimated glomerular filtration rate (eGFR), doubled of serum creatinine (SCr) from the baseline, eGFR less than 5 mL/min/1.73m2, or all-cause mortality). Exposure variables were circulating lipid profiles and lipid distribution measurements. Association were assessed using Relative risks (RRs) (95% confidence intervals (CIs)) computed by multivariate Poisson models combined with least absolute shrinkage and selection operator (LASSO) regression according to categories of lipid manifestations. The best model was selected via akaike information criterion (AIC), area under curve (AUC), receiver operating characteristic curve (ROC) and net reclassification index (NRI). Subgroup analysis and sensitivity analysis were performed to assess the interaction effects and robustness..Results255 individuals reached the composite outcome. Median follow-up duration was 2.03 [1.06, 3.19] years. Median age was 58.8 [48.7, 67.2] years with a median eGFR of 33.7 [17.6, 47.8] ml/min/1.73 m2. Five dataset were built after multiple imputation and five category-based Possion models were constructed for each dataset. Model 5 across five datasets had the best fitness with smallest AIC and largest AUC. The pooled results of Model 5 showed that total cholesterol (TC) (RR (95%CI) (per mmol/L) :1.143[1.023,1.278], P = 0.018) and percentage of body fat (PBF) (RR (95%CI) (per percentage):0.976[0.961,0.992], P = 0.003) were significant factors of composite outcome. The results indicated that comprehensive consideration of lipid metabolism and fat distribution is more critical in the prediction of CKD prognosis..ConclusionComprehensive consideration of lipid manifestations is optimal in predicting the prognosis of individuals with non-dialysis CKD stages 3-5.

Project description:ImportanceEarly identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.ObjectiveTo develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).Design, setting, and participantsIndividual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).ExposuresDemographic and clinical factors.Main outcomes and measuresIncident eGFR of less than 60 mL/min/1.73 m2.ResultsAmong 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.Conclusions and relevanceEquations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.

Dataset Information

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction.

Publications

OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction.

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