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• W2520592021 abstract "IntroductionHuman studies report conflicting results on the predictive power of serum lipids on the progression of chronic kidney disease. We aimed to systematically identify the lipids that predict progression to end-stage kidney disease.MethodsFrom the Chronic Renal Insufficiency Cohort, 79 patients with chronic kidney disease stages 2 to 3 who progressed to end-stage kidney disease over 6 years of follow-up were selected and frequency matched by age, sex, race, and diabetes with 121 nonprogressors with less than 25% decline in estimated glomerular filtration rate during the follow-up. The patients were randomly divided into training and test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples.ResultsWe identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the training set. From the top 10 lipids, the abundance of diacylglycerols and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol 16:0 was significantly higher in progressors. Using logistic regression models, a multimarker panel consisting of diacylglycerols and monoacylglycerol independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of estimated glomerular filtration rate and urine protein-to-creatinine ratio as compared with that of the base model was 0.92 (95% confidence interval: 0.88–0.97) and 0.83 (95% confidence interval: 0.76–0.90, P < 0.01), respectively, an observation that was validated in the test subset.DiscussionWe conclude that a distinct panel of lipids may improve prediction of progression of chronic kidney disease beyond estimated glomerular filtration rate and urine protein-to-creatinine ratio when added to the base model. Human studies report conflicting results on the predictive power of serum lipids on the progression of chronic kidney disease. We aimed to systematically identify the lipids that predict progression to end-stage kidney disease. From the Chronic Renal Insufficiency Cohort, 79 patients with chronic kidney disease stages 2 to 3 who progressed to end-stage kidney disease over 6 years of follow-up were selected and frequency matched by age, sex, race, and diabetes with 121 nonprogressors with less than 25% decline in estimated glomerular filtration rate during the follow-up. The patients were randomly divided into training and test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples. We identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the training set. From the top 10 lipids, the abundance of diacylglycerols and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol 16:0 was significantly higher in progressors. Using logistic regression models, a multimarker panel consisting of diacylglycerols and monoacylglycerol independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of estimated glomerular filtration rate and urine protein-to-creatinine ratio as compared with that of the base model was 0.92 (95% confidence interval: 0.88–0.97) and 0.83 (95% confidence interval: 0.76–0.90, P < 0.01), respectively, an observation that was validated in the test subset. We conclude that a distinct panel of lipids may improve prediction of progression of chronic kidney disease beyond estimated glomerular filtration rate and urine protein-to-creatinine ratio when added to the base model. According to the Center for Disease Control and Prevention, there are currently more than 20 million people above the age of 20 with chronic kidney disease (CKD) in the United States.1Centers for Disease Control. National Chronic Kidney Disease Fact Sheet 2010. Available at: www.cdc.gov/diabetes/projects/pdfs/ckd_summary.pdf. Accessed September 8, 2016.Google Scholar In spite of its public health burden, the clinical care of the patients with CKD is largely dependent on the application of traditional biomarkers including serum creatinine, urine protein-to-creatinine ratio (UPCR), and estimated glomerular filtration rate (eGFR), which are significantly limited by their precision, accuracy, and prognostic values especially early in the course of disease.2Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (15902) Google Scholar, 3Rule A.D. Larson T.S. Bergstralh E.J. et al.Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease.Ann Intern Med. 2004; 141: 929-937Crossref PubMed Scopus (909) Google Scholar In CKD, metabolic derangements start at early stages where these inherent deficiencies are most prominent. Such limitations necessitate a shift of paradigm from exclusive reliance on traditional biomarkers to systematic approaches for the identification of prognostic markers. Lipids are diverse and abundant molecules with significant links to different metabolic pathways along with diverse cellular and biological functions.4Fahy E. Subramaniam S. Murphy R.C. et al.Update of the LIPID MAPS comprehensive classification system for lipids.J Lipid Res. 2009; 50: S9-S14Crossref PubMed Scopus (1050) Google Scholar, 5Subramaniam S. Fahy E. Gupta S. et al.Bioinformatics and systems biology of the lipidome.Chem Rev. 2011; 111: 6452-6490Crossref PubMed Scopus (126) Google Scholar In the past, lipid studies in CKD have largely been limited to studying the changes at class level of a limited number of lipids such as total cholesterol, triglycerides, low-density lipoprotein (LDL), and high-density lipoprotein with conflicting results in terms of the association between dyslipidemia and progression of CKD.6Chawla V. Greene T. Beck G.J. et al.Hyperlipidemia and long-term outcomes in nondiabetic chronic kidney disease.Clin J Am Soc Nephrol. 2010; 5: 1582-1587Crossref PubMed Scopus (65) Google Scholar, 7Hadjadj S. Duly-Bouhanick B. Bekherraz A. et al.Serum triglycerides are a predictive factor for the development and the progression of renal and retinal complications in patients with type 1 diabetes.Diabetes Metab. 2004; 30: 43-51Abstract Full Text PDF PubMed Scopus (84) Google Scholar, 8Kaysen G.A. Lipid and lipoprotein metabolism in chronic kidney disease.J Ren Nutr. 2009; 19: 73-77Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar, 9Rahman M. Yang W. Akkina S. et al.Relation of serum lipids and lipoproteins with progression of CKD: the CRIC study.Clin J Am Soc Nephrol. 2014; 9: 1190-1198Crossref PubMed Scopus (102) Google Scholar, 10Samuelsson O. Mulec H. Knight-Gibson C. et al.Lipoprotein abnormalities are associated with increased rate of progression of human chronic renal insufficiency.Nephrol Dial Transplant. 1997; 12: 1908-1915Crossref PubMed Scopus (220) Google Scholar, 11Miller M. Stone N.J. Ballantyne C. et al.Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association.Circulation. 2011; 123: 2292-2333Crossref PubMed Scopus (1337) Google Scholar As a result of these limited approaches, the effect of diverse intraclass variation within these lipid classes as well as the alterations in various other classes of lipids on the progression of CKD has remained poorly understood. More recently, the use of conventional lipid measurements for the description of lipoprotein abnormalities in mild CKD has come into question.12de Boer I.H. Astor B.C. Kramer H. et al.Lipoprotein abnormalities associated with mild impairment of kidney function in the multi-ethnic study of atherosclerosis.Clin J Am Soc Nephrol. 2008; 3: 125-132Crossref PubMed Scopus (54) Google Scholar On the other hand, the application of the lipidomics and/or metabolomics approach in a number of diseases such as diabetes,13Rhee E.P. Cheng S. Larson M.G. et al.Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.J Clin Invest. 2011; 121: 1402-1411Crossref PubMed Scopus (446) Google Scholar, 14Dutta T. Chai H.S. Ward L.E. et al.Concordance of changes in metabolic pathways based on plasma metabolomics and skeletal muscle transcriptomics in type 1 diabetes.Diabetes. 2012; 61: 1004-1016Crossref PubMed Scopus (49) Google Scholar cardiovascular diseases,15Hinterwirth H. Stegemann C. Mayr M. Lipidomics: quest for molecular lipid biomarkers in cardiovascular disease.Circ Cardiovasc Genet. 2014; 7: 941-954Crossref PubMed Scopus (61) Google Scholar and other inflammatory processes16Zhou X. Mao J. Ai J. et al.Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics.PLoS One. 2012; 7: e48889Crossref PubMed Scopus (152) Google Scholar has provided characteristic lipid signatures and mechanistic insights to disease processes.17Sas K.M. Karnovsky A. Michailidis G. Pennathur S. Metabolomics and diabetes: analytical and computational approaches.Diabetes. 2015; 64: 718-732Crossref PubMed Scopus (111) Google Scholar These studies provide proof-of-principle on the clinical applicability of the candidate metabolites for risk prediction, an approach that is rarely taken in CKD. In a recently published report, Reis et al.18Reis A. Rudnitskaya A. Chariyavilaskul P. et al.Top-down lipidomics of low density lipoprotein reveal altered lipid profiles in advanced chronic kidney disease.J Lipid Res. 2015; 56: 413-422Crossref PubMed Scopus (55) Google Scholar have compared the lipid signature of LDL in patients at the advanced stage of CKD (stages 4 and 5) with the control group using the liquid chromatography-mass spectrometry-based lipidomics approach. To our knowledge, there is no study in CKD aimed at the identification of lipid signature predictive of incident end-stage kidney disease (ESKD) at early stages of CKD. Therefore, this study examines the systematic identification of prognostic serum lipid metabolites at CKD stages 2 and 3 to predict progression to ESKD using liquid chromatography-mass spectrometry-based lipidomics in the Chronic Renal Insufficiency Cohort (CRIC) patient population. This study is a case-control study nested in the core CRIC study. The design of CRIC is published previously.19Jassim F.A. Image denoising using interquartile range filter with local averaging.Int J Soft Comput Eng. 2013; 2: 2231-2307Google Scholar, 20Feldman H.I. Appel L.J. Chertow G.M. et al.The Chronic Renal Insufficiency Cohort (CRIC) Study: design and methods.J Am Soc Nephrol. 2003; 14: S148-S153Crossref PubMed Google Scholar CRIC is a multicenter cohort of patients with mild-to-moderate CKD, with recruitment starting in 2003 with the goals of examining risk factors for CKD and cardiovascular events, and developing predictive models that would identify high-risk subgroups. The core study has recruited 3939 subjects over a 5-year period through 2008. Inclusion criteria of the subcohort used for this study were eGFR ≥ 30 ml/min at visit year 1 and an age of 18 years or more with no racial or gender restriction. Cases were defined as patients who progressed to ESKD over the next 6 years of follow-up. ESKD is defined as needing chronic dialysis or having kidney transplantation. Controls were defined as patients who were frequency matched with cases by their baseline age, sex, race, and diabetes and had less than 25% decline in eGFR during the 6-year mean follow-up. One milliliter of fasting serum sample from visit year 1 as baseline was obtained from the selected subcohort. Demographic, clinical, and laboratory variables from baseline were retrieved from the corresponding patients. eGFR calculated by CKD Epidemiology Collaboration is used for multivariable adjustments. Liquid chromatography-mass spectrometry-based shotgun lipidomics using a TripleTOF 5600 was applied for lipid identification (see the Supplementary Methods for details). After data acquisition, the missing values for lipids were imputed using the K nearest-neighbor method.17Sas K.M. Karnovsky A. Michailidis G. Pennathur S. Metabolomics and diabetes: analytical and computational approaches.Diabetes. 2015; 64: 718-732Crossref PubMed Scopus (111) Google Scholar, 21Lash J.P. Go A.S. Appel L.J. et al.Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.Clin J Am Soc Nephrol. 2009; 4: 1302-1311Crossref PubMed Scopus (420) Google Scholar Then the data were log2 transformed followed by normalization using the cross-contribution compensating multiple internal standard normalization method.22Altman N.S. An introduction to kernel and nearest-neighbor nonparametric regression.Am Stat. 1992; 46: 175-185Crossref Google Scholar The cohort was randomly divided into the training and test sets with a 2:1 ratio in an attempt to develop the probabilistic predictive model of multimarker panel predictive of progression in the training set followed by its validation in the test set. The compound-by-compound t-test was applied to identify the top differentially regulated lipids that passed the nominal threshold P value of <0.05, followed by the Benjamini-Hochberg procedure for false discovery rate (FDR) correction23Redestig H. Fukushima A. Stenlund H. et al.Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data.Anal Chem. 2009; 81: 7974-7980Crossref PubMed Scopus (127) Google Scholar, 24Storey J.D. Tibshirani R. Statistical significance for genomewide studies.Proc Natl Acad Sci USA. 2003; 100: 9440-9445Crossref PubMed Scopus (7078) Google Scholar accounting for multiple comparisons. In parallel, the partial least square-discriminant analysis (PLS-DA)25Benjamini Y. Drai D. Elmer G. et al.Controlling the false discovery rate in behavior genetics research.Behav Brain Res. 2001; 125: 279-284Crossref PubMed Scopus (2701) Google Scholar, 26Eriksson L. Antti H. Gottfries J. et al.Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm).Anal Bioanal Chem. 2004; 380: 419-429Crossref PubMed Scopus (220) Google Scholar and Random Forest (RF)27Eriksson L. Johansson E. Lindgren F. et al.Megavariate analysis of hierarchical QSAR data.J Comput Aided Mol Des. 2002; 16: 711-726Crossref PubMed Scopus (51) Google Scholar classification methods were applied on the top lipids with nominal significance in the training set to generate the rank of the variable important in projection by each classification method separately (Figure 1). The rationale for using PLS-DA and RF classification methods besides the application of the Benjamini-Hochberg procedure for FDR correction was to assess concordance of the products of different classification methods and to compare if the proposed lipids by different methods differed. Then logistic regression models with and without adjusting for eGFR and UPCR as continuous variables were used to identify the independent predictors of progression from the ranking list of each classification method. The c-statistic,28Svetnik V. Liaw A. Tong C. et al.Random forest: a classification and regression tool for compound classification and QSAR modeling.J Chem Inf Comput Sci. 2003; 43: 1947-1958Crossref PubMed Scopus (2134) Google Scholar category-free continuous net reclassification improvement, and integrated discrimination improvement were calculated for the probabilistic model of the multimarker panels derived from logistic regression, and their improvement over the base model was tested in the training set and replicated in the test set. Linear regression analysis was applied to test the relationship between the mean ratio of lipid levels on the log2 scale in cases and controls with the number of carbons or double bonds within each lipid class. Over-representative enrichment analysis using the 2-sided Fisher exact test was applied to test the enrichment of lipid classes by taking into account the number of metabolites that have passed the FDR threshold and the number of metabolites that are detected within each class of lipids as compared with the rest of other lipids in the entire dataset. The lipid correlation network was built using Metscape.29DeLong E.R. DeLong D.M. Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.Biometrics. 1988; 44: 837-845Crossref PubMed Scopus (14119) Google Scholar We applied a sparse graphical modeling algorithm based on the desparsified graphical lasso modeling procedure30Karnovsky A. Weymouth T. Hull T. et al.Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.Bioinformatics. 2012; 28: 373-380Crossref PubMed Scopus (286) Google Scholar to calculate the Benjamini-Hochberg-adjusted partial correlations between each pair of lipids that displayed a significant difference between cases and controls using the “Correlation Calculator” tool (http://metscape.med.umich.edu/calculator.html).31Jankova J, van de Geer S. Honest confidence regions and optimality in high-dimensional precision matrix estimation. Available at: https://arxiv.org/pdf/1507.02061v2.pdf. Accessed September 8, 2016.Google Scholar The study has 80% power at α = 0.05 to detect an increase in the area under receiver operating characteristics from 0.8 to 0.9 using a 2-sided z-test.32Hanley J.A. McNeil B.J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases.Radiology. 1983; 148: 839-843Crossref PubMed Scopus (5979) Google Scholar MetaboAnalyst version 2.0,33Obuchowski N.A. McClish D.K. Sample size determination for diagnostic accuracy studies involving binormal ROC curve indices.Stat Med. 1997; 16: 1529-1542Crossref PubMed Scopus (221) Google Scholar, 34Xia J. Mandal R. Sinelnikov I.V. et al.MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis.Nucleic Acids Res. 2012; 40: W127-W133Crossref PubMed Scopus (929) Google Scholar R-Metabolomics version 0.1.3 (Melbourne, Australia),35Xia J. Psychogios N. Young N. Wishart D.S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation.Nucleic Acids Res. 2009; 37: W652-W660Crossref PubMed Scopus (1284) Google Scholar SPSS version 22 (Armonk, NY), and STATA version 10 (College Station, TX) were applied for the analysis. From patients aged ≥18 years with baseline eGFR ≥ 30 ml/min, 79 patients who progressed to ESKD over 6 years of follow-up were selected and frequency matched with 121 nonprogressors (<25% decline in eGFR during follow-up) by age, sex, race, and diabetes. Mean age was 59 years (SD = 10). There were 112 males (56%), 100 patients with diabetes (50%), and equal numbers of African American and Caucasians (100 in each group). The distribution of baseline demographic characteristics, medications, and comorbidities in cases and controls is presented for the entire cohort (Table 1) as well as in the training and the test subsets (Supplementary Table S1). Accordingly, the progressors are reasonably matched with nonprogressors as is evident by the lack of clinical and statistical differences in most of the baseline variables. However, the mean eGFR was lower by 10 ml/min, and the median UPCR was higher by 0.7 in progressors as compared with nonprogressors at baseline (P < 0.001).Table 1Comparison of baseline characteristics of the progressors and nonprogressorsVariableNonprogressorsProgressorsP valueN12179Age (yr)59 ± 1059 ± 100.705Male gender (%)68 (56.2)44 (55.7)0.944Race0.885 White (%)61 (50.4)39 (49.4) Black (%)60 (49.6)40 (50.6)Current smoking (%)23 (19.0)16 (20.3)0.828Medications ACEI (%)61 (50.4)41 (52.6)0.767 ARB (%)33 (27.3)23 (29.5)0.735 Beta blocker (%)56 (46.3)44 (56.4)0.163 Ca channel blocker (%)48 (39.7)42 (53.8)0.050 Diuretics (%)68 (56.2)48 (61.5)0.456 Statins (%)73 (60.6)51 (65.4)0.473 Other lipid-lowering agents (%)15 (12.4)8 (10.3)0.645 Steroids (%)80 (66.1)53 (67.1)0.887 Aspirin (%)66 (54.5)39 (50.0)0.531 Antiplatelets (%)67 (55.4)42 (53.8)0.833Comorbidities (history) Diabetes (%)57 (47.1)43 (54.4)0.311 Hypertension (%)103 (85.1)73 (92.4)0.121 PVD (%)9 (7.4)7 (9.0)0.717 CHF (%)9 (7.4)14 (17.7)0.026 Stroke (%)6 (5.0)9 (11.4)0.091 A-fib (%)19 (15.7)22 (27.8)0.038Height (m)1.7 ± 0.11.7 ± 0.10.492Weight (kg)93 ± 2197 ± 280.229BMI (kg/m2)32.0 ± 7.133.7 ± 8.90.139Waist (m)1.1 ± 0.21.1 ± 0.20.203Systolic BP (mm Hg)128 ± 22132 ± 190.162Diastolic BP (mm Hg)71 ± 1471 ± 130.774Pulse (per min)67 ± 1169 ± 110.205HbA1c (%)aN for HbA1c is 54 nonprogressors and 43 progressors all in diabetic patients.7.8 ± 1.88.2 ± 1.80.080Sodium (mmol/l)140 ± 2.3140 ± 2.90.455CO2 (mmol/l)24.8 ± 2.724.1 ± 2.80.110Chloride (mmol/l)104 ± 3105 ± 40.093ALT (IU/l)35 ± 2132 ± 130.249AST (IU/l)26 ± 1226 ± 130.695TAG (mg/dl)149 ± 104154 ± 770.699Total cholesterol (mg/dl)182 ± 47179 ± 470.648HDL (mg/dl)49 ± 1447 ± 150.409LDL (mg/dl)103 ± 3896 ± 310.226eGFR (ml/min)48 ± 1338 ± 8<0.001UPCRbValues are median and interquartile range.0.1 [0.1–0.4]1.8 [0.2–2.1]<0.001ACEI, angiotensin-converting-enzyme inhibitor; ALT, alanine aminotransferase; ARB, angiotensin receptor blocker; AST, aspartate aminotransferase; BMI, body mass index; BP, blood pressure; CHF, congestive heart failure; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; PVD, peripheral vascular disease; TAG, triacylglycerol; UPCR, urine protein-to-creatinine ratio.a N for HbA1c is 54 nonprogressors and 43 progressors all in diabetic patients.b Values are median and interquartile range. Open table in a new tab ACEI, angiotensin-converting-enzyme inhibitor; ALT, alanine aminotransferase; ARB, angiotensin receptor blocker; AST, aspartate aminotransferase; BMI, body mass index; BP, blood pressure; CHF, congestive heart failure; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; PVD, peripheral vascular disease; TAG, triacylglycerol; UPCR, urine protein-to-creatinine ratio. Figure 1 illustrates the flow of identification and validation of the independent candidates for predicting progression of CKD. First, the entire cohort was randomly divided into the discovery or training set (77 nonprogressors and 57 progressors) and the validation or test set (44 nonprogressors and 22 progressors) with a 2:1 ratio. Using the training set, from the 510 identified known lipids, 128 lipids that were unlikely to be used in the downstream analyses were filtered out using the interquartile range filtering protocol36De Livera AM, Bowne JB. Package ‘Metabolomics’. Available at: https://cran.r-project.org/web/packages/metabolomics/metabolomics.pdf. Accessed September 8, 2016.Google Scholar that left 382 lipids of which 49 passed the nominal significance by a t-test (P < 0.05). From the top 49 lipids, we also used PLS-DA and RF besides using the Benjamini-Hochberg procedure for FDR correction to explore if different classification methods nominate different candidates. Figure 2 illustrates the distribution of statistical significance by the log2 mean fold change of the identified lipids in progressors versus nonprogressors in the training set, suggesting lower abundance of differentially regulated diacylglycerols (DAGs) and cholesterol esters(CEs) in progressors. Supplementary Table S3 shows the compound-by-compound comparison of identified lipids by status of progression using a t-test, as well as the corresponding unadjusted and adjusted logistic regression models. Accordingly, from the FDR-projected list, the top 10 lipids coincide with q ≤ 0.058. We then identified the top lipid candidates by each classification method followed by internal validation in the test set. In the next step, we compared the products of different classification methods. Supplementary Table S4 suggests a high concordance between the top 10 lipids ranked by the 3 classification methods particularly for the DAGs. We then used logistic regression models on the top 10 lipids of each classification method to further narrow down to the independent predictors of progression after adjusting for eGFR, UPCR, age, sex, race, diabetes, hypertension, and congestive heart failure. As a result, the independent lipids projected by the FDR list were DAG 36:0, DAG 32:0, and monoacylglycerol (MAG) 16:0 (most conservative model). The independent lipids projected from the PLS-DA were DAG 36:0, DAG 32:0, MAG 26:2, MAG 20:0, MAG 16:0, and phosphatidylcholine (PC) 34:3 (most inclusive model), and those projected by RF were DAG 36:0, DAG 32:0, MAG 16:0, and PC 34:3 (a model in between), suggesting a high concordance in the final products of different classification methods. Figure 3 shows that the significance or direction of the risk associated with each 1 SD change in abundance was unchanged in unadjusted to fully adjusted models by age, sex, race, diabetes, hypertension, and congestive heart failure. According to the FDR-driven models and after full adjustment, each 1 SD increase in abundance of DAG 36:0 and DAG 32:0 was associated with the reduced risk of progression by 71% (95% CI: 43% to 85%, P < 0.001) and 66% (95% CI: 33% to 83%, P = 0.002), respectively. On the other hand, each 1 SD increase in abundance of MAG 16:0 was associated with increased risk of progression by 5.45-fold (95% CI: 2.51 to 11.86, P < 0.001), a result similar to the unadjusted model. Similar results were obtained from the RF- and PLS-DA-driven methods (Figure 3). Table 2 shows that in the training set, irrespective of the classification method, the addition of the multimarker panel to the base model (eGFR + UPCR) has significantly improved the c-statistic (P < 0.05). We also showed in Table 2 that such an improvement was reproducible in the test set by the application of the corresponding multivariable probabilistic model developed in the training set (P < 0.05). In addition, the net reclassification improvement and integrated discrimination improvement were highly significant in all multimarker lipid panels alone and when added to the base model in the training set (P ≤ 0.0005), an observation that was reproduced in the test set using the models that were developed in the training set. The statistically significant net reclassification improvement and integrated discrimination improvement imply that the sum of correctly classified progressors and correctly classified nonprogressors by the new models is significantly higher than what was obtained from the base model (eGFR + UPCR) alone.Table 2Comparison of c-statistic, category-free continuous NRI, and IDI and their 95% confidence intervals in the “training set,” “test set,” and the entire cohort by base models, lipids, and their combinations to predict progression of chronic kidney disease to end-stage kidney diseaseModelsTraining set (Npatient = 134)Test set (Npatient = 66)C (95% CI)NRIIDI (95% CI)c (95% CI)NRIIDI (95% CI)Base (eGFR + UPCR)0.83 (0.76–0.90)Sensitivity: 31/57–0.78 (0.67–0.89)Sensitivity: 6/22–Specificity: 41/77Specificity: 18/44FDR-driven models Lipids (n = 3)0.86 (0.79–0.92)aP < 0.05, bP < 0.001.Event NRI: 31/570.22 (0.15–0.29)bP < 0.05, bP < 0.001.0.81 (0.70–0.93)Event NRI: 10/220.28 (0.16–0.40)bP < 0.05, bP < 0.001.Nonevent NRI: 49/77Nonevent NRI: 26/44Overall NRI: 1.18bP < 0.05, bP < 0.001.Overall NRI: 1.05bP < 0.05, bP < 0.001. Lipids + base model0.92 (0.88–0.97)aP < 0.05, bP < 0.001.Event NRI: 53/570.23 (0.16–0.30)bP < 0.05, bP < 0.001.0.91 (0.83–0.99)aP < 0.05, bP < 0.001.Event NRI: 12/220.38 (0.25–0.52)bP < 0.05, bP < 0.001.Nonevent NRI: 49/77Nonevent NRI: 30/44Overall NRI: 1.34bP < 0.05, bP < 0.001.Overall NRI: 1.23bP < 0.05, bP < 0.001.RF-driven models Lipids (n = 4)0.89 (0.83–0.95)aP < 0.05, bP < 0.001.Event NRI: 35/570.28 (0.20–0.35)bP < 0.05, bP < 0.001.0.85 (0.76–0.95)aP < 0.05, bP < 0.001.Event NRI: 10/220.28 (0.16–0.41)bP < 0.05, bP < 0.001.Nonevent NRI: 53/77Nonevent NRI: 26/44Overall NRI: 1.30bP < 0.05, bP < 0.001.Overall NRI: 1.05bP < 0.05, bP < 0.001. Lipids + base model0.94 (0.90–0.98)aP < 0.05, bP < 0.001.Event NRI: 41/570.30 (0.22–0.39)bP < 0.05, bP < 0.001.0.93 (0.87–0.99)aP < 0.05, bP < 0.001.Event NRI: 12/220.40 (0.26–0.53)bP < 0.05, bP < 0.001.Nonevent NRI: 59/77Nonevent NRI: 30/44Overall NRI: 1.49bP < 0.05, bP < 0.001.Overall NRI: 1.23bP < 0.05, bP < 0.001.PLS-DA driven models Lipids (n = 6)0.92 (0.87–0.97)aP < 0.05, bP < 0.001.Event NRI: 37/570.36 (0.25–0.42)bP < 0.05, bP < 0.001.0.80 (0.69–0.91)Event NRI: 8/220.20 (0.09–0.31)bP < 0.05, bP < 0.001.Nonevent NRI: 53/77Nonevent NRI:24/44Overall NRI: 1.34bP < 0.05, bP < 0.001.Overall NRI: 0.91bP < 0.05, bP < 0.001. Lipids + base model0.95 (0.92–0.99)aP < 0.05, bP < 0.001.Event NRI: 43/570.37 (0.28–0.45)bP < 0.05, bP < 0.001.0.90 (0.82–0.97)aP < 0.05, bP < 0.001.Event NRI: 12/220.27 (0.15–0.39)bP < 0.05, bP < 0.001.Nonevent NRI: 65/77Nonevent NRI: 24/44Overall NRI: 1.60bP < 0.05, bP < 0.001.Overall NRI: 1.09bP < 0.05, bP < 0.001.P values are comparisons with the corresponding base model. The components of FDR-driven model were DAG 36:0, DAG 32:0, MAG 16:0. The components of the RF-driven model were DAG 36:0, DAG 32:0, MAG 16:0, and PC 34:3. The components of the PLS-DA-driven model were DAG 36:0, DAG 32:0, MAG 26:2, MAG 20:0, MAG 16:0, and PC 34:3.CI, confidence interval; DAG, diacylglycerol; eFGR, estimated glomerular filtration rate; FDR, false discovery rate; IDI, integrated discrimination improvement; MAG, monoacylglycerol; NRI, net reclassification improvement; PC, phosphatidylcholine; PLS-DA, partial least square-discriminant analysis; RF, Random Forest; UPCR, urine protein-to-creatinine ratio.a P < 0.05, bP < 0.001. Open table in a new tab P values are comparisons with the corresponding base model. The compon" @default.
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