title: A combined epidemiologic and metabolomic approach improves CKD prediction.
abstract: Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry-based metabolite profiling on plasma from 1434 participants in the Framingham Heart Study (FHS) who did not have CKD at baseline. During the following 8 years, 123 individuals developed CKD, defined by an estimated GFR of <60 ml/min per 1.73 m(2). Numerous metabolites were associated with incident CKD, including 16 that achieved the Bonferroni-adjusted significance threshold of P≤0.00023. To explore how the human kidney modulates these metabolites, we profiled arterial and renal venous plasma from nine individuals. Nine metabolites that predicted CKD in the FHS cohort decreased more than creatinine across the renal circulation, suggesting that they may reflect non-GFR-dependent functions, such as renal metabolism and secretion. Urine isotope dilution studies identified citrulline and choline as markers of renal metabolism and kynurenic acid as a marker of renal secretion. In turn, these analytes remained associated with incident CKD in the FHS cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, and proteinuria at baseline. Addition of a multimarker metabolite panel to clinical variables significantly increased the c-statistic (0.77-0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 to 0.95; P<0.0001). Thus, the addition of metabolite profiling to clinical data may significantly improve the ability to predict whether an individual will develop CKD by identifying predictors of renal risk that are independent of estimated GFR.
authors.affiliation: Nephrology Division, Massachusetts General Hospital, Boston, MA 02114, USA. eprhee@partners.org
authors.forename: Eugene P
authors.initials: EP
authors.lastname: Rhee
authors.forename: Clary B
authors.initials: CB
authors.lastname: Clish
authors.forename: Anahita
authors.initials: A
authors.lastname: Ghorbani
authors.forename: Martin G
authors.initials: MG
authors.lastname: Larson
authors.forename: Sammy
authors.initials: S
authors.lastname: Elmariah
authors.forename: Elizabeth
authors.initials: E
authors.lastname: McCabe
authors.forename: Qiong
authors.initials: Q
authors.lastname: Yang
authors.forename: Susan
authors.initials: S
authors.lastname: Cheng
authors.forename: Kerry
authors.initials: K
authors.lastname: Pierce
authors.forename: Amy
authors.initials: A
authors.lastname: Deik
authors.forename: Amanda L
authors.initials: AL
authors.lastname: Souza
authors.forename: Laurie
authors.initials: L
authors.lastname: Farrell
authors.forename: Carly
authors.initials: C
authors.lastname: Domos
authors.forename: Robert W
authors.initials: RW
authors.lastname: Yeh
authors.forename: Igor
authors.initials: I
authors.lastname: Palacios
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rosenfield
authors.forename: Ramachandran S
authors.initials: RS
authors.lastname: Vasan
authors.forename: Jose C
authors.initials: JC
authors.lastname: Florez
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Wang
authors.forename: Caroline S
authors.initials: CS
authors.lastname: Fox
authors.forename: Robert E
authors.initials: RE
authors.lastname: Gerszten
mesh_terms: D000368:Aged; D015415:Biomarkers / Q000097:blood; D002853:Chromatography, Liquid; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D007676:Kidney Failure, Chronic / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology*; D007677:Kidney Function Tests; D008297:Male; D013058:Mass Spectrometry; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D011446:Prospective Studies; D012307:Risk Factors
chemical_list: D015415:Biomarkers