Protein Biomarkers of Ultra-Processed Food Consumption and Risk of Coronary Heart Disease, Chronic Kidney Disease, and All-Cause Mortality.

BACKGROUND: There is a need to understand the underlying biological mechanisms through which ultra-processed foods negatively affect health. Proteomics offers a valuable tool with which to examine different aspects of ultra-processed foods and their impact on health. OBJECTIVES: The aim of this study was to identify protein biomarkers of usual ultra-processed food consumption and assess their relation to the incidence of coronary heart disease (CHD), chronic kidney disease (CKD), and all-cause mortality risk. METHODS: A total of 9361 participants from the Atherosclerosis Risk in Communities visit 3 (1993-1995) were included. Dietary intake was assessed using a 66-item food-frequency questionnaire and the processing levels were categorized on the basis of the Nova classification. Plasma proteins were detected using an aptamer-based proteomic assay. We used multivariable linear regressions to examine the association between ultra-processed food and proteins, and Cox proportional hazard models to identify associations between ultra-processed food-related proteins and health outcomes. Models extensively controlled for sociodemographic characteristics, health behaviors, and clinical factors. RESULTS: Eight proteins (6 positive, 2 negative) were identified as significantly associated with ultra-processed food consumption. Over a median follow-up of 22 y, there were 1276, 3084, and 5127 cases of CHD, CKD, and death, respectively. Three, 5, and 3 ultra-processed food-related proteins were associated with each outcome, respectively. One protein (ß-glucuronidase) was significantly associated with a higher risk of all 3 outcomes, and 3 proteins (receptor-type tyrosine-protein phosphatase U, C-C motif chemokine 25, and twisted gastrulation protein homolog 1) were associated with a higher risk of 2 outcomes. CONCLUSIONS: We identified a panel of protein biomarkers that were significantly associated with ultra-processed food consumption. These proteins may be considered potential biomarkers for ultra-processed food intake and may elucidate the biological processes through which ultra-processed foods impact health outcomes.

Serum Metabolomic Markers of Artificially Sweetened Beverage Consumption.

BACKGROUND: The consumption of artificially sweetened beverages is on the rise. Use of artificial sweeteners has been associated with adverse health outcomes. There is a need to identify novel objective biomarkers of artificially sweetened beverages in order to improve dietary assessment and to provide insight into their metabolic impact. OBJECTIVES: We aimed to identify serum metabolites that are associated with artificially sweetened beverage consumption. METHODS: In the Atherosclerosis Risk in Communities (ARIC) study, consumption of artificially sweetened beverages was assessed using a food frequency questionnaire and fasting serum samples were collected during the first study visit (1987-1989). Participants were categorized as nonusers if they reported almost never consumption of artificially sweetened beverages, moderate users for 1 glass/mo to 6 glasses/wk, and heavy users for ≥1 glasses/d. Untargeted metabolomic profiling was conducted in 2 subgroups (subgroup 1: n = 1866, profiled in 2010; subgroup 2 profiled in 2014: n = 2072), and 360 metabolites were analyzed. In this secondary data analysis, multivariable linear regression models were used, adjusting for demographics, health behaviors, health status, and dietary factors. Analyses were conducted in each subgroup and results meta-analyzed. RESULTS: In a meta-analysis of 3938 generally healthy participants (mean age, 54 y; 60% women; 62% Black participants) from ARIC study visit 1, 11 serum metabolites were significantly associated with artificially sweetened beverage consumption. Heavier consumption of artificially sweetened beverages was associated with higher concentrations of 10 metabolites (saccharin, threonate, erythronate, glycerate, gluconate, mannitol, glucose, tryptophan betaine, trehalose, and N6-acetyllysine) and lower concentrations of glycocholenate sulfate. CONCLUSIONS: Eleven serum metabolites are related to artificially sweetened beverage intake, which consist of known sugar substitutes, processed food additives, glucose-related compounds, and gut microbiome-related metabolites. These findings enhance our knowledge of the metabolic activity of artificial sweeteners and suggests new biomarkers for monitoring intake.

Proteome-wide analysis identifies plasma immune regulators of amyloid-beta progression.

While immune function is known to play a mechanistic role in Alzheimer's disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-ß (Aß) progression - a central feature of AD - remains unknown. In the Baltimore Longitudinal Study of Aging, we quantified 942 immunological proteins in plasma and identified 32 (including CAT [catalase], CD36 [CD36 antigen], and KRT19 [keratin 19]) associated with rates of cortical Aß accumulation measured with positron emission tomography (PET). Longitudinal changes in a subset of candidate proteins also predicted Aß progression, and the mid- to late-life (20-year) trajectory of one protein, CAT, was associated with late-life Aß-positive status in the Atherosclerosis Risk in Communities (ARIC) study. Genetic variation that influenced plasma levels of CAT, CD36 and KRT19 predicted rates of Aß accumulation, including causal relationships with Aß PET levels identified with two-sample Mendelian randomization. In addition to associations with tau PET and plasma AD biomarker changes, as well as expression patterns in human microglia subtypes and neurovascular cells in AD brain tissue, we showed that 31 % of candidate proteins were related to mid-life (20-year) or late-life (8-year) dementia risk in ARIC. Our findings reveal plasma proteins associated with longitudinal Aß accumulation, and identify specific peripheral immune mediators that may contribute to the progression of AD pathophysiology.

Plasma Metabolomics of Dietary Intake of Protein-Rich Foods and Kidney Disease Progression in Children.

OBJECTIVE: Evidence regarding the efficacy of a low-protein diet for patients with CKD is inconsistent and recommending a low-protein diet for pediatric patients is controversial. There is also a lack of objective biomarkers of dietary intake. The purpose of this study was to identify plasma metabolites associated with dietary intake of protein and to assess whether protein-related metabolites are associated with CKD progression. METHODS: Nontargeted metabolomics was conducted in plasma samples from 484 Chronic Kidney Disease in Children (CKiD) participants. Multivariable linear regression estimated the cross-sectional association between 949 known, nondrug metabolites and dietary intake of total protein, animal protein, plant protein, chicken, dairy, nuts and beans, red and processed meat, fish, and eggs, adjusting for demographic, clinical, and dietary covariates. Cox proportional hazards models assessed the prospective association between protein-related metabolites and CKD progression defined as the initiation of kidney replacement therapy or 50% eGFR reduction, adjusting for demographic and clinical covariates. RESULTS: One hundred and twenty-seven (26%) children experienced CKD progression during 5 years of follow-up. Sixty metabolites were significantly associated with dietary protein intake. Among the 60 metabolites, 10 metabolites were significantly associated with CKD progression (animal protein: n = 1, dairy: n = 7, red and processed meat: n = 2, nuts and beans: n = 1), including one amino acid, one cofactor and vitamin, 4 lipids, 2 nucleotides, one peptide, and one xenobiotic. 1-(1-enyl-palmitoyl)-2-oleoyl-glycerophosphoethanolamine (GPE, P-16:0/18:1) was positively associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 88% higher risk of CKD progression. 3-ureidopropionate was inversely associated with dietary intake of red and processed meat, and a doubling of its abundance was associated with 48% lower risk of CKD progression. CONCLUSIONS: Untargeted plasma metabolomic profiling revealed metabolites associated with dietary intake of protein and CKD progression in a pediatric population.

Proteomic analyses reveal plasma EFEMP1 and CXCL12 as biomarkers and determinants of neurodegeneration.

INTRODUCTION: Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes. METHODS: We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk. RESULTS: Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization. DISCUSSION: Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration. HIGHLIGHTS: Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.

Comparison of Proteomic Measurements Across Platforms in the Atherosclerosis Risk in Communities (ARIC) Study.

BACKGROUND: The plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays. METHODS: We assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011-2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status. RESULTS: There were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: -0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status. CONCLUSIONS: There is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.

Plasma Protein Biomarkers of Healthy Dietary Patterns: Results from the Atherosclerosis Risk in Communities Study and the Framingham Heart Study.

BACKGROUND: Molecular mechanisms underlying the benefits of healthy dietary patterns are poorly understood. Identifying protein biomarkers of dietary patterns can contribute to characterizing biological pathways influenced by food intake. OBJECTIVES: This study aimed to identify protein biomarkers associated with four indexes of healthy dietary patterns: Healthy Eating Index-2015 (HEI-2015); Alternative Healthy Eating Index-2010 (AHEI-2010); DASH diet; and alternate Mediterranean Diet (aMED). METHODS: Analyses were conducted on 10,490 Black and White men and women aged 49-73 y from the ARIC study at visit 3 (1993-1995). Dietary intake data were collected using a food frequency questionnaire, and plasma proteins were quantified using an aptamer-based proteomics assay. Multivariable linear regression models were used to examine the association between 4955 proteins and dietary patterns. We performed pathway overrepresentation analysis for diet-related proteins. An independent study population from the Framingham Heart Study was used for replication analyses. RESULTS: In the multivariable-adjusted models, 282 out of 4955 proteins (5.7%) were significantly associated with at least one dietary pattern (HEI-2015: 137; AHEI-2010: 72; DASH: 254; aMED: 35; P value < 0.05/4955 = 1.01 × 10-5). There were 148 proteins that were associated with only one dietary pattern (HEI-2015: 22; AHEI-2010: 5; DASH: 121; aMED: 0), and 20 proteins were associated with all four dietary patterns. Five unique biological pathways were significantly enriched by diet-related proteins. Seven out of 20 proteins associated with all dietary patterns in the ARIC study were available for replication analyses, and 6 out of these 7 proteins were consistent in direction and significantly associated with at least 1 dietary pattern in the Framingham Heart Study (HEI-2015: 2; AHEI-2010: 4; DASH: 6; aMED: 4; P value < 0.05/7 = 7.14 × 10-3). CONCLUSIONS: A large-scale proteomic analysis identified plasma protein biomarkers that are representative of healthy dietary patterns among middle-aged and older US adult population. These protein biomarkers may be useful objective indicators of healthy dietary patterns.

Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study.

BACKGROUND: Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake. OBJECTIVES: We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults. METHODS: Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785). RESULTS: In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS. CONCLUSIONS: We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN). METHODS: Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause. RESULTS: ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites. CONCLUSION: ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.

Metabolomics of Dietary Acid Load and Incident Chronic Kidney Disease.

OBJECTIVE: Blood biomarkers of dietary intake are more objective than self-reported dietary intake. Metabolites associated with dietary acid load were previously identified in 2 chronic kidney disease (CKD) populations. We aimed to extend these findings to a general population, replicating their association with dietary acid load, and investigating whether the individual biomarkers were prospectively associated with incident CKD. METHODS: Among 15,792 participants in the Atherosclerosis Risk in Communities cohort followed up from 1987 to 1989 (baseline) to 2019, we evaluated 3,844 black and white men and women with dietary and metabolomic data in cross-sectional and prospective analyses. We hypothesized that a higher dietary acid load (using equations for potential renal acid load and net endogenous acid production) was associated with lower serum levels of 12 previously identified metabolites: indolepropionylglycine, indolepropionate, N-methylproline, N-δ-acetylornithine, threonate, oxalate, chiro-inositol, methyl glucopyranoside, stachydrine, catechol sulfate, hippurate, and tartronate. In addition, we hypothesized that lower serum levels of these 12 metabolites were associated with higher risk of incident CKD. RESULTS: Eleven out of 12 metabolites were significantly inversely associated with dietary acid load, after adjusting for demographics, socioeconomic status, health behaviors, health status, and estimated glomerular filtration rate: indolepropionylglycine, indolepropionate, N-methylproline, threonate, oxalate, chiro-inositol, catechol sulfate, hippurate, methyl glucopyranoside (α + ß), stachydrine, and tartronate. N-methylproline was inversely associated with incident CKD (hazard ratio: 0.95, 95% confidence interval: 0.91, 0.99, P = .01). The metabolomic biomarkers of dietary acid load significantly improved prediction of elevated dietary acid load estimated using dietary data, beyond covariates (difference in C statistics: 0.021-0.077, P ≤ 1.08 × 10-3). CONCLUSION: Inverse associations between candidate biomarkers of dietary acid load were replicated in a general population. N-methylproline, representative of citrus fruit consumption, is a promising marker of dietary acid load and could represent an important pathway between dietary acid load and CKD.

Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases.

BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. METHODS: We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS ( n =765,348) and UK Biobank GWAS (90% of the cohort; n =451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data ( n =45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study ( n =8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR. RESULTS: The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α -1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR. CONCLUSIONS: A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.

Proteins Associated with Risk of Kidney Function Decline in the General Population.

BACKGROUND: Proteomic profiling may allow identification of plasma proteins that associate with subsequent changesin kidney function, elucidating biologic processes underlying the development and progression of CKD. METHODS: We quantified the association between 4877 plasma proteins and a composite outcome of ESKD or decline in eGFR by ≥50% among 9406 participants in the Atherosclerosis Risk in Communities (ARIC) Study (visit 3; mean age, 60 years) who were followed for a median of 14.4 years. We performed separate analyses for these proteins in a subset of 4378 participants (visit 5), who were followed at a later time point, for a median of 4.4 years. For validation, we evaluated proteins with significant associations (false discovery rate <5%) in both time periods in 3249 participants in the Chronic Renal Insufficiency Cohort (CRIC) and 703 participants in the African American Study of Kidney Disease and Hypertension (AASK). We also compared the genetic determinants of protein levels with those from a meta-analysis genome-wide association study of eGFR. RESULTS: In models adjusted for multiple covariates, including baseline eGFR and albuminuria, we identified 13 distinct proteins that were significantly associated with the composite end point in both time periods, including TNF receptor superfamily members 1A and 1B, trefoil factor 3, and ß-trace protein. Of these proteins, 12 were also significantly associated in CRIC, and nine were significantly associated in AASK. Higher levels of each protein associated with higher risk of 50% eGFR decline or ESKD. We found genetic evidence for a causal role for one protein, lectin mannose-binding 2 protein (LMAN2). CONCLUSIONS: Large-scale proteomic analysis identified both known and novel proteomic risk factors for eGFR decline.

Validation of a Metabolite Panel for a More Accurate Estimation of Glomerular Filtration Rate Using Quantitative LC-MS/MS.

BACKGROUND: Clinical practice guidelines recommend estimation of glomerular filtration rate (eGFR) using validated equations based on serum creatinine (eGFRcr), cystatin C (eGFRcys), or both (eGFRcr-cys). However, when compared with the measured GFR (mGFR), only eGFRcr-cys meets recommended performance standards. Our goal was to develop a more accurate eGFR method using a panel of metabolites without creatinine, cystatin C, or demographic variables. METHODS: An ultra-performance liquid chromatography-tandem mass spectrometry assay for acetylthreonine, phenylacetylglutamine, pseudouridine, and tryptophan was developed, and a 20-day, multiinstrument analytical validation was conducted. The assay was tested in 2424 participants with mGFR data from 4 independent research studies. A new GFR equation (eGFRmet) was developed in a random subset (n = 1615) and evaluated in the remaining participants (n = 809). Performance was assessed as the frequency of large errors [estimates that differed from mGFR by at least 30% (1 - P30); goal <10%]. RESULTS: The assay had a mean imprecision (≤10% intraassay, ≤6.9% interassay), linearity over the quantitative range (r 2 > 0.98), and analyte recovery (98.5%-113%). There was no carryover, no interferences observed, and analyte stability was established. In addition, 1 - P30 in the validation set for eGFRmet (10.0%) was more accurate than eGFRcr (13.1%) and eGFRcys (12.0%) but not eGFRcr-cys (8.7%). Combining metabolites, creatinine, cystatin C, and demographics led to the most accurate equation (7.0%). Neither equation had substantial variation among population subgroups. CONCLUSIONS: The new eGFRmet equation could serve as a confirmatory test for GFR estimation.

Metabolomic profiling to improve glomerular filtration rate estimation: a proof-of-concept study.

BACKGROUND: Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates. METHODS: We performed a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors >30% of mGFR (1-P30), before and after bias correction. RESULTS: Of untargeted metabolites in the AASK and MESA, 283 of 780 (36%) and 387 of 1447 (27%), respectively, were significantly correlated (P ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9%, respectively) than eGFRcr [11.2 and 18.5%, respectively (P < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6% (P = 0.02) and 9.1% (P < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7% (P > 0.05) and 9.1% (P < 0.05), respectively]. A panel excluding creatinine and demographics still performed well [1-P30 6.4% (P = 0.11) and 3.4% (P < 0.001) in the AASK and MESA] versus eGFRcr. CONCLUSIONS: Multimetabolite panels can enable accurate GFR estimation. Metabolomic equations, preferably excluding creatinine and demographic characteristics, should be tested for robustness and generalizability as a potential confirmatory test when eGFRcr is unreliable.

Novel "Predictor Patch" Method for Adding Predictors Using Estimates From Outside Datasets　- A Proof-of-Concept Study Adding Kidney Measures to Cardiovascular Mortality Prediction.

BACKGROUND: Cardiovascular guidelines include risk prediction models for decision making that lack the capacity to include novel predictors.Methods and Results:We explored a new "predictor patch" approach to calibrating the predicted risk from a base model according to 2 components from outside datasets: (1) the difference in observed vs. expected values of novel predictors and (2) the hazard ratios (HRs) for novel predictors, in a scenario of adding kidney measures for cardiovascular mortality. Using 4 US cohorts (n=54,425) we alternately chose 1 as the base dataset and constructed a base prediction model with traditional predictors for cross-validation. In the 3 other "outside" datasets, we developed a linear regression model with traditional predictors for estimating expected values of glomerular filtration rate and albuminuria and obtained their adjusted HRs of cardiovascular mortality, together constituting a "patch" for adding kidney measures to the base model. The base model predicted cardiovascular mortality well in each cohort (c-statistic 0.78-0.91). The addition of kidney measures using a patch significantly improved discrimination (cross-validated ∆c-statistic 0.006 [0.004-0.008]) to a similar degree as refitting these kidney measures in each base dataset. CONCLUSIONS: The addition of kidney measures using our new "predictor patch" approach based on estimates from outside datasets improved cardiovascular mortality prediction based on traditional predictors, providing an option to incorporate novel predictors to an existing prediction model.

Resistant hypertension and cardiovascular disease mortality in the US: results from the National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: Apparent treatment-resistant hypertension (aTRH) is a common condition associated with risk of cardiovascular events. However, the risk of cardiovascular mortality associated with aTRH in the US population is unknown. We aimed to assess the risk of cardiovascular disease (CVD) mortality associated with aTRH in the US population. METHODS: We analyzed data from 6357 adult hypertensive participants of the National Health and Nutrition Examination Survey (1988-1994 and 1999-2010) linked to the National Death Index. Based on presence of uncontrolled hypertension [blood pressure (BP) ≥140/90 mmHg] and the number of antihypertensives prescribed, we classified participants into the following groups: non-aTRH (BP < 140/90 mmHg and ≤ 3 antihypertensives); controlled aTRH (BP < 140/90 mmHg and ≥ 4 antihypertensives); and uncontrolled aTRH (BP ≥140/90 mmHg and ≥ 3 antihypertensives). RESULTS: Of the 6357 participants, 1522 had aTRH, representing a US prevalence of 7.6 million. Of the participants with aTRH, 432 had controlled aTRH and 1090 had uncontrolled aTRH. During follow-up (median 6 years), there were 550 CVD deaths. The cumulative incidence of CVD mortality was significantly higher in the aTRH group compared with non-aTRH group (log-rank p < 0.001). In fully adjusted models, aTRH was associated with a 47% higher risk of CVD mortality compared with the non-aTRH group [1.47 (1.1-1.96)]. Similar increase in risk of CVD mortality was noted across aTRH subgroups compared with the non-aTRH group: controlled aTRH [1.66 (1.03-2.68)] and uncontrolled aTRH [1.43 (1.05-1.94)]. Among non-aTRH subgroups, those on 3 antihypertensive medications had a 35% increased risk of CVD mortality than those on < 3 medications [1.35 (0.98-1.86)]. CONCLUSIONS: aTRH is a common condition, affecting approximately 7.6 million Americans. Regardless of BP control, people with aTRH remain at a higher risk of cardiovascular outcomes. The risk of cardiovascular disease mortality remains high among those with controlled BP on 3 medications (non-aTRH) or ≥ 4 medications (controlled aTRH), groups not generally considered at high risk. Future risk reduction interventions should consider focusing on these high-risk groups.

Serum metabolites are associated with all-cause mortality in chronic kidney disease.

Chronic kidney disease (CKD) involves significant metabolic abnormalities and has a high mortality rate. Because the levels of serum metabolites in patients with CKD might provide insight into subclinical disease states and risk for future mortality, we determined which serum metabolites reproducibly associate with mortality in CKD using a discovery and replication design. Metabolite levels were quantified via untargeted liquid chromatography and mass spectroscopy from serum samples of 299 patients with CKD in the Modification of Diet in Renal Disease (MDRD) study as a discovery cohort. Six among 622 metabolites were significantly associated with mortality over a median follow-up of 17 years after adjustment for demographic and clinical covariates, including urine protein and measured glomerular filtration rate. We then replicated associations with mortality in 963 patients with CKD from the African American Study of Kidney Disease and Hypertension (AASK) cohort over a median follow-up of ten years. Three of the six metabolites identified in the MDRD cohort replicated in the AASK cohort: fumarate, allantoin, and ribonate, belonging to energy, nucleotide, and carbohydrate pathways, respectively. Point estimates were similar in both studies and in meta-analysis (adjusted hazard ratios 1.63, 1.59, and 1.61, respectively, per doubling of the metabolite). Thus, selected serum metabolites were reproducibly associated with long-term mortality in CKD beyond markers of kidney function in two well characterized cohorts, providing targets for investigation.

Risk of end-stage renal disease in Japanese patients with chronic kidney disease increases proportionately to decline in estimated glomerular filtration rate.

Predominantly based on North American and European studies, 30% to 40% declines in estimated glomerular filtration rate (eGFR) over a few years are strongly associated with the risk of end-stage renal disease (ESRD) and have been proposed as surrogate endpoints of ESRD for clinical research. However, this association has not been systematically quantified in Asian populations. To do this we studied adult Japanese patients with baseline eGFR 10-59 ml/min/1.73m2. Changes in eGFR from baseline measured by centrally assessed serum creatinine were linked to subsequent ESRD in 2410 patients after one year and in 2079 patients after year 2. After year 1, 1.4% experienced a 53% decrease in eGFR (equivalent to doubling of serum creatinine), whereas 4.3% and 9.7% had eGFR decrease of 40% or 30% or more, respectively. The corresponding numbers after 2 years were 4.2%, 10.9%, and 19.3%, respectively. After year 1 baseline period, 498 patients developed ESRD over a median follow-up of 2.9 years (365 ESRD cases over a median follow-up of 2 years after year 2). In year 1, after accounting for potential confounders, a strong linear association was found between eGFR declines and subsequent ESRD, with adjusted hazard ratios of 20.7 (95% confidence interval 14.3-30.1) for a 53% decrease, 9.6 (7.4-12.5) for a 40% decrease, and 5.3 (4.1-6.9) for a 30% decrease compared to no change. Corresponding hazard ratios for year two analysis were 17.3 (11.8-25.3), 6.5 (4.7-9.1), and 3.1 (2.2-4.4), respectively. The associations were consistent across demographics and kidney diseases. Thus, 30% to 40% declines in eGFR are strongly associated with the risk of ESRD in Japanese patients with reduced eGFR, broadening global implications as a surrogate endpoint in clinical research.

Consistency of metabolite associations with measured glomerular filtration rate in children and adults.

Background: There is interest in identifying novel filtration markers that lead to more accurate GFR estimates than current markers (creatinine and cystatin C) and are more consistent across demographic groups. We hypothesize that large-scale metabolomics can identify serum metabolites that are strongly influenced by glomerular filtration rate (GFR) and are more consistent across demographic variables than creatinine, which would be promising filtration markers for future investigation. Methods: We evaluated the consistency of associations between measured GFR (mGFR) and 887 common, known metabolites quantified by an untargeted chromatography- and spectroscopy-based metabolomics platform (Metabolon) performed on frozen blood samples from 580 participants in Chronic Kidney Disease in Children (CKiD), 674 participants in Modification of Diet in Renal Disease (MDRD) Study and 962 participants in African American Study of Kidney Disease and Hypertension (AASK). We evaluated metabolite-mGFR correlation association with metabolite class, molecular weight, assay platform and measurement coefficient of variation (CV). Among metabolites with strong negative correlations with mGFR (r < -0.5), we assessed additional variation by age (height in children), sex, race and body mass index (BMI). Results: A total of 561 metabolites (63%) were negatively correlated with mGFR. Correlations with mGFR were highly consistent across study, sex, race and BMI categories (correlation of metabolite-mGFR correlations between 0.88 and 0.95). Amino acids, carbohydrates and nucleotides were more often negatively correlated with mGFR compared with lipids, but there was no association with metabolite molecular weight, liquid chromatography/mass spectrometry platform and measurement CV. Among 114 metabolites with strong negative associations with mGFR (r < -0.5), 27 were consistently not associated with age (height in children), sex or race. Conclusions: The majority of metabolite-mGFR correlations were negative and consistent across sex, race, BMI and study. Metabolites with consistent strong negative correlations with mGFR and non-association with demographic variables may represent candidate markers to improve estimation of GFR.

Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study.

Rationale & Objective: While urine excretion of nitrogen estimates the total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of 6 protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD). Study Design: Prospective cohort study. Setting & Participants: A total of 3,726 participants from the Atherosclerosis Risk in Communities study without CKD at baseline. Exposures: Dietary intake of 6 protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry), serum metabolites. Outcomes: Incident CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2 with ≥25% estimated glomerular filtration rate decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease). Analytical Approach: Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C statistics assessed the ability of the metabolites to improve the discrimination of highest versus lower 3 quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and the intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident CKD. Results: Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n = 8; nuts, n = 5; legumes, n = 0; red and processed meat, n = 5; eggs, n = 3; and poultry, n = 9). Metabolites collectively and significantly improved the discrimination of high intake of protein-rich foods compared with covariates alone (difference in C statistics = 0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; P < 1.00 × 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR, 0.82; 95% CI, 0.75-0.89; P = 7.81 × 10-6). Limitations: Residual confounding and sample-storage duration. Conclusions: We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with a lower risk of CKD.

In this study, we aimed to identify associations between protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry) and serum metabolites, which are small biological molecules involved in metabolism. Metabolites significantly associated with a protein-rich food individually and collectively improved the discrimination of the respective protein-rich food, suggesting that these metabolites should be prioritized in future diet biomarker research. We also studied associations between significant diet-related metabolites and incident kidney disease. One fish-related metabolite was associated with a lower kidney disease risk. This finding supports the recent nutritional guidelines recommending a Mediterranean diet, which includes fish as the main dietary protein source.