Statistical methods for building better biomarkers of chronic kidney disease

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer's disease

Understanding Alzheimer's disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity.

Metabolomics as a tool for the early diagnosis and prognosis of diabetic kidney disease

Diabetic kidney disease (DKD) is one of the most prevalent comorbidities of diabetes mellitus and the leading cause of the end-stage renal disease (ESRD). DKD results from chronic exposure to hyperglycemia, leading to progressive alterations in kidney structure and function. The early development of DKD is clinically silent and when albuminuria is detected the lesions are often at advanced stages, leading to rapid kidney function decline towards ESRD. DKD progression can be arrested or substantially delayed if detected and addressed at early stages. A major limitation of current methods is the absence of albuminuria in non-albuminuric phenotypes of diabetic nephropathy, which becomes increasingly prevalent and lacks focused therapy. Metabolomics is an ever-evolving omics technology that enables the study of metabolites, downstream products of every biochemical event that occurs in an organism. Metabolomics disclosures complex metabolic networks and provide knowledge of the very foundation of several physiological or pathophysiological processes, ultimately leading to the identification of diseases' unique metabolic signatures. In this sense, metabolomics is a promising tool not only for the diagnosis but also for the identification of pre-disease states which would confer a rapid and personalized clinical practice. Herein, the use of metabolomics as a tool to identify the DKD metabolic signature of tubule interstitial lesions to diagnose or predict the time-course of DKD will be discussed. In addition, the proficiency and limitations of the currently available high-throughput metabolomic techniques will be discussed.

Impact of Chronic Kidney Disease on the Associations of Cardiovascular Biomarkers With Adverse Outcomes in Patients With Suspected or Known Coronary Artery Disease: The EXCEED-J Study

Background

The impact of chronic kidney disease (CKD) on the prognostic utility of cardiovascular biomarkers in high‐risk patients remains unclear.

Methods and Results

We performed a multicenter, prospective cohort study of 3255 patients with suspected or known coronary artery disease (CAD) to investigate whether CKD modifies the prognostic utility of cardiovascular biomarkers. Serum levels of cardiovascular and renal biomarkers, including soluble fms‐like tyrosine kinase‐1 (sFlt‐1), N‐terminal pro‐brain natriuretic peptide (NT‐proBNP), high‐sensitivity cardiac troponin‐I (hs‐cTnI), cystatin C, and placental growth factor, were measured in 1301 CKD and 1954 patients without CKD. The urine albumin to creatinine ratio (UACR) was measured in patients with CKD. The primary outcome was 3‐point MACE (3P‐MACE) defined as a composite of cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke. The secondary outcomes were all‐cause death, cardiovascular death, and 5P‐MACE defined as a composite of 3P‐MACE, heart failure hospitalization, and coronary/peripheral artery revascularization. After adjustment for clinical confounders, sFlt‐1, NT‐proBNP, and hs‐cTnI, but not other biomarkers, were significantly associated with 3P‐MACE, all‐cause death, and cardiovascular death in the entire cohort and in patients without CKD. These associations were still significant in CKD only for NT‐proBNP and hs‐cTnI. NT‐proBNP and hs‐cTnI were also significantly associated with 5P‐MACE in CKD. The UACR was not significantly associated with any outcomes in CKD. NT‐proBNP and hs‐cTnI added incremental prognostic information for all outcomes to the model with potential clinical confounders in CKD.

Conclusions

NT‐proBNP and hs‐cTnI were the most powerful prognostic biomarkers in patients with suspected or known CAD and concomitant CKD.

Urine Metabolite Levels, Adverse Kidney Outcomes, and Mortality in CKD Patients: A Metabolome-wide Association Study

RATIONALE & OBJECTIVE

Mechanisms underlying the variable course of disease progression in patients with chronic kidney disease (CKD) are incompletely understood. The aim of this study was to identify novel biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiologic mechanisms.

STUDY DESIGN

Metabolome-wide association study.

SETTING & PARTICIPANTS

5,087 patients with CKD enrolled in the observational German Chronic Kidney Disease Study.

EXPOSURES

Measurements of 1,487 metabolites in urine.

OUTCOMES

End points of interest were time to kidney failure (KF), a combined end point of KF and acute kidney injury (KF+AKI), and overall mortality.

ANALYTICAL APPROACH

Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable-adjusted Cox regression models.

RESULTS

After a median follow-up of 4 years, 362 patients died, 241 experienced KF, and 382 experienced KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly associated with adverse kidney outcomes and/or mortality. Higher levels of C-glycosyltryptophan were consistently associated with all 3 main end points (hazard ratios of 1.43 [95% CI, 1.27-1.61] for KF, 1.40 [95% CI, 1.27-1.55] for KF+AKI, and 1.47 [95% CI, 1.33-1.63] for death). Metabolites belonging to the phosphatidylcholine pathway showed significant enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established Kidney Failure Risk Equation.

LIMITATIONS

Findings among patients of European ancestry with CKD may not be generalizable to the general population.

CONCLUSIONS

Our comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identifies metabolites that predict KF and represents a valuable resource for future studies of biomarkers of CKD progression.

Toward Personalized Radiation Therapy of Liver Metastasis: Importance of Serial Blood Biomarkers

PURPOSE

To assess the added value of serial blood biomarkers in liver metastasis stereotactic body radiation therapy (SBRT).

MATERIALS AND METHODS

Eighty-nine patients were retrospectively included. Pre- and midtreatment blood samples were analyzed for potential biomarkers of the treatment response. Three biomarker classes were studied: gene mutation status, complete blood count, and inflammatory cytokine concentration in plasma. One-year local failure (LF) and 2-year overall survival (OS) were chosen as study end points. Multivariate logistic regression was used for response prediction. Added predictive benefit was assessed by quantifying the difference between the predictive performance of a baseline model (clinicopathologic and dosimetric predictors) and that of the biomarker-enhanced model, using three metrics: (1) likelihood ratio, (2) predictive variance, and (3) area under the receiver operating characteristic curve (AUC).

RESULTS

The most important predictors of LF were mutation in KRAS gene (hazard ratio [HR] = 2.92, 95% CI, [1.17 to 7.28], P = .02) and baseline and midtreatment concentration of plasma interleukin-6 (HR = 1.15 [1.04 to 1.26] and 1.06 [1.01 to 1.13], P = .01). Absolute lymphocyte count and platelet-to-lymphocyte ratio at baseline as well as neutrophil-to-lymphocyte ratio at baseline and before fraction 3 (HR = 1.33 [1.16 to 1.51] and 1.19 [1.09 to 1.30]) had the most significant association with OS (P = .0003). Addition of baseline GEN and inflammatory plasma cytokine biomarkers in predicting LF, respectively, increased AUC by 0.06 (from 0.73 to 0.79) and 0.07 (from 0.77 to 0.84). In predicting OS, inclusion of midtreatment complete blood count biomarkers increased AUC from 0.72 to 0.80, along with significant boosts in likelihood ratio and predictive variance.

CONCLUSION

Inclusion of serial blood biomarkers leads to significant gain in predicting response to liver metastasis stereotactic body radiation therapy and can guide treatment personalization.

Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease

Chronic kidney disease (CKD), defined as the presence of albuminuria and/or reduction in estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m², is considered a growing public health problem, with its prevalence and incidence having almost doubled in the past three decades. The implementation of novel biomarkers in clinical practice is crucial, since it could allow earlier diagnosis and lead to an improvement in CKD outcomes. Nevertheless, a clear guidance on how to develop biomarkers in the setting of CKD is not yet available. The aim of this review is to report the framework for implementing biomarkers in observational and intervention studies. Biomarkers are classified as either prognostic or predictive; the first type is used to identify the likelihood of a patient to develop an endpoint regardless of treatment, whereas the second type is used to determine whether the patient is likely to benefit from a specific treatment. Many single assays and complex biomarkers were shown to improve the prediction of cardiovascular and kidney outcomes in CKD patients on top of the traditional risk factors. Biomarkers were also shown to improve clinical trial designs. Understanding the correct ways to validate and implement novel biomarkers in CKD will help to mitigate the global burden of CKD and to improve the individual prognosis of these high-risk patients.

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression.

STUDY DESIGN

Prospective cohort.

SETTING & PARTICIPANTS

1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m² were followed up prospectively for a median of 8 (range, 2-10) years.

PREDICTORS

13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A_1c level, blood pressure, urinary albumin, and eGFR.

OUTCOMES

Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant).

ANALYTICAL APPROACH

Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT.

RESULTS

During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m² per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]).

LIMITATIONS

Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches.

CONCLUSIONS

Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.