N-Terminal Pro-B-Type Natriuretic Peptide as a Biomarker for Loss of Muscle Mass in Prevalent Hemodialysis Patients

Ikeda, Minako; Honda, Hirokazu; Takahashi, Keiko; Shishido, Kanji; Shibata, Takanori

doi:10.1371/journal.pone.0166804

Cited by 23 publications

(35 citation statements)

References 43 publications

(47 reference statements)

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“…19 Multiple regression analysis adjusted with confounders revealed that NT-proBNP was an independent predictor of decrease in LBM and serial lower levels of CI. 19 CI can be calculated conveniently using a simple equation in HD patients based on routine clinical variables. Clinicians can calculate serial CIs using the simple equation in each HD patient, and the changes in CI over time can be used in nutritional and prognostic monitoring following dialysis.…”

Section: Discussionmentioning

confidence: 94%

“…Values of CI were 21.0 ± 2.2 in higher NT‐proBNP and 23.1 ± 2.9 in lower NT‐proBNP (P < 0.0001). The CI in the higher NT‐proBNP decreased serially and continuously for 12 months compared with lower NT‐proBNP . In our study, a CI cutoff of 22 distinguished normal but not severe and moderate malnutrition groups.…”

Section: Discussionmentioning

confidence: 94%

“…Higher CI was associated with lower mortality and longer survival in men but not in women in subgroup analysis in a HD population . N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) is a key factor in worsening of nutritional status . Multiple regression analysis adjusted with confounders revealed that NT‐proBNP was an independent predictor of decrease in LBM and serial lower levels of CI .…”

Section: Discussionmentioning

confidence: 99%

“…N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) is a key factor in worsening of nutritional status . Multiple regression analysis adjusted with confounders revealed that NT‐proBNP was an independent predictor of decrease in LBM and serial lower levels of CI . CI can be calculated conveniently using a simple equation in HD patients based on routine clinical variables.…”

Section: Discussionmentioning

confidence: 99%

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Comparison of creatinine index and geriatric nutritional risk index for nutritional evaluation of patients with hemodialysis

Hwang

Cho

et al. 2018

Hemodialysis International

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Comparison of creatinine index and geriatric nutritional risk index for nutritional evaluation of patients with hemodialysis

Hwang

Cho

et al. 2018

Hemodialysis International

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“…The relation between markers of FO and malnutrition has also been described in the literature [23, 45, 46]. However, sometimes the interpretation may be hampered by the fact that the ECW:total body water was used as a marker of FO [46], whereas this is likely a combined risk marker expressing either FO, malnutrition, or both [22].…”

Section: Fo and Inflammation: Part Of A Broader Spectrum?mentioning

confidence: 99%

Fluid Overload and Inflammation Axis

et al. 2018

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Extracellular fluid overload (FO), which is assessed using bioimpedance technologies, is an important predictor of outcome in dialysis patients and in patients with early stages of chronic kidney disease. While traditional cardiovascular abnormalities are assumed to mediate this risk, recently also, the importance of noncardiovascular factors, such as systemic inflammation and malnutrition has been shown. While both FO and inflammation are independent risk factors for mortality, recent studies have shown that their combined presence can lead to a cumulative risk profile. From a pathophysiologic viewpoint, FO and inflammation can also be mutually reinforcing. Inflammation could contribute to FO by hypoalbuminemia, capillary leakage, and a (unnoticed) decline in lean and/or fat tissue mass resulting in incorrect estimation of dry weight. Reciprocally, FO could lead to inflammation by the translocation of endotoxins through a congested bowel wall or by a proinflammatory effect of tissue sodium. The relative importance of these putative factors is, however, not clear yet and epidemiological studies have shown no clear temporal direction regarding the relationship between FO and inflammation. FO and inflammation appear to be part of (dynamic) clusters of risk factors, including malnutrition and hyponatremia. Technology-guided fluid management of the often vulnerable dialysis patient with FO and inflammation cannot yet be based on evidence from randomized controlled trials, in which these specific patients were in general not included. In the absence of those trials, treatment should be based on identifying actionable causes of inflammation and on the judicious removal of excess volume based on frequent clinical reassessment.

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Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study

Huemer

Bauer

Petrera

et al. 2021

J cachexia sarcopenia muscle

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Background The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High‐throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers. Methods Data derived from the prospective population‐based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow‐up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55–74 years in the cross‐sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow‐up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives‐controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100× bootstrapping by cross‐validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors. Results In the cross‐sectional analysis, we identified kallikrein‐6, C‐C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1‐SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37–1.95), 1.31 (1.14–1.51), 1.24 (1.06–1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61–0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08–1.61), 1.28 (1.03–1.59), respectively]. Including protein biomarkers selected in ≥90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13–0.20), 0.22 (0.18–0.25), 0.12 (0.08–0.17), respectively]. In the longitudinal analysis, N‐terminal prohormone brain natriuretic peptide (NT‐proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10–1.77), 1.60 (1.15–2.24), respectively]. Conclusions Proteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT‐proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.

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N-Terminal Pro-B-Type Natriuretic Peptide as a Biomarker for Loss of Muscle Mass in Prevalent Hemodialysis Patients

Cited by 23 publications

References 43 publications

Comparison of creatinine index and geriatric nutritional risk index for nutritional evaluation of patients with hemodialysis

Comparison of creatinine index and geriatric nutritional risk index for nutritional evaluation of patients with hemodialysis

Fluid Overload and Inflammation Axis

Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study

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