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. 2019 Oct 1;8(10):1576.
doi: 10.3390/jcm8101576.

Growth Differentiation Factor-15 (GDF-15) is a Biomarker of Muscle Wasting and Renal Dysfunction in Preoperative Cardiovascular Surgery Patients

Toshiaki Nakajima  1 , Ikuko Shibasaki  2 , Tatsuya Sawaguchi  3 , Akiko Haruyama  4 , Hiroyuki Kaneda  5 , Takafumi Nakajima  6 , Takaaki Hasegawa  7 , Takuo Arikawa  8 , Syotaro Obi  9 , Masashi Sakuma  10 , Hironaga Ogawa  11 , Shigeru Toyoda  12 , Fumitaka Nakamura  13 , Shichiro Abe  14 , Hirotsugu Fukuda  15 , Teruo Inoue  16
Affiliations

Growth Differentiation Factor-15 (GDF-15) is a Biomarker of Muscle Wasting and Renal Dysfunction in Preoperative Cardiovascular Surgery Patients

Toshiaki Nakajima et al. J Clin Med. .

Abstract

Frailty and sarcopenia increase the risk of complications and mortality when invasive treatment such as cardiac surgery is performed. Growth differentiation factor-15 (GDF-15) involves various pathophysiological conditions including renal dysfunction, heart failure and cachexia. We investigated the pathophysiological roles of preoperative GDF-15 levels in cardiovascular surgery patients. Preoperative skeletal muscle index (SMI) determined by bioelectrical impedance analysis, hand-grip strength, 4 m gait speed, and anterior thigh muscle thickness (TMth) measured by echocardiography were assessed in 72 patients (average age 69.9 years) who underwent cardiovascular surgery. The preoperative serum GDF-15 concentration was determined by enzyme-linked immunosorbent assay. Circulating GDF-15 level was correlated with age, brain natriuretic peptide, and estimated glomerular filtration rate (eGFR). It was also negatively correlated with SMI, hand-grip strength, and anterior TMth. In multivariate analysis, eGFR and anterior TMth were the independent determinants of GDF-15 concentration even after adjusting for age, sex, and body mass index. Alternatively, the GDF-15 level was an independent determinant of eGFR and anterior TMth. We concluded that preoperative GDF-15 levels reflect muscle wasting as well as renal dysfunction in preoperative cardiovascular surgery patients. GDF-15 may be a novel biomarker for identify high-risk patients with muscle wasting and renal dysfunction before cardiovascular surgery.

Keywords: GDF-15; biomarkers; cardiovascular surgery; chronic kidney disease; muscle wasting; operative risk; renal dysfunction; sarcopenia.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Correlations between clinical data (age, eGFR) and serum concentrations of GDF-15, TNF-α, and IGF-1. Relationships between laboratory data (age (a), eGFR (b)) and serum concentrations of GDF-15 (Aa,Ab), TNF-α (Ba,Bb) and IGF-1 (Ca,Cb) in males and females. ** p < 0.01, *** p < 0.001.
Figure 2
Figure 2
Correlations between the physical data (anterior thigh muscle thickness, grip strength) and serum concentrations of GDF-15, TNF-α, and IGF-1. Relationships between the laboratory data (anterior thigh muscle thickness (TMth, supine) (a), grip strength (b) and serum concentrations of GDF-15 (Aa,Ab), TNF-α (Ba,Bb) and IGF-1 (Ca,Cb) in males * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 3
Figure 3
ROC curves to identify the optimal cut-off level of GDF-15, TNFα, and Hb for detecting eGFR < 60. In the ROC curves shown, different cut-off values of GDF-15, and TNFα, and Hb were used to predict eGFR < 60, with true positives plotted on the vertical axis (sensitivity) and Figure 1. plotted on the horizontal axis.
See this image and copyright information in PMC

References

    1. Partridge J.S., Harari D., Dhesi J.K. Frailty in the older surgical patient: A review. Age Ageing. 2012;41:142–147. doi: 10.1093/ageing/afr182. - DOI - PubMed
    1. Clegg A., Young J., Iliffe S., Rikkert M.O., Rockwood K. Frailty in elderly people. Lancet. 2013;381:752–762. doi: 10.1016/S0140-6736(12)62167-9. - DOI - PMC - PubMed
    1. Makary M.A., Segev D.L., Pronovost P.J., Syin D., Bandeen-Roche K., Patel P., Takenaga R., Devgan L., Holzmueller C.G., Tian J., et al. Frailty as a predictor of surgical outcomes in older patients. J. Am. Coll. Surg. 2010;210:901–908. doi: 10.1016/j.jamcollsurg.2010.01.028. - DOI - PubMed
    1. Shimura T., Yamamoto M., Kano S., Kagase A., Kodama A., Koyama Y., Tsuchikane E., Suzuki T., Otsuka T., Kohsaka S., et al. OCEAN-TAVI Investigators. Impact of the Clinical Frailty Scale on Outcomes After Transcatheter Aortic Valve Replacement. Circulation. 2017;135:2013–2024. doi: 10.1161/CIRCULATIONAHA.116.025630. - DOI - PubMed
    1. Afilalo J., Alexander K.P., Mack M.J., Maurer M.S., Green P., Allen L.A. Frailty assessment in the cardiovascular care of older adults. J. Am. Coll. Cardiol. 2014;63:747–762. doi: 10.1016/j.jacc.2013.09.070. - DOI - PMC - PubMed