Prediction of Sarcopenia Using a Battery of Circulating Biomarkers Rizwan Qaisar 1*, Asima Karim1,2, Tahir Muhammad3, Islam Shah4 & Javaidullah Khan5
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www.nature.com/scientificreports OPEN Prediction of sarcopenia using a battery of circulating biomarkers Rizwan Qaisar 1*, Asima Karim1,2, Tahir Muhammad3, Islam Shah4 & Javaidullah Khan5 Loss of muscle mass and strength with aging, termed sarcopenia is accelerated in several comorbidities including chronic heart failure (CHF) and chronic obstructive pulmonary diseases (COPD). However, the efective circulating biomarkers to accurately diagnose and assess sarcopenia are not known. We recruited male healthy controls and patients with CHF and COPD (n = 81–87/group), aged 55–74 years. Sarcopenia was clinically identifed based on hand-grip strength, appendicular skeletal muscle index and physical capacity as recommended by the European working group for sarcopenia. The serum levels of amino-terminal pro-peptide of type-III procollagen, c-terminal agrin fragment-22, osteonectin, irisin, fatty acid-binding protein-3 and macrophage migration inhibitory factor were signifcantly diferent between healthy controls and patients with CHF and COPD. Risk scores for individual biomarkers were calculated by logistic regressions and combined into a cumulative risk score. The median cutof value of 3.86 was used to divide subjects into high- and low- risk groups for sarcopenia with the area under the curve of 0.793 (95% CI = 0.738–0.845, p < 0.001). A signifcantly higher incidence of clinical sarcopenia was found in high-risk group. Taken together, the battery of biomarkers can be an efective tool in the early diagnosis and assessment of sarcopenia. Sarcopenia is traditionally defned as the loss of muscle mass with aging 1. However, recent defnitions of sarco- penia consider the loss of muscle mass, strength and physical capacity with aging 2. For example, the European Working Group for Sarcopenia (EWGSOP) defnes sarcopenia as muscle atrophy (normalized for height) com- bined with muscle weakness (measured in hand-grip muscles) and/or reduced physical capacity (measured via gait speed)3. Among these parameters, muscle weakness has emerged as the primary determinant of sarcopenia and functional dependency in aging2. Elderly with low muscle mass (normalized for height) in the absence of muscle weakness and functional compromise are categorized in “pre-sarcopenia” phase, while “probable sarco- penia” is defned as reduced hand-grip strength (HGS) and/or compromised physical capacity (low chair stand test ability)2, which warrants further assessments. Tese assessments highlight the importance of sarcopenia as the muscle impairment in the elderly is ofen associated with a dependent lifestyle due to physical decline 4, falls5, fractures5 and increased morbidities6. Several methods are used to assess the indexes of sarcopenia. MRI and CT scans are considered the gold standards to measure muscle mass but are costly, require technical expertise and involve radiation exposure. Bioelectrical impedance is now widely implied due to its simple and cost-efective manner despite less reliability due to gender, ethnicity and hydration status 7. Muscle strength is routinely measured in the hand-grip muscles with a dynamometer and shows a correlation with several age-related diseases8–10. Additionally, a short physical performance battery (SPPB) has emerged as a promising tool to evaluate functional capacity and incorporates standing balance and chair stand test in addition to gait speed11. SPPB has shown high sensitivity for sarcopenia and maybe a favorable tool in clinical settings as it is a simple and quick test, which does not require any special- ized instruments. In addition to clinical measurements, sarcopenia is also defnable by simple functional questions. SARC-F questionnaire has emerged as a useful tool for the rapid assessment of sarcopenia 12. It considers various meas- ures of functional independence such as strength, assistance in walking, rising from a chair, climbing stairs and falls. Each component is awarded a score from 0 to 10, with a maximal score of 10 in the overall assessment. A score ≥ 4 is considered as a predictor of sarcopenia12. Several co-morbidities can induce and/or exacerbate the sarcopenia phenotype in aging. Tese diseases also share several common risk factors and health outcomes with sarcopenia. However, sarcopenia is frequently underdiagnosed in clinical practice despite the considerable overlap in the severities of sarcopenia phenotype and 1Basic Medical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates. 2University of Health Sciences, Lahore, Pakistan. 3Departmenr of Biochemistry, Gomal Medical College, Dera Ismail Khan, Pakistan. 4Department of Cardiology, Al Qassimi Hospital, Sharjah, United Arab Emirates. 5Department of Cardiology, Post Graduate Medical Institute, Hayatabad Medical Complex, Peshawar, Pakistan. *email: rqaisar@ sharjah.ac.ae Scientifc Reports | (2021) 11:8632 | https://doi.org/10.1038/s41598-021-87974-6 1 Vol.:(0123456789) www.nature.com/scientificreports/ Healthy COPD CHF Age at baseline (years) 62.6 ± 5.5 64.3 ± 3.7 66.9 ± 5.4 Body composition BMI (Kg/m2) 23.85 ± 2.76 22.27 ± 3.25 25.62 ± 3.32*# ASM (Kg) 20.29 ± 3.08 19.27 ± 2.61 19.41 ± 3.24 ASMI (Kg/m2) 7.24 ± 1.45 7.03 ± 1.38* 6.84 ± 1.44* Percent fat 27.79 ± 3.15 25.18 ± 3.85* 28.43 ± 3.63# Physical parameters HGS (kg) 25.57 ± 5.36 20.19 ± 4.37* 21.45 ± 3.31* HGS / ASM 1.26 ± 0.18 1.04 ± 0.14* 1.01 ± 0.17* Daily steps count 6873 ± 1394 3182 ± 949* 3648 ± 1203* 4-meter gait speed (m/s) 1.23 ± 0.21 0.87 ± 0.18* 0.83 ± 0.17* Chair stand for fve rises (s) 13.5 ± 2.54 15.4 ± 3.11 16.7 ± 3.69* Spirometry and oxygen saturation FEV1% predicted 97.39 ± 3.3 61.89 ± 5.7* 66.31 ± 4.3*# PEFR % predicted 93.53 ± 4.7 67.39 ± 4.5* 73.61 ± 4.1* SpO2 98.5 ± 1.5 95.3 ± 2.2 95.1 ± 2.4 Plasma biomarkers Plasma 8-isoprostanes (pg/ml) 58.4 ± 12.7 95.2 ± 16.9* 87.9 ± 13.4* CRP (mg/l) 1.91 ± 0.24 3.26 ± 0.43* 3.53 ± 0.47*# Creatine kinase (IU/L) 167.9 ± 25.7 293.5 ± 52.7* 279.9 ± 42.9* Table 1. Body composition, physical parameters and circulating biomarkers in healthy controls and patients with COPD and CHF. Values are expressed as mean ± SD, one-way analysis of variance. *p < 0.05 vs. healthy controls; #p < 0.05 vs. COPD participants; (N = 83–88/group). (BMI; body mass index, ASM; appendicular skeletal mass, ASMI; appendicular skeletal mass index, HGS; hand-grip strength, FEV1; forced expiratory volume in 1 s, PEFR; peak expiratory fow rate, CRP; C—reactive protein). comorbidities. Further, treatment protocols for sarcopenia do not take comorbidities into account13. Terefore, there is a need to determine the diagnosis and prevalence of sarcopenia in the elderly with age-related comor- bidities. Among them, chronic obstructive pulmonary diseases (COPD) and congestive heart failure (CHF) are common drivers of sarcopenia in the elderly10,14. We have previously shown that patients with respiratory diseases have accelerated loss of muscle mass and strength than the age-matched healthy elderly9. Tese diseases also share several common risk factors and health outcomes with sarcopenia. However, sar- copenia is frequently underdiagnosed in clinical practice despite the considerable overlap in the severities of sarcopenia phenotype and comorbidities. Further, treatment protocols for sarcopenia do not take comorbidities into account13. Terefore, there is a need to determine the diagnosis and prevalence of sarcopenia in the elderly with comorbidities such as COPD and CHF. Several molecular mechanisms contribute to the pathogenesis of sarcopenia. Both the intrinsic (generalized infammation, oxidative stress, calcium dysregulation, apoptosis, hypoxia, the disintegration of neuromuscular junction) and extrinsic (chronic inactivity, poor nutrition, endocrine dysfunction) factors contribute to the loss of muscle mass and strength in aging 15. However, considering the complex pathophysiology, no single molecular biomarker can accurately evaluate the sarcopenia phenotype, necessitating the use of multiple biomarkers to assess loss of muscle mass and strength in the elderly. Given the complexity of sarcopenia, we tested a battery of circulating biomarkers as a strategy to defne a biomarker panel specifc to diferent pathophysiological mechanisms of sarcopenia. We evaluated specifc biomarkers to assess the neuromuscular junction integrity (c-terminal agrin fragment 22 or CAF22), protein turnaround (amino-terminal pro-peptide of type III procollagen or P3NP), cell–matrix interaction (osteonectin), growth factors (irisin), cellular metabolism (fatty acid-binding protein 3 or FABP3) and systemic infammation (macrophage migration inhibitory factor or MIF). Te circulating biomarker levels were correlated with the sarcopenia as defned by clinical parameters or SARC-F questionnaire to assess their diagnostic signifcance. Te comparative analysis of the biomarkers was performed in the normal elderly with sarcopenia and the patients of COPD and CHF with advanced sarcopenia. Results Characteristics of the participants. Te basic characteristics of the study population are summarized in Table 1. Overall, the COPD and CHF patients had lower ASMI, HGS, gait speed and steps count than healthy con- trols (all p < 0.05). Moreover, the circulating levels of 8-isoprostanes, CRP and CK were higher in the patients with COPD and CHF than healthy controls (all p < 0.05). Tese patients also performed poorly on the SPPB and SARC-F scales. Generally, a higher score means a good functional capacity on SPPB scale, but poor functional capacity on SARC-F scale. Tus, the relative proportion of participants with SPPB score ≤ 8 (among the diagnostic indexes of sarcopenia) was higher in patients with COPD and CHF than healthy controls (≈64% and ≈68% vs. Scientifc Reports | (2021) 11:8632 | https://doi.org/10.1038/s41598-021-87974-6 2 Vol:.(1234567890) www.nature.com/scientificreports/ Figure 1. Relative proportions of the participants with diferent SPPB (A) and SARC-F (B) scores in the healthy controls (N = 87) and patients with COPD (N = 86) and CHF (N = 81).