Skip to main content

Sarcopenia, sarcopenic obesity and the clinical outcome of the older inpatients with COVID-19 infection: a prospective observational study

Abstract

Objective

We aimed to investigate the impact of sarcopenia and sarcopenic obesity (SO) on the clinical outcome in older patients with COVID-19 infection and chronic disease.

Methods

We prospectively collected data from patients admitted to Huadong Hospital for COVID-19 infection between November 1, 2022, and January 31, 2023. These patients were included from a previously established comprehensive geriatric assessment (CGA) cohort. We collected information on their pre-admission condition regarding sarcopenia, SO, and malnutrition, as well as their medical treatment. The primary endpoint was the incidence of intubation, while secondary endpoints included in-hospital mortality rates. We then utilized Kaplan-Meier (K-M) survival curves and the log-rank tests to compare the clinical outcomes related to intubation or death, assessing the impact of sarcopenia and SO on patient clinical outcomes.

Results

A total of 113 patients (age 89.6 ± 7.0 years) were included in the study. Among them, 51 patients had sarcopenia and 39 had SO prior to hospitalization. Intubation was required for 6 patients without sarcopenia (9.7%) and for 18 sarcopenia patients (35.3%), with 16 of these being SO patients (41%). Mortality occurred in 2 patients without sarcopenia (3.3%) and in 13 sarcopenia patients (25.5%), of which 11 were SO patients (28%). Upon further analysis, patients with SO exhibited significantly elevated risks for both intubation (Hazard Ratio [HR] 7.43, 95% Confidence Interval [CI] 1.26–43.90, P < 0.001) and mortality (HR 6.54, 95% CI 1.09–39.38, P < 0.001) after adjusting for confounding factors.

Conclusions

The prevalence of sarcopenia or SO was high among senior inpatients, and both conditions were found to have a significant negative impact on the clinical outcomes of COVID-19 infection. Therefore, it is essential to regularly assess and intervene in these conditions at the earliest stage possible.

Peer Review reports

Introduction

Sarcopenia, a degenerative condition commonly linked to the aging process, results in the deterioration of muscle mass, strength, and functionality. Its prevalence among the community-dwelling elderly varies with estimates ranging from roughly 6.6% to as high as 25.4% [1,2,3,4,5]. The incidence escalates in institutional settings such as nursing homes or among hospitalized older individuals, where it spans from 28 to 58% [6, 7]. Sarcopenia has been reported linked to longer hospital stays, diminished physical capabilities, a lower quality of life, and heightened long-term mortality across a broad spectrum of diseases, including COVID-19 [8,9,10]. Sarcopenic obesity (SO), a unique subtype of sarcopenia characterized by concurrent excess adiposity and diminished muscle mass or function, has garnered significant attention in the medical community. Since first described by Baumgartner in 2000 [11], the diagnostic criteria for SO have been refined in 2022 by the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) [12]. Extensive multi-center cohort studies have demonstrated a correlation between SO and a heightened risk of various comorbidities, including cardiovascular disease, metabolic disorders, cognitive decline, arthritis, functional impairments, and pulmonary conditions [13]. Furthermore, SO has been linked to detrimental health outcomes in individuals with pre-existing cardiovascular and oncological conditions, such as a higher susceptibility to falls, fractures, and diminished self-care capabilities [14,15,16]. The implications of SO are profound, notably increasing the risk of all-cause mortality [13], with particularly pronounced effects observed in elderly patients within hospital settings [17] and in those with severe infections [18].

The geriatric, representing about 34% of all infections and 23% of deaths in COVID-19 infection, with those aged 70 and above being particularly vulnerable [19,20,21,22]. Literatures [23,24,25] have consistently demonstrated that age and sarcopenia significantly contribute to the risk of adverse outcomes in COVID-19 patients. These outcomes included extended hospital stays, increased likelihood of intensive care unit (ICU) admission, the requirement for intermittent mandatory ventilation (IMV), and higher mortality rates. To date, only one study has reported that SO might elevate the risk of cardiovascular disease and mortality among older patients with COVID-19 [26].

Despite growing interest, the prevalence and influence of SO on disease prognosis remain contentious, primarily due to the variability in diagnostic criteria [13]. The scarcity of domestic research on the prognostic implications of SO, particularly in relation to pulmonary diseases such as COVID-19 infection in the geriatric, highlights a significant gap in the literature. In response to the situation, we conducted a prospective observational cohort study. Our primary objective was to investigate the independent effects of sarcopenia or SO on the adverse outcomes of older patients hospitalized with pulmonary infections. This study aimed to provide empirical evidence that could inform early, routine screening, evaluation, and intervention strategies for sarcopenia or SO in geriatric population.

Materials and methods

Study design and participants

From November 1, 2022, to January 31, 2023, we conducted a prospective observational study on the older patients who were consecutively admitted to Huadong Hospital due to acute COVID-19 infections. These patients were part of a previously established comprehensive geriatric assessment (CGA) cohort.

To be eligible for inclusion in the study, participants had to meet the following criteria: (1) age 65 years or older, (2) confirmed diagnosis of coronavirus-2 (SARS-CoV-2) infection, determined using polymerase chain reaction (PCR), (3) had CGA records within 6 months before admission. Patients who met any of the following criteria were excluded from the study: (1) diagnosed with end-stage renal disease(creatinine clearance rate(CCr) < 30 ml/min), moderate to severe liver failure (Child-Pugh B or C), and malignant tumors, (2) underwent surgery and radiotherapy within the last three months, (3) missing data on the assessment of sarcopenia, frailty, and malnutrition.

The study adhered to the principles and guidelines outlined in the Declaration of Helsinki and its subsequent amendments, and the protocol was approved by the Ethics Committee of Huadong Hospital (2023K199). Our methodology strictly followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines to ensure the transparency and rigor of our observational research.

Calculation of the sample size

We utilized PASS 15.0 software to calculate the sample size, employing the “Tests for Two Survival Curves Using Cox’s Proportional Hazards Model” function. Based on a prior study [27], we hypothesized a hazard ratio for intubation of 2.867, with an assumed progression to intubation of 22% among patients without sarcopenia and 68% among those with sarcopenia. The calculated minimum sample size was 80 patients to ensure adequate statistical power (α = 0.05, β = 0.1). Considering a potential dropout rate of 20%, the initial sample size was adjusted to a minimum of 107 participants.

Data collection

Demographic and clinical data

We obtained data on the participant’s age, sex, medical history, body mass index (BMI), as well as comorbidities such as hypertension (HBP), hyperlipidemia (HLP), coronary artery disease (CAD), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Blood test results within 48 h after hospitalization were recorded, which include virus PCR test, white blood cell count, lymphocyte count percentage, fibrinogen levels, and D-dimer level. Low lymphocyte count percentage (percentage of lymphocyte count less than 20%), hyperfibrinogenemia (fibrinogen levels above 4 g/L), and hyperdimeremia (D-dimer levels above 0.55 mg/L) were indicative of abnormalities. During the hospitalization, we documented the medical procedures and their results, such as the administration of immunoglobulins, glucocorticoids, antimicrobials, and Paxlovid. Additionally, we closely monitored intubation and assessed the in-hospital mortality rate. This comprehensive record-keeping ensures a thorough evaluation of the treatment and its outcomes.

CGA records on sarcopenia, SO, malnutrition, and frailty

CGA records within six-month prior to admission were reviewed. Patients’ height, weight were measured using Height and Weight Scale (BSM370, Inbody Inc. Korea). BMI was calculated by weight (kg) / height (m2). Body composition indices such as appendicular skeletal muscle mass (ASM) and percent body fat (PBF) were evaluated using Multi-Frequency Bioelectrical Impedance Measurement (DSM-BIA) method (Inbody S10, Inbody Inc. Korea) [28]. Appendicular skeletal muscle mass index (ASMI) was calculated by ASM (kg) / height (m2). Grip hand strength (HGS) was assessed using a grip dynamometer (Jamar® Hand Evaluation, USA) [29]. Based on the Asian Working Group for Sarcopenia (AWGS) [30], ASMI less than 7.0 kg/m2 (male) or 5.7 kg/m2 (female) was defined as decreased muscle mass. Diminished muscle strength is defined by a HGS measurement of less than 28 kg for males or 18 kg for females. Additionally, a 6-meter gait speed of less than 1.0 m/s was considered indicative of reduced muscular endurance. Sarcopenia was diagnosed through a reduction in either ASMI and HGS or ASMI and 6-meter gait speed. In accordance with the consensus statement by ESPEN & EASO [12], PBF exceeding 25% (male) or 35% (female) was considered obese status. SO was confirmed by a diagnosis of sarcopenia with obese status.

We employed the Global Leadership Initiative on Malnutrition (GLIM) framework [31] to assess the nutrition status of patients within the CGA cohort. The GLIM assessment incorporated three phenotypic factors: BMI, ASMI, and unintentional weight loss (within 6 months). For the etiologic type, the assessment considered dietary intake, gastrointestinal function, and the presence of elevated C-reactive protein (CRP > 10 mg/L). The presence and severity of malnutrition was diagnosed according to the established GLIM criteria.

Frailty was evaluated based on the five aspects of the Frail Scale [32, 33]: fatigue, resistance, ambulation, illnesses, and weight loss. A score of 3–5 indicates frailty, while a score of 1–2 suggests a pre-frail status.

Statistical analysis

Initially, we employed the Kolmogorov-Smirnov test to assess the normality of the data distribution. Normally distributed data were described by mean and standard deviation, and the independent t-test was used to compare the two groups. Non-normally distributed data were expressed by median and interquartile range (IQR) (25-75th percentile), and Wilcoxon rank-sum test was used for the comparison of two independent groups. Categorical count data were reported as absolute frequencies and percentages. Chi-square tests were performed to compare categorical variables between two groups.

To evaluate the effects of independent variables on sarcopenia or SO, we utilized multivariate forward stepwise logistic regression models to estimate odds ratios (ORs) with their corresponding 95% confidence intervals (CIs). The selection of independent variables—such as age, sex, chronic conditions including HBP, CAD, CKD, and COPD, as well as geriatric syndromes like frailty and malnutrition—were selected based on previous literature [2,3,4,5]. Frailty was categorized as a binary variable, identified by a Frail scale score exceeding 3, while the remaining variables were treated as continuous.

Additionally, Cox proportional hazards regression analysis was applied to determine hazard ratios (HRs) along with their 95% CIs. For the estimation of intubation and mortality rates, Kaplan-Meier (K-M) survival curves were constructed, and any statistically significant differences between groups were assessed using the log-rank test. Two-sided P-values < 0.05 were considered statistically significant. The K-M curves were plotted using R Foundation for Statistical Computing (Version 4.2.0, Vienna, Austria), while other statistical analyses were executed with the SPSS software package (Version 22, IBM, Chicago, USA).

Results

The study encompassed a total of 113 patients who fulfilled the eligibility criteria, as depicted in Fig. 1. These patients had an average age of 89.6 ± 7.0 years, consisting of 83 males and 30 females. As shown in Tables 1 and 51 (45.1%) of the older were diagnosed with sarcopenia while 39 (34.5%) as having SO. According to the CGA record, 18 participants (15.9%) were found to be malnourished, all of whom were among those with sarcopenia. There were 5 cases of patients with moderate malnutrition and 3 cases with severe malnutrition. Additionally, 64 individuals (57.5%) were identified as frail, as determined by the Frail scale score.

Fig. 1
figure 1

Flowchart describing participant recruitment

This flowchart illustrates the participant enrollment methodology for our study. We enrolled a cohort of 113 elderly patients hospitalized with COVID-19, all of whom underwent a comprehensive geriatric assessment prior to admission, specifically six months before. Utilizing the pre-admission evaluations for sarcopenia, participants were categorized into three groups: non-sarcopenia group, sarcopenia group, and sarcopenic obesity group

Table 1 Characteristics of baseline according to sarcopenia and sarcopenic obesity

Compared with the non-sarcopenia group, both the sarcopenia and SO groups had older age, higher prevalence of COPD, higher levels of PBF (P < 0.05). Notably, it was worth noting that both the sarcopenia and SO groups exhibited relatively higher rates of malnutrition (35% and 41%, respectively) and frailty (78% and 84%, respectively) (Table 1).

Factors associated with sarcopenia and SO

First, in the univariate analysis (Table 1), age, COPD, malnutrition as defined by the GLIM criteria, and frailty were identified as independent variables that differentiated between the sarcopenia and non-sarcopenia groups. Sex was found to be differed between the SO and non-sarcopenia groups. These variables were subsequently subjected to multivariate logistic regression analysis.

The results showed that age (OR 1.19, 95%CI 1.07–1.33, P = 0.001) and frailty (OR 3.43, 95%CI 1.02–11.53, P = 0.046) were found to be associated with sarcopenia. On the other hand, age (OR 1.25, 95%CI 1.10–1.42, P = 0.001), male (OR 7.96, 95%CI 1.61–39.47, P = 0.011), and malnutrition by GLIM (OR 20.2, 95%CI 3.14–130.2, P = 0.002) were identified as significant factors related to SO, as shown in Table 2. Additional univariate analysis on the single variable revealed that glucocorticoid use demonstrated a significant correlation with two critical clinical outcomes: intubation and mortality (P < 0.01).

Table 2 Assocociated factors for sarcopenia and sarcopenic obesity

Impact of sarcopenia and SO on intubation and mortality

Throughout the observation period, 24 patients (21.2%) necessitated intubation, and 15 (13.2%) succumbed to their illness. Among all, patients presenting with sarcopenia and SO exhibited a greater propensity for intubation, with 18 and 16 cases respectively, and these conditions were associated with increased mortality rates, accounting for 13 and 11 respectively (Table 3; Fig. 2). Following adjustments for age, sex, BMI, malnutrition, and glucocorticoid use, Cox regression analyses further illustrated that sarcopenia was an independent predictor of intubation (HR 3.26, 95%CI 1.03–10.34, P < 0.001) and mortality (HR 4.03, 95%CI 1.21–13.40, P < 0.001). As a subtype of sarcopenia, SO emerged as a significantly stronger risk factor for both intubation (HR 7.43, 95%CI 1.26–43.90, P < 0.001) and death (HR 6.54, 95%CI 1.09–39.38, P < 0.001) (Table 4). K-M survival curves distinctly revealed that patients with either sarcopenia or SO, when compared to the non-sarcopenia group, faced a higher likelihood of intubation and a reduced likelihood of survival (P < 0.05) (Fig. 3).

Fig. 2
figure 2

Intubation rate and mortality among three groups

This bar graph illustrates the rate of intubation and mortality among three groups of elderly COVID-19 patients: those with no sarcopenia, those with sarcopenia, and those with sarcopenic obesity. Compared to the non-sarcopenia group, the rate of intubation and mortality were both significantly higher in the sarcopenia group, and the sarcopenic obesity group (P = 0.001)

Fig. 3
figure 3

Kaplan-Meier survival estimates for intubation and mortality rates in patients

(a) Intubation rates as estimated by Kaplan-Meier for patients with sarcopenia versus those without. (b) Mortality rates as estimated by Kaplan-Meier for patients with sarcopenia versus those without. (c) Mortality rates as estimated by Kaplan-Meier for patients with SO versus those without SO. (d) Comparative mortality rates as estimated by Kaplan-Meier between patients with and without SO

Table 3 Characteristics after COVID-19 infection according to sarcopenia and sarcopenic obesity
Table 4 Associations between sarcopenia or sarcopenic obesity and clinical outcomes

Discussion

Our study revealed a remarkably high prevalence of sarcopenia and SO among older patients hospitalized with COVID-19, and the presence of sarcopenia, especially SO independently influenced adverse outcomes of COVID-19, including intubation or death.

45% of the participants had sarcopenia, which was consistent with previous findings by Gingrich [34]. He found that about 42% of 100 hospitalized patients with gastrointestinal diseases and malignant tumors suffered from sarcopenia. But due to the plethora of previous diagnostic techniques used to assess obesity, such as BMI, PBF, and visceral fat area, the occurrence of obesity in healthy populations showed significant variations [35]. Recognizing the limited accuracy of BMI, ESPEN and EASO established PBF as the standardized method for determining SO in 2022 [12]. In our cohort study, it was observed that despite patients having a BMI within the normal range, the utilization of PBF revealed a prevalence of obesity of 34.5%, which was comparable to the 34.1% prevalence observed in patients receiving chemotherapy for esophageal cancer [36]. Li [37] documented that aging, a sedentary lifestyle, an unhealthy diet, and insulin resistance were identified as risk factors for obesity. The development of SO involved a complex mechanism. With aging, adipose tissue in the body tends to accumulate ectopically and redistribute in the liver and muscle. The presence of ectopic adipocytes not only disrupted the alignment of muscle fibers but also led to the release of adipokines, which in turn triggered a heightened pro-inflammatory response. This chronic inflammation ultimately caused dysfunction and apoptosis of myocytes. Furthermore, these cytokines could worsen the atrophy of adipose tissue, leading to a vicious cycle of localized hyperlipidemia and inflammation [38].

In our study, we found that both sarcopenia and SO were strongly associated with malnutrition and frailty, and independently contributed to an increased risk of intubation or death. This finding highlights the crucial role these two factors play in determining patient outcomes. Previous studies [8,9,10, 39] on sarcopenia had documented not only its negative impact on clinical outcomes, but also the coexistence of sarcopenia with other geriatric syndromes had been shown to further exacerbate the risk of disease severity and death. These relationships often involve reciprocal causation, where the syndromes coexist and worsen each other’s effects. When two or more co-morbidities, such as frailty, sarcopenia, and malnutrition, were present, the mortality rate increased to nearly 75% [40]. These findings highlight the vulnerability of older individuals with multiple geriatric syndromes to severe illness and death during the COVID-19 pandemic.

So far, the impact of SO on clinical outcomes remained unclear. Some studies [14, 41, 42] found a strong association between SO and an increased risk of osteoporosis, falls, fractures, and other related conditions, while others did not [43]. As to the pulmonary disease, a Korean cohort study [44] has identified SO as a determinant affecting pulmonary function, with a significant association to the development of restrictive pulmonary disease (OR 2.81, 95% CI 1.72–4.59). A multi-center retrospective cohort study [45] in the United States recognized SO as a predictive risk factor for extended hospital stays and an increased incidence of postoperative complications following lobectomy for lung cancer. Regarding pulmonary infection, Mayoral er al [26, 46] reported that SO, as opposed to obesity alone, was found to have a higher likelihood of increasing the expression of inflammatory cytokines, and might be associated with a poor prognosis in COVID-19 patients. This heightened inflammatory response in SO might contribute to exacerbating the immune and metabolic stress caused by COVID-19 which partially explain why patients with SO had higher risks than those with sarcopenia alone in our study. Given the aforementioned information, it is imperative to prioritize early CGA evaluations that identify conditions such as sarcopenia—particularly SO—as well as malnutrition and frailty. Timely intervention is essential for enhancing the quality of life and averting poor prognoses in geriatric patients with pulmonary infections. Additionally, it is also essential to vigorously pursue research into intervention strategies aimed at combating sarcopenia and SO, such as measures to alleviate inflammation status and improve body composition by optimizing lifestyle factors [47, 48].

Furthermore, This study bears some limitations to be acknowledged. Firstly, there may have been selection bias as the participants were recruited from a single center, which may not be representative of the larger population. Additionally, the sample size was relatively small, which may affect the statistical power and precision of the study. Secondly, the study design itself might have inherent limitations. For example, the data collection methods used may introduce measurement bias or confounding variables that were not accounted for. Lastly, the prediction formulas used in the study might need further refinement to improve their accuracy and reliability.

Conclusions

The findings of our research underscore the prevalent incidence of sarcopenia and sarcopenic obesity among the geriatric population suffering from pulmonary diseases. It is noteworthy that both conditions have been established as independent predictors of adverse outcomes, including the necessity for intubation and the risk of mortality. Our study provides solid theoretical evidence advocating for the early adoption of routine screening, thorough assessment, and specific interventions for sarcopenia or SO in senior patients, extending to, but not limited to, those afflicted with pulmonary infections.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. Chianca V, Albano D, Messina C, Gitto S, Ruffo G, Guarino S, et al. Sarcopenia: imaging assessment and clinical application. Abdom Radiol. 2022;47:3205–16.

    Article  Google Scholar 

  2. Nakamura K, Yoshida D, Honda T, Hata J, Shibata M, Hirakawa Y, et al. Prevalence and mortality of Sarcopenia in a community-dwelling older Japanese population: the hisayama study. J Epidemiol. 2021;31:320–27.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Almohaisen N, Gittins M, Todd C, Sremanakova J, Sowerbutts AM, Aldossari A, et al. Prevalence of undernutrition, frailty and sarcopenia in community-dwelling people aged 50 years and above: systematic review and meta-analysis. Nutrients. 2022;14:1537–59.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bianchi L, Ferrucci L, Cherubini A, Maggio M, Bandinelli S, Savino E, et al. The predictive value of the EWGSOP definition of Sarcopenia: results from the InCHIANTI study. J Gerontol Biol. 2016;71:259–64.

    Article  Google Scholar 

  5. Landi F, Cruz-Jentoft AJ, Liperoti R, Russo A, Giovannini S, Tosato M, et al. Sarcopenia and mortality risk in frail older persons aged 80 years and older: results from ilSIRENTE study. Age Ageing. 2013;42:203–9.

    Article  PubMed  Google Scholar 

  6. Landi F, Liperoti R, Fusco D, Mastropaolo S, Quattrociocchi D, Proia A, et al. Sarcopenia and mortality among older nursing home residents. J Am Med Dir Assoc. 2012;13:121–6.

    Article  PubMed  Google Scholar 

  7. Nipp RD, Fuchs G, El-Jawahri A, Mario J, Troschel FM, Greer JA, et al. Sarcopenia is associated with quality of life and depression in patients with advanced cancer. Oncologist. 2018;23:97–104.

    Article  PubMed  Google Scholar 

  8. Cerri AP, Bellelli G, Mazzone A, Pittella F, Landi F, Zambon A, et al. Sarcopenia and malnutrition in acutely ill hospitalized elderly: prevalence and outcomes. Clin Nutr. 2015;34:745–51.

    Article  PubMed  Google Scholar 

  9. Beaudart C, Zaaria M, Pasleau F, Reginster JY, Bruyère O. Health outcomes of Sarcopenia: a systematic review and Meta-analysis. PLoS ONE. 2017;12:e0169548.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chang SF, Lin PL. Systematic literature review and meta-analysis of the association of Sarcopenia with mortality. Worldviews Evidence-Based Nurs. 2016;13:153–62.

    Article  Google Scholar 

  11. Baumgartner RN. Body composition in healthy aging. Ann N Y Acad Sci. 2000;904:437–48.

    Article  CAS  PubMed  Google Scholar 

  12. Donini LM, Busetto L, Bischoff SC, Cederholm T, Ballesteros-Pomar M, Batsis JA, et al. Definition and diagnostic criteria for sarcopenic obesity: ESPEN and EASO Consensus Statement. Obes Facts. 2022;15(3):321–35.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Liu C, Wong PY, Chung YL, Chow SK, Cheung WH, Law SW, et al. Deciphering the obesity paradox in the elderly: a systematic review and meta-analysis of sarcopenic obesity. Obes Rev. 2023;24(2):e13534.

    Article  CAS  PubMed  Google Scholar 

  14. Gao Q, Hu K, Gao J, Shang Y, Mei F, Zhao L, et al. Prevalence and prognostic value of sarcopenic obesity in patients with cancer: a systematic review and meta-analysis. Nutrition. 2022;101:111704.

    Article  PubMed  Google Scholar 

  15. Wei S, Nguyen T, Zhang Y, Ryu D, Gariani K. Sarcopenic obesity: epidemiology, pathophysiology, cardiovascular disease, mortality, and management. Front Endocrinol. 2023;14:1185221–32.

    Article  Google Scholar 

  16. Saino Y, Kawase F, Nagano A, Ueshima J, Kobayashi H, Murotani K, et al. Diagnosis and prevalence of sarcopenic obesity in patients with colorectal cancer: a scoping review. Clin Nutr. 2023;42(9):1595–601.

    Article  PubMed  Google Scholar 

  17. Atmis V, Yalcin A, Silay K, Ulutas S, Bahsi R, Turgut T, et al. The relationship between all-cause mortality Sarcopenia and sarcopenic obesity among hospitalized older people. Aging Clin Exp Res. 2019;31(11):1563–72.

    Article  PubMed  Google Scholar 

  18. Ji Y, Cheng B, Xu Z, Ye H, Lu W, Luo X, et al. Impact of sarcopenic obesity on 30-day mortality in critically ill patients with intra-abdominal sepsis. J Crit Care. 2018;46:50–4.

    Article  PubMed  Google Scholar 

  19. Zhang JJ, Dong X, Liu GH, Gao YD. Risk and protective factors for COVID-19 morbidity, severity, and mortality. Clin Rev Allergy Immunol. 2023;64:90–107.

    Article  CAS  PubMed  Google Scholar 

  20. Kowsar R, Rahimi AM, Sroka M, Mansouri A, Sadeghi K, Bonakdar E, et al. Risk of mortality in COVID-19 patients: a meta- and network analysis. Sci Rep. 2023;13:2138.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications. Eur J Epidemiol. 2020;35:1123–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wu ZY, McGoogan JM. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323:1239–42.

    Article  CAS  PubMed  Google Scholar 

  23. Greco M, De Corte T, Ercole A, Antonelli M, Azoulay E, Citerio G, et al. Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study. Intensive Care Med. 2022;48:690–705.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang Y, Tan S, Yan Q, Gao Y. Sarcopenia and COVID-19 outcomes. Clin Interv Aging. 2023;18:359–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Pérez-Campos Mayoral L, Matias-Cervantes CA, Pérez-Campos E, Romero Díaz C, Laguna Barrios LÁ, Pina, Canseco, et al. Associations of dynapenic obesity and sarcopenic obesity with the risk of complications in COVID-19. Int J Mol Sci. 2022;23(15):8277.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ma Y, He M, Hou LS, Xu S, Huang ZX, Zhao N, et al. The role of SARC-F scale in predicting progression risk of COVID-19 in elderly patients: a prospective cohort study in Wuhan. BMC Geriatr. 2021;21(1):355–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ling CH, de Craen AJ, Slagboom PE, Gunn DA, Stokkel MP, Westendorp RG, et al. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr. 2011;30(5):610–5.

    Article  PubMed  Google Scholar 

  29. Andrade MS, Honorato MP, Vargas JP, Galvez M, Rojas MR. Comparison of two handgrip dynamometers in older adults before elective surgery. Perioper Med (Lond). 2023;12(1):46–53.

    Article  PubMed  Google Scholar 

  30. Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 consensus update on Sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21:300–7.

    Article  PubMed  Google Scholar 

  31. Cederholm T, Jensen GL, Correia MI, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - a consensus report from the global clinical nutrition community. Clin Nutr. 2019;38:1–9.

    Article  CAS  PubMed  Google Scholar 

  32. Martin FC, O’Halloran AM. Tools for assessing Frailty in Older people: General concepts. Adv Exp Med Biol. 2020;1216:9–19.

    Article  PubMed  Google Scholar 

  33. Aguilar-Iglesias L, Merino-Merino A, Sanchez-Corral E, Garcia-Sanchez MJ, Santos-Sanchez I, Saez-Maleta R, et al. Differences according to Age in the diagnostic performance of cardiac biomarkers to predict frailty in patients with acute heart failure. Biomolecules. 2022;12(2):245–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gingrich A, Volkert D, Kiesswetter E, Thomanek M, Bach S, Sieber CC, et al. Prevalence and overlap of Sarcopenia, frailty, cachexia and malnutrition in older medical inpatients. BMC Geriatr. 2019;19:120–30.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wei S, Nguyen T, Zhang Y, Ryu D, Gariani K. Sarcopenic obesity: epidemiology, pathophysiology, cardiovascular disease, mortality, and management. Front Endocrinol. 2023;14:1185221.

    Article  Google Scholar 

  36. Onishi S, Tajika M, Tanaka T, Yamada K, Abe T, Higaki EJ, et al. Prognostic impact of sarcopenic obesity after neoadjuvant chemotherapy followed by surgery in elderly patients with esophageal squamous. J Clin Med. 2020;9:2974–88.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Li XH, Yuan MX. Research progress on sarcopenic obesity. Chin J Diabetes Mellitus. 2023;5:66–70.

    Google Scholar 

  38. Li CW, Yu K, Shyh-Chang N, Jiang Z, Liu T, Ma S, et al. Pathogenesis of Sarcopenia and the relationship with fat mass: descriptive review. J Cachexia Sarcopenia Muscle. 2022;13:781–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Chen WZ, Zhang XZ, Zhang FM, Yu DY, Chen WH, Lin F, et al. Coexistence of GLIM-defined malnutrition and Sarcopenia have negative effect on the clinical outcomes in the elderly gastric cancer patients after radical gastrectomy. Front Nutr. 2022;19:960670.

    Article  Google Scholar 

  40. Zhang C, Wang H, Wen Z, Bao Z, Li X. Collective and individual assessment of the risk of death from COVID-19 for the elderly, 2020–2022. China CDC Wkly. 2023;5(18):407–12.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wang H, Hai S, Liu Y, Li C, Liu Y, Liu P, et al. Associations between sarcopenic obesity and cognitive impairment in elderly Chinese communitydwelling individuals. J Nutr Health Aging. 2019;23:14–20.

    Article  CAS  PubMed  Google Scholar 

  42. Ji W, Liu X, Zheng K, Liu P, Zhao Y, Lu J, et al. Thresholds of visceral fat area and percent of body fat to define sarcopenic obesity and its clinical consequences in Chinese cancer patients. Clin Nutr. 2022;41(3):737–45.

    Article  CAS  PubMed  Google Scholar 

  43. Kroh A, Uschner D, Lodewick T, Eickhoff RM, Schöning W, Ulmer FT, et al. Impact of body composition on survival and morbidity after liver resection in hepatocellular carcinoma patients. Hepatobiliary Pancreat Dis. 2019;18:28–37.

    Article  CAS  Google Scholar 

  44. Lee SE, Park JH, Kim KA, Kang YS, Choi HS. Association between sarcopenic obesity and pulmonary function in Korean elderly: results from the Korean National Health and Nutrition Examination Survey. Calcif Tissue Int. 2020;106(2):124–30.

    Article  CAS  PubMed  Google Scholar 

  45. Best TD, Mercaldo SF, Bryan DS, Marquardt JP, Wrobel MM, Bridge CP, et al. Multilevel body composition analysis on chest computed tomography predicts hospital length of stay and complications after lobectomy for lung cancer: a multicenter study. Ann Surg. 2022;275(5):e708–15.

    Article  PubMed  Google Scholar 

  46. Mayoral LP, Matias-Cervantes CA, Pérez-Campos E, Díaz CR, Barrios LA, Canseco MD, et al. Associations of dynapenic obesity and sarcopenic obesity with the risk of complications in COVID-19. Int J Mol Sci. 2022;23:8277–90.

    Article  Google Scholar 

  47. Paoli A, Cerullo G. Investigating the link between Ketogenic diet, NAFLD, mitochondria, and oxidative stress: a narrative review. Antioxidants. 2023;12:1065.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li CW, Yu K, Shyh-Chang N, Li GX, Jiang LJ, Yu SL, et al. Circulating factors associated with sarcopenia during ageing and after intensive lifestyle intervention. J Cachexia Sarcopenia Muscle. 2019;10:586–600.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to sincerely acknowledge the contribution of the comprehensive geriatric assessment team and all the medical staff of the geriatric inpatient unit at Huadong Hospital.

Funding

This work was supported by the Shanghai Municipal Health System Project (No.202140517).

Author information

Authors and Affiliations

Authors

Contributions

Zong M designed the data collection instruments, collected and analyzed data, drafted the initial manuscript, and revised the manuscript. Zhao A analyzed data and revised the manuscript. Han W, Chen Y, Weng T, Li S, Tang L collected data and curation. Wu J conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Jiang Wu.

Ethics declarations

Ethics approval and consent to participate

The study adhered to the principles and guidelines outlined in the Declaration of Helsinki and its subsequent amendments, and the protocol was approved by the Ethics Committee of Huadong Hospital (2023K199). And informed consent was obtained from all subjects.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zong, M., Zhao, A., Han, W. et al. Sarcopenia, sarcopenic obesity and the clinical outcome of the older inpatients with COVID-19 infection: a prospective observational study. BMC Geriatr 24, 578 (2024). https://doi.org/10.1186/s12877-024-05177-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-024-05177-w

Keywords