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. 2024 Aug 9;103(32):e39257. doi: 10.1097/MD.0000000000039257

Impact of the COVID-19 pandemic on risk of sarcopenia: From lockdown and infection perspectives: A systematic review and meta-analysis

Xiang Wang a,b, Qiaofeng Guo b, Kai Huang b, Gouping Ma b, Lifeng Zhai b, Bingyuan Lin b, Haiyong Ren b, Ze Yang a,*
PMCID: PMC11315480  PMID: 39121262

Abstract

Background:

During the new coronavirus disease 2019 (COVID-19) pandemic, there are numerous symptoms in the skeletal muscular system, such as decreased skeletal muscle mass, strength, and muscle function, which are the main manifestations of sarcopenia. To investigate the impact of the COVID-19 pandemic on sarcopenia from the perspectives of COVID-19 pandemic lockdown and COVID-19 infection, we conducted this study.

Methods:

We searched for literature related to COVID-19 and sarcopenia published in PubMed, Embase, Cochrane Library, and Web of Science. Two researchers independently searched and screened the articles, extracted data, and assessed the quality of the final included literature. RevMan 5.4 was used for meta-analysis.

Results:

A total of 8 articles with a total of 1145 patients were included. There was a significant difference in SARC-F scores (MD = 0.67, 95%CI = [0.41, 0.93], Z = 5.00, P < .00001), handgrip (MD = ‐1.57, 95%CI = [‐2.41, ‐0.73], Z = 3.66, P = .0002), body weight (MD = ‐1.87, 95%CI = [‐3.69, ‐0.05], Z = 2.01, P = .04), and skeletal muscle mass index (MD = ‐0.28, 95%CI = [‐0.54, ‐0.02], Z = 2.13, P = .03) between the time before the COVID-19 pandemic and during the COVID-19 pandemic. However, the results showed that there was no significant difference in muscle mass between the 2 groups (MD = ‐1.72, 95%CI = [‐4.39, 0.94], Z = 1.27, P = .21).

Conclusion:

The COVID-19 pandemic has had an impact on sarcopenia. Both infection with COVID-19 and lockdown during the COVID-19 pandemic increase the risk of sarcopenia. Research should pay more attention to this disease during the COVID-19 pandemic and adopt effective interventions to minimize adverse outcomes.

Keywords: COVID-19 pandemic, infection, lockdown, sarcopenia, systematic review and meta-analysis

1. Introduction

The outbreak and rapid spread of the new coronavirus disease 2019 (COVID-19) in December 2019 is now prevalent worldwide.[1,2] Its pathogen, the 2019 novel coronavirus (2019-nCoV), is a highly infectious respiratory virus that not only causes significant damage to the respiratory system[3,4] but also impacts many other systems, including the nervous system,[5–7] cardiovascular system,[8,9] digestive system,[10,11] skeletal muscular system,[12,13] etc. In the case of the skeletal muscular system, it is because the cells of the skeletal muscles express ACE2, which interacts with SARS-CoV-2 in its spike domain, and thus skeletal muscles are likely more susceptible to direct viral invasion.[14] One of the common complications of the skeletal muscular system is sarcopenia, which is characterized by a progressive loss of muscle mass and strength, as well as decreased muscle function.[15] What’s more, with the spread of the COVID-19 pandemic, social lockdown measures were implemented. Physical activities conducted in collective environments were prohibited, and sitting time increased.[16] The decrease in physical activity levels and sedentary behavior due to measures adopted to reduce the spread of infection may also have a relationship with sarcopenia.

To the best of our knowledge, there is no meta-analysis on how the COVID-19 pandemic affects sarcopenia, even though numerous studies have suggested that the spread of the pandemic may result in a loss of muscle mass and strength. Therefore, the main objective of this study was to investigate the impact of the COVID-19 pandemic on sarcopenia from the perspectives of the COVID-19 pandemic lockdown and COVID-19 infection.

2. Materials and methods

2.1. Search strategy

A complete search strategy was designed to retrieve articles from PubMed, Embase, Web of Science, and the Cochrane Library, covering the period from the creation of each database to October 1st, 2023. The keywords were as follows: (“COVID-19” OR “2019-nCoV Infection” OR “SARS-CoV-2 Infection” OR “2019 Novel Coronavirus Disease” OR “COVID-19 Virus Infection” OR “Coronavirus Disease 2019”) AND (“skeletal muscles” OR “voluntary muscle” OR “muscle strength” OR “muscle mass” OR “sarcopenia”). At the same time, we searched for gray literature that met the inclusion criteria in Open Grey, ClinicalTrials.gov, and the WHO Clinical Trial Registration Center. The primary outcome indicator was SARC-F scores. Duplicate studies were removed at the initial stage. Titles and abstracts were screened independently by 2 investigators (ZY and KH) to identify potentially relevant studies that met the inclusion criteria. Then, the full text of each study was individually analyzed to determine if it met the eligibility criteria. Disagreements regarding the inclusion of studies were resolved through consultation with a third reviewer (XW).

2.2. Eligibility criteria

The protocols for this systematic review and meta-analysis were registered with PROSPERO (CRD 42023391252).

The inclusion criteria for this review were: (1) study design: observational study or randomized controlled trial; (2) participants: aged older than 18 years; (3) background: during the COVID-19 pandemic (during the pandemic lockdown or infected with COVID-19); (4) outcomes: SARC-F scores, skeletal muscle mass index (SMI), body weight, handgrip strength, and muscle mass.

The exclusion criteria were as follows: (1) studies with insufficient data or irrelevant outcomes; (2) type of article: conference abstracts, reviews, editorials, and letters. (3) Article language: not in English.

2.3. Data extraction

Two independent researchers (ZY and KH) extracted the following information from the included studies: author and publication year, study design, source of population, mean age or age range, sample size, the 2 groups, and outcomes. Then the results would be cross-checked. Disagreements between the 2 researchers regarding the extracted data were resolved by discussion or consultation with a third investigator (XW).

2.4. Quality assessment

The quality of the included articles was evaluated by two researchers using the Newcastle-Ottawa Scale, which scores based on selection, comparability, and outcome. Newcastle-Ottawa Scale scores of ≥7 were considered high quality, scores of 5 to 6 were considered moderate quality, and scores <5 were considered low quality.

2.5. Statistical analyses

This meta-analysis was performed using Revman5.4 software. I2 value and Q test were used to evaluate the heterogeneity between studies; if P > .1 and I2 < 50%, the fixed-effect model was selected for meta-analysis; if P ≤ .1 and I2 ≥ 50%, the random-effect model was selected. Subgroup analysis was performed to explore the sources of heterogeneity according to gender, age, etc. Sensitivity analysis was also performed to evaluate the sources of heterogeneity. Funnel plots were used to evaluate publication bias. The test level for this meta-analysis was set at α = 0.05.

3. Result

3.1. Search results

A total of 2292 relevant articles were retrieved. Among them, 622 duplicate publications were excluded, 1471 papers were excluded after the initial screening of titles and abstracts, and 191 papers were excluded from full-text screening due to noncompliance. Finally, 8 articles were included.[17–24] The flow chart of the literature screening and the results are presented in Figure 1.

Figure 1.

Figure 1.

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PRISMA flow diagram.

3.2. Characteristics of the literature and quality assessment

Seven of the included articles were observational study and one was a randomized controlled trial. Three compared it before and after the COVID-19 pandemic lockdown and the other 5 compared it between the short time of the infection (or just in acute infection) and the longer time of the infection. Some included articles had more than one set of data for one outcome based on gender (total, male, female) or other information and were labeled with A, B, and C to distinguish them. The participants in these studies were from different countries. The results of the characteristics of the literature were shown in Table 1. As for the quality of these articles, 3 articles ranked high quality and 5 ranked moderate quality. The quality assessment of the literature was shown in Table 2.

Table 1.

Characteristics of the 8 studies included in the meta-analysis.

Study Study design Source of population Mean age or age range Sample size (M/F) Groups Outcomes
Bakilan 2021[17] Cohort study Turkey 67 36/125 During COVID-19 pandemic lockdown; before COVID-19 pandemic lockdown ①
Hasegawa 2021[18] Cohort study Japan 75.2 ± 7.1 35/21 During COVID-19 pandemic lockdown; before COVID-19 pandemic lockdown ③⑤
Rocha 2021[19] Cohort study Brazil 65.5 ± 5.6 0/29 During COVID-19 pandemic lockdown; before COVID-19 pandemic lockdown ①③
Wierdsma 2021[20] Observational study Netherlands 64.8 ± 12.4 280/127 During COVID-19 infection hospital stay and after discharge ①
Almudena 2023 Prospective, longitudinal study Spain 76.8 ± 7.0 55/51 3 months after acute COVID-19 infection; 12 months after COVID-19 infection ①②③④
Tamara 2022 Randomized controlled trial Spain – 25/63 Long-term post-COVID-19 patients; 8 weeks after it ④
Robinson 2023 Cohort study Spain T: 47.38 ± 9.99
C: 52.22 ± 11.94		 75/121 Long-COVID syndrome patients; before COVID-19 pandemic lockdown ②④⑤
Androniki 2023 Observational study Greece 59 61/41 COVID-19 patients for days 1 and 7 ④
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① SARC-F score, ② muscle mass, ③ body weight, ④ handgrip, ⑤ SMI.

C = control group, F = female, M = male, SMI = skeletal muscle mass index, T = test group.

Table 2.

Risk of bias assessment of component studies using the modified Newcastle-Ottawa Quality Assessment Scale.

Study Selection Comparability Outcome Total Quality rating
1 2 3 4 1 1 2 3
Bakilan 2021[17] * * * * ** * * 8 High
Hasegawa 2021[18] * * * ** * 6 Moderate
Rocha 2021[19] * * * * ** * 7 High
Wierdsma 2021[20] * * * * ** 6 Moderate
Almudena 2023 * * ** * * 6 Moderate
Tamara 2022 * * * * * * 6 Moderate
Robinson 2023 * * * ** * 6 Moderate
Androniki 2023 * * * ** * * 7 High
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Selection included: 1. representativeness of the exposed cohort; 2. selection of the nonexposed cohort; 3. ascertainment of exposure; 4. demonstration that outcome of interest was not present at start of study.

Comparability included: comparability of cohorts on the basis of the design or analysis.

Outcome included: 1. assessment of outcome; 2. was follow-up long enough for outcomes to occur; 3. adequacy of follow up of cohorts.

*One score.

**Two scores.

3.3. SARC-F scores

Four studies reported the outcome of SARC-F scores. From the heterogeneity analysis (I2 = 77%, P = .0002) (Fig. 2), we learned that there was a high heterogeneity, thus sensitivity analysis was used to find its sources of it. After removing Wierdsma research, the heterogeneity decreased (I2 = 0%, P = .55), so the fix-effect model was selected. This meta-analysis showed that there was a significant difference between the group before the COVID-19 pandemic and during the COVID-19 pandemic (MD = 0.67, 95%CI = [0.41, 0.93], Z = 5.00, P < .00001) (Fig. 3).

Figure 2.

Figure 2.

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Forest plot of SARC-F scores between before the COVID-19 pandemic group and during the COVID-19 pandemic group. COVID-19 = new coronavirus disease 2019.

Figure 3.

Figure 3.

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Sensitivity analysis of SARC-F scores.

3.4. Body weight

Three studies were combined for analysis. There was no heterogeneity between the included studies (I2 = 0%, P = .74), so a fixed-effect model was employed for the analysis. There was significant difference between the time before COVID-19 pandemic group and during COVID-19 group (MD = ‐1.87, 95%CI = [‐3.69, ‐0.05], Z = 2.01, P = .04) (Fig. 4).

Figure 4.

Figure 4.

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Forest plot of body weight between before the COVID-19 pandemic group and during the COVID-19 pandemic group. COVID-19 = new coronavirus disease 2019.

3.5. Muscle mass

Two studies evaluated the impact of the COVID-19 pandemic on muscle mass. The results showed that there was no obvious difference between the 2 groups (MD = ‐1.72, 95%CI = [‐4.39, 0.94], Z = 1.27, P = .21) (Fig. 5).

Figure 5.

Figure 5.

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Forest plot of muscle mass between before the COVID-19 pandemic group and during the COVID-19 pandemic group. COVID-19 = new coronavirus disease 2019.

3.6. Handgrip

The handgrip was reported in 4 articles. Although subgroup analysis of gender did not show difference between the 2 groups, the evidence of the impact of the COVID-19 pandemic on handgrip (MD = ‐1.57, 95%CI = [‐2.41, ‐0.73], Z = 3.66, P = .0002) (Fig. 6) with little heterogeneity (I2 = 20%, P = .26) found in the total results.

Figure 6.

Figure 6.

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Forest plot of handgrip between before the COVID-19 pandemic group and during the COVID-19 pandemic group. COVID-19 = new coronavirus disease 2019.

3.7. Skeletal muscle mass index

Two studies reported this outcome. A heterogeneity test showed that there was heterogeneity across studies (I2 = 64%, P = .09). The random-effect model was used to consolidate the effect value and it had difference between the time before the COVID-19 pandemic and in COVID-19 pandemic (MD = ‐0.28, 95%CI = [‐0.54, ‐0.02], Z = 2.13, P = .03) (Fig. 7).

Figure 7.

Figure 7.

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Forest plot of SMI between before the COVID-19 pandemic group and during the COVID-19 pandemic group. COVID-19 = new coronavirus disease 2019, SMI = skeletal muscle mass index.

3.8. Publication bias

A funnel plot was not performed since the number of studies included in the literature for each outcome was <5, which may lead to inaccuracy.

4. Discussion

Sarcopenia was identified by assessing muscle mass and muscle strength, measured through indicators such as the SARC-F score, SMI, and grip strength.[21] The SARC-F score is a simple yet significant screening tool for sarcopenia. It is screened via questionnaire and is convenient and suitable for data collection during the COVID-19 pandemic.[25,26] In this study, we observed a significant increase in SARC-F scores during the COVID-19 pandemic compared to before the pandemic, indicating a higher risk of sarcopenia during this period. This trend was evident not only between groups before and during the COVID-19 pandemic lockdown, but also among patients with and without COVID-19 infection. The results showed a decrease in SMI and handgrip strength during the COVID-19 pandemic.

Regarding handgrip strength, subgroup analysis based on gender showed no significant difference between the 2 groups. However, in Almudena study,[21] it was reported that compared to groups 3 months after acute COVID-19 infection, an improvement in handgrip strength was observed globally (+1.2 kg), as well as by sex (males +2.5 kg and women +0.5 kg) in groups 12 months after COVID-19 infection. It is possible that the limited sample size affected the accuracy of the results. It seemed that there was no significant difference in muscle mass between the two groups. However, in Robinson study,[24] lean muscle tissue was significantly lower in patients with long-COVID-19 syndrome (P < .001). As we know, due to the global impact of COVID-19, personal isolation has become commonplace. Governments have recommended social distancing to prevent further infection from spreading through social gatherings and any group activities, leading to an inevitable reduction in outdoor activities.[27,28] The decrease in physical activity caused the loss of skeletal muscle mass and function to become more severe, inevitably leading to sarcopenia. In the case of COVID-19 infection, the results of our research were consistent with others. Numerous studies have mentioned that sarcopenia is one of the common complications of the skeletal muscular system in patients infected with COVID-19.[29,30] The probable reasons for this may relate to fever caused by viral infections, loss of appetite, hypoxemia, prolonged bed rest, and lack of exercise.[31] In particular, glucocorticoids are often used in patients with severe COVID-19. One of the main target tissues of these drugs is skeletal muscle, which can affect and inhibit glucose uptake, utilization, and gluconeogenesis in skeletal muscle, and can induce myoglycogenolysis.[32] The long-term use of glucocorticoids accelerates the process of muscle weakness and muscle atrophy.[33]

For body weight, studies reported that during the COVID-19 pandemic lockdown, the majority of people showed an increase in body weight, and only a small percentage of people lost weight.[34] Factors contributing to weight gain were probably related to increased snacking frequency and sedentary behavior, emotional eating, decreased physical activity, and poor sleep quality.[35] This is for those who were in the COVID-19 lockdown. For those infected with COVID-19, the more likely condition was losing weight due to the symptoms of COVID-19, which leads to a decrease in skeletal muscle mass.[36]

Without timely intervention or improper management, sarcopenia can lead to decreased ability to perform daily living activities and an increased risk of falls, fractures, bed rest, infection, and even death, placing a heavy burden on the patient’s family. Moreover, sarcopenia can cause other diseases. The muscle is the main organ for glucose metabolism, so a reduction in muscle mass leads to a reduction in blood glucose metabolism, which can result in more severe diabetes.[37,38] Moreover, a meta-analysis showed that low muscle quality and function were associated with COVID-19 disease severity.[39] Therefore, the adoption of preventive measures against sarcopenia, such as sufficient dietary protein consumption and resistance training, is crucial.[40]

This review is the first meta-analysis of the impact of the COVID-19 pandemic on the risk of sarcopenia, including observational studies and randomized controlled trials conducted in different countries. However, it inevitably had some limitations. It had a small sample size, which may cause the unknown possibility of bias brought on by insufficient data. Besides, due to the epidemic, some of the included studies used online surveys without field measurements of objective data such as body weight, which affected the accurate estimation of their changes. Subsequent studies should incorporate simple instruments such as grip strength and gait speed tests for screening, followed by further bioimpedance analysis for the accurate diagnosis of sarcopenia.

5. Conclusion

This systematic review and meta-analysis suggested that the COVID-19 pandemic had an impact on sarcopenia. Both infection with COVID-19 and lockdown during the COVID-19 pandemic increase the risk of sarcopenia. Researchers should pay more attention to this disease during the COVID-19 pandemic and adopt effective interventions to minimize the adverse outcomes.

Author contributions

Conceptualization: Xiang Wang.

Data curation: Xiang Wang, Ze Yang.

Formal analysis: Xiang Wang, Kai Huang, Ze Yang.

Funding acquisition: Kai Huang.

Investigation: Xiang Wang, Kai Huang, Ze Yang.

Methodology: Xiang Wang, Kai Huang.

Project administration: Gouping Ma.

Resources: Gouping Ma, Lifeng Zhai, Haiyong Ren.

Software: Gouping Ma, Lifeng Zhai, Haiyong Ren, Qiaofeng Guo.

Supervision: Gouping Ma, Lifeng Zhai, Bingyuan Lin, Haiyong Ren, Qiaofeng Guo, Ze Yang.

Validation: Bingyuan Lin, Qiaofeng Guo, Ze Yang.

Visualization: Bingyuan Lin, Ze Yang.

Writing – original draft: Xiang Wang.

Writing – review & editing: Ze Yang.

Abbreviations:

COVID-19
new coronavirus disease 2019
SMI
skeletal muscle mass index

The authors have no funding and conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

How to cite this article: Wang X, Guo Q, Huang K, Ma G, Zhai L, Lin B, Ren H, Yang Z. Impact of the COVID-19 pandemic on risk of sarcopenia: From lockdown and infection perspectives: A systematic review and meta-analysis. Medicine 2024;103:32(e39257).

XW, QFG, and KH contributed equally to this work and share the first authorship.

Contributor Information

Xiang Wang, Email: wangxianga1995@163.com.

Qiaofeng Guo, Email: hzgqf1971@163.com.

Kai Huang, Email: zjhzhuangkai@163.com.

Gouping Ma, Email: tdyymgp@163.com.

Lifeng Zhai, Email: 2644817118@qq.com.

Bingyuan Lin, Email: hzboneage@163.com.

Haiyong Ren, Email: renhaiyong1991@126.com.

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