Next Article in Journal
Longitudinal Associations between Healthy Eating Habits, Resilience, Insomnia, and Internet Addiction in Chinese College Students: A Cross-Lagged Panel Analysis
Previous Article in Journal
Effects of an Acute Dose of Zinc Monomethionine Asparate and Magnesium Asparate (ZMA) on Subsequent Sleep and Next-Day Morning Performance (Countermovement Jumps, Repeated Sprints and Stroop Test)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 16
Issue 15
10.3390/nu16152468
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Association between Body Composition Phenotype and Insulin Resistance in Post-COVID-19 Syndrome Patients without Diabetes: A Cross-Sectional, Single-Center Study

by
Dulce González-Islas
1,
Laura Flores-Cisneros
2,
Arturo Orea-Tejeda
1,*,
Candace Keirns-Davis
1,
Nadia Hernández-López
3,
Laura Patricia Arcos-Pacheco
1,
Andrea Zurita-Sandoval
1,
Frida Albarran-López
1,
Luis García-Castañeda
4,
Fernanda Salgado-Fernández
3,
Samantha Hernández-López
1,
Angelia Jiménez-Valentín
1 and
Ilse Pérez-García
1
1
Heart Failure and Respiratory Distress Clinic, Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas”, Mexico City 14080, Mexico
2
Department of Epidemiological Information Analysis, General Directorate of Epidemiology, Mexico City 01480, Mexico
3
Licenciatura en Nutriología, Facultad de Estudios Superiores Zaragoza, Universidad Nacional Autónoma de México, Mexico City 09230, Mexico
4
Clinical Nutrition Department, Hospital General “Dr. Manuel Gea González”, Mexico City 14080, Mexico
*
Author to whom correspondence should be addressed.
Nutrients 2024, 16(15), 2468; https://doi.org/10.3390/nu16152468
Submission received: 19 June 2024 / Revised: 24 July 2024 / Accepted: 27 July 2024 / Published: 30 July 2024
(This article belongs to the Section Nutrition and Diabetes)
Browse Figures
Versions Notes

Abstract

:
Background: The most frequent body composition alterations in post-COVID-19 syndrome include low muscle mass, dynapenia, sarcopenia, and obesity. These conditions share interconnected pathophysiological mechanisms that exacerbate each other. The relationship between body composition phenotypes and metabolic abnormalities in post-COVID-19 syndrome remains unclear. Objective: To evaluate the association between body composition phenotypes and insulin resistance (IR) and metabolic abnormalities in non-diabetic individuals with post-COVID-19 syndrome. Methods: A cross-sectional, single-center study involving 483 subjects with post-COVID-19 syndrome following moderate to severe acute COVID-19 requiring hospitalization. Individuals with diabetes, those who declined to participate, or those who could not be contacted were excluded. Body composition phenotypes were classified as normal weight, dynapenia, sarcopenia, dynapenic obesity, and sarcopenic obesity (SO). Results: The average age was 52.69 ± 14.75 years; of note, 67.08% were male. The prevalence of body composition phenotypes was as follows: 13.25% were of normal weight, 9.52% had dynapenia, 9.94% had sarcopenia, 43.69% had obesity, 18.84% had dynapenic obesity, and 4.76% had SO. Additionally, 58.18% had IR. Obesity (OR: 2.98, CI95%; 1.64–5.41) and dynapenic obesity (OR: 4.98, CI95%; 1.46–6.88) were associated with IR. Conclusion: The most common body composition phenotypes were obesity, dynapenic obesity, and dynapenia. Furthermore, obesity and dynapenic obesity were associated with IR in post-COVID-19 syndrome.

1. Introduction

Post-COVID-19 syndrome is characterized by symptoms and abnormalities that persist for more than 12 weeks and may continue for years after the initial acute viral infection caused by SARS-CoV-2 which cannot be attributed to other conditions [1]. The persistence of signs and symptoms is linked to the severity of the original COVID-19 infection [2]. The global prevalence of post-COVID-19 syndrome is 43%, with 54% of cases occurring in hospitalized subjects and 34% in non-hospitalized individuals [3]. The most frequently observed manifestations affect the pulmonary, hematologic, cardiovascular, endocrine, renal, gastrointestinal, and musculoskeletal systems [4].
The most characteristic musculoskeletal and body composition alterations in post-COVID-19 syndrome are fatigue, low muscle mass, dynapenia, sarcopenia, and obesity [2,5,6,7,8]. Dynapenia is defined as low muscle strength and can be assessed based on handgrip strength (HGS), which correlates with strength in other body compartments. Sarcopenia, on the other hand, is a progressive and generalized skeletal muscle disorder [9]. Additionally, body composition phenotypes such as low muscle mass, dynapenia, sarcopenia, and obesity increase the risk of adverse outcomes such as falls, physical disability, fractures, limited capacity for activities of daily living, impaired mobility, and mortality in diverse populations [10,11,12,13,14].
Several mechanisms may contribute to changes in body composition, including advanced age and pre-existing metabolic and pro-inflammatory conditions such as diabetes and pulmonary and cardiovascular diseases. During the acute phase of COVID-19, several factors contribute to changes in body composition, including the severity of the disease, anorexia, malnutrition, prolonged hospitalization, inflammatory states, oxidative stress, increased protein breakdown, the use of corticosteroids, and the use of mechanical ventilation and neuromuscular blockers in patients requiring intensive care, which can negatively impact respiratory muscles such as the intercostal muscles, diaphragm, and peripheral muscles [5,15,16,17].
Following SARS-CoV-2 infection, metabolic abnormalities have been observed, such as an increased risk of new-onset diabetes and hyperglycemia [18], as well as a higher prevalence of dynapenia, sarcopenia, and obesity [19,20,21].
Moreover, low muscle mass/strength, particularly sarcopenia, and obesity share strongly interconnected pathophysiological mechanisms that aggravate each other, creating a vicious cycle. These mechanisms include age-related reductions in protein synthesis and increased proteolysis, fat infiltration into skeletal muscle promoting lipotoxicity and aggravating inflammation, mitochondrial dysfunction, oxidative stress [22,23,24], and metabolic abnormalities such as insulin resistance (IR), which is commonly defined as diminished sensitivity or receptivity to the metabolic actions of insulin and is a risk factor for metabolic syndrome (MetS) and type 2 diabetes [25,26].
Excess adipose tissue and low muscle mass/strength coexist in sarcopenic obesity (SO), which has been associated with adverse outcomes [27]. Furthermore, previous studies have shown a relationship between body composition alterations and metabolic abnormalities, particularly IR, in diverse populations [28,29].
Despite the prevalence of post-COVID-19 syndrome and the observed metabolic abnormalities following SARS-CoV-2 infection, the relationship between body composition phenotypes and metabolic abnormalities in post-COVID-19 syndrome has not been fully examined. Therefore, this study aimed to evaluate the association between body composition phenotypes and IR, as well as metabolic abnormalities in subjects with post-COVID-19 syndrome without diabetes.

2. Materials and Methods

2.1. Study Design and Population

A cross-sectional single-center study was conducted at the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas” in Mexico City. Data were obtained from outpatient evaluations conducted and clinical records examined three months after SARS-CoV-2 infection as part of routine clinical examinations of post-COVID-19 subjects. The study period spanned from 1 June 2020 to 30 May 2023.
Patients were invited to participate in the follow-up post-COVID clinic around 12 weeks after hospital discharge. Subjects who required hospitalization during the acute phase due to moderate to severe COVID-19, with a confirmed diagnosis via PCR testing, were included. Individuals with diabetes, those who declined to participate, or those who could not be contacted were excluded from the study (Figure 1).

Outcome Measures

Clinical information and data about hospitalization for SARS-CoV-2 were obtained from medical records. Body composition, anthropometry, metabolic parameters, and clinical and demographic variables were measured as part of the post-COVID-19 clinical management implemented for the patients who came to our hospital.

2.2. Insulin Resistance (IR)

IR was defined as a Homeostatic Model Assessment of Insulin Resistance Index (HOMA-IR) > 2.5 [30].

2.3. Metabolic Syndrome (MetS)

MetS was assessed using the modified National Cholesterol Education Program Adult Treatment Panel (NCEP ATP III) criteria. MetS was defined as the presence of three or more of the following points: (1) elevated blood pressure (systolic blood pressure > 130 mmHg or diastolic blood pressure > 85 mmHg), (2) hypertriglyceridemia (triglycerides > 150 mg/dL), (3) low HDL cholesterol (HDL cholesterol < 50 mg/dL in men, <40 mg/dL in women), (4) hyperglycemia (plasma glucose > 100 mg/dL or treatment for diabetes), and (5) obesity (in men, fat mass > 25% and ≤16 kg; in women, fat mass > 35%) [31].

2.4. Anthropometry

Weight and height measurements were performed in accordance with the manual anthropometric standardization reference [32]. All of the subjects wore light clothing and were barefoot. Body mass index (BMI) was calculated by dividing weight (kilograms) by height squared (meters).

2.5. Handgrip Strength (HGS)

According to Rodríguez-García et al. [33], HGS was evaluated using a mechanical Smedley Hand Dynamometer (Stoelting, Wood Dale, UK).

2.6. Body Composition

Body composition was measured with whole-body bioelectrical impedance using four-pole mono-frequency equipment, the RJL Quantum IV analyzer (RJL Systems®, Clinton Township, MI, USA). Using a technique previously described [34], fat mass was estimated using RJL Systems’ software BC 4.2.2.
Appendicular skeletal muscle mass adjusted by Height2 (ASM/Height2) was assessed according to Sergi’s formula [35]: ASM/Height2 (Kg/m2) = [−3.964 + (0.227 * (Height2 (cm)/Resistance) + (0.095 * Weight) + (1.384 * Sex) + (0.064 * Reactance)/Height (m2)].

2.7. Body Composition Phenotype

Normal weight was defined as adequate muscle mass (in men, ASM/Height2 > 7.0 kg/m2 and in women, ASM/Height2 > 6.0 kg/m2), muscle strength (in men, HGS > 27 kg in men and in women, HGS > 16 kg), and fat mass (≤25% in men and ≤35% in women) [9,36].
Dynapenia was defined as low muscle strength conforming to the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) [9], with thresholds of ≤27 kg HGS in men and ≤16 kg HGS in women.
Sarcopenia was defined according to EWGSOP2 [9] as low muscle mass (in men, ASM/Height2 ≤ 7.0 kg/m2 and in women, ASM/Height2 ≤ 6.0 kg/m2) and low muscle strength (in men, HGS ≤ 27 kg and in women, HGS ≤ 16 kg).
Obesity was defined as fat mass > 25% in men and > 35% in women [36]. Dynapenic obesity was defined in men as low muscle strength (HGS ≤ 27 kg in men and women) and excess adiposity (fat mass > 25% in men and >35% in women) [9,36].
Sarcopenic obesity (SO) was defined as low muscle mass (in men ASM/Height2 ≤ 7.0 kg/m2 and ≤6.0 kg/m2 in women), low muscle strength (HGS ≤ 27 kg and ≤16 kg in women), and excess adiposity (in men fat mass > 25% and in women as fat mass > 35%) [9,36].

2.8. Statistical Analysis

Analyses were conducted using the commercially available STATA package, version 14 (Stata Corp., College Station, TX, USA). Categorical variables were expressed as frequencies and percentages. Normality in continuous variables was evaluated using the Shapiro–Wilk test. Normal continuous variables were expressed as the mean and standard deviation, while non-normal variables were expressed as the median and interquartile range (25th–75th percentiles). Comparisons among the study groups were analyzed using the Chi-square test for categorical variables and t-tests or Mann–Whitney U tests for continuous variables.
A simple logistic regression model was performed to evaluate the risk factors associated with insulin resistance (IR), and the odds ratios (OR) and 95% confidence intervals (CI) were estimated. The multivariate logistic regression model was adjusted for variables that exhibited significance at p < 0.20 in the crude model. Potential interactions and multicollinearity between variables were tested. A p-value < 0.05 was considered statistically significant.

3. Results

A total of 483 subjects diagnosed with post-COVID-19 syndrome were assessed, with a mean age of 52.69 ± 14.75 years; of note, 67.08% were male. Among the subjects, 13.25% were of normal weight, 9.52% had dynapenia, 9.94% had sarcopenia, 43.69% had obesity, 18.84% had dynapenic obesity, and 4.76% had SO. The insulin-resistant group (HOMA-IR > 2.5) was younger, with higher weight, height, BMI, HGS, phase angle, fat mass, and obesity and a lower prevalence of dynapenia and sarcopenia compared to the non-insulin-resistant group (Table 1).

3.1. Metabolic Alterations

Regarding metabolic alterations, 56.33% had MetS, 58.18% had IR (HOMA-IR > 2.5), 30.23% had glucose > 100 mg/dL, 67.86% had HDL cholesterol < 40 mg/dL, 40.99% had high blood pressure, and 66.38% had triglycerides > 150 mg/dL. Figure 2 shows the metabolic alterations among the different body composition phenotypes.

3.2. Risk Factors Associated with IR

Regarding risk factors associated with IR, a bivariate model showed that age (OR: 0.97, CI 95%: 0.96 to 0.98, p < 0.001), COPD (OR: 0.22, CI 95%: 0.07 to 0.71, p = 0.012), length of hospital stay (OR: 0.98, CI 95%: 0.97 to 0.99, p = 0.001), weight (OR: 1.04, CI 95%: 1.02 to 1.05, p < 0.001), height (OR: 1.02, CI 95%: 1.00 to 1.04, p = 0.013), BMI (OR: 1.12, CI 95%: 1.08 to 1.16, p < 0.001), HGS (OR: 1.03, CI 95%: 1.01 to 1.05, p = 0.001), phase angle (OR: 1.32, CI 95%: 1.13 to 1.52, p < 0.001), ASM/Height2 (OR: 1.37, CI 95%: 1.17 to 1.60, p < 0.001), fat mass % (OR: 1.05, CI 95%: 1.03 to 1.07, p < 0.001), obesity (OR: 3.10, CI 95%: 1.74 to 5.53, p < 0.001), and dynapenic obesity (OR: 3.75, CI 95%: 1.15 to 12.21, p = 0.028) are associated with IR (Table 2).
Regarding body composition phenotypes associated with IR, a multivariate model showed that subjects with obesity (OR: 2.98, CI 95%: 1.64 to 5.41, p < 0.001) and dynapenic obesity (OR: 4.98, CI 95%: 1.46 to 16.88, p = 0.01) are associated with a higher risk of IR after adjusting for age, COPD, and hospital stay (Figure 3).

4. Discussion

One of the main findings of our research highlights the association between body composition phenotypes and the risk of IR, as well as the prevalence and metabolic profiles across these body composition phenotypes in individuals with post-COVID-19 syndrome.
In our study population, a high prevalence of alterations in body composition and metabolic parameters was observed, with only 13.25% of individuals classified as having a normal weight.
Concerning muscle strength, 9.52% had dynapenia and 18.84% had dynapenic obesity. Dynapenia refers to a low level of muscle strength and functionality. It has been associated with lower pulmonary function [37], prolonged hospital stays [38], and mortality [12] in various populations. Moreover, Wilkinson and Cols. found that individuals with reduced muscle strength had a 1.63 times higher risk of hospitalization or death from COVID-19 [39]. Within the context of post-COVID-19 syndrome, there is a high prevalence of dynapenia. López-Sampalo et al. reported that nearly 80% of patients over 65 years age exhibited dynapenia three months after infection, with a prevalence of 57% persisting at 12 months [7]. Another study observed a prevalence of dynapenia of 17.5% in outpatient individuals [8].
Another body composition phenotype commonly observed in individuals with post-COVID-19 syndrome is sarcopenia, characterized by low muscle mass and strength. In patients with post-COVID-19 syndrome, the prevalence of sarcopenia can vary depending on population characteristics and the diagnostic method used. For example, LoMauro et al. found a 41% prevalence of sarcopenia, as assessed using the SARC-F questionnaire, in individuals with neurological and motor deficits [21]. In contrast, a study by Lopez-Sampalo et al. reported a prevalence of 32% at three months and 19% at twelve months of follow-up among both hospitalized and ambulatory older adults, also using the SARC-F questionnaire. Our study found a prevalence of sarcopenia of 9.94% and 4.76% of SO, according to EWGSOP2 [9].
In severe or critical COVID-19 survivors, a prevalence of obesity higher than 60% has been observed [40]. Additionally, in subjects with post-COVID-19 syndrome who did not require hospitalization, the prevalence of obesity appears to be lower at 28% [8]. However, the coexistence of obesity with dynapenia or sarcopenia was not evaluated in these populations. One contribution of our study is that we assessed the coexistence of obesity with dynapenia or sarcopenia, demonstrating a negative impact on the quality of life and prognosis of the subjects [39].
SARS-CoV-2 infection contributes to body composition alterations, including loss of muscle strength and mass and myosteatosis. These changes may be attributed to factors such as increased oxidative stress, inflammatory cytokines, and the use of glucocorticoids. These factors could promote higher accumulation of adipose tissue and more severe manifestations [17,22,23,24,41,42,43,44], as IR and MetS [10] increase the risk of cardiovascular events and all-cause mortality. Additionally, low skeletal muscle mass and intramuscular fat accumulation contribute to impaired muscle contractility [28,45].
Several studies have shown that patients who have recovered from COVID-19 exhibit alterations in body composition and metabolism [7,8]. Another contribution of our study was to evaluate the association between fat mass, muscle/strength mass, and IR using a multivariate analysis, which allows for adjustment for confounding variables. Our study revealed that obese individuals had a 2.98-fold higher risk of IR. However, this risk significantly increased in individuals with dynapenic obesity (OR: 4.98, 95% CI: 1.46–16.88), while SO (OR: 1.74, 95% CI: 0.48–6.30) did not show statistical significance, likely due to the smaller sample size.
Skeletal muscle is an organ implicated in insulin-induced glucose metabolism mediated by glucose transporter 4 (GLUT4), which accounts for approximately 80% of glucose clearance [46]. A decline in muscle mass has been related to IR, MetS, and diabetes [47,48]
In the context of IR, compensatory hyperinsulinemia occurs, leading to reduced suppression of glycogenesis, accelerated protein catabolism, decreased protein anabolism, and increased myostatin levels [49,50]. IR combined with muscle loss inhibits β-oxidation, promotes increased lipolysis, and releases free fatty acids, leading to the aggregation of triglycerides in skeletal muscle and the liver [49,51]. Moreover, adipose tissue produces pro-inflammatory cytokines such as TNF-α, IL-6, and C-reactive protein, which negatively impact IR and are linked with lower muscle mass and diminished muscle strength [52,53]. Our results show that subjects with IR had shorter hospital stays; this may be because these subjects were younger, which is also reflected in higher strength and ASM/height2.
Concerning MetS and IR, higher prevalences were found in patients with obesity, dynapenic obesity, and OS. Although the prevalence of metabolic disorders in the population was high, 56.33% had MetS and 58.18% had IR.
Although the number of SARS-CoV-2 infections has decreased considerably, the sequelae in these subjects continue, especially in those with moderate to severe disease. The findings of this study highlight the need to screen both body composition and metabolic variables in patients who present with COVID-19 to apply therapeutic measures to mitigate and reverse body composition and metabolic alterations, preventing adverse events and improving quality of life.

Strengths and Limitations

This study has inherent limitations due to its cross-sectional design, which makes it impossible to assess causality and the incidence of different body compositions. Nonetheless, it also exhibits notable strengths. The diagnosis of body composition phenotypes was based on electrical bioimpedance analysis, which allows for estimating different body compartments such as muscle and fat mass. Furthermore, this study considers the coexistence of dynapenia and sarcopenia with obesity, which carries significant implications.

5. Conclusions

In post-COVID-19 subjects, there is a high prevalence of metabolic alterations and changes in body composition, such as obesity and dynapenic obesity. Additionally, both obesity and dynapenic obesity were found to be associated with a higher risk of IR.

Author Contributions

Conceptualization, D.G.-I., L.F.-C. and A.O.-T.; methodology, D.G.-I., L.F.-C. and A.O.-T.; software, L.P.A.-P., A.Z.-S. and F.A.-L.; validation, L.G.-C., F.S.-F., S.H.-L., A.J.-V. and I.P.-G.; formal analysis, D.G.-I. and L.F.-C.; investigation, C.K.-D. and N.H.-L.; resources, L.G.-C., F.S.-F., S.H.-L., A.J.-V. and I.P.-G.; data curation, C.K.-D. and N.H.-L.; writing—original draft preparation, D.G.-I., L.F.-C. and A.O.-T.; writing—review and editing, C.K.-D. and N.H.-L.; visualization, L.P.A.-P., A.Z.-S. and F.A.-L.; supervision, L.P.A.-P., A.Z.-S. and F.A.-L.; project administration, D.G.-I., L.F.-C. and A.O.-T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was approved by the Institutional Ethics and Research Committee of Biomedical Research in Humans of the Instituto Nacional de Enfermedades Respiratorias “Ismael Cosío Villegas” (approval number C71-20, date: 1 August 2020). All participants signed informed consent, and the study was conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due ethical reasons.

Acknowledgments

We extend our gratitude to the Cardiology Service team and Ana Bertha Pimentel Aguilar for her invaluable support.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. National Institute for Health and Care Excellence. Clinical Guidelines. In COVID-19 Rapid Guideline: Managing the Long-Term Effects of COVID-19; National Institute for Health and Care Excellence (NICE): London, UK, 2020. [Google Scholar]
  2. Fernández-de-Las-Peñas, C.; Palacios-Ceña, D.; Gómez-Mayordomo, V.; Florencio, L.L.; Cuadrado, M.L.; Plaza-Manzano, G.; Navarro-Santana, M. Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis. Eur. J. Intern. Med. 2021, 92, 55–70. [Google Scholar] [CrossRef] [PubMed]
  3. Chen, C.; Haupert, S.R.; Zimmermann, L.; Shi, X.; Fritsche, L.G.; Mukherjee, B. Global Prevalence of Post-Coronavirus Disease 2019 (COVID-19) Condition or Long COVID: A Meta-Analysis and Systematic Review. J. Infect. Dis. 2022, 226, 1593–1607. [Google Scholar] [CrossRef] [PubMed]
  4. Gupta, A.; Madhavan, M.V.; Sehgal, K.; Nair, N.; Mahajan, S.; Sehrawat, T.S.; Bikdeli, B.; Ahluwalia, N.; Ausiello, J.C.; Wan, E.Y.; et al. Extrapulmonary manifestations of COVID-19. Nat. Med. 2020, 26, 1017–1032. [Google Scholar] [CrossRef] [PubMed]
  5. González-Islas, D.; Sánchez-Moreno, C.; Orea-Tejeda, A.; Hernández-López, S.; Salgado-Fernández, F.; Keirns-Davis, C.; Galicia-Amor, S.; Trejo-Mellado, E.; Gochicoa-Rangel, L.; Castorena-Maldonado, A. Body composition and risk factors associated with sarcopenia in post-COVID patients after moderate or severe COVID-19 infections. BMC Pulm. Med. 2022, 22, 223. [Google Scholar] [CrossRef]
  6. Huang, C.; Huang, L.; Wang, Y.; Li, X.; Ren, L.; Gu, X.; Kang, L.; Guo, L.; Liu, M.; Zhou, X.; et al. 6-month consequences of COVID-19 in patients discharged from hospital: A cohort study. Lancet 2023, 401, e21–e33. [Google Scholar] [CrossRef] [PubMed]
  7. López-Sampalo, A.; Cobos-Palacios, L.; Vilches-Pérez, A.; Sanz-Cánovas, J.; Vargas-Candela, A.; Mancebo-Sevilla, J.J.; Hernández-Negrín, H.; Gómez-Huelgas, R.; Bernal-López, M.R. COVID-19 in Older Patients: Assessment of Post-COVID-19 Sarcopenia. Biomedicines 2023, 11, 733. [Google Scholar] [CrossRef] [PubMed]
  8. Ramírez-Vélez, R.; Legarra-Gorgoñon, G.; Oscoz-Ochandorena, S.; García-Alonso, Y.; García-Alonso, N.; Oteiza, J.; Ernaga Lorea, A.; Correa-Rodríguez, M.; Izquierdo, M. Reduced muscle strength in patients with long-COVID-19 syndrome is mediated by limb muscle mass. J. Appl. Physiol. 2023, 134, 50–58. [Google Scholar] [CrossRef] [PubMed]
  9. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef] [PubMed]
  10. Arnlöv, J.; Ingelsson, E.; Sundström, J.; Lind, L. Impact of body mass index and the metabolic syndrome on the risk of cardiovascular disease and death in middle-aged men. Circulation 2010, 121, 230–236. [Google Scholar] [CrossRef] [PubMed]
  11. Beaudart, C.; Zaaria, M.; Pasleau, F.; Reginster, J.Y.; Bruyère, O. Health Outcomes of Sarcopenia: A Systematic Review and Meta-Analysis. PLoS ONE 2017, 12, e0169548. [Google Scholar] [CrossRef] [PubMed]
  12. Celis-Morales, C.A.; Welsh, P.; Lyall, D.M.; Steell, L.; Petermann, F.; Anderson, J.; Iliodromiti, S.; Sillars, A.; Graham, N.; Mackay, D.F.; et al. Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: Prospective cohort study of half a million UK Biobank participants. BMJ 2018, 361, k1651. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, D.X.M.; Yao, J.; Zirek, Y.; Reijnierse, E.M.; Maier, A.B. Muscle mass, strength, and physical performance predicting activities of daily living: A meta-analysis. J. Cachexia Sarcopenia Muscle 2020, 11, 3–25. [Google Scholar] [CrossRef]
  14. Gil, S.; Jacob Filho, W.; Shinjo, S.K.; Ferriolli, E.; Busse, A.L.; Avelino-Silva, T.J.; Longobardi, I.; de Oliveira Júnior, G.N.; Swinton, P.; Gualano, B.; et al. Muscle strength and muscle mass as predictors of hospital length of stay in patients with moderate to severe COVID-19: A prospective observational study. J. Cachexia Sarcopenia Muscle 2021, 12, 1871–1878. [Google Scholar] [CrossRef] [PubMed]
  15. Yang, T.; Li, Z.; Jiang, L.; Xi, X. Corticosteroid use and intensive care unit-acquired weakness: A systematic review and meta-analysis. Crit. Care 2018, 22, 187. [Google Scholar] [CrossRef] [PubMed]
  16. Forcina, L.; Miano, C.; Scicchitano, B.M.; Rizzuto, E.; Berardinelli, M.G.; De Benedetti, F.; Pelosi, L.; Musarò, A. Increased Circulating Levels of Interleukin-6 Affect the Redox Balance in Skeletal Muscle. Oxid. Med. Cell. Longev. 2019, 2019, 3018584. [Google Scholar] [CrossRef] [PubMed]
  17. Disser, N.P.; De Micheli, A.J.; Schonk, M.M.; Konnaris, M.A.; Piacentini, A.N.; Edon, D.L.; Toresdahl, B.G.; Rodeo, S.A.; Casey, E.K.; Mendias, C.L. Musculoskeletal Consequences of COVID-19. J. Bone Jt. Surg. 2020, 102, 1197–1204. [Google Scholar] [CrossRef]
  18. Li, J.; Li, Y.; Wang, Z.; Liu, N.; He, L.; Zhang, H. Increased risk of new-onset diabetes in patients with COVID-19: A systematic review and meta-analysis. Front. Public Health 2023, 11, 1170156. [Google Scholar] [CrossRef] [PubMed]
  19. Álvarez-Hernández, J.; Matía-Martín, P.; Cáncer-Minchot, E.; Cuerda, C. Long-term outcomes in critically ill patients who survived COVID-19: The NUTRICOVID observational cohort study. Clin. Nutr. 2023, 42, 2029–2035. [Google Scholar] [CrossRef] [PubMed]
  20. Joaquín, C.; Bretón, I.; Ocón Bretón, M.J.; Burgos, R.; Bellido, D.; Matía-Martín, P.; Martínez Olmos, M.; Zugasti, A.; Riestra, M.; Botella, F.; et al. Nutritional and Morphofunctional Assessment of Post-ICU Patients with COVID-19 at Hospital Discharge: NutriEcoMuscle Study. Nutrients 2024, 16, 886. [Google Scholar] [CrossRef] [PubMed]
  21. LoMauro, A.; Gervasoni, F.; Ricci, V.; Salce, G.; Andreoli, A.; Pantoni, L.; Özçakar, L. Sarcopenia and Multisensory Integration Deficit in Post-COVID Syndrome Patients: The Clinical, Ultrasound and Robotic Evaluation Protocol in a Cross-sectional Study. Am. J. Phys. Med. Rehabil. 2024, 103, 181–187. [Google Scholar] [CrossRef] [PubMed]
  22. Di Meo, S.; Iossa, S.; Venditti, P. Skeletal muscle insulin resistance: Role of mitochondria and other ROS sources. J. Endocrinol. 2017, 233, R15–R42. [Google Scholar] [CrossRef] [PubMed]
  23. Kalinkovich, A.; Livshits, G. Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis. Ageing Res. Rev. 2017, 35, 200–221. [Google Scholar] [CrossRef] [PubMed]
  24. Wu, H.; Ballantyne, C.M. Skeletal muscle inflammation and insulin resistance in obesity. J. Clin. Investig. 2017, 127, 43–54. [Google Scholar] [CrossRef] [PubMed]
  25. Gluvic, Z.; Zaric, B.; Resanovic, I.; Obradovic, M.; Mitrovic, A.; Radak, D.; Isenovic, E.R. Link between Metabolic Syndrome and Insulin Resistance. Curr. Vasc. Pharmacol. 2017, 15, 30–39. [Google Scholar] [CrossRef] [PubMed]
  26. Rubio-Ruiz, M.E.; Guarner-Lans, V.; Pérez-Torres, I.; Soto, M.E. Mechanisms Underlying Metabolic Syndrome-Related Sarcopenia and Possible Therapeutic Measures. Int. J. Mol. Sci. 2019, 20, 647. [Google Scholar] [CrossRef] [PubMed]
  27. Donini, L.M.; Busetto, L.; Bischoff, S.C.; Cederholm, T.; Ballesteros-Pomar, M.D.; Batsis, J.A.; Bauer, J.M.; Boirie, Y.; Cruz-Jentoft, A.J.; Dicker, D.; et al. Definition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement. Obes. Facts 2022, 15, 321–335. [Google Scholar] [CrossRef] [PubMed]
  28. Wu, H.; Liu, M.; Chi, V.T.Q.; Wang, J.; Zhang, Q.; Liu, L.; Meng, G.; Yao, Z.; Bao, X.; Gu, Y.; et al. Handgrip strength is inversely associated with metabolic syndrome and its separate components in middle aged and older adults: A large-scale population-based study. Metab. Clin. Exp. 2019, 93, 61–67. [Google Scholar] [CrossRef] [PubMed]
  29. Hwang, J.; Park, S. Gender-Specific Prevalence and Risk Factors of Sarcopenic Obesity in the Korean Elderly Population: A Nationwide Cross-Sectional Study. Int. J. Environ. Res. Public Health 2023, 20, 1140. [Google Scholar] [CrossRef] [PubMed]
  30. Durward, C.M.; Hartman, T.J.; Nickols-Richardson, S.M. All-cause mortality risk of metabolically healthy obese individuals in NHANES III. J. Obes. 2012, 2012, 460321. [Google Scholar] [CrossRef] [PubMed]
  31. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001, 285, 2486–2497. [CrossRef] [PubMed]
  32. Lohman, T.G.; Roche, A.F.; Martorell, R. Anthropometric Standardization Reference Manual; Human Kinetics Books: Champaign, IL, USA, 1988; Volume 177. [Google Scholar]
  33. Rodríguez-García, W.D.; García-Castañeda, L.; Orea-Tejeda, A.; Mendoza-Núñez, V.; González-Islas, D.G.; Santillán-Díaz, C.; Castillo-Martínez, L. Handgrip strength: Reference values and its relationship with bioimpedance and anthropometric variables. Clin. Nutr. ESPEN 2017, 19, 54–58. [Google Scholar] [CrossRef]
  34. Lukaski, H.C.; Johnson, P.E.; Bolonchuk, W.W.; Lykken, G.I. Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am. J. Clin. Nutr. 1985, 41, 810–817. [Google Scholar] [CrossRef] [PubMed]
  35. Sergi, G.; De Rui, M.; Veronese, N.; Bolzetta, F.; Berton, L.; Carraro, S.; Bano, G.; Coin, A.; Manzato, E.; Perissinotto, E. Assessing appendicular skeletal muscle mass with bioelectrical impedance analysis in free-living Caucasian older adults. Clin. Nutr. 2015, 34, 667–673. [Google Scholar] [CrossRef] [PubMed]
  36. Batsis, J.A.; Singh, S.; Lopez-Jimenez, F. Anthropometric measurements and survival in older Americans: Results from the third National Health and Nutrition Examination Survey. J. Nutr. Health Aging 2014, 18, 123–130. [Google Scholar] [CrossRef]
  37. Chen, L.; Liu, X.; Wang, Q.; Jia, L.; Song, K.; Nie, S.; Zhang, Y.; Cao, D.; Zhao, D.; Li, Z.; et al. Better pulmonary function is associated with greater handgrip strength in a healthy Chinese Han population. BMC Pulm. Med. 2020, 20, 114. [Google Scholar] [CrossRef] [PubMed]
  38. Chites, V.S.; Teixeira, P.P.; Lima, J.; Burgel, C.F.; Gattermann Pereira, T.; Silva, F.M. Reduced Handgrip Strength in Hospital Admission Predicts Prolonged Hospital Stay and Death but Is Not Accurate to Identify Malnutrition: A Longitudinal Study of Reduced Handgrip Strength in Hospitalized Patients. J. Parenter. Enter. Nutr. 2021, 45, 1016–1022. [Google Scholar] [CrossRef] [PubMed]
  39. Wilkinson, T.J.; Yates, T.; Baker, L.A.; Zaccardi, F.; Smith, A.C. Sarcopenic obesity and the risk of hospitalization or death from coronavirus disease 2019: Findings from UK Biobank. JCSM Rapid Commun. 2022, 5, 3–9. [Google Scholar] [CrossRef] [PubMed]
  40. Longobardi, I.; Goessler, K.; de Oliveira Júnior, G.N.; Prado, D.; Santos, J.V.P.; Meletti, M.M.; de Andrade, D.C.O.; Gil, S.; Boza, J.; Lima, F.R.; et al. Effects of a 16-week home-based exercise training programme on health-related quality of life, functional capacity, and persistent symptoms in survivors of severe/critical COVID-19: A randomised controlled trial. Br. J. Sports Med. 2023, 57, 1295–1303. [Google Scholar] [CrossRef] [PubMed]
  41. Reid, M.B.; Li, Y.P. Tumor necrosis factor-alpha and muscle wasting: A cellular perspective. Respir. Res. 2001, 2, 269–272. [Google Scholar] [CrossRef] [PubMed]
  42. Aon, M.A.; Bhatt, N.; Cortassa, S.C. Mitochondrial and cellular mechanisms for managing lipid excess. Front. Physiol. 2014, 5, 282. [Google Scholar] [CrossRef] [PubMed]
  43. Ali, A.M.; Kunugi, H. Skeletal Muscle Damage in COVID-19: A Call for Action. Medicina 2021, 57, 372. [Google Scholar] [CrossRef] [PubMed]
  44. Di Girolamo, F.G.; Fiotti, N.; Sisto, U.G.; Nunnari, A.; Colla, S.; Mearelli, F.; Vinci, P.; Schincariol, P.; Biolo, G. Skeletal Muscle in Hypoxia and Inflammation: Insights on the COVID-19 Pandemic. Front. Nutr. 2022, 9, 865402. [Google Scholar] [CrossRef] [PubMed]
  45. Baker, J.F.; Mostoufi-Moab, S.; Long, J.; Zemel, B.; Ibrahim, S.; Taratuta, E.; Leonard, M.B. Intramuscular Fat Accumulation and Associations With Body Composition, Strength, and Physical Functioning in Patients With Rheumatoid Arthritis. Arthritis Care Res. 2018, 70, 1727–1734. [Google Scholar] [CrossRef] [PubMed]
  46. Thiebaud, D.; Jacot, E.; DeFronzo, R.A.; Maeder, E.; Jequier, E.; Felber, J.P. The effect of graded doses of insulin on total glucose uptake, glucose oxidation, and glucose storage in man. Diabetes 1982, 31, 957–963. [Google Scholar] [CrossRef] [PubMed]
  47. Son, J.W.; Lee, S.S.; Kim, S.R.; Yoo, S.J.; Cha, B.Y.; Son, H.Y.; Cho, N.H. Low muscle mass and risk of type 2 diabetes in middle-aged and older adults: Findings from the KoGES. Diabetologia 2017, 60, 865–872. [Google Scholar] [CrossRef] [PubMed]
  48. Poggiogalle, E.; Lubrano, C.; Gnessi, L.; Mariani, S.; Di Martino, M.; Catalano, C.; Lenzi, A.; Donini, L.M. The decline in muscle strength and muscle quality in relation to metabolic derangements in adult women with obesity. Clin. Nutr. 2019, 38, 2430–2435. [Google Scholar] [CrossRef] [PubMed]
  49. Bonaldo, P.; Sandri, M. Cellular and molecular mechanisms of muscle atrophy. Dis. Models Mech. 2013, 6, 25–39. [Google Scholar] [CrossRef] [PubMed]
  50. Baczek, J.; Silkiewicz, M.; Wojszel, Z.B. Myostatin as a Biomarker of Muscle Wasting and other Pathologies-State of the Art and Knowledge Gaps. Nutrients 2020, 12, 2401. [Google Scholar] [CrossRef] [PubMed]
  51. Postic, C.; Girard, J. Contribution of de novo fatty acid synthesis to hepatic steatosis and insulin resistance: Lessons from genetically engineered mice. J. Clin. Investig. 2008, 118, 829–838. [Google Scholar] [CrossRef] [PubMed]
  52. Visser, M.; Pahor, M.; Taaffe, D.R.; Goodpaster, B.H.; Simonsick, E.M.; Newman, A.B.; Nevitt, M.; Harris, T.B. Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: The Health ABC Study. J. Gerontol. A Biol. Sci. Med. Sci. 2002, 57, M326–M332. [Google Scholar] [CrossRef] [PubMed]
  53. Hamer, M.; Molloy, G.J. Association of C-reactive protein and muscle strength in the English Longitudinal Study of Ageing. Age 2009, 31, 171–177. [Google Scholar] [CrossRef]
Nutrients 16 02468 g001
Figure 1. Study flowchart diagram.
Figure 1. Study flowchart diagram.
Nutrients 16 02468 g001
Nutrients 16 02468 g002
Figure 2. Prevalence of metabolic alterations according to body composition phenotype.
Figure 2. Prevalence of metabolic alterations according to body composition phenotype.
Nutrients 16 02468 g002
Nutrients 16 02468 g003
Figure 3. Association between body composition phenotype and insulin resistance.
Figure 3. Association between body composition phenotype and insulin resistance.
Nutrients 16 02468 g003
Table 1. Demographic and clinical characteristics according to insulin resistance in post-COVID-19 syndrome patients without diabetic subjects.
Table 1. Demographic and clinical characteristics according to insulin resistance in post-COVID-19 syndrome patients without diabetic subjects.
All
n = 483		HOMA-IR > 2.5
n = 281		HOMA-IR < 2.5
n = 202		p-Value
Age, y52.69 ± 14.7550.4 ± 14.0455.89 ± 15.16<0.001
Male, n (%)324 (67.08)188 (66.90)136 (67.33)0.922
Co-morbidities
Hypertension, n (%)153 (31.68)90 (32.03)63 (31.19)0.845
Ischemic cardiopathy, n (%)32 (6.63)17 (6.05)15 (7.43)0.549
Pulmonary disease, n (%)75 (15.53)39 (13.88)36 (17.08)0.238
Thyroid disease, n (%)32 (6.63)20 (7.12)12 (5.94)0.608
Hepatopathy, n (%)8 (1.66)3 (1.07)5 (2.48)0.232
HIV, n (%)7 (1.45)4 (1.42)3 (1.49)0.955
Asthma, n (%)13 (2.69)8 (2.85)5 (2.48)0.803
COPD, n (%)16 (3.31)4 (1.42)12 (5.94)0.006
Hospitalary parameters
Hospital stay, d17 [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]15 [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26]20 [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]0.006
Mechanical ventilation, n (%)289 (60.33)163 (58.63)126 (62.69)0.371
Body composition
Weight, kg81.6 ± 18.886.6 ± 17.9874.60 ± 17.80<0.001
Height, cm163.47 ± 9.63164.40 ± 8.78162.18 ± 10.590.012
BMI, kg/m230.44 ± 6.3732.01 ± 6.0528.27 ± 6.17<0.001
Handgrip strength, kg24.85 ± 9.8726.11 ± 9.4123.10 ± 10.24<0.001
Prediction marker, 200/5 kHz0.79 ± 0.050.79 ± 0.050.79 ± 0.050.811
Phase angle, °6.01 ± 1.526.24 ± 1.415.71 ± 1.60<0.001
ASM/height2, kg/m27.79 ± 1.408.01 ± 1.127.48 ± 1.67<0.001
Fat mass, kg30.08 ± 10.7032.23 ± 8.8427.09 ± 12.26<0.001
Body composition phenotype
Normal weight, n (%)64 (13.25)29 (10.32)35 (17.33)<0.001
Dynapenia, n (%)46 (9.52)23 (8.19)23 (11.39)
Sarcopenia, n (%)48 (9.94)11 (3.91)37 (18.32)
Obesity, n (%)211 (43.69)152 (54.09)59 (29.21)
Dynapenic obesity, n (%)91 (18.84)53 (18.86)38 (18.81)
Sarcopenic obesity, n (%)23 (4.76)13 (4.63)10 (4.95)
HIV—human immunodeficiency virus; COPD—chronic obstructive pulmonary disease; BMI—body mass index; ASM/height2—appendicular skeletal muscle mass adjusted by height.
Table 2. Risk factors associated with insulin resistance (HOMA-IR > 2.5) in post-COVID-19 syndrome patients without diabetes. Crude model.
Table 2. Risk factors associated with insulin resistance (HOMA-IR > 2.5) in post-COVID-19 syndrome patients without diabetes. Crude model.
OR95% CIp-Value
Age, y0.970.96 to 0.98<0.001
Male0.980.66 to 1.440.922
Co-morbidities
Hypertension1.030.70 to 1.530.845
Ischemic cardiopathy0.800.39 to 1.640.549
Pulmonary disease0.740.45 to 1.210.239
Thyroid disease1.210.57 to 2.540.608
Hepatopathy0.420.10 to 1.790.245
HIV0.950.21 to 4.320.955
Asthma1.150.37 to 3.580.804
COPD0.220.07 to 0.710.012
Hospitalary parameters
Length of hospital stay, d0.980.97 to 0.990.001
Mechanical ventilation0.840.58 to 1.220.371
Body composition
Weight, kg1.041.02 to 1.05<0.001
Height, cm1.021.00 to 1.040.013
BMI, kg/m21.121.08 to 1.16<0.001
Handgrip strength, kg1.031.01 to 1.050.001
Prediction marker, 200/5 kHz0.550.004 to 69.750.810
Phase angle, °1.321.13 to 1.52<0.001
ASM/height2, kg/m21.371.17 to 1.60<0.001
Fat mass, %1.051.03 to 1.07<0.001
Body composition phenotype
Normal weight1Reference
Dynapenia1.200.56 to 2.570.627
Sarcopenia0.350.15 to 0.820.016
Obesity3.101.74 to 5.53<0.001
Dynapenic obesity3.751.15 to 12.210.028
Sarcopenic obesity1.110.32 to 3.810.862
HIV—human immunodeficiency virus; COPD—chronic obstructive pulmonary disease; BMI—body mass index; ASM/heigth2—appendicular muscular mass adjusted by height.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

González-Islas, D.; Flores-Cisneros, L.; Orea-Tejeda, A.; Keirns-Davis, C.; Hernández-López, N.; Arcos-Pacheco, L.P.; Zurita-Sandoval, A.; Albarran-López, F.; García-Castañeda, L.; Salgado-Fernández, F.; et al. The Association between Body Composition Phenotype and Insulin Resistance in Post-COVID-19 Syndrome Patients without Diabetes: A Cross-Sectional, Single-Center Study. Nutrients 2024, 16, 2468. https://doi.org/10.3390/nu16152468

AMA Style

González-Islas D, Flores-Cisneros L, Orea-Tejeda A, Keirns-Davis C, Hernández-López N, Arcos-Pacheco LP, Zurita-Sandoval A, Albarran-López F, García-Castañeda L, Salgado-Fernández F, et al. The Association between Body Composition Phenotype and Insulin Resistance in Post-COVID-19 Syndrome Patients without Diabetes: A Cross-Sectional, Single-Center Study. Nutrients. 2024; 16(15):2468. https://doi.org/10.3390/nu16152468

Chicago/Turabian Style

González-Islas, Dulce, Laura Flores-Cisneros, Arturo Orea-Tejeda, Candace Keirns-Davis, Nadia Hernández-López, Laura Patricia Arcos-Pacheco, Andrea Zurita-Sandoval, Frida Albarran-López, Luis García-Castañeda, Fernanda Salgado-Fernández, and et al. 2024. "The Association between Body Composition Phenotype and Insulin Resistance in Post-COVID-19 Syndrome Patients without Diabetes: A Cross-Sectional, Single-Center Study" Nutrients 16, no. 15: 2468. https://doi.org/10.3390/nu16152468

APA Style

González-Islas, D., Flores-Cisneros, L., Orea-Tejeda, A., Keirns-Davis, C., Hernández-López, N., Arcos-Pacheco, L. P., Zurita-Sandoval, A., Albarran-López, F., García-Castañeda, L., Salgado-Fernández, F., Hernández-López, S., Jiménez-Valentín, A., & Pérez-García, I. (2024). The Association between Body Composition Phenotype and Insulin Resistance in Post-COVID-19 Syndrome Patients without Diabetes: A Cross-Sectional, Single-Center Study. Nutrients, 16(15), 2468. https://doi.org/10.3390/nu16152468

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop