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Sarcopenia as a predictor of nutritional status and comorbidities: a cross-sectional and mendelian randomization study

BMC Geriatrics volume 24, Article number: 752 (2024) Cite this article

Abstract

Background

With the advancement of world population aging, age-related sarcopenia (SP) imposes enormous clinical burden on hospital. Clinical research of SP in non-geriatric wards has not been appreciated, necessitating further investigation. However, observational studies are susceptible to confounders. Mendelian randomization (MR) can effectively mitigate bias to assess causality.

Objective

To investigate the correlation between SP and comorbidities in orthopedic wards, and subsequently infer the causality, providing a theoretical basis for developing strategies in SP prevention and treatment.

Methods

Logistic regression models were employed to assess the correlation between SP and comorbidities. The MR analysis was mainly conducted with inverse variance weighted, utilizing data extracted from the UK and FinnGen biobank (Round 9).

Results

In the cross-sectional analysis, SP exhibited significant associations with malnutrition (P = 0.013) and some comorbidities, including osteoporosis (P = 0.014), body mass index (BMI) (P = 0.021), Charlson Comorbidity Index (CCI) (P = 0.006). The MR result also provided supporting evidence for the causality between SP and hypertension, osteoporosis and BMI. These results also withstood multiple sensitivity analyses assessing the validity of MR assumptions.

Conclusion

The result indicated a significant association between SP and BMI, CCI, malnutrition, and osteoporosis. We highlighted that SP and comorbidities deserved more attention in non-geriatric wards, urging further comprehensive investigation.

Peer Review reports

Introduction

Sarcopenia (SP) is an age-related skeletal muscle disease characterized by the progressive decline of skeletal muscle mass, accompanied by diminished muscle strength and/or reduced physical performance [1]. The prevalence of SP in older adults worldwide ranges from 10 to 27% [2] and was notably higher among hospitalized older adults [3]. SP has been associated with severe physiological and clinical consequences, including cognitive impairment, functional decline, falls, fractures, disability, hospital admissions, postoperative complications and mortality [4,5,6,7,8]. The risk factors for SP are also diverse, encompassing aging, physical inactivity, malnutrition, cachexia, and various chronic diseases [9]. Furthermore, SP often coexists with other age-related or metabolic diseases, such as dementia, cardiovascular diseases, diabetes mellitus [9], cirrhosis [10], which imposes a growing economic and public health burden. Consequently, exploring the role of SP in routine clinical practice is of paramount importance to meet the public health challenge effectively.

SP has garnered growing interest across several hospital departments. However, most observational studies have been concentrated in acute geriatric wards [11], and geriatric rehabilitation wards [12], where inpatients exhibit a notably high prevalence of SP. Meanwhile, scant attention has been paid to the relationship between SP and common comorbidities in orthopedic inpatients. SP has the potential to serve as a valuable predictor for identifying orthopedic patients who need improved preoperative interventions and postoperative rehabilitation.

At present, SP related clinical studies were basically limited to cross-sectional investigations, lacking in-depth exploration of the genetic relationship between SP and comorbidities. The Mendelian randomization (MR) approach can effectively mitigate bias from uncontrolled confounders, reverse causality and selective bias, all of which are frequently encountered challenges in conventional observational epidemiological studies [13]. Therefore, MR was employed to deduce the causality between exposure and outcome, using independent single nucleotide polymorphisms (SNPs) as instrumental variables (IVs). Our primary objective was to briefly estimate the correlation between SP and comorbidities in orthopedic patients. After that, the causal relationship was verified by MR, based on the results of the observational study.

Materials and methods

Cross-sectional study design

Study Design and patients

This study was designed as a cross-sectional investigation, continuously recruiting older patients from orthopedic wards at the Second Xiangya Hospital of Central South University. Ethical approval was obtained from the Medical Ethics Committee of our institute (Ethics number: LYF2022221), and all participating patients signed the consent forms. Wave 1 data were analyzed, comprising patients admitted from March 1, 2023 to July 1, 2023. Exclusion criteria were (a) patients in intensive care unit, unable to participate; (b) patients with pacemakers or metal implants. (c) patients who had undergone amputation.

Patient characteristics

Age, sex, length of stay, hospitalization cost, living area, and duration of disease were extracted from the patients’ medical records. Mini-mental State Examination (MMSE) [14], The Barthel index (BI) [15], and The Mini Nutritional Assessment (MNA) [16] were completed by nurses to assess the patients’ status at admission. The MMSE is a valid test of cognitive function, which includes eleven questions, requires only 5–10 min to administer, and is therefore practical to use serially and routinely [14]. The BI is a tool for assessing the ability to perform basic activities of daily living. It consists of 10 items including eating, bathing, dressing, personal hygiene, walking, and so on [15]. The MNA was designed and validated to provide a single, rapid assessment of nutritional status in elderly patients. It has been translated into several languages and validated in many clinics around the world [16]. The Blood biochemical parameters were also measured within the first 24 h of hospitalization: albumin (ALB), total protein (TP), alanine transaminase (ALT), aspartate transaminase (AST), blood urea nitrogen (BUN), and serum creatinine (SCR).

Assessment of Sarcopenia and comorbidities

According to Asian Working Group on Sarcopenia in Older People criteria, bioelectrical impedance analysis (Biospace, InBody 770, Korea) was used to calculate skeletal muscle mass index and body mass index (BMI) [1]. Dynamometer(ASP Global, Jamar Plus+, USA)were used to measure handgrip strength. Experienced physicians completed the medical history and Charlson Comorbidity Index (CCI) [17]. CCI is designed to help doctors assess the patient’s chronic disease and comorbid burden through CCI score. Comorbidity severity was categorized based on the total CCI score: none = 0, mild = 1–2, moderate = 3–4, and severe ≥ 5 [18].

Bidirectional MR study design

Data source

The diagnostic criteria for SP primarily involved appendicular lean mass (ALM), grip strength, and walking speed [1]. The ALM-related summary-level genome‐wide association study (GWAS) datasets were sourced from the UK Biobank cohort, comprising 450,243 participants. These datasets were quantified by the sum of fat-free mass, and adjusted for appendicular fat mass and other covariates [19]. Walking pace statistics were extracted from an UK Biobank cohort of 459,915 individuals [20]. The low grip strength GWAS data, involving 256,523 individuals of European descent, were extracted from a genome-wide meta-analysis study, adjusted for sex, age, and population substructure [21]. The selection of comorbidities for Mendelian randomization (MR) analysis was informed by the results of our observational study. FinnGen combines imputed genotype data from Finnish biobanks and digital health record data from Finnish health registries, with nearly 500,000 participants. GWAS summary statistics for the chosen comorbidities were extracted from the FinnGen biobank (Round 9) (Supplementary Table 1) [22]. Further detail could be found in their website (https://www.finngen.fi/en) and the published article [19,20,21].

MR approaches

Genetic IVs with F statistics > 10 were meticulously chosen for exposure (p < 5 × 10−8, clumping r2 = 0.001 and kb = 10,000) [23]. The number of malnutrition-related IVs below 3, so we relaxed the P-value threshold to 1 × 10−5 and reevaluated it. After harmonization processes, MR-pleiotropy residual sum and outlier (MR-PRESSO) was executed to eliminate IVs with potential pleiotropy [24]. These valid IVs satisfied the three assumptions: [1] IVs were significantly associated with the exposure; [2] IVs were not related to any potential confounders; [3] IVs affect outcome solely through exposure [13]. The inverse-variance weighted (IVW) method was employed as the main MR analysis, which could effectively estimate the causality in the absence of directed pleiotropy (p > 0.05 in the MR-Egger intercept test) [25]. The Cochran’s Q test assessed these selected IVs to determine appropriate IVW effects model. Additionally, Weighted median and MR Robust Adjusted Profile Score(MR RAPS) were also performed to assess the robustness and sensitivity [26, 27]. MR Egger was utilized to calculate the intercept value to evaluate horizontal pleiotropy [28]. Finally, MR Steiger was employed to test whether causal relationship was valid.

Statistical analyses

All statistical analyses were conducted in the R statistical software (Version 4.1.3). In the analysis of observational study, Continuous variables are presented as means ± standard deviations, while categorical variables are expressed as numbers (percentages). The differences between the SP and non-SP groups were evaluated by independent sample t-tests for normally distributed data and chi-squared tests for categorical data. Backward stepwise binomial logistic regression (p < 0.10) was performed to correct for the possible influencing factors and determine the associations between potential risk factors and SP.

For MR analysis, a Bonferroni corrected significance level (p < 0.05/15 = 0.003) was applied. All analyses were conducted with the R packages “MendelianRandomization”, “TwosampleMR”, and “MRPRESSO”.

Results

Cross-sectional analysis

The patient characteristics across groups with and without SP are showed in Table 1. Patient in orthopedic wards diagnosed with SP exhibited reduced ASM/height2 and weaker handgrip strength. Compared with patient without SP, those with SP presented with older age, lower BMI, and higher CCI. The statistical difference result revealed that SP patients were more prone to require nursing care, suffer from hypertension, diabetes, osteoporosis, and malnutrition. Although there were notable differences in SCR in the blood biochemical indexes, but not in BUN. The backward stepwise binomial logistic regression model was conducted and five significant factors were detected (Table 2). Notably, the analysis revealed significant associations between SP and BMI, CCI, osteoporosis in orthopedic wards.

Table 1 Patient characteristics
Full size table
Table 2 Risk factors of Sarcopenia according to logistic regression analysis
Full size table

MR analysis between comorbidities and SP-related traits

The IVW result revealed a significant causal effect of BMI on SP after the Bonferroni correction (Table 3; Fig. 1) [ALM-related analysis: Beta = 0.180, 95% confidence interval (CI) = (0.153, 0.207), p = 1.779e-38; walking pace-related analysis: Beta (95% CI) =-0.104(-0.119, -0.089), p = 3.908e-42]. Furthermore, the IVW results also indicated that a significant negative causal effect of hypertension on SP [walking pace-related analysis: Beta (95% CI) =-0.023(-0.030, -0.016), p = 6.277e-11]. Consistently, the weighted median and the RAPS further reinforcing the hypothesized relationships. Osteoporosis and dementia exhibited nominal causal effects on SP-related traits.

Table 3 Primary mendelian randomization estimates of comorbidities on Sarcopenia-related traits
Full size table
Fig. 1
figure 1

Forest plot of the MR IVW analyses for comorbidities on SP-related traits. IVW, in verse-variance weighted; MR, mendelian randomization; SP, sarcopenia; AlM, appendicular lean mass; BMI, body mass index; CI, confidence interval

Full size image

The impact of SP on comorbidities was also thoroughly evaluated. The result showed that SP exerted a substantial causal effect on BMI after the Bonferroni correction (Table 4; Fig. 2) [ALM-related analysis: Beta (95% CI) = 0.049(0.028, 0.069), p = 3.975e-06; walking pace-related analysis: Beta (95% CI) =-1.120(-1.295, -0.945), p = 5.550e-36]. The IVW results also supported that SP had a significant negative causal effect on hypertension [ALM-related analysis: Beta (95% CI) =-0.096(-0.140, -0.052), p = 2.153e-05; walking pace-related analysis: Beta (95% CI) =-1.341(-1.716, -0.967), p = 2.207e-12], and SP had a noteworthy positive causal effect on osteoporosis [low grip strength-related analysis: Beta (95% CI) = 0.367(0.146, 0.589), p = 0.001]. SP also had a nominal causal effect on dementia. However, the current results did not support for the causality between malnutrition and SP-related traits.

Table 4 Primary mendelian randomization estimates of Sarcopenia-related traits on comorbidities
Full size table
Fig. 2
figure 2

Forest plot of the MR IVW analyses for SP-related traits on comorbidities. IVW, inverse-variance weighted; MR, mendelian randomization; SP, sarcopenia; AlM, appendicular lean mass; BMl, body mass index; Cl, confidence interval

Full size image

To ensure the robustness of our findings, comprehensive sensitivity analyses were conducted, (Table 5; Fig. 3). The Cochran’s Q test showed heterogeneity within each pair of datasets. The outliers identified through MR-PRESSO are documented in Supplementary Table 2. MR Egger indicated significantly horizontal pleiotropy of BMI on ALM. The F value of these IVs exceeded 10, indicating their validity in minimizing bias. The proportion of variance explained showed in Supplementary Tables 3, and each pair passed the MR Steiger test, enhancing the effectiveness and reliability of our analyses (Supplementary Table 3).

Table 5 Sensitivity analysis of the primary causal association between Sarcopenia-related traits and comorbidities
Full size table
Fig. 3
figure 3

Scatter plot of the MR estimate for A the effect of BMI on AlM; B the effect of BMI on walking pace: C the effect of hypertension on walking pace; D the effect of osteoporosis on walking pace: E the effect of AlM on BMI; F the effect of walking pace on BMI; G the effect of AlM on hypertension; H the effect of walking pace on hypertension; I the effect of low grip strength on osteoporosis; Abbreviations: BMI; body mass index, AlM: appendicular lean mass, MR: mendelian randomization

Full size image

Discussion

In this study, we conducted a comprehensive analysis combining a cross-sectional investigation in orthopedic wards and a two-sample MR analysis to explore the relationship between SP and comorbidities. The observational research results unveiled a robust association between a higher risk of SP and elevated BMI, as well as an increased risk of osteoporosis and malnutrition in orthopedic wards. The MR analysis further corroborated the causal link between SP and BMI, hypertension, osteoporosis. To our knowledge, this was also the first bi-directional MR study to investigate causality between SP and these comorbidities.

Actually, some observational studies with similar themes were reported, but most of these investigations occurred in geriatric wards [12], or several studies only focus on SP related postoperative complications [29]. Extensive meta-analyses have demonstrated that SP is highly prevalent as a comorbid disease in individuals with hypertension [30], diabetes mellitus [9], lower BMI [31], osteoporosis [32], liver cirrhosis [33], chronic kidney disease [34] and malnutrition [35]. Nevertheless, it is crucial to specify the scope to which the conclusions may be applied. When experienced orthopedic surgeons encounter older patients with uncontrolled diabetes, hypertension, or mental illness in outpatient settings, they may recommend the patient to control disease first, and then go to the orthopedic wards for surgery, which makes the condition of SP in orthopedics significantly different from that in geriatrics. Our result revealed a strong correlation between SP and BMI, osteoporosis, and malnutrition in orthopedics department. Suitable nutritional supplementation can be effective in managing fatigue, improving motor, cognitive performance and ALM [36,37,38]. Meanwhile, hypertension, diabetes, and the blood biochemical parameters cannot well predict SP in orthopedic wards. SP has not received adequate attention in clinical practice and our findings carry crucial implications for comprehending and forecasting SP in non-geriatric medical contexts.

Similarly, SP related MR studied are currently rare. Despite these differences, one MR letter reported a significant negative causal effect between BMI and walking pace [39]. Two MR studied revealed a mutual significant causal effect between osteoporosis and SP [39, 40]. Our MR analyses were the first to assess the causality between SP and common comorbidities, including hypertension, dementia, and malnutrition. Somewhat unexpectedly, the MR result did not support causality between SP and malnutrition, warranting scrupulous consideration. The reasons may be that the P-value threshold was relaxed to 1 × 10−5 instead of 1 × 10−8, which might downgrade the quality of GWAS datasets. Malabsorption, atherosclerosis and stroke may be potential confounding factors to affect the MR process [41,42,43]. Addressing this requires more rigorous MR methods and improved data sources. Our current MR results remain consistent with the majority of previous studies.

In our observational study, we focused on the role of SP in non-geriatric wards, indicating that older patients with osteoporosis, malnutrition, and lower BMI are more susceptible to developing SP in orthopedic wards. Combined with these observational findings, we identified common comorbidities to perform MR analyses, supporting the bidirectional causality between SP and BMI, hypertension, and osteoporosis. We retrieved data from the UK and FinnGen biobank (Round 9) to meticulously avoid sample overlapping and minimize bias. The validity of results was checked by strict parameters and diverse MR methods. However, our MR analysis still had several potential limitations. Firstly, more clinical samples in orthopedic wards are still essential to bolster our hypothesis. Secondly, more reliable data sources were still essential, especially for malnutrition. Regrettably, we were unable to find more robust statistic for malnutrition. Thirdly, MR analysis is valid only if the three assumptions mentioned above are strictly satisfied. Although we used six sensitivity analysis methods to ensure that these assumptions were met, more testing methods were also required to ensure the robustness of the results, with the updating of MR methods.

Conclusions

In conclusion, our cross-sectional analysis demonstrated that BMI, osteoporosis, and malnutrition are robust predictors of SP in orthopedic wards, while the MR analyses validated the causal association between SP and BMI, hypertension, osteoporosis. This novel discovery offers valuable insights for improving practices in identifying SP among older patients in non-geriatric wards, who had better absorb adequate nutrition, control weight, and prevent osteoporosis to effectively address SP.

Availability of data and materials

GWAS datasets of SP-related traits are available in [https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90007526/], [https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90000025/], [https://gwas.mrcieu.ac.uk/datasets/ukb-b-4711/]. GWAS datasets of comorbidities are available in [https://www.finngen.fi/en/access_results]. Further inquiries can be directed to the corresponding authors.

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Statement

An unauthorized version of the Chinese MMSE was used by the study team without permission, however this has now been rectified with PAR. The MMSE is a copyrighted instrument and may not be used or reproduced in whole or in part, in any form or language, or by any means without written permission of PAR (www.parinc.com).

Funding

This work was supported by Research Project of Human Health Commission (grant number 202204073071).

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Authors and Affiliations

  1. Department of Orthopedics, the Second Xiangya Hospital of Central South University, 139 Renmin Road, Changsha, Hunan , 410011, People’s Republic of China

    Chao Liu, Guanyi Chen, Yu Xia, Pingxiao Wang, Ziyue Zhao, JiaLin Zhang, Tao Xiao & Hui Li

  2. Orthopedic Biomedical Materials Engineering Laboratory of Hunan Province, Changsha, 410011, People’s Republic of China

    Chao Liu, Yu Xia, Pingxiao Wang, Ziyue Zhao, JiaLin Zhang, Tao Xiao & Hui Li

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  1. Chao Liu
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Contributions

L.C. had the idea and drafted the first draft, Guanyi Chen collected clinical data, Yu Xia performed data analysis, Pingxiao Wang and Ziyue Zhao created the table and figure, JiaLin Zhang gave constructive suggestions during the process. X.T. and L.H drafted the final manuscript. All authors contributed to the final manuscript and agreed to the published version of the manuscript.

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Correspondence to Hui Li.

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The study received the necessary ethical approvals from the Medical Ethics Committee of the Second Xiangya Hospital, Central South University (Ethics number: LYF2022221; date: 18 January 2023). The consent that was obtained from all of the participants was informed.

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

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Supplementary Information

12877_2024_5341_MOESM1_ESM.xlsx

Supplementary Material 1: Additional Table 1. Independent lVs of age-related diseases in the forward MR analysis. Additional Table 2. Independent lVs of sarcopenia-related traits in the reverse MR analysis.

12877_2024_5341_MOESM2_ESM.docx

Supplementary Material 2: Supplementary Table 1. Phenotype source and description of age-related comorbidities. Supplementary Table 2. IVs outliers in MR-PRESSO analysis.

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Liu, C., Chen, G., Xia, Y. et al. Sarcopenia as a predictor of nutritional status and comorbidities: a cross-sectional and mendelian randomization study. BMC Geriatr 24, 752 (2024). https://doi.org/10.1186/s12877-024-05341-2

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BMC Geriatrics

ISSN: 1471-2318

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