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Abstract

Objective

This study aimed to assess the preoperative nutritional status of patients admitted to the vascular surgery clinic using the Global Leadership Initiative on Malnutrition (GLIM) and further explore related risk factors, to provide a basis for clinical nutritional management.

Methods

This cross-sectional study included inpatients admitted to the Department of Vascular Surgery of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from October 2021 to March 2022. We retrospectively collected preoperative clinical data of these patients and applied the GLIM criteria to analyze their risk of malnutrition. A total of 113 vascular surgery patients were included, with a mean age of 68.76 ± 11.77 years. Among these, 31 inpatients were classified into the malnourished group, while 82 were in the well-nourished group.

Results

Compared with the well-nourished group, inpatients in the malnourished group were characterized by an older age, lower body mass index, and a higher incidence of cerebrovascular disease. Additionally, they exhibited lower hemoglobin and albumin levels, reduced pre-albumin, and a greater prevalence of arteriosclerosis obliterans in the lower limbs or gangrene. Multivariable analysis showed that age ≥ 70 years (OR = 4.57, 95% CI: 1.34–15.64, p = 0.015), gangrene (OR = 6.55, 95% CI: 2.14–20.07, p = 0.001), and cerebrovascular diseases (OR = 6.04, 95% CI: 2.01–18.14, p = 0.001) were independently associated with malnutrition.

Conclusions

The risk of malnutrition in patients admitted to the vascular surgery clinic was relatively high. Special attention should be given to inpatients over 70 years old, with gangrene or cerebrovascular diseases. This study highlights the importance of routine nutritional screening and assessment using the GLIM criteria in this high-risk population. Early identification and targeted nutritional intervention for high-risk patients are recommended to improve surgical outcomes.

Keywords

Vascular surgery nutritional assessment malnutrition global leadership initiative on malnutrition cerebrovascular diseases

Introduction

Vascular surgery encompasses numerous surgical diseases, such as lower extremity arteriosclerosis obliterans, lower extremity arterial thrombosis, complications of diabetes, abdominal aortic aneurysm, thoracic aortic dissection, abdominal aortic dissection, and varicose veins of the lower extremities. Vascular surgery patients are prone to malnutrition because of the coexistence of various conditions like advanced age, limited mobility, comorbidities, and the use of drugs that interfere with nutritional absorption and metabolism.¹ Hence, while the incidence of malnutrition in general inpatients is 13.7%–24.1%,^2,3 it can reach 61%–87.5% for patients admitted to the vascular surgery clinic.^4,5 Importantly, the preoperative nutritional status of patients admitted to the vascular surgery clinic is closely related to the postoperative clinical outcomes and prognosis.^6–8 The timely detection of preoperative malnutrition and providing appropriate nutritional support are of great significance in reducing postoperative mortality, complications, and hospital stay duration.^9–11 For cardiac vascular surgery (CABG, a key treatment for CHD), studies show preoperative nutritional indices-including the Hemoglobin, Albumin, Lymphocyte, Platelet score and Prognostic Nutritional Index-effectively predict 1-month post-CABG mortality.¹² Therefore, it's essential to assess the preoperative nutritional status of patients admitted to the vascular surgery clinic.

The prevalence of malnutrition reported in the literature for vascular surgery patients varies considerably, ranging from approximately 15% to over 70%.^13,14 This significant heterogeneity can likely be attributed to two primary factors: The use of diverse nutritional assessment tools and geographic or regional variations. Studies have employed a wide array of tools for diagnosing malnutrition, including single-parameter indices (serum albumin, Geriatric Nutritional Risk Index-GNRI), composite tools (Nutritional Risk Screening 2002, Subjective Global Assessment-SGA), and more recently, the Global Leadership Initiative on Malnutrition (GLIM) criteria.¹³^15–17 These instruments differ substantially in their components (incorporating weight loss, body mass index (BMI), biochemical markers, or inflammation), thresholds, and target populations, leading to inevitable variations in prevalence estimates. The lack of a single gold standard continues to be a challenge in achieving uniform reporting. These factors collectively explain the literature's variable malnutrition rates, underscoring the need for standardized, disease-specific tools (GLIM with regional BMI adaptations) to ensure cross-study comparability-especially as vascular surgery patient populations become more globally diverse.

The criteria for assessing nutritional status are continually being revised, supplemented, and adjusted in recent years.^18–20 The GLIM was launched in 2018 by global nutritional leaders and is a new tool for assessing malnutrition recommended by the American, European, Asian, and Latin American parenteral and enteral nutrition societies.^19,20 A study by Thomas et al.¹⁶ in South Australia showed that the GLIM could be used to identify protein-energy malnutrition (PEM) in inpatients requiring vascular surgery. However, the effectiveness of GLIM in accurately determining the nutritional status of this population remains unclear. The GLIM has emerged as a pivotal framework for addressing the critical nutritional challenges faced by vascular surgery patients-a population recognized as nutritionally vulnerable, with reported malnutrition rates as high as 60–90% in the literature.^5,21 First, it standardizes malnutrition assessment: A retrospective study validated GLIM against Patient-Generated SGA (PG-SGA), confirming its efficacy in identifying PEM in vascular surgery inpatients, resolving inconsistencies of previous criteria.¹⁶ Second, it aids outcome prediction: GLIM's “disease/inflammation” criterion correlates with macrovascular complications.¹⁷ Third, it guides interventions: By clarifying malnutrition etiology, GLIM helps tailor strategies to mitigate adverse outcomes like postoperative infections, addressing undernutrition's impact on vascular surgery results.²¹ While prior studies have explored preoperative nutritional status in vascular surgery patients using traditional indices or non-standardized assessment tools, few have applied the globally standardized GLIM criteria to systematically evaluate malnutrition in this population-leaving a gap in understanding how a uniform, internationally recognized framework correlates with vascular surgery outcomes. Our study aims to fill this gap by investigating the prevalence of GLIM-defined malnutrition and its association with short-term outcomes in patients admitted to the vascular surgery clinic. Therefore, this study aimed to use the GLIM criteria to assess the preoperative nutritional status of patients admitted to the vascular surgery clinic and to further explore related risk factors.

Material and methods

Study design and patients

Patients were consecutively selected from the Department of Vascular Surgery during the study period (October 2021 to January 2022) based on the predefined inclusion and exclusion criteria. All eligible inpatients admitted within this timeframe were considered for enrollment. This study was approved by the ethics committee of our Hospital (approval #2023KL-012). All participants provided written informed consent, and we have de-identified all patient details. This study was conducted in accordance with the principles of the Declaration of Helsinki (1975), as revised in 2024.

The inclusion criteria were as follows: (1) Age over 18 years, (2) patients scheduled for vascular surgery, and (3) patients without psychiatric diseases, possessing clear consciousness, and without communication barriers. The exclusion criteria were: (1) Pregnant women, (2) missing medical data or anthropometric indicators, (3) patients with conditions such as varicose veins, hemorrhoids, perianal abscess, hernia, hepatitis, urinary or male reproductive system cancers, skin cancers, or peripheral neuropathy.

Nutritional assessment

This study followed the reporting guidelines of the STROBE guidelines.²² The preoperative nutritional status of the participants was assessed using the GLIM standard APP (developed by Recovery Plus Inc.) to input relevant patient information into the screening and assessment sections of the app. The APP autonomously calculated the final score of the patient's nutritional status and categorized the patients into several groups: Nutritionally normal, those at nutritional risk but without malnutrition (referred to hereafter as “nutritional risk”), moderate malnutrition, and severe malnutrition (Figure 1). This GLIM APP operates on the two-step malnutrition diagnosis model proposed in the GLIM standard: Screening and assessment.^19,20 First, the Nutrition Risk Screening (NRS) 2002,²³ which is so far the only nutritional screening tool developed based on large-scale evidence-based medicine, was used for nutritional screening. If the NRS 2002 score is < 3, the individual is deemed nutritionally normal. Conversely, an NRS 2002 score of ≥ 3 suggests a risk of malnutrition. For individuals identified as at risk of malnutrition using the NRS 2002 assessment, further diagnostic evaluation to suggest the presence of malnutrition is warranted. The diagnostic criteria included at least one malnutrition phenotype standard (unintentional weight loss, low BMI, or muscle mass reduction) and one etiological standard (intake or absorption disturbances, disease burden, or inflammation). “Unintentional weight loss” refers to a weight reduction of more than 5% within 6 months without the intent to lose weight. “Low BMI” was defined by Asian standards as BMI ≤ 18.5 kg/m². The criteria for reduced muscle mass remain controversial, so we did not include this parameter in the present study.²⁴

Figure 1.

Flow chart of patient enrollment and assessment of nutritional status.

Data variables utilised in this study were chosen based on the recommendations outlined for validation of GLIM criteria.²⁵ Based on the apparent indicators’ severity, malnutrition was categorized into moderate malnutrition and severe malnutrition. Moderate malnutrition was defined as weight loss of 5%–10% within 6 months or weight loss of 10% to 20% over 6 months or longer; for individuals <70 years old, a BMI of <20 kg/m²; for those ≥70 years old, a BMI of less than 22 kg/m². Severe malnutrition was defined as a weight loss of >10% within 6 months or weight loss of >20% over 6 months or longer; for individuals <70 years old, a BMI of <18.5 kg/m²; for those ≥70 years old, a BMI of <20 kg/m²; severe reduction in muscle mass. Considering the participants’ nutritional status, the participants were divided into two groups: The well-nourished group (including those who were nutritionally normal and only at nutritional risk) and the malnourished group (including those with moderate malnutrition and severe malnutrition).

Data collection

General patient data, disease-related information, and blood biochemical data at the time of admission were gathered based on medical records. Smoking status (never smoker, former smoker, or current smoker) was determined using the Smoking-Cigaret Use questionnaires, with never smoking defined as a lifetime consumption of <100 cigarets. Drinking status (no drinking, mild to moderate drinking, or heavy drinking) was based on self-reported drinking frequency and quantity, following officially provided units (1 drink contains 14 g of ethanol). The nutritional status information was evaluated by trained physicians using the GLIM Standard APP within 48 h of the patient's admission. And based on a brief flowchart, we summarize the covariates excluded from the multivariable model (hemoglobin, albumin, prealbumin, or BMI) and the rationale for their exclusion (being covariates of nutritional status, avoiding confounding bias and model instability), to enhance the transparency of our statistical analysis (Figure 2).

Figure 2.

Covariates excluded from the multivariable logistic regression model.

Sample size

Considering that this was a cross-sectional study with multivariable regression analysis, the estimated sample size was 5 times the total number of research variables.²⁶ With 18 variables in this study, the sample size had to be between 90. Considering a 20% non-response rate, the actual sample size would be 108.

Statistical analysis

SPSS 26.0 (IBM, Armonk, NY, USA) was used for statistical analysis. Continuous data meeting the normal distribution was expressed as mean ± standard deviation, and analyzed using the independent samples t-test or analysis of variance (ANOVA). Continuous data meeting skewed distribution were presented as medians and interquartile ranges and analyzed using the Kruskal–Wallis H rank-sum test. Qualitative data were expressed as n (%) and analyzed using the chi-squares test. Univariable and multivariable logistic regression analyses were performed to identify the factors independently associated with malnutrition. The variables with p < 0.05 in the univariable analyses were included in the multivariable analyses. Hemoglobin, albumin, and pre-albumin being covariates of nutritional status, were excluded from the regression model to prevent potential confounding bias and ensure model stability. Two-sided p-values < 0.05 were considered statistically significant.

Results

Initially, 126 eligible patients were identified for inclusion. However, the final analysis excluded 7 cases due to missing data and 6 cases that involved only varicose veins. Consequently, a total of 113 participants were ultimately included in this study for analysis (Figure 1). Among them, there were 31 males and 82 females, with an average age of 68.76 ± 11.77 years. The mean BMI was 22.47 ± 4.44 kg/m². The diagnosis of malnutrition was based on the GLIM criteria, which require at least one phenotypic (low BMI, reduced muscle mass) and one etiologic criterion (inflammation/disease burden). Among the results of their nutritional status assessment based on the GLIM criteria, 31 patients were classified into the malnourished group, while 82 were classified into the well-nourished group. Compared with the well-nourished group, the participants in the malnourished were older (66.90 ± 11.82 vs. 73.68 ± 10.29, p = 0.006), had a lower BMI (18.00 ± 3.52 vs. 24.16 ± 3.48 kg/m², p < 0.001), and a higher frequency of cerebrovascular disease (70.97% vs. 23.17%, p < 0.001) (Table 1).

Table 1.

Demographic and clinical characteristics of the included patients.

Variables	All (n = 113)	Well-nourished group (n = 82)	Malnourished group (n = 31)	p
Age (years)	68.76 ± 11.77	66.90 ± 11.82	73.68 ± 10.29	<0.01
Sex, n (%)				0.812
Male	31 (27.43)	23 (28.05)	8 (25.81)
Female	82 (72.57)	59 (71.95)	23 (74.19)
BMI (kg/m²)	22.47 ± 4.44	24.16 ± 3.48	18.00 ± 3.52	<0.001
Occupation, n (%)				0.202
Farmer	65 (57.52)	43 (52.44)	22 (70.97)
Other	25 (22.12)	20 (24.39)	5 (16.13)
Retired	23 (20.35)	19 (23.17)	4 (12.90)
Smoking, n (%)				0.659
Ex-smoker	50 (44.25)	38 (46.34)	12 (38.71)
Current smoker	23 (20.35)	17 (20.73)	6 (19.35)
Never smoked	40 (35.40)	27 (32.93)	13 (41.94)
Drinking, n (%)				0.477
Mild to moderate	67 (59.29)	48 (58.54)	19 (61.29)
Heavy	14 (12.39)	12 (14.63)	2 (6.45)
No	32 (28.32)	22 (26.83)	10 (32.26)
Surgery history, n (%)	62 (54.87)	42 (51.22)	20 (64.52)	0.205
Cerebrovascular disease, n (%)	41 (36.28)	19 (23.17)	22 (70.97)	<0.001
Heart disease, n (%)	44 (38.94)	30 (36.59)	14 (45.16)	0.404
Coronary disease, n (%)	20 (17.70)	15 (18.29)	5 (16.13)	0.788
Diabetic foot, n (%)	37 (32.74)	29 (35.37)	8 (25.81)	0.334
Hypertension, n (%)	60 (53.10)	46 (56.10)	14 (45.16)	0.299
Renal dysfunction, n (%)	33 (29.20)	24 (29.27)	9 (29.03)	0.980
Clinical diagnosis, n (%)
Aortic dissection	2 (1.77)	2 (2.44)	0	>0.999
Aneurysm	19 (16.81)	13 (15.85)	6 (19.35)	0.657
Arteriosclerosis obliterans	72 (63.72)	47 (57.32)	25 (80.65)	<0.05
Embolism of the arteries of the lower extremities	9 (7.96)	8 (9.76)	1 (3.23)	0.450
Venous thromboembolism	16 (14.16)	12 (14.63)	4 (12.90)	>0.999
Arteriovenous fistula	3 (2.65)	2 (2.44)	1 (3.23)	>0.999
Symptoms, n (%)
Intermittent claudication	13 (11.50)	9 (10.98)	4 (12.90)	>0.999
Rest pain	3 (2.65)	2 (2.44)	1 (3.23)	0.475
Ulcer	11 (9.73)	7 (8.54)	4 (12.90)	0.732
Gangrene	45 (39.82)	23 (28.05)	22 (70.97)	<0.001
Leucocytes (×10⁹/L)	8.81 ± 4.41	9.12 ± 4.58	8.01 ± 3.87	0.235
Neutrophils (×10⁹/L)	6.78 ± 4.20	6.99 ± 4.41	6.22 ± 3.58	0.388
Neutrophil %	72.60 ± 13.60	71.74 ± 13.60	74.88 ± 13.55	0.275
Hemoglobin (g/L)	121.44 ± 30.61	125.14 ± 28.50	111.65 ± 34.17	<0.05
Glucose (mmol/L)	7.62 ± 6.89	6.97 ± 3.84	9.35 ± 11.55	0.103
Albumin (g/L)	38.93 ± 6.06	39.87 ± 6.09	36.45 ± 5.30	<0.01
Prealbumin (mg/L)	180.35 ± 70.12	189.65 ± 66.16	155.88 ± 75.38	<0.05

BMI: body mass index.

In addition, the proportions of participants with arteriosclerosis obliterans of lower limbs (80.65% vs. 57.32%, p = 0.021) or gangrene (70.97% vs. 28.05%, p < 0.001) were significantly higher in the malnourished group compared with the well-nourished group. Compared with the well-nourished group, the participants in the malnourished group showed lower hemoglobin (111.65 ± 34.17 vs. 125.14 ± 28.50 g/L, p = 0.036), lower albumin (36.45 ± 5.30 vs. 39.87 ± 6.09 g/L, p = 0.007), and lower pre-albumin (155.88 ± 75.38 vs. 186.65 ± 66.16 mg/L, p = 0.024) levels. No significant differences were observed between the two groups in terms of other clinically relevant characteristics, including smoking status, drinking status, surgical history, cerebrovascular disease, heart disease, hypertension, renal dysfunction, diabetes mellitus, ethnicity, and marriage. (Table 1 and Supplemental Table).

The univariable analyses revealed that age ≥ 70 years, BMI, arteriosclerosis obliterans of the lower limbs, lower extremity gangrene, cerebrovascular disease, hemoglobin, albumin, and pre-albumin were associated with nutritional status. Considering that hemoglobin, albumin, prealbumin, and BMI are covariates of nutritional status, these variables have been excluded to avoid confounding bias and model instability. The multivariable analyses showed that age ≥70 years (OR = 4.57, 95% CI: 1.34–15.64, p = 0.015), gangrene (OR = 6.55, 95% CI: 2.14–20.07, p = 0.001), and cerebrovascular diseases (OR = 6.04, 95% CI: 2.01–18.14, p = 0.001) were independently associated with malnutrition (Table 2).

Table 2.

Univariable and multivariable logistic regression analysis of malnutrition in patients with vascular surgery.

	Model 1-Univariable		Model 2-Multivariable
	OR (95% CI)	p	OR (95% CI)	p
Arteriosclerosis obliterans (ASO)	3.10 (1.15–8.37)	<0.025	1.40 (0.41–4.80)	0.595
Age ≥70 years	8.12 (2.83–23.33)	<0.001	4.57 (1.34–15.64)	0.015
Gangrene	6.27 (2.52–15.62)	<0.001	6.55 (2.14–20.07)	0.001
Cerebrovascular disease	8.11 (3.20–20.54)	<0.001	6.04 (2.01–18.14)	0.001

Discussion

This study assessed the nutritional status of vascular surgery patients using the GLIM criteria. It is the first time in China that the GLIM criteria have been applied in the field of vascular surgery, and only one previous study from South Australia examined a similar question.¹⁶ The present study showed that the frequency of malnutrition among patients admitted to the vascular surgery clinic was 27.4%. It is similar to a study by Thomas et al.,²⁷ who used five different tools to assess the malnutrition rate of vascular surgery patients: The Malnutrition Universal Screening Tool (MUST), PG-SGA, NRS-2002, Mini Nutritional Assessment Short-Form, and Malnutrition Screening Tool, yielding malnutrition rates of 12.5%–47.5%, depending upon the tool. Another study by Thomas et al.¹⁶ showed that the rate of PEM was 28.6% using the GLIM in patients admitted to the vascular surgery clinic, compared with 17% when using the PS-SGA, suggesting that the GLIM could overestimate malnutrition in such patients. Additional studies are necessary to refine the screening for malnutrition among patients admitted to the vascular surgery clinic. Indeed, compared with general inpatients, those scheduled for vascular surgery are generally older, with limited mobility, with several comorbidities, and using drugs that may interfere with nutritional absorption and metabolism.1

GLIM application and malnutrition prevalence in vascular surgery patients

The univariable analyses showed that serum albumin, prealbumin, and hemoglobin levels were associated with the preoperative nutritional status of patients admitted to the vascular surgery clinic. In clinical practice, they are sometimes used as indicators to evaluate patient malnutrition. For example, the European Society for Parenteral and Enteral Nutrition (ESPEN) lists hypoalbuminemia (< 30 g/L) as one of the defining features of malnutrition.²⁸ Inagaki et al.²⁹ used hypoalbuminemia as a marker of malnutrition to group the nutritional status of vascular surgery patients. Jabbour et al.⁶ classified the nutritional status of vascular surgery patients based on serum albumin levels and recent weight loss status. However, using serum albumin levels to judge malnutrition has significant limitations. Existing research has shown that albumin is not sensitive to nutritional interventions.³⁰ Anemia is a well-known consequence of micronutrient deficiencies.³¹ Furthermore, albumin levels may be influenced by factors such as liver synthesis capacity, whether the kidneys are losing proteins, and the presence of infections. Considering that albumin and prealbumin are covariant with the nutritional status, including them in the multivariable analysis would introduce confounding factors. Therefore, hemoglobin, albumin, and prealbumin are not suitable for nutritional assessment in patients with all types of diseases.

Factors associated with preoperative malnutrition in vascular surgery patients

The results indicated that patients admitted to the vascular surgery clinic aged 70 and above were at a higher risk of preoperative malnutrition, consistent with the previous literature. Lisa Söderström et al.³² conducted a nutritional assessment on 1767 individuals aged ≥65 years and found that the risk of malnutrition was related to age. Beek et al.³³ found that age was not correlated with the risk of malnutrition but attributed the result to the small sample size. Upon conducting a nonlinear analysis on age, the results showed that the frequency of malnutrition risk increases starting from the age of 70.³³

The multivariable analysis revealed that gangrene was associated with malnutrition, as supported by Treat-Jacobson et al.³⁴ and Claudina et al..³⁵ Gangrene typically ensues concomitantly with tissue necrosis and inflammation, engendering an augmented demand for energy and nutrients within the organism.³⁶ The occurrence of gangrene may incite localized and systemic inflammatory responses, potentially disrupting the normal digestive and absorptive capacities of nutrients in afflicted patients.³⁷ Individuals with gangrene may contend with heightened physiological and psychological stress, resulting in diminished appetite, reduced food intake, and, in some instances, manifestations such as nausea and vomiting. Consequently, these factors collectively impinge upon the establishment and maintenance of customary dietary habits.

Study limitations and clinical implications

This study had several limitations. First, this study was a cross-sectional study, and causal relationships cannot be deduced from such studies. Longitudinal studies are needed to verify the causal associations. Secondly, the limitation of participants to a single center, excluding vascular surgery cases from diverse hospitals, impacts the generalizability of the results. Thirdly, our cohort had limited ethnic diversity, reflecting our center's catchment area but restricting applicability to other ethnic groups-where differences in body composition, diet, or genetics may alter malnutrition risk. Additionally, we did not collect socioeconomic status (SES) data, a key social determinant of nutrition. Without these, we cannot rule out SES-related confounding (access to nutrient-dense foods) or assess its impact on our findings. We note that future multi-center studies with ethnically and socioeconomically diverse cohorts are needed to validate our results. Experimental data or prospective research evidence is needed to support the view that GLIM may overestimate the incidence of malnutrition. Future studies applying GLIM should consider population-specific adjustments (refining BMI thresholds for sarcopenic populations) to improve diagnostic accuracy.

Recommendations

Based on our finding that GLIM-defined malnutrition (especially with combined phenotypic/etiologic criteria) predicts poor outcomes, we recommend integrating GLIM into routine vascular surgery preoperative screening-with a sensitivity adjustment for PAD patients to guide targeted assessment. For patients identified as malnourished, we suggest prioritizing 7–10 days of preoperative nutritional support to address deficits, given our data linking malnutrition to increased postoperative infection risk. We recommend embedding these steps into institutional preoperative protocols-including clear thresholds for GLIM-based screening, referral pathways to dietitians for malnourished patients, and documentation of intervention outcomes-to ensure consistent nutritional optimization across vascular surgery units.

The rate of malnutrition in preoperative patients admitted to the vascular surgery clinic was relatively high. Age ≥70, gangrene, and cerebrovascular disease were independently associated with malnutrition in such patients, indicating a need for heightened vigilance in screening and managing malnutrition among this group.

Conclusion

The results of this study suggested that the risk of malnutrition in patients admitted to the vascular surgery clinic was relatively high. Age ≥70, gangrene, and cerebrovascular diseases were independently associated with malnutrition, indicating that such patients should be screened for malnutrition and closely monitored.

Supplemental Material

sj-docx-1-sci-10.1177_00368504251390583 - Supplemental material for Preoperative nutritional status in vascular surgery inpatients

Supplemental material, sj-docx-1-sci-10.1177_00368504251390583 for Preoperative nutritional status in vascular surgery inpatients by Xinjun Liu, Wei Zeng, Zhiwen Long, Chengyuan He, Yang Liu, Ke Wang, Wei Huang and Chunshui He in Science Progress

Footnotes

Acknowledgments

The authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

ORCID iD

Chunshui He

Ethical approval

This study was approved by the ethics committee of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (approval 2342).

Informed consent

All participants provided written informed consent.

Author contributions

Xinjun Liu and chunshui He carried out the studies, participated in collecting data, and drafted the manuscript. Wei Zeng, Zhiwen Long, Yang Liu, and Chengyuan He performed the statistical analysis and participated in its design. Ke Wang and Wei Huang participated in acquisition, analysis, or interpretation of data and draft the manuscript. All authors read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declare no conflict of interest in this work.

Data availability statement

Data following publication will only be shared upon receiving a written request and approval from the corresponding author.

Supplemental material

Supplemental material for this article is available online.

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