NRS2002 combined with nutritional, immune and inflammatory indicators for the nomogram to predict Sarcopenia

NRS2002 combined with nutritional, immune and inflammatory indicators for the nomogram to predict Sarcopenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article NRS2002 combined with nutritional, immune and inflammatory indicators for the nomogram to predict Sarcopenia Jie Liu, Jingjin Liu, Xuejiao Xian, Tao Hu, Zhengfeng Bi, Hongjun Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3868428/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives Sarcopenia is a geriatric syndrome characterized by age-related loss of muscle mass and strength, with or without physical function decline. In clinical work, it is complicated to consider it as a geriatric syndrome, and the diagnostic criteria are often ignored by clinical workers. This study aims to construct a predictive model for sarcopenia using commonly used clinical indicators. Design: By collecting the basic clinical data, NRS2002 score scale, nutrition, immunity, inflammation, and other blood indicators of the subjects, the diagnosis and prediction model of sarcopenia was established. The LASSO regression method was used to screen the variables and select predictors. logistic regression analysis was used to construct the modal map, and the discriminant ability of the model was determined by calculating the area under the curve (AUC). Finally, the training set and validation set were randomly split for internal verification, and the AUC was used to judge the verification effect. Participants: The study was conducted from June 2023 to September 2022 in the First Affiliated Hospital of Kunming Medical University; Elderly inpatients over 60 years old were included, and sarcopenia was diagnosed using the Asian Working Group for Sarcopenia (AWGS2019) diagnostic criteria. NRS2002 score, nutrition, immunity, and inflammation indexes were collected to construct the model. Results Four variables were selected and screened by the LASSO regression method, and a diagnostic and prediction model was established based on these variables. The AUC of the prediction model was 0.80. In the internal validation, the total number of samples was randomly divided into training set and validation set according to a 0.85 split ratio, and the ROC curve was used to verify the results, and the AUC was 0.8047 and 0.9065 respectively. Finally, the model was used to correct the curve, and the curve fit was good, the mean absolute error (MAE) was 0.014, and the prediction effect was good. The model can be used to diagnose and predict sarcopenia in clinical patients. Conclusion In this study, NRS2002 combined with BMI, lymphocyte count, and BNP were used to construct a diagnosis and prediction model for sarcopenia, which has important value for the prediction of sarcopenia. Sarcopenia Prediction NRS2002 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sarcopenia is a group of systemic, progressive skeletal muscle diseases characterized by loss of muscle mass and muscle function caused by a variety of causes. In 2010, the European Working Group on Sarcopenia in the Elderly (EWGSOP) defined sarcopenia as "a geriatric syndrome characterized by age-related loss of muscle mass, muscle strength and/or physical function", which mainly emphasized the decline of skeletal muscle mass, combined with the decline of skeletal muscle strength, or combined with the decline of skeletal muscle function [1]. With the increase of age, the human body is accompanied by the decline of skeletal muscle mass and function. This study uses the Asian Sarcopenia Working Group to diagnose according to the AWGS2019 diagnostic criteria [2], and through NRS2002(nutria-tionalriskscreening2002) combined with albumin, Hemoglobin, BMI, C-reactive protein, procalcitonin, neutrophil count, lymphocyte count, monocyte count, BNP and other related indicators for the diagnosis and prediction model of sarcopenia were established. Participants and Methods Participants Elderly inpatients over 60 years old in the First Affiliated Hospital of Kunming Medical University from June 2023 to September 2022 were enrolled in this study. The AWG2019 diagnostic algorithm was used to diagnose sarcopenia in hospitalized patients.This study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University, and all research subjects signed the informed consent. Diagnostic process . Screening: SARC-F(The Simple Five item Scoring Scale for Sarcopenia) is used to screen for sarcopenia by assessing muscle strength, walking ability, seat-standing test, stair climbing, and falls. A total score of≥4 points can screen for sarcopenia. Skeletal muscle mass was measured by the Inbody (S10) body composition analyzer (BIA) in Korea. The patient should empty the stool and urine, and remain in the supine position for at least 15 minutes before the measurement, and at least 2 hours after the last meal or water intake and intravenous fluid therapy. Standard measurements were performed as indicated on the device. Skeletal muscle strength: Skeletal muscle strength: grip strength; Skeletal muscle strength of both upper limbs was assessed by electronic handgrip dynamometer (WCS- 100), and the left and right hands were measured 3 times with an interval of 5 minutes. The maximum value was taken as the final measurement result. Grip strength was measured in two decimal places, and grip strength was measured in kg. Diagnostic criteria: (1) Muscle mass: ASM/ height 2(kg/m2) measured by body composition measurement (BIA): male ≤7.0, female ≤5.7; (2) Muscle strength: grip strength <28 kg for men and <18 kg for women; All patient variables included in the statistical analysis for the diagnostic prediction model. E stablishment: NRS2002 score, age, height, weight, BMI, grip strength, C-reactive protein, procalcitonin, neutrophil count, lymphocyte count, monocyte count, eosinophil count, basophil count, red blood cell count, hemoglobin, platelet count, albumin, BNP, neutrophil-to-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), CALLY index (calculated as follows: Albumin × lymphocytes ÷(CRP×10)), C-reactive protein/lymphocyte ratio (LCR), C-reactive protein/albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory response index (SIRI: neutrophil count ×monocyte count/lymphocyte count), systemic immune inflammation index (SI) I: platelet count ×neutrophil count/lymphocyte count), albumin/globulin ratio (A/G), Brain natriuretic peptide (BNP). All scales and values were collected on admission, and all blood indicators were collected from the fasting venous blood of patients in the morning. Statistical analysis: enumeration data and measurement data were expressed by median (quartile) and number (proportion), respectively. Unpaired t-test Wilcoxon rank sum test, Pearson chi-square test, or Fisher's exact test were used for comparison between sarcopenia patients and non-sarcopenia patients, as appropriate. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select data dimensions and predictors. Multivariate logistic regression analysis was used to establish the prediction model and the mode map of sarcopenia. The discriminant power of the model was determined by calculating the area under the curve (AUC). The receiver operating characteristic (ROC) curve and Calibration curve analysis were used to evaluate the clinical usefulness of the model. R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria), P < 0.05 was considered statistically significant. Results Among the patients in this study, 22.78% (54/237) patients had sarcopenia. According to the relevant data collected from the study (Table 1), univariate analysis was performed (Fig. 1). Among the 28 variables collected, lasso regression analysis was used to obtain 6 variables, which included: sex, WEIGHT, BMI, NRS2002, M (monocyte count), and BNP. BMI, NRS2002, M (monocyte count), and BNP were used to establish a diagnostic prediction model. Using multivariate logistic regression analysis, the AUC of the prediction model was 0.823, and the total sample number was randomly divided into a training set and validation set according to the 0.85 split ratio. The ROC curve was used for verification (Fig. 2 and Fig. 3 ), and the AUC was 0.8233 and 0.9278, respectively. For a more convenient presentation of the prediction model, a nomogram was constructed to predict the probability of diagnosis of sarcopenia. Finally, the model was corrected, and the calibration curve (Fig. 4 ) was obtained under the condition of 1000 replicates, which showed the consistency of the predicted probability and the observed probability. The curve fit was good, and the mean absolute error (MAE) was 0.014, indicating that the prediction effect was good. The model can be used to diagnose and predict ordinary clinical patients. Table 1 Baseline tables for inclusion of clinical data from subjects Characteristic patient P-value Non-sarcopenia=183 Sarcopenia=54 diagnosis = 1 (%) 0 (0.0) 54 (100.0) <0.001 SEX= 1 (%) male 94 (51.4) 31 (57.4) 0.531 female 89 (48.6) 23 (42.6) AGE (Y/O) 71.43 (8.26) 74.44 (8.57) 0.02 HEIGHT (cm) 162.15 (8.24) 158.83 (7.56) 0.009 WEIGHT (kg) 57.97 (11.84) 45.70 (11.32) <0.001 BMI (kg/m2) 21.99 (3.98) 18.04 (3.92) <0.001 GRIP (kg) 17.99 (9.38) 13.10 (7.00) <0.001 NRS2002 (%) <0.001 0 3 (1.6) 0 (0.0) 1 54 (29.5) 7 (13.0) 2 74 (40.4) 10 (18.5) 3 19 (10.4) 8 (14.8) 4 20 (10.9) 10 (18.5) 5 11 (6.0) 14 (25.9) 6 2 (1.1) 4 (7.4) 7 0 (0.0) 1 (1.9) PCT (ng/mL) 0.95 (3.36) 0.65 (1.65) 0.538 CRP (mg/L) 32.24 (48.86) 37.84 (45.53) 0.453 N (10^9/L) 6.02 (4.04) 5.24 (2.91) 0.184 NLR 7.60 (10.90) 4.95 (4.63) 0.084 MLR 0.44 (0.37) 0.53 (0.44) 0.172 CALLY index 3.32 (8.53) 2.23 (4.88) 0.369 CLR 44.45 (90.75) 49.70 (97.02) 0.714 CAR 0.97 (1.54) 1.17 (1.50) 0.399 PLR 230.95 (254.49) 226.00 (183.17) 0.894 SIRI 3.59 (6.89) 3.28 (4.33) 0.753 SII 1629.59 (2717.33) 1362.42 (1992.89) 0.503 L(10^9/L) 1.36 (0.80) 1.62 (1.26) 0.064 M (10^9/L) 0.46 (0.27) 0.57 (0.27) 0.011 E (10^9/L) 0.11 (0.12) 0.09 (0.11) 0.232 B (10^9/L) 0.03 (0.02) 0.03 (0.02) 0.758 RBC (10^12/L) 4.12 (0.81) 4.04 (0.74) 0.536 Hb (g/L) 122.67 (27.77) 116.35 (24.72) 0.134 PLT (10^9/ml) 214.72 (94.58) 249.89 (116.92) 0.024 Alb (g/L) 37.72 (6.42) 35.76 (6.51) 0.05 A/G 1.59 (3.05) 1.32 (0.36) 0.511 BNP (pg/mL) 90.37 (191.28) 183.74 (382.38) 0.016 NRS2002:nutriational risk screening 2002;N:neutrophil count；NLR:Neutrophils lymphocyte ratio; MLR: Mononuclear lymphocyte ratio；CALLY index: Crp-albumin-lymphocyte index； CLR: C-reactive protein lymphocyte ratio; CAR: C-reactive protein albumin ratio; PLR: Platelet to lymphocyte ratio; SIRI(systemic immune-response index):Formula: the platelet count is multiplied mononuclear cell count is multiplied NLR；SII(systemic immune-inflammation index):Formula: the platelet count is multiplied NLR .L: lymphocyte count; M: Monocyte count; E: Eosinophil count; B: Basophil Cell Count; A/G: the albumin and globulin ratio. Discussion The 2018 European Working Group on Sarcopenia in Older Adults (EWGSOP2) updated its definition of sarcopenia, defining sarcopenia as a progressive and systemic skeletal muscle disease associated with an increased likelihood of adverse outcomes such as falls, fractures, physical disability, and death[ 2 ]. However, considering the limited data in Asia and the controversy of the term sarcopenia, the Asian Sarcopenia Working Group in 2019 consensus felt that its original definition of "age-related loss of skeletal muscle mass plus loss of muscle strength and/or decline in physical function" should be retained, without referring to comorbidities. It does not follow the current trend to treat all muscle wasting as sarcopenia. The lower limit of their age is set for elderly patients aged 60 or 65 years [ 3 ]. With the increase of age, various body functions of people decline. At the same time, with the appearance of aging, studies have found that in the process of aging, inflammatory response and related inflammatory cells and inflammatory cytokines involved in inflammation play a large role. At the same time, some articles put forward the concept of "inflammatory aging", which believes that with the increase of age, the ability of the body to cope with various stressors gradually decreases, and chronic low-grade inflammation occurs at the same time [ 4 , 5 ]. This chronic low-grade inflammation is generally considered to be A chronic state with slightly elevated plasma levels of pro-inflammatory mediators associated with aging, including tumor necrosis factor A(TNFα)[ 6 ], interleukin-6 (IL-6), and C-reactive protein (CRP)[ 7 ].In a meta-analysis study, TNF-a, CRP, IL-6, and other indicators were included in a large number of data. Analysis of its relationship with sarcopenia found that there was a link between sarcopenia and elevated CRP [ 8 ]. The results of a population-based prospective study showed that higher levels of IL-6 and CRP increased the risk of muscle strength loss, while higher levels of ACT reduced the risk of muscle strength loss in elderly men and women[ 9 , 10 ]. However, with sarcopenia as an age-related disease, a large number of studies have shown that abnormal and unresolved changes in normal inflammatory processes in old age may ultimately act as a link that drives skeletal muscle to become more degenerative and dysfunctional in nature. This negative outcome of muscle wasting may be due to the disruption of central mechanisms that regulate muscle regeneration, repair, protein turnover, and apoptosis caused by inflammation. It is also believed that chronic inflammation, whether local or systemic, has a negative effect on skeletal muscle mass[ 11 ].Recent studies have found that by promoting the expression of autophagy genes, the ability of muscle autophagy degradation can be increased to prevent skeletal muscle aging and reduce the impact of age-related skeletal muscle dysfunction in the aging process [ 12 ]. However, impaired autophagy promotes the development of sarcopenia, such as oxidative stress and mitochondrial damage found in lipofuscin deposition during muscle aging[ 13 ]. However, even though extensive evidence has confirmed the important role of inflammation in sarcopenia, the relationship between serum inflammatory markers and sarcopenia in clinical practice is still unclear. In 2019, the GLIM consensus included muscle mass loss in the diagnostic criteria of malnutrition and suggested that the GLIM diagnostic criteria be used in the diagnosis of sarcopenia [ 14 ]. In the elderly, the risk of malnutrition gradually increases with the increase of age. Protein-energy malnutrition has a high incidence and can also lead to the occurrence of sarcopenia. Active nutritional treatment for patients with malnutrition and sarcopenia can significantly improve the nutritional status of patients, thereby promoting functional recovery and the improvement of mobility [ 15 ]. However, in clinical work, the diagnosis process of sarcopenia is complicated. In AWG2019, the criteria for screening and diagnosis are clarified. Screening is performed using calf circumference (M < 34 cm, F < 33 cm) or SARC-F ≥ 4 or ARC-CalF ≥ 11. The diagnostic criteria are based on the three negative aspects of muscle mass, muscle strength, and with or without decline in physical function [ 14 ]. In this study, common clinical indicators combined with nutritional screening 2002 scale for hospitalized patients were used to establish a diagnostic prediction model, which can only predict the diagnosis of sarcopenia, reduce the more complex diagnostic work, and put forward a new idea to provide a basis for early nutritional intervention for clinical patients. Conclusion In this study, the diagnosis of sarcopenia was predicted based on four variables of patients with sarcopenia under definite diagnosis. The variables in the fitting graph were screened by lasso regression analysis. BMI, M (monocyte count), NRS2002, and BNP were relatively easy to obtain in clinical work. The nomogram has good discrimination and calibration ability, which has good application value in clinical work. This study has several limitations. First, since this study is all clinical data, the sample size is small, and the lack of multi-center joint data verification, results in the screening variables being worse than expected. At the same time, CRP, Alb, and sarcopenia-related indicators have not been screened out. Despite the limitations of this study, this study proposes a new idea for the prediction and diagnosis of sarcopenia. Declarations Conflict of interest : Jie Liu: Conflicts of interest/ financial disclosures-none. Jingjin Liu；Conflicts of interest/ financial disclosures-none. Guiron He:Conflicts of interest/ financial disclosures-none. XianXuejiao：Conflicts of interest/ financial disclosures-none. Zhengfeng Bi:Conflicts of interest/ financial disclosures-none. Tao Hu:Conflicts of interest/ financial disclosures-none. Hongjun Yang:Conflicts of interest/ financial disclosures-none. Author Contribution Jie Liu::Original Draft Preparation Jingjin Liu:Investigation Guiron He:Supervision Xian Xuejiao:Formal Analysis Zhengfeng Bi:Project Administration Tao Hu:Data Curation; Hongjun Yang:Writing – Review & Editing References Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People[J]. JouralIusse. Jul 2010. 412 – 23: 10.1093/ageing/afq034 Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis[J]. JouralIusse. Jan 1 2019. 16–31: 10.1093/ageing/afy169 Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment[J]. JouralIusse. 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Oxidative stress and mitochondrial impairment can be separated from lipofuscin accumulation in aged human skeletal muscle[J]. JouralIusse. Apr 2007. 245 – 56: 10.1111/j.1474-9726.2007.00282.x Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community[J]. JouralIusse. Feb 2019. 207–217: 10.1002/jcsm.12383 Nakahara S, Takasaki M, Abe S, Kakitani C, Nishioka S, Wakabayashi H, et al. Aggressive nutrition therapy in malnutrition and sarcopenia[J]. JouralIusse. Apr 2021. 111109: 10.1016/j.nut.2020.111109 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In 2010, the European Working Group on Sarcopenia in the Elderly (EWGSOP) defined sarcopenia as \"a geriatric syndrome characterized by age-related loss of muscle mass, muscle strength and/or physical function\", which mainly emphasized the decline of skeletal muscle mass, combined with the decline of skeletal muscle strength, or combined with the decline of skeletal muscle function [1]. With the increase of age, the human body is accompanied by the decline of skeletal muscle mass and function. This study uses the Asian Sarcopenia Working Group to diagnose according to the AWGS2019 diagnostic criteria [2], and through NRS2002(nutria-tionalriskscreening2002) combined with albumin, Hemoglobin, BMI, C-reactive protein, procalcitonin, neutrophil count, lymphocyte count, monocyte count, BNP and other related indicators for the diagnosis and prediction model of sarcopenia were established."},{"header":"Participants and Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eElderly inpatients over 60 years old in the First Affiliated Hospital of Kunming Medical University from June 2023 to September 2022 were enrolled in this study. The AWG2019 diagnostic algorithm was used to diagnose sarcopenia in hospitalized patients.This study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University, and all research subjects signed the informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic process\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening:\u003c/strong\u003e SARC-F(The Simple Five item Scoring Scale for Sarcopenia) is used to screen for sarcopenia by assessing muscle strength, walking ability, seat-standing test, stair climbing, and falls. A total score of\u0026ge;4 points can screen for sarcopenia.\u003c/p\u003e\n\u003cp\u003eSkeletal muscle mass was measured by the Inbody (S10) body composition analyzer (BIA) in Korea. The patient should empty the stool and urine, and remain in the supine position for at least 15 minutes before the measurement, and at least 2 hours after the last meal or water intake and intravenous fluid therapy. Standard measurements were performed as indicated on the device.\u003c/p\u003e\n\u003cp\u003eSkeletal muscle strength:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSkeletal\u0026nbsp;muscle\u0026nbsp;strength:\u003c/strong\u003egrip strength; Skeletal muscle strength of both upper limbs was assessed by electronic handgrip dynamometer (WCS- 100), and the left and right hands were measured 3 times with an interval of 5 \u0026nbsp;minutes. The maximum value was taken as the final measurement result. Grip strength was measured in two decimal places, and grip strength was measured in kg.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic\u0026nbsp;criteria:\u0026nbsp;\u003c/strong\u003e(1) Muscle mass: ASM/ height 2(kg/m2) measured by body composition measurement (BIA): male \u0026nbsp;\u0026nbsp;\u0026le;7.0, female \u0026nbsp; \u0026nbsp;\u0026le;5.7; (2) Muscle strength: grip strength \u0026lt;28 kg for men and \u0026lt;18 kg for women; \u0026nbsp;All patient variables included in the statistical analysis for the diagnostic prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003cstrong\u003establishment:\u003c/strong\u003eNRS2002 score, age, height, weight, BMI, grip strength, C-reactive protein, procalcitonin, neutrophil count, lymphocyte count, monocyte count, eosinophil count, basophil count, red blood cell count, hemoglobin, platelet count, albumin, BNP, neutrophil-to-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), CALLY index (calculated as follows: Albumin \u0026times; lymphocytes \u0026nbsp; \u0026divide;(CRP\u0026times;10)), C-reactive protein/lymphocyte ratio (LCR), C-reactive protein/albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), systemic inflammatory response index (SIRI: neutrophil count \u0026times;monocyte count/lymphocyte count), systemic immune inflammation index (SI) I: platelet count \u0026times;neutrophil count/lymphocyte count), albumin/globulin ratio (A/G), Brain natriuretic peptide (BNP). All scales and values were collected on admission, and all blood indicators were collected from the fasting venous blood of patients in the morning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis:\u003c/strong\u003e enumeration data and measurement data were expressed by median (quartile) and number (proportion), respectively. Unpaired t-test Wilcoxon rank sum test, Pearson chi-square test, or Fisher\u0026apos;s exact test were used for comparison between sarcopenia patients and non-sarcopenia patients, as appropriate. \u0026nbsp; Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select data dimensions and predictors. Multivariate logistic regression analysis was used to establish the prediction model and the mode map of sarcopenia. The discriminant power of the model was determined by calculating the area under the curve (AUC). The receiver operating characteristic (ROC) curve and Calibration curve analysis were used to evaluate the clinical usefulness of the model. R software (version 4.3.2; R Foundation for Statistical Computing, Vienna, Austria), P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the patients in this study, 22.78% (54/237) patients had sarcopenia. According to the relevant data collected from the study (Table\u0026nbsp;1), univariate analysis was performed (Fig.\u0026nbsp;1). Among the 28 variables collected, lasso regression analysis was used to obtain 6 variables, which included: sex, WEIGHT, BMI, NRS2002, M (monocyte count), and BNP. BMI, NRS2002, M (monocyte count), and BNP were used to establish a diagnostic prediction model. Using multivariate logistic regression analysis, the AUC of the prediction model was 0.823, and the total sample number was randomly divided into a training set and validation set according to the 0.85 split ratio. The ROC curve was used for verification (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and the AUC was 0.8233 and 0.9278, respectively. For a more convenient presentation of the prediction model, a nomogram was constructed to predict the probability of diagnosis of sarcopenia. Finally, the model was corrected, and the calibration curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was obtained under the condition of 1000 replicates, which showed the consistency of the predicted probability and the observed probability. The curve fit was good, and the mean absolute error (MAE) was 0.014, indicating that the prediction effect was good. The model can be used to diagnose and predict ordinary clinical patients.\u003c/p\u003e \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"721\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Baseline tables for inclusion of clinical data from subjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.554016620498615%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003epatient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.342465753424655%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-sarcopenia=183\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.657534246575345%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSarcopenia=54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ediagnosis = 1 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e54 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEX= 1 (%) \u0026nbsp; \u0026nbsp; \u0026nbsp;male\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e94 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e31 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.39097744360902%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.96992481203007%\" valign=\"top\"\u003e\n \u003cp\u003e89 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.639097744360903%\" valign=\"top\"\u003e\n \u003cp\u003e23 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGE (Y/O)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e71.43 (8.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e74.44 (8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHEIGHT (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e162.15 (8.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e158.83 (7.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWEIGHT (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e57.97 (11.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e45.70 (11.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e21.99 (3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e18.04 (3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGRIP (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e17.99 (9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e13.10 (7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRS2002 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e54 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e7 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e74 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e10 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e19 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e8 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e20 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e10 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e11 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e14 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e4 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCT (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (3.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e0.65 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e32.24 (48.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e37.84 (45.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (10^9/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e6.02 (4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e5.24 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e7.60 (10.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e4.95 (4.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0.44 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e0.53 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCALLY index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e3.32 (8.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e2.23 (4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e44.45 (90.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e49.70 (97.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e230.95 (254.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e226.00 (183.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e3.59 (6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e3.28 (4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e1629.59 (2717.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e1362.42 (1992.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eL(10^9/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e1.36 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e1.62 (1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eM (10^9/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0.46 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e0.57 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eE (10^9/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e0.09 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB (10^9/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e0.03 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e0.03 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRBC (10^12/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e4.12 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e4.04 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e122.67 (27.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e116.35 (24.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLT (10^9/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e214.72 (94.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e249.89 (116.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlb (g/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e37.72 (6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e35.76 (6.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA/G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e1.59 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e1.32 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.130193905817176%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBNP (pg/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.977839335180054%\" valign=\"top\"\u003e\n \u003cp\u003e90.37 (191.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.57617728531856%\" valign=\"top\"\u003e\n \u003cp\u003e183.74 (382.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.31578947368421%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNRS2002:nutriational risk screening 2002;N:neutrophil count；NLR:Neutrophils lymphocyte ratio; MLR: Mononuclear lymphocyte ratio；CALLY index: Crp-albumin-lymphocyte index； CLR: C-reactive protein lymphocyte ratio; CAR: C-reactive protein albumin ratio; PLR: Platelet to lymphocyte ratio; SIRI(systemic immune-response index):Formula: the platelet count is multiplied mononuclear cell count is multiplied NLR；SII(systemic immune-inflammation index):Formula: the platelet count is multiplied NLR .L: lymphocyte count; M: Monocyte count; E: Eosinophil count; B: Basophil Cell Count; A/G: the albumin and globulin ratio.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe 2018 European Working Group on Sarcopenia in Older Adults (EWGSOP2) updated its definition of sarcopenia, defining sarcopenia as a progressive and systemic skeletal muscle disease associated with an increased likelihood of adverse outcomes such as falls, fractures, physical disability, and death[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, considering the limited data in Asia and the controversy of the term sarcopenia, the Asian Sarcopenia Working Group in 2019 consensus felt that its original definition of \"age-related loss of skeletal muscle mass plus loss of muscle strength and/or decline in physical function\" should be retained, without referring to comorbidities. It does not follow the current trend to treat all muscle wasting as sarcopenia. The lower limit of their age is set for elderly patients aged 60 or 65 years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. With the increase of age, various body functions of people decline. At the same time, with the appearance of aging, studies have found that in the process of aging, inflammatory response and related inflammatory cells and inflammatory cytokines involved in inflammation play a large role. At the same time, some articles put forward the concept of \"inflammatory aging\", which believes that with the increase of age, the ability of the body to cope with various stressors gradually decreases, and chronic low-grade inflammation occurs at the same time [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This chronic low-grade inflammation is generally considered to be A chronic state with slightly elevated plasma levels of pro-inflammatory mediators associated with aging, including tumor necrosis factor A(TNFα)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], interleukin-6 (IL-6), and C-reactive protein (CRP)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].In a meta-analysis study, TNF-a, CRP, IL-6, and other indicators were included in a large number of data. Analysis of its relationship with sarcopenia found that there was a link between sarcopenia and elevated CRP [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The results of a population-based prospective study showed that higher levels of IL-6 and CRP increased the risk of muscle strength loss, while higher levels of ACT reduced the risk of muscle strength loss in elderly men and women[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, with sarcopenia as an age-related disease, a large number of studies have shown that abnormal and unresolved changes in normal inflammatory processes in old age may ultimately act as a link that drives skeletal muscle to become more degenerative and dysfunctional in nature. This negative outcome of muscle wasting may be due to the disruption of central mechanisms that regulate muscle regeneration, repair, protein turnover, and apoptosis caused by inflammation. It is also believed that chronic inflammation, whether local or systemic, has a negative effect on skeletal muscle mass[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Recent studies have found that by promoting the expression of autophagy genes, the ability of muscle autophagy degradation can be increased to prevent skeletal muscle aging and reduce the impact of age-related skeletal muscle dysfunction in the aging process [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, impaired autophagy promotes the development of sarcopenia, such as oxidative stress and mitochondrial damage found in lipofuscin deposition during muscle aging[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, even though extensive evidence has confirmed the important role of inflammation in sarcopenia, the relationship between serum inflammatory markers and sarcopenia in clinical practice is still unclear.\u003c/p\u003e \u003cp\u003eIn 2019, the GLIM consensus included muscle mass loss in the diagnostic criteria of malnutrition and suggested that the GLIM diagnostic criteria be used in the diagnosis of sarcopenia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the elderly, the risk of malnutrition gradually increases with the increase of age. Protein-energy malnutrition has a high incidence and can also lead to the occurrence of sarcopenia. Active nutritional treatment for patients with malnutrition and sarcopenia can significantly improve the nutritional status of patients, thereby promoting functional recovery and the improvement of mobility [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, in clinical work, the diagnosis process of sarcopenia is complicated. In AWG2019, the criteria for screening and diagnosis are clarified. Screening is performed using calf circumference (M\u0026thinsp;\u0026lt;\u0026thinsp;34 cm, F\u0026thinsp;\u0026lt;\u0026thinsp;33 cm) or SARC-F\u0026thinsp;\u0026ge;\u0026thinsp;4 or ARC-CalF\u0026thinsp;\u0026ge;\u0026thinsp;11. The diagnostic criteria are based on the three negative aspects of muscle mass, muscle strength, and with or without decline in physical function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, common clinical indicators combined with nutritional screening 2002 scale for hospitalized patients were used to establish a diagnostic prediction model, which can only predict the diagnosis of sarcopenia, reduce the more complex diagnostic work, and put forward a new idea to provide a basis for early nutritional intervention for clinical patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the diagnosis of sarcopenia was predicted based on four variables of patients with sarcopenia under definite diagnosis. The variables in the fitting graph were screened by lasso regression analysis. BMI, M (monocyte count), NRS2002, and BNP were relatively easy to obtain in clinical work. The nomogram has good discrimination and calibration ability, which has good application value in clinical work.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, since this study is all clinical data, the sample size is small, and the lack of multi-center joint data verification, results in the screening variables being worse than expected. At the same time, CRP, Alb, and sarcopenia-related indicators have not been screened out. Despite the limitations of this study, this study proposes a new idea for the prediction and diagnosis of sarcopenia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Liu: Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003eJingjin Liu；Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003eGuiron He:Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003eXianXuejiao：Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003eZhengfeng Bi:Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003eTao Hu:Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003eHongjun\u0026nbsp;Yang:Conflicts of interest/ financial disclosures-none.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Liu::Original Draft Preparation\u003c/p\u003e\n\u003cp\u003eJingjin Liu:Investigation\u003c/p\u003e\n\u003cp\u003eGuiron He:Supervision\u003c/p\u003e\n\u003cp\u003eXian Xuejiao:Formal Analysis\u003c/p\u003e\n\u003cp\u003eZhengfeng Bi:Project Administration\u003c/p\u003e\n\u003cp\u003eTao Hu:Data Curation;\u003c/p\u003e\n\u003cp\u003eHongjun\u0026nbsp;Yang:Writing\u0026nbsp;\u0026ndash;\u0026nbsp;Review \u0026amp; Editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. 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Aggressive nutrition therapy in malnutrition and sarcopenia[J]. JouralIusse. Apr 2021. 111109: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.nut.2020.111109\u003c/span\u003e\u003cspan address=\"10.1016/j.nut.2020.111109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Prediction, NRS2002","lastPublishedDoi":"10.21203/rs.3.rs-3868428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3868428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eSarcopenia is a geriatric syndrome characterized by age-related loss of muscle mass and strength, with or without physical function decline. In clinical work, it is complicated to consider it as a geriatric syndrome, and the diagnostic criteria are often ignored by clinical workers. This study aims to construct a predictive model for sarcopenia using commonly used clinical indicators.\u003c/p\u003e\u003ch2\u003eDesign:\u003c/h2\u003e \u003cp\u003eBy collecting the basic clinical data, NRS2002 score scale, nutrition, immunity, inflammation, and other blood indicators of the subjects, the diagnosis and prediction model of sarcopenia was established. The LASSO regression method was used to screen the variables and select predictors. logistic regression analysis was used to construct the modal map, and the discriminant ability of the model was determined by calculating the area under the curve (AUC). Finally, the training set and validation set were randomly split for internal verification, and the AUC was used to judge the verification effect.\u003c/p\u003e\u003ch2\u003eParticipants:\u003c/h2\u003e \u003cp\u003eThe study was conducted from June 2023 to September 2022 in the First Affiliated Hospital of Kunming Medical University; Elderly inpatients over 60 years old were included, and sarcopenia was diagnosed using the Asian Working Group for Sarcopenia (AWGS2019) diagnostic criteria. NRS2002 score, nutrition, immunity, and inflammation indexes were collected to construct the model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour variables were selected and screened by the LASSO regression method, and a diagnostic and prediction model was established based on these variables. The AUC of the prediction model was 0.80. In the internal validation, the total number of samples was randomly divided into training set and validation set according to a 0.85 split ratio, and the ROC curve was used to verify the results, and the AUC was 0.8047 and 0.9065 respectively. Finally, the model was used to correct the curve, and the curve fit was good, the mean absolute error (MAE) was 0.014, and the prediction effect was good. The model can be used to diagnose and predict sarcopenia in clinical patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn this study, NRS2002 combined with BMI, lymphocyte count, and BNP were used to construct a diagnosis and prediction model for sarcopenia, which has important value for the prediction of sarcopenia.\u003c/p\u003e","manuscriptTitle":"NRS2002 combined with nutritional, immune and inflammatory indicators for the nomogram to predict Sarcopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 19:56:50","doi":"10.21203/rs.3.rs-3868428/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a87654b8-9fa5-469f-a203-dc86a127f2a3","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-22T05:52:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 19:56:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3868428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3868428","identity":"rs-3868428","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}