{"paper_id":"37efec8e-facb-47f4-93b1-51d2bfc75a2d","body_text":"Malnutrition and Muscle Loss Mediate the Association between NT-proBNP and Mortality in Hospitalized Older Adults | 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 Malnutrition and Muscle Loss Mediate the Association between NT-proBNP and Mortality in Hospitalized Older Adults Jun Tao, Xiaoyan Zhang, Niansong Wang, Dongsheng Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3863523/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 Background & Aims The purpose of this study was to assess the association between N-terminal prohormone of type B natriuretic peptide (NT-proBNP) and long-term mortality in hospitalized oldest-old adults and to explore the mediating role of malnutrition and muscle loss. Methods This prospective cohort study was conducted among 360 hospitalized patients ≥ 80 years of age (median age 87 [IQR 84–90] years, 24.4% women) in the Department of Geriatrics. The Geriatric Nutritional Risk Index (GNRI) and Mini Nutritional Assessment Short Form (MNA-SF) were used for nutritional assessment, while calf circumference was used as a measure of muscle mass. A Cox proportional hazard model was used to assess the relationship between NT-proBNP levels and mortality. Mediation analysis was used to explore the mediating effects of malnutrition and muscle loss. Results The median follow-up was 4.1 years with 159 (44.1%) deaths. Mortality risk increased by 32% per 2-fold increase in NT-proBNP levels (full adjusted hazard ratio: 1.32 [95% CI, 1.20–1.46]). A mediation analysis showed that a lower GNRI score and decreased calf circumference mediated the effects of high NT-proBNP and mortality risk, with an estimated relative effect size of 28.9%, while MNA-SF and calf circumference mediated the effect, with an estimated relative effect size of 25.3%. Conclusions NT-proBNP levels were associated with long-term mortality in hospitalized older patients. Moreover, the detrimental effects of NT-proBNP on survival were partly mediated by malnutrition and muscle loss. Natriuretic peptide Sarcopenia Malnutrition Hospitalization Oldest-old Figures Figure 1 Figure 2 Introduction Older hospitalized patients often suffer from malnutrition and muscle loss, which is associated with a poor quality of life and an increased mortality risk. Many factors (1–3) , including reduced food intake, the burden of chronic disease, systemic inflammation, and insulin resistance, are responsible for malnutrition and muscle loss in hospitalized older patients. Nutritional intervention combined with exercise training can help improve prognosis in older patients. Brain natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are important in the diagnosis and management of patients with heart failure (4) . Prior study also reported that higher circulating natriuretic peptide levels predicted mortality risk regardless of heart failure status (5) . Nevertheless, these findings bring to light further essential questions regarding the treatment strategies for patients who have raised plasma NT-proBNP levels but do not have heart failure. However, the mechanisms by which elevated BNP levels increase the risk of premature death, especially in general population, remain unclear. Recently, cardiac natriuretic peptides have been recognized as activators of browning in white adipose tissue and contribute to protein-energy wasting (6) . Meanwhile, higher circulating natriuretic peptide levels are associated with protein-energy wasting in patients on hemodialysis (7,8) and muscle loss in apparently healthy individuals (9) . Thus, we hypothesized that malnutrition and muscle loss may mediate the association between NT-proBNP levels and mortality. Therefore, the purpose of this study was to evaluate the association between NT-proBNP and long-term mortality in hospitalized oldest-old adults and to explore the mediating role of malnutrition and muscle loss. Materials and Methods Patient selection This was a prospective cohort study conducted in the Department of Geriatrics of an academic teaching hospital (Shanghai, China). Patients ≥ 80 years of age were screened between August 2017 and January 2018. Exclusion criteria were participants with terminal carcinomatous cachexia (clinical history), inability to communicate, bedridden or in wheelchairs, those receiving hemodialysis or peritoneal dialysis, acute severe infection, acute gastrointestinal bleeding, severe respiratory, liver, or heart failure, and those with incomplete comprehensive geriatric assessment data. For individuals with more than one admission to the geriatric unit, only the first admission during the study period was considered. A total of 381 participants were screened, and 21 participants were excluded (acute severe infection [n = 8]; severe respiratory, liver, or heart failure [n = 5]; acute gastrointestinal bleeding [n = 3]; incomplete data [n = 5]). Ultimately, 360 participants were included in the final analysis. This study was approved by our hospital's Ethics Committee. All participants provided written informed consent. Data collection and measurements In-hospital patients underwent a comprehensive geriatric assessment. Data on demographic and comorbid conditions were obtained from electronic medical records. Charlson comorbidity index (CCI) was calculated (10) . Baseline laboratory measurements during admission included serum albumin, prealbumin, serum creatinine, and NT-proBNP levels. The estimated glomerular filtration rate (eGFR) was calculated using CKD-EPI formula (11) . Anthropometric parameters included body weight, height, body mass index (BMI), mid arm circumference, waist circumference, abdominal skinfold thickness, calf circumference, and handgrip strength. Waist circumference was measured at the midpoint between the rib cage and the iliac crest. Mid arm circumference was measured with millimeter tape at the midpoint of the arm, between olecranon and acromion. Abdominal skinfold thickness was measured at the junction of the horizontal line of the umbilicus and the midline of the right clavicle using a skinfold caliper. Calf circumference refers to the measurement taken at the widest part of the right calf, typically at the fullest area below the knee. All these parameters were measured twice to obtain mean values. The dominant hand was chosen for three measurements to obtain maximum grip strength (WCS-100 electronic vibrometer, China). Definitions of exposure, mediator, and Outcome Serum NT-proBNP was measured using an electrochemiluminescence immunoassay (Elecsys proBNP II, Roche Diagnostics GmbH). Nutritional status was also assessed using the GNRI (12) and Mini Nutritional Assessment Short Form (MNA-SF; score 0–14) (13) . GNRI is derived from serum albumin, height, and weight, while MNA-SF assesses dietary intake, weight loss, mobility, acute illness, cognitive problems, and BMI. Calf circumference was also recorded. The primary outcome was long-term all-cause mortality, with data collected electronically from medical records and no missing primary outcome data. In-hospital mortality rates were recorded. Follow-up completed on January 1, 2022. GNRI (or MNA-SF) and calf circumference, key indices linked to nutritional status and muscle mass, are important determinants of long-term mortality. Studying these variables deepens understanding of the relationship between admission serum NT-proBNP levels and long-term mortality risk, contributing to more nuanced patient stratification. Statistical analyses Continuous data were summarized as mean ± standard deviation or median (interquartile range), while categorical data was presented as percentages. To explore the potential nonlinear relationship between NT-proBNP and mortality, restricted cubic splines were used for survival analysis, and Cox regression was employed to assess the association between NT-proBNP levels and mortality. Before entering the Cox regression model, log-transformed NT-proBNP values were evaluated as a continuous variable and in different tertiles. HRs and 95% confidence intervals (95% CIs) were determined through Cox proportional hazard models, considering baseline variables including age, sex, CCI, GNRI and eGFR. Subgroup analysis was conducted based on sex, BMI, diabetes mellitus history, eGFR levels, and severity of heart failure. Sensitivity analyses were also carried out by excluding participants with events occurring in the first 1–2 years or patients with cancer at baseline. In order to understand the mediating roles of the GNRI and calf circumference in the relationship between NT-proBNP and mortality, we established a two-part regression analysis framework, which includes mediator and outcome models (as shown in Fig. 1 ). The mediator model regressed potential mediating factors (GNRI and calf circumference) against NT-proBNP, while the outcome model estimated the association of mortality with NT-proBNP alongside the mediators. Regression coefficients reflecting the relationship of NT-proBNP with each mediator were obtained from these models. Indirect effects of NT-proBNP on mortality through each mediator were defined, and the total mortality effect attributable to NT-proBNP was calculated. All data were analyzed using SPSS version 22.0 software, and statistical significance was defined as a P-value < 0.05. Results Baseline characteristics Table 1 presents baseline patient characteristics by NT-proBNP tertiles. The study included 360 participants (median age: 87 [IQR 84–90] years), 88 (24.4%) of whom were women. Overall, 33.1% of patients had diabetes mellitus, and 85.0% had hypertension. NT-proBNP ranged from 12.6 pg/ml to 11930.0 pg/ml. Patients in the higher tertiles were significantly older, and had higher CCI scores, lower BMI, calf circumference, poorer handgrip strength and reduced serum albumin compared to patients in the lowest tertile. There were more likely to have lower GNRI and MNA-SF scores and to have increased risk of death when NT-proBNP was higher. Table 1 Baseline characteristics of hospitalized oldest-old patients according to NT-proBNP tertiles. Variables Total (n = 360) T1 (n = 120) T2 (n = 120) T3 (n = 120) NT-proBNP (pg/ml) 269.9 [115.5-638.4] 79.0 [52.0-116.3] 269.0 [195.9- 356.8] 1043.5[635.9–1894.0] log2-NT-proBNP (pg/ml) 8.16 ± 1.87 6.19 ± 0.78 8.04 ± 0.51 10.26 ± 1.16 Age (years) 87[84,90] 85 [82, 87] 88 [85, 90] 89 [85, 91] Female, n (%) 88(24.2%) 28 (23.3%) 30 (25.0%) 30 (25.0%) Comorbid conditions CCI scores 2[ 1 , 4 ] 2 [ 1 , 3 ] 3 [ 1 , 4 ] 3 [ 2 , 4 ] Diabetes, n (%) 119(33.1%) 34 (28.3%) 45 (37.5%) 40 (33.3%) Hypertension, n (%) 306(85.0%) 99 (82.5%) 106 (88.3%) 101 (84.2%) Anthropometric measure BMI (kg/m 2 ) 23.2 ± 3.6 23.92 ± 3.42 22.97 ± 3.48 22.62 ± 3.72 Mid arm circumference (cm) 24[22.5,26] 25.0 [23.0, 27.0] 24.0 [22.0, 26.0] 23.5 [21.0, 25.0] Waist circumference (cm) 89.1 ± 11.2 92.0 [85.0, 97.8] 90.0 [83.0, 96.0] 87.0 [79.0, 95.0] Skinfold thickness (mm) 18.7 ± 7.6 21.3 ± 6.91 18.54 ± 6.66 17.32 ± 7.64 Calf circumference (cm) 30[27,33] 32.0 [29.0, 34.0] 29.3 [27.0, 33.0] 28.5 [25.0, 31.0] Handgrip strength (kg) 18.93 ± 8.09 21.97 ± 7.78 17.47 ± 7.60 17.38 ± 8.06 Composite nutritional indices GNRI 100 [95, 103] 103 [100, 106] 98 [94, 103] 97 [91, 100] MNA-SF 12[ 9 , 13 ] 12 [ 11 , 13 ] 11 [ 9 , 12 ] 11 [ 6 , 12 ] Laboratory parameters Albumin, g/L 40[37,42] 41 [40, 43] 39[37, 42] 38 [35, 41] Prealbumin, g/L 200[165,231] 216 [190, 250] 201 [167, 230] 179 [141, 213] eGFR, ml/min/1.73 m 2 67.3[52.0,79.8] 72.2 [60.9, 80.5] 68.08 [49.8, 81.2] 60.9 [40.6, 78.1] Death Events 159 23 56 80 Death per 1000 person-yr 143.8 50.7 147.8 291.6 Continuous variables are expressed as median [interquartile range] or mean ± standard deviation and categorical variables as numbers (percentages). BMI, body mass index; CCI, Charlson comorbidity index; eGFR, estimated glomerular filtration rate; GNRI, geriatric nutritional risk index; MNA-SF, mini nutritional assessment short form; NRS-2002, nutrition risk screening 2002. Association between NT-ProBNP and long-term mortality During the median 4.1 years follow-up period, 159 patients died, totaling 1106 person-years. In total, 29 patients died during hospital admission. Compared to the first tertile (as a reference group), fully adjusted HRs for long-term mortality were 2.82 (95% CI, 1.74–4.59) and 5.34 (95% CI, 3.35–8.51) for the second and third tertiles, respectively (Table 2 ). A restricted multivariable cubic spline plot clearly showed a linear association between NT-proBNP levels and long mortality (Figure S1). Long-term mortality risk increased by 32% when per doubling increase in NT-proBNP (HR, 1.32 [95% CI, 1.20–1.46]). Table 2 Associations of NT-proBNP and mortality in hospitalized oldest-old patients. Per doubling increase Tertile 1 Tertile 2 Tertile 3 P for Trend Events, n (%) 23 (19.2) 56 (46.7) 80 (66.7) Model 1 1.46 (1.35, 1.59) 1.0 (reference) 2.82 (1.74, 4.59) 5.34 (3.35, 8.51) < 0.001 Model 2 1.38 (1.27, 1.51) 1.0 (reference) 1.802 (1.10, 2.98) 3.72 (2.30, 6.01) < 0.001 Model 3 1.32 (1.20, 1.46) 1.0 (reference) 1.15 (0.87, 2.41) 2.53 (1.51, 4.26) < 0.001 Model 1 was a univariate analysis; Model 2 was adjusted for age, sex, and Charlson Comorbidity Index; Model 3 was further adjusted for geriatric nutritional risk index and estimated glomerular filtration rate. A significant association between NT-proBNP level and long-term mortality was observed in all subgroups (Supplementary Table S1). There was a substantial subgroup interaction for sex ( P = 0.008), whereby the association was more evident in women than that in men (per doubling increase in NT-proBNP (HR 1.52 [95% CI, 1.21–1.93] and 1.27 [95% CI, 1.13–1.42], respectively). The exclusion of participants with events occurring during the first 1–2 years, or excluding participants with cancer at baseline, did not affect the relationship between NT-proBNP levels and mortality risk (Supplementary Table S2). Malnutrition and muscle loss mediated the association between NT-proBNP and mortality The potential mediation effects of malnutrition and muscle loss on the associations between NT-proBNP and mortality are described in Fig. 2 . A mediation analysis was performed, controlling for multiple confounders, including age, sex, BMI, CCI, and eGFR. The indirect effects of the GNRI score and calf circumference were 0.097 (95% CI, 0.026–0.188) and 0.061 (95% CI, 0.008–0.134), respectively (Figs. 2 A). Lower GNRI scores and a smaller calf circumference largely explained the effects of high proBNP levels as a predictor of increased mortality risk, with an estimated relative effect of 28.9% (17.7% and 11.2%, respectively). Significant results were also obtained when using the MNA-SF scores as nutritional assessment tools, with an estimated relative effect of 25.3% (Fig. 2 B). Discussion The major findings of our analysis were as followings: 1) increased NT-proBNP levels were associated with high long-term mortality in hospitalized older patients; 2) the detrimental effects of NT-proBNP on survival were partly mediated by malnutrition and muscle loss. By synthesizing these results, clinicians could better understand NT-proBNP levels and determine which patients face a greater risk; moreover, nutritional screening and intervention should be required for these patients with high circulating NT-proBNP. Circulating NT-proBNP is a crucial prognostic marker in patients with heart failure (14) . Additionally, the measurement of NT-proBNP could help identify individuals at high risk for cardiovascular disease (14) . In this study, NT-proBNP levels upon admission were associated with long-term mortality in hospitalized oldest-old adults. Importantly, these associations persisted after stratification by sex, BMI, history of diabetes mellitus, eGFR levels, and heart failure severity. Prior study also reported that higher BNP levels predicted a higher risk of death in heart failure patients as well as controls (5) . Thus, our findings support the role of NT-proBNP not only a diagnostic and prognostic biomarker for patients with heart failure, but also a good indicator of health status and prognosis in hospitalized oldest-old patients. Circulating natriuretic peptides released from the heart respond to cardiac wall stress and have diuretic, natriuretic, and vasodilatory effects (4) . However, the mechanism underlying the detrimental effects of elevated BNP levels on mortality risk remain poorly understood. In this study, of hospitalized oldest-old adults, increased NT-proBNP levels were associated with a poor nutritional status, as indicated by anthropometric measures, composite nutritional indices, and nutritional laboratory parameters. Participant with increased NT-proBNP levels had lower with muscle mass (lower calf circumference) and poorer handgrip strength. As previously reported, among 5,300 consecutively enrolled asymptomatic Asian participants, individuals with malnourishment had substantially higher NT-proBNP levels than the well-nourished group, regardless of the presence of obesity (15) . So based on the harmful effects of malnutrition and muscle loss on survival, we first tested their mediating role in the relationship between NT-proBNP levels and mortality risk. Fortunately, mediation analysis showed that the association between NT-proBNP and mortality was partly mediated by malnutrition (lower GNRI scores) and muscle mass loss (smaller calf circumference), with an estimated relative effect of 28.9%. Moreover, these effects remained when the GNRI scores were replaced by the MNA-SF scores for nutritional assessment. These findings may have significant clinical implications. Considering the mediating roles of malnutrition and muscle loss, preventive and treatment strategies, such as malnutrition screening, nutritional therapy, and exercise intervention, may be beneficial for hospitalized oldest-old patients with high NT-proBNP levels. Further prospective studies are required to clarify whether these strategies can improve patient outcomes. Our study had several limitations. First, NT-proBNP was only recorded at baseline, while dynamic monitoring of NT-proBNP might provide more useful evidence. Second, nutritional status was not evaluated using comprehensive assessment tools, such as the full Mini Nutritional Assessment or Subjective Global Assessment. However, the assessment tools used, including MNA-SF and NRS-2002, are common in clinical settings, were in acceptable agreement with a full assessment, and showed good performance in predicting clinical outcomes in hospitalized patients (12,16) . Third, muscle mass was not assessed using gold-standard techniques (dual-energy X-ray absorptiometry or computed tomography). Nonetheless, calf circumference is a recommended surrogate measure for muscle loss and independently predicted outcomes (17) . Finally, given that the patients were those hospitalized in a single center in China, the applicability of these findings to other patients in other countries needs to be further tested. Conclusion In hospitalized oldest-old patients, high NT-proBNP levels were significantly associated with poor survival. Moreover, malnutrition and muscle loss partly mediated the detrimental effects of NT-proBNP on survival. Nutritional screening and intervention may improve the prognosis of hospitalized oldest-old adults with high NT-proBNP level. Declarations Funding This work was sponsored by the Shanghai municipal health commission (grant number 20194y0347), and the Interdisciplinary Program of Shanghai Jiao Tong University (grant number YG2019QNB14). Conflicts of interest The authors declare that they have no competing financial interests. The results presented in this paper have not been published previously in whole or part, except in abstract format. Author's contributions Jun Tao: Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing - original draft; Xiaoyan Zhang: data curation, formal analysis, investigation, methodology, visualization; Niansong Wang: Conceptualization, funding acquisition, methodology, project administration, resources, supervision, writing - review & editing; Dongsheng Cheng: Conceptualization, formal analysis, funding acquisition, methodology, project administration, resources, supervision, writing - original draft, writing - review & editing. Ethical Approval Statement This research study was conducted in accordance with the ethical standards and guidelines established by the Ethics Committee of the Sixth People’s Hospital of Shanghai, which granted approval for this study under the ethical approval number 2016-141-(1). All procedures performed in the study involving human participants were in adherence to the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent to Publish All authors included in this manuscript have given their consent for the publication of this work. The authors confirm that the content of this article has not been published, or submitted for publication elsewhere. Furthermore, all authors concur with the submission of the manuscript to Nutrition Journal and there are no conflicts of interest to declare. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding authors upon reasonable request. References Martone AM, Bianchi L, Abete P, Bellelli G, Bo M, Cherubini A, Corica F, Di Bari M, Maggio M, Manca GM, et al. 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Available from: http://dx.doi.org/10.1016/j.nut.2009.07.010 Chen L-K, Woo J, Assantachai P, Auyeung T-W, Chou M-Y, Iijima K, Jang HC, Kang L, Kim M, Kim S, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Journal of the American Medical Directors Association [Internet] 2020;300-307.e2. Available from: http://dx.doi.org/10.1016/j.jamda.2019.12.012 Additional Declarations No competing interests reported. Supplementary Files Supportingfile.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3863523\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":267335823,\"identity\":\"de122294-26db-4a2c-b8e3-75abc1b7619a\",\"order_by\":0,\"name\":\"Jun Tao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Nephrology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jun\",\"middleName\":\"\",\"lastName\":\"Tao\",\"suffix\":\"\"},{\"id\":267335824,\"identity\":\"5fdbe2c8-9d69-4f99-aca3-370a7ee588af\",\"order_by\":1,\"name\":\"Xiaoyan Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Geriatrics, Shanghai JiaoTong University Affiliated Sixth People's Hospital, Shanghai, China\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiaoyan\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":267335825,\"identity\":\"e6980b14-4deb-4b9c-a95a-6c91b2f8b9f0\",\"order_by\":2,\"name\":\"Niansong Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Nephrology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Niansong\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":267335826,\"identity\":\"cce231c7-a574-4583-a8f6-dc6be9bb0d7e\",\"order_by\":3,\"name\":\"Dongsheng Cheng\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Department of Nephrology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, China\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Dongsheng\",\"middleName\":\"\",\"lastName\":\"Cheng\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-01-14 14:14:15\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3863523/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3863523/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":49768978,\"identity\":\"5833148b-5cf1-4cd9-9156-23972e6c8e0b\",\"added_by\":\"auto\",\"created_at\":\"2024-01-17 17:23:16\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":141732,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnderlying causal framework of the relationship between NT-proBNP and mortality. (The directed acyclic graph outlines a conceptual schema involving NT-proBNP (E), Malnutrition (M1), and Muscle Loss (M2) as indicated by GNRI and calf circumference, leading to Mortality (O). It includes key confounders such as age, sex, Charlson Comorbidity Index (CCI), and estimated glomerular filtration rate (eGFR). Dotted arrows suggest potential confounding, while solid arrows and dashed arrows represent indirect and direct effects of NT-proBNP, respectively. The schema also includes a vector from GNRI to calf circumference, reflecting the impact of malnutrition on muscle loss. All statistical analyses are based on this framework, with adjustments for variables such as age, sex, CCI and eGFR.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863523/v1/16849ac6aecee1f1fd6e3bfb.png\"},{\"id\":49768976,\"identity\":\"21e170ec-b5c4-4957-abec-628a29241569\",\"added_by\":\"auto\",\"created_at\":\"2024-01-17 17:23:16\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":164401,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMediation analysis exploring malnutrition (A. GNRI; B. MAN-SF) and muscle loss (calf circumference) as mediators of NT-proBNP induced high mortality in hospitalized oldest-old patients, adjusted for age, gender, Charlson comorbidity index, and estimated glomerular filtration rate.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863523/v1/a259ad66d2bfe42c7779b906.png\"},{\"id\":52993740,\"identity\":\"0022ae52-b662-4d8a-a9e3-8c27941e0fd4\",\"added_by\":\"auto\",\"created_at\":\"2024-03-19 12:50:59\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":412337,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863523/v1/df825b70-2c58-4cb7-9f78-0f36c72b5812.pdf\"},{\"id\":49768977,\"identity\":\"db589309-cd45-4880-b23e-aab816ff775a\",\"added_by\":\"auto\",\"created_at\":\"2024-01-17 17:23:16\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":1919867,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supportingfile.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3863523/v1/3b8c59ddaa00efe658c19787.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Malnutrition and Muscle Loss Mediate the Association between NT-proBNP and Mortality in Hospitalized Older Adults\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eOlder hospitalized patients often suffer from malnutrition and muscle loss, which is associated with a poor quality of life and an increased mortality risk. Many factors\\u003csup\\u003e(1\\u0026ndash;3)\\u003c/sup\\u003e, including reduced food intake, the burden of chronic disease, systemic inflammation, and insulin resistance, are responsible for malnutrition and muscle loss in hospitalized older patients. Nutritional intervention combined with exercise training can help improve prognosis in older patients.\\u003c/p\\u003e \\u003cp\\u003eBrain natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) are important in the diagnosis and management of patients with heart failure\\u003csup\\u003e(4)\\u003c/sup\\u003e. Prior study also reported that higher circulating natriuretic peptide levels predicted mortality risk regardless of heart failure status\\u003csup\\u003e(5)\\u003c/sup\\u003e. Nevertheless, these findings bring to light further essential questions regarding the treatment strategies for patients who have raised plasma NT-proBNP levels but do not have heart failure. However, the mechanisms by which elevated BNP levels increase the risk of premature death, especially in general population, remain unclear. Recently, cardiac natriuretic peptides have been recognized as activators of browning in white adipose tissue and contribute to protein-energy wasting\\u003csup\\u003e(6)\\u003c/sup\\u003e. Meanwhile, higher circulating natriuretic peptide levels are associated with protein-energy wasting in patients on hemodialysis\\u003csup\\u003e(7,8)\\u003c/sup\\u003e and muscle loss in apparently healthy individuals\\u003csup\\u003e(9)\\u003c/sup\\u003e. Thus, we hypothesized that malnutrition and muscle loss may mediate the association between NT-proBNP levels and mortality.\\u003c/p\\u003e \\u003cp\\u003eTherefore, the purpose of this study was to evaluate the association between NT-proBNP and long-term mortality in hospitalized oldest-old adults and to explore the mediating role of malnutrition and muscle loss.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePatient selection\\u003c/h2\\u003e \\u003cp\\u003eThis was a prospective cohort study conducted in the Department of Geriatrics of an academic teaching hospital (Shanghai, China). Patients\\u0026thinsp;\\u0026ge;\\u0026thinsp;80 years of age were screened between August 2017 and January 2018. Exclusion criteria were participants with terminal carcinomatous cachexia (clinical history), inability to communicate, bedridden or in wheelchairs, those receiving hemodialysis or peritoneal dialysis, acute severe infection, acute gastrointestinal bleeding, severe respiratory, liver, or heart failure, and those with incomplete comprehensive geriatric assessment data. For individuals with more than one admission to the geriatric unit, only the first admission during the study period was considered. A total of 381 participants were screened, and 21 participants were excluded (acute severe infection [n\\u0026thinsp;=\\u0026thinsp;8]; severe respiratory, liver, or heart failure [n\\u0026thinsp;=\\u0026thinsp;5]; acute gastrointestinal bleeding [n\\u0026thinsp;=\\u0026thinsp;3]; incomplete data [n\\u0026thinsp;=\\u0026thinsp;5]). Ultimately, 360 participants were included in the final analysis. This study was approved by our hospital's Ethics Committee. All participants provided written informed consent.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData collection and measurements\\u003c/h2\\u003e \\u003cp\\u003eIn-hospital patients underwent a comprehensive geriatric assessment. Data on demographic and comorbid conditions were obtained from electronic medical records. Charlson comorbidity index (CCI) was calculated\\u003csup\\u003e(10)\\u003c/sup\\u003e. Baseline laboratory measurements during admission included serum albumin, prealbumin, serum creatinine, and NT-proBNP levels. The estimated glomerular filtration rate (eGFR) was calculated using CKD-EPI formula\\u003csup\\u003e(11)\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eAnthropometric parameters included body weight, height, body mass index (BMI), mid arm circumference, waist circumference, abdominal skinfold thickness, calf circumference, and handgrip strength. Waist circumference was measured at the midpoint between the rib cage and the iliac crest. Mid arm circumference was measured with millimeter tape at the midpoint of the arm, between olecranon and acromion. Abdominal skinfold thickness was measured at the junction of the horizontal line of the umbilicus and the midline of the right clavicle using a skinfold caliper. Calf circumference refers to the measurement taken at the widest part of the right calf, typically at the fullest area below the knee. All these parameters were measured twice to obtain mean values. The dominant hand was chosen for three measurements to obtain maximum grip strength (WCS-100 electronic vibrometer, China).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDefinitions of exposure, mediator, and Outcome\\u003c/h2\\u003e \\u003cp\\u003eSerum NT-proBNP was measured using an electrochemiluminescence immunoassay (Elecsys proBNP II, Roche Diagnostics GmbH). Nutritional status was also assessed using the GNRI\\u003csup\\u003e(12)\\u003c/sup\\u003e and Mini Nutritional Assessment Short Form (MNA-SF; score 0\\u0026ndash;14)\\u003csup\\u003e(13)\\u003c/sup\\u003e. GNRI is derived from serum albumin, height, and weight, while MNA-SF assesses dietary intake, weight loss, mobility, acute illness, cognitive problems, and BMI. Calf circumference was also recorded.\\u003c/p\\u003e \\u003cp\\u003eThe primary outcome was long-term all-cause mortality, with data collected electronically from medical records and no missing primary outcome data. In-hospital mortality rates were recorded. Follow-up completed on January 1, 2022.\\u003c/p\\u003e \\u003cp\\u003eGNRI (or MNA-SF) and calf circumference, key indices linked to nutritional status and muscle mass, are important determinants of long-term mortality. Studying these variables deepens understanding of the relationship between admission serum NT-proBNP levels and long-term mortality risk, contributing to more nuanced patient stratification.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analyses\\u003c/h2\\u003e \\u003cp\\u003eContinuous data were summarized as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation or median (interquartile range), while categorical data was presented as percentages. To explore the potential nonlinear relationship between NT-proBNP and mortality, restricted cubic splines were used for survival analysis, and Cox regression was employed to assess the association between NT-proBNP levels and mortality. Before entering the Cox regression model, log-transformed NT-proBNP values were evaluated as a continuous variable and in different tertiles. HRs and 95% confidence intervals (95% CIs) were determined through Cox proportional hazard models, considering baseline variables including age, sex, CCI, GNRI and eGFR. Subgroup analysis was conducted based on sex, BMI, diabetes mellitus history, eGFR levels, and severity of heart failure. Sensitivity analyses were also carried out by excluding participants with events occurring in the first 1\\u0026ndash;2 years or patients with cancer at baseline.\\u003c/p\\u003e \\u003cp\\u003eIn order to understand the mediating roles of the GNRI and calf circumference in the relationship between NT-proBNP and mortality, we established a two-part regression analysis framework, which includes mediator and outcome models (as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The mediator model regressed potential mediating factors (GNRI and calf circumference) against NT-proBNP, while the outcome model estimated the association of mortality with NT-proBNP alongside the mediators. Regression coefficients reflecting the relationship of NT-proBNP with each mediator were obtained from these models. Indirect effects of NT-proBNP on mortality through each mediator were defined, and the total mortality effect attributable to NT-proBNP was calculated.\\u003c/p\\u003e \\u003cp\\u003eAll data were analyzed using SPSS version 22.0 software, and statistical significance was defined as a P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBaseline characteristics\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e presents baseline patient characteristics by NT-proBNP tertiles. The study included 360 participants (median age: 87 [IQR 84\\u0026ndash;90] years), 88 (24.4%) of whom were women. Overall, 33.1% of patients had diabetes mellitus, and 85.0% had hypertension. NT-proBNP ranged from 12.6 pg/ml to 11930.0 pg/ml.\\u003c/p\\u003e \\u003cp\\u003ePatients in the higher tertiles were significantly older, and had higher CCI scores, lower BMI, calf circumference, poorer handgrip strength and reduced serum albumin compared to patients in the lowest tertile. There were more likely to have lower GNRI and MNA-SF scores and to have increased risk of death when NT-proBNP was higher.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristics of hospitalized oldest-old patients according to NT-proBNP tertiles.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;360)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eT1\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;120)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eT2\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;120)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eT3\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;120)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNT-proBNP (pg/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e269.9 [115.5-638.4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79.0 [52.0-116.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e269.0 [195.9- 356.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1043.5[635.9\\u0026ndash;1894.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003elog2-NT-proBNP (pg/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e87[84,90]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e85 [82, 87]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e88 [85, 90]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e89 [85, 91]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e88(24.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28 (23.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30 (25.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30 (25.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eComorbid conditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCCI scores\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e119(33.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34 (28.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45 (37.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e40 (33.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e306(85.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e99 (82.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e106 (88.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e101 (84.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnthropometric measure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e22.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMid arm circumference (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24[22.5,26]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e25.0 [23.0, 27.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.0 [22.0, 26.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.5 [21.0, 25.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWaist circumference (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e89.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92.0 [85.0, 97.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e90.0 [83.0, 96.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e87.0 [79.0, 95.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSkinfold thickness (mm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e18.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCalf\\u0026nbsp;circumference (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30[27,33]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32.0 [29.0, 34.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e29.3 [27.0, 33.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.5 [25.0, 31.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHandgrip strength (kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eComposite nutritional indices\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGNRI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100 [95, 103]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103 [100, 106]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98 [94, 103]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e97 [91, 100]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMNA-SF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12 [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11 [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11 [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLaboratory parameters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAlbumin, g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40[37,42]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41 [40, 43]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39[37, 42]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38 [35, 41]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrealbumin, g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e200[165,231]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e216 [190, 250]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e201 [167, 230]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e179 [141, 213]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR, ml/min/1.73 m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e67.3[52.0,79.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e72.2 [60.9, 80.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e68.08 [49.8, 81.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60.9 [40.6, 78.1]\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDeath Events\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e159\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDeath per 1000 person-yr\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e143.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e147.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e291.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eContinuous variables are expressed as median [interquartile range] or mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation and categorical variables as numbers (percentages). BMI, body mass index; CCI, Charlson comorbidity index; eGFR, estimated glomerular filtration rate; GNRI, geriatric nutritional risk index; MNA-SF, mini nutritional assessment short form; NRS-2002, nutrition risk screening 2002.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eAssociation between NT-ProBNP and long-term mortality\\u003c/h3\\u003e\\n\\u003cp\\u003eDuring the median 4.1 years follow-up period, 159 patients died, totaling 1106 person-years. In total, 29 patients died during hospital admission. Compared to the first tertile (as a reference group), fully adjusted HRs for long-term mortality were 2.82 (95% CI, 1.74\\u0026ndash;4.59) and 5.34 (95% CI, 3.35\\u0026ndash;8.51) for the second and third tertiles, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). A restricted multivariable cubic spline plot clearly showed a linear association between NT-proBNP levels and long mortality (Figure S1). Long-term mortality risk increased by 32% when per doubling increase in NT-proBNP (HR, 1.32 [95% CI, 1.20\\u0026ndash;1.46]).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAssociations of NT-proBNP and mortality in hospitalized oldest-old patients.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePer doubling increase\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTertile 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTertile 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTertile 3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e for Trend\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEvents, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (19.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56 (46.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e80 (66.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.46 (1.35, 1.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.0 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.82 (1.74, 4.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.34 (3.35, 8.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.38 (1.27, 1.51)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.0 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.802 (1.10, 2.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.72 (2.30, 6.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.32 (1.20, 1.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.0 (reference)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.15 (0.87, 2.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.53 (1.51, 4.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eModel 1 was a univariate analysis; Model 2 was adjusted for age, sex, and Charlson Comorbidity Index; Model 3 was further adjusted for geriatric nutritional risk index and estimated glomerular filtration rate.\\u003c/p\\u003e \\u003cp\\u003eA significant association between NT-proBNP level and long-term mortality was observed in all subgroups (Supplementary Table S1). There was a substantial subgroup interaction for sex (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008), whereby the association was more evident in women than that in men (per doubling increase in NT-proBNP (HR 1.52 [95% CI, 1.21\\u0026ndash;1.93] and 1.27 [95% CI, 1.13\\u0026ndash;1.42], respectively).\\u003c/p\\u003e \\u003cp\\u003eThe exclusion of participants with events occurring during the first 1\\u0026ndash;2 years, or excluding participants with cancer at baseline, did not affect the relationship between NT-proBNP levels and mortality risk (Supplementary Table S2).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMalnutrition and muscle loss mediated the association between NT-proBNP and mortality\\u003c/h2\\u003e \\u003cp\\u003eThe potential mediation effects of malnutrition and muscle loss on the associations between NT-proBNP and mortality are described in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. A mediation analysis was performed, controlling for multiple confounders, including age, sex, BMI, CCI, and eGFR. The indirect effects of the GNRI score and calf circumference were 0.097 (95% CI, 0.026\\u0026ndash;0.188) and 0.061 (95% CI, 0.008\\u0026ndash;0.134), respectively (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Lower GNRI scores and a smaller calf circumference largely explained the effects of high proBNP levels as a predictor of increased mortality risk, with an estimated relative effect of 28.9% (17.7% and 11.2%, respectively). Significant results were also obtained when using the MNA-SF scores as nutritional assessment tools, with an estimated relative effect of 25.3% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe major findings of our analysis were as followings: 1) increased NT-proBNP levels were associated with high long-term mortality in hospitalized older patients; 2) the detrimental effects of NT-proBNP on survival were partly mediated by malnutrition and muscle loss. By synthesizing these results, clinicians could better understand NT-proBNP levels and determine which patients face a greater risk; moreover, nutritional screening and intervention should be required for these patients with high circulating NT-proBNP.\\u003c/p\\u003e \\u003cp\\u003eCirculating NT-proBNP is a crucial prognostic marker in patients with heart failure\\u003csup\\u003e(14)\\u003c/sup\\u003e. Additionally, the measurement of NT-proBNP could help identify individuals at high risk for cardiovascular disease\\u003csup\\u003e(14)\\u003c/sup\\u003e. In this study, NT-proBNP levels upon admission were associated with long-term mortality in hospitalized oldest-old adults. Importantly, these associations persisted after stratification by sex, BMI, history of diabetes mellitus, eGFR levels, and heart failure severity. Prior study also reported that higher BNP levels predicted a higher risk of death in heart failure patients as well as controls\\u003csup\\u003e(5)\\u003c/sup\\u003e. Thus, our findings support the role of NT-proBNP not only a diagnostic and prognostic biomarker for patients with heart failure, but also a good indicator of health status and prognosis in hospitalized oldest-old patients.\\u003c/p\\u003e \\u003cp\\u003eCirculating natriuretic peptides released from the heart respond to cardiac wall stress and have diuretic, natriuretic, and vasodilatory effects\\u003csup\\u003e(4)\\u003c/sup\\u003e. However, the mechanism underlying the detrimental effects of elevated BNP levels on mortality risk remain poorly understood. In this study, of hospitalized oldest-old adults, increased NT-proBNP levels were associated with a poor nutritional status, as indicated by anthropometric measures, composite nutritional indices, and nutritional laboratory parameters. Participant with increased NT-proBNP levels had lower with muscle mass (lower calf circumference) and poorer handgrip strength. As previously reported, among 5,300 consecutively enrolled asymptomatic Asian participants, individuals with malnourishment had substantially higher NT-proBNP levels than the well-nourished group, regardless of the presence of obesity\\u003csup\\u003e(15)\\u003c/sup\\u003e. So based on the harmful effects of malnutrition and muscle loss on survival, we first tested their mediating role in the relationship between NT-proBNP levels and mortality risk. Fortunately, mediation analysis showed that the association between NT-proBNP and mortality was partly mediated by malnutrition (lower GNRI scores) and muscle mass loss (smaller calf circumference), with an estimated relative effect of 28.9%. Moreover, these effects remained when the GNRI scores were replaced by the MNA-SF scores for nutritional assessment. These findings may have significant clinical implications. Considering the mediating roles of malnutrition and muscle loss, preventive and treatment strategies, such as malnutrition screening, nutritional therapy, and exercise intervention, may be beneficial for hospitalized oldest-old patients with high NT-proBNP levels. Further prospective studies are required to clarify whether these strategies can improve patient outcomes.\\u003c/p\\u003e \\u003cp\\u003eOur study had several limitations. First, NT-proBNP was only recorded at baseline, while dynamic monitoring of NT-proBNP might provide more useful evidence. Second, nutritional status was not evaluated using comprehensive assessment tools, such as the full Mini Nutritional Assessment or Subjective Global Assessment. However, the assessment tools used, including MNA-SF and NRS-2002, are common in clinical settings, were in acceptable agreement with a full assessment, and showed good performance in predicting clinical outcomes in hospitalized patients\\u003csup\\u003e(12,16)\\u003c/sup\\u003e. Third, muscle mass was not assessed using gold-standard techniques (dual-energy X-ray absorptiometry or computed tomography). Nonetheless, calf circumference is a recommended surrogate measure for muscle loss and independently predicted outcomes\\u003csup\\u003e(17)\\u003c/sup\\u003e. Finally, given that the patients were those hospitalized in a single center in China, the applicability of these findings to other patients in other countries needs to be further tested.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn hospitalized oldest-old patients, high NT-proBNP levels were significantly associated with poor survival. Moreover, malnutrition and muscle loss partly mediated the detrimental effects of NT-proBNP on survival. Nutritional screening and intervention may improve the prognosis of hospitalized oldest-old adults with high NT-proBNP level.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was sponsored by the Shanghai municipal health commission (grant number 20194y0347), and the Interdisciplinary Program of Shanghai Jiao Tong University (grant number YG2019QNB14).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing financial interests. The results presented in this paper have not been published previously in whole or part, except in abstract format.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor\\u0026apos;s contributions\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eJun Tao: Conceptualization,\\u0026nbsp; \\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;data curation, formal analysis, investigation, methodology, visualization, writing - original draft;\\u003c/p\\u003e\\n\\u003cp\\u003eXiaoyan Zhang: data curation, formal analysis, investigation, methodology, visualization;\\u003c/p\\u003e\\n\\u003cp\\u003eNiansong Wang: Conceptualization, funding acquisition, methodology, project administration, resources, supervision, writing - review \\u0026amp; editing;\\u003c/p\\u003e\\n\\u003cp\\u003eDongsheng Cheng: Conceptualization,\\u0026nbsp; \\u0026nbsp; formal analysis, funding acquisition, methodology, project administration, resources, supervision, writing - original draft, writing - review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Approval Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research study was conducted in accordance with the ethical standards and guidelines established by the Ethics Committee of the Sixth People\\u0026rsquo;s Hospital of Shanghai, which granted approval for this study under the ethical approval number 2016-141-(1). All procedures performed in the study involving human participants were in adherence to the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Publish\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors included in this manuscript have given their consent for the publication of this work. The authors confirm that the content of this article has not been published, or submitted for publication elsewhere. Furthermore, all authors concur with the submission of the manuscript to Nutrition Journal and there are no conflicts of interest to declare.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analyzed during the current study are available from the corresponding authors upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eMartone AM, Bianchi L, Abete P, Bellelli G, Bo M, Cherubini A, Corica F, Di Bari M, Maggio M, Manca GM, et al. The incidence of sarcopenia among hospitalized older patients: results from the Glisten study. Journal of Cachexia, Sarcopenia and Muscle [Internet] 2017;907\\u0026ndash;14. Available from: http://dx.doi.org/10.1002/jcsm.12224\\u003c/li\\u003e\\n\\u003cli\\u003eHu X, Zhang L, Wang H, Hao Q, Dong B, Yang M. Malnutrition-sarcopenia syndrome predicts mortality in hospitalized older patients. Scientific Reports [Internet] 2017; Available from: http://dx.doi.org/10.1038/s41598-017-03388-3\\u003c/li\\u003e\\n\\u003cli\\u003eXie L, Jiang J, Fu H, Zhang W, Yang L, Yang M. Malnutrition in Relation to Muscle Mass, Muscle Quality, and Muscle Strength in Hospitalized Older Adults. Journal of the American Medical Directors Association [Internet] 2022;722\\u0026ndash;8. Available from: http://dx.doi.org/10.1016/j.jamda.2021.11.025\\u003c/li\\u003e\\n\\u003cli\\u003eMueller C, McDonald K, de Boer RA, Maisel A, Cleland JGF, Kozhuharov N, Coats AJS, Metra M, Mebazaa A, Ruschitzka F, et al. Heart Failure Association of the European Society of Cardiology practical guidance on the use of natriuretic peptide concentrations. European Journal of Heart Failure [Internet] 2019;715\\u0026ndash;31. Available from: http://dx.doi.org/10.1002/ejhf.1494\\u003c/li\\u003e\\n\\u003cli\\u003eYork MK, Gupta DK, Reynolds CF, Farber-Eger E, Wells QS, Bachmann KN, Xu M, Harrell FE, Wang TJ. B-Type Natriuretic Peptide Levels and Mortality in Patients With and Without Heart Failure. Journal of the American College of Cardiology [Internet] 2018;2079\\u0026ndash;88. Available from: http://dx.doi.org/10.1016/j.jacc.2018.02.071\\u003c/li\\u003e\\n\\u003cli\\u003eLuce M, Barba C, Yi D, Mey A, Roussel D, Bres E, Benoit B, Pastural M, Granjon S, Szelag JC, et al. Accumulation of natriuretic peptides is associated with protein energy wasting and activation of browning in white adipose tissue in chronic kidney disease. Kidney International [Internet] 2020;663\\u0026ndash;72. Available from: http://dx.doi.org/10.1016/j.kint.2020.03.027\\u003c/li\\u003e\\n\\u003cli\\u003eIkeda M, Honda H, Takahashi K, Shishido K, Shibata T. N-Terminal Pro-B-Type Natriuretic Peptide as a Biomarker for Loss of Muscle Mass in Prevalent Hemodialysis Patients. PLOS ONE [Internet] 2016;e0166804. Available from: http://dx.doi.org/10.1371/journal.pone.0166804\\u003c/li\\u003e\\n\\u003cli\\u003eLuce M, Bres E, Yi D, Pastural M, Granjon S, Szelag JC, Laville M, Arkouche W, Bouchara A, Fouque D, et al. Natriuretic Peptides as Predictors of Protein-Energy Wasting in Hemodialysis Population. Journal of Renal Nutrition [Internet] 2022;234\\u0026ndash;42. Available from: http://dx.doi.org/10.1053/j.jrn.2021.03.002\\u003c/li\\u003e\\n\\u003cli\\u003eYamashita T, Kohara K, Tabara Y, Ochi M, Nagai T, Okada Y, Igase M, Miki T. Muscle Mass, Visceral Fat, and Plasma Levels of B-Type Natriuretic Peptide in Healthy Individuals (from the J-SHIPP Study). The American Journal of Cardiology [Internet] 2014;635\\u0026ndash;40. Available from: http://dx.doi.org/10.1016/j.amjcard.2014.05.050\\u003c/li\\u003e\\n\\u003cli\\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases [Internet] 1987;373\\u0026ndash;83. Available from: http://dx.doi.org/10.1016/0021-9681(87)90171-8\\u003c/li\\u003e\\n\\u003cli\\u003eLevey AS, Stevens LA, Schmid CH, Zhang Y (Lucy), Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, et al. A New Equation to Estimate Glomerular Filtration Rate. Annals of Internal Medicine [Internet] 2009;604. Available from: http://dx.doi.org/10.7326/0003-4819-150-9-200905050-00006\\u003c/li\\u003e\\n\\u003cli\\u003eBouillanne O, Morineau G, Dupont C, Coulombel I, Vincent J-P, Nicolis I, Benazeth S, Cynober L, Aussel C. Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. The American Journal of Clinical Nutrition [Internet] 2005;777\\u0026ndash;83. Available from: http://dx.doi.org/10.1093/ajcn/82.4.777\\u003c/li\\u003e\\n\\u003cli\\u003eRubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for Undernutrition in Geriatric Practice: Developing the Short-Form Mini-Nutritional Assessment (MNA-SF). The Journals of Gerontology Series A: Biological Sciences and Medical Sciences [Internet] 2001;M366\\u0026ndash;72. Available from: http://dx.doi.org/10.1093/gerona/56.6.m366\\u003c/li\\u003e\\n\\u003cli\\u003eHussain A, Sun W, Deswal A, de Lemos JA, McEvoy JW, Hoogeveen RC, Matsushita K, Aguilar D, Bozkurt B, Virani SS, et al. Association of NT-ProBNP, Blood Pressure, and Cardiovascular Events. Journal of the American College of Cardiology [Internet] 2021;559\\u0026ndash;71. Available from: http://dx.doi.org/10.1016/j.jacc.2020.11.063\\u003c/li\\u003e\\n\\u003cli\\u003eChien S-C, Chandramouli C, Lo C-I, Lin C-F, Sung K-T, Huang W-H, Lai Y-H, Yun C-H, Su C-H, Yeh H-I, et al. Associations of obesity and malnutrition with cardiac remodeling and cardiovascular outcomes in Asian adults: A cohort study. \\u003c/li\\u003e\\n\\u003cli\\u003eRaslan M, Gonzalez MC, Gon\\u0026ccedil;alves Dias MC, Nascimento M, Castro M, Marques P, Segatto S, Torrinhas RS, Cecconello I, Waitzberg DL. Comparison of nutritional risk screening tools for predicting clinical outcomes in hospitalized patients. Nutrition [Internet] 2010;721\\u0026ndash;6. Available from: http://dx.doi.org/10.1016/j.nut.2009.07.010\\u003c/li\\u003e\\n\\u003cli\\u003eChen L-K, Woo J, Assantachai P, Auyeung T-W, Chou M-Y, Iijima K, Jang HC, Kang L, Kim M, Kim S, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. Journal of the American Medical Directors Association [Internet] 2020;300-307.e2. Available from: http://dx.doi.org/10.1016/j.jamda.2019.12.012\\u003c/li\\u003e\\n\\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\":\"info@researchsquare.com\",\"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\":\"Natriuretic peptide, Sarcopenia, Malnutrition, Hospitalization, Oldest-old\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3863523/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3863523/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground \\u0026amp; Aims\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe purpose of this study was to assess the association between N-terminal prohormone of type B natriuretic peptide (NT-proBNP) and long-term mortality in hospitalized oldest-old adults and to explore the mediating role of malnutrition and muscle loss.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis prospective cohort study was conducted among 360 hospitalized patients ≥ 80 years of age (median age 87 [IQR 84–90] years, 24.4% women) in the Department of Geriatrics. The Geriatric Nutritional Risk Index (GNRI) and Mini Nutritional Assessment Short Form (MNA-SF) were used for nutritional assessment, while calf circumference was used as a measure of muscle mass. A Cox proportional hazard model was used to assess the relationship between NT-proBNP levels and mortality. Mediation analysis was used to explore the mediating effects of malnutrition and muscle loss.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe median follow-up was 4.1 years with 159 (44.1%) deaths. Mortality risk increased by 32% per 2-fold increase in NT-proBNP levels (full adjusted hazard ratio: 1.32 [95% CI, 1.20–1.46]). A mediation analysis showed that a lower GNRI score and decreased calf circumference mediated the effects of high NT-proBNP and mortality risk, with an estimated relative effect size of 28.9%, while MNA-SF and calf circumference mediated the effect, with an estimated relative effect size of 25.3%.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNT-proBNP levels were associated with long-term mortality in hospitalized older patients. Moreover, the detrimental effects of NT-proBNP on survival were partly mediated by malnutrition and muscle loss.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Malnutrition and Muscle Loss Mediate the Association between NT-proBNP and Mortality in Hospitalized Older Adults\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-01-17 17:23:11\",\"doi\":\"10.21203/rs.3.rs-3863523/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"b43ecaf3-90dc-40f5-ac21-601186298e7c\",\"owner\":[],\"postedDate\":\"January 17th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-03-19T12:42:53+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-01-17 17:23:11\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3863523\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3863523\",\"identity\":\"rs-3863523\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}