Abstract
Background
Chronic heart failure combined with sarcopenia is significantly associated with negative health outcomes in elderly patients. Therefore, early identification of sarcopenia risk in elderly patients with heart failure is crucial for their prognosis, however, there is currently no simple and practical predictive model available for clinicians.This study aimed to develop and externally validate a risk prediction model for sarcopenia in elderly patients with heart failure with preserved ejection fraction.
Methods
A retrospective study design was employed.A cohort of HFpEF patients from the Geriatrics Department of Ruijin Hospital was used as the development cohort (n = 272) for model construction and internal validation.Variables with significant differences in intergroup comparisons were initially screened, followed by variable compression using LASSO regression.A multivariable logistic regression analysis was ultimately performed to establish the prediction model.The discriminative ability and calibration of the model were assessed using ROC curve and calibration curve, respectively.Subsequently, an independent external validation cohort (n = 84) from Nursing Home in Changning District was used to validate the model’s generalizability and clinical utility through ROC curve analysis, calibration curve analysis, and decision curve analysis.
Results
The final model included five predictors: 1, 25OH-VitD3, BMI, NRS2002 score, handgrip strength, and homocysteine. In the development cohort, the model showed strong discriminative ability, with an AUC of 0.923 (95% CI: 0.892–0.954), and was well-calibrated. External validation confirmed its robust performance, yielding an AUC of 0.937 (95% CI: 0.890–0.984). The calibration curve indicated high agreement between predictions and observations, and decision curve analysis demonstrated a favorable net clinical benefit.
Conclusions
This study developed and validated the first risk prediction model for sarcopenia tailored to elderly HFpEF patients. The model performed excellently in both internal and external validation, enabling effective identification of high-risk individuals. It offers a practical quantitative tool for early screening and targeted intervention.
Keywords: Heart faliure with preserved ejection fraction, Sarcopenia, Risk prediction model, Nomogram, External validation
Introduction
Heart failure with preserved ejection fraction (HFpEF) constitutes the predominant variant of heart failure within the geriatric population, accounting for over 50% of cases and contributing to 27.8% of heart failure-related deaths [1–2]. Sarcopenia, a frequent comorbidity in heart failure, is characterized by a progressive and widespread reduction in muscle mass and strength, leading to numerous adverse outcomes [3–5]. Among Chinese adults over 60, sarcopenia prevalence ranges from 5.7% to 23.9% [5], while in chronic heart failure (CHF) patients of the same age, it rises to 31%-55.8% [6–7].The presence of sarcopenia exacerbates the clinical symptoms of chronic heart failure, further diminishing physical functions such as giant speed and hand grip strength, and significantly increasing readmission rates and all-cause mortality. Therefore, early identification of high-risk sarcopenia groups in elderly CHF patients is crucial for implementing comprehensive interventions to improve prognosis [8–9].
Because the symptoms of sarcopenia overlap with those of HFpEF, the identification of sarcopenia is often overlooked in clinical practice. Consequently, research targeting this patient population is of significant clinical importance, as it may pave the way for the development of more targeted interventions and improved patient outcomes.
Diagnosing sarcopenia presents challenges within the primary healthcare system due to the limited availability of measurement instruments like dual-energy X-ray absorptiometry(DXA) and bioimpedance analysis (BIA), which require standard interpretation by healthcare professionals. Previous studies [10–13] on the occurrence of community sarcopenia and its risk factors have yielded inconsistent results and remain highly controversial. Meta-analyses have mostly focused on specific influencing factors, such as cognitive function, smoking, and drinking, and most of the included studies are cross-sectional. Similarly, the risk factors for sarcopenia in elderly patients with HFpEF are also not very clear. despite the high prevalence and clinical impact of sarcopenia in this population, there is currently a lack of simple, integrated prediction tools specifically designed for screening sarcopenia in elderly HFpEF patients. To address these challenges, this investigation aims to establish and confirm a trustworthy nomogram model for predicting sarcopenia risk in elderly patients with HFpEF [14]. Through this model, we aim to identify high-risk sarcopenia populations in elderly HFpEF patients, enabling early screening and intervention to slow sarcopenia progression and improve patient outcomes.
Methods
Study population
The study comprised two independent cohorts: a development cohort and an external validation cohort.
The development cohort was consecutively enrolled from the Department of Geriatrics, Ruijin Hospital, between January 2018 and December 2023. Eligible participants were aged 60 years or older, capable of providing informed consent, and able to cooperate with comprehensive assessments including gait speed, handgrip strength, and bioelectrical impedance analysis (BIA). Additionally, participants were required to be available for follow-up visits and have a heart function classification of New York Heart Association (NYHA) class II to III. Exclusion criteria included incomplete medical histories or laboratory data, severe audio-visual impairment, severe dementia, active mental illness, or unstable acute medical conditions.
The external validation cohort was independently recruited from Shanghai RenShouTang WenJin Nursing Home in the Changning District during the same period. The same eligibility and exclusion criteria applied to this cohort to ensure consistency.
This study aimed to develop a cross-sectional screening model to identify patients at concurrent risk of sarcopenia.
Ethics approval and consent to participate
This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (Approval No: KY2021-108). Written informed consent was obtained from all participants or their legal guardians prior to their inclusion in the study.
Consent for publication
Not applicable. No individual person’s data or images are presented.
Clinical trial registration
Clinical trial number: not applicable.
Sample size calculation
The sample size for the development cohort was determined based on the widely accepted Events Per Variable (EPV) criterion in clinical prediction model development. The incidence of sarcopenia in the HFpEF training cohort was 39.7%. With five predictor variables planned for inclusion in the final multivariable logistic regression model, and to ensure model stability and prevent overfitting, an EPV value of 10 was targeted. The minimum number of required events was calculated as follows:
 |
Data collection and definitions
Participants’ clinical data encompassed gender, age, weight, height and body mass index (BMI). Health behaviors like current smoking, alcohol use, exercise habits, and comorbidities were documented. Current smoking and alcohol use were defined as consuming tobacco or alcohol more than once monthly in the past year. Comorbidities, including hypertension (HTN), diabetes mellitus (DM) and coronary heart disease (CHD) were diagnosed by medical professionals. Clinical laboratory parameters assessed included Blood routine, liver and kidney function, electrolytes, coagulation function, blood glucose, blood lipid, myocardial protein, etc. These tests were conducted at Ruijin Hospital using standardized protocols. The Beckman AU-5800 analyzer was utilized to assess lipid profiles, fasting blood glucose and liver and kidney function. Using InBody S10(InBody Co., Ltd, Korea) equipment by bioelectrical impedance analysis (BIA) measurement of body composition [15]. BIA is a non-invasive technique that assesses the electrical impedance of different body tissues to determine body composition. An electronic hand dynamometer (CAMRY EH101, Guangdong, China) was used to evaluate hand grip strength. Participants were instructed to maintain an upright posture, either standing or sitting, with feet shoulder-width apart and hands hanging freely at their sides, ensuring the dynamometer did not touch any clothing. Maximum grip strength for each hand was recorded after two separate measurements. Using a 6-meter walk test to measure gait speed, with two trials conducted and the average speed recorded. Professional medical staff conducted body composition analysis, grip strength, and gait speed measurements. In addition, Medical professionals employed comprehensive geriatric assessment scales, including the NRS2002 nutritional screening and FRAIL scales to assess patients’ nutritional risk and frailty levels respectively [16–18].
The 2019 AWGS criteria served as the benchmark for diagnosing sarcopenia, encompassing several key components: (1) reduced appendicular skeletal muscle mass (ASM), with a threshold of BIA elow 5.7 kg/m² for females and below 7.0 kg/m² for males; (2) diminished muscle strength, reflected by grip strength below 18 kg for females and below 28 kg for males; (3) impaired mobility, indicated by a walking speed (6 m walk) of less than 1.0 m/s. A diagnosis of sarcopenia necessitates low ASM in conjunction with either reduced muscular strength or impaired mobility.
The European Society of Cardiology 2019 criteria served as the diagnostic standard for HFpEF. Diagnosis required: 1) documented signs and/or symptoms of heart failure; 2) left ventricular ejection fraction (LVEF) ≥ 50% as measured by echocardiography; and 3) elevated levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP ≥ 125 pg/mL) and/or objective evidence of diastolic dysfunction/structural changes (e.g., average E/e’ ≥13). All echocardiograms were performed and interpreted by experienced cardiology sonographers and physicians to ensure diagnostic accuracy [19].
Statistical analysis
All statistical analyses were performed with the use of R software (version 4.3.2; www.R-project.org). For normally distributed data, results are presented as mean ± SD and compared between groups using independent sample t-tests. Continuous data with non-normal distribution are presented as median (25th − 75th percentiles) and were compared using the Mann-Whitney U test. Categorical variables are presented as percentages and were compared using the chi-square test. Variables with significant differences in univariate analyses were subsequently included in the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis to reduce dimensionality and select potential predictors. LASSO was employed as it effectively handles multicollinearity among variables and performs feature selection by penalizing the coefficients of less contributive predictors, thereby enhancing model generalizability. Variables selected by LASSO regression were then entered into a multivariable logistic regression analysis to identify independent risk factors and to develop the final predictive model for sarcopenia risk in HFpEF patients. This two-stage approach combines the strengths of machine learning for variable refinement with traditional regression for interpretable clinical parameter estimation. A nomogram was constructed based on the final logistic regression model to visualize the prediction model. The model’s discriminative ability was evaluated using receiver operating characteristic (ROC) curve analysis and expressed as the area under the curve (AUC) with 95% confidence intervals (CI). The model was internally validated using bootstrapping with 1,000 resamples to obtain calibrated performance metrics. For external validation, the model’s performance was evaluated in the independent validation cohort from community health centers using ROC curve analysis, calibration curves, and decision curve analysis (DCA) to comprehensively assess its discriminative ability, calibration, and clinical utility.A p-value of less than 0.05 was deemed indicative of statistical significance for comparative analyses (Fig. 1).
Fig. 1.
Flowchart of the study
Results
This study comprised two independent cohorts: a development cohort from Ruijin Hospital (RJ, n = 272) and an external validation cohort from Shanghai RenShouTang WenJin Nursing Home in Changning District (NH, n = 84). The incidence rate was 39.7% in the RJ cohort and 48.8% in the NH cohort.
In the RJ development cohort, the following variables showed significant differences between groups (P < 0.05): PAB, URIC, EGFR, GRIP, BMI, AGE, 1,25OH-VitD3, THCY, ALB, TG, HDL, NA, SCR, FINS, LA, SPEED, FRAIL, CFS, MORSE, SPPB, SSRS, NRS2002, APPETITE, EXERCISE, HTN.
In the SQ validation cohort, the following variables showed significant differences between groups (P < 0.05): PAB, GRIP, BMI, 1, 25OH-VitD3, THCY, ALB, LA, SPEED, SPPB, NRS2002, SMOKE, ALCOHOL, APPETITE, EXERCISE, EDUCATION (Table 1).
Table 1.
Baseline characteristics of the derivation and validation cohorts
| Variables | Internal Group(RJ) | External Group(NH) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Total (n = 272) | 0 (n = 164) | 1 (n = 108) | Statistic | P | Total (n = 84) | 0 (n = 41) | 1 (n = 43) | Statistic | P | |
| HB, Mean ± SD | 121.45 ± 21.67 | 123.36 ± 22.96 | 118.56 ± 19.30 | t = 1.80 | 0.074 | 109.30 ± 16.88 | 111.07 ± 16.58 | 107.60 ± 17.18 | t = 0.94 | 0.350 |
| PAB, Mean ± SD | 189.12 ± 56.18 | 196.82 ± 55.68 | 177.43 ± 55.16 | t = 2.82 | 0.005 | 153.85 ± 44.58 | 172.22 ± 35.16 | 136.33 ± 45.90 | t = 4.01 | < 0.001 |
| URIC, Mean ± SD | 326.77 ± 96.70 | 341.54 ± 98.01 | 304.34 ± 90.59 | t = 3.15 | 0.002 | 301.18 ± 109.56 | 306.80 ± 111.79 | 295.81 ± 108.43 | t = 0.46 | 0.649 |
| EGFR, Mean ± SD | 64.27 ± 17.88 | 62.02 ± 18.98 | 67.68 ± 15.53 | t=-2.58 | 0.010 | 67.84 ± 28.35 | 70.88 ± 28.84 | 64.95 ± 27.90 | t = 0.96 | 0.341 |
| GRIP, Mean ± SD | 25.62 ± 8.70 | 28.87 ± 8.68 | 20.67 ± 6.01 | t = 9.20 | < 0.001 | 20.34 ± 8.97 | 24.17 ± 8.55 | 16.69 ± 7.83 | t = 4.18 | < 0.001 |
| BMI, Mean ± SD | 24.04 ± 3.65 | 25.62 ± 2.94 | 21.63 ± 3.32 | t = 10.40 | < 0.001 | 22.93 ± 3.92 | 23.87 ± 4.71 | 22.03 ± 2.73 | t = 2.20 | 0.030 |
| AGE, M (Q₁, Q₃) | 88.00 (83.00, 93.00) | 87.00 (81.00, 91.00) | 90.00 (86.00, 95.00) | Z=-4.13 | < 0.001 | 88.00 (84.00, 92.00) | 89.00 (84.00, 91.00) | 88.00 (84.00, 93.00) | Z=-0.62 | 0.536 |
| 1, 25OH-VitD3, M (Q₁, Q₃) | 44.52 (32.70, 56.47) | 49.58 (37.81, 58.63) | 36.24 (25.98, 48.50) | Z=-5.12 | < 0.001 | 25.70 (19.76, 34.46) | 30.08 (24.14, 36.14) | 21.98 (18.01, 30.54) | Z=-3.15 | 0.002 |
| ALT, M (Q₁, Q₃) | 14.00 (10.00, 20.00) | 15.00 (10.00, 21.00) | 13.00 (10.00, 18.00) | Z=-1.62 | 0.105 | 11.50 (8.00, 23.00) | 12.00 (8.00, 21.00) | 11.00 (8.00, 24.00) | Z=-0.08 | 0.939 |
| AST, M (Q₁, Q₃) | 21.00 (17.00, 26.00) | 20.00 (17.00, 25.00) | 21.00 (17.00, 26.00) | Z=-1.12 | 0.264 | 16.00 (12.00, 23.00) | 15.00 (12.00, 22.00) | 17.00 (13.00, 24.00) | Z=-0.26 | 0.791 |
| THCY, M (Q₁, Q₃) | 12.80 (10.30, 17.97) | 11.35 (9.60, 13.90) | 17.85 (11.93, 22.15) | Z=-6.71 | < 0.001 | 17.40 (13.00, 22.42) | 13.40 (11.30, 16.10) | 21.80 (18.50, 23.80) | Z=-5.88 | < 0.001 |
| ALB, M (Q₁, Q₃) | 37.00 (33.00, 40.00) | 38.00 (34.00, 40.00) | 35.50 (32.00, 39.00) | Z=-3.43 | < 0.001 | 31.00 (28.00, 34.00) | 33.00 (29.00, 36.00) | 29.00 (27.00, 33.00) | Z=-2.86 | 0.004 |
| TG, M (Q₁, Q₃) | 1.03 (0.75, 1.33) | 1.08 (0.83, 1.42) | 0.92 (0.63, 1.20) | Z=-3.27 | 0.001 | 1.07 (0.83, 1.49) | 1.04 (0.83, 1.59) | 1.08 (0.81, 1.42) | Z=-0.09 | 0.929 |
| TC, M (Q₁, Q₃) | 3.71 (3.16, 4.38) | 3.79 (3.22, 4.41) | 3.64 (3.00, 4.36) | Z=-1.25 | 0.210 | 3.66 (2.99, 4.33) | 3.47 (2.79, 4.80) | 3.67 (3.17, 4.08) | Z=-0.43 | 0.671 |
| HDL, M (Q₁, Q₃) | 1.22 (1.02, 1.42) | 1.17 (1.00, 1.36) | 1.34 (1.05, 1.53) | Z=-3.03 | 0.002 | 1.09 (0.91, 1.29) | 1.15 (0.91, 1.32) | 1.02 (0.91, 1.22) | Z=-1.16 | 0.245 |
| LDL, M (Q₁, Q₃) | 2.11 (1.69, 2.70) | 2.13 (1.73, 2.73) | 2.03 (1.56, 2.61) | Z=-1.36 | 0.173 | 2.25 (1.63, 2.98) | 2.65 (1.49, 3.17) | 2.20 (1.75, 2.56) | Z=-0.70 | 0.482 |
| K, M (Q₁, Q₃) | 3.95 (3.65, 4.22) | 3.90 (3.62, 4.22) | 3.96 (3.75, 4.21) | Z=-0.82 | 0.413 | 4.45 (3.98, 4.82) | 4.41 (3.91, 4.81) | 4.47 (4.05, 4.83) | Z=-0.89 | 0.376 |
| NA, M (Q₁, Q₃) | 140.50 (138.00, 143.00) | 141.00 (138.00, 143.00) | 140.00 (137.75, 142.00) | Z=-2.05 | 0.040 | 138.50 (136.00, 141.00) | 138.00 (136.00, 141.00) | 139.00 (136.00, 141.00) | Z=-0.26 | 0.798 |
| CL, M (Q₁, Q₃) | 104.00 (102.00, 106.00) | 104.00 (102.00, 107.00) | 104.00 (102.00, 106.00) | Z=-1.55 | 0.122 | 102.00 (99.00, 106.00) | 102.00 (99.00, 105.00) | 102.00 (99.00, 107.50) | Z=-0.24 | 0.812 |
| CA, M (Q₁, Q₃) | 2.19 (2.11, 2.26) | 2.20 (2.11, 2.26) | 2.19 (2.11, 2.25) | Z=-0.91 | 0.365 | 2.16 (2.03, 2.22) | 2.15 (2.01, 2.24) | 2.16 (2.05, 2.21) | Z=-0.15 | 0.883 |
| SCR, M (Q₁, Q₃) | 87.00 (76.00, 105.25) | 91.00 (79.00, 111.00) | 81.50 (69.75, 100.00) | Z=-3.60 | < 0.001 | 77.50 (63.00, 99.00) | 74.00 (58.00, 96.00) | 82.00 (67.00, 99.50) | Z=-1.14 | 0.254 |
| FBG, M (Q₁, Q₃) | 5.75 (5.30, 6.70) | 5.81 (5.33, 6.74) | 5.71 (5.22, 6.51) | Z=-0.49 | 0.624 | 5.37 (4.88, 7.37) | 5.38 (4.96, 6.76) | 5.34 (4.81, 7.95) | Z=-0.06 | 0.954 |
| PBG, M (Q₁, Q₃) | 8.11 (6.96, 10.32) | 8.29 (6.91, 10.32) | 7.84 (7.13, 10.33) | Z=-0.07 | 0.945 | 9.09 (7.21, 12.72) | 8.32 (6.92, 12.72) | 9.96 (7.42, 12.72) | Z=-1.02 | 0.305 |
| FINS, M (Q₁, Q₃) | 8.68 (6.08, 12.74) | 9.23 (7.06, 13.83) | 7.69 (5.22, 12.28) | Z=-2.66 | 0.008 | 7.34 (5.46, 11.36) | 6.39 (5.29, 9.18) | 7.63 (5.67, 14.49) | Z=-1.21 | 0.227 |
| HBA1C, M (Q₁, Q₃) | 6.00 (5.60, 6.50) | 6.00 (5.60, 6.53) | 5.80 (5.50, 6.20) | Z=-1.78 | 0.075 | 6.40 (5.47, 7.60) | 6.00 (5.40, 7.20) | 6.40 (5.60, 8.95) | Z=-1.52 | 0.128 |
| CRP, M (Q₁, Q₃) | 2.77 (1.00, 12.00) | 2.79 (1.00, 10.85) | 2.65 (0.92, 12.29) | Z=-0.20 | 0.839 | 10.00 (4.75, 23.00) | 8.00 (4.00, 18.00) | 13.00 (5.50, 26.00) | Z=-1.59 | 0.112 |
| BNP, M (Q₁, Q₃) | 432.85 (195.35, 862.30) | 392.90 (187.00, 836.50) | 467.45 (212.53, 883.62) | Z=-0.95 | 0.340 | 886.50 (368.75, 1964.25) | 1183.00 (379.00, 2428.00) | 684.00 (338.50, 1567.00) | Z=-1.23 | 0.220 |
| LA, M (Q₁, Q₃) | 41.00 (38.00, 43.25) | 41.00 (39.00, 44.00) | 40.00 (36.75, 43.00) | Z=-2.31 | 0.021 | 41.00 (38.00, 43.25) | 39.00 (38.00, 41.00) | 42.00 (40.50, 44.00) | Z=-4.18 | < 0.001 |
| SPEED, M (Q₁, Q₃) | 0.86 (0.57, 1.12) | 1.02 (0.74, 1.21) | 0.64 (0.44, 0.88) | Z=-6.40 | < 0.001 | 0.46 (0.21, 0.86) | 0.77 (0.53, 1.03) | 0.30 (0.00, 0.45) | Z=-4.67 | < 0.001 |
| FRAIL, M (Q₁, Q₃) | 2.00 (1.00, 3.00) | 2.00 (1.00, 2.00) | 2.00 (1.00, 3.00) | Z=-3.39 | < 0.001 | 2.00 (1.00, 3.00) | 2.00 (1.00, 3.00) | 2.00 (1.00, 3.00) | Z=-1.08 | 0.282 |
| CFS, M (Q₁, Q₃) | 4.00 (3.00, 5.00) | 3.00 (3.00, 5.00) | 4.00 (3.00, 5.00) | Z=-3.55 | < 0.001 | 5.00 (3.00, 5.00) | 4.00 (3.00, 5.00) | 5.00 (4.00, 5.00) | Z=-1.30 | 0.193 |
| MORSE, M (Q₁, Q₃) | 42.50 (25.00, 65.00) | 40.00 (25.00, 50.00) | 50.00 (40.00, 70.00) | Z=-4.41 | < 0.001 | 50.00 (40.00, 70.00) | 50.00 (35.00, 65.00) | 55.00 (40.00, 77.50) | Z=-1.20 | 0.229 |
| SPPB, M (Q₁, Q₃) | 10.00 (6.00, 12.00) | 11.00 (8.00, 12.00) | 8.00 (5.00, 10.00) | Z=-5.82 | < 0.001 | 8.00 (5.00, 10.00) | 9.00 (6.00, 11.00) | 7.00 (4.00, 9.50) | Z=-2.06 | 0.040 |
| SSRS, M (Q₁, Q₃) | 39.00 (32.00, 45.00) | 39.00 (33.00, 45.00) | 37.00 (30.75, 42.25) | Z=-2.04 | 0.042 | 36.00 (30.75, 40.25) | 38.00 (31.00, 41.00) | 35.00 (28.50, 40.00) | Z=-1.23 | 0.217 |
| NRS2002, M (Q₁, Q₃) | 2.00 (2.00, 2.00) | 2.00 (1.00, 2.00) | 2.00 (2.00, 3.00) | Z=-7.63 | < 0.001 | 2.00 (2.00, 4.00) | 2.00 (2.00, 2.00) | 4.00 (2.00, 5.00) | Z=-5.62 | < 0.001 |
| SEX, n(%) | χ²=0.21 | 0.650 | χ²=1.22 | 0.270 | ||||||
| F | 42 (15.44) | 24 (14.63) | 18 (16.67) | 44 (52.38) | 24 (58.54) | 20 (46.51) | ||||
| M | 230 (84.56) | 140 (85.37) | 90 (83.33) | 40 (47.62) | 17 (41.46) | 23 (53.49) | ||||
| SMOKE, n(%) | χ²=1.33 | 0.249 | χ²=6.17 | 0.013 | ||||||
| NO | 192 (70.59) | 120 (73.17) | 72 (66.67) | 59 (70.24) | 34 (82.93) | 25 (58.14) | ||||
| YES | 80 (29.41) | 44 (26.83) | 36 (33.33) | 25 (29.76) | 7 (17.07) | 18 (41.86) | ||||
| ALCOHOL, n(%) | χ²=0.07 | 0.788 | χ²=6.07 | 0.014 | ||||||
| NO | 241 (88.60) | 146 (89.02) | 95 (87.96) | 69 (82.14) | 38 (92.68) | 31 (72.09) | ||||
| YES | 31 (11.40) | 18 (10.98) | 13 (12.04) | 15 (17.86) | 3 (7.32) | 12 (27.91) | ||||
| APPETITE, n(%) | χ²=83.68 | < 0.001 | χ²=21.16 | < 0.001 | ||||||
| NO | 164 (60.29) | 135 (82.32) | 29 (26.85) | 36 (42.86) | 28 (68.29) | 8 (18.60) | ||||
| YES | 108 (39.71) | 29 (17.68) | 79 (73.15) | 48 (57.14) | 13 (31.71) | 35 (81.40) | ||||
| EXERCISE, n(%) | χ²=56.02 | < 0.001 | χ²=9.66 | 0.002 | ||||||
| NO | 139 (51.10) | 114 (69.51) | 25 (23.15) | 22 (26.19) | 17 (41.46) | 5 (11.63) | ||||
| YES | 133 (48.90) | 50 (30.49) | 83 (76.85) | 62 (73.81) | 24 (58.54) | 38 (88.37) | ||||
| EDUCATION, n(%) | χ²=0.69 | 0.708 | χ²=8.06 | 0.018 | ||||||
| 0 | 11 (4.04) | 6 (3.66) | 5 (4.63) | 10 (11.90) | 2 (4.88) | 8 (18.60) | ||||
| 1 | 50 (18.38) | 28 (17.07) | 22 (20.37) | 23 (27.38) | 8 (19.51) | 15 (34.88) | ||||
| 2 | 211 (77.57) | 130 (79.27) | 81 (75.00) | 51 (60.71) | 31 (75.61) | 20 (46.51) | ||||
| MARRIAGE, n(%) | χ²=0.63 | 0.427 | χ²=0.41 | 0.524 | ||||||
| NO | 138 (50.74) | 80 (48.78) | 58 (53.70) | 38 (45.24) | 20 (48.78) | 18 (41.86) | ||||
| YES | 134 (49.26) | 84 (51.22) | 50 (46.30) | 46 (54.76) | 21 (51.22) | 25 (58.14) | ||||
| HTN, n(%) | χ²=13.98 | < 0.001 | χ²=0.01 | 0.916 | ||||||
| NO | 35 (12.87) | 11 (6.71) | 24 (22.22) | 16 (19.05) | 8 (19.51) | 8 (18.60) | ||||
| YES | 237 (87.13) | 153 (93.29) | 84 (77.78) | 68 (80.95) | 33 (80.49) | 35 (81.40) | ||||
| DM, n(%) | χ²=3.33 | 0.068 | χ²=1.81 | 0.179 | ||||||
| NO | 158 (58.09) | 88 (53.66) | 70 (64.81) | 47 (55.95) | 26 (63.41) | 21 (48.84) | ||||
| YES | 114 (41.91) | 76 (46.34) | 38 (35.19) | 37 (44.05) | 15 (36.59) | 22 (51.16) | ||||
| CHD, n(%) | χ²=1.31 | 0.252 | χ²=3.04 | 0.081 | ||||||
| NO | 204 (75.00) | 119 (72.56) | 85 (78.70) | 58 (69.05) | 32 (78.05) | 26 (60.47) | ||||
| YES | 68 (25.00) | 45 (27.44) | 23 (21.30) | 26 (30.95) | 9 (21.95) | 17 (39.53) | ||||
t t-test, Z: Mann-Whitney test, χ²: Chi-square test
SD standard deviation, M Median, Q₁ 1st Quartile, Q₃ 3st Quartile
These statistically significant variables from the univariate analyses in each cohort were subsequently included for further variable selection using LASSO regression.
LASSO regression analysis was employed to identify 14 significant predictors with non-zero coefficients: HTN, SSRS, HDL, SCR, THCY, SPEED, GRIP, BMI, EXERCISE, 1, 25OH-VitD3, PAB, CSF09, MORSE, NRS2002 (Fig. 2).
Fig. 2.
Lasso regression analysis and tenfold cross validation were used to select predictors. Tuning parameters (lambda) were selected according to the 1-SE criterion (right dashed line) and minimum criterion (left dashed line) in lasso regression. A coefficient profile plot was generated against the log (lambda) sequence. In this research, the selection of predictors followed the 1-SE criterion (right dashed line), resulting in the selection of five nonzero coefficients
According to the comparative analysis of basic information and LASSO regression analysis, a multiple Logistic regression analysis model was established to screen out five indicators closely related to sarcopenia, including 1, 25OH-VitD3, BMI, NRS2002, THCY and GRIP. Notably, handgrip strength (GRIP) was retained as a key component due to its clinical feasibility and established role as a primary indicator of muscle function, aligning with the goal of creating a pragmatic screening tool. The findings are presented in the form of a nomogram (Fig. 3).
Fig. 3.
Nomogram and predictors of sarcopenia in elderly patients with HFpEF
Within the training cohort, the predictive model demonstrated excellent discriminative ability with an AUC of 0.923 (95% CI: 0.892–0.954). The performance was further validated in the external validation cohort, where the model achieved an AUC of 0.937 (95% CI: 0.890–0.984) (Fig. 4). The calibration curve demonstrated excellent agreement between the nomogram-predicted probability and actual observed outcomes in both cohorts (Fig. 5). Furthermore, decision curve analysis indicated a favorable clinical net benefit across a reasonable threshold probability range, supporting the practical utility of the model in clinical settings (Fig. 6).
Fig. 4.
ROC curve and 95% confidence interval of internal validation and external validation. The model demonstrated excellent discrimination, with an AUC of 0.923 (95% CI: 0.892–0.954) in the internal cohort and 0.937 (95% CI: 0.890–0.984) in the external cohort
Fig. 5.
The calibration curves of internal validation and external validationof the prediction model are depicted. The model showed good calibration in the internal validation cohort (slope = 0.931, intercept = − 0.038), indicating close agreement between predicted and observed probabilities.In external validation, the calibration slope was 0.736 with an intercept of − 0.113, suggesting mild overestimation of risk but acceptable generalizability
Fig. 6.
The decision curve analysis curves of external validationof the prediction model are depicted. The decision curve analysis demonstrated that the model strategy yielded a consistently higher net benefit than both the “treat-all” and “treat-none” approaches across risk thresholds of 1–40%, confirming its clinical utility in the community setting
Discussion
For elderly patients with HFpEF, sarcopenia may occur due to various mechanisms [20]. The clinical symptoms of the two conditions overlap, making it easy to overlook sarcopenia and delay its diagnosis. HFpEF patients with sarcopenia have a worse prognosis [8], and timely detection and intervention of sarcopenia have been shown to significantly improve outcomes for elderly heart failure patients.
Despite the existence of sarcopenia screening scales such as SARC-F/SARC-CalF, our study found that the scores of these scales were not significant variables for identifying sarcopenia in elderly HFpEF patients. In contrast, our predictive model, which incorporates specific biochemical markers, provides a more precise and objective approach to assessing sarcopenia risk. This model not only improves diagnostic accuracy but also enhances understanding of the underlying pathophysiological processes, enabling more effective interventions for patients.
Our research findings can help identify the risk of sarcopenia. Therefore, they provide clinicians with a simple and feasible method for screening for sarcopenia, offering the possibility of early intervention and prevention.
This study developed a straightforward risk prediction model to evaluate sarcopenia risk in elderly patients with HFpEF. Key predictors included 1, 25OH-VitD3, BMI, NRS2002, THCY and GRIP. The nomogram demonstrated excellent predictive performance. Calibration curves indicated high agreement between predicted and observed probabilities in both cohorts, and decision curve analysis confirmed the clinical utility of the model. To our knowledge, this is the first simplified predictive model developed for sarcopenia risk screening in elderly patients with HFpEF, providing a precise, efficient and cost-effective tool to support early identification in both clinical and community settings.
Reduced appetite is identified as a distinct predictor, playing a pivotal role in the exacerbation of both the quantity and performance of skeletal muscle [21]. In elderly CHF patients, gastrointestinal congestion and edema often lead to decreased appetite and dyspepsia due to insufficient blood perfusion, low ghrelin levels, and adverse drug reactions [22–23]. This results in inadequate protein synthesis, leading to malnutrition and exacerbating sarcopenia. Previous studies have shown that nutritional supplementation, such as amino acids and exogenous testosterone, can maintain mineral homeostasis, benefiting muscle mass and physical performance [24–25]. Therefore, early nutritional screening and timely support are crucial for improving patient prognosis (such as NRS2002, A higher score indicates a poorer nutritional status [26]) .
Handgrip strength serves as a straight forward and non-invasive indicator of upper limb muscle strength, is very convenient for clinical application. Previous research has demonstrated that handgrip strength is strongly linked to negative consequences, such as cardiovascular incidents and all-cause mortality, in individuals with sarcopenia [27–29]. The decline in handgrip strength often parallels muscle mass reduction [30]. Although gait speed and SPPB scores are also related to muscle mass, they are more influenced by balance and coordination and require more space and time, potentially affecting test results [31]. Our findings indicate that handgrip strength serves as an independent predictor of muscle wasting in elderly HFpEF patients, corroborating the findings of additional studies. This makes grip strength a valuable indicator, recommended by both the EWGSOP and AWGS for sarcopenia diagnosis.
Homocysteine, an intermediate metabolite of methionine, affects protein synthesis and regulation when present in high concentrations, leading to tissue and organ changes [32]. Our study found that homocysteine is an independent factor of sarcopenia in elderly HFpEF patients, consistent with previous research [33–34]. The pathophysiological relevance of homocysteine may be particularly pronounced in this population. Beyond its direct effects on muscle—such as inhibiting satellite cell proliferation, enhancing oxidative damage via p38 MAPK signaling, and inducing myostatin production—homocysteine is integral to the cardiovascular pathology of HFpEF [35]. It contributes to endothelial dysfunction (by reducing nitric oxide bioavailability) and oxidative stress, which drive myocardial remodeling, diastolic impairment, and disease progression. Thus, in elderly HFpEF patients, elevated homocysteine likely reflects and exacerbates a shared pathway linking cardiac dysfunction and skeletal muscle loss, making it a highly pertinent biomarker and potential therapeutic target [36–38].
1, 25OH-VitD3—the biologically active form of vitamin D—was also identified as a significant predictor in the model.Beyond its classical role in calcium–phosphate homeostasis, calcitriol binds to the vitamin D receptor (VDR) expressed in human skeletal-muscle fibres, where it promotes myoblast proliferation, terminal differentiation and fast-twitch myosin expression [39]. Low serum levels diminish satellite-cell activation, accelerate protein ubiquitination via the atrogenes Atrogin-1 and MuRF-1, and impair mitochondrial oxidative capacity, collectively leading to losses in muscle mass and strength [40]. In elderly HFpEF patients, reduced cardiac output and heightened neuro-hormonal activation may further impair renal 1-α-hydroxylase activity, creating a vicious cycle in which vitamin D deficiency aggravates sarcopenia and, conversely, skeletal-muscle wasting limits outdoor activity and cutaneous vitamin D synthesis [41–42]. The inclusion of 1, 25OH-VitD3 in the final model therefore captures both a pathophysiological driver and a potentially modifiable therapeutic target.
The indicators in our nomogram are easily obtainable, imposing no additional burden, aiding clinicians in identifying high-risk sarcopenia populations among elderly HFpEF patients. However, this study acknowledges its inherent limitations. First, the external validation was conducted in a nursing home population, which may have a higher burden of functional impairment and multimorbidity compared to community-dwelling elderly. This could affect the generalizability of the model to other settings, such as outpatient clinics or hospitals. Second, the model includes handgrip strength, a component of the sarcopenia diagnostic criteria. This pragmatic choice optimizes its utility as a screening tool, but it is acknowledged that this influences the model’s performance characteristics as a diagnostic rather than an etiological predictor. Third, the model incorporates specific biomarkers (homocysteine and 1,25-dihydroxyvitamin D). While these are routinely available within Shanghai’s integrated healthcare system and were selected for their direct pathophysiological relevance, their inclusion may affect the model’s feasibility in settings where such tests are not readily accessible. Fourth, although patients with severe renal impairment were excluded, residual confounding by varying degrees of renal function remains possible, as it influences both homocysteine metabolism and vitamin D activation. Fifth, this study focused on the development and validation of the model; its practical integration into clinical workflows and its impact on patient management and outcomes require prospective evaluation in implementation studies.Furthermore, the predominantly urban, single-center (Shanghai) origin of our study population introduces potential geographic and demographic homogeneity bias, which may limit the generalizability of our findings. Future validation in ethnically, geographically, and socioeconomically diverse populations is essential to confirm the model’s broader applicability.
Conclusion
This study successfully developed and validated a predictive nomogram for sarcopenia risk in elderly patients with HFpEF. The model demonstrated high discriminative accuracy and reliable calibration in both internal and external validation cohorts, indicating its strong generalizability. As a practical and efficient clinical tool, it shows significant potential for early screening and targeted intervention in high-risk populations. Further prospective multicenter studies are warranted to verify its broader applicability and evaluate its impact on clinical outcomes.
Acknowledgements
We extend our heartfelt appreciation to all the study participants and their families for their collaboration with the research team. The authors are also grateful to the investigators for their dedication and cooperation throughout the fieldwork.
Abbreviations
- AWGS
Asian Working Group for Sarcopenia
- BIA
Bioelectrical impedance analysis
- BMI
Body mass index
- CI
Confidence interval
- DCA
Decision curve analysis
- eGFR
Estimated glomerular filtration rate
- ESC
European Society of Cardiology
- GRIP
Handgrip strength
- HFpEF
Heart failure with preserved ejection fraction
- LASSO
Least Absolute Shrinkage and Selection Operator
- LVEF
Left ventricular ejection fraction
- NRS2002
Nutritional Risk Screening 2002
- NT-proBNP
N-terminal pro-B-type natriuretic peptide
- ROC
Receiver operating characteristic
- SPPB
Short Physical Performance Battery
- THCY
Total homocysteine
- 1,25OH-VitD3
1,25-dihydroxyvitamin D3
Authors’ contributions
Qirui Yang, Qianwen Jiang, and Tingting Bai contributed equally to this work and should be considered co-first authors. Qirui Yang was responsible for conceptualization, methodology, investigation, data curation, and writing the original draft. Qianwen Jiang contributed to methodology, validation, formal analysis, investigation, and writing the original draft. Tingting Bai was involved in investigation, resources, data curation, visualization, and writing—review and editing. Yuanyue Zhu participated in investigation, resources, and supervision. Yajie Zhao contributed to software, validation, and formal analysis. Gang Xu was responsible for software, resources, and writing—review and editing. Fang Wu contributed to conceptualization, supervision, and project administration. Peijing Cui and Feika Li served as co-corresponding authors and were involved in conceptualization, methodology, writing—review and editing, supervision, and funding acquisition. All authors read and approved the final manuscript.
Funding
This research endeavor was financially supported by the the Chinese Medical Association Parenteral and Enteral Nutrition Society Medical Nutrition Special Research Fund (Grant No.Z-2017-24-2403 FKL) and National Natural Science Foundation of China (Grant No.82570400, JMC). The sponsors exerted no influence over the data gathering, study’s design, data gathering, analysis, methodology or the drafting of the manuscript.
Data availability
The datasets employed and/or analyzed within this study are accessible from the corresponding author upon the submission of a legitimate request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (Approval No: KY2021-108). Written informed consent was obtained from all participants or their legal guardians prior to their inclusion in the study.
Consent for publication
Not applicable. No individual person’s data or images are presented.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qirui Yang, QianWen Jiang and Tingting Bai contributed equally to this work.
Peijing Cui and Feika Li contributed equally as co-corresponding authors.
Contributor Information
Peijing Cui, Email: cpj11008@rjh.com.cn.
Feika Li, Email: feika2013@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets employed and/or analyzed within this study are accessible from the corresponding author upon the submission of a legitimate request.