Correspondence to Dr Yohei Sotomi; [email protected]
WHAT IS ALREADY KNOWN ON THIS TOPIC
Growth differentiation factor 15 (GDF15) increases in response to multifactorial stresses such as obesity, anaemia, renal dysfunction, inflammation and cardiac burden, and acts as a feedback mechanism against these stresses by suppressing appetite, exerting anti-inflammatory effect and protecting cardiomyocytes from apoptosis. High GDF15 levels have been reported to have a correlation with poor prognosis in young obese heart failure with preserved ejection fraction (HFpEF).
WHAT THIS STUDY ADDS
In elderly multimorbid HFpEF patients, GDF15 was associated with anaemia, inflammation, renal dysfunction, cardiac burden and malnutrition, and showed incremental prognostic utility over traditional risk assessment tools.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
GDF15 could serve as a complementary marker to traditional heart failure risk assessment score like MAGGIC score. In this study, a strong association between GDF15 and malnutrition was demonstrated, which should be kept in mind when considering GDF15 as a therapeutic target.
Introduction
Growth differentiation factor 15 (GDF15) is an oxidative stress and inflammation response cytokine of the glial cell line derived neurotrophic factor family belonging to the transforming growth factor beta superfamily. GDF15 is systemically activated in a variety of stress conditions like pregnancy, cancer, diabetes and cardiac disease.1–4
GDF15 acts primarily through a brainstem receptor, where it regulates appetite and energy balance.5 6 GDF15, because of its bioactivity, is thought to play both protective and detrimental roles depending on patients’ characteristics. In obese and patients with diabetes, GDF15 is thought to play protective roles, causing them to refrain from high caloric intake and regulating body weight through blood glucose homeostasis, whereas, in elderly multimorbid patients, GDF15 can be one of the molecules playing detrimental roles in weight loss by reduction of appetite and decreasing physical activity.3 7 High serum GDF15 levels have been correlated with the progression of frailty and cachexia, which characterise the last stage in human life.8 9
There are some reports about the prognostic value of GDF15 for patients with heart failure with preserved ejection fraction (HFpEF).10 11 However, these data were from the cohort who were relatively young, had preserved renal function, and were well nourished. In those patients, the detrimental roles of GDF15 may be less pronounced. The prognostic value of GDF15 in elderly multimorbid patients, in whom the detrimental roles of GDF15 can theoretically be more pronounced, is hypothetically different from known reports. Therefore, we aimed (1) to examine the association between GDF15 and clinical factors, and (2) to examine the prognostic value of GDF15 for relatively elderly multimorbid HFpEF patients.
Methods
Study subjects
This study represents a post hoc analysis of the database from the ongoing Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with preserved Ejection Fraction (PURSUIT-HFpEF) study, which is a multi-referral centre, prospective and observational study (UMIN-CTR ID: UMIN000021831). Consecutively enrolled patients with acute decompensated HF and preserved ejection fraction (≥50%) were registered. Diagnosis of acute decompensated HF was based on the clinical symptoms and signs using the Framingham Heart Study criteria, as well as a serum N-terminal pro-B-type natriuretic peptide (NT-proBNP) level of ≥400 pg/mL or brain natriuretic peptide level of ≥100 pg/mL. Comprehensive patient characteristics, echocardiography, laboratory tests and medication lists were collected at admission, discharge and during annual follow-up time points.
In the ongoing PURSUIT-HFpEF registry, a predetermined subgroup underwent multi-biomarker assessments. This subgroup consisted of patients who consented to additional blood sampling. Out of the overall cohort of 1231 patients, a total of 212 patients had biomarker data in the data set analysed in this study, which was finalised in April 2022. The study was conducted in compliance with the ethical principles stated in the Declaration of Helsinki, and the study protocol was approved by the ethics committees of all participating hospitals. All patients provided written informed consent before participating in the study.
Patient and public involvement
Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Data collection
We collected data including detailed medical history, comorbidities, clinical frailty scale, New York Heart Association class, laboratory data and transthoracic echocardiographic data. Laboratory data included complete blood count and serum chemistry. In echocardiography, we measured left atrial volume index, and left ventricular ejection fraction by the modified Simpson’s method. Left ventricular mass index was calculated by dividing left ventricular mass by body surface area. E/e’ was the mean of septal E/e’ and lateral E/e’. Tricuspid annular plane systolic excursion and inferior vena cava diameter were measured using the standard method.12 Tricuspid pressure gradient was measured using the simplified Bernoulli equation.
Measurement of GDF15
All study subjects were admitted to the hospital due to acute decompensated HF. Blood sampling for the biomarker test was conducted after completion of acute phase treatment. The blood samples were centrifuged within 30 min at 4°C and stored at −20°C until assay. GDF15 was assessed using the Quantikine Human GDF-15 immunoassay. This assay is a 3.5 hours solid phase ELISA designed to measure GDF-15. The minimum detectable dose of human GDF-15 ranged from 0 to 4.4 pg/mL.
Clinical endpoints
The primary endpoint of this study was a composite of all-cause death and hospitalisation for HF. After discharge, enrolled patients were followed-up at an outpatient clinic in each hospital. Clinical follow-up data was obtained either by direct contact with patients or giving a telephone or a mail with their families.
Statistical analysis
Data are presented with listwise deletion. Categorical variables are expressed as counts (percentages) and compared with the χ2 test or Fisher’s exact test. Continuous variables are expressed as mean (SD) or median (IQR) and compared using the Student’s t-test or the Mann-Whitney U test as appropriate. The normality of distribution of continuous data was examined with the Shapiro-Wilk test.
Multiple linear regression analysis was used to identify correlates of GDF15. Covariates in the multivariable linear regression model were age, sex, body mass index, smoking history, diabetes mellitus, atrial fibrillation, haemoglobin, estimated glomerular filtration rate, C-reactive protein, NT-proBNP and left ventricular mass index. These covariates were selected based on the previous findings.2 10 13
In addition, to assess the association between GDF15 and nutritional status, multiple linear regression analysis for the Geriatric Nutritional Risk Index and the Prognostic Nutritional Index and ordinal logistic regression analysis for the controlling nutritional status score were conducted. In the ordinal logistic regression model, the proportional odds assumption was tested using the Brant test and found to be satisfied. In all models, GDF15 was a variable of interest, and covariates were age, clinical frailty scale, body mass index, haemoglobin and estimated glomerular filtration rate. These covariates were selected based on the previous reports.14
Cox proportional hazards regression models were used to analyse the prognostic impact of the GDF15 level by calculating the multivariable-adjusted HR and 95% CI. The initial model included the following potential covariates: age, sex, body mass index, clinical frailty scale, New York Heart Association class, history of diabetes, atrial fibrillation, history of coronary artery disease, systolic blood pressure, haemoglobin, sodium, estimated glomerular filtration rate, albumin, C-reactive protein, NT-proBNP, left ventricular mass index, tricuspid annular plane systolic excursion, tricuspid pressure gradient and GDF15. These covariates were reported to have prognostic significance in HF patients.15 16
We used a stepwise selection method to determine the most parsimonious model. This approach combined forward selection and backward elimination procedures to iteratively add and remove covariates based on the Akaike information criterion (AIC). Specifically, the stepwise procedure started with no covariates and sequentially added covariates that significantly improved the model fit, as determined by a reduction in AIC. After each addition, all included covariates were re-evaluated, and any covariates that no longer contributed significantly to the model were removed. This process was repeated until no further covariates met the criteria for inclusion or exclusion. The final model included the following covariates: body mass index, clinical frailty scale, New York Heart Association class and GDF15. The HR for GDF15 is presented per one unit increase in the base-10 log-transformed GDF15. This increment corresponds to a 900% increase in the absolute GDF15 value. All variables had missing data <20%. The missing data were imputed by random forest imputation using ‘missForest’ package prior to these multivariable analyses.
The primary endpoint was assessed according to the stratification by tertile of GDF15 value in a time-to-first-event fashion with the Kaplan-Meier method and compared with the log-rank test.
The incremental discriminative utility of GDF15 for 1-year primary endpoint over the MAGGIC risk score, one of the most famous risk scores for HF patients was assessed by comparing the areas under the curve (AUC) of receiver operating characteristics curves of MAGGIC model and GDF15 model, using the DeLong test. MAGGIC model included the MAGGIC risk score and GDF15 model included MAGGIC model plus GDF15.17 One of the items for the MAGGIC risk score calculation, history of HF for more than 18 months, was substituted for a history of HF hospitalisation in this study. The net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were calculated when GDF15 was added to MAGGIC model.
All statistical analyses were performed using R software (V.4.3.1; R Foundation for Statistical Computing, Vienna, Austria). A p value of <0.05 was considered statistically significant.
Results
Baseline patient characteristics
Patient flowchart is shown in figure 1. This study analysed a subcohort of 212 patients who underwent biomarker testing, out of the overall cohort of 1231 patients. Of these, 181 patients with clinical follow-up data available were analysed to assess the prognostic value of GDF15 (figure 1). Online supplemental figure 1 illustrates the histograms showing the distribution of GDF15.
Enlarge this image.Figure 1. Patient flowchart. GDF15, growth differentiation factor 15; PURSUIT-HFpEF, Prospective mUlticenteR obServational stUdy of patIenTs with Heart Failure with preserved Ejection Fraction.
In this study population (n=181), the median age was 81 (75–85) years, male patients accounted for 48%, the median body mass index at discharge was 21.5 (19.3–24.3) kg/m2. Clinical characteristics of patients included and those excluded are summarised in online supplemental table 1. The study population was stratified by GDF15 level tertiles as follows: low GDF15 level ≤2360 pg/mL (N=61); 2360 pg/mL <middle GDF15 level <3880 pg/mL (N=60); and high GDF15 level ≥3880 pg/mL (N=60). Patients’ baseline characteristics are shown in table 1. Compared with patients with low GDF15 level, those with high GDF15 level had a significantly higher percentage of men, smoking history, higher C-reactive protein level, higher serum NT-proBNP level, lower haemoglobin level, lower estimated glomerular filtration rate level and lower albumin level.
Table 1Patient background
| Characteristic | GDF15 tertile | P value | Data missing (%) | ||
| ≤2360 pg/mL (N=61） | 2360 to 3880 pg/mL (N=60） | ≥3880 pg/mL (N=60） | |||
| GDF15 | 1690 (1290–2090) | 3135 (2675–3378) | 5170 (4448–6885) | <0.01 | 0 |
| Age (years) | 78.0 (73.0–83.0) | 82.5 (77.8–86.0) | 81.0 (73.8–86.0) | 0.06 | 0 |
| Male sex | 25 (41.0) | 21 (35.0) | 40 (66.7) | <0.01 | 0 |
| Body mass index (kg/m2) | 21.5 (19.2–24.3) | 21.3 (19.4–24.7) | 21.7 (19.6–24.2) | 0.87 | 0 |
| NYHA≥II | 32 (52.5) | 42 (70.0) | 42 (70.0) | 0.07 | 0 |
| Clinical frailty scale ≥5 | 8 (13.1) | 15 (25.0) | 9 (15.0) | 0.18 | 0 |
| Smoking history | 21 (34.4) | 17 (28.8) | 37 (61.7) | <0.01 | 0.6 |
| Systolic blood pressure | 124.0 (108.0–133.0) | 127.0 (112.5–135.2) | 128.5 (117.5–140.0) | 0.07 | 0 |
| Heart rate | 70.0 (61.0–76.0) | 69.5 (60.0–78.0) | 70.0 (61.8–76.0) | 0.93 | 0 |
| Medical history | |||||
| Atrial fibrillation | 25 (41.0) | 26 (43.3) | 19 (31.7) | 0.38 | 0 |
| Coronary artery disease | 8 (13.1) | 10 (16.9) | 17 (28.8) | 0.08 | 1.1 |
| Hypertension | 17 (27.9) | 24 (40.0) | 26 (44.1) | 0.16 | 0.6 |
| Diabetes mellitus | 51 (83.6) | 55 (91.7) | 56 (93.3) | 0.17 | 0 |
| Dyslipidaemia | 26 (43.3) | 27 (45.0) | 34 (56.7) | 0.28 | 0.6 |
| COPD | 5 (8.3) | 7 (11.9) | 6 (10.0) | 0.81 | 1.1 |
| Dialysis | 0 (0.0) | 0 (0.0) | 6 (10.0) | <0.01 | 0 |
| Medication at discharge | |||||
| ACE-Is/ARBs | 40 (65.6) | 28 (46.7) | 22 (36.7) | 0.01 | 0 |
| β blockers | 45 (73.8) | 41 (68.3) | 42 (70.0) | 0.80 | 0 |
| Diuretics | 46 (75.4) | 56 (93.3) | 53 (88.3) | 0.01 | 0 |
| MRAs | 25 (41.0) | 25 (41.7) | 20 (33.3) | 0.58 | 0 |
| SGLT2 inhibitors | 4 (6.6) | 9 (15.0) | 5 (8.3) | 0.26 | 0 |
| Biguanides | 3 (4.9) | 1 (1.7) | 3 (5.0) | 0.56 | 0 |
| Laboratory data | |||||
| Albumin (g/dL) | 3.4 (3.2–3.7) | 3.4 (3.1–3.7) | 3.3 (3.0–3.5) | 0.03 | 0.6 |
| Haemoglobin (g/L) | 126 (108–136) | 115 (98–134) | 104 (94–119) | <0.01 | 0 |
| Sodium (mEq/L) | 140.0 (139.0–142.0) | 140.0 (138.0–141.2) | 138.0 (136.8–141.0) | 0.02 | 0 |
| eGFR (mL/min/1.73 m2) | 56.2 (45.0–69.2) | 40.5 (31.2–56.0) | 25.5 (14.7–40.3) | <0.01 | 0 |
| Cholinesterase (U/L) | 242.0 (197.0–285.0) | 212.5 (194.5–250.5) | 179.5 (153.8–221.2) | <0.01 | 13.3 |
| C-reactive protein (mg/dL) | 0.3 (0.1–1.2) | 0.5 (0.2–1.0) | 1.1 (0.3–2.1) | <0.01 | 1.1 |
| Haemoglobin A1c | 6.0 (5.6–6.5) | 6.2 (5.8–6.8) | 5.9 (5.4–6.4) | 0.08 | 17.7 |
| LDL cholesterol | 99.0 (76.5–112.5) | 101.0 (82.2–124.2) | 78.5 (71.5–102.0) | 0.01 | 9.9 |
| NT-proBNP (pg/mL) | 672 (273–1345) | 1090 (569–2470) | 2431 (1280–6018) | <0.01 | 14.9 |
| Echocardiography at discharge | |||||
| LVDd (mm) | 45.8 (41.8–51.1) | 45.1 (40.2–49.6) | 49.0 (43.8–53.8) | 0.01 | 1.1 |
| LVEF (%) | 61.0 (57.0–64.7) | 61.0 (54.3–68.1) | 60.0 (53.5–64.8) | 0.30 | 3.9 |
| LAVI (mL/m2) | 50.0 (37.5–65.5) | 51.0 (35.5–62.5) | 45.0 (37.0–66.0) | >0.99 | 6.1 |
| LVMI (g/m2) | 113.1 (91.2–143.7) | 111.6 (97.8–143.9) | 125.7 (109.7–147.6) | 0.11 | 1.7 |
| TAPSE (mm) | 17.4 (15.6–20.4) | 17.6(14.8–20.4) | 17.1 (14.6–20.6) | 0.89 | 2.8 |
| Mean E/e’ | 11.6 (8.8–15.7) | 12.8 (10.9–17.6) | 13.7 (10.7–17.2) | 0.03 | 2.8 |
| TRPG (mm Hg) | 25.0 (20.0–29.3) | 26.3 (21.2–34.0) | 25.7 (20.1–32.7) | 0.36 | 9.4 |
| Nutritional status | |||||
| CONUT score | 3.0 (1.8–5.0) | 3.0 (2.0–5.0) | 4.0 (3.0–6.0) | 0.01 | 16 |
| GNRI | 91.1 (83.9–102.3) | 91.7 (85.7–96.4) | 91.0 (83.5–95.6) | 0.52 | 0.6 |
| PNI | 42.0 (38.5–45.5) | 40.8 (37.5–43.9) | 38.2 (34.8–42.2) | 0.01 | 2.2 |
Data are shown as median (IQR) or number (percentage).
ACE-Is, ACE inhibitors; ARBs, angiotensin II receptor blockers; CONUT, controlling nutritional status; COPD, chronic obstructive pulmonary disease; e’, early diastolic velocity of the mitral valve annulus; E, early diastolic velocity on transmitral doppler; eGFR, estimated glomerular filtration rate; GDF15, growth differentiation factor 15; GNRI, Geriatric Nutritional Risk Index; LAVI, left atrial volume index; LDL, low-density lipoprotein; LVDd, left ventricular diastolic diameter; LVEF, left ventricular ejection fraction; LVMI, left ventricular mass index; MRAs, mineralocorticoid receptor antagonists; NT-proBNP, N-terminal pro-brain natriuretic peptide; NYHA, New York Heart Association; PNI, Prognostic Nutritional Index; SGLT2 inhibitors, sodium glucose cotransporter-2 inhibitors; TAPSE, tricuspid annular plane systolic excursion; TRPG, tricuspid regurgitation pressure gradient.
Correlates of GDF15 with clinical factors
Correlates of GDF15 are shown in table 2. Age, male, diabetes mellitus, log-transformed C-reactive protein and log-transformed NT-proBNP were positively and haemoglobin, estimated glomerular filtration rate were negatively correlated with GDF15 level. Scatter plots and box-and-whisker plot with GDF15 are presented for factors that were found to be significantly correlated (figure 2).
Table 2Correlation between GDF15 and clinical factors
| β-coefficient (95% CI) | P value | |
| Age (years) | 0.0039 (0.0010 to 0.0067) | 0.008 |
| Male sex | 0.0984 (0.0431 to 0.1537) | 0.001 |
| Body mass index | −0.0038 (−0.0100 to 0.0024) | 0.229 |
| Smoking history | 0.0539 (−0.0029 to 0.1108) | 0.065 |
| Diabetes mellitus | 0.0582 (0.0063 to 0.1100) | 0.029 |
| Atrial fibrillation | −0.0067 (−0.0583 to 0.0449) | 0.799 |
| Haemoglobin (g/L) | −0.0018 (−0.0031 to 0.0004) | 0.010 |
| eGFR (mL/min/1.73 m2) | −0.0057 (−0.0071 to 0.0043) | <0.001 |
| Log-transformed C-reactive protein (mg/dL) | 0.0496 (0.0082 to 0.0909) | 0.020 |
| Log-transformed NT-proBNP (pg/mL) | 0.0742 (0.0184 to 0.1300) | 0.010 |
| LVMI | −0.0004 (−0.0011 to 0.0002) | 0.167 |
We used the multivariable linear regression model including 181 cases to assess the correlation between log-transformed GDF15 and clinical factors.
eGFR, estimated glomerular filtration rate; GDF15, growth differentiation factor 15; LVMI, left ventricular mass index; NT-proBNP, N-terminal pro-brain natriuretic peptide.
Enlarge this image.Figure 2. Correlation of GDF15 with clinical factors. Correlation of growth differentiation factor 15 with numeric clinical factors is shown in a scatter plot. Correlation of growth differentiation factor 15 with categorical clinical factors is shown in a box-and-whisker plot. CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; GDF15, growth differentiation factor 15.
Association between nutritional status and GDF15
GDF15 was independently associated with all nutritional scores, the Geriatric Nutritional Risk Index, the Prognostic Nutritional Index and the controlling nutritional status score, after adjustment with covariates (table 3).
Table 3Correlation between nutrition scores and GDF15
| GNRI | PNI | CONUT score | ||||
| β-coefficient (95% CI) | P value | β-coefficient (95% CI) | P value | OR (95% CI) | P value | |
| Log-transformed GDF15 (pg/mL) | −5.73 (−10.36 to 1.10) | 0.016 | −5.02 (−9.14 to 0.89) | 0.018 | 7.18 (1.68 to 31.76) | 0.008 |
| Age (years) | −0.05 (−0.14 to 0.05) | 0.323 | −0.05 (−0.13 to 0.04) | 0.257 | 1.02 (0.99 to 1.05) | 0.163 |
| Clinical frailty scale ≥5 | −1.87 (−4.20 to 0.47) | 0.119 | −2.20 (−4.28 to 0.13) | 0.039 | 2.56 (1.24 to 5.38) | 0.012 |
| Body mass index | 1.88 (1.68 to 2.08) | <0.001 | 0.14 (−0.04 to 0.32) | 0.124 | 0.97 (0.91 to 1.03) | 0.313 |
| Haemoglobin per 10 (g/L) | 1.02 (0.57 to 1.47) | <0.001 | −0.03 (−0.08 to 0.02) | 0.177 | 0.72 (0.62 to 0.83) | <0.001 |
| eGFR (mL/min/1.73 m2) | −0.06 (−0.12 to 0.01) | 0.032 | 0.69 (0.29 to 1.09) | 0.001 | 1.02 (1.00 to 1.03) | 0.091 |
We used the multivariable linear regression model to assess the correlation of GNRI and PNI with clinical factors and the ordinal logistic regression model to assess the correlation of CONUT score with clinical factors.
CONUT, controlling nutritional status; eGFR, estimated glomerular filtration rate; GDF15, growth differentiation factor 15; GNRI, Geriatric Nutritional Risk Index; PNI, Prognostic Nutritional Index.
Association of GDF15 with outcome
A mean follow-up duration was 1.6±1.0 years. The primary endpoint, a composite of all-cause death and hospitalisation for HF, occurred in 63 patients (all-cause death, 38 patients; and hospitalisation for HF, 41 patients).
GDF15 remained to be an independent predictor of the primary endpoint even after multivariable adjustment (adjusted HR for log-transformed GDF15; 13.67, 95% CI: 2.78 to 67.22, p=0.001) (table 4).
Table 4Prognostic impact of GDF15
| Multivariable Cox proportional hazard model | HR (95% CI) | P value |
| Log-transformed GDF15 (pg/mL) | 6.95 (2.44 to 19.80) | <0.001 |
| Body mass index | 0.91 (0.84 to 0.97) | 0.004 |
| Clinical frailty scale ≥5 | 2.04 (1.17 to 3.54) | 0.011 |
| New York Heart Association class ≥2 | 1.88 (1.00 to 3.55) | 0.051 |
We used the multivariable Cox regression analysis including 181 cases to assess the adjusted impact of GDF15 on the primary endpoint (a composite of all-cause death and rehospitalisation for heart failure).
GDF15, growth differentiation factor 15.
The Kaplan-Meier analysis revealed that GDF15 divided into tertile successfully stratified the patient prognosis (figure 3).
Enlarge this image.Figure 3. Kaplan-Meier curves demonstrating the composite endpoint stratified by GDF15 tertile. The Kaplan-Meier analysis for comparing the composite of all-cause death and hospitalisation for heart failure in three groups divided by GDF15 tertile. GDF15, growth differentiation factor 15.
To assess the incremental discriminative power of GDF15, we compared c-statistics of MAGGIC model and GDF15 model and found numerically higher AUC in GDF15 model (MAGGIC model; AUC=0.661 (95% CI: 0.563 to 0.759), GDF15 model; AUC=0.707 (95% CI: 0.621 to 0.793), DeLong test p=0.299) (figure 4). The NRI and the IDI for the addition of GDF15 to the MAGGIC model were significant (NRI=0.4955 (95% CI: 0.1367 to 0.8543), p=0.007; IDI=0.0278 (95% CI: 0.0013 to 0.0543), p=0.040).
Enlarge this image.Figure 4. Comparison of ROC curves for primary endpoint within 1 year. The AUCs of MAGGIC model and GDF15 model were compared. MAGGIC model included the MAGGIC risk score, and GDF15 model included MAGGIC model plus GDF15. The AUC of GDF15 model was higher than MAGGIC model (MAGGIC model; AUC=0.661 (95% CI: 0.563 to 0.759), GDF15 model; AUC=0.707 (95% CI: 0.621 to 0.793), DeLong test p=0.299). NRI and IDI were calculated for the addition of GDF15 to the MAGGIC model (NRI=0.4955, 95% CI: 0.1367 to 0.8543, p=0.007, IDI=0.0278, 95% CI: 0.0013 to 0.0543, p=0.040). AUC, area under the curve; GDF15, growth differentiation factor 15; IDI, integrated discrimination improvement; NRI, net reclassification improvement; ROC, receiver operating characteristics.
Discussion
Main findings of the present study can be summarised as follows: (1) GDF15 was correlated with cardiac burden, anaemia, renal dysfunction, inflammation and malnutrition; (2) GDF15 was associated with the poor prognosis in relatively elderly multimorbid cohort with HFpEF; and (3) significant incremental value of GDF15 added to the established risk assessment system (MAGGIC risk score) was identified.
Correlation factors and prognostic value of GDF15
Our study demonstrates that GDF15 levels are independently associated with adverse outcomes in relatively elderly multimorbid HFpEF cohort. The prognostic utility of GDF15 in HFpEF patients was first reported in Singapore Heart Failure Outcomes and Phenotypes study.10 This study, including 186 HFpEF patients, revealed that GDF15 had the significant prognostic value even after multivariable adjustment. Following this initial report, similar findings were also reported from several studies.11 18 These findings suggest important insights into the practical use of GDF15 in our HF care. However, there remains a large evidence gap in this topic. GDF15 levels have been known to have an association with various clinical status such as age, renal function, inflammation and metabolism. Although they are the same HFpEF cohort, they are younger, better-nourished and have better renal function than our cohort.
Possibly due to such differences in patients background, median GDF15 levels seemed to show noticeable differences between our study and other studies (3130 pg/mL in our study vs about 2600–2900 pg/mL in other studies). In our study, we have observed significant correlations between GDF15 levels and several clinical indicators such as age, sex, diabetes mellitus, anaemia, C-reactive protein, renal function and NT-proBNP, which is consistent with previous studies.10 11 Notably, our cohort, which is characterised by elderly and multiple comorbidities, uniquely showed significant negative correlations between GDF15 and nutritional status represented by the controlling nutritional status score and the Geriatric Nutritional Risk Index. In HF patients, it is well recognised that inflammation and malnutrition are interrelated.19 GDF15, which is associated with inflammation, has been suggested to promote appetite loss, potentially leading to malnutrition. These points presumably suggest that the prognostic role of GDF15 may be universally significant in both cohorts, although the cause of GDF15 increase can be distinct.
The incremental prognostic value of GDF15 in combination with the MAGGIC risk score was found in our study as presented by the NRI and the IDI. It suggested that GDF15 could serve as a complementary marker to traditional HF risk assessment score. The MAGGIC risk score was developed from a broader HF population. However, it does not incorporate the pathophysiological dimensions such as systemic stress and inflammation, which may drive the progression of frailty and cachexia. GDF15 may capture such pathophysiological dimensions, potentially offering an incremental prognostic advantage beyond the MAGGIC risk score in our multimorbid elderly HFpEF cohort. The development of a more comprehensive, HFpEF-specific risk prediction model incorporating GDF15 would be one of the future scientific topics.
Pathophysiological roles of GDF15 in HFpEF
HFpEF presents a variety of pathological states, not only cardiac load but also systemic stress such as inflammation, anaemia, arteriosclerosis, renal failure and metabolic disorders, consequently elevating the GDF15 levels. GDF15 has been known to regulate appetite, tissue homeostasis, inflammation and to protect cardiomyocytes from apoptosis.6 20 21 GDF15 is basically similar to BNP, as its levels increase in response to multifactorial stresses and act in a feedback mechanism to these stresses. Recent therapeutic interventions in HF patients have established the efficacy of sacubitril/valsartan for brain natriuretic peptide. Similarly, GDF15 may also emerge as a target for therapeutic intervention.
However, the physiological activity of GDF15 warrants careful consideration. In type II diabetics and obese patients, it can suppress their appetite and contribute to the metabolic homeostasis, whereas in elderly and patients with cancer, it can suppress their appetite and therefore promote cachexia and frailty.3 7 9 There is a duality of protective and detrimental roles depending on patients’ characteristics. In HFpEF with diverse pathologies, it is plausible that GDF15 could act beneficially in some populations and detrimentally in other populations. Current report has shown that the blood levels of GDF15 are increased by sodium glucose cotransporter-2 inhibitors (SGLT2-Is) which have cardioprotective effects and improve the prognosis of HFpEF.22 23 However, the beneficial effects of SGLT2-Is on prognosis vary among different populations. The benefit of SGLT2-Is in an Asian cohort of the Dapagliflozin Evaluation to Improve the Lives of Patients with Preserved Ejection Fraction Heart Failure (DELIVER) trial was reported.22 The population was relatively young (mean age 71.4 years), met the diagnostic criteria for obesity in Asia (mean body mass index 25 kg/m2), had preserved renal function (mean estimated glomerular filtration rate 61 mL/min/1.73 m2). In this population, the protective roles of GDF15, appetite control and metabolic homeostasis, may be pronounced rather than the detrimental roles of GDF15, appetite suppression and promoting cachexia and frailty. On the other hand, a recent report indicated that the effectiveness of SGLT2-Is diminished in HFpEF patients with more advanced frailty.24 In highly vulnerable HFpEF patients represented by those with severe frailty, elevating GDF15 levels due to SGLT2-Is may play detrimental roles and reduce the overall benefit of SGLT2-Is.
As discussed, GDF15 can have both detrimental and protective roles depending on the patients’ condition, and this duality suggests a variable impact on prognosis. Therefore, caution may be warranted when considering GDF15 as a target for therapeutic intervention.
Study limitation
Several limitations must be acknowledged in this study. First, only a small proportion of patients (14.7%) were included in this study. As presented in online supplemental table 1, there were some differences in clinical characteristics between the patients included and excluded, which might have resulted in a potential selection bias. Second, GDF15 is a bioactive biomarker, but its causal role, whether as cause or effect, in relation to prognostic factors is largely unknown. Third, we have demonstrated the potential prognostic utility of GDF15 by integrating it into the MAGGIC risk score. Nonetheless, the development of a more comprehensive, HFpEF-specific risk prediction model incorporating GDF15 would be highly desirable. Unfortunately, the limited sample size of our current study population precludes the construction of such a specialised predictive model. Finally, the generalisability of the findings to other regions and ethnicities is limited due to differing races, social healthcare systems and dietary habits in Japan compared with other countries. Further large-scale global studies and basic research to explore the mechanistic pathways of GDF15 in HFpEF are needed to address these limitations.
Conclusions
GDF15 was associated with anaemia, inflammation, renal dysfunction, cardiac burden and malnutrition. It demonstrated prognostic value in elderly multimorbid HFpEF patients, suggesting its potential role as a complementary marker for the prognostic risk assessment of HFpEF patients.
The authors thank Nagisa Yoshioka, Satomi Kishimoto, Kyoko Tatsumi and Yumi Yoshida for their excellent assistance in data collection, data management and secretarial works. English editing was assisted by the open artificial intelligence (AI) chatbot, ChatGPT, developed by OpenAI in San Francisco, California, USA.
Data availability statement
Data are available upon reasonable request. The data supporting the findings of this study are not publicly available, but are available upon reasonable request from the corresponding author.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and was approved by Kansai Rosai Hospital Institutional Review Board (approval ID: 16co10g), Kawachi General Hospital Ethics Committee (approval ID was not available, but approved on 26 April 2016), Osaka Rosai Hospital Ethics Committee(approval ID: 28-5), Higashiosaka City Medical Center Institutional Review Board (approval ID: 02-0313), Osaka Prefectural Hospital Organization Osaka General Medical Center Institutional Review Board (approval ID: 28-2002), Hyogo Prefectural Nishinomiya Hospital Ethics Committee (approval ID: H28-3), Ikeda Municipal Hospital Ethics Committee (approval ID: 3280), Kawanishi City Hospital Institutional Review Board (approval ID: 28001), Rinku General Medical Center Ethics Committee (approval ID: 27-40), Saiseikai Senri Hospital Ethics Committee (approval ID: 280304), Yao Municipal Hospital Institutional Review Board (approval ID: H28-6), Kawasaki Hospital Ethics Committee (approval ID was not available, but approved on 12 May 2016), Minoh City Hospital Ethics Committee (approval ID was not available, but approved on 24 May 2016), National Hospital Organization Osaka National Hospital Second Institutional Review Board (approval ID: 16024), Kano General Hospital Ethics Committee (approval ID was not available, but approved on 9 June 2016), Toyonaka Municipal Hospital Ethics Committee (approval ID: 2016-04-02), Kinan Hospital Ethics Committee 121, Japan Community Health Care Organization Osaka Hospital Ethics Committee (approval ID: 2016-2), Kobe Ekisaikai Hospital Ethics Committee (approval ID: 2016-3), Sakurabashi Watanabe Hospital Ethics Committee (approval ID: 16-15), Sumitomo Hospital Research Ethics Committee (approval ID: 28-01), Suita Municipal Hospital Institutional Review Board (approval ID: 2017-8), Kinki Central Hospital Ethics Committee (approval ID: 288), Osaka Police Hospital Institutional Review Board (approval ID: 593), Japan Community Health Care Organization Hoshigaoka Medical Center Institutional Review Board (approval ID: 1618), National Hospital Organization Osaka Minami Medical Center Institutional Review Board (approval ID: 28-3), Japan Community Health Care Organization Osaka Minato Central Hospital Ethics Committee (approval ID was not available, but approved on 10 June 2016), Amagasaki Chuo Hospital Ethics Committee (approval ID was not available, but approved on 1 August 2017), Otemae Hospital Institutional Review Board (approval ID: 2017-020), Osaka University Hospital Clinical Research Review Committee (approval ID: 15471), Osaka International Cancer Institute Institutional Review Board (No.20097). Participants gave informed consent to participate in the study before taking part.
Collaborators The OCVC-Heart Failure Investigators Chair: Yasushi Sakata, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, 2–2 Yamada-oka, Suita 565–0871, Japan. Study manager: Yohei Sotomi (Chief), Daisaku Nakatani, Katsuki Okada, Tomoharu Dohi, Akihiro Sunaga, Hirota Kida, Taiki Sato, Yuki Matsuoka, and Daisuke Sakamoto; Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Suita, Japan. Collaborators: Masahiro Seo, Tetsuya Watanabe, and Takahisa Yamada, Osaka General Medical Center, Osaka, Japan; Takaharu Hayashi and Yoshiharu Higuchi, Osaka Police Hospital, Osaka, Japan; Masaharu Masuda, Mitsutoshi Asai, and Toshiaki Mano, Kansai Rosai Hospital, Amagasaki, Japan; Hisakazu Fuji, Kobe Ekisaikai Hospital, Kobe, Japan; Shunsuke Tamaki, Daisaku Masuda, Ryu Shutta, and Shizuya Yamashita, Rinku General Medical Center, Izumisano, Japan; Masami Sairyo and Yusuke Nakagawa, Kawanichi City Medical Center, Kawanishi, Japan; Haruhiko Abe, Yasunori Ueda, and Yasushi Matsumura, National Hospital Organization Osaka National Hospital, Osaka, Japan; Kunihiko Nagai, Ikeda Municipal Hospital, Ikeda, Japan; Masamichi Yano, Masami Nishino, and Jun Tanouchi, Osaka Rosai Hospital, Sakai, Japan; Yoh Arita and, Nobuyuki Ogasawara, Japan Community Health Care Organization Osaka Hospital, Osaka, Japan; Takamaru Ishizu, Minoru Ichikawa and Yuzuru Takano, Higashiosaka City Medical Center, Higashiosaka, Japan; Eisai Rin, Kawachi General Hospital, Higashiosaka, Japan; Yukinori Shinoda, Koichi Tachibana and Shiro Hoshida, Yao Municipal Hospital, Yao, Japan; Masahiro Izumi, Kinki Central Hospital, Itami, Japan; Hiroyoshi Yamamoto and Hiroyasu Kato, Japan Community Health Care Organization, Osaka Minato Central Hospital, Osaka, Japan; Kazuhiro Nakatani and Yuji Yasuga, Sumitomo Hospital, Osaka, Japan; Mayu Nishio and Keiji Hirooka, Saiseikai Senri Hospital, Suita, Japan; Takahiro Yoshimura, Kazunori Kashiwase and Shinji Hasegawa, National Hospital Organization Osaka Minami Medical Center, Kawachinagano, Japan; Akihiro Tani, Kano General Hospital, Osaka, Japan; Yasushi Okumoto, Kinan Hospital, Tanabe, Japan; Yasunaka Makino, Hyogo Prefectural Nishinomiya Hospital, Nishinomiya, Japan; Toshinari Onishi and Katsuomi Iwakura, Sakurabashi Watanabe Hospital, Osaka, Japan; Yoshiyuki Kijima, Japan Community Health Care Organization, Hoshigaoka Medical Center, Hirakata, Japan; Takashi Kitao, Minoh City Hospital, Minoh, Japan; Masashi Fujita, Osaka International Cancer Institute, Osaka, Japan; Koichiro Harada, Suita Municipal Hospital, Suita, Japan; Masahiro Kumada and Osamu Nakagawa, Toyonaka Municipal Hospital, Toyonaka, Japan; Ryo Araki and Takayuki Yamada, Otemae Hospital, Osaka, Japan; Akito Nakagawa and Yoshio Yasumura, Amagasaki Chuo Hospital, Amagasaki, Japan; and Daisuke Sakamoto, Yuki Matsuoka, Taiki Sato, Akihiro Sunaga, Hirota Kida, Yohei Sotomi, Tomoharu Dohi, Yasuhiro Akazawa, Kei Nakamoto, Katsuki Okada, Fusako Sera, Hidetaka Kioka, Tomohito Ohtani, Toshihiro Takeda, Daisaku Nakatani, Shungo Hikoso, and Yasushi Sakata, Osaka University Graduate School of Medicine, Suita, Japan.
Contributors DS: Study design, data analysis, manuscript drafting, data collection, interpretation. YM: Data collection, interpretation. DN: Data collection, interpretation. KO: Study design, data analysis, manuscript drafting, data collection, interpretation. AS: Study design, data collection, interpretation. HK: Study design, data collection, interpretation. TS: Study design, data collection, interpretation. TK: Study design, data collection, interpretation. ST: Study design, data collection, interpretation. MS: Study design, data collection, interpretation. MY: Study design, data collection, interpretation. TH: Study design, data collection, interpretation. AN: Study design, data collection, interpretation. YN: Study design, data collection, interpretation. YY: Study design, data collection, interpretation. TY: Study design, data collection, interpretation. SH: Study design, data collection, interpretation. YSo: Study design, data analysis, manuscript drafting, critical revisions, final manuscript approval, the guarantor of this work. YSa: Study design, data collection, interpretation, critical revisions, final manuscript approval. OCVC-Heart Failure Investigators: Study design, data collection, interpretation. Guarantor: YSo (corresponding author).
Funding This work was funded by Roche Diagnostics K.K., Fuji Film Toyama Chemical Co. Ltd., and Bristol-Myers Squibb, Co.
Competing interests YSo has received grants from Abbott Medical Japan, Biosense Webster, Boston Scientific Japan, Bristol-Myers Squibb, FUJIFILM Toyama Chemical, Roche Diagnostics, TERUMO, TOA EIYO and NIPRO, and personal fees from Abbott Medical Japan, Amgen Astellas BioPharma, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific Japan, Bristole-Myers Squibb, Daiichi Sankyo, Novartis, TERUMO, Eli Lilly and Company, ONO PHARMACEUTICAL and Pfizer Pharmaceuticals. SH has received personal fees from Daiichi Sankyo Company, Bayer, Astellas Pharma, Pfizer Pharmaceuticals, Novartis Pharmaceuticals, Kowa Company, Otsuka Pharmaceutical, AstraZeneca, Eli Lilly Japan, Ono Pharmaceutical, TOA EIYO, Kyowa Kirin and Boehringer Ingelheim Japan that include speaking and lecture fees. SH has received grants from Roche Diagnostics, FUJIFILM Toyama Chemical, TOA EIYO and Bristol Myers Squibb. DN has received personal fees from Roche Diagnostics. YSa has received personal fees from Otsuka Pharmaceutical, Ono Pharmaceutical, Daiichi Sankyo, Mitsubishi Tanabe Pharma Corporation, AstraZeneca K.K. and Actelion Pharmaceuticals, and grants from Roche Diagnostic, FUJIFILM Toyama Chemical, Bristol-Myers Squibb, Co, Biosense Webster, Inc., Abbott Medical Japan, Otsuka Pharmaceutical, Daiichi Sankyo Company, Mitsubishi Tanabe Pharma Corporation, Astellas Pharma, Kowa Company, Boehringer Ingelheim Japan, and Biotronik. The other authors have nothing to disclose.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
1 Wollert KC, Kempf T, Wallentin L. Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease. Clin Chem 2017; 63: 140–51. doi:10.1373/clinchem.2016.255174
2 Luan HH, Wang A, Hilliard BK, et al. GDF15 Is an Inflammation-Induced Central Mediator of Tissue Tolerance. Cell 2019; 178: 1231–44. doi:10.1016/j.cell.2019.07.033
3 Ling T, Zhang J, Ding F, et al. Role of growth differentiation factor 15 in cancer cachexia (Review). Oncol Lett 2023; 26: 462. doi:10.3892/ol.2023.14049
4 Adela R, Banerjee SK. GDF-15 as a Target and Biomarker for Diabetes and Cardiovascular Diseases: A Translational Prospective. J Diabetes Res 2015; 2015: 490842. doi:10.1155/2015/490842
5 Tsai VW-W, Manandhar R, Jørgensen SB, et al. The anorectic actions of the TGFβ cytokine MIC-1/GDF15 require an intact brainstem area postrema and nucleus of the solitary tract. PLoS ONE 2014; 9: e100370. doi:10.1371/journal.pone.0100370
6 Ago T, Sadoshima J. GDF15, a cardioprotective TGF-beta superfamily protein. Circ Res 2006; 98: 294–7. doi:10.1161/01.RES.0000207919.83894.9d
7 Zhang SY, Danaei Z, Bruce K, et al. Acute Activation of GFRAL in the Area Postrema Contributes to Glucose Regulation Independent of Weight. Diabetes 2024; 73: 426–33. doi:10.2337/db23-0705
8 Raffin J, Rolland Y, Parini A, et al. Association between physical activity, growth differentiation factor 15 and bodyweight in older adults: A longitudinal mediation analysis. J Cachexia Sarcopenia Muscle 2023; 14: 771–80. doi:10.1002/jcsm.13152
9 Oba K, Ishikawa J, Tamura Y, et al. Serum Growth Differentiation Factor 15 Levels Predict the Incidence of Frailty among Patients with Cardiometabolic Diseases. Gerontology 2024; 70: 517–25. doi:10.1159/000536150
10 Chan MMY, Santhanakrishnan R, Chong JPC, et al. Growth differentiation factor 15 in heart failure with preserved vs. reduced ejection fraction. Eur J Heart Fail 2016; 18: 81–8. doi:10.1002/ejhf.431
11 Otaki Y, Shimizu M, Watanabe T, et al. Growth Differentiation Factor 15 and Clinical Outcomes in Japanese Patients With Heart Failure. Circ J 2023; 87: 1120–9. doi:10.1253/circj.CJ-23-0088
12 Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr 2015; 28: 1–39. doi:10.1016/j.echo.2014.10.003
13 Benes J, Kotrc M, Wohlfahrt P, et al. The Role of GDF-15 in Heart Failure Patients With Chronic Kidney Disease. Can J Cardiol 2019; 35: 462–70. doi:10.1016/j.cjca.2018.12.027
14 Doğan Akagündüz D, Şahin H, Akagündüz B. Malnutrition and Related Factors in Older Patients With Gastrointestinal Cancer Receiving Chemotherapy. Cureus 2024; 16: e58252. doi:10.7759/cureus.58252
15 Sotomi Y, Iwakura K, Hikoso S, et al. Prognostic significance of the HFA-PEFF score in patients with heart failure with preserved ejection fraction. ESC Heart Fail 2021; 8: 2154–64. doi:10.1002/ehf2.13302
16 Sunaga A, Hikoso S, Yamada T, et al. Prognostic impact of Clinical Frailty Scale in patients with heart failure with preserved ejection fraction. ESC Heart Fail 2021; 8: 3316–26. doi:10.1002/ehf2.13482
17 Pocock SJ, Ariti CA, McMurray JJV, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 2013; 34: 1404–13. doi:10.1093/eurheartj/ehs337
18 Lyu L, Xu J, Xv C, et al. Prognostic value of growth differentiation factor-15 in heart failure among whole ejection fraction phenotypes. ESC Heart Fail 2024; 11: 2295–304. doi:10.1002/ehf2.14807
19 Nakagomi A, Kohashi K, Morisawa T, et al. Nutritional Status is Associated with Inflammation and Predicts a Poor Outcome in Patients with Chronic Heart Failure. J Atheroscler Thromb 2016; 23: 713–27. doi:10.5551/jat.31526
20 Kempf T, Eden M, Strelau J, et al. The transforming growth factor-beta superfamily member growth-differentiation factor-15 protects the heart from ischemia/reperfusion injury. Circ Res 2006; 98: 351–60. doi:10.1161/01.RES.0000202805.73038.48
21 Hsu J-Y, Crawley S, Chen M, et al. Non-homeostatic body weight regulation through a brainstem-restricted receptor for GDF15. Nature New Biol 2017; 550: 255–9. doi:10.1038/nature24042
22 Wang X, Lam CSP, Vaduganathan M, et al. Effects of Dapagliflozin in Patients in Asia: A Post Hoc Subgroup Analysis From the DELIVER Trial. JACC Asia 2024; 4: 108–18. doi:10.1016/j.jacasi.2023.10.005
23 Omar M, Jensen J, Kistorp C, et al. The effect of empagliflozin on growth differentiation factor 15 in patients with heart failure: a randomized controlled trial (Empire HF Biomarker). Cardiovasc Diabetol 2022; 21: 34. doi:10.1186/s12933-022-01463-2
24 Coats AJS, Butler J, Tsutsui H, et al. Efficacy of empagliflozin in heart failure with preserved ejection fraction according to frailty status in EMPEROR-Preserved. J Cachexia Sarcopenia Muscle 2024; 15: 412–24. doi:10.1002/jcsm.13393
© 2025 Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. http://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Yano, Masamichi 6 ; Hayashi, Takaharu 7 ; Nakagawa, Akito 8 ; Nakagawa, Yusuke 9 ; Yasumura, Yoshio 10 ; Yamada, Takahisa 5 ; Hikoso, Shungo 11
; Sotomi, Yohei 1
; Sakata, Yasushi 1 1 Osaka University Graduate School of Medicine Department of Cardiovascular Medicine, Suita, Japan
2 Osaka University Graduate School of Medicine Department of Cardiovascular Medicine, Suita, Japan; Department of Medical Informatics, Osaka University Graduate School of Medicine, Osaka, Japan
3 Environmental Medicine, Osaka University, Suita, Japan
4 Department of Cardiology, Rinku General Medical Center, Izumisano, Japan; Department of Cardiology, Pulmonology, Hypertension and Nephrology, Ehime University Graduate School of Medicine, Toon, Japan
5 Osaka General Medical Center, Osaka, Japan
6 Osaka Rosai Hospital, Sakai, Japan
7 Cardiology, Osaka Police Hospital, Osaka, Japan
8 Shoushoukai healthcare corporation, Nakagawa Clinic, Suita, Japan
9 Kawanishi City Medical Center, Kawanishi, Japan
10 Amagasaki Chuo Hospital, Amagasaki, Japan
11 Department of Cardiology, Nara Medical University, Kashihara, Japan