Association Between Serum N-Terminal Prohormone of Brain Natriuretic Peptide (NT-proBNP) Levels and Frailty in Community-Dwelling Old-Old Older Adults　― SONIC Study ―

Abstract

Background: Heart failure (HF) and frailty are increasing among aging populations, but because data on the association between potential cardiac overload or asymptomatic HF, measured by the serum level of N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and frailty among community-dwelling old-old older adults (≥75 years) are limited, we examined this association.

Methods and Results: A cross-sectional analysis was conducted using data from a longitudinal cohort study. Frailty was assessed using the Japanese version of the Cardiovascular Health Study (J-CHS) criteria. Association between log-transformed NT-proBNP levels and frailty were examined using multinomial logistic regression. The discriminative ability of NT-proBNP for frailty was assessed using receiver operating characteristic (ROC) curve analysis. A total of 588 participants (46.9% female, median age: 77 (76–86) years) were included. Log-transformed NT-proBNP was significantly associated with frailty compared to robust (OR 1.69; 95% CI 1.23–2.32; P=0.001), even after adjusting for potential confounding factors. NT-proBNP had modest discriminative ability for frailty (AUC 0.64; 95% CI 0.59–0.70; P<0.001), with an optimal cutoff of 94.5 pg/mL.

Conclusions: Elevated serum NT-proBNP levels are independently associated with frailty onset in community-dwelling old-old older adults, driven by the interaction between potential cardiac overload or asymptomatic HF and frailty. Serum NT-proBNP may be a useful tool for identifying frailty associated with cardiac overload.

Central Figure

As the global population ages,¹ the prevalence of heart failure (HF) among older adults is rising rapidly.^2,3 Consequently, many community-dwelling older adults may experience potential cardiac over load or asymptomatic HF.^4,5 Similarly, the prevalence of frailty increases with age,⁶ particularly among individuals aged ≥75 years.⁷ Both HF and frailty are independently associated with adverse outcomes, such as reduced quality of life or death.^8–13 However, these conditions interact, exacerbating each other’s effects^14–16 and leading to worse health outcomes, including hospitalization and death.^17–20

Early diagnosis of both HF and frailty is crucial,²¹ particularly for old-old older adults aged ≥75 years, who comprise approximately 30% of the population in Japan.²² Serum N-terminal prohormone of brain natriuretic peptide (NT-proBNP) is a well-established biomarker for HF screening and severity assessment.^23–27 In addition to frailty conventional assessment tools,^10,28 several biomarkers have been proposed to aid in the identification of frailty.^29,30 Furthermore, some previous studies have explored the association between serum NT-proBNP levels and frailty in individuals with apparent HF.^30,31

Despite these advances, limited data exist on the relationship between potential cardiac overload or asymptomatic HF, as measured by serum NT-proBNP, and frailty in community-dwelling old-old older adults aged ≥75 years without a diagnosed HF condition.

Therefore, in this study we aimed to investigate the relationship between potential cardiac overload or asymptomatic HF (evaluated using serum NT-proBNP levels) and frailty, as well as assessing the discriminative value of NT-proBNP for frailty in this population.

Methods

Study Design and Participants

This research was conducted as part of the Septuagenarian, Octogenarian, Nonagenarian Investigation of Centenarian (SONIC) study, a Japanese longitudinal cohort study examining factors associated with healthy longevity in community-dwelling older adults. The study includes participants from 4 regions in Japan, covering both urban and non-urban areas in the western and eastern parts of the country. Participants are assessed every 3 years and categorized into 3 age groups: 70s, 80 s and 90s. A detailed description of the study design and methods has been published previously.³²

For this cross-sectional analysis, the study population consisted of old-old older adults aged ≥75 years who participated in the western regions of the SONIC study between July 2016 and December 2018. Eligibility criteria required participants to have completed a full frailty assessment (described later in this section) and undergone measurement of serum NT-proBNP levels, which was performed only in the survey of the western region. Individuals with HF were excluded. The selection process for study participants is illustrated in Figure 1.

Figure 1.

Selection of study participants. HF, heart failure; J-CHS, Japanese version of Cardiovascular Health Study; NT-proBNP, N-terminal prohormone of brain natriuretic peptide.

Data Collection

All data, including serum NT-proBNP levels and frailty assessments, were collected during the same period. Demographic and clinical data, including medical history, smoking status (categorized as never, past, and current), and alcohol consumption (categorized as never, moderate, and excessive), were collected through participant questionnaires. Medication data were obtained from prescription records. Blood pressure (BP) was measured twice for each arm using mercury sphygmomanometers by clinical doctors or registered nurses at the survey venue, and the mean of the 4 measurements was calculated for analysis. Body mass index (BMI) was determined using participants’ height and weight measured at the survey venue.

Blood samples for serum NT-proBNP and other biomarkers were collected by clinical doctors or registered nurses using EDTA-2K, sodium fluoride, and separation gel-containing vacuum tubes, followed by centrifugation at 3,000 g for 10 min at 4℃. Serum NT-proBNP levels were measured using an electrochemical luminescence immunoassay (ECLIA: Cobas 8000 Analyzer; Roche Diagnostics, Ltd.). Hemoglobin (Hb) levels were analyzed using an automated hematology analyzer (Cell-Dyne Sapphire; Abbott Japan LLC) via laser flow cytometry, and the high-sensitivity C-reactive protein (hs-CRP) levels were measured using a latex nephelometry assay (Behring Nephelometer II (BNII): Siemens Healthcare, Inc.). Total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were determined by enzymatic methods using an automated analyzer (Hitachi High-Tech Co., Ltd.). HbA1c levels were measured by an enzymatic method using a JCA-BM9130 analyzer (JEOL Ltd.); plasma glucose levels were evaluated using a hexokinase-glucose 6 phosphate dehydrogenase (G6PDH) coupled assay on the same platform (Nihon Rinsho, Inc.). Serum cystatin C levels were estimated using a JCA-BM8040 analyzer (JEOL Ltd.), and albumin (Alb) levels was measured using a modified bromocresol purple (BCP) method on an automated analyzer (Hitachi High-Tech Co., Ltd.).

Frailty was assessed using the Japanese version of the Cardiovascular Health Study (J-CHS) criteria, which have been validated for reliability and validity.³³ The J-CHS criteria included 5 components: shrinking, weakness, exhaustion, slowness, and low activity. Weakness was evaluated using grip strength measurements, and slowness was measured using gait speed assessed at the survey venue; the remaining components were collected via self-reported questionnaires.

Definitions

Frailty Classification Frailty classification was based on the J-CHS criteria, with participants meeting ≥3 items defined as frailty, those meeting 1–2 items classified as prefrailty, and the remaining participants categorized as robust.³³ In some analyses, participants were further dichotomized into 2 groups: those with frailty were defined as “frailty”, while those with prefrailty or robust status were categorized as “non-frailty”.

Other Diseases and Clinical Conditions Hypertension was defined as the use of antihypertensive medications, systolic BP (SBP) ≥140 mmHg, or diastolic BP (DBP) ≥90 mmHg, according to the Japanese Society of Hypertension 2014 guidelines.³⁴ Diabetes was classified as the use of antidiabetic medications, HbA1c ≥6.5%, or casual plasma glucose ≥200 mg/dL.³⁵ Dyslipidemia was defined as the use of antihyperlipidemic drugs, LDL-C ≥140 mg/dL, HDL-C <40 mg/dL or TG ≥150 mg/dL.³⁶ Estimated glomerular filtration rate (eGFR) calculated from cystatin C (eGFRcys) was derived using the following formula: eGFRcys (mL/min/1.73 m²) = [104 × cystatin C (mg/L) ^ (−1.019) × 0.996 ^ (age) (×0.929 if female)] − 8.³⁷ Renal dysfunction was defined as eGFRcys <60 mL/min/1.73 m². Anemia was defined as Hb <13 g/dL in males and <12 g/dL in females.³⁸ Malnutrition was defined as Alb ≤3.8 g/dL.^39,40 Smoking status was categorized as past and/or current smokers. Alcohol consumption was classified as moderate or excessive for current drinkers.

Statistical Analysis

Categorical variables are expressed as number and percentages, while continuous variables are presented as medians with interquartile ranges (IQRs). To assess the relationship between participants’ characteristics and serum NT-proBNP levels, univariate linear regression analysis was performed. Post hoc comparisons of serum NT-proBNP levels across the frailty classifications were conducted using the Dann-Bonferroni test following the Kruskal-Wallis test.

The association between serum NT-proBNP levels with frailty was evaluated using multinomial logistic regression analysis. Potential confounding factors were selected based on previous studies and included as covariates: sex, age, smoker, alcohol consumption, BMI, hs-CRP, atrial fibrillation (AF), arrythmia (without AF), hypertension, diabetes, stroke, rheumatoid arthritis, renal dysfunction, anemia and malnutrition.^41–50 The variance inflation factor (VIF) were calculated to assess multicollinearity among these variables. Binomial logistic regression analysis was also conducted to examine the association between serum NT-proBNP levels and frailty, with non-frailty (the latter combining prefrailty and robust participants) as the reference group. In all regression analyses, NT-proBNP and hs-CRP levels were log-transformed using the natural logarithm (base e) due to their right-skewed distributions (log (NT-proBNP) and log (hs-CRP)).

To assess the discriminative ability of serum NT-proBNP levels for frailty, receiver operating characteristic (ROC) curve analysis was performed. Participants were grouped into 2 categories: frailty and non-frailty (prefrailty and robust). The area under the ROC curve (AUC) was calculated to evaluate the accuracy of NT-proBNP in distinguishing frailty from non-frailty. The optimal cutoff points for serum NT-proBNP levels were determined using the Youden Index. Additionally, the discriminative performance of binomial logistic regression model was also evaluated using AUC. A two-tailed P value <0.05 was considered statistically significant. All analyses were conducted using SPSS version 30 software (IBM Japan, Tokyo, Japan).

Results

Basic Characteristics of the Study Participants

The total number of participants was 588, with 46.9% being female and a median age of 77 years (IQR: 76–86). The median serum NT-proBNP levels were 126 pg/mL (IQR: 72–246). The prevalence of AF was 3.9% and that of renal dysfunction was 52.2%. In the univariate linear regression analysis, age, anemia, and malnutrition were significantly associated with log (NT-proBNP) (standardized regression coefficient [β], 0.34; 95% confidence interval [CI] 0.04–0.07; β, 0.19; 95% CI 0.24–0.61; β, 0.18; 95% CI 0.32–0.86, respectively) (Table 1). The prevalence of the frailty, prefrailty and robust classifications was 21.1%, 52.4% and 26.5%, respectively. Serum NT-proBNP levels significantly increased as frailty classification worsened (P<0.001), as shown in Figure 2.

Table 1.

Basic Characteristics of Study Participants and Association With Serum NT-proBNP Levels

	All (n=588)	Log (NT-proBNP)
		β	95% CI
Sex (female), n (%)	276 (46.9)	−0.04	−0.24, 0.08
Age (years)	77 (76–86)	0.34	0.04, 0.07
Smoker, n (%)	231 (40.2)	−0.01	−0.38, 0.30
Alcohol consumption, n (%)	245 (42.6)	0.03	−0.11, 0.22
History of falls within the past year, n (%)	131 (22.3)	0.12	0.09, 0.47
BMI (kg/m2)	22.4 (20.4–24.3)	−0.06	−0.05, 0.01
NT-proBNP (pg/mL)	126 (72–246)
hs-CRP (mg/L)	0.486 (0.236–1.030)	0.07	−0.01, 0.13
Comorbidities, n (%)
Heart disease	145 (24.7)	0.25	0.40, 0.76
Coronary artery disease	43 (7.3)	0.04	−0.14, 0.47
AF	23 (3.9)	0.27	0.98, 1.78
Arrythmia (without AF)	42 (7.1)	0.14	0.24, 0.86
Cardiomyopathy	2 (0.3)	0.08	−0.06, 2.69
Valvular heart disease	8 (1.4)	0.09	0.08, 1.46
Hypertension	434 (73.8)	0.10	0.03, 0.40
Diabetes	111 (18.9)	−0.08	−0.41, −0.01
Stroke	61 (10.4)	0.01	−0.22, 0.31
COPD	6 (1.0)	0.00	−0.78, 0.82
Rheumatoid arthritis	7 (1.2)	0.04	−0.36, 1.12
Renal dysfunction	302 (52.2)	0.24	0.33, 0.64
Anemia	150 (25.5)	0.19	0.24, 0.61
Malnutrition	55 (9.4)	0.18	0.32, 0.86

Categorical variables are expressed as number (%), and continuous variables are shown as median (interquartile range). Univariate linear regression analysis was used. NT-proBNP and hs-CRP were log-transformed in the univariate linear regression analysis. β, standardized regression coefficient; AF, atrial fibrillation; BMI, body mass index; CI, confidence interval; COPD, chronic obstructive pulmonary disease; hs-CRP, high-sensitivity C-reactive protein; NT-proBNP, N-terminal prohormone of brain natriuretic peptide.

Figure 2.

Serum NT-proBNP levels and proportion of participants by frailty classification. The number and percentage of participants in each frailty classification are shown below each box. The Kruskal-Wallis test was used to compare groups (P<0.001) and the Dann-Bonferroni test was used to compare each groups. NT-proBNP, N-terminal-pro brain natriuretic peptide.

Association of Serum NT-proBNP Levels and Frailty

Multinomial logistic regression analysis demonstrated that log (NT-proBNP) was significantly associated with frailty, even after adjusting for potential confounding factors (odds ratio [OR], 1.69; 95% CI 1.23–2.32; P=0.001) (Table 2). When using non-frailty as the reference group, binomial logistic regression analysis yielded a similar result, confirming the association between log (NT-proBNP) and frailty (OR 1.45; 95% CI 1.12–1.88; P=0.004) (Supplementary Table). Conversely, no significant association was found between log (NT-proBNP) and prefrailty (OR 1.23; 95% CI 0.95–1.58; P=0.111).

Table 2.

Association of Serum NT-proBNP Levels With Frailty Using Multinomial Logistic Regression Analysis

	Prefrailty*		Frailty*		VIF
	OR (95% CI)	P value	OR (95% CI)	P value
Log (NT-proBNP)	1.23 (0.95–1.58)	0.111	1.69 (1.23–2.32)	0.001	1.5
Sex (Ref: male)	0.89 (0.56–1.42)	0.631	1.18 (0.63–2.20)	0.603	1.6
Age	1.05 (1.01–1.10)	0.012	1.12 (1.06–1.18)	<0.001	1.6
Smoker	1.28 (0.44–3.76)	0.654	7.63 (2.32–25.18)	<0.001	1.1
Alcohol drinker	0.76 (0.48–1.19)	0.229	0.55 (0.30–1.02)	0.057	1.4
History of falls within the past year	1.49 (0.86–2.58)	0.155	3.02 (1.56–5.85)	0.001	1.2
BMI	0.95 (0.89–1.03)	0.207	0.99 (0.90–1.09)	0.823	1.2
Log (hs-CRP)	0.88 (0.73–1.05)	0.157	0.98 (0.77–1.24)	0.839	1.1
AF	0.34 (0.11–0.98)	0.047	0.20 (0.04–1.01)	0.051	1.2
Arrythmia (without AF)	1.28 (0.52–3.15)	0.591	1.51 (0.49–4.61)	0.470	1.1
Hypertension	1.13 (0.72–1.80)	0.596	1.12 (0.59–2.14)	0.726	1.1
Diabetes	0.86 (0.51–1.46)	0.578	0.88 (0.43–1.79)	0.719	1.1
Stroke	1.45 (0.72–2.94)	0.302	1.33 (0.53–3.35)	0.545	1.1
Rheumatoid arthritis	1.72 (0.18–15.99)	0.636	0.59 (0.03–11.28)	0.724	1.1
Renal dysfunction	1.08 (0.70–1.67)	0.743	1.40 (0.77–2.55)	0.275	1.3
Anemia	1.28 (0.73–2.25)	0.384	1.75 (0.88–3.47)	0.109	1.3
Malnutrition	1.13 (0.46–2.75)	0.796	1.61 (0.60–4.33)	0.348	1.2

Reference category: *Robust. Multinomial logistic regression analysis was used. NT-proBNP and hs-CRP were log-transformed. OR, odds ratio; VIF, variance inflation factor. Other abbreviations as in Table 1.

Discriminative Ability of Serum NT-proBNP Levels for Frailty

The discriminative potential of the serum NT-proBNP levels for identifying frailty was assessed using ROC curve analysis, as shown in Figure 3. The AUC for serum NT-proBNP was 0.64 (95% CI 0.59–0.70; P<0.001), indicating modest discriminative accuracy. The optimal cutoff point for serum NT-proBNP levels, determined using the Youden Index, was identified as 94.5 pg/mL. Binomial logistic regression model incorporating multiple variables demonstrated moderate discriminative performance, with AUC of 0.76 (95% CI 0.71–0.81; P<0.001) (Supplementary Figure).

Figure 3.

Receiver operating characteristic curve (ROC) analysis of the potential of serum NT-proBNP levels to discriminate frailty. AUC, area under the curve; CI, confidence interval; NT-proBNP, N-terminal prohormone of brain natriuretic peptide.

Discussion

The results of this study demonstrated that the serum NT-proBNP levels were independently associated with frailty in community-dwelling old-old older adults aged ≥75 years without an apparent HF diagnosis. Furthermore, ROC analysis revealed that serum NT-proBNP had only modest discriminative accuracy for frailty, with an optimal cutoff point of 94.5 pg/mL. These findings suggest that serum NT-proBNP could be a valuable standalone biomarker for identifying frailty, particularly in the presence of elevated levels.

The prevalence of frailty in this study was 21.1%. Despite the absence of a definitive HF diagnosis, the finding is consistent with previous reports of a frequency of frailty among HF outpatients ranging from 19% to 52%.^20,51,52 The interaction between frailty and HF has been shown to exacerbate adverse outcomes.^17,19,20 Additionally, Ogita et al. demonstrated that elderly individuals who participated in health and frailty check-ups had a significantly lower risk of requiring long-term care or death compared to those who did not participate.⁵³ Consequently, evaluating frailty in individuals with potential cardiac overload is also critical to mitigating these risks.²¹

The mutual influence of HF, often measured by serum NT-proBNP, and frailty is well documented, with each condition worsening the prognosis of the other.^18,20,21,31 Shared pathways, such as systemic inflammation, neurohormonal dysregulation, sarcopenia, and malnutrition, contribute to their development.^30,54–56 The independent association between serum NT-proBNP levels and frailty may be driven by these underlying pathophysiological pathways, even in individuals without a formal HF diagnosis. Furthermore, subclinical elevations in the serum NT-proBNP levels may reflect systemic vulnerability, including muscle mass loss and increased energy expenditure, both of which contribute to frailty.^57–59

Given this relationship between the serum NT-proBNP levels and frailty, it would appear to be a potential biomarker for frailty. To our knowledge, no previous studies have evaluated the usefulness of the serum NT-proBNP levels in distinguishing between individuals with and without frailty, nor has a specific cutoff point been proposed. Although ROC analysis indicated poor discriminative accuracy for the serum NT-proBNP levels alone in detecting frailty, the biomarker still holds promise for simple screening in scenarios where cardiac overload contributes to frailty. As the frailty phenotype in this study was assessed using the J-CHS criteria, mainly focusing on physical components, the serum NT-proBNP levels may particularly reflect the biological aspects of physical frailty, such as muscle wasting and altered energy metabolism.^57–59 Moreover, the identified optimal cutoff level (94.5 pg/mL) was lower than the conventional diagnostic threshold for HF (typically 125 and 300 pg/mL),^60,61 suggesting that even slight elevation of the serum NT-proBNP level within the normal range may signal early physiological vulnerability in the context of frailty. Miyazaki et al. showed that even slight elevations in NT-proBNP (i.e., levels ≥52.4 pg/mL) can be associated with the future risk of pre-HF,⁶² supporting the potential relevance of subdiagnostic NT-proBNP elevations in identifying early pathological states, including frailty.

Current frailty assessment scales often include items such as muscle strength and walking speed, which may be challenging to evaluate in certain settings, particularly among cognitively impaired individuals and community-dwelling older adults.^10,63,64 In this context, serum NT-proBNP measurement offers a practical alternative for initial frailty screening in the community. Although its standalone discriminative power is limited, it may serve as a useful adjunct to traditional assessment methods, particularly for frailty associated with cardiac overload.

Study Strengths and Limitations

This study has several notable strengths. First, it focused on participants aged ≥75 years including nonagenarians, a demographic rarely examined in previous studies. Second, the independent association between serum NT-proBNP level and frailty in this population was elucidated. Third, our findings demonstrated a correlation between serum NT-proBNP levels and frailty, highlighting the potential utility of serum NT-proBNP as a standalone screening tool for frailty associated with potential cardiac overload. However, the study also has limitations. Being cross-sectional in design, it cannot establish causality between serum NT-proBNP levels and frailty, nor definitely assess the ability of the serum NT-proBNP to discriminate frailty. Additionally, although multinomial logistic regression analysis was performed, not all potential confounders could be adjusted for due to the limited sample size, despite using covariates commonly cited in previous studies. Furthermore, the influence of medications on serum NT-proBNP levels was not accounted for in the analysis. Moreover, participants were drawn from the western region survey of the SONIC study because insufficient serum samples were available to measure serum NT-proBNP levels in the eastern region survey; this may limit generalizability. Individuals with a diagnosed HF were excluded, but some participants may have had undiagnosed or asymptomatic HF. Conversely, this could reflect a real-would issue that HF is sometimes undiagnosed among community-dwelling old-old older adults.⁵ Further longitudinal studies are needed to explore the causal relationship between potential cardiac overload or asymptomatic HF and frailty, as well as to evaluate the discriminative utility of the serum NT-proBNP levels in frailty screening.

Conclusions

Our results demonstrate that elevated serum NT-proBNP levels were significantly associated with frailty in community-dwelling old-old older adults aged ≥75 years without a history of HF, highlighting its potential as a single biomarker for frailty and an independent risk indicator. Although the discriminative accuracy of serum NT-proBNP alone for frailty was low (AUC=0.64), the findings suggest that elevated levels may be associated with an interaction between potential cardiac overload and frailty, aiding in the identification of at-risk individuals. Measurement of the serum NT-proBNP levels could serve as a valuable tool for initial frailty screening, prompting comprehensive assessments. Further research is necessary to explore the prognostic value of NT-proBNP in frailty, establish standardized threshold values for risk assessment, and enhance its clinical applicability. The current findings not only contribute to a deeper understanding of the relationship between cardiac overload and frailty, but also emphasize the need for additional studies to refine clinical practices and improve health outcomes in older adults.

Acknowledgments

We are grateful to all the staff members involved in the SONIC study. We sincerely appreciate the kind cooperation of all the SONIC participants.

Sources of Funding

This work was supported by JSPS KAKENHI [grant numbers 22H00089, 20H03576, 23K24672]; the Ministry of Health Labour and Welfare [grant numbers K24FA1005, 23AA2006]; by AMED [grant number 24dk0110049 h0002]; Center for infectious Disease Education and Research from the Nippon Foundation [grant number JM00000160]; Osaka University’s International Joint Research Promotion Program Support Type A [grant number J219713005]; and JST SPRING [grant number JPMJSP2138].

Disclosures

The authors declare they have no conflicts of interest.

IRB Information

This study was reviewed and approved by the institutional review boards of University of Osaka Graduate Schools of Medicine, Dentistry, and Human Sciences, as well as the Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology (approval numbers: 266, H22-9, 22 018 and 38, respectively). Study procedures were followed in accordance with the “Declaration of Helsinki” and the ethical standards of the responsible committee on human experimentation. Written informed consent was given by all participants prior to their enrollment in the study.

Data Availability

The deidentified participant data will not be shared.

Author Contributions

S.T.: Writing - original draft, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, K.G.: Conceptualization, Investigation, Methodology, Software, Project administration, Writing - review & editing, M. Kabayama: Funding acquisition, Investigation, Project administration, M. Kido: Investigation, Y. Akagi: Methodology, Software, Investigation, Project administration, Writing - review & editing, M.M.: Writing - review & editing, H.A.: Methodology, Investigation, Writing - review & editing, Y.T.: Investigation, T.N.: Investigation, Project administration, S.Y.: Investigation, Project administration, Y.G.: Funding acquisition, Investigation, Resources, Project administration, K.I.: Investigation, Y. Arai: Investigation, Y.M.: Investigation, Project administration, T.H.; Investigation, Writing - review & editing, K.Y.: Investigation, K.K.: Conceptualization, Funding acquisition, Methodology, Investigation, Writing - review & editing.

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circrep.CR-25-0110

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