Relationship of interleukin‐16 with different phenogroups in acute heart failure with preserved ejection fraction

Abstract

Aims

Interleukin‐16 (IL‐16) has been reported to mediate left ventricular myocardial fibrosis and stiffening in patients with heart failure with preserved ejection fraction (HFpEF). We sought to elucidate whether IL‐16 has a distinct impact on pathophysiology and prognosis across different subphenotypes of acute HFpEF.

Methods and results

We analysed 211 patients enrolled in a prospective multicentre registry of acute decompensated HFpEF for whom serum IL‐16 levels after stabilization were available (53% female, median age 81 [interquartile range 75–85] years). We divided this sub‐cohort into four phenogroups using our established clustering algorithm. The study endpoint was all‐cause death. Patients were subclassified into phenogroup 1 (‘rhythm trouble’ [n = 69]), phenogroup 2 (‘ventricular‐arterial uncoupling’ [n = 49]), phenogroup 3 (‘low output and systemic congestion’ [n = 41]), and phenogroup 4 (‘systemic failure’ [n = 52]). After a median follow‐up of 640 days, 38 patients had died. Among the four phenogroups, phenogroup 2 had the highest IL‐16 level. The IL‐16 level showed significant associations with indices of cardiac hypertrophy, diastolic dysfunction, and congestion only in phenogroup 2. Furthermore, the IL‐16 level had a significant predictive value for all‐cause death only in phenogroup 2 (C‐statistic 0.750, 95% confidence interval 0.606–0.863, P = 0.017), while there was no association between the IL‐16 level and the endpoint in the other phenogroups.

Conclusions

Our results indicated that the serum IL‐16 level had a significant association with indices that reflect the pathophysiology and prognosis of HFpEF in a specific phenogroup in acute HFpEF.

Introduction

Although the incidence of heart failure with preserved ejection fraction (HFpEF) is increasing, its prognosis remains poor.¹ HFpEF encompasses a heterogeneous syndrome with a complex pathophysiology and manifestations, which is thought to be responsible for its extremely limited available therapeutic options.² Categorization of patients with HFpEF based on its underlying pathophysiology may lead to the development of phenotype-specific therapies.

In HFpEF, a systemic proinflammatory state driven by a plethora of comorbidities is suggested to cause coronary microvascular inflammation and resultant dysfunction, which is responsible for the stiffening of cardiomyocytes and interstitial fibrosis, leading to cardiac stiffening and increased left ventricular (LV) filling pressure.^3,4 Previously, we showed that interleukin-16 (IL-16) is an inflammatory mediator of LV myocardial fibrosis and stiffening in HFpEF.⁵ However, it is not clear which patient groups are more affected by inflammatory mediators in HFpEF.

Phenomapping emerged as a method to identify distinct patient subgroups using statistical techniques and a variety of patient data.⁶ Previously, we employed a machine-learning-based clustering method (latent class analysis) on data from patients with HFpEF admitted for acute decompensated heart failure (ADHF), and established a clustering model.^7,8 Furthermore, we demonstrated the potential of this approach to identify populations in which specific medications would be effective.⁹ Identifying inflammatory mediators that have a distinct impact on pathophysiology and prognosis across different phenogroups may help identify phenogroup-specific therapeutic targets.

Accordingly, we sought to elucidate the impact of serum IL-16 levels on the indices that reflect LV diastolic function and prognosis across different phenogroups of acute HFpEF.

Methods

Study patients and data collection

The Prospective Multicenter Observational Study of Patients with Heart Failure with Preserved Ejection Fraction (PURSUIT-HFpEF) is a study in which collaborating hospitals in the Osaka urban area record clinical, echocardiographic, and outcome data from patients with ADHF with preserved LV ejection fraction (University Hospital Medical Information Network Clinical Trials Registry ID: UMIN000021831).¹⁰ Consecutive patients were prospectively registered and agreed to be followed up for the collection of outcome data. Anonymized data were transferred to the data center of Osaka University Hospital for analysis. Inclusion criteria were acute decompensated HFpEF diagnosed using the Framingham criteria¹¹ and the following: (i) LV ejection fraction ≥50% and (ii) N-terminal pro-B-type natriuretic peptide ≥400 pg/mL or brain natriuretic peptide ≥100 pg/mL on admission. Exclusion criteria were age <20 years, severe valvular disease (aortic stenosis, aortic regurgitation, mitral stenosis, or mitral regurgitation), acute coronary syndrome on admission, life expectancy of <6 months because of non-cardiac diseases, and previous heart transplantation. Written informed consent was obtained from all patients prior to study participation. The ethics committee of each participating hospital approved the study protocol. This study conformed to the ethical guidelines outlined in the Declaration of Helsinki.

Details of the data collection have been described elsewhere.¹⁰ In brief, basic patient characteristics, echocardiography, laboratory tests, and lists of medications were obtained on admission, at discharge, and 1 year after discharge. In this study, patient data on admission were analysed. Echocardiography was performed according to standard techniques using a commercially available machine, as previously reported.^10,12 Anaemia was defined as a haemoglobin level of <13.0 g/dL in men and <12.0 g/dL in women, according to the World Health Organization criteria.¹³ The estimated glomerular filtration rate was calculated using the modified isotope dilution mass spectrometry traceable modification of diet in renal disease study equation with a Japanese coefficient.¹⁴ Chronic kidney disease was defined as an estimated glomerular filtration rate of ≤60 mL/min per 1.73 m². Patients were classified according to the clinical scenario classification, based on systolic blood pressure on admission and other symptoms.¹⁵ Plasma volume status was calculated as an index of congestion, which was defined as follows: actual plasma volume (PV) = (1 − haematocrit) × [a + (b × body weight)] (a = 1530 in men and 864 in women, b = 41.0 in men and 47.9 in women); ideal PV = c × body weight (c = 39 in men and 40 in women); and plasma volume status = [(actual PV − ideal PV)/ideal PV] × 100 (%).¹⁶ The Get With The Guidelines-Heart Failure (GWTG-HF) risk score was calculated by summing points assigned to the values of the following seven predictors: age, systolic blood pressure, heart rate, blood urea nitrogen, sodium, chronic obstructive pulmonary disease, and race.¹⁷ The Controlling Nutritional Status (CONUT) score was calculated with serum albumin and total cholesterol levels and total lymphocyte count, as previously reported.¹⁸

From the overall cohort, we analysed a subgroup of patients who consented to additional blood sampling for the measurement of IL-16 in this study. Blood samples for IL-16 measurement were obtained after completion of acute phase treatment. The blood samples were centrifuged at 4°C within 30 minutes of collection and stored at −20°C until assay. The serum concentration of IL-16 was measured by commercially available enzyme-linked immunosorbent assay kits specific for human IL-16 (R&D Systems).

We have applied latent class analysis (‘VarSelLCM’ package in R 4.0.5) to the PURSUIT-HFpEF dataset and have established a machine-learning-based clustering model with the following 16 variables on hospital admission: C-reactive protein, creatinine, gamma-glutamyl transferase, brain natriuretic peptide, white blood cells, systolic blood pressure, fasting blood sugar, triglycerides, clinical scenario classification, infection-triggered ADHF, estimated glomerular filtration rate, platelets, neutrophils, GWTG-HF risk score, chronic kidney disease, and CONUT score.^7,8 In this study, we applied this clustering model to the subgroup of patients with IL-16 measurements, and classified them into four subgroups.⁸

Endpoint and follow-up

The endpoint of this study was all-cause death. The duration of follow-up was calculated from the day of admission until the endpoint. Survival data were obtained by dedicated coordinators and investigators by one of the following methods: direct contact with patients and their physicians at the hospital; in an outpatient setting, by a telephone interview with the patient's families, or by mail.

Statistical analysis

Baseline characteristics were summarized with medians and interquartile ranges (IQRs) (or means and standard deviations) for continuous variables, depending on the distributions, and with percentages for categorical variables. Continuous variables were compared using the Mann–Whitney U test and analysis of variance or the Kruskal–Wallis test with the Steel-Dwass post hoc test as appropriate. Categorical variables were compared with the chi-square test. Determinants of IL-16 levels were analysed by multivariate linear regression analysis. Correlations between variables were determined by Pearson's correlation coefficient. The predictive value of IL-16 for the endpoint was evaluated using receiver operating characteristic curve analysis, and the results were expressed in terms of the area under the curve and its 95% confidence interval. Skewed continuous variables were natural log-transformed prior to inclusion in the analysis. All statistical analyses were performed with EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria),¹⁹ or MedCalc® Statistical Software version 20.009 (MedCalc Software Ltd, Ostend, Belgium).

Results

Among the 1231 consecutive patients admitted from June 2016 to December 2021 and enrolled in the PURSUIT-HFpEF study, those with measurement of IL-16 not available (n = 1020) were excluded. Ultimately, data from 211 patients were analysed. Patients were subclassified into 4 subgroups: phenogroup 1 (‘rhythm trouble’ [n = 69]), phenogroup 2 (‘ventricular-arterial uncoupling’ [n = 49]), phenogroup 3 (‘low output and systemic congestion’ [n = 41]), and phenogroup 4 (‘systemic failure’ [n = 52]) (Figure 1).

[IMAGE OMITTED. SEE PDF]

Baseline characteristics

The baseline characteristics of the 211 patients analysed are summarized in Table 1. Characteristics of the phenogroups were almost identical to those previously reported.^7,8 In phenogroup 1, arrhythmia triggering (especially atrial fibrillation) was the most common reason for ADHF. The prevalence of comorbidities, such as diabetes, chronic kidney disease, and dyslipidemia was lower in this phenogroup. Phenogroup 2 had the highest frequency of clinical scenario 1, the highest blood pressure, and the highest brain natriuretic peptide levels among the phenogroups. Diabetes and chronic kidney disease were frequently observed in this group, and patients in this group had a higher LV mass index and ratio of early transmitral flow velocity to septal mitral annular early diastolic velocity (E/e′ ratio). Phenogroup 3 exhibited the highest gamma-glutamyl transferase level and the lowest blood pressure and heart rate among the groups. Most of the patients in this phenogroup were classified as clinical scenario 2. Phenogroup 4 was characterized by a high C-reactive protein level, infection-triggered hospitalization, and impaired nutritional status. As shown in Figure 2, phenogroup 2 had the highest median IL-16 levels among the phenogroups (phenogroup 1, 205 [IQR 158–274] pg/mL; phenogroup 2, 297 [219–442] pg/mL; phenogroup 3, 249 [201–294] pg/mL; phenogroup 4, 253 [193–368] pg/mL), although only the difference between phenogroups 1 and 2 was statistically significant by a post hoc test. Multivariate linear regression analysis including comorbidities and phenogroups demonstrated that phenogroup 2 was an independent determinant of IL-16 levels in total patients (Table S1).

Table 1 Patient baseline characteristics on admission

Characteristics	Total (n = 211)	Phenogroup 1 ‘Rhythm trouble’ (n = 69)	Phenogroup 2 ‘Ventricular-arterial uncoupling’ (n = 49)	Phenogroup 3 ‘Low output and systemic congestion’ (n = 41)	Phenogroup 4 ‘Systemic failure’ (n = 52)	P-value
Age, year	81 (75–85)	82 (76–86)	77 (67–82)	82 (78–85)	82 (76–87)	0.001
Female sex	53%	62%	45%	46%	52%	0.218
Co-morbidities
Atrial fibrillation	35%	41%	18%	46%	35%	0.027
Hypertension	90%	87%	100%	76%	94%	0.001
Diabetes	38%	33%	46%	24%	46%	0.086
Dyslipidaemia	50%	49%	63%	32%	52%	0.029
Hyperuricemia	42%	35%	45%	54%	39%	0.264
Coronary artery disease	19%	13%	25%	27%	16%	0.197
Chronic kidney disease	52%	27%	71%	71%	52%	<0.001
COPD	10%	7%	10%	12%	10%	0.849
Anaemia	66%	62%	63%	63%	77%	0.324
Prior HF-related hospitalization	16%	12%	12%	29%	14%	0.065
Trigger of acute decompensated HF
Infection	16%	6%	10%	2%	44%	<0.001
Arrhythmia	33%	41%	16%	54%	21%	<0.001
Uncontrollable blood pressure	27%	28%	51%	5%	21%	<0.001
Body mass index, kg/m2	23.9 (21.5–27.8)	24.2 (21.4–27.4)	24.3 (21.9–27.8)	23.9 (20.2–27.5)	23.1 (21.6–28.0)	0.732
CS classification
CS1/CS2/CS3/CS5	69/30/1/0%	81/19/0/0%	96/4/0/0%	27/71/2/0%	60/38/2/0%	<0.001
Systolic blood pressure, mmHg	158 (136–181)	158 (146–181)	183 (169–203)	134 (115–150)	146 (131–169)	<0.001
Diastolic blood pressure, mmHg	83 (69–100)	82 (70–100)	102 (83–128)	74 (58–88)	81 (70–90)	<0.001
Heart rate, b.p.m.	87 (70–110)	86 (69–106)	96 (76–115)	76 (60–111)	92 (77–107)	0.082
Echocardiographic data
LVEDD, mm	47 ± 7	46 ± 7	50 ± 6	46 ± 8	47 ± 7	0.009
LVMI, g/m2	110 (94–135)	109 (92–130)	118 (101–148)	116 (94–135)	103 (89–130)	0.098
RWT	0.44 (0.39–0.52)	0.46 (0.39–0.53)	0.43 (0.40–0.50)	0.45 (0.40–0.56)	0.44 (0.38–0.51)	0.608
LVEF, %	61 (55–66)	63 (56–68)	58 (53–64)	60 (55–67)	61 (57–66)	0.054
LAD, mm	43 (39–48)	43 (38–49)	41 (38–46)	46 (41–51)	42 (40–46)	0.126
E, m/s	0.95 (0.74–1.11)	0.96 (0.76–1.23)	0.84 (0.70–1.09)	0.96 (0.77–1.14)	0.95 (0.74–1.11)	0.587
e′, m/s e′, m/s e′, m/s	0.06 (0.05–0.08)	0.06 (0.05–0.08)	0.05 (0.04–0.06)	0.07 (0.05–0.08)	0.06 (0.05–0.09)	0.003
E/e′ ratio	14.9 (10.8–19.6)	14.6 (11.3–19.1)	16.4 (13.5–22.6)	13.7 (10.3–17.7)	13.5 (9.7–19.8)	0.102
TRPG, mmHg	35 (26–44)	37 (26–47)	32 (25–43)	35 (26–41)	35 (29–43)	0.568
PASP, mmHg	43 (33–51)	45 (34–54)	39 (30–49)	43 (34–51)	43 (33–50)	0.530
Plasma volume status, %	5.8 ± 12.9	4.5 ± 11.2	3.1 ± 14.6	7.0 ± 10.2	9.1 ± 14.7	0.080
GWTG-HF risk score	39 ± 8	36 ± 6	35 ± 7	44 ± 7	42 ± 7	<0.001
CONUT score	4 (2–5)	3 (1–4)	2 (1–4)	4 (3–6)	5 (3–6)	<0.001
Laboratory data
White blood cell, ×103/μL	7.1 (5.6–9.6)	6.5 (5.5–7.4)	9.9 (7.8–13.2)	5.6 (5.1–6.3)	8.8 (6.7–11.2)	<0.001
Neutrophil, ×103/μL	4.7 (3.8–6.8)	4.2 (3.3–5.1)	6.0 (4.5–8.5)	3.9 (3.4–4.5)	7.0 (5.2–9.1)	<0.001
Lymphocyte, ×103/μL	1.2 (0.8–1.7)	1.3 (0.9–1.9)	2.2 (1.2–4.2)	0.9 (0.7–1.2)	1.0 (0.7–1.3)	<0.001
Platelet, ×103/μL	198 (159–257)	200 (167–258)	212 (184–280)	166 (124–215)	205 (168–257)	0.005
NLR	4.1 (2.6–6.5)	3.4 (2.1–4.7)	3.2 (1.5–5.4)	4.3 (3.2–6.1)	6.7 (4.6–9.2)	<0.001
PLR	162 (105–253)	172 (110–233)	97 (56–167)	167 (114–274)	221 (146–303)	<0.001
Haemoglobin, g/dL	11.5 ± 2.3	11.7 ± 2.0	11.8 ± 2.5	11.3 ± 2.3	10.9 ± 2.4	0.167
Sodium, mEq/L	140 (138–142)	142 (139–143)	139 (138–142)	141 (136–142)	139 (136–141)	0.002
Creatinine, mg/dL	1.2 (0.8–1.7)	0.9 (0.8–1.1)	1.5 (1.0–3.8)	1.5 (1.2–1.9)	1.2 (0.8–1.7)	<0.001
BUN, mg/dL	24 (17–35)	18 (14–22)	27 (18–44)	33 (27–41)	27 (19–36)	<0.001
eGFR, mL/min/1.73 m2	42 (27–53)	52 (44–61)	28 (12–51)	30 (25–37)	40 (27–52)	<0.001
BNP, pg/mL	520 (312–761)	444 (281–608)	721 (441–1387)	576 (327–1114)	472 (301–611)	<0.001
NT-proBNP, pg/mL	4135 (2073–7915)	2474 (1546–5430)	8280 (2995–20 145)	5031 (2438–8165)	4095 (2223–7178)	<0.001
Uric acid, mg/dL	6.2 (5.0–7.4)	5.6 (4.7–6.8)	6.2 (5.0–7.4)	6.8 (5.5–9.6)	6.3 (5.4–7.3)	0.019
Albumin, mg/dL	3.6 (3.3–3.9)	3.7 (3.3–3.9)	3.6 (3.3–3.9)	3.6 (3.4–3.8)	3.4 (3.2–3.8)	0.015
GGT, IU/L	39 (23–68)	36 (19–70)	32 (20–50)	58 (26–133)	43 (30–76)	0.023
Triglyceride, mg/dL	75 (58–108)	70 (52–91)	116 (73–156)	72 (56–108)	71 (57–87)	<0.001
HDL-C, mg/dL	47 (39–58)	49 (42–59)	46 (35–57)	47 (40–53)	47 (38–62)	0.371
CRP, mg/dL	0.72 (0.21–2.40)	0.28 (0.16–0.74)	0.59 (0.21–1.75)	0.46 (0.18–1.03)	4.84 (2.88–8.54)	<0.001
HDL-C/CRP ratio	69 (20–219)	141 (60–327)	88 (22–264)	100 (42–212)	10 (4–20)	<0.001
FBS, mg/dL	131 (112–179)	123 (106–149)	199 (131–242)	120 (110–129)	145 (115–202)	<0.001
Oral medications
Loop diuretic	46%	39%	39%	68%	46%	0.015
ACE inhibitor/ARB	47%	41%	45%	54%	52%	0.482
Beta-blocker	52%	48%	51%	56%	56%	0.784
Aldosterone antagonist	17%	16%	4%	29%	19%	0.014
SGLT2 inhibitor	3%	1%	4%	2%	4%	0.808
Statin	30%	30%	31%	22%	37%	0.510

[IMAGE OMITTED. SEE PDF]

Correlation between interleukin-16 level and cardiac hypertrophy, diastolic dysfunction, and congestion

Associations between the serum IL-16 level and indices of cardiac hypertrophy, diastolic dysfunction, and congestion in total patients and each phenogroup are shown in Figure 3 and 4. There were weak positive correlations between the serum IL-16 level and LV mass index, E/e′ ratio, and plasma volume status in total patients (Figure 3). When patients were classified into phenogroups, the serum IL-16 level was positively correlated with LV mass index, E/e′ ratio, and plasma volume status in phenogroup 2, whereas there was no association between the IL-16 level and LV mass index, E/e′ ratio, and plasma volume status in the other phenogroups (Figure 4).

[IMAGE OMITTED. SEE PDF]

[IMAGE OMITTED. SEE PDF]

Prognostic analysis

After a median follow-up of 640 days, 38 patients died (phenogroup 1, n = 7; phenogroup 2, n = 6; phenogroup 3, n = 11; phenogroup 4, n = 14); 16 of these deaths were attributed to cardiac causes (exacerbation of HF, n = 8; fatal arrhythmia or sudden cardiac death, n = 3; and other causes of death, n = 5), and 22 were attributed to non-cardiac causes (infection, n = 12; cancer, n = 3; stroke, n = 3; renal failure, n = 1; and other causes of death, n = 3). Causes of death among the phenogroups are shown in Table 2.

Table 2 Causes of death among the phenogroups

	Phenogroup 1	Phenogroup 2	Phenogroup 3	Phenogroup 4
Cardiac death
Exacerbation of heart failure	2	1	3	2
Fatal arrhythmia or sudden cardiac death	1	0	1	1
Other causes	0	0	2	3
Non-cardiac death
Infection	2	3	2	5
Cancer	2	1	0	0
Stroke	0	1	2	0
Renal failure	0	0	0	1
Other causes	0	0	1	2

There was no difference in the serum IL-16 level between patients with and without all-cause death in total patients (273 [193–446] vs. 246 [187–328] pg/mL, P = 0.199). In addition, receiver operating characteristic curve analysis showed that the IL-16 level had no predictive value for all-cause death in total patients (Figure 5). When patients were classified into phenogroups, the serum IL-16 level of the patients with all-cause death was significantly higher than those without in phenogroup 2 (531 [372–639] vs. 282 [208–368] pg/mL, P = 0.049), whereas there was no difference between the two groups in the other phenogroups (phenogroup 1, 243 [189–286] vs. 205 [153–268] pg/mL, P = 0.585; phenogroup 3, 248 [168–288] vs. 259 [204–316] pg/mL, P = 0.462; phenogroup 4, 258 [195–502] vs. 253 [195–345] pg/mL, P = 0.317). Receiver operating characteristic curve analysis showed that the serum IL-16 level predicted all-cause death with high accuracy in phenogroup 2, whereas there was no association between the serum IL-16 level and the occurrence of all-cause death in the other phenogroups (Figure 6).

[IMAGE OMITTED. SEE PDF]

[IMAGE OMITTED. SEE PDF]

Discussion

In this study, we found a significant difference in the serum levels of IL-16 among the 4 phenogroups identified by a machine learning-based clustering model, and phenogroup 2 was demonstrated to be an independent determinant of IL-16 levels. The relationship between serum IL-16 levels and indices of cardiac hypertrophy, diastolic function, and congestion varied among the different phenogroups. Furthermore, the prognostic value of the serum IL-16 level was significant only for phenogroup 2, ‘ventricular-arterial uncoupling’, which has typical echocardiographic features, such as higher LV mass index and LV diastolic dysfunction.^5,7 To the best of our knowledge, this is the first report to elucidate a distinct association between an inflammatory mediator and indices reflecting the pathophysiology of HFpEF and prognosis across different phenogroups in acute HFpEF.

Phenomapping emerged as a tool to develop and understand various phenotypes within HFpEF.⁶ Although many efforts have been made to identify phenogroups in HFpEF, few data exist regarding differences in biomarkers or inflammatory mediators across phenogroups.^20–22 We recently revealed the distinctive characteristics of biomarkers in HFpEF phenogroups.²³ However, the detailed association between these biomarkers and indices reflecting the pathophysiology and prognosis of HFpEF remains unknown. We reported previously that IL-16 mediates LV myocardial fibrosis and stiffening in HFpEF,⁵ and other previous reports have shown that blood levels of IL-16 are significantly different among the phenogroups in patients with chronic HFpEF.^21,22 Therefore, in this study, we conducted an additional measurement of IL-16 and investigated the relationship between the serum IL-16 levels and each phenogroup. Our findings expand on those of the earlier reports, not only by demonstrating the significant difference in circulating IL-16 levels among the phenogroups, but also by showing a significant association of serum IL-16 levels with indices of cardiac hypertrophy, diastolic dysfunction, and congestion only in a specific phenogroup in acute HFpEF. Furthermore, the association of the serum IL-16 level and prognosis in phenogroup 2 ‘ventricular-arterial uncoupling’ may suggest that use of IL-16 blockade could be a potential therapeutic option in this phenogroup.

Previous phenomapping studies have identified the three most common phenogroups in patients with HFpEF, labelled as ‘older, vascular aging’, ‘metabolic, obese’, and ‘relatively younger, natriuretic peptide deficiency’ phenotypes.^6,20,24 Phenogroup 2 ‘ventricular-arterial uncoupling’ in our clustering model seems to correspond with the ‘metabolic, obese’ phenotype, in terms of the higher prevalence of hypertension, diabetes, dyslipidaemia, chronic kidney disease, LV hypertrophy, and LV diastolic dysfunction.⁷ Lower body mass index in the ‘ventricular-arterial uncoupling’ phenogroup compared with the ‘metabolic, obese’ phenotype might be explained by the lean diabetic phenotype in patients with a cardiometabolic phenotype of HFpEF in Asia, in whom, rather than body weight per se, increased visceral and epicardial adiposity is suggested to play a prominent pathophysiological role.^25–27 Sabbah et al. showed that the pan-inflammatory phenotype, which has many similarities to the ‘metabolic, obese’ phenotype, has significantly higher circulating levels of IL-16 in HFpEF.²² In a report by Cohen et al., spironolactone use was associated with better outcomes in the ‘metabolic, obese’ phenotype.²⁰ Intriguingly, in agreement with the report by Cohen et al., we recently found that mineralocorticoid receptor antagonists were specifically effective in phenogroup 2 ‘ventricular-arterial uncoupling’, although the prescription rate of mineralocorticoid receptor antagonists in phenogroup 2 was lower than that in the ‘metabolic, obese’ phenotype in their study.⁹ These facts support the similarities in the underlying pathophysiology between the ‘metabolic, obese’ phenotype in previous reports and the ‘ventricular-arterial uncoupling’ phenogroup in our clustering model.

Cohen and coworkers found the highest levels of biomarkers of tumour necrosis factor-mediated inflammation and intermediary metabolism in the ‘metabolic, obese’ phenotype.²⁰ In agreement with their report, our recent study showed the highest value of tumour necrosis factor-α and growth differentiation factor-15 in phenogroup 2 ‘ventricular-arterial uncoupling’.²³ Our current results suggest that IL-16 might be another inflammatory mediator associated with this phenogroup. Visceral and epicardial adipose tissue has been reported to be a metabolically active pro-inflammatory adipose tissue that may serve a pivotal role in the pathophysiology of HFpEF through the production of several inflammatory mediators.²⁸ Furthermore, recent reports indicate the potential production of IL-16 from visceral and epicardial adipose tissue.^29,30 We previously showed that IL-16 promotes cardiac fibrosis in HFpEF possibly through chemoattraction of macrophages into the myocardium and subsequent stimulation of the release of profibrotic mediators such as transforming growth factor-beta 1 from macrophages.⁵ Therefore, IL-16 generated from visceral and epicardial adipose tissue might induce microvascular inflammation and dysfunction leading to cardiac fibrosis and LV myocardial stiffening accompanied by left atrial enlargement in this phenogroup, akin to the observations in a rat model of HFpEF and in IL-16 transgenic mice.^5,31,32

Phenogroup 3 ‘low output and systemic congestion’ in our clustering model might correspond with the ‘older, vascular aging’ phenogroup among the three most common phenogroups in patients with HFpEF, in terms of older age and higher prevalence of atrial fibrillation, chronic kidney disease, left atrial dilatation, and right ventricular dysfunction represented by higher total bilirubin levels.^6,20,24 This ‘older, vascular aging’ phenogroup is reported to be characterized by high levels of biomarkers of innate immunity.^20,24 Therefore, although inflammation also plays a prominent role in this phenogroup, the underlying inflammatory pathophysiology and pathways might differ slightly between the ‘older, vascular aging’ and the ‘metabolic, obese’ phenogroups. There appears to be no similar phenogroup among the three most common phenogroups identified in patients with HFpEF that corresponds with phenogroups 1 ‘rhythm trouble’ and 4 ‘systemic failure’ in our clustering model,^7,24 which might be because these two phenogroups are mainly characterized by triggers of acute decompensation in patients with HFpEF, while most previous phenogroup studies in HFpEF were performed in patients with chronic HFpEF. Although phenogroup 4 ‘systemic failure’ in our clustering model was characterized by infection-triggered hospitalization and a systemic inflammatory state represented by the highest C-reactive protein level among the four phenogroups, the serum IL-16 level of this phenogroup was lower than that of phenogroup 2 ‘ventricular-arterial uncoupling’. This underscores the need for identification of the inflammatory pathway specific to each phenogroup of patients with acute HFpEF.

This study had several limitations. First, the small and empirically chosen sample size, which might have resulted in a lack of power to detect differences in serum IL-16 levels among the phenogroups by post hoc test, and the relatively short follow-up period are major limitations. Second, because this study utilized a multicenter prospective East-Asian HFpEF registry, possible ethnic differences should be considered when attempting to generalize the results to non-Japanese populations. Specifically, in this study, we used our clustering model that has not yet been externally validated, and we could not find a ‘relatively younger, natriuretic peptide deficiency’ phenotype, which is frequently identified in phenotype analyses in patients with HFpEF in Western countries.²⁴ A comprehensive and prospective phenogroup analysis that encompasses patients from various regions would be needed to provide a universal understanding and definition of the phenogroups of acute HFpEF and to clarify the pathophysiological role of IL-16 in each phenogroup. Third, owing to the enrollment period of the study patients, the prescription rate of sodium-glucose cotransporter type 2 (SGLT2) inhibitors was low. Based on the recent positive results of randomized controlled trials, SGLT2 inhibitors have now received the strong recommendation for the treatment of patients with HFpEF.³³ As SGLT2 inhibitors are suggested to have an anti-inflammatory effect in patients with HFpEF,³⁴ association between IL-16 and phenogroups remain to be elucidated in the contemporary cohort of HFpEF patients with higher prescription rate of SGLT2 inhibitors. Fourth, because of the observational nature of this study and the absence of data regarding the increase in IL-16 levels or the extent of fibrosis in the myocardium, whether elevation of serum IL-16 levels in phenogroup 2 ‘ventricular-arterial uncoupling’ is merely a marker or a causal factor for worsening heart failure remains unknown. Lastly, although we demonstrated a distinct association between IL-16 and indices reflecting the pathophysiology of HFpEF and prognosis across different phenogroups, relationship between phenogroups and multiple inflammatory and biological pathways, which have been implicated in the pathophysiology of HFpEF remains unknown. As previously mentioned, we recently reported the characteristics of 10 biomarkers in HFpEF phenogroups and demonstrated the substantial contribution of systemic inflammation in phenogroup 2 ‘ventricular-arterial uncoupling’; however, in that study, many of the other biomarkers representing key inflammatory and biological pathways, such as angiogenesis, atherothrombosis, extracellular matrix turnover, cell-matrix interactions, adipocyte biology, mineral metabolism/calcification, and neurohormonal regulation, were not measured. Moreover, although we observed a significant association between the serum IL-16 level and the indices that reflect the pathophysiology and prognosis of HFpEF in phenogroup 2, the incidence of cardiac death was too low to support the causative role of IL-16 in phenogroup 2. Therefore, further comprehensive larger long follow-up study is needed to draw conclusion regarding the role of IL-16 blockade.

In conclusion, analysis of patients in our multicentre East-Asian HFpEF registry showed that the serum IL-16 level has a distinct impact on indices that reflect LV diastolic function and prognosis across different phenogroups of patients with HFpEF admitted for ADHF. Further large-scale studies are warranted to identify the inflammatory pathway specific for each phenogroup of patients with acute HFpEF.

Acknowledgements

The authors thank Sugako Mitsuoka, Masako Terui, Nagisa Yoshioka, Satomi Kishimoto, Kyoko Tatsumi, and Noriko Murakami for their excellent assistance in data collection, data management, and secretarial work.

Conflict of interest

Y. Sotomi has received grants from Roche Diagnostics, Fujifilm Toyama Chemical, TOA EIYO, Bristol-Myers Squibb, Biosense Webster, Abbott Medical Japan, and Nipro, and personal fees from Abiomed, AstraZeneca, Amgen Astellas BioPharma, Biosensors, Boehringer Ingelheim, Bristole-Myers Squibb, Abbott Medical Japan, Boston Scientific Japan, Bayer, Daiichi Sankyo, Novartis, TERUMO, Medtronic, and Pfizer Pharmaceuticals. S. Hikoso has received grants from Roche Diagnostics, FUJIFILM Toyama Chemical, Actelion Pharmaceuticals; and personal fees from Daiichi Sankyo, Astellas Pharma, Bayer, Pfizer Pharmaceuticals, Boehringer Ingelheim Japan, Kowa Company, and Ono Pharmaceutical. D. Nakatani has received personal fees from Roche Diagnostics. Y. Sakata 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.

Funding

This work was supported by Roche Diagnostics K.K. and Fuji Film Toyama Chemical Co. Ltd.

References

Tsao CW, Lyass A, Enserro D, Larson MG, Ho JE, Kizer JR, et al. Temporal trends in the incidence of and mortality associated with heart failure with preserved and reduced ejection fraction. JACC Heart Fail 2018;6:678–685. doi:10.1016/j.jchf.2018.03.006

Shah SJ, Katz DH, Deo RC. Phenotypic spectrum of heart failure with preserved ejection fraction. Heart Fail Clin 2014;10:407–418. doi:10.1016/j.hfc.2014.04.008

Paulus WJ, Tschope C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol 2013;62:263–271. doi:10.1016/j.jacc.2013.02.092

Paulus WJ, Zile MR. From systemic inflammation to myocardial fibrosis: the heart failure with preserved ejection fraction paradigm revisited. Circ Res 2021;128:1451–1467. doi:10.1161/CIRCRESAHA.121.318159

Tamaki S, Mano T, Sakata Y, Ohtani T, Takeda Y, Kamimura D, et al. Interleukin‐16 promotes cardiac fibrosis and myocardial stiffening in heart failure with preserved ejection fraction. PLoS ONE 2013;8: [eLocator: e68893]. doi:10.1371/journal.pone.0068893

Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 2015;131:269–279. doi:10.1161/CIRCULATIONAHA.114.010637

Sotomi Y, Hikoso S, Komukai S, Sato T, Oeun B, Kitamura T, et al. OCVC‐heart failure investigators. Phenotyping of acute decompensated heart failure with preserved ejection fraction. Heart 2022;108:1553–1561. doi:10.1136/heartjnl‐2021‐320270

Sotomi Y, Sato T, Hikoso S, Komukai S, Oeun B, Kitamura T, et al. OCVC‐heart failure investigators. Minimal subphenotyping model for acute heart failure with preserved ejection fraction. ESC Heart Fail 2022;9:2738–2746. doi:10.1002/ehf2.13928

Sotomi Y, Hikoso S, Nakatani D, Okada K, Dohi T, Sunaga A, et al. OCVC‐heart failure investigators. Medications for specific phenotypes of heart failure with preserved ejection fraction classified by a machine learning‐based clustering model. Heart 2023;109:1231–1240. doi:10.1136/heartjnl‐2022‐322181

Suna S, Hikoso S, Yamada T, Uematsu M, Yasumura Y, Nakagawa A, et al. OCVC‐heart failure investigators. Study protocol for the PURSUIT‐HFpEF study: a prospective, multicenter, observational study of patients with heart failure with preserved ejection fraction. BMJ Open 2020;10: [eLocator: e038294]. doi:10.1136/bmjopen‐2020‐038294

McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of congestive heart failure: the Framingham study. N Engl J Med 1971;285:1441–1446. doi:10.1056/NEJM197112232852601

Nakagawa A, Yasumura Y, Yoshida C, Okumura T, Tateishi J, Yoshida J, et al. OCVC‐heart failure investigators. Prognostic importance of right ventricular‐vascular uncoupling in acute decompensated heart failure with preserved ejection fraction. Circ Cardiovasc Imaging 2020;13: [eLocator: e011430]. doi:10.1161/CIRCIMAGING.120.011430

McLean E, Cogswell M, Egli I, Wojdyla D, de Benoist B. Worldwide prevalence of anaemia, WHO vitamin and mineral nutrition information system, 1993‐2005. Public Health Nutr 2009;12:444–454. doi:10.1017/S1368980008002401

Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al. Collaborators developing the Japanese equation for estimated GFR. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009;53:982–992. doi:10.1053/j.ajkd.2008.12.034

Mebazaa A, Gheorghiade M, Pina IL, Harjola VP, Hollenberg SM, Follath F, et al. Practical recommendations for prehospital and early in‐hospital management of patients presenting with acute heart failure syndromes. Crit Care Med 2008;36:S129–S139. doi:10.1097/01.CCM.0000296274.51933.4C

Ling HZ, Flint J, Damgaard M, Bonfils PK, Cheng AS, Aggarwal S, et al. Calculated plasma volume status and prognosis in chronic heart failure. Eur J Heart Fail 2015;17:35–43. doi:10.1002/ejhf.193

Peterson PN, Rumsfeld JS, Liang L, Albert NM, Hernandez AF, Peterson ED, et al. American Heart Association get with the guidelines‐heart failure program. A validated risk score for in‐hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program. Circ Cardiovasc Qual Outcomes 2010;3:25–32. doi:10.1161/CIRCOUTCOMES.109.854877

Ignacio de Ulibarri J, Gonzalez‐Madrono A, de Villar NG, Gonzalez P, Gonzalez B, Mancha A, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp 2005;20:38–45.

Kanda Y. Investigation of the freely available easy‐to‐use software 'EZR' for medical statistics. Bone Marrow Transplant 2013;48:452–458. doi:10.1038/bmt.2012.244

Cohen JB, Schrauben SJ, Zhao L, Basso MD, Cvijic ME, Li Z, et al. Clinical Phenogroups in heart failure with preserved ejection fraction: detailed phenotypes, prognosis, and response to spironolactone. JACC Heart Fail 2020;8:172–184. doi:10.1016/j.jchf.2019.09.009

Hedman AK, Hage C, Sharma A, Brosnan MJ, Buckbinder L, Gan LM, et al. Identification of novel pheno‐groups in heart failure with preserved ejection fraction using machine learning. Heart 2020;106:342–349. doi:10.1136/heartjnl‐2019‐315481

Sabbah MS, Fayyaz AU, de Denus S, Felker GM, Borlaug BA, Dasari S, et al. Obese‐inflammatory phenotypes in heart failure with preserved ejection fraction. Circ Heart Fail 2020;13: [eLocator: e006414]. doi:10.1161/CIRCHEARTFAILURE.119.006414

Sotomi Y, Tamaki S, Hikoso S, Nakatani D, Okada K, Dohi T, et al. OCVC‐heart failure investigators. Pathophysiological insights into machine learning‐based subphenotypes of acute heart failure with preserved ejection fraction. Heart 2024;110:441–447. doi:10.1136/heartjnl‐2023‐323059

Peters AE, Tromp J, Shah SJ, Lam CSP, Lewis GD, Borlaug BA, et al. Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions. Cardiovasc Res 2023;118:3403–3415. doi:10.1093/cvr/cvac179

Tromp J, Tay WT, Ouwerkerk W, Teng TK, Yap J, MacDonald MR, et al. Multimorbidity in patients with heart failure from 11 Asian regions: a prospective cohort study using the ASIAN‐HF registry. PLoS Med 2018;15: [eLocator: e1002541]. doi:10.1371/journal.pmed.1002541

Chandramouli C, Tay WT, Bamadhaj NS, Tromp J, Teng TK, Yap JJL, et al. Association of obesity with heart failure outcomes in 11 Asian regions: a cohort study. PLoS Med 2019;16: [eLocator: e1002916]. doi:10.1371/journal.pmed.1002916

Tromp J, Bryant JA, Jin X, van Woerden G, Asali S, Yiying H, et al. Epicardial fat in heart failure with reduced versus preserved ejection fraction. Eur J Heart Fail 2021;23:835–838. doi:10.1002/ejhf.2156

Rao VN, Fudim M, Mentz RJ, Michos ED, Felker GM. Regional adiposity and heart failure with preserved ejection fraction. Eur J Heart Fail 2020;22:1540–1550. doi:10.1002/ejhf.1956

Huang Y, Liu Y, Ma Y, Tu T, Liu N, Bai F, et al. Associations of visceral adipose tissue, circulating protein biomarkers, and risk of cardiovascular diseases: a Mendelian randomization analysis. Front Cell Dev Biol 2022;10: [eLocator: 840866]. doi:10.3389/fcell.2022.840866

Karastergiou K, Evans I, Ogston N, Miheisi N, Nair D, Kaski JC, et al. Epicardial adipokines in obesity and coronary artery disease induce atherogenic changes in monocytes and endothelial cells. Arterioscler Thromb Vasc Biol 2010;30:1340–1346. doi:10.1161/ATVBAHA.110.204719

Masuyama T, Yamamoto K, Sakata Y, Doi R, Nishikawa N, Kondo H, et al. Evolving changes in Doppler mitral flow velocity pattern in rats with hypertensive hypertrophy. J Am Coll Cardiol 2000;36:2333–2338. doi:10.1016/s0735‐1097(00)01000‐7

Gallet R, de Couto G, Simsolo E, Valle J, Sun B, Liu W, et al. Cardiosphere‐derived cells reverse heart failure with preserved ejection fraction (HFpEF) in rats by decreasing fibrosis and inflammation. JACC Basic Transl Sci 2016;1:14–28. doi:10.1016/j.jacbts.2016.01.003

McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, et al. 2023 focused update of the 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 2023;44:3627–3639. doi:10.1093/eurheartj/ehad195

Pandey AK, Bhatt DL, Pandey A, Marx N, Cosentino F, Pandey A, et al. Mechanisms of benefits of sodium‐glucose cotransporter 2 inhibitors in heart failure with preserved ejection fraction. Eur Heart J 2023;44:3640–3651. doi:10.1093/eurheartj/ehad389