Low Plasma Levels of Irisin Predict Acutely Decompensated Heart Failure in Type 2 Diabetes Mellitus Patients with Chronic Heart Failure

The aim of this study was to determine the discriminative value of irisin for acutely decompensated heart failure (ADHF) in type 2 diabetes mellitus (T2DM) patients with chronic HF. We included 480 T2DM patients with any phenotype of HF and followed them for 52 weeks. Hemodynamic performances and the serum levels of biomarkers were detected at the study entry. The primary clinical end-point was ADHF that led to urgent hospitalization. We found that the serum levels of N-terminal natriuretic pro-peptide (NT-proBNP) were higher (1719 [980–2457] pmol/mL vs. 1057 [570–2607] pmol/mL, respectively) and the levels of irisin were lower (4.96 [3.14–6.85] ng/mL vs. 7.95 [5.73–9.16] ng/mL) in ADHF patients than in those without ADHF. The ROC curve analysis showed that the estimated cut-off point for serum irisin levels (ADHF versus non-ADHF) was 7.85 ng/mL (area under curve [AUC] = 0.869 (95% CI = 0.800–0.937), sensitivity = 82.7%, specificity = 73.5%; p = 0.0001). The multivariate logistic regression yielded that the serum levels of irisin < 7.85 ng/mL (OR = 1.20; p = 0.001) and NT-proBNP > 1215 pmol/mL (OR = 1.18; p = 0.001) retained the predictors for ADHF. Kaplan–Meier plots showed a significant difference of clinical end-point accumulations in patients with HF depending on irisin levels (<7.85 ng/mL versus ≥7.85 ng/mL). In conclusion, we established that decreased levels of irisin were associated with ADHF presentation in chronic HF patients with T2DM independently from NT-proBNP.


Introduction
Acutely decompensated heart failure (ADHF) is defined as rapidly progressive preexisting cardiomyopathy often due to the dysregulation of neuro-humoral adaptive mechanisms, which act to maintain hemodynamic and perfusion of target organs despite worsening cardiac function [1]. The patients with ADHF demonstrate a high variability of signs and symptoms of congestion and fluid retention which, in the majority of cases, lead to urgent hospital admission [2,3]. ADHF continues to be associated with unacceptably increasing in-hospital mortality rates (7.5%) and one-year mortality rates (20.1-23.3%) [4]. Although a short-term prognosis of ADHF remains to be poor, hemodynamically stable patients after ADHF continue to be at higher risk of unfavorable post-discharge clinical outcomes [5,6]. Indeed, 30-day HF readmission rates were 4.8-5.4%, one-year HF readmission average rates were from 23.6% to 26.2%, and the 60-day rate of readmission or cardiovascular (CV) death is between 31% and 50% [4][5][6][7].
Distinct scenarios of the natural course of ADHF relate to clinical heterogeneity among patients admitted to hospitals, cardiac dysfunction etiology, precipitating factors contributing to heart failure (HF) decompensation, and the current implementation of guidelinebased medical therapy [8][9][10]. Euro Heart Failure Survey II revealed that 40% of ADHF patients had type 2 diabetes mellitus (T2DM) and that almost half of individuals exerted multiple co-morbidities including atrial fibrillation (AF), chronic kidney disease (CKD), hypertension, and coronary artery disease [11]. Moreover, the majority of ADHF patients had polypharmacy and variable side effects of medications [12].
Despite several factors, such as clinical phenotypes of patients with known HF, phenotypes of HF, comorbidity profile, natriuretic peptides (NPs), and a personally adjusted care program for HF, short-and long-term clinical outcomes do not seem to be concisely predicted [13,14]. Yet, a lung ultrasound and echocardiography with measurements of cardiac features including left ventricular ejection fraction (LVEF) and diastolic dysfunction parameters were the most useful tools for affirming the presence of ADHF, but not for predicting the condition [15]. In addition, NPs were more valuable in excluding ADHF than in the prediction of the occurrence of the disease in patients with any phenotype of HF [16]. Indeed, despite the utilization of NPs as routine, costly, affordable, easy-to-use tests for an HF diagnosis in emergency departments, their predictive potency for ADHF appears to be sufficiently variable and dependent on the HF phenotype and T2DM presence [17]. In this context, there is a need to discover new approaches to identify chronic HF patients with concomitant T2DM at a higher risk of ADHF depending on their comorbidity status [18].
Irisin is a multifunctional peptide, which is proteolytically cleaved from its precursor fibronectin type III domain-containing protein 5 which is mainly secreted by skeletal muscles and cardiac myocytes [19,20]. Irisin plays a crucial role in energy homeostasis and regulates glucose metabolism, insulin sensitivity, and mitochondrial oxidation of free fatty acids [21]. Therefore, irisin maintains cardiac function and prevents cardiac injury, cardiac myocyte necrosis and apoptosis, extracellular matrix remodeling, and inflammatory reaction [22]. Yet, irisin seems to show a cardiac protective effect in T2DM patients with chronic HF treated with SGLT2 inhibitors [23]. Previously, it has been reported that low levels of irisin predicted mortality risk in acute HF patients [24] and chronic HFrEF/HFpEF [25,26]. However, the role of irisin in predicting ADHF in T2DM patients with chronic HF remains as not fully understood. The aim of the study is to determine the discriminative value of irisin for ADHF in T2DM patients with chronic HF.

Study Design and Cohorts of Participants
A total of 738 patients with T2DM were prescreened using the local database of "Vita Center" (Zaporozhye, Ukraine). Using criteria of inclusion (male/female with age of ≥18 years, established T2DM with hemodynamically stable chronic HF, glycosylated hemoglobin < 6.9%, informed consent to participate in the study), we enrolled 489 patients with T2DM with concomitant chronic HF I-IV New York Heart Association (NYHA) functional classes (Figure 1). The exclusion criteria were acute de novo HF, acute coronary syndrome/myocardial infarction or unstable angina pectoris, recent stroke/transient ischemic attack, acute myocarditis/endocarditis/pericarditis, known malignancy and/or chemotherapy, acute viral/bacterial/fungal infections, severe co-morbidities (anemia, chronic lung and liver diseases, known inherited and acquired heart defect, symptomatic severe hypoglycemia, morbid obesity, systemic connective tissue diseases, autoimmune disease, cognitive dysfunction, and thyroid disorders), pregnancy, type 1 diabetes mellitus, or current therapy with insulin. Then, we excluded nine patients who were not able to be under continuous monitoring for 52 weeks. Finally, we selected in the study 480 patients with T2DM with any phenotype of chronic HF who were followed from June 2021 to August 2022. During the 52-week observation period, we pooled patient data from different sources including physician records, databases, discharge reports, autopsy reports, and direct calls to patients and/or their relatives. continuous monitoring for 52 weeks. Finally, we selected in the study 480 patients with T2DM with any phenotype of chronic HF who were followed from June 2021 to August 2022. During the 52-week observation period, we pooled patient data from different sources including physician records, databases, discharge reports, autopsy reports, and direct calls to patients and/or their relatives. Figure 1. Flow chart of the study design. Abbreviations: ADHF, acutely decompensated heart failure; CV, cardiovascular; Echo-CG, echocardiography; GFR, glomerular filtration rate; HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HFmrEF, heart failure with mildly reduced ejection fraction; HFrEF, heart failure with reduced ejection fraction; hs-CRP, high sensitivity C-reactive protein; HOMA-IR, Homeostatic Assessment Model of Insulin Resistance; HbA1c, glycosylated hemoglobin; NT-proBNP, N-terminal brain natriuretic pro-peptide; T2DM, type 2 diabetes mellitus; TIA, transient ischemic attack; and TNF-alpha, tumor necrosis factor-alpha.

Determination of Study End-Points
The primary clinical end-point was ADHF that led to urgent hospitalization. ADHF was defined as the clinical presentation of signs and symptoms of congestion (elevated jugular venous pressure, orthopnea, bilateral leg edema, pulmonary rales, third heart sound, pulmonary edema on chest X-ray, nocturnal cough, dyspnea with exertion, recent diuresis, and onset of hepatomegaly and/or pleural effusion) [1].

Determination of Study End-Points
The primary clinical end-point was ADHF that led to urgent hospitalization. ADHF was defined as the clinical presentation of signs and symptoms of congestion (elevated jugular venous pressure, orthopnea, bilateral leg edema, pulmonary rales, third heart sound, pulmonary edema on chest X-ray, nocturnal cough, dyspnea with exertion, recent diuresis, and onset of hepatomegaly and/or pleural effusion) [1].

Concomitant Medical Information Collection
Basic clinical data, including age, gender, height, weight, waist circumference, hipto-waist ratio (WHR), body mass index (BMI), and body surface area (BSA) comorbidities (hypertension, T2DM history, and dyslipidemia), and smoking were collected. T2DM was established according to the conventional criteria provided by the American Diabetes Association [27]. The European Society of Cardiology (ESC) clinical guidelines were used to detect HF [1], hypertension [28], dyslipidemia [29], and stable coronary artery disease [30]. Chronic kidney disease in T2DM patients was established in accordance with the Kidney Disease Improving Global Outcomes (KDIGO) Consensus Report [31].

Echocardiography Examination
Enrolled patients underwent transthoracic B-mode echocardiography and Doppler examination, which was performed by a blinded high qualified ultra-sonographer using the diagnostic system Vivid T8 ("General Electric Medical Systems", Freiburg, Germany) Cardiac volumes including left ventricular end-diastolic (LVEDV) and end-systolic (LVESV) volumes and left atrial volume (LAV) were measured in the standard apical 4-chamber view in compliance with current guidelines [32,33]. The LAV index (LAVI) was calculated as a ratio of LAV to BSA. Left ventricular (LV) ejection fraction (LVEF) was estimated by the Simpson method. Diastolic parameters included early diastolic blood filling (E), longitudinal strain ratio (e'), and their ratio (E/e') at baseline and at the 52nd week of the follow-up. Estimated E/e' ratio was expressed as the ratio equation of E wave velocity to averaged medial and lateral e' velocity [33]. Left ventricular hypertrophy (LVH) was detected according to conventional recommendations [33], which used LV myocardial mass index (LVMMI) ≥ 125 g/m 2 or ≥110 g/m 2 in males and females, respectively, as a marker of LVH.

Blood Sampling and Biomarker Measurements
Fasting blood samples from patients were collected from an antecubital vein (3-5 mL) and maintained at 4 • C at baseline and in the 52-week interval of the follow-up. After centrifugation (3000 r/min, 30 min), polled serum aliquots were immediately stored at ≤−70 • C until analysis. Serum concentrations of NT-proBNP, irisin, tumor necrosis factoralpha (TNF-alpha), and high-sensitivity C-reactive protein (hs-CRP) were determined using commercially available enzyme-linked immunosorbent assay (ELISA) kits (Elabscience, Houston, TX, USA) according to the manufacturer's instructions. Both the intra-and interassay coefficient of variability for each biomarker were <10%. Conventional biochemistry parameters were routinely measured at the local biochemical laboratory of Vita Center (Zaporozhye, Ukraine) using a Roche P800 analyzer (Basel, Switzerland).

Estimation of Glomerular Filtration Rate
We used CKD-EPI formula to estimate the glomerular filtration rate (GFR) [34].

Determination of Insulin Resistance
Insulin resistance was evaluated using the Homeostatic Assessment Model of Insulin Resistance (HOMA-IR) [35].

Statistics
V. 23 Statistical Packages for Social Sciences (SPSS; IBM, Armonk, New York, NY, USA) software and v. 9 GraphPad Prism (GraphPad Software, San Diego, CA, USA) software for statistical analysis were used. Normal distribution of variables was evaluated with the Kolmogorov-Smirnov test. Mean (M) ± standard deviation (SD) and median (I) and 25-75% interquartile range (IQR), respectively, characterized continuous normally and non-normally distributed variables. The difference between categorical values was assessed with the chi-square test. Student's t-tests or one-way analyses of variance (ANOVA) with the Mann-Whitney U test were used for the comparison between groups depending on variable distribution. Spearman's correlation coefficient was calculated to ascertain the relationship between variables. Receive Operation Curve (ROC) curves with a separate analysis of the Youden Index were performed to assess the reliability of predictive models. Predictors for ADHF were determined by a univariate logistic regression. All variables with p < 0.1 were entered in a backward stepwise multivariate logistic regression analysis and then variables with the highest p value were eliminated from the whole model. The selection was stopped when the p value was smaller than the pre-specified threshold determined by Bayesian information criterion. An odds ratio (OR) and 95% confidence interval (CI) were reported for each predictor. Predictors of ADHF were confirmed using integrated discrimination indices (IDI) and net reclassification improvement (NRI). Kaplan-Meyer curve analysis was used with the aim of elucidating plausible benefit in clinical outcome occurrence depending of irisin cutoff levels (≥7.85 ng/mL versus <7.85 ng/mL). The intra-class correlation coefficient was used to determine both inter-and intra-observer reproducibility for irisin levels and echocardiographic parameters from 50 randomly selected HF patients using an identical cine-loop for each view. Differences were considered significant at the level of statistical significance p < 0.05.

Clinical Features, Echocrdiographic Parameters, and Biomarkers' Levels during the Follow-Up
A dynamic of several characteristics of the entire patient population is reported in Table 2. Notes: Data of variables are given as mean ± SD and median (25-75% interquartile range). Abbreviations: DBP, diastolic blood pressure; E/e', early diastolic blood filling to longitudinal strain ratio; GFR, glomerular filtration rate; hs-CRP, high-sensitivity C-reactive protein; HFpEF, heart failure with preserved ejection fraction; HFmrEF, heart failure with mildly reduced ejection fraction; HFrEF, heart failure with reduced ejection fraction; LVESV, left ventricular end-systolic volume; LVEF, left ventricular ejection fraction; LVMMI, left ventricle myocardial mass index, left atrial volume index, LAVI; left atrial volume index; NT-proBNP, N-terminal brain natriuretic pro-peptide; TNF-alpha, tumor necrosis factor-alpha.
In fact, in the entire group there were no significant changes in BMI, systolic and diastolic BP, cardiohemodynamic performances apart from LVESV (∆% = −6.9%, p = 0.04), eGFR, fasting glucose, HbAc1, creatinine, NT-proBNP, irisin, and TNF-alpha. Along with it, hs-CRP levels were found to be increased up to 13.1% (p = 0.026). In the ADHF group, an elevation of creatinine, hs-CRP, and NT-proBNP were detected, whereas the levels of irisin remained lower. On the contrary, in the non-ADHF group a significant decrease in creatinine, hs-CRP, NT-proBNP, TNF-alpha, and irisin were noticed. These changes in circulating biomarkers corresponded to favorable dynamics of LVESV.

Comparison of the Predictive Models
We compared predictive models for ADHF and found that the discriminative value of irisin < 7.85 ng/mL was superior to that NT-proBNP > 1215 pg/mL, whereas there was no significant difference between Model 2 and Model 3 for ADHF (Table 4). Thus, decreased levels of irisin were associated with ADHF independently from NT-proBNP. Abbreviations: AUC, area under curve; NT-proBNP, N-terminal brain natriuretic pro-peptide; HF, heart failure; IDI, integrated discrimination indices; NRI, net reclassification improvement. Note: p value indicates a significant difference in terms of Model 1.

Kaplan-Meier Curve Analysis
To confirm our hypothesis of predictive ability of low levels of irisin for ADNF, we performed the Kaplan-Meier analysis of clinical outcome. Kaplan-Meier plots showed a significant difference of clinical end-point accumulations in patients with HF depending on irisin levels (<7.85 ng/mL versus ≥7.85 ng/mL) (Figure 3). We found that patients with irisin levels ≥ 7.85 ng/mL had a benefit in ADHF occurrence when compared with those who had irisin levels < 7.85 ng/mL (OR = 2.667; 95% CI = 1.177-6.043; log rank test = 0.0144). To confirm our hypothesis of predictive ability of low levels of irisin for ADNF, we performed the Kaplan-Meier analysis of clinical outcome. Kaplan-Meier plots showed a significant difference of clinical end-point accumulations in patients with HF depending on irisin levels (<7.85 ng/mL versus ≥7.85 ng/mL) (Figure 3). We found that patients with irisin levels ≥ 7.85 ng/mL had a benefit in ADHF occurrence when compared with those who had irisin levels < 7.85 ng/mL (OR = 2.667; 95% CI = 1.177-6.043; log rank test = 0.0144).

Reproducibility of Biomarkers
The evaluation of the reproducibility of irisin was performed in comparison with NT-proBNP. The intra-class correlation coefficient for the inter-observer reproducibility of NT-proBNP was 0.92 (95% CI = 0.86-0.97), whereas the intra-class correlation coefficient for intra-observer reproducibility of irisin was 0.91 (95% CI = 0.88-0.95).

Discussion
The results of the study revealed that the levels of irisin < 7.85 ng/mL in chronic hemodynamically stable HF patients with T2DM seem to show discriminative values for ADHF. Along with it, irisin exhibited much better predictive potency than NT-proBNP, whereas a combination of both biomarkers did not add any prognostic information for

Reproducibility of Biomarkers
The evaluation of the reproducibility of irisin was performed in comparison with NT-proBNP. The intra-class correlation coefficient for the inter-observer reproducibility of NT-proBNP was 0.92 (95% CI = 0.86-0.97), whereas the intra-class correlation coefficient for intra-observer reproducibility of irisin was 0.91 (95% CI = 0.88-0.95).

Discussion
The results of the study revealed that the levels of irisin < 7.85 ng/mL in chronic hemodynamically stable HF patients with T2DM seem to show discriminative values for ADHF. Along with it, irisin exhibited much better predictive potency than NT-proBNP, whereas a combination of both biomarkers did not add any prognostic information for ADHF to irisin. The Kaplan-Meier curve analysis yielded sufficient benefits in clinical outcome occurrence in patients with irisin levels ≥ 7.85 ng/mL than those with <7.85 ng/mL. The majority of previous studies have reported that patients with T2DM had low levels of irisin and that irisin might be a biomarker with plausible predictive values for clinical outcomes, although there are controversial issues [17,25,26]. Indeed, Shen S. et al. (2017) [17] found that increased levels of irisin predicted mortality risk in patients with acute HF, while the authors did not especially evaluate a role of T2DM as a cofactor in the discriminative potency of the biomarker. A meta-analysis of 26 studies (number of participants = 3667) of Song R, et al. (2021) [36] showed that patients with T2DM had lower levels of irisin than healthy volunteers. In another meta-analysis of 23 studies involving 1745 diabetic patients (T2DM, T2DM, and gestational diabetes mellitus [GDM]) and 1337 non-diabetic controls, circulating irisin levels were decreased in patients with T2DM and GDM, but not in patients with T1DM [37]. Yet, an effective cardiac rehabilitation program was associated with an increase in irisin levels in peripheral blood in patients with T2DM [38]. There is strong evidence of the fact that low levels of irisin are related to reduced eGFR in T2DM patients and predicted T2DM-induced nephropathy [39]. Recently, we reported that low levels of irisin being associated with any phenotypes of chronic HF predicted poor clinical outcomes among HF patients with concomitant T2DM [25,26]. However, a link between low concentrations of irisin and a risk of ADHF in chronic HF population patients with T2DM has been detected here first.
Despite conventional clinical protocols for HF diagnosis and therapy, including a limiting number of biomarkers having validated predictive values for mainly NPs, a discovery of new biomarkers is in a loop of investigations because several selective populations of HF patients such as diabetics cannot be thoroughly stratified as at risk of HF-related complications by NPs [1,40]. Yet, there is limiting evidence for low levels of NPs in the prediction of clinical outcomes among HF patients treated with the four-pillar guidelinerecommended combination, which includes the renin-angiotensin-aldosterone system mainly ARNI and MCA, beta-blocker, and SGLT2 inhibitor [41]. Irisin seems to show high reliability of its discriminative value beyond NT-proBNP for HF-related events, adverse cardiac remodeling, and mortality [42][43][44][45]. Therefore, low concentrations of irisin are found in T2DM and predict a high risk of cardiovascular complications in this population [46,47].
In our study, the main causes which contribute to the decompensation of chronic HF and led to ADHF with subsequent hospitalizations were progression of T2DM and chronic kidney disease, uncontrolled hypertension, and acute myocardial infarction. Other causes included malignant arrhythmia, loop diuretic intolerance, transient ischemia attack/stroke, pneumonia, pulmonary thromboembolism, and dilation cardiomyopathy. In fact, the majority of ADHF patients (59 individuals, 55.6%) had direct cardiovascular reasons for cardiac decompensations, whereas other patients might exhibit clinical signs and symptoms of ADHF due to numerous factors indirectly affecting cardiac injury. Therefore, uncontrolled hypertension and chronic kidney disease may be a result of an escape of glycaemia control in T2DM patients, as well as that acute myocardial infarction is a frequent complication of accelerating atherosclerosis in T2DM [48]. Thus, irisin as a multifunctional regulator of energetic homeostasis, inflammation, tissue reparation, and endothelial function is able to participate in the pathogenesis of these complications and to link cardiac remodeling in T2DM patients with a risk of ADHF [49][50][51]. Indeed, high glycemic variability, poor glycemic control, acute coronary syndromes, declining diuretic response, and uncontrolled hypertension were found to the most important factors leading to ADHF in T2DM patients with HF [52][53][54][55]. In this connection, low levels of circulating irisin, which were found in patients with T2DM, cardiovascular diseases, and chronic kidney disease in numerous previous studies [56][57][58], appear to be a promising indicator of a higher risk of ADHF regardless of the etiology of the condition.
There are numerous underlying molecular mechanisms which explain the involvement of irisin in the regulation of inflammatory response and tissue reparation with a subsequent impact on adverse cardiac remodeling, microvascular inflammation, endothelial function, kidney parenchyma survival, and browning adipose tissue [59]. Irisin acts through up-regulating the Expression of Uncoupling Protein 2 and macrophage-stimulating 1/c-Jun N-terminal kinase pathway and thereby prevents ischemia/reperfusion, suppresses inflammation, oxidative stress, and apoptosis, promotes cardiomyocyte survival, and mitochondrial homeostasis [60,61]. Yet, irisin markedly decreased the activation of the mitogenactivated protein kinase (MAPK) signaling pathway and suppressed pro-informatory cytokine expression, cellular senescence in TNF-α-stimulated cardiomyocytes, and NLRP3 inflammasome [62,63].
In our study, we did not find a significant difference between both groups of patients in the levels of hs-CRP, whereas concentrations of TNF-alpha were higher in ADHF patients than in non-ADHF individuals. Yet, we found a moderate negative correlation between irisin and TNF-alpha, whereas an association between irisin and hs-CRP was mild. In addition to that, fasting glucose and HbA1c did not correlate with irisin at the baseline of the study. To note, hs-CRP was previously found to be an independent predictor of cardiovascular death in T2DM patients regardless of HF presentation [60]. However, derangements in adrenergic-adipokine signaling in a case of a deficiency of irisin production may be more valuable for adverse cardiac remodeling and cardiovascular outcomes among T2DM patients [64][65][66].
We suggested that differences in the levels of irisin and TNF-alpha at baseline in ADHF and non-ADHF was a result of particularities in the signature of cardiac and noncardiac comorbidities along with therapy of HF. However, higher levels of NT-proBNP in ADHF patients when compared with non-ADHF individuals clearly indicated that there was a pre-existing risk of congestion due to certain conditions, which were able to increase the risks of hospitalization (arrhythmia, chronic kidney disease, and coronary artery disease). Indeed, ADHF patients presented higher AF, coronary artery disease, and dilated cardiomyopathy than non-ADHF. Moreover, this signature of comorbidities may explain the majority of cases of ADHF. In addition, clinical status in ADHF patients was respectively worse than those with non-ADHF, because IV NYHA class occurred frequently, but I NYHA class was detected rarely in ADHF compared to non-ADHF. Therefore, ADHF patients sufficiently differed from non-ADHF in LVEDS, LVMMI, and LAVI. Perhaps all these may explain why the baseline levels of NT-proBNP were higher in ADHF than in non-ADHF. Altogether, the results of our study confirmed that irisin has a unique capability to improve the prognostic information of NT-proBNP for ADHF and that it is a promising marker for serial monitoring.

Study Limitations
This study has several limitations. First, we included the patients with good control for T2DM who did not receive insulin. Consequently, we did not compare irisin levels and their predictive values for depending variables between T2DM and non-T2DM individuals. Second, the patients from both cohorts have been treated with guideline-recommended therapy and the majority of them received antagonists of the renin-angiotensin-aldosterone system in combination with beta-blockers and SGLT2 inhibitors. Third, we did not provide continuous monitoring of the biomarkers with the aim of clearly elucidating the dynamics of them. Perhaps this is an aim for investigations in the future. Yet, we had no possibilities to extend the discovery over other potential conditions affecting the risks of hospitalizations including iron deficiency, depression, missed drug intake, and the use of prohibited drugs, such as non-steroidal anti-inflammatory agents and appetite suppressants. We believe that these limitations would not be serious arguments against the interpretation of the study results.

Conclusions
We established that in T2DM patients with concomitant chronic HF, serum irisin levels were associated with the risk of ADHF. Yet, irisin added a discriminatory value to NT-proBNP for ADHF in this population. Additionally, T2DM patients with levels of irisin ≥ 7.85 ng/mL demonstrated benefits in clinical outcomes related to ADHF than those with <7.85 ng/mL. This finding may open a new prospective for the prediction of ADHF in T2DM patients with chronic HF regardless of the levels of NT-proBNP.