Effects of SGLT2 Ablation or Inhibition on Corticosterone Secretion in High-Fat-Fed Mice: Exploring a Nexus with Cytokine Levels.

Despite recent therapeutic advances, achieving optimal glycaemic control remains a challenge in managing Type 2 Diabetes (T2D). Sodium-glucose co-transporter type 2 (SGLT2) inhibitors have emerged as effective treatments by promoting urinary glucose excretion. However, the full scope of their mechanisms extends beyond glycaemic control. At present, their immunometabolic effects remain elusive. To investigate the effects of SGLT2 inhibition or deletion, we compared the metabolic and immune phenotype between high fat diet-fed control, chronically dapagliflozin-treated mice and total-body SGLT2/Slc5a2 knockout mice. SGLT2 null mice exhibited superior glucose tolerance and insulin sensitivity compared to control or dapagliflozin-treated mice, independent of glycosuria and body weight. Moreover, SGLT2 null mice demonstrated physiological regulation of corticosterone secretion, with lowered morning levels compared to control mice. Systemic cytokine profiling also unveiled significant alterations in inflammatory mediators, particularly interleukin 6 (IL-6). Furthermore, unbiased proteomic analysis demonstrated downregulation of acute-phase proteins and upregulation of glutathione-related proteins, suggesting a role in the modulation of antioxidant responses. Conversely, IL-6 increased SGLT2 expression in kidney HK2 cells suggesting a role for cytokines in the effects of hyperglycemia. Collectively, our study elucidates a potential interplay between SGLT2 activity, immune modulation, and metabolic homeostasis.

Transcriptional, epigenetic and metabolic signatures in cardiometabolic syndrome defined by extreme phenotypes.

BACKGROUND: This work is aimed at improving the understanding of cardiometabolic syndrome pathophysiology and its relationship with thrombosis by generating a multi-omic disease signature. METHODS/RESULTS: We combined classic plasma biochemistry and plasma biomarkers with the transcriptional and epigenetic characterisation of cell types involved in thrombosis, obtained from two extreme phenotype groups (morbidly obese and lipodystrophy) and lean individuals to identify the molecular mechanisms at play, highlighting patterns of abnormal activation in innate immune phagocytic cells. Our analyses showed that extreme phenotype groups could be distinguished from lean individuals, and from each other, across all data layers. The characterisation of the same obese group, 6 months after bariatric surgery, revealed the loss of the abnormal activation of innate immune cells previously observed. However, rather than reverting to the gene expression landscape of lean individuals, this occurred via the establishment of novel gene expression landscapes. NETosis and its control mechanisms emerge amongst the pathways that show an improvement after surgical intervention. CONCLUSIONS: We showed that the morbidly obese and lipodystrophy groups, despite some differences, shared a common cardiometabolic syndrome signature. We also showed that this could be used to discriminate, amongst the normal population, those individuals with a higher likelihood of presenting with the disease, even when not displaying the classic features.

Proenkephalin and prognosis in heart failure with preserved ejection fraction: a GREAT network study.

BACKGROUND: Proenkephalin (PENK), a stable endogenous opioid biomarker related to renal function, has prognostic utility in acute and chronic heart failure. We investigated the prognostic utility of PENK in heart failure with preserved ejection fraction (HFpEF), and its relationship to renal function, Body Mass Index (BMI), and imaging measures of diastolic dysfunction. METHODS: In this multicentre study, PENK was measured in 522 HFpEF patients (ejection fraction > 50%, 253 male, mean age 76.13 ± 10.73 years) and compared to 47 age and sex-matched controls. The primary endpoint was 2-years composite of all-cause mortality and/or heart failure rehospitalisation (HF). A subset (n = 163) received detailed imaging studies. RESULTS: PENK levels were raised in HFpEF (median [interquartile range] 88.9 [62.1-132.0]) compared to normal controls (56.3 [47.9-70.5]). PENK was correlated to urea, eGFR, Body Mass Index and E/e' (rs 0.635, - 0.741, - 0.275, 0.476, respectively, p < 0.0005). During 2 years follow-up 144 patients died and 220 had death/HF endpoints. Multivariable Cox regression models showed PENK independently predicted 2 year death/HF [hazard ratio (for 1 SD increment of log-transformed biomarker) HR 1.45 [95% CI 1.12-1.88, p = 0.005]], even after adjustment for troponin (HR 1.59 [1.14-2.20, p = 0.006]), and Body Mass Index (HR 1.63 [1.13-2.33, p = 0.009]). PENK showed no interaction with ejection fraction status for prediction of poor outcomes. Net reclassification analyses showed PENK significantly improved classification of death/HF outcomes for multivariable models containing natriuretic peptide, troponin and Body Mass Index (p < 0.05 for all). CONCLUSIONS: In HFpEF, PENK levels are related to BMI, and measures of diastolic dysfunction and are prognostic for all-cause mortality and heart failure rehospitalisation.

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure.

BACKGROUND: Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. METHODS: A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. RESULTS: After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). CONCLUSIONS: The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

Proenkephalin in Heart Failure.

The opioid system is activated in heart failure, which may be cardioprotective but may also be counter-regulatory. Recently, systemic proenkephalin activation has been investigated in various conditions predicting mortality and kidney injury. In acute heart failure, proenkephalin independently predicts mortality and heart failure rehospitalization in addition to traditional risk markers. It also predicts worsening renal function, increasingly recognized as an important risk predictor for poor outcome in heart failure. This article explores the role of enkephalins and delta-opioid receptors in the heart, then reviews studies measuring proenkephalin levels in the circulation and their associations with prognosis.

Proenkephalin, Renal Dysfunction, and Prognosis in Patients With Acute Heart Failure: A GREAT Network Study.

BACKGROUND: Proenkephalin A (PENK) and its receptors are widely distributed. Enkephalins are cardiodepressive and difficult to measure directly. PENK is a stable surrogate analyte of labile enkephalins that is correlated inversely with renal function. Cardiorenal syndrome is common in acute heart failure (HF) and portends poor prognosis. OBJECTIVES: This study assessed the prognostic value of PENK in acute HF, by identifying levels that may be useful in clinical decisions, and evaluated its utility for predicting cardiorenal syndrome. METHODS: This multicenter study measured PENK in 1,908 patients with acute HF (1,186 male; mean age 75.66 ± 11.74 years). The primary endpoint was 1-year all-cause mortality; secondary endpoints were in-hospital mortality, all-cause mortality or HF rehospitalization within 1 year, and in-hospital worsening renal function, defined as a rise in plasma creatinine ≥26.5 µmol/l or 50% higher than the admission value within 5 days of presentation. RESULTS: During 1-year follow-up, 518 patients died. Measures of renal function were the major determinants of PENK levels. PENK independently predicted worsening renal function (odds ratio: 1.58; 95% confidence interval [CI]: 1.24 to 2.00; p < 0.0005) with a model receiver-operating characteristic area of 0.69. PENK was associated with the degree of worsening renal function. Multivariable Cox regression models showed that PENK level was an independent predictor of 1-year mortality (p < 0.0005) and 1-year death and/or HF (hazard ratio: 1.27; 95% CI: 1.10 to 1.45; p = 0.001). PENK levels independently predicted outcomes at 3 or 6 months and were independent predictors of in-hospital mortality, predominantly down-classifying risk in survivors when added to clinical scores; levels <133.3 pmol/l and >211.3 pmol/l detected low-risk and high-risk patients, respectively. CONCLUSIONS: PENK levels reflect cardiorenal status in acute HF and are prognostic for worsening renal function and in-hospital mortality as well as mortality during follow-up.

Identification of novel biomarkers in plasma for prediction of treatment response in patients with heart failure.

BACKGROUND: Heart failure is a complex clinical syndrome that occurs at the end stage of heart disease. Despite advances in therapy for heart failure, improvement of clinical outcomes remains a challenge for physicians. The identification of treatment response early in the course of disease would be useful to improve management of these patients. The aim of this study was to identify novel biomarkers in plasma that could predict treatment response in patients with heart failure. METHODS: Patients with heart failure who met inclusion and exclusion criteria according to the guidelines of the European Society of Cardiology were recruited. Uptitration of angiotensin-converting enzyme inhibitors and ß blockers was performed over 6 months. Patients were followed up for clinical events within the next 24 months. Plasma proteins in patients who responded to standard treatment (responders) were compared with patients who died or were re-admitted for heart failure (non-responders). Plasma samples were depleted of 14 high abundance proteins with a multiple affinity removal system column (MARS). Then plasma samples were analysed on two-dimensional liquid chromatography coupled to a tandem mass spectrometry (2D LC-ESI-MS/MS) in high definition mode (HDMS(E)) to identify and quantify the different expression of proteins in plasma. Finally, ELISA was used to verify candidate biomarkers. FINDINGS: Participants were 100 patients with heart failure matched for sex and age (50 responders [25 women], 50 non-responders [25 women], mean age 76·6 years [SD 8·1]). Of the non-responders, 18 died and 32 were re-admitted to hospital. 2D LC-ESI-MS/MS showed that the expression of neurotrimin (NTM) was highly upregulated, by 26·5 times (p<0·0001), in the responder group compared with the non-responder group. ELISA in the verification phase showed that the concentrations of NTM in plasma were significantly higher in the responders and lower in the non-responders (mean 4·73 log10 relative light units [SD 0·07] vs 4·70 [0·08], p=0·036). When ANOVA with Bonferroni post-hoc comparisons was used in three outcome subgroups (responders, patients re-admitted to hospital, and deaths), NTM concentrations were significantly different between death and the other groups (higher in responder vs death group, p<0·0001; higher in re-admission vs death group, p=0·001). INTERPRETATION: Our findings suggest that NTM as a novel biomarker in heart failure will not only add information to understand the pathophysiological mechanisms of heart failure better, but also might provide a more accurate prediction of treatment response to guide medical therapy. In addition, a novel therapeutic target could be identified for design of drugs to improve outcomes. Futher work is required in larger populations to confirm this biomarker. FUNDING: European Union's Seventh Framework Programme (BIOSTAT-CHF), John and Lucille van Geest Foundation.