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Aims The HFA-PEFF score was developed to optimize diagnosis and to aid in early recognition of heart failure (HF) with preserved ejection fraction (HFpEF) in patients who present with HF-like symptoms. Recognizing early-HFpEF phenogroups is essential to better understand progression towards overt HFpEF and pave the way for early intervention and treatment. Whether the HFA-PEFF domain scores can identify ‘early-HFpEF’ phenogroups remains unknown. The aims of this pilot study are to (i) identify distinct phenogroups by cluster analysis of HFA-PEFF domain scores in subjects that present with HF-like symptoms and (ii) study whether these phenogroups may be associated with distinct blood proteome profiles. Methods and results Subjects referred to the Cardiology Centers of the Netherlands, location Utrecht, with non-acute possibly cardiac-related symptoms (such as dyspnoea or fatigue) were prospectively enrolled in the HELPFul cohort (N = 507) and were included in the current analysis. Inclusion criteria for this study were (i) age ≥ 45 years and (ii) a left ventricular ejection fraction (LVEF) ≥ 50%, in the absence of a history of HF, coronary artery disease, congenital heart disease, or any previous cardiac interventions. Multinominal-based clustering with latent class model using the HFA-PEFF domain scores (functional, structural, and biomarker scores) as input was used to detect distinct phenotypic clusters. For each bootstrapping run, the 92 Olink proteins were analysed for their association with the identified phenogroups. Four distinct phenogroups were identified in the current analysis (validated by bootstrapping 1000×): (i) no left ventricular diastolic dysfunction (no LVDD, N = 102); (ii) LVDD with functional left ventricular (LV) abnormalities (N = 204); (iii) LVDD with functional and structural LV abnormalities (N = 204); and (iv) LVDD with functional and structural LV abnormalities and elevated BNP (N = 107). The HFA-PEFF total score risk categories significantly differed between the phenogroups (P < 0.001), with an increase of the HFA-PEFF score from Phenogroup 1 to 4 (low/intermediate/high HFA-PEFF risk score: Phenogroup 1: 88%/12%/0%; Phenogroup 2: 9%/91%/0%; Phenogroup 3: 0%/92%/8%; Phenogroup 4: 5%/83%/12%). Thirty-two out of the 92 Olink protein biomarkers significantly differed among the phenogroups. The top eight biomarkers—N-terminal prohormone brain natriuretic peptide, growth differentiation factor-15, matrix metalloproteinase-2, osteoprotegerin, tissue inhibitor of metalloproteinase-4, chitinase-3-like protein 1, insulin-like growth factor-binding protein 2, and insulin-like growth factor-binding protein 7—are mainly involved in inflammation and extracellular matrix remodelling, which are currently proposed key processes in HFpEF pathophysiology. Conclusions This study identified distinct phenogroups by using the HFA-PEFF domain scores in ambulant subjects referred for HF-like symptoms. The newly identified phenogroups accompanied by their circulating biomarkers profile might aid in a better understanding of the pathophysiological processes involved during the early stages of the HFpEF syndrome. [ABSTRACT FROM AUTHOR]

Carriers of multidrug-resistant bacteria are at risk of infections with these bacteria; the precise size of this risk is unclear. We aimed to quantify the effect of gut colonisation on subsequent risk of infection with multidrug-resistant bacteria. We performed a systematic review and meta-regression analysis. We searched PubMed, Embase, Web of Science Core Collection, and Google Scholar for follow-up studies published from Jan 1, 1995, to March 17, 2022, that measured the incidence of infections with multidrug-resistant Gram-negative bacteria (MDR-GNB) and from Jan 1, 1995, to March 15, 2022, that measured the incidence of infections with vancomycin-resistant enterococci (VRE). We included original cohort studies and case-control studies that used incidence-density sampling, included 50 or more patients with enteric colonisation or positive urinary samples as a surrogate marker of colonisation, or both, and analysed infections clearly preceded by colonisation. We did not use any language restrictions. We excluded studies not reporting length of follow-up. Summary data were extracted and independently cross-verified by two authors. Carriage was defined as MDR-GNB or VRE, detected in faecal or urinary cultures. Our primary outcomes were cumulative incidence and incidence density of infection in patients colonised by multidrug-resistant bacteria. To estimate pooled incidences, general linearised mixed-effects meta-regressions were used, adjusting for varying follow-up durations. This study is registered with PROSPERO, CRD42020222415. Of the 301 studies identified, 44 studies (26 on MDR-GNB, 14 on VRE, and four on both MDR-GNB and VRE) from 14 countries were retained for qualitative synthesis, 40 of which were analysed with meta-regression, comprising data for 14 049 patients colonised with multidrug-resistant bacteria. The pooled cumulative incidence of infection was 14% (95% CI 10–18; p<0·0001) at a median follow-up time of 30 days for MDR-GNB (845 cases of infection in 9034 patients colonised) and 8% (5–13; p<0·0001) at 30 days for VRE (229 cases of infection in 4747 patients colonised). Infection incidence density (4·26 infections per 1000 patient-days; 95% CI 1·69–6·82) and cumulative incidence of infection (19%, 95% CI 15–25; p<0·0001; 602 cases of infection in 4547 patients colonised) were highest for carbapenem-resistant Gram-negative bacteria at 30 days. Risk of bias was rated low to moderate. The risk of infection was substantial, with the highest risk for patients colonised with carbapenem-resistant Gram-negative bacteria and the lowest in patients with VRE. These data might help to guide prophylactic and treatment decisions and form a valuable resource for planning clinical trials on targeted prevention. The Netherlands Organization for Health Research and Development [ABSTRACT FROM AUTHOR]

Background and Aims: This study aims to investigate the relationship of serum and dietary advanced glycation endproducts (AGEs) with cardiac function and structure after eight years of follow-up., Methods and Results: We included 370 Hoorn Study participants (aged 66.4 ± 6.1, 47% women). Serum protein-bound AGEs [N ε -(carboxymethyl)lysine, N ε -(carboxyethyl)lysine, and pentosidine], as well as echocardiography to assess left atrium volume index (LAVI), left ventricle ejection fraction (LVEF), and left ventricle mass index (LVMI), were measured at baseline and after 8 years of follow-up. Dietary AGEs [N ε -(carboxymethyl)lysine and N ε -(carboxyethyl)lysine] were estimated at baseline with a validated food-frequency questionnaire and an AGEs database. Increased pentosidine [-1.4% (-2.6;-0.2)] and overall serum AGEs Z-scores over time [-2.1% (-3.8;-0.5)] were associated with decreased LVEF at follow-up, adjusted for confounders. Glucose metabolism status was an effect modifier (P-for-interaction = 0.04). In participants with impaired glucose metabolism, but not type 2 diabetes, increased pentosidine was associated with decreased LVEF [-4.2 (-8.0;-0.3)%]. Higher dietary N ε -(carboxyethyl)lysine [1.9 (0.1; 3.7)%] and overall dietary AGEs Z-scores [2.1 (0.1; 4.2)%] were associated with higher LVEF at follow-up. However, prior cardiovascular disease (CVD) was an effect modifier (P = 0.02). We found a stronger, non-significant, association of higher dietary (carboxyethyl)lysine with higher LVEF at follow-up in participants without CVD [2.3 (-0.1; 4.7)%] compared to participants with CVD [0.6 (-2.1; 3.4)%]., Conclusion: Overall serum AGEs were longitudinally associated with impaired systolic function. Future research should focus on including changes in dietary AGEs intake over time and the relation of dietary AGEs with cardiac measures needs to be established in intervention studies using low AGEs diets., Competing Interests: Declaration of competing interest None declared., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)