Novel insights about albumin in cardiovascular diseases: Focus on heart failure

Zoanni, Beatrice; Brioschi, Marcos Leal; Mallia, Alice; Gianazza, Erica; Eligini, Sonia; Carini, Marina; Aldini, Giancarlo; Banfi, Cristina

doi:10.1002/mas.21743

Cited by 27 publications

(21 citation statements)

References 110 publications

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“…35 Serum albumin levels have been reported to be inversely associated with the cardiovascular disease risk and aging, supporting its possible causal relationships with the oxidative stress-related status and diseases. [36][37][38] The mechanisms underlying the close associations of serum bilirubin and albumin levels with the progression to ESKD should be clari ed in future studies.…”

Section: Discussionmentioning

confidence: 99%

A Simplified Prediction Model for End-stage Kidney Disease in Patients With Diabetes

Inoguchi

Okui

Nojiri

et al. 2022

Preprint

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This study aimed to develop a simplified model for predicting end-stage kidney disease (ESKD) in patients with diabetes. The cohort included 2,549 individuals who were followed up at Kyushu University Hospital (Japan) between January 1, 2008 and December 31, 2018. The outcome was a composite of ESKD, defined as an eGFR < 15 mL min− 1 [1.73 m]−2, dialysis, or renal transplantation. The mean follow-up was 5.6 \(\pm\) 3.7 years, and ESKD occurred in 176 (6.2 ) individuals. Both a machine learning random forest model and a Cox proportional hazard model selected eGFR, proteinuria, hemoglobin A1c, serum albumin levels, and serum bilirubin levels in a descending order as the most important predictors among 20 baseline variables. A model using eGFR, proteinuria and hemoglobin A1c showed a relatively good performance in discrimination (C-statistic: 0.842) and calibration (Nam and D’Agostino \(\chi\)2 statistic: 22.4). Adding serum albumin and bilirubin levels to the model further improved it, and a model using 5 variables showed the best performance in the predictive ability (C-statistic: 0.895, \(\chi\)2 statistic: 7.7). The accuracy of this model was validated in an external cohort (n = 5,153). This novel simplified prediction model may be clinically useful for predicting ESKD in patients with diabetes.

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Section: Discussionmentioning

confidence: 99%

A Simplified Prediction Model for End-stage Kidney Disease in Patients With Diabetes

Inoguchi

Okui

Nojiri

et al. 2022

Preprint

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“…Various genes such as PDK4 [140], ALB (albumin) [141], EGR1 [142], RYR3 [48], CYP27B1 [143], GATA6 [144], NR4A3 [145], TET2 [146], CACNA1D [147], KLF15 [148], CYP4A11 [149], ATOH8 [150], ATF3 [151], EGF (epidermal growth factor) [152], LPL (lipoprotein lipase) [153], PPARGC1A [154], PLG (plasminogen) [155], USP2 [156], PDE4D [157], RGS2 [158], ILK (integrin linked kinase) [159], SLC26A4 [160], PER1 [161], LGR4 [72], GABRB3 [162], TRPC1 [163], KL (klotho) [164], NOX4 [165], S100A12 [166], NEDD9 [167], ANKRD36 [168], IRF9 [169], GIMAP5 [170], ND2 [171], CHGA (chromogranin A) [172], DBH (dopamine beta-hydroxylase) [173], LTA (lymphotoxin alpha) [174], MPO (myeloperoxidase) [175], CD70 [176], LCN2 [177], TLR6 [178], TREM2 [179], NLRP12 [180], CCR5 [181], IL1RN [182], CX3CR1 [183], TRPM2 [184], TNF (tumor necrosis factor) [185], TLR7 [186], NOD2 [187], TNFRSF1B [188], CCR2 [181], CCL5 [189], PLA2G7 [190], TH (tyrosine hydroxylase) [191], WNT7A [192], ADRB3 [193], IL10RA [194], NOS3 [195], PIK3R5 [196], APLNR (apelin receptor) [197], COMP (cartilage oligomeric matrix protein) [198], RETN (resistin) [199], S100B [200], IGFBP1 [201] and COL1A1 [202] are crucial in the progression of hypertension. Evidence suggests that PDK4 [203], ALB (albumin) [204], EGR1 [205], RYR3 [206], DUSP1 [207], EYA4 [208], CYP27B1 [143<...…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Focal segmental glomerulosclerosis (FSGS) is a renal disease leading threat to human health around the world. Here we aimed to explore novel molecular and potential therapeutic targets in FSGS through adopting integrated bioinformatics tools. Next generation sequencing (NGS) data of GSE197307 was available from Gene Expression Omnibus (GEO) database. Furthermore, differentially expressed genes (DEGs) were screened using the DESeq2 package in R software. Gene ontology (GO) and REACTOME pathway enrichment analyses of DEGs were performed via g:Profiler. Then, the protein-protein interaction (PPI) network, miRNA- hub gene regulatory network and TF-hub gene regulatory network were constructed using the HIPPIE, miRNet and NetworkAnalyst databases. Hub genes were validated using the receiver operating characteristic curve (ROC) analysis. By performing DEGs analysis, 488 up regulated genes and 488 down regulated genes were successfully identified from GSE197307, respectively. And they were mainly enriched in the terms of multicellular organismal process, cell periphery, protein binding, metabolism, immune system process, signaling receptor binding and immune system. Based on the data of protein-protein interaction (PPI), miRNA-hub gene regulatory network and TF-hub gene regulatory network, the top hub genes were ranked, including ILK, DDX5, MATR3, ALB, FOS, MKI67, PLK1, TNF, LCK and GTSE1. Bioinformatics analysis of NGS data identified signaling pathways and hub genes, potentially representing molecular mechanisms for the occurrence, progression, and risk prediction in FSGS.

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“…16,17) Serum albumin is often involved in the anti-inflammatory, antioxidant, and anticoagulant processes in the acute phase of the body and is associated with the incidence of long-term adverse cardiovascular events. 18) It has been demonstrated that GNRI is a better predictor of poor prognosis in patients with heart failure than any other model comparing BMI, serum albumin, and GNRI. 19) In a study of 213 elderly heart failure patients aged "80 years, mortality was found to be significantly higher in the low GNRI group than in the normal GNRI group and was significantly associated with poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

Association of GNRI, NLR, and FT3 with the Clinical Prognosis of Older Patients with Heart Failure

2022

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We investigated the relationship between heart failure and malnutrition, inflammation, and thyroid function and evaluated the predictive potential of these markers for major adverse cardiovascular events (MACEs).This study included 454 patients aged over 65 years with heart failure as the main diagnosis for 18 months follow-up. The nutritional and inflammatory status were assessed using the geriatric nutritional risk index (GNRI) and neutrophil-to-lymphocyte ratio (NLR), respectively. Free triiodothyronine (FT3) in thyroid hormone was divided into low, medium, and high FT3. Older patients were divided into two groups according to whether they had endpoint events. Differences in nutrition, inflammation, and thyroid hormone were compared between the two groups. The prognostic value of the combination of GNRI, NLR, and FT3 was analyzed.Older patients in the MACEs (+) group had lower levels of GNRI and FT3 and higher NLR than those in the MACEs (−) group. Low GNRI and FT3 and high NLR were associated with MACEs (P < 0.05). Multivariate Cox regression analysis revealed that low FT3 was an independent predictor of MACEs (P < 0.05). Regardless of how the LVEF changed, when patients had low GNRI and FT3 and high NLR risk factors, the risk of developing MACEs significantly increased. The addition of GNRI, NLR, and FT3 to the basic model significantly increased the predictability of MACEs in patients.Low GNRI and FT3 and high NLR were associated with MACEs. The combination of GNRI, NLR, and FT3 increased the predictive value of MACEs in older patients with heart failure.

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Novel insights about albumin in cardiovascular diseases: Focus on heart failure

Cited by 27 publications

References 110 publications

A Simplified Prediction Model for End-stage Kidney Disease in Patients With Diabetes

A Simplified Prediction Model for End-stage Kidney Disease in Patients With Diabetes

Identification of biomarkers and pathways for focal segmental glomerulosclerosis using next generation sequencing data and bioinformatics analysis

Association of GNRI, NLR, and FT3 with the Clinical Prognosis of Older Patients with Heart Failure

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