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A notable shift in understanding the human microbiome's influence on cardiovascular disease (CVD) is underway, although the causal association remains elusive. A systematic review and meta-analysis were conducted to synthesise current knowledge on microbial taxonomy and metabolite variations between healthy controls (HCs) and those with CVD. An extensive search encompassing three databases identified 67 relevant studies (2012–2023) covering CVD pathologies from 4707 reports. Metagenomic and metabolomic data, both qualitative and quantitative, were obtained. Analysis revealed substantial variability in microbial alpha and beta diversities. Moreover, specific changes in bacterial populations were shown, including increased Streptococcus and Proteobacteria and decreased Faecalibacterium in patients with CVD compared with HC. Additionally, elevated trimethylamine N-oxide levels were reported in CVD cases. Biochemical parameter analysis indicated increased fasting glucose and triglycerides and decreased total cholesterol and low- and high-density lipoprotein cholesterol levels in diseased individuals. This study revealed a significant relationship between certain bacterial species and CVD. Additionally, it has become clear that there are substantial inconsistencies in the methodologies employed and the reporting standards adhered to in various studies. Undoubtedly, standardising research methodologies and developing extensive guidelines for microbiome studies are crucial for advancing the field. [ABSTRACT FROM AUTHOR]

The associations of plasma metabolites with adverse cardiovascular (CV) outcomes are still underexplored and may be useful in CV risk stratification. We performed a systematic review and meta-analysis to establish correlations between blood metabolites and adverse CV outcomes in patients with heart failure (HF). Four cohorts were included, involving 83 metabolites and 37 metabolite ratios, measured in 1158 HF patients. Hazard ratios (HR) of 42 metabolites and 3 metabolite ratios, present in at least two studies, were combined through meta-analysis. Higher levels of histidine (HR 0.74, 95% CI [0.64; 0.86]) and tryptophan (HR 0.82 [0.71; 0.96]) seemed protective, whereas higher levels of symmetric dimethylarginine (SDMA) (HR 1.58 [1.30; 1.93]), N-methyl-1-histidine (HR 1.56 [1.27; 1.90]), SDMA/arginine (HR 1.38 [1.14; 1.68]), putrescine (HR 1.31 [1.06; 1.61]), methionine sulfoxide (HR 1.26 [1.03; 1.52]), and 5-hydroxylysine (HR 1.25 [1.05; 1.48]) were associated with a higher risk of CV events. Our findings corroborate important associations between metabolic imbalances and a higher risk of CV events in HF patients. However, the lack of standardization and data reporting hampered the comparison of a higher number of studies. In a future clinical scenario, metabolomics will greatly benefit from harmonizing sample handling, data analysis, reporting, and sharing. [ABSTRACT FROM AUTHOR]

Gestational diabetes, affecting about 10% of pregnancies, is characterized by impaired glucose regulation and can lead to complications for health of pregnant women and their offspring. The microbiota, the resident microbes within the body, have been linked to the development of several metabolic conditions. This systematic review with meta-analysis aims to summarize the evidence on the differences in microbiota composition in pregnant women with gestational diabetes and their offspring compared to healthy pregnancies. A thorough search was conducted in the PubMed, Scopus, and Web of Science databases, and data from 21 studies were analyzed utilizing 41 meta-analyses. In the gut microbiota, Bifidobacterium and Alistipes were found to be more abundant in healthy pregnancies, while Roseburia appears to be more abundant in gestational diabetes. The heterogeneity among study findings regarding the microbiota in the meconium is considerable. The placental microbiota exhibited almost no heterogeneity, with an increased abundance of Firmicutes in the gestational diabetes group and a higher abundance of Proteobacteria in the control. The role of the microbiota in gestational diabetes is reinforced by these findings, which additionally point to the potential of microbiome-targeted therapies. To completely comprehend the interactions between gestational diabetes and the microbiome, standardizing methodologies and further research is necessary. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we examined how age impacts the outcomes of breast cancer by comparing three age groups: patients 45 years old or younger, patients between 46 and 69 years old, and patients 70 years old or older. Despite similar cancer staging and tumor characteristics among the age groups, the study found that older patients were prone to suboptimal treatment. Older patients also had a lower overall survival rate, but this was not related to cancer itself. Instead, we found that undertreatment was a factor that negatively impacted survival for older women with breast cancer. This study suggests that tumor characteristics and treatment compliance are more important predictors of survival than chronological age. Age as a breast cancer (BC) prognostic factor remains debatable. Several studies have investigated clinicopathological features at different ages, but few make an age group direct comparison. The European Society of Breast Cancer Specialists quality indicators (EUSOMA-QIs) allow a standardized quality assurance of BC diagnosis, treatment, and follow-up. Our objective was to compare clinicopathological features, compliance to EUSOMA-QIs and BC outcomes in three age groups (≤45 years, 46–69 years, and ≥70 years). Data from 1580 patients with staged 0–IV BC from 2015 to 2019 were analyzed. The minimum standard and desirable target on 19 mandatory and 7 recommended QIs were studied. The 5-year relapse rate, overall survival (OS), and BC-specific survival (BCSS) were also evaluated. No meaningful differences in TNM staging and molecular subtyping classification between age groups were found. On the contrary, disparities in QIs compliance were observed: 73.1% in ≤45 years and 46–69 years women vs. 54% in older patients. No differences in loco-regional or distant progression were observed between age groups. Nevertheless, lower OS was found in older patients due to concurrent non-oncological causes. After survival curves adjustment, we underscored evidence of undertreatment impacting BCSS in ≥70 years women. Despite a unique exception—more invasive G3 tumors in younger patients—no age-specific differences in BC biology impacting outcome were found. Although increased noncompliance in older women, no outcome correlation was observed with QIs noncompliance in any age group. Clinicopathological features and differences in multimodal treatment (not the chronological age) are predictors of lower BCSS. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) and the frequently coexisting aortic valve stenosis (AVS) are heart diseases accounting for most cardiac surgeries. These share many risk factors, such as age, diabetes, hypertension, or obesity, and similar pathogenesis, including endothelial disruption, lipid and immune cell infiltration, inflammation, fibrosis, and calcification. Unsuspected CAD and AVS are sometimes detected opportunistically through echocardiography, coronary angiography, and magnetic resonance. Routine biomarkers for early detection of either of these atherosclerotic-rooted conditions would be important to anticipate the diagnosis. With a noninvasive collection, urine is appealing for biomarker assessment. We conducted a shotgun proteomics exploratory analysis of urine from 12 CAD and/or AVS patients and 11 controls to identify putative candidates to differentiate these diseases from healthy subjects. Among the top 20 most dysregulated proteins, TIMP1, MMP2 and vWF stood out, being at least 2.5× increased in patients with CAD/AVS and holding a central position in a network of protein-protein interactions. Moreover, their assessment in an independent cohort (19 CAD/AVS and 10 controls) evidenced strong correlations between urinary TIMP1 and vWF levels and a common cardiovascular risk factor - HDL (r = 0.59, p < 0.05, and r = 0.64, p < 0.01, respectively). [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer (PCa) is one of the most common cancers. Due to the limited and invasive approaches for PCa diagnosis, it is crucial to identify more accurate and non-invasive biomarkers for its detection. The aim of our study was to non-invasively uncover new protein targets for detecting PCa using a proteomics and proteogenomics approach. This work identified several dysregulated mutant protein isoforms in urine from PCa patients, some of them predicted to have a protective or an adverse role in these patients. These results are promising given urine's non-invasive nature and offers an auspicious opportunity for research and development of PCa biomarkers. To identify new protein targets for PCa detection, first, a shotgun discovery experiment was performed to characterize the urinary proteome of PCa patients. This revealed 18 differentially abundant urinary proteins in PCa patients. Second, selected targets were clinically tested by immunoblot, and the soluble E-cadherin fragment was detected for the first time in the urine of PCa patients. Third, the proteogenome landscape of these PCa patients was characterized, revealing 1665 mutant protein isoforms. Statistical analysis revealed 6 differentially abundant mutant protein isoforms in PCa patients. Analysis of the likely effects of mutations on protein function and PPIs involving the dysregulated mutant protein isoforms suggests a protective role of mutations HSPG2*Q1062H and VASN*R161Q and an adverse role of AMBP*A286G and CD55*S162L in PCa patients. This work originally characterized the urinary proteome, focusing on the proteogenome profile of PCa patients, which is usually overlooked in the analysis of PCa and body fluids. Combined analysis of mass spectrometry data using two different software packages was performed for the first time in the context of PCa, which increased the robustness of the data analysis. The application of proteogenomics to urine proteomic analysis can be very enriching in mutation-related diseases such as cancer. [ABSTRACT FROM AUTHOR]

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine. [ABSTRACT FROM AUTHOR]