Sex-specific differences in intestinal microbiota associated with cardiovascular diseases.

BACKGROUND: Cardiovascular diseases (CVD), including coronary heart disease (CHD), display a higher prevalence in men than women. This study aims to evaluate the variations in the intestinal microbiota between men and women afflicted with CHD and delineate these against a non-CVD control group for each sex. METHODS: Our research was conducted in the framework of the CORDIOPREV study, a clinical trial which involved 837 men and 165 women with CHD. We contrasted our findings with a reference group of 375 individuals (270 men, 105 women) without CVD. The intestinal microbiota was examined through 16S metagenomics on the Illumina MiSeq platform and the data processed with Quiime2 software. RESULTS: Our results showed a sex-specific variation (beta diversity) in the intestinal microbiota, while alpha-biodiversity remained consistent across both sexes. Linear discriminant analysis effect size (LEfSe) analysis revealed sex-centric alterations in the intestinal microbiota linked to CVD. Moreover, using random forest (RF) methodology, we identified seven bacterial taxa-g_UBA1819 (Ruminococcaceae), g_Bilophila, g_Subdoligranulum, g_Phascolarctobacterium, f_Barnesiellaceae, g_Ruminococcus, and an unknown genus from the Ruminococcaceae family (Ruminococcaceae incertae sedis)-as key discriminators between men and women diagnosed with CHD. The same taxa also emerged as critical discriminators between CHD-afflicted and non-CVD individuals, when analyzed separately by sex. CONCLUSION: Our findings suggest a sex-specific dysbiosis in the intestinal microbiota linked to CHD, potentially contributing to the sex disparity observed in CVD incidence. Trial registration Clinical Trials.gov.Identifier NCT00924937.

The frequency with which cardiovascular diseases occur differs in men and women as it appears with greater frequency in men. Moreover, it has been known for years that the community of bacteria living in our intestine, also known as the gut microbiota, influences the development of these diseases. Indeed, nowadays it known the influence of the intestinal microbiota in the development of atherosclerosis, the pathological process which is responsible for the three main causes of cardiovascular diseases: coronary heart disease, cerebrovascular disease and peripheral arterial disease. This study shows the differences in the community of bacteria living in the gut of men and those living in the gut of women, so that these differences could explain, at least in part, the differences in the frequency with which cardiovascular diseases appear between men and women. Our results suggest that the dysbiosis of the intestinal microbiota associated with CHD seems to be partially sex-specific, which may influence the sexual dimorphism in its incidence. Moreover, the identification of the mechanisms responsible for sexual dimorphism in the incidence of metabolic and cardiovascular disease is of particular importance when developing effective strategies and therapies aimed at reducing their incidence and recurrence. Indeed, the strategies and therapies used to treat the dysbiosis of the gut microbiota should be sex-specific.

Analysis of Connectome Graphs Based on Boundary Scale.

The purpose of this work is to advance in the computational study of connectome graphs from a topological point of view. Specifically, starting from a sequence of hypergraphs associated to a brain graph (obtained using the Boundary Scale model, BS2), we analyze the resulting scale-space representation using classical topological features, such as Betti numbers and average node and edge degrees. In this way, the topological information that can be extracted from the original graph is substantially enriched, thus providing an insightful description of the graph from a clinical perspective. To assess the qualitative and quantitative topological information gain of the BS2 model, we carried out an empirical analysis of neuroimaging data using a dataset that contains the connectomes of 96 healthy subjects, 52 women and 44 men, generated from MRI scans in the Human Connectome Project. The results obtained shed light on the differences between these two classes of subjects in terms of neural connectivity.

An altered microbiota pattern precedes Type 2 diabetes mellitus development: From the CORDIOPREV study.

Introduction: A distinctive gut microbiome have been linked to type 2 diabetes mellitus (T2DM). Objectives: We aimed to evaluate whether gut microbiota composition, in addition to clinical biomarkers, could improve the prediction of new incident cases of diabetes in patients with coronary heart disease. Methods: All the patients from the CORDIOPREV (Clinical Trials.gov.Identifier: NCT00924937) study without T2DM at baseline were included (n = 462). Overall, 107 patients developed it after a median of 60 months. The gut microbiota composition was determined by 16S rRNA gene sequencing and predictive models were created using hold-out method. Results: A gut microbiota profile associated with T2DM development was determined through a microbiome-based predictive model. The addition of microbiome data to clinical parameters (variables included in FINDRISC risk score and the diabetes risk score of the American Diabetes Association, HDL, triglycerides and HbA1c) improved the prediction increasing the area under the curve from 0.632 to 0.946. Furthermore, a microbiome-based risk score including the ten most discriminant genera, was associated with the probability of develop T2DM. Conclusion: These results suggest that a microbiota profile is associated to the T2DM development. An integrate predictive model of microbiome and clinical data that can improve the prediction of T2DM is also proposed, if is validated in independent populations to prevent this disease.

Interplay between gonadal hormones and postnatal overfeeding in defining sex-dependent differences in gut microbiota architecture.

Aging is associated with a decline in sex hormones, variable between sexes, that has an impact on many different body systems and might contribute to age-related disease progression. We aimed to characterize the sex differences in gut microbiota, and to explore the impact of depletion of gonadal hormones, alone or combined with postnatal overfeeding, in rats. Many of the differences in the gut microbiota between sexes persisted after gonadectomy, but removal of gonadal hormones shaped several gut microbiota features towards a more deleterious profile, the effect being greater in females than in males, mainly when animals were concurrently overfed. Moreover, we identified several intestinal miRNAs as potential mediators of the impact of changes in gut microbiota on host organism physiology. Our study points out that gonadal hormones contribute to defining sex-dependent differences of gut microbiota, and discloses a potential role of gonadal hormones in shaping gut microbiota, as consequence of the interaction between sex and nutrition. Our data suggest that the changes in gut microbiota, observed in conditions of sex hormone decline, as those caused by ageing in men and menopause in women, might exert different effects on the host organism, which are putatively mediated by gut microbiota-intestinal miRNA cross-talk.

Neonatal exposure to androgens dynamically alters gut microbiota architecture.

Gonadal steroids strongly contribute to the metabolic programming that shapes the susceptibility to the manifestation of diseases later in life, and the effect is often sexually dimorphic. Microbiome signatures, together with metabolic traits and sex steroid levels, were analyzed at adulthood in neonatally androgenized female rats, and compared with those of control male and female rats. Exposure of female rats to high doses of androgens on early postnatal life resulted in persistent alterations of the sex steroid profile later on life, namely lower progesterone and higher estradiol and estrone levels, with no effect on endogenous androgens. Neonatally androgenized females were heavier (10% at early adulthood and 26% at adulthood) than controls and had impaired glucose homeostasis observed by higher AUC of glucose in GTT and ITT when subjected to obesogenic manipulations. Androgenized female displayed overt alterations in gut microbiota, indicated especially by higher Bacteroidetes and lower Firmicutes abundance at early adulthood, which disappeared when animals were concurrently overfed at adulthood. Notably, these changes in gut microbiota were related with the intestinal expression of several miRNAs, such as miR-27a-3p, miR-29a-5p, and miR-100-3p. Our results suggest that nutritional and hormonal disruption at early developmental periods not only alters the metabolic programming of the individual later in life but also perturbs the architecture of gut microbiota, which may interact with the host by a cross-talk mediated by intestinal miRNAs; phenomena that may contribute to amplify the metabolic derangement caused by obesity, as seen in neonatally androgenized female rats.

Deregulation of miR-324/KISS1/kisspeptin in early ectopic pregnancy: mechanistic findings with clinical and diagnostic implications.

BACKGROUND: Ectopic pregnancy is a life-threatening condition for which novel screening tools that would enable early accurate diagnosis would improve clinical outcomes. Kisspeptins, encoded by KISS1, play an essential role in human reproduction, at least partially by regulating placental function and possibly embryo implantation. Kisspeptin levels are elevated massively in normal pregnancy and reportedly altered in various gestational pathologic diseases. Yet, the pathophysiologic role of KISS1/kisspeptin in ectopic pregnancy has not been investigated previously. OBJECTIVE: The purpose of this study was to evaluate changes of KISS1/kisspeptin levels in ectopic pregnancy and their underlaying molecular mechanisms and to ascertain the diagnostic implications of these changes. STUDY DESIGN: A total of 122 women with normal pregnancy who underwent voluntary termination of pregnancy and 84 patients who experienced tubal ectopic pregnancy were recruited. Measurements of plasma kisspeptins and KISS1 expression analyses in human embryonic/placental tissue were conducted in ectopic pregnancy and voluntary termination of pregnancy control subjects during the early gestational window (<12 weeks). Putative microRNA regulators of KISS1 were predicted in silico, followed by expression analyses of selected microRNAs and validation of repressive interactions in vitro. Circulating levels of these microRNAs were also assayed in ectopic pregnancy vs voluntary termination of pregnancy. RESULTS: Circulating kisspeptins gradually increased during the first trimester of normal pregnancy but were reduced markedly in ectopic pregnancy. This profile correlated with the expression levels of KISS1 in human embryonic/placental tissue, which increased in voluntary termination of pregnancy but remained suppressed in ectopic pregnancy. Bioinformatic predictions and expression analyses identified miR-27b-3p and miR-324-3p as putative repressors of KISS1 in human embryonic/placental tissue at <12 weeks gestation, when expression of microRNAs was low in voluntary termination of pregnancy control subjects but significantly increased in ectopic pregnancy. Yet, a significant repressive interaction was documented only for miR-324-3p, occurring at the predicted 3'-UTR of KISS1. Interestingly, circulating levels of miR-324-3p, but not of miR-27b-3p, were suppressed distinctly in ectopic pregnancy, despite elevated tissue expression of the pre-microRNA. A decision-tree model that used kisspeptin and miR-324-3p levels was successful in discriminating ectopic pregnancy vs voluntary termination of pregnancy, with a receiver-operating characteristic area under the curve of 0.95±0.02 (95% confidence interval). CONCLUSION: Our results document a significant down-regulation of KISS1/kisspeptins in early stages of ectopic pregnancy via, at least partially, a repressive interaction with miR-324-3p. Our data identify circulating kisspeptins and miR-324-3p as putative biomarkers for accurate screening of ectopic pregnancy at early gestational ages.

Sex Differences in the Gut Microbiota as Potential Determinants of Gender Predisposition to Disease.

SCOPE: Dysbiosis of gut microbiota is involved in metabolic syndrome (MetS) development, which has a different incidence between men (M) and women (W). The differences in gut microbiota in MetS patients are explored according to gender, and whether consuming two healthy diets, Mediterranean (MED) and low-fat (LF), may, over time, differentially shape the gut microbiota dysbiosis according to gender is evaluated. MATERIALS AND METHODS: All the women from the CORDIOPREV study whose feces samples were available and a similar number of men, matched by the main metabolic variables (N = 246, 123 women and 123 men), and categorized according to the presence or not of MetS are included. Gut microbiota is analyzed at baseline and after 3 years of dietary intervention. RESULTS: Higher abundance of Collinsella, Alistipes, Anaerotruncus, and Phascolarctobacterium genera is observed in MetS-W than in MetS-M, whereas the abundance of Faecalibacterium and Prevotella genera is higher in MetS-M than in MetS-W. Moreover, higher levels of Desulfovibrio, Roseburia, and Holdemania are observed in men than in women after the consumption of the LF diet. CONCLUSION: The results suggest the potential involvement of differences in gut microbiota in the unequal incidence of metabolic diseases between genders, and a sex-dependent effect on shaping the gut microbiota according to diet.

Circulating miRNAs as Predictive Biomarkers of Type 2 Diabetes Mellitus Development in Coronary Heart Disease Patients from the CORDIOPREV Study.

Circulating microRNAs (miRNAs) have been proposed as type 2 diabetes biomarkers, and they may be a more sensitive way to predict development of the disease than the currently used tools. Our aim was to identify whether circulating miRNAs, added to clinical and biochemical markers, yielded better potential for predicting type 2 diabetes. The study included 462 non-diabetic patients at baseline in the CORDIOPREV study. After a median follow-up of 60 months, 107 of them developed type 2 diabetes. Plasma levels of 24 miRNAs were measured at baseline by qRT-PCR, and other strong biomarkers to predict diabetes were determined. The ROC analysis identified 9 miRNAs, which, added to HbA1c, have a greater predictive value in early diagnosis of type 2 diabetes (AUC = 0.8342) than HbA1c alone (AUC = 0.6950). The miRNA and HbA1c-based model did not improve when the FINDRISC was included (AUC = 0.8293). Cox regression analyses showed that patients with low miR-103, miR-28-3p, miR-29a, and miR-9 and high miR-30a-5p and miR-150 circulating levels have a higher risk of disease (HR = 11.27; 95% CI = 2.61-48.65). Our results suggest that circulating miRNAs could potentially be used as a new tool for predicting the development of type 2 diabetes in clinical practice.

European Randomized Study of Screening for Prostate Cancer Risk Calculator: External Validation, Variability, and Clinical Significance.

OBJECTIVE: To externally validate the European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC) and to evaluate its variability between 2 consecutive prostate-specific antigen (PSA) values. MATERIALS AND METHODS: We prospectively catalogued 1021 consecutive patients before prostate biopsy for suspicion of prostate cancer (PCa). The risk of PCa and significant PCa (Gleason score ≥7) from 749 patients was calculated according to ERSPC-RC (digital rectal examination-based version 3 of 4) for 2 consecutive PSA tests per patient. The calculators' predictions were analyzed using calibration plots and the area under the receiver operating characteristic curve (area under the curve). Cohen kappa coefficient was used to compare the ability and variability. RESULTS: Of 749 patients, PCa was detected in 251 (33.5%) and significant PCa was detected in 133 (17.8%). Calibration plots showed an acceptable parallelism and similar discrimination ability for both PSA levels with an area under the curve of 0.69 for PCa and 0.74 for significant PCa. The ERSPC showed 226 (30.2%) unnecessary biopsies with the loss of 10 significant PCa. The variability of the RC was 16% for PCa and 20% for significant PCa, and a higher variability was associated with a reduced risk of significant PCa. CONCLUSION: We can conclude that the performance of the ERSPC-RC in the present cohort shows a high similitude between the 2 PSA levels; however, the RC variability value is associated with a decreased risk of significant PCa. The use of the ERSPC in our cohort detects a high number of unnecessary biopsies. Thus, the incorporation of ERSPC-RC could help the clinical decision to carry out a prostate biopsy.

Learning Biomarker Models for Progression Estimation of Alzheimer's Disease.

Being able to estimate a patient's progress in the course of Alzheimer's disease and predicting future progression based on a number of observed biomarker values is of great interest for patients, clinicians and researchers alike. In this work, an approach for disease progress estimation is presented. Based on a set of subjects that convert to a more severe disease stage during the study, models that describe typical trajectories of biomarker values in the course of disease are learned using quantile regression. A novel probabilistic method is then derived to estimate the current disease progress as well as the rate of progression of an individual by fitting acquired biomarkers to the models. A particular strength of the method is its ability to naturally handle missing data. This means, it is applicable even if individual biomarker measurements are missing for a subject without requiring a retraining of the model. The functionality of the presented method is demonstrated using synthetic and--employing cognitive scores and image-based biomarkers--real data from the ADNI study. Further, three possible applications for progress estimation are demonstrated to underline the versatility of the approach: classification, construction of a spatio-temporal disease progression atlas and prediction of future disease progression.

Multi-stage Biomarker Models for Progression Estimation in Alzheimer's Disease.

The estimation of disease progression in Alzheimer's disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1) subjects that progress from a cognitively normal (CN) stage to mild cognitive impairment (MCI) and (2) subjects that progress from MCI to AD during the observation window of a longitudinal study. These models are then automatically combined to develop a multi-stage disease progression model for the whole disease course. A probabilistic approach is derived to estimate the current disease progress (DP) and the disease progression rate (DPR) of a given individual by fitting any acquired biomarkers to these models. A particular strength of this method is that it is applicable even if individual biomarker measurements are missing for the subject. Employing cognitive scores and image-based biomarkers, the presented method is used to estimate DP and DPR for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Further, the potential use of these values as features for different classification tasks is demonstrated. For example, accuracy of 64% is reached for CN vs. MCI vs. AD classification.

Topo-geometric filtration scheme for geometric active contours and level sets: application to cerebrovascular segmentation.

One of the main problems of the existing methods for the segmentation of cerebral vasculature is the appearance in the segmentation result of wrong topological artefacts such as the kissing vessels. In this paper, a new approach for the detection and correction of such errors is presented. The proposed technique combines robust topological information given by Persistent Homology with complementary geometrical information of the vascular tree. The method was evaluated on 20 images depicting cerebral arteries. Detection and correction success rates were 81.80% and 68.77%, respectively.