Are There Interindividual Responses of Cardiovascular Disease Risk Markers to Acute Exercise? A Replicate Crossover Trial.

PURPOSE: Using a replicated crossover design, we quantified the response heterogeneity of postprandial cardiovascular disease risk marker responses to acute exercise. METHODS: Twenty men (mean (SD) age, 26 (6) yr; body mass index, 23.9 (2.4) kg·m -2 ) completed four 2-d conditions (two control, two exercise) in randomized orders. On days 1 and 2, participants rested and consumed two high-fat meals over 9 h. Participants ran for 60 min (61 (7)% of peak oxygen uptake) on day 1 (6.5 to 7.5 h) of both exercise conditions. Time-averaged total area under the curve (TAUC) for triacylglycerol, glucose, and insulin were calculated from 11 venous blood samples on day 2. Arterial stiffness and blood pressure responses were calculated from measurements at baseline on day 1 and at 2.5 h on day 2. Consistency of individual differences was explored by correlating the two replicates of control-adjusted exercise responses for each outcome. Within-participant covariate-adjusted linear mixed models quantified participant-by-condition interactions and individual response SDs. RESULTS: Acute exercise reduced mean TAUC-triacylglycerol (-0.27 mmol·L -1 ·h; Cohen's d = 0.29, P = 0.017) and TAUC-insulin (-25 pmol·L -1 ·h; Cohen's d = 0.35, P = 0.022) versus control, but led to negligible changes in TAUC-glucose and the vascular outcomes (Cohen's d ≤ 0.36, P ≥ 0.106). Small-to-moderate, but nonsignificant, correlations were observed between the two response replicates ( r = -0.42 to 0.15, P ≥ 0.066). We did not detect any individual response heterogeneity. All participant-by-condition interactions were P ≥ 0.137, and all individual response SDs were small with wide 95% confidence intervals overlapping zero. CONCLUSIONS: Large trial-to-trial within-subject variability inhibited detection of consistent interindividual variability in postprandial metabolic and vascular responses to acute exercise.

The influence of physical activity on neural responses to visual food cues in humans: A systematic review of functional magnetic resonance imaging studies.

This systematic review examined whether neural responses to visual food-cues measured by functional magnetic resonance imaging (fMRI) are influenced by physical activity. Seven databases were searched up to February 2023 for human studies evaluating visual food-cue reactivity using fMRI alongside an assessment of habitual physical activity or structured exercise exposure. Eight studies (1 exercise training, 4 acute crossover, 3 cross-sectional) were included in a qualitative synthesis. Structured acute and chronic exercise appear to lower food-cue reactivity in several brain regions, including the insula, hippocampus, orbitofrontal cortex (OFC), postcentral gyrus and putamen, particularly when viewing high-energy-density food cues. Exercise, at least acutely, may enhance appeal of low-energy-density food-cues. Cross-sectional studies show higher self-reported physical activity is associated with lower reactivity to food-cues particularly of high-energy-density in the insula, OFC, postcentral gyrus and precuneus. This review shows that physical activity may influence brain food-cue reactivity in motivational, emotional, and reward-related processing regions, possibly indicative of a hedonic appetite-suppressing effect. Conclusions should be drawn cautiously given considerable methodological variability exists across limited evidence.

Prevalence of abnormal and borderline electrocardiogram changes in 13, 079 Chinese amateur marathon runners.

BACKGROUND: The 12-lead electrocardiogram (ECG) has been adopted as an important component of preparticipation cardiovascular screening. However, there are still controversies in the screening and few studies with a large sample size have reported the results of ECGs of marathon runners. Therefore, the purpose of this study was to assess the prevalence of normal, borderline, and abnormal ECG changes in marathon runners. METHODS: The 12-lead ECG data of 13,079 amateur marathon runners between the ages of 18 and 35 years were included for analysis. The prevalence of ECG abnormalities among different gender groups was compared with chi-square tests. RESULTS: In terms of training-related changes, sinus bradycardia, sinus arrhythmia, and left ventricular high voltage were found in approximately 15, 5, and 3.28% of the participants, respectively. The incidence of right axis deviation in the marathon runners was 1.78%, which was slightly higher than the incidence of left axis deviation (0.88%). No more than 0.1% of the amateur marathon runners exhibited ST-segment depression, T wave inversion (TWI), premature ventricular contraction, pathologic Q waves, and prolonged QT interval. CONCLUSIONS: Training-related ECG changes, including sinus bradycardia, sinus arrhythmia, and left ventricular high voltage, were common in amateur marathon runners. Most abnormal ECG changes, including ST-segment depression, TWI, premature ventricular contraction, pathologic Q waves, and prolonged QT interval, were infrequently found in amateur marathon runners. The data also suggested Chinese amateur marathon runners may have a relatively lower prevalence of ECG abnormalities than black and white runners.

HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou's Five-Step Rule.

Identifying new treatments for existing drugs can help reduce drug development costs and explore novel indications of drugs. The prediction of associations between drugs and diseases is challenging because their similarities and relations are complicated and non-linear. We propose a HeteroDualNet model to address this issue. Firstly, three types of matrices are extracted to represent intra-drug similarities, intra-disease similarity and drug-disease associations. The intra-drug similarities consider three drug features and a newly introduced drug-related disease correlation. Secondly, an embedding mechanism is proposed to integrate these matrices in a heterogenous drug-disease association layer (hetero-layer). Further, a neighbouring heterogeneous layer (hetero-layer-N) is constructed to incorporate the biological premise that similar drugs can often treat related diseases. Finally, a dual convolutional neural network is built with hetero-layer and hetero-layer-N as two branches to learn from characteristics of drug-disease and the relations of their neighbours simultaneously. HeteroDualNet outperformed the other four methods in comparison over a public dataset of 763 drugs and 681 diseases in terms of Areas Under the Curves of Receiver Operating Characteristics and Precision-Recall, and recall rate at top k. Case study of five drugs further proved the capacity of HeteroDualNet in finding reliable disease candidates of drugs as validated by database records or literature. Our findings show that the embedded heterogenous layers of original and neighbouring drug-disease representations in a dual neural network improved the association prediction performance.

Heart-rate-based prediction of velocity at lactate threshold in ordinary adults.

BACKGROUND: Velocity lactate threshold (VLT) is commonly used as a standard for exercise intensity, although previous studies of VLT have focused mostly on well-trained athletes. Heart rate (HR) is an important physiological index which is easy to measure, the heart rate-workload relationship curve is the reported coincidence with lactate threshold (LT). This study aimed to develop valid, simple and economical velocity at lactate threshold prediction methods for general Chinese adults. METHODS: Eighty-four Chinese adults (49 males and 35 females aged 27.1 ±â€¯8.3 years) were recruited to perform a graded exercise test on a treadmill. A 20-s rest time for the blood sample collection was set between each 3-min successive stage. Blood lactate concentration and heart rate (HR) were determined using a blood lactate analyser and HR monitors. Multiple linear regressions were applied to develop VLT prediction models using velocity at different HR levels, genders, BMI and ages as dependent variables. RESULTS: Eight VLT prediction models were established, in which 47%-65% of variance of VLT could be explained. The results of the paired t-tests showed no significant difference can be observed between estimated and measured VLTs. CONCLUSION: In conclusion, simple and convenient VLT prediction models were established, and the models are valid in predicting VLT for general population.

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.

Determining the target genes that interact with drugs-drug-target interactions-plays an important role in drug discovery. Identification of drug-target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug-target interactions. We constructed a drug-target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug-target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug-target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug-target interactions.

Drug repositioning through integration of prior knowledge and projections of drugs and diseases.

MOTIVATION: Identifying and developing novel therapeutic effects for existing drugs contributes to reduction of drug development costs. Most of the previous methods focus on integration of the heterogeneous data of drugs and diseases from multiple sources for predicting the candidate drug-disease associations. However, they fail to take the prior knowledge of drugs and diseases and their sparse characteristic into account. It is essential to develop a method that exploits the more useful information to predict the reliable candidate associations. RESULTS: We present a method based on non-negative matrix factorization, DisDrugPred, to predict the drug-related candidate disease indications. A new type of drug similarity is firstly calculated based on their associated diseases. DisDrugPred completely integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different levels including the chemical structures of drugs, the target proteins of drugs, the diseases associated with drugs and the side effects of drugs. The prior knowledge of drugs and diseases and the sparse characteristic of drug-disease associations provide a deep biological perspective for capturing the relationships between drugs and diseases. Simultaneously, the possibility that a drug is associated with a disease is also dependant on their projections in the low-dimension feature space. Therefore, DisDrugPred deeply integrates the diverse prior knowledge, the sparse characteristic of associations and the projections of drugs and diseases. DisDrugPred achieves superior prediction performance than several state-of-the-art methods for drug-disease association prediction. During the validation process, DisDrugPred also can retrieve more actual drug-disease associations in the top part of prediction result which often attracts more attention from the biologists. Moreover, case studies on five drugs further confirm DisDrugPred's ability to discover potential candidate disease indications for drugs. AVAILABILITY AND IMPLEMENTATION: The fourth type of drug similarity and the predicted candidates for all the drugs are available at https://github.com/pingxuan-hlju/DisDrugPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inferring disease-associated microRNAs in heterogeneous networks with node attributes.

Identification of disease-associated microRNAs (disease miRNAs) is an essential step towards discovering causal miRNAs and understanding disease pathogenesis. Two sources of information can be exploited for predicting disease miRNAs: one includes the connections between miRNAs, between diseases, and between miRNAs and diseases, and the other has the attributes of miRNA nodes. The former contains information of miRNA similarities, disease similarities, and miRNA-disease associations. The latter includes the information of the families and clusters that miRNAs belong to. Similar diseases are usually associated with miRNAs that have similar functions and common attributes. However, most of the existing methods for disease miRNA prediction focus only on the connections of miRNAs and diseases. It remains challenging to adequately integrate the connections and miRNA node attributes to identify more reliable candidate disease miRNAs. We propose a non-negative matrix factorization based method, FamCluRank, for predicting disease miRNAs in heterogeneous networks with node attributes. One of the novelties of FamCluRank is to fully utilize these two oversighted characteristics of miRNAs and focuses particularly on a deep integration of miRNA families and cluster attributes. In particular, the integration was achieved by three different means. We first constructed a miRNA-disease heterogeneous network with node attributes where the miRNA nodes have their family and cluster attributes. Second, miRNAs sharing more common families and clusters are more likely to be associated with the diseases that are also related to these families and clusters. On the basis of the biological premise, we constructed a novel prediction model of FamCluRank to deeply integrate the family and cluster attributes of miRNAs. Third, two similar diseases tend to be associated with more common miRNA families and clusters, and vice versa. Hence FamCluRank's prediction model is constructed by concerning not only the possible associations between miRNAs and diseases but also the possible disease-family and disease-cluster associations. Comparison with the state-of-the-art methods showed FamCluRank's superior performance not only on the well-characterized diseases but also on the new ones. Case studies on colorectal neoplasms, pancreatic neoplasms, lung neoplasms, and 32 new diseases demonstrated its ability for discovering potential disease miRNAs. FamCluRank is a potent prioritization tool for screening the reliable candidates for subsequent studies concerning their involvement in the pathogenesis of diseases. The web service of FamCluRank, the candidate disease miRNAs for 329 diseases, and the dataset used to develop FamCluRank are available at http://www.famclurank.top.