O3-03-05: INTEGRATIVE NETWORK ANALYSIS IDENTIFIES RELATIONSHIPS BETWEEN METABOLOMICS, GENOMICS, AND RISK FACTORS FOR AD

IntroductionBody fat distribution is strongly associated with cardiometabolic disease (CMD), but the relative importance of hepatic fat as an underlying driver remains unclear. Here, we applied a systems biology approach to compare the clinical and molecular subnetworks that correlate with hepatic fat, visceral fat, and abdominal subcutaneous fat distribution.Research design and methodsThis was a cross-sectional sub-study of 283 children/adolescents (7–19 years) from the Yale Pediatric NAFLD Cohort. Untargeted, high-resolution metabolomics (HRM) was performed on plasma and combined with existing clinical variables including hepatic and abdominal fat measured by MRI. Integrative network analysis was coupled with pathway enrichment analysis and multivariable linear regression (MLR) to examine which metabolites and clinical variables associated with each fat depot.ResultsThe data divided into four communities of correlated variables (|r|>0.15, p<0.05) after integrative network analysis. In the largest community, hepatic fat was associated with eight clinical biomarkers, including measures of insulin resistance and dyslipidemia, and 878 metabolite features that were enriched predominantly in amino acid (AA) and lipid pathways in pathway enrichment analysis (p<0.05). Key metabolites associated with hepatic fat included branched-chain AAs (valine and isoleucine/leucine), aromatic AAs (tyrosine and tryptophan), serine, glycine, alanine, and pyruvate, as well as several acylcarnitines and glycerophospholipids (all q<0.05 in MLR adjusted for covariates). The other communities detected in integrative network analysis consisted of abdominal visceral, superficial subcutaneous, and deep subcutaneous fats, but no clinical variables, fewer metabolite features (280, 312, and 74, respectively), and limited findings in pathway analysis.ConclusionsThese data-driven findings show a stronger association of hepatic fat with key CMD risk factors compared with abdominal fats. The molecular network identified using HRM that associated with hepatic fat provides insight into potential mechanisms underlying the hepatic fat–insulin resistance interface in youth.

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The Application of Network Analysis to Dynamic Risk Factors in Adult Male Sex Offenders

Clinical Psychological Science ◽

10.1177/2167702620901720 ◽

2020 ◽

Vol 8 (3) ◽

pp. 539-554 ◽

Cited By ~ 3

Author(s):

Jan Willem van den Berg ◽

Wineke Smid ◽

Jolanda J. Kossakowski ◽

Daan van Beek ◽

Denny Borsboom ◽

...

Keyword(s):

Risk Factors ◽

Network Analysis ◽

Sex Offenders ◽

Adult Male ◽

Sexual Contact ◽

Central Position ◽

Dynamic Risk Factors ◽

Dynamic Risk ◽

Male Sex ◽

Male Sex Offenders

Although dynamic risk factors are considered important in the assessment and treatment of adult male sex offenders, little is known about their interrelationships. We apply network analysis to assess their associations and to provide an analysis of their shortest pathways to sexual and violent (including sexual contact) recidivism. Analyses revealed a central position for general rejection/loneliness (in all networks), poor cognitive problem solving (in networks containing sexual or violent—including sexual contact—recidivism), and impulsive acts (only in the network including sexual recidivism). These variables represented links between clusters of dynamic risk factors composed of factors relating to sexual self-regulation, emotionally intimate relationships, antisocial traits, and self-management. Impulsive acts showed the strongest independent association with sexual and violent (including sexual contact) recidivism.

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Toward a Stakeholder Perspective on Safety Risk Factors of Metro Construction: A Social Network Analysis

Complexity ◽

10.1155/2020/8884304 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Ying Lu ◽

Yu Zhang

Keyword(s):

Risk Factors ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Government Regulation ◽

Rapid Development ◽

Construction Site ◽

Safety Risk ◽

Stakeholder Perspective ◽

Bad Weather

The rapid development of the metro has greatly relieved the traffic pressure on the urban ground system, but the frequency of metro construction accidents is also increasing year by year. Due to the complex construction process of the metro, once an accident occurs, casualties and property damage are extremely serious. The safety risk factors triggered by different stakeholders were the primary cause of accidents during the metro construction phase. This paper builts a social analysis network of safety risk factors in metro construction from a stakeholder’s perspective. Based on 42 accident cases and related literature, 6 stakeholders and 25 safety risk factors were identified and the relationships between stakeholders and safety risk factors were also determined. Through the application of social network analysis, a social network of safety risk factors in metro construction was constructed, and quantitative analysis was carried out based on density, degree centrality, betweenness centrality, and cohesive subgroup. The results showed that the key safety risk factors in the construction phase of the metro were in action of the contractor’s construction site managers, lack of safety protection at the construction site, insufficient detailed survey and design information provided by the designer, unfavorable government regulation, and bad weather. Moreover, the results of 20 cohesive subgroups illustrated the interrelationship between safety risk factors. S1H2 (“violations by operatives” related to contractor) and S1H4 (“lack of safety precautions” related to contractor) and S5H5 (“ineffective supervision” related to supervisor) both belonged to subgroup G1, which means that there is a high probability that these three safety risk factors would occur simultaneously. This paper provided a basis to improve the level of safety risk management and control from the stakeholder’s perspective.

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Integrative network analysis reveals biological pathways associated with Williams syndrome

Journal of Child Psychology and Psychiatry ◽

10.1111/jcpp.12999 ◽

2018 ◽

Vol 60 (5) ◽

pp. 585-598 ◽

Cited By ~ 11

Author(s):

Ryo Kimura ◽

Vivek Swarup ◽

Kiyotaka Tomiwa ◽

Michael J. Gandal ◽

Neelroop N. Parikshak ◽

...

Keyword(s):

Network Analysis ◽

Williams Syndrome ◽

Biological Pathways ◽

Integrative Network

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Integrative network analysis identifies potential targets and drugs for ovarian cancer

BMC Medical Genomics ◽

10.1186/s12920-020-00773-2 ◽

2020 ◽

Vol 13 (S9) ◽

Author(s):

Tianyu Zhang ◽

Liwei Zhang ◽

Fuhai Li

Keyword(s):

Ovarian Cancer ◽

Transcription Factors ◽

Network Analysis ◽

Cancer Treatment ◽

Signaling Pathways ◽

Cancer Patients ◽

Cancer Death ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Integrative Network

Abstract Background Though accounts for 2.5% of all cancers in female, the death rate of ovarian cancer is high, which is the fifth leading cause of cancer death (5% of all cancer death) in female. The 5-year survival rate of ovarian cancer is less than 50%. The oncogenic molecular signaling of ovarian cancer are complicated and remain unclear, and there is a lack of effective targeted therapies for ovarian cancer treatment. Methods In this study, we propose to investigate activated signaling pathways of individual ovarian cancer patients and sub-groups; and identify potential targets and drugs that are able to disrupt the activated signaling pathways. Specifically, we first identify the up-regulated genes of individual cancer patients using Markov chain Monte Carlo (MCMC), and then identify the potential activated transcription factors. After dividing ovarian cancer patients into several sub-groups sharing common transcription factors using K-modes method, we uncover the up-stream signaling pathways of activated transcription factors in each sub-group. Finally, we mapped all FDA approved drugs targeting on the upstream signaling. Results The 427 ovarian cancer samples were divided into 3 sub-groups (with 100, 172, 155 samples respectively) based on the activated TFs (with 14, 25, 26 activated TFs respectively). Multiple up-stream signaling pathways, e.g., MYC, WNT, PDGFRA (RTK), PI3K, AKT TP53, and MTOR, are uncovered to activate the discovered TFs. In addition, 66 FDA approved drugs were identified targeting on the uncovered core signaling pathways. Forty-four drugs had been reported in ovarian cancer related reports. The signaling diversity and heterogeneity can be potential therapeutic targets for drug combination discovery. Conclusions The proposed integrative network analysis could uncover potential core signaling pathways, targets and drugs for ovarian cancer treatment.

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