Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses

AbstractBackground:Recent studies have shown that epigenetic differences can increase the risk of spontaneous preterm birth (PTB). However, little is known about heterogeneity underlying such epigenetic differences, which could lead to hypotheses for biological pathways in specific patient subgroups, and corresponding targeted interventions critical for precision medicine. Using bipartite network analysis of fetal DNA methylation data we demonstrate a novel method for classification of PTB.Methods:The data consisted of DNA methylation across the genome (HumanMethylation450 BeadChip) in cord blood from 50 African-American subjects consisting of 22 cases of early spontaneous PTB (24–34 weeks of gestation) and 28 controls (>39 weeks of gestation). These data were analyzed using a combination of (1) a supervised method to select the top 10 significant methylation sites, (2) unsupervised “subject-variable” bipartite networks to visualize and quantitatively analyze how those 10 methylation sites co-occurred across all the subjects, and across only the cases with the goal of analyzing subgroups and their underlying pathways, and (3) a simple linear regression to test whether there was an association between the total methylation in the cases, and gestational age.Results:The bipartite network analysis of all subjects and significant methylation sites revealed statistically significant clustering consisting of an inverse symmetrical relationship in the methylation profiles between a case-enriched subgroup and a control-enriched subgroup: the former was predominantly hypermethylated across seven methylation sites, and hypomethylated across three methylation sites, whereas the latter was predominantly hypomethylated across the above seven methylation sites and hypermethylated across the three methylation sites. Furthermore, the analysis of only cases revealed one subgroup that was predominantly hypomethylated across seven methylation sites, and another subgroup that was hypomethylated across all methylation sites suggesting the presence of heterogeneity in PTB pathophysiology. Finally, the analysis found a strong inverse linear relationship between total methylation and gestational age suggesting that methylation differences could be used as predictive markers for gestational length.Conclusions:The results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.

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How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach (Preprint)

10.2196/preprints.13567 ◽

2019 ◽

Author(s):

Suresh K Bhavnani ◽

Bryant Dang ◽

Rebekah Penton ◽

Shyam Visweswaran ◽

Kevin E Bassler ◽

...

Keyword(s):

Hip Fracture ◽

High Risk ◽

Prediction Models ◽

Significant Risk ◽

Training Data ◽

Integrated Analysis ◽

Bipartite Network ◽

Data Set ◽

Targeted Interventions ◽

Patient Subgroups

BACKGROUND When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. OBJECTIVE This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. METHODS We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. RESULTS The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. CONCLUSIONS The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

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Bipartite network analysis of ant-task associations reveals task groups and absence of colonial daily activity

10.1101/2020.03.28.013128 ◽

2020 ◽

Author(s):

Haruna Fujioka ◽

Yasukazu Okada ◽

Masato S. Abe

Keyword(s):

Network Analysis ◽

Task Allocation ◽

Daily Rhythm ◽

Systems Science ◽

Bipartite Network ◽

Individual Behaviour ◽

Task Groups ◽

Network Analyses ◽

Allocation Patterns ◽

The Individual

AbstractSocial insects are one of the best examples of complex self-organised systems exhibiting task allocation. How task allocation is achieved is the most fascinating question in behavioural ecology and complex systems science. However, it is difficult to comprehensively characterise task allocation patterns due to behavioural complexity, such as the individual variation, context dependency, and chronological variation. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we applied bipartite network analyses to characterise individual-behaviour relationships. We recorded the behaviours of all individuals with verified age in ant colonies and analysed the individual-behaviour relationship at the individual, module, and network levels. Bipartite network analysis successfully detected the module structures, illustrating that certain individuals performed a subset of behaviours (i.e., task groups). We confirmed age polyethism by comparing age between modules. Additionally, to test the daily rhythm of the executed tasks, the data were partitioned between daytime and nighttime, and a bipartite network was re-constructed. This analysis supported that there was no daily rhythm in the tasks performed. These findings suggested that bipartite network analyses could untangle complex task allocation patterns and provide insights into understanding the division of labour.

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How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach

JMIR Medical Informatics ◽

10.2196/13567 ◽

2020 ◽

Vol 8 (10) ◽

pp. e13567

Author(s):

Suresh K Bhavnani ◽

Bryant Dang ◽

Rebekah Penton ◽

Shyam Visweswaran ◽

Kevin E Bassler ◽

...

Keyword(s):

Hip Fracture ◽

High Risk ◽

Prediction Models ◽

Significant Risk ◽

Training Data ◽

Integrated Analysis ◽

Bipartite Network ◽

Data Set ◽

Targeted Interventions ◽

Patient Subgroups

Background When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. Objective This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. Methods We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. Results The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. Conclusions The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

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Bipartite network analysis of ant-task associations reveals task groups and absence of colonial daily activity

Royal Society Open Science ◽

10.1098/rsos.201637 ◽

2021 ◽

Vol 8 (1) ◽

pp. 201637

Author(s):

Haruna Fujioka ◽

Yasukazu Okada ◽

Masato S. Abe

Keyword(s):

Network Analysis ◽

Task Allocation ◽

Daily Rhythm ◽

Systems Science ◽

Bipartite Network ◽

Individual Behaviour ◽

Task Groups ◽

Network Analyses ◽

Allocation Patterns ◽

The Individual

Social insects are one of the best examples of complex self-organized systems exhibiting task allocation. How task allocation is achieved is the most fascinating question in behavioural ecology and complex systems science. However, it is difficult to comprehensively characterize task allocation patterns due to behavioural complexity, such as the individual variation, context dependency and chronological variation. Thus, it is imperative to quantify individual behaviours and integrate them into colony levels. Here, we applied bipartite network analyses to characterize individual-behaviour relationships. We recorded the behaviours of all individuals with verified age in ant colonies and analysed the individual-behaviour relationship at the individual, module and network levels. Bipartite network analysis successfully detected the module structures, illustrating that certain individuals performed a subset of behaviours (i.e. task groups). We confirmed age polyethism by comparing age between modules. Additionally, to test the daily rhythm of the executed tasks, the data were partitioned between daytime and nighttime, and a bipartite network was re-constructed. This analysis supported that there was no daily rhythm in the tasks performed. These findings suggested that bipartite network analyses could untangle complex task allocation patterns and provide insights into understanding the division of labour.

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Birthweight DNA methylation signatures in infant saliva

Clinical Epigenetics ◽

10.1186/s13148-021-01053-1 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Chiara Moccia ◽

Maja Popovic ◽

Elena Isaevska ◽

Valentina Fiano ◽

Morena Trevisan ◽

...

Keyword(s):

Dna Methylation ◽

Cord Blood ◽

Gestational Age ◽

Low Birthweight ◽

Continuous Variable ◽

Linear Regression Models ◽

Association Analyses ◽

Cpg Sites ◽

Adverse Health Outcomes ◽

The Look

Abstract Background Low birthweight has been repeatedly associated with long-term adverse health outcomes and many non-communicable diseases. Our aim was to look-up cord blood birthweight-associated CpG sites identified by the PACE Consortium in infant saliva, and to explore saliva-specific DNA methylation signatures of birthweight. Methods DNA methylation was assessed using Infinium HumanMethylation450K array in 135 saliva samples collected from children of the NINFEA birth cohort at an average age of 10.8 (range 7–17) months. The association analyses between birthweight and DNA methylation variations were carried out using robust linear regression models both in the exploratory EWAS analyses and in the look-up of the PACE findings in infant saliva. Results None of the cord blood birthweight-associated CpGs identified by the PACE Consortium was associated with birthweight when analysed in infant saliva. In saliva EWAS analyses, considering a false discovery rate p-values < 0.05, birthweight as continuous variable was associated with DNA methylation in 44 CpG sites; being born small for gestational age (SGA, lower 10th percentile of birthweight for gestational age according to WHO reference charts) was associated with DNA methylation in 44 CpGs, with only one overlapping CpG between the two analyses. Despite no overlap with PACE results at the CpG level, two of the top saliva birthweight CpGs mapped at genes associated with birthweight with the same direction of the effect also in the PACE Consortium (MACROD1 and RPTOR). Conclusion Our study provides an indication of the birthweight and SGA epigenetic salivary signatures in children around 10 months of age. DNA methylation signatures in cord blood may not be comparable with saliva DNA methylation signatures at about 10 months of age, suggesting that the birthweight epigenetic marks are likely time and tissue specific.

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Associations of circulating folate, vitamin B12 and homocysteine concentrations in early pregnancy and cord blood with epigenetic gestational age: the Generation R Study

Clinical Epigenetics ◽

10.1186/s13148-021-01065-x ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Giulietta S. Monasso ◽

Leanne K. Küpers ◽

Vincent W. V. Jaddoe ◽

Sandra G. Heil ◽

Janine F. Felix

Keyword(s):

Dna Methylation ◽

Vitamin B12 ◽

Cord Blood ◽

Gestational Age ◽

Fetal Development ◽

Maternal Plasma ◽

Plasma Homocysteine ◽

Training Population ◽

Epigenetic Aging ◽

Pregnancy Dating

Abstract Background Circulating folate, vitamin B12 and homocysteine concentrations during fetal development have been associated with health outcomes in childhood. Changes in fetal DNA methylation may be an underlying mechanism. This may be reflected in altered epigenetic aging of the fetus, as compared to chronological aging. The difference between gestational age derived in clinical practice and gestational age predicted from neonatal DNA methylation data is referred to as gestational age acceleration. Differences in circulating folate, vitamin B12 and homocysteine concentrations during fetal development may be associated with gestational age acceleration. Results Up to 1346 newborns participating in the Generation R Study, a population-based prospective cohort study, had both cord blood DNA methylation data available and information on plasma folate, serum total and active B12 and plasma homocysteine concentrations, measured in early pregnancy and/or in cord blood. A subgroup of 380 newborns had mothers with optimal pregnancy dating based on a regular menstrual cycle and a known date of last menstrual period. For comparison, gestational age acceleration was calculated based the method of both Bohlin and Knight. In the total study population, which was more similar to Bohlin’s training population, one standard deviation score (SDS) higher maternal plasma homocysteine concentrations was nominally associated with positive gestational age acceleration [0.07 weeks, 95% confidence interval (CI) 0.02, 0.13] by Bohlin’s method. In the subgroup with pregnancy dating based on last menstrual period, the method that was also used in Knight’s training population, one SDS higher cord serum total and active B12 concentrations were nominally associated with negative gestational age acceleration [(− 0.16 weeks, 95% CI − 0.30, − 0.02) and (− 0.15 weeks, 95% CI − 0.29, − 0.01), respectively] by Knight’s method. Conclusions We found some evidence to support associations of higher maternal plasma homocysteine concentrations with positive gestational age acceleration, suggesting faster epigenetic than clinical gestational aging. Cord serum vitamin B12 concentrations may be associated with negative gestational age acceleration, indicating slower epigenetic than clinical gestational aging. Future studies could examine whether altered fetal epigenetic aging underlies the associations of circulating homocysteine and vitamin B12 blood concentrations during fetal development with long-term health outcomes.

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Pregnancy Outcome during the First COVID 19 Lockdown in Vienna, Austria

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18073782 ◽

2021 ◽

Vol 18 (7) ◽

pp. 3782

Author(s):

Sylvia Kirchengast ◽

Beda Hartmann

Keyword(s):

Birth Weight ◽

Preterm Birth ◽

Low Birth Weight ◽

Pregnancy Outcome ◽

Gestational Age ◽

Very Low Birth Weight ◽

Extremely Low Birth Weight ◽

Negative Effects ◽

Gestational Length ◽

Live Births

The COVID 19 pandemic represents a major stress factor for non-infected pregnant women. Although maternal stress during pregnancy increases the risk of preterm birth and intrauterine growth restriction, an increasing number of studies yielded no negative effects of COVID 19 lockdowns on pregnancy outcome. The present study focused on pregnancy outcome during the first COVID 19 lockdown phase in Austria. In particular, it was hypothesized that the national lockdown had no negative effects on birth weight, low birth weight rate and preterm birth rate. In a retrospective medical record-based single center study, the outcome of 669 singleton live births in Vienna Austria during the lockdown phase between March and July 2020 was compared with the pregnancy outcome of 277 live births at the same hospital during the pre-lockdown months of January and February 2020 and, in addition, with the outcome of 28,807 live births between 2005 and 2019. The rate of very low gestational age was significantly lower during the lockdown phase than during the pre-lockdown phase. The rate of low gestational age, however, was slightly higher during the lockdown phase. Mean birth weight was significantly higher during the lockdown phase; the rates of low birth weight, very low birth weight and extremely low birth weight were significantly lower during the lockdown phase. In contrast, maternal gestational weight gain was significantly higher during the lockdown phase. The stressful lockdown phase in Austria seems to have no negative affect on gestational length and newborn weight among non-infected mothers.

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Effects of anacetrapib on plasma lipids in specific patient subgroups in the DEFINE (Determining the Efficacy and Tolerability of CETP INhibition with AnacEtrapib) trial

Journal of Clinical Lipidology ◽

10.1016/j.jacl.2014.10.005 ◽

2015 ◽

Vol 9 (1) ◽

pp. 65-71 ◽

Cited By ~ 21

Author(s):

Eliot A. Brinton ◽

Uma Kher ◽

Sukrut Shah ◽

Christopher P. Cannon ◽

Michael Davidson ◽

...

Keyword(s):

Plasma Lipids ◽

Specific Patient ◽

Cetp Inhibition ◽

Patient Subgroups

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Novel Antipsychotics and Patient Care in State Hospitals

Annals of Pharmacotherapy ◽

10.1345/aph.17409 ◽

1998 ◽

Vol 32 (9) ◽

pp. 906-914 ◽

Cited By ~ 4

Author(s):

Peter F Buckley

Keyword(s):

State Hospital ◽

Cost Savings ◽

Preliminary Evidence ◽

The State ◽

New Agents ◽

Hospital System ◽

Specific Patient ◽

State Hospitals ◽

Additional Information ◽

Patient Subgroups

OBJECTIVE: To review and highlight the opportunities and challenges of pharmacologic advances in the use of antipsychotics for the state hospital system. METHODS: A critical review was performed of studies published either as articles or abstracts, on the use of novel antipsychotics, particularly as they relate to the patient population within the state mental hospital system. FINDINGS: The recent availability of new antipsychotic medications within state facilities has resulted in more progressive treatment, reduced recidivism (and consequently cost savings), and preliminary evidence of preferential and superior treatment response in specific patient subgroups (e.g., those with aggression). At the same time, inpatient pharmacy budget increases and uncertainty in guiding the use of novel antipsychotics have influenced the availability of these agents in state hospitals. CONCLUSIONS: State hospital services have, by and large, embraced the developments in pharmacotherapy of schizophrenia. Optimal use of these new agents in this population requires additional information on their relative efficacy in specific patient subgroups.

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Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

PLoS ONE ◽

10.1371/journal.pone.0256696 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256696

Author(s):

Anna Keuchenius ◽

Petter Törnberg ◽

Justus Uitermark

Keyword(s):

Machine Learning ◽

Social Media ◽

Network Analysis ◽

Language Processing ◽

User Interaction ◽

User Interactions ◽

Online Interactions ◽

Network Analyses ◽

Social Media Platforms ◽

Signed Network

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis.

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