Network analysis of multiple risk factors for mental health in young Colombian adults

Juntos hacemos la diferencia [together we make the difference]: A network analysis of Latinx caregivers' use of youth support services

Latinx families face unique barriers to accessing traditional youth mental health services and may instead rely on a wide range of supports to meet youth emotional or behavioral concerns. Previous studies have typically focused on patterns of utilization for discrete services, classified by setting, specialization, or level of care (e.g., specialty outpatient, inpatient, informal supports), yet little is known about how youth support services might be accessed in tandem. This analysis used data from the Pathways to Latinx Mental Health study - a national sample of Latinx caregivers (N = 598) from across the United States collected at the start of the coronavirus pandemic (i.e., May-June 2020) - to describe the broad network of available supports that are used by Latinx caregivers. Using exploratory network analysis, we found that the use of youth psychological counseling, telepsychology, and online support groups was highly influential on support service utilization in the broader network. Specifically, Latinx caregivers who used one or more of these services for their child were more likely to report utilizing other related sources of support. We also identified five support clusters within the larger network that were interconnected through specific sources of support (i.e., outpatient counseling, crisis, religious, informal, and non-specialty). Findings offer a foundational look at the complex system of youth supports available to Latinx caregivers, highlighting areas for future study, opportunities to advance the implementation of evidence-based interventions, and channels through which to disseminate information about available services.

Using network analysis to illuminate the intergenerational transmission of adversity

Objective: The effects of maternal exposure to adverse childhood experiences (ACEs) may be transmitted to subsequent generations through various biopsychosocial mechanisms. However, studies tend to focus on exploring one or two focal pathways with less attention paid to links between different pathways. Using a network approach, this paper explores a range of core prenatal risk factors that may link maternal ACEs to infant preterm birth (PTB) and low birthweight (LBW). Methods: We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 8379) to estimate two mixed graphical network models: Model 1 was constructed using adverse infant outcomes, biopsychosocial and environmental risk factors, forms of ACEs, and sociodemographic factors. In Model 2, ACEs were combined to represent a threshold ACEs score (≥4). Network indices (i.e., shortest path and bridge expected influence [1-step & 2-step]) were estimated to determine the shortest pathway from ACEs to infant outcomes, and to identify the risk factors that are vital in activating other risk factors and adverse outcomes. Results: Network analyses estimated a mutually reinforcing web of childhood and prenatal risk factors, with each risk connected to at least two other risks. Bridge influence indices suggested that childhood physical and sexual abuse and multiple ACEs were highly interconnected to others risks. Overall, risky health behaviours during pregnancy (i.e., smoking & illicit drug use) were identified as 'active' risk factors capable of affecting (directly and indirectly) other risk factors and contributing to the persistent activation of the global risk network. These risks may be considered priority candidate targets for interventions to disrupt intergenerational risk transmission. Our study demonstrates the promise of network analysis as an approach for illuminating the intergenerational transmission of adversity in its full complexity.

HIGHLIGHTS

We took a network approach to assessing links between ACEs and birth outcomes.ACEs, other prenatal risk factors, and birth outcomes had complex inter-connectionsHealth behaviours in pregnancy were indicated as optimal intervention targets.

Personalized Prediction of Kidney Function Decline and Network Analysis of the Risk Factors after Kidney Transplantation Using Nationwide Cohort Data

We developed a machine-learning-based model that could predict a decrease in one-year graft function after kidney transplantation, and investigated the risk factors of the decreased function. A total of 4317 cases were included from the Korean Organ Transplant Registry (2014−2019). An XGBoost model was trained to predict the recipient’s one-year estimated glomerular filtration rate (eGFR) below 45 mL/min/1.73 m2 using 112 pre- and peri-transplantation variables. The network of model factors was drawn using inter-factor partial correlations and the statistical significance of each factor. The model with seven features achieved an area under the curve of 0.82, sensitivity of 0.73, and specificity of 0.79. The model prediction was associated with five-year graft and rejection-free survival. Post-transplantation hospitalization >25 days and eGFR ≥ 88.0 were the prominent risk and preventive factors, respectively. Donor age and post-transplantation eGFR < 59.8 were connected to multiple risk factors on the network. Therefore, careful donor−recipient matching in older donors, and avoiding pre-transplantation risk factors, would reduce the risk of graft dysfunction. The model might improve long-term graft outcomes by supporting early detection of graft dysfunction, and proactive risk factor control.

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis

BACKGROUND

In epidemiological studies, finding the best subset of factors is challenging when the number of explanatory variables is large.

OBJECTIVE

Our study had two aims. First, we aimed to identify essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Second, we aimed to achieve a comprehensive understanding of multifactorial features in depression using network analysis.

METHODS

An XGBoost model was trained and tested to classify "current depression" and "no lifetime depression" for a data set of 120 variables for 12,596 cases. The optimal XGBoost hyperparameters were set by an automated machine learning tool (TPOT), and a high-performance sparse model was obtained by feature selection using the feature importance value of XGBoost. We performed statistical tests on the model and nonmodel factors using survey-weighted multiple logistic regression and drew a correlation network among factors. We also adopted statistical tests for the confounder or interaction effect of selected risk factors when it was suspected on the network.

RESULTS

The XGBoost-derived depression model consisted of 18 factors with an area under the weighted receiver operating characteristic curve of 0.86. Two nonmodel factors could be found using the model factors, and the factors were classified into direct (P<.05) and indirect (P≥.05), according to the statistical significance of the association with depression. Perceived stress and asthma were the most remarkable risk factors, and urine specific gravity was a novel protective factor. The depression-factor network showed clusters of socioeconomic status and quality of life factors and suggested that educational level and sex might be predisposing factors. Indirect factors (eg, diabetes, hypercholesterolemia, and smoking) were involved in confounding or interaction effects of direct factors. Triglyceride level was a confounder of hypercholesterolemia and diabetes, smoking had a significant risk in females, and weight gain was associated with depression involving diabetes.

CONCLUSIONS

XGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.

Comorbidity Between Depression and Anxiety in Adolescents: Bridge Symptoms and Relevance of Risk and Protective Factors

Depression and anxiety are highly prevalent and comorbid in adolescents, and this co-occurrence leads to worse prognosis and additional difficulties. The relationship between depression and anxiety must be delineated to, in turn, reduce and prevent the comorbidity, however our knowledge is still limited. We used network analysis to investigate bridge symptoms; symptoms that connect individual depression and anxiety symptoms and thus can help explain the comorbidity. We also examined the role of relevant risk and protective factors in explaining these symptom-level associations between these disorders. We analyzed data from the Avon Longitudinal Study of Children and Parents (n = 3670). Depression and anxiety symptoms, peer victimization, bullying, peer relational problems, prosocial behavior, and parental monitoring were assessed at a single time point around age 13 years. Stressful life events (SLEs) were assessed at age 11 years. We identified the most prominent bridge symptoms among depression ("feeling unhappy", "feeling lonely") and anxiety symptoms ("worrying about past", "worrying about future"). Peer relational difficulties and SLEs were strongly associated with several depression and anxiety symptoms, such that these two risk factors created a link between individual depression and anxiety symptoms. Prosocial behavior had several negative associations with symptoms of both disorders, suggesting it can be an important protective factor.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10862-021-09880-5.

On Penalty Parameter Selection for Estimating Network Models

Network models are gaining popularity as a way to estimate direct effects among psychological variables and investigate the structure of constructs. A key feature of network estimation is determining which edges are likely to be non-zero. In psychology, this is commonly achieved through the graphical lasso regularization method that estimates a precision matrix of Gaussian variables using an ℓ1-penalty to push small values to zero. A tuning parameter, λ, controls the sparsity of the network. There are many methods to select λ, which can lead to vastly different graphs. The most common approach in psychological network applications is to minimize the extended Bayesian information criterion, but the consistency of this method for model selection has primarily been examined in high dimensional settings (i.e., n < p) that are uncommon in psychology. Further, there is some evidence that alternative selection methods may have superior performance. Here, using simulation, we compare four different methods for selecting λ, including the stability approach to regularization selection (StARS), K-fold cross-validation, the rotation information criterion (RIC), and the extended Bayesian information criterion (EBIC). Our results demonstrate that penalty parameter selection should be made based on data characteristics and the inferential goal (e.g., to increase sensitivity versus to avoid false positives). We end with recommendations for selecting the penalty parameter when using the graphical lasso.

Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey

BACKGROUND

Dry eye disease (DED) is a complex disease of the ocular surface, and its associated factors are important for understanding and effectively treating DED.

OBJECTIVE

This study aimed to provide an integrative and personalized model of DED by making an explanatory model of DED using as many factors as possible from the Korea National Health and Nutrition Examination Survey (KNHANES) data.

METHODS

Using KNHANES data for 2012 (4391 sample cases), a point-based scoring system was created for ranking factors associated with DED and assessing patient-specific DED risk. First, decision trees and lasso were used to classify continuous factors and to select important factors, respectively. Next, a survey-weighted multiple logistic regression was trained using these factors, and points were assigned using the regression coefficients. Finally, network graphs of partial correlations between factors were utilized to study the interrelatedness of DED-associated factors.

RESULTS

The point-based model achieved an area under the curve of 0.70 (95% CI 0.61-0.78), and 13 of 78 factors considered were chosen. Important factors included sex (+9 points for women), corneal refractive surgery (+9 points), current depression (+7 points), cataract surgery (+7 points), stress (+6 points), age (54-66 years; +4 points), rhinitis (+4 points), lipid-lowering medication (+4 points), and intake of omega-3 (0.43%-0.65% kcal/day; -4 points). Among these, the age group 54 to 66 years had high centrality in the network, whereas omega-3 had low centrality.

CONCLUSIONS

Integrative understanding of DED was possible using the machine learning-based model and network-based factor analysis. This method for finding important risk factors and identifying patient-specific risk could be applied to other multifactorial diseases.

Anxiety-related Endophenotypes and Hazardous Alcohol Use in Young Adults are Associated with a Functional Polymorphism in the SLC6A4 Gene

Background:

A functional polymorphism (5-HTTLPR, rs4795541) in the serotonin transporter (SLC6A4) gene has been shown as an important candidate for several psychiatric and behavioral traits.

Objective:

The objective of this study was to examine the possible interaction of this polymorphism with physical neglect in childhood on the presentation of anxiety traits and hazardous alcohol consumption in young Colombian subjects.

Methods:

272 young adults (mean age: 21.3 years) were evaluated with the Childhood Trauma Questionnaire, the Zung Self-rating Anxiety Scale, the Big Five Inventory, the Cohen’s Perceived Stress Scale, the Alcohol, Smoking, Substance Involvement Screening Test and the Alcohol Use Disorders Identification Test. Genotyping for the 5-HTTLPR polymorphism was carried out using conventional PCR. A linear regression model, corrected by age and gender, was used.

Results:

We found that individuals with the L/L genotype showed higher scores on physical neglect (p=0.0047), anxiety symptoms (p=0.028), neuroticism (p=0.019) and perceived stress (p=0.035). L/L genotype was a risk factor for hazardous alcohol use in young adults (OR=3.06, p=0.0003). No GxE interactions were observed in our data.

Conclusion:

Our results provide novel evidence for the role of a functional polymorphism in the SLC6A4 gene on the relationship of childhood trauma, anxiety-related traits and risky consumption of alcohol.

The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

BACKGROUND

Network analysis (NA) is an analytical tool that allows one to explore the map of connections and eventual dynamic influences among symptoms and other elements of mental disorders. In recent years, the use of NA in psychopathology has rapidly grown, which calls for a systematic and critical analysis of its clinical utility.

METHODS

Following PRISMA guidelines, a systematic review of published empirical studies applying NA in psychopathology, between 2010 and 2017, was conducted. We included the literature published in PubMed and PsycINFO using as keywords any combination of "network analysis" with the terms "anxiety," "affective disorders," "depression," "schizophrenia," "psychosis," "personality disorders," "substance abuse" and "psychopathology."

RESULTS

The review showed that NA has been applied in a plethora of mental disorders in adults (i.e., 13 studies on anxiety disorders; 19 on mood disorders; 7 on psychosis; 1 on substance abuse; 1 on borderline personality disorder; 18 on the association of symptoms between disorders), and 6 on childhood and adolescence.

CONCLUSIONS

A critical examination of the results of each study suggests that NA helps to identify, in an innovative way, important aspects of psychopathology like the centrality of the symptoms in a given disorder as well as the mutual dynamics among symptoms. Yet, despite these promising results, the clinical utility of NA is still uncertain as there are important limitations on the analytic procedures (e.g., reliability of indices), the type of data included (e.g., typically restricted to secondary analysis of already published data), and ultimately, the psychometric and clinical validity of the results.

Depression and multimorbidity: Considering temporal characteristics of the associations between depression and multiple chronic diseases

OBJECTIVES

Depression frequently co-occurs with multiple chronic diseases in complex, costly, and dangerous patterns of multimorbidity. The field of health psychology may benefit from evaluating the temporal characteristics of depression's associations with common diseases, and from determining whether depression is a central connector in multimorbid disease clusters. The present review addresses these issues by focusing on 4 of the most prevalent diseases: hypertension, ischemic heart disease, arthritis, and diabetes.

METHOD

Study 1 assessed how prior chronic disease diagnoses were associated with current depression in a large, cross-sectional, population-based study. It assessed depression's centrality using network analysis accounting for disease prevalence. Study 2 presents a systematic scoping review evaluating the extent to which depression was prospectively associated with the onset of the 4 prevalent chronic diseases.

RESULTS

In Study 1 depression had the fourth highest betweenness centrality ranking of 26 network nodes and centrally connected many existing diseases and unhealthy behaviors. In Study 2 depression was associated with subsequent incidence of ischemic heart disease and diabetes across multiple meta-analyses. Insufficient information was available about depression's prospective associations with incident hypertension and arthritis.

CONCLUSIONS

Depression is central in patterns of multimorbidity and is associated with incident disease for several of the most common chronic diseases, justifying the focus on screening and treatment of depression in those at risk for developing chronic disease. Future research should investigate the mediating and moderating roles of health behaviors in the association between depression and the staggered emergence over time of clusters of multimorbid chronic diseases. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Early maladaptive schemas and borderline personality disorder features in a nonclinical sample: A network analysis

Borderline personality disorder (BPD) is a challenging problem. Early maladaptive schemas (EMSs) are considered as important vulnerability factors for the development and maintenance of BPD. Literature suggests a complex relationship between BPD and EMSs. The current study employed network analysis to model the complex associations between central BPD features (i.e., affective instability, identity problems, negative relations, and self-harm) and EMSs in 706 undergraduate students. The severity of BPD symptoms was assessed using the Personality Assessment Inventory-Borderline subscale; the Young Schema Questionnaire-Short Form was used to assess EMSs. Results suggest that specific EMSs show unique associations with different BPD features. Interestingly, affective instability showed no unique associations with EMSs. Identity problems were uniquely associated with abandonment, insufficient self-control, dependence/incompetence, and vulnerability to harm/illness schemas. Negative relations in BPD showed unique connections with mistrust/abuse and abandonment. Finally, BPD self-harm was connected to emotional deprivation and failure. These findings indicate potential pathways between EMSs and specific BPD features that could improve our understanding of BPD theoretically and clinically.

Mood rhythmicity is associated with depressive symptoms and caffeinated drinks consumption in South American young adults

Among the factors that contribute to the onset and maintenance of depressive disorders, rhythmicity of symptoms and consumption of caffeine have recently gained attention. The current study aimed to examine the differential rhythmicity of relevant variables in a sample of young participants, considering the presence of depressive symptomatology and the frequency of caffeinated drinks consumption. A significant 24-hour differential rhythmicity of mood, cognitive and physiological variables was found indicating an evening peak pattern in the participants with depressive symptoms. Interestingly, caffeinated drinks consumption was differentially associated with self-perceived peaks, according to the presence of depressive symptomatology. Our findings are among the first reports about the potential association of the 24-hours rhythmicity of relevant mood-related variables, depressive symptoms, and caffeine intake. These results support the view that the identification of risk factors for depression, and the application of novel measurements and analysis methods in the development of new preventive strategies should be a public health priority.