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PubMed2026

Social disconnection integrates genetic and proteomic risks in suicidal ideation and depression.

Suicidal ideation (SI) and major depressive disorder (MDD) are complex psychiatric conditions arising from the interplay of genetic liability, molecular processes, and psychosocial factors. While these dimensions have been extensively studied in isolation, their joint contribution to SI and MDD remains unclear. This study integrates multi-modal data to elucidate these synergistic effects and develop robust models for individual-level risk stratification. Leveraging longitudinal multi-modal data from 13,085 UK Biobank participants, we integrated genomic, proteomic, and social connection profiles. We developed interpretable risk scores using a rigorous supervised machine learning framework encompassing diverse linear and ensemble classifiers. Permutation importance was employed to quantify feature contributions and derive transparent, weighted risk metrics across diverse classifiers. These scores were validated through association, interaction, and mediation analyses. Social connection-based risk scores significantly differentiated cases and controls across the two suicidal ideation phenotypes at 2017 and 2023 with cross-sectional analyses (AUCs: 0.70 - 0.73), outperforming proteomic-only models. Functional dimensions of social connection emerged as the most informative predictors. Longitudinal analyses revealed that social risk scores at baseline predicted suicidal ideation onset six years later, independent of demographic covariates. Interaction analyses demonstrated that polygenic risk for suicide attempt significantly interacted with both social and proteomic risk features in relation to depression. Structural equation models further confirmed that social disconnection acts as a key mediator linking genetic predisposition to MDD and SI. Social disconnection is a critical risk factor mediating the impact of genetic vulnerability on psychiatric outcomes. Integrating social, genetic, and molecular data supports a multilevel framework for risk stratification and highlights the potential of socially oriented interventions to mitigate biological risk.

Humans↗

PubMed1984

Construct validity of heroin abuse estimators.

The construct validity of two methodologically unrelated procedures for estimating the prevalence of heroin abuse was studied using a covariance structure model-fitting approach. A factor analytic estimation procedure and a log-linear-based capture-recapture method of estimation were simultaneously applied to 24 major metropolitan areas of the nation. A test of the construct validity of these procedures for estimating the prevalence of heroin abuse was carried out in the context of structural equation models where the results of the two methods were compared directly.

Data Collection↗

PubMed1994

A longitudinal model of maternal self-efficacy, depression, and difficult temperament during toddlerhood.

The purpose of this study was to test a model of maternal self-efficacy during toddlerhood using a longitudinal sequential design. Participants were 126 mothers of 1-year olds (Cohort 1) and 126 mothers of 2-year olds (Cohort 2) who completed questionnaires measuring maternal self-efficacy, depression, and perceived difficult toddler temperament three times over 1 year. Data were analyzed using structural equation modeling and maximum likelihood estimation. Findings support a model whereby (a) the more depressed the mother feels, the more likely she is to rate her toddler's temperament as difficult, (b) the more difficult the child's temperament is perceived to be, the lower the mother's estimates of her parenting self-efficacy, (c) the lower the mother's self-efficacy, the greater her depression, and (d) the more depressed the mother feels at one point in time, the more likely she is to remain depressed 6 months later. Implications of the findings are discussed as they relate to self-efficacy theory and nursing intervention with parents of difficult toddlers.

Adult↗

PubMedDate not supplied

Factors promoting cigarette smoking among black youth: a causal modeling approach.

A longitudinal model of Black adolescent smoking was tested using 223 seventh-grade students attending public schools in northern New Jersey. Interpersonal and intrapersonal factors were hypothesized to have an impact on Black seventh graders' decision to smoke. After conducting an exploratory Principal Factor Analysis (PFA) using a varimax rotation with the Time 1 data, a structural equation model was developed and refined through successive iterations. The final model revealed friends' smoking to be the most significant predictor of Black adolescent smoking at Time 1, but perceived smoking norms and intrapersonal factors such as decision making, self-efficacy, and self-esteem at home and at school exerted an important influence on smoking at Time 2. These results suggest that social influence factors may be important early in the smoking initiation process, but factors such as perceived smoking norms and intrapersonal factors may play an important role in maintaining the smoking habit in Black adolescents.

Adolescent↗

PubMed1992

Personality, life events and coping in the oldest-old.

This paper compares older adults in their sixties, eighties, and 100s on personality, experience of life events, and coping. A secondary goal was to test a structural model of adaptation. Participants (165) filled out a personality inventory, life-event lists, and coping and mental health measures. Results revealed differences in personality: centenarians scored higher on dominance, suspiciousness, and imagination. While centenarians scored lower on active behavioral coping than other age groups, they used cognitive strategies when coping with health and family events. Results from the structural equation model indicated that extraversion and anxiety predicted morale and mental health.

Adaptation, Psychological↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

PubMed1994

Structural equation analyses of clinical subpopulation differences and comparative treatment outcomes: characterizing the daily lives of drug addicts.

The use of structural equation modeling (SEM) is illustrated for comparative treatment outcome research conducted with heterogeneous clinical subpopulations within large multimodality treatment settings. All analyses are accomplished with SEM analogs of more familiar classical multivariate techniques. The effect of the early period of treatment on the daily lives of 486 clients in two drug abuse treatment modalities (methadone maintenance and outpatient counseling) is evaluated. Structured means analysis is used to assess initial differences between modalities on the latent means of 6 latent constructs reflecting daily life. The effect of treatment modality and attrition from the program on daily life latent constructs is evaluated while initial selection differences are statistically controlled. Effect sizes are computed on the basis of SEM parameter estimates. The advantage of SEM over classic multivariate approaches for correcting for selection bias when assessing comparative outcomes is explained.

Activities of Daily Living↗

PubMedDate not supplied

A practical and theoretical guide to measurement invariance in aging research.

We describe mathematical and statistical models for factor invariance. We demonstrate that factor invariance is a condition of measurement invariance. In any study of change (as over age) measurement invariance is necessary for valid inference and interpretation. Two important forms of factorial invariance are distinguished: "configural" and "metric". Tests for factorial invariance and the range of tests from strong to weak are illustrated with multiple group factor and structural equation modeling analyses (with programs such as LISREL, COSAN, and RAM). The tests are for models of the organization and age changes of intellectual abilities. The models are derived from current theory of fluid (Gf) and crystallized (Gc) abilities. The models are made manifest with measurements of the WAIS-R in the standardization sample. Although this is a methodological paper, the key issues and major principles and conclusions are presented in basic English, devoid of technical details and obscure notation. Conceptual principles of multivariate methods of data analysis are presented in terms of substantive issues of importance for the science of the psychology of aging.

Aged↗

PubMed1995

The trait-state-error model for multiwave data.

Although researchers in clinical psychology routinely gather data in which many individuals respond at multiple times, there is not a standard way to analyze such data. A new approach for the analysis of such data is described. It is proposed that a person's current standing on a variable is caused by 3 sources of variance: a term that does not change (trait), a term that changes (state), and a random term (error). It is shown how structural equation modeling can be used to estimate such a model. An extended example is presented in which the correlations between variables are quite different at the trait, state, and error levels.

Humans↗

PubMed1995

The factorial structure and stability of the McGill Pain Questionnaire in patients experiencing oral mucositis following bone marrow transplantation.

The McGill Pain Questionnaire (MPQ) (Melzack 1975) is an important assessment tool for multidimensional pain measurement in both clinical practice and research. Despite widespread acceptance, empirical analyses have not consistently verified the 3 a-priori factors that guided the subclass construction of the Pain Rating Index (PRI) of the MPQ. This study compared the a-priori model with 2 qualitatively different factor models in 191 patients with oral mucositis pain at 3 days and 10 days following bone marrow transplantation. A semantic model defined by Sensory Action, Sensory Evaluation, and Affective Evaluation factors of subclass descriptor content fit better than the a-priori model and a model positing a single general pain factor. The 3 semantic PRI factors were highly intercorrelated, with the sensory factors correlating more highly with an independent visual analogue (VAS) pain scale. Standardized factor regression coefficients between the two occasions of measurement ranged between 0.4 and 0.5. Mean factor change was greatest for Sensory Evaluation and lowest for Affective Evaluation. All analyses were conducted with the LISREL 7 structural equation modeling program. Although the factor analyses indicated an unambiguous ranking of PRI models according to statistical criteria, these theoretical results generalize poorly to simple scores formed by direct addition of the PRI subclasses. Summary scores can only approximate the unobserved factors and cannot retain the fine discriminations revealed by the theoretical factors. Psychometric considerations suggest that a single PRI total score will yield better practical measurement than any scoring rules based on multiple factors.

Adolescent↗

PubMed1993

The prediction of major depression in women: toward an integrated etiologic model.

OBJECTIVE: The authors develop an exploratory, integrated etiologic model for the prediction of episodes of major depression in an epidemiologic sample of women. METHOD: Both members of 680 female-female twin pairs of known zygosity from a population-based register were assessed three times at greater than 1-year intervals. The last two assessments included a structured interview evaluation for presence of episodes of major depression, defined by DSM-III-R, in the preceding year. The final structural equation model contained nine predictor variables: genetic factors, parental warmth, childhood parental loss, lifetime traumas, neuroticism, social support, past depressive episodes, recent difficulties, and recent stressful life events. RESULTS: The best-fitting model predicted 50.1% of the variance in the liability to major depression. The strongest predictors of this liability were, in descending order, 1) stressful life events, 2) genetic factors, 3) previous history of major depression, and 4) neuroticism. While 60% of the effect of genetic factors on the liability to major depression was direct, the remaining 40% was indirect and mediated largely by a history of prior depressive episodes, stressful life events, lifetime traumas, and neuroticism. The model suggested that at least four major and interacting risk factor domains are needed to understand the etiology of major depression: traumatic experiences, genetic factors, temperament, and interpersonal relations. CONCLUSIONS: Major depression is a multifactorial disorder, and understanding its etiology will require the rigorous integration of genetic, temperamental, and environmental risk factors.

Adult↗

PubMed2026

Integrated bioinformatics and SEM analysis reveal GPAM as a key mediator of fibrosis in NAFLD with metabolic dysfunction.

Nonalcoholic fatty liver disease (NAFLD) is a complex condition influenced by metabolic and genetic factors, yet the shared genetic architecture underlying its progression remains poorly understood. The aim of this study was to employ genomic structural equation modeling (GSEM) to elucidate the genetic architecture linking NAFLD with key metabolic traits-including insulin resistance, body mass index (BMI), hemoglobin A1c (HbA1c), and liver fibrosis using summary statistics from large-scale genome-wide association studies. By harmonizing 2.18 million variants across five genome-wide association studies (GWAS) datasets, we identified 134 genome-wide significant loci that mapped to 24 genes. GSEM revealed a latent genetic structure composed of two distinct dimensions: a metabolic regulation factor primarily driven by insulin resistance, BMI, and HbA1c; and a structural pathology factor specifically associated with liver fibrosis. These factors explained 65.5% and 78.1% of the genetic variance in BMI and fibrosis, respectively, with minimal correlation (rg = 0:07), indicating their genetic distinctness. Additionally, integrating Mendelian randomization with liver transcriptome profiling, we characterized how the 24 genes contribute to disease and identified mitochondrial glycerol-3-phosphate acyltransferase (GPAM) as the key gene that causally links lipid metabolism to fibrogenesis. In conclusion, we present the first genetically grounded mechanism for the progression of NAFLD to fibrosis. This mechanism encompasssses genetic variants, dysregulated gene expression, metabolic disturbances, and the processes involved in fibrotic remodeling. This research establishes a genetic framework for understanding the pathogenesis of NAFLD and highlights novel therapeutic targets for intervention.

Non-alcoholic Fatty Liver Disease↗

PubMed1994

Risk, protection, and vulnerability to adolescent drug use: latent-variable models of three age groups.

Much research has focused on the relationships between risk factors and adolescent drug use (DU). Less is known regarding the role of protective factors and how they may inoculate youth from initiating or escalating their DU. Using latent-variable modeling and a risk factor method, we examined the cross-sectional role of risk and protective factors in predicting teenage DU for three age groups, separately by gender. Data are from a biannual statewide survey of California students. A Vulnerability latent construct was reflected in three unit-weighted indexes: risk for initiation to DU, risk for problem DU, and protection from DU. A Polydrug Use construct was reflected in eight measures of alcohol and drug use. Structural equation models revealed that for all age/gender groups, Vulnerability was strongly related to Polydrug Use as well as having specific effects on the DU measures. Effects between Vulnerability and DU were more numerous for seventh and eleventh grade than ninth grade students. Ninth grade females had the fewest effects overall. Number of specific effects between protection and DU remained stable with increasing age. Results underscore two important foci for prevention: 1) the importance of considering age-related developmental phenomena in the overall context of DU prevention; and 2) that programs continue to emphasize risk reduction, while simultaneously developing and reinforcing protective agents.

Adolescent↗

PubMed1994

A model of homelessness among male veterans of the Vietnam War generation.

OBJECTIVE: This study explored a multifactorial model of vulnerability to homelessness among male veterans of the Vietnam war generation. METHOD: Data from 1,460 male veterans who participated in the National Vietnam Veterans Readjustment Study were used to evaluate hypotheses about the causes of homelessness grouped into four sets of sequential variables: 1) premilitary risk factors, 2) war related and non-war-related traumatic experiences, 3) lack of social support at the time of discharge from military service, and 4) postmilitary psychiatric disorder and social dysfunction. Structural equation modeling was used to explore the posited model of risk factors for homelessness. RESULTS: Postmilitary social isolation, psychiatric disorder, and substance abuse had the strongest direct effects on homelessness, although substantial indirect effects from stressors related to being in the war zone and from premilitary conduct disorder were observed. Several premilitary factors--year of birth, childhood physical or sexual abuse, other childhood traumas, and placement in foster care during childhood--also had direct effects on homelessness. CONCLUSIONS: Individual vulnerability to homelessness is most likely due to a multiplicity of psychiatric and nonpsychiatric factors, with independent influences emerging at each of four discrete time periods. In view of this complex pattern of influences, prevention efforts directed at individuals must address a very broad range of adjustment problems.

Adolescent↗

PubMedDate not supplied

Multidimensional causal model of dental caries development in low-income preschool children.

Despite the decline in the incidence of dental caries in the United States over the past several years, the condition remains a significant problem for the nation's poor children. Efforts to identify the factors responsible for caries development in samples of children of low socioeconomic status have primarily focused on a limited number of variables, and those have been predominantly biological (mutans streptococci, for example). Resulting models of caries development have usually shown good sensitivity but poor specificity. They have had limited implications for treatment. In an effort to produce a comprehensive model of caries development, 184 low-income preschool children were clinically assessed for mutans streptococci and for decayed, missing, or filled surfaces of deciduous teeth twice, first at age 4 years (baseline) and again a year later (year 1 assessment). As the clinical assessments were being done, caretakers were being interviewed to obtain data from five domains: demographics, social status, dental health behaviors, cognitive factors such as self-efficacy (self-confidence) and controllability, and perceived life stress. Data were analyzed using a structural equations modeling approach in which variables from all domains, plus baseline decayed missing and filled surfaces and baseline mutants, were used together to create a model of caries development in the year 1 assessment. Results confirmed earlier work that suggested that caries development at a 1-year followup was strongly dependent on earlier caries development. Early caries development in this sample was determined in part by mutans levels and by dental health behaviors. These behaviors themselves were accounted for partly by a cognitive factor. The results support the advantages of employing multidimensional models and provide some direction for intervention to reduce caries incidence.

Causality↗

PubMed2026

From fear to empowerment: the impact of employees AI awareness on workplace well-being - a new insight from the JD-R model.

PURPOSE: The primary purpose of the study was to explore the impact of health workers' awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD-R) model, the study aimed to provide insights into how health workers' perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study's findings could inform strategies for supporting healthcare workers during technological transformations. DESIGN/METHODOLOGY/APPROACH: The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling. FINDINGS: The study findings revealed that health workers' AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers' workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers' AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour. RESEARCH LIMITATIONS/IMPLICATIONS: While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables. PRACTICAL IMPLICATIONS: The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers' AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives. ORIGINALITY/VALUE: The study contributes to the existing literature by addressing a relatively unexplored area - the impact of AI awareness on healthcare workers' well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers' perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD-R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

Humans↗

PubMed2026

Innovation-related perception as a key driver of alternative protein acceptance: evidence from an early-stage model for cultivated meat and algae-/microalgae-based alternative protein products in Italy.

Alternative proteins are increasingly considered part of the transition toward more sustainable food systems, yet their diffusion depends critically on consumer acceptance. This study investigates the early-stage acceptance of two alternative protein categories in Italy-cultivated meat and algae-/microalgae-based alternative protein products. Focusing on the first three phases of acceptance, the analysis examines how innovation-related perception (IRP) shapes consumer perceived value (CPV), consumer perceived risk (CPR), and subsequent affective (AFF), cognitive (COG), and conative (CON) responses. Data were collected through an online survey administered to 238 Italian respondents and analysed using partial least squares structural equation modelling (PLS-SEM). The results show that IRP is the main upstream driver of early-stage acceptance in both product domains: more favourable perceptions strongly increase perceived value and reduce perceived risk. In turn, CPV exerts a much stronger influence than CPR on both affective and cognitive attitudes. A tentative cross-model comparison suggests only a descriptive variation in the final transition toward conative acceptance: affective and cognitive responses were both significant in the two models, with a relatively larger affective coefficient for cultivated meat and more balanced coefficients for algae-/microalgae-based products. Overall, the findings support a process-based interpretation of alternative protein acceptance and highlight the central role of innovation-related perception in shaping early consumer responses. These results provide relevant implications for communication strategies, product positioning, and policy actions aimed at improving the acceptability of alternative proteins in food cultures characterised by strong culinary traditions.

Italy↗

PubMed2026

Long-term petroleum pollution alters soil microbial communities via electron transfer capacity: Evidence from a 35-year chronosequence.

Petroleum pollution poses a serious threat to soil ecosystems, especially in areas surrounding oil wells, where contamination should not be overlooked. Through a 35-year longitudinal study of soils surrounding oil wells, we demonstrate that petroleum hydrocarbons accumulate predominantly in the top 10 cm of soil, reducing the electron acceptor capacity (EAC) by 61.59 % (from 12.68 to 4.87 μmole-/gC) and decreasing the electron transfer capacity (ETC) by 43 %. Structural equation modeling identified ETC as the critical mediator of microbial community shifts, with EAC playing a pivotal role in sustaining redox processes. Notably, hydrocarbon accumulation triggered a microbial succession: The abundance of Actinomycetota (including genera Rhodococcus, Arthrobacter, and Rubrobacter) showed the most significant fluctuations within 2 years, while Pseudomonadota (genera Methylobacter, Thiobacillus, and Pseudomonas), which were dominant in uncontaminated soils, decreased markedly during this period. This transition coincided with peak microbial dysbiosis (microbial dysbiosis index in 2022 reached 31.41 times that of controls). Within two to four years following mild petroleum stress, the bacterial community established a new structural configuration, revealing a crucial window for ecological recovery. The coupling between ETC reduction and microbial succession highlights the pivotal role of electron flux in soil recovery. Our findings establish a mechanistic framework for ETC-targeted restoration strategies to enhance bioremediation in petroleum-contaminated soils.

Soil Microbiology↗