Multi-omics in stress and health research: study designs that will drive the field forward

Figures & data

Figure 1. Multi-Omics Data Types. (a) Common multi-omics data types include genomics (i.e., full set of genes within an organism), transcriptomics (i.e., genome-wide RNA levels), proteomics (i.e., protein and peptide levels), lipidomics (i.e., cellular lipids), metabolomics (i.e., small molecules; cellular metabolites), and microbiome (i.e., community of microorganisms within a sample). (b) Collection and assay recommendations. Note: This is not an exhaustive list of all possible sample types, collection kits, or assay methodologies, but rather, one option for each ome.

(a) contains a visualization of common multi-omics data types, while (b) provides recommendations for collection and assaying samples for each ome.

Figure 2. Longitudinal study design recommendations in stress and multi-omics research. (a) Studies beginning in the perinatal period should examine associations between maternal and child multi-omics and birth outcomes, growth, and cognitive and emotional development, including measures of maternal stress, diet, and social support. Parents should be surveyed for children’s adverse childhood experiences (ACEs). Monitoring environmental exposures in the home for two-week periods using exposome monitoring and parental surveys is recommended to give context to multi-omics analyses. Here, researchers have the power to examine trajectories of molecular change across the entire lifespan. (b) Studies of early childhood should pair multi-omics and analyses with developmental milestones of interest, and behavioral measures and tasks. Environmental exposure assessments should also include both home and school monitoring. Both parents and children should be surveyed about the child’s ACEs. (c) When examining the transition from adolescence to adulthood, associations between multi-omics and pubertal timing/transition, physical and mental health, and school performance should be assessed. Children can be asked to self-report psychological data, including ACEs. Parental surveys and environmental exposure assessments can be included to add additional context to analyses. (d) In any longitudinal studies occurring in adulthood, researchers should examine the stability of analytes across the study as they relate to stress, health, and disease, including interactions between ACEs and current stress levels on multi-omics and stress-related pathologies. Analyte change in response to experimental (e.g., social evaluation) stressors can be examined to provide insight into how stress response systems function. Researchers may also consider assessing analyte levels before and after immersive interventions to observe the molecular changes that occur during the transition from stress-related dysfunction to health. (e) In intensive longitudinal study designs, stress levels can be tracked multiple times per day, daily, or weekly, providing a higher-resolution assessment of current stress levels as well as the variability in these levels over time. These designs can be cost effective in that high numbers of repeated measures in stress observations can compensate for smaller overall sample size for multi-omics analysis (Moriarity & Slavich, 2023). Although adult populations are easier to recruit for these types of study designs, valuable gains could be made from intensive longitudinal designs which monitor the perinatal or childhood periods as well.

A timeline of developmental stages from perinatal to adulthood, highlighting key study design options, such as focusing on the first 1,000 days of life, studying early childhood, and the adolescent to adult transition.

Table

Research Question	What is the biology underlying the development of mental health problems during stressful life transitions, and can insights into this biology inform predictive models that forecast risk of developing mental health problems?
Participants	Young adults transitioning into their first year of college (N = 300+)
Study Design	Longitudinal: baseline assessment prior to beginning of first semester, then monthly assessments during first semester (approximately 5 measurements); an assessment after school break, but prior to the beginning of the second semester, then monthly assessments during second semester (approximately 5 measurements). Collect demographic measures, mental and physical health history, and lifetime stressor exposure and perceived stress surveys (i.e., Stress and Adversity Inventory [STRAIN; Slavich & Shields, 2018], Perceived Stress Scale [PSS; Cohen et al., 1994]) at baseline sessions. Follow-up sessions will assess whether students have received a diagnosis for a mental health problem since their last session (and if they are taking medication for that condition), and will also involve completion of perceived stress, depressive symptomology (e.g., Beck’s Depression Inventory; Beck et al., 1987), and anxiety scales (e.g., Generalized Anxiety Disorder-7 scale; Spitzer et al., 2006). TASSO devices or venipuncture for blood collection will be completed at each occasion for multi-omics analysis. Students will also wear Fitbits or other wearable devices to track activity and sleep during each semester.
Analytic Approach	First, generalized linear mixed effects models or joint longitudinal survival models will be used to identify which multi-omic analytes change alongside mental health disorder diagnoses over time. Next, mixed effect models will be used to identify which analytes covary with responses to stress, depression, and anxiety scales. Models that disaggregate within- and between-individual effects can help parse stable (i.e., trait) and time-dependent (i.e., state) relationships between analytes and risk for mental health problems (e.g., Curran & Bauer, 2011). Further, introducing time lags or using cross-lagged models (e.g., latent change score model) may lend insights into the temporal order of changes in symptoms and analyte levels (although these models should be interpreted with caution when using observational data). Downstream integrative multi-omics analyses will be used to identify focal biological pathways and disease processes. Machine learning approaches and development of prediction models (e.g., fused Lasso regression, elastic net, Bayesian network algorithms) will be developed to identify features that forecast the onset of mental health problems (e.g., using baseline measures).
Possible Outcomes	Identify multi-omic analytes associated with onset of mental health problems, stress, and both overall and specific depression/anxiety symptoms. Determine if correlates of diagnosis events (e.g., inflammation) are also associated with continuous ratings of symptomology. The analytes and biological pathways identified can be compared and contrasted with those found for stress levels. Exploratory analyses may examine whether multi-omic signatures predict both likelihood and type of mental health problem (e.g., anxiety vs. depression). The identified genes and pathways can be augmented with regulatory genomics data from the PsychENCODE Consortium (Akbarian et al., 2015) to further the understanding of the biological mechanisms underlying the development of psychiatric disorders. Ancillary data (e.g., wearable) might provide insights into moderators and mediators (e.g., sleep quality) of the impact that stressful life transitions have on key biological pathways, and the effects that changes to these pathways have on mental health. Such information could also be used to strengthen risk prediction algorithms and lay the groundwork for developing just-in-time interventions to mitigate this risk.
Limitations / Considerations	Developing a standard workflow for multi-omic data reduction, regularization, and quality control is critical to minimize risk of bias or error introduced by sample assaying and data processing. More complex longitudinal models may be unfeasible due to computational burden, limited sample size, or aspects of data distributions (e.g., negative binomial distribution of RNAseq data; BinTayyash et al., 2021). Analytes might differ greatly in their autocorrelation or variance, which could require custom model fitting. Given that the study is observational, a strong adherence to best practices is necessary for reliable insights into mechanistic pathways. Domain knowledge is also important for interpreting results and assessing biological plausibility/relevance.
Implications	Results will elucidate the biological changes underlying the development of mental health problems during stressful life transitions in young adults.
Costs/Benefits of Multi-omics (What did multi-omics buy us here?)	Multi-omics analysis provides an opportunity to comprehensively assess contributions of diverse biological pathways, rather than inferring relevance of a pathway from only a few biomarkers. In addition to improving chances of discovery, this approach also takes a more systems-focused perspective that reflects the realities of how biology works. Using multi-omics in prediction models as opposed to single omes (Ghaemi et al., 2019) or clinical data alone (Schüssler-Fiorenza Rose et al., 2019) has been shown in other studies to improve predictive power of the models, potentially identifying biomarkers that predict poor mental health trajectories. Alternatively, it is possible that different people have different molecular entry points to poor mental health (e.g., depression) as has been shown in type II diabetes (Schüssler-Fiorenza Rose et al., 2019) and aging (Ahadi et al., 2020) and multi-omics can help identify different subtypes. Longitudinal multi-omics profiling can also identify shifts in biology that occur with different clinical states (Stelzer et al., 2021) and potentially provide biological markers of poor mental health onset which, with further validation, could eventually be used for diagnostics.

Table

Research Question	How does a three-day multicomponent immersive intervention designed to reduce stress and enhance well-being impact perceived stress?
Participants	Adults with elevated perceived stress levels (PSS-10 > 14); N = 100+
Study Design	Baseline day (7-10 days pre-intervention): Participants will be shipped a study package with a TASSO device, a smartwatch (measuring changes in activity, sleep, heart rate, heart rate variability, electrodermal activity, and caloric intake), and a QR code to access surveys. They will collect blood microsamples in the morning, mid-day, and evening using TASSO devices. Participants will be asked to wear a smartwatch for the full study period. Participant will complete a baseline questionnaire, which contains demographic questions; the STRAIN; trait measures of stress-related processes, mental health, physical health; and other key outcomes of interest to researchers. Participants will complete state survey measures using a smartphone at the same time as the midday blood sample. All samples will be stored in the participant’s freezer, then shipped to study team the next day. Intervention period: Participants will take 3 blood microsamples per day during the intervention period, in the morning before starting and after each component (midday and evening) of the intervention. They will complete a brief ecological momentary assessment (EMA) state measure survey with each blood draw. The evening survey will contain additional questions assessing participation and response to the intervention activities. Participants will provide one saliva sample for genomic analysis. Study staff will be on site to collect samples. Follow-up 1 (7-10 days post intervention): Participants will provide 3 blood samples collected throughout the course of the day, concurrent with brief EMA surveys, collected at home and continue wearing smartwatch. The Follow-up 1 survey will contain assessments of perceived stress, and other key outcomes of interest to researchers. Follow-up 2 (1-3 months post intervention): Participants will complete a similar protocol to the Follow-up 1 Timepoint to assess lasting change in outcomes of interest.
Analytic Approach	Multi-level and mixed effects models to examine effects of the intervention on both perceived stress and changes in multi-omics analytes. Exploratory follow-up analyses may explore moderation by demographic and individual difference factors (e.g., sex, STRAIN), as well as time-varying covariance of changes to perceived stress and analytes. Exploratory analyses to identify unique biological responses to different intervention components. Multi-omics: Identify analytes that change in response to the intervention (after false discovery rate/familywise error correction), then conduct integrative enrichment analyses to determine relevant biological and disease pathways. Multi-omic predictive modeling to identify subpopulations that may be more likely to respond overall or respond to specific components of the intervention.
Possible Outcomes	Identify the biological pathways through which the intervention acts to result in reduced stress levels during the intervention, alongside the biological pathways that predict lasting change in stress levels (i.e., at follow-up 2). Determine which analytes are associated with baseline levels of, and changes to, PSS scores across the study. This information can be used to determine biological systems involved in stress processes (e.g., innate immunity), as well as outcomes that might be impacted by stress biology. Determine the key individual differences that impact intervention efficacy and whether these are associated with biological processes as well as determine differential responses to components of the intervention that will enable development of personalized interventions in future studies/therapies (Grant et al., 2022).
Limitations / Considerations	Always use past theoretical research as a guide and consider biological plausibility when conducting exploratory analyses. Achieving adequate power for random assignment may be challenging without a large funding source, thus researchers may need to focus on within-person analyses instead.
Implications	Results will advance understanding of the biological mechanisms associated with changes in perceived stress and advance a precision medicine approach to stress reduction.
Costs/Benefits of Multi-omics (What did multi-omics buy us here?)	By using multi-omics, it is possible to elucidate mechanistic pathways through which the intervention impacted biological processes, perceived stress, and possibly later health, and the timing of changes. A more traditional approach might assess a few analytes before and after the intervention period. Knowing how an intervention impacts a few analytes might lead to a publishable finding that the intervention reduced stress and this effect was mediated by reductions in IL-6, for example, but will not allow us to answer more nuanced mechanistic questions about the links between IL-6 and stress reduction. A multi-omics approach will enable the identification of shared or subtype specific signatures of IL-6 reduction. In addition, it allows one to assess a more comprehensive picture illuminating mechanisms other than IL-6 that are important in stress-related inflammatory processes and enables the identification of novel analytes and mechanisms that may not have been previously considered. Similar to the example in Box 1, the additional biological information that multi-omics approaches provide compared to single omes and clinical data alone often enables the development of superior prediction models that can be used to predict responses to treatments and stratify people into different therapeutic interventions. Combining multi-omics data with wearable data may improve predictive power of such models. Wearables provide real-time continuous monitoring of the effect of therapeutic interventions and provide context to biological changes with physiological data.

Table

Description	Tool / Key Reference
Reviews of analysis techniques used in multi-omics studies of psychiatric disorders	Amasi-Hartoonian et al., 2022 Sathyanarayanan et al., 2023
Guides to multi-omics study design, data integration, and data analysis for translational medicine and psychoneuroimmunology research	Athieniti & Spyrou, 2023 Mengelkoch et al., 2023
Catalog to help researchers select social determinants of health measures to include in studies	NIH PhenX Toolkit (Hamilton et al., 2011)
Review of unsupervised multi-omics data integration methods	Vahabi & Michailidis, 2022
Review of network based integrative multi-omics analysis	Agamah et al., 2022
Tools for determining appropriate sample size in multi-omics studies	MultiPower/MultiML (Tarazona et al., 2020) PowerTools (Acharjee et al., 2020)
Platforms that contain common multi-omics data analytic tools for both supervised and unsupervised analytic approaches	mixOmics (Cao et al., 2008) MetaboAnalyst (Pang et al., 2022) 3omics (Kuo et al., 2013) PaintOmics (Liu et al., 2022) OmicsNet2.0 (Zhou et al., 2022) Mergeomics2.0 (Ding et al., 2021) TidyMass (Shen et al., 2022)
Tools for integrative multi-omics analyses (e.g., pathway enrichment and overrepresentation analysis)	IMPaLA (Kamburov et al., 2011) clusterProfiler (Yu et al., 2012) MOFA2 (Argelaguet et al., 2018) PathwayMultiomics (Odom et al., 2021)

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