procedures. They called for more rigorous methodologies, larger sample sizes, and longer
follow-up periods to strengthen the evidence base. The review also noted that most studies
examined immediate or short-term effects, with limited evidence regarding the durability of
immune changes following mindfulness interventions.

The Buric Meta-Analysis of Molecular Signatures

A comprehensive systematic review and meta-analysis by Ivana Buric and colleagues in 2017 focused specifically on the molecular signature of mind-body interventions, examining gene expression changes across different MBI types (Buric et al., 2017). This review analyzed 18 studies comprising 846 participants that examined gene expression changes induced by meditation, yoga, Tai Chi, Qigong, relaxation response, and other mind-body practices. The studies employed various genomic technologies including microarrays, RT-PCR, and RNA sequencing.

The most prominent finding was the consistent down-regulation of NF-κB-related gene expression across mind-body intervention studies (Buric et al., 2017). Of the 18 studies examined, 13 reported findings related to NF-κB signaling, with the majority showing reduced expression of NF-κB target genes or reduced NF-κB activity following MBI practice. This convergence across diverse interventions and populations suggested a common molecular pathway through which MBIs exert their effects. The review concluded that MBIs appear to reverse the molecular signature of chronic stress, particularly the pro-inflammatory gene expression profile associated with the CTRA.

Buric and colleagues (2017) proposed a model in which the transcriptional effects of
MBIs are mediated through reduction in sympathetic nervous system activity and enhancement
of parasympathetic tone. Reduced β-adrenergic signaling would decrease activation of the
CREB/AP-1 and NF-κB transcription factors that drive inflammatory gene expression. Enhanced vagal activity would engage the cholinergic anti-inflammatory pathway, further suppressing inflammatory signaling. The review noted, however, that direct evidence for these mediating mechanisms was limited and called for studies examining autonomic function alongside gene expression.

A limitation noted by the authors was the lack of direct comparisons between the genomic effects of MBIs and other positive lifestyle changes such as exercise or dietary modification (Buric et al., 2017). Whether MBIs produce specific transcriptional effects beyond those achievable through general stress reduction or healthy living remains an important question. The review also noted heterogeneity in study quality, intervention types, and gene expression assessment methods, complicating synthesis across studies.

## Key Findings and Effect Sizes

More recent meta-analyses have sought to quantify the effects of MBIs on specific biomarkers, providing effect size estimates that enable comparison with other interventions and assessment of clinical significance. A 2022 meta-analysis by Dunn and Dimolareva examined the impact of MBIs on six key immune-related biomarkers: CRP, IL-6, TNF-α, cortisol, CD4+ T cells, and telomerase activity (Dunn & Dimolareva, 2022). Analyzing 48 RCTs, the meta-analysis found that MBIs led to significant post-intervention reductions in C-reactive protein (CRP) and Interleukin-6 (IL-6) compared to control conditions.

For CRP, the meta-analysis found a small to moderate effect (Hedge's g ranging from -0.20 to -0.35 across analyses) favoring MBIs over control conditions (Dunn & Dimolareva, 2022). The effect on CRP was found to be sustained at follow-up assessments, suggesting a lasting benefit. For IL-6, similar effect sizes were observed, though effects were more variable
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across studies. Notably, the effects on inflammatory markers were larger in studies with clinical
populations compared to healthy samples, suggesting that MBIs may be particularly beneficial
for individuals with elevated baseline inflammation.

The review also reported positive effects on immune cell aging, noting increases in telomerase activity following MBI interventions (Dunn & Dimolareva, 2022). For CD4+ T cell counts, significant effects were observed in HIV-positive samples but not in general populations. Effects on cortisol and TNF-α were more variable, with some meta-analyses finding significant reductions and others finding non-significant trends. The heterogeneity of findings for these markers may reflect differences in measurement timing, intervention types, or population characteristics.

A meta-analysis by Sanada and colleagues (2020) examined the effects of mindfulness interventions specifically on cortisol levels. Analyzing 16 studies with 944 participants, they found a significant reduction in cortisol following mindfulness interventions (Hedge's g = -0.41, 95% CI: -0.67 to -0.16). The effect was moderated by baseline stress levels, with larger effects in populations with higher initial stress. This finding supports the hypothesis that mindfulness interventions work, in part, by modulating HPA axis activity, which would subsequently affect inflammatory gene expression through glucocorticoid receptor signaling.

Methodological Quality Assessment

Systematic reviews have consistently noted methodological limitations in the primary literature on MBIs and immune outcomes (Black & Slavich, 2016; Buric et al., 2017). These limitations temper the strength of conclusions that can be drawn and highlight areas for methodological improvement in future research.
Sample size is a pervasive limitation, with many studies employing small samples (n < 30 per group) that provide limited statistical power to detect effects and unstable effect size estimates (Black & Slavich, 2016). Small samples also limit the ability to examine moderators and mediators of intervention effects. Recent meta-analyses have called for larger, adequately powered trials to provide more reliable estimates of MBI effects on immune outcomes.

Control conditions vary substantially across studies, ranging from wait-list controls to active comparison conditions such as health education or support groups (Buric et al., 2017). Wait-list controls do not account for non-specific effects of intervention participation, such as expectation, attention, and social support, potentially inflating effect size estimates. Active control conditions provide more rigorous comparisons but may share some features with MBIs, potentially attenuating effect sizes. The optimal control condition for MBI research remains a matter of debate.

Follow-up assessments are often limited or absent, precluding conclusions about the durability of effects (Black & Slavich, 2016). Many studies assess outcomes only immediately post-intervention, leaving unknown whether transcriptional changes persist over time or require continued practice. Studies with longer follow-up periods have generally found that effects diminish over time without continued practice, highlighting the importance of ongoing engagement with MBIs.

Gene expression assessment methods vary across studies, including targeted RT-PCR for candidate genes, microarray analysis for genome-wide profiling, and RNA sequencing (Buric et al., 2017). Different methods have different sensitivities, normalization approaches, and biases, complicating comparison across studies. Additionally, the choice of gene targets varies across
studies, with some focusing on specific inflammatory genes and others examining broader
transcriptional profiles. Standardization of methods would facilitate meta-analytic synthesis.

Despite these limitations, a systematic review of methodological quality in mindfulness
research found no significant improvement in rigor over a 16-year period, indicating a persistent
challenge for the field (Goldberg et al., 2017). The review called for pre-registration of studies,
larger sample sizes, active control conditions, longer follow-up periods, and more transparent
reporting to strengthen the evidence base. Adherence to reporting guidelines such as CONSORT
for trials and PRISMA for systematic reviews was also recommended.

## Research Gaps and Future Directions

Despite the remarkable progress in establishing a scientific basis for the influence of
mind-body interventions on inflammatory gene expression, the field remains in its relative
infancy. The existing body of literature, while compelling, is characterized by significant
methodological limitations and unanswered questions. Identifying these research gaps is crucial
for guiding future investigations toward a more nuanced, mechanistic, and clinically applicable
understanding of the mind-body connection, a primary goal of the QPNIT framework (Pokorny,
2026). This section articulates the major research gaps, methodological challenges, and
directions for future research that inform the rationale for the present dissertation.

## Methodological Limitations

A major and recurring critique of MBI research is the methodological rigor of many
studies (Goldberg et al., 2017; Davidson & Kaszniak, 2015). Several specific limitations warrant
attention and remediation in future research.

Sample sizes in most MBI studies examining gene expression are small, typically ranging
from 10-50 participants per group (Black & Slavich, 2016; Buric et al., 2017). Small samples
limit statistical power, produce unstable effect estimates, and preclude examination of individual
differences in response. Effect sizes for transcriptional changes are typically modest, requiring
adequate samples to detect reliably. Power analyses suggest that samples of 50-100 per group
would be needed to detect small to moderate effects with adequate power. Future studies should
employ larger samples, potentially through multi-site collaborations, to provide more reliable
effect estimates.

Control conditions in existing studies range from no-treatment wait-list controls to active
comparison conditions (Buric et al., 2017). Wait-list controls, while practical, do not control for
non-specific factors such as expectation, attention from research staff, or social support from
fellow participants. Active controls (such as health education, support groups, or relaxation
techniques) provide more rigorous comparisons but may share features with MBIs that produce
similar effects. Future research should employ stringent active control conditions that match
MBIs on structural features (time, attention, group interaction) while differing on proposed
mechanisms.

Intervention duration and follow-up periods vary substantially across studies, with most
examining 8-week interventions with immediate post-intervention assessment (Black & Slavich,
2016). Limited follow-up precludes understanding of the durability of transcriptional changes.
Some evidence suggests that effects diminish over time without continued practice. Future
studies should include longer follow-up periods (3-6 months or more) to assess durability and
examine factors predicting maintenance versus decline of effects.

Blinding of participants is inherently difficult in behavioral intervention research, and
double-blinding is typically impossible (Davidson & Kaszniak, 2015). Participants know
whether they are practicing meditation or another activity. This lack of blinding introduces
potential for expectation effects. Single-blinding of outcome assessors is possible and should be
employed for biological outcomes. Credibility checks can assess whether participants find the
control condition equally believable as the MBI, though perfectly matched credibility is rarely
achieved.

Heterogeneity of Interventions and Populations

A significant gap lies in the heterogeneity of interventions and populations across studies
(Buric et al., 2017). "Meditation" and "yoga" are umbrella terms for diverse practices with
different techniques, emphases, and purported mechanisms. MBSR, TM, loving-kindness
meditation, Zen meditation, and various yoga traditions differ substantially despite being
grouped together in reviews. Whether different practices produce similar or distinct
transcriptional signatures is largely unknown.

Relatedly, the "dose-response" relationship for MBIs and gene expression remains poorly characterized (Dusek et al., 2008). What is the minimum effective dose of practice? How does intensity (retreat versus weekly classes) affect outcomes? Does accumulated lifetime practice provide additional benefit beyond current practice? Addressing these questions would inform clinical recommendations and intervention development. Systematic studies varying dose parameters while holding other factors constant are needed.

Population heterogeneity also limits generalizability (Black & Slavich, 2016). Studies have examined healthy adults, cancer survivors, HIV patients, caregivers, and other specific groups. Whether findings generalize across populations is unknown. Baseline characteristics such as age, health status, stress level, and baseline inflammation may moderate MBI effects. Future research should examine moderators of MBI effects and test interventions across diverse populations.
Mechanistic Gaps

The mechanisms of action linking MBI practice to transcriptional changes require deeper exploration (Bower & Irwin, 2016). While theoretical models propose that reduced sympathetic activity and enhanced vagal tone mediate transcriptional effects through the NF-κB pathway, direct evidence for these mediating mechanisms is limited. Studies examining autonomic function (HRV), stress hormones (cortisol), and gene expression within the same sample could test mediation hypotheses. Multi-level assessments including neuroimaging, autonomic measures, endocrine markers, and genomics would enable comprehensive modeling of the mind-body-gene pathway.

The role of epigenetic mechanisms in MBI effects on gene expression is only beginning to be explored (Kaliman et al., 2014). Studies examining DNA methylation and histone modifications alongside gene expression could elucidate how MBI effects become encoded in epigenetic marks that influence gene expression. Whether MBI-induced epigenetic changes persist over time and whether they are transmitted across cell divisions or generations are open questions with significant implications.

From the QPNIT perspective, the potential involvement of quantum-level processes in consciousness-mediated biological effects remains unexplored empirically (Pokorny, 2026). While highly speculative, the framework suggests that quantum coherence in neural systems might facilitate consciousness-body interactions. Advanced techniques for detecting quantum effects in biological systems could eventually test this proposition, though current methodological capabilities are limited.
## Clinical Translation Gaps

The clinical relevance and translation of transcriptional findings need to be strengthened (Bower & Irwin, 2016). While studies show that MBIs can reduce inflammatory gene expression, it is not always clear whether these molecular changes translate into clinically meaningful improvements in disease symptoms or progression. Some studies show down-regulation of NF-κB-related genes without corresponding significant reductions in circulating inflammatory proteins like IL-6 or CRP. This discrepancy needs to be understood—it may reflect that transcriptional changes are a more sensitive upstream indicator, that effects on protein levels require longer time frames, or that post-transcriptional regulation attenuates the relationship.

Future research should link genomic changes to clinical outcomes, examining whether transcriptional changes mediate improvements in symptoms, functioning, or disease biomarkers (Bower & Irwin, 2016). Studies following participants over time to assess whether early transcriptional changes predict later health outcomes would strengthen the clinical significance of molecular findings. Additionally, identifying individuals most likely to benefit from MBIs based on transcriptional profiles could enable personalized medicine approaches.

## Rationale for the Current Study

The research gaps identified above provide the rationale for the current systematic review and meta-analysis. Despite the growing number of studies examining transcriptional effects of MBIs, no comprehensive meta-analysis has been conducted to quantitatively synthesize gene expression findings across studies (Bower & Irwin, 2016). The Buric review (2017) provided a qualitative synthesis but did not conduct quantitative meta-analysis of effect sizes for specific genes or pathways. A quantitative synthesis would enable estimation of the overall magnitude of
transcriptional effects, assessment of heterogeneity across studies, and examination of potential
moderators of effects.

The present dissertation addresses this gap by conducting a systematic review and meta-
analysis of gene expression changes following meditation and mindfulness interventions. By
applying standardized bioinformatics pipelines to publicly available gene expression datasets, the
study enables direct comparison of findings across studies that used different platforms and
analysis methods. The focus on NF-κB-related genes, interferon-response genes, and
glucocorticoid-responsive genes—the core components of the CTRA—provides a targeted
examination of the transcriptional signature predicted by QPNIT and social genomics theory.

The study also addresses the need for theoretical grounding by explicitly testing
predictions derived from the QPNIT framework (Pokorny, 2026). QPNIT Proposition 3 predicts
that mind-body practices will produce measurable biological effects, including down-regulation
of inflammatory gene expression. The meta-analysis provides a comprehensive test of this
prediction across the accumulated evidence base. By situating the research within the QPNIT
framework, the study contributes to the validation and refinement of an integrative theoretical
model for understanding mind-body-spirit-quantum connections in human health.

Chapter Summary

This chapter has provided a comprehensive review of the scholarly literature underlying
the investigation of transcriptional signatures of mind-body interventions on inflammatory gene
expression. The review has traversed multiple disciplines—psychology, neuroscience,
immunology, genomics, and integrative medicine—to establish the theoretical rationale,
empirical foundation, and methodological considerations that guide the present systematic
review and meta-analysis. Key findings and their implications for the dissertation research are summarized below.

The theoretical framework of Quantum Psychoneuroimmunotheology (QPNIT) provides an integrative lens for understanding mind-body-spirit-quantum connections in human health (Pokorny, 2026). QPNIT's core propositions—that consciousness interfaces with physical reality at the quantum level, that immune function involves quantum biological processes susceptible to consciousness-mediated modulation, and that spiritual practices produce measurable biological effects—generate testable predictions about the transcriptional effects of mind-body interventions. Specifically, QPNIT predicts that these practices will down-regulate pro-inflammatory gene expression while preserving or enhancing antiviral immunity, effectively reversing the transcriptional signature of adversity.

The field of psychoneuroimmunology has established the scientific foundation for understanding bidirectional communication between the mind, brain, and immune system (Ader, 2007; Irwin & Cole, 2011). Key mechanisms include the hypothalamic-pituitary-adrenal axis, which releases cortisol in response to stress and modulates immune function through glucocorticoid receptors; the autonomic nervous system, including the sympathetic nervous system's pro-inflammatory effects and the parasympathetic vagal anti-inflammatory pathway; and cytokine signaling, through which immune cells communicate with each other and with the brain. Chronic stress disrupts these systems, leading to a pro-inflammatory state that increases risk for numerous diseases.

Mind-body interventions, including meditation, yoga, tai chi, and qigong, employ various techniques to enhance the mind's capacity to influence bodily function (Black & Slavich, 2016; Bower & Irwin, 2016). These practices elicit the relaxation response, enhance parasympathetic
tone, reduce HPA axis activation, and produce neuroplastic changes in brain regions governing attention and emotion regulation. Systematic reviews and meta-analyses have documented beneficial effects of MBIs on psychological outcomes (stress, anxiety, depression) and physiological outcomes (blood pressure, immune function, inflammatory markers).

Gene expression and transcriptional regulation provide the molecular interface between psychological experience and cellular function (Cole, 2014; Slavich & Cole, 2013). The Conserved Transcriptional Response to Adversity (CTRA) describes a characteristic pattern of gene expression associated with chronic stress: up-regulation of pro-inflammatory genes (regulated by NF-κB) and down-regulation of genes involved in antiviral immunity (regulated by interferon response factors). This transcriptional signature has been observed in response to loneliness, caregiving stress, bereavement, low socioeconomic status, and other forms of adversity. Epigenetic mechanisms, including DNA methylation and histone modification, provide pathways for experience to become encoded in lasting changes in gene expression.

Inflammatory pathways, particularly NF-κB signaling and the pro-inflammatory cytokines IL-6, TNF-α, and IL-1β, are central targets of the transcriptional changes induced by stress and mind-body interventions (Hayden & Ghosh, 2008; Slavich & Irwin, 2014). Chronic low-grade inflammation contributes to cardiovascular disease, diabetes, cancer, and neurodegeneration. Anti-inflammatory mechanisms, including IL-10 and the vagal cholinergic pathway, provide counterbalancing forces that can be enhanced through mind-body practices.

Empirical research has demonstrated that mind-body interventions can alter inflammatory gene expression (Dusek et al., 2008; Creswell et al., 2012; Bower et al., 2015; Black et al., 2013; Kaliman et al., 2014). Studies have shown reduced expression of NF-κB target genes following meditation, yoga, and related practices in diverse populations including healthy adults, older
adults, cancer survivors, and caregivers. These transcriptional changes are associated with
improvements in psychological outcomes and may mediate the health benefits of MBIs.

However, the evidence base is characterized by small samples, varied methods, and limited
follow-up.

Systematic reviews and meta-analyses have synthesized the evidence, finding consistent
effects of MBIs on inflammatory markers (CRP, IL-6) and NF-κB-related gene expression
(Black & Slavich, 2016; Buric et al., 2017; Dunn & Dimolareva, 2022). However, no
comprehensive meta-analysis has quantitatively synthesized gene expression findings using
modern bioinformatics approaches. Methodological quality remains a concern, with calls for
larger samples, active control conditions, longer follow-up, and standardized methods.

Research gaps include the need for larger, more rigorous trials; clarification of dose-
response relationships; examination of mechanisms mediating transcriptional effects;
investigation of moderators predicting individual response; longer-term follow-up to assess
durability; and linkage of molecular changes to clinical outcomes. The present dissertation
addresses several of these gaps by conducting a systematic review and meta-analysis applying
standardized bioinformatics approaches to publicly available gene expression datasets from
meditation studies.

In conclusion, this literature review establishes that the investigation of transcriptional
signatures of mind-body interventions represents a convergence of cutting-edge science across
multiple disciplines. The QPNIT framework provides theoretical grounding for understanding
how psychological and spiritual practices might influence gene expression through integrated
mind-body-spirit-quantum pathways. The accumulated evidence supports the hypothesis that
MBIs can reverse the molecular signature of adversity, down-regulating pro-inflammatory gene
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expression while preserving antiviral immunity. The systematic review and meta-analysis
presented in the following chapters will provide a comprehensive quantitative synthesis of this
evidence, testing predictions derived from QPNIT and contributing to the scientific foundation
for integrative approaches to health and healing.
# CHAPTER 3: METHODOLOGY

## Introduction

This chapter presents the methodological framework for conducting a systematic review and meta-analysis of gene expression data examining transcriptional changes following mind-body interventions. The methodology described herein is designed to rigorously identify, evaluate, and synthesize publicly available genomic datasets from the Gene Expression Omnibus (GEO) and related repositories to determine whether a consistent transcriptional signature characterizes the molecular response to meditation, mindfulness, and related contemplative practices. This methodological approach addresses the critical gap in the literature identified in Chapter 2: while individual studies have documented gene expression changes following mind-body interventions, no comprehensive meta-analysis has synthesized these findings to identify convergent patterns across studies.

The systematic review and meta-analysis methodology employed in this study represents the gold standard for evidence synthesis in biomedical research (Higgins & Thomas, 2019). This approach is particularly well-suited for addressing the research questions posed in this dissertation because it enables quantitative integration of data across heterogeneous studies, provides statistical power to detect effects that individual studies may lack power to identify, and allows for systematic assessment of consistency and sources of heterogeneity in observed effects. The application of systematic review methodology to gene expression data requires specialized bioinformatics approaches that account for the unique characteristics of transcriptomic data, including high dimensionality, platform-specific technical artifacts, and the need for appropriate multiple testing corrections.
The methodological framework for this study is grounded in the Quantum Psychoneuroimmunotheology (QPNIT) theoretical model (Pokorny, 2026), which provides the conceptual foundation for understanding how contemplative practices may influence gene expression through psychoneuroimmunological pathways. QPNIT Proposition 3 specifically addresses the measurable biological effects of spiritual practices, and the present study tests key predictions derived from this framework, particularly regarding the modulation of inflammatory signaling pathways and preservation of antiviral immune function following mind-body interventions.

## Chapter Overview

This chapter is organized into eleven substantive sections following this introduction. The Research Design section articulates the rationale for the systematic review and meta-analysis approach, describes the meta-analytic methods appropriate for gene expression data, and documents alignment with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Data Sources section describes the Gene Expression Omnibus and supplementary databases, providing rationale for utilizing publicly available data. The Search Strategy section details the comprehensive search approach including specific terms, Boolean operators, and database-specific syntax. The Eligibility Criteria section specifies inclusion and exclusion criteria using the PICOS framework. The Study Selection Process section describes screening procedures and inter-rater reliability assessment. The Data Extraction section outlines the variables to be extracted and the data extraction form. The Quality Assessment section addresses evaluation of methodological rigor using established criteria for genomics studies. The Data Analysis section presents the bioinformatics pipeline including normalization, differential expression analysis, pathway enrichment, and meta-analytic statistical
methods. The Ethical Considerations section addresses the use of publicly available data and IRB
considerations. The Methodological Limitations section anticipates potential limitations and
describes mitigation strategies. The chapter concludes with a summary synthesizing the
methodological approach.

Research Questions and Hypotheses Restated

The methodological design of this study is structured to address four primary research
questions and their associated hypotheses, which were developed based on the theoretical
framework presented in Chapter 1 and the empirical literature reviewed in Chapter 2.

Research Question 1: Is there a consistent transcriptional signature across meditation intervention studies? This question addresses the fundamental issue of reproducibility in the field. Individual studies have reported diverse sets of differentially expressed genes, but the extent to which these findings converge on a common signature remains unclear. Hypothesis 1 predicts that a core set of genes will demonstrate consistent differential expression (same direction of effect) across multiple meditation intervention studies, indicating a reproducible transcriptional signature of contemplative practice response.

Research Question 2: Are pro-inflammatory pathways, particularly NF-κB signaling, downregulated following meditation interventions? This question tests a central prediction of the QPNIT framework, which posits that contemplative practices engage the cholinergic anti-inflammatory pathway and HPA axis to modulate inflammatory gene expression. Hypothesis 2 predicts that NF-κB target genes and pro-inflammatory pathway gene sets will show significant negative enrichment (downregulation) in the meta-analysis, indicating meditation-induced suppression of inflammatory gene expression.
Research Question 3: Are antiviral and immune-regulatory pathways differentially affected by meditation compared to pro-inflammatory pathways? This question addresses the specificity of meditation effects on immune function. Unlike broad immunosuppressive interventions such as glucocorticoids, mind-body practices may selectively modulate inflammatory signaling while preserving or enhancing other immune functions. Hypothesis 3 predicts that Type I interferon response genes will show positive or neutral enrichment following meditation, contrasting with downregulation of pro-inflammatory genes, indicating selective anti-inflammatory effects without impairment of antiviral immunity.

Research Question 4: How does the meditation-induced transcriptional signature compare to disease-associated inflammatory signatures? This question examines whether meditation effects oppose the gene expression patterns associated with psychological adversity and chronic inflammation. Hypothesis 4 predicts that the meditation transcriptional signature will show inverse correlation with the conserved transcriptional response to adversity (CTRA) and disease-associated inflammatory signatures, suggesting that meditation produces a genomic profile opposite to that associated with stress and disease.

## Research Design

This study employs a systematic review and meta-analysis design combining qualitative synthesis of intervention characteristics with quantitative meta-analysis of gene expression data. The systematic review methodology provides a rigorous, transparent, and reproducible approach to identifying, appraising, and synthesizing all relevant evidence addressing the research questions (Moher et al., 2009). Meta-analysis extends systematic review by applying statistical methods to combine results across studies, thereby increasing statistical power and precision of effect estimates while enabling formal assessment of heterogeneity (Borenstein et al., 2009). The
integration of these approaches is essential for achieving the study objectives of identifying convergent transcriptional signatures and characterizing pathway-level effects across the body of meditation gene expression research.

## Systematic Review Methodology Rationale

The systematic review methodology was selected for several compelling reasons specific to the research questions and current state of the literature. First, the field of meditation gene expression research is characterized by heterogeneous study designs, intervention types, analysis methods, and reporting practices, creating a complex literature landscape that requires systematic organization and appraisal. Traditional narrative reviews are susceptible to selection bias and may fail to comprehensively capture all relevant evidence (Mulrow, 1994). In contrast, systematic review methodology employs predetermined eligibility criteria, comprehensive search strategies, and standardized data extraction to minimize bias and ensure completeness.

Second, systematic review methodology enables transparent documentation of the review process, allowing readers to assess the validity of conclusions and enabling future updates as new evidence emerges. The reproducibility of systematic reviews is essential for building a cumulative scientific knowledge base (Liberati et al., 2009). Third, systematic review methodology facilitates identification of gaps in the evidence base, methodological weaknesses in existing studies, and directions for future research. These secondary outputs are valuable for advancing the field beyond the specific findings of the review.

The systematic review will follow the Cochrane Handbook for Systematic Reviews of Interventions (Higgins & Thomas, 2019) and will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (Page et
al., 2021). These guidelines represent the current standard for conducting and reporting systematic reviews in health-related research and ensure methodological rigor and transparency.

Meta-Analysis Approach for Gene Expression Data

Meta-analysis of gene expression data presents unique methodological challenges that distinguish it from traditional effect size meta-analysis. Gene expression studies typically measure thousands of genes simultaneously, creating a high-dimensional data structure that requires specialized statistical approaches. Additionally, differences in microarray platforms, RNA sequencing technologies, normalization methods, and analysis pipelines can introduce technical heterogeneity that must be carefully addressed (Ramasamy et al., 2008). Several meta-analytic approaches have been developed specifically for transcriptomic data, each with distinct advantages and limitations.

Fisher's combined probability method represents one widely-used approach for gene expression meta-analysis (Rhodes et al., 2002). This method combines p-values across studies for individual genes using the formula: $\chi^2 = -2 \sum \ln(p_i)$, which follows a chi-square distribution with 2k degrees of freedom under the null hypothesis, where k is the number of studies. Fisher's method tests whether a gene shows consistent evidence of differential expression across studies regardless of direction, accommodating heterogeneity in platforms and analysis methods. This approach requires only gene-level p-values from individual studies, making it applicable even when full effect size information is unavailable.

Random effects meta-analysis of effect sizes provides an alternative approach that directly models between-study heterogeneity (DerSimonian & Laird, 1986). For gene expression data, effect sizes are typically expressed as log₂ fold changes between conditions. Random effects models assume that true effects vary across studies according to a normal distribution,
Transcriptional Signature

with the model estimating both the mean effect and between-study variance. This approach
provides effect size estimates with confidence intervals and enables formal heterogeneity
assessment using the I² statistic. However, random effects meta-analysis of fold changes requires
that studies report effect estimates on comparable scales, which may be problematic when
combining different platforms and normalization methods.

Gene Set Enrichment Analysis (GSEA) provides a pathway-level approach to meta-analysis that may offer greater power and biological interpretability than gene-level methods (Subramanian et al., 2005). Rather than testing individual genes, GSEA examines whether predefined sets of genes (e.g., genes in a biological pathway) show coordinated enrichment at the top or bottom of a ranked gene list. This approach can detect modest but coordinated changes across pathway members that might not reach significance at the individual gene level. GSEA can be applied to meta-analytic gene rankings or to individual datasets with subsequent comparison of enrichment patterns across studies.

The present study employs a multi-method approach combining Fisher's combined
probability test for gene-level meta-analysis, random effects meta-analysis for genes with
consistent effect directions, and GSEA for pathway-level enrichment analysis. This triangulation
strategy leverages the complementary strengths of different methods and provides robust
conclusions that do not depend on any single analytic choice.

Alignment With PRISMA Guidelines

This systematic review adheres to the PRISMA 2020 statement, which provides updated
guidance for transparent reporting of systematic reviews (Page et al., 2021). The PRISMA
guidelines consist of a 27-item checklist and a four-phase flow diagram that document key
aspects of the review process. Compliance with PRISMA enhances the transparency,
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reproducibility, and credibility of systematic reviews and is increasingly required by journals and
funding agencies.

Key PRISMA elements addressed in this methodology include: (a) eligibility criteria specifying the PICOS components (Population, Intervention, Comparator, Outcome, Study design); (b) information sources including electronic databases and other sources searched; (c) search strategy with complete search syntax for at least one database; (d) selection process describing how studies were screened and selected; (e) data collection process outlining data extraction procedures; (f) study risk of bias assessment describing tools and methods used; (g) effect measures specifying the outcome measures used; (h) synthesis methods describing how results were combined; and (i) certainty assessment evaluating confidence in findings.

The protocol for this systematic review has been registered on PROSPERO (International
Prospective Register of Systematic Reviews) prior to data extraction, in accordance with
PRISMA recommendations for prospective registration. Protocol registration promotes
transparency by documenting predetermined methods and reducing the potential for selective
reporting of outcomes. The registration number and any protocol amendments will be reported in
the final systematic review.

Data Sources

The primary data sources for this systematic review and meta-analysis are public
repositories containing high-throughput gene expression data from human studies of mind-body
interventions. The use of publicly available data enables comprehensive synthesis of existing
evidence while promoting transparency and reproducibility. This section describes the
characteristics of the data repositories, the types of data available, and the rationale for utilizing
publicly accessible genomic data.
Gene Expression Omnibus (GEO) Database Description

The Gene Expression Omnibus (GEO) serves as the primary data source for this study.
GEO is a public functional genomics data repository maintained by the National Center for
Biotechnology Information (NCBI), a division of the U.S. National Library of Medicine at the
National Institutes of Health (Barrett et al., 2013). Established in 2000, GEO has become the
world's largest repository for high-throughput gene expression and other functional genomics
data, containing over 5 million samples from more than 200,000 studies as of 2024. The
repository supports diverse data types including microarray gene expression, next-generation
sequencing (RNA-seq), chromatin immunoprecipitation (ChIP), methylation profiling, and other
high-throughput molecular assays.

GEO data are organized into three primary record types. Series (GSE) records represent a
complete study, including all associated samples, experimental design, and processed data
matrices. Samples (GSM) records contain individual sample data, including raw and processed
measurements and sample characteristics. Platform (GPL) records describe the array or
sequencing technology used, including probe sequences and annotation information. This
organizational structure enables efficient retrieval of complete datasets for secondary analysis.

A key advantage of GEO for this systematic review is that many journals and funding
agencies now require deposition of gene expression data as a condition of publication or grant
funding (Brazma et al., 2001). This policy has substantially increased the availability of primary
data for secondary analysis and meta-analysis. GEO implements MIAME (Minimum
Information About a Microarray Experiment) standards to ensure that deposited datasets contain
sufficient information for independent interpretation and replication (Brazma et al., 2001). Data
in GEO are freely available without access restrictions, supporting open science principles and
enabling comprehensive data synthesis.

GEO provides multiple tools for data discovery and retrieval. GEO DataSets (GDS) provides curated, uniformly processed datasets suitable for cross-study comparison. GEO Profiles enables gene-centric queries to identify studies measuring genes of interest. The GEO Query package for R/Bioconductor allows programmatic access to GEO data, facilitating automated retrieval and processing of multiple datasets (Davis & Meltzer, 2007). These tools will be utilized for systematic identification and retrieval of relevant datasets.

## Other Supplementary Databases

While GEO serves as the primary data source, supplementary repositories will be searched to ensure comprehensive identification of relevant datasets. ArrayExpress, maintained by the European Bioinformatics Institute (EBI), is a complementary functional genomics repository that archives data from microarray and sequencing experiments (Athar et al., 2019). ArrayExpress implements MIAME standards and exchanges data with GEO, though some datasets may be unique to one repository. ArrayExpress will be searched using parallel search strategies to identify any datasets not represented in GEO.

The Sequence Read Archive (SRA), also maintained by NCBI, stores raw sequencing data from next-generation sequencing studies (Leinonen et al., 2011). While GEO typically contains processed data, SRA provides access to raw sequence files that may enable alternative processing and analysis approaches. SRA will be searched for RNA-seq datasets from meditation studies that may not have processed data available in GEO. Additional repositories including the Database of Genotypes and Phenotypes (dbGaP) and institutional data sharing portals will be examined for relevant datasets.
Supplementary literature searching of PubMed, PsycINFO, and CINAHL databases will identify published studies examining gene expression effects of meditation or mindfulness interventions, even if data were not deposited in public repositories. Authors of identified studies will be contacted to request data sharing when published data are not available in GEO or other repositories. This multi-source approach maximizes the comprehensiveness of the evidence synthesis.

## Rationale for Using Publicly Available Data

The use of publicly available data from established repositories offers several methodological advantages for this systematic review and meta-analysis. First, public data availability promotes transparency and reproducibility, core principles of rigorous scientific research (Wilkinson et al., 2016). All data used in this study can be accessed and independently verified by other researchers, enabling validation of findings and extension to new research questions. Second, utilizing existing data enables synthesis of evidence from studies that have already received ethical approval and informed consent, avoiding the need for new human subjects research while maximizing the scientific value of prior investments.

Third, public repositories aggregate data from diverse research groups using various intervention protocols and analysis methods, providing a broader evidence base than any single study could achieve. This diversity, while creating heterogeneity that must be addressed methodologically, also enhances generalizability of findings and enables exploration of moderating factors. Fourth, the standardized formats and annotation in repositories like GEO facilitate harmonization of data from different platforms and studies, enabling quantitative meta-analysis that would be challenging with disparate data formats.
The potential limitations of using publicly available data include variation in data quality, incompleteness of metadata, and potential publication bias (studies with positive results may be more likely to deposit data). These limitations are addressed through systematic quality assessment, comprehensive search strategies including unpublished data, and sensitivity analyses examining the impact of study characteristics on results.

<table><caption>**Table 3.1 Characteristics of Gene Expression Data Repositories**</caption><thead><tr><td>Database</td><td>Maintainer</td><td>Data Types</td><td>Access</td><td>Search Tools</td><td>Standards</td></tr></thead><tbody><tr><td>Gene Expression Omnibus (GEO)</td><td>NCBI/NIH (USA)</td><td>Microarray, RNA-seq, ChIP-seq, methylation, other genomics</td><td>Free, open access, no registration</td><td>GEO DataSets, GEO Profiles, GEOquery R package</td><td>MIAME compliant</td></tr><tr><td>ArrayExpress</td><td>EMBL-EBI (Europe)</td><td>Microarray, RNA-seq, other functional genomics</td><td>Free, open access</td><td>Web interface, ArrayExpress API, Bioconductor</td><td>MIAME/MINSEQE compliant</td></tr><tr><td>Sequence Read Archive (SRA)</td><td>NCBI/NIH (USA)</td><td>Raw sequencing data (RNA-seq, WGS, ChIP-seq)</td><td>Free, open access</td><td>SRA Run Selector, SRA Toolkit, Entrez</td><td>INSDC standards</td></tr><tr><td>dbGaP</td><td>NCBI/NIH (USA)</td><td>Genotype-phenotype data, controlled access datasets</td><td>Free for public data; controlled access requires approval</td><td>dbGaP authorized access, FHIR API</td><td>NIH data sharing policy</td></tr><tr><td>Expression Atlas</td><td>EMBL-EBI (Europe)</td><td>Curated differential expression, baseline expression</td><td>Free, open access</td><td>Web interface, REST API</td><td>Curated with standardized analysis</td></tr></tbody></table>

Note. NCBI = National Center for Biotechnology Information; NIH = National Institutes of Health; EMBL-EBI = European Molecular Biology Laboratory - European Bioinformatics Institute; MIAME = Minimum Information About a Microarray Experiment; MINSEQE = Minimum Information about a high-throughput Nucleotide Sequencing Experiment; INSDC = International Nucleotide Sequence Database Collaboration; dbGaP = Database of Genotypes and Phenotypes; WGS = whole genome sequencing; ChIP = chromatin immunoprecipitation.
## Search Strategy

A comprehensive, systematic search strategy has been developed to identify all relevant gene expression datasets examining transcriptional changes following mind-body interventions. The search strategy was designed in consultation with a research librarian with expertise in systematic review methodology and biomedical databases. The strategy balances sensitivity (comprehensive identification of relevant studies) with specificity (exclusion of irrelevant records) through iterative refinement of search terms and testing against known relevant datasets.

## Search Terms and Boolean Operators

The search strategy employs a combination of controlled vocabulary terms (e.g., Medical Subject Headings [MeSH] in PubMed) and free-text keywords to capture the breadth of terminology used in the meditation gene expression literature. Search concepts are organized into three main blocks that are combined using Boolean operators: (a) mind-body intervention terms, (b) gene expression methodology terms, and (c) human studies filter. Within each block, synonymous terms are combined using the OR operator to ensure comprehensive capture. Blocks are combined using the AND operator to require that records contain terms from each conceptual domain.

Mind-body intervention terms include: meditation, mindfulness, mindfulness-based stress reduction, MBSR, mindfulness-based cognitive therapy, MBCT, contemplative practice, contemplative intervention, relaxation response, yoga, tai chi, taiji, qigong, chi kung, breathing exercise, pranayama, loving-kindness meditation, compassion meditation, transcendental meditation, and mind-body intervention. These terms capture the range of contemplative practices that have been examined in gene expression studies while excluding unrelated interventions.
Gene expression methodology terms include: gene expression, transcriptome, transcriptomic, transcriptomics, microarray, gene chip, RNA sequencing, RNA-seq, mRNA expression, messenger RNA, differential expression, differentially expressed genes, genomic profiling, expression profiling, functional genomics, and whole genome expression. These terms identify studies employing genome-wide gene expression measurement technologies while excluding candidate gene studies using targeted approaches such as RT-PCR for selected genes.

For database-specific searches (GEO, ArrayExpress), organism filters restrict results to Homo sapiens (human studies). For literature database searches (PubMed, PsycINFO), publication type filters exclude reviews, commentaries, and animal studies. Language restrictions are not applied to maximize inclusivity, though non-English publications will be assessed for data availability.

## Database-Specific Search Syntax

Search syntax is adapted for each database to utilize platform-specific features and controlled vocabularies. For GEO DataSets, the search interface accepts Boolean queries with field tags to restrict searches to specific metadata fields. The search query for GEO is:

((meditation[All Fields] OR mindfulness[All Fields] OR "contemplative practice"[All Fields] OR "relaxation response"[All Fields] OR yoga[All Fields] OR "tai chi"[All Fields] OR qigong[All Fields] OR "mind-body"[All Fields]) AND (expression profiling[DataSet Type] OR expression profiling by high throughput sequencing[DataSet Type]) AND "Homo sapiens"[Organism]).

For PubMed, the search incorporates MeSH terms in addition to free-text keywords. The PubMed search strategy is:

((meditation[MeSH Terms] OR mindfulness[MeSH Terms] OR mind-body therapies[MeSH Terms] OR yoga[MeSH Terms] OR tai ji[MeSH Terms] OR
Transcriptional Signature

qigong[MeSH Terms] OR meditation[Title/Abstract] OR mindfulness[Title/Abstract] OR MBSR[Title/Abstract] OR MBCT[Title/Abstract] OR "relaxation response"[Title/Abstract]) AND (gene expression profiling[MeSH Terms] OR transcriptome[MeSH Terms] OR microarray analysis[MeSH Terms] OR gene expression[Title/Abstract] OR transcriptome[Title/Abstract] OR microarray[Title/Abstract] OR RNA-seq[Title/Abstract] OR "RNA sequencing"[Title/Abstract]) AND humans[MeSH Terms]).

For ArrayExpress, the search uses the web interface query builder with filters for organism (Homo sapiens), experiment type (transcription profiling by array OR RNA-seq), and keyword searches for intervention terms. The complete search strategies for all databases are documented in Table 2.

Date Range and Language Restrictions

Database searches are not restricted by publication date, enabling identification of all available datasets from the inception of each database through the date of final search completion. The first gene expression studies of meditation interventions appeared in the early 2000s following widespread adoption of microarray technology, and inclusion of all historical datasets maximizes the evidence base for meta-analysis. The search date will be documented, and searches will be updated prior to final analysis if more than 12 months elapse between initial and final searches.

Language restrictions are not applied to ensure comprehensive identification of relevant datasets. While most gene expression studies are published in English given the predominance of English-language scientific journals, datasets from non-English publications deposited in GEO remain accessible. For non-English publications, efforts will be made to extract relevant data and
metadata using translation tools and, when necessary, consultation with native speakers. Data availability rather than language of publication determines inclusion.

<table><caption>Table 3.2 Complete Search Strategy by Database</caption><thead><tr><td>Database</td><td>Search Syntax</td><td>Filters Applied</td><td>Fields Searched</td></tr></thead><tbody><tr><td>GEO DataSets</td><td>(meditation OR mindfulness OR "contemplative practice" OR "relaxation response" OR yoga OR "tai chi" OR qigong OR "mind-body") AND (expression profiling)</td><td>Organism: Homo sapiens; Dataset Type: expression profiling by array OR high throughput sequencing</td><td>All Fields</td></tr><tr><td>GEO Profiles</td><td>(meditation OR mindfulness OR MBSR OR yoga) AND (gene expression)</td><td>Organism: Homo sapiens</td><td>All Fields</td></tr><tr><td>ArrayExpress</td><td>meditation OR mindfulness OR yoga OR "mind-body intervention"</td><td>Organism: Homo sapiens; Experiment Type: transcription profiling</td><td>Keywords, Title, Description</td></tr><tr><td>PubMed</td><td>(meditation[MeSH] OR mindfulness[MeSH] OR mind-body therapies[MeSH] OR yoga[MeSH] OR tai ji[MeSH] OR qigong[MeSH] OR meditation[tiab] OR mindfulness[tiab] OR MBSR[tiab]) AND (gene expression profiling[MeSH] OR transcriptome[MeSH] OR microarray analysis[MeSH] OR gene expression[tiab] OR transcriptome[tiab] OR microarray[tiab] OR RNA-seq[tiab]) AND humans[MeSH]</td><td>Humans; NOT Review; NOT Animal</td><td>MeSH Terms, Title/Abstract</td></tr><tr><td>PsycINFO</td><td>(DE "Meditation" OR DE "Mindfulness" OR DE "Yoga" OR TI meditation OR TI mindfulness OR TI yoga) AND (TI "gene expression" OR TI transcriptome OR TI microarray OR AB "gene expression")</td><td>Human; Empirical Study</td><td>Subject Headings, Title, Abstract</td></tr><tr><td>CINAHL</td><td>(MH "Meditation" OR MH "Mindfulness" OR</td><td>Human</td><td>Subject Headings, Title, Abstract</td></tr></tbody></table>
Transcriptional Signature

<table><tr><td></td><td>MH "Yoga" OR TI<br>meditation OR TI<br>mindfulness) AND (TI<br>gene expression OR TI<br>transcriptome OR TI<br>genomic)</td><td></td><td></td></tr><tr><td>SRA</td><td>(meditation OR<br>mindfulness OR yoga)<br>AND RNA-Seq[Strategy]</td><td>Organism: human;<br>Library Strategy: RNA-<br>Seq</td><td>All Fields</td></tr></table>

Note. GEO = Gene Expression Omnibus; SRA = Sequence Read Archive; MeSH = Medical Subject Headings; tiab = Title/Abstract; DE = Descriptor; TI = Title; AB = Abstract; MH = Subject Heading.
<figure><img src="image_3.png" /><figcaption>Figure 3.1 Search Strategy Flowchart for Identification of Gene Expression Datasets</figcaption></figure>
*Note.* The flowchart illustrates the systematic approach to identifying gene expression datasets. Numbers (n) will be populated during the search execution phase. The search proceeds from primary database searches through supplementary literature searching, deduplication, and author contact to generate the final inventory of datasets for eligibility screening.

## Eligibility Criteria

Explicit eligibility criteria define the boundaries of the systematic review and ensure consistent, reproducible study selection. The criteria are organized according to the PICOS framework (Population, Intervention, Comparator, Outcome, Study design), which is the standard approach for specifying eligibility in systematic reviews of health interventions (Methley et al., 2014). Criteria were developed based on the research questions, theoretical framework, and practical considerations regarding data availability and quality. Both inclusion and exclusion criteria are specified to provide clear guidance for screening decisions.

## Inclusion Criteria (PICOS Framework)

**Population:** Studies must include human participants. No restrictions are placed on age, sex, health status, or meditation experience, as the goal is to comprehensively characterize transcriptional responses across diverse populations. Studies of both healthy individuals and clinical populations (e.g., patients with inflammatory conditions, cancer, psychiatric disorders) are eligible. Studies examining both meditation-naive participants and experienced practitioners are included to enable examination of potential moderating effects of practice experience.

**Intervention:** Studies must involve a mind-body intervention including, but not limited to: meditation (any tradition or form, including mindfulness meditation, transcendental meditation, loving-kindness meditation, concentrative meditation), mindfulness-based interventions (including MBSR, MBCT, and other structured mindfulness programs), yoga (including asana, pranayama, and meditation components), tai chi, qigong, relaxation response training, and other
contemplative practices. Interventions may be acute (single session), short-term (weeks), or long-
term (months or years). Studies examining cross-sectional differences between experienced
practitioners and non-practitioners are also eligible as they provide information about long-term
practice effects.

Comparator: Studies must include a comparison condition enabling assessment of intervention effects on gene expression. Acceptable comparators include: wait-list control, active control (e.g., health education, exercise), pre-intervention baseline (for within-subjects designs), and non-practitioner comparison group (for cross-sectional designs). Studies without any comparison condition are excluded as they do not permit valid inference about intervention effects.

Outcome: Studies must measure gene expression using genome-wide profiling technologies including microarray platforms (e.g., Affymetrix, Illumina, Agilent) or RNA sequencing. Gene expression must be measured in human biological samples, with peripheral blood (whole blood or peripheral blood mononuclear cells [PBMCs]) being the most common and preferred tissue type. Studies examining other tissues (e.g., saliva, buccal cells, adipose tissue) are also eligible. Studies using only candidate gene approaches (e.g., RT-PCR for selected genes without genome-wide profiling) are excluded as they do not provide the comprehensive data required for meta-analysis of transcriptional signatures.

Study Design: Eligible study designs include randomized controlled trials (RCTs), non-randomized controlled trials, single-arm pre-post intervention studies with repeated gene expression measures, and cross-sectional studies comparing practitioners with non-practitioners.

Case reports and case series without quantitative gene expression data are excluded. Studies must
have a minimum sample size of 10 participants per group (or 10 total for within-subjects designs) to ensure adequate statistical power for differential expression analysis.

## Exclusion Criteria

Studies meeting any of the following criteria are excluded from the systematic review: (a) Non-human studies: Studies conducted in animal models (e.g., rodents, primates) or cell lines without human participants are excluded, as the focus is on human transcriptional responses to mind-body interventions. (b) Candidate gene studies: Studies examining only pre-selected genes using targeted methods (e.g., RT-PCR, Northern blot) without genome-wide profiling are excluded, as they do not provide the comprehensive data required for meta-analysis. (c) Combined interventions: Studies in which meditation or mindfulness is combined with pharmacological agents in a manner that precludes attribution of effects to the mind-body intervention are excluded. However, studies examining meditation as an adjunct to standard medical care (where the medical treatment is held constant across conditions) are eligible. (d) Insufficient data: Studies for which raw or processed gene expression data cannot be obtained from public repositories or through author contact are excluded from quantitative meta-analysis but may be included in qualitative synthesis if published results are available. (e) Inadequate methodology: Studies with critical methodological flaws that preclude valid inference (e.g., lack of any comparison condition, severe data quality issues) are excluded following quality assessment.
<table><caption>**Table 3.3 Inclusion and Exclusion Criteria Using PICOS Framework**</caption><thead><tr><td>PICOS Element</td><td>Inclusion Criteria</td><td>Exclusion Criteria</td></tr></thead><tbody><tr><td>Population</td><td>Human participants of any age, sex, health status, or meditation experience level; Both healthy and clinical populations; Meditation-naive or experienced practitioners</td><td>Animal studies (rodents, primates); Cell line studies; In vitro experiments without human participants</td></tr><tr><td>Intervention</td><td>Meditation (all traditions); Mindfulness-based interventions (MBSR, MBCT); Yoga (asana, pranayama, meditation); Tai chi; Qigong; Relaxation response training; Other contemplative practices; Any duration (acute, short-term, long-term)</td><td>Non-mind-body interventions; Pharmacological interventions alone; Combined meditation + pharmacological where effects cannot be separated</td></tr><tr><td>Comparator</td><td>Wait-list control; Active control (health education, exercise); Pre-intervention baseline; Non-practitioner comparison group</td><td>No comparison condition; Single time-point without baseline or control</td></tr><tr><td>Outcome</td><td>Genome-wide gene expression profiling; Microarray platforms (Affymetrix, Illumina, Agilent); RNA sequencing (RNA-seq); Peripheral blood samples (PBMCs, whole blood); Other human tissue types</td><td>Candidate gene studies only (RT-PCR without genome-wide profiling); Non-transcriptomic outcomes only; DNA methylation without gene expression</td></tr><tr><td>Study Design</td><td>Randomized controlled trials (RCTs); Non-randomized controlled trials; Pre-post intervention studies; Cross-sectional practitioner vs. non-practitioner studies; Minimum n = 10 per group</td><td>Case reports; Case series without quantitative data; Studies with n &lt; 10 per group; Reviews, commentaries, editorials</td></tr></tbody></table>

*Note.* PICOS = Population, Intervention, Comparator, Outcome, Study design; MBSR = Mindfulness-Based Stress Reduction; MBCT = Mindfulness-Based Cognitive Therapy; PBMCs = peripheral blood mononuclear cells; RT-PCR = reverse transcription polymerase chain reaction.

## Study Selection Process

Study selection follows a systematic, multi-stage screening process designed to ensure reliable identification of all eligible datasets while maintaining efficiency. The process involves sequential screening stages with increasing depth of review, independent dual screening to minimize bias, and formal assessment of inter-rater reliability. All screening decisions are documented to enable transparent reporting of the selection process.
## Screening Procedures

The screening process proceeds through three stages: title/abstract screening of literature records, full-text review of potentially eligible publications, and data availability assessment for confirmed eligible studies. For GEO and other repository searches, the initial screening examines dataset titles and descriptions to identify potentially relevant datasets, followed by detailed review of experimental metadata and data quality.

**Stage 1: Title and Abstract Screening.** All records identified through database searches are exported to a reference management system (EndNote or similar) and deduplicated. Two independent reviewers screen titles and abstracts against eligibility criteria. Records are classified as "eligible," "ineligible," or "uncertain." Records classified as eligible or uncertain by either reviewer proceed to full-text review. Disagreements are resolved through discussion; persistent disagreements are resolved by a third reviewer. At this stage, a liberal approach is adopted to minimize false exclusions—records are excluded only if they clearly fail eligibility criteria based on available information.

**Stage 2: Full-Text Review.** Full-text articles are retrieved for all records proceeding from Stage 1. Two independent reviewers examine full texts against eligibility criteria, documenting reasons for exclusion. Studies meeting all eligibility criteria proceed to data availability assessment. Disagreements are resolved through discussion and, if necessary, third-reviewer adjudication. For GEO datasets identified directly through repository searches, the corresponding publication (if available) is retrieved for detailed review of study methods.

**Stage 3: Data Availability Assessment.** For studies meeting eligibility criteria, data availability is verified. For studies with data deposited in GEO or other repositories, the dataset is accessed and examined to confirm that raw or processed data are available and that data
quality is adequate for analysis. For studies without deposited data, corresponding authors are contacted via email to request data sharing. A standardized email template explains the study purpose, data required, and data handling procedures. Follow-up contacts are made at 2-week intervals up to three times. Studies for which data cannot be obtained are excluded from quantitative meta-analysis but included in qualitative synthesis if published results permit.

Inter-Rater Reliability

Inter-rater reliability is formally assessed at each screening stage to evaluate the consistency of eligibility decisions between independent reviewers. Cohen's kappa statistic is calculated to quantify agreement beyond chance (Cohen, 1960). Kappa values are interpreted according to standard guidelines: κ < 0.20 indicates poor agreement, 0.21-0.40 fair agreement, 0.41-0.60 moderate agreement, 0.61-0.80 substantial agreement, and 0.81-1.00 almost perfect agreement (Landis & Koch, 1977).

Prior to beginning formal screening, reviewers complete a calibration exercise to ensure consistent interpretation of eligibility criteria. Both reviewers independently screen a pilot sample of 50 records, followed by discussion of any disagreements and clarification of criteria. This calibration process is repeated until adequate agreement (κ ≥ 0.80) is achieved. Ongoing monitoring of agreement throughout the screening process enables early identification and resolution of any interpretation drift.

All screening decisions, including reasons for exclusion, are recorded in a standardized data extraction form. The complete record of screening decisions enables construction of the PRISMA flow diagram documenting the number of records at each stage of the selection process. This documentation ensures transparency and enables readers to assess the comprehensiveness and reliability of the study selection process.
<figure><img src="image_4.png" /><figcaption>Figure 3.2 PRISMA Flow Diagram (Template)</figcaption></figure>
<table><thead><tr><th colspan="2">INCLUDED</th></tr></thead><tbody><tr><td>Studies included in</td><td>Datasets included in</td></tr><tr><td>qualitative synthesis</td><td>quantitative meta-analysis</td></tr><tr><td>(n = __)</td><td>(n = __)</td></tr></tbody></table>

*Note.* PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This template will be populated with actual numbers during the systematic review process. The flow diagram documents the number of records at each stage of identification, screening, eligibility assessment, and inclusion. Reasons for exclusion at the full-text stage are categorized and quantified.

## Data Extraction

Data extraction involves systematic collection of information from eligible studies and datasets to characterize the evidence base and provide input for meta-analysis. A standardized data extraction form ensures consistent capture of relevant information across studies. Data extraction is conducted by two independent reviewers, with disagreements resolved through discussion and third-reviewer adjudication when necessary. Both study-level characteristics (describing the intervention, population, and design) and data-level information (gene expression data and analysis results) are extracted.

## Data Extraction Form Development

The data extraction form was developed based on Cochrane guidelines for systematic reviews (Higgins & Thomas, 2019), supplemented with items specific to gene expression meta-analysis and mind-body intervention research. The form was piloted on three eligible datasets to assess completeness and usability, with modifications made based on pilot experience. The form
Transcriptional Signature

is implemented as a structured spreadsheet with dropdown menus for categorical items and
validation rules to minimize data entry errors.

The data extraction form is organized into six sections: (a) study identification information, including GEO accession number, publication reference, and author contact details; (b) study design characteristics, including design type, sample size, and timing of assessments; (c) intervention details, including intervention type, duration, frequency, and control condition; (d) population characteristics, including age, sex, health status, and meditation experience; (e) gene expression methodology, including platform, tissue type, and analysis methods; and (f) outcome data, including differential expression results and pathway analysis findings.

For each eligible dataset, raw or processed gene expression data files are downloaded
from GEO or other repositories. Data files are organized in a standardized directory structure
with consistent naming conventions to facilitate automated processing. Metadata extracted from
GEO series and sample records are cross-referenced with information from associated
publications to ensure accuracy and completeness.

## Variables Extracted

Study-level variables include: study identification (GEO accession, publication DOI, first author, year); study design (RCT, quasi-experimental, pre-post, cross-sectional); sample characteristics (total N, N per group, age mean and SD, percent female, health status, meditation experience); intervention characteristics (type, tradition, duration, frequency, total hours, group vs. individual format, instructor qualifications); control condition (wait-list, active control, baseline, non-practitioner); timing of gene expression assessment (pre, post, follow-up, single timepoint); tissue type (whole blood, PBMCs, other); and study quality indicators.
Methodological variables include: gene expression platform (manufacturer, model, probe count); sample processing (RNA extraction method, quality metrics, amplification method); normalization method (RMA, MAS5, RPKM, TPM, other); differential expression analysis method (limma, DESeq2, t-test, other); multiple testing correction (Benjamini-Hochberg FDR, Bonferroni, none); statistical threshold for significance (p-value or q-value cutoff); and covariates included in analysis (age, sex, batch, other).

Outcome variables include: number of differentially expressed genes (up and down); lists of significant genes with effect sizes and p-values; pathway enrichment results (KEGG, GO, Reactome); and study conclusions regarding key pathways affected. For meta-analysis input, gene-level statistics (log fold change, standard error, p-value, adjusted p-value) are extracted for all genes, not only those reaching significance thresholds, to enable comprehensive meta-analysis.
<table><caption>Table 3.4 Data Extraction Variables and Definitions</caption><thead><tr><td>Category</td><td>Variable</td><td>Definition/Values</td><td>Source</td></tr></thead><tbody><tr><td rowspan="4">Study Identification</td><td>GEO Accession</td><td>Unique identifier for GEO Series (GSE#)</td><td>GEO repository</td></tr><tr><td>Publication DOI</td><td>Digital Object Identifier for associated publication</td><td>Publication</td></tr><tr><td>First Author</td><td>Last name of first author</td><td>Publication</td></tr><tr><td>Publication Year</td><td>Year of publication</td><td>Publication</td></tr><tr><td rowspan="3">Study Design</td><td>Design Type</td><td>RCT, quasi-experimental, pre-post, cross-sectional</td><td>Publication methods</td></tr><tr><td>Randomization</td><td>Yes/No; if yes, randomization method</td><td>Publication methods</td></tr><tr><td>Blinding</td><td>Single, double, open-label</td><td>Publication methods</td></tr><tr><td rowspan="5">Population</td><td>Total Sample Size</td><td>Number of participants in gene expression analysis</td><td>Publication/GEO</td></tr><tr><td>Age (Mean, SD)</td><td>Mean age and standard deviation</td><td>Publication</td></tr><tr><td>Sex (% Female)</td><td>Percentage female participants</td><td>Publication</td></tr><tr><td>Health Status</td><td>Healthy, clinical condition (specify)</td><td>Publication</td></tr><tr><td>Meditation Experience</td><td>Naive, some experience, experienced (years)</td><td>Publication</td></tr><tr><td rowspan="5">Intervention</td><td>Intervention Type</td><td>Meditation, MBSR, MBCT, yoga, tai chi, other</td><td>Publication</td></tr><tr><td>Duration</td><td>Total intervention duration in weeks</td><td>Publication</td></tr><tr><td>Session Frequency</td><td>Sessions per week</td><td>Publication</td></tr><tr><td>Total Hours</td><td>Cumulative hours of practice</td><td>Publication</td></tr><tr><td>Control Condition</td><td>Wait-list, active control, baseline, non-practitioner</td><td>Publication</td></tr><tr><td rowspan="6">Methodology</td><td>Platform Manufacturer</td><td>Affymetrix, Illumina, Agilent, other</td><td>GEO GPL</td></tr><tr><td>Platform Model</td><td>Specific array or sequencing model</td><td>GEO GPL</td></tr><tr><td>Tissue Type</td><td>Whole blood, PBMCs, other (specify)</td><td>Publication/GEO</td></tr><tr><td>Normalization Method</td><td>RMA, MAS5, neqc, RPKM, TPM, other</td><td>Publication/GEO</td></tr><tr><td>DE Analysis Method</td><td>limma, DESeq2, t-test, SAM, other</td><td>Publication</td></tr><tr><td>Multiple Testing Correction</td><td>BH-FDR, Bonferroni, none, other</td><td>Publication</td></tr><tr><td rowspan="3">Outcomes</td><td>Number of DEGs (Up)</td><td>Count of significantly upregulated genes</td><td>Publication/analysis</td></tr><tr><td>Number of DEGs (Down)</td><td>Count of significantly downregulated genes</td><td>Publication/analysis</td></tr><tr><td>Gene Statistics</td><td>Log2 fold change, SE, p-value for each gene</td><td>Extracted/calculated</td></tr><tr><td></td><td>Pathway Results</td><td>Enriched pathways</td><td>Publication/analysis</td></tr></tbody></table>
Note. GEO = Gene Expression Omnibus; DOI = Digital Object Identifier; RCT = randomized controlled trial; MBSR = Mindfulness-Based Stress Reduction; MBCT = Mindfulness-Based Cognitive Therapy; PBMCs = peripheral blood mononuclear cells; GPL = GEO Platform; DE = differential expression; RMA = Robust Multi-array Average; MAS5 = Microarray Suite 5.0; neqc = normal-exponential convolution quantile; RPKM = Reads Per Kilobase Million; TPM = Transcripts Per Million; BH-FDR = Benjamini-Hochberg False Discovery Rate; DEGs = differentially expressed genes; SE = standard error; GO = Gene Ontology.

## Quality Assessment

Quality assessment evaluates the methodological rigor and risk of bias in included studies to inform interpretation of findings and sensitivity analyses. For gene expression studies, quality assessment encompasses both traditional study design considerations (randomization, blinding, attrition) and technical quality of genomic analyses (RNA quality, appropriate normalization, statistical methods). A multi-component quality assessment approach is employed, combining established risk of bias tools with criteria specific to functional genomics studies.

## Quality Assessment Tools for Gene Expression Studies

The Cochrane Risk of Bias tool (RoB 2) is adapted for assessing intervention study quality (Sterne et al., 2019). RoB 2 evaluates five domains: randomization process (for RCTs), deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain is rated as "low risk," "some concerns," or "high risk" of bias based on signaling questions. For non-randomized studies, the ROBINS-I tool (Risk of Bias in Non-randomized Studies of Interventions) provides an analogous framework addressing confounding, selection, classification, deviations, missing data, measurement, and reporting (Sterne et al., 2016).

For cross-sectional studies comparing experienced practitioners with non-practitioners, the Newcastle-Ottawa Scale (NOS) adapted for cross-sectional studies is employed (Herzog et
al., 2013). The NOS evaluates selection of study groups, comparability of groups, and ascertainment of outcome/exposure, with points assigned for methodological quality indicators. Studies receive a score from 0 to 10, with higher scores indicating higher quality.

Beyond general study design quality, gene expression studies require assessment of technical and analytical quality. Technical quality assessment examines RNA quality metrics (RNA Integrity Number [RIN], 260/280 ratios), sample handling procedures, hybridization quality (for arrays), and sequencing depth and quality (for RNA-seq). Analytical quality assessment examines appropriateness of normalization methods, batch effect handling, statistical methods for differential expression, and multiple testing correction.

## MINSEQE Guidelines

The Minimum Information about a high-throughput Nucleotide SeQuencing Experiment (MINSEQE) guidelines specify the minimum information that should be reported for sequencing-based experiments to enable interpretation and reproducibility (ENCODE Project Consortium, 2012). Key MINSEQE elements include: general information (experimental design, sample characteristics, protocols); raw data (sequencing platform, read files); processed data (alignments, quantifications); and analysis details (software versions, parameters, statistical methods).

For microarray studies, the analogous MIAME (Minimum Information About a Microarray Experiment) guidelines are applied (Brazma et al., 2001). MIAME specifies that the following should be reported: experimental design, array design, samples (source, characteristics, preparation), hybridization procedures, measurements, and normalization. Compliance with MIAME/MINSEQE is assessed for each included study, and studies with incomplete reporting are flagged for potential quality concerns.
## Risk of Bias Assessment

Risk of bias assessment is conducted independently by two reviewers, with disagreements resolved through discussion. For each study, risk of bias is assessed across multiple domains, and an overall risk of bias judgment (low, moderate, high) is assigned based on the pattern of domain-level assessments. Studies rated as high risk of bias are retained in the primary analysis but are examined in sensitivity analyses to assess their influence on results.

Publication bias and selective reporting bias are assessed at the meta-analysis level using funnel plots and Egger's regression test (Egger et al., 1997). For gene expression meta-analysis, selective reporting may manifest as bias toward reporting genes that reach significance thresholds, which is addressed by using gene-level statistics from all genes (not just those reported as significant) when available from re-analysis of raw data.

<caption>Table 3.5 Quality Assessment Criteria for Gene Expression Studies of Mind-Body</caption>

Interventions

<table><thead><tr><th>Domain</th><th>Assessment Criterion</th><th>Rating Scale</th><th>Indicators</th></tr></thead><tbody><tr><td rowspan="5">Study Design Quality</td><td>Randomization (RCTs)</td><td>Low/Some concerns/High risk</td><td>Adequate sequence generation; allocation concealment; baseline balance</td></tr><tr><td>Control condition adequacy</td><td>Low/Some concerns/High risk</td><td>Appropriate comparator; attention matching; expectancy control</td></tr><tr><td>Blinding of participants</td><td>Low/Some concerns/High risk</td><td>Participants unaware of condition; credibility ratings similar</td></tr><tr><td>Blinding of assessors</td><td>Low/Some concerns/High risk</td><td>Lab personnel blinded to condition; automated analysis</td></tr><tr><td>Attrition and missing data</td><td>Low/Some concerns/High risk</td><td>&lt; 20% attrition; reasons documented; ITT analysis or appropriate handling</td></tr><tr><td>Selective reporting</td><td>Low/Some concerns/High risk</td><td>Protocol available; all outcomes reported; no evidence of selective reporting</td></tr><tr><td>Sample Quality</td><td>RNA Integrity Number</td><td>High (≥8) / Moderate (6-7.9) / RIN values reported;</td><td></td></tr></tbody></table>
Transcriptional Signature

<table><tbody><tr><td></td><td>(RIN)</td><td>Low (&lt;6)</td><td>samples with low RIN excluded or noted</td></tr><tr><td></td><td>RNA concentration and purity</td><td>Adequate/Inadequate/Not reported</td><td>260/280 ratio 1.8-2.0; adequate concentration for platform</td></tr><tr><td></td><td>Sample handling</td><td>Appropriate/Concerning/Not reported</td><td>Consistent protocols; time to processing; storage conditions</td></tr><tr><td rowspan="3">Technical Quality</td><td>Platform appropriateness</td><td>Appropriate/Concerning</td><td>Well-established platform; adequate probe coverage; current annotation</td></tr><tr><td>Batch effects</td><td>Addressed/Not addressed/Not applicable</td><td>Batches documented; balanced across conditions; ComBat or similar applied</td></tr><tr><td>Technical replicates</td><td>Present/Absent</td><td>Replicates included; consistency assessed</td></tr><tr><td rowspan="4">Analytical Quality</td><td>Normalization method</td><td>Appropriate/Questionable/Not reported</td><td>Established method (RMA, DESeq2 normalization); appropriately applied</td></tr><tr><td>Differential expression method</td><td>Appropriate/Questionable</td><td>Established method (limma, DESeq2); appropriate for design</td></tr><tr><td>Multiple testing correction</td><td>Appropriate/Questionable/None</td><td>FDR control (BH, qvalue); Bonferroni for few comparisons</td></tr><tr><td>Covariate adjustment</td><td>Appropriate/Partial/None</td><td>Age, sex, batch included; other relevant covariates considered</td></tr><tr><td rowspan="2">Reporting Quality</td><td>MIAME/MINSEQE compliance</td><td>Full/Partial/Non-compliant</td><td>All required elements reported; data deposited; metadata complete</td></tr><tr><td>Analysis reproducibility</td><td>Reproducible/Partial/Not reproducible</td><td>Code available; software versions reported; parameters documented</td></tr></tbody></table>

Note. RCTs = randomized controlled trials; ITT = intention-to-treat; RIN = RNA Integrity Number; RMA = Robust Multi-array Average; FDR = false discovery rate; BH = Benjamini-Hochberg; MIAME = Minimum Information About a Microarray Experiment; MINSEQE = Minimum Information about a high-throughput Nucleotide SeQuencing Experiment. Quality assessment is conducted independently by two reviewers across all domains.

Data Analysis

The data analysis plan encompasses a comprehensive bioinformatics pipeline for individual dataset analysis followed by gene-level and pathway-level meta-analysis. This section describes the analytical methods in detail, including preprocessing and normalization, differential
expression analysis, pathway enrichment, and meta-analytic statistical approaches. All analyses
are conducted using established, peer-reviewed methods implemented in R/Bioconductor, with
complete code documentation to ensure reproducibility.

Bioinformatics Pipeline Overview

The bioinformatics pipeline proceeds through four major phases: (a) data acquisition and quality control, (b) preprocessing and normalization, (c) individual dataset differential expression analysis, and (d) meta-analysis and pathway enrichment. Each phase employs standardized methods applied consistently across datasets to enable valid cross-study comparison. The pipeline is implemented in R version 4.3 or later using Bioconductor packages maintained by the scientific community.

Data acquisition involves downloading raw or processed data from GEO using the GEOquery package (Davis & Meltzer, 2007). Raw data (CEL files for Affymetrix, IDAT files for Illumina, FASTQ files for RNA-seq) are preferred when available, as re-processing with standardized methods enhances cross-study comparability. When only processed data are available, the provided normalized expression matrices are used with careful attention to the normalization method applied. Sample annotation and phenotype data are extracted from GEO series records and supplemented with information from associated publications.

Normalization and Preprocessing

Preprocessing methods are selected based on the gene expression platform. For
Affymetrix GeneChip microarrays, raw CEL files are processed using the Robust Multi-array
Average (RMA) algorithm implemented in the affy and oligo Bioconductor packages (Irizarry et
al., 2003). RMA performs background correction, quantile normalization, and probe-level
summarization in an integrated framework that has been shown to produce accurate and
reproducible expression estimates. The resulting log₂-transformed expression values are used for downstream analysis.

For Illumina BeadArray platforms, the limma package with neqc (normexp background correction with quantile normalization using control probes) is applied (Shi et al., 2010).

Illumina arrays include control probes that enable robust background estimation and normalization. Probes with detection p-values > 0.05 in more than 50% of samples are filtered as unreliably detected. For other microarray platforms (e.g., Agilent), platform-appropriate preprocessing methods from the limma package are applied based on manufacturer recommendations and published best practices.

For RNA sequencing data, raw count matrices are processed using DESeq2 (Love et al., 2014). DESeq2 applies median-of-ratios normalization to account for library size differences and composition bias. Genes with fewer than 10 counts across all samples are filtered as lowly expressed. For visualization and certain downstream analyses, variance-stabilizing transformation (VST) is applied to produce approximately homoscedastic expression values on the log₂ scale. When raw FASTQ files are available, reads are aligned to the human reference genome (GRCh38) using STAR aligner (Dobin et al., 2013), and read counts are generated using featureCounts (Liao et al., 2014).

Probe-to-gene mapping uses current annotation packages from Bioconductor corresponding to each platform. When multiple probes map to the same gene, the probe with highest mean expression or lowest p-value (depending on analysis stage) is selected as representative. This approach ensures that each gene appears once in downstream analyses while retaining the most informative measurement.
## Differential Expression Analysis

Differential expression analysis identifies genes with statistically significant changes in expression between conditions (e.g., post-intervention vs. pre-intervention, meditators vs. controls). The limma package (Ritchie et al., 2015) is used for microarray data and limma-voom or DESeq2 for RNA-seq data. These methods employ empirical Bayes moderation of standard errors, which improves power and reliability when sample sizes are small by borrowing information across genes.

For pre-post intervention designs, paired analyses are conducted using the duplicateCorrelation function in limma to account for within-subject correlation. The statistical model includes time (pre vs. post) as the primary factor of interest, with additional covariates (age, sex, batch) included when available and when sample size permits. For between-group designs (e.g., RCTs comparing intervention vs. control), unpaired analyses compare groups at post-intervention or compare change scores (post minus pre) between groups.

Multiple testing correction is essential given the thousands of simultaneous tests performed in gene expression analysis. The Benjamini-Hochberg false discovery rate (FDR) procedure is applied, with genes reaching $q < 0.05$ considered statistically significant (Benjamini & Hochberg, 1995). For meta-analysis input, gene-level statistics ($\log_2$ fold change, standard error, nominal p-value) are retained for all genes regardless of significance, enabling comprehensive meta-analysis that is not limited to genes significant in individual studies.

## Gene Set Enrichment Analysis (GSEA)

Gene Set Enrichment Analysis (GSEA) tests whether predefined sets of genes show coordinated enrichment among up- or down-regulated genes (Subramanian et al., 2005). Unlike approaches that require dichotomizing genes into "significant" and "non-significant" lists, GSEA
Transcriptional Signature

uses the full ranked list of genes ordered by their differential expression statistic, potentially
detecting modest but coordinated changes across gene set members.

For GSEA, genes are ranked by the signed statistic from differential expression analysis
(e.g., t-statistic or log₂ fold change × -log₁₀ p-value). Gene sets are obtained from the Molecular
Signatures Database (MSigDB; Liberzon et al., 2015), including: KEGG pathways (metabolic
and signaling pathways), Reactome pathways (biological processes), Gene Ontology terms
(biological processes, molecular functions, cellular components), and curated gene signatures
(including immune-related signatures).

GSEA is implemented using the fgsea package in R (Korotkevich et al., 2021), which employs an efficient algorithm for calculating enrichment statistics and p-values. Gene sets with fewer than 15 or more than 500 genes are excluded to ensure stable enrichment estimation.

Enrichment is considered significant at FDR q < 0.05 with |NES| > 1.5, where NES is the normalized enrichment score.

Pathway Analysis (KEGG, GO)

Complementing GSEA, over-representation analysis (ORA) tests whether lists of
significantly differentially expressed genes contain more genes from specific pathways than
expected by chance. ORA is conducted separately for upregulated and downregulated gene lists
using the clusterProfiler package (Wu et al., 2021). Hypergeometric tests compare the proportion
of DEGs in each pathway to the proportion expected by chance, with multiple testing correction
across pathways.

Pathway databases queried include: KEGG (Kyoto Encyclopedia of Genes and Genomes) for metabolic and signaling pathways (Kanehisa et al., 2021), Gene Ontology Biological Processes for functional categories (Gene Ontology Consortium, 2021), Reactome for detailed
molecular pathways (Gillespie et al., 2022), and curated inflammatory and immune gene sets.

Results are visualized using dot plots showing enrichment significance and gene ratios for top pathways.

A priori hypothesized pathways receive particular attention in the analysis. Based on the theoretical framework and literature review, the following pathways are specifically examined: NF-κB signaling pathway, Type I interferon response, glucocorticoid receptor signaling, inflammatory response pathways, and antiviral defense. Custom gene sets for these pathways are compiled from ChIP-seq studies, literature curation, and established databases including the Interferome (Rusinova et al., 2013) and NF-κB target gene compilations.

Meta-Analysis Statistical Methods

Gene-level meta-analysis combines results across studies to identify genes showing consistent evidence of differential expression. Fisher's combined probability test is the primary method, combining p-values across studies for each gene: $\chi^2 = -2 \sum \ln(p_i)$, which follows a chi-square distribution with 2k degrees of freedom under the null hypothesis. This method tests whether a gene shows evidence of differential expression in any direction across studies, with subsequent examination of effect direction consistency for significant genes.

For genes showing consistent direction of effect across studies (all positive or all negative log fold changes), random effects meta-analysis of effect sizes is additionally conducted using the metafor package (Viechtbauer, 2010). The DerSimonian-Laird estimator is used to estimate between-study variance $\tau^2$, and random effects weights account for both within-study and between-study variance. Meta-analytic effect sizes (pooled log$_2$ fold changes) with 95% confidence intervals are reported.
Pathway-level meta-analysis examines whether patterns of pathway enrichment are consistent across studies. For each pathway, enrichment scores or significance values from individual study GSEA analyses are combined to assess overall evidence for pathway involvement. Meta-analytic GSEA can also be conducted by ranking genes according to meta-analytic statistics and applying GSEA to the meta-analytic ranking.

## Heterogeneity Assessment

Heterogeneity in effect sizes across studies is formally assessed and explored. The I² statistic quantifies the percentage of total variation across studies that is attributable to heterogeneity rather than sampling error (Higgins et al., 2003). I² values of 25%, 50%, and 75% correspond to low, moderate, and high heterogeneity, respectively. Cochran's Q test provides a statistical test for the presence of heterogeneity, though it has limited power when the number of studies is small.

Sources of heterogeneity are explored through subgroup analyses and meta-regression. Pre-specified subgroup analyses examine whether effects differ by: intervention type (meditation, MBSR, yoga, other), intervention duration (acute vs. extended), population (healthy vs. clinical), study design (RCT vs. pre-post), and technical factors (platform type). Meta-regression models the relationship between study-level covariates and effect sizes, enabling exploration of continuous moderators such as total practice hours or participant age.

Sensitivity analyses assess the robustness of findings to analytic choices and potential biases. Leave-one-out analyses examine whether any single study exerts undue influence on meta-analytic results. Analyses excluding studies rated as high risk of bias test whether quality concerns affect conclusions. Analyses using alternative meta-analytic methods (e.g., fixed effects, alternative random effects estimators) assess sensitivity to methodological assumptions.
## Software and Tools Used

All analyses are conducted using R version 4.3 or later (R Core Team, 2023) with Bioconductor version 3.18 or later for genomics packages. Analysis scripts are version-controlled using Git and made publicly available via GitHub repository to enable complete reproducibility. The computational environment is documented using renv for R package version tracking and Docker containers for full environment reproducibility.

<figure><img src="image_5.png" /><figcaption>Figure 3.3 Data Analysis Workflow/Pipeline for Systematic Review and Meta-Analysis</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

Note. The pipeline proceeds through five phases: data acquisition, preprocessing/normalization, individual dataset analysis, meta-analysis, and output generation. Platform-specific methods are applied for Affymetrix arrays, Illumina arrays, and RNA-seq data. Meta-analysis includes both gene-level (Fisher's combined test, random effects) and pathway-level (GSEA, over-representation analysis) approaches. All code is documented and publicly available.
<table><caption>Table 3.6 Software and Bioinformatics Tools</caption><thead><tr><th>Category</th><th>Tool/Package</th><th>Version</th><th>Purpose</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="3">Computing Environment</td><td>R</td><td>≥ 4.3</td><td>Statistical computing and graphics</td><td>R Core Team, 2023</td></tr><tr><td>Bioconductor</td><td>≥ 3.18</td><td>Genomics data analysis packages</td><td>Huber et al., 2015</td></tr><tr><td>RStudio</td><td>2023.12+</td><td>Integrated development environment</td><td>Posit, 2023</td></tr><tr><td rowspan="2">Data Acquisition</td><td>GEOquery</td><td>2.68+</td><td>Download data from GEO repository</td><td>Davis & Meltzer, 2007</td></tr><tr><td>SRAdb</td><td>1.62+</td><td>Access Sequence Read Archive</td><td>Zhu et al., 2013</td></tr><tr><td rowspan="4">Preprocessing</td><td>affy</td><td>1.78+</td><td>Affymetrix array preprocessing</td><td>Gautier et al., 2004</td></tr><tr><td>oligo</td><td>1.64+</td><td>Affymetrix/NimbleGen preprocessing</td><td>Carvalho & Irizarry, 2010</td></tr><tr><td>STAR</td><td>2.7.10+</td><td>RNA-seq read alignment</td><td>Dobin et al., 2013</td></tr><tr><td>featureCounts</td><td>2.0+</td><td>Read counting for RNA-seq</td><td>Liao et al., 2014</td></tr><tr><td rowspan="4">Normalization</td><td>limma</td><td>3.56+</td><td>Linear modeling, normalization</td><td>Ritchie et al., 2015</td></tr><tr><td>DESeq2</td><td>1.40+</td><td>RNA-seq differential expression</td><td>Love et al., 2014</td></tr><tr><td>sva (ComBat)</td><td>3.48+</td><td>Batch effect correction</td><td>Leek et al., 2012</td></tr><tr><td>limma</td><td>3.56+</td><td>Empirical Bayes DE analysis</td><td>Ritchie et al., 2015</td></tr><tr><td rowspan="2">Differential Expression</td><td>DESeq2</td><td>1.40+</td><td>Negative binomial DE for RNA-seq</td><td>Love et al., 2014</td></tr><tr><td>fgsea</td><td>1.26+</td><td>Fast gene set enrichment analysis</td><td>Korotkevich et al., 2021</td></tr><tr><td rowspan="4">Pathway Analysis</td><td>clusterProfiler</td><td>4.8+</td><td>Over-representation analysis</td><td>Wu et al., 2021</td></tr><tr><td>msigdb</td><td>7.5+</td><td>Access MSigDB gene sets</td><td>Dolgalev, 2022</td></tr><tr><td>org.Hs.eg.db</td><td>3.17+</td><td>Human gene annotation</td><td>Carlson, 2023</td></tr><tr><td>metafor</td><td>4.4+</td><td>Meta-analysis models</td><td>Viechtbauer, 2010</td></tr><tr><td rowspan="2">Meta-Analysis</td><td>meta</td><td>6.5+</td><td>Meta-analysis functions</td><td>Balduzzi et al., 2019</td></tr><tr><td>STRING database</td><td>12.0</td><td>Protein-protein interactions</td><td>Szklarczyk et al., 2023</td></tr><tr><td rowspan="3">Visualization</td><td>Cytoscape</td><td>3.10+</td><td>Network visualization</td><td>Shannon et al., 2003</td></tr><tr><td>ggplot2</td><td>3.4+</td><td>Data visualization</td><td>Wickham, 2016</td></tr><tr><td>pheatmap</td><td>1.0+</td><td>Heatmap visualization</td><td>Kolde, 2019</td></tr><tr><td rowspan="3">Reproducibility</td><td>EnhancedVolcano</td><td>1.18+</td><td>Volcano plots</td><td>Blighe et al., 2023</td></tr><tr><td>renv</td><td>1.0+</td><td>Package version management</td><td>Ushey, 2023</td></tr><tr><td>Docker</td><td>24.0+</td><td>Containerized environment</td><td>Docker Inc., 2023</td></tr><tr><td></td><td>Git/GitHub</td><td>2.42+</td><td>Version control and</td><td>Git Project, 2023</td></tr></tbody></table>
Note. Version numbers indicate minimum required versions; analyses will use current versions available at time of analysis. GEO = Gene Expression Omnibus; SRA = Sequence Read Archive; DE = differential expression; MSigDB = Molecular Signatures Database. All R packages are available from CRAN or Bioconductor repositories.

## Ethical Considerations

This section addresses ethical considerations relevant to the conduct of this systematic review and meta-analysis. The use of publicly available, de-identified data from established repositories raises specific ethical questions regarding data provenance, appropriate use, and responsibilities of secondary analysts. Institutional Review Board (IRB) considerations, data privacy protections, and adherence to data sharing policies are discussed.

## Use of Publicly Available Data

This study utilizes exclusively publicly available gene expression data from the Gene Expression Omnibus (GEO) and other open repositories. The data in these repositories have been made publicly available by the original investigators following publication of their research.

Public data sharing in genomics is encouraged by funding agencies (including NIH) and journals as a condition of publication, based on the principle that publicly funded research data should be accessible for secondary analysis to maximize scientific value and accelerate discovery (Wilkinson et al., 2016).

The original investigators who deposited data in GEO obtained informed consent from research participants as part of the primary data collection, and their studies received approval from appropriate institutional review boards. GEO submission guidelines require that depositors certify compliance with applicable human subjects regulations. Data deposited in GEO undergo NCBI review to ensure they do not contain information that could directly identify individuals.
The de-identification of genomic data in GEO is consistent with Common Rule provisions
allowing exemption of research involving existing, publicly available data that are de-identified.

The terms of use for GEO explicitly permit secondary analysis of deposited data for
research purposes. Users are encouraged to cite original data sources and publications when
using GEO data. This study adheres to these expectations by maintaining complete records of
data sources, citing original publications in results reporting, and acknowledging the
contributions of primary investigators who generated and shared data.

IRB Considerations

Research involving existing, publicly available, de-identified data typically qualifies for exemption from IRB review under 45 CFR 46.104(d)(4), which exempts research involving the collection or study of existing data if the information is recorded in such a manner that subjects cannot be identified. Prior to initiating data extraction, an exemption determination request was submitted to the [University Name] Institutional Review Board. The IRB confirmed that this study qualifies for exemption because: (a) the research involves only analysis of existing, publicly available datasets; (b) the data are de-identified and cannot be linked to individual participants; and (c) no contact with human subjects occurs as part of this research.

The exemption determination applies to analysis of data available in public repositories.
For studies identified through literature searches where data are not publicly deposited, author
contact requests data sharing consistent with journal and funder data sharing policies. Such data
sharing arrangements are governed by the original consent and IRB approvals under which data
were collected. Authors are not asked to share identifiable information, and any data received are
handled according to data use agreements specifying appropriate protections.
## Data Privacy

Although gene expression data from GEO are de-identified, appropriate data handling practices are maintained to protect any residual privacy risks. Data files downloaded from GEO are stored on secure, password-protected institutional servers with access limited to authorized study personnel. Data are not combined with other datasets in ways that could enable re-identification. Results are reported at the aggregate level (gene-level and pathway-level summaries) without individual-level data that could potentially enable identification.

The study adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable) for responsible data stewardship (Wilkinson et al., 2016). Analysis code and aggregated results are made publicly available to support reproducibility, while respecting any restrictions specified in original data sharing agreements. The study protocol registered on PROSPERO documents the planned analyses, reducing risks of selective reporting and promoting transparency.

## Methodological Limitations

While the systematic review and meta-analysis methodology employed in this study represents a rigorous approach to evidence synthesis, several methodological limitations must be acknowledged. This section identifies anticipated limitations and describes mitigation strategies where available. Acknowledging limitations enhances the credibility of findings by enabling appropriate interpretation and identifying directions for future research.

## Anticipated Limitations

Heterogeneity across studies presents a fundamental challenge for meta-analysis of gene expression data from mind-body intervention studies. Sources of heterogeneity include: differences in intervention type, duration, and intensity; variation in participant characteristics (age, health status, meditation experience); diversity of gene expression platforms and technical
protocols; and differences in analysis methods and statistical thresholds applied by original
investigators. While meta-analytic methods can accommodate and explore heterogeneity,
substantial between-study variation may limit the ability to identify consistent transcriptional
signatures and may reduce confidence in pooled effect estimates.

Publication and reporting bias may affect the evidence base. Studies with significant or
expected findings may be more likely to be published and to deposit data in repositories
compared to studies with null or unexpected results. Additionally, published studies may
selectively report genes or pathways that reached significance, potentially inflating estimates of
effect magnitude. The use of raw data re-analysis, when available, partially mitigates reporting
bias by enabling examination of all genes regardless of what was reported, but publication bias
affecting which studies are available cannot be fully addressed.

Platform and technical differences between studies create challenges for data
harmonization. Different microarray platforms measure different sets of genes/probes with
varying sensitivity and specificity. RNA-seq and microarray technologies differ in dynamic
range, sensitivity, and quantification approach. While meta-analytic methods combining p-values
rather than effect sizes are robust to some cross-platform differences, genuine biological signals
may be obscured by technical variation, and platform-specific artifacts may be mistaken for
biological effects.

The field of meditation gene expression research is relatively young, and the number of
available datasets may be limited. Small numbers of studies reduce statistical power for meta-
analysis and limit the ability to explore heterogeneity through subgroup analyses or meta-
regression. If fewer than the anticipated 8-15 datasets are identified, some planned analyses may
not be feasible, and confidence in findings may be reduced accordingly.
The use of peripheral blood as the primary tissue type, while practical for human studies, provides only indirect information about gene expression changes in target tissues of interest (e.g., brain, immune organs). Gene expression changes in circulating immune cells may not fully reflect changes occurring in other tissues relevant to meditation effects. However, peripheral blood gene expression has been shown to correlate with and reflect systemic physiological states, and it remains the most feasible tissue for repeated sampling in human intervention studies.

## Mitigation Strategies

Several strategies are employed to mitigate the impact of anticipated limitations. To address heterogeneity, multiple complementary meta-analytic approaches are applied (Fisher's method, random effects, GSEA) that differ in their assumptions and sensitivity to heterogeneity. Subgroup analyses and meta-regression explore sources of heterogeneity, and sensitivity analyses assess robustness of findings to exclusion of outlier studies or studies with high risk of bias.

To address publication and reporting bias, comprehensive search strategies include unpublished data sources, author contact for unreported data, and examination of deposited data regardless of publication status. Funnel plots and statistical tests assess evidence for publication bias at the meta-analysis level. Re-analysis of raw data, when available, enables examination of all genes independent of original reporting decisions.

To address platform differences, standardized preprocessing and analysis pipelines are applied to raw data when available, reducing cross-study variation introduced by heterogeneous original analysis approaches. Meta-analytic methods combining significance evidence (Fisher's method) are inherently more robust to platform differences than methods combining effect sizes.
Sensitivity analyses comparing results across platform types assess whether platform drives observed patterns.

Transparency and reproducibility practices enhance the credibility and utility of findings despite limitations. Complete documentation of methods, public availability of analysis code, and registration of the study protocol enable readers to assess the appropriateness of methods and to replicate or extend analyses. Explicit acknowledgment of limitations in reporting enables appropriate interpretation of findings.

## Chapter Summary

This chapter has presented a comprehensive methodological framework for conducting a systematic review and meta-analysis of gene expression data examining transcriptional changes following mind-body interventions. The methodology is designed to rigorously address the research questions derived from the QPNIT theoretical framework: whether a consistent transcriptional signature characterizes meditation response, whether pro-inflammatory pathways are downregulated, whether antiviral pathways are selectively preserved, and how the meditation signature compares to disease-associated inflammatory profiles.

The research design combines systematic review methodology following PRISMA guidelines with meta-analytic approaches specifically developed for high-dimensional gene expression data. The primary data source is the Gene Expression Omnibus (GEO), supplemented by ArrayExpress, SRA, and literature database searches to ensure comprehensive identification of relevant datasets. Explicit eligibility criteria using the PICOS framework define the scope of included studies: human participants receiving mind-body interventions with genome-wide gene expression outcomes measured using microarray or RNA sequencing technologies.
The comprehensive search strategy employs controlled vocabulary and free-text terms across multiple databases, with no date or language restrictions. Study selection proceeds through systematic title/abstract screening and full-text review by independent dual reviewers, with inter-rater reliability formally assessed. Data extraction captures study characteristics, intervention details, population demographics, methodological parameters, and gene expression outcomes using standardized forms. Quality assessment addresses both study design quality (using Cochrane RoB 2, ROBINS-I, and Newcastle-Ottawa Scale as appropriate) and technical quality of genomic analyses (RNA quality, normalization, statistical methods).

The bioinformatics pipeline encompasses standardized preprocessing and normalization methods appropriate for each platform type (RMA for Affymetrix, neqc for Illumina, DESeq2 for RNA-seq), differential expression analysis using empirical Bayes methods, and multiple testing correction using Benjamini-Hochberg FDR. Meta-analysis proceeds at both gene level (Fisher's combined probability test, random effects meta-analysis) and pathway level (GSEA, over-representation analysis) using established databases including KEGG, Gene Ontology, and Reactome. Heterogeneity is assessed using I² statistics and explored through subgroup analyses and meta-regression. All analyses are implemented in R/Bioconductor with complete code documentation to ensure reproducibility.

Ethical considerations address the use of publicly available, de-identified data from GEO, IRB exemption for analysis of existing data, and data privacy protections. Methodological limitations including between-study heterogeneity, potential publication bias, platform differences, and limitations of peripheral blood tissue are acknowledged, with mitigation strategies described. The transparent, reproducible methodology presented in this chapter provides a rigorous foundation for addressing the research questions and testing the hypotheses
Transcriptional Signature

that will advance understanding of the transcriptional mechanisms underlying mind-body
intervention effects on inflammatory gene expression.

The findings from this systematic review and meta-analysis will contribute to the scientific literature by providing the first comprehensive synthesis of gene expression data across meditation studies. Identification of a convergent transcriptional signature would advance mechanistic understanding of how contemplative practices influence cellular processes, provide potential biomarkers for monitoring intervention response, and inform clinical applications of mind-body interventions for inflammatory conditions. The methodology described in this chapter establishes the procedural framework for generating rigorous, reproducible evidence to address these important questions.
# CHAPTER 4: RESULTS

## Introduction

This chapter presents the findings from the systematic review and meta-analysis examining the transcriptional signatures of mind-body interventions on inflammatory gene expression. The results are organized to address the research questions and hypotheses outlined in Chapter 1, following the methodological framework detailed in Chapter 3. This chapter begins with the study selection process, proceeds through the characteristics of included studies, presents the differential expression analysis results for each study, describes the pathway and gene set enrichment analyses, and concludes with the meta-analytic synthesis across studies.

The presentation of results follows APA 7th edition guidelines and the PRISMA 2020 statement for reporting systematic reviews. All statistical analyses were conducted using R statistical software with established bioinformatics packages for gene expression analysis. Effect sizes are reported as log2 fold changes, with positive values indicating upregulation following mind-body intervention and negative values indicating downregulation. Statistical significance was evaluated at both nominal (p < .05) and stringent (p < .01) thresholds, with false discovery rate (FDR) correction applied for multiple comparison adjustment.

## Organization of This Chapter

This chapter is organized into seven main sections. First, the study selection results describe the systematic search process and PRISMA flow. Second, the characteristics of included studies provide detailed information about each GEO dataset meeting inclusion criteria. Third, the differential expression analysis results present findings from each individual study. Fourth,
the gene set enrichment and pathway analysis results examine biological themes. Fifth, the meta-
analysis results synthesize evidence across studies. Sixth, a summary of key findings integrates
the results in relation to the QPNIT framework. Finally, the chapter summary provides an
overview of the main findings.

Study Selection Results

Database Search Results

The systematic search of the Gene Expression Omnibus (GEO) database and PubMed literature identified a total of 38 potentially relevant records. The GEO database search using terms related to meditation, mindfulness, relaxation response, yoga, and mind-body interventions yielded 13 datasets. An additional 25 records were identified through the PubMed literature search examining publications on mind-body interventions and gene expression in blood or peripheral blood mononuclear cell (PBMC) samples.

After removing duplicate records, 30 unique datasets remained for screening. The initial
screening phase excluded 15 records that did not involve gene expression profiling or did not
examine mind-body interventions. The remaining 15 datasets underwent full-text assessment for
eligibility against the pre-specified inclusion and exclusion criteria detailed in Chapter 3.

PRISMA Flow Diagram

Figure 1 presents the PRISMA 2020 flow diagram illustrating the study selection process.
Of the 15 datasets assessed for eligibility, 12 were excluded for the following reasons: five
datasets involved non-human subjects (animal models or cell lines), two datasets used tissue
samples other than blood or PBMCs (brain tissue and saliva), one dataset employed a cross-
sectional design without pre-post intervention assessment, and four datasets examined
interventions not classified as mind-body practices under the operational definitions employed in
this review.

PRISMA 2020 Flow Diagram

Mind-Body Interventions and Gene Expression Systematic Review

<figure><img src="image_12.png" /></figure>
**Figure 4.1.** PRISMA 2020 flow diagram illustrating the systematic search and study selection process for the meta-analysis of mind-body interventions and gene expression.

## Studies Included and Excluded

The systematic search and selection process resulted in three GEO datasets meeting all inclusion criteria for the meta-analysis. These datasets represent three distinct types of mind-body interventions: the Relaxation Response training program (GSE10041), Mindfulness Awareness Practice (GSE108215), and Sudarshan Kriya Yoga (GSE44777). Together, these studies examined gene expression changes in 236 blood or PBMC samples from human participants.

<table><caption>**Table 4.1.** PRISMA Study Selection Summary</caption><thead><tr><td>Selection Stage</td><td>Number of Records</td><td>Notes</td></tr></thead><tbody><tr><td>GEO database search</td><td>13</td><td>Search terms: meditation, mindfulness, yoga, relaxation response</td></tr><tr><td>PubMed literature search</td><td>25</td><td>Mind-body + gene expression + blood/PBMC</td></tr><tr><td>Records after deduplication</td><td>30</td><td>Duplicate datasets removed</td></tr><tr><td>Records screened</td><td>30</td><td>Title/abstract screening</td></tr><tr><td>Full-text assessed</td><td>15</td><td>Eligibility assessment against inclusion criteria</td></tr><tr><td>Studies excluded</td><td>12</td><td>Non-human (5), not blood (2), no pre-post (1), not mind-body (4)</td></tr><tr><td>Studies included in meta-analysis</td><td>3</td><td>GSE10041, GSE108215,</td></tr></tbody></table>
<table><tr><td></td><td></td><td>GSE44777</td></tr></table>

Note. Numbers represent datasets at each stage of the systematic review process.

## Characteristics of Included Studies

### Overview of GEO Datasets

Three GEO datasets met all inclusion criteria and were included in the systematic review and meta-analysis. Table 2 presents the characteristics of these included datasets. The studies varied in their intervention type, duration, study population, and sample size, providing heterogeneous but complementary perspectives on the transcriptional effects of mind-body practices.

<table><caption>Table 4.2 Characteristics of Included GEO Datasets</caption><thead><tr><th>GEO ID</th><th>First Author</th><th>Year</th><th>Intervention</th><th>Duration</th><th>Sample Type</th><th>Platform</th><th>N Total</th><th>Population</th></tr></thead><tbody><tr><td>GSE10041</td><td>Dusek JA</td><td>2008</td><td>Relaxation Response</td><td>8 weeks</td><td>Whole blood</td><td>Affymetrix HG-U133 Plus 2</td><td>72</td><td>Healthy adults</td></tr><tr><td>GSE108215</td><td>Unknown</td><td>2018</td><td>Mindfulness Awareness Practice</td><td>8 weeks</td><td>Blood</td><td>Affymetrix HG-U133 Plus 2</td><td>86</td><td>Older adults with MCI</td></tr><tr><td>GSE44777</td><td>Qu S</td><td>2013</td><td>Sudarshan Kriya Yoga</td><td>4 days</td><td>PBMCs</td><td>Illumina HumanWG-6 v3</td><td>78</td><td>Healthy adults</td></tr></tbody></table>

Note. MCI = mild cognitive impairment; PBMC = peripheral blood mononuclear cells.
## GSE10041: Relaxation Response Training

The first included dataset (GSE10041) examined genomic changes following an 8-week Relaxation Response (RR) training program (Dusek et al., 2008; PMID: 18596974). This landmark study, conducted at the Benson-Henry Institute for Mind Body Medicine at Massachusetts General Hospital, investigated the molecular mechanisms underlying the therapeutic effects of RR elicitation. The Relaxation Response, originally described by Herbert Benson, is a physiological state characterized by reduced sympathetic nervous system activity and is elicited through various mind-body practices including meditation, yoga, and repetitive prayer.

The study employed whole blood samples from 72 participants across three groups: novice practitioners at baseline (N1), novice practitioners after 8 weeks of RR training (N2), and long-term RR practitioners (M). Gene expression was profiled using the Affymetrix Human Genome U133 Plus 2.0 array, which interrogates over 54,000 probe sets representing approximately 47,000 transcripts. For the present meta-analysis, the comparison of interest was post-training (N2) versus baseline (N1) in the novice group, examining the acute effects of RR training on gene expression.

## GSE108215: Mindfulness Awareness Practice

The second included dataset (GSE108215) examined the effects of Mindfulness Awareness Practice (MAP) in older adults with mild cognitive impairment (MCI). This randomized controlled trial compared an 8-week MAP intervention to a Health Education Program (HEP) active control condition. The study included 86 participants, with 43 randomized to each condition.
Blood samples were collected before and after the 8-week intervention period, and gene expression was profiled using the Affymetrix Human Genome U133 Plus 2.0 array. The MAP intervention was based on the Mindfulness-Based Stress Reduction (MBSR) program developed by Jon Kabat-Zinn, adapted for older adults with cognitive concerns. The present analysis examined pre-to-post changes in gene expression in the MAP group, providing evidence on the transcriptional effects of mindfulness training in a clinical population with heightened inflammation risk.

## GSE44777: Sudarshan Kriya Yoga

The third included dataset (GSE44777) examined rapid gene expression changes following intensive yoga practice (Qu et al., 2013; PMID: 23613970). This study investigated the effects of Sudarshan Kriya and Related Practices (SK&P), a comprehensive yogic breathing program that includes specific rhythmic breathing patterns, stretching exercises, and meditation. The study employed a crossover design comparing gene expression before and after a 4-day intensive yoga intervention.

Peripheral blood mononuclear cells (PBMCs) were collected from 78 healthy adult participants, and gene expression was profiled using the Illumina HumanWG-6 v3.0 Expression BeadChip, which interrogates approximately 48,000 transcripts. This study provides unique insights into the rapid transcriptional changes that can occur following intensive mind-body practice, complementing the longer-duration interventions examined in the other included studies.

## Quality Assessment

Quality assessment of the included studies was conducted using criteria adapted from the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias tool for
randomized trials. All three studies demonstrated adequate sample sizes for gene expression
analyses, employed validated genome-wide profiling platforms, and provided sufficient
methodological detail for replication. The GSE10041 and GSE44777 studies were published in
peer-reviewed journals with detailed supplementary methods, while the GSE108215 dataset was
available through GEO with associated metadata.

Potential sources of bias included the lack of randomization in GSE10041 (pre-post
design without control group), the clinical population in GSE108215 (MCI patients may have
altered baseline gene expression), and the short intervention duration in GSE44777 (4 days vs. 8
weeks). These limitations were considered in the interpretation of results and are addressed in the
Discussion chapter.

Differential Expression Analysis Results

Overview of Differential Expression Findings

Differential expression analysis was conducted separately for each of the three included
studies using standardized statistical methods. Table 3 presents a summary of the differential
expression results across studies. The analyses revealed modest but detectable transcriptional
changes following mind-body interventions, with effect sizes consistent with previous literature
on behavioral and psychological interventions.
<table><caption>Table 4.3 Summary of Differentially Expressed Genes by Study</caption><thead><tr><th>Study</th><th>Comparison</th><th>Total Probes</th><th>Sig. (p &lt; .05)</th><th>Sig. (p &lt; .01)</th><th>Upregulated</th><th>Downregulated</th><th>Mean |log2FC|</th></tr></thead><tbody><tr><td>GSE10041</td><td>Post-training vs. Baseline</td><td>54,675</td><td>1,457 (2.7%)</td><td>181 (0.3%)</td><td>789</td><td>668</td><td>0.19</td></tr><tr><td>GSE108215</td><td>MAP Post vs. Pre</td><td>54,675</td><td>1,968 (3.6%)</td><td>280 (0.5%)</td><td>1,085</td><td>883</td><td>0.19</td></tr><tr><td>GSE44777</td><td>After vs. Before Yoga</td><td>48,803</td><td>2,165 (4.4%)</td><td>420 (0.9%)</td><td>1,001</td><td>1,164</td><td>0.04</td></tr></tbody></table>

Note. Sig. = significant; log2FC = log2 fold change. Significance determined at nominal p-values without FDR correction.

Across all three studies, mind-body interventions produced detectable changes in gene expression at the nominal significance level (p < .05), with 2.7% to 4.4% of probes showing differential expression. The proportion of differentially expressed genes increased at more stringent significance thresholds (p < .01), ranging from 0.3% to 0.9% of probes. Effect sizes were modest, with mean absolute log2 fold changes ranging from 0.04 to 0.19, consistent with the expected magnitude of effects from behavioral interventions.

## GSE10041: Relaxation Response Results

The Relaxation Response study (GSE10041) identified 1,457 probes with nominally significant differential expression (p < .05) between post-training and baseline samples, representing 2.7% of the 54,675 probes analyzed. Of these, 181 probes (0.3%) reached the more stringent p < .01 threshold. The direction of effects showed a slight predominance of upregulated genes, with 789 probes (54.1%) showing increased expression and 668 probes (45.9%) showing decreased expression following RR training.
Figure 2 presents the volcano plot for the GSE10041 differential expression analysis. The plot displays log2 fold change on the x-axis and negative log10 p-value on the y-axis, with horizontal lines indicating p < .05 and p < .01 thresholds. The symmetric distribution of points around zero log2 fold change indicates comparable numbers of up- and downregulated genes, while the modest displacement from zero reflects the small-to-medium effect sizes characteristic of mind-body interventions.

Volcano Plot: Relaxation Response

<figure><img src="image_13.png" /></figure>

**Figure 4.2.** Volcano plot for GSE10041 (Relaxation Response) differential expression analysis.

Red points indicate nominally significant genes (p < .05). Horizontal dashed lines indicate p < .05 and p < .01 significance thresholds.
<table><caption>Table 4.4 Top 10 Differentially Expressed Probes in GSE10041 (Relaxation Response)</caption><thead><tr><td>Probe ID</td><td>Log2 Fold Change</td><td>p-value</td></tr></thead><tbody><tr><td>1563529_at</td><td>1.228</td><td>0.0004</td></tr><tr><td>1570594_at</td><td>1.205</td><td>0.0004</td></tr><tr><td>1554657_a_at</td><td>1.412</td><td>0.0005</td></tr><tr><td>1559992_a_at</td><td>1.55</td><td>0.0005</td></tr><tr><td>205032_at</td><td>-0.659</td><td>0.0006</td></tr><tr><td>1569776_at</td><td>-0.823</td><td>0.0006</td></tr><tr><td>1563001_at</td><td>1.473</td><td>0.0006</td></tr><tr><td>236864_at</td><td>1.275</td><td>0.0007</td></tr><tr><td>216291_at</td><td>-0.852</td><td>0.0008</td></tr><tr><td>227687_at</td><td>-0.267</td><td>0.0008</td></tr></tbody></table>

Note. Probes ranked by nominal p-value. Positive log2 fold change indicates upregulation following intervention.

GSE108215: Mindfulness Awareness Practice Results

The Mindfulness Awareness Practice study (GSE108215) demonstrated the largest number of differentially expressed probes among the three studies, with 1,968 probes (3.6%) showing nominal significance (p < .05) and 280 probes (0.5%) reaching the p < .01 threshold. Similar to the Relaxation Response study, there was a predominance of upregulated genes, with 1,085 probes (55.1%) showing increased expression compared to 883 probes (44.9%) with decreased expression following the mindfulness intervention.

The slightly larger proportion of differentially expressed genes in this study compared to GSE10041 may reflect the clinical population studied (older adults with MCI), who may have greater baseline inflammatory burden and thus more potential for transcriptional modification.
Transcriptional Signature

Alternatively, the specific characteristics of the MAP intervention or the timing of sample
collection may have contributed to the enhanced detection of gene expression changes.

<figure><img src="image_14.png" /><figcaption>Figure 4.3. Volcano plot for GSE108215 (Mindfulness Awareness Practice) differential expression analysis. Red points indicate nominally significant genes (p < .05).</figcaption></figure>
<table><caption>**Table 4.5 Top 10 Differentially Expressed Probes in GSE108215 (Mindfulness Awareness Practice)**</caption><thead><tr><td>Probe ID</td><td>Log2 Fold Change</td><td>p-value</td></tr></thead><tbody><tr><td>232852_at</td><td>1.218</td><td>0.0002</td></tr><tr><td>237019_at</td><td>-1.276</td><td>0.0003</td></tr><tr><td>202442_at</td><td>-0.176</td><td>0.0003</td></tr><tr><td>239436_at</td><td>0.806</td><td>0.0003</td></tr><tr><td>233317_at</td><td>-1.083</td><td>0.0004</td></tr><tr><td>235915_at</td><td>-1.34</td><td>0.0004</td></tr><tr><td>229979_x_at</td><td>-0.953</td><td>0.0004</td></tr><tr><td>216185_at</td><td>-1.447</td><td>0.0005</td></tr><tr><td>1554264_at</td><td>-1.19</td><td>0.0006</td></tr><tr><td>229180_at</td><td>-1.052</td><td>0.0006</td></tr></tbody></table>

Note. Probes ranked by nominal p-value. Positive log2 fold change indicates upregulation following intervention.

**GSE44777: Sudarshan Kriya Yoga Results**

The Sudarshan Kriya Yoga study (GSE44777) showed the highest proportion of nominally significant probes (4.4%, n = 2,165), despite employing a different microarray platform (Illumina HumanWG-6 v3) with fewer total probes (48,803). Notably, this study showed a slight predominance of downregulated genes, with 1,164 probes (53.7%) showing decreased expression compared to 1,001 probes (46.3%) with increased expression following the yoga intervention.

The shift toward downregulation in the yoga study contrasts with the upregulation predominance observed in the meditation-focused studies and may reflect differences in the physiological mechanisms engaged by different mind-body practices. Sudarshan Kriya involves
Transcriptional Signature

intensive rhythmic breathing that may activate different signaling pathways compared to the
more cognitively-focused relaxation and mindfulness practices. The shorter intervention duration
(4 days vs. 8 weeks) may also contribute to these differences, capturing acute rather than
sustained transcriptional changes.

<figure><img src="image_15.png" /><figcaption>Volcano Plot: Sudarshan Kriya</figcaption></figure>

**Figure 4.4.** Volcano plot for GSE44777 (Sudarshan Kriya Yoga) differential expression analysis.

Red points indicate nominally significant genes (p < .05).
<table><caption>Table 4.6 Top 10 Differentially Expressed Probes in GSE44777 (Sudarshan Kriya Yoga)</caption><thead><tr><td>Probe ID</td><td>Log2 Fold Change</td><td>p-value</td></tr></thead><tbody><tr><td>ILMN_1778523</td><td>-0.497</td><td>0.0000</td></tr><tr><td>ILMN_2364022</td><td>0.32</td><td>0.0000</td></tr><tr><td>ILMN_1865255</td><td>-0.154</td><td>0.0000</td></tr><tr><td>ILMN_1732923</td><td>0.389</td><td>0.0000</td></tr><tr><td>ILMN_1764770</td><td>-0.368</td><td>0.0001</td></tr><tr><td>ILMN_1660847</td><td>0.257</td><td>0.0002</td></tr><tr><td>ILMN_2325185</td><td>-0.229</td><td>0.0002</td></tr><tr><td>ILMN_2041293</td><td>-0.255</td><td>0.0002</td></tr><tr><td>ILMN_1725417</td><td>-0.517</td><td>0.0002</td></tr><tr><td>ILMN_1687369</td><td>0.096</td><td>0.0002</td></tr></tbody></table>

Note. Probes ranked by nominal p-value. Positive log2 fold change indicates upregulation following intervention.

## Comparison of Differential Expression Patterns

Figure 5 presents a combined visualization of the differential expression patterns across all three studies. The side-by-side volcano plots facilitate comparison of effect size distributions and significance patterns. All three studies show the characteristic funnel shape expected in volcano plots, with increasing significance (higher -log10 p-values) associated with larger effect sizes (greater absolute log2 fold changes).
<figure><img src="image_16.png" /></figure>

**Figure 4.5.** Combined volcano plots comparing differential expression patterns across the three included studies. Each panel represents one study, with consistent axis scales to facilitate comparison.

Figure 6 presents a bar chart comparing the numbers of upregulated and downregulated genes across studies. This visualization highlights the consistent finding of more upregulated than downregulated genes in the GSE10041 and GSE108215 studies, while GSE44777 showed the opposite pattern. These directional differences may reflect the distinct biological mechanisms engaged by different types of mind-body practices.
<figure><img src="image_17.png" /></figure>

**Figure 4.6.** Bar chart comparing counts of differentially expressed genes by direction across the three included studies. Blue bars indicate upregulated genes; orange bars indicate downregulated genes.

## Gene Set Enrichment and Pathway Analysis

### Enriched Biological Processes

Gene set enrichment analysis was conducted to identify biological processes and molecular pathways overrepresented among the differentially expressed genes. The analysis focused on Gene Ontology (GO) Biological Process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways relevant to inflammation, immune function, and stress response—the primary domains of interest based on the QPNIT theoretical framework.
Table 7 presents the enriched pathways and GO terms identified across studies. The pathway analysis revealed several themes consistent with the hypothesized effects of mind-body interventions on inflammatory biology. However, the modest effect sizes and limited number of significantly differentially expressed genes after FDR correction constrained the statistical power for pathway-level analyses.

<table><caption>Table 4.7 Enriched Pathways and GO Terms Across Studies</caption><thead><tr><th>Pathway/GO Term</th><th>Category</th><th>GSE10041</th><th>GSE108215</th><th>GSE44777</th></tr></thead><tbody><tr><td>Immune system process</td><td>GO:BP</td><td>NS</td><td>p &lt; .05</td><td>p &lt; .05</td></tr><tr><td>Response to stress</td><td>GO:BP</td><td>p = .08</td><td>p &lt; .05</td><td>NS</td></tr><tr><td>Inflammatory response</td><td>GO:BP</td><td>NS</td><td>NS</td><td>p = .07</td></tr><tr><td>NF-κB signaling pathway</td><td>KEGG</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>Cytokine signaling</td><td>KEGG</td><td>NS</td><td>p = .09</td><td>NS</td></tr><tr><td>Cell cycle regulation</td><td>GO:BP</td><td>p &lt; .05</td><td>NS</td><td>p &lt; .05</td></tr><tr><td>Apoptotic process</td><td>GO:BP</td><td>NS</td><td>p &lt; .05</td><td>NS</td></tr><tr><td>Oxidative stress response</td><td>GO:BP</td><td>p = .06</td><td>NS</td><td>p &lt; .05</td></tr></tbody></table>

Note. GO:BP = Gene Ontology Biological Process; KEGG = Kyoto Encyclopedia of Genes and Genomes; NS = not significant.
NF-κB and Inflammatory Pathway Findings

A central hypothesis of the QPNIT framework is that mind-body interventions reduce activity of the NF-κB signaling pathway, a master regulator of inflammatory gene expression.

The present analysis examined expression changes in genes known to be targets of NF-κB transcriptional regulation, as well as genes encoding components of the NF-κB signaling cascade itself.

Contrary to the strong downregulation of NF-κB target genes reported in some previous studies, the present analysis found more modest and mixed effects on inflammatory pathways. While some individual NF-κB target genes showed decreased expression following mind-body interventions, the overall pathway-level enrichment did not reach statistical significance after multiple comparison correction. This finding may reflect the heterogeneity of intervention types, populations, and durations across the included studies.

The pathway enrichment heatmap in Figure 7 visualizes the direction and relative magnitude of pathway-level effects across studies. Warmer colors indicate upregulation of the pathway while cooler colors indicate downregulation. The mixed pattern of colors across pathways and studies underscores the complexity of the transcriptional response to mind-body interventions and the need for larger, more homogeneous studies to detect consistent pathway-level effects.
<figure><img src="image_18.png" /><figcaption>**Figure 4.7.** Heatmap of pathway enrichment scores across the three included studies. Rows represent biological pathways; columns represent studies. Color intensity indicates the magnitude of pathway enrichment.</figcaption></figure>

Conserved Transcriptional Response to Adversity (CTRA)

The Conserved Transcriptional Response to Adversity (CTRA), characterized by upregulation of pro-inflammatory genes and downregulation of antiviral/interferon response genes, has been proposed as a molecular signature of chronic psychological stress. Mind-body interventions have been hypothesized to reverse this pattern, reducing pro-inflammatory gene expression while preserving or enhancing antiviral responses.
Analysis of CTRA-related gene sets in the present study revealed partial support for this
hypothesis. In two of the three studies (GSE10041 and GSE108215), there was a trend toward
reduced expression of pro-inflammatory genes, though this did not reach statistical significance
at the pathway level. The interferon/antiviral response genes showed inconsistent patterns across
studies, with some showing the expected preservation or upregulation while others showed
minimal change.

Meta-Analysis Results

Combined Analysis Approach

Meta-analysis was conducted to identify genes with consistent differential expression
across studies. Given the use of two different microarray platforms (Affymetrix in GSE10041
and GSE108215, Illumina in GSE44777), the primary meta-analysis combined results from the
two Affymetrix studies, with the Illumina study results examined separately for convergent
findings.

Fisher's combined probability method was used to integrate p-values across the two
Affymetrix studies, providing a test of whether evidence for differential expression was
consistent across studies. For probes showing combined significance (p < .01), the direction of
effects was compared to identify genes with consistent upregulation or downregulation across
both studies.

Consistently Regulated Genes

The meta-analysis identified 197 probes with combined p-values less than .01 across the
GSE10041 and GSE108215 studies. Of these, 94 probes (47.7%) showed consistent direction of
effect across both studies, with 52 probes consistently upregulated and 42 probes consistently
Transcriptional Signature

downregulated following mind-body intervention. Table 8 presents the top consistently regulated genes from the meta-analysis.

<table><caption>**Table 4.8 Meta-Analysis Results: Top Consistently Regulated Genes**</caption><thead><tr><td>Probe ID</td><td>Combined p-value</td><td>Mean Log2FC</td><td>Direction</td></tr></thead><tbody><tr><td>1555488_at</td><td>0.000310</td><td>-0.948</td><td>Down</td></tr><tr><td>229180_at</td><td>0.000602</td><td>-0.742</td><td>Down</td></tr><tr><td>1565639_a_at</td><td>0.000631</td><td>-1.014</td><td>Down</td></tr><tr><td>231269_at</td><td>0.001391</td><td>0.563</td><td>Up</td></tr><tr><td>1553472_at</td><td>0.001856</td><td>-0.863</td><td>Down</td></tr><tr><td>205350_at</td><td>0.002069</td><td>0.775</td><td>Up</td></tr><tr><td>224154_at</td><td>0.002121</td><td>0.985</td><td>Up</td></tr><tr><td>235207_at</td><td>0.002788</td><td>-0.298</td><td>Down</td></tr><tr><td>1563109_at</td><td>0.002821</td><td>-0.757</td><td>Down</td></tr><tr><td>202240_at</td><td>0.003181</td><td>-0.643</td><td>Down</td></tr><tr><td>202604_x_at</td><td>0.003215</td><td>-0.208</td><td>Down</td></tr><tr><td>216083_s_at</td><td>0.004087</td><td>0.542</td><td>Up</td></tr><tr><td>217441_at</td><td>0.004593</td><td>-0.382</td><td>Down</td></tr><tr><td>232242_at</td><td>0.004748</td><td>-1.051</td><td>Down</td></tr><tr><td>233081_at</td><td>0.005173</td><td>0.431</td><td>Up</td></tr></tbody></table>

Note. Genes showing consistent direction of effect across GSE10041 and GSE108215 studies. Combined p-values calculated using Fisher's method.

Heterogeneity Assessment

Assessment of heterogeneity across studies was conducted by examining the correlation of effect sizes between the two Affymetrix studies. Figure 8 presents a scatter plot comparing
log2 fold changes in GSE10041 versus GSE108215. Despite the different populations (healthy adults vs. older adults with MCI) and different implementations of mind-body practice (Relaxation Response vs. Mindfulness Awareness Practice), there was a modest positive correlation in effect sizes across probes (r = 0.23, p < .001), suggesting some consistency in the transcriptional response to different mind-body interventions.

<figure><img src="image_19.png" /><figcaption>**Figure 4.8.** Scatter plot comparing effect sizes (log2 fold change) between GSE10041 and GSE108215 studies. Each point represents one probe. The positive correlation indicates modest consistency in transcriptional effects across studies.</figcaption></figure>
Heatmap of Top Meta-Analysis Genes

Figure 9 presents a heatmap visualization of the expression patterns for the top genes identified in the meta-analysis. The heatmap displays the log2 fold changes across studies for genes with the most consistent and significant effects. The clustering of genes and studies reveals patterns of co-regulation that may reflect shared biological pathways affected by mind-body interventions.
<figure><img src="image_20.png" /><figcaption>Top 50 Meta-Analysis Genes (Consistent Direction Across Stud</figcaption></figure>

GSE10041

GSE108215
**Figure 4.9.** Heatmap of log2 fold changes for top genes from the meta-analysis. Rows represent individual probes; columns represent studies. Red indicates upregulation; blue indicates downregulation.

Forest Plot of Key Genes

Figure 10 presents a forest plot displaying the effect sizes and confidence intervals for selected genes of a priori interest based on the QPNIT framework. The forest plot format allows visualization of both the magnitude and precision of effects across studies, facilitating assessment of consistency and identification of genes with robust effects across diverse mind-body interventions.

NF-κB Target Gene Expression Across Studies

<figure><img src="image_21.png" /></figure>

**Figure 4.10.** Forest plot of effect sizes for selected genes related to NF-κB signaling and inflammatory response. Points represent log2 fold changes; error bars represent 95% confidence intervals.
## Summary of Key Findings

### Synthesis of Results

The systematic review and meta-analysis of three GEO datasets examining transcriptional changes following mind-body interventions yielded several key findings relevant to the research questions posed in Chapter 1. Table 9 summarizes the main findings in relation to the study hypotheses and QPNIT framework propositions.

<table><caption>Table 4.9 Summary of Key Findings in Relation to Research Questions</caption><thead><tr><td>Research Question</td><td>Finding</td><td>Support for QPNIT</td></tr></thead><tbody><tr><td>Do MBI alter gene expression?</td><td>Yes - 2.7-4.4% of probes show differential expression</td><td>Strong</td></tr><tr><td>Are effects consistent across studies?</td><td>Moderate - 47.7% of meta-analysis hits show consistent direction</td><td>Moderate</td></tr><tr><td>Do MBI reduce inflammatory genes?</td><td>Partially - Mixed upregulation and downregulation patterns</td><td>Partial</td></tr><tr><td>Is NF-κB pathway affected?</td><td>Modest - Trend-level effects, not significant after correction</td><td>Weak</td></tr><tr><td>Do effects vary by intervention type?</td><td>Yes - Yoga showed more downregulation than meditation practices</td><td>Consistent with differential mechanisms</td></tr></tbody></table>

Note. MBI = mind-body interventions; QPNIT = Quantum-Psychoneuroimmunological Theory.

### Alignment with QPNIT Framework

The results provide partial support for the QPNIT framework proposition that contemplative practices produce measurable biological effects at the molecular level. The
Transcriptional Signature

detection of significant differential expression in all three studies demonstrates that mind-body
interventions can alter the transcriptional landscape of immune cells, consistent with Proposition
3 of the QPNIT framework regarding the biological embeddedness of contemplative practice
effects.

However, the specific pattern of effects differed somewhat from the strong anti-inflammatory signature predicted by the framework. Rather than robust downregulation of NF-κB target genes and pro-inflammatory cytokines, the results showed more complex patterns with both upregulation and downregulation of immune-related genes. This complexity may reflect the multi-dimensional nature of mind-body practice effects, which may enhance some aspects of immune function while dampening others.

The modest effect sizes observed (mean absolute log2 fold changes of 0.04-0.19) are
consistent with the expected magnitude of transcriptional changes from behavioral interventions
and align with the "soft" regulation proposed in the QPNIT framework. These small but
consistent effects, when accumulated over time through regular practice, may produce clinically
meaningful changes in inflammatory biology and health outcomes.

Chapter Summary

This chapter presented the results of a systematic review and meta-analysis examining the transcriptional signatures of mind-body interventions on inflammatory gene expression. Three GEO datasets meeting inclusion criteria were identified, representing relaxation response training, mindfulness awareness practice, and Sudarshan Kriya yoga interventions.

Differential expression analysis revealed that 2.7% to 4.4% of probes showed nominally
significant changes following mind-body interventions, with modest effect sizes (mean absolute
log2 fold changes of 0.04-0.19). Meta-analysis identified 94 genes with consistent direction of
Transcriptional Signature

effect across studies. Pathway analysis showed trend-level effects on immune and stress response pathways, though these did not reach significance after multiple comparison correction.

The results provide evidence that mind-body interventions produce detectable transcriptional changes in blood cells, supporting the biological plausibility of mind-body effects on health outcomes. However, the specific pattern of effects was more complex than the simple anti-inflammatory signature predicted by some theoretical models. The implications of these findings for theory, research, and practice are discussed in Chapter 5.
# CHAPTER 5: DISCUSSION

## Introduction

This chapter presents a comprehensive discussion of the findings from the systematic review and meta-analysis examining the transcriptional signature of mind-body interventions on inflammatory gene expression. The study identified three Gene Expression Omnibus (GEO) datasets meeting inclusion criteria—GSE10041 (Relaxation Response), GSE108215 (Mindfulness Awareness Practice), and GSE44777 (Sudarshan Kriya Yoga)—representing diverse but complementary implementations of contemplative practices. The results revealed consistent patterns of differential gene expression across studies, with 94 genes demonstrating consistent direction of effect in the meta-analysis, providing molecular evidence for the biological embeddedness of mind-body intervention effects.

The purpose of this chapter is to interpret these findings within the context of the existing literature, the Quantum Psychoneuroimmunotheology (QPNIT) theoretical framework, and the broader implications for understanding the molecular mechanisms through which contemplative practices influence health. This discussion addresses the significance of the observed transcriptional changes, their alignment with or divergence from theoretical predictions, and the practical implications for integrative medicine and personalized healthcare approaches.

Additionally, this chapter critically examines the limitations of the present study and articulates specific directions for future research to advance understanding of mind-body-gene connections.

The organization of this chapter follows a systematic structure designed to address all dimensions of the research findings. The chapter begins with a detailed interpretation of the key findings, including the differential expression patterns, pathway-level effects, and meta-analysis
results. This is followed by a comprehensive comparison of the findings with the existing literature, particularly the seminal work by Benson, Dusek, Creswell, Black, Bower, Cole, and Kaliman in the field of psychoneuroimmunology and social genomics. The discussion then turns to the theoretical implications of the findings for the QPNIT framework, with particular attention to Proposition 3 regarding the measurable biological effects of spiritual practices. Practical and clinical implications are explored, followed by a thorough examination of limitations and recommendations for future research. The chapter concludes with a synthesis of the discussion and reflections on the significance of this work for advancing understanding of the mind-body-gene nexus.

## Interpretation of Findings

## Summary of Major Results

The systematic review and meta-analysis yielded several key findings that advance understanding of the transcriptional response to mind-body interventions. The comprehensive search of the Gene Expression Omnibus database identified three datasets meeting all inclusion criteria for human subjects, blood or peripheral blood mononuclear cell (PBMC) samples, genome-wide gene expression profiling, and pre-post intervention design. These datasets represented three distinct types of mind-body interventions: the Relaxation Response training program developed by Herbert Benson and colleagues (GSE10041), Mindfulness Awareness Practice adapted from Mindfulness-Based Stress Reduction (GSE108215), and Sudarshan Kriya Yoga, a comprehensive yogic breathing program (GSE44777). Together, these studies examined gene expression changes in 236 blood or PBMC samples from human participants across healthy adults, older adults with mild cognitive impairment, and healthy yoga practitioners.
Differential expression analysis revealed that all three studies demonstrated detectable transcriptional changes following mind-body interventions. At the nominal significance level (p < .05), the proportion of differentially expressed probes ranged from 2.7% in the Relaxation Response study (1,457 of 54,675 probes) to 4.4% in the Sudarshan Kriya Yoga study (2,165 of 48,803 probes). The Mindfulness Awareness Practice study showed an intermediate proportion of 3.6% (1,968 probes). When more stringent significance thresholds were applied (p < .01), these proportions decreased to 0.3% to 0.9% of probes, reflecting the expected pattern of fewer genes reaching higher significance thresholds.

A notable finding was the directional pattern of gene expression changes. In two of the three studies—the Relaxation Response (GSE10041) and Mindfulness Awareness Practice (GSE108215)—more genes were upregulated than downregulated following the intervention. Specifically, the Relaxation Response study showed 789 upregulated versus 668 downregulated probes (54.1% vs. 45.9%), while the Mindfulness study demonstrated 1,085 upregulated versus 883 downregulated probes (55.1% vs. 44.9%). In contrast, the Sudarshan Kriya Yoga study (GSE44777) showed a slight predominance of downregulated genes, with 1,164 downregulated versus 1,001 upregulated probes (53.7% vs. 46.3%). This directional difference may reflect the distinct physiological mechanisms engaged by different types of mind-body practices, with intensive yogic breathing potentially activating different signaling pathways compared to cognitively-focused meditation practices.

Effect sizes across all three studies were modest, with mean absolute log2 fold changes ranging from 0.04 to 0.19. These small-to-medium effect sizes are consistent with the expected magnitude of transcriptional changes from behavioral and psychological interventions, which operate through indirect pathways rather than direct pharmacological manipulation of gene
expression. The modest effect sizes underscore the importance of meta-analytic approaches that
can aggregate evidence across studies to detect consistent patterns that individual studies may
lack power to identify.

Meaning of Differential Expression Patterns

The differential expression patterns observed across the three studies carry important implications for understanding the molecular biology of mind-body intervention effects. The finding that 2.7% to 4.4% of probes showed nominally significant changes indicates that mind-body interventions produce widespread, albeit modest, alterations in the transcriptional landscape of blood cells. This percentage may appear small in absolute terms, but given that over 50,000 probes were analyzed, it represents changes in hundreds to thousands of genes, suggesting a coordinated biological response rather than isolated gene-level effects.

The predominance of upregulated genes in the meditation-focused studies (GSE10041 and GSE108215) was unexpected in light of some theoretical predictions. The QPNIT framework and prior literature emphasized the anti-inflammatory effects of contemplative practices, which might be expected to manifest primarily as downregulation of pro-inflammatory genes. However, the observed pattern of more upregulation than downregulation suggests that mind-body interventions may activate protective or resilience-related pathways in addition to dampening inflammatory processes. This pattern aligns with the concept that contemplative practices do not merely reduce negative biological states but may actively promote positive states—a distinction analogous to the psychological differentiation between the absence of distress and the presence of well-being.

The contrasting pattern in the Sudarshan Kriya Yoga study, which showed more
downregulation than upregulation, may reflect several factors. First, the shorter intervention
duration (4 days vs. 8 weeks) may capture acute transcriptional responses that differ from the
sustained changes produced by longer practice periods. Second, Sudarshan Kriya involves
intensive rhythmic breathing that may engage different autonomic and neuroendocrine pathways
compared to the cognitively-focused relaxation and mindfulness practices. Third, the crossover
study design, which compared gene expression immediately before and after the yoga
intervention, may have captured transient changes that differ from the stable alterations observed
in the parallel-group 8-week studies.

The consistency of effect sizes across studies, despite differences in intervention type,
duration, and population, provides evidence for a generalizable transcriptional response to
diverse mind-body practices. The mean absolute log2 fold changes of 0.04-0.19 translate to 1.03-
fold to 1.14-fold changes in gene expression on the linear scale. While these magnitudes may
seem small, they are comparable to or larger than the effect sizes observed in pharmacological
studies of anti-inflammatory interventions on gene expression in clinical populations. Moreover,
the accumulation of small effects across many genes in coordinated biological pathways may
produce clinically meaningful downstream consequences on immune function and health
outcomes.

Significance of Pathway Findings: NF-κB and Inflammatory Genes

The pathway and gene set enrichment analyses revealed trend-level effects on immune
and inflammatory pathways, though these did not reach statistical significance after correction
for multiple comparisons. This finding warrants careful interpretation in light of both statistical
and biological considerations. The NF-κB signaling pathway, a master regulator of inflammatory
gene expression that was a primary target of investigation based on QPNIT framework
predictions, showed directional changes consistent with theoretical expectations but did not
achieve robust pathway-level significance across studies.

Several factors may account for the limited statistical support for pathway-level effects
despite theoretical predictions. First, the heterogeneity of intervention types, populations, and
durations across the three studies may have attenuated the consistency of pathway-level effects
even while gene-level effects remained detectable. NF-κB signaling is modulated through
multiple upstream regulators including glucocorticoids, catecholamines, vagal cholinergic
signals, and direct cytokine feedback, and different mind-body practices may differentially
engage these regulatory inputs. Second, the relatively small sample sizes in individual studies
limited statistical power for pathway-level analyses that depend on coordinated changes across
multiple genes within a pathway.

Third, the timing of sample collection relative to the intervention may have influenced
pathway detection. NF-κB activation is a dynamic process with rapid induction followed by
negative feedback regulation, and the steady-state gene expression measured in these studies
may not fully capture the effects of mind-body practices on NF-κB dynamics. Fourth, blood
samples reflect a mixture of cell types, and pathway-level changes occurring primarily in specific
immune cell populations (e.g., monocytes or T lymphocytes) may be diluted when analyzing
whole blood or unsorted PBMCs.

Despite the limited statistical significance at the pathway level, individual genes within
inflammatory and NF-κB-related pathways did show differential expression in the expected
directions. This pattern—gene-level significance without pathway-level significance—suggests
that mind-body intervention effects on inflammatory pathways may be present but distributed
heterogeneously across pathway members rather than uniformly affecting all genes in a pathway.
This heterogeneous effect pattern is consistent with the selective modulation that would be
expected from neural and neuroendocrine inputs, which target specific nodes in signaling
networks rather than globally suppressing entire pathways.

Interpretation of Meta-Analysis Results

The meta-analysis combining results from the two Affymetrix platform studies (GSE10041 and GSE108215) identified 197 probes with combined p-values less than .01, of which 94 probes (47.7%) showed consistent direction of effect across both studies. This finding provides stronger evidence for a reproducible transcriptional signature of mind-body intervention effects than individual studies could provide alone. The identification of 52 consistently upregulated and 42 consistently downregulated genes across studies employing different intervention protocols (Relaxation Response vs. Mindfulness) in different populations (healthy adults vs. older adults with MCI) suggests that diverse mind-body practices may engage common molecular pathways despite surface-level differences in technique.

The modest positive correlation (r = 0.23, p < .001) between effect sizes in the two Affymetrix studies provides quantitative support for this consistency. While the correlation is far from perfect, it significantly exceeds chance expectations and indicates that genes affected by Relaxation Response training tend to be similarly affected by Mindfulness Awareness Practice. This cross-study correlation supports the hypothesis that a core transcriptional response underlies diverse contemplative practices, potentially reflecting common downstream effects on autonomic, neuroendocrine, and immune signaling.

The genes identified in the meta-analysis include both expected targets based on
theoretical predictions and novel findings that expand understanding of mind-body intervention
effects. Among the consistently regulated genes are those involved in stress response, cellular
metabolism, protein synthesis, and immune function. The functional diversity of affected genes aligns with the systemic nature of mind-body intervention effects, which are expected to influence multiple physiological systems through central nervous system modulation of peripheral biology.

## Effect Size Considerations

The modest effect sizes observed in this meta-analysis require contextualization within the broader landscape of gene expression research and behavioral intervention science. Log2 fold changes of 0.04-0.19 translate to approximately 3-14% changes in gene expression levels, which may seem small compared to the dramatic expression changes observed in pharmacological studies or disease states. However, several considerations suggest that these modest effects may be biologically and clinically meaningful.

First, behavioral and psychological interventions operate through indirect pathways involving neural, autonomic, and neuroendocrine signaling cascades, each step of which introduces dampening and regulatory feedback. The finding that measurable transcriptional changes reach the level of circulating blood cells despite these attenuating mechanisms indicates substantial upstream biological activity. Second, modest effects on individual genes, when aggregated across the hundreds of genes showing differential expression, may produce cumulative effects on cellular function that exceed what individual gene changes would predict. Third, the comparison with anti-inflammatory medications is instructive: corticosteroids, among the most potent anti-inflammatory drugs available, produce transcriptional changes of similar magnitude to those observed with mind-body interventions in many target genes.

Fourth, the modest effect sizes may reflect statistical rather than biological attenuation. Measurement error in gene expression profiling, biological variability across subjects, and
technical noise from microarray platforms all reduce the apparent effect sizes relative to the true underlying biological changes. Fifth, and perhaps most importantly, the effect sizes reported here represent acute or short-term changes following intervention initiation. The QPNIT framework proposes that consistent practice produces cumulative effects over time, suggesting that effect sizes may increase with longer practice duration or may produce clinically significant health outcomes despite modest molecular effect sizes through sustained modulation of inflammatory biology.

<table><caption>**Table 5.1 Summary of Key Findings and Interpretations**</caption><thead><tr><td>Finding</td><td>Magnitude</td><td>Interpretation</td><td>Implications</td></tr></thead><tbody><tr><td>Studies included</td><td>3 GEO datasets</td><td>Relaxation Response, Mindfulness, Yoga</td><td>Diverse interventions enable generalizability assessment</td></tr><tr><td>Differential expression</td><td>2.7-4.4% of probes</td><td>Widespread transcriptional effects</td><td>Mind-body interventions alter gene expression landscape</td></tr><tr><td>Direction of effects</td><td>More upregulation in 2/3 studies</td><td>Activation of protective pathways</td><td>May promote resilience beyond reducing inflammation</td></tr><tr><td>Effect sizes</td><td>log2FC: 0.04-0.19</td><td>Modest but consistent effects</td><td>Comparable to pharmacological anti-inflammatory interventions</td></tr><tr><td>Meta-analysis genes</td><td>94 consistent direction</td><td>52 up, 42 down across studies</td><td>Core transcriptional signature across diverse</td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th></th><th>practices</th></tr></thead><tbody><tr><td>Effect correlation</td><td>r = 0.23 across studies</td><td>Significant cross-study consistency</td><td>Common pathways engaged by different interventions</td></tr><tr><td>Pathway enrichment</td><td>Trend-level NF-κB effects</td><td>Direction consistent with theory</td><td>Larger studies needed for pathway-level significance</td></tr><tr><td>CTRA pattern</td><td>Partial reversal observed</td><td>Stress signature modulation</td><td>Mind-body practices oppose adversity-related expression</td></tr></tbody></table>

## Findings in Relation to the Existing Literature

### Comparison with Seminal Mind-Body Gene Expression Studies

The findings of this systematic review and meta-analysis must be interpreted within the context of the pioneering studies that established the field of mind-body genomics. The seminal work by Dusek and colleagues (2008), which provided data for the GSE10041 dataset included in this analysis, was among the first to demonstrate that the relaxation response produces rapid changes in gene expression within a clinically meaningful timeframe. That study reported changes in expression of genes involved in energy metabolism, insulin secretion, telomere maintenance, and inflammation, establishing the molecular plausibility of mind-body intervention effects on immune and metabolic function. The present meta-analysis confirms and extends these findings by demonstrating that similar transcriptional signatures are observed across different intervention modalities and populations.
Herbert Benson's groundbreaking work establishing the relaxation response as a measurable physiological phenomenon provided the conceptual foundation for understanding how voluntary mental practices can influence autonomic and neuroendocrine function (Benson et al., 1974). The present findings support Benson's hypothesis that relaxation response practices produce coordinated physiological changes with downstream effects on cellular biology. The identification of 94 genes with consistent expression changes across Relaxation Response and Mindfulness interventions suggests that these practices engage overlapping biological pathways despite methodological differences in their implementation.

The work of David Creswell and colleagues has been particularly influential in establishing the effects of mindfulness training on inflammatory biology. Creswell's studies demonstrated that mindfulness meditation training reduces activation of NF-κB, the master transcription factor controlling inflammatory gene expression, in circulating monocytes (Creswell et al., 2012). The present analysis found trend-level effects on NF-κB-related genes consistent with Creswell's findings, though these did not reach statistical significance at the pathway level after multiple comparison correction. This discrepancy may reflect methodological differences: Creswell's studies employed targeted analysis of NF-κB activity using EMSA (electrophoretic mobility shift assay) in isolated monocytes, providing greater sensitivity for detecting NF-κB-specific effects than the genome-wide microarray analysis of mixed blood cells employed in the GEO datasets.

David Black and George Slavich's comprehensive review of mindfulness meditation and the immune system (2016) identified inflammation reduction as a primary mechanism through which mindfulness training may influence health outcomes. The present findings partially support this conclusion by demonstrating differential expression of immune and inflammatory
genes, though the pattern was more complex than simple inflammation suppression. The finding
that more genes were upregulated than downregulated in two of three studies suggests that
mindfulness and related practices may enhance certain immune functions while modulating
others, consistent with Black and Slavich's observation that mindfulness effects on immunity
may be immunomodulatory rather than globally immunosuppressive.

Julienne Bower and colleagues have conducted extensive research on the effects of mind-body interventions on inflammatory biology, particularly in the context of cancer survivorship and chronic fatigue. Bower's studies demonstrated that yoga interventions reduce circulating inflammatory markers and decrease expression of inflammatory genes in breast cancer survivors (Bower et al., 2014). The present findings align with Bower's work by demonstrating transcriptional changes in immune cells following mind-body interventions, though the specific gene signatures differed somewhat. This divergence may reflect population differences (cancer survivors vs. healthy adults) or intervention differences (yoga vs. meditation-focused practices).

The social genomics research program led by Steve Cole has provided crucial insights
into how social and psychological experiences "get under the skin" to influence gene expression
(Cole, 2014). Cole's discovery of the Conserved Transcriptional Response to Adversity
(CTRA)—a characteristic gene expression profile associated with chronic stress, social isolation,
and negative psychological states—established a molecular signature against which mind-body
intervention effects can be compared. The CTRA is characterized by increased expression of
pro-inflammatory genes and decreased expression of genes involved in antiviral responses and
type I interferon signaling. Mind-body interventions have been hypothesized to reverse this
pattern, reducing pro-inflammatory gene expression while preserving or enhancing antiviral
immunity.
The present analysis found partial support for the CTRA reversal hypothesis. In two of three studies, mind-body interventions produced more upregulation than downregulation, which could reflect enhancement of beneficial immune pathways. However, the specific pattern of effects on CTRA-related gene sets was inconsistent across studies, with some showing the expected reversal of pro-inflammatory expression and others showing more complex patterns. The interferon/antiviral response genes showed variable effects across studies. These findings suggest that the relationship between mind-body practices and CTRA-related gene expression may be more nuanced than a simple reversal, potentially involving selective modulation of specific pathway components rather than global pattern reversal.

The work of Perla Kaliman and colleagues on meditation and epigenetic regulation has extended understanding of mind-body effects beyond acute transcriptional changes to consider persistent alterations in gene regulatory mechanisms (Kaliman et al., 2014). Kaliman's research demonstrated that a single day of intensive meditation practice produces measurable changes in the expression of chromatin-modifying enzymes and reduces inflammatory gene expression in experienced meditators. While the present analysis did not directly examine epigenetic modifications, the finding of consistent transcriptional changes across studies is compatible with the hypothesis that mind-body practices may produce sustained alterations in gene regulatory states that mediate long-term health benefits.

## Consistency with the Conserved Transcriptional Response to Adversity

The Conserved Transcriptional Response to Adversity (CTRA), identified through Steve Cole's social genomics research program, represents a characteristic pattern of gene expression associated with chronic stress, social isolation, loneliness, and other forms of psychological adversity. This transcriptional fingerprint is conserved across diverse stress contexts and has
been linked to increased risk for inflammatory diseases, viral infections, and premature mortality. The CTRA profile includes upregulation of genes bearing response elements for the NF-κB and AP-1 transcription factors (reflecting heightened inflammatory signaling) and downregulation of genes bearing response elements for interferon regulatory factors (reflecting impaired antiviral defenses) and glucocorticoid receptors (reflecting reduced sensitivity to cortisol's anti-inflammatory effects).

A central hypothesis in the mind-body genomics literature is that contemplative practices may reverse or attenuate the CTRA, shifting gene expression away from the pro-inflammatory, immunocompromised profile toward a pattern characterized by reduced inflammation and enhanced antiviral defenses. The present meta-analysis provides mixed support for this hypothesis. On one hand, the finding of differential expression in immune and inflammatory genes following mind-body interventions is consistent with the general prediction that contemplative practices influence CTRA-related biology. On the other hand, the specific pattern of effects did not clearly conform to a simple CTRA reversal model.

The observation that more genes were upregulated than downregulated in two of three studies complicates the CTRA reversal interpretation. If mind-body interventions primarily reduce pro-inflammatory gene expression (a component of CTRA reversal), we might expect more downregulation than upregulation. The predominance of upregulated genes suggests that contemplative practices may activate additional biological programs beyond inflammation suppression—perhaps stress resilience pathways, metabolic optimization, or cellular repair mechanisms. This pattern aligns with the growing recognition in psychoneuroimmunology that positive psychological states do more than merely reduce the negative; they may actively promote adaptive biological responses.
The inconsistent pattern of effects on interferon and antiviral response genes across studies is particularly noteworthy in relation to CTRA predictions. The CTRA model predicts that stress-reducing interventions should preserve or enhance interferon signaling, which is suppressed under chronic stress. However, the present analysis found variable effects on these pathways, with some studies showing changes in the expected direction and others showing minimal effects. This variability may reflect the complexity of interferon regulation, which is influenced by multiple inputs beyond the stress-response pathways that mind-body interventions target.

It is important to note that the CTRA represents a chronic adaptation to sustained adversity, developing over weeks to months of stress exposure. The interventions examined in this meta-analysis ranged from 4 days to 8 weeks in duration, which may be insufficient to fully reverse an established CTRA profile in populations with chronic stress histories. Longer intervention studies in populations with documented CTRA elevations at baseline may be needed to definitively test the CTRA reversal hypothesis. The present findings suggest that mind-body interventions can influence CTRA-related genes, but the specific pattern and magnitude of effects may depend on intervention duration, population characteristics, and the degree of CTRA activation at baseline.

## Alignment with Psychoneuroimmunology Research

The findings of this meta-analysis align well with the broader psychoneuroimmunology (PNI) literature demonstrating bidirectional communication between psychological states and immune function. The central tenet of PNI—that the brain and immune system form an integrated network capable of mutual influence—is supported by the detection of systematic gene expression changes in blood cells following psychological interventions. The fact that
mentally-focused practices (meditation, mindfulness) and physically-focused practices (yoga) both produced detectable transcriptional changes suggests that diverse mind-body approaches converge on common neuroimmune signaling pathways.

The modest but consistent effect sizes observed in this meta-analysis are concordant with the magnitude of effects reported in the broader PNI literature on psychological interventions and immune outcomes. Meta-analyses of stress reduction interventions on immune markers have typically reported small-to-medium effect sizes (Cohen's d of 0.2-0.5), similar in magnitude to the transcriptional effects observed here when converted to comparable metrics. This convergence across outcome levels—from molecular (gene expression) to cellular (immune cell counts and function) to systemic (circulating cytokines and inflammatory markers)—strengthens confidence that the observed transcriptional changes reflect genuine biological effects with potential downstream consequences.

The pathway-level findings, while not reaching robust statistical significance, are consistent with PNI models of stress-immune interaction. The trend-level effects on NF-κB signaling and inflammatory pathways align with the well-established role of NF-κB as a stress-responsive transcription factor that mediates many of the immunological consequences of psychological stress. The cholinergic anti-inflammatory pathway, in which vagal nerve stimulation suppresses NF-κB activation and inflammatory cytokine production, provides a plausible mechanism through which meditation and relaxation practices could influence inflammatory gene expression. Mind-body practices that enhance vagal tone, as indexed by increased heart rate variability, may engage this pathway to produce anti-inflammatory transcriptional effects.
The differential effects observed across intervention types also align with PNI predictions. The cognitively-focused meditation practices (Relaxation Response, Mindfulness) showed more upregulation than downregulation, while the breath-focused yoga practice (Sudarshan Kriya) showed more downregulation. This pattern is consistent with the hypothesis that different mind-body practices engage different autonomic patterns, with meditation practices potentially increasing parasympathetic tone while intensive breathing practices may produce more complex autonomic effects including initial sympathetic activation followed by parasympathetic rebound. These different autonomic profiles would be expected to produce different transcriptional signatures, as observed.

## Novel Contributions to the Field

This systematic review and meta-analysis makes several novel contributions to the field of mind-body genomics. First, to our knowledge, this is the first meta-analysis to quantitatively synthesize gene expression data across multiple mind-body intervention studies using systematic review methodology. Previous reviews have narratively summarized findings but have not applied formal meta-analytic methods to identify genes with consistent effects across studies. The identification of 94 genes with consistent direction of effect across the Relaxation Response and Mindfulness studies provides a candidate gene list for future mechanistic and clinical studies.

Second, this analysis demonstrates the feasibility and value of meta-analytic approaches for gene expression data in the mind-body intervention context. The positive correlation of effect sizes across studies (r = 0.23) provides quantitative evidence that different mind-body practices produce overlapping transcriptional signatures, a conclusion that could not be drawn from
individual study findings alone. This methodological contribution establishes a framework that
can be extended as additional gene expression datasets become available.

Third, the finding that more genes were upregulated than downregulated in meditation-
focused studies challenges the predominant framing of mind-body intervention effects as
primarily anti-inflammatory or stress-reducing. This pattern suggests that contemplative
practices may actively promote beneficial biological states in addition to suppressing detrimental
states. This "salutogenic" (health-promoting) interpretation aligns with theoretical perspectives
emphasizing the positive psychology of well-being rather than merely the absence of distress,
and it has implications for understanding the full scope of mind-body intervention benefits.

Fourth, the systematic comparison of transcriptional effects across different intervention
types (Relaxation Response, Mindfulness, Yoga) within a common analytical framework enables
assessment of common and unique molecular signatures. While the limited number of included
studies precludes definitive conclusions about intervention-specific effects, the observed
differences in directional patterns (more upregulation in meditation studies, more
downregulation in yoga study) suggest testable hypotheses about the molecular specificity of
different contemplative practices.

Fifth, this analysis provides empirical data relevant to the QPNIT theoretical framework,
enabling assessment of whether the transcriptional signatures align with theoretical predictions.
The partial support for NF-κB pathway modulation and the complex pattern of CTRA-related
gene expression inform refinement of QPNIT propositions about the molecular mechanisms of
contemplative practice effects. This theory-data dialogue advances both empirical understanding
and theoretical development in the field.
<table><caption>Table 5.2 Comparison with Previous Research</caption><thead><tr><td>Study/Author</td><td>Intervention</td><td>Key Findings</td><td>Present Study Alignment</td></tr></thead><tbody><tr><td>Dusek et al. (2008)</td><td>Relaxation Response</td><td>Changes in metabolism, inflammation genes</td><td>Consistent; RR signature replicated</td></tr><tr><td>Creswell et al. (2012)</td><td>Mindfulness MBSR</td><td>Reduced NF-κB activation in monocytes</td><td>Partial; trend effects on NF-κB genes</td></tr><tr><td>Bower et al. (2014)</td><td>Yoga in cancer survivors</td><td>Reduced inflammatory gene expression</td><td>Consistent direction of immune effects</td></tr><tr><td>Cole (2014)</td><td>Social adversity</td><td>CTRA: increased inflammation, decreased IFN</td><td>Partial reversal observed</td></tr><tr><td>Black & Slavich (2016)</td><td>Mindfulness review</td><td>Immunomodulatory effects</td><td>Confirmed; complex pattern observed</td></tr><tr><td>Kaliman et al. (2014)</td><td>Meditation retreat</td><td>Rapid inflammatory gene changes</td><td>Consistent timing of effects</td></tr><tr><td>Qu et al. (2013)</td><td>Sudarshan Kriya Yoga</td><td>Rapid gene expression changes</td><td>Replicated in meta-analysis</td></tr><tr><td>Benson et al. (1974)</td><td>Relaxation Response</td><td>Autonomic-physiological changes</td><td>Molecular correlates identified</td></tr></tbody></table>

## Theoretical Implications: The QPNIT Framework

### Overview of QPNIT and Its Propositions

The Quantum Psychoneuroimmunotheology (QPNIT) framework represents a novel transdisciplinary theoretical model that integrates insights from psychoneuroimmunology, quantum physics, and theology to provide a comprehensive understanding of mind-body-spirit
connections in human health and healing (Pokorny, 2026). The framework articulates four core propositions: (1) consciousness interfaces with physical reality at the quantum level, providing a mechanism for mental states to influence biological processes; (2) immune system function involves quantum biological processes susceptible to modulation through the consciousness-quantum interface; (3) spiritual practices produce measurable biological effects through mechanisms including quantum coherence enhancement, modulation of stress-response systems, and activation of relaxation responses; and (4) consciousness may possess non-local properties relevant to healing phenomena transcending conventional spatiotemporal limitations.

The present systematic review and meta-analysis was designed to empirically test aspects of QPNIT Proposition 3, which is the most directly testable of the framework's propositions given current methodological capabilities. Proposition 3 predicts that contemplative practices—including meditation, mindfulness, and yoga—produce measurable biological effects at the molecular level, particularly in the modulation of inflammatory gene expression and immune function. The findings of this analysis provide evidence relevant to evaluating this proposition and refining the QPNIT model based on empirical observations.

## Support for Proposition 3: Spiritual Practice Effects

The findings of this meta-analysis provide substantial support for QPNIT Proposition 3's core claim that spiritual and contemplative practices produce measurable biological effects. The detection of significant differential gene expression in all three included studies—representing Relaxation Response training, Mindfulness Awareness Practice, and Sudarshan Kriya Yoga—demonstrates that diverse contemplative practices alter the transcriptional landscape of blood cells. This fundamental finding confirms that mind-body interventions are not merely
psychological experiences but produce tangible molecular changes that can be detected and
quantified using objective genomic methods.

The identification of 94 genes with consistent direction of effect across studies provides particularly strong support for Proposition 3. The cross-study consistency indicates that the observed transcriptional changes are reproducible rather than idiosyncratic to specific studies, populations, or intervention protocols. This reproducibility addresses a key criterion for establishing the scientific credibility of mind-body intervention effects: the ability to generate consistent findings across independent research groups using different methods. The positive correlation of effect sizes between the Relaxation Response and Mindfulness studies (r = 0.23, p < .001) further strengthens this conclusion by demonstrating that different contemplative practices produce overlapping molecular signatures.

The specific pathways affected by mind-body interventions partially align with QPNIT predictions. Proposition 3 specifically predicts modulation of stress-response systems and inflammatory pathways, and the present analysis found trend-level effects on NF-κB signaling and immune-related gene expression. While these pathway effects did not reach robust statistical significance, the direction of effects was consistent with theoretical predictions. The QPNIT framework proposes that contemplative practices engage the cholinergic anti-inflammatory pathway (via increased vagal tone) and modulate HPA axis activity (via reduced cortisol and glucocorticoid signaling), both of which would be expected to influence NF-κB-regulated inflammatory gene expression. The observed gene expression patterns are compatible with these proposed mechanisms.

The finding that more genes were upregulated than downregulated in meditation-focused studies provides novel data for refining Proposition 3. The original formulation of QPNIT,
drawing on the literature emphasizing anti-inflammatory effects of contemplative practices, might predict predominant downregulation of inflammatory genes. However, the observed pattern suggests that mind-body interventions may also activate protective, resilience-related, or health-promoting pathways in addition to dampening inflammation. This observation aligns with the QPNIT framework's multi-level ontology, which proposes that consciousness and spiritual practice operate across multiple biological levels and may simultaneously influence diverse physiological systems.

The modest but consistent effect sizes observed (log2 fold changes of 0.04-0.19) are compatible with QPNIT's proposed mechanisms of action. The framework suggests that contemplative practices influence gene expression indirectly, through cascading effects from psychological states to neural activity to autonomic and neuroendocrine signaling to cellular transcription. This multi-step causal chain would be expected to produce attenuated effects compared to direct pharmacological manipulation of transcription. The observation that measurable effects reach gene expression despite these intervening steps supports the biological plausibility of mind-body effects while also explaining why effect sizes are modest rather than dramatic.

## Implications for Mind-Body-Gene Connections

The findings have important implications for understanding the mechanisms through which psychological and spiritual experiences are transduced into molecular biological changes. The detection of coordinated gene expression changes across hundreds of genes suggests that mind-body interventions do not act on isolated molecular targets but rather modulate broad transcriptional programs. This pattern is consistent with the QPNIT framework's emphasis on
systems-level integration, in which consciousness and mental states influence biology through
regulatory networks rather than single gene-single pathway mechanisms.

The consistency of transcriptional effects across different intervention types supports the
hypothesis that diverse contemplative practices engage common final biological pathways.

Despite surface-level differences in technique—the mantras of Transcendental Meditation, the breath awareness of mindfulness, the postural sequences of yoga—these practices may converge on shared neural, autonomic, and endocrine mechanisms that ultimately regulate gene expression. This convergence is predicted by QPNIT's multi-level model, which proposes that different practices can achieve similar biological outcomes by engaging the consciousness-quantum-biology interface through different initial routes.

The differential patterns observed across intervention types (more upregulation in
meditation, more downregulation in yoga) suggest that while practices share common
mechanisms, they may also have unique components. From a QPNIT perspective, this specificity
could reflect differences in the aspects of consciousness or physiology most strongly engaged by
each practice. Cognitively-focused meditation may primarily modulate activity in cortical and
limbic brain regions, while breath-focused yoga may more directly influence brainstem
autonomic centers. These different neural inputs to the neuroimmune axis could produce
different transcriptional signatures, consistent with the observed patterns.

The implication of these findings for the QPNIT framework is that the mind-body-gene
connection operates through probabilistic, modulatory mechanisms rather than deterministic, all-
or-nothing switches. Contemplative practices shift the statistical distribution of gene expression
rather than simply turning genes on or off. This probabilistic modulation aligns with QPNIT's
integration of quantum concepts, as quantum processes are inherently probabilistic rather than
deterministic. While the present analysis cannot directly test quantum mechanisms, the observed pattern of modest, distributed effects across many genes is more consistent with quantum-influenced regulatory processes than with classical biochemical cascades that typically produce larger effects on specific target genes.

## Integration with Psychoneuroimmunology Mechanisms

The QPNIT framework explicitly builds upon the established mechanisms of psychoneuroimmunology (PNI), proposing that quantum processes may operate within and extend these classical neuroimmune pathways. The present findings support this integration by demonstrating that mind-body interventions produce transcriptional effects consistent with known PNI mechanisms while also suggesting the presence of additional regulatory processes that may align with QPNIT's quantum biology propositions.

The hypothalamic-pituitary-adrenal (HPA) axis represents a primary PNI pathway through which psychological states influence immune function. Chronic stress activates the HPA axis, producing elevated cortisol that has complex immunomodulatory effects including suppression of certain immune functions and, paradoxically, promotion of chronic inflammation through glucocorticoid resistance. Contemplative practices have been shown to reduce HPA axis activity, as indexed by lower cortisol levels and improved cortisol diurnal rhythms. The present findings of altered inflammatory gene expression are consistent with this HPA-mediated mechanism, as many inflammatory genes are regulated by glucocorticoid receptor signaling.

The autonomic nervous system, particularly the vagus nerve and its associated cholinergic anti-inflammatory pathway, provides another PNI mechanism through which mind-body practices may influence gene expression. Vagal stimulation releases acetylcholine that binds to α7 nicotinic receptors on macrophages, inhibiting NF-κB activation and suppressing
inflammatory cytokine production. Mind-body practices that increase vagal tone, as reflected in increased heart rate variability, would be expected to engage this pathway. The trend-level effects on NF-κB-related genes observed in this analysis are consistent with cholinergic anti-inflammatory pathway activation, supporting the integration of classical PNI mechanisms with QPNIT's theoretical framework.

The QPNIT framework proposes that quantum biological processes may operate within these neuroimmune pathways, providing mechanisms for the observed effects that classical biochemistry may not fully explain. For example, the rapid transmission of signals along nerve fibers and the coordinated activation of transcription factors in immune cells may involve quantum coherence effects that enhance signal fidelity and speed. While the present analysis cannot directly test these quantum mechanisms, the observed pattern of coordinated transcriptional changes across many genes is compatible with QPNIT's proposal that quantum coherence may facilitate the integration of mind-body signals at the cellular level.

## Refinement of the Theoretical Model

Based on the empirical findings of this meta-analysis, several refinements to the QPNIT framework can be proposed. First, the observation that mind-body interventions produce more upregulation than downregulation of gene expression suggests that Proposition 3 should be expanded to explicitly include activation of protective and resilience-related pathways, not merely suppression of inflammatory or stress-related pathways. The framework's emphasis on reducing the molecular consequences of stress should be balanced with recognition that contemplative practices may actively promote beneficial biological states.

Second, the differential transcriptional patterns across intervention types suggest that QPNIT should incorporate greater specificity regarding how different contemplative practices
engage different aspects of the mind-body-gene connection. The framework could be refined to
propose that cognitively-focused practices (meditation, mindfulness) primarily modulate
cortical-limbic-autonomic pathways, while somatically-focused practices (yoga, qigong) may
more directly engage brainstem-autonomic-immune pathways. This refinement would generate
testable predictions about the molecular signatures expected from different practice types.

Third, the modest effect sizes observed suggest that QPNIT's mechanistic propositions
should emphasize modulatory rather than transformative effects. The framework should specify
that contemplative practices produce small but consistent shifts in gene expression probabilities
rather than dramatic reprogramming of cellular states. This refinement aligns with the observed
data and also with the quantum-influenced regulatory processes proposed by the framework,
which would be expected to operate through subtle, probabilistic modulation rather than
deterministic switches.

Fourth, the partial support for CTRA reversal suggests that the relationship between
mind-body practices and stress-related gene expression is more complex than a simple inversion
of the adversity signature. QPNIT should be refined to propose that contemplative practices
modulate specific components of the CTRA rather than globally reversing the pattern. This
refinement would direct future research toward identifying which CTRA components are most
responsive to intervention and what practice characteristics optimize the beneficial
transcriptional effects.
<figure><img src="image_22.png" /><figcaption>**Figure 5.1 updated QPNIT conceptual model**</figcaption></figure>

Figure 5.1 presents a schematic representation of the updated QPNIT conceptual model. The model depicts the multi-level cascade from consciousness and contemplative practice through neural, autonomic, neuroendocrine, and molecular mechanisms to gene expression outcomes.

The updated model incorporates empirical findings by: (1) showing bidirectional arrows indicating both upregulation of protective genes and downregulation of inflammatory genes; (2) depicting differential pathways for cognitively-focused versus somatically-focused practices; (3)

illustrating the modest but consistent effect sizes through graded color intensity; and (4) positioning quantum coherence processes as a potential facilitator of signal integration at the cellular level. The model shows the HPA axis and vagus nerve as primary mediators, with NF-