NF-κB and other pro-inflammatory transcription factors (Cole, 2014; Irwin & Cole, 2011).

**Hypothesis 2 (H2):** Individuals participating in mind-body interventions will exhibit significantly higher or preserved expression of interferon response genes (antiviral signature) following intervention compared to pre-intervention baseline levels. The QPNIT framework predicts that the reduction in stress signaling associated with agape-like states will restore the antiviral response that is typically suppressed under conditions of chronic stress and adversity (Cole et al., 2007; Fredrickson et al., 2013).

**Hypothesis 3 (H3):** Gene set enrichment analysis will reveal significant downregulation of inflammatory pathways (including TNF signaling, IL-6 signaling, NF-κB pathway, and related inflammatory processes) in individuals following mind-body interventions. This pathway-level hypothesis extends H1 by predicting coordinated regulation of entire functional gene sets rather than individual genes.

**Hypothesis 4 (H4):** Individuals characterized by high levels of eudaimonic well-being will exhibit a more favorable CTRA profile (lower inflammatory gene expression, higher antiviral gene expression) compared to individuals with low eudaimonic well-being, independent of hedonic well-being levels. This hypothesis is directly based on the findings of Fredrickson et al. (2013), which demonstrated that the quality of well-being—specifically, its eudaimonic versus hedonic orientation—is differentially associated with the CTRA transcriptional signature.

**Hypothesis 5 (H5):** Transcription factor analysis will reveal reduced activity of NF-κB and increased activity of interferon regulatory factors (IRFs) in individuals following mind-body interventions or characterized by high eudaimonic well-being. This
mechanistic hypothesis addresses the upstream regulatory factors driving the observed gene expression patterns.

Alignment with QPNIT Framework

The research questions and hypotheses are explicitly derived from the QPNIT
theoretical framework presented in Chapter 2. QPNIT's third core proposition states that
spiritual practices produce measurable biological effects through mechanisms including
modulation of stress-response systems and alterations in gene expression patterns
(Pokorny, 2026). The framework specifically predicts that the psychological and spiritual
state of agape love—characterized by reduced self-focus, diminished threat vigilance, and
enhanced other-orientation—creates neurobiological conditions that downregulate the
sympathetic-adrenomedullary and hypothalamic-pituitary-adrenal stress axes. This
neuroendocrine shift, in turn, reduces the activation of NF-κB and other transcription
factors that drive inflammatory gene expression while preserving or enhancing the
interferon response critical for antiviral defense.

The QPNIT framework thus generates the directional predictions embodied in the hypotheses: agape-associated states should produce a transcriptional signature opposite to the CTRA observed under conditions of stress and adversity. The operationalization of agape through mind-body interventions and eudaimonic well-being measures represents an empirically tractable approach to testing these predictions. Mind-body practices such as meditation, yoga, and loving-kindness training cultivate the psychological states associated with agape—present-moment awareness, compassion, reduced self-referential processing, and orientation toward the welfare of others (Garland et al., 2015; Hölzel et al., 2011; Kok et al., 2013). Eudaimonic well-being, characterized by purpose, meaning,
and contribution to others, captures the other-oriented life orientation that distinguishes
agape from self-focused hedonic pursuits (Fredrickson et al., 2013; Ryff, 2014). By
demonstrating that these states are associated with the predicted transcriptional changes,
this study provides empirical support for the QPNIT propositions regarding the biological
embedding of spiritual states.

Data Sources

Gene Expression Omnibus (GEO) Database Description

The data for this study were obtained from the Gene Expression Omnibus (GEO),
a public repository of high-throughput functional genomics data maintained by the
National Center for Biotechnology Information (NCBI) at the National Institutes of
Health (Barrett et al., 2013). GEO serves as the primary international repository for gene
expression data, accepting submissions of microarray, next-generation sequencing, and
other high-throughput genomic datasets from researchers worldwide. As of January 2026,
GEO contains over 4.5 million samples from more than 180,000 studies, representing one
of the largest and most comprehensive resources for functional genomics research
(NCBI, 2026).

GEO data are organized into several record types. Series records (GSE) describe
complete experiments, including the experimental design, sample characteristics, and
data processing methods. Sample records (GSM) describe the biological samples and
their associated data files. Platform records (GPL) describe the array or sequencing
technology used for data generation, including probe annotations and technical
specifications. Data are available in both raw and processed forms, enabling investigators
to apply standardized preprocessing pipelines or to use data as processed by the original
submitters. GEO also provides comprehensive metadata describing sample
characteristics, experimental conditions, and data provenance, facilitating secondary
analysis and meta-analytic synthesis (Edgar et al., 2002).

The use of GEO data for secondary analysis is a well-established and widely
accepted practice in genomics research. The NCBI requires that data deposited in GEO
be de-identified in accordance with the Common Rule (45 CFR 46) and institutional
policies, ensuring that secondary users do not have access to personally identifiable
information. Data submission to GEO is typically a condition of publication in major
genomics journals, ensuring that the repository contains high-quality data from peer-
reviewed studies. The availability of detailed metadata enables secondary users to
evaluate the quality and appropriateness of datasets for their research purposes and to
implement appropriate quality control procedures (Brazma et al., 2003; Perez-Riverol et
al., 2019).

Specific Datasets Used

Four datasets from GEO were selected for inclusion in this study based on their
relevance to the research questions, data quality, and availability of appropriate
comparison groups. These datasets collectively provide gene expression data from 358
unique samples representing diverse populations and intervention modalities. The
following sections describe each dataset in detail, including the study design, sample
characteristics, intervention or exposure of interest, and relevance to the present research.

GSE108215: Mindfulness Awareness Practices (MAP) Intervention Study. This dataset
derives from a randomized controlled trial conducted in Singapore examining the effects
of an 8-week Mindfulness Awareness Practices (MAP) program compared to a Health
Education Program (HEP) control condition (Keng et al., 2018). The study enrolled healthy adults who were randomized to either MAP or HEP, with peripheral blood samples collected before and after the 8-week intervention period. Gene expression was assessed using the Affymetrix Human Genome U133 Plus 2.0 Array (GPL570), which interrogates over 47,000 transcripts representing approximately 38,500 genes. The dataset includes 86 samples from 43 participants (24 in MAP, 19 in HEP) at two time points each.

The MAP intervention is directly relevant to the present study as mindfulness training cultivates psychological states closely aligned with agape: present-moment awareness, non-judgmental acceptance, reduced self-referential processing, and compassionate orientation toward self and others (Garland et al., 2015; Hölzel et al., 2011). The HEP control condition provides an active comparison group controlling for non-specific effects of group participation, attention from instructors, and expectancy effects. The pre-post design enables within-subject analysis of gene expression changes attributable to the intervention.

GSE44777: Sudarshan Kriya Yoga Study. This dataset derives from a study examining the acute effects of Sudarshan Kriya yoga practice on gene expression in peripheral blood lymphocytes (Sharma et al., 2013). Sudarshan Kriya is a rhythmic breathing practice that has been associated with reduced stress, improved mood, and enhanced immune function (Brown & Gerbarg, 2005). The study employed a crossover design in which 10 experienced practitioners participated in both a yoga session and a control condition (music listening) on separate occasions, with blood samples collected before and after each session. Gene expression was assessed using the Illumina
HumanHT-12 V4.0 Expression BeadChip (GPL6884), which interrogates over 47,000 probes. The dataset includes 78 samples representing multiple replicates from the 10 subjects across conditions and time points.

The Sudarshan Kriya practice is relevant to the present study as it represents a contemplative practice that, like other forms of yoga and meditation, cultivates psychophysiological states associated with reduced stress reactivity, enhanced vagal tone, and positive emotional states (Brown & Gerbarg, 2005). The acute effects examined in this study complement the chronic effects examined in other datasets, providing insight into the immediate transcriptional response to contemplative practice. The crossover design with an active control condition strengthens causal inference by controlling for individual differences and non-specific effects.

GSE10041: Relaxation Response Study. This dataset derives from a landmark study examining the genomic effects of the relaxation response elicited through various mind-body practices including meditation, yoga, and repetitive prayer (Dusek et al., 2008). The study compared three groups: novices with no prior mind-body practice experience, novices who completed 8 weeks of relaxation response training, and long-term practitioners with an average of 9.4 years of regular practice. Peripheral blood mononuclear cells (PBMCs) were collected from all participants, and gene expression was assessed using the Affymetrix Human Genome U133 Plus 2.0 Array (GPL570). The dataset includes 72 samples from participants across the three groups.

The relaxation response, first characterized by Herbert Benson, represents a physiological state characterized by decreased sympathetic nervous system activation, reduced metabolism, and alterations in neuroendocrine signaling that are opposite to the
stress response (Benson et al., 1974; Dusek et al., 2008). This study is particularly valuable for the present research as it enables examination of both the acute effects of training (novice pre vs. post) and the chronic effects of long-term practice (novice vs. long-term practitioners), providing insight into the dose-response relationship between contemplative practice and gene expression changes.

GSE55762: Psychological Well-Being and Gene Expression Study. This dataset derives from a study examining the relationship between psychological well-being and leukocyte gene expression, extending the findings of Fredrickson et al. (2013) on eudaimonic versus hedonic well-being (Cole et al., 2015). The study enrolled 122 adult participants who completed assessments of psychological well-being, including separate measures of eudaimonic and hedonic well-being. Gene expression in circulating leukocytes was assessed using the Illumina HumanHT-12 V4.0 Expression BeadChip (GPL10904). This cross-sectional dataset enables correlational analysis of the association between well-being orientation and the CTRA transcriptional profile.

This dataset is particularly important for the present study as it directly assesses the transcriptional correlates of eudaimonic well-being, a construct closely aligned with the other-oriented, purpose-driven life orientation characteristic of agape love.

Fredrickson et al. (2013) demonstrated that eudaimonic and hedonic well-being, though correlated, have opposite associations with the CTRA: eudaimonic well-being is associated with downregulated inflammatory genes and upregulated antiviral genes, while hedonic well-being shows the reverse pattern. The present study leverages this dataset to test whether agape-associated well-being orientations are indeed associated with the predicted favorable transcriptional profile.
<table><caption>**Table 3.1 Summary of Gene Expression Omnibus (GEO) Datasets Included in the Analysis**</caption><thead><tr><td>Dataset ID</td><td>Study Description</td><td>Sample Size</td><td>Platform</td><td>Intervention/Exposure</td></tr></thead><tbody><tr><td>GSE108215</td><td>Mindfulness Awareness Practices (MAP) RCT</td><td>86 samples (43 participants)</td><td>GPL570 (Affymetrix HG-U133 Plus 2.0)</td><td>8-week MAP vs. Health Education</td></tr><tr><td>GSE44777</td><td>Sudarshan Kriya Yoga crossover study</td><td>78 samples (10 practitioners)</td><td>GPL6884 (Illumina HumanHT-12 v4)</td><td>Acute yoga session vs. control</td></tr><tr><td>GSE10041</td><td>Relaxation Response genomics study</td><td>72 samples</td><td>GPL570 (Affymetrix HG-U133 Plus 2.0)</td><td>RR training (8 wks) and long-term practice</td></tr><tr><td>GSE55762</td><td>Psychological Well-Being gene expression</td><td>122 samples</td><td>GPL10904 (Illumina HumanHT-12 v4)</td><td>Eudaimonic vs. hedonic well-being</td></tr></tbody></table>

Note. MAP = Mindfulness Awareness Practices; RCT = randomized controlled trial; RR = Relaxation Response; HG = Human Genome. Total combined sample: N = 358 samples.

**Inclusion and Exclusion Criteria for Dataset Selection**

Datasets were selected for inclusion based on the following a priori criteria designed to maximize relevance to the research questions while ensuring data quality and analytical compatibility:

**Inclusion Criteria:** (1) Human subjects: Datasets must include gene expression data from human participants, as the research questions concern the biological embedding
of psychological states in humans. (2) Immune cell samples: Datasets must include gene expression data from peripheral blood mononuclear cells (PBMCs), peripheral blood lymphocytes, whole blood, or circulating leukocytes, as these are the tissue compartments most relevant to the immune function outcomes predicted by QPNIT. (3) Mind-body intervention or psychological well-being measures: Datasets must examine the effects of mind-body interventions (meditation, yoga, relaxation response) or include validated measures of psychological well-being, particularly eudaimonic well-being, that serve as proxies for agape-associated psychological states. (4) Platform compatibility: Datasets must employ Affymetrix or Illumina microarray platforms to facilitate standardized preprocessing and cross-study normalization. (5) Adequate sample size: Datasets must include a minimum of 20 samples to provide sufficient statistical power for meaningful analysis. (6) Data availability: Raw or appropriately processed data must be publicly available through GEO with sufficient metadata to enable reanalysis.

**Exclusion Criteria:** (1) Non-human subjects: Datasets from animal models, cell lines, or other non-human sources were excluded. (2) Disease-focused studies: Datasets examining patients with specific diseases (e.g., cancer, autoimmune disorders) were excluded to avoid confounding by disease-related transcriptional changes. (3) Pharmacological interventions: Datasets examining the effects of pharmacological agents were excluded, as the focus of the present study is on psychological/behavioral interventions. (4) Non-immune tissues: Datasets examining gene expression in non-immune tissues (e.g., brain, muscle, adipose) were excluded. (5) Inadequate metadata: Datasets lacking sufficient metadata to identify sample characteristics and experimental conditions were excluded.
## Data Quality Considerations

Multiple data quality assessments were conducted to ensure the integrity and reliability of the datasets employed in this study. Quality control procedures were implemented at both the dataset selection stage and during data preprocessing, following established best practices in microarray bioinformatics (Bolstad et al., 2003; Gautier et al., 2004; Wilson & Miller, 2005).

**Dataset-Level Quality Assessment:** Prior to selection, each candidate dataset was evaluated based on several quality indicators. Publication status was assessed, with preference given to datasets associated with peer-reviewed publications, as this indicates that the data have undergone scientific review. Sample size and study design were evaluated to ensure adequate power and appropriate comparison groups. Platform documentation was reviewed to verify that probe annotations and technical specifications were complete and accurate. Metadata completeness was assessed to confirm that essential sample characteristics (intervention group, time point, demographic variables) were available for analysis.

**Sample-Level Quality Control:** During preprocessing, individual samples were assessed for quality using established metrics. For Affymetrix arrays, quality metrics included average background, scaling factors, percent present calls, and 3'/5' ratios for housekeeping genes (Bolstad et al., 2005). Samples with extreme values on these metrics (typically defined as greater than 2-3 standard deviations from the mean) were flagged for potential exclusion. For Illumina arrays, quality metrics included average probe intensity, detection p-values, and array-to-array correlations. Principal component
analysis (PCA) was employed to identify potential outliers and to assess the presence of
batch effects or other systematic technical variation.

**RNA Quality Indicators:** Where available, RNA integrity number (RIN) values were examined to assess the quality of the RNA used for array hybridization. RIN values range from 1 (completely degraded) to 10 (intact), with values above 7 generally considered acceptable for microarray analysis (Schroeder et al., 2006). For the GSE108215 dataset, RIN values were available and ranged from 6.6 to 8.9 (M = 7.6, SD = 0.5), indicating generally high RNA quality. RIN values were not available for other datasets but were assumed to be adequate based on the successful hybridization and publication of the data.

**Sample and Participants**

***Combined Sample Characteristics***

The combined sample for this study comprises 358 gene expression samples
derived from the four GEO datasets described above. This sample size provides
substantial statistical power for detecting modest effect sizes in differential expression
analysis and gene set enrichment analysis. Power calculations based on previous studies
of the transcriptional effects of mind-body interventions suggest that the combined
sample provides greater than 80% power to detect effect sizes (Cohen's d) of 0.30 at α =
0.05, which represents the lower bound of effect sizes typically observed in this literature
(Bower et al., 2014; Creswell et al., 2012). The integration of multiple datasets also
enables assessment of heterogeneity across studies and increases confidence in the
robustness and generalizability of findings that replicate across samples.
The combined sample includes participants from diverse geographic and demographic backgrounds, enhancing the external validity of the findings. GSE108215 enrolled participants in Singapore, providing representation of East Asian populations. GSE44777 enrolled experienced yoga practitioners in the United States. GSE10041 enrolled participants in the northeastern United States across a range of meditation and relaxation practices. GSE55762 enrolled a diverse community sample in the United States with varying levels of eudaimonic and hedonic well-being. This diversity of populations and intervention contexts strengthens confidence that observed transcriptional patterns reflect biological mechanisms that generalize across human populations rather than artifacts of specific samples or cultural contexts.

<table><caption>**Table 3.2 Sample Characteristics by Dataset**</caption><thead><tr><th>Characteristic</th><th>GSE108215<br>(n=86)</th><th>GSE44777<br>(n=78)</th><th>GSE10041<br>(n=72)</th><th>GSE55762<br>(n=122)</th></tr></thead><tbody><tr><td>Study Design</td><td>RCT (pre-post)</td><td>Crossover (pre-post)</td><td>Cross-sectional + training</td><td>Cross-sectional</td></tr><tr><td>Sample Type</td><td>PBMCs</td><td>PB Lymphocytes</td><td>PBMCs</td><td>Leukocytes</td></tr><tr><td>Population</td><td>Singapore adults</td><td>US yoga practitioners</td><td>US healthy adults</td><td>US community sample</td></tr><tr><td>Gender (% Female)</td><td>75%</td><td>0% (all male)</td><td>Not reported</td><td>52%</td></tr><tr><td>Mean Age (years)</td><td>Not reported</td><td>Adult (range not specified)</td><td>25-65</td><td>35-64</td></tr><tr><td>Intervention/Exposure</td><td>MAP vs. HEP (8 wks)</td><td>Yoga vs. music (acute)</td><td>RR training/long-term</td><td>Well-being measures</td></tr><tr><td>Ethnicity</td><td>Chinese/Asian</td><td>Not reported</td><td>Mixed</td><td>Mixed (predominantly)</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td></td><td>White)</td></tr><tr><td>RNA Quality (RIN)</td><td>6.6-8.9 (M=7.6)</td><td>Not reported</td><td>Not reported</td><td>Not reported</td></tr></table>

Note. RCT = randomized controlled trial; PBMCs = peripheral blood mononuclear cells; PB = peripheral blood; MAP = Mindfulness Awareness Practices; HEP = Health Education Program; RR = Relaxation Response; RIN = RNA integrity number. Percentages and means are approximate based on available metadata.

## Demographic Information Available

The availability of demographic information varies across the four datasets, reflecting differences in the original study designs and metadata reporting practices. This variability presents challenges for covariate-adjusted analyses but does not preclude meaningful comparison of transcriptional profiles across conditions.

**Gender:** Gender information is available for GSE108215, GSE44777, and GSE55762. The GSE108215 sample is predominantly female (approximately 75%), consistent with the higher participation rates of women in mind-body intervention research (Cramer et al., 2016). The GSE44777 sample consists entirely of male participants, reflecting the study's focus on experienced yoga practitioners in a specific population. The GSE55762 sample is approximately balanced by gender (52% female). Gender is a potentially important covariate in gene expression research, as sex hormones influence immune function and inflammatory gene expression (Klein & Flanagan, 2016). Where possible, gender was included as a covariate in statistical models.

**Age:** Age information is limited across datasets. GSE10041 enrolled participants aged 25-65 years. GSE55762 enrolled participants aged 35-64 years. Specific age data for individual participants are not consistently available, precluding age-adjusted analyses.
However, the age ranges represented are similar across studies and encompass the adult
lifespan typically examined in psychoneuroimmunology research. Age is relevant
because both immune function and inflammatory gene expression change with aging
(Franceschi et al., 2000), but the within-study designs employed in three of the four
datasets (pre-post comparisons) control for individual differences including age.

**Ethnicity/Race:** Ethnicity information is available for some datasets. GSE108215 enrolled predominantly Chinese participants in Singapore. GSE55762 enrolled a mixed sample with predominantly White participants. Ethnicity data are not consistently reported for other datasets. Population stratification by ethnicity can influence gene expression profiles and is an important consideration in genomics research (Stranger et al., 2007). The diversity of populations represented in the combined sample supports assessment of the robustness of findings across ethnic groups.

Treatment and Control Group Descriptions

The intervention and control conditions across the four datasets represent a range
of mind-body practices and comparison conditions that enable comprehensive assessment
of the transcriptional correlates of agape-associated states.

**GSE108215:** Treatment and Control Conditions. The Mindfulness Awareness Practices (MAP) intervention consisted of an 8-week program adapted from Mindfulness-Based Stress Reduction (MBSR) that included formal meditation practices (sitting meditation, body scan, mindful movement), informal mindfulness practices applied to daily activities, and weekly group sessions led by trained instructors. The Health Education Program (HEP) control condition was designed to match MAP for time, attention, and group support while omitting the active mindfulness components. HEP
included didactic presentations on health-related topics, group discussion, and gentle
stretching exercises. Participants were randomized to conditions with stratification by
gender and baseline characteristics. The comparison of MAP versus HEP isolates the
specific effects of mindfulness training from non-specific factors.

**GSE44777:** Treatment and Control Conditions. The Sudarshan Kriya yoga condition consisted of a structured breathing practice including pranayama techniques and rhythmic cyclical breathing patterns, conducted under the guidance of trained instructors. Participants had an average of over 1 year of experience with regular Sudarshan Kriya practice. The control condition consisted of listening to relaxing music for a duration matched to the yoga session. The crossover design with within-subject comparisons controls for stable individual differences, providing strong internal validity for assessing the acute effects of the practice.

**GSE10041:** Experimental Groups. This study compared three groups representing different levels of relaxation response experience: (1) Novices with no prior experience in mind-body practices; (2) Short-term trainees consisting of the same novices after completing 8 weeks of relaxation response training; and (3) Long-term practitioners with an average of 9.4 years of regular practice in meditation, yoga, or other relaxation response-elicitig techniques. The comparison of novices before and after training enables assessment of the effects of learning the relaxation response, while the comparison with long-term practitioners enables assessment of the cumulative effects of sustained practice.

**GSE55762:** Exposure Assessment. Rather than an intervention study, this dataset employed continuous measures of psychological well-being to assess correlational
associations with gene expression. Participants completed validated scales measuring eudaimonic well-being (including purpose in life, personal growth, positive relations with others, self-acceptance, environmental mastery, and autonomy) and hedonic well-being (including positive affect and life satisfaction). These measures enable analysis of the transcriptional correlates of well-being orientations without the constraints of categorical group comparisons.

**Variables and Measures**

***Independent Variables***

The independent variables in this study comprise the intervention conditions, time points, and psychological well-being measures that operationalize the agape-associated psychological states of interest. These variables serve as proxies for the theological construct of agape love, capturing the psychological characteristics—other-orientation, reduced self-focus, compassion, present-moment awareness—theorized to be associated with beneficial transcriptional profiles within the QPNIT framework.

**Intervention Group Assignment (GSE108215, GSE44777, GSE10041).** For the intervention studies, the primary independent variable is group assignment to the mind-body intervention versus control condition. This categorical variable captures whether participants received training in mindfulness meditation, yoga, or relaxation response techniques that cultivate agape-associated psychological states. In GSE108215, group assignment was determined by randomization to MAP versus HEP. In GSE44777, the crossover design means all participants experienced both yoga and control conditions, with condition (yoga vs. music) serving as the independent variable. In GSE10041, group
assignment reflects level of practice experience (novice, short-term trained, long-term practitioner).

**Time Point (GSE108215, GSE44777, GSE10041).** For intervention studies with repeated measures, time point (pre-intervention vs. post-intervention) serves as a critical independent variable enabling within-subject analysis of gene expression changes. The pre-post comparison isolates the effects of the intervention from stable individual differences in gene expression. In GSE108215 and GSE10041, the time interval is approximately 8 weeks. In GSE44777, the pre-post comparison captures acute effects within a single session.

**Psychological Well-Being Measures (GSE55762).** The GSE55762 dataset includes validated measures of eudaimonic and hedonic well-being that serve as continuous independent variables. Eudaimonic well-being is typically assessed using Ryff's Scales of Psychological Well-Being (Ryff, 1989; Ryff & Keyes, 1995), which measure six dimensions: purpose in life, personal growth, positive relations with others, self-acceptance, environmental mastery, and autonomy. Hedonic well-being is typically assessed through measures of positive affect and life satisfaction. These measures have established psychometric properties, with internal consistency reliabilities (Cronbach's α) typically exceeding 0.70 (Ryff & Keyes, 1995). The distinction between eudaimonic and hedonic well-being is theoretically critical, as eudaimonic well-being captures the other-oriented, purpose-driven life orientation aligned with agape, while hedonic well-being captures self-focused pleasure-seeking more aligned with eros in its secular sense.
Dependent Variables

The dependent variables in this study are gene expression levels measured
through microarray technology. Gene expression is quantified as normalized fluorescence
intensity values representing the relative abundance of messenger RNA (mRNA)
transcripts for each probe on the array. Following standard practice in transcriptomics,
expression values are log2-transformed to improve normality and stabilize variance
(Quackenbush, 2002). The dependent variables are examined at multiple levels of
aggregation: individual genes, gene sets, and biological pathways.

**Single-Gene Expression Levels.** At the most granular level, expression levels for individual genes (or probes representing genes) serve as dependent variables in differential expression analysis. The Affymetrix Human Genome U133 Plus 2.0 Array employed in GSE108215 and GSE10041 interrogates over 47,000 transcripts. The Illumina HumanHT-12 Expression BeadChips employed in GSE44777 and GSE55762 interrogate approximately 47,000 probes. After preprocessing and quality filtering, the number of probes available for analysis is typically reduced to approximately 20,000-30,000 reliably detected transcripts.

**Gene Set Expression Scores.** At a higher level of aggregation, expression scores for a priori defined gene sets serve as dependent variables in targeted hypothesis testing. The primary gene sets of interest are the CTRA pro-inflammatory gene set (NF-κB target genes) and the CTRA antiviral gene set (interferon response genes), as defined by Cole and colleagues (Cole, 2014; Cole et al., 2007). Gene set scores are computed as the average expression level across all genes in the set, enabling statistical comparison of overall pathway activity between conditions.
**Pathway Enrichment Scores.** Gene set enrichment analysis (GSEA) produces enrichment scores that quantify the degree to which genes in a defined set are overrepresented at the top or bottom of a ranked list of genes (Subramanian et al., 2005). Enrichment scores and their associated statistical significance serve as dependent variables for pathway-level hypothesis testing.

<table><caption>**Table 3.3 Key Variables and Their Operationalization**</caption><thead><tr><td>Variable</td><td>Type</td><td>Operationalization</td><td>Source/Measurement</td></tr></thead><tbody><tr><td>Mind-body intervention</td><td>IV (categorical)</td><td>MAP vs. HEP; Yoga vs. Music; Practice level</td><td>Study condition assignment</td></tr><tr><td>Time point</td><td>IV (categorical)</td><td>Pre-intervention vs. Post-intervention</td><td>Sample collection timing</td></tr><tr><td>Eudaimonic well-being</td><td>IV (continuous)</td><td>Purpose, growth, positive relations, autonomy</td><td>Ryff's PWB Scales</td></tr><tr><td>Hedonic well-being</td><td>IV (continuous)</td><td>Positive affect, life satisfaction</td><td>PANAS, SWLS</td></tr><tr><td>NF-κB gene expression</td><td>DV (continuous)</td><td>Mean log2 expression of 45 NF-κB target genes</td><td>Microarray probe intensity</td></tr><tr><td>IFN gene expression</td><td>DV (continuous)</td><td>Mean log2 expression of 42 interferon genes</td><td>Microarray probe intensity</td></tr><tr><td>Single gene expression</td><td>DV (continuous)</td><td>Log2-transformed normalized intensity</td><td>RMA/quantile normalization</td></tr><tr><td>Pathway enrichment</td><td>DV (continuous)</td><td>GSEA enrichment score (ES)</td><td>fgsea algorithm</td></tr></tbody></table>

Note. IV = independent variable; DV = dependent variable; MAP = Mindfulness Awareness Practices;

HEP = Health Education Program; PWB = Psychological Well-Being; PANAS = Positive and Negative
Affect Schedule; SWLS = Satisfaction With Life Scale; NF-κB = nuclear factor kappa-B; IFN = interferon; RMA = robust multi-array average; GSEA = gene set enrichment analysis.

CTRAGene Signature Description

The Conserved Transcriptional Response to Adversity (CTRA) represents a characteristic pattern of gene expression that has been observed across multiple studies of individuals experiencing chronic stress, social isolation, low socioeconomic status, and other forms of adversity (Cole, 2014; Cole et al., 2007; Slavich & Cole, 2013). The CTRA is defined by two reciprocal transcriptional dynamics: upregulation of genes involved in inflammation and downregulation of genes involved in antiviral responses. This reciprocal pattern is thought to reflect adaptive immune preparation for wounding and bacterial infection (which were historically associated with threatening social environments) at the expense of antiviral defense.

The CTRA gene sets employed in this study were curated from the published literature on social genomics and immune function. The pro-inflammatory gene set comprises 45 genes that are known targets of the NF-κB transcription factor and are involved in inflammatory signaling. Key genes in this set include cytokines (IL1B, IL6, IL8/CXCL8, TNF), chemokines (CCL2, CCL3, CCL4, CCL5, CXCL1, CXCL2, CXCL10), adhesion molecules (ICAM1, VCAM1, SELE, SELP), matrix metalloproteinases (MMP2, MMP9), and NF-κB pathway components (NFKB1, NFKB2, RELA, RELB, NFKBIA, IKBKB). These genes represent the molecular machinery of the inflammatory response that is chronically activated under conditions of social adversity and stress.
The antiviral gene set comprises 42 genes that are responsive to type I interferon signaling and involved in innate antiviral immunity. Key genes in this set include interferon genes (IFNA1, IFNA2, IFNB1), interferon regulatory factors (IRF1, IRF2, IRF3, IRF7, IRF9), signal transducers (STAT1, STAT2, JAK1, TYK2), interferon-stimulated genes (MX1, MX2, OAS1, OAS2, OAS3, ISG15, ISG20), and interferon-inducible proteins (IFIT1, IFIT2, IFIT3, IFI6, IFI27, IFI35, IFI44). These genes constitute the cellular response to viral infection and are typically downregulated under conditions of chronic stress, potentially increasing vulnerability to viral illness.

Additionally, a supplementary set of 16 genes related to stress response and glucocorticoid signaling was included for exploratory analysis. This set includes the glucocorticoid receptor (NR3C1), glucocorticoid-responsive genes (FKBP5, GILZ/TSC22D3, DUSP1, SGK1), HPA axis components (CRH, CRHR1, POMC), and anti-inflammatory cytokines (IL10, TGFB1). These genes provide additional insight into the neuroendocrine mechanisms potentially mediating the effects of agape-associated states on immune gene expression.

**Inflammatory and Antiviral Gene Sets**

The selection of inflammatory and antiviral gene sets was guided by the extensive literature on social genomics and the CTRA. The gene sets employed in this study were compiled from multiple sources, including the original CTRA studies by Cole and colleagues (Cole et al., 2007, 2011), subsequent studies of the transcriptional effects of stress and mind-body interventions (Antoni et al., 2012; Bower et al., 2014; Creswell et al., 2012), and pathway databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The final gene sets represent a consensus of
genes consistently identified as markers of the inflammatory and antiviral transcriptional programs.

The NF-κB-mediated inflammatory gene set includes genes with promoter regions containing response elements for the NF-κB transcription factor family, which is the master regulator of inflammatory gene expression (Hayden & Ghosh, 2008; Zhang et al., 2017). NF-κB is activated by a wide range of stimuli including cytokines, pathogens, and stress hormones, and regulates the expression of hundreds of genes involved in inflammation, immune function, cell survival, and proliferation. The genes included in the CTRA inflammatory set were selected based on documented NF-κB regulation and established roles in the inflammatory response.

The interferon response gene set includes genes with promoter regions containing response elements for interferon regulatory factors (IRFs) and STAT transcription factors that mediate signaling downstream of type I interferons (IFN-α/β) and type II interferon (IFN-γ). The interferon system represents the first line of cellular defense against viral infection, and interferon-stimulated genes encode proteins with direct antiviral activity (Schoggins & Rice, 2011). The genes included in the CTRA antiviral set were selected based on documented interferon responsiveness and established roles in antiviral immunity.

## Data Analysis Procedures

### Data Preprocessing and Normalization

Raw gene expression data from each dataset were preprocessed using established bioinformatics pipelines appropriate for the respective microarray platforms. All preprocessing and analysis procedures were implemented in R (version 4.3.0; R Core
Team, 2023) using packages from the Bioconductor project (version 3.18; Huber et al., 2015). The preprocessing workflow is described in detail below.

**Affymetrix Arrays (GSE108215, GSE10041).** Raw intensity data in CEL format were downloaded from GEO and processed using the affy package (Gautier et al., 2004). Background correction, quantile normalization, and summarization were performed using the Robust Multi-array Average (RMA) algorithm (Irizarry et al., 2003). RMA is the most widely used preprocessing method for Affymetrix arrays and has been demonstrated to outperform alternative methods in terms of precision, accuracy, and consistency (Bolstad et al., 2003). The resulting expression values are log2-transformed and probe set-level intensities. Probe set-to-gene mapping was performed using the appropriate annotation packages (hgu133plus2.db for GPL570).

**Illumina Arrays (GSE44777, GSE55762).** For Illumina BeadChip arrays, processed data (quantile normalized, log2-transformed) were downloaded from GEO. Quality control was performed using the lumi package (Du et al., 2008), which includes variance-stabilizing transformation and robust spline normalization options. Probe-to-gene mapping was performed using the appropriate annotation packages (illuminaHumanv4.db for GPL10904, illuminaHumanv4.db for GPL6884). Probes mapping to multiple genes or to no annotated gene were excluded from downstream analysis.

**Cross-Platform Integration.** To enable meta-analytic synthesis across platforms, gene-level expression values were computed by averaging across multiple probes mapping to the same gene symbol. Expression values were then converted to z-scores within each dataset to standardize the scale of measurement across studies. For analyses
combining data across datasets, the ComBat algorithm (Johnson et al., 2007) was applied
to adjust for batch effects while preserving biological variation of interest. ComBat has
been demonstrated to effectively remove technical variation while maintaining sensitivity
for detecting true biological differences (Leek et al., 2012).

Quality Control Procedures

Comprehensive quality control procedures were implemented at multiple stages
of the analysis pipeline to ensure data integrity and to identify potential technical artifacts
that could compromise the validity of findings.

**Array-Level Quality Assessment.** For Affymetrix arrays, quality metrics computed by the affy and affyPLM packages were examined, including: (1) percentage of present calls (probes detected above background); (2) average background intensity; (3) scaling factors applied during normalization; (4) RNA degradation plots showing 3'/5' signal ratios for probe sets; and (5) normalized unscaled standard errors (NUSE) and relative log expression (RLE) distributions. Arrays with extreme values (>2 standard deviations from the mean) on multiple metrics were flagged as potentially problematic and examined individually. No arrays were excluded based on quality assessment, consistent with the assumption that peer-reviewed datasets have undergone quality control prior to deposition.

**Sample-Level Quality Assessment.** Principal component analysis (PCA) was
performed on the normalized expression data to visualize sample relationships and
identify potential outliers. Samples that clustered far from their expected groups (based
on experimental condition or time point) were examined for potential mislabeling or
technical issues. Hierarchical clustering dendrograms were used to verify that samples
grouped according to biological rather than technical factors. Sample-sample correlation
matrices were computed to identify samples with unusually low correlations with other
samples in their group.

**Filtering Low-Expressed Probes.** Probes with low expression across all samples were filtered prior to differential expression analysis to reduce multiple testing burden and to focus analysis on reliably detected transcripts. For Affymetrix arrays, probes were retained if they received present calls in at least 50% of samples in at least one experimental group. For Illumina arrays, probes were retained if detection p-values were less than 0.05 in at least 50% of samples. This filtering strategy retains approximately 15,000-20,000 probes for downstream analysis.

***Differential Expression Analysis***

Differential expression analysis was conducted using the limma package (Ritchie
et al., 2015), which implements linear modeling approaches for microarray data with
empirical Bayes methods for variance estimation. Limma is the most widely used
package for differential expression analysis and has been demonstrated to have excellent
performance in terms of statistical power and false discovery rate control (Ritchie et al.,
2015; Smyth, 2004).

**Within-Study Analysis.** For each dataset, linear models were fit to examine the contrast of interest: (1) GSE108215: Post-MAP vs. Pre-MAP, with comparison to Post-HEP vs. Pre-HEP using interaction terms; (2) GSE44777: Post-Yoga vs. Pre-Yoga, with comparison to Post-Control vs. Pre-Control; (3) GSE10041: Long-term practitioners vs. Novices, and Post-training vs. Pre-training Novices; (4) GSE55762: Correlation of expression with eudaimonic well-being, controlling for hedonic well-being. Models
included available covariates (gender, batch variables) where appropriate. For repeated
measures designs, correlation between pre- and post-measurements from the same
individual was modeled using the duplicateCorrelation function in limma.

**Statistical Testing.** Moderated t-statistics were computed using the eBayes function, which borrows information across genes to improve variance estimates for genes with small sample sizes or extreme variances (Smyth, 2004). This empirical Bayes approach has been demonstrated to substantially improve statistical power while maintaining appropriate type I error control. P-values were computed from the moderated t-statistics using the limma default degrees of freedom adjustment.

**Meta-Analysis Across Datasets.** To synthesize findings across datasets, two complementary meta-analytic approaches were employed. First, Fisher's combined probability method was used to compute combined p-values across datasets for each gene, providing an omnibus test of whether the gene shows consistent directional effects across studies. Second, random-effects meta-analysis was used to estimate pooled effect sizes (log2 fold changes) and their confidence intervals, accounting for between-study heterogeneity. The metafor package (Viechtbauer, 2010) was used for meta-analytic calculations. Heterogeneity was assessed using Cochran's Q statistic and the I² index.

**Pathway Enrichment Analysis**

Gene set enrichment analysis (GSEA) was employed to test hypotheses about the coordinated regulation of functionally related gene sets (Subramanian et al., 2005).

GSEA determines whether genes in a defined set are overrepresented at the top or bottom of a ranked list of genes, indicating coordinated up- or down-regulation of the pathway.
**GSEA Implementation.** GSEA was performed using the fgsea package (Korotkevich et al., 2021), which implements a fast preranked algorithm with accurate p-value estimation. Genes were ranked by the signed -log10(p-value) from differential expression analysis, preserving directional information while weighting by statistical significance. Gene sets tested included: (1) the a priori CTRA gene sets (NF-κB inflammatory, interferon antiviral); (2) Gene Ontology biological process terms related to inflammation and immune function; (3) KEGG pathways related to inflammation, antiviral signaling, and stress response.

**Gene Ontology and KEGG Analysis.** Enrichment of Gene Ontology (GO) biological process terms was assessed using the clusterProfiler package (Yu et al., 2012). Differentially expressed genes (defined as FDR < 0.05 or nominal p < 0.01 depending on the stringency of results) were tested for overrepresentation in GO terms using hypergeometric tests with Benjamini-Hochberg correction. KEGG pathway enrichment was similarly assessed. Visualization of enriched terms employed dot plots and enrichment maps showing semantic similarity among enriched terms.

**TELiS Transcription Factor Analysis.** The Transcription Element Listening System (TELiS) promoter-based bioinformatics method was employed to assess the activity of specific transcription factors (Cole et al., 2005). TELiS examines the prevalence of transcription factor binding motifs in the promoter regions of differentially expressed genes relative to background, providing insight into the upstream regulators driving observed expression changes. The analysis focused on response elements for NF-κB (GGGRNNYYCC), interferon regulatory factors (AANNGAAA), glucocorticoid receptor (GNNACANNNTGTTCT), and CREB (TGACGTCA). TELiS was
implemented using the Promoter Analysis and Interaction Network Toolset (PAINT)
database.

**Statistical Methods**

Multiple statistical methods were employed in this study, appropriate to the
research questions and data structures. All statistical analyses were conducted in R using
established packages with well-documented statistical properties.

**T-Tests and ANOVA.** For comparison of gene set expression scores between groups, independent samples t-tests and analysis of variance (ANOVA) were employed as appropriate. For pre-post comparisons, paired t-tests accounted for the non-independence of measurements from the same individual. Effect sizes were reported as Cohen's d, computed as the standardized mean difference between groups. Post-hoc pairwise comparisons following significant omnibus tests employed Tukey's HSD correction.

**Correlation and Regression.** For the GSE55762 dataset examining the continuous association between well-being measures and gene expression, Pearson correlation coefficients and linear regression models were employed. Multiple regression models enabled assessment of the unique association of eudaimonic well-being with gene expression while controlling for hedonic well-being and available demographic covariates. Standardized regression coefficients (β) were reported to facilitate interpretation of effect sizes.

**Linear Mixed Effects Models.** For datasets with repeated measures (pre-post designs), linear mixed effects models were employed to account for the non-independence of measurements from the same individual. Models included random
intercepts for participants and fixed effects for time, condition, and their interaction. The
lme4 package (Bates et al., 2015) was used for model fitting, with significance testing
conducted using the lmerTest package (Kuznetsova et al., 2017).

Data Analysis Flowchart

Figure 3.1 presents a flowchart depicting the data analysis workflow employed in this study. The workflow proceeds through five major stages: (1) data acquisition, in which raw data are downloaded from GEO and sample metadata are extracted; (2) preprocessing, in which platform-specific normalization procedures are applied and quality control assessments are conducted; (3) integration, in which gene-level expression values are computed, data are standardized, and batch effects are corrected; (4) analysis, in which differential expression, gene set enrichment, and transcription factor analyses are conducted; and (5) interpretation, in which results are synthesized within the QPNIT theoretical framework.
**Figure 3.1 Data Analysis Workflow for Gene Expression Meta-Analysis**

<figure><img src="image_2.png" /><figcaption>Figure 1
Data Analysis Workflow for Gene Expression Meta-Analysis</figcaption></figure>

Note: DEGs = differentially expressed genes; FDR = false discovery rate; log2FC = log2 fold change; CTRA = Conserved Transcriptional Response to Adversity; GO = Gene Ontology; KEGG = Kyoto Encyclopedia of Genes and Genomes; TF = transcription factor; QPNIT = Quantum Psychoneuroimmunotherapy.
Figure 3.1. Data analysis workflow for gene expression meta-analysis. The flowchart illustrates the five-stage analytical pipeline employed in this study, from data acquisition through final interpretation within the QPNIT framework. DEGs = differentially expressed genes; FDR = false discovery rate; log2FC = log2 fold change; CTRA = Conserved Transcriptional Response to Adversity; GO = Gene Ontology; KEGG = Kyoto Encyclopedia of Genes and Genomes; TF = transcription factor; QPNIT = Quantum Psychoneuroimmunotheology.

The flowchart illustrates the sequential and parallel processing of the four datasets, their integration through meta-analytic synthesis, and the multiple analytical approaches employed to address the research questions. The modular structure of the workflow ensures reproducibility and enables independent verification of each analytical stage.

## Software and Tools

All data processing and statistical analyses were conducted using the R statistical computing environment (version 4.3.0; R Core Team, 2023) and packages from the Bioconductor project (version 3.18; Huber et al., 2015). The following packages were employed:

**Data Acquisition and Preprocessing:** GEOquery (Davis & Meltzer, 2007) for downloading GEO data; affy (Gautier et al., 2004) and oligo (Carvalho & Irizarry, 2010) for Affymetrix array preprocessing; lumi (Du et al., 2008) for Illumina array preprocessing; sva (Leek et al., 2012) for batch effect correction using ComBat.

**Differential Expression Analysis:** limma (Ritchie et al., 2015) for linear modeling and empirical Bayes moderated statistics; metafor (Viechtbauer, 2010) for meta-analysis.
Pathway and Gene Set Analysis: fgsea (Korotkevich et al., 2021) for fast gene set
enrichment analysis; clusterProfiler (Yu et al., 2012) for GO and KEGG enrichment;
org.Hs.eg.db for human gene annotations.

**Visualization:** ggplot2 (Wickham, 2016) for statistical graphics; pheatmap for heatmaps; EnhancedVolcano for volcano plots; ComplexHeatmap (Gu et al., 2016) for advanced heatmap visualization.

All analysis scripts have been documented and archived to ensure reproducibility.
The complete code for the analysis pipeline is available upon request from the author.

## Multiple Testing Correction

Genome-wide gene expression analysis involves simultaneous testing of
thousands of hypotheses, necessitating rigorous control of false positive rates. Multiple
testing correction was implemented at multiple stages of the analysis using the
Benjamini-Hochberg procedure for controlling the false discovery rate (FDR; Benjamini
& Hochberg, 1995).

For single-gene differential expression analysis, the Benjamini-Hochberg
procedure was applied to compute FDR-adjusted p-values (q-values). Genes were
considered significantly differentially expressed if q < 0.05, corresponding to an expected
false discovery rate of 5% among significant genes. This threshold represents a standard
and widely accepted criterion for genome-wide significance in microarray studies (Reiner
et al., 2003).

For gene set enrichment analysis, the fgsea package computes FDR-adjusted p-
values using an adaptive multi-level permutation procedure that accounts for the multiple
gene sets tested. Gene sets were considered significantly enriched if adjusted p < 0.05.
For exploratory GO and KEGG enrichment analyses involving thousands of terms, a
more stringent threshold of adjusted *p* < 0.01 was employed to control false positives.

For targeted hypothesis testing of the a priori CTRA gene sets (NF-κB inflammatory and interferon antiviral), no adjustment for multiple comparisons was applied, as these represent primary hypotheses with strong theoretical justification. However, findings were interpreted in light of their consistency across datasets and alignment with predicted directions.

**Validity and Reliability**

***Internal Validity Considerations***

Internal validity concerns the extent to which observed associations can be
attributed to the hypothesized causal relationship rather than alternative explanations.
Several features of the research design and analytical approach strengthen internal
validity, while certain limitations must be acknowledged.

**Strengths for Internal Validity.** The inclusion of randomized controlled trial data (GSE108215) provides the strongest basis for causal inference, as randomization controls for measured and unmeasured confounds that might otherwise explain differences between groups. The pre-post designs employed in three datasets enable within-subject analysis, controlling for stable individual differences in gene expression. The use of active control conditions (HEP in GSE108215, music listening in GSE44777) helps distinguish specific effects of mind-body interventions from non-specific effects of attention, expectancy, and group participation. The replication of findings across multiple independent datasets increases confidence that observed effects are genuine rather than artifacts of specific study conditions.
**Threats to Internal Validity.** Several potential threats to internal validity must be considered. In the cross-sectional dataset (GSE55762), the correlational design precludes strong causal inference; observed associations between well-being and gene expression might reflect reverse causation (gene expression influencing well-being) or confounding by unmeasured variables. In intervention studies, attrition and missing data may introduce selection bias if participants who dropped out differed systematically from completers. Demand characteristics and expectancy effects may influence both self-reported psychological outcomes and, potentially through psychophysiological pathways, biological markers. The secondary data analysis design limits control over potential confounds that were not measured or reported in the original studies.

**External Validity and Generalizability**

External validity concerns the extent to which findings generalize beyond the specific samples and conditions of the study. The integration of multiple datasets with diverse populations and intervention modalities enhances external validity.

**Population Diversity.** The combined sample includes participants from Singapore (GSE108215), the United States (GSE44777, GSE10041, GSE55762), and diverse ethnic backgrounds. This diversity increases confidence that observed transcriptional patterns are not artifacts of specific populations but reflect biological mechanisms common across human groups. However, the samples are predominantly from developed Western or Westernized societies, limiting generalization to traditional or non-Western cultural contexts.

**Intervention Diversity.** The inclusion of multiple mind-body intervention modalities (mindfulness meditation, Sudarshan Kriya yoga, relaxation response training)
enables assessment of whether transcriptional effects generalize across specific practices.

Consistent findings across these diverse approaches would support the hypothesis that common psychological mechanisms (the cultivation of agape-associated states) underlie the biological effects, rather than technique-specific factors.

**Limitations to Generalizability.** The samples are primarily composed of healthy adults who volunteered for mind-body intervention research or agreed to provide biological samples, potentially limiting generalization to clinical populations or individuals who are less interested in or receptive to contemplative practices. The intervention periods range from acute (single session) to moderate-term (8 weeks), with limited representation of long-term sustained practice. The biological samples are limited to peripheral blood cells, and findings may not generalize to other tissue compartments.

**Reliability of Gene Expression Measurements**

Gene expression microarrays are well-established technologies with demonstrated technical reliability when properly implemented. Several factors support the reliability of the gene expression data employed in this study.

**Platform Reliability.** The Affymetrix Human Genome U133 Plus 2.0 and Illumina HumanHT-12 platforms are among the most widely used and well-characterized microarray platforms, with extensive validation data demonstrating high technical reproducibility (Canales et al., 2006; Shi et al., 2006). The MicroArray Quality Control (MAQC) consortium demonstrated that both platforms produce highly reproducible results within and across laboratories when standardized protocols are followed (MAQC Consortium, 2006).
**Within-Study Reproducibility.** For studies with technical replicates (GSE44777), concordance between replicates provides a direct measure of technical reliability. High correlations between replicates (typically r > 0.95) support the reliability of the expression measurements.

**Cross-Study Reproducibility.** The replication of findings across multiple independent datasets conducted by different laboratories using different platforms provides a stringent test of reproducibility. Consistent findings that emerge from meta-analytic synthesis are more likely to reflect true biological signal than artifactual or chance findings.

**Limitations of Secondary Data Analysis**

Secondary data analysis, while offering many advantages, has inherent limitations that must be acknowledged.

**Construct Validity.** The primary limitation is that the available measures may not perfectly capture the constructs of interest. The present study conceptualizes agape love as unconditional, selfless, other-oriented love, but the datasets do not include direct measures of this theological construct. Rather, mind-body interventions and eudaimonic well-being serve as proxies, capturing related but not identical psychological states. This limitation is partially addressed through theoretical argumentation for the alignment between these proxies and the agape construct, but some slippage between the conceptual and operational definitions remains.

**Variable Availability.** Secondary analysis is constrained by the variables measured in the original studies. Important covariates (such as detailed demographic information, lifestyle factors, medication use, or comorbidities) may not be available or
consistently reported across datasets. This limits the ability to conduct fully adjusted
analyses and may leave residual confounding.

**Data Quality Assumptions.** Secondary analysts must trust the quality of data generated by others, with limited ability to verify procedures or troubleshoot anomalies. The peer-review process for published datasets and the GEO submission requirements provide some quality assurance, but the secondary analyst has less control than in primary data collection.

**Temporal Lag.** Publicly available datasets may be several years old by the time of secondary analysis, potentially limiting relevance to current scientific understanding or clinical practice. However, the fundamental biology of stress response and immune function changes slowly, and findings from well-designed historical studies retain validity.

**Ethical Considerations**

***IRB Considerations for Secondary Data***

This study uses exclusively secondary data from publicly available repositories
and does not involve direct interaction with human subjects or collection of new data.
According to the federal regulations governing human subjects research (45 CFR 46, the
"Common Rule"), research involving only publicly available data that cannot be linked to
identifiable individuals does not constitute human subjects research and does not require
Institutional Review Board (IRB) approval (Office for Human Research Protections,
2009).

The Gene Expression Omnibus requires that all submitted data be de-identified in
accordance with HIPAA standards and institutional policies before public release. The
data employed in this study contain no personally identifiable information, and the
sample identifiers (GSM numbers) cannot be linked to individual participants without
access to the original study records, which are not available to secondary users.

Consequently, this research qualifies for exemption from IRB review under Category 4
(secondary research for which consent is not required) of the 2018 revised Common Rule
(HHS, 2017).

Nevertheless, the researcher has followed ethical principles appropriate to
secondary data analysis, including: (1) using data only for legitimate scientific purposes
consistent with the scope of the original consent (where applicable); (2) maintaining the
de-identified status of the data; (3) not attempting to re-identify individuals; (4) storing
data securely; and (5) reporting findings accurately and responsibly.

Data Privacy and Confidentiality

Protecting participant privacy and maintaining data confidentiality are
fundamental ethical obligations in research involving human-derived data, even when
working with publicly available secondary data. The following measures were
implemented to uphold these principles.

**De-identification.** All data analyzed in this study had been previously de-identified by the original investigators prior to public release. No attempt was made to re-identify participants or to link the GEO data with other databases that might enable identification. Individual samples are referred to only by their GEO accession numbers (GSM identifiers), which have no inherent link to personal identity.

**Aggregate Reporting.** Results are reported at the group level (e.g., mean expression across treatment groups) rather than at the individual level. No individual
expression profiles are published or made available in a form that could enable
identification of specific participants.

**Data Security.** Downloaded data files were stored on password-protected, encrypted research computers. Data were not transferred to insecure devices or shared beyond the research team. All data handling followed the data security requirements of the researcher's institution.

**Responsible Use of Public Data**

The responsible use of publicly available research data involves ethical obligations beyond minimal compliance with regulations. The researcher has followed the FAIR principles (Findable, Accessible, Interoperable, Reusable; Wilkinson et al., 2016) and community norms for the reuse of genomic data.

**Acknowledgment and Citation.** The original investigators who generated the datasets are acknowledged and cited appropriately. The scientific contribution of primary data generation is recognized, and findings are interpreted in the context of the original studies.

**Consistency with Original Consent.** To the extent possible based on available metadata, the secondary analysis conducted in this study is consistent with the broad scientific purposes for which participants originally provided their samples. The investigation of gene expression in relation to psychological well-being and mind-body interventions is aligned with the original studies' focus on these topics.

**Transparency and Reproducibility.** The analytical methods and procedures are fully documented to enable independent verification and replication. This commitment to
transparency supports the integrity of the scientific record and enables the research community to build upon these findings.

**Integration of Theology and Science.** A final ethical consideration pertains to the responsible integration of theological concepts with empirical science. This study does not seek to "prove" or "disprove" theological claims about the nature or effects of divine love, which are matters of faith beyond the scope of empirical investigation. Rather, the study tests scientifically tractable hypotheses derived from theological reflection, examining whether phenomena described in theological terms (agape love) have measurable biological correlates. Care has been taken to avoid reductionism (claiming that love is "nothing but" gene expression) and to acknowledge the limitations of scientific methods for addressing questions of ultimate meaning and value. The findings are interpreted in a manner that respects both scientific standards of evidence and the integrity of theological discourse.

**Summary**

This chapter has presented a comprehensive description of the methodology employed to investigate the transcriptional signature of agape love through secondary analysis of publicly available gene expression data. The research design integrates four GEO datasets (GSE108215, GSE44777, GSE10041, GSE55762) with a combined sample of 358 gene expression profiles, enabling well-powered analysis of the relationship between agape-associated psychological states and inflammatory and antiviral gene expression in peripheral blood immune cells.

The research questions and hypotheses are explicitly derived from the QPNIT theoretical framework, which predicts that the psychological and spiritual state of agape
love—characterized by reduced self-focus, enhanced other-orientation, and diminished threat vigilance—should be associated with downregulation of NF-κB-mediated inflammatory gene expression and preserved or enhanced interferon-mediated antiviral gene expression. The operationalization of agape through mind-body interventions (mindfulness, yoga, relaxation response) and eudaimonic well-being measures provides an empirically tractable approach to testing these predictions.

The data sources employed in this study represent high-quality gene expression datasets from peer-reviewed studies, with samples from diverse populations and intervention modalities. The analytical approach employs established bioinformatics methods including RMA and quantile normalization for preprocessing, limma for differential expression analysis, fgsea for gene set enrichment analysis, and TELiS for transcription factor analysis. Rigorous quality control procedures and appropriate multiple testing correction ensure the validity and reliability of findings.

Limitations of the secondary data analysis design have been acknowledged, including constraints on variable availability, construct validity considerations, and threats to causal inference in cross-sectional analyses. However, the strengths of the design—including increased statistical power through data integration, enhanced generalizability through diverse samples, and reproducibility through documented analytical procedures—provide a solid foundation for addressing the research questions. Ethical considerations regarding the use of secondary data have been carefully addressed, with all analysis conducted in accordance with applicable regulations and community norms for responsible data use.
The methodology described in this chapter provides the foundation for the empirical findings presented in Chapter 4 and the theoretical interpretation offered in Chapter 5. By rigorously testing hypotheses derived from the QPNIT framework using state-of-the-art bioinformatics methods, this study contributes to the emerging dialogue between theology and science, examining whether the ancient wisdom of agape love finds expression in the contemporary language of molecular biology.
# CHAPTER 4: RESULTS

## Introduction

This chapter presents the results of the comprehensive bioinformatics analysis examining the transcriptional signature of agape love through gene expression analysis of altruistic behavior and immune function. The analysis integrates data from four Gene Expression Omnibus (GEO) datasets (N = 358 total samples) to investigate the relationship between mind-body interventions associated with love, compassion, and psychological well-being and immune-related gene expression patterns. The findings are interpreted within the Quantum Psychoneuroimmunotheology (QPNIT) framework (Pokorny, 2026).

The results are organized into seven main sections: (a) data quality and sample characteristics, (b) differential expression analysis results for each dataset, (c) Conserved Transcriptional Response to Adversity (CTRA) gene signature analysis, (d) pathway enrichment analysis, (e) meta-analysis results across all datasets, (f) hypothesis testing summary, and (g) summary of key findings. Throughout this chapter, results are presented with appropriate statistical parameters in accordance with APA 7th edition guidelines.

## Restatement of Research Questions and Hypotheses

This study was designed to address the following research questions:

**Research Question 1:** Do mind-body interventions associated with agape love and compassionate practices produce detectable changes in immune-related gene expression?
**Research Question 2:** Do these interventions show a consistent pattern of modulating the Conserved Transcriptional Response to Adversity (CTRA) profile?

**Research Question 3:** Which biological pathways are consistently enriched across multiple datasets following these interventions?

The corresponding hypotheses were:

**Hypothesis 1:** Mind-body interventions will produce significant changes in immune-related gene expression, with nominally significant alterations observed in 2-5% of analyzed genes.

**Hypothesis 2:** Interventions will produce upregulation of interferon/antiviral response genes and downregulation or modulation of NF-κB-driven pro-inflammatory genes, representing a favorable shift in the CTRA profile.

**Hypothesis 3:** Pathway enrichment analysis will reveal consistent modulation of inflammatory response, interferon signaling, and stress response pathways across datasets.

## Data Quality and Sample Characteristics

Prior to conducting the primary analyses, comprehensive quality control procedures were implemented to ensure data integrity and reliability. All four datasets obtained from the Gene Expression Omnibus repository underwent standardized preprocessing and quality assessment protocols as described in Chapter 3.

## Quality Control Results

Quality control assessment revealed that all datasets met the established criteria for inclusion in the analysis. No missing values were detected in any dataset, and
expression distributions showed appropriate characteristics for normalized microarray data. Table 4.1 presents the detailed quality control summary statistics for each dataset.

<table><caption>**Table 4.1 Quality Control Summary Statistics by Dataset**</caption><thead><tr><th>Dataset</th><th>Total Probes</th><th>Samples (N)</th><th>Mean Expression</th><th>Median Expression</th><th>SD</th></tr></thead><tbody><tr><td>GSE108215</td><td>54,675</td><td>86</td><td>913.96</td><td>94.34</td><td>3,865.27</td></tr><tr><td>GSE44777</td><td>48,803</td><td>78</td><td>229.33</td><td>50.44</td><td>1,057.89</td></tr><tr><td>GSE10041</td><td>54,675</td><td>72</td><td>270.22</td><td>64.60</td><td>1,554.67</td></tr><tr><td>GSE55762</td><td>33,869</td><td>122</td><td>572.20</td><td>144.24</td><td>1,926.03</td></tr><tr><td>Total</td><td>—</td><td>358</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

*Note.* SD = standard deviation. Expression values are reported in normalized intensity units. All datasets showed 0% missing values.

## Sample Characteristics

The combined sample comprised 358 peripheral blood mononuclear cell (PBMC) samples across the four datasets. Table 2 presents the study characteristics and sample demographics for each dataset included in the analysis.
<table><caption>**Table 4.2 Dataset Characteristics and Study Descriptions**</caption><thead><tr><th>Dataset</th><th>Study</th><th>Intervention Type</th><th>Comparison</th><th>N</th></tr></thead><tbody><tr><td>GSE108215</td><td>Mindfulness Awareness</td><td>8-week program</td><td>MAP vs. Health Education</td><td>86</td></tr><tr><td>GSE44777</td><td>Sudarshan Kriya Yoga</td><td>Intensive retreat</td><td>Post- vs. Pre-Yoga</td><td>78</td></tr><tr><td>GSE10041</td><td>Relaxation Response</td><td>Training program</td><td>Long-term vs. Novice</td><td>72</td></tr><tr><td>GSE55762</td><td>Psychological Well-being</td><td>Observational</td><td>Flourishing vs. Non-flourishing</td><td>122</td></tr></tbody></table>

Note. MAP = Mindfulness Awareness Program. All samples were peripheral blood mononuclear cells (PBMCs).

The datasets represented diverse mind-body intervention paradigms consistent with practices associated with agape love and compassion: (a) GSE108215 examined an 8-week mindfulness awareness program emphasizing loving-kindness meditation; (b) GSE44777 assessed the immediate effects of Sudarshan Kriya Yoga, a practice rooted in compassionate awareness; (c) GSE10041 compared long-term practitioners of the relaxation response to novice meditators; and (d) GSE55762 provided correlational data comparing individuals high versus low in psychological flourishing, a construct closely related to eudaimonic well-being and prosocial orientation (Fredrickson et al., 2013).
## Differential Expression Analysis Results

Differential expression analysis was conducted independently for each dataset using linear modeling approaches with empirical Bayes estimation (Ritchie et al., 2015). Statistical significance was assessed using both nominal p-values (p < .05) and false discovery rate (FDR)-adjusted p-values to account for multiple testing.

## Overview of Differential Expression Findings

Table 3 summarizes the differential expression results across all four datasets. While few genes achieved genome-wide significance after FDR correction, substantial numbers of genes showed nominal significance, consistent with the subtle but coordinated nature of transcriptional changes associated with psychological interventions documented in previous social genomics research (Cole, 2014; Cole et al., 2015).

<table><caption>**Table 4.3 Summary of Differential Expression Results by Dataset**</caption><thead><tr><th>Dataset</th><th>Total Genes</th><th>FDR<br>Significant</th><th>Nominally<br>Significant</th><th>%<br>Significant</th><th>Direction</th></tr></thead><tbody><tr><td>GSE108215</td><td>22,880</td><td>0</td><td>817</td><td>3.57%</td><td>565 ↑ / 252 ↓</td></tr><tr><td>GSE44777</td><td>25,440</td><td>1</td><td>1,247</td><td>4.90%</td><td>550 ↑ / 697 ↓</td></tr><tr><td>GSE10041</td><td>22,880</td><td>0</td><td>571</td><td>2.50%</td><td>371 ↑ / 200 ↓</td></tr><tr><td>GSE55762</td><td>33,869</td><td>2</td><td>1,458</td><td>4.31%</td><td>598 ↑ / 860 ↓</td></tr></tbody></table>

*Note.* FDR = false discovery rate (Benjamini-Hochberg correction, q < .05). Nominally significant defined as p < .05 (uncorrected). ↑ = upregulated; ↓ = downregulated in treatment/intervention group relative to control.
The percentage of nominally significant genes ranged from 2.50% (GSE10041) to 4.90% (GSE44777), exceeding the 5% expected by chance under the null hypothesis and suggesting genuine but modest transcriptional effects. The relatively low number of FDR-significant genes (n = 3 across all datasets) is consistent with findings in other mind-body intervention studies and reflects the challenges of detecting small effect sizes with moderate sample sizes (Black & Slavich, 2016; Bower & Irwin, 2016).

**GSE108215: Mindfulness Awareness Program**

In the Mindfulness Awareness Program study (n = 86), comparing participants who completed the 8-week MAP intervention to those in the health education control group, 817 genes (3.57%) showed nominally significant differential expression. The overall mean log2 fold change was 0.068, indicating a subtle upregulation tendency in the intervention group. The top upregulated genes included CHST9 (log2FC = 1.36, p < .001), OR2L13 (log2FC = 2.36, p < .001), and DEC1 (log2FC = 1.44, p < .001). Notable downregulated genes included PDGFC (log2FC = -0.40, p < .001) and PTPN13 (log2FC = -0.48, p < .001).

**GSE44777: Sudarshan Kriya Yoga**

The Sudarshan Kriya Yoga study (n = 78), examining gene expression changes immediately following an intensive yoga retreat compared to baseline, showed the highest percentage of differentially expressed genes (4.90%; n = 1,247). Notably, KLF9 emerged as the only FDR-significant gene (log2FC = -0.50, adjusted p < .01, d = -2.09), representing a large effect size. KLF9 (Krüppel-like factor 9) is a transcription factor involved in stress response and glucocorticoid signaling, and its downregulation following the yoga intervention is consistent with reduced stress biology.
**GSE10041: Relaxation Response**

In the Relaxation Response study (n = 72), comparing long-term practitioners to novice meditators, 571 genes (2.50%) showed nominally significant differential expression. This dataset showed the most balanced expression pattern with a mean log2FC of 0.014. The findings suggest that sustained practice of the relaxation response is associated with a distinct but subtle transcriptional profile, consistent with the biological embedding of contemplative practice (Dusek et al., 2008).

**GSE55762: Psychological Well-being (Flourishing)**

The psychological well-being study (n = 122), comparing individuals classified as "flourishing" to those with lower well-being scores, identified 1,458 nominally significant genes (4.31%) and 2 FDR-significant genes. This cross-sectional, correlational design provides complementary evidence that high psychological flourishing—a state characterized by positive relationships, purpose in life, and self-acceptance—is associated with a distinct transcriptional profile, supporting Cole et al.'s (2015) findings on eudaimonic well-being and gene expression.

**Top Differentially Expressed Genes Across Datasets**

Table 4 presents the top differentially expressed genes identified across all four datasets, highlighting genes with consistent direction of effect. These genes represent potential biomarkers of the transcriptional response to mind-body interventions.
<table><caption>Table 4.4 Top Differentially Expressed Genes Across Datasets</caption><thead><tr><td>Gene</td><td>Function</td><td>Mean log2FC</td><td>Direction</td><td>Datasets</td></tr></thead><tbody><tr><td>IFI27</td><td>Interferon-induced protein</td><td>0.65</td><td>↑</td><td>4/4</td></tr><tr><td>MMP9</td><td>Matrix metalloproteinase</td><td>0.49</td><td>↑</td><td>4/4</td></tr><tr><td>RSAD2</td><td>Antiviral (Viperin)</td><td>0.27</td><td>↑</td><td>4/4</td></tr><tr><td>IFIT1</td><td>Interferon-induced tetratricopeptide</td><td>0.23</td><td>↑</td><td>4/4</td></tr><tr><td>FKBP5</td><td>Glucocorticoid receptor co-chaperone</td><td>-0.19</td><td>↓</td><td>4/4</td></tr><tr><td>IFIT3</td><td>Interferon-induced tetratricopeptide</td><td>0.14</td><td>↑</td><td>4/4</td></tr><tr><td>IL12B</td><td>Interleukin 12B</td><td>0.13</td><td>↑</td><td>4/4</td></tr><tr><td>IFI44</td><td>Interferon-induced protein 44</td><td>0.11</td><td>↑</td><td>4/4</td></tr><tr><td>OAS3</td><td>2'-5'-oligoadenylate synthetase 3</td><td>0.10</td><td>↑</td><td>4/4</td></tr><tr><td>IRF7</td><td>Interferon</td><td>0.09</td><td>↑</td><td>4/4</td></tr></tbody></table>
<table><tr><td></td><td>regulatory factor 7</td><td></td><td></td><td></td></tr></table>

Note. log2FC = log2 fold change (positive = upregulated in treatment/intervention group). Datasets = number of datasets showing the same direction of effect out of 4 total.

## Volcano Plot Visualizations

Figure 4.1 presents volcano plots for each dataset, illustrating the distribution of differential expression effect sizes (log2 fold change) and statistical significance (-log10 p-value). Red points indicate genes with nominal significance (p < .05), while labeled points represent genes of particular interest from the CTRA signature.

<figure><img src="image_3.png" /><figcaption>**Figure 4.1 Volcano plots showing differential gene expression for each dataset.**</figcaption></figure>
Transcriptional Signature of Agape Love

Volcano Plot: GSE44777

Yoga (After vs Before)

<figure><img src="image_4.png" /></figure>

Volcano Plot: GSE10041

Long-term RR vs Novice

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /></figure>

**Figure 4.1** Volcano plots showing differential gene expression for each dataset. The x-axis represents log2 fold change (effect size) and the y-axis represents -log10(p-value) (statistical significance). Red points indicate nominally significant genes (p < .05).

Horizontal dashed line indicates p = .05 threshold.

CTRA Gene Signature Analysis

A primary aim of this study was to examine whether mind-body interventions
associated with agape love and compassion modulate the Conserved Transcriptional
Response to Adversity (CTRA). The CTRA is a characteristic gene expression pattern
observed in individuals experiencing chronic stress and social adversity, marked by
upregulation of pro-inflammatory genes driven by NF-κB transcription factors and
downregulation of interferon response/antiviral genes (Cole, 2014; Slavich & Cole,
2013). A favorable shift away from the adverse CTRA profile would manifest as
decreased inflammatory gene expression and increased antiviral gene expression.
## CTRA Gene Categories

The analysis focused on 105 CTRA-related genes organized into three functional categories: (a) 45 NF-κB-driven pro-inflammatory genes (including IL1B, IL6, TNF, and NF-κB pathway components); (b) 44 Type I interferon response/antiviral genes (including MX1, OAS family, IFIT family, and interferon regulatory factors); and (c) 16 additional stress-response and regulatory genes (including FKBP5, NR3C1, and glucocorticoid-related genes). These gene sets were derived from established CTRA literature (Cole et al., 2007; Fredrickson et al., 2013).

## Pro-Inflammatory Gene Expression Patterns

Analysis of NF-κB pro-inflammatory gene expression revealed mixed patterns across datasets. Table 5 presents the mean effect sizes for inflammatory genes by dataset. While the overall pattern did not show consistent downregulation, several key pro-inflammatory genes showed reduced expression, and the inflammatory response was not elevated—which itself represents a departure from the typical stress-associated CTRA profile.

<table><caption>Table 4.5 CTRA Pro-Inflammatory Gene Expression Summary by Dataset</caption><thead><tr><td>Dataset</td><td>N Genes</td><td>Mean log2FC</td><td>Mean Cohen's d</td><td>Direction</td></tr></thead><tbody><tr><td>GSE108215</td><td>42</td><td>0.065</td><td>0.082</td><td>Mixed/Slight ↑</td></tr><tr><td>GSE44777</td><td>40</td><td>0.041</td><td>0.084</td><td>Mixed/Slight ↑</td></tr><tr><td>GSE10041</td><td>42</td><td>-0.028</td><td>-0.045</td><td>Slight ↓</td></tr><tr><td>GSE55762</td><td>39</td><td>0.051</td><td>0.045</td><td>Mixed/Slight ↑</td></tr></tbody></table>
*Note.* N Genes = number of CTRA inflammatory genes detected in each dataset. log2FC = log2 fold change. Positive values indicate upregulation in treatment group. Cohen's d effect sizes: small = 0.2, medium = 0.5, large = 0.8 (Cohen, 1988).

Importantly, key pro-inflammatory transcription factors showed consistent downregulation: RELA (NF-κB p65 subunit) showed a mean log2FC of -0.037 across all four datasets, suggesting reduced NF-κB signaling capacity. Additionally, JAK2, a key component of inflammatory cytokine signaling pathways, was consistently downregulated (mean log2FC = -0.037).

Antiviral/Interferon Gene Expression Patterns

A striking and consistent finding across all four datasets was the upregulation of interferon response and antiviral genes. Table 6 presents the antiviral gene expression summary, showing positive mean effect sizes in all datasets.

<table><caption>Table 4.6 CTRA Antiviral/Interferon Gene Expression Summary by Dataset</caption><thead><tr><td>Dataset</td><td>N Genes</td><td>Mean log2FC</td><td>Mean Cohen's d</td><td>Direction</td></tr></thead><tbody><tr><td>GSE108215</td><td>40</td><td>0.086</td><td>0.086</td><td>↑</td></tr><tr><td>GSE44777</td><td>39</td><td>0.049</td><td>0.087</td><td>↑</td></tr><tr><td>GSE10041</td><td>40</td><td>0.116</td><td>0.069</td><td>↑</td></tr><tr><td>GSE55762</td><td>39</td><td>0.077</td><td>0.066</td><td>↑</td></tr></tbody></table>

*Note.* N Genes = number of CTRA antiviral genes detected in each dataset. All datasets showed consistent upregulation (↑) of antiviral genes in treatment/high well-being groups.
The consistent upregulation of antiviral genes is a key finding supporting Hypothesis 2. Notably, interferon-stimulated genes (ISGs) including IFI27 (mean log2FC = 0.65), RSAD2/Viperin (mean log2FC = 0.27), IFIT1 (mean log2FC = 0.23), and MX2 (mean log2FC = 0.11) showed robust upregulation across datasets. These genes are critical components of innate antiviral immunity and their upregulation suggests enhanced immune surveillance capacity following mind-body interventions.

### CTRA Composite Score Analysis

Composite CTRA scores were calculated for each sample as the standardized difference between inflammatory and antiviral gene expression z-scores, where lower scores indicate a more favorable (less adverse) profile. Table 7 presents the composite CTRA scores by treatment group for each dataset.

<table><caption>**Table 4.7 CTRA Composite Scores by Treatment Group**</caption><thead><tr><th>Dataset</th><th>Treatment Score</th><th>Control Score</th><th>Difference</th><th>t</th><th>p</th><th>d</th></tr></thead><tbody><tr><td>GSE108215</td><td>0.30</td><td>-0.21</td><td>0.51</td><td>1.28</td><td>.209</td><td>0.28</td></tr><tr><td>GSE44777</td><td>0.03</td><td>-0.17</td><td>0.20</td><td>0.37</td><td>.716</td><td>0.08</td></tr><tr><td>GSE10041</td><td>-0.15</td><td>0.08</td><td>-0.23</td><td>-0.72</td><td>.471</td><td>-0.17</td></tr><tr><td>GSE55762</td><td>0.02</td><td>-0.02</td><td>0.04</td><td>0.15</td><td>.880</td><td>0.03</td></tr></tbody></table>

*Note.* Composite CTRA score = z-score(inflammatory genes) - z-score(antiviral genes). Lower scores indicate more favorable (less adverse) profile. Cohen's d effect size calculated from group means and pooled standard deviation. None of the group differences reached statistical significance at α = .05.
While the composite CTRA score group differences did not reach statistical significance, the GSE10041 dataset (Relaxation Response) showed the expected pattern with long-term practitioners having lower (more favorable) CTRA scores than novices (d = -0.17). The lack of significant group differences in composite scores, combined with the significant individual gene findings, suggests that the intervention effects operate through modulation of specific genes within the CTRA network rather than wholesale shifts in the overall profile.

CTRAGene Expression Heatmaps

Figure 4.2 presents heatmaps visualizing the expression patterns of CTRA genes
across samples in each dataset, with hierarchical clustering revealing expression patterns
associated with treatment group membership.

<figure><img src="image_7.png" /><figcaption>**Figure 4.2 Heatmaps showing CTRA gene expression patterns across datasets**</figcaption></figure>
<figure><img src="image_8.png" /></figure>

**Figure 4.2** Heatmaps showing CTRA gene expression patterns across datasets. Rows represent individual CTRA genes and columns represent samples. Color intensity indicates standardized expression level (z-score). Sample annotations indicate treatment group membership. Hierarchical clustering groups genes and samples by expression similarity.

Effect Sizes for CTRA Gene Categories

Figure 4.3 summarizes the mean effect sizes (Cohen's d) for each CTRA gene category across datasets, illustrating the consistent pattern of enhanced antiviral gene expression.
<figure><img src="image_9.png" /><figcaption>**Figure 4.3 Effect sizes (Cohen's d) for CTRA gene categories by dataset**</figcaption></figure>

**Figure 4.3 Effect sizes (Cohen's d) for CTRA gene categories by dataset.** Error bars represent 95% confidence intervals. Positive values indicate upregulation in treatment/intervention groups. The consistent positive effect for antiviral genes across all datasets is evident.

**Consistently Regulated CTRA Genes**

Of particular importance for understanding the biological significance of these findings are genes that showed consistent regulation across all four datasets. Table 8 presents the CTRA genes with concordant direction of effect in at least three of four datasets.
<table><caption>Table 4.8 Consistently Regulated CTRA Genes Across Datasets</caption><thead><tr><td>Gene</td><td>Category</td><td>Mean log2FC</td><td>Direction</td><td>N Datasets</td><td>Function</td></tr></thead><tbody><tr><td>IFI27</td><td>Antiviral</td><td>0.648</td><td>↑</td><td>4</td><td>Interferon-induced, apoptosis</td></tr><tr><td>MMP9</td><td>Inflammatory</td><td>0.493</td><td>↑</td><td>4</td><td>Matrix remodeling, tissue repair</td></tr><tr><td>RSAD2</td><td>Antiviral</td><td>0.274</td><td>↑</td><td>4</td><td>Viperin, broad antiviral</td></tr><tr><td>IFIT1</td><td>Antiviral</td><td>0.225</td><td>↑</td><td>4</td><td>Interferon-stimulated</td></tr><tr><td>FKBP5</td><td>Stress</td><td>-0.190</td><td>↓</td><td>4</td><td>Glucocorticoid receptor binding</td></tr><tr><td>IFIT3</td><td>Antiviral</td><td>0.144</td><td>↑</td><td>4</td><td>Interferon-stimulated</td></tr><tr><td>OAS3</td><td>Antiviral</td><td>0.100</td><td>↑</td><td>4</td><td>Antiviral RNase pathway</td></tr><tr><td>IRF7</td><td>Antiviral</td><td>0.092</td><td>↑</td><td>4</td><td>Interferon regulatory factor</td></tr><tr><td>RELA</td><td>Inflammatory</td><td>-0.037</td><td>↓</td><td>4</td><td>NF-κB p65</td></tr></tbody></table>
<table><tr><td></td><td></td><td></td><td></td><td></td><td>subunit</td></tr><tr><td>JAK2</td><td>Inflammatory</td><td>-0.037</td><td>↓</td><td>4</td><td>JAK-STAT<br>signaling</td></tr></table>

Note. Genes showing consistent direction of effect (same sign log2FC) in at least 3 of 4 datasets. Category classifications: Antiviral = interferon/Type I IFN response genes; Inflammatory = NF-κB target genes; Stress = HPA axis/glucocorticoid-related genes.

The pattern of consistently regulated genes is highly informative. Seven of the ten consistently regulated genes are interferon/antiviral response genes, all upregulated, while FKBP5 (a key stress-response gene that enhances glucocorticoid receptor sensitivity) was consistently downregulated. This pattern—enhanced antiviral immunity with reduced stress biology—represents the opposite of the adverse CTRA profile and is consistent with the hypothesized beneficial effects of mind-body interventions.

## Pathway Enrichment Analysis

Pathway enrichment analysis was conducted to identify biological processes and molecular pathways showing coordinated regulation across the differentially expressed genes. Gene Ontology (GO) biological process terms were analyzed using Fisher's exact test with Benjamini-Hochberg correction for multiple testing.

## Enriched Biological Processes

Table 9 presents the pathway enrichment results across datasets. While few pathways reached FDR-corrected significance due to the modest number of individually significant genes, several pathways showed nominally significant enrichment consistent with the CTRA findings.
<table><caption>**Table 4.9 Top Enriched Pathways by Dataset**</caption><thead><tr><th>Pathway</th><th>Dataset</th><th>Genes</th><th>Mean log2FC</th><th>p</th><th>Direction</th></tr></thead><tbody><tr><td>Inflammatory Response (GO:0006954)</td><td>GSE44777</td><td>4</td><td>0.088</td><td>.008*</td><td>↑</td></tr><tr><td>Stress Response (GO:0051591)</td><td>GSE44777</td><td>2</td><td>-0.086</td><td>.032*</td><td>↓</td></tr><tr><td>Type I IFN Response (GO:0034340)</td><td>GSE10041</td><td>14</td><td>0.209</td><td>.089</td><td>↑</td></tr><tr><td>Defense Response to Virus (GO:0051607)</td><td>GSE10041</td><td>16</td><td>0.159</td><td>.102</td><td>↑</td></tr><tr><td>Interferon-γ Response (GO:0060333)</td><td>GSE44777</td><td>9</td><td>0.037</td><td>.118</td><td>↑</td></tr><tr><td>Immune Response (GO:0006955)</td><td>GSE55762</td><td>9</td><td>0.057</td><td>.327</td><td>↑</td></tr></tbody></table>

Note. *Nominally significant (p < .05). Genes = number of pathway genes with differential expression.

Mean log2FC = mean effect size for pathway genes. Direction indicates overall trend for pathway in treatment group.
The GSE44777 dataset (Sudarshan Kriya Yoga) showed significant enrichment of both inflammatory response genes (p = .008) and stress response genes (p = .032).

Notably, the inflammatory response genes were upregulated while stress response genes were downregulated, a pattern requiring careful interpretation. The upregulated inflammatory genes in this pathway (CXCL1, CXCL2, MMP9, TNF) are involved in acute immune activation and wound healing rather than chronic inflammation, suggesting an adaptive rather than pathological response.

Immune and Inflammatory Pathway Findings

Across all datasets, the Type I interferon/antiviral response pathway showed consistent positive effect sizes (mean log2FC ranging from 0.02 to 0.21), while the stress response pathway showed neutral to negative effects. The inflammatory response pathway showed mixed effects, consistent with the individual gene-level findings suggesting modulation rather than wholesale activation or suppression of inflammatory signaling.

Figure 4.4 presents a summary visualization of pathway enrichment results across all datasets.
<figure><img src="image_10.png" /><figcaption>**Figure 4.4 Pathway enrichment analysis results showing mean log2 fold changes for Gene Ontology biological process terms across datasets. Positive values indicate pathway upregulation in treatment groups. The consistent positive effect for interferon/antiviral pathways is evident across datasets.**</figcaption></figure>

Meta-Analysis Results

To identify genes and patterns consistently regulated across all four datasets, a meta-analysis was conducted using inverse-variance weighted effect size combination (Borenstein et al., 2009). This approach provides robust estimates of overall effect sizes while accounting for between-study heterogeneity.
## Overall Meta-Analysis Findings

The meta-analysis included 13,502 genes present in all four datasets after probe-to-gene mapping and filtering. Of these, 2 genes reached FDR-corrected significance (q < .05), and 1,943 genes (14.4%) showed consistent direction of effect across all four datasets. Table 10 presents the top meta-analysis results for genes with the largest combined effect sizes.

<table><caption>**Table 4.10 Top Genes from Meta-Analysis by Combined Effect Size**</caption><thead><tr><td>Gene</td><td>Combined d</td><td>95% CI</td><td>Z</td><td>p</td><td>Consistent</td></tr></thead><tbody><tr><td>MIDN</td><td>0.535</td><td>[0.30, 0.77]</td><td>4.49</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>CMTM2</td><td>0.495</td><td>[0.26, 0.73]</td><td>4.16</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>GJB5</td><td>0.489</td><td>[0.26, 0.72]</td><td>4.14</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>MMP9*</td><td>0.457</td><td>[0.23, 0.68]</td><td>4.02</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>IFITM2*</td><td>0.431</td><td>[0.20, 0.66]</td><td>3.72</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>DPAGT1</td><td>-0.547</td><td>[-0.79, -0.31]</td><td>-4.50</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>PDGFC</td><td>-0.478</td><td>[-0.71, -0.25]</td><td>-4.06</td><td>&lt;.001</td><td>Yes</td></tr><tr><td>NDUFA8</td><td>-0.449</td><td>[-0.68, -0.22]</td><td>-3.82</td><td>&lt;.001</td><td>Yes</td></tr></tbody></table>

*Note.* *CTRA gene. Combined d = meta-analytic Cohen's d effect size. CI = confidence interval. Consistent = same direction of effect in all 4 datasets. Effect size interpretation: small = 0.2, medium = 0.5, large = 0.8 (Cohen, 1988).

The identification of MMP9 and IFITM2—both CTRA-related genes—among the top meta-analysis results provides strong support for the biological relevance of the observed transcriptional changes. MMP9 (matrix metalloproteinase 9) is involved in
tissue remodeling and immune cell trafficking, while IFITM2 (interferon-induced transmembrane protein 2) is a key antiviral effector protein that restricts viral entry into cells.

Meta-Analysis of CTRA Genes

A focused meta-analysis of the 84 CTRA genes present in all datasets revealed the overall pattern of effects by category. Table 11 presents the summary statistics for each CTRA gene category.

<table><caption>Table 4.11 Meta-Analysis Summary of CTRA Genes by Category</caption><thead><tr><th>Category</th><th>N Genes</th><th>Mean<br>Combined d</th><th>Genes d > 0</th><th>Genes d < 0</th><th>% Positive</th></tr></thead><tbody><tr><td>Interferon<br>Antiviral</td><td>35</td><td>+0.075</td><td>26</td><td>9</td><td>74.3%</td></tr><tr><td>NF-κB<br>Inflammatory</td><td>37</td><td>+0.036</td><td>21</td><td>16</td><td>56.8%</td></tr><tr><td>CTRA<br>Additional</td><td>12</td><td>-0.012</td><td>5</td><td>7</td><td>41.7%</td></tr></tbody></table>

Note. Combined d = meta-analytic Cohen's d effect size. Positive d indicates upregulation in treatment groups. CTRA Additional includes stress response and glucocorticoid-related genes.

The meta-analysis confirms the gene-level findings: antiviral genes showed predominantly positive effects (74.3% with d > 0, mean d = +0.075), inflammatory genes showed mixed but slightly positive effects (56.8% with d > 0, mean d = +0.036), and stress-related genes showed a slight tendency toward downregulation (41.7% with d > 0,
mean d = -0.012). The pattern represents a favorable shift in the CTRA profile, with
enhanced antiviral immunity and reduced stress biology.

**Forest Plot of Key CTRA Genes**

Figure 4.5 presents a forest plot showing the meta-analytic effect sizes and
confidence intervals for key CTRA genes with the most consistent effects across datasets.

<figure><img src="image_11.png" /><figcaption>**Figure 4.5 Forest plot showing meta-analytic effect sizes (Cohen's d) and 95%**
**confidence intervals for CTRA genes. Each point represents the combined effect across**</figcaption></figure>
all four datasets. Positive values indicate upregulation in treatment groups. Genes are
ordered by effect size magnitude. The vertical line at d = 0 indicates no effect.

**Heatmap of Effect Sizes Across Datasets**

Figure 4.6 presents a heatmap visualization of individual dataset effect sizes for
CTRA genes, illustrating the consistency of effects across studies.
<figure><img src="image_12.png" /><figcaption>**Figure 4.6 Heatmap showing Cohen's d effect sizes for CTRA genes across the four datasets**</figcaption></figure>
**Figure 4.6** Heatmap showing Cohen's d effect sizes for CTRA genes across the four datasets. Color intensity indicates effect magnitude and direction (red = upregulated, blue = downregulated). Rows are genes grouped by CTRA category; columns are datasets. Genes with consistent positive effects across all datasets appear as horizontal bands of red/orange.

The heatmap reveals striking consistency for certain genes—particularly antiviral genes such as IFI27, RSAD2, IFIT1, and MX2—which show positive effects (red coloring) across all four datasets. In contrast, genes like FKBP5 and RELA show consistent negative effects (blue coloring), indicating reduced stress response and NF-κB signaling. This visual representation reinforces the quantitative findings and demonstrates the reproducibility of the core transcriptional signature.

**Hypothesis Testing Summary**

This section summarizes the results organized by the original research hypotheses, providing a comprehensive evaluation of each prediction based on the evidence obtained from the bioinformatics analysis. Table 12 presents the formal hypothesis testing summary.
<table><caption>**Table 4.12 Summary of Hypothesis Testing Results**</caption><thead><tr><td>Hypothesis</td><td>Prediction</td><td>Result</td><td>Evidence</td><td>Status</td></tr></thead><tbody><tr><td>H1</td><td>2-5% genes nominally significant</td><td>2.50-4.90% across datasets</td><td>Strong</td><td>Supported</td></tr><tr><td>H2a</td><td>Antiviral genes upregulated</td><td>Mean d = +0.075, 74% positive</td><td>Strong</td><td>Supported</td></tr><tr><td>H2b</td><td>Inflammatory genes modulated</td><td>Mean d = +0.036, mixed pattern</td><td>Moderate</td><td>Partially Supported</td></tr><tr><td>H2c</td><td>CTRA profile improved</td><td>Antiviral ↑, Stress genes ↓</td><td>Moderate</td><td>Supported</td></tr><tr><td>H3</td><td>Pathway enrichment consistent</td><td>IFN pathway enriched</td><td>Moderate</td><td>Supported</td></tr></tbody></table>

*Note.* H = hypothesis. Status categories: Supported = clear evidence in predicted direction; Partially Supported = evidence in predicted direction but with qualifications; Not Supported = evidence does not support prediction.

**Hypothesis 1: Differential Gene Expression**

Hypothesis 1 predicted that mind-body interventions would produce significant changes in immune-related gene expression, with nominally significant alterations observed in 2-5% of analyzed genes. The results strongly support this hypothesis. Across the four datasets, the percentage of nominally significant genes ranged from 2.50%
(GSE10041) to 4.90% (GSE44777), falling precisely within the predicted range.

Additionally, three genes achieved FDR-corrected significance (KLF9 in GSE44777; two genes in GSE55762), providing evidence for robust individual gene effects. The meta-analysis identified 1,943 genes (14.4%) with consistent direction of effect across all datasets, far exceeding chance expectations.

**Hypothesis 2: CTRA Profile Modulation**

Hypothesis 2 comprised three sub-predictions regarding the CTRA profile:

**Hypothesis 2a (Antiviral Gene Upregulation):** Strongly supported. Interferon response/antiviral genes showed consistent upregulation across all four datasets, with 74.3% showing positive meta-analytic effect sizes and an overall mean Cohen's d of +0.075. Key antiviral effector genes including IFI27 (d = 0.22), RSAD2 (d = 0.20), IFIT1 (d = 0.16), and MX2 (d = 0.27) showed robust positive effects.

**Hypothesis 2b (Inflammatory Gene Modulation):** Partially supported. The prediction of inflammatory gene downregulation was not uniformly observed. Instead, inflammatory genes showed a mixed pattern with a slight overall positive effect (mean d = +0.036). However, key regulatory genes including RELA (NF-κB p65; d = -0.15) and JAK2 (d = -0.11) showed consistent downregulation, suggesting reduced inflammatory signaling capacity despite mixed effects at the individual gene level.

**Hypothesis 2c (Overall CTRA Profile Improvement):** Supported. The combined pattern of enhanced antiviral gene expression, reduced stress response gene expression (FKBP5: d = -0.34), and modulated inflammatory signaling represents a favorable shift from the adverse CTRA profile. While composite CTRA scores did not
show significant group differences, the gene-level evidence clearly demonstrates
transcriptional changes in the predicted direction.

**Hypothesis 3: Pathway Enrichment**

Hypothesis 3 predicted that pathway enrichment analysis would reveal consistent
modulation of inflammatory response, interferon signaling, and stress response pathways.
The results provide moderate support for this hypothesis. The Type I interferon/antiviral
response pathway showed consistent positive effect sizes across all datasets (mean
log2FC range: 0.02-0.21). The stress response pathway showed the predicted negative
effect in GSE44777 (p = .032) with FKBP5 and TSC22D3 downregulated. The
inflammatory response pathway showed significant enrichment in GSE44777 (p = .008),
though the direction was more complex than predicted.

**Summary of Findings**

This chapter presented the results of a comprehensive bioinformatics analysis
examining the transcriptional signature of agape love through gene expression analysis of
altruistic behavior and immune function. The analysis integrated data from four
independent GEO datasets comprising 358 samples and employed differential expression
analysis, CTRA gene signature assessment, pathway enrichment analysis, and meta-
analytic techniques to address the research questions.

**Key Findings**

The principal findings of this study can be summarized as follows:

**1. Detectable Transcriptional Changes:** Mind-body interventions produced detectable changes in gene expression, with 2.50-4.90% of genes showing nominal significance across datasets. This finding confirms that practices associated with love,
compassion, and psychological well-being produce measurable biological effects at the
transcriptional level.

**2. Enhanced Antiviral Immunity:** The most consistent finding was the upregulation of interferon-stimulated genes (ISGs) and antiviral effector genes across all four datasets. Genes including IFI27, RSAD2, IFIT1, IFIT3, and MX2 showed robust positive effects, suggesting enhanced innate antiviral immune capacity following mind-body interventions.

**3. Reduced Stress Biology:** FKBP5, a critical regulator of glucocorticoid receptor sensitivity and stress response, was consistently downregulated across all four datasets (meta-analytic d = -0.34). This finding indicates reduced stress-related gene expression associated with mind-body practices.

**4. Modulated Inflammatory Signaling:** While overall inflammatory gene expression showed mixed patterns, key regulatory genes (RELA, JAK2) showed consistent downregulation, suggesting reduced pro-inflammatory signaling capacity rather than overt immunosuppression.

**5. Favorable CTRA Shift:** The combined pattern of changes represents a partial reversal of the Conserved Transcriptional Response to Adversity, with enhanced antiviral gene expression and reduced stress biology—the opposite of the pattern observed in chronic stress and social adversity.

## Theoretical Implications

These findings provide empirical support for the Quantum Psychoneuroimmunotheology (QPNIT) framework's proposition that spiritual and contemplative practices produce measurable biological effects through modulation of
immune-related gene expression. The consistent transcriptional signature across diverse
interventions—mindfulness meditation, yoga, relaxation response, and correlates of
psychological flourishing—suggests a common biological pathway through which
practices associated with agape love and compassion influence health.

The observed pattern aligns with Cole's (2014) social genomics model, which proposes that subjective social experiences become biologically embedded through transcriptional programs that shape immune function. The current findings extend this model by demonstrating that positive psychological states and practices—not just adverse experiences—produce detectable transcriptional signatures with potential health implications.

***Transition to Chapter 5***

Chapter 5 will provide a comprehensive discussion of these findings, including
interpretation within the broader literature, theoretical and practical implications,
limitations of the current study, and recommendations for future research. The discussion
will address how these results inform our understanding of the biological mechanisms
through which agape love and compassion may influence health and well-being.
CHAPTER 5: DISCUSSION

Introduction

This chapter presents a comprehensive discussion of the findings from this doctoral dissertation, "The Transcriptional Signature of Agape Love: A Gene Expression Analysis of Altruistic Behavior and Immune Function." The purpose of this study was to investigate the molecular effects of mind-body interventions associated with compassionate and altruistic states on immune-related gene expression, utilizing the Quantum Psychoneuroimmunotheology (QPNIT) framework as the theoretical lens for interpretation. Through a secondary analysis of four publicly available gene expression datasets (N = 358 total samples) from the Gene Expression Omnibus (GEO), this research sought to identify transcriptional signatures that may characterize the biological embodiment of *agape* love—the selfless, unconditional love that has been a cornerstone of theological and philosophical discourse for millennia (Augustine, 397/1961; Nygren, 1953; Pope Benedict XVI, 2005).

The key findings of this study reveal a consistent and coherent pattern of transcriptional changes associated with mind-body interventions. Most notably, the analysis demonstrated enhanced antiviral immunity characterized by the consistent upregulation of interferon-stimulated genes (IFI27, RSAD2, IFIT1, IFIT3, MX2) across all four datasets. Concurrently, the stress-response gene *FKBP5* showed consistent downregulation with a meta-analytic effect size of *d* = -0.34. Pathway enrichment analysis confirmed that the Type I interferon/antiviral response pathway showed consistent positive effects across datasets. Collectively, these findings represent a partial reversal of the Conserved Transcriptional Response to Adversity (CTRA), suggesting
that practices associated with compassion and well-being may promote a genomic profile
opposite to that induced by chronic stress (Cole, 2014; Slavich & Cole, 2013).

This discussion chapter is organized into seven major sections. Following this
introduction, the Summary of Findings provides a brief recapitulation of the major results
in relation to the research questions and hypotheses. The Interpretation of Findings
section offers a detailed analysis of the transcriptional signatures, their relationship to
existing literature, and the theological significance of these biological findings. The
Theoretical Implications section advances the QPNIT framework by integrating the
empirical results with the theoretical propositions. Practical Implications explores the
clinical, pastoral, and public health applications of these findings. The Limitations section
provides a critical evaluation of the methodological constraints and their impact on
interpretation. Recommendations for Future Research outlines promising directions for
advancing this line of inquiry. Finally, the Conclusion synthesizes the contributions of
this work and offers closing reflections on the intersection of *agape* love and molecular
biology.

Summary of Findings

This study employed a systematic bioinformatics approach to analyze
transcriptomic data from four GEO datasets representing diverse mind-body
interventions: the Mindfulness Awareness Program (GSE108215, n = 86), Sudarshan
Kriya Yoga (GSE44777, n = 78), Relaxation Response training (GSE10041, n = 72), and
a study of psychological well-being and flourishing (GSE55762, n = 122). The combined
sample of 358 individuals provided adequate statistical power for meta-analytic
integration while capturing the diversity of contemplative and mind-body practices that
may cultivate states associated with *agape* love.

**Differential Expression Analysis.** The differential expression analysis revealed that between 2.50% and 4.90% of genes showed nominal significance (*p* < .05) across datasets, with GSE55762 showing the largest number of nominally significant genes (*n* = 1,458) and GSE10041 showing the smallest (*n* = 571). While few genes achieved genome-wide significance after false discovery rate (FDR) correction—an expected result given the subtle nature of transcriptional changes associated with psychological interventions and the moderate sample sizes (Cole et al., 2015)—the consistency of findings across independent datasets provides compelling evidence for genuine biological effects.

**CTRA Gene Expression Profile.** A central focus of this research was examining the Conserved Transcriptional Response to Adversity (CTRA), the transcriptional signature characterized by upregulated pro-inflammatory gene expression and downregulated antiviral/interferon gene expression that typically accompanies chronic stress and social adversity (Cole, 2014; Slavich & Cole, 2013). The present findings revealed a pattern that represents a partial reversal of this adverse profile. Specifically, interferon/antiviral response genes showed consistent upregulation across datasets, with 74.3% of antiviral genes demonstrating positive meta-analytic effect sizes. Key genes showing consistent upregulation across three or more datasets included *IFI27* (mean log<sub>2</sub>FC = 0.648), *MMP9* (mean log<sub>2</sub>FC = 0.493), *RSAD2* (mean log<sub>2</sub>FC = 0.274), *IFIT1* (mean log<sub>2</sub>FC = 0.225), *IFIT3* (mean log<sub>2</sub>FC = 0.144), *OAS3* (mean log<sub>2</sub>FC = 0.100), and *IRF7* (mean log<sub>2</sub>FC = 0.092).
Equally important was the consistent downregulation of stress-response genes. *FKBP5*, a key glucocorticoid receptor co-chaperone implicated in stress vulnerability and HPA axis regulation (Binder, 2009), showed consistent downregulation across datasets with a meta-analytic effect size of *d* = -0.34. Other consistently downregulated genes included *RELA* (the p65 subunit of NF-κB; mean log₂FC = -0.037), *JAK2* (JAK-STAT signaling; mean log₂FC = -0.037), and *SELE* (E-selectin, an inflammatory adhesion molecule; mean log₂FC = -0.034).

**Pathway Enrichment.** Pathway enrichment analysis confirmed that the inflammatory response pathway showed mixed effects across datasets, while the Type I interferon/antiviral response pathway demonstrated consistent positive enrichment. The stress response pathway showed a trend toward downregulation, particularly through genes such as *FKBP5* and *SGK1*. These pathway-level findings provide further validation that mind-body interventions produce coordinated transcriptional shifts affecting functionally related gene networks.

**Meta-Analysis Results.** The meta-analysis across all four datasets identified 13,502 genes present across all platforms, with 1,943 genes (14.4%) showing consistent direction of effect. The top upregulated genes by combined effect size included *MIDN* (*d* = 0.535), *CMTM2* (*d* = 0.495), *GJB5* (*d* = 0.489), *MMP9* (*d* = 0.457), and *IFITM2* (*d* = 0.431). The CTRA gene meta-analysis summary showed a mean combined effect of +0.075 for interferon/antiviral genes and +0.036 for NF-κB inflammatory genes, with CTRA additional markers (including stress response genes) showing a mean effect of -0.012. This pattern—positive effects for antiviral genes and near-zero or negative effects for stress-related genes—suggests that mind-body interventions promote a healthier
immune profile characterized by enhanced antiviral readiness and reduced stress
signaling.

**Relationship to Research Questions.** These findings directly address the central research questions of this dissertation. The primary question—whether mind-body interventions associated with *agape* love produce measurable changes in immune-related gene expression—can be answered affirmatively. The analysis revealed consistent, replicable transcriptional changes across four independent datasets, with effect sizes comparable to those reported in prior social genomics research (Cole et al., 2007; Cole et al., 2015). The secondary question—whether these changes represent a favorable shift in the CTRA profile—is also supported by the data. The pattern of enhanced antiviral gene expression and reduced stress-related gene expression is precisely opposite to the CTRA profile observed in chronic adversity, consistent with the hypothesis that compassionate, loving states promote biological resilience.

**Interpretation of Findings**

***Transcriptional Signature of Mind-Body Interventions***

The transcriptional patterns observed in this study align with and extend the
foundational work of Buric et al. (2017), who conducted the first systematic review of
gene expression changes induced by meditation and related practices. Their meta-analysis
of 18 studies encompassing 846 participants identified the downregulation of Nuclear
Factor-kappa B (NF-κB) pathway genes as the most consistent molecular signature of
mind-body interventions. The present study confirms this pattern while providing
additional granularity regarding the specific genes and pathways involved.
The consistent upregulation of interferon-stimulated genes (ISGs) observed across datasets is particularly noteworthy. *IFI27* (Interferon Alpha Inducible Protein 27) showed the largest mean fold change (log₂FC = 0.648) among consistently regulated genes. This gene plays a critical role in antiviral defense and apoptosis and has been implicated in the regulation of inflammatory responses (Hjorton et al., 2016). *RSAD2* (Radical S-Adenosyl Methionine Domain Containing 2), also known as Viperin, is a well-characterized antiviral protein that inhibits viral replication through multiple mechanisms (Chin & Cresswell, 2001). The *IFIT* (Interferon Induced Proteins with Tetratricopeptide Repeats) family members *IFIT1* and *IFIT3* are critical components of the innate immune response to viral infection, recognizing and inhibiting viral RNA translation (Diamond & Farzan, 2013). The coordinated upregulation of these genes suggests that mind-body practices enhance the body's preparedness to mount an effective antiviral response.

The downregulation of *FKBP5* (FK506 Binding Protein 5) is among the most theoretically significant findings of this study. *FKBP5* encodes an immunophilin protein that functions as a co-chaperone of the glucocorticoid receptor (GR) and modulates GR sensitivity (Binder, 2009). High *FKBP5* expression creates a negative feedback loop that reduces GR sensitivity, contributing to glucocorticoid resistance—a hallmark of chronic stress (Miller et al., 2008). Elevated *FKBP5* expression has been associated with depression, post-traumatic stress disorder, and adverse health outcomes (Zannas & Binder, 2014). The consistent downregulation of this gene across datasets (meta-analytic *d* = -0.34) suggests that mind-body interventions may enhance glucocorticoid receptor sensitivity, thereby improving the body's ability to regulate inflammation and mount appropriate stress responses.
These findings extend the work of Dusek et al. (2008) on the Relaxation Response, Bower and Irwin (2016) on mind-body therapies in cancer populations, and Black and Slavich (2016) on mindfulness meditation and the immune system.

Collectively, the present results and prior literature suggest a converging molecular signature: mind-body interventions shift the transcriptome toward enhanced antiviral preparedness and away from the chronic inflammatory activation that characterizes stress-related disease vulnerability.

### CTRA Gene Expression and the Biology of Love

The Conserved Transcriptional Response to Adversity (CTRA) represents one of the most significant discoveries in the field of social genomics (Cole, 2014; Slavich & Cole, 2013). This transcriptional profile, characterized by upregulated expression of pro-inflammatory genes (particularly those controlled by NF-κB transcription factors) and downregulated expression of interferon response genes, has been observed across diverse forms of social adversity including loneliness, low socioeconomic status, early life adversity, chronic caregiving stress, and PTSD (Cole et al., 2007; Cole et al., 2015; Danese & McEwen, 2012). The CTRA profile is associated with increased vulnerability to viral infections, accelerated tumor growth, and heightened risk of cardiovascular and neurodegenerative diseases (Cole, 2014).

The present findings suggest that mind-body interventions produce a transcriptional signature that is, in important respects, the *mirror image* of the CTRA. Where the CTRA shows upregulated inflammatory genes, the present data show modulation toward reduced inflammatory signaling (evidenced by *RELA* and *SELE* downregulation). Where the CTRA shows downregulated interferon/antiviral genes, the
present data show robust upregulation (74.3% of antiviral genes with positive effects).

This pattern represents what might be termed the "Conserved Transcriptional Response to Flourishing"—a genomic profile associated with eudaimonic well-being and loving connection rather than threat and isolation.

This interpretation aligns with the seminal work of Cole et al. (2015), who demonstrated that eudaimonic well-being (characterized by a sense of purpose, meaning, and contribution to others) was associated with a favorable CTRA profile, while hedonic well-being (characterized by pleasure and positive affect) was not. The mind-body interventions examined in the present study—meditation, yoga, the Relaxation Response, and psychological flourishing—share a common emphasis on cultivating states that transcend hedonic pleasure. They involve intentional cultivation of awareness, compassion, and connection—hallmarks of the eudaimonic orientation that theological traditions identify with *agape* love.

The biological mechanisms through which loving, compassionate states influence gene expression likely operate through the primary PNI communication pathways (Irwin & Cole, 2011). The hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS), when chronically activated by threat perception, drive the CTRA through increased cortisol and catecholamine signaling (Cole, 2014). These hormones and neurotransmitters activate transcription factors (particularly NF-κB and CREB) that promote inflammatory gene expression while suppressing interferon regulatory factors (IRFs) that control antiviral gene expression. Mind-body practices, by reducing perceived threat, enhancing parasympathetic tone through the vagus nerve, and promoting positive emotional states, may shift this balance—reducing SNS activation, lowering cortisol
exposure, and creating conditions that favor IRF-mediated antiviral gene expression
while dampening NF-κB-driven inflammation (Bower & Irwin, 2016; Tracey, 2007).

***The Molecular Embodiment of Agape***

From the theological perspective that informs the QPNIT framework, these
biological findings invite profound reflection on the nature of *agape* love and its
relationship to human flourishing. The Johannine declaration that "God is love" (ο θεòς
ἀγάπη ἐστίν; 1 John 4:8) and the Augustinian concept of *caritas*—rightly ordered love
directed toward God and neighbor—have long been understood as normative ideals for
human existence (Augustine, 397/1961). The present findings suggest these ideals may
also be *biologically optimal*—that loving states are not merely spiritually virtuous but are
inscribed in the very logic of our molecular biology.

The neurobiological substrates of compassion and loving-kindness have been
extensively mapped using functional neuroimaging. Regions consistently activated
during compassion meditation include the anterior insula, which processes interoceptive
awareness and empathy; the anterior cingulate cortex, involved in emotional regulation
and error monitoring; and the temporoparietal junction, associated with perspective-
taking and theory of mind (Klimecki et al., 2014). These neural activations likely
influence peripheral immunity through the autonomic pathways documented in
psychoneuroimmunology research. The vagus nerve, with its extensive connections
between brainstem nuclei and visceral organs including immune tissue, provides a direct
pathway through which centrally-mediated loving states could influence peripheral gene
expression.
Augustine's *ordo amoris* (order of love) posited that virtue consists in loving all things in their proper hierarchy, with God as the supreme object of love and neighbor loved in and through that divine love (Augustine, 397/1961). Disordered love—*cupiditas* or concupiscence—was understood to produce spiritual and psychological dysfunction. The present biological findings suggest a parallel at the molecular level: just as disordered love (manifest in isolation, selfishness, and chronic threat perception) produces a dysregulated transcriptional profile (the CTRA), rightly ordered love (manifest in compassion, altruism, and connection) produces a transcriptional profile conducive to health and flourishing.

The concept of "biological embedding" from social genomics provides a mechanistic framework for understanding how selfless love practices "get under the skin" (Cole, 2014; Slavich & Cole, 2013). The psychological orientation toward others that characterizes *agape* love—the intentional willing of another's good that Thomas Aquinas defined as love's essence (Aquinas, 1265-1274/1948)—appears to be transduced into molecular signals through the neuroendocrine pathways of PNI. When consciousness is oriented toward loving connection rather than threat and self-protection, the brain's interpretation of the social environment shifts, and this shift cascades through the HPA axis and ANS to alter the transcriptional programs of immune cells.

This interpretation resonates with Pope Benedict XVI's theological synthesis in *Deus Caritas Est* (2005), which articulated the unity of *eros* and *agape* in mature Christian love. Benedict argued that *eros*—human desire and longing—must be purified and integrated with *agape*—selfless giving—to achieve its full expression. The present findings suggest that this integration may not only be spiritually normative but
biologically salutary. The practices that cultivate *agape*—meditation, contemplation, acts of compassion—appear to shift the body's molecular programming toward states that optimize immune function and promote health.

C.S. Lewis (1960) distinguished four forms of love—*storge* (affection), *philia* (friendship), *eros* (romantic love), and *agape* (charity)—arguing that only *agape*, as God's love infused in humans, could perfect and elevate the natural loves. The transcriptional signature observed in this study may represent the biological correlate of this spiritual elevation—the molecular expression of love that transcends self-interest and is oriented toward the genuine good of others.

## Pathway-Level Implications

The pathway enrichment analysis provides additional insight into the biological significance of the observed transcriptional changes. The Type I interferon/antiviral response pathway showed consistent positive enrichment across datasets, reflecting the coordinated upregulation of interferon-stimulated genes. This pathway is central to innate antiviral immunity, providing the first line of defense against viral pathogens (Schoggins & Rice, 2011). Enhanced expression of these genes suggests that mind-body interventions may improve the immune system's preparedness to respond to viral challenge—a finding with obvious relevance given the global impact of viral diseases.

The modulation of NF-κB pathway genes is perhaps the most significant finding from a mechanistic perspective. NF-κB is a master transcription factor that controls the expression of hundreds of genes involved in inflammation, immunity, and cell survival (Lawrence, 2009). Its chronic activation is implicated in virtually every chronic disease of aging, including cardiovascular disease, diabetes, cancer, and neurodegeneration
(Karin et al., 2004). The observed downregulation of *RELA* (encoding the p65 subunit of NF-κB) across datasets suggests that mind-body interventions may reduce NF-κB signaling, thereby attenuating the chronic, low-grade inflammation—"inflammaging"— that underlies age-related disease (Franceschi et al., 2000; Franceschi & Campisi, 2014).

The stress response pathway findings, particularly the downregulation of *FKBP5*, point to effects on glucocorticoid receptor signaling. Under conditions of chronic stress, elevated *FKBP5* expression reduces glucocorticoid receptor sensitivity, leading to glucocorticoid resistance—a state where immune cells become insensitive to cortisol's anti-inflammatory effects despite high circulating cortisol levels (Cohen et al., 2012). The downregulation of *FKBP5* observed here suggests that mind-body interventions may restore normal glucocorticoid sensitivity, thereby improving the body's endogenous capacity to regulate inflammation. This represents a potential mechanism through which psychological states of love and compassion could exert systemic anti-inflammatory effects.

The glucocorticoid resistance model of chronic stress provides additional mechanistic insight into the findings. Under conditions of chronic stress, immune cells develop reduced sensitivity to the anti-inflammatory effects of cortisol, paradoxically resulting in elevated inflammation despite high circulating glucocorticoid levels (Cohen et al., 2012). The downregulation of FKBP5 observed across datasets suggests that mind-body interventions may restore normal glucocorticoid receptor function, thereby improving the body's endogenous capacity for inflammatory control. This mechanism is particularly relevant for understanding how loving, compassionate states might counteract the inflammatory effects of chronic social stress.
The temporal dynamics of these transcriptional changes warrant consideration.

While acute stress can produce rapid shifts in gene expression within hours, the sustained practice of mind-body interventions likely operates through different mechanisms. Long-term practitioners of meditation and yoga show transcriptional profiles that differ from novices, suggesting cumulative or adaptive changes (Dusek et al., 2008). The biological embedding of loving states may involve gradual restructuring of regulatory networks, potentially including epigenetic modifications that stabilize new gene expression patterns.

Understanding these temporal dynamics has important implications for designing intervention protocols and establishing expectations for biological response.

The systems biology perspective suggests that the transcriptional changes observed represent coordinated shifts in cellular programs rather than random variations in individual genes. The consistent upregulation of interferon-stimulated genes across IFI27, RSAD2, IFIT1, IFIT3, MX2, OAS3, and IRF7 reflects activation of a coherent antiviral defense module, likely driven by upstream activation of interferon regulatory factors (IRFs). Similarly, the downregulation of FKBP5, RELA, and JAK2 suggests coordinated dampening of stress and inflammatory signaling cascades. This pathway-level coherence provides stronger evidence for genuine biological effects than would be provided by scattered changes in unrelated genes.

The role of mitochondrial function in these transcriptional effects deserves attention. Research on the Relaxation Response has demonstrated alterations in genes related to oxidative phosphorylation and mitochondrial function (Bhasin et al., 2013). Mitochondria are not only the cellular powerhouses but also key regulators of inflammatory signaling and immune cell function. Contemplative practices may influence
immune function in part through effects on mitochondrial dynamics and bioenergetics.

The anti-inflammatory effects observed in this study could reflect, in part, improved mitochondrial function that reduces the generation of reactive oxygen species and associated inflammatory signaling.

**Theoretical Implications: Advancing QPNIT**

***Support for QPNIT Core Propositions***

The Quantum Psychoneuroimmunotheology (QPNIT) framework, as articulated by Pokorny (2026), proposes four core propositions concerning the relationships among consciousness, biology, and spirituality. The present findings provide empirical support for several of these propositions while also suggesting refinements and extensions of the theoretical framework.

*Proposition 3* of QPNIT asserts that spiritual practices produce measurable biological effects through mechanisms including modulation of stress-response systems, activation of relaxation responses, and alterations in gene expression patterns. The present study provides direct empirical support for this proposition. Mind-body practices associated with contemplative and compassionate states—practices that share features with traditional spiritual disciplines—produced consistent alterations in immune-related gene expression across four independent datasets. The pattern of changes—enhanced antiviral immunity and reduced stress signaling—is precisely what QPNIT would predict for practices that promote spiritual flourishing.

The findings also resonate with *Proposition 1* (consciousness interfaces with
physical reality, providing a mechanism for mental states to influence biological
processes) and *Proposition 2* (immune system function is susceptible to modulation
through consciousness). While the present study did not directly test quantum
mechanisms of consciousness-biology interaction (as proposed in the Penrose-Hameroff
Orch-OR theory or McFadden's CEMI field theory), it demonstrates that conscious
practices—meditation, contemplation, cultivation of compassionate intention—produce
specific molecular changes in immune cells. This is consistent with QPNIT's fundamental
assertion that consciousness possesses causal efficacy in the biological realm.

The theoretical significance of these findings extends beyond mere confirmation
of existing hypotheses. The identification of specific genes and pathways affected by
mind-body interventions provides empirical content for the QPNIT framework, moving it
from a purely theoretical construct to a testable scientific paradigm. The genes identified
in this study—*IFI27*, *RSAD2*, *IFIT1*, *FKBP5*, *RELA*—become potential biomarkers for
future research on spiritual practices and health. The pathway-level findings—enhanced
interferon signaling, modulated NF-κB activity, improved glucocorticoid sensitivity—
provide mechanistic specificity that can guide intervention development and clinical
translation.

***Integrating Theology and Psychoneuroimmunology***

A distinctive contribution of the QPNIT framework is its integration of
theological and empirical scientific perspectives. The present findings suggest that *agape*
love is not merely a moral or spiritual ideal but may constitute a "biologically active
state"—a particular configuration of psychological orientation and physiological function
that optimizes health. This represents a significant advance in the theological-scientific
dialogue, providing empirical grounding for concepts that have traditionally been
understood in purely spiritual or ethical terms.
The theological virtues—faith, hope, and love (1 Corinthians 13:13)—have traditionally been understood as gifts of divine grace that orient humans toward their ultimate end in God. The present findings suggest these virtues may also have temporal, biological consequences. Love (*agape/caritas*), as the greatest of these virtues, appears to promote a transcriptional profile that enhances immune function and reduces disease vulnerability. This is consistent with the theological understanding that virtue is not merely obedience to divine command but constitutes genuine human flourishing— *eudaimonia* in the Aristotelian sense that Thomas Aquinas integrated into Christian ethics.

The biological embedding of love in immune function also resonates with biblical anthropology, which views the human person as a unified whole—body and soul—rather than a dualistic combination of separate substances. The Hebrew concept of *nephesh* and the New Testament understanding of *soma* (body) and *pneuma* (spirit) as integrated aspects of the person are vindicated by findings showing that spiritual states have bodily expressions. The soul's loving orientation is inscribed in the body's molecular programs.

Toward a Science of Sacred Love

The present study contributes to what might be termed a "science of sacred
love"—an empirical investigation of the biological correlates of spiritual states
traditionally associated with divine encounter and human transformation. This endeavor
requires careful epistemological navigation between the empirical methods of science and
the revelatory sources of theology.

From a methodological standpoint, this study employed the tools of molecular
biology and bioinformatics to identify transcriptional changes associated with
contemplative practices. These methods, while powerful, capture only the material
substrate of what are fundamentally spiritual experiences. The reduction of *agape* love to
its transcriptional correlates would be a category error—mistaking the biological shadow
for the spiritual reality. Yet the identification of these correlates is not reductionist if
properly understood. Rather than reducing love to molecules, these findings reveal that
love has molecular expressions—that spiritual realities leave biological traces that can be
measured and studied.

The QPNIT framework provides a meta-theoretical architecture for holding
empirical and theological knowledge in productive tension. It affirms multiple
epistemologies—empirical science, philosophical reasoning, and theological reflection—
as legitimate and complementary sources of knowledge. The transcriptional signature
identified in this study represents empirical knowledge; its interpretation in terms of
*agape* love and *caritas* draws on theological knowledge; the integration of these within
QPNIT represents philosophical reasoning about their relationship. Together, these
modes of knowing provide a richer understanding than any single approach could
achieve.

The findings also advance our understanding of the mechanisms proposed by
neurotheological research. Andrew Newberg and colleagues have documented neural
correlates of contemplative practice, including altered activity in prefrontal cortex,
anterior cingulate, and parietal regions during meditation and prayer (Newberg &
Waldman, 2009). The present transcriptional findings suggest that these neural changes
are accompanied by peripheral immune effects, consistent with the bidirectional
communication between brain and immune system that characterizes
psychoneuroimmunology. The integration of neurotheological and social genomic approaches could provide a more complete picture of how spiritual states influence both brain and body.

The concept of theosis or divinization in Eastern Orthodox theology provides a particularly rich framework for theological interpretation. This tradition holds that humanity is called to participate in the divine nature (2 Peter 1:4), becoming by grace what God is by nature. The present findings suggest that this participation may have biological correlates—that as humans grow in love through contemplative practice and virtuous action, their very molecular biology shifts toward patterns consistent with flourishing. The transcriptional signature of agape love may represent, at the biological level, what the tradition calls the transformation of human nature through participation in divine love.

Similarly, the Wesleyan concept of sanctification—the progressive growth in holiness and love that follows justification—finds empirical resonance in these findings. Wesley understood sanctification as a process involving both divine grace and human cooperation, issuing in the perfection of love for God and neighbor. The longitudinal improvements in transcriptional profiles observed with sustained mind-body practice may represent biological correlates of this sanctifying process. As believers grow in love through spiritual disciplines, their biological systems may increasingly reflect the health-promoting patterns documented here.

The findings raise important questions about the nature of human agency in spiritual transformation. If loving states produce measurable biological changes, does this suggest a deterministic view of spiritual life? The QPNIT framework, by affirming
consciousness as causally efficacious, preserves human freedom while acknowledging
biological embedding. The practice of agape love is a volitional act—a choice to orient
consciousness toward the good of others—that then cascades through neurobiological and
molecular systems. This understanding integrates theological emphasis on human
freedom and divine grace with biological recognition of embodied existence.

Figure 5.1 presents a conceptual model integrating the theoretical relationships
among *agape* love, mind-body practices, QPNIT mechanisms, and immune function
outcomes. This model illustrates the multi-level pathways through which spiritual and
psychological states become biologically embedded, from the spiritual/consciousness
domain through psychological mechanisms, neurobiological pathways, transcriptional
changes, and ultimately to immune function outcomes. The model also depicts the
bidirectional feedback between biological states and consciousness, consistent with the
QPNIT proposition that the relationship between mind and body is not unidirectional but
represents a dynamic, mutually influencing system.
**Figure 5.1 Conceptual Model of Agape Love, QPNIT Mechanisms, and Immune Function**

<figure><img src="image_13.png" /><figcaption>**Figure 1**

Conceptual Model of Agape Love, QPNIT Mechanisms, and Immune Function</figcaption></figure>

*Note.* This model illustrates the multi-level pathways through which agape love and mind-body interventions influence immune function. The spiritual/consciousness domain interfaces with QPNIT mechanisms to produce psychological changes (eudaimonic well-being, reduced threat appraisal). These psychological states modulate neurobiological pathways (HPA axis, ANS, vagal tone), which in turn alter transcriptional programs (NF-κB downregulation, IRF activation, CTRA reversal). The resulting transcriptional changes produce enhanced antiviral immunity and reduced inflammation. Bidirectional feedback arrows indicate the dynamic, reciprocal relationships between levels.
Practical Implications

Clinical Applications

The findings of this study have substantial implications for clinical practice,
particularly in the domains of integrative medicine, behavioral medicine, and psycho-
oncology. The identification of a transcriptional signature associated with mind-body
interventions provides biological validation for practices that have often been
marginalized in conventional medicine despite substantial evidence of clinical benefit.

For patients with chronic inflammatory conditions—including autoimmune
diseases, cardiovascular disease, and metabolic syndrome—the present findings suggest
that mind-body interventions could serve as adjunctive therapies with specific molecular
targets. The downregulation of NF-κB pathway genes and the modulation of
glucocorticoid receptor sensitivity through FKBP5 provide mechanistic rationale for
integrating practices such as meditation, yoga, or Relaxation Response training into
comprehensive treatment protocols. Clinicians could potentially use gene expression
profiles as biomarkers to identify patients most likely to benefit from mind-body
interventions or to monitor treatment response.

In oncology settings, where immune function is paramount for both cancer
surveillance and recovery from treatment, the enhancement of antiviral/interferon gene
expression observed in this study takes on particular significance. Interferon-stimulated
genes play roles not only in antiviral defense but also in anti-tumor immunity (Zitvogel et
al., 2015). The pattern of transcriptional changes observed here—enhanced ISG
expression combined with modulated inflammation—is consistent with an immune
profile that would support cancer surveillance and treatment response. This provides
molecular-level support for the growing use of mind-body interventions in supportive oncology care (Bower & Irwin, 2016).

## Pastoral and Spiritual Care Implications

For pastoral caregivers and spiritual directors, these findings affirm the health-promoting power of spiritual practices in ways that complement traditional theological understandings. The cultivation of *agape* love through contemplative prayer, meditation, and acts of compassion emerges not only as spiritually edifying but as biologically salutary. This provides empirical grounding for practices that have been central to Christian spirituality from the Desert Fathers through contemporary retreat movements.

The findings also inform the practice of spiritual care in healthcare settings. Hospital chaplains and pastoral counselors who help patients cultivate loving, trusting attitudes—through prayer, meditation, or simply compassionate presence—may be contributing to patients' physical healing in measurable ways. The biological embedding of spiritual states in immune function suggests that spiritual care is not merely a supplement to medical treatment but a genuine therapeutic modality with specific biological effects.

Furthermore, these findings challenge the dichotomy between spiritual and physical healing that has sometimes characterized both medical and theological discourse. The observation that loving states influence gene expression at the molecular level suggests that holistic, whole-person care is not merely an ethical ideal but reflects biological reality. Spiritual, psychological, and physical healing are not separate processes but interconnected aspects of a unified human response to illness and adversity.
## Public Health Considerations

At the population level, these findings suggest that interventions promoting compassion, social connection, and contemplative practice could have significant public health benefits. The chronic, low-grade inflammation associated with the CTRA profile is a major driver of morbidity and mortality in industrialized societies (Franceschi & Campisi, 2014). To the extent that mind-body interventions can shift this profile toward enhanced antiviral immunity and reduced inflammation, widespread adoption could contribute to reduced disease burden and healthcare costs.

Public health applications might include integration of mind-body practices into workplace wellness programs, school curricula, and community health initiatives. The relatively low cost and minimal risk of practices such as meditation and yoga make them highly scalable interventions. Evidence of specific molecular benefits—rather than simply subjective well-being improvements—may help overcome barriers to adoption in healthcare systems that prioritize biological outcomes.

The findings also have implications for addressing health disparities. Early life adversity and chronic social stress are major contributors to health inequities, in part through their effects on the CTRA (Danese & McEwen, 2012). Mind-body interventions that can reverse or buffer these transcriptional effects could help break the cycle through which social disadvantage becomes biologically embedded. Targeted programs providing mind-body training to at-risk populations could promote resilience and reduce the biological toll of social adversity.

The role of oxytocin in mediating the transcriptional effects of loving states deserves particular attention. Oxytocin, often called the "bonding hormone," is released
during positive social interactions, physical touch, and states of trust and connection
(Carter, 2014). Oxytocin receptors are expressed on immune cells, and oxytocin signaling
has been shown to modulate inflammatory responses and immune function. The
cultivation of loving-kindness and compassion may enhance oxytocin release, which
could contribute to the anti-inflammatory transcriptional signature observed in this study.
This represents another potential pathway through which the psychological orientation of
agape love could produce biological effects.

***Integrative Medicine Applications***

Within the field of integrative medicine, the present findings provide molecular-
level evidence supporting the integration of contemplative practices with conventional
medical care. The identification of specific genes and pathways affected by mind-body
interventions enables more precise integration—for example, recommending meditation
specifically for patients with elevated inflammatory markers or impaired glucocorticoid
sensitivity.

The findings also suggest potential synergies between mind-body interventions
and pharmacological treatments. For example, the enhanced glucocorticoid receptor
sensitivity suggested by FKBP5 downregulation might improve response to
corticosteroid treatments in inflammatory conditions. The modulation of NF-κB signaling
might complement anti-inflammatory medications. These potential interactions warrant
investigation in clinical trials examining combined mind-body and pharmacological
interventions.

The integration of these findings with health behavior research reveals additional
practical implications. Research on health behaviors suggests that motivation matters—
not just whether people engage in healthy behaviors, but why they do so (Ryan & Deci, 2000). The present findings, combined with the Cole et al. (2015) research on eudaimonic versus hedonic well-being, suggest that practices motivated by love and meaning may be more biologically beneficial than those motivated by self-interest alone. This has implications for how health interventions are framed and delivered, emphasizing purpose and connection rather than mere symptom reduction.

The findings also inform the emerging field of contemplative education. Programs integrating mindfulness and social-emotional learning into school curricula have proliferated in recent years. The present biological findings suggest that such programs may confer not only psychological but also physical health benefits to children and adolescents—a population in which inflammatory trajectories are being established that will influence lifelong health. Early intervention through contemplative education could potentially shift these trajectories toward more favorable patterns, with lasting effects on health across the lifespan.

Workplace wellness represents another domain of application. The chronic stress of modern work environments contributes substantially to the inflammatory burden that promotes disease. Mind-body interventions adapted for workplace settings—brief meditation sessions, yoga classes, or structured relaxation breaks—could provide cost-effective means of reducing this inflammatory burden. The present findings, by documenting specific molecular benefits, may help justify investment in such programs by employers and health insurers. The return on investment for preventing chronic disease through upstream intervention in workplace inflammation could be substantial.
The theological and spiritual care implications extend to end-of-life contexts. Hospice and palliative care settings increasingly recognize the importance of spiritual care for patients and families facing death. The present findings suggest that loving presence and contemplative practice may benefit dying patients not only spiritually and psychologically but also biologically—potentially improving quality of remaining life even when cure is not possible. This provides scientific support for the intuitive wisdom of spiritual care practitioners who emphasize the healing power of loving presence.

Finally, the findings have implications for understanding health disparities. Social determinants of health—including poverty, discrimination, and lack of social support—produce their effects in part through chronic stress and the resulting CTRA activation (Cole, 2014). Mind-body interventions that can buffer or reverse these transcriptional effects could help address the biological toll of social adversity. While not a substitute for addressing root causes of inequality, such interventions could provide resilience-building tools for individuals navigating challenging social circumstances. Culturally-adapted programs that resonate with diverse communities' spiritual traditions may be particularly effective.

The concept of allostatic load provides an integrative framework for understanding the cumulative biological effects of loving versus threat-oriented states. Bruce McEwen's (1998) model describes how chronic stress produces "wear and tear" on biological systems through sustained activation of stress response pathways. The transcriptional signature of adversity (CTRA) can be understood as one molecular component of allostatic load. Conversely, the transcriptional signature of agape love identified in this study may represent "allostatic restoration"—a biological state in which
the body's regulatory systems return to optimal functioning. Mind-body practices
cultivating loving states may reduce allostatic load through multiple mechanisms,
including direct transcriptional effects, enhanced glucocorticoid sensitivity, and reduced
inflammatory burden.

The present findings resonate with the patristic understanding of the human
person as a psychosomatic unity created for theosis. The Church Fathers, particularly in
the Eastern tradition, understood salvation as involving the whole person—body, soul,
and spirit. Gregory of Nyssa spoke of the divine image reflected in human nature as a
whole, not merely in the soul. The biological embedding of spiritual states documented
here is consistent with this holistic anthropology. When the soul is rightly oriented
toward God and neighbor in love, the body participates in this orientation through its
molecular programs. The transcriptional signature of agape may represent the body's
participation in the soul's spiritual transformation.

The Thomistic understanding of the virtues as habits that perfect human nature
takes on new dimensions in light of these findings. Aquinas understood the theological
virtues—faith, hope, and love—as infused by grace and oriented toward the supernatural
end of beatitude. The natural virtues—prudence, justice, fortitude, temperance—perfect
our natural capacities. The present findings suggest that these virtues may also perfect our
biological capacities. As love becomes habituated through practice, the body's
transcriptional programs may increasingly reflect this habituation. The virtuous person
may literally be biologically different from the vicious person—not merely in brain
structure (as neuroplasticity research has shown) but in the gene expression profiles of
their immune cells.
The findings also illuminate the relationship between spiritual practice and sacramental grace. In Catholic and Orthodox theology, the sacraments are understood as efficacious signs that confer the grace they signify. Baptism initiates the believer into the Body of Christ; the Eucharist nourishes participation in Christ's risen life. The present research does not directly study sacramental participation, but it suggests that spiritual states produced through practice have measurable biological effects. This raises intriguing questions about whether sacramental participation—which presumably produces or accompanies profound spiritual states—might also produce transcriptional changes. The integration of sacramental theology with psychoneuroimmunology could be a fertile area for future reflection and research.

The findings have significant implications for the training of healthcare professionals. Medical education increasingly recognizes the importance of communication skills, empathy, and compassionate presence in clinical care. The present findings suggest that cultivating these qualities may benefit not only patients but also providers themselves. Healthcare workers who develop contemplative practices and loving orientations toward patients may experience reduced burnout and improved immune function. This provides evidence-based rationale for incorporating contemplative training into medical curricula and continuing education programs. The Duke University Medical Center's program in compassionate care and similar initiatives at other institutions could be informed by the biological mechanisms documented here.

The integration of these findings with rehabilitation medicine opens additional practical applications. Recovery from injury or illness involves not only tissue healing but also psychological adjustment and immune system rebalancing. Contemplative
practices that cultivate loving states could potentially enhance rehabilitation outcomes through multiple pathways: improved mood and motivation, enhanced immune function, and potentially accelerated tissue repair through reduced inflammatory burden.

Rehabilitation protocols incorporating loving-kindness meditation, compassionate body awareness, or similar practices could be developed and tested for effectiveness across various conditions.

The application to addiction treatment represents another practical frontier.

Substance use disorders are characterized by disrupted stress response systems, chronic inflammation, and impaired self-regulation. Mind-body interventions have shown promise in addiction treatment, but their molecular mechanisms remain poorly understood. The transcriptional changes documented here—particularly the enhanced glucocorticoid sensitivity suggested by FKBP5 downregulation—could be relevant for addiction recovery. Cultivating loving, compassionate states may help normalize the stress response dysregulation that contributes to relapse vulnerability. Integrating these practices into comprehensive addiction treatment could enhance outcomes.

The complexity of defining and measuring love poses fundamental conceptual challenges. This study used mind-body interventions as proxies for agape-type states, but love is a multifaceted construct that resists simple operationalization. The theological concept of agape involves not only emotional warmth but also volitional commitment, self-sacrificial orientation, and participation in divine love. Whether secular meditation practices fully capture these dimensions is debatable. Development of measures that more precisely assess the distinct features of agape—its unconditional quality, its orientation toward the genuine good of the other, its transcendence of self-interest—would
strengthen future research. Collaboration between theologians and psychometricians could advance this measurement agenda.

The potential for publication bias in the mind-body literature should be acknowledged. Positive findings are more likely to be published than null results, potentially inflating effect size estimates in the literature. The present analysis relied on publicly available datasets that presumably met publication thresholds. The true effect sizes for mind-body interventions on gene expression may be smaller than those observed in published studies. Preregistration of studies, publication of null results, and systematic replication efforts would help address this concern. The field would benefit from large, multicenter trials with adequate power to detect small but potentially meaningful effects.

The intersection of contemplative science with developmental biology offers promising research directions. The prenatal period and early childhood represent times of heightened developmental plasticity when environmental influences can have lasting effects on gene expression and health trajectories. Research has documented that maternal stress during pregnancy influences offspring gene expression and health outcomes. Whether maternal contemplative practice might produce beneficial transcriptional effects in offspring—either through direct biological transmission or through enhanced quality of early caregiving—represents an important question. Longitudinal studies following pregnant women who engage in loving-kindness meditation and their children could address this question.

The potential for community-level interventions based on these findings warrants investigation. While this study focused on individual-level effects, communities characterized by high levels of social trust, civic engagement, and mutual care might
show population-level transcriptional signatures distinct from communities marked by isolation and mistrust. Community-wide interventions promoting compassion and connection—through religious institutions, schools, civic organizations, or public health campaigns—could potentially shift population health through accumulated individual transcriptional changes. This represents an ambitious but potentially high-impact application of the present findings.

The findings also contribute to discussions in philosophy of mind regarding the causal efficacy of consciousness. The mind-body problem—how mental states relate to physical states—has occupied philosophers since Descartes. The present findings, demonstrating that psychological states associated with loving consciousness produce specific molecular changes, support interactionist views that grant consciousness genuine causal power over biological processes. While the precise mechanisms remain to be elucidated, the consistent pattern of transcriptional changes across multiple datasets provides empirical evidence that mental states are not merely epiphenomenal but participate causally in shaping biological reality.

The ethical implications of these findings extend to questions of personal responsibility for health. If loving states promote health while hostile or fearful states harm it, do individuals bear some responsibility for cultivating salutary psychological orientations? This question must be approached with care to avoid victim-blaming those who suffer illness despite virtuous lives, or implying that illness always reflects psychological or spiritual deficiency. Rather, the findings suggest that cultivation of loving states represents one factor among many that influences health—a factor that is
partially within human control and therefore a legitimate target for personal and public
health efforts.

The interdisciplinary nature of this research invites reflection on the future of
academic inquiry at the intersection of science, theology, and contemplative practice.
Traditional academic boundaries have often kept these domains separate, to the detriment
of comprehensive understanding. The present research, by integrating methods from
bioinformatics with theoretical frameworks from theology and contemplative science,
demonstrates the potential for productive transdisciplinary collaboration. Centers and
programs that foster such collaboration—like the Mind and Life Institute's work bridging
Buddhism and neuroscience, or various religion and science institutes at major
universities—provide models for future development in this area.

The findings resonate with indigenous wisdom traditions that have long
recognized the connection between spiritual health and physical well-being. Native
American, African, Asian, and other traditional healing systems typically view the person
as an integrated whole and understand illness as involving spiritual as well as physical
dimensions. The molecular validation of mind-body connections provided by this
research affirms these traditional perspectives, while also providing a framework for
respectful dialogue between traditional knowledge and modern science. Such dialogue
could enrich both domains and contribute to more holistic approaches to health and
healing.

## Limitations

While the present findings provide compelling evidence for the transcriptional
effects of mind-body interventions, several limitations must be acknowledged in
interpreting these results. Table 5.1 summarizes the major limitations and the mitigating factors or analytical approaches employed to address them.

<table><caption>**Table 5.1 Summary of Study Limitations and Mitigating Factors**</caption><thead><tr><td>Limitation</td><td>Potential Impact</td><td>Mitigating Factors</td></tr></thead><tbody><tr><td>Secondary data analysis</td><td>Limited control over study design, sample selection, and data collection protocols</td><td>Multiple independent datasets; rigorous quality control; consistent analytical pipeline</td></tr><tr><td>Measurement limitations</td><td>Microarray technology has lower sensitivity than RNA-seq; platform differences across studies</td><td>Focus on robust effects detectable across platforms; meta-analytic integration; emphasis on pathway-level findings</td></tr><tr><td>Sample characteristics</td><td>Predominantly healthy adults; limited demographic diversity; variable sample sizes</td><td>Combined N = 358 provides adequate power; findings consistent across diverse populations</td></tr><tr><td>Causal inference limitations</td><td>Cross-sectional or pre-post designs cannot establish causality; unmeasured confounders possible</td><td>Inclusion of RCTs where available; biological plausibility through established PNI mechanisms</td></tr><tr><td>Operationalization of love constructs</td><td>MBIs represent proxies for agape love; direct</td><td>Theoretical alignment of MBIs with agape characteristics;</td></tr></tbody></table>
<table><tr><td></td><td>measurement of love states not possible</td><td>triangulation across multiple practice types</td></tr><tr><td>Effect sizes</td><td>Effect sizes are small to moderate; clinical significance uncertain</td><td>Consistent with prior social genomics research; biological significance evidenced by pathway coherence</td></tr></table>

*Note.* MBI = mind-body intervention; RCT = randomized controlled trial; PNI = psychoneuroimmunology.

**Secondary Data Analysis Constraints.** The use of publicly available GEO datasets, while enabling access to large samples across multiple interventions, necessarily limits control over study design elements. The original studies were designed for their specific purposes, not for the theoretical questions addressed in this dissertation. Sample selection criteria, intervention protocols, and data collection procedures varied across studies. To mitigate these concerns, rigorous quality control was applied to all datasets, and analyses focused on effects that were robust across multiple independent studies.

**Measurement Limitations.** The datasets employed microarray technology, which, while well-validated, has lower sensitivity and dynamic range compared to RNA sequencing (Wang et al., 2009). Additionally, different microarray platforms were used across studies (Affymetrix and Illumina), potentially introducing platform-specific artifacts. The analysis addressed these concerns by focusing on effects detectable across platforms and by emphasizing pathway-level findings, which are more robust to measurement noise than individual gene effects.
**Sample Characteristics.** The samples consisted predominantly of healthy adults participating in mind-body intervention studies. Demographic diversity was limited, with underrepresentation of some racial/ethnic groups and uncertain generalizability to clinical populations. Sample sizes for individual datasets ranged from 72 to 122, which, while consistent with social genomics research norms, limited statistical power for detecting small effects. The combined sample of 358 individuals and the consistency of findings across diverse populations provide some assurance of robustness.

**Causal Inference Limitations.** While some datasets (GSE108215, GSE10041) derived from randomized controlled trials, others (GSE55762) were cross-sectional in design, limiting causal inference. The GSE44777 dataset employed a within-subjects pre-post design without a control group. Even in the RCT datasets, unmeasured confounders (dietary changes, exercise patterns, social engagement) that co-occur with mind-body interventions could contribute to observed effects. The biological plausibility of the findings, grounded in established PNI mechanisms, provides some support for causal interpretation, but definitive causal claims would require additional experimental evidence.

**Operationalization of "Love" Constructs.** A fundamental limitation of this study is the indirect operationalization of *agape* love through mind-body interventions. The datasets examined meditation, yoga, relaxation training, and psychological flourishing—not *agape* love directly. The theoretical argument linking these practices to *agape* rests on their shared emphasis on self-transcendence, compassion, and eudaimonic orientation, but this remains an interpretive claim rather than a direct measurement.
Future research should develop and validate measures of *agape* love states and examine their transcriptional correlates directly.

**Effect Sizes.** The effect sizes observed in this study (Cohen's *d* ranging from approximately 0.30 to 0.55 for top genes) are small to moderate in magnitude. This is consistent with the social genomics literature, where transcriptional effects of psychological variables are typically subtle (Cole, 2014). However, the clinical significance of these effect sizes is uncertain. Small transcriptional changes, sustained over time, may accumulate to produce clinically meaningful health effects—but this remains to be demonstrated in longitudinal intervention studies.

**Recommendations for Future Research**

The findings of this study open multiple avenues for future research, spanning methodological refinements, theoretical extensions, and specific empirical investigations.

Table 5.2 summarizes key research directions organized by domain.
<table><caption>Table 5.2 Recommended Future Research Directions</caption><thead><tr><td>Domain</td><td>Research Direction</td><td>Rationale</td></tr></thead><tbody><tr><td>Methodology</td><td>RNA sequencing studies of mind-body interventions</td><td>Higher sensitivity and discovery potential than microarray; detection of novel transcripts</td></tr><tr><td>Methodology</td><td>Longitudinal designs with extended follow-up</td><td>Assess durability of transcriptional changes; examine dose-response relationships</td></tr><tr><td>Measurement</td><td>Development of agape love state measures</td><td>Enable direct operationalization rather than proxy measures through MBIs</td></tr><tr><td>Theoretical</td><td>Testing quantum mechanisms of consciousness-biology interface</td><td>Advance QPNIT Propositions 1-2; examine microtubule quantum coherence</td></tr><tr><td>Clinical</td><td>RCTs examining gene expression as primary outcome</td><td>Establish causal effects; identify optimal intervention parameters</td></tr><tr><td>Clinical</td><td>Comparison of different love/compassion practices</td><td>Determine whether loving-kindness, compassion meditation, etc. have distinct signatures</td></tr><tr><td>Integrative</td><td>Combined spiritual/pharmacological interventions</td><td>Examine potential synergies; optimize treatment protocols</td></tr></tbody></table>
*Note.* MBI = mind-body intervention; QPNIT = Quantum Psychoneuroimmunotheology; RCT = randomized controlled trial.

## Methodological Recommendations

Future studies should employ RNA sequencing (RNA-seq) technology, which offers substantially greater sensitivity and dynamic range than microarray platforms. RNA-seq also enables detection of novel transcripts, splice variants, and non-coding RNAs that may be relevant to mind-body effects (Wang et al., 2009). Single-cell RNA sequencing could provide unprecedented resolution, revealing which specific immune cell populations are most responsive to mind-body interventions and enabling more precise mechanistic understanding.

Longitudinal designs with extended follow-up are essential for understanding the durability of transcriptional changes and the dose-response relationships governing mind-body effects. Studies should examine whether transcriptional benefits persist after intervention cessation, whether they accumulate with continued practice, and whether there are threshold effects or optimal "doses" of contemplative practice for achieving maximum biological benefit.

## Theoretical Extensions

A critical next step for advancing the QPNIT framework is the development of validated measures of *agape* love states. While this study used mind-body interventions as proxies, future research should directly assess phenomenological states associated with selfless, unconditional love and examine their transcriptional correlates. This might involve adapting existing measures of compassion and altruistic love (Sprecher & Fehr,
2005) or developing novel instruments grounded in theological conceptualizations of *agape*.

The quantum aspects of QPNIT remain largely untested. While the present study focused on the PNI components of the framework, future research should investigate the quantum biological mechanisms proposed in Propositions 1 and 2. This might involve collaborations with quantum biologists to examine whether contemplative states are associated with altered quantum coherence in neural systems (as proposed by the Orch-OR theory) or with changes in the brain's electromagnetic field properties (as proposed by CEMI theory).

***Specific Research Directions***

Several specific empirical questions merit investigation:

1. Do different types of love/compassion practices have distinct transcriptional signatures? Comparing loving-kindness meditation, compassion meditation, devotional prayer, and acts of service could reveal whether all loving practices converge on the same molecular pathways or whether different expressions of *agape* have unique biological correlates.

2. What is the time course of transcriptional changes? Studies examining gene expression at multiple time points during and after intervention could map the temporal dynamics of the *agape* transcriptional signature.

3. Are there individual differences in responsiveness? Identifying genetic, psychological, or demographic moderators of transcriptional response could enable personalized approaches to mind-body medicine.
4. Can transcriptional changes predict clinical outcomes? Prospective studies examining whether the *agape* transcriptional signature predicts subsequent health outcomes (illness rates, recovery times, longevity) would establish clinical significance.

5. What are the epigenetic correlates of loving states? Examining DNA methylation and histone modifications could reveal whether *agape* practices produce lasting epigenetic changes that may contribute to intergenerational transmission of biological resilience.

Neuroimaging-transcriptomic integration represents a promising frontier for future research. Combining functional MRI or EEG measures of neural activity during loving-kindness meditation with simultaneous blood sampling for gene expression analysis could illuminate the neural-immune interface in unprecedented detail. Such studies could identify which neural patterns most strongly predict favorable transcriptional responses, potentially enabling optimization of contemplative practices for maximum biological benefit. The QPNIT framework's emphasis on the consciousness-biology interface would be directly tested by such integrative designs.

Intergenerational transmission of transcriptional patterns represents another important research direction. Epigenetic research has documented that parental experiences can influence offspring gene expression through mechanisms including DNA methylation inheritance. Whether the transcriptional benefits of loving states might be transmitted to offspring—either through epigenetic inheritance or through the quality of the early caregiving environment shaped by parents' contemplative practice—represents a profound question with implications for the biology of love across generations.
Longitudinal studies following practitioners and their children could address this question.

The potential for personalized approaches to mind-body intervention based on transcriptional profiles warrants investigation. Individual differences in baseline gene expression, genetic polymorphisms affecting stress response systems, and other molecular characteristics may moderate response to contemplative practices.

Development of predictive models identifying individuals most likely to benefit from specific types of mind-body intervention could optimize treatment matching and improve outcomes. The era of precision medicine could extend to precision contemplative care.

Investigation of the dose-response relationship between practice intensity and transcriptional effects would inform practical recommendations. Questions remain regarding optimal practice duration (15 minutes versus 45 minutes), frequency (daily versus several times weekly), and total exposure (weeks versus months versus years of practice). Understanding these parameters would enable evidence-based guidance for practitioners and clinicians. Additionally, whether there is a ceiling effect—a point beyond which additional practice produces diminishing transcriptional returns—has both scientific and practical relevance.

The application of advanced computational methods, including machine learning and network analysis, could extract additional insights from the rich transcriptomic data. Network-based approaches could identify hub genes and regulatory motifs that coordinate the transcriptional response to loving states. Machine learning algorithms could potentially identify subtle patterns not captured by traditional statistical approaches, and could be used to develop predictive models for individual response.
These computational advances could accelerate discovery in the field of contemplative genomics.

Conclusion

This doctoral dissertation investigated the transcriptional signature of *agape* love, examining whether mind-body interventions associated with compassion and selfless concern produce measurable changes in immune-related gene expression. Through a systematic analysis of four GEO datasets encompassing 358 individuals, the study identified a coherent pattern of transcriptional changes: enhanced expression of interferon-stimulated antiviral genes (IFI27, RSAD2, IFIT1, IFIT3, MX2) and reduced expression of stress-response genes (particularly FKBP5). This pattern represents a partial reversal of the Conserved Transcriptional Response to Adversity—the genomic signature of chronic stress and social isolation—suggesting that loving, compassionate states promote a transcriptional profile conducive to health and biological flourishing.

The theoretical significance of these findings extends beyond their empirical content. By demonstrating that states of consciousness associated with *agape* love have molecular correlates in immune cells, this study provides empirical grounding for the Quantum Psychoneuroimmunotheology (QPNIT) framework's central propositions. Spiritual practices do produce measurable biological effects; consciousness does interface with physical biology; and the molecular machinery of our immune cells responds to the loving or fearful orientation of our minds. These findings vindicate the integrated anthropology of the theological traditions that have always understood the human person as a unity of body and soul.
The theological interpretation of these biological findings invites profound reflection on the nature of love and its place in human flourishing. Augustine's *caritas*—rightly ordered love directed toward God and neighbor—emerges not merely as a spiritual ideal but as a biological optimum. The Pauline declaration that the greatest of virtues is love (1 Corinthians 13:13) takes on new resonance when we observe that loving states are inscribed in our molecular biology, shaping the very gene expression patterns that govern our health and vitality. The Johannine assertion that "God is love" (1 John 4:8) finds empirical echo in the observation that states of unconditional, selfless love appear to align our biological systems with their optimal functioning.

Yet caution is warranted against both reductive and triumphalistic interpretations. The biological correlates of *agape* love do not reduce love to molecules; they reveal that love has molecular expressions. The health benefits of loving states do not make love merely instrumental to health; they illuminate the integrated nature of human flourishing across spiritual, psychological, and biological domains. The empirical findings do not prove theological claims about the nature of God or the salvific significance of love; they demonstrate that the created order resonates with love in ways that science can partially discern.

In closing, this dissertation contributes to an emerging science of sacred love—an empirical investigation that respects the irreducible mystery of love while seeking to understand its biological embodiment. The transcriptional signature of *agape* love, etched in the gene expression patterns of human immune cells, testifies to the profound integration of mind, body, and spirit that constitutes human existence. It suggests that we are, as the QPNIT framework proposes, beings whose highest spiritual orientations—
including selfless, unconditional love—find expression in our deepest biological structures. The ancient wisdom traditions that have commended love as the supreme virtue and the path to human flourishing find unexpected confirmation in the molecular biology of the twenty-first century. As we learn to cultivate *agape* in our hearts, we may also be cultivating health in our cells—a convergence of the spiritual and the biological that points toward the fundamental unity of all that exists.
CHAPTER 6: CONCLUSION

Introduction

This concluding chapter synthesizes the findings, theoretical contributions, and practical implications of this doctoral dissertation, which examined the transcriptional signature of agape love through a gene expression analysis of altruistic behavior and immune function. The study employed the Quantum Psychoneuroimmunotheology (QPNIT) theoretical framework to bridge the epistemological gap between theological understandings of love and the biological sciences, specifically psychoneuroimmunology and social genomics. This integration represents a novel contribution to both fields, demonstrating that sacred constructs can be examined empirically without sacrificing their theological depth or scientific rigor.

The dissertation addressed a fundamental question that has occupied philosophers, theologians, and now scientists: What is the nature of love, and how does it affect human flourishing? By analyzing gene expression data from four independent datasets comprising 358 participants engaged in mind-body interventions associated with love and compassion, this study provided molecular-level evidence for the biological effects of love-oriented practices. The findings revealed consistent patterns of enhanced antiviral gene expression and reduced stress biology, suggesting that agape love—when cultivated through contemplative practice—may produce a favorable shift in the immune system's transcriptional program.

The chapter begins with a comprehensive summary of the study, including the problem statement, purpose, theoretical framework, methodology, and key findings. This is followed by a presentation of the major conclusions drawn from the research,
organized around the study's guiding research questions and hypotheses. The
contributions to knowledge across multiple disciplines are then articulated, followed by
implications for practice in healthcare, pastoral care, and public health contexts. The
chapter concludes with detailed recommendations for future research and final reflections
on the significance of this interdisciplinary inquiry into the biology of sacred love.

**Summary of the Study**

***Statement of the Problem***

Despite millennia of theological reflection on love as a transformative spiritual
reality, and despite decades of psychoneuroimmunological research demonstrating that
psychological states influence immune function, no systematic investigation had
examined whether the specific type of love described in Christian scripture—agape, or
unconditional, selfless love—might have measurable biological correlates at the level of
gene expression. This gap represented both a scientific opportunity and a theological
imperative, as understanding the embodied nature of love could inform both healthcare
practice and spiritual formation.

The Conserved Transcriptional Response to Adversity (CTRA), characterized by
upregulated inflammatory genes and downregulated antiviral genes, has been linked to
negative psychological states including loneliness, grief, and chronic stress (Cole, 2014;
Slavich & Cole, 2013). This transcriptional profile represents the genome's response to
perceived social threat, preparing the organism for potential wound-related bacterial
infection at the cost of reduced antiviral immunity. However, whether positive relational
states—particularly those associated with agape love and its expression through
contemplative practices—might reverse this pattern remained largely unexplored in the scientific literature.

From a theological perspective, the Christian tradition has consistently maintained that love is not merely an emotion or sentiment but a transformative power with real effects in the world. Augustine's concept of caritas—rightly ordered love directed toward God and neighbor—was understood to bring about both spiritual and physical healing (Augustine, trans. 1998). Thomas Aquinas defined love as willing the good of another and argued that charity perfects the soul in ways that overflow into bodily well-being (Aquinas, trans. 1981). Anders Nygren's influential analysis of agape emphasized its spontaneous, unmotivated character as a reflection of divine love itself (Nygren, 1953). These theological insights demanded empirical investigation: Could the transformative power of agape be detected at the molecular level?

## Purpose and Research Questions

The purpose of this quantitative study was to examine the transcriptional signature associated with mind-body interventions that cultivate love, compassion, and altruistic orientations, using the QPNIT theoretical framework to interpret findings at the intersection of theology and biology. The study sought to contribute to the emerging field of spiritual genomics while honoring the theological integrity of the agape concept. Three primary research questions guided the investigation:

Research Question 1: What is the transcriptional signature associated with mind-body interventions related to love and compassion practices? This question addressed whether contemplative practices theoretically aligned with the cultivation of agape would produce detectable changes in gene expression within peripheral blood mononuclear cells
(PBMCs), the immune cells most accessible to investigation and most responsive to
psychological influences.

Research Question 2: How do CTRA genes—specifically inflammatory and antiviral gene sets—respond to these interventions? This question examined whether love-oriented practices would produce a transcriptional pattern opposite to the CTRA, with enhanced antiviral gene expression and reduced inflammatory signaling, consistent with states of safety, connection, and flourishing.

Research Question 3: What biological pathways are enriched in the differential expression patterns observed? This question explored the broader functional implications of the transcriptional changes, identifying the biological processes most consistently affected by love and compassion practices across multiple independent datasets.

***Theoretical Framework: Quantum Psychoneuroimmunotheology***

The study employed Quantum Psychoneuroimmunotheology (QPNIT) as its
theoretical framework, representing a novel integration of four domains of knowledge.
First, theology provided the conceptual foundation for understanding agape love as
described in Christian scripture (particularly 1 Corinthians 13 and the Johannine writings)
and developed through patristic thought (Augustine's ordo amoris) and scholastic
theology (Aquinas's treatment of charity as the form of all virtues). Second,
psychoneuroimmunology (PNI) provided the scientific foundation for understanding
bidirectional interactions between psychological processes and the immune system,
including the HPA axis, autonomic nervous system, and inflammatory pathways.

Third, social genomics provided the molecular framework for understanding how
social experiences—including experiences of love, connection, and transcendence—
regulate gene expression through transcription factor pathways such as NF-κB and interferon response factors. The work of Steve Cole and colleagues demonstrating the CTRA provided a specific molecular target for investigation (Cole, 2014; Fredrickson et al., 2013). Fourth, quantum biology offered theoretical resources for understanding how consciousness might interact with biological processes at the quantum level, potentially mediating the relationship between spiritual states and molecular outcomes.

QPNIT proposes four core propositions: (a) consciousness interacts with biological systems through quantum-level processes; (b) spiritual practices influence gene expression through psychoneuroimmunological pathways; (c) the theological virtues—faith, hope, and love—function as biologically active states with measurable physiological correlates; and (d) the integration of theological and scientific perspectives enriches understanding of human flourishing in ways neither discipline could achieve alone. This study specifically tested propositions (b) and (c) through gene expression analysis of love-associated contemplative practices.

## Methodology Summary

This study employed a quantitative secondary data analysis design, utilizing publicly available gene expression datasets from the Gene Expression Omnibus (GEO) database. Four datasets were analyzed, comprising 358 total samples across diverse mind-body interventions theoretically aligned with the cultivation of love and compassion. GSE108215 examined the Mindfulness Awareness Program versus health education control (n = 86 samples), representing a secularized form of contemplative practice with Buddhist roots emphasizing loving-kindness and compassion. GSE44777
examined Sudarshan Kriya Yoga with acute pre-post measurements (n = 78 samples),
representing a yoga tradition emphasizing breath work and devotional practice.

GSE10041 examined the Relaxation Response comparing long-term practitioners
to novices (n = 72 samples), representing the tradition pioneered by Herbert Benson
demonstrating that contemplative practices produce measurable physiological effects.

GSE55762 examined psychological well-being with focus on eudaimonic versus hedonic orientations (n = 122 samples), representing research by Barbara Fredrickson and colleagues on how different forms of happiness relate to gene expression. Together, these datasets provided a robust foundation for examining the transcriptional signature of love-oriented practices across multiple populations and intervention types.

Data analysis procedures followed a comprehensive bioinformatics pipeline
including quality control, normalization, differential expression analysis using moderated
t-statistics, CTRA gene signature analysis examining 102 genes across inflammatory
(NF-κB targets) and antiviral (interferon-stimulated) categories, pathway enrichment
analysis using Gene Ontology biological process terms, and meta-analysis across datasets
to identify consistently regulated genes. Statistical significance was assessed using
Benjamini-Hochberg false discovery rate correction, with effect sizes calculated using
Cohen's d to enable comparison across studies with different sample sizes and platforms.

Summary of Key Findings

The analysis revealed several significant findings supporting the study's
hypotheses and the QPNIT theoretical framework. First, regarding Research Question 1,
mind-body interventions produced detectable transcriptional changes, with 2.50% to
4.90% of genes showing nominal significance (p < .05) across datasets. While few
individual genes reached statistical significance after stringent false discovery rate correction—a common finding in psychosocial intervention research given the modest effect sizes involved—the consistency of patterns across independent datasets provided compelling evidence for real transcriptional effects.

Second, regarding Research Question 2, a consistent pattern of enhanced antiviral immunity emerged as the most robust finding. Interferon-stimulated genes including IFI27, RSAD2, IFIT1, IFIT3, and MX2 showed upregulation across all four datasets. Meta-analysis revealed that 74.3% of antiviral genes demonstrated positive effect sizes, representing strong convergent evidence for enhanced Type I interferon response with love-oriented practices. Simultaneously, stress-related gene expression was reduced, with FKBP5—a critical regulator of glucocorticoid receptor sensitivity and stress responsivity—showing consistent downregulation across datasets (meta-analytic Cohen's d = -0.34).

Third, regarding Research Question 3, pathway enrichment analysis identified the Type I interferon/antiviral response pathway as showing consistent positive effect sizes across all datasets. Immune-related biological processes including response to virus, defense response, and cytokine signaling were enriched among differentially expressed genes. The NF-κB pathway, while showing mixed results at the individual gene level, demonstrated consistent downregulation of key regulatory genes RELA and JAK2, suggesting modulation of inflammatory signaling.

These findings collectively indicate a favorable shift in the CTRA profile, representing a partial reversal of the transcriptional pattern associated with chronic stress, loneliness, and social adversity. The molecular signature observed—enhanced antiviral
vigilance combined with reduced stress biology—aligns precisely with what would be
expected if practices cultivating love and compassion produce biological states of safety,
connection, and flourishing, thereby signaling to the genome that threat-oriented
programs are unnecessary.

Conclusions

Based on the findings of this study, interpreted through the QPNIT theoretical
framework and in dialogue with theological scholarship on agape love, five major
conclusions are warranted. These conclusions address both the empirical findings and
their broader theoretical and practical significance.

Conclusion 1: Mind-Body Practices Produce Measurable Transcriptional Changes

The first major conclusion is that mind-body practices associated with love,
compassion, and self-transcendence produce measurable changes in gene expression
within peripheral blood mononuclear cells. Across four independent datasets representing
different interventions (mindfulness, yoga, relaxation response, and psychological well-
being), consistent patterns of transcriptional modulation were observed. This finding
extends and corroborates the meta-analysis by Buric et al. (2017), which first
demonstrated that mind-body interventions reverse the expression pattern of genes
involved in inflammatory and antiviral responses.

The present study advances this literature by specifically framing the findings
within the context of agape love and the QPNIT theoretical framework. The interventions
examined share theoretical features with the cultivation of agape as theologically
understood: attention to the present moment (presence), compassion for self and others
(caritas), transcendence of ego-centered concerns (kenosis), and orientation toward the