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. 2025 Apr 11;230(3):iyaf072. doi: 10.1093/genetics/iyaf072

Living in poverty is associated with gene expression changes in immune cells

Nicole S Arnold 1,2, Justyna Resztak 3, David Witonsky 4, Adnan Alazizi 5, Nicole Noren Hooten 6, Michele K Evans 7, Valerie Odero-Marah 8, Douglas F Dluzen 9,✉,4, Roger Pique-Regi 10,11,✉,4, Francesca Luca 12,✉,4
Editor: Y Li
PMCID: PMC12239207  PMID: 40211598

Abstract

Social factors influence health outcomes and life expectancy. Individuals living in poverty often have adverse health outcomes related to chronic inflammation that affect the cardiovascular, renal, and pulmonary systems. Negative psychosocial experiences are associated with transcriptional changes in genes associated with complex traits. However, the underlying molecular mechanisms by which poverty increases the risk of disease and health disparities are still not fully understood. To bridge the gap in our understanding of the link between living in poverty and adverse health outcomes, we performed RNA-sequencing of blood immune cells from 204 participants of the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study in Baltimore, Maryland. We identified 138 genes differentially expressed in association with poverty. Genes differentially expressed were enriched in wound healing and coagulation processes. Of the genes differentially expressed in individuals living in poverty, EEF1DP7 and VIL1 are also associated with hypertension in transcriptome-wide association studies. Our results suggest that living in poverty influences inflammation and the risk for cardiovascular disease through gene expression changes in immune cells.

Keywords: poverty, gene expression, RNA-seq, health, transcriptome

This research aimed to understand how living in poverty affects the immune system and the risk of diseases. Arnold et al. measured gene activity of blood immune cells from 204 participants of the Healthy Aging in Neighborhoods of Diversity across the Life Span study in Baltimore, Maryland. The authors found that gene expression is associated with poverty for 138 genes, with stronger effects in women and with functions related to wound healing. Two of these genes had been previously associated with hypertension. Their findings suggest that poverty influences inflammation and cardiovascular risk through changes in gene activity in immune cells.

Introduction

Socioeconomic disparities in health and mortality are very well documented (Antonovsky 1967; Sorlie et al. 1995; Kunst et al. 1998). These inequalities have been observed at similar levels since the early nineteenth century. More recently, health disparities are also reflected in current major causes of death including cancer and cardiovascular disease (Phelan et al. 2010). Individuals living in poverty are more frequently exposed to risk factors for these conditions including poor diet, inadequate exercise, and smoking. Additionally, psychosocial factors that may influence health status are also common when living in poverty, such as housing insecurity, discrimination, structural racism, and lack of social support (Shahu et al. 2021). Living below the United States federal poverty line increases cardiovascular risk (Odutayo et al. 2017) and low socioeconomic status (SES) is associated with an increase of cardiovascular disease biomarkers in the circulation, including red-blood-cell width (Tajuddin et al. 2017), high-density lipoprotein cholesterol (HDL-C) (Waldstein et al. 2016), intimal medial thickness of the arteries, and pulse-wave velocity (Wendell et al. 2017). These risks are also further amplified in vulnerable groups such as African American adults (Wendell et al. 2017). Low SES is associated with high prevalence of Cytomegalovirus seropositivity, which is also associated with higher chronic conditions (Aiello et al. 2017). Low SES has consistently been associated with a greater risk of infectious and cardiovascular diseases, more rapid progression, and decreased survival (Phelan et al. 2010). The biological mechanisms underlying these disparities still need to be better understood. Elevated innate immunity responsiveness is observed in low SES children when compared with children that live in higher SES (Azad et al. 2012). In a broad cohort across the United States, low childhood SES was linked to poorer metabolic outcomes in midlife (Miller et al. 2011).

Adverse health is consistently linked to lower SES, but little is known about the molecular mechanisms behind this disparity. Individuals from low-income families have higher cortisol output in daily life, higher expression of transcripts containing NFKappaBeta response components, and higher production of the proinflammatory cytokine Interleukin 6 (Miller et al. 2009). Children from low-income homes have increased cortisol levels in adulthood, regardless of social standing (Li et al. 2007; Miller et al. 2009), which could potentially lead to resistance to glucocorticoid signaling and allow for increased adrenocortical and inflammatory responses (Miller et al. 2009). Additionally, metabolic syndrome components such as central obesity, insulin resistance, and blood pressure can worsen with chronic cortisol exposure (Björntorp and Rosmond 1999).

Social genomics investigates the various dimensions of a person's social and psychological environment that influence gene expression (Cole 2009, 2014; Galea et al. 2011; Toyokawa et al. 2012; Slavich and Cole 2013). Several studies have linked changes in gene expression in whole blood to SES, psychosocial experiences, and adverse social conditions (Chen et al. 2009; Segman et al. 2010; Powell et al. 2013; Stanton et al. 2017; Farrell et al. 2018; Robles et al. 2018; Noren Hooten and Evans 2019; Arnold et al. 2020; Resztak et al. 2021). Earlier studies in the field (Cole 2009, 2014; Slavich and Cole 2013) identified a pattern of differentially expressed genes known as the conserved transcriptional response to adversity (CTRA) that are altered in response to various aspects of social adversity. The CTRA comprises genes in Inflammatory pathways as well as innate antiviral response and antibody production genes (Cole 2019; Thames et al. 2019). However, SES and psychosocial experiences have a broader effect on immune cell gene expression and impact additional pathways beyond those included in the CTRA (Resztak et al. 2021).

Therefore, there is a need in the field for genome-wide transcriptomic studies of individuals living in poverty and in a sample balanced by age, race, and sex. A recent analysis of a cohort of 1,069 young adults from the National Longitudinal Study of Adolescent to Adult Health (Add Health) found gene expression changes with demographic and behavioral factors; yet family poverty was not found to be associated with changes in the expression of individual genes unless aggregated by functional groups (Cole et al. 2020). Characterizing whether living in poverty induces genome-wide transcriptional changes thus remains essential to understand the biological mechanisms underlying poor health outcomes and health disparities in immune and cardiovascular diseases among individuals living in poverty. Here, we hypothesized that differential gene expression is associated with poverty status and that understanding the molecular pathways affected by poverty may shed light on health disparities in disease risk and outcome. We analyzed a sub-cohort of participants from the National Institute on Aging's (NIA) Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) study in Baltimore, MD. To determine the association between poverty status and gene expression, we studied 204 participants from the HANDLS cohort and performed RNA-sequencing on peripheral blood mononuclear cells (PBMCs) collected longitudinally at two different time points with a 4–5 years interval (Fig. 1 and Supplementary Table 1).

Fig. 1.

Fig. 1.

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Study design. a) Conceptual framework showing the workflow of the study to examine the relationship between poverty status and gene expression patterns leading to health disparities. b) Study cohort. The final sample cohort consists of 204 participants. Blood samples were collected at two different time points ∼4–5 years apart.

Materials and methods

Study participants and sample collection

The HANDLS study (Evans et al. 2010) is a longitudinal, epidemiological study of a diverse cohort of individuals living in Baltimore City and was designed and implemented by the National Institute on Aging Intramural Research Program (NIA IRP). Poverty status was assigned based on self-reported household income, by grouping participants as living either 125% above or below the 2004 federal poverty line. Race was self-reported as either African American or White. Samples were collected at two different time points ∼4–5 years apart for each participant (time point 1 and time point 2). Participant blood samples were collected in the morning. PBMCs were isolated within 3 h of blood draw, aliquoted, and stored at −80°C. The Institutional Review Boards of the National Institute of Environmental Health Sciences, National Institutes of Health and Morgan State University approved this study and a written informed consent was signed by all participants.

In the present study, HANDLS participants were selected across 4 variables: sex, race, poverty status, and age group (10-yr groups between 35 and 65). The cohort was balanced for all variables and divided in 24 batches for a total of 240 individuals across two time points, and 480 samples. Following RNA-sequencing QC (see section below), the final sample size consisted of 204 participants (Fig. 1b; Supplementary Table 1).

RNA extraction, library preparation, and sequencing

Cells were thawed and transferred into RPMI1640 (Gibco Cat# 11835030) medium containing 10% FBS (Gibco Cat# 10082147). Cells were then collected by centrifugation at 1,200 RPM for 7 min and washed twice using ice-cold PBS (Gibco Cat# 10010002). Collected pellets were lysed using Lysis/Binding Buffer (Invitrogen Cat# 61012), and frozen at −80°C. Polyadenylated mRNAs were subsequently isolated from thawed lysates using the Dynabeads mRNA Direct Kit (Invitrogen), following the manufacturer's instructions. RNA-seq libraries were prepared using a protocol modified from the NEBNext Ultradirectional (Illumina—NEB Cat# E7760L) library preparation protocol. RNA was fragmented at 94°C for 5 min to obtain 200–1,500 bp fragments. SPRISelect beads (Beckman Coulter) were used in all purification steps and size selection. Barcodes from PerkinElmer were added by ligation. The individual libraries were quantified using the KAPA real-time PCR system (ROCHE DIAGNOSTICS Cat # 07960441001), following the manufacturer's instructions and using a custom-made series of standards obtained from serial dilutions of the PhiX DNA (Illumina). Next, libraries were pooled and sequenced using the Novaseq instrument. Median number of reads/sample was 33,015,582 (Supplementary Table 2).

Data preprocessing

We aligned the RNA-seq reads with HISAT2 (Kim et al. 2015) to the human genome version “grch38_snp_tran”, which considers splicing and common genetic variants. Aligned and cleaned (deduplicated) reads were counted using HTSeq and Homo_sapiens.GRCh38.103 (Ensembl) transcriptome assembly genes. We started with an initial sample size of 478 libraries corresponding to 239 individuals. Due to poor RNA quality 26 libraries failed to sequence, which resulted in 452 libraries. For all gene expression analyses, genes on sex chromosomes and genes with expression below six reads or 0.1 counts per million in at least 20% of samples were dropped, which resulted in 21,251 genes.

To validate the identity of the two time point samples from the same individual, we first used the program ANGSD (Korneliussen et al. 2014) to infer genotype likelihoods from the aligned and cleaned mapped reads. The program NgsRelate (Korneliussen and Moltke 2015) was then used on the genotype likelihoods to estimate relatedness between all sample pairs. Individual sample pairs from the two time points were retained if the pair had an estimated KING coefficient >0.30. This resulted in a final set of 204 individuals (408 samples) that were used in all subsequent analyses.

We obtained genotype information from the RNA-seq data by using samtools pileup and bcftools on known genotypes from the 1,000 genomes project (1000 Genomes Project Consortium et al. 2010). The resulting VCF file was filtered for biallelic SNPs and the samples were further analyzed with PLINK2 (Purcell et al. 2007). The first three genotype principal components were added in the gene expression model to control for genetic ancestry, similar to the GTEx analysis workflow. Note that 26.9% of variance is explained by the first PC, and it flattens for subsequent PCs (Supplementary Fig. 1). Additionally, the following covariates were included in the model to correct for confounders: library preparation batch, age, sex, percent reads mapping to exons, percent nonduplicate reads.

Differential gene expression analysis

We used DESeq2 v1.22.1 (Love et al. 2014) to test for differential gene expression across 204 individuals at each time point.

Model 1 (Poverty Model): gene expression ∼ library preparation batch + age + sex + percent reads mapping to exons + percent nonduplicate reads + PC1 + PC2 + PC3 + poverty status

We also examined each sex separately:

Model 2 (Women and Men Split Poverty Model): gene expression ∼ library preparation batch + age + percent reads mapping to exons + percent nonduplicate reads + PC1 + PC2 + PC3 + poverty status

To control for FDR, we used the Benjamin–Hochberg independent filtering step and multiple test correction implemented in DESeq2. Supplementary Tables 3 and 4 contain the full results of the analysis. Supplementary Tables 5 and 6 contain the results when testing for age and sex effects on gene expression.

Replication in an independent cohort

To compare the results of the differential expression analysis to those from an independent cohort, we downloaded File S3 from Cole et al. (2020). This study analyzed transcriptional variation as a function of individuals' sociodemographic condition (family poverty) in the ADD Health cohort, consisting of 1,069 young adults (age 32–42). To determine the overlap between genes differentially expressed in our cohort and those differentially expressed in the ADD Health cohort, we first relaxed the significance threshold to nominal P < 0.05 and identified a total of 1,757 differentially expressed genes in HANDLS and 1,126 in ADD Health. These sets of genes were then used to calculate the overlap between the two datasets (190 genes). We used Pearson's correlation on the intersection of all genes tested in the two datasets (11,304) to determine the overall degree of concordance in the gene expression changes associated with poverty in the two cohorts.

Gene ontology and pathway enrichment analyses

To identify relevant pathways related to the significantly differentially expressed genes in individuals living in poverty, we performed an enrichment analysis (hypergeometric test) for GO, KEGG, and REACTOME terms with clusterProfiler in R (Yu et al. 2012). We tested each list of differentially expressed genes against a background of all tested genes in our dataset. Significant biological processes contained a minimum of 10 genes and a maximum of 500 genes with a P adjusted <0.1.

TWAS analyses

To directly investigate whether discovered effects on gene expression may contribute to differential health outcomes, we used the results from PTWAS (Probabilistic Transcriptome-Wide Association Study; Zhang et al. 2020; 5% FDR) for cardiovascular disease traits such as: Coronary Artery Disease and hypertension. We overlapped the significantly differentially expressed genes associated with poverty and focused on those genes that had a z-score > 2 in absolute value (Zhang et al. 2020).

Results

Poverty status influences gene expression in the immune system

To identify genes differentially expressed between individuals living in poverty and those living above poverty, we analyzed differential gene expression using DESeq2 (Supplementary Fig. 1). We observed 138 differentially expressed genes at the first time point (FDR < 10%). We found that 70% of the differentially expressed genes with time point 1 had lower expression in individuals living in poverty (Fig. 2a).

Fig. 2.

Fig. 2.

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Poverty status influences gene expression in immune cells. a) Volcano plot of the differentially expressed genes at time point 1. b) Scatter plot of the correlation between gene expression changes associated with poverty (z-scores) in each time point separately. Significantly differentially expressed genes at time point 1 are indicated in red (FDR < 10%). c) Scatter plot of z-scores for poverty effects in the current study and the ADD Health study (Cole et al. 2020). Each point represents a gene and the color indicates if it is nominally significant (|z| > 2) in: ADD Health study only, HANDLS study only, shared by both studies. The dotted line is the regression line.

To assess whether the association between poverty and gene expression is stable over time and replicate our findings, we leveraged the longitudinal nature of the cohort. We found that 74 of the 138 genes differentially expressed at the first time point are also associated with poverty status at the second time point (P < 0.05), and overall, we find consistent effects between the two time points as indicated by a significant correlation in the gene expression z-score (r = 0.47, P < 10−16) (Fig. 2b). This result confirms that the gene expression changes associated with poverty are largely consistent across time points.

We then asked whether the time point 1 gene expression changes associated with poverty are specific to our cohort or shared with an independent cohort of 1,069 young adults from the ADD Health Study (Cole et al. 2020), where the effect of poverty on gene expression was also tested. We found a significant correlation between the z-scores from the two studies (ρ = 0.258 and P-value < 10−16, Fig. 3). Additionally, we found 190 differentially expressed genes shared between the two cohorts at nominal P < 0.05 (Fig. 2c).

Fig. 3.

Fig. 3.

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Pathways and gene expression changes associated with poverty. a) Results of the enrichment analysis for the gene ontology terms in the differentially expressed genes. The x-axis displays the proportion of gene (gene ratio), y-axis top ten (by FDR) enriched gene ontology terms (biological processes), legend indicates the number of associated genes (n = 10–20), and the P adjusted. b) Heatmap of significantly differentially expressed genes in the same gene ontology terms as in a). Blue indicates that a gene is downregulated in individuals living in poverty; gray indicates that the gene is not part of the gene ontology term. c, d). Examples of differentially expressed genes. The x-axis indicates the poverty status (as in Fig. 1), the y-axis indicates the normalized log10 expression counts.

We performed an over-representation analysis on the DEGs identified in the HANDLS cohort compared with all genes that were tested, independently of whether they were up- or downregulated. Our goal was to identify the biological processes impacted by poverty. We found an enrichment for biological processes pertaining to wound healing, blood coagulation, and platelet activation (Fig. 3a, b) with largely overlapping sets of differentially expressed genes in these biological categories. Among these genes, ALOX12 (log2 fold change (LFC) = −0.57, P-adj = 0.055) and MYL9 (LFC = −0.53, P-adj = 0.087) have the largest absolute change in expression and are in several enriched GO categories, including response to wounding, blood coagulation, and platelet aggregation (Fig. 3c, d; Supplementary Table 7). ALOX12 (Arachidonate 12-Lipoxygenase, 12S Type) encodes lipid metabolizing enzymes that regulate inflammation, platelet aggregation, cellular migration, and modulation of vascular permeability (Kulkarni et al. 2021). Depletion of the gene ALOX12 has been associated with chronic inflammation via effects on proinflammatory activity (Kulkarni et al. 2021). Direct evidence links ALOX12 to chronic diseases characterized by unregulated inflammation (Kohli and Levy 2009; Duvall and Levy 2016). Promoter methylation changes at ALOX12 result in potential epigenetic alterations in the etiology of atherosclerosis plaques (Kim et al. 2020). ALOX12 is expressed in various cell types and organs throughout the body, linking to several diseases, including atherosclerosis, hypertension, diabetes, obesity, and neurodegenerative disorders (Singh and Rao 2019). Genetic variants in the ALOX12 gene are associated with myocardial infarction, increased cardiovascular events, and mortality (Roumeliotis et al. 2018). Myosin-9 MYL9 is a motor protein that plays a role in cellular movement and contraction (Kumar et al. 1989; Iwasaki et al. 2001).

Effects of poverty on gene expression in men and women

Sex differences in physiology and pathology, along with sex steroid receptor signaling have an influence on health outcomes and contribute to life expectancy differences between men and women (Spence and Pilote 2015; Boese et al. 2017; Ahmed and Spence 2021). Sex hormones modulate the inflammatory system, and sexual dimorphism drives inflammatory activity (Rainville et al. 2018; Dolsen et al. 2019). It has been shown that sex influences gene expression levels and cellular composition of tissue samples across the human body, including immune cells (Oliva et al. 2020). To investigate whether transcriptional differences associated with poverty may affect men and women differently, we split our study cohort by sex and investigated gene expression patterns associated with poverty in time point 1 men and time point 1 women separately. We found 104 differentially expressed genes in women, 60 upregulated and 44 downregulated with poverty (Fig. 4a). Of these DEGs, 31 overlapped with those differentially expressed in the entire cohort at time point 1. However, we did not observe any genes differentially expressed in men living in poverty (Fig. 4b). This is also reflected in the observation that a larger proportion of genes have high z-scores in women (16.2% of tested genes with |z| > 2), compared with men (3.6% of tested genes with |z| > 2). This difference may be due to unmeasured confounding factors in men, or to a stronger effect of poverty on gene expression in immune cells in women.

Fig. 4.

Fig. 4.

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Association between poverty and gene expression in men and women. a) Volcano plot of the differentially expressed genes in women. b) Scatter plot of the correlation between gene expression z-scores of time point 1 men and time point 1 women. Significantly differentially expressed genes are indicated in red (FDR < 10%).

Understanding diseases and conditions influenced by poverty through transcriptional changes

To examine the adverse effects of poverty on disease risk that may be mediated by changes in gene expression in immune cells (Fig. 1a), we considered genes associated with disease risk through a PTWAs (Zhang et al. 2020). Because of the enrichment in genes associated with coagulation and wound healing and the importance of inflammation in cardiovascular health, we focused on cardiovascular disease. We considered all genes significantly differentially expressed at time point 1 and found two genes associated with hypertension (Fig. 5a–c). Lower expression of EEF1DP7 and VIL1 was associated with increased risk for hypertension in PTWAS (Fig. 5a), and we also found lower expression of these genes in individuals living in poverty compared with those living above poverty, thus suggesting that poverty may impact the molecular pathways associated with hypertension. Furthermore, poverty status and disease risk both correlate in the same direction with gene expression, thus showing concordant (Findley et al. 2019) effects. Note that the direction of the PTWAS effect also reflects the original context of the eQTL study and the LD structure in the discovery sample does not capture epistasis and coregulation, therefore, caution should be exercised in over-interpreting the sign of the z-score. VIL1 encodes calcium-regulated actin-binding proteins that cap, sever, and build actin filaments (Revenu et al. 2007; Khurana et al. 2008; Tomar et al. 2009). In addition, VIL1 encodes for the tissue-specific protein villin expressed in renal and gastrointestinal epithelial cells (Bretscher et al. 1981). VIL1 (LFC = −0.51, P-adj = 0.07) is in the pathways for response and regulation of wound healing. Lower expression of VIL1 is associated with prolonged stress and ineffective recovery of cell stress (Roy et al. 2018).

Fig. 5.

Fig. 5.

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Genes associated with poverty status are also associated with disease risk. a) Table of the differentially expressed genes for time point 1 that are also associated with hypertension in PTWAS. The table columns list the genes, trait, TWAS z-scores, and DESeq z-scores. b, c) Violin plots of the two differentially expressed genes associated with hypertension. The x-axis indicates the poverty status, and the y-axis indicates the normalized log10 gene expression counts.

Discussion

Our study advances the knowledge of how a social determinant of health, poverty, may influence gene expression, using a well-balanced, inner-city cohort. Using RNA-sequencing in a cohort of 204 individuals, we found 138 genes differentially expressed between individuals living either above or below the federal poverty line. We discovered that these genes were enriched for coagulation and wound healing processes. We also found overall stronger effects in women, compared with men. Our results suggest that poverty may contribute to health disparities in hypertension through gene expression changes in immune cells. Our findings identify important pathways that are differentially regulated by poverty.

Our previously published work conducted on a smaller subset of 52 individuals (Arnold et al. 2020) found suggestive indication of an association between poverty and immune cell gene expression. Here, using a larger cohort study design and examining genome-wide transcriptional changes, we found significant gene expression changes in individuals living in poverty. Our results show that it is possible to identify transcriptional signatures associated with poverty in well-powered population studies. Furthermore, these effects are detected consistently across two time points. A larger study in a population of young adults (32–42 years old) (Cole et al. 2020), only found suggestive evidence of transcriptional changes associated with poverty. Nevertheless, these changes are largely consistent with our results, but we identify stronger effects, probably because of our balanced study design.

We identified wound healing among the significant biological processes dysregulated in individuals that live in poverty. The role of immune cells in wound healing is critical for integrity restoration of damaged tissues (Boniakowski et al. 2017). Chronic inflammation of immune cells delays tissue wound healing repair (Boniakowski et al. 2017; Kimball et al. 2018). Monocytes are one of the cell types in PBMCs and single-cell studies have shown that their transcriptional status influences other PBMC components (e.g. T cells) (Randolph et al. 2021; Resztak et al. 2022). Lack of plasticity of monocytes/macrophages in the inflammatory to antiinflammatory signaling promotes ineffective wound healing, which is a main complication of type 2 Diabetes (Jordan et al. 2015). In addition, chronic proinflammatory states of monocytes have been linked to cardiovascular diseases like coronary artery disease and myocardial infarction (van der Laan et al. 2012; Peet et al. 2020). Here we analyzed gene expression within a mixed population of PBMCs and did not discern whether transcriptional profiles were associated with unique cell types. Follow-up analyses are warranted to experimentally validate these initial findings and identify if transcriptional profiles in specific cell types serve as better biological biomarkers for the effect of poverty on gene expression.

Men and women differ in their risk for many complex diseases (e.g. coronary artery disease and systemic lupus erythematosus (Arslan et al. 2021; Kim et al. 2022)). These sex differences are attributed to hormones, sex chromosomes, genotype x sex effects, behavioral differences, and differential environmental exposures; however, the mechanisms and underlying biology need clarification. Oliva et al. (2020), studied differences in gene expression between men and women in the Genotype-Tissue Expression (GTEx) dataset and identified 13,294 genes differentially expressed in at least one tissue. In blood, they found 1,710 differentially expressed genes. The sex-specific differences in gene expression observed between individuals living in poverty or above poverty may reflect differences between sexes in average life expectancy of at-risk populations (Chetty et al. 2016). Compared with individuals who are not living in poverty, we did not observe gene expression changes in men living in poverty, but we did find significant changes in women (Fig. 2). Gene expression differences between men and women could reflect that living in poverty has a less profound effect on gene expression levels in PBMCs in men.

Poverty is consistently linked to social and environmental conditions that act as stressors and are associated with risk of chronic illness (Gaye et al. 2017). Earlier studies in social genomics identified a pattern of differentially expressed genes known as the CTRA, comprised of genes in inflammatory pathways (Cole 2009, 2014; Slavich and Cole 2013). SES and psychosocial experiences have a broader effect on immune cell gene expression and impact additional pathways beyond those included in the CTRA (Resztak et al. 2021). We identified 138 differentially expressed genes, of which only PTGS1 was also included in the CTRA. Furthermore, the genes identified in our study were enriched in pathways related to wound healing and coagulation. Our study thus identifies novel genes and biological processes and pathways associated with poverty that were previously unknown.

Here we focused on poverty strictly defined as household income, however, several factors of living in poverty may be directly associated with gene expression differences, including for example food insecurity or nutritional factors, psychosocial stress or perceived violence, or any other complex environmental influences resulting from living in an impoverished urban area. Future studies aiming to dissect the relative effect of each of these factors will be important to guide interventions aimed at improving health outcomes for inner-city residents.

Conclusion

Our study provides evidence that poverty influences gene expression patterns in a large diverse cohort. Impoverished environments have long been associated with the prevalence of poor health outcomes. This work helps elucidate the molecular pathways that potentially transduce the effects of poverty into poor health outcomes, thus providing an understanding of how social determinants of health alter gene expression and can provide additional information about pathways that contribute to disparities in health outcomes in disadvantaged populations.

Supplementary Material

iyaf072_Supplementary_Data
iyaf072_supplementary_data.zip (2.5MB, zip)
iyaf072_Peer_Review_History
iyaf072_peer_review_history.pdf (198.3KB, pdf)

Acknowledgments

We thank Samuele Zilioli and members of the Luca/Pique-Regi group for helpful discussions and comments on an early version of this manuscript. We would like to thank the HANDLS participants and clinical staff, and Nicolle Mode for helping with cohort design.

Contributor Information

Nicole S Arnold, Department of Biology, Morgan State University, Baltimore 21251, USA; Center for Molecular Medicine and Genetics, Wayne State University, Detroit 48201, USA.

Justyna Resztak, Center for Molecular Medicine and Genetics, Wayne State University, Detroit 48201, USA.

David Witonsky, Department of Human Genetics, The University of Chicago, Chicago 60637, USA.

Adnan Alazizi, Center for Molecular Medicine and Genetics, Wayne State University, Detroit 48201, USA.

Nicole Noren Hooten, National Institute on Aging, National Institutes of Health, Baltimore 21224, USA.

Michele K Evans, National Institute on Aging, National Institutes of Health, Baltimore 21224, USA.

Valerie Odero-Marah, Department of Biology, Morgan State University, Baltimore 21251, USA.

Douglas F Dluzen, Department of Biology, Morgan State University, Baltimore 21251, USA.

Roger Pique-Regi, Center for Molecular Medicine and Genetics, Wayne State University, Detroit 48201, USA; Department of Obstetrics and Gynecology, Wayne State University, Detroit 48201, USA.

Francesca Luca, Department of Human Genetics, The University of Chicago, Chicago 60637, USA.

Data availability

All RNA-seq files are available from the Gene Expression Omnibus database (accession number GSE221766). Participant data were collected under NIA's IRB-approved protocol no. 09-AG-N248 entitled, “Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS)”. This study has also been approved by the IRB of Morgan State University no.19/06-0083.

Supplemental material available at GENETICS online.

Funding

This work was supported by the National Institutes of Health grant no. U54MD013376 and by the National Institute on Aging Intramural Research Program, National Institutes of Health project no. AG000513.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

iyaf072_Supplementary_Data
iyaf072_supplementary_data.zip (2.5MB, zip)
iyaf072_Peer_Review_History
iyaf072_peer_review_history.pdf (198.3KB, pdf)

Data Availability Statement

All RNA-seq files are available from the Gene Expression Omnibus database (accession number GSE221766). Participant data were collected under NIA's IRB-approved protocol no. 09-AG-N248 entitled, “Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS)”. This study has also been approved by the IRB of Morgan State University no.19/06-0083.

Supplemental material available at GENETICS online.

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