Loneliness is a painful feeling that arises when there is a mismatch between one’s desired social connections and actual relationships.
Chronic loneliness is increasingly prevalent in modern societies and strongly tied to poor mental and physical health outcomes, including higher risks of depression, cardiovascular disease, and mortality.
New genetic research provides key insights into the biological underpinnings of loneliness and its widespread health impacts.
Key Facts:
- Researchers conducted a large genome-wide association study (GWAS) on loneliness involving over 500,000 people.
- They identified 16 genetic loci significantly linked to loneliness. Many of the genes are active in brain regions involved in threat/stress response and social cognition.
- Genetic factors accounted for around 7% of individual differences in loneliness, confirming loneliness has a partly heritable basis.
- Loneliness showed significant genetic correlations with traits like neuroticism, depression, and poor cardiovascular health.
- A genetic predisposition to loneliness was tied to higher risks for depression, heart disease, diabetes, and metabolic problems in a clinical health records study.
Source: Human Molecular Genetics. 2019 Nov 15; 28(22): 3853-3865.
Unraveling the Genetic Architecture of Loneliness
Loneliness is a complex human experience. Both external situational factors and internal traits shape feelings of social isolation.
Past twin studies already indicated genetics influence loneliness.
Researchers conducted a massive GWAS analysis to unravel loneliness’ genetic architecture.
The GWAS meta-analysis included over half a million adults of European ancestry across 5 cohorts from the UK, US, Sweden, and the Netherlands.
Participants completed surveys on loneliness. One cohort had a binary lonely/not lonely measure, while others used multi-item scales.
GWAS examines associations between genetic variants (SNPs) across the genome and traits to pinpoint genetic influences.
The meta-analysis identified 16 independent genetic loci significantly linked to loneliness.
The top hit was on chromosome 18 in the TCF4 gene important for brain development.
12 other loci were within or near genes active in the central nervous system. Gene-based tests highlighted 58 genes significantly associated with loneliness.
The combined genetic factors (SNP heritability) accounted for around 7% of individual differences in loneliness.
Environmental influences and gene-environment interplay shape the remainder of loneliness variation.
Loneliness-Linked Genes Active in Brain’s Social Pain and Threat Circuits
The researchers next tested whether loneliness genetics are enriched for genes expressed in certain tissues using GTEx RNA-seq data.
Brain regions involved in processing social pain, threats, emotions, and rewards were significantly enriched.
The anterior cingulate cortex is key node of brain’s social pain network activated when people feel ostracized.
The prefrontal cortex supports complex social perceptions related to loneliness.
The substantia nigra produces dopamine, influencing social motivation and learning.
The cerebellum modulates social cognition and abstract thinking.
The enrichment highlights that genes influencing loneliness are preferentially active in neural circuits underlying threat surveillance, emotional resonance, social cognition, and motivation.
Isolation engages these circuits, while social support regulates their activity. The results provide a neurogenetic framework to understand loneliness.
Widespread Genetic Overlap With Health and Behavioral Traits
The team examined genetic correlations between loneliness and over 60 traits across 9 domains to uncover shared genetic roots.
After correction for multiple testing, 39 traits showed significant genetic correlations with loneliness.
The strongest correlations were with mental health traits like depression, neuroticism, and low subjective well-being.
This confirms close biological ties between poor mental health and loneliness.
Loneliness genetics also overlapped with personality, substance use, cardiovascular risks, and socioeconomic status.
Lower income and education levels showed robust negative genetic correlations, indicating shared genetic influences with social disadvantage.
Loneliness genetics correlated with worse self-rated health and father’s/mother’s age of death.
Interestingly, having children and reproducing earlier were genetically linked to greater loneliness, contrasting with some demographic research.
Overall, the widespread genetic correlations reveal shared roots between loneliness and an array of health outcomes across mental, cardiovascular, personality, and socioeconomic domains.
Loneliness genetics also relate in complex ways to social ties and reproduction.
Clinical Health Records Analysis Confirms Health Impacts
The researchers built a “polygenic score” based on the millions of loneliness-linked genetic variants to test whether a genetic propensity to loneliness predicts real-world health outcomes.
They analyzed medical records from over 18,000 patients of European ancestry.
As expected, a higher loneliness polygenic score associated with significantly increased risks for clinical depression, heart disease, and metabolic disorders like obesity and type 2 diabetes.
It also related to unfavorable blood lipid profiles tied to cardiovascular risk.
This analysis confirms associations between loneliness genetics and health using clinical diagnoses and objective health measures.
It avoids biases and subjectivity of self-reports.
The approach demonstrates the utility of polygenic scores for probing links between genes, tough-to-measure behaviors like loneliness, and health phenotypes in the clinic.
Causal Relationships: How Health Factors Contribute to Loneliness
While observing genetic correlations is informative, it cannot discern the causal direction between associated traits.
The team also conducted Mendelian randomization (MR) analyses to shed light on potential causal relationships.
They focused on links between loneliness and cardiometabolic traits it is genetically correlated with, including coronary artery disease, BMI, and blood lipids.
MR assesses whether genetic variants associated with probable causal risk factors (exposures) are linked to an outcome in a manner consistent with causation while minimizing bias.
MR found evidence that higher BMI and increased body fat causally raise loneliness levels.
This aligns with their known causal effects increasing risks for depression.
It suggests excess weight biologically contributes to social isolation and associated health consequences.
However, the analyses did not support causal effects of loneliness raising cardiovascular risks.
Elucidating Biological Pathways From Isolation to Health
This landmark study provides crucial insights into the biology of loneliness.
Identifying specific genes and brain circuits involved creates a framework to better understand the distressing phenomenon of social isolation.
Loneliness genetics’ broad links with mental and physical health traits help explain its profound health impacts.
The findings confirm social pain pathways are hyperactive when people feel alone and socially disconnected.
Chronic loneliness engages threat surveillance processes, instilling anxiety and hypervigilance. It may also directly dysregulate reward, motivation and immune pathways.
New MR results suggest obesity and weight gain can directly feed into loneliness.
More research is needed to elucidate the full range of causal pathways and biological mechanisms linking loneliness to adverse health outcomes.
Expanding large genomic studies across diverse populations will also help generalize findings.
Tackling the modern loneliness epidemic requires bridging social, psychological, and biological insights.
These seminal findings provide a strong foundation for translational research to develop more effective, personalized prevention and treatment approaches targeting loneliness’ biological roots and health risks.
References
- Study: Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness
- Authors: Abdel Abdellaoui et al. (2019)
