Genetic analysis challenges the idea that loneliness directly causes diseases

By Hugo Francisco de SouzaReviewed by Susha Cheriyedath, M.Sc.Sep 18 2024

While loneliness has been tied to higher disease risks, new genetic findings reveal it might be more of a warning signal than a direct trigger, reshaping our understanding of its true health impact.

Study: Observational and genetic evidence disagree on the association between loneliness and risk of multiple diseases. Image Credit: Alphavector / Shutterstock

In a recent study published in the journal Nature Human Behaviour, researchers used an extensive sample cohort (476,100 participants from the UK Biobank) under extensive follow-up (median 12.2 years, range 10.6-13.8 years)  to unravel the associations between the subjective feeling of loneliness and the subsequent risk of diseases.

While a growing body of observational evidence suggests an association between loneliness and heightened risk of multiple diseases, the study employed Mendelian randomization (MR) to formally test the hypothesis and address potential reverse causality and confounding factors.

Study findings reveal a significant discordance between results from observational and genetic analysis. While observational evidence highlights an association between loneliness and the increased risk of contracting at least 30 out of the 56 individual diseases tested across 13 of the 14 disease categories, genetic data does not verify a causal relationship between these factors.

Instead, genetic evidence suggests that loneliness may serve as a potential surrogate marker for preexisting disease incidence rather than a catalyst for disease genesis. This was particularly evident as the genetic MR analysis only identified potentially causal relationships between loneliness and six specific diseases—hypothyroidism, asthma, depression, psychoactive substance abuse, sleep apnea, and hearing loss—out of the 26 diseases analyzed. Together, these findings challenge our understanding of loneliness's clinical implications, prompting novel methodologies to address this complex public health concern.

The study underscores the importance of considering factors such as baseline depressive symptoms, socioeconomic status, and health behaviors, which explained a substantial proportion of the observed associations. It debunks longstanding beliefs that loneliness may herald the advent of new diseases. Rather, loneliness may highlight the presence of diseases that have already invaded but remain asymptomatic, thereby allowing for timely diagnosis and treatment.

Background

Loneliness is a common unpleasant emotional response to the subjective notion of insufficient social connections and perceived isolation.

A growing body of observational research suggests that loneliness may play a direct role in altering individuals' risk of common diseases and comorbidities such as cardiovascular disease (CVD), obesity, type 2 diabetes mellitus, and neurological conditions.

Unfortunately, given its observational nature, assessing the potential for reverse causation and similar confounds using solely observational epidemiological evidence is impossible and requires studies with specific bidirectional causation design.

Understanding the nature of the observed associations between loneliness and multiple disease risks would better equip clinicians and caretakers to treat their patients and prepare for potential clinical conditions.

The present study leverages a novel model design named Mendelian randomization (MR), which uses genetic variants to establish causal associations between factors under investigation.

Unfortunately, previous attempts to employ MR in loneliness evaluations have presented conflicting results – while some studies have established causal relationships between loneliness and depression, others have found no such associations between the former and cardiovascular traits.

The present study aims to overcome previous limitations using significantly larger sample sizes and more extended follow-up periods than previously employed.

About the study

Data for the present study was obtained from the United Kingdom (UK) Biobank, a nationwide population-based cohort study comprising more than 500,000 UK nationals aged 37-73 years. Participants from the UK Biobank were screened using a modified short University of California, Los Angeles (UCLA) Loneliness Scale questionnaire consisting of two questions whose scores comprise participants' baseline' loneliness index.' Screening was used to categorize participants into 'loneliness' and 'no loneliness' subcohorts.

Hospital admission and death registry records data were used to determine the incidence and severity of disease during the follow-up period (median = 12.2 yrs, range = 10.6-13.8 yrs).

Diseases recorded were identified using the International Classification of Diseases-10th Revision (ICD-10) codes (n = 56 individual diseases) and further classified into 14 disease categories (e.g., CVD, respiratory, neurological, etc.). Demographic data (age, sex, ethnicity, education level, employment status, alcohol consumption, physical activity, and body mass index [BMI]) were considered potential covariates and were carefully adjusted in the analysis to reduce confounding effects.

Demographic data (age, sex, ethnicity, education level, employment status, alcohol consumption, physical activity, and body mass index [BMI]) were considered potential covariates and adjusted during model analysis.

A meta-analysis of bidirectional MR studies using genome-wide association studies (GWASs) data was carried out to establish a genetic library of disease risk. GWAS data was derived from the FinnGen consortium (Round 8) and the European Bioinformatics Institute (EBI) databases.

Study findings

The final study cohort comprised 476,100 participants (54.6% women) with a mean age of 56.5 years. Summary statistics reveal that 23,136 (4.9%) participants had loneliness index scores indicative of loneliness.

These participants were more likely to be females, obese, with lower education levels, unhealthy habits (e.g., smoking), and low physical activity.

As expected, observational evidence established an association between loneliness and multiple disease risks, with 13 out of the 14 disease categories (30 out of 56 individual diseases) appearing to be significantly correlated with loneliness.

Loneliness was most often associated with risks of developing mental and behavioral disorders, infectious diseases, and respiratory distress.

Posttraumatic stress disorder (adjusted Hazard Ratio - aHR = 2.18), depression (aHR = 2.15), anxiety (aHR = 1.82), schizophrenia (aHR = 1.81), and chronic obstructive pulmonary disease (aHR = 1.51) were the conditions most often cooccurring with loneliness.

Surprisingly, however, genetic MR analysis revealed minimal to no causal relationship between loneliness and multiple disease risks. Of the 26 diseases screened with GWAS scores, only 6 scored a borderline statistical association with loneliness, emphasizing the discordance between observational and genetic evidence.

Negative controls and sensitivity analyses confirmed these results, highlighting the potential limitations of observational studies and the dangers of confusing correlations with causality.

Conclusions

The present study used an extensive dataset with 12.2 median years of follow-up to investigate the causal associations between loneliness and multiple disease risk.

Study findings confirmed the observational relationship between the factors but failed to provide genetic causality for the same. This suggests that loneliness is likely a surrogate marker of underlying asymptomatic disease rather than a direct cause of disease onset or progression.

The study also underscores the importance of addressing associated risk factors, such as depression and unhealthy behaviors, to improve health outcomes. progression.