In a recent study published in Communications Biology, researchers investigated how spouses influence each other’s sleep patterns and circadian preferences, using data from large-scale studies, including the United Kingdom (UK) Biobank and 23andMe.
Study: Correlations in sleeping patterns and circadian preference between spouses. Image Credit: Gorodenkoff/Shutterstock.com
Insufficient and disturbed sleep, including insomnia and short sleep duration, are widespread in society, affecting over a quarter of United States (US) adults and linked to productivity loss, occupational accidents, and increased risks of cardiovascular and metabolic diseases, depression, and certain cancers.
Sleep patterns, influenced by age, gender, and lifestyle, also show interconnectedness in couples, potentially impacting familial health and providing avenues for targeted interventions.
Further research is needed because understanding the interdependence of sleep patterns in couples is crucial for addressing widespread sleep issues with significant health and societal implications, including increased risks of accidents, productivity loss, and various health conditions.
About the study
The genetic data in the UK Biobank includes genotypes of 488,377 individuals using two different arrays. The present study focused on 463,827 individuals of recent European descent, excluding non-European ancestries based on genetic analysis.
Participants reported their household composition at baseline, identifying whether they lived with a spouse, someone else, or alone. Spouse pairs were identified using detailed criteria, including shared household characteristics and genetic unrelatedness, resulting in a final sample of 47,549 pairs.
At baseline, participants completed a touchscreen questionnaire covering various topics, including sleep. This questionnaire included questions on chronotype, ease of waking, insomnia symptoms, sleep duration, and snoring, with responses categorized for analysis.
Additionally, 103,711 individuals wore a triaxial accelerometer device several years after the baseline, providing detailed sleep data. This data was processed to derive sleep quality, quantity, and timing measures, focusing on the least active period, number of sleep episodes, sleep duration, and efficiency. Data with issues in recording or calibration were excluded to ensure accuracy.
The UK Biobank study, with participants aged 40-70, collected detailed data including age, sex, and birthplace, excluding those with sex mismatches or chromosome anomalies. It factored in assessment locations and the season of accelerometer wear, integrating genetic components as covariates.
In contrast, the 23andMe dataset consisted of customers from a personal genomics company, focusing on European ancestry to minimize confounding and identify spouse pairs through genetic analysis.
Both studies surveyed sleep traits like chronotype and insomnia. The UK Biobank used categorical responses, while 23andMe employed binary variables.
The UK Biobank further employed Mendelian randomization (MR), using genetic risk scores to investigate how an individual’s sleep traits affect their spouse. This included adjusting for confounders and conducting sensitivity analyses to address horizontal pleiotropy and Winner’s curse, ensuring the study’s vitality and validity.
The UK Biobank study comprehensively analyzed sleep characteristics among 47,549 spouse pairs. Of these, 47,420 pairs provided self-reported sleep information through a baseline questionnaire, and 3,454 pairs had valid accelerometer data, which was collected between 2.8 and 8.7 years after the initial study. This data allowed for a detailed assessment of various sleep measures.
The average age of female and male spouses at the study’s outset was approximately 56.8 and 58.5 years, respectively. Both groups reported similar sleep durations, slightly varying their chronotype preferences.
Men were more inclined towards no preference or an evening preference, whereas women showed a stronger morning preference. Women also reported more insomnia symptoms and difficulty waking up, while men were more often reported to snore by their partners.
Interestingly, spouses who participated in the accelerometer assessment were, on average, older than those who did not. They also exhibited healthier lifestyle choices, such as lower smoking rates and alcohol consumption. This cohort reflected a subset of the broader UK Biobank population, with notable differences in employment and education levels.
Among the UK Biobank participants with genetic data, those living with spouses were less likely to have extreme evening preferences or difficulty waking up and experienced insomnia less frequently. However, snoring was more commonly reported, possibly influenced by the nature of the question about snoring.
The 23andMe dataset included spouse pairs with an average age slightly older than the UK Biobank group. Similar to the UK Biobank findings, sleep duration was consistent between genders, but there were differences in the prevalence of insomnia and snoring.
A key finding from both datasets was the correlation of sleep traits between spouses. While there were weak positive correlations for sleep duration and daily activity, an inverse correlation was observed for chronotype. These correlations were generally smaller than those for other sociodemographic and lifestyle factors.
MR analysis in the UK Biobank indicated that one spouse’s sleep duration and activity levels might affect the other’s, with one’s chronotype potentially inducing the opposite in their partner. The study revealed complex interplays between different sleep traits in couples, underscoring the intricate genetic and behavioral factors in spousal sleep patterns.
In the UK Biobank study, the correlation between genetic risk scores (GRS) for sleep traits in spouses showed limited evidence of genotypic correlations.
These correlations, derived from single nucleotide polymorphisms (SNPs) associated with sleep traits, ranged between -0.007 and 0.010. Even when different p-value thresholds from genome-wide association studies (GWAS) were applied, the correlations remained subtle, with insomnia showing only a weak, consistent correlation.
The research also explored whether various factors could modify the effects observed between spouses’ sleep traits. The analysis considered factors like age as a proxy for relationship length, birth location for potential population structure effects, and lifestyle aspects such as employment status and household composition.
Notably, sleep duration effects appeared stronger in older age groups, and activity timing effects were more pronounced in couples without children.
Regarding horizontal pleiotropy, which refers to a genetic variant influencing multiple traits, the findings showed little evidence of this phenomenon impacting the results. Tests like the Sargan test and the MR-Egger intercept test supported this conclusion.
Additionally, analyses accounting for pleiotropy yielded consistent effect directions with broader confidence intervals, indicating vitality against horizontal pleiotropy. However, the presence of weak genetic instruments cautioned against overinterpreting these findings.
Finally, the study addressed potential biases from Winner’s curse, where overlapping GWAS and spouse samples might overestimate SNP effects.
Genetic risk scores based on replicated SNPs maintained consistency with the main analysis results, further affirming the reliability of the findings.