Genetic Liability to Insomnia and Lung Cancer Risk: A Mendelian Randomization Analysis

The association between sleep quantity, insomnia and lung cancer risk - A systematic review and meta-analysis

PURPOSE

The effect of various sleep traits on the risk of lung cancer differs among pre-existing studies. This study aims to systematically review and synthesise the association between sleep duration and insomnia with the incidence of lung cancer.

METHODS

PubMed, Embase and The Cochrane Library were searched from inception to 23 April 2023 for observational studies examining the effect of sleep quantity or insomnia on lung cancer incidence. We pooled maximally-adjusted hazard ratios and odds ratios separately using random effects inverse variance weighted models. The risk of bias of observational studies was assessed using the Newcastle-Ottawa Scale.

RESULTS

We included 11 observational studies with a pooled cohort of 5,049,141 patients. The mean age of the patients was 49.5 ± 17.7 years, and 51.4% were males. The risk of bias ranged from low-moderate. Individuals who slept for a shorter or longer duration than the reference range of sleep per night showed an increased risk of lung cancer by 11% (HR:1.11; 95%CI:1.00-1.23) and 16% (HR:1.16; 95%CI:1.06-1.27) respectively. Furthermore, individuals with insomnia symptoms had a 9% greater risk of lung cancer than those without symptoms (HR:1.09; 95%CI:1.05-1.13).

CONCLUSION

This study suggests that insufficient sleep, excessive sleep and insomnia may be associated with an increased risk of lung cancer. Physicians should be mindful of this association and encourage healthy sleep practises among patients. Given the observed heterogeneity among some pre-existing studies, future research with longer periods of follow-up, greater control for covariates and objective testing of sleep parameters may add value to this topic.

Causal effects between personality and psychiatric traits and lung cancer: a bidirectional two-sample Mendelian randomization and bibliometric study

Introduction

The causality between personality and psychiatric traits and lung cancer (LC) remains unclear. Therefore, we aimed to elucidate the causality between these traits and LC.

Methods

Bidirectional two-sample Mendelian randomization (MR) and bibliometric approaches were conducted to estimate the causality between personality (neuroticism, extraversion, agreeableness, conscientiousness, and openness) and psychiatric (schizophrenia, attention-deficit/hyperactivity disorder [ADHD], major depressive disorder, autism spectrum disorder, bipolar disorder, insomnia, and anxiety) traits and LC and its subtypes (lung squamous cell carcinoma, lung adenocarcinoma, and small cell LC). Summary data of these traits were extracted from large datasets (17,375-462,341 participants). Inverse variance weighting was used as the primary MR analysis, with supplementary models, including MR-Egger and weighted medians. Sensitivity analyses were conducted to detect pleiotropy. Bibliometric data were retrieved from the Web of Science Core Collection, Scopus, and PubMed. The main mapping techniques adopted were co-word, collaboration, and citation analyses.

Results

Schizophrenia was associated with an increased risk of LC (odds ratio [OR] = 1.077, 95% confidence interval [CI] = 1.030-1.126, P = 0.001). Moreover, LC increased the risk of ADHD (OR = 1.221, 95% CI = 1.096-1.362, P < 0.001). No significant bidirectional associations were observed between other mental traits and LC and its subtypes. Causality, psychiatry, and psychiatric comorbidity are emerging keywords. Research dynamics and landscapes were revealed.

Conclusion

This study suggests that schizophrenia is a risk factor for LC and that LC is a risk factor for ADHD. Furthermore, causality, psychiatry, and psychiatric comorbidity have become emerging research trends in related fields.

Genetic correlation and causal associations between psychiatric disorders and lung cancer risk

BACKGROUND

Patients with certain psychiatric disorders have increased lung cancer incidence. However, establishing a causal relationship through traditional epidemiological methods poses challenges.

METHODS

Available summary statistics of genome-wide association studies of cigarette smoking, lung cancer, and eight psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism, depression, major depressive disorder, bipolar disorder, insomnia, neuroticism, and schizophrenia (range N: 46,350-1,331,010) were leveraged to estimate genetic correlations using Linkage Disequilibrium Score Regression and assess causal effect of each psychiatric disorder on lung cancer using two-sample Mendelian randomization (MR) models, comprising inverse-variance weighted (IVW), weighted median, MR-Egger, pleiotropy residual sum and outlier testing (MR-PRESSO), and a constrained maximum likelihood approach (cML-MR).

RESULTS

Significant positive correlations were observed between each psychiatric disorder and both smoking and lung cancer (all FDR < 0.05), except for the correlation between autism and lung cancer. Both univariable and the cML-MA MR analyses demonstrated that liability to schizophrenia, depression, ADHD, or insomnia was associated with an increased risk of overall lung cancer. Genetic liability to insomnia was linked specifically to squamous cell carcinoma (SCC), while genetic liability to ADHD was associated with an elevated risk of both SCC and small cell lung cancer (all P < 0.05). The later was further supported by multivariable MR analyses, which accounted for smoking.

LIMITATIONS

Participants were constrained to European ancestry populations. Causal estimates from binary psychiatric disorders may be biased.

CONCLUSION

Our findings suggest appropriate management of several psychiatric disorders, particularly ADHD, may potentially reduce the risk of developing lung cancer.

Selection of genetic instruments in Mendelian randomisation studies of sleep traits

This review explores the criteria used for the selection of genetic instruments of sleep traits in the context of Mendelian randomisation studies. This work was motivated by the fact that instrument selection is the most important decision when designing a Mendelian randomisation study. As far as we are aware, no review has sought to address this to date, even though the number of these studies is growing rapidly. The review is divided into the following sections which are essential for genetic instrument selection: 1) Single-gene region vs polygenic analysis; 2) Polygenic analysis: biologically-vs statistically-driven approaches; 3) P-value; 4) Linkage disequilibrium clumping; 5) Sample overlap; 6) Type of exposure; 7) Total (R2) and average strength (F-statistic) metrics; 8) Number of single-nucleotide polymorphisms; 9) Minor allele frequency and palindromic variants; 10) Confounding. Our main aim is to discuss how instrumental choice impacts analysis and compare the strategies that Mendelian randomisation studies of sleep traits have used. We hope that our review will enable more researchers to take a more considered approach when selecting genetic instruments for sleep exposures.

Associations of Insomnia With Insulin Resistance Traits: A Cross-sectional and Mendelian Randomization Study

CONTEXT

Insomnia is associated with insulin resistance (IR) in observational studies; however, whether insomnia is causally associated with IR remains unestablished.

OBJECTIVE

This study aims to estimate the causal associations of insomnia with IR and its related traits.

METHODS

In primary analyses, multivariable regression (MVR) and 1-sample Mendelian randomization (1SMR) analyses were performed to estimate the associations of insomnia with IR (triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol [TG/HDL-C] ratio) and its related traits (glucose level, TG, and HDL-C) in the UK Biobank. Thereafter, 2-sample MR (2SMR) analyses were used to validate the findings from primary analyses. Finally, the potential mediating effects of IR on the pathway of insomnia giving rise to type 2 diabetes (T2D) were examined using a 2-step MR design.

RESULTS

Across the MVR, 1SMR, and their sensitivity analyses, we found consistent evidence suggesting that more frequent insomnia symptoms were significantly associated with higher values of triglyceride-glucose index (MVR, β = 0.024, P < 2.00E-16; 1SMR, β = 0.343, P < 2.00E-16), TG/HDL-C ratio (MVR, β = 0.016, P = 1.75E-13; 1SMR, β = 0.445, P < 2.00E-16), and TG level (MVR, β = 0.019 log mg/dL, P < 2.00E-16, 1SMR: β = 0.289 log mg/dL, P < 2.00E-16) after Bonferroni adjustment. Similar evidence was obtained by using 2SMR, and mediation analysis suggested that about one-quarter (25.21%) of the association between insomnia symptoms and T2D was mediated by IR.

CONCLUSIONS

This study provides robust evidence supporting that more frequent insomnia symptoms are associated with IR and its related traits across different angles. These findings indicate that insomnia symptoms can be served as a promising target to improve IR and prevent subsequent T2D.

Aberrant expression of KDM1A inhibits ferroptosis of lung cancer cells through up-regulating c-Myc

Ferroptosis is a cell death process caused by metabolic dysfunction with the feature of aberrant iron accumulation. Emerging studies have identified that ferroptosis is an important biological function involving in the tumorigenesis, and targeting ferroptosis could provide promising therapeutic targets for lung cancer. However, such therapeutic strategies show limited therapeutic effect owing to drug resistance and other unknown underlying mechanisms. In this study, lysine-specific demethylase 1 (LSD1/KDM1A) was found to be significantly upregulated in lung cancer cells and tissues. The patients with KDM1A downregulation displayed the good prognosis. Using gene set enrichment analysis (GSEA), we demonstrated that KDM1A-associated genes might participate in the regulation of cell ferroptosis and Myc signaling in lung cancer. Knockdown of KDM1A inhibited the level of c-Myc and increased the concentration of malondialdehyde (MDA) and irons in human lung cancer cells H1299 and A549. Downregulation of c-Myc could facilitate KDM1A knockdown-mediated ferroptosis. Our study has elucidated the effect of KDM1A/c-Myc regulatory axis in the ferroptosis resistance of lung cancer cells.

Leveraging genetic discoveries for sleep to determine causal relationships with common complex traits

Sleep occurs universally and is a biological necessity for human functioning. The consequences of diminished sleep quality impact physical and physiological systems such as neurological, cardiovascular, and metabolic processes. In fact, people impacted by common complex diseases experience a wide range of sleep disturbances. It is challenging to uncover the underlying molecular mechanisms responsible for decreased sleep quality in many disease systems owing to the lack of suitable sleep biomarkers. However, the discovery of a genetic component to sleep patterns has opened a new opportunity to examine and understand the involvement of sleep in many disease states. It is now possible to use major genomic resources and technologies to uncover genetic contributions to many common diseases. Large scale prospective studies such as the genome wide association studies (GWAS) have successfully revealed many robust genetic signals associated with sleep-related traits. With the discovery of these genetic variants, a major objective of the community has been to investigate whether sleep-related traits are associated with disease pathogenesis and other health complications. Mendelian Randomization (MR) represents an analytical method that leverages genetic loci as proxy indicators to establish causal effect between sleep traits and disease outcomes. Given such variants are randomly inherited at birth, confounding bias is eliminated with MR analysis, thus demonstrating evidence of causal relationships that can be used for drug development and to prioritize clinical trials. In this review, we outline the results of MR analyses performed to date on sleep traits in relation to a multitude of common complex diseases.