The genetic etiology of periodic limb movement in sleep

Proteomic insights into the pathophysiology of periodic limb movements and restless legs syndrome

OBJECTIVES

We used a high-throughput assay of 5000 plasma proteins to identify biomarkers associated with periodic limb movements (PLM) and restless legs syndrome (RLS) in adults.

METHODS

Participants (n = 1410) of the Stanford Technology Analytics and Genomics in Sleep (STAGES) study had blood collected, completed a sleep questionnaire, and underwent overnight polysomnography with the scoring of PLMs. An aptamer-based array (SomaScan) was used to quantify 5000 proteins in plasma. A second cohort (n = 697) that had serum assayed using a previous iteration of SomaScan (1300 proteins) was used for replication and in a combined analysis (n = 2107). A 5% false discovery rate was used to assess significance.

RESULTS

Multivariate analyses in STAGES identified 68 proteins associated with the PLM index after correction for multiple testing (ie, base model). Most significantly decreased proteins were iron-related and included Hepcidin (LEAP-1), Ferritin, and Ferritin light chain. Most significantly increased proteins included RANTES, Cathepsin A, and SULT 1A3. Of 68 proteins significant in the base model, 17 were present in the 1300 panel, and 15 of 17 were replicated. The most significant proteins in the combined model were Hepcidin (LEAP-1), Cathepsin A, Ferritin, and RANTES. Exploration of proteins in RLS versus non-RLS identified Cathepsin Z, Heme oxygenase 2 (HO-2), Interleukin-17A (upregulated in the combined cohort), and Megalin (upregulated in STAGES only) although results were less significant than for proteins associated with PLM index.

CONCLUSIONS

These results confirm the association of PLM with low iron status and suggest the involvement of catabolic enzymes in PLM/RLS.

The accuracy and difference of scoring rules and methods to score respiratory event-related leg movements in obstructive sleep apnea patients

OBJECTIVE

To compare manual scoring: 1) to the American Academy of Sleep Medicine (AASM) auto-scoring rules. 2) to the AASM and World Association of Sleep Medicine (WASM) rules, and evaluate the accuracy of the AASM and WASM for respiratory event-related limb movements (RRLM) in diagnostic and continuous positive airway pressure (CPAP) titration polysomnography (PSG).

METHODS

We retrospectively, re-scored diagnostic and CPAP titration PSGs of 16 patients with obstructive sleep apnea (OSA), using manual re-scoring by the AASM (mAASM) and WASM (mWASM) criteria for RRLM, periodic limb movements during sleep (PLMS), and limb movements (LM), which were compared to auto-scoring by the AASM (aAASM).

RESULTS

In diagnostic PSG, significant differences were found in LMs (p < 0.05), RRLM (p = 0.009) and the mean duration of PLMS sequences (p = 0.013). In CPAP titration PSG, there was a significant difference in RRLM (p = 0.008) and PLMS with arousal index (p = 0.036). aAASM underestimated LM and RRLM, especially in severe OSA. Changes in RRLM and PLMS with arousal index between diagnostic and titration PSG were significantly different between aAASM and mAASM, but there was no significant difference between scoring by mAASM and mWASM. The ratio of PLMS and RRLM changes between diagnostic and CPAP titration PSG was 0.257 in mAASM and 0.293 in mWASM.

CONCLUSIONS

In addition to the overestimation of RRLM by mAASM compared to aAASM, mAASM may also be more sensitive than aAASM in detecting RRLM changes in the titration PSG. Despite intuitive differences in the definition of RRLM between AASM and WASM rules, RRLM results between mAASM and mWASM were not significant and about 30% of RRLMs might be scored as PLMS by both scoring rules.

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.