SUMMARY Wrist actigraphy is employed increasingly in sleep research and clinical sleep medicine.Critical evaluation of the performance of new actigraphs and software is needed. Actigraphic sleep-wake estimation was compared with polysomnographic (PSG) scoring as the standard in a clinical sleep laboratory. A convenience sample of 116 patients undergoing clinical sleep recordings volunteered to participate. Actiwatch-L recordings were obtained from 98 participants, along with 18 recordings using the newer Spectrum model (Philips Electronics), but some of the actigraphic recordings could not be adequately aligned with the simultaneous PSGs. Of satisfactory alignments, 40 Actiwatch recordings were used as a training set to empirically develop a new Scripps Clinic algorithm for sleep-wake scoring. The Scripps Clinic algorithm was then prospectively evaluated in 39 Actiwatch recordings and 16 Spectrum recordings, producing epoch-by-epoch sleep-wake agreements of 85-87% and kappa statistics averaging 0.52 (indicating moderate agreement). Wake was underestimated by the scoring algorithm. The correlations of PSG versus actigraphic wake percentage estimates were r = 0.6690 for the Actiwatch and r = 0.2197 for the Spectrum. In general, using a different weighting of activity counts from previous and subsequent epochs, the Scripps Clinic algorithm discriminated sleep-wake more successfully than the manufacturerÕs Actiware algorithms. Neither algorithm had fully satisfactory agreement with PSG. Further evaluations of algorithms for these actigraphs are needed, along with controlled comparisons of different actigraphic designs and software.

Objective The diagnostic boundaries of sleep disorders are under considerable debate. The main sleep disorders are partly heritable; therefore, defining heritable pathophysiologic mechanisms could delineate diagnoses and suggest treatment. We collected clinical data and DNA from consenting patients scheduled to undergo clinical polysomnograms, to expand our understanding of the polymorphisms associated with the phenotypes of particular sleep disorders. Methods Patients at least 21 years of age were recruited to contribute research questionnaires, and to provide access to their medical records, saliva for deoxyribonucleic acid (DNA), and polysomnographic data. From these complex data, 38 partly overlapping phenotypes were derived indicating complaints, subjective and objective sleep timing, and polysomnographic disturbances. A custom chip was used to genotype 768 single-nucleotide polymorphisms (SNPs). Additional assays derived ancestry-informative markers (eg, 751 participants of European ancestry). Linear regressions controlling for age, gender, and ancestry were used to assess the associations of each phenotype with each of the SNPs, highlighting those with Bonferroni-corrected significance. Results In peroxisome proliferator-activated receptor gamma, coactivator 1 beta (PPARGC1B), rs6888451 was associated with several markers of obstructive sleep apnea. In aryl hydrocarbon receptor nuclear translocator-like (ARNTL), rs10766071 was associated with decreased polysomnographic sleep duration. The association of rs3923809 in BTBD9 with periodic limb movements in sleep was confirmed. SNPs in casein kinase 1 delta (CSNK1D rs11552085), cryptochrome 1 (CRY1 rs4964515), and retinoic acid receptor-related orphan receptor A (RORA rs11071547) were less persuasively associated with sleep latency and time of falling asleep. Conclusions SNPs associated with several sleep phenotypes were suggested, but due to risks of false discovery, independent replications are needed before the importance of these associations can be assessed, followed by investigation of molecular mechanisms.

BackgroundThere are several indications that malfunctions of the circadian clock contribute to depression. To search for particular circadian gene polymorphisms associated with depression, diverse polymorphisms were genotyped in two samples covering a range of depressed volunteers and participants with normal mood.MethodsDepression mood self-ratings and DNA were collected independently from a sample of patients presenting to a sleep disorders center (1086 of European origin) and from a separate sample consisting of 399 participants claiming delayed sleep phase symptoms and 406 partly-matched controls. A custom Illumina Golden Gate array of 768 selected single nucleotide polymorphisms (SNPs) was assayed in both samples, supplemented by additional SNPlex and Taqman assays, including assay of 41 ancestry-associated markers (AIMs) to control stratification.ResultsIn the Sleep Clinic sample, these assays yielded Bonferroni-significant association with depressed mood in three linked SNPs of the gene FMR1: rs25702 (nominal P=1.77E-05), rs25714 (P=1.83E-05), and rs28900 (P=5.24E-05). This FMR1 association was supported by 8 SNPs with nominal significance and a nominally-significant gene-wise set test. There was no association of depressed mood with FMR1 in the delayed sleep phase case–control sample or in downloaded GWAS data from the GenRED 2 sample contrasting an early-onset recurrent depression sample with controls. No replication was located in other GWAS studies of depression. Our data did weakly replicate a previously-reported association of depression with PPARGC1B rs7732671 (P=0.0235). Suggestive associations not meeting strict criteria for multiple testing and replication were found with GSK3B, NPAS2, RORA, PER3, CRY1, MTNR1A and NR1D1. Notably, 16 SNPs nominally associated with depressed mood (14 in GSK3B) were also nominally associated with delayed sleep phase syndrome (P=3E10-6).ConclusionsConsidering the inconsistencies between samples and the likelihood that the significant three FMR1 SNPs might be linked to complex polymorphisms more functionally related to depression, large gene resequencing studies may be needed to clarify the import for depression of these circadian genes.