CLOCK Genetic Variations Are Associated With Age-Related Changes in Sleep Duration and Brain Volume.

BACKGROUND: Although a connection between sleep disruption and brain aging has been documented, biological mechanisms need to be further clarified. Intriguingly, aging is associated with circadian rhythm and/or sleep dysfunction in a key gene regulating circadian rhythm, Circadian Locomotor Output Cycles Kaput (CLOCK), has been linked to both aging-related sleep disturbances and neurodegenerative diseases. This study aims to investigate how CLOCK genetic variation associates with sleep duration changes and/or volumetric brain alteration. METHODS: This population-based cross-sectional study used data from the Korean Genome Epidemiology Study and analyzed sleep characteristics and genetic and brain imaging data in 2 221 participants (mean 58.8 ± 6.8 years, 50.2% male). Eleven single-nucleotide polymorphisms (SNPs) in CLOCK were analyzed using PLINK software v1.09 to test for their association with sleep duration and brain volume. Haplotype analysis was performed by using pair-wise linkage disequilibrium of CLOCK polymorphisms, and multivariate analysis of covariance was for statistical analysis. RESULTS: Decreased sleep duration was associated with several SNPs in CLOCK intronic regions, with the highest significance for rs10002541 (p = 1.58 × 10-5). Five SNPs with the highest significance (rs10002541, rs6850524, rs4580704, rs3805151, rs3749474) revealed that CGTCT was the most prevalent. In the major CGTCT haplotype, decreased sleep duration over time was associated with lower cortical volumes predominantly in frontal and parietal regions. Less common haplotypes (GCCTC/CGTTC) had shorter sleep duration and more decreases in sleep duration over 8 years, which revealed smaller total and gray matter volumes, especially in frontal and temporal regions of the left hemisphere. CONCLUSION: CLOCK genetic variations could be involved in age-related sleep and brain volume changes.

Cerebellar White Matter Abnormalities in Charcot-Marie-Tooth Disease: A Combined Volumetry and Diffusion Tensor Imaging Analysis.

Charcot-Marie-Tooth disease (CMT) is a genetically heterogeneous hereditary peripheral neuropathy. Brain volumetry and diffusion tensor imaging (DTI) were performed in 47 controls and 47 CMT patients with PMP22 duplication (n = 10), MFN2 (n = 15), GJB1 (n = 11), or NEFL mutations (n = 11) to investigate for structural changes in the cerebellum. Volume of cerebellar white matter (WM) was significantly reduced in CMT patients with NEFL mutations. Abnormal DTI findings were observed in the superior, middle, and inferior cerebellar peduncles, predominantly in NEFL mutations and partly in GJB1 mutations. Cerebellar ataxia was more prevalent in the NEFL mutation group (72.7%) than the GJB1 mutation group (9.1%) but was not observed in other genotypic subtypes, which indicates that structural cerebellar abnormalities were associated with the presence of cerebellar ataxia. However, NEFL and GJB1 mutations did not affect cerebellar gray matter (GM), and neither cerebellar GM nor WM abnormalities were observed in the PMP22 duplication or MFN2 mutation groups. We found structural evidence of cerebellar WM abnormalities in CMT patients with NEFL and GJB1 mutations and an association between cerebellar WM involvement and cerebellar ataxia in these genetic subtypes, especially in the NEFL subgroup. Therefore, we suggest that neuroimaging, such as MRI volumetry or DTI, for CMT patients could play an important role in detecting abnormalities of cerebellar WM.

Sex differences in deterioration of sleep properties associated with aging: a 12-year longitudinal cohort study.

STUDY OBJECTIVES: The sleep patterns of humans are greatly influenced by age and sex and have various effects on overall health as they change continuously during the lifespan. We investigated age-dependent changes in sleep properties and their relation to sex in middle-aged individuals. METHODS: We analyzed data from 2,640 participants (mean age of 49.8 ± 6.8 years at baseline, 50.6% women) in the Korean Genome and Epidemiology Study, which assessed sleep habits using the Pittsburgh Sleep Quality Index and other clinical characteristics. We analyzed the sleep habit changes that occurred between baseline and a follow-up point (mean interval: 12.00 ± 0.16 years). Associations of age and sex with 9 sleep characteristics were evaluated. RESULTS: Age was associated with most of the sleep characteristics cross-sectionally and longitudinally (P < .05), except for the time in bed at baseline (P = .455) and change in sleep duration (P = .561). Compared with men, women had higher Pittsburgh Sleep Quality Index scores, shorter time in bed, shorter sleep duration, and longer latency at baseline (P ≤ .001). Longitudinal deterioration in Pittsburgh Sleep Quality Index score, habitual sleep efficiency, duration, and latency was more prominent in women (P < .001). The sex differences in these longitudinal sleep changes were mainly noticeable before age 60 years (P < .05). Worsening of Pittsburgh Sleep Quality Index scores, habitual sleep efficiency, and latency was most evident in perimenopausal women. Men presented with greater advancement of chronotype (P = .006), with the peak sex-related difference occurring when they were in their late 40s (P = .048). CONCLUSIONS: Aging is associated with substantial deterioration in sleep quantity and quality as well as chronotype advancement, with the degree and timing of these changes differing by sex.

Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls.

To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n = 69; test set; n = 31) and left TLE from HC (training set, n = 66; test set, n = 30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values < 0.05). The radiomics model from hippocampus can be a potential biomarker for identifying TLE.