Integrative Modeling of Accelerometry-Derived Sleep, Physical Activity, and Circadian Rhythm Domains With Current or Remitted Major Depression.

Importance: Accelerometry has been increasingly used as an objective index of sleep, physical activity, and circadian rhythms in people with mood disorders. However, most prior research has focused on sleep or physical activity alone without consideration of the strong within- and cross-domain intercorrelations; and few studies have distinguished between trait and state profiles of accelerometry domains in major depressive disorder (MDD). Objectives: To identify joint and individual components of the domains derived from accelerometry, including sleep, physical activity, and circadian rhythmicity using the Joint and Individual Variation Explained method (JIVE), a novel multimodal integrative dimension-reduction technique; and to examine associations between joint and individual components with current and remitted MDD. Design, Setting, and Participants: This cross-sectional study examined data from the second wave of a population cohort study from Lausanne, Switzerland. Participants included 2317 adults (1164 without MDD, 185 with current MDD, and 968 with remitted MDD) with accelerometry for at least 7 days. Statistical analysis was conducted from January 2021 to June 2023. Main Outcomes and Measures: Features derived from accelerometry for 14 days; current and remitted MDD. Logistic regression adjusted for age, sex, body mass index, and anxiety and substance use disorders. Results: Among 2317 adults included in the study, 1261 (54.42%) were female, and mean (SD) age was 61.79 (9.97) years. JIVE reduced 28 accelerometry features to 3 joint and 6 individual components (1 sleep, 2 physical activity, 3 circadian rhythms). Joint components explained 58.5%, 79.5%, 54.5% of the total variation in sleep, physical activity, and circadian rhythm domains, respectively. Both current and remitted depression were associated with the first 2 joint components that were distinguished by the salience of high-intensity physical activity and amplitude of circadian rhythm and timing of both sleep and physical activity, respectively. MDD had significantly weaker circadian rhythmicity. Conclusions and Relevance: Application of a novel multimodal dimension-reduction technique demonstrates the importance of joint influences of physical activity, circadian rhythms, and timing of both sleep and physical activity with MDD; dampened circadian rhythmicity may constitute a trait marker for MDD. This work illustrates the value of accelerometry as a potential biomarker for subtypes of depression and highlights the importance of consideration of the full 24-hour sleep-wake cycle in future studies.

Objectively assessed sleep and physical activity in depression subtypes and its mediating role in their association with cardiovascular risk factors.

The aims of this study were to investigate the associations of major depressive disorder (MDD) and its subtypes (atypical, melancholic, combined, unspecified) with actigraphy-derived measures of sleep, physical activity and circadian rhythms; and test the potentially mediating role of sleep, physical activity and circadian rhythms in the well-established associations of the atypical MDD subtype with Body Mass Index (BMI) and the metabolic syndrome (MeS). The sample consisted of 2317 participants recruited from an urban area, who underwent comprehensive somatic and psychiatric evaluations. MDD and its subtypes were assessed via semi-structured diagnostic interviews. Sleep, physical activity and circadian rhythms were measured using actigraphy. MDD and its subtypes were associated with several actigraphy-derived variables, including later sleep midpoint, low physical activity, low inter-daily stability and larger intra-individual variability of sleep duration and relative amplitude. Sleep midpoint and physical activity fulfilled criteria for partial mediation of the association between atypical MDD and BMI, and physical activity also for partial mediation of the association between atypical MDD and MeS. Our findings confirm associations of MDD and its atypical subtype with sleep and physical activity, which are likely to partially mediate the associations of atypical MDD with BMI and MeS, although most of these associations are not explained by sleep and activity variables. This highlights the need to consider atypical MDD, sleep and sedentary behavior as cardiovascular risk factors.

Cellular Diversity in Human Subgenual Anterior Cingulate and Dorsolateral Prefrontal Cortex by Single-Nucleus RNA-Sequencing.

Regional cellular heterogeneity is a fundamental feature of the human neocortex; however, details of this heterogeneity are still undefined. We used single-nucleus RNA-sequencing to examine cell-specific transcriptional features in the dorsolateral PFC (DLPFC) and the subgenual anterior cingulate cortex (sgACC), regions implicated in major psychiatric disorders. Droplet-based nuclei-capture and library preparation were performed on replicate samples from 8 male donors without history of psychiatric or neurologic disorder. Unsupervised clustering identified major neural cell classes. Subsequent iterative clustering of neurons further revealed 20 excitatory and 22 inhibitory subclasses. Inhibitory cells were consistently more abundant in the sgACC and excitatory neuron subclusters exhibited considerable variability across brain regions. Excitatory cell subclasses also exhibited greater within-class transcriptional differences between the two regions. We used these molecular definitions to determine which cell classes might be enriched in loci carrying a genetic signal in genome-wide association studies or for differentially expressed genes in mental illness. We found that the heritable signals of psychiatric disorders were enriched in neurons and that, while the gene expression changes detected in bulk-RNA-sequencing studies were dominated by glial cells, some alterations could be identified in specific classes of excitatory and inhibitory neurons. Intriguingly, only two excitatory cell classes exhibited concomitant region-specific enrichment for both genome-wide association study loci and transcriptional dysregulation. In sum, by detailing the molecular and cellular diversity of the DLPFC and sgACC, we were able to generate hypotheses on regional and cell-specific dysfunctions that may contribute to the development of mental illness.SIGNIFICANCE STATEMENT Dysfunction of the subgenual anterior cingulate cortex has been implicated in mood disorders, particularly major depressive disorder, and the dorsolateral PFC, a subsection of the PFC involved in executive functioning, has been implicated in schizophrenia. Understanding the cellular composition of these regions is critical to elucidating the neurobiology underlying psychiatric and neurologic disorders. We studied cell type diversity of the subgenual anterior cingulate cortex and dorsolateral PFC of humans with no neuropsychiatric illness using a clustering analysis of single-nuclei RNA-sequencing data. Defining the transcriptomic profile of cellular subpopulations in these cortical regions is a first step to demystifying the cellular and molecular pathways involved in psychiatric disorders.

Hepatitis C cure and medications for opioid use disorder improve health-related quality of life in patients with opioid use disorder actively engaged in substance use.

BACKGROUND: This study aims to determine whether Hepatitis C (HCV) treatment improves health-related quality of life (HRQL) in patients with opioid use disorder (OUD) actively engaged in substance use, and which variables are associated with improving HRQL in patients with OUD during HCV treatment. METHODS: Data are from a prospective, open-label, observational study of 198 patients with OUD or opioid misuse within 1 year of study enrollment who received HCV treatment with the primary endpoint of Sustained Virologic Response (SVR). HRQL was assessed using the Hepatitis C Virus Patient Reported Outcomes (HCV-PRO) survey, with higher scores denoting better HRQL. HCV-PRO surveys were conducted at Day 0, Week 12, and Week 24. A mixed-effects model investigated which variables were associated with changing HCV-PRO scores from Day 0 to Week 24. RESULTS: Patients had a median age of 57 and were predominantly male (68.2%) and Black (83.3%). Most reported daily-or-more drug use (58.6%) and injection drug use (IDU) (75.8%). Mean HCV-PRO scores at Day 0 and Week 24 were 64.0 and 72.9, respectively. HCV-PRO scores at Week 24 improved compared with scores at Day 0 (8.7; p<0.001). Achieving SVR (10.4; p<0.001) and receiving medications for OUD (MOUD) at Week 24 (9.5; p<0.001) were associated with improving HCV-PRO scores. HCV-PRO scores increased at Week 24 for patients who experienced no decline in IDU frequency (8.1; p<0.001) or had a UDS positive for opioids (8.0; p<0.001) or cocaine (7.5; p=0.003) at Week 24. CONCLUSION: Patients with OUD actively engaged in substance use experience improvement in HRQL from HCV cure unaffected by ongoing substance use. Interventions to promote HCV cure and MOUD engagement could improve HRQL for patients with OUD.

Comparison of the serum fibrin-fibrinogen degradation products with cytokeratin 19 fragment as biomarkers in patients with lung cancer.

Lung cancer is one of the main causes of cancer-related mortality. The identification of early diagnostic biomarkers improved outcomes for lung cancer patients. Serum fibrin-fibrinogen degradation products (FDP) levels are elevated in numerous malignancies due to hemostatic alterations. The serum FDP levels were compared to the levels of cytokeratin 19 fragment antigen (CYFRA 21-1), another well-established biomarker. The serum samples from 193 lung cancer patients, 84 healthy controls and 106 patients with benign respiratory diseases were obtained. The serum FDP level was measured using the DR-70 immunoassay and the CYFRA 21-1 level was measured by electrochemiluminescence using the Roche Analytics E170. Receiver operating characteristics curves were used to assess the predictive sensitivity and specificity. The mean serum FDP level in lung cancer patients (35.01±229.02 µg/ml) was significantly higher compared to the 190 non-cancerous subjects (0.60±0.75 µg/ml; P=0.039). The mean serum CYFRA 21-1 level in lung cancer patients (4.50±6.67 ng/ml) was also significantly higher compared to the non-cancerous subjects (1.40±0.83 ng/ml; P<0.05). FDP exhibited clinical sensitivity and specificity of 86 and 75%, respectively, at an optimal cut-off at 0.67 µg/ml. CYFRA 21-1 exhibited clinical sensitivity and specificity of 77 and 74%, respectively, at a cut-off of 1.65 ng/ml. The serum FDP area under the curve (0.87) was slightly higher compared to CYFRA 21-1 (0.83). Therefore, it is apparent that serum FDP is comparable to CYFRA 21-1 as a lung cancer biomarker and can be used for clinical practice.

Principal components ancestry adjustment for Genetic Analysis Workshop 17 data.

Statistical tests on rare variant data may well have type I error rates that differ from their nominal levels. Here, we use the Genetic Analysis Workshop 17 data to estimate type I error rates and powers of three models for identifying rare variants associated with a phenotype: (1) by using the number of minor alleles, age, and smoking status as predictor variables; (2) by using the number of minor alleles, age, smoking status, and the identity of the population of the subject as predictor variables; and (3) by using the number of minor alleles, age, smoking status, and ancestry adjustment using 10 principal component scores. We studied both quantitative phenotype and a dichotomized phenotype. The model with principal component adjustment has type I error rates that are closer to the nominal level of significance of 0.05 for single-nucleotide polymorphisms (SNPs) in noncausal genes for the selected phenotype than the model directly adjusting for population. The principal component adjustment model type I error rates are also closer to the nominal level of 0.05 for noncausal SNPs located in causal genes for the phenotype. The power for causal SNPs with the principal component adjustment model is comparable to the power of the other methods. The power using the underlying quantitative phenotype is greater than the power using the dichotomized phenotype.

Assessing the impact of global versus local ancestry in association studies.

BACKGROUND: To account for population stratification in association studies, principal-components analysis is often performed on single-nucleotide polymorphisms (SNPs) across the genome. Here, we use Framingham Heart Study (FHS) Genetic Analysis Workshop 16 data to compare the performance of local ancestry adjustment for population stratification based on principal components (PCs) estimated from SNPs in a local chromosomal region with global ancestry adjustment based on PCs estimated from genome-wide SNPs. METHODS: Standardized height residuals from unrelated adults from the FHS Offspring Cohort were averaged from longitudinal data. PCs of SNP genotype data were calculated to represent individual's ancestry either 1) globally using all SNPs across the genome or 2) locally using SNPs in adjacent 20-Mbp regions within each chromosome. We assessed the extent to which there were differences in association studies of height depending on whether PCs for global, local, or both global and local ancestry were included as covariates. RESULTS: The correlations between local and global PCs were low (r < 0.12), suggesting variability between local and global ancestry estimates. Genome-wide association tests without any ancestry adjustment demonstrated an inflated type I error rate that decreased with adjustment for local ancestry, global ancestry, or both. A known spurious association was replicated for SNPs within the lactase gene, and this false-positive association was abolished by adjustment with local or global ancestry PCs. CONCLUSION: Population stratification is a potential source of bias in this seemingly homogenous FHS population. However, local and global PCs derived from SNPs appear to provide adequate information about ancestry.

The evaluation of individual dispersion of single-walled carbon nanotubes using absorption and fluorescence spectroscopic techniques.

We have investigated the degree of dispersion of single-walled carbon nanotubes (SWNTs) in solution using laser spectroscopic techniques. SWNTs were suspended in aqueous media using a sodium dodecyl sulfate (SDS) surfactant. SWNTs with different dispersion states were prepared by controlling the intensity and duration of sonication and centrifugation. The absorption and fluorescence spectroscopic techniques were employed to characterize the different dispersion state of the prepared samples. Nanotube suspensions with better dispersion showed higher fluorescence and sharper absorption peaks. The fluorescence data were characterized as a function of the nanotube chirality, and absorption peak shifts were analyzed depending on the first and second van Hove singularities (vHs) of semiconducting nanotubes.

What SNP genotyping errors are most costly for genetic association studies?

Which genotype misclassification errors are most costly, in terms of increased sample size necessary (SSN) to maintain constant asymptotic power and significance level, when performing case/control studies of genetic association? We answer this question for single-nucleotide polymorphisms (SNPs), using the 2x3 chi(2) test of independence. Our strategy is to expand the noncentrality parameter of the asymptotic distribution of the chi(2) test under a specified alternative hypothesis to approximate SSN, using a linear Taylor series in the error parameters. We consider two scenarios: the first assumes Hardy-Weinberg equilibrium (HWE) for the true genotypes in both cases and controls, and the second assumes HWE only in controls. The Taylor series approximation has a relative error of less than 1% when each error rate is less than 2%. The most costly error is recording the more common homozygote as the less common homozygote, with indefinitely increasing cost coefficient as minor SNP allele frequencies approach 0 in both scenarios. The cost of misclassifying the more common homozygote to the heterozygote also becomes indefinitely large as the minor SNP allele frequency goes to 0 under both scenarios. For the violation of HWE modeled here, the cost of misclassifying a heterozygote to the less common homozygote becomes large, although bounded. Therefore, the use of SNPs with a small minor allele frequency requires careful attention to the frequency of genotyping errors to ensure that power specifications are met. Furthermore, the design of automated genotyping should minimize those errors whose cost coefficients can become indefinitely large.

Quantifying the percent increase in minimum sample size for SNP genotyping errors in genetic model-based association studies.

Kang et al. [Genet Epidemiol 2004;26:132-141] addressed the question of which genotype misclassification errors are most costly, in terms of minimum percentage increase in sample size necessary (%MSSN) to maintain constant asymptotic power and significance level, when performing case/control studies of genetic association in a genetic model-free setting. They answered the question for single nucleotide polymorphisms (SNPs) using the 2 x 3 chi2 test of independence. We address the same question here for a genetic model-based framework. The genetic model parameters considered are: disease model (dominant, recessive), genotypic relative risk, SNP (marker) and disease allele frequency, and linkage disequilibrium. %MSSN coefficients of each of the six possible error rates are determined by expanding the non-centrality parameter of the asymptotic distribution of the 2 x 3 chi2 test under a specified alternative hypothesis to approximate %MSSN using a linear Taylor series in the error rates. In this work we assume errors misclassifying one homozygote as another homozygote are 0, since these errors are thought to rarely occur in practice. Our findings are that there are settings of the genetic model parameters that lead to large total %MSSN for both dominant and recessive models. As SNP minor allele approaches 0, total %MSSN increases without bound, independent of other genetic model parameters. In general, %MSSN is a complex function of the genetic model parameters. Use of SNPs with small minor allele frequency requires careful attention to frequency of genotyping errors to insure that power specifications are met. Software to perform these calculations for study design is available, and an example of its use to study a disease is given.