Five million nights: temporal dynamics in human sleep phenotypes.

Sleep monitoring has become widespread with the rise of affordable wearable devices. However, converting sleep data into actionable change remains challenging as diverse factors can cause combinations of sleep parameters to differ both between people and within people over time. Researchers have attempted to combine sleep parameters to improve detecting similarities between nights of sleep. The cluster of similar combinations of sleep parameters from a night of sleep defines that night's sleep phenotype. To date, quantitative models of sleep phenotype made from data collected from large populations have used cross-sectional data, which preclude longitudinal analyses that could better quantify differences within individuals over time. In analyses reported here, we used five million nights of wearable sleep data to test (a) whether an individual's sleep phenotype changes over time and (b) whether these changes elucidate new information about acute periods of illness (e.g., flu, fever, COVID-19). We found evidence for 13 sleep phenotypes associated with sleep quality and that individuals transition between these phenotypes over time. Patterns of transitions significantly differ (i) between individuals (with vs. without a chronic health condition; chi-square test; p-value < 1e-100) and (ii) within individuals over time (before vs. during an acute condition; Chi-Square test; p-value < 1e-100). Finally, we found that the patterns of transitions carried more information about chronic and acute health conditions than did phenotype membership alone (longitudinal analyses yielded 2-10× as much information as cross-sectional analyses). These results support the use of temporal dynamics in the future development of longitudinal sleep analyses.

Population Pharmacokinetics and Pharmacodynamics of Disulfiram on Inducing Latent HIV-1 Transcription in a Phase IIb Trial.

Disulfiram (DSF) was well tolerated and activated viral transcription (cell-associated unspliced (CA-US) and plasma human immunodeficiency virus (HIV) RNA) in a phase II dose-escalation trial in HIV+ antiretroviral therapy (ART)-suppressed participants. Here, we investigated whether exposure to DSF and its metabolites predicted these changes in HIV transcription. Participants were administered 500 (N = 10), 1,000 (N = 10), or 2,000 (N = 10) mg of DSF for 3 consecutive days. DSF and four metabolites were measured by ultraperformance liquid chromatography-tandem mass spectrometry. Changes in CA-US and plasma HIV RNA were quantified by polymerase chain reaction (PCR) and analyzed in NONMEM. A seven-compartment pharmacokinetic (PK) model demonstrated nonlinear elimination kinetics. The fitted median area under the curve values for 72 hours (AUC0-72 ) were 3,816, 8,386, and 22,331 mg*hour/L, respectively. Higher exposure predicted greater increases in CA-US (maximum effect (Emax ) = 78%, AUC50  = 1,600 µg*hour/L, P = 0.013) but not plasma HIV RNA. These results provide support for further development of DSF as an important drug for future HIV cure strategies.

Persistence of integrated HIV DNA in CXCR3 + CCR6 + memory CD4+ T cells in HIV-infected individuals on antiretroviral therapy.

BACKGROUND: HIV latent infection can be established in vitro by treating resting CD4 T cells with chemokines that bind to chemokine receptors (CKR), CCR7, CXCR3, and CCR6, highly expressed on T cells. OBJECTIVE: To determine if CKR identify CD4 T cells enriched for HIV in HIV-infected individuals receiving suppressive antiretroviral therapy (ART). DESIGN: A cross-sectional study of CKR expression and HIV persistence in blood from HIV-infected individuals on suppressive ART for more than 3 years (n = 48). A subset of 20 individuals underwent leukapheresis and sorting of specific CD4 T-cell subsets. METHODS: We used flow cytometry to quantify CCR5, CCR6, CXCR3, and CXCR5 expression on CD4 T cells. HIV persistence was quantified using real-time Polymerase Chain Reaction to detect total, integrated HIV DNA, 2-long terminal repeat circles and cell-associated unspliced (CA-US) HIV RNA in total CD4 T cells from blood or sorted T-cell subsets. Associations between CKR and HIV persistence in CD4 T cells in blood were determined using regression models and adjusted for current and nadir CD4 T-cell counts. RESULTS: The frequency of cells harbouring integrated HIV DNA was inversely associated with current CD4 T-cell count and positively associated with CCR5+ CD4 T cells, CXCR3+CCR6+ and CXCR3+CCR6- expression on total memory CD4 T cells (P < 0.001, 0.048, 0.015, and 0.016, respectively). CXCR3+CCR6+ CM CD4 T cells contained the highest amount of integrated HIV DNA and lowest ratio of CA-US HIV RNA to DNA compared to all T-cell subsets examined. CONCLUSION: CXCR3 and CCR6 coexpression defines a subset of CD4 T cells that are preferentially enriched for HIV DNA in HIV-infected individuals on ART.

Programmed death-1 expression on CD4⁺ and CD8⁺ T cells in treated and untreated HIV disease.

BACKGROUND: There is intense interest in the role of programmed death 1 (PD-1) in causing persistent T-cell dysfunction in HIV infection. However, the impact of HIV infection and antiretroviral treatment (ART) on the expression of PD-1 on T cells is still poorly defined. METHODS: PD-1 was measured longitudinally in a cohort of recently HIV-infected individuals (n = 121) who started ART early (<6 months after infection) vs. later (≥2 years after infection). PD-1 was also measured cross-sectionally in a diverse cohort of chronically HIV-infected adults (n = 206). RESULTS: PD-1 expression levels were high on CD8⁺ T cells during early HIV infection. PD-1 levels increased on both CD4⁺ and CD8⁺ T cells populations in those who delayed therapy (11 and 10%/year, respectively). PD-1 levels declined and were similar in those treated early vs. late after 1 year of ART. In both cohorts, PD-1 expression on CD4⁺ T cells was associated with CD4⁺ T-cell activation (CD38⁺HLA-DR⁺) and inversely with CD4⁺ cell count. In contrast, PD-1 expression on CD8⁺ T cells was most strongly associated with CD8⁺ T-cell activation and with plasma viral load in viremic individuals. CONCLUSION: Across two large cohorts of untreated and treated individuals, we found consistent associations between HIV RNA levels, CD8⁺ T-cell activation and PD-1 expression on CD8⁺ T cells. In contrast, CD4⁺ T-cell counts and CD4⁺ T-cell activation were more consistent correlates of PD-1 expression on CD4⁺ T cells. PD-1 expression appears to be driven by both direct antigen and homeostatic pathways.