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Abstract In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

AbstractBackground: There is a pressing need for effective treatments for major depressive disorder (MDD).Objective To examine the feasibility of an integrated mind-body MDD treatment combining cognitive behavioral therapy (CBT) and whole-body hyperthermia (WBH).Methods In this single-arm trial, 16 adults with MDD initially received 8 weekly CBT sessions and 8 weekly WBH sessions. Outcomes included WBH sessions completed (primary), self-report depression assessments completed (secondary), and pre-post intervention changes in depression symptoms (secondary). We also explored changes in mood and cognitive processes and assessed changes in mood as predictors of overall treatment response.Results Thirteen participants (81.3%) completed ≥ 4 WBH sessions (primary outcome); midway through the trial, we reduced from 8 weekly to 4 bi-weekly WBH sessions to increase feasibility. The n = 12 participants who attended the final assessment visit completed 100% of administered self-report depression assessments; all enrolled participants (n = 16) completed 89% of these assessments. Among the n = 12 who attended the final assessment visit, the average pre-post-intervention BDI-II reduction was 15.8 points (95% CI: −22.0, −9.70), p = 0.0001, with 11 no longer meeting MDD criteria (secondary outcomes). Pre-post intervention improvements in negative automatic thinking, but not cognitive flexibility, achieved statistical significance. Improved mood from pre-post the initial WBH session predicted pre-post treatment BDI-II change (36.2%; rho = 0.60, p = 0.038); mood changes pre-post the first CBT session did not.Limitations Small sample size and single-arm design limit generalizability.Conclusion An integrated mind-body intervention comprising weekly CBT sessions and bi-weekly WBH sessions was feasible. Results warrant future larger controlled clinical trials.Clinivaltrials.gov Registration: NCT05708976

Abstract 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

Abstract Correlations between altered body temperature and depression have been reported in small samples; greater confidence in these associations would provide a rationale for further examining potential mechanisms of depression related to body temperature regulation. We sought to test the hypotheses that greater depression symptom severity is associated with (1) higher body temperature, (2) smaller differences between body temperature when awake versus asleep, and (3) lower diurnal body temperature amplitude. Data collected included both self-reported body temperature (using standard thermometers), wearable sensor-assessed distal body temperature (using an off-the-shelf wearable sensor that collected minute-level physiological data), and self-reported depressive symptoms from > 20,000 participants over the course of ~ 7 months as part of the TemPredict Study. Higher self-reported and wearable sensor-assessed body temperatures when awake were associated with greater depression symptom severity. Lower diurnal body temperature amplitude, computed using wearable sensor-assessed distal body temperature data, tended to be associated with greater depression symptom severity, though this association did not achieve statistical significance. These findings, drawn from a large sample, replicate and expand upon prior data pointing to body temperature alterations as potentially relevant factors in depression etiology and may hold implications for development of novel approaches to the treatment of major depressive disorder.

Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term “manifestations,” as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.

Abstract Background Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales. Methods Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments. Results Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales. Conclusions These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them.

Abstract Background Optimal carbohydrate intake is an important and controversial area in the nutritional management of type 2 diabetes. Some evidence indicates that reducing overall carbohydrate intake with a low- or very low-carbohydrate eating plan can improve glycemic control compared to following eating plans that involve greater carbohydrate intake. However, critical knowledge gaps currently prevent clear recommendations about carbohydrate intake levels. Methods The LEGEND (Lifestyle Education about Nutrition for Diabetes) Trial aims to compare a very low-carbohydrate diet to a moderate-carbohydrate plate-method diet for glycemic control in adults with type 2 diabetes. This two-site trial plans to recruit 180 adults with type 2 diabetes. We will randomize participants to either a 20-session group-based diet and lifestyle intervention that teaches either a very low-carbohydrate diet or a moderate-carbohydrate plate-method diet. We will assess participants at study entry and 4 and 12 months later. The primary outcome is HbA1c, and secondary outcomes include inflammation (high sensitivity C-reactive protein), body weight, changes in diabetes medications, lipids (small particle LDL, HDL, triglycerides), skeletal metabolism (bone mineral density from dual-energy x-ray absorptiometry and bone turnover markers serum procollagen type I N propeptide and serum C-terminal telopeptide of type I collagen), and body composition (percent body fat, percent lean body mass). Discussion The LEGEND trial is a randomized controlled trial to assess optimal carbohydrate intake in type 2 diabetes by evaluating the effects of a very low-carbohydrate diet vs. a moderate-carbohydrate plate-method diet over a year-long period. The research addresses important gaps in the evidence base for the nutritional management of type 2 diabetes by providing data on potential benefits and adverse effects of different levels of carbohydrate intake. Trial registration ClinicalTrials.gov NCT05237128. Registered on February 11, 2022

There is a substantial body of clinical evidence supporting the beneficial effects of lower-carbohydrate dietary patterns on multiple established risk factors associated with insulin resistance and cardiovascular diseases in adult populations. Nutrition and health researchers, clinical practitioners, and stakeholders gathered for, “The Scientific Forum on Nutrition, Wellness, and Lower-Carbohydrate Diets: An Evidence- and Equity-Based Approach to Dietary Guidance” to discuss the evidence base around lower-carbohydrate diets, health outcomes, and dietary guidance. Consensus statements were agreed upon to identify current areas of scientific agreement and spotlight gaps in research, education, and practice to help define and prioritize future pathways. Given the evidence base and considering that most American adults are living with at least one nutrition-related chronic disease, there was consensus that including a lower-carbohydrate dietary pattern as one part of the Dietary Guidelines for Americans could help promote health equity among the general population.

Abstract As concerns related to the COVID-19 pandemic continue, it is critical to understand the impact of vaccination type on neutralizing antibody response durability as well as to identify individual difference factors related to decline in neutralization. This was a head-to-head comparison study following 498 healthy, community volunteers who received the BNT162b2 (n = 287), mRNA-1273 (n = 149), and Ad26.COV2.S (n = 62). Participants completed questionnaires and underwent blood draws prior to vaccination, 1 month, and 6 months after the vaccination series, and neutralizing antibody (nAB) titers at 1- and 6-months post vaccination were quantified using a high-throughput pseudovirus assay. Over 6 months of follow-up, nABs declined in recipients of BNT162b2 and mRNA-1273, while nABs in recipients of Ad26.COV2.S showed a significant increase. At the 6-month time point, nABs to Ad26.COV2.S were significantly higher than nABs to BNT162b2 and equivalent to mRNA-1273. Irrespective of follow-up timing, being older was associated with lower nAB for participants who received BNT162b2 and Ad26.COV2.S but not for those who received mRNA-1273. A higher baseline BMI was associated with a lower nAB for Ad26.COV2.S recipients but not for recipients of other vaccines. Women and non-smokers showed higher nAB compared to men and current smokers, respectively. The durability of neutralizing antibody responses differed by vaccine type and several sociodemographic factors that predicted response. These findings may inform booster recommendations in the future.

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

Abstract Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.

IntroductionHIV-1 persists in resting CD4+ T-cells despite antiretroviral therapy (ART). Determining the cell surface markers that enrich for genetically-intact HIV-1 genomes is vital in developing targeted curative strategies. Previous studies have found that HIV-1 proviral DNA is enriched in CD4+ T-cells expressing the immune checkpoint markers programmed cell death protein-1 (PD-1) or cytotoxic T-lymphocyte associated protein-4 (CTLA-4). There has also been some success in blocking these markers in an effort to reverse HIV-1 latency. However, it remains unclear whether cells expressing PD-1 and/or CTLA-4 are enriched for genetically-intact, and potentially replication-competent, HIV-1 genomes. MethodsWe obtained peripheral blood from 16 HIV-1-infected participants, and paired lymph node from four of these participants, during effective ART. Memory CD4+ T-cells from either site were sorted into four populations: PD-1-CTLA-4- (double negative, DN), PD-1+CTLA-4- (PD-1+), PD-1-CTLA-4+ (CTLA-4+) and PD-1+CTLA-4+ (double positive, DP). We performed an exploratory study using the full-length individual proviral sequencing (FLIPS) assay to identify genetically-intact and defective genomes from each subset, as well as HIV-1 genomes with specific intact open reading frames (ORFs). Results and DiscussionIn peripheral blood, we observed that proviruses found within PD-1+ cells are more likely to have intact ORFs for genes such as tat, rev and nef compared to DN, CTLA-4+ and DP cells, all of which may contribute to HIV-1 persistence. Conversely, we observed that CTLA-4 expression is a marker for cells harbouring HIV-1 provirus that is more likely to be defective, containing low levels of these intact ORFs. In the lymph node, we found evidence that CTLA-4+ cells contain lower levels of HIV-1 provirus compared to the other cell subsets. Importantly, however, we observed significant participant variation in the enrichment of HIV-1 proviruses with intact genomes or specific intact ORFs across these memory CD4+ T-cell subsets, and therefore consideration of additional cellular markers will likely be needed to consistently identify cells harbouring latent, and potentially replication-competent, HIV-1.

BackgroundDaily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks.ObjectiveUse data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex.MethodsWe used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model.ResultsAlthough the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females.ConclusionWeb-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

Abstract Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported COVID-19 infections. These cases provide the first evidence that illness-associated elevations in peripheral temperature are observable using wearable devices and correlate with self-reported fever. Our analyses support the hypothesis that wearable sensors can detect illnesses in the absence of symptom recognition. Finally, these data support the hypothesis that prediction of illness onset is possible using continuously generated physiological data collected by wearable sensors. Our findings should encourage further research into the role of wearable sensors in public health efforts aimed at illness detection, and underscore the importance of integrating temperature sensors into commercially available wearables.

Purpose A metastatic cancer diagnosis is associated with high levels of distress in patients and caregivers, which may be alleviated by mindfulness interventions. Research on scalable, tailored, online mindfulness training programs is needed. We sought to test the feasibility and acceptability of a remotely delivered 8-week mindfulness-based intervention, Being Present 2.0 (BP2.0). Methods We performed a single-arm feasibility study of BP2.0 among patients with any metastatic gastrointestinal cancer receiving chemotherapy, with or without an informal caregiver. Participants were instructed to practice mindfulness using pre-recorded guided meditations 5 times per week using a study-specific website and to attend a weekly live, interactive virtual meeting facilitated by a trained instructor. The web-based platform enabled direct measurement of adherence. Results The study enrolled 46 of 74 (62%) patients contacted, together with 23 caregivers (69 participants total), from May to October 2018. Median patient age was 52 (range 20-70 years), 39% were male, 67% non-Hispanic white, 65% had colorectal cancer, and 78% lived outside of San Francisco. The top reasons cited for participation were to reduce stress/anxiety and learn how to meditate. Mean baseline National Comprehensive Cancer Network Distress Thermometer (NCCN DT) scores were 4.7 (patients) and 5.8 (caregivers). The study discontinuation rate was 20% (eight patients and six caregivers). Among the remaining 55 participants, 43 (78%) listened to at least one audio recording and/or attended at least one virtual meeting, although adherence data was incomplete. The retention rate was 71%, with 39 participants completing at least one follow-up assessment. In post-intervention qualitative interviews, 88% of respondents reported a positive experience. Compared to baseline, participants reported significantly reduced post-intervention NCCN DT scores (mean 3.1; P = .012). Conclusion The BP2.0 online mindfulness-based program is feasible and acceptable for patients with metastatic gastrointestinal cancer and caregivers. These results will guide plans for a follow-up efficacy study. ClinicalTrials.gov Identifier: NCT03528863.

The design of future HIV-1 curative therapies requires a more thorough understanding of the distribution of genetically intact HIV-1 within T-cell subsets as well as the cellular mechanisms that maintain this reservoir. These genetically intact and presumably replication-competent proviruses make up the latent HIV-1 reservoir.

Abstract Background A carbohydrate-restricted (CR) diet can improve glycemic control in people with type 2 diabetes mellitus (T2DM). There are concerns, however, that the high dietary fat content of CR diets can increase low-density lipoprotein cholesterol (LDL-C), thus increasing cardiovascular disease (CVD) risk. Quantifying CVD risk associated with changes in LDL-C in the context of CR diets is complicated by the fact that LDL-C reflects heterogeneous lipids. For example, small LDL particle number (sLDL-P) is more closely associated with CVD risk than is total LDL-C, and CR diets tend to decrease the proportion of sLDL-C in LDL-C, which standard lipid measures do not indicate. Advanced lipoprotein assays, such as nuclear magnetic resonance (NMR) testing, can subfractionate lipoproteins by size and density and may better depict the effects of CR diets on CVD risk. Methods Adults (N = 58) with T2DM (n = 37 women; baseline HbA1c ≥ 6.5%) completed a 6-month group-based CR diet intervention. We obtained a standard lipid panel, advanced lipoprotein assays (NMR testing), and two 24-h diet recalls at baseline and post-intervention (6 months). Participants also completed home-based blood ketone testing (a biological index of dietary adherence) during the final five weeks of the intervention. Results From baseline to post-intervention, participants had increased mean HDL-C, decreased triglycerides and triglyceride/HDL ratio, decreased mean sLDL-P, and increased LDL size, which reflect reductions in CVD risk (ps

The immune checkpoint molecule PD-1 is expressed on a fraction of CD4+ T cells latently infected with HIV, but whether PD-1 plays a functional role in reservoir persistence remains unknown. Here, Fromentin et al. show that PD-1 blockade potentiates latency reversal ex vivo in CD4+ T cells from ART suppressed individuals.

Although many HIV cure strategies seek to expand HIV-specific CD8+ T cells to control the virus, all are likely to fail if cellular exhaustion is not prevented. A loss in stem-like memory properties (i.e., the ability to proliferate and generate secondary effector cells) is a key feature of exhaustion; little is known, however, about how these properties are regulated in human virus–specific CD8+ T cells. We found that virus-specific CD8+ T cells from humans and nonhuman primates naturally controlling HIV/SIV infection express more of the transcription factor TCF-1 than noncontrollers. HIV-specific CD8+ T cell TCF-1 expression correlated with memory marker expression and expansion capacity and declined with antigenic stimulation. CRISPR-Cas9 editing of TCF-1 in human primary T cells demonstrated a direct role in regulating expansion capacity. Collectively, these data suggest that TCF-1 contributes to the regulation of the stem-like memory property of secondary expansion capacity of HIV-specific CD8+ T cells, and they provide a rationale for exploring the enhancement of this pathway in T cell–based therapeutic strategies for HIV.

Mindfulness and compassion meditation are thought to cultivate prosocial behavior. However, the lack of diverse representation within both scientific and participant populations in contemplative neuroscience may limit generalizability and translation of prior findings. To address these issues, we propose a research framework called Intersectional Neuroscience which adapts research procedures to be more inclusive of under-represented groups. Intersectional Neuroscience builds inclusive processes into research design using two main approaches: 1) community engagement with diverse participants, and 2) individualized multivariate neuroscience methods to accommodate neural diversity. We tested the feasibility of this framework in partnership with a diverse U.S. meditation center (East Bay Meditation Center, Oakland, CA). Using focus group and community feedback, we adapted functional magnetic resonance imaging (fMRI) screening and recruitment procedures to be inclusive of participants from various under-represented groups, including racial and ethnic minorities, gender and sexual minorities, people with disabilities, neuropsychiatric disorders, and/or lower income. Using person-centered screening and study materials, we recruited and scanned 15 diverse meditators (80% racial/ethnic minorities, 53% gender and sexual minorities). The participants completed the EMBODY task – which applies individualized machine learning algorithms to fMRI data – to identify mental states during breath-focused meditation, a basic skill that stabilizes attention to support interoception and compassion. All 15 meditators’ unique brain patterns were recognized by machine learning algorithms significantly above chance levels. These individualized brain patterns were used to decode the internal focus of attention throughout a 10-min breath-focused meditation period, specific to each meditator. These data were used to compile individual-level attention profiles during meditation, such as the percentage time attending to the breath, mind wandering, or engaging in self-referential processing. This study provides feasibility of employing an intersectional neuroscience approach to include diverse participants and develop individualized neural metrics of meditation practice. Through inclusion of more under-represented groups while developing reciprocal partnerships, intersectional neuroscience turns the research process into an embodied form of social action.

Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health via cognitive and emotional regulation of bodily signals. However, it remains unclear how meditation impacts internal attention (IA) states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to: (1) learn and recognize internal attentional states relevant for meditation during a directed IA task; and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N = 16), we first used MVPA to develop single-subject brain classifiers for five modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states [i.e., meditation-related states: breath attention, mind wandering (MW), and self-referential processing, and control states: attention to feet and sounds]. Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all volumes were tested, N = 2,160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, MW, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to MW or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses.

Latent replication-competent HIV-1 persists in individuals on long-term antiretroviral therapy (ART). We developed the Full-Length Individual Proviral Sequencing (FLIPS) assay to determine the distribution of latent replication-competent HIV-1 within memory CD4+ T cell subsets in six individuals on long-term ART. FLIPS is an efficient, high-throughput assay that amplifies and sequences near full-length (∼9 kb) HIV-1 proviral genomes and determines potential replication competency through genetic characterization. FLIPS provides a genome-scale perspective that addresses the limitations of other methods that also genetically characterize the latent reservoir. Using FLIPS, we identified 5% of proviruses as intact and potentially replication competent. Intact proviruses were unequally distributed between T cell subsets, with effector memory cells containing the largest proportion of genetically intact HIV-1 proviruses. We identified multiple identical intact proviruses, suggesting a role for cellular proliferation in the maintenance of the latent HIV-1 reservoir.

To date, most assays for measuring the human immunodeficiency virus (HIV-1) reservoir do not include memory CD4+ T-cells expressing the activation marker, human leukocyte antigen-antigen D related (HLA-DR). However, little is known concerning the role these cells play in maintaining persistent HIV-1 during effective antiretroviral therapy (ART). To address this issue, we examined, cellular activation/exhaustion markers (Ki67, CCR5, PD-1, Lag-3 and Tim-3) and viral gag-pol DNA sequences within HLA-DR− and HLA-DR+ memory CD4+ T-cell subsets longitudinally from the peripheral blood of six participants over 3 to ≥15 years of effective therapy. HLA-DR expression was readily detected during the study period in all participants. The average expression levels of CCR5, PD-1 and Tim-3 were higher on the HLA-DR+ T-cell subset whereas the average of LAG-3 expression was higher on their HLA-DR− counterpart. The proportion of HIV-infected cells increased within the HLA-DR+ subset by an average of 18% per year of ART whereas the frequency of infected HLA-DR− T-cells slightly decreased over time (5% per year). We observed that 20–33% of HIV-DNA sequences from the early time points were genetically identical to viral sequences from the last time point within the same cell subset during ART. This indicates that a fraction of proviruses persists within HLA-DR+ and HLA-DR− T-cell subsets during prolonged ART. Our HIV-DNA sequence analyses also revealed that cells transitioned between the HLA-DR+ and HLA-DR− phenotypes. The Ki67 expression, a marker for cellular proliferation, and the combined markers of Ki67/PD-1 averaged 19-fold and 22-fold higher on the HLA-DR+ T-cell subset compared to their HLA-DR− counterpart. Moreover, cellular proliferation, as reflected by the proportion of genetically identical HIV-DNA sequences, increased within both T-cell subsets over the study period; however, this increase was greater within the HLA-DR+ T-cells. Our research revealed that cellular transition and proliferation contribute to the persistence of HIV in HLA-DR+ and HLA-DR− T-cell subsets during prolonged therapy. As such, the HIV reservoir expands during effective ART when both the HLA-DR+ and HLA-DR− cell subsets are included, and therapeutic interventions aimed at reducing the HIV-1 reservoir should target HLA-DR+ and HLA-DR− T-cells.

Gut microbes can profoundly modulate mucosal barrier-promoting Th17 cells in mammals. A salient feature of HIV/simian immunodeficiency virus (SIV) immunopathogenesis is the loss of Th17 cells, which has been linked to increased activity of the immunomodulatory enzyme, indoleamine 2,3-dioxygenase 1 (IDO 1). The role of gut microbes in this system remains unknown, and the SIV-infected rhesus macaque provides a well-described model for HIV-associated Th17 loss and mucosal immune disruption. We observed a specific depletion of gut-resident Lactobacillus during acute and chronic SIV infection of rhesus macaques, which was also seen in early HIV-infected humans. This depletion in rhesus macaques correlated with increased IDO1 activity and Th17 loss. Macaques supplemented with a Lactobacillus-containing probiotic exhibited decreased IDO1 activity during chronic SIV infection. We propose that Lactobacillus species inhibit mammalian IDO1 and thus may help to preserve Th17 cells during pathogenic SIV infection, providing support for Lactobacillus species as modulators of mucosal immune homeostasis.

Background: Limited data exist comparing viral quasispecies between cerebrospinal fluid (CSF) and plasma compartments during primary HIV infection. Deep sequencing is a new method to examine the HIV plasma and CSF quasispecies. Methods: In this pilot study, deep sequencing of protease (PR) and reverse transcriptase (RT) was performed in plasma and CSF from participants during primary HIV infection. Estimated mutational load was calculated by mutant variant frequency multiplied by HIV-RNA level. Results: Paired plasma and CSF samples were studied from five antiretroviral therapy-naïve male participants with median 109 days post estimated transmission, age 32 years, CD4 cell count 580 cells/μL, HIV-RNA 5.18 log10 copies/mL in plasma and 3.67 log10 copies/mL in CSF. Plasma samples averaged 7,124 reads of PR and 2,448 reads of RT, whereas CSF samples averaged 7,082 and 2,792 reads, respectively. A distinct drug-resistance pattern with linked mutations present at significant levels (5–10%) was detected in one participant in CSF. Other low abundance variants (>0.2%) were detected in plasma and CSF of four out of five participants. Conclusions: Deep sequencing of CSF HIV is technically possible with sufficient HIV-RNA levels. Differences between the quasispecies in the two compartments detected in one participant, which were present with a high mutational load in CSF at an estimated 3.6 months after HIV infection, suggest that early CNS compartmentalisation may be revealed by sensitive deep-sequencing methods. The presence of distinct low abundance (

Background: Quantifying latently infected cells is critical to evaluate the efficacy of therapeutic strategies aimed at reducing the size of the long-lived viral reservoir, but the low frequency of these cells makes this very challenging. Methods: We developed TILDA (Tat/rev Induced Limiting Dilution Assay) to measure the frequency of cells with inducible multiply-spliced HIV RNA, as these transcripts are usually absent in latently infected cells but induced upon viral reactivation. TILDA requires less than a million cells, does not require RNA extraction and can be completed in two days. Findings: In suppressed individuals on ART, we found the median frequency of latently infected CD4+ T cells as estimated by TILDA to be 24 cells/million, which was 48 times more than the frequency measured by the quantitative viral outgrowth assay, and 6–27 times less than the frequencies of cells harbouring viral DNA measured by PCR-based assays. TILDA measurements strongly correlated with most HIV DNA assays. The size of the latent reservoir measured by TILDA was lower in subjects who initiated ART during the early compared to late stage of infection (p = 0.011). In untreated HIV disease, the frequency of CD4+ cells carrying latent but inducible HIV largely exceeded the frequency of actively producing cells, demonstrating that the majority of infected cells are transcriptionally silent even in the absence of ART. Interpretations: Our results suggest that TILDA is a reproducible and sensitive approach to measure the frequency of productively and latently infected cells in clinical settings. We demonstrate that the latent reservoir represents a substantial fraction of all infected cells prior to ART initiation. Research in context: In this manuscript, we describe the development of a novel assay that measures the magnitude of the latent HIV reservoir, the main barrier to HIV eradication. This novel assay, termed TILDA for Tat/rev Induced Limiting Dilution Assay, requires only 10 ml of blood, does not necessitate extraction of viral nucleic acids, is highly reproducible, covers a wide dynamic range of reservoir sizes and can be completed in two days. As such, TILDA may represent an alternative to existing assays used to evaluate the efficacy of therapeutic strategies aimed at reducing the size of the latent HIV reservoir.

A diversity of scales capture facets of reward-related eating (RRE). These scales assess food cravings, uncontrolled eating, addictive behavior, restrained eating, binge eating, and other eating behaviors. However, these scales differ in terms of the severity of RRE they capture. We sought to incorporate the items from existing scales to broaden the 9-item Reward-based Eating Drive scale (RED-9; Epel et al., 2014), which assesses three dimensions of RRE (lack of satiety, preoccupation with food, and lack of control over eating), in order to more comprehensively assess the entire spectrum of RRE. In a series of 4 studies, we used Item Response Theory models to consider candidate items to broaden the RED-9. Studies 1 and 2 evaluated the abilities of additional items from existing scales to increase the RED-9’s coverage across the spectrum of RRE. Study 3 evaluated candidate items identified in Studies 1 and 2 in a new sample to assess the extent to which they accounted for more variance in areas less well-covered by the RED-9. Study 4 tested the ability of the RED-13 to provide consistent coverage across the range of the RRE spectrum. The resultant RED-13 accounted for greater variability than the RED-9 by reducing gaps in coverage of RRE in middle-to-low ranges. Like the RED-9, the RED-13 was positively correlated with BMI. The RED-13 was also positively related to a diagnosis of type 2 diabetes as well as cravings for sweet and savory foods. In summary, the RED-13 is a brief self-report measure that broadly captures the spectrum of RRE and may be a useful tool for identifying individuals at risk for overweight or obesity.

Following publication of the original article [1], the author reported that the co-author’s name was missing in the original article.

Psychological distress and elevated cortisol secretion promote abdominal fat, a feature of the Metabolic Syndrome. Effects of stress reduction interventions on abdominal fat are unknown. Forty-seven overweight/obese women (mean BMI =31.2) were randomly assigned to a 4-month intervention or waitlist group to explore effects of a mindfulness program for stress eating. We assessed mindfulness, psychological distress, eating behavior, weight, cortisol awakening response (CAR), and abdominal fat (by dual-energy X-ray absorptiometry) pre- and posttreatment. Treatment participants improved in mindfulness, anxiety, and external-based eating compared to control participants. Groups did not differ on average CAR, weight, or abdominal fat over time. However, obese treatment participants showed significant reductions in CAR and maintained body weight, while obese control participants had stable CAR and gained weight. Improvements in mindfulness, chronic stress, and CAR were associated with reductions in abdominal fat. This proof of concept study suggests that mindfulness training shows promise for improving eating patterns and the CAR, which may reduce abdominal fat over time.