Evidence-Based Therapist-Supported Digital Mental Health Intervention for Patients Experiencing Medical Multimorbidity: A Retrospective Cohort Intent-to-Treat Study.

OBJECTIVE: Multimorbidity or the co-occurrence of multiple health conditions is increasing globally and is associated with significant psychological complications. It is unclear whether digital mental health (DMH) interventions for patients experiencing multimorbidity are effective, particularly given that this patient population faces more treatment resistance. The goal of the current study was to examine the impact of smartphone-delivered DMH interventions for patients presenting with elevated internalizing symptoms that have reported multiple lifetime medical conditions. METHODS: This preregistered (see https://osf.io/vh2et/ ) retrospective cohort intent-to-treat study with 2819 patients enrolled in a therapist-supported DMH intervention examined the associations between medical multimorbidity (MMB) and mental health outcomes. RESULTS: Results indicated that more MMB was significantly associated with greater presenting mental health symptom severity. MMB did not have a deleterious influence on depressive symptom trajectories across treatment, although having one medical condition was associated with a steeper decrease in anxiety symptoms compared to patients with no medical conditions. Finally, MMB was not associated with time to dropout, but was associated with higher dropout and was differentially associated with fewer beneficial treatment outcomes, although this is likely attributable to higher presenting symptom severity, rather than lesser symptom reductions during treatment. CONCLUSIONS: Overall, the Meru Health Program was associated with large effect size decreases in depressive and anxiety symptoms regardless of the number of MMB. Future DMH treatments and research might investigate tailored barrier reduction and extended treatment lengths for patients experiencing MMB to allow for greater treatment dose to reduce symptoms below clinical outcome thresholds.

Real-time heart rate variability biofeedback amplitude during a large-scale digital mental health intervention differed by age, gender, and mental and physical health.

Heart rate variability biofeedback (HRVB) is an efficacious treatment for depression and anxiety. However, translation to digital mental health interventions (DMHI) requires computing and providing real-time HRVB metrics in a personalized and user-friendly fashion. To address these gaps, this study validates a real-time HRVB feedback algorithm and characterizes the association of the main algorithmic summary metric-HRVB amplitude-with demographic, psychological, and health factors. We analyzed HRVB data from 5158 participants in a therapist-supported DMHI incorporating slow-paced breathing to treat depression or anxiety symptoms. A real-time feedback metric of HRVB amplitude and a gold-standard research metric of low-frequency (LF) power were computed for each session and then averaged within-participants over 2 weeks. We provide HRVB amplitude values, stratified by age and gender, and we characterize the multivariate associations of HRVB amplitude with demographic, psychological, and health factors. Real-time HRVB amplitude correlated strongly (r = .93, p < .001) with the LF power around the respiratory frequency (~0.1 Hz). Age was associated with a significant decline in HRVB (ß = -0.46, p < .001), which was steeper among men than women, adjusting for demographic, psychological, and health factors. Resting high- and low-frequency power, body mass index, hypertension, Asian race, depression symptoms, and trauma history were significantly associated with HRVB amplitude in multivariate analyses (p's < .01). Real-time HRVB amplitude correlates highly with a research gold-standard spectral metric, enabling automated biofeedback delivery as a potential treatment component of DMHIs. Moreover, we identify demographic, psychological, and health factors relevant to building an equitable, accurate, and personalized biofeedback user experience.

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention.

OBJECTIVE: Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes. METHODS: We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics. RESULTS: Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals. CONCLUSIONS: Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

Trajectories of depression symptoms in a therapist-supported digital mental health intervention: a repeated measures latent profile analysis.

PURPOSE: Major depression affects 10% of the US adult population annually, contributing to significant burden and impairment. Research indicates treatment response is a non-linear process characterized by combinations of gradual changes and abrupt shifts in depression symptoms, although less is known about differential trajectories of depression symptoms in therapist-supported digital mental health interventions (DMHI). METHODS: Repeated measures latent profile analysis was used to empirically identify differential trajectories based upon biweekly depression scores on the Patient Health Questionnaire-9 (PHQ-9) among patients engaging in a therapist-supported DMHI from January 2020 to July 2021. Multivariate associations between symptom trajectories with sociodemographics and clinical characteristics were examined with multinomial logistic regression. Minimal clinically important differences (MCID) were defined as a five-point change on the PHQ-9 from baseline to week 12. RESULTS: The final sample included 2192 patients aged 18 to 82 (mean = 39.1). Four distinct trajectories emerged that differed by symptom severity and trajectory of depression symptoms over 12 weeks. All trajectories demonstrated reductions in symptoms. Despite meeting MCID criteria, evidence of treatment resistance was found among the trajectory with the highest symptom severity. Chronicity of major depressive episodes and lifetime trauma exposures were ubiquitous across the trajectories in a multinomial logistic regression model. CONCLUSIONS: These data indicate that changes in depression symptoms during DMHI are heterogenous and non-linear, suggesting a need for precision care strategies to address treatment resistance and increase engagement. Future efforts should examine the effectiveness of trauma-informed treatment modules for DMHIs as well as protocols for continuation treatment and relapse prevention.

An immunogenomic phenotype predicting behavioral treatment response: Toward precision psychiatry for mothers and children with trauma exposure.

Inflammatory pathways predict antidepressant treatment non-response among individuals with major depression; yet, this phenomenon may have broader transdiagnostic and transtherapeutic relevance. Among trauma-exposed mothers (Mage = 32 years) and their young children (Mage = 4 years), we tested whether genomic and proteomic biomarkers of pro-inflammatory imbalance prospectively predicted treatment response (PTSD and depression) to an empirically-supported behavioral treatment. Forty-three mother-child dyads without chronic disease completed Child Parent Psychotherapy (CPP) for roughly 9 months. Maternal blood was drawn pre-treatment, CD14 + monocytes isolated, gene expression derived from RNA sequencing (n = 34; Illumina HiSeq 4000;TruSeqcDNA library), and serum assayed (n = 43) for C-Reactive Protein (CRP) and interleukin-1ß (IL-1ß). Symptoms of PTSD and depression decreased significantly from pre- to post-treatment for both mothers and children (all p's < 0.01). Nonetheless, a higher pre-treatment maternal pro-inflammatory imbalance of M1-like versus M2-like macrophage-associated RNA expression (M1/M2) (ß = 0.476, p = .004) and IL-1ß (ß=0.333, p = .029), but not CRP, predicted lesser improvements in maternal PTSD symptoms, unadjusted and adjusting for maternal age, BMI, ethnicity, antidepressant use, income, education, and US birth. Only higher pre-treatment M1/M2 predicted a clinically-relevant threshold of PTSD non-response among mothers (OR = 3.364, p = .015; ROC-AUC = 0.78). Additionally, higher M1/M2 predicted lesser decline in maternal depressive symptoms (ß = 0.556, p = .001), though not independent of PTSD symptoms. For child outcomes, higher maternal IL-1ß significantly predicted poorer PTSD and depression symptom trajectories (ß's = 0.318-0.429, p's < 0.01), while M1/M2 and CRP were marginally associated with poorer PTSD symptom improvement (ß's = 0.295-0.333, p's < 0.056). Pre-treatment pro-inflammatory imbalance prospectively predicts poorer transdiagnostic symptom response to an empirically-supported behavioral treatment for trauma-exposed women and their young children.

Stress resilience: Narrative identity may buffer the longitudinal effects of chronic caregiving stress on mental health and telomere shortening.

BACKGROUND: Chronic caregiving stress may accelerate biological aging; however, the ability to integrate the meaning of caregiving through self-awareness, adaptation, and growth can buffer the negative effects of stress. Narrative researchers have shown that people who coherently integrate difficult experiences into their life story tend to have better mental health, but no prior study has examined the prospective association between narrative identity and biological indicators, such as telomere length. We tested whether narrative identity might be prospectively associated with resilience to long-term parenting stress, depressive symptoms, and protection from telomere shortening, especially among caregivers. METHODS: We conducted a semi-structured interview about parenting and quantified narrative themes by applying well-validated, standardized coding systems with high inter-rater reliability among 88 mothers: 32 "caregivers" (mothers with a child diagnosed with an autism spectrum disorder), and 56 "controls" (mothers with a neurotypical child). To assess longitudinal changes, we measured mental health (parenting stress [PS], depressive symptoms [DS]) and leukocyte telomere length [LTL], a biomarker of aging, at baseline and again 18 months later. We examined whether narrative identity themes were related to these outcomes and whether associations differed across caregivers versus controls. RESULTS: Caregivers exhibited significantly higher basal levels of PS and DS relative to controls (all p's < .05), but no significant difference in LTL (p > .05). Caregivers rated higher in the narrative theme of integration showed healthier future 18-month trajectories in PS (B = -0.832, 99% CI: [-1.315, -0.155], p < .01) and LTL (B = 1.193, 99% CI: [0.526, 2.130], p < .01), but no differences in depressive symptoms (p > .05), adjusting for age and antidepressant use. Analyses examining affective themes in caregiver narratives did not demonstrate significant associations. Narrative themes did not predict outcomes in controls. CONCLUSIONS: The data suggest that narratives reflecting coherent integration, but not necessarily affect, prospectively relate to psychological and biological stress resilience. Maternal caregivers' ability to tell an integrated story of their parenting experiences forecasts lower parenting stress and telomere shortening over time. This study suggests the possibility that helping individuals better integrate the meaning of stressful experiences, but not necessarily to affectively redeem them, may constitute a potential novel target for intervention among chronically stressed populations such as caregivers.

Temporal patterns of self-weighing behavior and weight changes assessed by consumer purchased scales in the Health eHeart Study.

Self-weighing may promote attainment and maintenance of healthy weight; however, the natural temporal patterns and factors associated with self-weighing behavior are unclear. The aims of this secondary analysis were to (1) identify distinct temporal patterns of self-weighing behaviors; (2) explore factors associated with temporal self-weighing patterns; and (3) examine differences in percent weight changes by patterns of self-weighing over time. We analyzed electronically collected self-weighing data from the Health eHeart Study, an ongoing longitudinal research study coordinated by the University of California, San Francisco. We selected participants with at least 12 months of data since the day of first use of a WiFi- or Bluetooth-enabled digital scale. The sample (N = 1041) was predominantly male (77.5%) and White (89.9%), with a mean age of 46.5 ± 12.3 years and a mean BMI of 28.3 ± 5.9 kg/m2 at entry. Using group-based trajectory modeling, six distinct temporal patterns of self-weighing were identified: non-users (n = 120, 11.5%), weekly users (n = 189, 18.2%), rapid decliners (n = 109, 10.5%), increasing users (n = 160, 15.4%), slow decliners (n = 182, 17.5%), and persistent daily users (n = 281, 27.0%). Individuals who were older, female, or self-weighed 6-7 days/week at week 1 were more likely to follow the self-weighing pattern of persistent daily users. Predicted self-weighing trajectory group membership was significantly associated with weight change over time (p < .001). In conclusion, we identified six distinct patterns of self-weighing behavior over the 12-month period. Persistent daily users lost more weight compared with groups with less frequent patterns of scale use.

When anger expression might be beneficial for African Americans: The moderating role of chronic discrimination.

OBJECTIVES: Anger expression is assumed to have mostly negative health effects. Yet, evidence is mixed on how anger expression influences African Americans' cardiovascular health. The present research aimed to clarify this link by examining moderating effects of chronic discrimination on the relationship between anger expression and cardiovascular risk among African Americans in experimental (Study 1) and epidemiological (Study 2) studies. METHOD: Study 1 examined how African Americans' trait anger expression was linked to (a) physiologic reactivity to acute social rejection during an interracial encounter (Session 1); and (b) total/HDL cholesterol assessed two months later (Session 2). Study 2 examined the relationship between anger expression and total/HDL cholesterol with a larger sample of African Americans from the Midlife in the United States (MIDUS) survey. Both studies examined perceptions of chronic discrimination as a moderator of the relationships between anger expression and biological responses. RESULTS: In Study 1 higher anger expression was associated with quicker cortisol recovery and greater testosterone reactivity following outgroup social rejection in Session 1 and lower total/HDL cholesterol in Session 2. Study 2 replicated the relationship between anger expression and lower total/HDL cholesterol and further showed that this relationship was unique to the expressive aspect of anger. Importantly, in both studies, these potentially beneficial effects of anger expression were only evident among individuals with lower perceptions of chronic discrimination. CONCLUSIONS: These findings suggest that anger expression, when coupled with low levels of chronic discrimination, is associated with adaptive patterns of physiologic responses among African Americans. (PsycINFO Database Record

Chronic stress is associated with reduced circulating hematopoietic progenitor cell number: A maternal caregiving model.

BACKGROUND: Chronic psychological stress is a risk factor for cardiovascular disease and mortality. Circulating hematopoietic progenitor cells (CPCs) maintain vascular homeostasis, correlate with preclinical atherosclerosis, and prospectively predict cardiovascular events. We hypothesize that (1) chronic caregiving stress is related to reduced CPC number, and (2) this may be explained in part by negative interactions within the family. METHODS: We investigated levels of stress and CPCs in 68 healthy mothers - 31 of these had children with an autism spectrum disorder (M-ASD) and 37 had neurotypical children (M-NT). Participants provided fasting blood samples, and CD45+CD34+KDR+ and CD45+CD133+KDR+ CPCs were assayed by flow cytometry. We averaged the blom-transformed scores of both CPCs to create one index. Participants completed the perceived stress scale (PSS), the inventory for depressive symptoms (IDS), and reported on daily interactions with their children and partners, averaged over 7 nights. RESULTS: M-ASD exhibited lower CPCs than M-NT (Cohen's d=0.83; p⩽0.01), controlling for age, BMI, and physical activity. Across the whole sample, positive interactions were related to higher CPCs, and negative interactions to lower CPCs (allp's<0.05). The adverse effects of group on CPCs were significantly mediated through negative interactions with the child (indirect ß=-0.24, p⩽0.01). In the full model, greater age (ß=-0.19, p=0.04), BMI (ß=-0.18, p=0.04), and negative interactions with the child (ß=-0.33, p<0.01) were independently associated with lower CPCs. M-ASD had a less healthy lipid profile (total cholesterol/HDL), which in turn, was associated with lower CPCs. CONCLUSIONS: Chronic stress adversely impacts CPC number, an early-stage biomarker that predicts subclinical atherosclerosis and future CVD events, independent of traditional cardiovascular risk factors and inflammatory factors. Among maternal caregivers, child-related interpersonal stress appears to be a key psychological predictor of stress-related CVD risk.

A population of atypical CD56(-)CD16(+) natural killer cells is expanded in PTSD and is associated with symptom severity.

INTRODUCTION: Post-traumatic stress disorder (PTSD) has been associated with immune disturbances, including a higher incidence of infections and autoimmune diseases as well as a net pro-inflammatory state. Natural killer (NK) cells, a key component of the innate immune system, have been less well-studied in PTSD despite their importance in immunity. METHODS: We studied two independent samples of combat-exposed male war veterans with or without PTSD, the first ("Discovery Sample") to generate hypotheses, and the second ("Validation Sample") to replicate the findings. The Discovery Sample was comprised of 42 PTSD subjects and 42 controls. The Validation Sample was comprised of 25 PTSD subjects and 30 controls. Participants had fasting, morning blood samples collected for examination of the frequency of NK cell subsets, determined by flow cytometry. The current and lifetime Clinician Administered PTSD Scale (CAPS) was used to assess symptom severity. Statistical analyses were adjusted for age and BMI. RESULTS: PTSD subjects compared to controls had (i) a significantly higher relative frequency of atypical CD56(-)CD16(+) NK cells in the Discovery Sample (p=0.027), which was replicated in the Validation Sample (p=0.004) and the combined sample (p<0.001), and (ii) a non-significantly lower relative frequency of CD56(bright)CD16(-) NK cells in the two samples (p=0.082; p=0.118), which became statistically significant in the combined sample (p=0.020). Further, within subjects with PTSD of both samples, the relative frequency of atypical CD56(-)CD16(+) NK cells was near significantly positively correlated with lifetime PTSD severity (p=0.074). DISCUSSION: This study is the first to characterize NK cell subsets in individuals with PTSD. The results suggest that combat-exposed men with PTSD exhibit an aberrant profile of NK cells with significantly higher frequencies of an atypical population of CD56(-)CD16(+) cells and possibly lower frequencies of the functional CD56(bright)CD16(-) NK cell subsets. Higher proportions of dysfunctional CD56(-)CD16(+) cells have been reported in certain chronic viral infections and in senescent individuals. It is possible that this could contribute to immune dysfunctions and prematurely senescent phenotypes seen in PTSD.

Reduced reward-driven eating accounts for the impact of a mindfulness-based diet and exercise intervention on weight loss: Data from the SHINE randomized controlled trial.

Many individuals with obesity report over eating despite intentions to maintain or lose weight. Two barriers to long-term weight loss are reward-driven eating, which is characterized by a lack of control over eating, a preoccupation with food, and a lack of satiety; and psychological stress. Mindfulness training may address these barriers by promoting awareness of hunger and satiety cues, self-regulatory control, and stress reduction. We examined these two barriers as potential mediators of weight loss in the Supporting Health by Integrating Nutrition and Exercise (SHINE) randomized controlled trial, which compared the effects of a 5.5-month diet and exercise intervention with or without mindfulness training on weight loss among adults with obesity. Intention-to-treat multiple mediation models tested whether post-intervention reward-driven eating and psychological stress mediated the impact of intervention arm on weight loss at 12- and 18-months post-baseline among 194 adults with obesity (BMI: 30-45). Mindfulness (relative to control) participants had significant reductions in reward-driven eating at 6 months (post-intervention), which, in turn, predicted weight loss at 12 months. Post-intervention reward-driven eating mediated 47.1% of the total intervention arm effect on weight loss at 12 months [ß = -0.06, SE(ß) = 0.03, p = .030, 95% CI (-0.12, -0.01)]. This mediated effect was reduced when predicting weight loss at 18 months (p = .396), accounting for 23.0% of the total intervention effect, despite similar weight loss at 12 months. Psychological stress did not mediate the effect of intervention arm on weight loss at 12 or 18 months. In conclusion, reducing reward-driven eating, which can be achieved using a diet and exercise intervention that includes mindfulness training, may promote weight loss (clinicaltrials.gov registration: NCT00960414).

Brief exposure to secondhand smoke reversibly impairs endothelial vasodilatory function.

INTRODUCTION: We sought to determine the effects of brief exposures to low concentrations of tobacco secondhand smoke (SHS) on arterial flow-mediated dilation (FMD, a nitric oxide-dependent measure of vascular endothelial function), in a controlled animal model never before exposed to smoke. In humans, SHS exposure for 30 min impairs FMD. It is important to gain a better understanding of the acute effects of exposure to SHS at low concentrations and for brief periods of time. METHODS: We measured changes in FMD in rats exposed to a range of real-world levels of SHS for durations of 30 min, 10 min, 1 min, and 4 breaths (roughly 15 s). RESULTS: We observed a dose-response relationship between SHS particle concentration over 30 min and post-exposure impairment of FMD, which was linear through the range typically encountered in smoky restaurants and then saturated at higher concentrations. One min of exposure to SHS at moderate concentrations was sufficient to impair FMD. CONCLUSIONS: Brief SHS exposure at real-world levels reversibly impairs FMD. Even 1 min of SHS exposure can cause reduction of endothelial function.

Anger is associated with increased IL-6 stress reactivity in women, but only among those low in social support.

BACKGROUND: Social connections moderate the effects of high negative affect on health. Affective states (anger, fear, and anxiety) predict interleukin-6 (IL-6) reactivity to acute stress; in turn, this reactivity predicts risk of cardiovascular disease progression. PURPOSE: Here, we examined whether perceived social support mitigates the relationship between negative affect and IL-6 stress reactivity. METHOD: Forty-eight postmenopausal women completed a standardized mental lab stressor with four blood draws at baseline and 30, 50, and 90 min after the onset of the stressor and anger, anxiety, and fear were assessed 10 min after task completion. Participants self-rated levels of social support within a week prior to the stressor. RESULTS: Only anger was related to IL-6 stress reactivity-those experiencing high anger after the stressor had significant increases in IL-6. IL-6 reactivity was marginally associated with perceived support, but more strikingly, perceived support mitigated anger associations with IL-6 stress reactivity. CONCLUSION: Supportive ties can dampen the relationship of anger to pro-inflammatory reactivity to acute stress. Implications to cardiovascular disease are discussed.

Linking disease symptoms and subtypes with personalized systems-based phenotypes: a proof of concept study.

A dynamic systems model was used to generate parameters describing a phenotype of Hypothalamic-Pituitary-Adrenal (HPA) behavior in a sample of 36 patients with chronic fatigue syndrome (CFS) and/or fibromyalgia (FM) and 36 case-matched healthy controls. Altered neuroendocrine function, particularly in relation to somatic symptoms and poor sleep quality, may contribute to the pathophysiology of these disorders. Blood plasma was assayed for cortisol and ACTH every 10 min for 24h. The dynamic model was specified with an ordinary differential equation using three parameters: (1) ACTH-adrenal signaling, (2) inhibitory feedback, and (3) non-ACTH influences. The model was "personalized" by estimating an individualized set of parameters from each participant's data. Day and nighttime parameters were assessed separately. Two nocturnal parameters (ACTH-adrenal signaling and inhibitory feedback) significantly differentiated the two patient subgroups ("fatigue-predominant" patients with CFS only versus "pain-predominant" patients with FM and comorbid chronic fatigue) from controls (all p's<.05), whereas daytime parameters and diurnal/nocturnal slopes did not. The same nocturnal parameters were significantly associated with somatic symptoms among patients (p's<.05). There was a significantly different pattern of association between nocturnal non-ACTH influences and sleep quality among patients versus controls (p<.05). Although speculative, the finding that patient somatic symptoms decreased when more cortisol was produced per unit ACTH, is consistent with cortisol's anti-inflammatory and sleep-modulatory effects. Patients' HPA systems may compensate by promoting more rapid or sustained cortisol production. Mapping "behavioral phenotypes" of stress-arousal systems onto symptom clusters may help disentangle the pathophysiology of complex disorders with frequent comorbidity.

Sparse System Identification of Leptin Dynamics in Women With Obesity.

The prevalence of obesity is increasing around the world at an alarming rate. The interplay of the hormone leptin with the hypothalamus-pituitary-adrenal axis plays an important role in regulating energy balance, thereby contributing to obesity. This study presents a mathematical model, which describes hormonal behavior leading to an energy abnormal equilibrium that contributes to obesity. To this end, we analyze the behavior of two neuroendocrine hormones, leptin and cortisol, in a cohort of women with obesity, with simplified minimal state-space modeling. Using a system theoretic approach, coordinate descent method, and sparse recovery, we deconvolved the serum leptin-cortisol levels. Accordingly, we estimate the secretion patterns, timings, amplitudes, number of underlying pulses, infusion, and clearance rates of hormones in eighteen premenopausal women with obesity. Our results show that minimal state-space model was able to successfully capture the leptin and cortisol sparse dynamics with the multiple correlation coefficients greater than 0.83 and 0.87, respectively. Furthermore, the Granger causality test demonstrated a negative prospective predictive relationship between leptin and cortisol, 14 of 18 women. These results indicate that increases in cortisol are prospectively associated with reductions in leptin and vice versa, suggesting a bidirectional negative inhibitory relationship. As dysregulation of leptin may result in an abnormality in satiety and thereby associated to obesity, the investigation of leptin-cortisol sparse dynamics may offer a better diagnostic methodology to improve better treatments plans for individuals with obesity.

Corrigendum: Sparse system identification of leptin dynamics in women with obesity.

[This corrects the article DOI: 10.3389/fendo.2022.769951.].

Adversity in early life and pregnancy are immunologically distinct from total life adversity: macrophage-associated phenotypes in women exposed to interpersonal violence.

Early childhood and pregnancy are two sensitive periods of heightened immune plasticity, when exposure to adversity may disproportionately increase health risks. However, we need deeper phenotyping to disentangle the impact of adversity during sensitive periods from that across the total lifespan. This study examined whether retrospective reports of adversity during childhood or pregnancy were associated with inflammatory imbalance, in an ethnically diverse cohort of 53 low-income women seeking family-based trauma treatment following exposure to interpersonal violence. Structured interviews assessed early life adversity (trauma exposure ≤ age 5), pregnancy adversity, and total lifetime adversity. Blood serum was assayed for pro-inflammatory (TNF-a, IL-1ß, IL-6, and CRP) and anti-inflammatory (IL-1RA, IL-4, and IL-10) cytokines. CD14+ monocytes were isolated in a subsample (n = 42) and gene expression assayed by RNA sequencing (Illumina HiSeq 4000; TruSeq cDNA library). The primary outcome was a macrophage-associated M1/M2 gene expression phenotype. To evaluate sensitivity and specificity, we contrasted M1/M2 gene expression with a second, clinically-validated macrophage-associated immunosuppressive phenotype (endotoxin tolerance) and with pro-inflammatory and anti-inflammatory cytokine levels. Adjusting for demographics, socioeconomic status, and psychopathology, higher adversity in early life (ß = .337, p = 0.029) and pregnancy (ß = .332, p = 0.032) were each associated with higher M1/M2 gene expression, whereas higher lifetime adversity (ß = -.341, p = 0.031) was associated with lower immunosuppressive gene expression. Adversity during sensitive periods was uniquely associated with M1/M2 imbalance, among low-income women with interpersonal violence exposure. Given that M1/M2 imbalance is found in sepsis, severe COVID-19 and myriad chronic diseases, these findings implicate novel immune mechanisms underlying the impact of adversity on health.

Longitudinal hair cortisol in low-income young children: A useful biomarker of behavioral symptom change?

Early childhood is a developmental period characterized by significant plasticity, heterogeneity in behaviors and biological functioning. Yet, cumulative cortisol secretion, as measured by hair cortisol, has not been examined longitudinally in relation to change in behavioral problems in young children. The current study examined cross-sectional and longitudinal associations between hair cortisol and changes in behavior problems in a combined sample (N = 88) of two groups of young children from low-income families: 1) A trauma-exposed sample that participated in Child-Parent Psychotherapy (CPP) (n = 43; Mean Age = 4.31, SD = 1.16; 53% Female; 77% Hispanic), and 2) A community sample of children from families experiencing high stress (n = 45; Mean Age = 3.20, SD = 0.29; 67% Female; 58% Hispanic). Cortisol was assayed from hair collected from children at baseline and, on average, one year later. Mothers completed the Child Behavior Checklist at the same time hair samples were collected. Baseline hair cortisol in children was not associated with maternally-reported child behavioral problems at baseline and did not predict change in behavior problems over time. In contrast, increases in cortisol were associated with greater improvement in child behavior problems (b = -2.98, p < 0.05), controlling for group status and relevant covariates. Subgroup analyses showed that cortisol change across one year significantly differed between the two groups (p = 0.043): on average, community children exhibited a decrease, whereas CPP children demonstrated no change. Hair cortisol concentration was similarly related to improvements in mother-reported behavior problems across both CPP and community groups over time. In summary, there were no cross-sectional associations with hair cortisol, whereas increases were associated with improved child well-being. Findings demonstrate an important link between this increasingly common biomarker and child health, but suggest that changes over time may be more informative than cross-sectional associations.

Machine learning prediction of blood alcohol concentration: a digital signature of smart-breathalyzer behavior.

Excess alcohol use is an important determinant of death and disability. Machine learning (ML)-driven interventions leveraging smart-breathalyzer data may help reduce these harms. We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals' breath alcohol concentration (BrAC) levels trained on data from a smart breathalyzer. We analyzed roughly one million datapoints from 33,452 users of a commercial smart-breathalyzer device, collected between 2013 and 2017. For validation, we analyzed the associations between state-level observed smart-breathalyzer BrAC levels and impaired-driving motor vehicle death rates. Behavioral, geolocation-based, and time-series-derived features were fed to an ML algorithm using training (70% of the cohort), development (10% of the cohort), and test (20% of the cohort) sets to predict the likelihood of a BrAC exceeding the legal driving limit (0.08 g/dL). States with higher average BrAC levels had significantly higher alcohol-related driving death rates, adjusted for the number of users per state B (SE) = 91.38 (15.16), p < 0.01. In the independent test set, the ML algorithm predicted the likelihood of a given user-initiated BrAC sample exceeding BrAC ≥ 0.08 g/dL, with an area under the curve (AUC) of 85%. Highly predictive features included users' prior BrAC trends, subjective estimation of their BrAC (or AUC = 82% without the self-estimate), engagement and self-monitoring, time since the last measure, and hour of the day. In conclusion, an ML algorithm successfully quantified a digital phenotype of behavior, predicting naturalistic BrAC levels exceeding 0.08 g/dL (a threshold associated with alcohol-related harm) with good discrimination capability. This result establishes a foundation for future research on precision behavioral medicine digital health interventions using smart breathalyzers and passive monitoring approaches.

A digital biomarker of diabetes from smartphone-based vascular signals.

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.