INTRODUCTION: Physicians die by suicide at rates higher than the general population, with the increased risk beginning in medical school. To better understand why, this study examined the prevalence of mental distress (e.g., depressive symptoms and suicide risk) and behavioral and psychosocial risk factors for distress, as well as the associations between mental distress and risk factors among a sample of medical students in a pre-COVID-19-era. METHODS: Students enrolled in a large California medical school in 2018-2019 (N = 134; 52% female) completed questionnaires assessing sociodemographic characteristics, depression and suicide family history, health behaviors, and psychosocial wellbeing. Assessment scores indexing mental distress (e.g., depressive symptoms, thoughts of suicide in the past 12 months, suicide risk, and history of suicidality) and risk factors (e.g., stress, subjective sleep quality, alcohol use, impostor feelings, and bill payment difficulty) were compared across biological sex using chi-squared tests, and associations between mental distress and risk factors were determined through logistic regression. RESULTS: Elevated mental distress indicators were observed relative to the general public (e.g., 16% positive depression screen, 17% thought about suicide in previous 12 months, 10% positive suicide risk screen, and 34% history of suicidality), as well as elevated risk factors [e.g., 55% moderate or high stress, 95% at least moderate impostor feelings, 59% poor sleep quality, 50% screened positive for hazardous drinking (more likely in females), and 25% difficulty paying bills]. A positive depression screen was associated with higher stress, higher impostor feelings, poorer sleep quality, and difficulty paying bills. Suicidal ideation in the previous 12 months, suicide risk, and a history of suicidality were independently associated with higher levels of impostor feelings. DISCUSSION: Higher scores on assessments of depressive symptoms and suicidal thoughts and behaviors were related to several individual-level and potentially modifiable risk factors (e.g., stress, impostor feelings, sleep quality, and bill payment difficulties). Future research is needed to inform customized screening and resources for the wellbeing of the medical community. However, it is likely that the modification of individual-level risk factors is limited by the larger medical culture and systems, suggesting that successful interventions mitigate suicide risk for medical providers need to address multiple socio-ecological levels.

Best practice standards for measuring analyte levels in saliva recommend that all biospecimens be tested in replicate with mean concentrations used in statistical analyses. This approach prioritizes minimizing laboratory-based measurement error but, in the process, expends considerable resources. We explore the possibility that, due to advances in salivary assay precision, the contribution of laboratory-based measurement error in salivary analyte data is very small relative to more important and meaningful variability in analyte levels across biological replicates (i.e., between different specimens). To evaluate this possibility, we examine the utility of the repeatability intra-class correlation (rICC) as an additional index of salivary analyte data precision. Using randomly selected subsamples (Ns=200 and 60) of salivary analyte data collected as part of a larger epidemiologic study, we compute the rICCs for seven commonly assayed salivary measures in biobehavioral research - cortisol, alpha-amylase, c-reactive protein, interlekin-6, uric acid, secretory immunoglobulin A, and testosterone. We assess the sensitivity of rICC estimates to assay type and the unique distributions of the underlying analyte data. We also use simulations to examine the bias, precision, and coverage probability of rICC estimates calculated for small to large sample sizes. For each analyte, the rICCs revealed that less than 5% of variation in analyte levels was attributable to laboratory-based measurement error. rICC estimates were similar across all analytes despite differences in analyte levels, average intra-assay coefficients of variation, and in the distributional properties of the data. Guidelines for calculating rICC are provided to enable investigators and laboratory staff to apply this metric and more accurately quantify, and communicate, the magnitude of laboratory-based measurement error in their data. By helping investigators scale measurement error relative to more scientifically meaningful variability between biological replicates, the application of the rICC has the potential to influence research strategies and tactics such that resources (e.g., finances, effort, number/volume of biospecimens) are allocated more efficiently and effectively.

Left censoring in salivary bioscience data occurs when salivary analyte determinations fall below the lower limit of an assay's measurement range. Conventional statistical approaches for addressing censored values (i.e., recoding as missing, substituting or extrapolating values) may introduce systematic bias. While specialized censored data statistical approaches (i.e., Maximum Likelihood Estimation, Regression on Ordered Statistics, Kaplan-Meier, and general Tobit regression) are available, these methods are rarely implemented in biobehavioral studies that examine salivary biomeasures, and their application to salivary data analysis may be hindered by their sensitivity to skewed data distributions, outliers, and sample size. This study compares descriptive statistics, correlation coefficients, and regression parameter estimates generated via conventional and specialized censored data approaches using salivary C-reactive protein data. We assess differences in statistical estimates across approach and across two levels of censoring (9% and 15%) and examine the sensitivity of our results to sample size. Overall, findings were similar across conventional and censored data approaches, but the implementation of specialized censored data approaches was more efficient (i.e., required little manipulations to the raw analyte data) and appropriate. Based on our review of the findings, we outline preliminary recommendations to enable investigators to more efficiently and effectively reduce statistical bias when working with left-censored salivary biomeasure data.

Despite the integration of salivary inflammatory cytokines into research across the biobehavioral, psychological, clinical, and health-related disciplines, there is little guidance regarding the biospecimen collection, handling, and storage practices that maximize the quality and validity of salivary cytokine data. Furthermore, associations between salivary cytokines and measures related to oral health are rarely assessed and accounted for in studies outside the oral health fields. To address these gaps, we examine the sensitivity of salivary interleukin-1β (IL-1β), IL-6, IL-8, and tumor necrosis factor-α (TNF-α) to changes in saliva sample collection technique and cold chain management procedures. Using subsets of saliva samples collected from 150 healthy adults, we measure salivary IL-1β, IL-6, IL-8, TNF-α, and other oral health-related indices (i.e., blood contamination [transferrin], and salivary matrixmallotprotienase-8). In addition to examining changes in cytokine levels associated with sample collection technique and cold chain management procedures, we assess relations between cytokine concentrations and levels of other oral health-related measures. We found that IL-1β, IL-6, and IL-8 were more robust to changes in sample collection and cold chain management procedures than TNF-α, and all cytokines were positively associated with other oral health-related measures. Based on our findings, we recommend analyte-specific guidance for measuring and interpreting salivary cytokine concentrations.

BACKGROUND: Exposure to environmental tobacco smoke (ETS) has been associated with detectable levels of cotinine (a nicotine metabolite) in childrens saliva. However, tobacco smoke also contains toxic and essential trace metals, including chromium (Cr), copper (Cu), lead (Pb), manganese (Mn), nickel (Ni) and zinc (Zn). OBJECTIVE: The current study examines whether there is a relationship between ETS exposure, as gauged by salivary cotinine, and salivary levels of these metals in a subset (n = 238) of children from the Family Life Project. METHODS: Using inductively-coupled-plasma optical emission spectrophotometry, we measured levels of metals in saliva from children at ~90 months of age. Salivary cotinine was measured using a commercial immunoassay. RESULTS: We found that Cr, Cu, Mn, and Zn were detected in most samples (85-99%) with lower levels of detection for Pb and Ni (9.3% and 13.9% respectively). There were no significant differences in any of the metal concentrations between males and females, nor were levels associated with body mass index, although significant differences in salivary Cr and Mn by race, state and income-to-needs ratio were observed. Children with cotinine levels >1 ng/ml had higher levels of Zn (b = 0.401, 95% CI: 0.183 to 0.619; p = 0.0003) and Cu (b = 0.655, 95% CI: 0.206 to 1.104; p = 0.004) compared to children with levels <1 ng/ml, after controlling for multiple confounders, including sex, race, BMI and income-to-needs ratio. Further, we show that children whose cotinine levels were >1 μg/L were more likely to have detectable levels of Pb in their saliva (b = 1.40, 95% CI: 0.424 to 2.459; p = 0.006) compared to children with cotinine levels <1 ng/ml, also considering confounders. IMPACT STATEMENT: This is the first study to demonstrate significant associations between salivary cotinine and salivary levels of Cu, Zn and Pb, suggesting that environmental tobacco smoke exposure my be one source of increased childrens exposure to heavy metals. This study also demonstrates that saliva samples can be used to measure heavy metal exposure, and thus serve as a non-invasive tool for assessing a broader range of risk indicators.

Human cytomegalovirus (HCMV) infects more than 80% of the global population. While mostly asymptomatic, HCMV infection can be serious among the immunocompromised, and it is implicated in chronic disease pathophysiology in adulthood. Large-scale minimally invasive HCMV screening could advance research and public health efforts to monitor infection prevalence and prevent or mitigate downstream risks associated with infection. We examine the utility of measuring HCMV immunoglobulin-G (IgG) levels in saliva as an index of serum levels. Matched serum and saliva samples from healthy adults (N = 98; 44% female; 51% white) were assayed for HCMV IgG, total salivary protein, and salivary markers related to oral inflammation, blood, and tissue integrity. We examine the serum-saliva association for HCMV IgG and assess the influence of participant characteristics and factors specific to the oral compartment (e.g., oral inflammation) on HCMV IgG levels and cross-specimen relations. We found a robust serum-saliva association for HCMV IgG with serum antibody levels accounting for >60% of the variance in salivary levels. This relation remained after adjusting for key demographic and oral immune-related variables. Compared to the serum test, the salivary HCMV IgG test had 51% sensitivity and 97% specificity. With improvements in assay performance and sample optimization, HCMV antibody levels in oral fluids may be a useful proxy for serum levels.

BACKGROUND: Alcohol and tobacco are known teratogens. Historically, more severe prenatal alcohol exposure (PAE) and prenatal tobacco exposure (PTE) have been examined as the principal predictor of neurodevelopmental alterations, with little incorporation of lower doses or ecological contextual factors that can also impact neurodevelopment, such as socioeconomic resources (SER) or adverse childhood experiences (ACEs). Here, a novel analytical approach informed by a socio-ecological perspective was used to examine the associations between SER, PAE and/or PTE, and ACEs, and their effects on neurodevelopment. METHODS: N = 313 mother-child dyads were recruited from a prospective birth cohort with maternal report of PAE and PTE, and cross-sectional structural brain neuroimaging of child acquired via 3T scanner at ages 8-11 years. In utero SER was measured by maternal education, household income, and home utility availability. The childs ACEs were measured by self-report assisted by the researcher. PAE was grouped into early exposure (<12 weeks), continued exposure (>=12 weeks), and no exposure controls. PTE was grouped into exposed and non-exposed controls. RESULTS: Greater access to SER during pregnancy was associated with fewer ACEs (maternal education: β = -0.293,p = 0.01; phone access: β = -0.968,p = 0.05). PTE partially mediated the association between SER and ACEs, where greater SER reduced the likelihood of PTE, which was positively associated with ACEs (β = 1.110,p = 0.01). SER was associated with alterations in superior frontal (β = -1336.036, q = 0.046), lateral orbitofrontal (β = -513.865, q = 0.046), caudal anterior cingulate volumes (β = -222.982, q = 0.046), with access to phone negatively associated with all three brain volumes. Access to water was positively associated with superior frontal volume (β=1569.527, q = 0.013). PTE was associated with smaller volumes of lateral orbitofrontal (β = -331.000, q = 0.033) and nucleus accumbens regions (β = -34.800, q = 0.033). CONCLUSION: Research on neurodevelopment following community-levels of PAE and PTE should more regularly consider the ecological context to accelerate understanding of teratogenic outcomes. Further research is needed to replicate this novel conceptual approach with varying PAE and PTE patterns, to disentangle the interplay between dose, community-level and individual-level risk factors on neurodevelopment.