Behavioral Diagnostics (United States)

Background: Due to variability in patterns of consumption as well as well-known difficulties in obtaining valid self-report from heavy drinkers, quantifying the effects of heavy alcohol consumption on mortality is challenging. Using a DNA methylation biomarker of chronic heavy alcohol consumption (HAC) named the alcohol T-score (ATS), we previously showed that chronic HAC was a strong predictor of mortality. However, whether there is a similar effect when measures of shorter-term heavy alcohol use, i.e., recent “binge” drinking, were used to predict mortality was not examined. This is a critical issue because most biomarkers of HAC assess only short-term HAC. Methods: Therefore, we examined the prediction of all-cause mortality from a DNA methylation biomarker of smoking (cg05575921), the ATS and a short-term biomarker of recent heavy alcohol use (cg07375256) in 708 subjects from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial using Cox Proportional Hazards Regression Modeling. Models were compared using Akaike’s Information Criterion. Results: The ATS was the best single predictor among three-variable models that included controls for sex and age. Of the possible four variable models, the model consisting of age, sex, cg05575921 methylation and ATS best predicted mortality. The addition of cg07375256 methylation did not improve model performance. In sensitivity analyses using only participants who provided alcohol SR (n = 639), the importance of the ATS and cg05575921 was replicated. We also found that ATS values were higher among those who declined to provide self-report alcohol use, indicating that missing self-report data about alcohol intake are not missing at random, and sometimes reflects elevated alcohol consumption. Finally, cg05575921 methylation was strongly associated with ATS values but only weakly with alcohol SR and not at all with cg07375256 methylation. Conclusions: Accordingly, this study indicates a strong effect of chronic HAC, but not short-term HAC, on mortality, further highlights the limitations of self-reported alcohol use in the prediction of all-cause mortality and indicates the value of assessing HAC in addition to smoking.

<bold>Introduction:</bold> Patients with advanced COPD and pulmonary emphysema undergo endoscopic valve implantation to improve symptoms and quality of life. As a side effect, some patients report an increase in mucus production due to the implantation of foreign material. Mucus scoring on chest computed tomography (CT) is an objective method of quantifying mucus load. <bold>Aim:</bold> The prospective, single-center MMCOPD study aims to characterize the microbiome, metabolome, mucus load, symptoms (COPD assessment test, CAT) and exacerbation history of COPD patients before and after bronchoscopic interventions. Chest CT scan-based mucus scoring was performed to assess mucus load before and after endoscopic valve implantation, separately for each lung segment. <bold>Results:</bold> 50 patients (emphysema index 34 ± 10%) were analyzed with a mean total mucus score of 5.4 ± 4.9 plugs. 2.9% of all segments were unscorable due to highly diseased regions or motion artifact. 58% of all patients had ≥3 mucus plugs in total. Mucus plugs were more prevalent in lower lobes (highest score in segment LB10) than in upper lobes. For 42 patients with a follow up CT scan, mucus score decreased significantly from 5.5 ± 3.6 to 3.6 ± 4.4 plugs (p=0.002). After exclusion of all unscorable segments the results were still significant. The first two questions on cough and sputum in the CAT score did not change significantly. <bold>Conclusion:</bold> Mucus load is high in patients with pulmonary emphysema. Unexpectedly and fortunately, the mucus score on the chest CT scan decreased after endoscopic valve implantation. This finding needs further evaluation and interpretation.

Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.

ABSTRACT Smoking and Heavy Alcohol Consumption (HAC) are established risk factors for myriad complex disorders of ageing. Yet many prior studies of Epigenetic Ageing (EA) have shown only modest effects of smoking and drinking on accelerated ageing. One potential reason for this conundrum might be the reliance of some prior EA studies on self-reported substance use, which may be unreliable in many samples. To test whether novel, non-self-reported indices would show a stronger association of smoking and HAC to EA, we used methylation sensitive digital PCR (MSdPCR) and data from 437 African American subjects from Wave 7 of the Family and Community Health Study Offspring Cohort to examine the effects of subjective and objective measures of smoking and HAC on 7 indices of EA. Because of limited overall correlations between the various EA indices, we examined patterns of association separately for each index. Consistent with expectations, MSdPCR assessments of smoking and HAC, but not self-reported alcohol consumption, were strongly correlated with accelerated EA. MSdPCR assessments of smoking and HAC accounted for 57% of GrimAge acceleration and the shared variance in GrimAge and DunedinPOAM accelerated EA. We conclude that MSdPCR assessments of smoking and HAC are valuable tools for understanding EA, represent directly targetable conditions for the prevention of premature ageing, and substantially improve upon self-reported assessment of smoking and HAC.

Healthcare is a large contributor to greenhouse gas (GHG) emissions around the world, given current power generation mix. Telemedicine, with its reduced travel for providers and patients, has been proposed to reduce emissions. Artificial intelligence (AI), and especially autonomous AI, where the medical decision is made without human oversight, has the potential to further reduce healthcare GHG emissions, but concerns have also been expressed about GHG emissions from digital technology, and AI training and inference. In a real-world example, we compared the marginal GHG contribution of an encounter performed by an autonomous AI to that of an in-person specialist encounter. Results show that an 80% reduction may be achievable, and we conclude that autonomous AI has the potential to reduce healthcare GHG emissions.

ABSTRACT Increasing use of non-combusted forms of nicotine such as e-cigarettes poses important public health questions regarding their specific risks relative to combusted tobacco products such as cigarettes. To fully delineate these risks, improved biomarkers that can distinguish between these forms of nicotine use are needed. Prior work has suggested that methylation status at cg05575921 may serve as a specific biomarker of combusted tobacco smoke exposure. We hypothesized combining this epigenetic biomarker with conventional metabolite assays could classify the type of nicotine product consumption. Therefore, we determined DNA methylation and serum cotinine values in samples from 112 smokers, 35 e-cigarette users, 19 smokeless tobacco users, and 269 controls, and performed mass spectroscopy analyses of urine samples from all nicotine users and 22 verified controls to determine urinary levels of putatively nicotine product-specific substances; propylene glycol, 2-cyanoethylmercapturic acid (CEMA), and anabasine. 1) Cigarette smoking was associated with a dose dependent demethylation of cg05575921 and increased urinary CEMA and anabasine levels, 2) e-cigarette use did not demethylate cg05575921, 3) smokeless tobacco use also did not demethylate cg05575921 but was positively associated with anabasine levels 4) CEMA and cg05575921 levels were highly correlated and 5) propylene glycol levels did not reliably distinguish use groups. Cg05575921 assessments distinguish exposure to tobacco smoke from smokeless sources of nicotine including e-cigarettes and smokeless tobacco, neither of which are associated with cg05575921 demethylation. A combination of methylomic and metabolite profiling may allow for accurate classification use status of a variety of nicotine containing products.

Identifying the mechanisms linking early experiences, genetic risk factors, and their interaction with later health consequences is central to the development of preventive interventions and identifying potential boundary conditions for their efficacy. In the current investigation of 412 African American adolescents followed across a 20-year period, we examined change in body mass index (BMI) across adolescence as one possible mechanism linking childhood adversity and adult health. We found associations of childhood adversity with objective indicators of young adult health, including a cardiometabolic risk index, a methylomic aging index, and a count of chronic health conditions. Childhood adversities were associated with objective indicators indirectly through their association with gains in BMI across adolescence and early adulthood. We also found evidence of an association of genetic risk with weight gain across adolescence and young adult health, as well as genetic moderation of childhood adversity's effect on gains in BMI, resulting in moderated mediation. These patterns indicated that genetic risk moderated the indirect pathways from childhood adversity to young adult health outcomes and childhood adversity moderated the indirect pathways from genetic risk to young adult health outcomes through effects on weight gain during adolescence and early adulthood.

The gut microbiome is now considered an organ unto itself and plays an important role in health maintenance and recovery from critical illness. The commensal organisms responsible for the framework of the gut microbiome are valuable in protection against disease and various physiological tasks. Critical illness and the associated interventions have a detrimental impact on the microbiome. While antimicrobials are one of the fundamental and often life-saving modalities in septic patients, they can also pave the way for subsequent harm because of the resulting damage to the gut microbiome. Contributing to many of the non-specific signs and symptoms of sepsis, the balance between the overuse of antimicrobials and the clinical need in these situations is often difficult to delineate. Given the potency of antimicrobials utilized to treat septic patients, the effects on the gut microbiome are often rapid and long-lasting, in which case full recovery may never be observed. The overgrowth of opportunistic pathogens is of significant concern as they can lead to infections that become increasingly difficult to treat. Continued research to understand the disturbances within the gut microbiome of critically ill patients and their outcomes is essential to help develop future therapies to circumvent damage to, or restore, the microbiome. In this review, we discuss the impact of the antimicrobials often used for the treatment of sepsis on the gut microbiota.

The look and feel of Dialectical Behaviour Therapy (DBT) is heavily influenced by the structure of the programme on which each session and meeting rest. Programme structure has always been a focus for Linehan, the treatment developer, who wove recommendations throughout her descriptions of the treatment. This chapter attempts to pull together in one place recommendations from the literature, real-world practice, and guidance of the model developer and experts to assist team leaders who are creating a DBT programme, or who wish to review their existing programme structure. Decisions about programme structure should be driven by a deep understanding of Linehan’s Functions of Treatment, as well as the principles and practices of behaviour therapy, and DBT in particular. A programme manual and routinization of administrative practices ensure that programme structure is well defined, supportive for clinicians and clients, and resistant to treatment drift.