Plasma proteomic biomarkers and the association between poor cardiovascular health and incident dementia: The UK Biobank study.

BACKGROUND: The study examined how plasma proteome indicators may explain the link between poor cardiovascular health (CVH) and dementia risk. METHODS: The present study involved 28,974 UK Biobank participants aged 50-74y at baseline (2006-2010) who were followed-up for ≤ 15 y for incidence of dementia. CVH was calculated using Life's Essential 8 (LE8) total scores. The scores were standardized and reverse coded to reflect poor CVH (LE8z_rev). OLINK proteomics was available on this sample (k = 1,463 plasma proteins). The study primarily tested the mediating effects of the plasma proteome in LE8z_rev-dementia effect. The total effect was decomposed into "mediation only" or pure indirect effect (PIE), "interaction only" or interaction referent (INTREF), "neither mediation nor interaction" or controlled direct effect (CDE), and "both mediation and interaction" or mediated interaction (INTMED). RESULTS: The study found poorer CVH assessed by LE8z_rev increased the risk of all-cause dementia by 11 % [per 1 SD, hazard ratio, (HR) = 1.11, 95 % CI: 1.03-1.20, p = 0.005). The study identified 11 plasma proteins with strong mediating effects, with GDF15 having the strongest association with dementia risk (per 1 SD, HR = 1.24, 95 % CI: 1.16, 1.33, P < 0.001 when LE8z_rev is set at its mean value) and the largest proportion mediated combining PIE and INTMED (62.6 %; 48 % of TE is PIE), followed by adrenomedullin or ADM. A first principal component with 10 top mediators (TNFRSF1A, GDF15, FSTL3, COL6A3, PLAUR, ADM, GFRAL, ACVRL1, TNFRSF6B, TGFA) mediated 53.6 % of the LE8z_rev-dementia effect. Using all the significant PIE (k = 526) proteins, we used OLINK Insight pathway analysis to identify key pathways, which revealed the involvement of the immune system, signal transduction, metabolism, disease, protein metabolism, hemostasis, membrane trafficking, extracellular matrix organization, developmental biology, and gene expression among others. STRING analysis revealed that five top consistent proteomic mediators were represented in two larger clusters reflecting numerous interconnected biological gene ontology pathways, most notably cytokine-mediated signaling pathway for GDF15 cluster (GO:0019221) and regulation of peptidyl-tyrosine phosphorylation for the ADM cluster (GO:0050730). CONCLUSION: Dementia is linked to poor CVH mediated by GDF15 and ADM among several key proteomic markers which collectively explained âˆ¼ 54 % of the total effect.

Pathways explaining racial/ethnic and socio-economic disparities in brain white matter integrity outcomes in the UK Biobank study.

Pathways explaining racial/ethnic and socio-economic status (SES) disparities in white matter integrity (WMI) reflecting brain health, remain underexplored, particularly in the UK population. We examined racial/ethnic and SES disparities in diffusion tensor brain magnetic resonance imaging (dMRI) markers, namely global and tract-specific mean fractional anisotropy (FA), and tested total, direct and indirect effects through lifestyle, health-related and cognition factors using a structural equations modeling approach among 36,184 UK Biobank participants aged 40-70 y at baseline assessment (47% men). Multiple linear regression models were conducted, testing independent associations of race/ethnicity, socio-economic and other downstream factors in relation to global mean FA, while stratifying by Alzheimer's Disease polygenic Risk Score (AD PRS) tertiles. Race (Non-White vs. White) and lower SES predicted poorer WMI (i.e. lower global mean FA) at follow-up, with racial/ethnic disparities in FAmean involving multiple pathways and SES playing a central role in those pathways. Mediational patterns differed across tract-specific FA outcomes, with SES-FAmean total effect being partially mediated (41% of total effect = indirect effect). Furthermore, the association of poor cognition with FAmean was markedly stronger in the two uppermost AD PRS tertiles compared to the lower tertile (T2 and T3: ß±SE: -0.0009 ± 0.0001 vs. T1: ß±SE: -0.0005 ± 0.0001, P < 0.001), independently of potentially confounding factors. Race and lower SES were generally important determinants of adverse WMI outcomes, with partial mediation of socio-economic disparities in global mean FA through lifestyle, health-related and cognition factors. The association of poor cognition with lower global mean FA was stronger at higher AD polygenic risk.

Chronic Non-cancer Pain and Associated Risks of Incident Mild Cognitive Impairment and Alzheimer's Disease and Related Dementias in Middle-Aged and Older Adults.

The goal of this study is to investigate the association between chronic non-cancer pain (CNCP) and mild cognitive impairment (MCI)/Alzheimer's disease and related dementias (ADRDs) development among adults aged ≥50 using administrative claims data from a national commercial health insurance company during 2007-2017. To reduce selection bias, propensity-score matching was applied to select comparable CNCP and non-CNCP patients. Time-dependent Cox proportional-hazards regressions were conducted to estimate the hazard ratios (HRs) of incident MCI/ADRDs. Of 170,900 patients with/without CNCP, 0.61% developed MCI and 2.33% had been diagnosed with ADRDs during the follow-up period. Controlling for potential confounders, CNCP patients had a 123% increase in MCI risk (HR = 2.23; 95% CI = 1.92-2.58) and a 44% increase in ADRDs risk (HR = 1.44; 95% CI = 1.34-1.54) relative to non-CNCP patients. CNCP is a risk factor for MCI/ADRDs. Promoting awareness and improving early CNCP diagnosis in middle-aged and older adults should be incorporated into cognitive impairment and dementia prevention.

Helicobacter pylori, persistent infection burden and structural brain imaging markers.

Persistent infections, whether viral, bacterial or parasitic, including Helicobacter pylori infection, have been implicated in non-communicable diseases, including dementia and other neurodegenerative diseases. In this cross-sectional study, data on 635 cognitively normal participants from the UK Biobank study (2006-21, age range: 40-70 years) were used to examine whether H. pylori seropositivity (e.g. presence of antibodies), serointensities of five H. pylori antigens and a measure of total persistent infection burden were associated with selected brain volumetric structural MRI (total, white, grey matter, frontal grey matter (left/right), white matter hyperintensity as percent intracranial volume and bi-lateral sub-cortical volumes) and diffusion-weighted MRI measures (global and tract-specific bi-lateral fractional anisotropy and mean diffusivity), after an average 9-10 years of lag time. Persistent infection burden was calculated as a cumulative score of seropositivity for over 20 different pathogens. Multivariable-adjusted linear regression analyses were conducted, whereby selected potential confounders (all measures) and intracranial volume (sub-cortical volumes) were adjusted, with stratification by Alzheimer's disease polygenic risk score tertile when exposures were H. pylori antigen serointensities. Type I error was adjusted to 0.007. We report little evidence of an association between H. pylori seropositivity and persistent infection burden with various volumetric outcomes (P > 0.007, from multivariable regression models), unlike previously reported in past research. However, H. pylori antigen serointensities, particularly immunoglobulin G against the vacuolating cytotoxin A, GroEL and outer membrane protein antigens, were associated with poorer tract-specific white matter integrity (P < 0.007), with outer membrane protein serointensity linked to worse outcomes in cognition-related tracts such as the external capsule, the anterior limb of the internal capsule and the cingulum, specifically at low Alzheimer's disease polygenic risk. Vacuolating cytotoxin A serointensity was associated with greater white matter hyperintensity volume among individuals with mid-level Alzheimer's disease polygenic risk, while among individuals with the highest Alzheimer's disease polygenic risk, the urease serointensity was consistently associated with reduced bi-lateral caudate volumes and the vacuolating cytotoxin A serointensity was linked to reduced right putamen volume (P < 0.007). Outer membrane protein and urease were associated with larger sub-cortical volumes (e.g. left putamen and right nucleus accumbens) at middle Alzheimer's disease polygenic risk levels (P < 0.007). Our results shed light on the relationship between H. pylori seropositivity, H. pylori antigen levels and persistent infection burden with brain volumetric structural measures. These data are important given the links between infectious agents and neurodegenerative diseases, including Alzheimer's disease, and can be used for the development of drugs and preventive interventions that would reduce the burden of those diseases.

Metallothionein Alleviates Glutathione Depletion-Induced Oxidative Cardiomyopathy through CISD1-Dependent Regulation of Ferroptosis in Murine Hearts.

This study was designed to discern the effect of heavy scavenger metallothionein on glutathione (GSH) deprivation-evoked cardiac anomalies and mechanisms involved with an emphasis on ferroptosis. Wild-type and cardiac metallothionein transgenic mice received GSH synthase inhibitor buthionine sulfoximine (BSO; 30 mmol/L in drinking water) for 14 days before assessment of myocardial morphology and function. BSO evoked cardiac remodeling and contractile anomalies, including cardiac hypertrophy, interstitial fibrosis, enlarged left ventricular chambers, deranged ejection fraction, fraction shortening, cardiomyocyte contractile capacity, intracellular Ca2+ handling, sarcoplasmic reticulum Ca2+ reuptake, loss of mitochondrial integrity (mitochondrial swelling, loss of aconitase activity), mitochondrial energy deficit, carbonyl damage, lipid peroxidation, ferroptosis, and apoptosis. Metallothionein itself did not affect myocardial morphology and function, although it mitigated BSO-provoked myocardial anomalies, loss of mitochondrial integrity and energy, and ferroptosis. Immunoblotting revealed down-regulated sarco(endo)plasmic reticulum Ca2+-ATPase 2a, glutathione peroxidase 4, ferroptosis-suppressing CDGSH iron-sulfur domain 1 (CISD1), and mitochondrial regulating glycogen synthase kinase-3ß phosphorylation with elevated p53, myosin heavy chain-ß isozyme, IκB phosphorylation, and solute carrier family 7 member 11 (SLC7A11) as well as unchanged SLC39A1, SLC1A5, and ferroptosis-suppressing protein 1 following BSO challenge, all of which, except glutamine transporter SLC7A11 and p53, were abrogated by metallothionein. Inhibition of CISD1 using pioglitazone nullified GSH-offered benefit against BSO-induced cardiomyocyte ferroptosis and contractile and intracellular Ca2+ derangement. Taken together, these findings support a regulatory modality for CISD1 in the impedance of ferroptosis in metallothionein-offered protection against GSH depletion-evoked cardiac aberration.

Infection burden and its association with neurite orientation dispersion and density imaging markers in the UK Biobank.

BACKGROUND: Infection burden (IB), although linked to neurodegeneration, including Alzheimer's Disease (AD), has not been examined against neurite orientation, dispersion, and density imaging (NODDI) measures. METHODS: Among 38,803 UK Biobank adults (Age:40-70 years), we tested associations of total IB (IBtotal, 47.5 %) and hospital-treated IB (IBhosp, 9.7 %) with NODDI measures (5-15 years later), including volume fraction of Gaussian isotropic diffusion (ISOVF), intra-cellular volume fraction (ICVF) and orientation dispersion (OD) indices, using multiple linear regression models. RESULTS: Total and hospital-treated infection burdens (IBtotal and IBhosp) were associated with increased ISOVF, indicating increased free-water component. IBtotal was positively associated with OD, indicating that at higher IBtotal there was greater fanning of neurites. This was more evident in the lower cardiovascular health group. IBhosp was associated with higher OD, and lower ICVF at higher AD polygenic risk. Together, these findings indicate that both total and hospital-treated infections have effects on NODDI outcomes in the direction of poor brain health. These effects were largely homogeneous across cardiovascular health and AD polygenic risk groups, with some effects shown to be stronger at poor cardiovascular health and/or higher AD risk. CONCLUSIONS: Total and hospital-treated infections were associated with poorer white matter microstructure (higher ISOVF or OD or lower ICVF), with some heterogeneity across cardiovascular health and AD risk. Longitudinal studies with multiple repeats on neuroimaging markers in comparable samples are needed.

Comparative study of machine learning methods for modeling associations between risk factors and future dementia cases.

A substantial portion of dementia risk can be attributed to modifiable risk factors that can be affected by lifestyle changes. Identifying the contributors to dementia risk could prove valuable. Recently, machine learning methods have been increasingly applied to healthcare data. Several studies have attempted to predict dementia progression by using such techniques. This study aimed to compare the performance of different machine-learning methods in modeling associations between known cognitive risk factors and future dementia cases. A subset of the AGES-Reykjavik Study dataset was analyzed using three machine-learning methods: logistic regression, random forest, and neural networks. Data were collected twice, approximately five years apart. The dataset included information from 1,491 older adults who underwent a cognitive screening process and were considered to have healthy cognition at baseline. Cognitive risk factors included in the models were based on demographics, MRI data, and other health-related data. At follow-up, participants were re-evaluated for dementia using the same cognitive screening process. Various performance metrics for all three machine learning algorithms were assessed. The study results indicate that a random forest algorithm performed better than neural networks and logistic regression in predicting the association between cognitive risk factors and dementia. Compared to more traditional statistical analyses, machine-learning methods have the potential to provide more accurate predictions about which individuals are more likely to develop dementia than others.

Dementia prediction in the general population using clinically accessible variables: a proof-of-concept study using machine learning. The AGES-Reykjavik study.

BACKGROUND: Early identification of dementia is crucial for prompt intervention for high-risk individuals in the general population. External validation studies on prognostic models for dementia have highlighted the need for updated models. The use of machine learning in dementia prediction is in its infancy and may improve predictive performance. The current study aimed to explore the difference in performance of machine learning algorithms compared to traditional statistical techniques, such as logistic and Cox regression, for prediction of all-cause dementia. Our secondary aim was to assess the feasibility of only using clinically accessible predictors rather than MRI predictors. METHODS: Data are from 4,793 participants in the population-based AGES-Reykjavik Study without dementia or mild cognitive impairment at baseline (mean age: 76 years, % female: 59%). Cognitive, biometric, and MRI assessments (total: 59 variables) were collected at baseline, with follow-up of incident dementia diagnoses for a maximum of 12 years. Machine learning algorithms included elastic net regression, random forest, support vector machine, and elastic net Cox regression. Traditional statistical methods for comparison were logistic and Cox regression. Model 1 was fit using all variables and model 2 was after feature selection using the Boruta package. A third model explored performance when leaving out neuroimaging markers (clinically accessible model). Ten-fold cross-validation, repeated ten times, was implemented during training. Upsampling was used to account for imbalanced data. Tuning parameters were optimized for recalibration automatically using the caret package in R. RESULTS: 19% of participants developed all-cause dementia. Machine learning algorithms were comparable in performance to logistic regression in all three models. However, a slight added performance was observed in the elastic net Cox regression in the third model (c = 0.78, 95% CI: 0.78-0.78) compared to the traditional Cox regression (c = 0.75, 95% CI: 0.74-0.77). CONCLUSIONS: Supervised machine learning only showed added benefit when using survival techniques. Removing MRI markers did not significantly worsen our model's performance. Further, we presented the use of a nomogram using machine learning methods, showing transportability for the use of machine learning models in clinical practice. External validation is needed to assess the use of this model in other populations. Identifying high-risk individuals will amplify prevention efforts and selection for clinical trials.

Gut microbiome and stages of diabetes in middle-aged adults: CARDIA microbiome study.

BACKGROUND: Animal and human studies suggest the gut microbiome is linked to diabetes but additional data are needed on the associations of the gut microbiome to specific diabetes characteristics. The aim of this study was to examine the associations of gut microbiome composition to insulin resistance [Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)], duration of diabetes, and 4 stages of diabetes [normoglycemia, pre-diabetes, and diabetes with (+) and without (-) medication for diabetes]. METHODS: Data are from a sub-sample (n = 605) of Black and White participants from the 30-year follow-up exam of the prospectively followed community-based Coronary Artery Risk Development in Young Adults cohort (2015-2016; aged 48-60 years). Stool samples were collected and sequenced using the 16S ribosomal RNA method. Microbial measures included: α diversity (within-person), ß diversity (between-person), and taxonomies. All analyses were adjusted for demographic, clinical, lifestyle factors, and use of relevant medications (full adjustment). Multivariate linear regression models were used to assess the association of diabetes characteristics with α diversity and genera abundance, while the association with ß diversity was analyzed using permutational multivariate analysis of variance. Statistical significance was set to p-value < 0.05 for α and ß diversity analyses and to q-value < 0.1 for genera abundance analyses. RESULTS: There were 16.7% of participants with pre-diabetes, and 14.4% with diabetes (9% diabetes+) with median (interquartile range) diabetes duration of 5 (5-10) years. In the fully adjusted models, compared to those with no diabetes, longer diabetes duration and the diabetes + group had a lower α diversity. There were significant differences in ß diversity across diabetes-related characteristics. A significantly reduced abundance of butyrate-producing genera was associated with higher HOMA-IR (ex., Anaerostipes and Lachnospiraceae_UCG.004), longer diabetes duration (ex., Agathobacter and Ruminococcus), and diabetes + (ex., Faecalibacterium and Romboutsia). CONCLUSIONS: Our results suggest that an adverse alteration of gut microbiome composition is related to higher insulin resistance, longer diabetes duration, and is present in those persons with diabetes using medications. These diabetes-related characteristics were also associated with lower levels of certain butyrate-producing bacteria that produce health-promoting short-chain fatty acids. Understanding the role of gut microbiota in glucose regulation may provide new strategies to reduce the burden of diabetes.

Characteristics of statewide prescription drug monitoring programs and potentially inappropriate opioid prescribing to patients with non-cancer chronic pain: A machine learning application.

Unnecessary/unsafe opioid prescribing has become a major public health concern in the U.S. Statewide prescription drug monitoring programs (PDMPs) with varying characteristics have been implemented to improve safe prescribing practice. Yet, no studies have comprehensively evaluated the effectiveness of PDMP characteristics in reducing opioid-related potentially inappropriate prescribing (PIP) practices. The objective of the study is to apply machine learning methods to evaluate PDMP effectiveness by examining how different PDMP characteristics are associated with opioid-related PIPs for non-cancer chronic pain (NCCP) treatment. This was a retrospective observational study that included 802,926 adult patients who were diagnosed NCCP, obtained opioid prescriptions, and were continuously enrolled in plans of a major U.S. insurer for over a year. Four outcomes of opioid-related PIP practices, including dosage ≥50 MME/day and ≥90 MME/day, days supply ≥7 days, and benzodiazepine-opioid co-prescription were examined. Machine learning models were applied, including logistic regression, least absolute shrinkage and selection operation regression, classification and regression trees, random forests, and gradient boost modeling (GBM). The SHapley Additive exPlanations (SHAP) method was applied to interpret model results. The results show that among 1,886,146 NCCP opioid-related claims, 22.8% had an opioid dosage ≥50 MME/day and 8.9% ≥90 MME/day, 70.3% had days supply ≥7 days, and 10.3% were when benzodiazepine was filled ≤7 days ago. GBM had superior model performance. We identified the most salient PDMP characteristics that predict opioid-related PIPs (e.g., broader access to patient prescription history, monitoring Schedule IV controlled substances), which could be informative to the states considering the redesign of PDMPs.

Association of Early Adulthood 25-Year Blood Pressure Trajectories With Cerebral Lesions and Brain Structure in Midlife.

Importance: Midlife elevated blood pressure (BP) is an important risk factor associated with brain structure and function. Little is known about trajectories of BP that modulate this risk. Objective: To identify BP trajectory patterns from young adulthood to midlife that are associated with brain structure in midlife. Design, Setting, and Participants: This cohort study used data of US adults from Coronary Artery Risk Development in Young Adults (CARDIA), a prospective longitudinal study of Black and White men and women (baseline age 18 to 30 years) examined up to 8 times over 30 years (1985-1986 to 2015-2016). There were 885 participants who underwent brain magnetic resonance imaging (MRI) in the 25th or 30th year examinations. Analyses were conducted November 2019 to December 2020. Exposures: Using group-based trajectory modeling, 5 25-year BP trajectories for 3 BP traits were identified in the total CARDIA cohort of participants with 3 or more BP measures, which were then applied to analyses of the subset of 853 participants in the Brain MRI substudy. Mean arterial pressure (MAP) was examined as an integrative measure of systolic and diastolic BP. With linear regression, the associations of the BP trajectories with brain structures were examined, adjusting sequentially for demographics, cardiovascular risk factors, and antihypertensive medication use. Main Outcomes and Measures: Brain MRI outcomes include total brain, total gray matter, normal-looking and abnormal white matter volumes, gray matter cerebral blood flow, and white matter fractional anisotropy. Results: Brain MRI analyses were conducted on 853 participants (mean [SD] age, 50.3 [3.6] years; 399 [46.8%] men; 354 [41.5%] Black and 499 [58.5%] White individuals). The MAP trajectory distribution was 187 individuals (21.1%) with low-stable, 385 (43.5%) with moderate-gradual, 71 (8.0%) with moderate-increasing, 204 (23.1%) with elevated-stable, and 38 (4.3%) with elevated-increasing. Compared with the MAP low-stable trajectory group, individuals in the moderate-increasing and elevated-increasing groups were more likely to have higher abnormal white matter volume (moderate: ß, 0.52; 95% CI, 0.23 to 0.82; elevated: ß, 0.57; 95% CI, 0.19 to 0.95). Those in the MAP elevated-increasing group had lower gray matter cerebral blood flow (ß, -0.42; 95% CI, -0.79 to -0.05) after adjusting for sociodemographics and cardiovascular risk factors. After adjustment for antihypertensive medication use, the difference was consistent for abnormal white matter volume, but results were no longer significant for gray matter cerebral blood flow. Conclusions and Relevance: Among young adults with moderate to high levels of BP, a gradual increase in BP to middle-age may increase the risk in diffuse small vessel disease and lower brain perfusion.

Circulating Metabolome and White Matter Hyperintensities in Women and Men.

BACKGROUND: White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites. METHODS: We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)<0.05 were considered significant. RESULTS: In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.full.adj=1.65×10-6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047). CONCLUSIONS: Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Assessing the Associations between Religiosity and Alcohol Use Stages in a Representative U.S. Sample.

Background: Problematic alcohol drinking has been a public health concern in the United States. Studies showed that religiosity serves as a protective factor, delaying the onset of alcohol use, and reducing the frequency of drinking. Few studies, however, have examined these associations with large, nationally representative samples, and even fewer have assessed the impact of religiosity on drinking behavior transitions/changes. Objectives: This study examined a national adult sample to investigate the associations between religiosity and alcohol use stages including initiation, reinitiation, and persistence of alcohol use. Methods: Data from the National Epidemiologic Survey of Alcohol and Related Conditions Waves 1-2 were used. Wave 1 sample included 6113 nondrinkers, 6189 prior drinkers, and 21,950 current drinkers who were at risk for initiation, reinitiation, and persistent use of alcohol, respectively. Religiosity constructs included importance of religious and frequency of practice. Three logistic regressions examined the aforementioned associations. Results: Compared to those not attending religious services, the most frequent attenders exhibited lower odds of initiating alcohol use, reinitiation after prior use, and persistent drinking (ORs = 0.23, 0.51, 0.55, respectively; ps < .01). Those identifying religious beliefs as very important exhibited lower odds of initiation and reinitiation of alcohol use (both ORs = 0.63, ps < .05). Conclusions: Religiosity plays an important role in preventing/delaying alcohol use initiation, reinitiation, and persistence. Incorporating religiosity aspects (e.g. meditation) into alcohol prevention and control programs may serve to increase protective effects. Future studies should seek to delineate what religiosity factors can be leveraged and embedded into secular prevention programs delivered to youth and adolescents.

A Cataluminescence Sensor Based on NiO Nanoparticles for Sensitive Detection of Acetaldehyde.

Sensitive and selective detection of harmful gas is an important task in environmental monitoring. In this work, a gas sensor based on cataluminescence (CTL) for detection of acetaldehyde was designed by using nano-NiO as the sensing material. The sensor shows sensitive response to acetaldehyde at a relatively low working temperature of 200 °C. The linear range of CTL intensity versus acetaldehyde concentration is 0.02-2.5 mg/L, with a limit of detection of 0.006 mg/L at a signal-to-noise ratio of three. Mechanism study shows that electronically excited CO2 is the excited intermediate for CTL emission during the catalytic oxidation of acetaldehyde on the NiO surface. The proposed sensor has promising application in monitoring acetaldehyde in residential buildings and in the workplace.

Comparing American college and noncollege young adults on e-cigarette use patterns including polysubstance use and reasons for using e-cigarettes.

Objective: Existing literature on young adults' e-cigarette and polysubstance use focused on college students. This study examined the differences between college and noncollege groups on prevalence and patterns of e-cigarette and other substance use using data from a national survey. Participants: Adults aged 18-24 from the 2013-2014 Population Assessment of Tobacco and Health Study (n = 6,608). Methods: Independent sample t-tests and Chi-square tests were conducted to examine group differences. Results: Noncollege young adults had higher prevalence of cigarette, e-cigarette, and marijuana use; college students had higher prevalence of alcohol use. Among current e-cigarette users, college students had higher prevalence of polysubstance use of alcohol and marijuana. College students used e-cigarettes for socializing purposes more. Conclusions: Differences in prevalence and patterns of e-cigarette and other substance use between college and noncollege groups exist. Future interventions should target the social context of college life and reach out to noncollege young adults in workplaces.

The association between e-cigarette use characteristics and combustible cigarette consumption and dependence symptoms: Results from a national longitudinal study.

BACKGROUND: Existing longitudinal surveys focused on the association between ever use of e-cigarettes and combustible cigarette consumption, making it difficult to infer what characteristics of e-cigarette use could potentially change combustible cigarette use behavior, which may have long-term health consequences. Although e-cigarettes' efficacy of alleviating dependence symptoms was supported by studies conducted in laboratory settings, whether the results can be translated into symptom reduction in the real world and over time is an open question. METHODS: This study conducted secondary analysis on the Waves 1-2 data of the Population Assessment of Tobacco and Health (PATH) Study to examine the association between e-cigarette use characteristics (frequency, flavoring, and voltage adjustment) and combustible cigarette use outcomes (frequency, quantity, and symptoms), using the Heckman 2-step selection procedure with the selection bias controlled. The inclusion criteria ensured that we followed an adult cohort of exclusive combustible cigarette users at Wave 1. RESULTS: The result shows that higher frequency of e-cigarette use was associated with lower combustible cigarette consumption and dependence symptoms, controlling for the corresponding baseline cigarette use variable and other confounders. Given the frequency of e-cigarette use, the feature of voltage adjustment was not significantly associated with any of the cigarette use outcomes. Flavoring, on the other hand, was associated with lower quantity of cigarette use. CONCLUSIONS: Exclusive smokers who start using e-cigarettes do indeed change the frequency and quantity with which they smoke cigarettes. E-cigarette use may also help reduce dependence symptoms.

Effects of alcohol and cigarette use on the initiation, reinitiation, and persistence of cannabis use from adolescence to emerging adulthood.

OBJECTIVE: Adolescent cannabis use has been associated with several negative outcomes. A previous study on an adult sample found alcohol and cigarette use to be associated with three cannabis use stages: initiation, reinitiation, and persistence, which represent distinct periods of use regarding progression and severity. Yet, the risk factors associated with these important stages have never been examined in a longitudinal study spanning adolescence to emerging adulthood. METHODS: Using longitudinal data from Add Health Waves 1-3, 1775 nonusers, 200 prior users, and 384 current users of cannabis were identified who were at risk of cannabis use initiation, reinitiation, and persistence, respectively. Three logistic regressions were conducted to examine the effects of prior cigarette and alcohol use on the three cannabis use stages, controlling for sociodemographic factors. RESULTS: Early onset of cigarette use (OR=2.04, p=0.006) and higher alcohol use frequency (OR=1.40, p<0.001) were associated with cannabis use initiation. Greater cigarette use quantity was associated with a lower likelihood of reinitiation of cannabis use (OR=0.58, p=0.02). Increased cannabis use frequency (OR=1.72, p=0.006) and higher alcohol use frequency (OR=1.32, p=0.048) were associated with persistence of cannabis use. Sociodemographic factors such as household income, sex, and being older adolescents were associated with different cannabis use stages. CONCLUSIONS: Prior cigarette and alcohol use affect the risk of initiation, reinitiation, and persistence of cannabis use. The specific risk factors vary across different cannabis use stages. Interventions to prevent adolescent cannabis use should recognize these different risk factors and tailor to the stages of cannabis use.

The Role of Leisure Activities in the Relationship Between Marital Transition in Later Midlife and Psychological Well-Being Trajectories.

This study examined the levels and rates of changes in psychological well-being for middle-aged adults of different statuses or marital transitions. The moderating effects of different leisure activities were also tested. Longitudinal data on 1,270 persons aged 50 to 65 years at baseline from the Taiwan longitudinal study on aging were analyzed. Adults who were stably unmarried or unpartnered reported worse mental health at baseline, but their psychological well-being improved over time. The trajectory of depressive symptoms fluctuated markedly in adults who became widowed during our observation period. Engagement in physical, cognitive, or social activities was significantly associated with participants' psychological well-being. Participation in religious activities was significantly associated with life satisfaction and decreased depressive symptoms for those undergoing bereavement. Findings from this study suggest that social and physical activities, among the four selected leisure activities, have the greatest association between decreasing depressive symptoms and increasing life satisfaction, respectively. Religious activities, in particular, may improve psychological well-being in bereaved middle-aged and older adults.

Predictive validity of cannabis consumption measures: Results from a national longitudinal study.

BACKGROUND: Validating the utility of cannabis consumption measures for predicting later cannabis related symptomatology or progression to cannabis use disorder (CUD) is crucial for prevention and intervention work that may use consumption measures for quick screening. This study examined whether cannabis use quantity and frequency predicted CUD symptom counts, progression to onset of CUD, and persistence of CUD. METHODS: Data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) at Wave 1 (2001-2002) and Wave 2 (2004-2005) were used to identify three risk samples: (1) current cannabis users at Wave 1 who were at risk for having CUD symptoms at Wave 2; (2) current users without lifetime CUD who were at risk for incident CUD; and (3) current users with past-year CUD who were at risk for persistent CUD. Logistic regression and zero-inflated Poisson models were used to examine the longitudinal effect of cannabis consumption on CUD outcomes. RESULTS: Higher frequency of cannabis use predicted lower likelihood of being symptom-free but it did not predict the severity of CUD symptomatology. Higher frequency of cannabis use also predicted higher likelihood of progression to onset of CUD and persistence of CUD. Cannabis use quantity, however, did not predict any of the developmental stages of CUD symptomatology examined in this study. CONCLUSIONS: This study has provided a new piece of evidence to support the predictive validity of cannabis use frequency based on national longitudinal data. The result supports the common practice of including frequency items in cannabis screening tools.