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.

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.

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.

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.

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.