A Cohort Study of Anticholinergic Medication Burden and Incident Dementia and Stroke in Older Adults

BackgroundAnticholinergic medications may increase risk of dementia and stroke, but prospective studies in healthy older people are lacking.ObjectiveCompare risk of incident dementia and stroke by anticholinergic burden among initially healthy older people.DesignProspective cohort study.SettingPrimary care (Australia and USA).Participants19,114 community-dwelling participants recruited for the ASPREE trial, aged 70+ years (65+ if US minorities) without major cardiovascular disease, dementia diagnosis, or Modified Mini-Mental State Examination score below 78/100.MeasurementsBaseline anticholinergic exposure was calculated using the Anticholinergic Cognitive Burden (ACB) score. Dementia was adjudicated using Diagnostic and Statistical Manual of Mental Disorders volume IV criteria, and stroke using the World Health Organization definition.ResultsAt baseline, 15,000 participants (79%) had an ACB score of zero, 2930 (15%) a score of 1–2, and 1184 (6%) a score of ≥ 3 (indicating higher burden). After a median follow-up of 4.7 years and adjusting for baseline covariates, a baseline ACB score of ≥ 3 was associated with increased risk of ischemic stroke (adjusted HR 1.58, 95% CI 1.06, 2.35), or dementia (adjusted HR 1.36, 95% CI 1.01, 1.82), especially of mixed etiology (adjusted HR 1.53, 95% CI 1.06, 2.21). Results were similar for those exposed to moderate/highly anticholinergic medications.LimitationsResidual confounding and reverse causality are possible. Assessment of dose or duration was not possible.ConclusionsHigh anticholinergic burden in initially healthy older people was associated with increased risk of incident dementia and ischemic stroke. A vascular effect may underlie this association. These findings highlight the importance of minimizing anticholinergic exposure in healthy older people.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06550-2.KEY WORDS: anticholinergic burden, dementia, stroke, potentially inappropriate medication     

Content-Driven Analysis of an Online Community for Smoking Cessation: Integration of Qualitative Techniques,Automated Text Analysis,and Affiliation Networks

Objectives. We identified content-specific patterns of network diffusion underlying smoking cessation in the context of online platforms, with the aim of generating targeted intervention strategies.Methods. QuitNet is an online social network for smoking cessation. We analyzed 16 492 de-identified peer-to-peer messages from 1423 members, posted between March 1 and April 30, 2007. Our mixed-methods approach comprised qualitative coding, automated text analysis, and affiliation network analysis to identify, visualize, and analyze content-specific communication patterns underlying smoking behavior.Results. Themes we identified in QuitNet messages included relapse, QuitNet-specific traditions, and cravings. QuitNet members who were exposed to other abstinent members by exchanging content related to interpersonal themes (e.g., social support, traditions, progress) tended to abstain. Themes found in other types of content did not show significant correlation with abstinence.Conclusions. Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.Epidemiological evidence indicates that modifiable risky health behaviors place a substantial socioeconomic burden on human health and wellness.1 Understanding human behavior in real-time settings is essential to improving health outcomes related to these behaviors.2,3 Technological advances in connectivity offer the means to obtain potentially valuable data sets in the form of electronic traces of the activities of online social communities. These data may help us to understand the intra- and interindividual intricacies of health-related behaviors. Studies of online and offline social networks provide valuable insight into social influence, information spread, and behavioral diffusion.4–6 Most of these analyses have paid more attention to the frequency of communication between members than to its content. The content, however, is relevant to behavior change theories, which address the use of specialized content to stimulate and support individuals to achieve a desired change.7,8 Contemporary work on social media data rarely addresses this fundamental concern of behavior change theorists.Outside the context of online networks, several theories have been formulated to explain behavior change. Some, such as the Transtheoretical Model,9 belong to the intrapersonal category; others, such as Social Cognitive Theory10 and social network and support models,11 are classified as interpersonal. (Appendix A, available as a supplement to the online version of this article at http://www.ajph.org, provides an overview of the theoretical constructs.) Empirical research on the applicability of these models to behavior change of health consumers in the digital era is minimal.12 Recent research showed that participation in health issue–specific social networking sites significantly influenced social factors such as identification, perceived subjective norms, and social support, which in turn resulted in greater smoking cessation self-efficacy.13 Content inclusion in analytical models of social networks can enable us to examine the content-specific patterns of social factors underlying behavior change. Through mapping of the specific content to theories, such content inclusion can facilitate the development of network interventions for health behavior changes by harnessing the power of social relationships.Studies of QuitNet, an online social network for smoking cessation, have examined the structure of peer-to-peer communication patterns and provided insights into social integrators and opinion leaders.5,14 Previous work showed the applicability of affiliation networks to real-world diffusion networks, enriching our understanding of the affiliation-based sources of influence on individuals’ behavior. Examples include the diffusion of (1) ratification of the World Health Organization Framework Convention on Tobacco Control by comembership with an online forum among countries15; (2) gunshot victimization by co-offending with victims among Chicago, Illinois, gangsters16; (3) substance use by coparticipating in school-sponsored sports or co-identifying with the same crowd types17,18; and (4) sexual behavior by coaffiliating with venues among male sex workers.19We used affiliation networks to analyze messages for content-specific patterns of network diffusion. We took an interdisciplinary approach, integrating methods from sociobehavioral sciences, social network analytics, and biomedical informatics. We employed qualitative techniques derived from grounded theory, automated text analysis, and affiliation network analysis to investigate the communication patterns underlying human behavior in online environments. Our study had 3 major components: (1) a qualitative study of human communication within user-generated data in QuitNet, (2) computational text analysis to further extend this analysis, and (3) identification of communication patterns pertinent to behavior change in affiliation networks. We anticipate that the insights gained from this research will enhance our understanding of behavior change and will have implications for the design of sociobehavioral interventions that draw upon social influence.     

Risk-Taking in Disorders of Natural and Drug Rewards: Neural Correlates and Effects of Probability,Valence, and Magnitude

Pathological behaviors toward drugs and food rewards have underlying commonalities. Risk-taking has a fourfold pattern varying as a function of probability and valence leading to the nonlinearity of probability weighting with overweighting of small probabilities and underweighting of large probabilities. Here we assess these influences on risk-taking in patients with pathological behaviors toward drug and food rewards and examine structural neural correlates of nonlinearity of probability weighting in healthy volunteers. In the anticipation of rewards, subjects with binge eating disorder show greater risk-taking, similar to substance-use disorders. Methamphetamine-dependent subjects had greater nonlinearity of probability weighting along with impaired subjective discrimination of probability and reward magnitude. Ex-smokers also had lower risk-taking to rewards compared with non-smokers. In the anticipation of losses, obesity without binge eating had a similar pattern to other substance-use disorders. Obese subjects with binge eating also have impaired discrimination of subjective value similar to that of the methamphetamine-dependent subjects. Nonlinearity of probability weighting was associated with lower gray matter volume in dorsolateral and ventromedial prefrontal cortex and orbitofrontal cortex in healthy volunteers. Our findings support a distinct subtype of binge eating disorder in obesity with similarities in risk-taking in the reward domain to substance use disorders. The results dovetail with the current approach of defining mechanistically based dimensional approaches rather than categorical approaches to psychiatric disorders. The relationship to risk probability and valence may underlie the propensity toward pathological behaviors toward different types of rewards.     

Insulin resistance-associated genetic variants in type 1 diabetes

AimsTo examine candidate insulin resistance single nucleotide polymorphisms (SNPs) for associations with glycemic control, insulin resistance, BMI, and complications in an observational type 1 diabetes (T1D) cohort: the Pittsburgh Epidemiology of Diabetes Complications (EDC) study.MethodsIn 422 European-ancestry participants, we assessed associations using additive models between 15 candidate SNPs and 25-year mortality, cardiovascular disease, microalbuminuria, overt nephropathy and proliferative retinopathy, and 25-year mean HbA1c, estimated glucose disposal rate (eGDR, inverse measure of insulin resistance), and BMI.ResultsThe A allele of rs12970134 was associated with higher mean HbA1c (β = +0.34 ± 0.09, p = 0.00009) and nominally associated with worse eGDR (p = 0.02). Further analyses suggest the HbA1c association may be modified by diabetes therapy regimen: rs12970134 AA genotype was associated with higher HbA1c under non-intensive therapy conditions (<3 insulin injections/day or monitoring blood glucose<3 times/day [p = 0.004]), but not under intensive therapy (≥3 injections/day or insulin pump and monitoring glucose≥3 times/day [p = 0.71]). There were no significant associations between any SNPs and BMI or complications.Conclusionsrs12970134, near MC4R, is strongly associated with HbA1c in this cohort. Further exploration of this genomic region is warranted, as it may hold promise for discovering new therapeutic targets to improve glycemic control in T1D.     

Reliable anticoagulation with enoxaparin in patients undergoing percutaneous coronary intervention: The pharmacokinetics of enoxaparin in PCI (PEPCI) study.

The objective of this study was to evaluate the pharmacokinetic response to intravenous (IV) enoxaparin given 8-12 hr after subcutaneous (SC) dosing in patients undergoing percutaneous coronary intervention (PCI). Fifty-five patients received SC enoxaparin (1 mg/kg every 12 hr) followed by an IV bolus (0.3 mg/kg) 8-12 hr after the last SC dose, at the start of PCI or during catheterization. Anti-Xa levels were within the target range in 98% of patients 2-8 hr after the last SC dose, in 96% of patients following the IV bolus, and in 91% of patients for a further 2 hr. Subcutaneous enoxaparin (1 mg/kg every 12 hr) provides sufficient anti-Xa levels for PCI 2-8 hr after the last dose. An additional 0.3 mg/kg enoxaparin dose given IV 8-12 hr after the last SC dose reliably maintains anti-Xa levels within the target for at least 2 additional hr.     

Age, hormones, and cognitive functioning among middle-aged and elderly men: cross-sectional evidence from the Massachusetts Male Aging Study

BACKGROUND: This study examines interrelationships among age, hormones, and cognition for middle-aged and elderly men, and tests whether hormones predict lower cognitive functioning and mediate the age-cognition relationship. METHODS: We analyzed Time 2 data from the Massachusetts Male Aging Study, a population-based cohort study. Selection criteria included complete information on cognition and hormones (n = 981). Cognitive measures included working memory (Backward Digit Span test), speed/attention (Digit Symbol Substitution test), and spatial ability (Figural Relations test). Hormones included free testosterone, total testosterone, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulfate (DHEAS), androstanediol glucuronide (3 alpha-A-diol-gluc), luteinizing hormone (LH), follicle-stimulating hormone (FSH), sex hormone-binding globulin (alternatively known as a "binding protein") (SHBG), prolactin (PRL), estrone (E1), and cortisol (CRT). Age was measured in years. Adjusted analyses added educational attainment, health conditions and behaviors, body mass index, and depression. RESULTS: Older age was associated with lower cognitive functioning. In unadjusted models, logged free and total testosterone, DHEA, and DHEAS related to higher functioning in at least one cognitive domain; logged FSH, SHBG, and LH related to lower functioning in at least one cognitive domain; and logged E1, CRT, and PRL were not significant. In adjusted models, logged hormones did not relate to cognitive function except for logged E1 and CRT, which had negative effects. Logged hormones did not mediate the age-cognition relationship. CONCLUSIONS: The direct effects of hormones on cognition are not significant when salient factors are considered. Further, hormones do not mediate the age-cognition relationship; it is necessary to look to other explanatory pathways.