In vitro anti-hyperglycemia properties of the aqueous stem bark extract from Strychnos henningsii (Gilg)

Strychnos henningsii (SH) is a plant commonly used in southern African traditional medicine for the management of diabetes mellitus. Previous in vivo studies showed that a stem bark extract improves glycemic control in a diabetic animal model. Meanwhile, the mechanism of action has not been elucidated. The present study therefore investigated various in vitro models known to target glucose homeostasis and its direct complications. The plant extract was found to stimulate both basal and insulin stimulated glucose uptake in differentiated 3T3-L1 cells but not in Chang liver cells. The effect on 3T3-L1 cells appears independent of PPARγ as the extract did not stimulate adipogenesis. Although, SH extract was inhibitory toward intestinal alpha glucosidase, the physiological relevance is doubtful based on the recommended dosages. The extract strongly inhibited protein glycation which, at least in part, may be explained by the antioxidant and phenolic content of this plant. Cytotoxicity in Chang liver cells yielded an IC₅₀ value of 130.0 μg/mL raising concern that continual exposure to this herbal remedy may initiate hepatotoxicity. The finding from this study suggests that SH extract may attenuate hyperglycemia through enhanced peripheral tissue glucose utilization.     

Glycemia (or, in women, estimated glucose disposal rate) predict lower extremity arterial disease events in type 1 diabetes

The purpose of this study was to determine the predictors of lower extremity arterial disease (LEAD) events in a type 1 diabetes population. Data are from the Pittsburgh Epidemiology of Diabetes Complications Study of childhood onset type 1 diabetes. At baseline, the study population had a mean age 28 (range, 8 to 47) years and duration 19 (range, 7 to 37) years. LEAD events, assessed by questionnaire or clinical examination, were defined as claudication (Rose questionnaire), foot ulceration, or lower extremity amputation. Estimated glucose disposal rate (eGDR), a measure of insulin resistance, was calculated from glycosylated hemoglobin (HbA(1)), waist-to-hip ratio (WHR), and hypertension using an equation previously validated with hyperinsulinemic euglycemic clamp studies. There were incident LEAD events in 70 of 586 subjects during 10 years follow-up, giving an incidence density of 1.3 events/100 person-years. Incidence did not differ by gender. Major predictors of LEAD events were diabetes duration, low-density lipoprotein-cholesterol (LDL-C), heart rate, eGDR, log albumin excretion rate (AER), systolic blood pressure (SBP), hypertension, proliferative retinopathy, distal symmetric polyneuropathy, and overt nephropathy (each P <.001). HbA(1), low ankle brachial index (ABI) (<0.9), and a high ankle brachial difference (ABD) (SBP > or = 75 mm Hg) also predicted LEAD events. Cox modeling suggested that duration (P <.001), HbA(1) (P <.001), hypertension (P =.006), log albumin excretion rate (P =.011), and heart rate (P =.028) predicted events independently. The overall model with HbA(1) and hypertension was significantly better than with eGDR, while the alternate models in men were similar. In women, the model with eGDR showed a significantly better fit. Glycemia, insulin resistance, hypertension and renal disease are powerful predictors of symptomatic lower extremity arterial disease in type 1 diabetes.     

Risk factors for progressive cartilage loss in the knee: a longitudinal magnetic resonance imaging study in forty-three patients

OBJECTIVE: To evaluate the rate of progression of cartilage loss in the knee joint using magnetic resonance imaging (MRI) and to evaluate potential risk factors for more rapid cartilage loss. METHODS: We evaluated baseline and followup MRIs of the knees in 43 patients (minimum time interval of 1 year, mean 1.8 years, range 52-285 weeks). Cartilage loss was graded in the anterior, central, and posterior regions of the medial and lateral knee compartments. Knee joints were also evaluated for other pathology. Data were analyzed using analysis of variance models. RESULTS: Patients who had sustained meniscal tears showed a higher average rate of progression of cartilage loss (22%) than that seen in those who had intact menisci (14.9%) (P 相似文献   

Improved treatment completion for tuberculosis patients: The case for a dedicated social care team

ObjectivesThe increasing social needs of people with Tuberculosis (TB), and the poor adherence to anti-TB therapy (ATT) associated with homelessness, drug or alcohol abuse, and prison history, led us to introduce a social care team (SCT) to support patient engagement with care within this low TB incidence setting.MethodsUsing a risk assessment, patients with social risk factors (SRF) for non-adherence to ATT are identified and a referral made to the SCT, who then provide intensive casework support for areas including homelessness, housing, benefits, debt and immigration. Retrospective data analysis of the social care database from 2017 to 2019 was conducted. Patients who were (n = 170) and were not referred to the SCT (n = 734) were compared.ResultsPatients referred were significantly more likely to complete treatment for TB than those not (88.2% versus 77.7% respectively, p = 0.0025), irrespective of receipt of Directly/Video Observed Therapy and adjusting for confounders.ConclusionsThis paper demonstrates important evidence for the positive impact of a dedicated SCT within a TB service, and these improved treatment outcomes provide a strong argument for development of similar SCTs within UK TB services and similar healthcare settings.     

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.