How patient and community involvement in diabetes research influences health outcomes : a realist review

Background Patient and public involvement in diabetes research is an international requirement, but little is known about the relationship between the process of involvement and health outcomes. Objective This realist review identifies who benefits from different types of involvement across different contexts and circumstances. Search strategies Medline, CINAHL and EMBASE were searched to identify interventions using targeted, embedded or collaborative involvement to reduce risk and promote self‐management of diabetes. People at risk/with diabetes, providers and community organizations with an interest in addressing diabetes were included. There were no limitations on date, language or study type. Data extraction and synthesis Data were extracted from 29 projects using elements from involvement frameworks. A conceptual analysis of involvement types was used to complete the synthesis. Main results Projects used targeted (4), embedded (8) and collaborative (17) involvement. Productive interaction facilitated over a sufficient period of time enabled people to set priorities for research. Partnerships that committed to collaboration increased awareness of diabetes risk and mobilized people to co‐design and co‐deliver diabetes interventions. Cultural adaptation increased relevance and acceptance of the intervention because they trusted local delivery approaches. Local implementation produced high levels of recruitment and retention, which project teams associated with achieving diabetes health outcomes. Discussion and Conclusions Achieving understanding of community context, developing trusting relationships across sectors and developing productive partnerships were prerequisites for designing research that was feasible and locally relevant. The proportion of diabetes studies incorporating these elements is surprisingly low. Barriers to resourcing partnerships need to be systematically addressed

Identifying postpartum intervention approaches to prevent type 2 diabetes in women with a history of gestational diabetes

<p>Abstract</p> <p>Background</p> <p>Women who develop gestational diabetes mellitus (GDM) have an increased risk for the development of type 2 diabetes. Despite this "window of opportunity," few intervention studies have targeted postpartum women with a history of GDM. We sought perspectives of women with a history of GDM to identify a) barriers and facilitators to healthy lifestyle changes postpartum, and b) specific intervention approaches that would facilitate participation in a postpartum lifestyle intervention program.</p> <p>Methods</p> <p>We used mixed methods to gather data from women with a prior history of GDM, including focus groups and informant interviews. Analysis of focus groups relied on grounded theory and used open-coding to categorize data by themes, while frequency distributions were used for the informant interviews.</p> <p>Results</p> <p>Of 38 women eligible to participate in focus groups, only ten women were able to accommodate their schedules to attend a focus group and 15 completed informant interviews by phone. We analyzed data from 25 women (mean age 35, mean pre-pregnancy BMI 28, 52% Caucasian, 20% African American, 12% Asian, 8% American Indian, 8% refused to specify). Themes from the focus groups included concern about developing type 2 diabetes, barriers to changing diet, and barriers to increasing physical activity. In one focus group, women expressed frustration about feeling judged by their physicians during their GDM pregnancy. Cited barriers to lifestyle change were identified from both methods, and included time and financial constraints, childcare duties, lack of motivation, fatigue, and obstacles at work. Informants suggested facilitators for lifestyle change, including nutrition education, accountability, exercise partners/groups, access to gyms with childcare, and home exercise equipment. All focus group and informant interview participants reported access to the internet, and the majority expressed interest in an intervention program delivered primarily via the internet that would include the opportunity to work with a lifestyle coach.</p> <p>Conclusion</p> <p>Time constraints were a major barrier. Our findings suggest that an internet-based lifestyle intervention program should be tested as a novel approach to prevent type 2 diabetes in postpartum women with a history of GDM.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01102530">NCT01102530</a></p

Generational status and duration of residence predict diabetes prevalence among Latinos: the California Men's Health Study

<p>Abstract</p> <p>Background</p> <p>Diabetes disproportionately affects Latinos. However, examining Latinos as one group obscures important intra-group differences. This study examined how generational status, duration of US residence, and language preference are associated with diabetes prevalence and to what extent these explain the higher prevalence among Latinos.</p> <p>Methods</p> <p>We determined nativity, duration of US residence, language preference, and diabetes prevalence among 11 817 Latino, 6109 black, and 52 184 white participants in the California Men's Health Study. We combined generational status and residence duration into a single migration status variable with levels: ≥ third generation; second generation; and immigrant living in the US for > 25, 16-25, 11-15, or ≤ 10 years. Language preference was defined as language in which the participant took the survey. Logistic regression models were specified to assess the associations of dependent variables with prevalent diabetes.</p> <p>Results</p> <p>Diabetes prevalence was 22%, 23%, and 11% among Latinos, blacks, and whites, respectively. In age-adjusted models, we observed a gradient of risk of diabetes by migration status among Latinos. Further adjustment for socioeconomic status, obesity and health behaviors only partially attenuated this gradient. Language preference was a weak predictor of prevalent diabetes in some models and not significant in others. In multivariate models, we found that odds of diabetes were higher among US-born Latinos than US-born blacks.</p> <p>Conclusion</p> <p>Generational status and residence duration were associated with diabetes prevalence among middle-aged Latino men in California. As the Latino population grows, the burden of diabetes-associated disease is likely to increase and demands public health attention.</p

P301S Mutant Human Tau Transgenic Mice Manifest Early Symptoms of Human Tauopathies with Dementia and Altered Sensorimotor Gating

Tauopathies are neurodegenerative disorders characterized by the accumulation of abnormal tau protein leading to cognitive and/or motor dysfunction. To understand the relationship between tau pathology and behavioral impairments, we comprehensively assessed behavioral abnormalities in a mouse tauopathy model expressing the human P301S mutant tau protein in the early stage of disease to detect its initial neurological manifestations. Behavioral abnormalities, shown by open field test, elevated plus-maze test, hot plate test, Y-maze test, Barnes maze test, Morris water maze test, and/or contextual fear conditioning test, recapitulated the neurological deficits of human tauopathies with dementia. Furthermore, we discovered that prepulse inhibition (PPI), a marker of sensorimotor gating, was enhanced in these animals concomitantly with initial neuropathological changes in associated brain regions. This finding provides evidence that our tauopathy mouse model displays neurofunctional abnormalities in prodromal stages of disease, since enhancement of PPI is characteristic of amnestic mild cognitive impairment, a transitional stage between normal aging and dementia such as Alzheimer's disease (AD), in contrast with attenuated PPI in AD patients. Therefore, assessment of sensorimotor gating could be used to detect the earliest manifestations of tauopathies exemplified by prodromal AD, in which abnormal tau protein may play critical roles in the onset of neuronal dysfunctions

Quasispecies Theory and the Behavior of RNA Viruses

A large number of medically important viruses, including HIV, hepatitis C virus, and influenza, have RNA genomes. These viruses replicate with extremely high mutation rates and exhibit significant genetic diversity. This diversity allows a viral population to rapidly adapt to dynamic environments and evolve resistance to vaccines and antiviral drugs. For the last 30 years, quasispecies theory has provided a population-based framework for understanding RNA viral evolution. A quasispecies is a cloud of diverse variants that are genetically linked through mutation, interact cooperatively on a functional level, and collectively contribute to the characteristics of the population. Many predictions of quasispecies theory run counter to traditional views of microbial behavior and evolution and have profound implications for our understanding of viral disease. Here, we discuss basic principles of quasispecies theory and describe its relevance for our understanding of viral fitness, virulence, and antiviral therapeutic strategy

Cigarette smoking and risk of gestational diabetes: a systematic review of observational studies

<p>Abstract</p> <p>Background</p> <p>Gestational diabetes is a prevalent disease associated with adverse outcomes of pregnancy. Smoking as been associated with glucose intolerance during pregnancy in some but not all studies. Therefore, we aimed to systematically review all epidemiological evidence to examine the association between cigarette smoking during pregnancy and risk of developing gestational diabetes mellitus.</p> <p>Methods</p> <p>We conducted a systematic review of articles published up to 2007, using PubMed, Embase, LILACS e CINAHL to identify the articles. Because this review focuses on studies of smoking during pregnancy, we excluded studies evaluating smoking outside pregnancy. Two investigators independently abstracted information on participant's characteristics, assessment of exposure and outcome, and estimates for the association under study. We evaluated the studies for publication bias and performed heterogeneity analyses. We also assessed the effect of each study individually through sensitivity analysis.</p> <p>Results</p> <p>We found and critically reviewed 32 studies, of which 12 met the criteria for inclusion in the review. Most of the studies provided only unadjusted measurements. Combining the results of the individual studies, we obtained a crude odds ratio of 1.03 (99% CI 0.85–1.25). Only 4 studies presented adjusted measurements of association, and no association was found when these alone were analyzed (OR 0.95; 99% CI 0.85–1.07). Subgroup analysis could not be done due to small sample size.</p> <p>Conclusion</p> <p>The number of studies is small, with major heterogeneity in research design and findings. Taken together, current data do not support an association between cigarette smoking during pregnancy and the risk of gestational diabetes.</p

Unravelling higher order chromatin organisation through statistical analysis

Recent technological advances underpinned by high throughput sequencing have given new insights into the three-dimensional structure of mammalian genomes. Chromatin conformation assays have been the critical development in this area, particularly the Hi-C method which ascertains genome-wide patterns of intra and inter-chromosomal contacts. However many open questions remain concerning the functional relevance of such higher order structure, the extent to which it varies, and how it relates to other features of the genomic and epigenomic landscape. Current knowledge of nuclear architecture describes a hierarchical organisation ranging from small loops between individual loci, to megabase-sized self-interacting topological domains (TADs), encompassed within large multimegabase chromosome compartments. In parallel with the discovery of these strata, the ENCODE project has generated vast amounts of data through ChIP-seq, RNA-seq and other assays applied to a wide variety of cell types, forming a comprehensive bioinformatics resource. In this work we combine Hi-C datasets describing physical genomic contacts with a large and diverse array of chromatin features derived at a much finer scale in the same mammalian cell types. These features include levels of bound transcription factors, histone modifications and expression data. These data are then integrated in a statistically rigorous way, through a predictive modelling framework from the machine learning field. These studies were extended, within a collaborative project, to encompass a dataset of matched Hi-C and expression data collected over a murine neural differentiation timecourse. We compare higher order chromatin organisation across a variety of human cell types and find pervasive conservation of chromatin organisation at multiple scales. We also identify structurally variable regions between cell types, that are rich in active enhancers and contain loci of known cell-type specific function. We show that broad aspects of higher order chromatin organisation, such as nuclear compartment domains, can be accurately predicted in a variety of human cell types, using models based upon underlying chromatin features. We dissect these quantitative models and find them to be generalisable to novel cell types, presumably reflecting fundamental biological rules linking compartments with key activating and repressive signals. These models describe the strong interconnectedness between locus-level patterns of local histone modifications and bound factors, on the order of hundreds or thousands of basepairs, with much broader compartmentalisation of large, multi-megabase chromosomal regions. Finally, boundary regions are investigated in terms of chromatin features and co-localisation with other known nuclear structures, such as association with the nuclear lamina. We find boundary complexity to vary between cell types and link TAD aggregations to previously described lamina-associated domains, as well as exploring the concept of meta-boundaries that span multiple levels of organisation. Together these analyses lend quantitative evidence to a model of higher order genome organisation that is largely stable between cell types, but can selectively vary locally, based on the activation or repression of key loci