Diabetes and the socioeconomic and built environment: geovisualization of disease prevalence and potential contextual associations using ring maps

<p>Abstract</p> <p>Background</p> <p>Efforts to stem the diabetes epidemic in the United States and other countries must take into account a complex array of individual, social, economic, and built environmental factors. Increasingly, scientists use information visualization tools to "make sense" of large multivariate data sets. Recently, ring map visualization has been explored as a means of depicting spatially referenced, multivariate data in a single information graphic. A ring map shows multiple attribute data sets as separate rings of information surrounding a base map of a particular geographic region of interest. In this study, ring maps were used to evaluate diabetes prevalence among adult South Carolina Medicaid recipients. In particular, county-level ring maps were used to evaluate disparities in diabetes prevalence among adult African Americans and Whites and to explore potential county-level associations between diabetes prevalence among adult African Americans and five measures of the socioeconomic and built environment—persistent poverty, unemployment, rurality, number of fast food restaurants per capita, and number of convenience stores per capita. Although Medicaid pays for the health care of approximately 15 percent of all diabetics, few studies have examined diabetes in adult Medicaid recipients at the county level. The present study thus addresses a critical information gap, while illustrating the utility of ring maps in multivariate investigations of population health and environmental context.</p> <p>Results</p> <p>Ring maps showed substantial racial disparity in diabetes prevalence among adult Medicaid recipients and suggested an association between adult African American diabetes prevalence and rurality. Rurality was significantly positively associated with diabetes prevalence among adult African American Medicaid recipients in a multivariate statistical model.</p> <p>Conclusions</p> <p>Efforts to reduce diabetes among adult African American Medicaid recipients must extend to rural African Americans. Ring maps can be used to integrate diverse data sets, explore attribute associations, and achieve insights critical to the promotion of population health.</p

Artificial drainage of peatlands: hydrological and hydrochemical process and wetland restoration

Peatlands have been subject to artificial drainage for centuries. This drainage has been in response to agricultural demand, forestry, horticultural and energy properties of peat and alleviation of flood risk. However, the are several environmental problems associated with drainage of peatlands. This paper describes the nature of these problems and examines the evidence for changes in hydrological and hydrochemical processes associated with these changes. Traditional black-box water balance approaches demonstrate little about wetland dynamics and therefore the science of catchment response to peat drainage is poorly understood. It is crucial that a more process-based approach be adopted within peatland ecosystems. The environmental problems associated with peat drainage have led, in part, to a recent reversal in attitudes to peatlands and we have seen a move towards wetland restoration. However, a detailed understanding of hydrological, hydrochemical and ecological process-interactions will be fundamental if we are to adequately restore degraded peatlands, preserve those that are still intact and understand the impacts of such management actions at the catchment scale

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Effect of collaborative depression treatment on risk for diabetes: A 9-year follow-up of the IMPACT randomized controlled trial

Considerable epidemiologic evidence and plausible biobehavioral mechanisms suggest that depression is an independent risk factor for diabetes. Moreover, reducing the elevated diabetes risk of depressed individuals is imperative given that both conditions are leading causes of death and disability. However, because no prior study has examined clinical diabetes outcomes among depressed patients at risk for diabetes, the question of whether depression treatment prevents or delays diabetes onset remains unanswered. Accordingly, we examined the effect of a 12-month collaborative care program for late-life depression on 9-year diabetes incidence among depressed, older adults initially free of diabetes. Participants were 119 primary care patients [M (SD) age: 67.2 (6.9) years, 41% African American] with a depressive disorder but without diabetes enrolled at the Indiana sites of the Improving Mood-Promoting Access to Collaborative Treatment (IMPACT) trial. Incident diabetes cases were defined as diabetes diagnoses, positive laboratory values, or diabetes medication prescription, and were identified using electronic medical record and Medicare/Medicaid data. Surprisingly, the rate of incident diabetes in the collaborative care group was 37% (22/59) versus 28% (17/60) in the usual care group. Even though the collaborative care group exhibited greater reductions in depressive symptom severity (p = .024), unadjusted (HR = 1.29, 95% CI: 0.69-2.43, p = .428) and adjusted (HR = 1.18, 95% CI: 0.61-2.29, p = .616) Cox proportional hazards models indicated that the risk of incident diabetes did not differ between the treatment groups. Our novel preliminary findings raise the possibility that depression treatment alone may be insufficient to reduce the excess diabetes risk of depressed, older adults

Use of an innovative model to evaluate mobility in seniors with lower-limb amputations of vascular origin: a pilot study

<p>Abstract</p> <p>Background</p> <p>The mobility of older individuals has often been only partially assessed, without considering all important aspects such as potential (available) versus effective (used) mobilities and the physical and psychosocial factors that modulate them. This study proposes a new model for evaluating mobility that considers all important aspects, applied here to lower-limb amputees with vascular origin. This model integrates the concepts of potential mobility (e.g. balance, speed of movement), effective mobility (e.g. life habits, movements in living areas) and factors that modulate these two types of mobility (e.g. strength, sensitivity, social support, depression). The main objective was to characterize potential and effective mobility as well as mobility modulators in a small sample of people with lower-limb amputations of vascular origin with different characteristics. The second objective of this pilot study was to assess the feasibility of measuring all variables in the model in a residential context.</p> <p>Methods</p> <p>An observational and transversal design was used with a heterogeneous sample of 10 participants with a lower-limb amputation of vascular origin, aged 51 to 83, assessed between eight and 18 months after discharge from an acute care hospital. A questionnaire of participant characteristics and 16 reliable and valid measurements were used.</p> <p>Results</p> <p>The results show that the potential mobility indicators do not accurately predict effective mobility, i.e., participants who perform well on traditional measures done in the laboratory or clinic are not always those who perform well in the real world. The model generated 4 different profiles (categories) of participants ranging from reduced to excellent potential mobility and low to excellent effective mobility, and characterized the modulating factors. The evaluations were acceptable in terms of the time taken (three hours) and the overall measurements, with a few exceptions, which were modified to optimize the data collected and the classification of the participants. For the population assessed, the results showed that some of the negative modulators (particularly living alone, no rehabilitation, pain, limited social support, poor muscle strength) played an important role in reducing effective mobility.</p> <p>Conclusion</p> <p>The first use of the model revealed interesting data that add to our understanding of important aspects linked to potential and effective mobility as well as modulators. The feasibility of measuring all variables in the model in a residential context was demonstrated. A study with a large number of participants is now warranted to rigorously characterize mobility levels of lower-limb amputees with vascular origin.</p

CropPol: a dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved

Is exposure to formaldehyde in air causally associated with leukemia?—A hypothesis-based weight-of-evidence analysis

Recent scientific debate has focused on the potential for inhaled formaldehyde to cause lymphohematopoietic cancers, particularly leukemias, in humans. The concern stems from certain epidemiology studies reporting an association, although particulars of endpoints and dosimetry are inconsistent across studies and several other studies show no such effects. Animal studies generally report neither hematotoxicity nor leukemia associated with formaldehyde inhalation, and hematotoxicity studies in humans are inconsistent. Formaldehyde's reactivity has been thought to preclude systemic exposure following inhalation, and its apparent inability to reach and affect the target tissues attacked by known leukemogens has, heretofore, led to skepticism regarding its potential to cause human lymphohematopoietic cancers. Recently, however, potential modes of action for formaldehyde leukemogenesis have been hypothesized, and it has been suggested that formaldehyde be identified as a known human leukemogen. In this article, we apply our hypothesis-based weight-of-evidence (HBWoE) approach to evaluate the large body of evidence regarding formaldehyde and leukemogenesis, attending to how human, animal, and mode-of-action results inform one another. We trace the logic of inference within and across all studies, and articulate how one could account for the suite of available observations under the various proposed hypotheses. Upon comparison of alternative proposals regarding what causal processes may have led to the array of observations as we see them, we conclude that the case fora causal association is weak and strains biological plausibility. Instead, apparent association between formaldehyde inhalation and leukemia in some human studies is better interpreted as due to chance or confounding