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- Diabetes mellitus (1)
- Diabetic nephropathies (1)
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Institute
Motivation
The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.
Main types of variables included
The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.
Spatial location and grain
BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).
Time period and grain
BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.
Major taxa and level of measurement
BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
Software format
.csv and .SQL.
Aim
Plant functional groups are widely used in community ecology and earth system modelling to describe trait variation within and across plant communities. However, this approach rests on the assumption that functional groups explain a large proportion of trait variation among species. We test whether four commonly used plant functional groups represent variation in six ecologically important plant traits.
Location
Tundra biome.
Time period
Data collected between 1964 and 2016.
Major taxa studied
295 tundra vascular plant species.
Methods
We compiled a database of six plant traits (plant height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, seed mass) for tundra species. We examined the variation in species-level trait expression explained by four traditional functional groups (evergreen shrubs, deciduous shrubs, graminoids, forbs), and whether variation explained was dependent upon the traits included in analysis. We further compared the explanatory power and species composition of functional groups to alternative classifications generated using post hoc clustering of species-level traits.
Results
Traditional functional groups explained significant differences in trait expression, particularly amongst traits associated with resource economics, which were consistent across sites and at the biome scale. However, functional groups explained 19% of overall trait variation and poorly represented differences in traits associated with plant size. Post hoc classification of species did not correspond well with traditional functional groups, and explained twice as much variation in species-level trait expression.
Main conclusions
Traditional functional groups only coarsely represent variation in well-measured traits within tundra plant communities, and better explain resource economic traits than size-related traits. We recommend caution when using functional group approaches to predict tundra vegetation change, or ecosystem functions relating to plant size, such as albedo or carbon storage. We argue that alternative classifications or direct use of specific plant traits could provide new insights for ecological prediction and modelling.
Background: Cardiovascular (CV) outcome trials in type 2 diabetes (T2D) have underrepresented patients with chronic kidney disease (CKD), leading to uncertainty regarding their kidney efficacy and safety. The CARMELINA (R) trial aims to evaluate the effects of linagliptin, a DPP-4 inhibitor, on both CV and kidney outcomes in a study population enriched for cardio-renal risk.
Methods: CARMELINA (R) is a randomized, double-blind, placebo-controlled clinical trial conducted in 27 countries in T2D patients at high risk of CV and/or kidney events. Participants with evidence of CKD with or without CV disease and HbA1c 6.5-10.0% (48-86 mmol/mol) were randomized 1:1 to receive linagliptin once daily or matching placebo, added to standard of care adjusted according to local guidelines. The primary outcome is time to first occurrence of CV death, non-fatal myocardial infarction, or non-fatal stroke. The key secondary outcome is a composite of time to first sustained occurrence of end-stage kidney disease, >= 40% decrease in estimated glomerular filtration rate (eGFR) from baseline, or renal death. CV and kidney events are prospectively adjudicated by independent, blinded clinical event committees. CARMELINA (R) was designed to continue until at least 611 participants had confirmed primary outcome events. Assuming a hazard ratio of 1.0, this provides 90% power to demonstrate non-inferiority of linagliptin versus placebo within the pre-specified non-inferiority margin of 1.3 at a one-sided a-level of 2.5%. If non-inferiority of linagliptin for the primary outcome is demonstrated, then its superiority for both the primary outcome and the key secondary outcome will be investigated with a sequentially rejective multiple test procedure.
Results: Between July 2013 and August 2016, 6980 patients were randomized and took >= 1 dose of study drug (40.6, 33.1, 16.9, and 9.4% from Europe, South America, North America, and Asia, respectively). At baseline, mean +/- SD age was 65.8 +/- 9.1 years, HbA1c 7.9 +/- 1.0%, BMI 31.3 +/- 5.3 kg/m(2), and eGFR 55 +/- 25 mL/min/1.73 m(2). A total of 5148 patients (73.8%) had prevalent kidney disease (defined as eGFR < 60 mL/min/1.73 m(2) or macroalbuminuria [albumin-to-creatinine ratio > 300 mg/g]) and 3990 patients (57.2%) had established CV disease with increased albuminuria; these characteristics were not mutually exclusive. Microalbuminuria (n = 2896 [41.5%]) and macroalbuminuria (n = 2691 [38.6%]) were common.
Conclusions: CARMELINA (R) will add important information regarding the CV and kidney disease clinical profile of linagliptin by including an understudied, vulnerable cohort of patients with T2D at highest cardio-renal risk.