Systematic review and network meta-analysis on the efficacy of evolocumab and other therapies for the management of lipid levels in hyperlipidemia

Background: The proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors evolocumab and alirocumab substantially reduce low‐density lipoprotein cholesterol (LDL‐C) when added to statin therapy in patients who need additional LDL‐C reduction. Methods and Results: We conducted a systematic review and network meta‐analysis of randomized trials of lipid‐lowering therapies from database inception through August 2016 (45 058 records retrieved). We found 69 trials of lipid‐lowering therapies that enrolled patients requiring further LDL‐C reduction while on maximally tolerated medium‐ or high‐intensity statin, of which 15 could be relevant for inclusion in LDL‐C reduction networks with evolocumab, alirocumab, ezetimibe, and placebo as treatment arms. PCSK9 inhibitors significantly reduced LDL‐C by 54% to 74% versus placebo and 26% to 46% versus ezetimibe. There were significant treatment differences for evolocumab 140 mg every 2 weeks at the mean of weeks 10 and 12 versus placebo (−74.1%; 95% credible interval −79.81% to −68.58%), alirocumab 75 mg (−20.03%; 95% credible interval −27.32% to −12.96%), and alirocumab 150 mg (−13.63%; 95% credible interval −22.43% to −5.33%) at ≥12 weeks. Treatment differences were similar in direction and magnitude for PCSK9 inhibitor monthly dosing. Adverse events were similar between PCSK9 inhibitors and control. Rates of adverse events were similar between PCSK9 inhibitors versus placebo or ezetimibe. Conclusions: PCSK9 inhibitors added to medium‐ to high‐intensity statin therapy significantly reduce LDL‐C in patients requiring further LDL‐C reduction. The network meta‐analysis showed a significant treatment difference in LDL‐C reduction for evolocumab versus alirocumab

Network meta‐analysis of randomized trials evaluating the comparative efficacy of lipid‐lowering therapies added to maximally tolerated statins for the reduction of low‐density lipoprotein cholesterol

Background: Lowering low‐density lipoprotein cholesterol (LDL‐C) levels decreases major cardiovascular events and is recommended for patients at elevated cardiovascular risk. However, appropriate doses of statin therapy are often insufficient to reduce LDL‐C in accordance with current guidelines. In such cases, treatment could be supplemented with nonstatin lipid‐lowering therapy. Methods and Results: A systematic literature review and network meta‐analysis were conducted on randomized controlled trials of nonstatin lipid‐lowering therapy added to maximally tolerated statins, including statin‐intolerant patients. The primary objective was to assess relative efficacy of nonstatin lipid‐lowering therapy in reducing LDL‐C levels at week 12. Secondary objectives included the following: LDL‐C level reduction at week 24 and change in non–high‐density lipoprotein cholesterol and apolipoprotein B at week 12. There were 48 randomized controlled trials included in the primary network meta‐analysis. All nonstatin agents significantly reduced LDL‐C from baseline versus placebo, regardless of background therapy. At week 12, evolocumab, 140 mg every 2 weeks (Q2W)/420 mg once a month, and alirocumab, 150 mg Q2W, were the most efficacious regimens, followed by alirocumab, 75 mg Q2W, alirocumab, 300 mg once a month, inclisiran, bempedoic acid/ezetimibe fixed‐dose combination, and ezetimibe and bempedoic acid used as monotherapies. Primary end point results were generally consistent at week 24, and for other lipid end points at week 12. Conclusions: Evolocumab, 140 mg Q2W/420 mg once a month, and alirocumab, 150 mg Q2W, were consistently the most efficacious nonstatin regimens when added to maximally tolerated statins to lower LDL‐C, non–high‐density lipoprotein cholesterol, and apolipoprotein B levels and facilitate attainment of guideline‐recommended risk‐stratified lipoprotein levels

Designing multifunctional landscapes for forest conservation

A multifunctional landscape approach to forest protection has been advocated for tropical countries. Designing such landscapes necessitates that the role of different land uses in protecting forest be evaluated, along with the spatial interactions between land uses. However, such evaluations have been hindered by a lack of suitable analysis methodologies and data with fine spatial resolution over long time periods. We demonstrate the utility of a matching method with multiple categories to evaluate the role of alternative land uses in protecting forest. We also assessed the impact of land use change trajectories on the rate of deforestation. We employed data from Kalimantan (Indonesian Borneo) at three different time periods during 2000–2012 to illustrate our approach. Four single land uses (protected areas (PA), natural forest logging concessions (LC), timber plantation concessions (TC) and oil-palm plantation concessions (OC)) and two mixed land uses (mixed concessions and the overlap between concessions and PA) were assessed. The rate of deforestation was found to be lowest for PA, followed by LC. Deforestation rates for all land uses tended to be highest for locations that share the characteristics of areas in which TC or OC are located (e.g. degraded areas), suggesting that these areas are inherently more susceptible to deforestation due to foregone opportunities. Our approach provides important insights into how multifunctional landscapes can be designed to enhance the protection of biodiversity

Correlating Gene Expression Variation with cis-Regulatory Polymorphism in Saccharomyces cerevisiae

Identifying the nucleotides that cause gene expression variation is a critical step in dissecting the genetic basis of complex traits. Here, we focus on polymorphisms that are predicted to alter transcription factor binding sites (TFBSs) in the yeast, Saccharomyces cerevisiae. We assembled a confident set of transcription factor motifs using recent protein binding microarray and ChIP-chip data and used our collection of motifs to predict a comprehensive set of TFBSs across the S. cerevisiae genome. We used a population genomics analysis to show that our predictions are accurate and significantly improve on our previous annotation. Although predicting gene expression from sequence is thought to be difficult in general, we identified a subset of genes for which changes in predicted TFBSs correlate well with expression divergence between yeast strains. Our analysis thus demonstrates both the accuracy of our new TFBS predictions and the feasibility of using simple models of gene regulation to causally link differences in gene expression to variation at individual nucleotides

Soil microbial CNP and respiration responses to organic matter and nutrient additions: evidence from a tropical soil incubation

Soil nutrient availability has a strong influence on the fate of soil carbon (C) during microbial decomposition, contributing to Earth's C balance. While nutrient availability itself can impact microbial physiology and C partitioning between biomass and respiration during soil organic matter decomposition, the availability of labile C inputs may mediate the response of microorganisms to nutrient additions. As soil organic matter is decomposed, microorganisms retain or release C, nitrogen (N) or phosphorus (P) to maintain a stoichiometric balance. Although the concept of a microbial stoichiometric homeostasis has previously been proposed, microbial biomass CNP ratios are not static, and this may have very relevant implications for microbial physiological activities. Here, we tested the hypothesis that N, P and potassium (K) nutrient additions impact C cycling in a tropical soil due to microbial stoichiometric constraints to growth and respiration, and that the availability of energy-rich labile organic matter in the soil (i.e. leaf litter) mediates the response to nutrient addition. We incubated tropical soil from French Guiana with a ¹³C labeled leaf litter addition and with mineral nutrient additions of +K, +N, +NK, +PK and +NPK for 30 days. We found that litter additions led to a ten-fold increase in microbial respiration and a doubling of microbial biomass C, along with greater microbial N and P content. We found some evidence that P additions increased soil CO² fluxes. Additionally, we found microbial biomass CP and NP ratios varied more widely than CN in response to nutrient and organic matter additions, with important implications for the role of microorganisms in C cycling. The addition of litter did not prime soil organic matter decomposition, except in combination with +NK fertilization, indicating possible P-mining of soil organic matter in this P-poor tropical soil. Together, these results point toward an ultimate labile organic substrate limitation of soil microorganisms in this tropical soil, but also indicate a complex interaction between C, N, P and K availability. This highlights the difference between microbial C cycling responses to N, P, or K additions in the tropics and explains why coupled C, N and P cycling modeling efforts cannot rely on strict microbial stoichiometric homeostasis as an underlying assumption

Adipose Tissue Fatty Acid Patterns and Changes in Anthropometry: A Cohort Study

INTRODUCTION: Diets rich in n-3 long chain polyunsaturated fatty acids (LC-PUFA), but low in n-6 LC-PUFA and 18:1 trans-fatty acids (TFA), may lower the risk of overweight and obesity. These fatty acids have often been investigated individually. We explored associations between global patterns in adipose tissue fatty acids and changes in anthropometry. METHODS: 34 fatty acid species from adipose tissue biopsies were determined in a random sample of 1100 men and women from a Danish cohort study. We used sex-specific principal component analysis and multiple linear regression to investigate the associations of adipose tissue fatty acid patterns with changes in weight, waist circumference (WC), and WC controlled for changes in body mass index (WC(BMI)), adjusting for confounders. RESULTS: 7 principal components were extracted for each sex, explaining 77.6% and 78.3% of fatty acid variation in men and women, respectively. Fatty acid patterns with high levels of TFA tended to be positively associated with changes in weight and WC for both sexes. Patterns with high levels of n-6 LC-PUFA tended to be negatively associated with changes in weight and WC in men, and positively associated in women. Associations with patterns with high levels of n-3 LC-PUFA were dependent on the context of the rest of the fatty acid pattern. CONCLUSIONS: Adipose tissue fatty acid patterns with high levels of TFA may be linked to weight gain, but patterns with high n-3 LC-PUFA did not appear to be linked to weight loss. Associations depended on characteristics of the rest of the pattern

Finding regulatory elements and regulatory motifs: a general probabilistic framework

Over the last two decades a large number of algorithms has been developed for regulatory motif finding. Here we show how many of these algorithms, especially those that model binding specificities of regulatory factors with position specific weight matrices (WMs), naturally arise within a general Bayesian probabilistic framework. We discuss how WMs are constructed from sets of regulatory sites, how sites for a given WM can be discovered by scanning of large sequences, how to cluster WMs, and more generally how to cluster large sets of sites from different WMs into clusters. We discuss how 'regulatory modules', clusters of sites for subsets of WMs, can be found in large intergenic sequences, and we discuss different methods for ab initio motif finding, including expectation maximization (EM) algorithms, and motif sampling algorithms. Finally, we extensively discuss how module finding methods and ab initio motif finding methods can be extended to take phylogenetic relations between the input sequences into account, i.e. we show how motif finding and phylogenetic footprinting can be integrated in a rigorous probabilistic framework. The article is intended for readers with a solid background in applied mathematics, and preferably with some knowledge of general Bayesian probabilistic methods. The main purpose of the article is to elucidate that all these methods are not a disconnected set of individual algorithmic recipes, but that they are just different facets of a single integrated probabilistic theory

Evidence of Disseminated Intravascular Coagulation in a Hemorrhagic Fever with Renal Syndrome—Scoring Models and Severe Illness

Background: Viral hemorrhagic fevers (VHF) are considered to be a serious threat to public health worldwide with up to 100 million cases annually. The general hypothesis is that disseminated intravascular coagulation (DIC) is an important part of the pathogenesis. The study objectives were to study the variability of DIC in consecutive patients with acute hemorrhagic fever with renal syndrome (HFRS), and to evaluate if different established DIC-scores can be used as a prognostic marker for a more severe illness. Method and Findings: In a prospective study 2006–2008, data from 106 patients with confirmed HFRS were analyzed and scored for the presence of DIC according to six different templates based on criteria from the International Society on Thrombosis and Haemostasis (ISTH). The DIC-scoring templates with a fibrinogen/CRP-ratio were most predictive, with predictions for moderate/severe illness (p,0.01) and bleeding of moderate/major importance (p,0.05). With these templates, 18.9–28.3 % of the patients were diagnosed with DIC. Conclusions: DIC was found in about one fourth of the patients and correlated with a more severe disease. This supports that DIC is an important part of the pathogenesis in HFRS. ISTH-scores including fibrinogen/CRP-ratio outperform models without. The high negative predictive value could be a valuable tool for the clinician. We also believe that our findings coul