Nationwide Study to Predict Colonic Ischemia after Abdominal Aortic Aneurysm Repair in The Netherlands

BACKGROUND: Colonic ischemia remains a severe complication after abdominal aortic aneurysm (AAA) repair and is associated with a high mortality. With open repair being one of the main risk factors of colonic ischemia, deciding between endovascular or open aneurysm repair should be based on tailor-made medicine. This study aims to identify high-risk patients of colonic ischemia, a risk that can be taken into account while deciding on AAA treatment strategy.METHODS: A nationwide population-based cohort study of 9,433 patients who underwent an AAA operation between 2014 and 2016 was conducted. Potential risk factors were determined by reviewing prior studies and univariate analysis. With logistic regression analysis, independent predictors of intestinal ischemia were established. These variables were used to form a prediction model.RESULTS: Intestinal ischemia occurred in 267 patients (2.8%). Occurrence of intestinal ischemia was seen significantly more in open repair versus endovascular aneurysm repair (7.6% vs. 0.9%; P &lt; 0.001). This difference remained significant after stratification by urgency of the procedure, in both intact open (4.2% vs. 0.4%; P &lt; 0.001) and ruptured open repair (15.0% vs. 6.2%); P &lt; 0.001). Rupture of the AAA was the most important predictor of developing intestinal ischemia (odds ratio [OR], 5.9, 95% confidence interval [CI] 4.4-8.0), followed by having a suprarenal AAA (OR 3.4; CI 1.1-10.6). Associated procedural factors were open repair (OR 2.8; 95% CI 1.9-4.2), blood loss &gt;1L (OR 3.6; 95% CI 1.7-7.5), and prolonged operating time (OR 2.0; 95% CI 1.4-2.8). Patient characteristics included having peripheral arterial disease (OR 2.4; 95% CI 1.3-4.4), female gender (OR 1.7; 95% CI 1.2-2.4), renal insufficiency (OR 1.7; 1.3-2.2), and pulmonary history (OR 1.6; 95% CI 1.2-2.2). Age &lt;68 years proved to be a protective factor (OR 0.5; 95% CI 0.4-0.8). Associated mortality was higher in patients with intestinal ischemia versus patients without (50.6% vs. 5.1%, P &lt; 0.001). Each predictor was given a score between 1 and 4. Patients with a score of ≥10 proved to be at high risk. A prediction model with an excellent AUC = 0.873 (95% CI 0.855-0.892) could be formed.CONCLUSIONS: One of the main risk factors is open repair. Several other risk factors can contribute to developing colonic ischemia after AAA repair. The proposed prediction model can be used to identify patients at high risk for developing colonic ischemia. With the current trend in AAA repair leaning toward open repair for better long-term results, our prediction model allows a better informed decision can be made in AAA treatment strategy.</p

Nationwide Study to Predict Colonic Ischemia after Abdominal Aortic Aneurysm Repair in The Netherlands

BACKGROUND: Colonic ischemia remains a severe complication after abdominal aortic aneurysm (AAA) repair and is associated with a high mortality. With open repair being one of the main risk factors of colonic ischemia, deciding between endovascular or open aneurysm repair should be based on tailor-made medicine. This study aims to identify high-risk patients of colonic ischemia, a risk that can be taken into account while deciding on AAA treatment strategy. METHODS: A nationwide population-based cohort study of 9,433 patients who underwent an AAA operation between 2014 and 2016 was conducted. Potential risk factors were determined by reviewing prior studies and univariate analysis. With logistic regression analysis, independent predictors of intestinal ischemia were established. These variables were used to form a prediction model. RESULTS: Intestinal ischemia occurred in 267 patients (2.8%). Occurrence of intestinal ischemia was seen significantly more in open repair versus endovascular aneurysm repair (7.6% vs. 0.9%; P 1L (OR 3.6; 95% CI 1.7-7.5), and prolonged operating time (OR 2.0; 95% CI 1.4-2.8). Patient characteristics included having peripheral arterial disease (OR 2.4; 95% CI 1.3-4.4), female gender (OR 1.7; 95% CI 1.2-2.4), renal insufficiency (OR 1.7; 1.3-2.2), and pulmonary history (OR 1.6; 95% CI 1.2-2.2). Age <68 years proved to be a protective factor (OR 0.5; 95% CI 0.4-0.8). Associated mortality was higher in patients with intestinal ischemia versus patients without (50.6% vs. 5.1%, P < 0.001). Each predictor was given a score between 1 and 4. Patients with a score of ≥10 proved to be at high risk. A prediction model with an excellent AUC = 0.873 (95% CI 0.855-0.892) could be formed. CONCLUSIONS: One of the main risk factors is open repair. Several other risk factors can contribute to developing colonic ischemia after AAA repair. The proposed prediction model can be used to identify patients at high risk for developing colonic ischemia. With the current trend in AAA repair leaning toward open repair for better long-term results, our prediction model allows a better informed decision can be made in AAA treatment strategy

Steroidogenic control of liver metabolism through a nuclear receptor-network

Objective: Coupling metabolic and reproductive pathways is essential for the survival of species. However, the functions of steroidogenic enzymes expressed in metabolic tissues are largely unknown. Methods and results: Here, we show that in the liver, the classical steroidogenic enzyme Cyp17a1 forms an essential nexus for glucose and ketone metabolism during feed-fast cycles. Both gain- and loss-of-function approaches are used to show that hepatic Cyp17a1 is induced by fasting, catalyzes the production of at least one hormone-ligand (DHEA) for the nuclear receptor PPARα, and is ultimately required for maintaining euglycemia and ketogenesis during nutrient deprivation. The feedback-loop that terminates Cyp17a1-PPARα activity, and re-establishes anabolic liver metabolism during re-feeding is mapped to postprandial bile acid-signaling, involving the receptors FXR, SHP and LRH-1. Conclusions: Together, these findings represent a novel paradigm of homeostatic control in which nutritional cues feed-forward on to metabolic pathways by influencing extragonadal steroidogenesis

Steroidogenic control of liver metabolism through a nuclear receptor-network

Objective: Coupling metabolic and reproductive pathways is essential for the survival of species. However, the functions of steroidogenic enzymes expressed in metabolic tissues are largely unknown. Methods and results: Here, we show that in the liver, the classical steroidogenic enzyme Cyp17a1 forms an essential nexus for glucose and ketone metabolism during feed-fast cycles. Both gain- and loss-of-function approaches are used to show that hepatic Cyp17a1 is induced by fasting, catalyzes the production of at least one hormone-ligand (DHEA) for the nuclear receptor PPARα, and is ultimately required for maintaining euglycemia and ketogenesis during nutrient deprivation. The feedback-loop that terminates Cyp17a1-PPARα activity, and re-establishes anabolic liver metabolism during re-feeding is mapped to postprandial bile acid-signaling, involving the receptors FXR, SHP and LRH-1. Conclusions: Together, these findings represent a novel paradigm of homeostatic control in which nutritional cues feed-forward on to metabolic pathways by influencing extragonadal steroidogenesis

Mutations in DDHD2, Encoding an Intracellular Phospholipase A(1), Cause a Recessive Form of Complex Hereditary Spastic Paraplegia

Contains fulltext : 108770.pdf (publisher's version ) (Closed access)We report on four families affected by a clinical presentation of complex hereditary spastic paraplegia (HSP) due to recessive mutations in DDHD2, encoding one of the three mammalian intracellular phospholipases A(1) (iPLA(1)). The core phenotype of this HSP syndrome consists of very early-onset (<2 years) spastic paraplegia, intellectual disability, and a specific pattern of brain abnormalities on cerebral imaging. An essential role for DDHD2 in the human CNS, and perhaps more specifically in synaptic functioning, is supported by a reduced number of active zones at synaptic terminals in Ddhd-knockdown Drosophila models. All identified mutations affect the protein's DDHD domain, which is vital for its phospholipase activity. In line with the function of DDHD2 in lipid metabolism and its role in the CNS, an abnormal lipid peak indicating accumulation of lipids was detected with cerebral magnetic resonance spectroscopy, which provides an applicable diagnostic biomarker that can distinguish the DDHD2 phenotype from other complex HSP phenotypes. We show that mutations in DDHD2 cause a specific complex HSP subtype (SPG54), thereby linking a member of the PLA(1) family to human neurologic disease

The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study.

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR<5%) age-specific effects, of which 11 had larger effects in younger (<50y) than in older adults (≥50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may provide further insights into the biology that underlies weight change with age or the sexually dimorphism of body shape

Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study

[This corrects the article DOI: 10.1371/journal.pgen.1005378.].status: publishe

Age-dependent BMI loci.

<p>Effect estimates (beta ±95CI) per standard deviation in BMI and risk allele for loci showing age-differences in men & women ≤50y compared to men & women >50y. Loci are ordered by greater magnitude of effect in men & women ≤50y compared to men & women >50y. (95%CI: 95% confidence interval; BMI: body mass index; SD: standard deviation, *Newly identified loci).</p

Forty-four WHR_adjBMI loci showing significant sex-differences.

<p>Chr: Chromosome; Pos: position; EAF: Effect Allele Frequency; EA: Effect allele; OA: Other allele</p><p>^a ‘Yes’ if the locus is mentioned as WHR_adjBMI locus for the first time</p><p>^b ‘Yes’ if the sex-difference in the effect on WHR_adjBMI is reported for the first time</p><p>^c Effect allele is according to the WHR_adjBMI increasing allele according to the associated sex.</p><p>The table shows the sex-specific (age-group combined) results, ordered by largest, positive effect in women to largest, negative effect in women. The age- and sex-specific results (four strata), more detailed information on the loci and on the screens for which they were detected are given in <b><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005378#pgen.1005378.s021" target="_blank">S5 Table</a></b>.</p