The mouth, stomach and intestines

Food is chewed into digestible portions in the mouth and swallowed – a complex reflex process involving several cranial nerves. The stomach homogenizes food, begins digestion and regulates the rate at which food enters the duodenum. Pancreatic juices containing powerful digesting enzymes are added and digestion is completed in the small intestine. The large bowel dehydrates the gastrointestinal contents

Short-term prognostic implications of serum and urine neutrophil gelatinase-associated lipocalin in acute heart failure:findings from the AKINESIS study

AIMS: Kidney impairment has been associated with worse outcomes in acute heart failure (AHF), although recent studies challenge this association. Neutrophil gelatinase-associated lipocalin (NGAL) is a novel biomarker of kidney tubular injury. Its prognostic role in AHF has not been evaluated in large cohorts. The present study aimed to determine if serum NGAL (sNGAL) or urine NGAL (uNGAL) is superior to creatinine for predicting short-term outcomes in AHF. METHODS AND RESULTS: The study was conducted in an international, multicentre, prospective cohort consisting of 927 patients with AHF. Admission and peak values of sNGAL, uNGAL and uNGAL/urine creatinine (uCr) ratio were compared to admission and peak serum creatinine (sCr). The composite endpoints were death, initiation of renal replacement therapy, heart failure (HF) readmission and any emergent HF-related outpatient visit within 30 and 60 days, respectively. The mean age of the cohort was 69 years and 62% were male. The median length of stay was 6 days. The composite endpoint occurred in 106 patients and 154 patients within 30 and 60 days, respectively. Serum NGAL was more predictive than uNGAL and the uNGAL/uCr ratio but was not superior to sCr (area under the curve [AUC]; admission sNGAL 0.61 [95% confidence interval (CI) 0.55-0.67] and 0.59 [95% CI 0.54-0.65], peak sNGAL 0.60 [95% CI 0.54-0.66] and 0.57 [95% CI 0.52-0.63], admission sCr 0.60 [95% CI 0.54-0.64] and 0.59 [95% CI 0.53-0.64] [area under the curve: admission sNGAL 0.61, 95% confidence interval (CI) 0.55-0.67, and 0.59, 95% CI 0.54-0.65; peak sNGAL: 0.60, 95% CI 0.54-0.66, and 0.57, 95% CI 0.52-0.63; admission sCr: 0.60, 95% CI 0.54-0.64, and 0.59, 95% CI 0.53-0.64, at 30 and 60 days, respectively], peak sCr 0.61 [95% CI 0.55-0.67] and 0.59 [95% CI 0.54-0.64] at 30 and 60 days, respectively). NGAL was not predictive of the composite endpoint in multivariate analysis. CONCLUSIONS: Serum NGAL outperformed uNGAL but neither was superior to admission or peak sCr for predicting adverse events

Potential Utility of Cardiorenal Biomarkers for Prediction and Prognostication of Worsening Renal Function in Acute Heart Failure

Background: Multiple different pathophysiologic processes can contribute to worsening renal function (WRF) in acute heart failure. Methods and Results: We retrospectively analyzed 787 patients with acute heart failure for the relationship between changes in serum creatinine and biomarkers including brain natriuretic peptide, high sensitivity cardiac troponin I, galectin 3, serum neutrophil gelatinase-associated lipocalin, and urine neutrophil gelatinase-associated lipocalin. WRF was defined as an increase of greater than or equal to 0.3 mg/dL or 50% in creatinine within first 5 days of hospitalization. WRF was observed in 25% of patients. Changes in biomarkers and creatinine were poorly correlated (r < 0.21) and no biomarker predicted WRF better than creatinine. In the multivariable Cox analysis, brain natriuretic peptide and high sensitivity cardiac troponin I, but not WRF, were significantly associated with the 1-year composite of death or heart failure hospitali-zation. WRF with an increasing urine neutrophil gelatinase-associated lipocalin predicted an increased risk of heart failure hospitalization. Conclusions: Biomarkers were not able to predict WRF better than creatinine. The 1-year outcomes were associated with biomarkers of cardiac stress and injury but not with WRF, whereas a kidney injury bio-marker may prognosticate WRF for heart failure hospitalization

Relation of Decongestion and Time to Diuretics to Biomarker Changes and Outcomes in Acute Heart Failure

Prompt treatment may mitigate the adverse effects of congestion in the early phase of heart failure (HF) hospitalization, which may lead to improved outcomes. We analyzed 814 acute HF patients for the relationships between time to first intravenous loop diuretics, changes in biomarkers of congestion and multiorgan dysfunction, and 1-year composite end point of death or HF hospitalization. B-type natriuretic peptide (BNP), high sensitivity cardiac troponin I (hscTnI), urine and serum neutrophil gelatinase-associated lipocalin, and galectin 3 were measured at hospital admission, hospital day 1, 2, 3 and discharge. Time to diuretics was not correlated with the timing of decongestion defined as BNP decrease >= 30% compared with admission. Earlier BNP decreases but not time to diuretics were associated with earlier and greater decreases in hscTnI and urine neutrophil gelatinase-associated lipocalin, and lower incidence of the composite end point. After adjustment for confounders, only no BNP decrease at discharge was significantly associated with mortality but not the composite end point (p = 0.006 and p = 0.062, respectively). In conclusion, earlier time to decongestion but not the time to diuretics was associated with better biomarker trajectories. Residual congestion at discharge rather than the timing of decongestion predicted a worse prognosis. (C) 2021 The Authors. Published by Elsevier Inc

Decongestion, kidney injury and prognosis in patients with acute heart failure

Background: In patients with acute heart failure (AHF), the development of worsening renal function with appropriate decongestion is thought to be a benign functional change and not associated with poor prognosis. We investigated whether the benefit of decongestion outweighs the risk of concurrent kidney tubular damage and leads to better outcomes.& nbsp;Methods: We retrospectively analyzed data from the AKINESIS study, which enrolled AHF patients requiring intravenous diuretic therapy. Urine neutrophil gelatinase-associated lipocalin (uNGAL) and B-type natriuretic peptide (BNP) were serially measured during the hospitalization. Decongestion was defined as >= 30% BNP decrease at discharge compared to admission. Univariable and multivariable Cox models were assessed for oneyear mortality.& nbsp;Results: Among 736 patients, 53% had >= 30% BNP decrease at discharge. Levels of uNGAL and BNP at each collection time point had positive but weak correlations (r = 30% BNP decrease was a significant predictor after multivariable adjustment.& nbsp;Conclusions: Among AHF patients treated with diuretic therapy, decongestion was generally not associated with kidney tubular damage assessed by uNGAL. Kidney tubular damage with adequate decongestion does not impact outcomes; however, kidney injury without adequate decongestion is associated with a worse prognosis

A framework for human microbiome research

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies

Structure, function and diversity of the healthy human microbiome

Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University