Context Morbidity and mortality rates in hemodialysis patients remain excessive. Alterations in the delivery of dialysis may lead to improved patient outcomes. Objective To compare the effects of frequent nocturnal hemodialysis vs conventional hemodialysis on change in left ventricular mass and health-related quality of life over 6 months. Design, Setting, and Participants A 2-group, parallel, randomized controlled trial conducted at 2 Canadian university centers between August 2004 and December 2006. A total of 52 patients undergoing hemodialysis were recruited. Intervention Participants were randomly assigned in a 1:1 ratio to receive nocturnal hemodialysis 6 times weekly or conventional hemodialysis 3 times weekly. Main Outcome Measures The primary outcome was change in left ventricular mass, as measured by cardiovascular magnetic resonance imaging. The secondary outcomes were patient-reported quality of life, blood pressure, mineral metabolism, and use of medications. Results Frequent nocturnal hemodialysis significantly improved the primary outcome (mean left ventricular mass difference between groups, 15.3 g, 95% confidence interval [CI], 1.0 to 29.6 g; P=.04). Frequent nocturnal hemodialysis did not significantly improve quality of life (difference of change in EuroQol 5-D index from baseline, 0.05; 95% CI, −0.07 to 0.17; P=.43). However, frequent nocturnal hemodialysis was associated with clinically and statistically significant improvements in selected kidney-specific domains of quality of life (P=.01 for effects of kidney disease and P=.02 for burden of kidney disease). Frequent nocturnal hemodialysis was also associated with improvements in systolic blood pressure (P=.01 after adjustment) and mineral metabolism, including a reduction in or discontinuation of antihypertensive medications (16/26 patients in the nocturnal hemodialysis group vs 3/25 patients in the conventional hemodialysis group; PϽ.001) and oral phosphate binders (19/26 patients in the nocturnal hemodialysis group vs 3/25 patients in the conventional dialysis group; PϽ.001). No benefit in anemia management was seen with nocturnal hemodialysis. Conclusion This preliminary study revealed that, compared with conventional hemodialysis (3 times weekly), frequent nocturnal hemodialysis improved left ventricular mass, reduced the need for blood pressure medications, improved some measures of mineral metabolism, and improved selected measures of quality of life.

The study demonstrated that five weekly intra-articular injections of sodium hyaluronate (Hyalgan) were superior to placebo and well tolerated in patients with osteoarthritis of the knee with a symptomatic benefit which persisted for 6 months.

To date, there is no clinically agreed-upon diagnostic test for acute respiratory distress syndrome (ARDS): the condition is still diagnosed on the basis of a constellation of clinical findings, laboratory tests, and radiological images. Development of ARDS biomarkers has been in a state of continuous flux during the past four decades. To address ARDS heterogeneity, several studies have recently focused on subphenotyping the disease on the basis of observable clinical characteristics and associated blood biomarkers. However, the strong correlation between identified biomarkers and ARDS subphenotypes has yet to establish etiology; hence, there is a need for the adoption of other methodologies for studying ARDS. In this review, we will shed light on ARDS metabolomics research in the literature and discuss advances and major obstacles encountered in ARDS metabolomics research. Generally, the ARDS metabolomics studies focused on identification of differentiating metabolites for diagnosing ARDS, but they were performed to different standards in terms of sample size, selection of control cohort, type of specimens collected, and measuring technique utilized. Virtually none of these studies have been properly validated to identify true metabolomics biomarkers of ARDS. Though in their infancy, metabolomics studies exhibit promise to unfold the biological processes underlying ARDS and, in our opinion, have great potential for pushing forward our present understanding of ARDS.

Aims: In this study we aimed to identify ARDS metabolic fingerprints in selected patient cohorts, compare the metabolic profiles of direct vs indirect ARDS and hypoinflammatory vs hyperinflammatory ARDS. Hypothesis: We hypothesize that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints which set ARDS apart from other ICU conditions, help examine ARDS subphenotypes and clinical subgroups.Subjects: 108 ARDS patients and 27 ICU ventilated controls were analyzed. Samples were randomly divided into 2/3 training and 1/3 test sets. Methods: Samples were analyzed using proton nuclear magnetic resonance spectroscopy (1H-NMR) and gas chromatography mass spectrometry (GC-MS). 12 proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct vs indirect ARDS subgroups and hypoinflammatory vs hyperinflammatory ARDS subgroups were compared.Results: Serum metabolomics and proteins/cytokines have similar AUROC when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibit correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups are metabolically distinct.Limitations: Our identified metabolic fingerprints are not ARDS diagnostic biomarkers. Further research is required to ascertain generalizability.Conclusions: ARDS patients are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.

Metabolomics in critical care medicine: a new approach to biomarker discovery Abstract Purpose: To present an overview and comparison of the main metabolomics techniques ( 1 H NMR, GC-MS, and LC-MS) and their current and potential use in critical care medicine.Source: This is a focused review, not a systematic review, using the PubMed database as the predominant source of references to compare metabolomics techniques.Principal Findings: 1 H NMR, GC-MS, and LC-MS are complementary techniques that can be used on a variety of biofluids for metabolomics analysis of patients in the Intensive Care Unit (ICU). These techniques have been successfully used for diagnosis and prognosis in the ICU and other clinical settings; for example, in patients with septic shock and community-acquired pneumonia. Conclusion:Metabolomics is a powerful tool that has strong potential to impact diagnosis and prognosis and to examine responses to treatment in critical care medicine through diagnostic and prognostic biomarker and biopattern identification. REVIEW ARTICLE © 2014 CIMClin Invest Med • Vol 37, no 6, December 2014 E363 Metabolomics refers to the systems level analysis of the metabolism and metabolites in response to physiological stimuli, such as disease or drug administration [1]. The application of clinical metabolomics to the complex illnesses of critical care medicine is relatively new [1,2]. Metabolomics, which is most commonly based on nuclear magnetic resonance (NMR) spectroscopy, gas chromatography-mass spectrometry (GC-MS) and/or liquid chromatography-mass spectrometry (LC-MS) platforms, provides an accurate reflection of the metabolic processes and pathways at play within the human body at a particular moment in time [1]. History of metabolomicsWhile the terms metabolomics and metabonomics are now used interchangeably, their original definitions focused on the level of analysis and the type of analytical technology used. This paper uses the term metabolomics as the terms are often treated as interchangeable and it is more commonly used in the literature than the term metabonomics [1,2]. Nicholson, who first defined metabonomics in 1999, described it as a systemsbased strategy to "measure the global, dynamic metabolic response of living systems to biological stimuli or genetic manipulation" [2]. Conversely, metabolomics was defined as the identification, analysis, and quantification of each metabolite within a biosample [3]. Metabolomics and metabonomics can also be defined by their method of analysis: metabolomics was originally described as a GC-MS-based approach in the study of plant metabolomes [3] and metabonomics was originally defined as NMR-based study of mammalian systems [2]. Although metabolomics and metabonomics were not defined until the late 1990s, biofluid analysis by GC-MS was first reported in the 1960s and 1970s [4]. The basis of today's metabolomics field started in the early 1980s, when NMR technology became adequately sensitive for biofluid metabolite identification. Combined with the use of statisti...