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Autosomal dominant polycystic kidney disease (ADPKD) is characterized by the formation of numerous fluid-filled cysts that lead to progressive loss of functional nephrons. Currently, there is an unmet need for diagnostic and prognostic indicators of early stages of the disease. Metabolites were extracted from the urine of patients with early-stage ADPKD ( n = 48 study participants) and age- and sex-matched normal controls ( n = 47) and analyzed by liquid chromatography-mass spectrometry. Orthogonal partial least squares-discriminant analysis was used to generate a global metabolomic profile of early ADPKD for the identification of metabolic pathway alterations and discriminatory metabolites as candidates of diagnostic and prognostic biomarkers. The global metabolomic profile exhibited alterations in steroid hormone biosynthesis and metabolism, fatty acid metabolism, pyruvate metabolism, amino acid metabolism, and the urea cycle. A panel of 46 metabolite features was identified as candidate diagnostic biomarkers. Notable putative identities of candidate diagnostic biomarkers for early detection include creatinine, cAMP, deoxycytidine monophosphate, various androgens (testosterone; 5-α-androstane-3,17,dione; trans -dehydroandrosterone), betaine aldehyde, phosphoric acid, choline, 18-hydroxycorticosterone, and cortisol. Metabolic pathways associated with variable rates of disease progression included steroid hormone biosynthesis and metabolism, vitamin D3 metabolism, fatty acid metabolism, the pentose phosphate pathway, tricarboxylic acid cycle, amino acid metabolism, sialic acid metabolism, and chondroitin sulfate and heparin sulfate degradation. A panel of 41 metabolite features was identified as candidate prognostic biomarkers. Notable putative identities of candidate prognostic biomarkers include ethanolamine, C20:4 anandamide phosphate, progesterone, various androgens (5-α-dihydrotestosterone, androsterone, etiocholanolone, and epiandrosterone), betaine aldehyde, inflammatory lipids (eicosapentaenoic acid, linoleic acid, and stearolic acid), and choline. Our exploratory data support metabolic reprogramming in early ADPKD and demonstrate the ability of liquid chromatography-mass spectrometry-based global metabolomic profiling to detect metabolic pathway alterations as new therapeutic targets and biomarkers for early diagnosis and tracking disease progression of ADPKD. NEW & NOTEWORTHY To our knowledge, this study is the first to generate urinary global metabolomic profiles from individuals with early-stage ADPKD with preserved renal function for biomarker discovery. The exploratory dataset reveals metabolic pathway alterations that may be responsible for early cystogenesis and rapid disease progression and may be potential therapeutic targets and pathway sources for candidate biomarkers. From these results, we generated a panel of candidate diagnostic and prognostic biomarkers of early-stage ADPKD for future validation.

Objectives: The objective of this study was to analyse the metabolomic profiles of rheumatoid arthritis synovial fluid to test the use of global metabolomics by liquid chromatography-mass spectrometry for clinical analysis of synovial fluid., Methods: Metabolites were extracted from rheumatoid arthritis (n=3) and healthy (n=5) synovial fluid samples using 50:50 water: acetonitrile. Metabolite extracts were analysed in positive mode by normal phase liquid chromatography-mass spectrometry for global metabolomics. Statistical analyses included hierarchical clustering analysis, principal component analysis, Student's t-test, and volcano plot analysis. Metabolites were matched with known metabolite identities using METLIN and enriched for relevant pathways using IMPaLA., Results: 1018 metabolites were detected by LC-MS analysis in synovial fluid from rheumatoid arthritis and healthy patients, with 162 metabolites identified as significantly different between diseased and control. Pathways upregulated with disease included ibuprofen metabolism, glucocorticoid and mineralocorticoid metabolism, alpha-linolenic acid metabolism, and steroid hormone biosynthesis. Pathways downregulated with disease included purine and pyrimidine metabolism, biological oxidations, arginine and proline metabolism, the citrulline-nitric oxide cycle, and glutathione metabolism. Receiver operating characteristic analysis identified 30 metabolites as putative rheumatoid arthritis biomarkers including various phospholipids, diol and its derivatives, arsonoacetate, oleananoic acid acetate, docosahexaenoic acid methyl ester, and linolenic acid and eicosatrienoic acid derivatives., Conclusions: This study supports the use of global metabolomic profiling by liquid chromatography-mass spectrometry for synovial fluid analysis to provide insight into the aetiology of disease.

Osteoarthritis affects over 250 million individuals worldwide. Currently, there are no options for early diagnosis of osteoarthritis, demonstrating the need for biomarker discovery. To find biomarkers of osteoarthritis in human synovial fluid, we used high performance liquid-chromatography mass spectrometry for global metabolomic profiling. Metabolites were extracted from human osteoarthritic (n = 5), rheumatoid arthritic (n = 3), and healthy (n = 5) synovial fluid, and a total of 1233 metabolites were detected. Principal components analysis clearly distinguished the metabolomic profiles of diseased from healthy synovial fluid. Synovial fluid from rheumatoid arthritis patients contained expected metabolites consistent with the inflammatory nature of the disease. Similarly, unsupervised clustering analysis found that each disease state was associated with distinct metabolomic profiles and clusters of co-regulated metabolites. For osteoarthritis, co-regulated metabolites that were upregulated compared to healthy synovial fluid mapped to known disease processes including chondroitin sulfate degradation, arginine and proline metabolism, and nitric oxide metabolism. We utilized receiver operating characteristic analysis to determine the diagnostic value of each metabolite and identified 35 metabolites as potential biomarkers of osteoarthritis, with an area under the receiver operating characteristic curve >0.9. These metabolites included phosphatidylcholine, lysophosphatidylcholine, ceramides, myristate derivatives, and carnitine derivatives. This pilot study provides strong justification for a larger cohort-based study of human osteoarthritic synovial fluid using global metabolomics. The significance of these data is the demonstration that metabolomic profiling of synovial fluid can identify relevant biomarkers of joint disease., (Copyright © 2018 Elsevier Inc. All rights reserved.)

We present the first long-term, highly resolved prokaryotic cell concentration record obtained from a polar ice core. This record, obtained from the West Antarctic Ice Sheet (WAIS) Divide (WD) ice core, spanned from the Last Glacial Maximum (LGM) to the early Holocene (EH) and showed distinct fluctuations in prokaryotic cell concentration coincident with major climatic states. The time series also revealed a ~1,500-year periodicity with greater amplitude during the Last Deglaciation (LDG). Higher prokaryotic cell concentration and lower variability occurred during the LGM and EH than during the LDG. A sevenfold decrease in prokaryotic cell concentration coincided with the LGM/LDG transition and the global 19 ka meltwater pulse. Statistical models revealed significant relationships between the prokaryotic cell record and tracers of both marine (sea-salt sodium [ssNa]) and burning emissions (black carbon [BC]). Collectively, these models, together with visual observations and methanosulfidic acid (MSA) measurements, indicated that the temporal variability in concentration of airborne prokaryotic cells reflected changes in marine/sea-ice regional environments of the WAIS. Our data revealed that variations in source and transport were the most likely processes producing the significant temporal variations in WD prokaryotic cell concentrations. This record provided strong evidence that airborne prokaryotic cell deposition differed during the LGM, LDG, and EH, and that these changes in cell densities could be explained by different environmental conditions during each of these climatic periods. Our observations provide the first ice-core time series evidence for a prokaryotic response to long-term climatic and environmental processes., (© 2018 John Wiley & Sons Ltd.)

In this paper we describe a coherent multiple testing procedure for correlated test statistics such as are encountered in functional linear models. The procedure makes use of two different p -value combination methods: the Fisher combination method and the Šidák correction-based method. P -values for Fisher's and Šidák's test statistics are estimated through resampling to cope with the correlated tests. Building upon these two existing combination methods, we propose the smallest p -value as a new test statistic for each hypothesis. The closure principle is incorporated along with the new test statistic to obtain the overall p -value and appropriately adjust the individual p -values. Furthermore, a shortcut version for the proposed procedure is detailed, so that individual adjustments can be obtained even for a large number of tests. The motivation for developing the procedure comes from a problem of point-wise inference with smooth functional data where tests at neighboring points are related. A simulation study verifies that the methodology performs well in this setting. We illustrate the proposed method with data from a study on the aerial detection of the spectral effect of below ground carbon dioxide leakage on vegetation stress via spectral responses.

While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods - SKAT and a previously proposed method based on functional linear models (FLM), - especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.

Study Objective: To evaluate the effect of levofloxacin coadministration on the international normalized ratio (INR) in patients receiving warfarin therapy., Design: Prospective analysis., Setting: Outpatient clinic at a Veterans Affairs medical center., Patients: Eighteen adult patients receiving warfarin., Intervention: On the basis of clinical diagnosis and judgment, levofloxacin was prescribed to the 18 patients for treatment of various types of infection. The INR was measured before and at 2-8-day intervals after the coadministration of levofloxacin therapy, and once after completing therapy. Warfarin dosages were adjusted when necessary., Measurements and Main Results: Warfarin dosages were changed in seven patients as a result of the first nontherapeutic INR values obtained after start of levofloxacin therapy. Owing to a concern regarding noncompliance and the adverse effect of bleeding, warfarin dosage was adjusted in one patient even though his first INR value was in the high end of the therapeutic range (2.98, therapeutic range 2-3). One patient withdrew from the study after the first INR measurement after levofloxacin coadministration. Because of a concern about the possible bleeding complication, warfarin dosage was also adjusted in this patient after obtaining his first INR value. Therefore, only the INR values obtained before and the first INR values obtained after levofloxacin administration were compared to evaluate the effect of levofloxacin on INR determination of warfarin therapy. The INR values obtained before levofloxacin administration did not differ significantly from the first INR values obtained after levofloxacin coadministration (mean +/- SD 2.61 +/- 0.44 vs 2.74 +/- 0.83, 95% confidence interval -0.449-0.196, p=0.419)., Conclusion: The INR values measured before and after concomitant levofloxacin therapy were not significantly different. However, the ability to detect a significant difference may be affected by the small number of patients studied. Further studies with a larger sample are required to better determine the effect of levofloxacin coadministration on INR monitoring during warfarin therapy