[ Nutrition ]

Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits

Hello, I have the same symptom as mentioned by sanjeev_84. Most urine infections are caused by germs (bacteria) that come from your own bowel. We utilized the microdialysis technique to monitor early changes in tumor necrosis-alpha (TNF-alpha) levels in the renal interstitial fluid and urine of conscious Sprague-Dawley rats (N = 8) before and after induction of diabetes with streptozotocin (STZ). Thirty-eight diabetics (DM) with or without KD and five healthy subjects (NL) received a single meal of egg white (56 g protein), cooked with (AGE-diet) or without fructose (100 g) (CL-diet). Clear and profuse urine in an exogenous febrile illness means the evil has not yet entered the interior part of the body. I was 28 when I was dx’d T1, rapid onset, probably triggered by immune system overreacting to a nasty virus–a very typical pattern. However, approximately 15% of excreted urine creatinine is derived from proximal tubular secretion.

In multivariate models, adults in the lowest urinary potassium quintile were more than twice as likely to develop diabetes as their counterparts in the highest quintile (HR 2.45; 95% CI 1.08, 5.59). Further understanding of the structure of the body (anatomy) and of the physical and chemical processes involved in organ function (physiology), as well as the invention and development of the microscope, led to additional advances in urine testing as a diagnostic tool. The liver is the organ that breaks down any ammonia in the body and sends it for excretion. 2002), which is diagnosed based on the results of an oral glucose tolerance test (oGTT) according to the threshold criteria defined by the World Health Organization/the International Diabetes Federation, or the American Diabetes Association (Nathan et al. The study was conducted with the approval of the Institutional Review Board of Pusan National University Hospital. Total MMP activity can be easily measured in human urine. Independent of an elevated risk to develop T2DM affected individuals experience an increased risk of cardiovascular events and mortality (Festa et al.

2004; Novoa et al. 2005; Petersen and McGuire 2005; Sorkin et al. 2005; Waugh et al. I am curious to know the cause of this issue. Take nitrofurantoin tablets/capsules exactly as your doctor tells you to. It is well known that the metabolites in plasma, urine and cerebrospinal fluid reflect both normal variation and the pathophysiological impact of diseases. These differences in metabolite patterns can give insight into underlying molecular mechanisms.

In the elderly, if the bladder is distended but unable to void or only excrete a few drops, this is due to kidney-qi deficiency. I would definitely get the antibody testing, see what type you are and then go from there with the diabetes treatment. 2008b; Lindon et al. 2003; Nicholson and Lindon 2008; van der Greef et al. Orange urine may be caused by medications (e.g., pyridium [used to treat urinary tract infections], warfarin, laxatives), B complex vitamins, and carotene (found in yellow vegetables). This is the easiest and main step to do away with the ammonia smell in urine. 2008), coronary heart disease (Brindle et al.

2002), bowel disease (Marchesi et al. MMP-3) specifically degrade fibronectin and laminin [9]. 2008), kidney cancer (Kind et al. 2007), and prostate cancer (Sreekumar et al. 2009). Thus, metabonomics investigations of body fluids serve in two distinct but closely related modes: as a metabolic fingerprinting tool separating groups based on altered metabolic patterns and as a means to identify metabolites as potential biomarkers discriminating between normal and pathological states. In the field of diabetes research recently a number of animal and a few human metabonomics studies had be published investigating the metabolic effects of an oral glucose challenge (Shaham et al.

Some antacids can interfere with nitrofurantoin and stop it from working properly. 2009; Zhao et al. 2009), insulin resistance (Chen et al. 2008; Plumb et al. 2006; Shaham et al. 2008; Shearer et al. 2008; Toye et al.

2007; Williams et al. 2006a, b), type 1 (Makinen et al. 2008; Zhang et al. 2008) or T2DM (Gipson et al. 2008; Huo et al. 2009; Salek et al. 2007; van Doorn et al.

2007; Zhang et al. 2009). However, currently only two studies investigated pre-diabetic metabolic pattern in humans (Shaham et al. EMIS has used all reasonable care in compiling the information but make no warranty as to its accuracy. 2009). Shaham et al. investigated in detail the time-dependent response to a glucose challenge on distinct axes of insulin sensitivity by targeted metabolic profiling of 191 plasma metabolites (Shaham et al.

2008). In the study performed by Zhang et al. 1H-NMR was applied for non-targeted metabonomic analysis of serum samples from controls, pre-diabetic subjects with impaired glucose regulation and individuals with T2DM. A distinct clustering of controls and diabetic subjects was demonstrated, however the analysis of pre-diabetic individuals and controls resulted in no clear separation (Zhang et al. 2009). Applying non-targeted metabonomics by UPLC-qTOF-MS we aimed to investigate metabolic fingerprints of pre-diabetic subjects vs normal glucose tolerant controls in plasma as well as spot urine. Based on the individual metabolic fingerprint, subjects with IGT were clearly separated from controls either in plasma or spot urine.

We identified metabolites reflecting specific alterations of metabolic pathways. The detected pathway alterations offer new insights in the complex dysregulation of the metabolism in the pathogenesis of T2DM during the long asymptomatic period of IGT giving prospects for novel interventions targets like the gut flora or fatty acid metabolism. In total 51 subjects were included in the study group (age: 46.9 SE ± 11.9 years). All individuals underwent a 75 g oGTT according to the recommendations of the WHO/IDF to define normal (NGT) or IGT (WHO 2006). 39 were diagnosed to have NGT and 12 to have IGT. For the metabolomics investigations blood and spot urine samples were collected after an overnight fasting under standardized conditions and immediately stored in aliquots at −80°C. Analysis of routine laboratory parameters was performed as described recently (Schafer et al.

2007). The protocol of the study was approved by the Ethics Committee of the University Tuebingen conformed to the Declaration of Helsinki, and all subjects gave written informed consent. The investigation was conducted in accordance with the ethical principles of Good Clinical Practice. Plasma or urine sample aliquots were deproteinized with two volume parts of acetonitrile, centrifuged (13000 × rpm for 20 min), run to dryness in a vacuum centrifuge and stored at −20°C. For analysis, the plasma samples were reconstituted in 200 μl acetonitrile and water (4:1), and urine samples were reconstituted in 200 μl acetonitrile and water (1:4). The chromatographic separation was performed on a 100 × 2.1 mm ACQUITY 1.7 μm/C18 column at 35°C using an ACQUITY-UPLC system (Waters Corp, Milford, USA). We applied our recently reported UPLC-qTOF-MS approach, established and validated for the metabonomics analysis of serum and urine (Yin et al.

2008; Zhao et al. 2008). For plasma analysis the gradient program was 95% A (A = 0.1% formic acid in water) for 0.5 min, changed to 100% B (B = acetonitrile) linearly within 24 min and held for 4 min, finally back to 95% A (flow rate 0.35 ml/min). Urine samples were analyzed applying a gradient program starting at 98% A (A = 0.1% formic acid in water) for 0.5 min, changed to 70% B (B = acetonitrile) linearly within 25 min, then changed to 100% B and held for 3.5 min, finally back to 98% A (flow rate 0.35 ml/min). The UPLC system was coupled to a qTOF-MS (Micromass, Manchester, UK) equipped with an electrospray source operating in either positive or negative ion mode The metabolites were detected and identified following our recently published strategy for the identification of metabolite biomarkers (Chen et al. 2008). For an efficient evaluation of the metabolic variability between NGT and IGT subjects, mass spectra were digitally analyzed using the Micromass MarkerLynx Applications Manager version 4.0 (Waters Ltd, Manchester, UK).

The data were combined into a single matrix by aligning peaks with the same mass and retention time together from each data file in the data set. The intensity for each peak was normalized to the sum of the peak intensities for each data set. Metabolites which did not exist in 80% of NGT or IGT subjects were filtered. After that, the data were exported into Soft Independent Modelling of Class Analogy (SIMCA)-P (version 11.0, Umetrics AB, Umea, Sweden) for analysis and visualization by multivariate statistical methods.

Tags: , ,