Jan C. Wiemer

Metabolite Profiling of Alzheimer's Disease Cerebrospinal Fluid

Alzheimers disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine.

Metabolite Profiling Identifies Candidate Markers Reflecting the Clinical Adaptations Associated with Roux-en-Y Gastric Bypass Surgery

Background Roux-en-Y gastric bypass (RYGB) surgery is associated with weight loss, improved insulin sensitivity and glucose homeostasis, and a reduction in co-morbidities such as diabetes and coronary heart disease. To generate further insight into the numerous metabolic adaptations associated with RYGB surgery, we profiled serum metabolites before and after gastric bypass surgery and integrated metabolite changes with clinical data. Methodology and Principal Findings Serum metabolites were detected by gas and liquid chromatography-coupled mass spectrometry before, and 3 and 6 months after RYGB in morbidly obese female subjects (n = 14; BMI = 46.2±1.7). Subjects showed decreases in weight-related parameters and improvements in insulin sensitivity post surgery. The abundance of 48% (83 of 172) of the measured metabolites changed significantly within the first 3 months post RYGB (p<0.05), including sphingosines, unsaturated fatty acids, and branched chain amino acids. Dividing subjects into obese (n = 9) and obese/diabetic (n = 5) groups identified 8 metabolites that differed consistently at all time points and whose serum levels changed following RYGB: asparagine, lysophosphatidylcholine (C18:2), nervonic (C24:1) acid, p-Cresol sulfate, lactate, lycopene, glucose, and mannose. Changes in the aforementioned metabolites were integrated with clinical data for body mass index (BMI) and estimates for insulin resistance (HOMA-IR). Of these, nervonic acid was significantly and negatively correlated with HOMA-IR (p = 0.001, R = −0.55). Conclusions Global metabolite profiling in morbidly obese subjects after RYGB has provided new information regarding the considerable metabolic alterations associated with this surgical procedure. Integrating clinical measurements with metabolomics data is capable of identifying markers that reflect the metabolic adaptations following RYGB.

A New Metabolomic Signature in Type-2 Diabetes Mellitus and Its Pathophysiology

Objective The objective of the current study was to find a metabolic signature associated with the early manifestations of type-2 diabetes mellitus. Research Design and Method Modern metabolic profiling technology (MxP™ Broad Profiling) was applied to find early alterations in the plasma metabolome of type-2 diabetic patients. The results were validated in an independent study. Eicosanoid and single inon monitoring analysis (MxP™ Eicosanoid and MxP™ SIM analysis) were performed in subsets of samples. Results A metabolic signature including significantly increased levels of glyoxylate as a potential novel marker for early detection of type-2 diabetes mellitus was identified in an initial study (Study1). The signature was significantly altered in fasted diabetic and pre-diabetic subjects and in non-fasted subjects up to three years prior to the diagnosis of type-2 diabetes; most alterations were also consistently found in an independent patient group (Study 2). In Study 2 diabetic and most control subjects suffered from heart failure. In Study 1 a subgroup of diabetic subjects, with a history of use of anti-hypertensive medication further showed a more pronounced increase of glyoxylate levels, compared to a non-diabetic control group when tested in a hyperglycemic state. In the context of a prior history of anti-hypertensive medication, alterations in hexosamine and eicosanoid levels were also found. Conclusion A metabolic signature including glyoxylate was associated with type-2 diabetes mellitus, independent of the fasting status and of occurrence of another major disease. The same signature was also found to be associated with pre-diabetic subjects. Glyoxylate levels further showed a specifically strong increase in a subgroup of diabetic subjects. It could represent a new marker for the detection of medical subgroups of diabetic subjects.

Glyoxylate, a new marker metabolite of type 2 diabetes.

Type 2 diabetes (T2D) is characterized by a variety of metabolic impairments that are closely linked to nonenzymatic glycation reactions of proteins and peptides resulting in advanced glycation end-products (AGEs). Reactive aldehydes derived from sugars play an important role in the generation of AGEs. Using metabolite profiling to characterize human plasma from diabetic versus nondiabetic subjects we observed in a recent study that the reactive aldehyde glyoxylate was increased before high levels of plasma glucose, typical for a diabetic condition, could be measured. Following this observation, we explored the relevance of increased glyoxylate in diabetic subjects and in diabetic C57BLKS/J-Leprdb/db−/− mice in the pathophysiology of diabetes. A retrospective study using samples of long-term blood donors revealed that glyoxylate levels unlike glucose levels became significantly elevated up to 3 years prior to diabetes diagnosis (difference to control P = 0.034). Elevated glyoxylate levels impact on newly identified mechanisms linking hyperglycemia and AGE production with diabetes-associated complications such as diabetic nephropathy. Glyoxylate in its metabolic network may serve as an early marker in diabetes diagnosis with predictive qualities for associated complications and as potential to guide the development of new antidiabetic therapies.

P3-066: Metabolic profiling of Alzheimer's disease cerebrospinal fluid

Background: Diagnosis of Alzheimer’s disease (AD) is difficult, expensive and is only late in the disease. It requires a combination of neuropsychological testing and the exclusion of other neurological diseases. The currently best biochemical disease parameters in the cerebrospinal fluid (CSF) are Abeta, Tau, phosphorylated Tau and combinations thereof. However, there is a huge overlap in these markers between healthy controls, AD patients, and patients with other dementias and there is urgent need for the identification of easy and cost effective tests for the early identification of AD. Moreover, as new disease modification treatment paradigms for AD are being developed, there are assays needed suitable to follow the disease progression to facilitate clinical development and to show clinical efficacy of these approaches. Methods: To study the complex metabolic consequences of the disease processes and to identify candidates useful as biomarkers for diagnosis and disease progression, information-rich, non-selective but specific analytical approaches are required. Metabolic profiling offer the prospect of efficiently distinguishing individuals with particular disease or toxic states. Here we show the metabolic profile in human CSF samples of AD patients and age matched healthy controls. For the analysis we have used in total 130 CSF samples, 79 samples from AD patients and 51 age matched healthy controls. Controls and AD patients had comparable age distribution and derived from five different clinical centers in Europe. Results: The applied combination of different metabolic profiling technologies allowed to identify and quantitate in total 343 analytes in human CSF. From these, 83 metabolites could be structurally identified. By applying univariate and multivariate statistical methods, the metabolite profiling analysis allowed for substantial differentiation of the metabolite profiles between AD patients and healthy controls showing significant differences. The statistically most significant biomarker candidates in CSF were cysteine, biotin and cortisol. Further promising biomarker candidates include tyrosine, methionine, serine, pyruvate, taurin, creatine and dopamine. Conclusions: With this approach we have identified candidates for biomarkers traced to metabolites or pathways specific for AD or the underlying neurodegenerative process and to be used as a starting point to for further validation in independent sample sets and subsequent studies.