Metabolomics/Applications/Nutrition/Personal Metabolomics

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  1. Phenotypes
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Contents

Personal Metabolomics[edit]

As technology progress and new algorithms for computer programs are discovered, we will see the ability for medical researchers to detect changes in the concentrations of a person's metabolites. This could lead to the discovery of new bio-markers for diseases such as schizophrenia. These ideas were shared between the articles about schizophrenia bio-markers and potentials of personal metabolomics by Elain Holmes and Leroy Hood and colleagues.
Personal metabolomics will be an easy method in the future to diagnose and treat metabolic disorders on an individual basis. Metabolites in urine or blood can be analyzed and through the data collected, illnesses that the individual may have can be examined. Our review focus was mostly on diabetes, as it is one of the most studied and well known metabolic disorders.
In the paper “Correlative and quantitativate (1)H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats”, rats were induced to develop diabetes by utilizing streptozotocin. Afterwards, urine and plasma were analyzed to discover metabolites that may indicate diabetes. Seventeen different metabolites where found, many in excess. By taking this information to a further level, in the future, it could be used to easily diagnose or treat diabetes in humans.
Similary, in the article “Comprehensive two-dimensional gas chromatography/time of flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus”, five potential biomarkers were found in human patients. However, in contrast to the first paper, instead of NMR, two dimensional gas chromatography was used. Potential biomarkers found included glucose and linoleic acid. Again, these discoveries are useful for further diagnoses and treatments.
Universal blue symbol for diabetes
The third article, “Nitric Oxide Synthesis and Isoprostane Production in Subjects With Type 1 Diabetes and Normal Urinary Albumin Excretion” showed that in type 1 diabetic patients, nitric oxide (NO) levels are higher than in normal healthy individuals. However, this increase in NO has no effect on renal function, as the diabetic patients had normal albumin excretion in their urine. NO is a metabolite that could be used further for diagnosis and study of diabetes.
The article "Personal Metabolics as a Next Generation Nutritional Assessment" discusses how current and future technologies as well as collaborating laboratories and databases on aspects of metabolism such as lipids will be the key to assessing metabolic disease as well as personalizing health and diet in humans in the very near future.
The article "Prospective health care: the second transformation of medicine" describes how current databases and standards for predicting disease are inadequate. Instead predictive modeling, such as using the Gail model with breast cancer, should be used to not only assess risk of disease or adverse effects of a disease but to also help work towards an appropriate treatment based upon an individual's personalized assessment and predictive model.
Websites found were mostly resource websites directed towards researchers and professionals rather than normal consumers. Chenomx Inc is a life sciences company offering metabolomics researchers for pharmaceutical companies chemistry software, and data analysis solutions among other things. They have a patented NMR suite 5.1, which is comprised of five different functional tools like Chenomx Profiler, and Chenomx Compound Builder. The software they provide is very reliable and accurate, capable of quickly identifying and quantifying metabolites. Currently, there are over 250 metabolites in their database, which provides a wealth of information to metabolomic researchers.
The PreDX was the only website directed towards consumers or the average Joe, rather than research facilities or large companies. The website offers a new type of blood test assesses different metabolites that have been found present in individuals at risk for diabetes. This is extremely important because some cases of diabetes can be prevented through diet and exercise change. Knowing the risks of developing diabetes can greatly aid individuals in the prevention process. The technology provided is easily accessible through phone, online or fax.
The last website belongs to The Society of Metabolomics, which is a group of well known metabolomic scientists that are trying to expand their field further. They offer tutorials and workshops on new technologies and methods in the field of metabolomics in addition to, providing resources. The website has links to various types of software used for metabolomics. Although though this is not useful for the general public, it is a good resource for metabolomic researchers or medical doctors hoping to use metabolomics to help diagnose their patients.

Website Sources[edit]

Chenomx.com[edit]

http://www.chenomx.com

General Overview[edit]

Chenomx NMR Suite helps scientists correlate metabolic responses with pathology, toxicity, drug efficacy, and genetics.

Main Focus:

To provide access to technology for analysis of metabolites found in various biological samples through the use of NMR spectroscopy and by unique, innovative software.

Summary:

Chenomx Inc is a life sciences company that has much to offer metabolomics researchers for pharmaceutical companies and institutions all over the world. Through partnership with some of the leading providers in specific areas of expertise, including chemistry software and data analysis solutions in systems biology, Chenomx grants access to a bevy of efficient, cost-effective, and timely services through their website. These services consist of nuclear magnetic resonance (NMR) spectroscopy data acquisition, targeted profiling of metabolite analysis and statistical analysis of numerous biological samples. This is all obtained through the their one-of-a-kind, patented Chenomx NMR suite 5.1; a suite compiled of five different functional tools such as the Chenomx Profiler, Chenomx Compound Builder, Chenomx Spin Simulator, Chenomx Library Manager, and Chenomx Processor.
Chenomx employ highly trained and skilled scientists to carry out all their services. Over the years, Chenomx has gained experience in the handling and analysis of a variety of biological samples. Standard protocols for urine, plasma, serum, saliva and cell extracts exist at the Chenomx labs. Chenomx continues to improve and expand their knowledge in working with new samples that may be analyzed for specific metabolite detection from NMR spectroscopy. The NMR spectroscopy at Chenomx is a powerful tool in quick detection of the contents of biofluids. The NMR spectrometer utilized at Chenomx has field strengths of 400 to 800 MHz. Coupling NMR spectroscopy with their software provides efficient one-step biofluid analysis. The Chenomx software accurately, reliably, and quickly identifies and quantifies metabolites giving researchers complete and thorough analysis presented in various formats or databases: delimited text, Microsoft Excel, XML, SIMCA-P, Mat lab, and much more.
Currently Chenomx provides sample preparation services for alcohols, fatty acids, amino acids, sugars, organic acids, and nucleic acid components to name a few. With over 250 compounds in the Chenomx database, metabolomic researchers in need of interpretation of compounds or pathways they study are just a few clicks away from accessing an advantageous tool provided at www.chenomx.com.

New Terms[edit]

NMR 
a family of scientific methods that exploit nuclear magnetic resonance to study molecules ("NMR spectroscopy") ( http://en.wikipedia.org/wiki/NMR )
chemometric 
The use of mathematical statistics in the design of experiments, and the evaluation of the resulting data (http://en.wiktionary.org/wiki/chemometrics)
metabolite 
Any substance produced by, or taking part in, a metabolic reaction (http://en.wiktionary.org/wiki/metabolite)
field strength 
the vector sum of all the forces exerted by an electrical or magnetic field (on a unit mass or unit charge or unit magnetic pole) at a given point in the field (http://wordnetweb.princeton.edu/perl/webwn?s=field%20strength)
serum 
The clear yellowish fluid obtained upon separating whole blood into its solid and liquid components after it has been allowed to clot (http://en.wiktionary.org/wiki/serum)

Course Relevance[edit]

This website offers technology to analyze various metabolites, some of which we have discussed in this course.

PredictMyRisk.com[edit]

http://predictmyrisk.com/about.html

General Overview[edit]

Identifies patients in danger of contracting diabetes withing five years.

Main Focus:

The main focus is to use metabolite blood testing to find patients at risk for diabetes, and to do so using metabolic indicators other then glucose.

Summary:

Diabetes is a major health concern for many. It can lead to other health problems such as high blood pressure, blood clots, loss of vision, stroke, and many other maladies. Today doctors have some ability to test for diabetes risk factors in an effort to prevents this condition before it happens. Doctors use a blood test that tests for the level of glucose in the blood during a period of fasting. Unfortunately this test has been found to be not as accurate as previously assumed.
PreDX is a website offering a new type of blood test that will test for the appearance of many different metabolites that have been found to be present in persons at risk for diabetes. This new test could be extremely helpful to doctors because it analyzes the blood for metabolites, which gives a much more accurate measure of diabetes risk then the traditional fasting blood glucose test. The website claims that its blood test is capable of identifying patients at risk for diabetes as much as five years before they would contract it. PreDX claims to be a simple to run and sensitive test that gives an easy to interpret readout of the patients risk and the reasons for that risk. This test could also be used on current diabetic patients to more fully test how well their diabetes is being controlled.
The test is preformed on a fasting blood sample. An algorithm analyzes a number of proteins and blood born biomarkers. This algorithm then compiles this data into a single numerical score that can be converted into a percentage of risk. This technology can be be obtained by phone, on line, or by faxing information to a number on the website.

New Terms[edit]

Biomarkers 
a substance used as an indicator of a biologic state. (http://en.wikipedia.org/wiki/Biomarkers)
Protein 
organic compounds made of amino acids arranged in a linear chain and joined together by peptide bonds between the carboxyl and amino groups of adjacent amino acid residues. (http://en.wikipedia.org/wiki/Protein)
Stroke 
the rapidly developing loss of brain functions due to a disturbance in the blood vessels supplying blood to the brain. (http://en.wikipedia.org/wiki/Stroke)
Macrovascular
referring to the large blood vessels. (http://diabetes.org.au/glossary.htm)
Fasting blood glucose
a method for learning how much glucose there is in a blood sample taken after an overnight fast. (http://www.medterms.com/script/main/art.asp?articlekey=3393)
Genetic marker
a specific gene that produces a recognizable trait and can be used in family or population studies. (http://wordnetweb.princeton.edu/perl/webwn?s=genetic%20marker)
Retinopathy
is a general term that refers to some form of non-inflammatory damage to the retina of the eye. Most commonly it is a problem with the blood supply that is the cause for this condition. (http://en.wikipedia.org/wiki/Retinopathy)

Course Relevance[edit]

This website offers a test to analyze metabolites pertaining to diabetes. This is relevant because it shows the complex interactions of metabolism and how they affect the body.

Metabolomics Society[edit]

http://www.metabolomicssociety.org

General Overview[edit]

The Metabolomics Society is a website commited to the growth of the metabolomics field. It is a non-profit organization containing more than 500 members in 20 countries. The society also publishes its very own journal titled Metabolomics, which is a peer-reviewed journal published by Springer that is released every 3-4months. This site provides multiple metabolomics resources, including numerous software and databases. However, these sources are more for research users rather than “everyday” individuals. In other words, it is NOT the WebMD of metabolomics, but still provides information that could be used by doctors or researchers to aid the “everyday” person with personal metabolomics.
Almost all software listed on the site uses either NMR or various types of mass spectrometry. Using this, they are able to detect certain metabolites by comparing to a large database, and sometimes even structuralize new metabolites found. XCMS(2) is capable of using a “similarity search” which can take an unknown metabolite and come up with possible structural motifs, allowing for possible identification of an unknown metabolite. MetaboMiner is a program capable of “identifying metabolites in complex biofluids”, which would be useful in a medical setting. HORA is probably the most relevant, because it’s a database made of up metabolites specifically in human blood. It allows you to tell which metabolites are abnormal, and conveniently also provides graphs to manage data. Although there is more software available, these last two were the most relevant to Personal Metabolomics. The website also provides databases for metabolites, including some related to diseases such as the OMIM. Aside from software, the Metabolomics Society also offers a variety of tutorial workshops, including “The NIH Roadmap to Understanding Biological Pathways and Networks with Metabolomics” and “PubChem: A Public Repository for Chemical Biology Screening Results”. The most important part of this website are the software resources, although there are other useful aspects of the site.

New Terms[edit]

NIH 
National Institutes of Health (http://en.wikipedia.org/wiki/NIH)
Metabolic profiling
Metabolic profiling employs a range of analytical approaches (e.g., mass spectrometry and high- resolution 1H nuclear magnetic resonance spectroscopy) suited to the chemical properties of the metabolite class(es) of interest. (http://www.genomicglossaries.com/content/metabolic_engineering.asp)
Metabolic fingerprinting
a rapid classification of samples according to their origin or their biological relevance. (http://www.genomicglossaries.com/content/metabolic_engineering.asp)
Footprinting
is a technique for identifying the site on DNA bound by some protein by virtue of the protection of bonds in this region against attack by nucleases. (http://www.hgsc.bcm.tmc.edu/docs/HGSC_glossary.html)
Transcriptome analysis
Analysis of the global gene expression of a cell by identification of all the messenger RNA present in the cell. (http://www.nature.com/nrmicro/journal/v2/n12/glossary/nrmicro1046_glossary.html)
Metabolic flux analysis
an analysis technique similar to Flux Base Analysis used to determine the rate at which a metabolite is produced during a bioprocess. (http://en.wikipedia.org/wiki/Metabolic_flux_analysis)
Metabolome
refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signalling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism. (http://en.wikipedia.org/wiki/Metabolome)

Article Sources[edit]

Systems Medicine: The Future of Medical Genomics and Healthcare[edit]

Auffray, Charles; Chen, Zhu; Hood, Leroy. Systems medicine: the Future of medical genomics and healthcare. Genome Medicine 2009, I:2. http://genomemedicine.com/content.I/I/2.

General Overview:[edit]

Systems biology can be used in the determination and early warnings of certain diseases, made possible through the advances in computation and technology.

Main Focus:[edit]

By making robust hypothesis, a biological system can be monitored by taking samples of certain metabolites and using well thought out mathematical methodologies to make informative decisions about data. The data can then be shared with other scientist through a series of networks.

Summary[edit]

Through well thought out hypothesis-driven methods synthetic biology and dynamic processes can be created that allow the user to change the parameters of the metabolites to predict effects of different concentrations. Using these well-thought out methods, the personal genome project would be able to determine the differences between normal and diseased phenotypes. Without high quality design and assessment the usefulness of the resulting biomarkers would be compromised. However, recent advances in micro array and PCR technology along with advances in proteomic tools allow for accurate readings and high quality data. Using robust computer programs, network processes can be produced that show protein to protein interactions. Some limitations on computing are the variations in annotated data. Different languages of programming, along with the fact that a cell's system is continuously changing is what makes it difficult to write an ideal program that will encompasses all changes in a cel. As technologies progress new computational methods will allow for the modeling of entire cell systems and organs. This project is very dependent on annotated information so all organizations should use a standard for annotation and pay close attention to the quality of their experiments.

Terms:[edit]

Systems biology - using complex biological systems knowledge can be determined by the behavior and differing conditions.

Synthetic biology - using modular processes biological systems can be designed and modeled.

Stratification - appearance

www.dictionary.com

allometric - measure of growth

www.dictionary.com

elucidate - make clear

www.dictionary.com

systematic - having a plan

www.dictionary.com

cytometry - cell counting

www.dictionary.com

Metabolic Profiling of Patients with Schizophrenia[edit]

Kaddurah-Daouk, Rima. Metabolic Profiling of Patients with Schizophrenia. PLoS Medicine. August 2006, V.3, I.8; pg 1222-1223.

General Overview:[edit]

Metabolomics can be used to monitor and develop biomakers in different human diseases.

Main Focus:[edit]

This article provides the idea that with proper measurement tools, environmental factors can be used to discovery new bio-markers for diseases, such as schizophrenia.

Summary:[edit]

In this time of developing medicine, new ideas for discovering and preventing diseases are proposed. Mental illnesses, more specifically schizophrenia, hinders the daily activity of many people around the world. This disease has a treatment course, but many Schizophrenics find it hard to continue on the course. Often stopping their treatments, only to relapse and make symptoms worse. Elaine Holmes and her colleagues presented the fact that schizophrenia has no known biomarkers. They focus their work on identifying biomarkers for schizophrenia, by identifying changes in samples of cerebrospinal fluid. To do this Nuclear Magnetic Resonance (NMR) was used to record several resonance coefficients. They tracked the metabolites for two different groups of schizophrenics and treated one group with anti psychotics. What they found was that once the treatment was given, the subset of the metabolom being tested stabilized to normal levels. Elaine Holmes and her colleagues believe that by studying metabolomics, scientists can figure impairments in energy and lipid biosynthesized metabolism. In further testing of this study, it should include a larger sample population as well as the ability to replicate and validate results. This would reduce confounding effects and allow for more meaningful biological hypothesis.

Terms:[edit]

aberrant - deviations from normal.

leading coefficients - constant factor in multiplication

Cerebrospinal fluid(CSF)- clear fluid by the spine around the brain

nuclear magnetic resonance(NMR)- physical resonance using quantum magnetics

Type 2 diabetes mellitus - non insulin dependent diabetes


Correlative and quantitative 1H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats[edit]

Zhang, Shucha. Nagana Gowda,GA. Asiago, V. Shanaiah, N. Barbas, C. "Correlative and quantitative (1)H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats". Analytical Biochemistry 383. May 2008. 76-84. 11 Feb 2009 http://www.ncbi.nlm.nih.gov/pubmed/18775407?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum

General Overview[edit]

Streptozotocin - a glucosamine-nitrosourea compound that is toxic to the insulin-producing beta cells of the mammalian pancreas. It is use to treat cancers cells in the Islet of Langerhans and has been experimentally use in animal models to treat Type 1 diabetes.
Type 1 diabetes is an autoimmune illness caused by the body’s destruction of beta cells in the pancreas. Even though diabetes is a well studied topic, the causes and preventions of it are still not well understood. In this study, researchers used the metabolomic approach to study Type 1 diabetes. Specifically, researchers combined nuclear magnetic resonance (NMR) and mass spectroscopy with multivariate statistical analysis (MSA). These techniques enable the researchers to screen large samples of metabolites and collect data that pertains to the normal individuals as well as diabetic patients in a relatively cost-effective and time-efficient fashion. The use of metabolomics as a way to study diseases is very common. For instance, it has been employed to study cancer, Type 2 diabetes, inborn errors of metabolism, and even diet and nutrition.
In this study, researchers injected rats with streptozotocin (STZ) to induce Type 1 diabetes. They were then examined for glucose level increases of more than 200 mg/dl after 4 days to confirm that they are diabetic. The controls chosen were both equivalent in age and gender. Both groups of rats were kept in proper condition with appropriate food and water supply. Urine samples were collected every 8 h after seven days after the initial injection. Blood samples were collected by cardiac puncture before sacrificing the rats. Data were collected from urine and blood samples and analyzed using NMR spectrometer supplied with HCN 1H inverse detection probe. After analysis, 17 metabolites were identified and quantified.
In diabetic rats, glucose, alpha-tocopherol, urea, triglycerides, TBARS, and liver alpha-tocopherol were all higher than the control. In addition, the diabetic rats consumed and secreted 10 times more urine volume than the control in the 24 h time frame.
The diabetic rats had high-intensity peaks from glucose along with a variety of other smaller molecules. Specific quantities of glucose averaged to about 7500-fold higher than in control rats. Lactate was observed to be the second highest increase with about 40-fold.
In order to confirm that the data compiled was accurate, researchers carried out a multivariate analysis: principal component analysis (PCA). The PCA results showed that the controls and the diabetic rats were well distinguished due to the large quantities of metabolites. The removal of glucose did not affect the analysis distinguishing diabetic rats from control rats.
Using the metabolomics approach to studying Type 1 diabetes, researchers found that even after the removal of the most significant marker (glucose) from the samples, there was still a significant difference in metabolites that separate the control rats from the diabetic rats. Furthermore, the researchers developed a network showing the metabolite changes and its correlation with each other.

New Terms[edit]

Autoimmune 
the failure of an organism to recognize its own constituent parts as self, which results in an immune response against its own cells and tissues (http://en.wikipedia.org/wiki/Autoimmune)
Glucose 
a monosaccharide (or simple sugar) also known as grape sugar, blood sugar, or corn sugar, is a very important carbohydrate in biology (http://en.wikipedia.org/wiki/Glucose)
Inborn errors of metabolism 
comprise a large class of genetic diseases involving disorders of metabolism (http://en.wikipedia.org/wiki/Inborn_errors_of_metabolism)
Mass spectroscopy 
a charged particle passing through a magnetic field is deflected along a circular path on a radius that is proportional to the mass to charge ratio, m/e (http://www.chem.ucalgary.ca/courses/351/Carey/Ch13/ch13-ms.html)
Metabolites 
is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind" : specifically, the study of their small-molecule metabolite profiles (http://en.wikipedia.org/wiki/Metabolites)
Nuclear magnetic resonance (NMR) 
is a physical phenomenon based upon the quantum mechanical magnetic properties of an atom's nucleus (http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance)
Principal components analysis 
determining a smaller set of synthetic variables that could explain the original set (http://en.wikipedia.org/wiki/Principal_components_analysis)
Streptozotocin 
a naturally occurring chemical that is particularly toxic to the insulin-producing beta cells of the pancreas in mammals(http://en.wikipedia.org/wiki/Streptozotocin)
Triglyceride 
chemical form in which most fat exists in food as well as in the body (http://www.americanheart.org/presenter.jhtml?identifier=4778)
Type 1 diabetes 
Type 1 diabetes is an autoimmune disease that results in destruction of insulin-producing beta cells of the pancreas (http://en.wikipedia.org/wiki/Type_1_diabetes)

Course Relevance[edit]

This pertains to the overall study of metabolism.

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus[edit]

Li, Xiang. Xu,Z. Lu, X. Yang, X. Yin, P. Kong, H. Xu, G. "Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus." Analytica Chimica Acta 663. Nov. 2008. 257-262. 11 Feb 2009 <http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TF4-4V2NKGK-2&_user=47004&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000005018&_version=1&_urlVersion=0&_userid=47004&md5=bc0216cfd9f5107aa5fd79797ede270b>.

General Overview[edit]

Metabolomics can be utilized to diagnose disease and help with mechanism research. Researchers have used linear chromatography – mass spectroscopy (LC-MS) and gas chromatography-mass spectroscopy (GC-MS) to examine metabolite contents that required high sensitivity, selectivity, and that had a large linear range. In this study, researchers investigated levels of plasma phospholipids in Type 2 diabetes mellitus (T2DB) patients by LC-MS and multivariate statistical analysis (MSA). Some standard methods were modified to examine the metabolite profile differences between healthy and diabetic patients. For example, researchers combined GC X GC-MS with ultra performance liquid chromatography mass spectroscopy (UPLC-MS) to acquire global metabolite profiles in rats. GC X GC has been used in a variety of ways. However, when it is coupled with MS, metabolic profiles can be contrasted among samples.
Metabolites were extracted from blood plasma and analyzed by GC X GC-TOFMS. The data was submitted to data processing software. Peak alignment adjustments and pattern recognition were performed. Potential biomarker metabolites were obtained according to their variable importance in the projection (VIP). They were identified by ChromaTOF and NIST MS search 2.0 software.
Forty-eight diabetes mellitus patients and thirty-one healthy control volunteers participated in this study. Blood samples were collected and plasma proteins were obtained. The plasma samples were then analyzed by LECO Pegasus 4D GC X GC-TOFMS device. After that, differences between the healthy and diabetic patients were revealed using the partial least-square discriminant analysis (PLSDA). Orthogonal signal correction (OSC) was used to exclude the variations between the two sample types.
As mentioned above, VIP values of greater than 1.0 were chosen as potential biomarkers. After further analysis and exclusion of unrelated data, the researchers concluded that if similarity were greater than 750, then it would be a positive match with the published data. They found that 4/9 of potential biomarkers had a positive match. Palmitic acid, phosphate, 2-hydroxyisobutyric acid, and linoleic acid were all identified as positive matches.
It is known that glucose and lipids are key features of Type 2 diabetes mellitus. In T2DM’s, an increase level of free fatty acids (FFA) is detected circulating the blood. This could be a cause of T2DM development or it could just be the result of T2DM. In addition, FFAs could compete with glucose for substrate level oxidation, thus interfering with the activities of pyruvate dehydrogenase. This leads to elevated levels of glucose intracellularly. The increased levels of FFA could also lead to hyperinsulinemia. Hyperinsulinemia could be the beginning of insulin resistance in T2DM patients. Since the biomarkers found were either associated with hyperglycemia or problems with beta-oxidation. They may be utilized to help with diagnosis or for further research.

New Terms[edit]

Gas chromatography 
specifically gas-liquid chromatography - involves a sample being vaporized and injected onto the head of the chromatographic column (http://teaching.shu.ac.uk/hwb/chemistry/tutorials/chrom/gaschrm.htm)
Multivariate statistical analysis 
describes a collection of procedures which involve observation and analysis of more than one statistical variable at a time (http://en.wikipedia.org/wiki/Multivariate_statistical_analysis)
Time of flight mass spectroscopy (TOFMS) 
ions are accelerated by an electrical field to the same kinetic energy with the velocity of the ion depending on the mass-to-charge ratio (http://en.wikipedia.org/wiki/Time-of-flight_mass_spectrometry)
Type 2 Diabetes 
a metabolic disorder that is characterized by high blood glucose in the context of insulin resistance and relative insulin deficiency. (http://en.wikipedia.org/wiki/Diabetes_mellitus_type_2)
Hyperinsulinemia 
present in people with diabetes mellitus type 2 or insulin resistance where excess levels of circulating insulin are in the blood. (http://en.wikipedia.org/wiki/Hyperinsulinemia)
Ultra performance liquid chromatography 
a column that holds chromatographic packing material (stationary phase), a pump that moves the mobile phase(s) through the column, and a detector that shows the retention times of the molecules (http://en.wikipedia.org/wiki/Ultra_performance_liquid_chromatography)

Course Relevance[edit]

This pertains to the overall study of metabolism.

Nitric Oxide Synthesis and Isoprostane Production in Subjects With Type 1 Diabetes and Normal Urinary Albumin Excretion[edit]

O'Byrne, Sharon, P Forte, LJ Roberts II, JD Morrow, A Johnston, E Anggard, RDG Leslie, and Nigel Benjamin. "Nitric Oxide Synthesis and Isoprostane Production in Subjects With Type 1 Diabetes and Normal Urinary Albumin Excretion." Diabetes. 49. 5, 857-862. May 2000. http://diabetes.diabetesjournals.org/cgi/reprint/49/5/857

General Overview[edit]

Nitric-oxide-2D.png
Peroxynitrite-ion-2D.png
People with type 1 diabetes are at a high risk of developing serious microvascular complications. Investigations into these complications have been conducted with considerable emphasis on endothelium and nitric oxide (NO) production. NO plays an important role in everyday normal functioning of the body’s microvasculature. NO action is tightly regulated by the balance between its own production and the production of the free radical, superoxide (O2-). When NO and O2- interact, a highly reactive peroxynitrite (ONOO-) forms, which catalyzes isoprostane formation in LDL cholesterol. Isoprostanes serve as markers for hyperglycemia; a disorder associated with diabetes that can induce proliferation of tissues vital for maintaining vasculature, thus causing complications. In this study, researchers designed a method to accurately quantify NO synthesis in order to delve into the relationship between NO and free radical production in type 1 diabetics with normal urinary albumin excretion (UAER) and matching healthy diabetic-free individuals.
The methodology required injection of the stable isotope L-[15N]2-arginine, which converts into 15N-nitrate, into each subject with their urine collected every 12 hours over a 36 hour period. Subjects followed strict guidelines, such as refraining from physical exercise for 3 days prior to and during the study. The major metabolite of a certain isoprostane, 2,3-dinor-5,6-dihydro-F2-IsoP, was used to quantify free radical production for the first 12h period through isotope dilution mass spectrometric assay. Measuring whole-body NO production was detected through levels of 15N-nitrate, excreted in urine, using isotope ratio mass spectrometry. Careful considerations and actions were taken in order to limit factors of variability between diabetic and healthy subjects that could possibly alter results, including age, BMI, blood pressure, and cholesterol.
According to results, in comparison to the control group, a significant increase in whole-body NO synthesis was exhibited by type 1 diabetics, particularly those individuals with a history of diabetes greater than 20 years. All variables pertaining to individual characteristics, creatinine clearance and rate of elimination were negligible. Only differences in sex had an effect on 15N-nitrate levels regardless of diabetes, with females showing the highest production of NO overall. Levels of the F2-isoprostanes, which determined oxidative stress in vivo, revealed an inverse relationship between NO synthesis and free radicals. This was consistent with earlier hypotheses stating that presence of oxidative species, free radicals, inactivates NO synthesis. Isoprostrane concentration was similar in both diabetic and control groups, therefore one explanation for higher NO production in the diabetic group could possibly be due to the antioxidant and protective activity of NO. NO inhibits free radicals, which are built up during hyperglycemic conditions developed by diabetic patients. This study’s results show promising new insights into the role of NO in people with type 1 diabetes.

New Terms[edit]

Microvascular 
referring to small blood vessels (http://diabetes.org.au/glossary.htm)
Anigiopathy 
any disease of the blood vessels or lymph ducts (http://wordnetweb.princeton.edu/perl/webwn?s=angiopathy)
Microalbuminuria 
leakage of small amounts of protein (albumin) into the urine; an early warning of kidney damage (http://diabetes.org.au/glossary.htm)
Mitogenesis 
induction of mitosis in a cell (http://medical-dictionary.thefreedictionary.com/mitogenesis)
Euglycemic 
of or pertaining to euglycemia; having the standard blood glucose level in the body (http://en.wiktionary.org/wiki/euglycemic)

Course Relevance[edit]

To further understand the relationship between NO, from the metabolism of arginine to praline, production and free radicals and the emergence of microvascular diseases in individuals with type 1 diabetes.

Personal Metabolomics as a Next Generation Nutritional Assessment[edit]

German, J. Bruce. Roberts, Matthew-Alan. and Watkins, Steven M. “Personal Metabolomics as a Next Generation Nutritional Assessment” The American Society for Nutritional Sciences. J. Nutr. May 2009. 133:4260-4266, December 2003. http://jn.nutrition.org/cgi/content/full/133/12/4260

General Overview[edit]

Image:FAB_MS.jpg

Every human differs in their metabolic regulation and because of this there is not necessarily an optimum diet that each person must follow. Personalized assessment of a person’s unique metabolism will be necessary in the future. The ultimate goal will be to individualize each person’s health in order to better predict and manage disease. Now, with the challenges of understanding metabolic health within individuals, it is necessary to take a more precise and more general approach. It is important to define both the input variables of foods as parts of complete diets and the outcome variables of integrated metabolism in order to judge a person’s health. To date, nutrition researchers have not addressed the acquisition of metabolism-wide data sets as output variables in nutrition clinical trials. The goal of metabolomics is to prepare a comprehensive dataset of every metabolite within a given biological sample. This is not yet possible because of the wide dynamic and chemical range of small metabolites within biological samples. However, it is possible to divide metabolites into specific classes, analyze these and then reassemble the data electronically. All lipid classes in blood, for example, can be quantified according to the mass of each fatty acid constituent. The technologies such as mass spectrometry provide a very efficient and relatively cheap way to implement systems to collect such data. The technologies that are available to address most of the metabolite classes are equally as available as those for fatty acids and complex lipids. Thus, no significant technological hurdle stands in the way of using these technologies to assemble metabolite databases of humans and experimental animals for amino acids and small peptides, sterols, organic acids, sugars and alcohols, vitamins, nucleotides, etc. So long as the data are qualitative and quantitative, such data from various human and animal investigations are directly comparable. Studies conducted in separate laboratories, using entirely different analytical technologies years apart, will produce directly comparable data if the data are both qualitative and quantitative. The primary causative factors in disease are often the altered biochemical composition of cells and tissues. Thus, the link between the gene regulatory control and the primary causative factors will be crucial for application in drug development, medicine, nutrition and other therapeutic courses of action. The identification of relationships between genes, transcripts, proteins and metabolites are essential components to understand integrative metabolism. Software is now available to superimpose analytical data onto said pathways, providing a powerful means to identify biological regulation of metabolism through the coexpression of gene data obtained from microarrays. GenMAPP is a particularly useful tool for such purpose, allowing the user to link pathway information to gene expression data. Overall the goal is to collaborate with various laboratories to interpret differences in blood lipids and thus provide predictive knowledge of potential interventions using food, drugs and lifestyle to improve lipid metabolism.

New Terms[edit]

Environment

The sum of all external variables, including diet, lifestyle and not to be forgotten, coexisting organisms.

Lipomics

Study and research of lipids

Lipids

Broadly defined as any fat-soluble (lipophilic), naturally-occurring molecule, such as fats, oils, waxes, cholesterol, sterols, fat-soluble vitamins (such as vitamins A, D, E and K), monoglycerides, diglycerides, phospholipids, and others. The main biological functions of lipids include energy storage, as structural components of cell membranes, and as important signaling molecules. (http://en.wikipedia.org/wiki/Lipid)

Nutrigenetics

refers to the specific gene sequence differences between humans and how these affect the differences in responses to diet and particular needs for nutrients.

Nutrigenomics

is the study of the effects of diet on the expression of all genes and their functions.

Course Relevance[edit]

Can be related to every human being as the study and research of lipids and personalized diets according to one’s own metabolic construct could impact our personal health. Could revolutionize how we think about health and diet.

Prospective health care: the second transformation of medicine[edit]

Snyderman, Ralph and Langheier, Jason. “Prospective health care: the second transformation of medicine” Genome Biology 2006. May 2009. 7:104. 27 March 2006. http://genomebiology.com/2006/7/2/104

General Overview[edit]

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The term “Prospective health care” refers to the personalized risk prediction and strategic health-care planning which will facilitate a new form of care. The current approach to health care is based upon the reductionist method which simplifies causal reasons for infectious disease as well as chronic disease. Instead of disease being caused by one microbe (as this is far too simplistic) diseases develop as a consequence of inherited susceptibilities and environmental exposure. Over time, pathology increases, reversibility decreases and costs of care increases. Earlier intervention could clearly reduce the costs and the disease burden. Thus, it appears as though current research is on curing chronic illness and not preventing it. With modern science and technology including rapid evolving fields such as genomics, proteomics, and metabolomics, the ability to predict events and interfere before damage occurs is possible. Prospective health care is a new approach that incorporates all the power of current disease-oriented medicine but is based on the concept of strategic health planning, a proactive, prospective approach to care. In this system, individuals will be evaluated to determine their baseline risk for various diseases, their current health status, and the likelihood of their developing specific clinical problems given their risks. In order for this to be possible one must acquire the tools necessary such as predictive biomarkers, such as low-density lipoprotein (LDL) for cardiovascular disease. These biomarkers need to be identified and tracked over time to determine whether the individual’s likelihood of developing any particular disease is increasing or decreasing. A model of prediction is thus needed to accomplish this task. Predictive modeling encompasses various procedures for creating models that distinguish predictors from many other factors that are not as valuable for anticipating the outcome. Mathematical models can serve as guidelines to raise the overall standard of care, but not to determine the final diagnosis or treatment plan as humans are sensitive to appreciating outlying issues that the model might not be able to account for. The best course of action would be for healthcare to use these math models as a guideline to help standardize care which is not currently being done. To be the most useful, clinical medicine requires predictive models that can predict events accurately over far shorter timeframes, rather than the likelihood of recurrence in 10 years. To achieve this, more relevant and specific data will need to be collected for analysis as shown in Figure 5 which states that clinical data and the results of biomarker analyses (such as gene expression, protein array, and EKG) be collected from a cohort of people and stored in disease model libraries and then models are developed from them. The models can then be used to identify risk prediction factors for particular diseases or events and can thus be compared against a specific person’s profile to determine their risk, or to diagnose disease progression. Biomarkers such as SNPs that are highly associated with causal genes will serve as much better predictors of adverse outcomes, as well as provide for better predictive models, than much of the current data being collected. For individuals that are identified to be high risk they will undergo extensive surveillance to track the disease as much as possible and to provide therapeutic support, such as with breast cancer. With any disease, and specifically breast cancer, for personalized prevention and early intervention, it is necessary to predict baseline risks, provide surveillance for early detection, and facilitate optimal individualized therapy if disease develops. In order to do this with breast cancer there are specific models called the Gail and the Claus models, as well as BRCAPRO, which are used to predict risk and also used to facilitate appropriate treatment. The application of these new technologies to health care will not only provide a far more detailed understanding of health and its evolution toward disease, but will also support the ability to predict events and anticipate appropriate interventions.

New Terms[edit]

Reductionist Method
Simplifies the concept of pathogenesis to the smallest number of causal factors
Biomarkers
Measurable biological factors that predict disease development
BRCA1/BRCA2
Human genes that with specific mutations can increase a person’s risk of breast and ovarian cancers in women (up to 86% in breast cancer) as well as breast and prostate cancers in men.
Claus Model
A computer program that uses statistics to predict a person’s risk for developing breast cancer based on family history (http://www.cancer.gov/Templates/db_alpha.aspx?CdrID=446553)
Gail Model
A computer program that uses personal and family history to estimate a woman’s chance of developing breast cancer. Also called Gail risk model. (http://www.cancer.gov/Templates/db_alpha.aspx?searchTxt=gail&sgroup=Starts+with&lang=)
SNP
Single nucleotide Polymorphism -- DNA sequence variation occurring when a single nucleotide — A, T, C, or G — in the genome (or other shared sequence) differs between members of a species (or between paired chromosomes in an individual). (http://en.wikipedia.org/wiki/Single_nucleotide_polymorphism)

Course Relevance[edit]

Prospective health care if put into effect could influence how modern health care works. We could be the guinea pigs to see if this type of intervention is possible, and we could ultimately benefit from it if successful. Overall relates to how metabolism and body regulations affects health.

Resources[edit]

O'Byrne, Sharon, P Forte, LJ Roberts II, JD Morrow, A Johnston, E Anggard, RDG Leslie, and Nigel Benjamin. "Nitric Oxide Synthesis and Isoprostane Production in Subjects With Type 1 Diabetes and Normal Urinary Albumin Excretion." Diabetes. 49. 5, 857-862. May 2000.
Li, Xiang. Xu,Z. Lu, X. Yang, X. Yin, P. Kong, H. Xu, G. "Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus." Analytica Chimica Acta 663. Nov. 2008. 257-262. 11 Feb 2009 <http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TF4-4V2NKGK-2&_user=47004&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000005018&_version=1&_urlVersion=0&_userid=47004&md5=bc0216cfd9f5107aa5fd79797ede270b>.
Zhang, Shucha. Nagana Gowda,GA. Asiago, V. Shanaiah, N. Barbas, C. "Correlative and quantitative (1)H NMR-based metabolomics reveals specific metabolic pathway disturbances in diabetic rats". Analytical Biochemistry 383. May 2008. 76-84. 11 Feb 2009 <http://www.ncbi.nlm.nih.gov/pubmed/18775407?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum>.
"Diabetes Risk Test". PreDX. 2008. Tethys Bioscience, Inc.. 15 Feb 2009 <http://predictmyrisk.com/about.html>.
"Home". Metabolomics Society. Dec. 2008. Thermo Scientific. 13 Feb 2009 <129.128.185.121/metabolomics_society>
German, J. Bruce. Roberts, Matthew-Alan. and Watkins, Steven M. “Personal Metabolomics as a Next Generation Nutritional Assessment” The American Society for Nutritional Sciences. J. Nutr. May 2009. 133:4260-4266, December 2003. http://jn.nutrition.org/cgi/content/full/133/12/4260
Snyderman, Ralph and Langheier, Jason. “Prospective health care: the second transformation of medicine” Genome Biology 2006. May 2009. 7:104. 27 March 2006. http://genomebiology.com/2006/7/2/104

Articles for future review as Metabolism class assignments[edit]

  1. Personalized Exposure Assessment: Promising Approaches for Human Environmental Health Research

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Identify the main focus of the resource. Possible answers include specific organisms, database design, intergration of information, but there are many more possibilities as well.

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Summary[edit]

Enter your article summary here. Please note that the punctuation is critical at the start (and sometimes at the end) of each entry. It should be 300-500 words. What are the main points of the article? What questions were they trying to answer? Did they find a clear answer? If so, what was it? If not, what did they find or what ideas are in tension in their findings?

Relevance to a Traditional Metabolism Course[edit]

Enter a 100-150 word description of how the material in this article connects to a traditional metabolism course. Does the article relate to particular pathways (e.g., glycolysis, the citric acid cycle, steroid synthesis, etc.) or to regulatory mechanisms, energetics, location, integration of pathways? Does it talk about new analytical approaches or ideas? Does the article show connections to the human genome project (or other genome projects)?
  1. Monitoring Environmental Exposures: Now It’s Personal

Main Focus[edit]

Identify the main focus of the resource. Possible answers include specific organisms, database design, intergration of information, but there are many more possibilities as well.

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Summary[edit]

Enter your article summary here. Please note that the punctuation is critical at the start (and sometimes at the end) of each entry. It should be 300-500 words. What are the main points of the article? What questions were they trying to answer? Did they find a clear answer? If so, what was it? If not, what did they find or what ideas are in tension in their findings?

Relevance to a Traditional Metabolism Course[edit]

Enter a 100-150 word description of how the material in this article connects to a traditional metabolism course. Does the article relate to particular pathways (e.g., glycolysis, the citric acid cycle, steroid synthesis, etc.) or to regulatory mechanisms, energetics, location, integration of pathways? Does it talk about new analytical approaches or ideas? Does the article show connections to the human genome project (or other genome projects)?
  1. Systems medicine: the future of medical genomics and healthcare

Main Focus[edit]

Identify the main focus of the resource. Possible answers include specific organisms, database design, intergration of information, but there are many more possibilities as well.

New Terms[edit]

New Term 1
Definition. (source: http://)
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Summary[edit]

Enter your article summary here. Please note that the punctuation is critical at the start (and sometimes at the end) of each entry. It should be 300-500 words. What are the main points of the article? What questions were they trying to answer? Did they find a clear answer? If so, what was it? If not, what did they find or what ideas are in tension in their findings?

Relevance to a Traditional Metabolism Course[edit]

Enter a 100-150 word description of how the material in this article connects to a traditional metabolism course. Does the article relate to particular pathways (e.g., glycolysis, the citric acid cycle, steroid synthesis, etc.) or to regulatory mechanisms, energetics, location, integration of pathways? Does it talk about new analytical approaches or ideas? Does the article show connections to the human genome project (or other genome projects)?
  1. Metabolic Profiling of Patients with Schizophrenia

Main Focus[edit]

Identify the main focus of the resource. Possible answers include specific organisms, database design, intergration of information, but there are many more possibilities as well.

New Terms[edit]

New Term 1
Definition. (source: http://)
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Definition. (source: http://)
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Summary[edit]

Enter your article summary here. Please note that the punctuation is critical at the start (and sometimes at the end) of each entry. It should be 300-500 words. What are the main points of the article? What questions were they trying to answer? Did they find a clear answer? If so, what was it? If not, what did they find or what ideas are in tension in their findings?

Relevance to a Traditional Metabolism Course[edit]

Enter a 100-150 word description of how the material in this article connects to a traditional metabolism course. Does the article relate to particular pathways (e.g., glycolysis, the citric acid cycle, steroid synthesis, etc.) or to regulatory mechanisms, energetics, location, integration of pathways? Does it talk about new analytical approaches or ideas? Does the article show connections to the human genome project (or other genome projects)?

Websites for future review as Metabolism class assignments[edit]