Summary of Study ST001411
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000967. The data can be accessed directly via it's Project DOI: 10.21228/M8PX1S This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
Study ID | ST001411 |
Study Title | Plasma metabolites of lipid metabolism associate with diabetic polyneuropathy in a cohort with screen-tested type 2 diabetes: ADDITION-Denmark |
Study Summary | The global rise in type 2 diabetes (T2D) is associated with a concomitant increase in diabetic complications. Diabetic polyneuropathy (DPN), the most frequent T2D complication, is characterized by sensory peripheral nerve damage. Although managing glucose effectively slows DPN progression in type 1 diabetes patients, it has limited efficacy in neuropathic T2D patients. The metabolic syndrome (MetS) recently emerged as a major risk factor for DPN; however, the metabolites associated with MetS that correlate with DPN are unknown. We conducted a global plasma metabolomics analysis from a cohort of patients enrolled in the Anglo-Danish-Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION), including healthy control subjects, T2D patients, and T2D DPN patients. We identified 15 total plasma metabolites that were altered in T2D DPN patients, including lipids, amino acids, and energy-related metabolites. We evaluated the correlation between these metabolites and all lipid species to identify major changes in both plasma free fatty acids and complex lipids in T2D DPN patients, and found significant alterations in the abundance of long-chain saturated fatty acids, acylcarnitines, and sphingolipids. Our study suggests that DPN in T2D is associated with novel alterations in plasma metabolites related to lipid metabolism. |
Institute | University of Michigan |
Last Name | Feldman |
First Name | Eva |
Address | 5017 AATBSRB, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200 |
efeldman@med.umich.edu | |
Phone | 7347637274 |
Submit Date | 2020-06-22 |
Num Groups | 3 |
Total Subjects | 106 |
Num Males | 83 |
Num Females | 23 |
Raw Data File Type(s) | wiff |
Analysis Type Detail | LC-MS |
Release Date | 2021-06-15 |
Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
Project ID: | PR000967 |
Project DOI: | doi: 10.21228/M8PX1S |
Project Title: | Plasma metabolites of lipid metabolism associate with diabetic polyneuropathy in a cohort with screen-tested type 2 diabetes: ADDITION-Denmark |
Project Summary: | The global rise in type 2 diabetes (T2D) is associated with a concomitant increase in diabetic complications. Diabetic polyneuropathy (DPN), the most frequent T2D complication, is characterized by sensory peripheral nerve damage. Although managing glucose effectively slows DPN progression in type 1 diabetes patients, it has limited efficacy in neuropathic T2D patients. The metabolic syndrome (MetS) recently emerged as a major risk factor for DPN; however, the metabolites associated with MetS that correlate with DPN are unknown. We conducted a global plasma metabolomics analysis from a cohort of patients enrolled in the Anglo-Danish-Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION), including healthy control subjects, T2D patients, and T2D DPN patients. We identified 15 total plasma metabolites that were altered in T2D DPN patients, including lipids, amino acids, and energy-related metabolites. We evaluated the correlation between these metabolites and all lipid species to identify major changes in both plasma free fatty acids and complex lipids in T2D DPN patients, and found significant alterations in the abundance of long-chain saturated fatty acids, acylcarnitines, and sphingolipids. Our study suggests that DPN in T2D is associated with novel alterations in plasma metabolites related to lipid metabolism. |
Institute: | University of Michigan |
Department: | Neurology |
Laboratory: | Feldman's lab |
Last Name: | Feldman |
First Name: | Eva |
Address: | 5017 AATBSRB, 109 Zina Pitcher Place, Ann Arbor, MI 48109-2200 |
Email: | efeldman@med.umich.edu |
Phone: | 7347637274 |
Funding Source: | Support for this research was provided by the Novo Nordisk Foundation through a Novo Nordisk Foundation Challenge Programme grant (NNF14OC0011633), the National Institutes of Health (1R24082841, 1R21NS102924, and 1DP3DK094292), the National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) (1F32DK112642 and 1K99DK119366), the American Diabetes Association (7-12-BS-045), the NeuroNetwork for Emerging Therapies, and the A. Alfred Taubman Medical Research Institute. |
Subject:
Subject ID: | SU001485 |
Subject Type: | Human |
Subject Species: | Homo sapiens |
Taxonomy ID: | 9606 |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
mb_sample_id | local_sample_id | Group |
---|---|---|
SA115723 | MICH-01469 | Diabetic Neuropathy |
SA115724 | MICH-01468 | Diabetic Neuropathy |
SA115725 | MICH-01466 | Diabetic Neuropathy |
SA115726 | MICH-01470 | Diabetic Neuropathy |
SA115727 | MICH-01419 | Diabetic Neuropathy |
SA115728 | MICH-01481 | Diabetic Neuropathy |
SA115729 | MICH-01473 | Diabetic Neuropathy |
SA115730 | MICH-01465 | Diabetic Neuropathy |
SA115731 | MICH-01464 | Diabetic Neuropathy |
SA115732 | MICH-01454 | Diabetic Neuropathy |
SA115733 | MICH-01453 | Diabetic Neuropathy |
SA115734 | MICH-01455 | Diabetic Neuropathy |
SA115735 | MICH-01459 | Diabetic Neuropathy |
SA115736 | MICH-01463 | Diabetic Neuropathy |
SA115737 | MICH-01462 | Diabetic Neuropathy |
SA115738 | MICH-01482 | Diabetic Neuropathy |
SA115739 | MICH-01483 | Diabetic Neuropathy |
SA115740 | MICH-01510 | Diabetic Neuropathy |
SA115741 | MICH-01507 | Diabetic Neuropathy |
SA115742 | MICH-01511 | Diabetic Neuropathy |
SA115743 | MICH-01512 | Diabetic Neuropathy |
SA115744 | MICH-01515 | Diabetic Neuropathy |
SA115745 | MICH-01514 | Diabetic Neuropathy |
SA115746 | MICH-01504 | Diabetic Neuropathy |
SA115747 | MICH-01502 | Diabetic Neuropathy |
SA115748 | MICH-01490 | Diabetic Neuropathy |
SA115749 | MICH-01485 | Diabetic Neuropathy |
SA115750 | MICH-01491 | Diabetic Neuropathy |
SA115751 | MICH-01492 | Diabetic Neuropathy |
SA115752 | MICH-01496 | Diabetic Neuropathy |
SA115753 | MICH-01495 | Diabetic Neuropathy |
SA115754 | MICH-01452 | Diabetic Neuropathy |
SA115755 | MICH-01472 | Diabetic Neuropathy |
SA115756 | MICH-01430 | Diabetic Neuropathy |
SA115757 | MICH-01426 | Diabetic Neuropathy |
SA115758 | MICH-01444 | Diabetic Neuropathy |
SA115759 | MICH-01429 | Diabetic Neuropathy |
SA115760 | MICH-01441 | Diabetic Neuropathy |
SA115761 | MICH-01428 | Diabetic Neuropathy |
SA115762 | MICH-01439 | Diabetic Neuropathy |
SA115763 | MICH-01447 | Diabetic Neuropathy |
SA115764 | MICH-01446 | Diabetic Neuropathy |
SA115765 | MICH-01436 | Diabetic Neuropathy |
SA115766 | MICH-01422 | Diabetic Neuropathy |
SA115767 | MICH-01432 | Diabetic Neuropathy |
SA115768 | MICH-01431 | Diabetic Neuropathy |
SA115769 | MICH-01434 | Diabetic Neuropathy |
SA115770 | MICH-01425 | Diabetic Neuropathy |
SA115771 | MICH-01433 | Diabetic non neuropathy |
SA115772 | MICH-01499 | Diabetic non neuropathy |
SA115773 | MICH-01500 | Diabetic non neuropathy |
SA115774 | MICH-01497 | Diabetic non neuropathy |
SA115775 | MICH-01494 | Diabetic non neuropathy |
SA115776 | MICH-01493 | Diabetic non neuropathy |
SA115777 | MICH-01498 | Diabetic non neuropathy |
SA115778 | MICH-01427 | Diabetic non neuropathy |
SA115779 | MICH-01423 | Diabetic non neuropathy |
SA115780 | MICH-01424 | Diabetic non neuropathy |
SA115781 | MICH-01513 | Diabetic non neuropathy |
SA115782 | MICH-01421 | Diabetic non neuropathy |
SA115783 | MICH-01420 | Diabetic non neuropathy |
SA115784 | MICH-01509 | Diabetic non neuropathy |
SA115785 | MICH-01508 | Diabetic non neuropathy |
SA115786 | MICH-01503 | Diabetic non neuropathy |
SA115787 | MICH-01451 | Diabetic non neuropathy |
SA115788 | MICH-01505 | Diabetic non neuropathy |
SA115789 | MICH-01506 | Diabetic non neuropathy |
SA115790 | MICH-01501 | Diabetic non neuropathy |
SA115791 | MICH-01489 | Diabetic non neuropathy |
SA115792 | MICH-01445 | Diabetic non neuropathy |
SA115793 | MICH-01461 | Diabetic non neuropathy |
SA115794 | MICH-01488 | Diabetic non neuropathy |
SA115795 | MICH-01467 | Diabetic non neuropathy |
SA115796 | MICH-01440 | Diabetic non neuropathy |
SA115797 | MICH-01442 | Diabetic non neuropathy |
SA115798 | MICH-01460 | Diabetic non neuropathy |
SA115799 | MICH-01448 | Diabetic non neuropathy |
SA115800 | MICH-01449 | Diabetic non neuropathy |
SA115801 | MICH-01450 | Diabetic non neuropathy |
SA115802 | MICH-01456 | Diabetic non neuropathy |
SA115803 | MICH-01457 | Diabetic non neuropathy |
SA115804 | MICH-01458 | Diabetic non neuropathy |
SA115805 | MICH-01471 | Diabetic non neuropathy |
SA115806 | MICH-01443 | Diabetic non neuropathy |
SA115807 | MICH-01435 | Diabetic non neuropathy |
SA115808 | MICH-01437 | Diabetic non neuropathy |
SA115809 | MICH-01438 | Diabetic non neuropathy |
SA115810 | MICH-01486 | Diabetic non neuropathy |
SA115811 | MICH-01487 | Diabetic non neuropathy |
SA115812 | MICH-01480 | Diabetic non neuropathy |
SA115813 | MICH-01484 | Diabetic non neuropathy |
SA115814 | MICH-01474 | Diabetic non neuropathy |
SA115815 | MICH-01479 | Diabetic non neuropathy |
SA115816 | MICH-01476 | Diabetic non neuropathy |
SA115817 | MICH-01475 | Diabetic non neuropathy |
SA115818 | MICH-01478 | Diabetic non neuropathy |
SA115819 | MICH-01477 | Diabetic non neuropathy |
SA115820 | MICH-01523 | Normal |
SA115821 | MICH-01522 | Normal |
SA115822 | MICH-01524 | Normal |
Collection:
Collection ID: | CO001480 |
Collection Summary: | At the mean 13 year follow up after T2D diagnosis, blood samples were collected from patients the same day as anthropometrics and DPN assessment. Plasma was collected in purple EDTA tubes with 10 mL of buffy coat, inverted 10 times, incubated for 30-90 minutes at room temperature, and centrifuged at 3000 rpm for 10 minutes. The plasma supernatant was collected by aspirating plasma to approximately 5 mm above the buffy coat and plasma samples were stored in 0.5 mL aliquots at -80C prior to metabolomics analysis. |
Sample Type: | Blood (plasma) |
Treatment:
Treatment ID: | TR001500 |
Treatment Summary: | NA |
Sample Preparation:
Sampleprep ID: | SP001493 |
Sampleprep Summary: | Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. Several recovery standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent. The sample extracts were stored overnight under nitrogen before preparation for analysis. |
Combined analysis:
Analysis ID | AN002361 |
---|---|
Analysis type | MS |
Chromatography type | HILIC |
Chromatography system | Waters Acquity |
Column | Waters Acquity CSH C18 (100 x 2.1mm,1.7um) |
MS Type | ESI |
MS instrument type | UPLC-MS |
MS instrument name | Waters Acquity |
Ion Mode | POSITIVE |
Units | peak area |
Chromatography:
Chromatography ID: | CH001731 |
Instrument Name: | Waters Acquity |
Column Name: | Waters Acquity CSH C18 (100 x 2.1mm,1.7um) |
Chromatography Type: | HILIC |
MS:
MS ID: | MS002203 |
Analysis ID: | AN002361 |
Instrument Name: | Waters Acquity |
Instrument Type: | UPLC-MS |
MS Type: | ESI |
MS Comments: | The informatics system consisted of four major components, the Laboratory Information Management System (LIMS), the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for use by data analysts. The hardware and software foundations for these informatics components were the LAN backbone, and a database server running Oracle 10.2.0.1 Enterprise Edition. |
Ion Mode: | POSITIVE |