Summary of Study ST002733
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 PR001697. The data can be accessed directly via it's Project DOI: 10.21228/M8BT6J This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002733 |
| Study Title | Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome |
| Study Summary | Direct diagnosis and accurate assessment of metabolic syndrome (MetS) would allow for prompt clinical interventions. However, diagnostic strategies use only traditional risk factors, without considering the complex heterogeneity of MetS. Here, we performed an advanced ferric particle-assisted laser desorption/ionization mass spectrometry (LDI-MS)-based metabolomic analysis of 100 nL of plasma per participant collected from the largest general community cohort (n=13,554) reported to date and extracted a set of 26 hub plasma metabolic fingerprints (PMFs) for MetS and its early identification (pre-MetS). We develop machine learning-based diagnostic models for pre-MetS and MetS with convincing performance through independent validation. These PMFs were applied to assess the contributions of four MetS risk factors in the general population as follows, from large to small contribution: hyperglycemia, hypertension, dyslipidemia, and obesity. We devised a personalized three-dimensional plasma metabolic risk (PMR) stratification to decode the individual metabolic risk into three patterns. During the 4-year follow-up period of 13,554 participants, the accumulation analysis of all-cause death events showed that patients with medium and high risk had HRs of 1.54 (95% CI 1.05-2.28, p = 0.029) and 1.85 (95% CI 1.22-2.79, p = 0.004), respectively, compared to those with low risk. Overall, we provided efficient screening tools to identify patients with pre-MetS and MetS who require treatments in the general community and defined the heterogeneous risk stratification of metabolic phenotypes in real-world settings. |
| Institute | Shanghai Jiao Tong University affiliated Renji Hospital |
| Last Name | Chen |
| First Name | Yifan |
| Address | dongfang road.1630 |
| yifanchen@sjtu.edu.cn | |
| Phone | +8613917129357 |
| Submit Date | 2023-05-21 |
| Analysis Type Detail | MALDI-MS |
| Release Date | 2023-06-15 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN004431 |
|---|---|
| Analysis type | MS |
| Chromatography type | None (Direct infusion) |
| Chromatography system | Bruker Autoflex speed TOF/TOF |
| Column | None |
| MS Type | MALDI |
| MS instrument type | TOF |
| MS instrument name | Bruker Autoflex speed TOF/TOF |
| Ion Mode | POSITIVE |
| Units | Peak Area |
