Plasma metabolic profiles predict future dementia and dementia subtypes: a prospective analysis of 274,160 participants DOI Creative Commons

Yi‐Xuan Qiang,

Jia You,

Xiao‐Yu He

et al.

Alzheimer s Research & Therapy, Journal Year: 2024, Volume and Issue: 16(1)

Published: Jan. 22, 2024

Abstract Background Blood-based biomarkers for dementia are gaining attention due to their non-invasive nature and feasibility in regular healthcare settings. Here, we explored the associations between 249 metabolites with all-cause (ACD), Alzheimer’s disease (AD), vascular (VaD) assessed predictive potential. Methods This study included 274,160 participants from UK Biobank. Cox proportional hazard models were employed investigate longitudinal dementia. The importance of these was quantified using machine learning algorithms, a metabolic risk score (MetRS) subsequently developed each type. We further investigated how MetRS stratified onset its performance, both alone combination demographic cognitive predictors. Results During median follow-up 14.01 years, 5274 Of examined, 143 significantly associated incident ACD, 130 AD, 140 VaD. Among dementia, lipoprotein lipid concentrations, linoleic acid, sphingomyelin, glucose, branched-chain amino acids ranked top importance. Individuals within tertile faced greater developing than those lowest tertile. When combined predictors, model yielded area under receiver operating characteristic curve (AUC) values 0.857 0.861 0.873 Conclusions conducted largest metabolome investigation date, first time revealed metabolite ranking, highlighted contribution plasma prediction.

Language: Английский

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights DOI Creative Commons
Zhiqiang Pang,

Jasmine Chong,

Guangyan Zhou

et al.

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 49(W1), P. W388 - W396

Published: April 28, 2021

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce version 5.0, aiming to narrow gap from raw functional insights global based on high-resolution mass spectrometry (HRMS). Three modules have been developed help achieve this goal, including: (i) LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization resumable significantly lower barriers LC-MS1 spectra processing; (ii) Functional Analysis expands previous MS Peaks Pathways allow users intuitively select any peak groups of interest evaluate their enrichment potential functions as defined by metabolic pathways metabolite sets; (iii) Meta-Analysis combine multiple datasets obtained under complementary conditions or similar studies arrive at insights. There are many other new including weighted joint-pathway analysis, data-driven network batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics underlying codebase also refactored improve performance user experience. At end session, now easily switch compatible more streamlined analysis. 5.0 is freely available https://www.metaboanalyst.ca.

Language: Английский

Citations

3285
MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation DOI Creative Commons
Zhiqiang Pang, Yao Lü,

Guangyan Zhou

et al.

Nucleic Acids Research, Journal Year: 2024, Volume and Issue: 52(W1), P. W398 - W406

Published: April 8, 2024

Abstract We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted well untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC–MS). The two main objectives in developing are to support tandem MS (MS2) processing annotation, the analysis of exposomics related experiments. Key features include: (i) significantly enhanced Spectra Processing module with MS2 asari algorithm; (ii) Peak Annotation based on comprehensive reference databases fragment-level annotation; (iii) new Statistical Analysis dedicated handling complex study design multiple factors or phenotypic descriptors; (iv) Causal estimating metabolite phenotype causal relations two-sample Mendelian randomization, (v) Dose-Response benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database sets, expanded pathway around 130 species. is freely available at https://www.metaboanalyst.ca.

Language: Английский

Citations

447
Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research DOI
Leonardo Perez de Souza, Saleh Alseekh, Federico Scossa

et al.

Nature Methods, Journal Year: 2021, Volume and Issue: 18(7), P. 733 - 746

Published: May 10, 2021

Language: Английский

Citations

257
After another decade: LC–MS/MS became routine in clinical diagnostics DOI Creative Commons
Christoph Seger,

Linda Salzmann

Clinical Biochemistry, Journal Year: 2020, Volume and Issue: 82, P. 2 - 11

Published: March 15, 2020

Tandem mass spectrometry – especially in combination with liquid chromatography (LC–MS/MS) is applied a multitude of important diagnostic niches laboratory medicine. It unquestioned its routine use and often unreplaceable by alternative technologies. This overview illustrates the development past decade (2009–2019) intends to provide insight into current standing future directions field. The instrumentation matured significantly, applications are well understood, vitro diagnostics (IVD) industry shaping market providing assay kits, certified instruments, first automated LC–MS/MS instruments as an analytical core. In many settings application still burdensome locally lab developed test (LDT) designs relying on highly specialized staff. cover wide range analytes therapeutic drug monitoring, endocrinology including newborn screening, toxicology. tasks that remain be mastered are, for example, quantification proteins means transition from targeted untargeted omics approaches pattern recognition/pattern discrimination key technology establishment decisions.

Language: Английский

Citations

227
Biostimulants for Plant Growth and Mitigation of Abiotic Stresses: A Metabolomics Perspective DOI Creative Commons
Lerato Nephali, Lizelle A. Piater, Ian A. Dubery

et al.

Metabolites, Journal Year: 2020, Volume and Issue: 10(12), P. 505 - 505

Published: Dec. 10, 2020

Adverse environmental conditions due to climate change, combined with declining soil fertility, threaten food security. Modern agriculture is facing a pressing situation where novel strategies must be developed for sustainable production and Biostimulants, conceptually defined as non-nutrient substances or microorganisms the ability promote plant growth health, represent potential provide economically favorable solutions that could introduce approaches improve agricultural practices crop productivity. Current knowledge phenotypic observations suggest biostimulants potentially function in regulating modifying physiological processes plants growth, alleviate stresses, quality yield. However, successfully develop biostimulant-based formulations programs, understanding biostimulant-plant interactions, at molecular, cellular levels, prerequisite. Metabolomics, multidisciplinary omics science, offers unique opportunities predictively decode mode of action on plants, identify signatory markers biostimulant action. Thus, this review intends highlight current scientific efforts gaps research industry, context promotion stress responses. The firstly revisits models have been elucidated describe molecular machinery employed by coping stresses. Furthermore, definitions, claims applications are pointed out, also indicating lack biological basis accurately postulate mechanisms biostimulants. articulates briefly key aspects metabolomics workflow (potential) science industry.

Language: Английский

Citations

181
Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies DOI Open Access
Jakub Morze, Clemens Wittenbecher, Lukas Schwingshackl

et al.

Diabetes Care, Journal Year: 2022, Volume and Issue: 45(4), P. 1013 - 1024

Published: March 29, 2022

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update meta evidence on and type 2 diabetes risk is warranted.

Language: Английский

Citations

176
A cross-platform approach identifies genetic regulators of human metabolism and health DOI
Luca A. Lotta, Maik Pietzner,

Isobel D. Stewart

et al.

Nature Genetics, Journal Year: 2021, Volume and Issue: 53(1), P. 54 - 64

Published: Jan. 1, 2021

Language: Английский

Citations

162
Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies DOI Creative Commons
Qiao Jin, Ronald C.W.

Cells, Journal Year: 2021, Volume and Issue: 10(11), P. 2832 - 2832

Published: Oct. 21, 2021

The increasing prevalence of diabetes and its complications, such as cardiovascular kidney disease, remains a huge burden globally. Identification biomarkers for the screening, diagnosis, prognosis complications better understanding molecular pathways involved in development progression can facilitate individualized prevention treatment. With advancement analytical techniques, metabolomics identify quantify multiple simultaneously high-throughput manner. Providing information on underlying metabolic pathways, further mechanisms progression. application epidemiological studies have identified novel type 2 (T2D) branched-chain amino acids, metabolites phenylalanine, energy metabolism, lipid metabolism. Metabolomics also been applied to explore potential modulated by medications. Investigating using systems biology approach integrating with other omics data, genetics, transcriptomics, proteomics, clinical data present comprehensive network causal inference. In this regard, deepen understanding, help therapeutic targets, improve management T2D complications. current review focused metabolomic disease from studies, will provide brief overview investigations T2D.

Language: Английский

Citations

162
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine DOI Open Access
Xiujing He, Xiaowei Liu,

Fengli Zuo

et al.

Seminars in Cancer Biology, Journal Year: 2022, Volume and Issue: 88, P. 187 - 200

Published: Dec. 31, 2022

Language: Английский

Citations

156
Multi-Omics Profiling for Health DOI Creative Commons
Mohan Babu, M Snyder

Molecular & Cellular Proteomics, Journal Year: 2023, Volume and Issue: 22(6), P. 100561 - 100561

Published: April 28, 2023

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting cross-disciplinary approach to understand treating disease. Current medical care focuses on people after they become patients rather than preventing illness, leading high costs late-stage diseases. Additionally, "one-size-fits all" health does not take into account individual differences genetics, environment, or lifestyle factors, decreasing the number of benefiting from interventions. Rapid advances omics technologies progress computational capabilities have led development multi-omics deep phenotyping, which profiles interaction multiple levels biology over time empowers precision approaches. This review highlights current emerging modalities for discusses applications following areas: genetic variation, cardio-metabolic cancer, organ transplantation, pregnancy, longevity/aging. We will briefly discuss potential approaches disentangling host-microbe host-environmental interactions. touch areas electronic record clinical imaging integration with muti-omics health. Finally, we challenges implementation its future prospects.

Language: Английский

Citations

148