Integrating metabolomics and genomics to identify biomarkers and drug targets for diseases
Notice bibliographique
Résumé
Despite the advances in measuring a large spectrum of metabolites, their causal roles in disease development remain unclear. This doctoral thesis explores the genetic architecture of circulating metabolites in adults, examines the modifying effect of sex on the association between variants and metabolites, and investigates the causal relationships between metabolites and diseases. First, using the Canadian Longitudinal Study on Aging (CLSA), I executed genome-wide association studies (GWAS) and uncovered associations between genetic variants at 248 loci with 690 metabolites. I also found genetic associations at 69 loci involving 143 metabolite ratios. Employing these associations in Mendelian randomization (MR) analysis, my analysis pinpointed 22 metabolites and 20 metabolite ratios that had estimated causal effects on at least one of 12 traits and diseases that were studied. One of these associations was replicated in an independent cohort, namely that an increase in orotate levels leads to impaired bone health. This study revealed genetic contributions to circulating metabolite levels and demonstrated the potential of genetics in identifying biomarkers or intervention targets for disease.In my second study, I aimed to explore how sex influences the genetics of metabolites and subsequently disease risks. I carried out a sex-stratified metabolomics GWAS meta-analysis by combining results from the CLSA and the EPIC-Norfolk study. I then assessed the role of sex in 2,504 significant variant-metabolite associations, involving 625 metabolites, identified in either males or females. My analysis revealed that merely 3% of these associations, at 13 loci, demonstrated sex-biased effects. Some sex-biased associations were involved in disease risks in a sex-specific manner. Using sex-specific MR, I identified 12 metabolites that appeared to exert different effects in males and females on at least one of 11 diseases. Together, this study revealed limited but important sex-specific genetic influences in circulating metabolites. My findings also suggested that these metabolites might contribute partially to some of the known sex-specific disease risks.Finally, I investigated the relationship between hypothyroidism and metabolites. Despite adequate thyroxine replacement, many patients with hypothyroidism continue to experience residual symptoms, possibly due to other uncorrected metabolic changes. My approach started with using MR to identify metabolites affected by hypothyroidism. I then examined which of these metabolites, were not corrected by treatment of hypothyroidism. Out of 458 plasma metabolites screened, I found four adrenal androstane steroids and two adrenal pregnane steroids that were reduced by hypothyroidism according to MR analyses and continued to be lower after appropriate levothyroxine treatment. The decreased levels of these six steroids were associated with impaired cognitive function and poorer self-rated general health. Collectively, my findings suggested these six metabolites might underlie some of the residual symptoms reported among patients treated for hypothyroidism.In summary, this doctoral thesis contributes to understanding the genetic architecture of metabolomics. The MR analysis findings also provide resources to identify metabolite targets for therapeutical interventions
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».