Integrating metabolomics and genomics to identify biomarkers and drug targets for diseases
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".