| 1. |
- Bodén, Stina, et al.
(författare)
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Dietary patterns, untargeted metabolite profiles and their association with colorectal cancer risk
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322 .- 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- We investigated data-driven and hypothesis-driven dietary patterns and their association to plasma metabolite profiles and subsequent colorectal cancer (CRC) risk in 680 CRC cases and individually matched controls. Dietary patterns were identified from combined exploratory/confirmatory factor analysis. We assessed association to LC–MS metabolic profiles by random forest regression and to CRC risk by multivariable conditional logistic regression. Principal component analysis was used on metabolite features selected to reflect dietary exposures. Component scores were associated to CRC risk and dietary exposures using partial Spearman correlation. We identified 12 data-driven dietary patterns, of which a breakfast food pattern showed an inverse association with CRC risk (OR per standard deviation increase 0.89, 95% CI 0.80–1.00, p = 0.04). This pattern was also inversely associated with risk of distal colon cancer (0.75, 0.61–0.96, p = 0.01) and was more pronounced in women (0.69, 0.49–0.96, p = 0.03). Associations between meat, fast-food, fruit soup/rice patterns and CRC risk were modified by tumor location in women. Alcohol as well as fruit and vegetables associated with metabolite profiles (Q2 0.22 and 0.26, respectively). One metabolite reflecting alcohol intake associated with increased CRC risk, whereas three metabolites reflecting fiber, wholegrain, and fruit and vegetables associated with decreased CRC risk.
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| 2. |
- González-Domínguez, Álvaro, et al.
(författare)
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QComics: Recommendations and Guidelines for Robust, Easily Implementable and Reportable Quality Control of Metabolomics Data
- 2024
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Ingår i: Analytical Chemistry. - 0003-2700 .- 1520-6882. ; 96:3, s. 1064-1072
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Tidskriftsartikel (refereegranskat)abstract
- The implementation of quality control strategies is crucial to ensure the reproducibility, accuracy, and meaningfulness of metabolomics data. However, this pivotal step is often overlooked within the metabolomics workflow and frequently relies on the use of nonstandardized and poorly reported protocols. To address current limitations in this respect, we have developed QComics, a robust, easily implementable and reportable method for monitoring and controlling data quality. The protocol operates in various sequential steps aimed to (i) correct for background noise and carryover, (ii) detect signal drifts and “out-of-control” observations, (iii) deal with missing data, (iv) remove outliers, (v) monitor quality markers to identify samples affected by improper collection, preprocessing, or storage, and (vi) assess overall data quality in terms of precision and accuracy. Notably, this tool considers important issues often neglected along quality control, such as the need of separately handling missing values and truly absent data to avoid losing relevant biological information, as well as the large impact that preanalytical factors may elicit on metabolomics results. Altogether, the guidelines compiled in QComics might contribute to establishing gold standard recommendations and best practices for quality control within the metabolomics community.
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| 3. |
- Hartvigsson, Olle, 1991, et al.
(författare)
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Associations of the placental metabolome with immune maturation up to one year of age in the Swedish NICE-cohort
- 2024
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Ingår i: Metabolomics. - : Springer Nature. - 1573-3882 .- 1573-3890. ; 20:2
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Allergies and other immune-mediated diseases are thought to result from incomplete maturation of the immune system early in life. We previously showed that infants’ metabolites at birth were associated with immune cell subtypes during infancy. The placenta supplies the fetus with nutrients, but may also provide immune maturation signals. Objectives: To examine the relationship between metabolites in placental villous tissue and immune maturation during the first year of life and infant and maternal characteristics (gestational length, birth weight, sex, parity, maternal age, and BMI). Methods: Untargeted metabolomics was measured using Liquid Chromatography-Mass Spectrometry. Subpopulations of T and B cells were measured using flow cytometry at birth, 48 h, one, four, and 12 months. Random forest analysis was used to link the metabolomics data with the T and B cell sub populations as well as infant and maternal characteristics. Results: Modest associations (Q2 = 0.2–0.3) were found between the placental metabolome and kappa-deleting recombination excision circles (KREC) at birth and naïve B cells and memory T cells at 12 months. Weak associations were observed between the placental metabolome and sex and parity. Still, most metabolite features of interest were of low intensity compared to associations previously found in cord blood, suggesting that underlying metabolites were not of placental origin. Conclusion: Our results indicate that metabolomic measurements of the placenta may not effectively recognize metabolites important for immune maturation.
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| 4. |
- Nordin, Elise, 1985, et al.
(författare)
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Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study
- 2024
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 20:2
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Tidskriftsartikel (refereegranskat)abstract
- There is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood. Objectives Our aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS. Methods Data were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods. Results Regression and classification RF models were in general of low predictive power (Q2 <= 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms. Conclusion Differential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www.clinicaltrials.gov as NCT03653689 31/08/2018.
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| 5. |
- Rostgaard-Hansen, Agnetha, 1986, et al.
(författare)
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Temporal gut microbiota variability and association with dietary patterns : from the one-year observational Diet, cancer, and health - Next generations MAX study
- 2024
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Ingår i: American Journal of Clinical Nutrition. - : Elsevier. - 0002-9165 .- 1938-3207. ; 119:4, s. 1015-1026
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Tidskriftsartikel (refereegranskat)abstract
- Background: Knowledge about the variability of gut microbiota within an individual over time is important to allow meaningful investigations of the gut microbiota in relation to diet and health outcomes in observational studies. Plant-based dietary patterns have been associated with a lower risk of morbidity and mortality and may alter gut microbiota in a favorable direction.Objectives: To assess the gut microbiota variability during one year and investigate the association between adherence to diet indexes and the gut microbiota in a Danish population.Methods: Four hundred forty-four participants were included in the Diet, Cancer, and Health - Next Generations MAX study (DCH-NG MAX). Stool samples collected up to three times during a year were analyzed by 16S ribosomal ribonucleic acid gene sequencing. Diet was obtained by 24-hour dietary recalls. Intraclass correlation coefficient (ICC) was calculated to assess temporal microbial variability based on 214 individuals. Diet indexes (Nordic, Mediterranean, and plant-based diets) and food groups thereof were associated with gut microbiota using linear regression analyses.Results: We found that 91 out of 234 genera had an ICC >0.5. We identified three subgroups dominated by Bacteroides, Prevotella 9, and Ruminococcaceae and adherence to diet indexes differed between subgroups. Higher adherence to diet indexes was associated with the relative abundance of 22 genera. Across diet indexes, higher intakes of fruit, vegetables, whole grains/cereals, and nuts were most frequently associated with these genera.Conclusions: In the DCH-NG MAX study, 39% of the genera had an ICC >0.5 over one year, suggesting that these genera could be studied with health outcomes in prospective analyses with acceptable precision. Adherence to the Nordic, Mediterranean, and plant-based diets differed between bacterial subgroups and was associated with a higher abundance of genera with fiber-degrading properties. Fruits, vegetables, whole grains/cereals, and nuts were frequently associated with these genera.
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| 6. |
- Yan, Yingxiao, 1997, et al.
(författare)
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Adjusting for covariates and assessing modeling fitness in machine learning using MUVR2
- 2024
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Ingår i: Bioinformatics Advances. - 2635-0041. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: Machine learning (ML) methods are frequently used in Omics research to examine associations between molecular data and for example exposures and health conditions. ML is also used for feature selection to facilitate biological interpretation. Our previous MUVR algorithm was shown to generate predictions and variable selections at state-of-the-art performance. However, a general framework for assessing modeling fitness is still lacking. In addition, enabling to adjust for covariates is a highly desired, but largely lacking trait in ML. We aimed to address these issues in the new MUVR2 framework. Results: The MUVR2 algorithm was developed to include the regularized regression framework elastic net in addition to partial least squares and random forest modeling. Compared with other cross-validation strategies, MUVR2 consistently showed state-of-the-art performance, including variable selection, while minimizing overfitting. Testing on simulated and real-world data, we also showed that MUVR2 allows for the adjustment for covariates using elastic net modeling, but not using partial least squares or random forest.
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