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Abstract Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR). Gut microbiome-associated metabolites were identified through a comprehensive search of Pubmed, Exposome Explorer and Human Metabolome Database. Single Nucleotide Polymorphisms (SNPs) associated by Genome-Wide Association Studies (GWAS) with circulating levels of 109 of these metabolites were collated from Pubmed and the GWAS catalogue. SNPs for 119 taxonomically defined gut genera were selected from a meta-analysis performed by the MiBioGen consortium. Two-sample MR was conducted using GWAS summary statistics from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including a total of 8,769 cases and 7,055 controls. Inverse variance-weighted MR analyses were performed along with sensitivity analyses to assess potential violations of MR assumptions. Nominally significant associations were noted for genetically predicted circulating concentrations of mannitol (odds ratio per standard deviation [ORSD] = 0.97; 95% confidence interval [CI]: 0.95–0.99, p = 0.006), methionine (ORSD= 0.97; 95%CI: 0.94-1.00, p = 0.031), stearic acid (ORSD= 0.93; 95%CI: 0.87–0.99, p = 0.027), carnitine = (ORSD=1.01; 95% CI: 1.00-1.03, p = 0.027), hippuric acid (ORSD= 1.02; 95%CI: 1.00-1.04, p = 0.038) and 3-methylhistidine (ORSD= 1.05; 95%CI: 1.01–1.10, p = 0.02). Two gut microbiome genera were associated with reduced PDAC risk; Clostridium sensu stricto 1 (OR: 0.88; 95%CI: 0.78–0.99, p = 0.027) and Romboutsia (OR: 0.87; 95%CI: 0.80–0.96, p = 0.004). These results, though based only on genetically predicted gut microbiome characteristics and circulating bacteria-related metabolite concentrations, provide evidence for causal associations with pancreatic carcinogenesis.

Abstract Purpose High levels of physical activity have been documented in eating disorder patients. Our aim was to examine whether adolescent leisure-time physical activity is prospectively associated with eating disorders in adolescence and young adulthood. Methods Finnish twins born in 1983–1987 reported their physical activity frequency at ages 12, 14, and 17. A subsample of participants underwent structured, retrospective interviews for eating disorders at the mean age of 22.4 years. Associations between female twins’ physical activity and future eating disorders (571–683 twins/wave) were investigated with the Cox proportional hazards model. To illustrate the physical activity similarity of the co-twins in a twin pair, we used cross-tabulation of eating disorder–discordant twin pairs (13–24 pairs/wave). Results After adjusting for several covariates, we found no statistically significant longitudinal association between physical activity and eating disorders. This applied when all eating disorders were combined but also when assessed separately as restrictive and non-restrictive eating disorders. Co-twins’ physical activity in adolescence tended to be similar irrespective of their future eating disorder, supporting the results of the regression analysis. Conclusion We observed no evidence of adolescent physical activity frequency being prospectively associated with eating disorders in female twins. Further longitudinal studies with larger sample sizes and more detailed physical activity data are needed. Level of evidence: III, evidence obtained from cohort or case–control analytic studies.

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

BackgroundGenetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche.MethodsA secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed, which included 9335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age of onset, early-onset AN (

Abstract Background Testicular germ cell tumours (TGCTs) are the most common malignancy in men aged 15–40 years, with increasing incidence worldwide. About 33 ~ 50% of the patients present with metastatic disease at diagnosis. TGCT survivors experience short- and long-term sequelae, including cancer-related fatigue (CRF). Physical activity (PA) has established effects on reducing CRF and other sequelae and improving health-related quality of life (HRQoL). However, its impact on TGCT survivors has so far received little attention. The gut microbiota plays a crucial role in various physiological functions, including cognition and metabolism, and may mediate the effects of PA on CRF and other sequelae, but this has not been investigated in randomized controlled trials. Methods This national, multicentre, phase-III trial will evaluate the impact of a one-year supervised PA program on CRF and other short- and long-term sequelae in metastatic TGCT patients receiving cisplatin-based chemotherapy combined with etoposide+/-bleomycin. It will also investigate potential mediating effects of the gut microbiota and its metabolites involved in the gut-brain axis on the relationship between PA and CRF and other sequelae. A total of 236 men ≥ 18 years of age with metastatic TGCT (seminoma and non-seminoma) will be enrolled before starting first-line chemotherapy in several French hospitals. The primary (CRF) and secondary (cognitive/psychological/metabolic sequelae, HRQoL, etc.) outcomes and gut microbiota and relevant metabolites will be assessed at inclusion, during and at the end of the one-year intervention, and annually until 10 years since inclusion to assess long-term sequelae, more specifically CRF, cardiovascular toxicities, and second primary cancer occurrence in this population. Discussion This trial will provide comprehensive and novel insights into the effects of a long-term supervised PA program on CRF and other sequelae in metastatic TGCT patients receiving first-line chemotherapy. It will also contribute to understanding the potential role of the gut microbiota and its metabolites in mediating the effects of PA on these outcomes. The findings of this study will help the development of effective PA interventions to improve the health of TGCT survivors and may have implications for other cancer populations as well. Trial registration The study was registered on ClinicalTrials.gov (NCT05588700) on 20 Oct. 2022.

Summary: Background: Altered lipid metabolism is a hallmark of cancer development. However, the role of specific lipid metabolites in colorectal cancer development is uncertain. Methods: In a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC), we examined associations between pre-diagnostic circulating concentrations of 97 lipid metabolites (acylcarnitines, glycerophospholipids and sphingolipids) and colorectal cancer risk. Circulating lipids were measured using targeted mass spectrometry in 1591 incident colorectal cancer cases (55% women) and 1591 matched controls. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between concentrations of individual lipid metabolites and metabolite patterns with colorectal cancer risk. Findings: Of the 97 assayed lipids, 24 were inversely associated (nominally p

Obesity is a major health concern for breast cancer survivors, being associated with high recurrence and reduced efficacy during cancer treatment. Metformin treatment is associated with reduced breast cancer incidence, recurrence and mortality. To better understand the underlying mechanisms through which metformin may reduce recurrence, we aimed to conduct metabolic profiling of overweight/obese breast cancer survivors before and after metformin treatment. Fasting plasma samples from 373 overweight or obese breast cancer survivors randomly assigned to metformin (n = 194) or placebo (n = 179) administration were collected at baseline, after 6 months (Reach For Health trial), and after 12 months (MetBreCS trial). Archival samples were concurrently analyzed using three complementary methods: untargeted LC-QTOF-MS metabolomics, targeted LC-MS metabolomics (AbsoluteIDQ p180, Biocrates), and gas chromatography phospholipid fatty acid assay. Multivariable linear regression models and family-wise error correction were used to identify metabolites that significantly changed after metformin treatment. Participants (n = 352) with both baseline and study end point samples available were included in the analysis. After adjusting for confounders such as study center, age, body mass index and false discovery rate, we found that metformin treatment was significantly associated with decreased levels of citrulline, arginine, tyrosine, caffeine, paraxanthine, and theophylline, and increased levels of leucine, isoleucine, proline, 3-methyl-2-oxovalerate, 4-methyl-2-oxovalerate, alanine and indoxyl-sulphate. Long-chain unsaturated phosphatidylcholines (PC ae C36:4, PC ae C38:5, PC ae C36:5 and PC ae C38:6) were significantly decreased with the metformin treatment, as were phospholipid-derived long-chain n-6 fatty acids. The metabolomic profiles of metformin treatment suggest change in specific biochemical pathways known to impair cancer cell growth including activation of CYP1A2, alterations in fatty acid desaturase activity, and altered metabolism of specific amino acids, including impaired branched chain amino acid catabolism. Our results in overweight breast cancer survivors identify new metabolic effects of metformin treatment that may mechanistically contribute to reduced risk of recurrence in this population and reduced obesity-related cancer risk reported in observational studies. ClinicalTrials.gov identifier: NCT01302379 and EudraCT Protocol #: 2015-001001-14.

The intricate relationship between electrons and the crystal lattice is a linchpin in condensed matter, traditionally described by the Fröhlich model encompassing the lowest-order lattice-electron coupling. Recently developed quantum acoustics, emphasizing the wave nature of lattice vibrations, has enabled the exploration of previously uncharted territories of electron–lattice interaction not accessible with conventional tools such as perturbation theory. In this context, our agenda here is two-fold. First, we showcase the application of machine learning methods to categorize various interaction regimes within the subtle interplay of electrons and the dynamical lattice landscape. Second, we shed light on a nebulous region of electron dynamics identified by the machine learning approach and then attribute it to transient localization, where strong lattice vibrations result in a momentary Anderson prison for electronic wavepackets, which are later released by the evolution of the lattice. Overall, our research illuminates the spectrum of dynamics within the Fröhlich model, such as transient localization, which has been suggested as a pivotal factor contributing to the mysteries surrounding strange metals. Furthermore, this paves the way for utilizing time-dependent perspectives in machine learning techniques for designing materials with tailored electron–lattice properties.

Recent theoretical investigations have revealed unconventional transport mechanisms within high Brillouin zones of two-dimensional superlattices. Electrons can navigate along channels we call superwires, gently guided without brute force confinement. Such dynamical confinement is caused by weak superlattice deflections, markedly different from the static or energetic confinement observed in traditional wave guides or one-dimensional electron wires. The quantum properties of superwires give rise to elastic dynamical tunneling, linking disjoint regions of the corresponding classical phase space, and enabling the emergence of several parallel channels. This paper provides the underlying theory and mechanisms that facilitate dynamical tunneling assisted by chaos in periodic lattices. Moreover, we show that the mechanism of dynamical tunneling can be effectively conceptualized through the lens of a paraxial approximation. Our results further reveal that superwires predominantly exist within flat bands, emerging from eigenstates that represent linear combinations of conventional degenerate Bloch states. Finally, we quantify tunneling rates across various lattice configurations and demonstrate that tunneling can be suppressed in a controlled fashion, illustrating potential implications in future nanodevices.

Endogenous sex hormones and DNA methylation both play important roles in various diseases. However, their interplay is largely unknown. A deeper understanding of their interrelationships could provide new insights into the pathology of disease development. We, therefore, investigated associations between circulating sex hormones, sex hormone binding globulin (SHBG), and DNA methylation in blood, using samples from 77 men (65 with repeated samples), from the population-based Northern Sweden Health and Disease Study (NSHDS). DNA methylation was measured in buffy coat using the Infinium Methylation EPIC BeadChip (Illumina). Sex hormone (oestradiol, oestrone, testosterone, androstenedione, dehydroepiandrosterone, and progesterone) and SHBG concentrations were measured in plasma using a high-performance liquid chromatography tandem mass spectrometry (LC/MS-MS) method and an enzyme-linked immunoassay, respectively. Associations between sex hormones, SHBG, and DNA methylation were estimated using both linear regression and mixed-effects models. Additionally, we used the comb-p method to identify differentially methylated regions based on nearby P values. We identified one novel CpG site (cg14319657), at which DNA methylation was associated with dehydroepiandrosterone, surpassing a genome-wide significance level. In addition, more than 40 differentially methylated regions were associated with levels of sex hormones and SHBG and several of these mapped to genes involved in hormone-related diseases. Our findings support a relationship between circulating sex hormones and DNA methylation and suggest that further investigation is warranted, both for validation, further exploration and to gain a deeper understanding of the mechanisms and potential consequences for health and disease.

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.

Abstract Background Rapid postnatal growth may result from exposure in utero or early life to adverse conditions and has been associated with diseases later in life and, in particular, with childhood obesity. DNA methylation, interfacing early-life exposures and subsequent diseases, is a possible mechanism underlying early-life programming. Methods Here, a meta-analysis of Illumina HumanMethylation 450K/EPIC-array associations of cord blood DNA methylation at single CpG sites and CpG genomic regions with rapid weight growth at 1 year of age (defined with reference to WHO growth charts) was conducted in six European-based child cohorts (ALSPAC, ENVIRONAGE, Generation XXI, INMA, Piccolipiù, and RHEA, N = 2003). The association of gestational age acceleration (calculated using the Bohlin epigenetic clock) with rapid weight growth was also explored via meta-analysis. Follow-up analyses of identified DNA methylation signals included prediction of rapid weight growth, mediation of the effect of conventional risk factors on rapid weight growth, integration with transcriptomics and metabolomics, association with overweight in childhood (between 4 and 8 years), and comparison with previous findings. Results Forty-seven CpGs were associated with rapid weight growth at suggestive p-value

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Abstract Background Obesity is a major health concern for breast cancer survivors, being associated with high recurrence and reduced efficacy during cancer treatment. Metformin treatment is associated with reduced breast cancer incidence, recurrence and mortality. To better understand the underlying mechanisms through which metformin may reduce recurrence, we aimed to conduct metabolic profiling of overweight/obese breast cancer survivors before and after metformin treatment. Methods Fasting plasma samples from 373 overweight or obese breast cancer survivors randomly assigned to metformin (n = 194) or placebo (n = 179) administration were collected at baseline, after 6 months (Reach For Health trial), and after 12 months (MetBreCS trial). Archival samples were concurrently analyzed using three complementary methods: untargeted LC–QTOF-MS metabolomics, targeted LC–MS metabolomics (AbsoluteIDQ p180, Biocrates), and gas chromatography phospholipid fatty acid assay. Multivariable linear regression models and family-wise error correction were used to identify metabolites that significantly changed after metformin treatment. Results Participants (n = 352) with both baseline and study end point samples available were included in the analysis. After adjusting for confounders such as study center, age, body mass index and false discovery rate, we found that metformin treatment was significantly associated with decreased levels of citrulline, arginine, tyrosine, caffeine, paraxanthine, and theophylline, and increased levels of leucine, isoleucine, proline, 3-methyl-2-oxovalerate, 4-methyl-2-oxovalerate, alanine and indoxyl-sulphate. Long-chain unsaturated phosphatidylcholines (PC ae C36:4, PC ae C38:5, PC ae C36:5 and PC ae C38:6) were significantly decreased with the metformin treatment, as were phospholipid-derived long-chain n-6 fatty acids. The metabolomic profiles of metformin treatment suggest change in specific biochemical pathways known to impair cancer cell growth including activation of CYP1A2, alterations in fatty acid desaturase activity, and altered metabolism of specific amino acids, including impaired branched chain amino acid catabolism. Conclusions Our results in overweight breast cancer survivors identify new metabolic effects of metformin treatment that may mechanistically contribute to reduced risk of recurrence in this population and reduced obesity-related cancer risk reported in observational studies. Trial registration ClinicalTrials.gov identifier: NCT01302379 and EudraCT Protocol #: 2015-001001-14.

Abstract Background Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. Methods We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty. Results Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk. Conclusions These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness1, affecting 0.9-4% of women and 0.3% of men2-4, with twin-based heritability estimates of 50-60%5. Mortality rates are higher than those in other psychiatric disorders6, and outcomes are unacceptably poor7. Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI)8,9 and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.

ABSTRACT Numerous studies have described specific metabolites as biomarkers of severe liver diseases, but very few have measured gut microbiota (GM)-produced metabolites in fatty liver disease. We aimed at finding GM signatures and metabolite markers in plasma and feces related to high liver fat content. Based on imaging, we divided study participants into low (5%, HF, n = 39) liver fat groups. Fecal (LF n = 14, HF n = 25) and plasma (LF n = 11, HF n = 7) metabolomes of subsets of participants were studied using liquid chromatography/high resolution mass spectrometry. The GM were analyzed using 16S rRNA gene sequencing. Additionally, blood clinical variables and diet were studied. Dyslipidemia, higher liver enzymes and insulin resistance characterized the HF group. No major differences in diet were found between the groups. In the GM, the HF group had lower abundance of Bacteroides and Prevotellaceae NK3B31 group than the LF group after adjusting for metformin use or obesity. In feces, the HF group had higher levels of lysine and histidine degradation products, while 6-hydroxybetatestosterone (metabolized by CYP3A4) was low. Higher plasma levels of caffeine and its metabolites in the HF group indicate that the activity of hepatic CYP1A2 was lower than in the LF group. Our results suggest, that low fecal Prevotellaceae NK3B31 and Bacteroides abundance, and increased lysine and histidine degradation may serve as GM biomarkers of high liver fat. Altered plasma caffeine metabolites and lowered testosterone metabolism may specify decreased CYP activities, and their potential utility, as biomarkers of fatty liver disease. IMPORTANCE Because the high prevalence of nonalcoholic fatty liver disease sets diagnostic challenges to health care, identification of new biomarkers of the disease that in the future could have potential utility as diagnostic biomarkers of high liver fat content is important. Our results show that increased amino acid degradation products in the feces may be such biomarkers. In the blood, molecules that indicate defective hepatic metabolic enzyme activities were identified in individuals with high liver fat content.

BackgroundExposure to disinfection by-products (DBPs) in drinking water and chlorinated swimming pools are associated with adverse health outcomes, but biological mechanisms remain poorly understood.ObjectivesEvaluate short-term changes in metabolic profiles in response to DBP exposure while swimming in a chlorinated pool.Materials and methodsThe PISCINA-II study (EXPOsOMICS project) includes 60 volunteers swimming 40 min in an indoor pool. Levels of most common DBPs were measured in water and in exhaled breath before and after swimming. Blood samples, collected before and 2 h after swimming, were used for metabolic profiling by liquid-chromatography coupled to high-resolution mass-spectrometry. Metabolome-wide association between DBP exposures and each metabolic feature was evaluated using multivariate normal (MVN) models. Sensitivity analyses and compound annotation were conducted.ResultsExposure levels of all DBPs in exhaled breath were higher after the experiment. A total of 6,471 metabolic features were detected and 293 features were associated with at least one DBP in exhaled breath following Bonferroni correction. A total of 333 metabolic features were associated to at least one DBP measured in water or urine. Uptake of DBPs and physical activity were strongly correlated and mutual adjustment reduced the number of statistically significant associations. From the 293 features, 20 could be identified corresponding to 13 metabolites including compounds in the tryptophan metabolism pathway.ConclusionOur study identified numerous molecular changes following a swim in a chlorinated pool. While we could not explicitly evaluate which experiment-related factors induced these associations, molecular characterization highlighted metabolic features associated with exposure changes during swimming.Highlights•MWAS of exposure to Disinfection By-Products from swimming in a chlorinated pool•6471 metabolic features were detected by UHPLC-QTOF- Mass Spectrometry.•DBP exposures were measured in exhaled breath, urine and in swimming water.•293 metabolic features associated to at least one DPB, and 137 to all exposures•Annotation revealed 13 compounds involved in the tryptophan metabolism pathway.

Abstract Background Many genes and molecular pathways are associated with obesity, but the mechanisms from genes to obesity are less well known. Eating behaviors represent a plausible pathway, but because the relationships of eating behaviors and obesity may be bi-directional, it remains challenging to resolve the underlying pathways. A longitudinal approach is needed to assess the contribution of genetic risk during the development of obesity in childhood. In this study we aim to examine the relationships between the polygenic risk score for body mass index (PRS-BMI), parental concern of overeating and obesity indices during childhood. Methods The IDEFICS/I.Family study is a school-based multicenter pan-European cohort of children observed for 6 years (mean ± SD follow-up 5.8 ± 0.4). Children examined in 2007/2008 (wave 1) (mean ± SD age: 4.4 ± 1.1, range: 2–9 years), in 2009/2010 (wave 2) and in 2013/2014 (wave 3) were included. A total of 5112 children (49% girls) participated at waves 1, 2 and 3. For 2656 children with genome-wide data we constructed a PRS based on 2.1 million single nucleotide polymorphisms. Z-score BMI and z-score waist circumference (WC) were assessed and eating behaviors and relevant confounders were reported by parents via questionnaires. Parental concern of overeating was derived from principal component analyses from an eating behavior questionnaire. Results In cross-lagged models, the prospective associations between z-score obesity indices and parental concern of overeating were bi-directional. In mediation models, the association between the PRS-BMI and parental concern of overeating at wave 3 was mediated by baseline z-BMI (β = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC (β = 0.17, 95% CI: 0.11, 0.23). To a lesser extent, baseline parental concern of overeating also mediated the association between the PRS-BMI and z-BMI at wave 3 (β = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 (β = 0.09, 95% CI: 0.07, 0.12). Conclusions The findings suggest that the prospective associations between obesity indices and parental concern of overeating are likely bi-directional, but obesity indices have a stronger association with future parental concern of overeating than vice versa. The findings suggest parental concern of overeating as a possible mediator in the genetic susceptibility to obesity and further highlight that other pathways are also involved. A better understanding of the genetic pathways that lead to childhood obesity can help to prevent weight gain. Trial registration Registry number: ISRCTN62310987 Retrospectively registered 17 September 2018.