Untargeted urinary metabolomics by CREBRF rs373863828 (p.Arg457Gln) variant among individuals without type 2 diabetes

Untargeted urinary metabolomics by CREBRF rs373863828 (p.Arg457Gln) variant among individuals without type 2 diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Untargeted urinary metabolomics by CREBRF rs373863828 (p.Arg457Gln) variant among individuals without type 2 diabetes Zanetta Toomata, Nicola Dalbeth, Lisa Stamp, Tony Merriman, Stephane Castel, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6372155/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background/Objectives : The CREBRF rs373863828-A (p.Arg457Gln) variant is associated with increased body mass index (BMI) but paradoxically reduced odds of type 2 diabetes (T2D). This study used untargeted urinary metabolomics to investigate metabolic pathways influenced by the CREBRF variant in Māori and Pacific peoples without T2D. Methods : Untargeted metabolomic analysis was conducted on urine samples from 980 adult participants of Māori and Pacific descent from the Genetics of Gout, Diabetes, and Kidney disease (GoGDK) study, all of whom did not have T2D or significant kidney disease. Of these, 325 (33.2%) were carriers of the CREBRF variant. Urine samples were analysed using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS). Linear modelling using the limma package was used to identify differentially expressed metabolites between carriers and non-carriers, with and without adjustment for BMI. Results were stratified by sex, and pathway enrichment analysis was performed using MetaboAnalyst 6.0. Results : Four metabolites differed significantly between carriers and non-carriers before BMI adjustment (false discovery rate [FDR]-adjusted P < 0.05), including N-acetylhistamine (log 2 FC = 0.25, adjusted p = 0.002) and dimethylglycine (log 2 FC = 0.19, p = 0.002). Two metabolic pathways were significantly enriched: glycine, serine, and threonine metabolism (adjusted p = 0.047; impact score = 0.19), and histidine metabolism (adjusted p = 0.047; impact score = 0.19). After BMI adjustment, no metabolites remained significant. Conclusions : Urinary metabolomic differences between CREBRF rs373863828-A carriers and non-carriers appear to be driven by differences in BMI. These findings highlight the need to further explore the role of body composition in mediating the metabolic effects of CREBRF . Biological sciences/Genetics Biological sciences/Physiology/Metabolism Biological sciences/Physiology/Metabolism/Metabolic diseases/Diabetes/Type 2 diabetes Biological sciences/Physiology/Metabolism/Metabolic diseases/Obesity Biological sciences/Physiology/Metabolism/Metabolic diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The minor A-allele in the CREBRF gene variant rs373863828 (Arg457Gln, c.1370G > A) is essentially specific to Pacific peoples [ 1 – 5 ]. This variant has been associated with several phenotypic traits, including greater body mass index (BMI), greater height, lower adiposity measures, and lower risk of type 2 diabetes (T2D) [ 1 – 12 ]. In Aotearoa/New Zealand (NZ), each copy of the A-allele contributes to a 1.4 kg/m 2 increase in BMI, while paradoxically an approximately two-fold reduced likelihood of T2D [ 4 ]. This paradox has prompted further study to understand the biological and molecular mechanisms that may confer this protective effect against T2D. Initial hypotheses suggested that the minor A-allele acted as a “thrifty” gene variant by promoting increased adiposity to facilitate energy storage, a trait advantageous for survival in energy-scarce environments [ 5 ]. Recent evidence has shifted this perspective, indicating that this variant may increase BMI by enhancing lean mass rather than fat mass [ 6 , 7 , 9 , 11 ]. A study conducted by Lee and Colleagues (2022) on young, healthy adult males of Māori and Pacific ancestry in Aotearoa found the variant was associated with reduced levels of the muscle inhibitory hormone, myostatin, and lower fat mass [ 6 ]. Furthermore, this was linked to enhanced early insulin release, independent of insulin sensitivity differences [ 9 ]. There are interactions between the rs373863828-A variant and various metabolic parameters that necessitates further investigation to unravel these relationships. Untargeted metabolomics offers one approach to understanding the specific metabolic pathways and processes associated with genetic variation [ 13 ]. By comprehensively identifying and quantifying a wide range of metabolites, untargeted metabolomics provides a holistic view of metabolic changes, enabling the detection of perturbations attributable to the genetic variant under study [ 14 ]. This approach allows for a single assay to probe multiple biochemical pathways simultaneously, providing insights into the underlying metabolic changes [ 14 ]. In this study, we applied untargeted metabolomics to investigate the metabolic pathways potentially influenced by the CREBRF rs373863828-A variant, particularly those related to body composition and beta-cell function. We assessed the urinary metabolite profiles of Māori and Pacific peoples without diabetes, or chronic kidney disease from the Genetics of Gout, Diabetes, and Kidney disease (GoGDK) in Aotearoa study [ 4 , 15 ]. Using data from over 1000 metabolites analysed on the Metabolon platform, the metabolic profiles of those with and without the CREBRF rs373863828 minor A-allele were compared, with and without BMI adjustment. 2. Materials and Methods 2.1 Study Participants The GoGDK of Aotearoa study recruited participants between 2006 and 2017 from the Auckland, Waikato, and Christchurch regions of Aotearoa [ 4 , 15 ]. Participants provided their phenotypic and genetic information to investigate the genetic factors underlying gout, diabetes, and kidney disease. Ancestry was determined based on self-reported ancestries of their grandparents, complemented by genome-wide principal component clustering. This process identified 2124 adults with Polynesian ancestry. For this metabolomics study, Polynesian participants who did not have diabetes or chronic kidney disease at the time of recruitment were selected, given these two conditions would likely influence urinary metabolites. Diabetes and chronic kidney disease ascertainment has been previously [ 4 ]. Urine samples were collected from these participants at the point of recruitment and used for this analysis. Genotypes for the rs373863828 variant were extracted from the whole-genome imputed sequence data constructed for Māori and Pacific participants from the GoGDK using BCFtools v1.9-94-g9589876 [ 16 ]. All participants provided written informed consent for the collection of their samples and subsequent analyses. The ethical approval for this study was provided by the NZ Multi-Region Ethics Committee (reference numbers: MEC/05/10/130; MEC/10/09/092; MEC/ 11/04/036). 2.2 Urine metabolomic analysis 2.2.1 Sample preparation and quality control Sample treatments and metabolomic measurements were conducted by Metabolon Inc. (Morrisville, NC, USA) using their established protocols. Briefly, samples underwent protein precipitation with methanol followed by centrifugation. The resulting extract was then analysed using a standardised multi-method approach incorporating both reverse phase and hydrophilic liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Quality control measures were implemented throughout the process, including technical replicates, process blanks, and a pooled matrix sample, as per Metabolon’s standard procedures. 2.2.2 Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy Chromatographic separation was achieved using a Waters ACQUITY UPLC system. Mass spectrometric detection was performed on a Thermo Scientific Q-Exactive high-resolution mass spectrometer. The analyses were optimised for a broad range of compound classes by employing different ionisation modes (positive and negative) and adjusting solvent gradients across multiple chromatographic runs. Detailed information regarding the specific solvent systems and gradients employed can be found in Metabolon’s standard documentation. 2.2.3 Data processing and statistical analysis Raw data were processed using Metabolon’s software for peak identification and quantification. Metabolite identification was based on three criteria: retention time, accurate mass measurement, and matching of MS/MS fragmentation spectra against Metabolon’s curated library of known standards. Data normalisation procedures were implemented to mitigate inter-run variability. The “Batch-norm-Imputed” data file provided by Metabolon was used for downstream analysis. The R package POMA v1.12.0 was used for metabolomic data pre-processing [ 17 ]. Missing values were imputed using the minimum observed method, where each missing value for a metabolite was replaced with the minimum value observed for that metabolite. Data normalisation was conducted by applying the log transformation and Pareto scaling method [ 18 ]. Supplementary Figs. 1 and 2 showcase the distribution of data before and after normalisation. The R package limma v3.58.1 was used to identify differentially expressed metabolites between CREBRF variant carriers and non-carriers [ 19 ]. Limma fits a linear model to each metabolite separately, allowing for covariate adjustment. Age, sex, and gout status were used as covariates. To assess the role of body composition in metabolomic differences, results were presented both before and after adjustment for BMI. Metabolites achieving a false discovery rate (FDR) adjusted p-value < 0.05 were considered significant. Analyses included overall and sex-stratified comparisons to identify potential sex-specific effects. Baseline demographics and clinical characteristics were compared between CREBRF carriers and non-carriers using the Wilcoxon rank sum test for continuous variables and the Pearson’s chi-square test for categorical variables. All analyses were performed using R software v4.3.1 [ 20 ]. 2.3 Pathway analysis Pathway analysis was performed using the online tool, MetaboAnalyst 6.0 [ 21 ]. All metabolites differentially expressed between CREBRF carriers and non-carriers with a raw p-value < 0.05 were included. This approach allowed for the inclusion of potentially relevant metabolites in the initial pathway analysis that might have been excluded due to stringent multiple testing corrections. The hypergeometric test was performed against the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database and a FDR < 5% was used as a threshold to identify significantly altered pathways. Additionally, MetaboAnalyst 6.0 calculates an impact score, which is the sum of the importance measures of identified metabolites divided by the total sum of the importance measures of all the identified and unidentified metabolites in the pathway (range 0–1). Pathway topology analysis was used to calculate the importance measures based on the degree of relative-betweeness centrality. The reported impact score represents an objective estimate of a pathway’s significance within the global metabolic network, with a score closer to 1.00 indicating a pathway’s greater importance and more pronounced effect on metabolic processes. 3. Results 3.1 Participant characteristics Of the 2258 participants in GoGDK study, 1436 had urinary metabolomics results that passed quality control, and a further 436 were excluded due to the presence of T2D ( Supplementary Fig. 3 ). Of the 980 individuals without T2D, 325 individuals (33.2%) were identified as carriers of the CREBRF rs373863828-A allele, while the remaining 655 individuals (66.8%) were non-carriers. Table 1 presents the baseline demographics and clinical characteristics of the study participants stratified by CREBRF variant status. Table 1 Baseline demographics and clinical characteristics of CREBRF carriers vs. non-carriers in metabolomics analysis Characteristic N CREBRF carriers Mean ± SD N Non-carriers Mean ± SD p-value N 325 655 Age (years) 325 46.14 ± 15.52 655 44.68 ± 15.90 0.179 Female (n(%)) 325 114 (35.1%) 655 201 (30.7%) 0.189 BMI (kg/m 2 ) 319 35.25 ± 7.78 641 32.41 ± 6.78 < 0.0001 eGFR (ml/min/1.73/m 2 ) 308 80.45 ± 18.90 612 82.39 ± 19.52 0.113 Systolic BP (mmHg) 292 132.31 ± 17.94 611 131.03 ± 18.76 0.231 Diastolic BP (mmHg) 292 82.34 ± 12.41 611 80.86 ± 13.26 0.023 Total cholesterol (mmol/L) 300 5.06 ± 1.04 598 5.05 ± 1.08 0.526 Triglycerides (mmol/L) 301 2.11 ± 1.32 597 2.06 ± 1.46 0.152 HDL (mmol/L) 300 1.17 ± 0.37 597 1.22 ± 0.39 0.045 LDL (mmol/L) 286 2.99 ± 0.95 568 2.96 ± 0.95 0.459 Gout (n (%)) 325 184 (56.6%) 655 358 (54.7%) 0.608 Quantitative data are presented as mean ± SD and qualitative data are presented as n (%). P-values come from a Wilcox rank sum test for continuous values and Pearson’s chi-squared test for categorical values. P-values that are statistically significant are shown in bold ( p < 0.05). The mean age (± standard deviation [SD]) of CREBRF carriers was 46.14 ± 15.52 years, compared to 44.68 ± 15.90 years for non-carriers ( p = 0.179). Females represented 35.1% of carriers and 30.7% of non-carriers ( p = 0.189). CREBRF carriers had a significantly higher mean BMI of 35.25 ± 7.78 kg/m² compared to 32.41 ± 6.78 kg/m² in non-carriers ( p < 0.0001). The estimated glomerular filtration rate (eGFR) did not significantly differ between groups ( p = 0.113), with all participants having an eGFR well above 30 ml/min/1.73/m 2 . Carriers exhibited higher diastolic blood pressure (82.34 ± 12.41 mmHg) compared to non-carriers (80.86 ± 13.26 mmHg, p = 0.023). High-density lipoprotein cholesterol (HDL) levels were lower in carriers (1.17 ± 0.37 mmol/L) than in non-carriers (1.22 ± 0.39 mmol/L, p = 0.045), while other lipid measurements showed no statistically significant differences. 3.2 Differentially expressed metabolites in the overall group A total of 1095 compounds were identified from the 980 participants’ urine samples, of which 771 compounds were identified as known metabolites. Prior to BMI adjustment, the limma t-test revealed four metabolites were significantly different between CREBRF carriers and non-carriers in the overall group (Table 2 ). Two metabolites were upregulated in CREBRF carriers, N-acetylhistamine (KEGG: C05135), involved in histidine metabolism, with a log2 fold change (log 2 FC) of 0.25 (adjusted p = 0.002), and dimethylglycine (DMG), involved in glycine, serine, and threonine metabolism, with a log 2 FC of 0.19 (adjusted p = 0.002; Fig. 1 ). Conversely, two metabolites were significantly downregulated in carriers, including N2-acetyl,N6,N6-dimethyllysine (log 2 FC = -0.17, adjusted p = 0.049) involved in lysine metabolism, and catechol glucuronide (log 2 FC = -0.21, adjusted p = 0.049) involved in tyrosine metabolism (Table 2 ; Fig. 1 ). Table 2 Differential metabolite analysis in CREBRF carriers prior to BMI adjustment: overall group comparison Metabolite Super Pathway Sub Pathway KEGG HMDB log2FC p-value Adjusted p-value N-acetylhistamine Amino Acid Histidine Metabolism C05135 HMDB0013253 0.25 1.6e-06 0.002 dimethylglycine Amino Acid Glycine, Serine and Threonine Metabolism C03626 HMDB0000092 0.19 4.4e-06 0.002 N2-acetyl,N6,N6-dimethyllysine Amino Acid Lysine Metabolism NA NA -0.17 1.5e-04 0.049 catechol glucuronide Amino Acid Tyrosine Metabolism NA HMDB0240490 -0.21 1.8e-04 0. 049 Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes; HMDB, Human Metabolome Database; FC, fold change; NA, not applicable. 3.3 Sex-stratified differential metabolite analysis Similar to the overall analysis, DMG was upregulated in male CREBRF carriers (log 2 FC = 0.20, adjusted p = .027), and N2-acetyl,N6,N6-dimethyllysine (log 2 FC = -0.23, adjusted p = 0.025) and catechol glucuronide (log 2 FC = -0.26, adjusted p = 0.025) were downregulated (Table 3 ). Only found in the male analysis was the downregulation of guaiacol sulfate with a log 2 FC of -0.19 (adjusted p = 0.025), which is involved in benzoate metabolism (Fig. 2; Table 3 ). Table 3 Differential metabolite analysis in CREBRF carriers prior to BMI adjustment: sex-stratified comparison Metabolite Super Pathway Sub Pathway KEGG HMDB log2FC p-value Adjusted p-value Male group N2-acetyl,N6,N6-dimethyllysine Amino Acid Lysine Metabolism NA NA -0.23 3.0e-05 0.025 catechol glucuronide Amino Acid Tyrosine Metabolism NA HMDB0240490 -0.26 6.5e-05 0.025 guaiacol sulfate Xenobiotics Benzoate Metabolism NA HMDB0060013 -0.19 6.8e-05 0.025 dimethylglycine Amino Acid Glycine, Serine and Threonine Metabolism C03626 HMDB0000092 0.20 9.7e-05 0.027 N2-acetyl,N6-methyllysine Amino Acid Lysine Metabolism NA HMDB0242186 -0.20 1.5e-04 0.033 Female group gamma-CEHC sulfate Cofactors and Vitamins Tocopherol Metabolism NA NA 0.34 1.9e-05 0.021 Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes; HMDB, Human Metabolome Database; FC, fold change; NA, not applicable. In the female group, gamma-carboxyethyl hydroxychroman (CEHC) sulfate, involved in tocopherol metabolism, was significantly upregulated with a log 2 FC of 0.34 and an adjusted p-value of 0.021 (Fig. 3; Table 3 ) 3.4 Pathway analysis Pathway analysis was conducted using MetaboAnalyst 6.0 to investigate the biological relevance of the observed metabolite changes in CREBRF carriers. Metabolites with a raw p-value < 0.05 were included in the analysis. 3.4.1 Pathway analysis in the overall group Several statistically significant pathway alterations were identified in CREBRF carriers (Fig. 4). The glycine, serine, and threonine metabolism pathway exhibited significant enrichment with an adjusted p-value of 0.047 and an impact score of 0.19 (match status 4/33; Table 4 ). Histidine metabolism was also noteworthy, with three out of 16 expected metabolites identified (padj-value = 0.047, impact score = 0.19; Table 4 ). While other pathways, such as taurine and hypotaurine metabolism, arginine metabolism, and starch and sucrose metabolism, showed enrichment, they did not reach statistical significance after FDR adjustment. Table 4 Pathway topology results in the overall group analysis prior to BMI adjustment Pathway Total Expected Hits Raw p -log10(p) FDR Impact Glycine, serine and threonine metabolism 33 0.46 4 0.001 3.05 0.047 0.19 Histidine metabolism 16 0.22 3 0.001 2.93 0.047 0.19 Taurine and hypotaurine metabolism 8 0.11 2 0.01 2.30 0.13 0.43 Arginine biosynthesis 14 0.20 2 0.02 1.81 0.31 0.00 Neomycin, kanamycin and gentamicin biosynthesis 2 0.03 1 0.03 1.56 0.44 0.00 Cysteine and methionine metabolism 33 0.46 2 0.08 1.12 1.00 0.18 Arginine and proline metabolism 36 0.50 2 0.09 1.05 1.00 0.05 Phenylalanine metabolism 8 0.11 1 0.11 0.97 1.00 0.24 Caffeine metabolism 10 0.14 1 0.13 0.88 1.00 0.31 Primary bile acid biosynthesis 46 0.64 2 0.13 0.88 1.00 0.02 Butanoate metabolism 15 0.21 1 0.19 0.72 1.00 0.00 Glycerolipid metabolism 16 0.22 1 0.20 0.69 1.00 0.09 Starch and sucrose metabolism 18 0.25 1 0.23 0.65 1.00 0.42 Citrate cycle (TCA cycle) 20 0.28 1 0.25 0.61 1.00 0.06 beta-Alanine metabolism 21 0.29 1 0.26 0.59 1.00 0.00 Pentose phosphate pathway 23 0.32 1 0.28 0.56 1.00 0.00 Galactose metabolism 27 0.38 1 0.32 0.50 1.00 0.03 Lipoic acid metabolism 28 0.39 1 0.33 0.48 1.00 0.00 Alanine, aspartate and glutamate metabolism 28 0.39 1 0.33 0.48 1.00 0.05 Lysine degradation 30 0.42 1 0.35 0.46 1.00 0.00 Sphingolipid metabolism 32 0.45 1 0.37 0.44 1.00 0.01 Glyoxylate and dicarboxylate metabolism 32 0.45 1 0.37 0.44 1.00 0.08 Glycerophospholipid metabolism 36 0.50 1 0.40 0.40 1.00 0.02 Tryptophan metabolism 41 0.57 1 0.44 0.35 1.00 0.04 Tyrosine metabolism 42 0.59 1 0.45 0.35 1.00 0.02 Steroid hormone biosynthesis 87 1.22 1 0.72 0.15 1.00 0.00 P-values are derived from pathway enrichment analysis and pathway impact values are from pathway topology analysis. P-values that are statistically significant are shown in bold ( p < 0.05). Abbreviation: FDR, false discovery rate. 3.4.2 Sex-specific pathway analysis The pathway analysis conducted in the male group did not result in any significant pathways ( Supplementary Table 1 ). However, in the female group histidine metabolism was significant with an adjusted p-value of 0.047 and an impact score of 0.19 (match status 3/16; Table 5 ). Table 5 Pathway topology results in the female group analysis prior to BMI adjustment Pathway Total Expected Hits Raw p -log10(p) FDR Impact Histidine metabolism 16 0.26 4 0.00 4.03 0.01 0.14 Glycine, serine and threonine metabolism 33 0.54 3 0.02 1.80 0.41 0.17 Arginine and proline metabolism 36 0.59 3 0.02 1.70 0.41 0.23 Arginine biosynthesis 14 0.23 2 0.02 1.68 0.41 0.00 Purine metabolism 70 1.16 4 0.03 1.59 0.41 0.00 Cysteine and methionine metabolism 33 0.54 2 0.10 1.00 1.00 0.23 Phenylalanine metabolism 8 0.13 1 0.13 0.90 1.00 0.24 Vitamin B6 metabolism 9 0.15 1 0.14 0.86 1.00 0.49 Tryptophan metabolism 41 0.68 2 0.15 0.84 1.00 0.00 Nicotinate and nicotinamide metabolism 15 0.25 1 0.22 0.65 1.00 0.00 Glutathione metabolism 28 0.46 1 0.38 0.43 1.00 0.01 Lysine degradation 30 0.5 1 0.40 0.40 1.00 0.00 Glycerophospholipid metabolism 36 0.59 1 0.46 0.34 1.00 0.00 Pyrimidine metabolism 39 0.64 1 0.48 0.32 1.00 0.07 Tyrosine metabolism 42 0.69 1 0.51 0.29 1.00 0.02 Primary bile acid biosynthesis 46 0.76 1 0.54 0.27 1.00 0.01 Steroid hormone biosynthesis 87 1.44 1 0.78 0.11 1.00 0.00 P-values are derived from pathway enrichment analysis and pathway impact values are from pathway topology analysis. P-values that are statistically significant are shown in bold ( p < 0.05). Abbreviation: FDR, false discovery rate. 4. Discussion This study utilised untargeted metabolomics to investigate the metabolic impacts of the CREBRF rs373863828-A variant within a cohort of Māori and Pacific peoples from the GoGDK study. Initially identified were distinct urinary metabolomic profiles between CREBRF variant carriers and non-carriers. Notably, there was a significant upregulation of N-acetylhistamine, a metabolite involved in histidine metabolism, in variant carriers, suggesting a potential connection between CREBRF and histidine signalling pathways. However, these differences were attenuated after adjusting for BMI, indicating that the observed metabolic variations largely reflect differences in body composition. Prior to BMI adjustment, alterations in several other amino acids were observed. Lysine metabolites, N2-acetyl,N6-methyllysine and N2-acetyl,N6,N6-dimethyllysine, were consistently downregulated in variant carriers across both the overall and male-specific analyses. This is consistent with the established association between higher levels of lysine metabolites and increased T2D risk, and aligning with the lower T2D risk observed in CREBRF variant carriers [ 1 – 5 ]. Furthermore, the initial findings of altered histidine metabolism in both the overall and female-specific pathway analyses, combined with the observed upregulation of N-acetylhistamine in CREBRF carriers, is an interesting one. Despite the loss of significance after BMI adjustment, this does not negate the potential biological relevance of this pathway. Instead, it highlights the importance of considering BMI as a significant modifier of genetic effects on metabolism. Histidine, an essential amino acid, undergoes various metabolic transformations, including its conversion to histamine through L-histidine decarboxylase. Histamine and its metabolites, including N-acetylhistamine (formed from the acetylation of histamine), are highly pleiotropic biogenic amines implicated in metabolic processes important for regulating body weight and maintaining glucose homeostasis, including energy metabolism, appetite regulation, and inflammation [ 22 , 23 ]. Dysregulation of histamine signalling has been associated with various metabolic conditions, such as obesity and T2D [ 24 – 26 ]. Notably, histamine demonstrates anti-inflammatory effects through its interaction with H2 and H4 G-protein coupled receptors, counteracting excessive inflammation driven by M1 macrophages in adipose tissue [ 27 ]. This is particularly relevant in obesity, which is associated with a milieu of inflammatory responses, including chronic inflammation in adipose tissue [ 28 ]. This aligns with CREBRF ’s involvement in cellular stress responses, which can be triggered by inflammation [ 29 , 30 ]. Additionally, evidence suggests that histamine may negatively regulate CREB activity [ 31 ]. Given that CREBRF negatively regulates CREB3 (CAMP Responsive Element Binding Protein 3 CREB3), the observed upregulation of N-acetylhistamine in variant carriers may reflect this regulatory dynamic. Further investigation is needed to elucidate potential mechanisms by which CREBRF influences histidine metabolism. Does CREBRF directly or indirectly affect the expression or activity of enzymes involved in histamine synthesis or degradation? Could CREBRF influence histamine receptor expression or signalling? The interactions between CREBRF , histidine metabolism, and histamine signalling may present a potential pathway through which the CREBRF variant may contribute to the paradoxical phenotype of increased BMI but reduced odds of T2D, warranting further investigation. In addition to alterations in histidine metabolism, changes in glycine, serine and threonine metabolism were observed in CREBRF variant carriers, which may be relevant to body composition. Glycine and threonine are essential for creatine synthesis and influence both muscle protein synthesis and beta-cell function [ 32 – 35 ]. Serine serves as an energy substrate for muscle cells and is involved in protein synthesis [ 35 , 36 ]. While glycine is often decreased in T2D and associated with insulin resistance [ 37 , 38 ], serine and threonine influence gluconeogenesis and enhance beta-cell function [ 39 – 41 ]. The upregulation of DMG, a metabolite in this pathway, is noteworthy. DMG and its precursor, betaine, have been inversely correlated with fasting glucose and insulin resistance, suggesting a protective effect against diabetes [ 38 , 42 , 43 ]. The increased DMG levels in variant carriers further support this protective effect [ 1 – 5 ]. DMG also has a role in muscle metabolism and function, with positive correlations observed between DMG levels and fat-free mass [ 44 ]. This aligns with the role of CREBRF and the rs373863828-A variant in muscle metabolism, which have been associated with reduced myostatin levels (promoting muscle growth) [ 6 , 30 ], and increased lean and fat-free mass [ 11 , 45 ]. Furthermore, the sex-specific analyses revealed additional metabolic differences. Males showed increased levels of DMG and decreased N2-acetyl,N6,N6-dimethyllysine and catechol glucuronide, while females exhibited increased gamma-CEHC sulfate, a Vitamin E antioxidant. [ 46 ]. Additionally, only the female pathway analysis showed significant alterations in histidine metabolism. These findings demonstrate sex-specific metabolic differences and highlight the importance of considering sex in future investigations of this variant [ 11 , 12 , 47 , 48 ]. Despite the attenuation of significance after adjusting for BMI, the initial findings highlight potential metabolic pathways influenced by the CREBRF variant. The fact that these associations diminished post-BMI adjustment underscores the role of BMI as a mediator of the CREBRF variant’s metabolic effects. This suggests that the metabolic differences observed between variant carriers and non-carriers in this study may be largely attributable to differences in BMI and body composition, rather than direct effects of the variant on metabolic pathways. Nevertheless, the biological relevance of these pathways cannot be entirely discounted. It is possible that the CREBRF variant exerts subtle effects on metabolism that are magnified or mediated by changes in body composition. The interactions between genetic variants, metabolism, and body composition are complex, and BMI does not capture all aspects of body composition, such as lean mass versus fat mass distribution. Therefore further studies that consider more precise measures of body composition are needed to fully elucidate the CREBRF variant’s effects on metabolism, and how these related to BMI and T2D risk. Declarations Acknowledgements We acknowledge the participants of the Genetics of Gout, Diabetes, and Kidney Disease in Aotearoa study for their invaluable contributions. Zanetta L.L. Toomata was supported by the Pacific Health Research PhD Scholarship from the Health Research Council of New Zealand (ref 21/197). We thank members of Variant Bio for their contributions to the project, including Stephane E. Castel at Variant Bio for reviewing the manuscript. Author Contributions ZLLT contributed to study conceptualisation, performed the data analysis and interpretation, and drafted the manuscript. RM conceptualised the study, supervised the overall research, provided oversight of the GoGDK study, and contributed to manuscript revisions. PW and OD supervised the overall research. ND, LKS, and TRM provided oversight of the GoGDK study and contributed to manuscript feedback. SEC provided the metabolomic data and contributed to manuscript input. All authors reviewed and approved the final manuscript. Competing Interests The authors declare no competing financial interests Data Availability The datasets generated during and/or analysed during the current study are not publicly available due to consent restrictions but can be requested from the corresponding author under an appropriate arrangement. References Naka I, Furusawa T, Kimura R, Natsuhara K, Yamauchi T, Nakazawa M, et al. A missense variant, rs373863828-A (p.Arg457Gln), of CREBRF and body mass index in Oceanic populations. Journal of Human Genetics 2017 62:9 2017;62:847–9. https://doi.org/10.1038/JHG.2017.44. Lin M, Caberto C, Wan P, Li Y, Lum-Jones A, Tiirikainen M, et al. Population-specific reference panels are crucial for genetic analyses: an example of the CREBRF locus in Native Hawaiians. 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Inflamm Res 2007;56:339–44. https://doi.org/10.1007/S00011-007-7049-7. Yan-Do R, Duong E, Manning Fox JE, Dai X, Suzuki K, Khan S, et al. A Glycine-Insulin Autocrine Feedback Loop Enhances Insulin Secretion From Human β-Cells and Is Impaired in Type 2 Diabetes. Diabetes 2016;65:2311–21. https://doi.org/10.2337/db15-1272. Yan-Do R, MacDonald PE. Impaired “Glycine”-mia in Type 2 Diabetes and Potential Mechanisms Contributing to Glucose Homeostasis. Endocrinology 2017;158:1064–73. https://doi.org/10.1210/en.2017-00148. Sun K, Wu Z, Ji Y, Wu G. Glycine Regulates Protein Turnover by Activating Protein Kinase B/Mammalian Target of Rapamycin and by Inhibiting MuRF1 and Atrogin-1 Gene Expression in C2C12 Myoblasts. J Nutr 2016;146:2461–7. https://doi.org/10.3945/JN.116.231266A. Gheller BJ, Blum JE, Lim EW, Handzlik MK, Hannah Fong EH, Ko AC, et al. Extracellular serine and glycine are required for mouse and human skeletal muscle stem and progenitor cell function. Mol Metab 2021;43:101106. https://doi.org/10.1016/j.molmet.2020.101106. He L, Ding Y, Zhou X, Li T, Yin Y. Serine signaling governs metabolic homeostasis and health. Trends in Endocrinology and Metabolism 2023;34:361–72. https://doi.org/10.1016/J.TEM.2023.03.001/ASSET/49AAB019-7C80-49F6-83B1-18DCE7AD223E/MAIN.ASSETS/GR2.JPG. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost H-G, et al. Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach. Diabetes 2013;62:639–48. https://doi.org/10.2337/db12-0495. Guasch-Ferré M, Hruby A, Toledo E, Clish CB, Martínez-González MA, Salas-Salvadó J, et al. Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis. Diabetes Care 2016;39:833–46. https://doi.org/10.2337/DC15-2251. Handzlik MK, Gengatharan JM, Frizzi KE, McGregor GH, Martino C, Rahman G, et al. Insulin-regulated serine and lipid metabolism drive peripheral neuropathy. Nature 2023;614:118–24. https://doi.org/10.1038/s41586-022-05637-6. Vangipurapu J, Stancáková A, Smith U, Kuusisto J, Laakso M. Nine Amino Acids Are Associated With Decreased Insulin Secretion and Elevated Glucose Levels in a 7.4-Year Follow-up Study of 5,181 Finnish Men. Diabetes 2019;68:1353–8. https://doi.org/10.2337/DB18-1076. Ardestani A, Lupse B, Kido Y, Leibowitz G, Maedler K. mTORC1 Signaling: A Double-Edged Sword in Diabetic β Cells. Cell Metab 2018;27:314–31. https://doi.org/10.1016/J.CMET.2017.11.004. Magnusson M, Wang TJ, Clish C, Engström G, Nilsson P, Gerszten RE, et al. Dimethylglycine Deficiency and the Development of Diabetes. Diabetes 2015;64:3010–6. https://doi.org/10.2337/DB14-1863. Konstantinova S V., Tell GS, Vollset SE, Nygård O, Bleie Ø, Ueland PM. Divergent Associations of Plasma Choline and Betaine with Components of Metabolic Syndrome in Middle Age and Elderly Men and Women,. J Nutr 2008;138:914–20. https://doi.org/10.1093/JN/138.5.914. Ubhi BK, Riley JH, Shaw PA, Lomas DA, Tal-Singers R, MacNeef W, et al. Metabolic profiling detects biomarkers of protein degradation in COPD patients. European Respiratory Journal 2012;40:345–55. https://doi.org/10.1183/09031936.00112411. Arslanian KJ, Fidow UT, Atanoa T, Unasa-Apelu F, Naseri T, Wetzel AI, et al. A missense variant in CREBRF, rs373863828, is associated with fat-free mass, not fat mass in Samoan infants. International Journal of Obesity 2020 45:1 2020;45:45–55. https://doi.org/10.1038/s41366-020-00659-4. Ge Q, Lu H, Geng X, Chen X, Liu X, Sun H, et al. Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness. Chin Neurosurg J 2024;10:1–12. https://doi.org/10.1186/S41016-024-00365-4/TABLES/4. Carlson JC, Rosenthal SL, Russell EM, Hawley NL, Sun G, Cheng H, et al. A missense variant in CREBRF is associated with taller stature in Samoans. Am J Hum Biol 2020;32. https://doi.org/10.1002/ajhb.23414. Metcalfe LK, Krishnan M, Turner N, Yaghootkar H, Merry TL, Dewes O, et al. The Māori and Pacific specific CREBRF variant and adult height. International Journal of Obesity 2019 44:3 2019;44:748–52. https://doi.org/10.1038/S41366-019-0437-6. Additional Declarations There is NO conflict of interest to disclose Supplementary Files CREBRFMetabolomicsSupplementaryMaterials.docx Supplementary Materials Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6372155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451822003,"identity":"ddc79f8a-a7c8-40b7-afad-449ed189191c","order_by":0,"name":"Zanetta 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Auckland","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Dalbeth","suffix":""},{"id":451822005,"identity":"ee2aa747-9d65-4af5-9a0a-6e307cfb62ba","order_by":2,"name":"Lisa Stamp","email":"","orcid":"https://orcid.org/0000-0003-0138-2912","institution":"University of Otago Christchurch","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Stamp","suffix":""},{"id":451822006,"identity":"65bf4c28-adb7-4078-82e0-3f5c691a9181","order_by":3,"name":"Tony Merriman","email":"","orcid":"https://orcid.org/0000-0003-0844-8726","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Tony","middleName":"","lastName":"Merriman","suffix":""},{"id":451822007,"identity":"6a3f0ec6-2604-4774-99ff-a190fec22ac3","order_by":4,"name":"Stephane Castel","email":"","orcid":"","institution":"Variant Bio 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Auckland","correspondingAuthor":false,"prefix":"","firstName":"Rinki","middleName":"","lastName":"Murphy","suffix":""}],"badges":[],"createdAt":"2025-04-03 21:50:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6372155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6372155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82558216,"identity":"88c0d940-7d0d-45ec-8af1-b94e7a5bec78","added_by":"auto","created_at":"2025-05-13 01:21:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52287,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot for the overall group analysis prior to BMI adjustment\u003c/p\u003e\n\u003cp\u003eVolcano plot depicts upregulation (right direction) or downregulation (left direction) of metabolites in CREBRF carriers compared to non-carriers.\u003c/p\u003e\n\u003cp\u003eThe presented p-values and log2fold changes are obtained from the limma t-test.\u003c/p\u003e\n\u003cp\u003eAbbreviation: FC, fold change\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372155/v1/f6fb668013327dc1b9fef9b6.jpg"},{"id":82558215,"identity":"d1edffc7-453b-4fae-b417-baf652973e45","added_by":"auto","created_at":"2025-05-13 01:21:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47048,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot for the male group analysis prior to BMI adjustment\u003c/p\u003e\n\u003cp\u003eVolcano plot depicts upregulation (right direction) or downregulation (left direction) of metabolites in CREBRF carriers compared to non-carriers in the male group.\u003c/p\u003e\n\u003cp\u003eThe presented p-values and log2fold changes are obtained from the limma t-test.\u003c/p\u003e\n\u003cp\u003eAbbreviation: FC, fold change.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372155/v1/63d927efc87adb52eb7e48c5.jpg"},{"id":82558218,"identity":"09c2f75f-eee6-4f02-980b-10f8e2b38b54","added_by":"auto","created_at":"2025-05-13 01:21:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43425,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot for the female group analysis prior to BMI adjustment\u003c/p\u003e\n\u003cp\u003eVolcano plot depicts upregulation (right direction) or downregulation (left direction) of metabolites in CREBRF carriers compared to non-carriers in the female group.\u003c/p\u003e\n\u003cp\u003eThe presented p-values and log2fold changes are obtained from the limma t-test.\u003c/p\u003e\n\u003cp\u003eAbbreviation: FC, fold change.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372155/v1/f04ddb6edf0b9f2e8c793664.jpg"},{"id":82558217,"identity":"aaf928ea-8746-4d9c-aa16-50bf5385d030","added_by":"auto","created_at":"2025-05-13 01:21:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72846,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic pathway plot in the overall group analysis prior to BMI adjustment\u003c/p\u003e\n\u003cp\u003eTwo significant pathway alterations are observed between CREBRF carriers and non-carriers.\u003c/p\u003e\n\u003cp\u003eX-axis represents the pathway impact value computed from pathway topological analysis, and the y-axis is the -log of the p-value obtained from pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6372155/v1/78775bb946bd600c3d30770d.jpg"},{"id":89074085,"identity":"d19c2543-b60e-4db0-8a56-2d0ad45c34a7","added_by":"auto","created_at":"2025-08-14 11:49:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1570182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6372155/v1/3876f9ee-53d4-4c93-847d-787462bcbad6.pdf"},{"id":82560870,"identity":"fe1fa270-275a-403b-a3f1-9163ec051067","added_by":"auto","created_at":"2025-05-13 01:37:06","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":515119,"visible":true,"origin":"","legend":"Supplementary Materials","description":"","filename":"CREBRFMetabolomicsSupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6372155/v1/f93be0ef7743aa08215eeaff.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"\u003cp\u003eUntargeted urinary metabolomics by \u003cem\u003eCREBRF\u003c/em\u003e rs373863828 (p.Arg457Gln) variant among individuals without type 2 diabetes\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe minor A-allele in the \u003cem\u003eCREBRF\u003c/em\u003e gene variant rs373863828 (Arg457Gln, c.1370G\u0026thinsp;\u0026gt;\u0026thinsp;A) is essentially specific to Pacific peoples [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This variant has been associated with several phenotypic traits, including greater body mass index (BMI), greater height, lower adiposity measures, and lower risk of type 2 diabetes (T2D) [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In Aotearoa/New Zealand (NZ), each copy of the A-allele contributes to a 1.4 kg/m\u003csup\u003e2\u003c/sup\u003e increase in BMI, while paradoxically an approximately two-fold reduced likelihood of T2D [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This paradox has prompted further study to understand the biological and molecular mechanisms that may confer this protective effect against T2D.\u003c/p\u003e \u003cp\u003eInitial hypotheses suggested that the minor A-allele acted as a \u0026ldquo;thrifty\u0026rdquo; gene variant by promoting increased adiposity to facilitate energy storage, a trait advantageous for survival in energy-scarce environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent evidence has shifted this perspective, indicating that this variant may increase BMI by enhancing lean mass rather than fat mass [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A study conducted by Lee and Colleagues (2022) on young, healthy adult males of Māori and Pacific ancestry in Aotearoa found the variant was associated with reduced levels of the muscle inhibitory hormone, myostatin, and lower fat mass [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, this was linked to enhanced early insulin release, independent of insulin sensitivity differences [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There are interactions between the rs373863828-A variant and various metabolic parameters that necessitates further investigation to unravel these relationships.\u003c/p\u003e \u003cp\u003eUntargeted metabolomics offers one approach to understanding the specific metabolic pathways and processes associated with genetic variation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By comprehensively identifying and quantifying a wide range of metabolites, untargeted metabolomics provides a holistic view of metabolic changes, enabling the detection of perturbations attributable to the genetic variant under study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach allows for a single assay to probe multiple biochemical pathways simultaneously, providing insights into the underlying metabolic changes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we applied untargeted metabolomics to investigate the metabolic pathways potentially influenced by the \u003cem\u003eCREBRF\u003c/em\u003e rs373863828-A variant, particularly those related to body composition and beta-cell function. We assessed the urinary metabolite profiles of Māori and Pacific peoples without diabetes, or chronic kidney disease from the Genetics of Gout, Diabetes, and Kidney disease (GoGDK) in Aotearoa study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Using data from over 1000 metabolites analysed on the Metabolon platform, the metabolic profiles of those with and without the \u003cem\u003eCREBRF\u003c/em\u003e rs373863828 minor A-allele were compared, with and without BMI adjustment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Participants\u003c/h2\u003e \u003cp\u003eThe GoGDK of Aotearoa study recruited participants between 2006 and 2017 from the Auckland, Waikato, and Christchurch regions of Aotearoa [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Participants provided their phenotypic and genetic information to investigate the genetic factors underlying gout, diabetes, and kidney disease. Ancestry was determined based on self-reported ancestries of their grandparents, complemented by genome-wide principal component clustering. This process identified 2124 adults with Polynesian ancestry. For this metabolomics study, Polynesian participants who did not have diabetes or chronic kidney disease at the time of recruitment were selected, given these two conditions would likely influence urinary metabolites. Diabetes and chronic kidney disease ascertainment has been previously [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Urine samples were collected from these participants at the point of recruitment and used for this analysis. Genotypes for the rs373863828 variant were extracted from the whole-genome imputed sequence data constructed for Māori and Pacific participants from the GoGDK using BCFtools v1.9-94-g9589876 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. All participants provided written informed consent for the collection of their samples and subsequent analyses. The ethical approval for this study was provided by the NZ Multi-Region Ethics Committee (reference numbers: MEC/05/10/130; MEC/10/09/092; MEC/ 11/04/036).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Urine metabolomic analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Sample preparation and quality control\u003c/h2\u003e \u003cp\u003eSample treatments and metabolomic measurements were conducted by Metabolon Inc. (Morrisville, NC, USA) using their established protocols. Briefly, samples underwent protein precipitation with methanol followed by centrifugation. The resulting extract was then analysed using a standardised multi-method approach incorporating both reverse phase and hydrophilic liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). Quality control measures were implemented throughout the process, including technical replicates, process blanks, and a pooled matrix sample, as per Metabolon\u0026rsquo;s standard procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy\u003c/h2\u003e \u003cp\u003eChromatographic separation was achieved using a Waters ACQUITY UPLC system. Mass spectrometric detection was performed on a Thermo Scientific Q-Exactive high-resolution mass spectrometer. The analyses were optimised for a broad range of compound classes by employing different ionisation modes (positive and negative) and adjusting solvent gradients across multiple chromatographic runs. Detailed information regarding the specific solvent systems and gradients employed can be found in Metabolon\u0026rsquo;s standard documentation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Data processing and statistical analysis\u003c/h2\u003e \u003cp\u003eRaw data were processed using Metabolon\u0026rsquo;s software for peak identification and quantification. Metabolite identification was based on three criteria: retention time, accurate mass measurement, and matching of MS/MS fragmentation spectra against Metabolon\u0026rsquo;s curated library of known standards. Data normalisation procedures were implemented to mitigate inter-run variability. The \u0026ldquo;Batch-norm-Imputed\u0026rdquo; data file provided by Metabolon was used for downstream analysis.\u003c/p\u003e \u003cp\u003eThe R package POMA v1.12.0 was used for metabolomic data pre-processing [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Missing values were imputed using the minimum observed method, where each missing value for a metabolite was replaced with the minimum value observed for that metabolite. Data normalisation was conducted by applying the log transformation and Pareto scaling method [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cb\u003eSupplementary Figs.\u0026nbsp;1 and 2\u003c/b\u003e showcase the distribution of data before and after normalisation.\u003c/p\u003e \u003cp\u003eThe R package limma v3.58.1 was used to identify differentially expressed metabolites between \u003cem\u003eCREBRF\u003c/em\u003e variant carriers and non-carriers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Limma fits a linear model to each metabolite separately, allowing for covariate adjustment. Age, sex, and gout status were used as covariates. To assess the role of body composition in metabolomic differences, results were presented both before and after adjustment for BMI. Metabolites achieving a false discovery rate (FDR) adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Analyses included overall and sex-stratified comparisons to identify potential sex-specific effects.\u003c/p\u003e \u003cp\u003eBaseline demographics and clinical characteristics were compared between \u003cem\u003eCREBRF\u003c/em\u003e carriers and non-carriers using the Wilcoxon rank sum test for continuous variables and the Pearson\u0026rsquo;s chi-square test for categorical variables. All analyses were performed using R software v4.3.1 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Pathway analysis\u003c/h2\u003e \u003cp\u003ePathway analysis was performed using the online tool, MetaboAnalyst 6.0 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. All metabolites differentially expressed between \u003cem\u003eCREBRF\u003c/em\u003e carriers and non-carriers with a raw p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included. This approach allowed for the inclusion of potentially relevant metabolites in the initial pathway analysis that might have been excluded due to stringent multiple testing corrections. The hypergeometric test was performed against the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database and a FDR\u0026thinsp;\u0026lt;\u0026thinsp;5% was used as a threshold to identify significantly altered pathways.\u003c/p\u003e \u003cp\u003eAdditionally, MetaboAnalyst 6.0 calculates an impact score, which is the sum of the importance measures of identified metabolites divided by the total sum of the importance measures of all the identified and unidentified metabolites in the pathway (range 0\u0026ndash;1). Pathway topology analysis was used to calculate the importance measures based on the degree of relative-betweeness centrality. The reported impact score represents an objective estimate of a pathway\u0026rsquo;s significance within the global metabolic network, with a score closer to 1.00 indicating a pathway\u0026rsquo;s greater importance and more pronounced effect on metabolic processes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant characteristics\u003c/h2\u003e \u003cp\u003eOf the 2258 participants in GoGDK study, 1436 had urinary metabolomics results that passed quality control, and a further 436 were excluded due to the presence of T2D (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Of the 980 individuals without T2D, 325 individuals (33.2%) were identified as carriers of the \u003cem\u003eCREBRF\u003c/em\u003e rs373863828-A allele, while the remaining 655 individuals (66.8%) were non-carriers. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline demographics and clinical characteristics of the study participants stratified by \u003cem\u003eCREBRF\u003c/em\u003e variant status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographics and clinical characteristics of \u003cem\u003eCREBRF\u003c/em\u003e carriers vs. non-carriers in metabolomics analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCREBRF\u003c/em\u003e carriers\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-carriers\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.14 \u0026plusmn; 15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.68 \u0026plusmn; 15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale (n(%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e201 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.25 \u0026plusmn; 7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.41 \u0026plusmn; 6.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR (ml/min/1.73/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.45 \u0026plusmn; 18.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.39 \u0026plusmn; 19.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.31 \u0026plusmn; 17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131.03 \u0026plusmn; 18.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiastolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.34 \u0026plusmn; 12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.86 \u0026plusmn; 13.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.06 \u0026plusmn; 1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.05 \u0026plusmn; 1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11 \u0026plusmn; 1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06 \u0026plusmn; 1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 \u0026plusmn; 0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22 \u0026plusmn; 0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99 \u0026plusmn; 0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96 \u0026plusmn; 0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGout (n (%))\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (56.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e358 (54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eQuantitative data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and qualitative data are presented as n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eP-values come from a Wilcox rank sum test for continuous values and Pearson\u0026rsquo;s chi-squared test for categorical values.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eP-values that are statistically significant are shown in bold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean age (\u0026plusmn;\u0026thinsp;standard deviation [SD]) of \u003cem\u003eCREBRF\u003c/em\u003e carriers was 46.14\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52 years, compared to 44.68 \u0026plusmn; 15.90 years for non-carriers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.179). Females represented 35.1% of carriers and 30.7% of non-carriers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.189). \u003cem\u003eCREBRF\u003c/em\u003e carriers had a significantly higher mean BMI of 35.25\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78 kg/m\u0026sup2; compared to 32.41 \u0026plusmn; 6.78 kg/m\u0026sup2; in non-carriers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The estimated glomerular filtration rate (eGFR) did not significantly differ between groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.113), with all participants having an eGFR well above 30 ml/min/1.73/m\u003csup\u003e2\u003c/sup\u003e. Carriers exhibited higher diastolic blood pressure (82.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.41 mmHg) compared to non-carriers (80.86 \u0026plusmn; 13.26 mmHg, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). High-density lipoprotein cholesterol (HDL) levels were lower in carriers (1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 mmol/L) than in non-carriers (1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 mmol/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), while other lipid measurements showed no statistically significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Differentially expressed metabolites in the overall group\u003c/h2\u003e \u003cp\u003eA total of 1095 compounds were identified from the 980 participants\u0026rsquo; urine samples, of which 771 compounds were identified as known metabolites. Prior to BMI adjustment, the limma t-test revealed four metabolites were significantly different between \u003cem\u003eCREBRF\u003c/em\u003e carriers and non-carriers in the overall group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Two metabolites were upregulated in \u003cem\u003eCREBRF\u003c/em\u003e carriers, N-acetylhistamine (KEGG: C05135), involved in histidine metabolism, with a log2 fold change (log\u003csub\u003e2\u003c/sub\u003eFC) of 0.25 (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and dimethylglycine (DMG), involved in glycine, serine, and threonine metabolism, with a log\u003csub\u003e2\u003c/sub\u003eFC of 0.19 (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e). Conversely, two metabolites were significantly downregulated in carriers, including N2-acetyl,N6,N6-dimethyllysine (log\u003csub\u003e2\u003c/sub\u003eFC = -0.17, adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) involved in lysine metabolism, and catechol glucuronide (log\u003csub\u003e2\u003c/sub\u003eFC = -0.21, adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) involved in tyrosine metabolism (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential metabolite analysis in \u003cem\u003eCREBRF\u003c/em\u003e carriers prior to BMI adjustment: overall group comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuper Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSub Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-acetylhistamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHistidine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC05135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0013253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edimethylglycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycine, Serine and Threonine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC03626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0000092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.4e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2-acetyl,N6,N6-dimethyllysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecatechol glucuronide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0240490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0. 049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes; HMDB, Human Metabolome Database; FC, fold change; NA, not applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sex-stratified differential metabolite analysis\u003c/h2\u003e \u003cp\u003eSimilar to the overall analysis, DMG was upregulated in male \u003cem\u003eCREBRF\u003c/em\u003e carriers (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.20, adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.027), and N2-acetyl,N6,N6-dimethyllysine (log\u003csub\u003e2\u003c/sub\u003eFC = -0.23, adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) and catechol glucuronide (log\u003csub\u003e2\u003c/sub\u003eFC = -0.26, adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) were downregulated (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Only found in the male analysis was the downregulation of guaiacol sulfate with a log\u003csub\u003e2\u003c/sub\u003eFC of -0.19 (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), which is involved in benzoate metabolism (Fig.\u0026nbsp;2; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential metabolite analysis in \u003cem\u003eCREBRF\u003c/em\u003e carriers prior to BMI adjustment: sex-stratified comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSuper Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSub Pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elog2FC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2-acetyl,N6,N6-dimethyllysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecatechol glucuronide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0240490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eguaiacol sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXenobiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenzoate Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0060013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.8e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edimethylglycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino\u0026nbsp;Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlycine, Serine and Threonine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC03626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0000092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.7e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2-acetyl,N6-methyllysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino\u0026nbsp;Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLysine Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHMDB0242186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egamma-CEHC sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCofactors and Vitamins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTocopherol Metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes; HMDB, Human Metabolome Database; FC, fold change; NA, not applicable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the female group, gamma-carboxyethyl hydroxychroman (CEHC) sulfate, involved in tocopherol metabolism, was significantly upregulated with a log\u003csub\u003e2\u003c/sub\u003eFC of 0.34 and an adjusted p-value of 0.021 (Fig.\u0026nbsp;3; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Pathway analysis\u003c/h2\u003e \u003cp\u003ePathway analysis was conducted using MetaboAnalyst 6.0 to investigate the biological relevance of the observed metabolite changes in \u003cem\u003eCREBRF\u003c/em\u003e carriers. Metabolites with a raw p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in the analysis.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Pathway analysis in the overall group\u003c/h2\u003e \u003cp\u003eSeveral statistically significant pathway alterations were identified in \u003cem\u003eCREBRF\u003c/em\u003e carriers (Fig.\u0026nbsp;4). The glycine, serine, and threonine metabolism pathway exhibited significant enrichment with an adjusted p-value of 0.047 and an impact score of 0.19 (match status 4/33; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Histidine metabolism was also noteworthy, with three out of 16 expected metabolites identified (padj-value\u0026thinsp;=\u0026thinsp;0.047, impact score\u0026thinsp;=\u0026thinsp;0.19; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). While other pathways, such as taurine and hypotaurine metabolism, arginine metabolism, and starch and sucrose metabolism, showed enrichment, they did not reach statistical significance after FDR adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathway topology results in the overall group analysis prior to BMI adjustment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-log10(p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine, serine and threonine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaurine and hypotaurine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeomycin, kanamycin and gentamicin biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine and proline metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaffeine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary bile acid biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButanoate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarch and sucrose metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitrate cycle (TCA cycle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebeta-Alanine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePentose phosphate pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalactose metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipoic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine, aspartate and glutamate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlyoxylate and dicarboxylate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptophan metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroid hormone biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eP-values are derived from pathway enrichment analysis and pathway impact values are from pathway topology analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eP-values that are statistically significant are shown in bold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviation: FDR, false discovery rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Sex-specific pathway analysis\u003c/h2\u003e \u003cp\u003eThe pathway analysis conducted in the male group did not result in any significant pathways (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). However, in the female group histidine metabolism was significant with an adjusted p-value of 0.047 and an impact score of 0.19 (match status 3/16; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathway topology results in the female group analysis prior to BMI adjustment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-log10(p)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine, serine and threonine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine and proline metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCysteine and methionine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B6 metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptophan metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicotinate and nicotinamide metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutathione metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyrimidine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary bile acid biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroid hormone biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eP-values are derived from pathway enrichment analysis and pathway impact values are from pathway topology analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eP-values that are statistically significant are shown in bold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviation: FDR, false discovery rate.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study utilised untargeted metabolomics to investigate the metabolic impacts of the \u003cem\u003eCREBRF\u003c/em\u003e rs373863828-A variant within a cohort of Māori and Pacific peoples from the GoGDK study. Initially identified were distinct urinary metabolomic profiles between \u003cem\u003eCREBRF\u003c/em\u003e variant carriers and non-carriers. Notably, there was a significant upregulation of N-acetylhistamine, a metabolite involved in histidine metabolism, in variant carriers, suggesting a potential connection between \u003cem\u003eCREBRF\u003c/em\u003e and histidine signalling pathways. However, these differences were attenuated after adjusting for BMI, indicating that the observed metabolic variations largely reflect differences in body composition.\u003c/p\u003e \u003cp\u003ePrior to BMI adjustment, alterations in several other amino acids were observed. Lysine metabolites, N2-acetyl,N6-methyllysine and N2-acetyl,N6,N6-dimethyllysine, were consistently downregulated in variant carriers across both the overall and male-specific analyses. This is consistent with the established association between higher levels of lysine metabolites and increased T2D risk, and aligning with the lower T2D risk observed in \u003cem\u003eCREBRF\u003c/em\u003e variant carriers [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the initial findings of altered histidine metabolism in both the overall and female-specific pathway analyses, combined with the observed upregulation of N-acetylhistamine in \u003cem\u003eCREBRF\u003c/em\u003e carriers, is an interesting one. Despite the loss of significance after BMI adjustment, this does not negate the potential biological relevance of this pathway. Instead, it highlights the importance of considering BMI as a significant modifier of genetic effects on metabolism.\u003c/p\u003e \u003cp\u003eHistidine, an essential amino acid, undergoes various metabolic transformations, including its conversion to histamine through L-histidine decarboxylase. Histamine and its metabolites, including N-acetylhistamine (formed from the acetylation of histamine), are highly pleiotropic biogenic amines implicated in metabolic processes important for regulating body weight and maintaining glucose homeostasis, including energy metabolism, appetite regulation, and inflammation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Dysregulation of histamine signalling has been associated with various metabolic conditions, such as obesity and T2D [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Notably, histamine demonstrates anti-inflammatory effects through its interaction with H2 and H4 G-protein coupled receptors, counteracting excessive inflammation driven by M1 macrophages in adipose tissue [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This is particularly relevant in obesity, which is associated with a milieu of inflammatory responses, including chronic inflammation in adipose tissue [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This aligns with \u003cem\u003eCREBRF\u003c/em\u003e\u0026rsquo;s involvement in cellular stress responses, which can be triggered by inflammation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, evidence suggests that histamine may negatively regulate CREB activity [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Given that \u003cem\u003eCREBRF\u003c/em\u003e negatively regulates CREB3 (CAMP Responsive Element Binding Protein 3 CREB3), the observed upregulation of N-acetylhistamine in variant carriers may reflect this regulatory dynamic. Further investigation is needed to elucidate potential mechanisms by which \u003cem\u003eCREBRF\u003c/em\u003e influences histidine metabolism. Does \u003cem\u003eCREBRF\u003c/em\u003e directly or indirectly affect the expression or activity of enzymes involved in histamine synthesis or degradation? Could \u003cem\u003eCREBRF\u003c/em\u003e influence histamine receptor expression or signalling? The interactions between \u003cem\u003eCREBRF\u003c/em\u003e, histidine metabolism, and histamine signalling may present a potential pathway through which the \u003cem\u003eCREBRF\u003c/em\u003e variant may contribute to the paradoxical phenotype of increased BMI but reduced odds of T2D, warranting further investigation.\u003c/p\u003e \u003cp\u003eIn addition to alterations in histidine metabolism, changes in glycine, serine and threonine metabolism were observed in \u003cem\u003eCREBRF\u003c/em\u003e variant carriers, which may be relevant to body composition. Glycine and threonine are essential for creatine synthesis and influence both muscle protein synthesis and beta-cell function [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Serine serves as an energy substrate for muscle cells and is involved in protein synthesis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. While glycine is often decreased in T2D and associated with insulin resistance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], serine and threonine influence gluconeogenesis and enhance beta-cell function [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The upregulation of DMG, a metabolite in this pathway, is noteworthy. DMG and its precursor, betaine, have been inversely correlated with fasting glucose and insulin resistance, suggesting a protective effect against diabetes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The increased DMG levels in variant carriers further support this protective effect [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDMG also has a role in muscle metabolism and function, with positive correlations observed between DMG levels and fat-free mass [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This aligns with the role of \u003cem\u003eCREBRF\u003c/em\u003e and the rs373863828-A variant in muscle metabolism, which have been associated with reduced myostatin levels (promoting muscle growth) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and increased lean and fat-free mass [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the sex-specific analyses revealed additional metabolic differences. Males showed increased levels of DMG and decreased N2-acetyl,N6,N6-dimethyllysine and catechol glucuronide, while females exhibited increased gamma-CEHC sulfate, a Vitamin E antioxidant. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, only the female pathway analysis showed significant alterations in histidine metabolism. These findings demonstrate sex-specific metabolic differences and highlight the importance of considering sex in future investigations of this variant [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the attenuation of significance after adjusting for BMI, the initial findings highlight potential metabolic pathways influenced by the \u003cem\u003eCREBRF\u003c/em\u003e variant. The fact that these associations diminished post-BMI adjustment underscores the role of BMI as a mediator of the \u003cem\u003eCREBRF\u003c/em\u003e variant\u0026rsquo;s metabolic effects. This suggests that the metabolic differences observed between variant carriers and non-carriers in this study may be largely attributable to differences in BMI and body composition, rather than direct effects of the variant on metabolic pathways.\u003c/p\u003e \u003cp\u003eNevertheless, the biological relevance of these pathways cannot be entirely discounted. It is possible that the \u003cem\u003eCREBRF\u003c/em\u003e variant exerts subtle effects on metabolism that are magnified or mediated by changes in body composition. The interactions between genetic variants, metabolism, and body composition are complex, and BMI does not capture all aspects of body composition, such as lean mass versus fat mass distribution. Therefore further studies that consider more precise measures of body composition are needed to fully elucidate the \u003cem\u003eCREBRF\u003c/em\u003e variant\u0026rsquo;s effects on metabolism, and how these related to BMI and T2D risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the participants of the Genetics of Gout, Diabetes, and Kidney Disease in Aotearoa study for their invaluable contributions. Zanetta L.L. Toomata was supported by the Pacific Health Research PhD Scholarship from the Health Research Council of New Zealand (ref 21/197).\u0026nbsp;We thank members of Variant Bio for their contributions to the project, including\u0026nbsp;Stephane E. Castel at Variant Bio for reviewing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZLLT contributed to study conceptualisation, performed the data analysis and interpretation, and drafted the manuscript. RM conceptualised the study, supervised the overall research, provided oversight of the GoGDK study, and contributed to manuscript revisions. PW and OD supervised the overall research. ND, LKS, and TRM provided oversight of the GoGDK study and contributed to manuscript feedback. SEC provided the metabolomic data and contributed to manuscript input. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to consent restrictions but can be requested from the corresponding author under an appropriate arrangement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNaka I, Furusawa T, Kimura R, Natsuhara K, Yamauchi T, Nakazawa M, et al. A missense variant, rs373863828-A (p.Arg457Gln), of CREBRF and body mass index in Oceanic populations. Journal of Human Genetics 2017 62:9 2017;62:847\u0026ndash;9. https://doi.org/10.1038/JHG.2017.44.\u003c/li\u003e\n\u003cli\u003eLin M, Caberto C, Wan P, Li Y, Lum-Jones A, Tiirikainen M, et al. 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Impaired \u0026ldquo;Glycine\u0026rdquo;-mia in Type 2 Diabetes and Potential Mechanisms Contributing to Glucose Homeostasis. Endocrinology 2017;158:1064\u0026ndash;73. https://doi.org/10.1210/en.2017-00148.\u003c/li\u003e\n\u003cli\u003eSun K, Wu Z, Ji Y, Wu G. Glycine Regulates Protein Turnover by Activating Protein Kinase B/Mammalian Target of Rapamycin and by Inhibiting MuRF1 and Atrogin-1 Gene Expression in C2C12 Myoblasts. J Nutr 2016;146:2461\u0026ndash;7. https://doi.org/10.3945/JN.116.231266A.\u003c/li\u003e\n\u003cli\u003eGheller BJ, Blum JE, Lim EW, Handzlik MK, Hannah Fong EH, Ko AC, et al. Extracellular serine and glycine are required for mouse and human skeletal muscle stem and progenitor cell function. Mol Metab 2021;43:101106. https://doi.org/10.1016/j.molmet.2020.101106.\u003c/li\u003e\n\u003cli\u003eHe L, Ding Y, Zhou X, Li T, Yin Y. Serine signaling governs metabolic homeostasis and health. Trends in Endocrinology and Metabolism 2023;34:361\u0026ndash;72. https://doi.org/10.1016/J.TEM.2023.03.001/ASSET/49AAB019-7C80-49F6-83B1-18DCE7AD223E/MAIN.ASSETS/GR2.JPG.\u003c/li\u003e\n\u003cli\u003eFloegel A, Stefan N, Yu Z, M\u0026uuml;hlenbruch K, Drogan D, Joost H-G, et al. Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach. Diabetes 2013;62:639\u0026ndash;48. https://doi.org/10.2337/db12-0495.\u003c/li\u003e\n\u003cli\u003eGuasch-Ferr\u0026eacute; M, Hruby A, Toledo E, Clish CB, Mart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Salas-Salvad\u0026oacute; J, et al. Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis. Diabetes Care 2016;39:833\u0026ndash;46. https://doi.org/10.2337/DC15-2251.\u003c/li\u003e\n\u003cli\u003eHandzlik MK, Gengatharan JM, Frizzi KE, McGregor GH, Martino C, Rahman G, et al. Insulin-regulated serine and lipid metabolism drive peripheral neuropathy. Nature 2023;614:118\u0026ndash;24. https://doi.org/10.1038/s41586-022-05637-6.\u003c/li\u003e\n\u003cli\u003eVangipurapu J, Stanc\u0026aacute;kov\u0026aacute; A, Smith U, Kuusisto J, Laakso M. Nine Amino Acids Are Associated With Decreased Insulin Secretion and Elevated Glucose Levels in a 7.4-Year Follow-up Study of 5,181 Finnish Men. Diabetes 2019;68:1353\u0026ndash;8. https://doi.org/10.2337/DB18-1076.\u003c/li\u003e\n\u003cli\u003eArdestani A, Lupse B, Kido Y, Leibowitz G, Maedler K. mTORC1 Signaling: A Double-Edged Sword in Diabetic \u0026beta; Cells. Cell Metab 2018;27:314\u0026ndash;31. https://doi.org/10.1016/J.CMET.2017.11.004.\u003c/li\u003e\n\u003cli\u003eMagnusson M, Wang TJ, Clish C, Engstr\u0026ouml;m G, Nilsson P, Gerszten RE, et al. Dimethylglycine Deficiency and the Development of Diabetes. Diabetes 2015;64:3010\u0026ndash;6. https://doi.org/10.2337/DB14-1863.\u003c/li\u003e\n\u003cli\u003eKonstantinova S V., Tell GS, Vollset SE, Nyg\u0026aring;rd O, Bleie \u0026Oslash;, Ueland PM. Divergent Associations of Plasma Choline and Betaine with Components of Metabolic Syndrome in Middle Age and Elderly Men and Women,. J Nutr 2008;138:914\u0026ndash;20. https://doi.org/10.1093/JN/138.5.914.\u003c/li\u003e\n\u003cli\u003eUbhi BK, Riley JH, Shaw PA, Lomas DA, Tal-Singers R, MacNeef W, et al. Metabolic profiling detects biomarkers of protein degradation in COPD patients. European Respiratory Journal 2012;40:345\u0026ndash;55. https://doi.org/10.1183/09031936.00112411.\u003c/li\u003e\n\u003cli\u003eArslanian KJ, Fidow UT, Atanoa T, Unasa-Apelu F, Naseri T, Wetzel AI, et al. A missense variant in CREBRF, rs373863828, is associated with fat-free mass, not fat mass in Samoan infants. International Journal of Obesity 2020 45:1 2020;45:45\u0026ndash;55. https://doi.org/10.1038/s41366-020-00659-4.\u003c/li\u003e\n\u003cli\u003eGe Q, Lu H, Geng X, Chen X, Liu X, Sun H, et al. Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness. Chin Neurosurg J 2024;10:1\u0026ndash;12. https://doi.org/10.1186/S41016-024-00365-4/TABLES/4.\u003c/li\u003e\n\u003cli\u003eCarlson JC, Rosenthal SL, Russell EM, Hawley NL, Sun G, Cheng H, et al. A missense variant in CREBRF is associated with taller stature in Samoans. Am J Hum Biol 2020;32. https://doi.org/10.1002/ajhb.23414.\u003c/li\u003e\n\u003cli\u003eMetcalfe LK, Krishnan M, Turner N, Yaghootkar H, Merry TL, Dewes O, et al. The Māori and Pacific specific CREBRF variant and adult height. International Journal of Obesity 2019 44:3 2019;44:748\u0026ndash;52. https://doi.org/10.1038/S41366-019-0437-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6372155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6372155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives\u003c/strong\u003e: The CREBRF rs373863828-A (p.Arg457Gln) variant is associated with increased body mass index (BMI) but paradoxically reduced odds of type 2 diabetes (T2D). This study used untargeted urinary metabolomics to investigate metabolic pathways influenced by the \u003cem\u003eCREBRF\u003c/em\u003e variant in Māori and Pacific peoples without T2D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Untargeted metabolomic analysis was conducted on urine samples from 980 adult participants of Māori and Pacific descent from the Genetics of Gout, Diabetes, and Kidney disease (GoGDK) study, all of whom did not have T2D or significant kidney disease. Of these, 325 (33.2%) were carriers of the \u003cem\u003eCREBRF \u003c/em\u003evariant. Urine samples were analysed using ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS). Linear modelling using the limma package was used to identify differentially expressed metabolites between carriers and non-carriers, with and without adjustment for BMI. Results were stratified by sex, and pathway enrichment analysis was performed using MetaboAnalyst 6.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Four metabolites differed significantly between carriers and non-carriers before BMI adjustment (false discovery rate [FDR]-adjusted P \u0026lt; 0.05), including N-acetylhistamine (log\u003csub\u003e2\u003c/sub\u003eFC = 0.25, adjusted \u003cem\u003ep\u003c/em\u003e = 0.002) and dimethylglycine (log\u003csub\u003e2\u003c/sub\u003eFC = 0.19, \u003cem\u003ep\u003c/em\u003e = 0.002). Two metabolic pathways were significantly enriched: glycine, serine, and threonine metabolism (adjusted \u003cem\u003ep\u003c/em\u003e = 0.047; impact score = 0.19), and histidine metabolism (adjusted \u003cem\u003ep\u003c/em\u003e = 0.047; impact score = 0.19). After BMI adjustment, no metabolites remained significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Urinary metabolomic differences between \u003cem\u003eCREBRF\u003c/em\u003e rs373863828-A carriers and non-carriers appear to be driven by differences in BMI. These findings highlight the need to further explore the role of body composition in mediating the metabolic effects of \u003cem\u003eCREBRF\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Untargeted urinary metabolomics by CREBRF rs373863828 (p.Arg457Gln) variant among individuals without type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 01:21:01","doi":"10.21203/rs.3.rs-6372155/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67432920-08f7-4354-962e-b017152a535e","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48347803,"name":"Biological sciences/Genetics"},{"id":48347804,"name":"Biological sciences/Physiology/Metabolism"},{"id":48347805,"name":"Biological sciences/Physiology/Metabolism/Metabolic diseases/Diabetes/Type 2 diabetes"},{"id":48347806,"name":"Biological sciences/Physiology/Metabolism/Metabolic diseases/Obesity"},{"id":48347807,"name":"Biological sciences/Physiology/Metabolism/Metabolic diseases"}],"tags":[],"updatedAt":"2025-08-14T11:41:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 01:21:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6372155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6372155","identity":"rs-6372155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}