Circulating metabolome landscape in Lynch Syndrome

Circulating metabolome landscape in Lynch Syndrome | 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 Research Article Circulating metabolome landscape in Lynch Syndrome Tiina Jokela, Jari Karppinen, Minta Kärkkäinen, Jukka-Pekka Mecklin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3561844/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Feb, 2024 Read the published version in Cancer & Metabolism → Version 1 posted 8 You are reading this latest preprint version Abstract Circulating metabolites systemically reflect cellular processes and can modulate the tissue microenvironment in complex ways, potentially impacting cancer initiation processes. Genetic background increases cancer risk in individuals with Lynch syndrome; however, not all carriers develop cancer. Various lifestyle factors can influence Lynch syndrome cancer risk, and lifestyle choices actively shape systemic metabolism, with circulating metabolites potentially serving as the mechanical link between lifestyle and cancer risk. This study aims to characterize the circulating metabolome of Lynch syndrome carriers, shedding light on the energy metabolism status in this cancer predisposition syndrome. This study consists of a three-group cross-sectional analysis to compare the circulating metabolome of cancer-free Lynch syndrome carriers, sporadic colorectal cancer (CRC) patients, and healthy non-carrier controls. We detected elevated levels of circulating cholesterol, lipids, and lipoproteins in LS carriers. Furthermore, we unveiled that Lynch syndrome carriers and CRC patients displayed similar alterations compared to healthy non-carriers in circulating amino acid and ketone body profiles. Both groups exhibited increased systemic inflammation based on higher levels of global N-acetyl glycosylation (GlycA). Overall, a remarkable similarity between the circulating metabolome of healthy Lynch syndrome carriers and CRC patients suggests shared metabolic perturbations that may contribute to Lynch syndrome cancer susceptibility. This study provides valuable insights into systemic metabolic landscape of Lynch syndrome individuals. The findings hint at shared metabolic patterns between cancer-free Lynch syndrome carriers and CRC patients. Metabolomic biomarkers DNA mismatch repair deficiency hereditary cancer lipid metabolism Cholesterol metabolism Circulating amino acids ketone bodies GlycA Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Lynch syndrome (LS) is a hereditary condition caused by specific pathogenic mutations in DNA mismatch repair ( MMR ) genes, including MLH1 , MSH2 , MSH6 , or PMS2 . These mutations impair the cells' ability to correct errors that occur during DNA replication. Individuals with LS face a significantly increased lifetime risk of developing cancers, with up to a 16-fold higher risk depending on the specific MMR gene affected 1 , 2 . Colorectal cancer (CRC) is the most common cancer with a 52–97% lifetime risk when mutations occur in the MLH1 and MSH2 genes, 13–19% with mutated MSH6 gene, and 10% with mutated PMS2 gene 1 , 2 . However, it is worth noting that not all individuals with LS develop cancer. The fact that some LS carriers remain cancer-free throughout their lives shows that cancer risk can be modified. Lifestyle factors, such as engaging in regular physical activity and maintaining healthy body weight, are associated with a reduced cancer risk within the LS population 3 . The circulating metabolome reflects whole-body metabolic processes, which are influenced by genes, lifestyle factors and health status 4 – 7 . Based on findings that adiposity-linked circulating metabolite signature is associated with elevated CRC risk 8 , while a metabolite profile reflecting a heathy lifestyle is associated with lower CRC risk 5 , 9 circulating metabolome holds the potential for characterizing a phenotype susceptible to CRC development. Compelling evidence suggests that some circulating metabolites are causally related to cancer development. Lipids and amino acids were the most abundant circulating metabolites associated with CRC risk 5 , 8 – 10 . Elevated levels of triglycerides, phospholipids, and cholesterol may promote cancer cell growth and proliferation by serving as an energy source and inhibiting CD8 + T cell proliferation 11 . Amino acids function as building blocks of proteins, precursors of various signaling molecules, and energy sources. Levels of certain amino acids, such as Alanine and Histidine, have been shown to inversely associate with the cancer stage 12 . In addition, Histidine concentration in blood was shown to be inversely associated with CRC risk 12 . Furthermore, circulating amino acid levels can influence immune cell activity, potentially impacting cancer development, as amino acids are vital for the basal metabolism of immune cells, and activated immune cells require more amino acids 13 . Collectively, these findings suggest that changes in circulating metabolite levels can precede CRC development. However, it remains unexplored whether LS genotype affects the circulating metabolome. Therefore, our study investigated the circulating metabolome in cancer-free LS carriers. In this study, we examined the circulating metabolome in a cohort of cancer-free LS carriers. We compared their metabolome to a control group of cancer-free non-carriers, as well as to a group of non-carriers with CRC. Our two main findings were that healthy LS carriers and CRC patients had broadly similar metabolite profiles that differed from controls. Notably, both LS and CRC participants exhibited similar patterns in circulating amino acids, ketone bodies, and global N-acetyl glycosylation (GlycA) levels. Second, we identified altered lipid metabolism in LS carriers compared with controls, which may play a role in the regulation of adiposity-related cancer risk. Overall, our study sheds light on the shared metabolic signatures of LS carriers, emphasizing the potential systemic factors at play in cancer susceptibility. Materials and methods Sample collection Samples to three-group cross-sectional analysis were collected from different study cohorts; LS (n = 80), CRC (n = 89), Control (total n = 103). LS cohort included registered participants in the Finnish Lynch Syndrome Research Registry (LSRFi), with confirmed pathological MMR gene ( path_MMR ) variants (classes 4 and 5 by InSiGHT criteria) 14 . Sporadic CRC patients were enrolled at the time of their initial surgical appointment for CRC at the local tertiary center responsible for the management of CRC. Healthy non-carrier control samples were acquired from the Biobank of Eastern Finland (n = 76) and studies of the University of Jyväskylä (JYU) (n = 27) 4 Informed consent was obtained from all participants, and ethical approval of sample collections was from: the Ethics committees of the Helsinki and Uusimaa Health Care District, Central Finland Health Care District the University of Jyväskylä. The study was conducted according to the guidelines of the Declaration of Helsinki. All samples were taken in a fasted state. However, fasting instructions had slight differences. Control cohort participants fasted overnight and had no diet restrictions for the previous days. We do not have information about the length of the fasting of biobank samples. Samples of LS and CRC participants were taken after surveillance colonoscopy. According to colonoscopy protocol, LS and CRC participants were instructed to avoid eating high-fiber food (for example, fruit, berries, vegetables and seeds) two days before the surveillance visit, to eat only easily digestible foods (for example yogurt, porridge, potato, pasta, fish and white bread) a day before the surveillance visit and to abstain from solid food 12 hours and any liquids 2 hours before colonoscopy. From all participants, venous blood samples were taken from the antecubital vein to standard serum tubes. The samples were aliquoted and stored at − 80°C until analysis. Metabolomics analysis Metabolites were analyzed with a targeted proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy platform (Nightingale Health Ltd., Helsinki, Finland; biomarker quantification version 2020). The technical details of the method have been reported previously 15 . The platform quantifies 250 metabolite measures. Of them, metabolome-wide analyses were conducted with 171 variables representing lipoproteins and lipids, glycolysis-related metabolites as well as amino acids, ketone bodies and some other metabolites including GlycA, which is a measure of global N-acetyl glycosylation. For individual metabolite analyses, we concentrated on 65 key metabolites representing these metabolite groups. Statistical analysis Descriptive statistics of each metabolite are reported in supplement Table 1 , and Table 1 shows the statistical analyses used in this study. Table 1 The statistical analyses used in this study. Analysis Data type Software/package Principal Coordinate analysis (PCoA) of the Euclidean distances calculated from circulating metabolite values Raw data R version 4.0.0 or newer / ape package 16 PERMANOVA analysis was used to test whether cohorts' centroids/mean in the PCoA distance matrix were significantly different from each other. Beta-dispersion (PERMDISP) test was used to examine whether the variance of cohorts was significantly different. ANOSIM test was used to determine whether there is more similarity within the cohorts than between cohorts PcoA distance matrix R version 4.0.0 or newer / hagis package 17 Hierarchical clustering and heatmapping the Euclidean distance metric and complete linkage method were used to create clusters based on similarity. The raw metabolite data was scaled column-wise to ensure that metabolite expression values were comparable across samples. R version 4.0.0 or newer / Pheatmap package 18 Box-Cox data transformation was performed to ensure normally distributed data to follow up analysis. The Box-Cox transformation with lambda parameter estimated from data for each variable separately. Raw data R version 4.0.0 or newer / MASS-package 19 Equality was tested using Levene’s test, and if at least one group showed heteroscedasticity, the ANCOVA test was replaced with a generalized linear model (GLiM) Box Cox transformed data SPSS 20 ANCOVA analysis, with covariates (age, sex, BMI), was used to evaluate whether the means of metabolite values are equal or not. ANCOVA was performed on metabolites that had equal variances between groups. Box Cox transformed data SPSS 20 A generalized linear model (GLiM) test, with covariates (age, sex, BMI), was used to evaluate whether the means of metabolites values are equal or not. GLiM test was performed on metabolites that had non equal variances between groups. Box Cox transformed data SPSS 20 For data visualization, standardized mean differences (SMD) and SMD 95% confidence intervals were calculated and visualized in forest plot. Box Cox transformed data R version 4.0.0 or newer / MBESS-package 21 , ggforestplot-package 22 Results Descriptive characteristics of study subjects in LS carrier, control and CRC cohorts are presented in Table 2 . Table 2 Descriptive characteristics of study subjects. LS = path_MMR carrier currently cancer-free, Control = Non-carrier currently cancer-free, CRC = Non-carrier colorectal cancer patient. Variable LS control CRC N (total = 272) 80 103 89 Sex (N(%)) Female 42(52.5%) 54 (52.4%) 39(43.8%) Male 38(47.5%) 49 (47.6%) 50(56.2%) Age, years (mean±SD) 58.2±13.3 59.7±14.3 70.8±9.6 Body mass index, kg/m2 (mean±SD) 26.6±5.5 27.6±6.0 26.7±4.9 path_MMR (N(%)) MLH1 52(65%) MSH2 13(16.25%) MSH6 14 (17.5%) PMS2 1(1.25%) Circulating Metabolome level results Cancer-free LS carriers’ circulating metabolome profile showed more similarity with CRC patients’ profile than controls’ − 1 71 circulating metabolites were studied using NMR-based targeted analysis. The dimension reduction method PCoA and PERMANOVA test indicate that circulating metabolite profiles differed between the three groups (Fig. 1 ). Pairwise comparisons further showed that the metabolite profile of the LS and CRC groups differed from the control group (Fig. 1 ). However, no clear difference was found between the LS and CRC groups (Fig. 1 ). In summary, our results suggest that both CRC and LS are associated with a similar circulating metabolome signature. Path_MMR gene variants do not show clearly differing associations with circulating metabolome – cancer risk in LS is strongly associated with path_MMR genes, where MLH1 is the most aggressive gene to increase cancer risk 1 , 2 . MLH1 is also the primary mutation found in our Finnish LS cohort 23 , which is why our path_MMR carrier groups are not equally sized (Table 2 ). These unbalanced group sizes need to be considered when interpreting the following results. Nevertheless, we considered it important to study whether different path_MMR genes have a different effect on circulating concentrations of the 171 metabolites and performed Euclidean clustering and heatmap visualization within the LS cohort (Fig. 2). No apparent clustering was detected based on path_MMR genes (Fig. 2). To study specific differences between groups carrying each of the path_MMR genes we excluded PSM2 , since we only had one carrier in the cohort. When comparing MLH1 , MSH2 and MSH6 carrier groups PCoA and PERMANOVA analyses did not indicate notable differences between groups (p-value = 0.319)(supplement Fig. 1 .). The significant difference between the groups' variances (PERMDISP p-value = 0.006** and ANOSIM p-value = 0.01**) is likely due to large differences in group sizes (see Table 2 ). Circulating metabolites-specific results Lipoprotein- and lipid-related alterations in LS and CRC cohorts compared to controls. ANCOVA or GLiM analysis was employed, with covariates (age, sex, BMI), to examine 65 key metabolites (Fig. 3, supplemental table 2 ). Analyses revealed distinct metabolic alterations within the LS cohort compared to the control cohort, particularly in relation to lipoprotein particles and their lipid content (Fig. 3). Notably, the mean total cholesterol in the LS cohort was 6.5% higher compared to the control cohort. Similarly, cholesterol bound to low-density lipoprotein (LDL) were elevated in LS compared to control, while no significant differences were observed in cholesterol bound to high-density lipoprotein (HDL) particles (Fig. 3). Apolipoprotein A1 (ApoA1), a key constituent of HDL particles, displayed higher levels in the LS relative to the control. Related to this, LS cohort had higher amounts of total HDL particles but when particle sizes were inspected separately, only the amount of small and medium size HDL particles differed compared to controls (Fig. 3). Total LDL particle consentration was not altered, but small and medium size LDL particle levels were elevated in the LS cohort compared to control. Very low-density lipoprotein (VLDL) displayed an enlargement in average particle size in the LS relative to the control cohort. Furthermore, the LS compared to the control cohort exhibited heightened levels of triglycerides specifically localized within VLDL particles (Fig. 3). Elevated concentrations of total cholines, phosphatidylcholines, and phosphoglycerides were detected in the LS cohort when compared to the control group (Fig. 3). In contrast, the CRC cohort did not exhibit any significant alterations in lipoprotein and lipid metabolism-related metabolites when compared to the control cohort (Fig. 3). Lipoprotein and lipid- levels vary between different path_MMR carriers . ANCOVA and GLiM analyses, with covariates (age, sex, BMI) were used to determine whether different path_MMR carriers express different levels of 65 selected non-redundant key metabolites (supplemental table 3). A finding was that MLH1 carriers had the highest circulating cholesterol levels (mean of total cholesterol, MLH1 = 5.44 mmol/l, MSH2 = 4.81 mmol/l and MSH6 = 4.72 mmol/l) and MLH1 carriers had significantly higher cholesterol levels than MSH6 carriers (Fig. 4E). Of the cholesterol transportation particles, the amounts of very low-density lipoprotein (VLDL) (Fig. 4B) and LDL (Fig. 4C) and the concentration of ApoB, main lipoprotein in these particles (Fig. 4A), were highest in MLH1 -cohort, and significantly lower in MSH6 -cohort when compared to MLH1 -cohort (Fig. 4A, B, C). VLDL and LDL-bound cholesterol levels were also highest in the MLH1 cohort (Fig. 4F, G). Additionally, fatty acids (Fig. 4I), LDL-bound triglycerides (Fig. 4K) and phospholipids were upregulated in the MLH1 cohort (Fig. 4D, H, L). In conclusion, the levels of most circulating metabolites exhibited similarity among different path-MMR carriers (supplement table 3). However, MLH1 carriers demonstrated higher mean levels of lipoprotein and lipid-related metabolites when compared to MSH6 carriers. Circulating amino acids, ketone bodies and GlycA show similarity between LS and CRC cohorts . In the LS and CRC cohort, glutamine levels were elevated, whereas all other studied amino acids; alanine, histidine, isoleucine, phenylalanine, tyrosine, valine, and total branced-chain amino acids (BCAAs) were curtailed compared to the control group (Fig. 3). GlycA levels were higher in LS and CRC cohorts when compared with the control group (Fig. 3). When examining ketogenesis products, both CRC and LS cohorts had altered ketone body expression levels compared to the control cohort (Fig. 3). In summary, these results revealed that the LS cohort shows similarity with CRC cohort regarding circulating amino acids, ketone bodies and inflammation marker GlycA signatures. There was no significant difference in these metabolite levels when comparing different path_MMR carrier cohorts to each other (supplementary table 3). Discussion In this study, we investigated the circulating metabolome signature of 80 cancer-free carriers of LS and compared it to two distinct groups: a cancer-free non-carrier control cohort and a cohort of individuals with sporadic CRC. Our findings showed that the metabolomic signatures of LS carriers more closely resembled those in the CRC cohort than in the control cohort. No significant omics-level differences were found within LS carriers based on different path_MMR gene variants. However, our individual metabolite level inspections revealed notably higher total cholesterol levels and other significant alterations related to lipoprotein – and lipid metabolism in LS carriers compared to control, which was not evident in the CRC-control comparison. Furthermore, we also identified within LS cohort differences; path_MLH1 carriers showing the highest levels of specific lipid and lipoprotein metabolite. Similar alterations in lipoprotein- and lipid metabolism were not detected in individuals with sporadic CRC. Additionally, both LS and CRC cohorts exhibited distinct yet parallel alterations in the circulating amino acids, ketone bodies and GlycA levels. The circulating metabolome is associated with cancer risk 5 , 8 , 9 . Germline mutations in DNA repair genes elevate cancer risk by imposing a high mutation load on fast-proliferating epithelial tissues. However, there is a limited understanding of the interaction between germline mutations in the DNA repair system and systemic metabolomics. DNA repair gene BRCA1 has been shown to impact cellular metabolism 24 , 25 . Additionally, women with this breast cancer predisposition gene exhibit an altered circulating metabolome signature 26 . In the context of CRC, MLH1 deficiency in the CRC cell model has been found to disrupt mitochondrial metabolism 27 . Our findings revealed that LS carriers had a significantly altered circulating metabolome signature compared to the control cohort. Interestingly, this signature closely resembled the circulating metabolome signature observed in sporadic CRC patients. These results, together with previous findings related to BRCA1 and path_MMR , suggest that these cancer-predisposing germline mutations not only increase the mutation load in epithelial cells but also impact systemic metabolomic status. The association between cancer risk and lipoprotein and lipid levels has been extensively studied, but the results remain controversial. A recent systemic meta-analysis showed that triglycerides and total cholesterol positively correlated with CRC incident rate, while high levels of HDL cholesterol negatively correlated with CRC incidences 28 . This analysis did not show an association between LDL cholesterol and CRC risk. However, some studies indicate a U-shaped association, suggesting that intermediate LDL cholesterol levels are related to the lowest cancer risk 29 . In the LS carriers with type two diabetes, triglyceride level was not, but cholesterol level was associated with higher CRC risk 30 . While there is no clear consensus on whether lipoprotein and lipid metabolism associates with CRC risk or not, it is evident that lipoprotein and lipids play a critical role as functional molecules in various carcinogenesis-related processes. Dysregulation of lipid metabolism represents an important metabolic alteration in cancer. Lipoproteins and lipids act as energy producers, signaling molecules and source material for the biogenesis of cell membranes 31 . Cholesterol is a key component of cell membrane lipid rafts, which play a vital role in cancer signaling. It can directly activate oncogenic signaling pathways 29 , 32 . Moreover, cholesterol and lipoproteins are essential in triggering immune responses 32 . Our findings revealed that in comparison to the control cohort cancer-free carriers of LS exhibited higher cholesterol levels and alterations in the distribution of cholesterol-transporting lipoprotein particles. MLH1 carriers with the highest cancer risk had the highest cholesterol levels. The elevated lipoprotein and lipid levels in LS carriers could be the response to the high levels of immune activity known to be present in LS. It is possible that increased lipoprotein and lipid levels support immune cell functions, aiding in the elimination of premalignant cells. On the other hand, elevated levels might also provide growth advantages to malignant cells by boosting oncogenic signaling and overall cell proliferation. The exact role of elevated lipoprotein and lipid levels in LS carcinogenesis, whether protective or oncogenic, remains to be thoroughly investigated in future studies. Amino acids and ketone bodies have links to cancer progression. Amino acids are necessary building blocks for cancer cell protein synthesis, and the cancer cell ketone body’s metabolism has been shown to be disrupted 33 , 34 . Interestingly, the circulating amino acid histidine has been found to have an inverse association with CRC risk 10 . Alterations in circulating amino acid levels have been reported in many cancers. A decrease in circulating amino acids is often suggested to be caused by cachexia. However, this may not be the sole reason, as similar decreases have been observed in cancer patients without weight loss or cachexia 34 . The reduction in amino acids could potentially be attributed to the high demand for amino acids by cancer cells 34 . Our results showed that LS carriers compared to the control group exhibited a similar decrease in circulating amino acids. This suggests that amino acids might be consumed by non-tumorous cells, for example, immune cells, in LS carriers. Ketone bodies serve as an energy source and can be involved in various metabolic pathways. Produced in the liver, ketone bodies are transported to other tissues when needed. Due to impaired ketone metabolism in cancer cells, most of cancer cells cannot utilize ketone bodies as an energy source, and ketone bodies can even impose reactive oxygen species production, inhibiting cancer cell growth 35 . Thus, ketone bodies possess anti-cancer properties 35 . However, the reasons behind LS carriers expressing a similar circulating ketone bodies’ profile as CRC patients remain unclear. Inflammation and its biomarkers have been strongly associated with cancer risk, progression, and survival 36 , 37 . GlycA, a novel inflammation marker has been linked to CRC incidence and mortality 38 . Elevated GlycA levels indicate both acute and chronic inflammation, serving as predictive markers for overall mortality. These levels remain persistently elevated and stable for an extended period, spanning up to a decade 39 . Our results showed increased GlycA levels in the CRC cohort, and in addition to that LS carriers displayed a modest yet statistically significant increase in GlycA levels. This rise in GlycA level could be attributed to heightened immune activity in LS. Our study provides valuable insights into the circulating metabolome of LS. It is important to acknowledge certain limitations attached to our study. For instance, sample size, although comprehensive, is still relatively small, which may have limited detecting subtle differences in certain metabolomic parameters. More in-depth discussions of limitations and strengths of this study are presented in the supplemental file. Conclusions The results demonstrate that the oncogenic stress imposed by path_MMR genes is reflected at the systemic metabolomic level. These constitutional alterations in energy metabolism may play a significant role in the etiology of LS-related cancers. The findings of our study raise several intriguing questions regarding the interaction between systemic metabolism and pre-carcinogenic processes. Abbreviations Lynch Syndrome (LS), Colorectal cancer (CRC), DNA mismatch repair gene (MMR), Pathogenic DNA mismatch repair gene (path_MMR), global N-acetyl glycosylation (GlycA), very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), high-density lipoprotein (HDL), Principal coordinate analysis (PCoA), generalized linear model (GLiM). Declarations Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Funding This work was supported by: T.J.-the EU Marie Skłodowska-Curie Actions [Grant agreement ID: 101026706], S.W.- the Finnish Cultural Foundation (grant #00211177), T.T.S.-research grants from Jane and Aatos Erkko Foundation, Sigrid Juselius Foundation, Finnish Medical Foundation, Emil Aaltonen Foundation, Cancer Foundation Finland, Relander Foundation, and state research funding. E.K.L Academy of Finland #335249 and #330281 and E.K.L: University of Jyväskylä. Conflict of interest T.T.S. reports a consultation fee from Amgen Finland and is a co-owner and CEO of Healthfund Finland Ltd, and the Clinical Advisory Board of LS Cancer Diag Ltd. Ethical Approval The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Central Finland Health Care District (KSSHP 3/2016). Informed consent was obtained from all participants, and ethical approval of sample collections was from: the Ethics committees of the Helsinki and Uusimaa Health Care District HUS/155/2021), Central Finland Health Care District the University of Jyväskylä (KSSHP D# 1U/2018, 1/2019 and KSSHP 3/2016). Authors contribution T.J. and E.K.L. contributed to the conception and design of the work. T.T.S. organized clinical sample collection. S.W. and E.K.L. organized the JYU control sample collection. T.T.S. and J-P.M. offered medical expertise and guidance. J.K. and M.K. took part in the data analysis and visualization. T.J. did the main part of the data analysis and drafted the manuscript. All authors critically revised the manuscript. All approved final version of the manuscript. Acknowledgments We thank all study participants from LS, control and CRC cohorts. References Win AK, Dowty JG, Reece JC, Lee G, Templeton AS, Plazzer JP et al. 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GlycA, a novel pro-inflammatory glycoprotein biomarker is associated with mortality: results from the PREVEND study and meta-analysis. J Intern Med 2019; 286 : 596–609. Chandler PD, Akinkuolie AO, Tobias DK, Lawler PR, Li C, Moorthy MV et al. Association of N-Linked Glycoprotein Acetyls and Colorectal Cancer Incidence and Mortality. PLoS One 2016; 11 : e0165615-. Ritchie SC, Würtz P, Nath AP, Abraham G, Havulinna AS, Fearnley LG et al. The Biomarker GlycA Is Associated with Chronic Inflammation and Predicts Long-Term Risk of Severe Infection. Cell Syst 2015; 1 : 293–301. Additional Declarations No competing interests reported. Supplementary Files SupplementFigure1.docx Supplementary figure1. Circulating metabolome visualized in PCoA distance matrix and comparison between different path_MMR gene carrier cohorts done by using PERMANOVA, PERMDISP and ANOSIM tests. Supplementarydatatables.pdf Supplementary table 1. Descriptive statistics for all 171 metabolites Supplementary table 2. ANCOVA& GLiM analysis for 65 key metabolites comparing LS, control and CRC cohorts. Supplementary table 3. Descriptive statistics and ANCOVA& GLiM analysis for 65 key metabolites comparing different path_MMR gene carriers. Supplementaryfile1.Strengthsandlimitations.docx Supplementary file 1. Strength and limitations Cite Share Download PDF Status: Published Journal Publication published 05 Feb, 2024 Read the published version in Cancer & Metabolism → Version 1 posted Editorial decision: Revision requested 07 Dec, 2023 Reviews received at journal 01 Dec, 2023 Reviewers agreed at journal 30 Nov, 2023 Reviewers agreed at journal 30 Nov, 2023 Reviewers invited by journal 27 Nov, 2023 Submission checks completed at journal 06 Nov, 2023 Editor assigned by journal 06 Nov, 2023 First submitted to journal 05 Nov, 2023 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-3561844","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":246492278,"identity":"9c035aba-6fb2-43ca-9653-4a436576ecbc","order_by":0,"name":"Tiina Jokela","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3RsQqCQBjA8e84yOVoVorsEXQPe5WTA3uFoCBbzkVq9XEKQZeoNWi5lqYGp3CISK8gWk7HoPsvdw4//D4OQKf7xTAK5YnCDoCAEa4/CqoQ5ItQCGqCEiX5XCVJJU9Uc40NzEUJHuCI+4LOD4ZzpCgU6sEiNwYGKM5Sh2YnLEnDLtwkgKthJtykHUkM0USsOyxqEpX0sW/3lx6ptkZJkIHPNy1IWpG+k5NqF2b6K4at3XmpJMY6v1jX6WzgRtwtipvHujnbLksFeeUAccPXlQ0378dtzH6fnt0S6HQ63f/0BKe4SfwbaRhWAAAAAElFTkSuQmCC","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tiina","middleName":"","lastName":"Jokela","suffix":""},{"id":246492281,"identity":"3d1a23df-16bf-4d06-b62f-e223fdfd68ea","order_by":1,"name":"Jari Karppinen","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jari","middleName":"","lastName":"Karppinen","suffix":""},{"id":246492282,"identity":"4de0d5a9-2f06-4fe3-ba28-f67e31ad4da2","order_by":2,"name":"Minta Kärkkäinen","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minta","middleName":"","lastName":"Kärkkäinen","suffix":""},{"id":246492285,"identity":"476e71ff-9692-4c43-89b4-f8a32d0af716","order_by":3,"name":"Jukka-Pekka Mecklin","email":"","orcid":"","institution":"The Wellbeing Services County of Central Finland","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jukka-Pekka","middleName":"","lastName":"Mecklin","suffix":""},{"id":246492287,"identity":"2db42a3a-8b5b-4b15-82e9-8dc024e1b4f5","order_by":4,"name":"Simon Walker","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Walker","suffix":""},{"id":246492289,"identity":"29571d7b-776d-4512-bec4-9f50920fbc0b","order_by":5,"name":"Toni T. Seppälä","email":"","orcid":"","institution":"University of Tampere","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Toni","middleName":"T.","lastName":"Seppälä","suffix":""},{"id":246492294,"identity":"4018835e-76a1-407e-91c1-4d2ec42e66d1","order_by":6,"name":"Eija K. Laakkonen","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eija","middleName":"K.","lastName":"Laakkonen","suffix":""}],"badges":[],"createdAt":"2023-11-05 10:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3561844/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3561844/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40170-024-00331-9","type":"published","date":"2024-02-05T15:01:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":46101349,"identity":"08fc4741-3f0a-48c1-b729-899e550be81f","added_by":"auto","created_at":"2023-11-08 15:59:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240281,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinate analysis (PCoA) of the Euclidean distances calculated from 171 circulating metabolome values. After data dimension reduction the difference between cohorts; cancer-free non-carrier controls (CTRL), cancer-free Lynch syndrome carriers (LS) and sporadic colorectal cancer patients (CRC) was tested for significance using PERMANOVA on the PcoA distance matrices. PERMDISP test was used to test if the variance of cohorts was significantly different or not. ANOSIM test was used to test if there is more similarity within the cohorts than between cohorts compared to all cohorts and each cohort paired. The table shows p-values for PERMANOVA, PERMDISP and ANOSIM analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/8889a67c399e070713ebf6af.png"},{"id":46101355,"identity":"3424bab7-4899-4aae-9017-34d2ddc4fe9c","added_by":"auto","created_at":"2023-11-08 15:59:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392348,"visible":true,"origin":"","legend":"\u003cp\u003eClustered heatmap based on Euclidean distance metric clustering and metabolite-wise scaled values. Different \u003cem\u003epath_MMR\u003c/em\u003e gene variant carriers are presented in the right side color bar; \u003cem\u003eMLH1 \u003c/em\u003e= yellow, \u003cem\u003eMSH2 \u003c/em\u003e= blue, \u003cem\u003eMSH6 \u003c/em\u003e= green and \u003cem\u003ePMS2\u003c/em\u003e = red.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/ab3f33285c2457e2b2a093aa.png"},{"id":46101351,"identity":"ee576198-701f-4b10-8ea6-691e37186ff7","added_by":"auto","created_at":"2023-11-08 15:59:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187593,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots illustrate standardized mean differences (SDM) relative to the control cohort, along with their corresponding 95% confidence intervals, calculated using box-cox transformed metabolite values. Significant differences between the control cohort and both LS and CRC cohorts are evaluated using ANCOVA or GLiM analysis, incorporating covariates age, sex, and BMI; a colored dot indicates p-value \u0026lt;0.05. Test and values for all 65 key metabolites comparisons are shown in supplementary table 2.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/a4ad67336c6bb449fdb74a14.png"},{"id":46102661,"identity":"dcf3fbc9-c94d-4daf-9abb-a5854084492d","added_by":"auto","created_at":"2023-11-08 16:07:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":181159,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolite levels in different \u003cem\u003epath_MMR \u003c/em\u003egene cohorts, g/l(A) and mmol/l (B-L). ANCOVA (B, D, E-H, J, L) or GLiM (A, C, I, K) test, incorporating covariates age, sex, and BMI, was used to test the difference between the \u003cem\u003eMLH1\u003c/em\u003e cohort and other \u003cem\u003epath_MMR\u003c/em\u003e cohorts and significant p-values were found only in comparison between \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eMSH6 \u003c/em\u003ecohorts, p-values shown in figures. Test and values for all 65 key metabolites comparisons are shown in supplementary table 3.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/c752d55344b693b3a00b9dc1.png"},{"id":51005652,"identity":"d24b613a-ba6b-41fe-a8b7-6400c4357db6","added_by":"auto","created_at":"2024-02-12 15:11:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1326668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/852abdb7-254f-4b64-9529-17e96061fe3f.pdf"},{"id":46101358,"identity":"d2c6ed74-6a41-4957-b83b-bc7377c4ae10","added_by":"auto","created_at":"2023-11-08 15:59:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":351321,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure1. Circulating metabolome visualized in PCoA distance matrix and comparison between different \u003cem\u003epath_MMR\u003c/em\u003e gene carrier cohorts done by using PERMANOVA, PERMDISP and ANOSIM tests.\u003c/p\u003e","description":"","filename":"SupplementFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/3ab183c8f5e3203be809a33a.docx"},{"id":46101357,"identity":"36d9db9e-c472-4f41-bc0c-c18cf1a1dddf","added_by":"auto","created_at":"2023-11-08 15:59:29","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":203368,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table 1. Descriptive statistics for all 171 metabolites\u003c/p\u003e\n\u003cp\u003eSupplementary table 2. ANCOVA\u0026amp; GLiM analysis for 65 key metabolites comparing LS, control and CRC cohorts.\u003c/p\u003e\n\u003cp\u003eSupplementary table 3. Descriptive statistics and ANCOVA\u0026amp; GLiM analysis for 65 key metabolites comparing different \u003cem\u003epath_MMR\u003c/em\u003e gene carriers.\u003c/p\u003e","description":"","filename":"Supplementarydatatables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/b52fa351e6e8762811940c75.pdf"},{"id":46102662,"identity":"91436a0d-3b73-4984-b99b-6723ccdc4e42","added_by":"auto","created_at":"2023-11-08 16:07:29","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14542,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary file 1. Strength and limitations\u003c/p\u003e","description":"","filename":"Supplementaryfile1.Strengthsandlimitations.docx","url":"https://assets-eu.researchsquare.com/files/rs-3561844/v1/9f50eaee20dc150cd847d375.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating metabolome landscape in Lynch Syndrome","fulltext":[{"header":"Background","content":"\u003cp\u003eLynch syndrome (LS) is a hereditary condition caused by specific pathogenic mutations in DNA mismatch repair (\u003cem\u003eMMR\u003c/em\u003e) genes, including \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e, \u003cem\u003eMSH6\u003c/em\u003e, or \u003cem\u003ePMS2\u003c/em\u003e. These mutations impair the cells' ability to correct errors that occur during DNA replication. Individuals with LS face a significantly increased lifetime risk of developing cancers, with up to a 16-fold higher risk depending on the specific \u003cem\u003eMMR\u003c/em\u003e gene affected \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Colorectal cancer (CRC) is the most common cancer with a 52\u0026ndash;97% lifetime risk when mutations occur in the \u003cem\u003eMLH1\u003c/em\u003e and \u003cem\u003eMSH2\u003c/em\u003e genes, 13\u0026ndash;19% with mutated \u003cem\u003eMSH6\u003c/em\u003e gene, and 10% with mutated \u003cem\u003ePMS2\u003c/em\u003e gene\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, it is worth noting that not all individuals with LS develop cancer. The fact that some LS carriers remain cancer-free throughout their lives shows that cancer risk can be modified. Lifestyle factors, such as engaging in regular physical activity and maintaining healthy body weight, are associated with a reduced cancer risk within the LS population\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe circulating metabolome reflects whole-body metabolic processes, which are influenced by genes, lifestyle factors and health status \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Based on findings that adiposity-linked circulating metabolite signature is associated with elevated CRC risk \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, while a metabolite profile reflecting a heathy lifestyle is associated with lower CRC risk \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e circulating metabolome holds the potential for characterizing a phenotype susceptible to CRC development.\u003c/p\u003e \u003cp\u003eCompelling evidence suggests that some circulating metabolites are causally related to cancer development. Lipids and amino acids were the most abundant circulating metabolites associated with CRC risk\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Elevated levels of triglycerides, phospholipids, and cholesterol may promote cancer cell growth and proliferation by serving as an energy source and inhibiting CD8\u0026thinsp;+\u0026thinsp;T cell proliferation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Amino acids function as building blocks of proteins, precursors of various signaling molecules, and energy sources. Levels of certain amino acids, such as Alanine and Histidine, have been shown to inversely associate with the cancer stage \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In addition, Histidine concentration in blood was shown to be inversely associated with CRC risk\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, circulating amino acid levels can influence immune cell activity, potentially impacting cancer development, as amino acids are vital for the basal metabolism of immune cells, and activated immune cells require more amino acids\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings suggest that changes in circulating metabolite levels can precede CRC development. However, it remains unexplored whether LS genotype affects the circulating metabolome. Therefore, our study investigated the circulating metabolome in cancer-free LS carriers.\u003c/p\u003e \u003cp\u003eIn this study, we examined the circulating metabolome in a cohort of cancer-free LS carriers. We compared their metabolome to a control group of cancer-free non-carriers, as well as to a group of non-carriers with CRC. Our two main findings were that healthy LS carriers and CRC patients had broadly similar metabolite profiles that differed from controls. Notably, both LS and CRC participants exhibited similar patterns in circulating amino acids, ketone bodies, and global N-acetyl glycosylation (GlycA) levels. Second, we identified altered lipid metabolism in LS carriers compared with controls, which may play a role in the regulation of adiposity-related cancer risk. Overall, our study sheds light on the shared metabolic signatures of LS carriers, emphasizing the potential systemic factors at play in cancer susceptibility.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eSamples to three-group cross-sectional analysis were collected from different study cohorts; LS (n\u0026thinsp;=\u0026thinsp;80), CRC (n\u0026thinsp;=\u0026thinsp;89), Control (total n\u0026thinsp;=\u0026thinsp;103).\u003c/p\u003e \u003cp\u003eLS cohort included registered participants in the Finnish Lynch Syndrome Research Registry (LSRFi), with confirmed pathological \u003cem\u003eMMR\u003c/em\u003e gene (\u003cem\u003epath_MMR\u003c/em\u003e) variants (classes 4 and 5 by InSiGHT criteria)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Sporadic CRC patients were enrolled at the time of their initial surgical appointment for CRC at the local tertiary center responsible for the management of CRC. Healthy non-carrier control samples were acquired from the Biobank of Eastern Finland (n\u0026thinsp;=\u0026thinsp;76) and studies of the University of Jyv\u0026auml;skyl\u0026auml; (JYU) (n\u0026thinsp;=\u0026thinsp;27)\u003csup\u003e4\u003c/sup\u003e Informed consent was obtained from all participants, and ethical approval of sample collections was from: the Ethics committees of the Helsinki and Uusimaa Health Care District, Central Finland Health Care District the University of Jyv\u0026auml;skyl\u0026auml;. The study was conducted according to the guidelines of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eAll samples were taken in a fasted state. However, fasting instructions had slight differences. Control cohort participants fasted overnight and had no diet restrictions for the previous days. We do not have information about the length of the fasting of biobank samples. Samples of LS and CRC participants were taken after surveillance colonoscopy. According to colonoscopy protocol, LS and CRC participants were instructed to avoid eating high-fiber food (for example, fruit, berries, vegetables and seeds) two days before the surveillance visit, to eat only easily digestible foods (for example yogurt, porridge, potato, pasta, fish and white bread) a day before the surveillance visit and to abstain from solid food 12 hours and any liquids 2 hours before colonoscopy. From all participants, venous blood samples were taken from the antecubital vein to standard serum tubes. The samples were aliquoted and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics analysis\u003c/h2\u003e \u003cp\u003eMetabolites were analyzed with a targeted proton nuclear magnetic resonance (\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003eH-NMR) spectroscopy platform (Nightingale Health Ltd., Helsinki, Finland; biomarker quantification version 2020). The technical details of the method have been reported previously\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The platform quantifies 250 metabolite measures. Of them, metabolome-wide analyses were conducted with 171 variables representing lipoproteins and lipids, glycolysis-related metabolites as well as amino acids, ketone bodies and some other metabolites including GlycA, which is a measure of global N-acetyl glycosylation. For individual metabolite analyses, we concentrated on 65 key metabolites representing these metabolite groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics of each metabolite are reported in supplement Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the statistical analyses used in this study.\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\u003eThe statistical analyses used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoftware/package\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrincipal Coordinate analysis (PCoA) of the Euclidean distances calculated from circulating metabolite values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR version 4.0.0 or newer / \u003cem\u003eape\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePERMANOVA analysis was used to test whether cohorts' centroids/mean in the PCoA distance matrix were significantly different from each other. Beta-dispersion (PERMDISP) test was used to examine whether the variance of cohorts was significantly different. ANOSIM test was used to determine whether there is more similarity within the cohorts than between cohorts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePcoA distance matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR version 4.0.0 or newer / \u003cem\u003ehagis\u003c/em\u003e package\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHierarchical clustering and heatmapping the Euclidean distance metric and complete linkage method were used to create clusters based on similarity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe raw metabolite data was scaled column-wise to ensure that metabolite expression values were comparable across samples.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR version 4.0.0 or newer / Pheatmap package\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBox-Cox data transformation was performed to ensure normally distributed data to follow up analysis. The Box-Cox transformation with lambda parameter estimated from data for each variable separately.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR version 4.0.0 or newer / MASS-package\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquality was tested using Levene\u0026rsquo;s test, and if at least one group showed heteroscedasticity, the ANCOVA test was replaced with a generalized linear model (GLiM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBox Cox transformed data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANCOVA analysis, with covariates (age, sex, BMI), was used to evaluate whether the means of metabolite values are equal or not.\u003c/p\u003e \u003cp\u003eANCOVA was performed on metabolites that had equal variances between groups.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBox Cox transformed data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA generalized linear model (GLiM) test, with covariates (age, sex, BMI), was used to evaluate whether the means of metabolites values are equal or not.\u003c/p\u003e \u003cp\u003eGLiM test was performed on metabolites that had non equal variances between groups.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBox Cox transformed data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPSS \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFor data visualization, standardized mean differences (SMD) and SMD 95% confidence intervals were calculated and visualized in forest plot.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBox Cox transformed data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR version 4.0.0 or newer / MBESS-package\u003csup\u003e21\u003c/sup\u003e, ggforestplot-package\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive characteristics of study subjects in LS carrier, control and CRC cohorts are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive characteristics of study subjects. LS\u0026thinsp;=\u0026thinsp;\u003cem\u003epath_MMR\u003c/em\u003e carrier currently cancer-free, Control\u0026thinsp;=\u0026thinsp;Non-carrier currently cancer-free, CRC\u0026thinsp;=\u0026thinsp;Non-carrier colorectal cancer patient.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003econtrol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCRC\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN (total\u0026thinsp;=\u0026thinsp;272)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex (N(%))\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42(52.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 (52.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39(43.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38(47.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (47.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50(56.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years (mean\u0026plusmn;SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.2\u0026plusmn;13.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.7\u0026plusmn;14.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.8\u0026plusmn;9.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBody mass index, kg/m2 (mean\u0026plusmn;SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.6\u0026plusmn;5.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.6\u0026plusmn;6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.7\u0026plusmn;4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003epath_MMR\u003c/em\u003e (N(%))\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMLH1\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52(65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMSH2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13(16.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (17.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1(1.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eCirculating Metabolome level results\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eCancer-free LS carriers\u0026rsquo; circulating metabolome profile showed more similarity with CRC patients\u0026rsquo; profile than controls\u0026rsquo; \u0026minus;\u0026thinsp;1\u003c/em\u003e71 circulating metabolites were studied using NMR-based targeted analysis. The dimension reduction method PCoA and PERMANOVA test indicate that circulating metabolite profiles differed between the three groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Pairwise comparisons further showed that the metabolite profile of the LS and CRC groups differed from the control group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). However, no clear difference was found between the LS and CRC groups (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In summary, our results suggest that both CRC and LS are associated with a similar circulating metabolome signature.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePath_MMR gene variants do not show clearly differing associations with circulating metabolome \u0026ndash;\u003c/em\u003e cancer risk in LS is strongly associated with \u003cem\u003epath_MMR\u003c/em\u003e genes, where \u003cem\u003eMLH1\u003c/em\u003e is the most aggressive gene to increase cancer risk\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMLH1\u003c/em\u003e is also the primary mutation found in our Finnish LS cohort\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which is why our path_MMR carrier groups are not equally sized (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These unbalanced group sizes need to be considered when interpreting the following results. Nevertheless, we considered it important to study whether different \u003cem\u003epath_MMR\u003c/em\u003e genes have a different effect on circulating concentrations of the 171 metabolites and performed Euclidean clustering and heatmap visualization within the LS cohort (Fig.\u0026nbsp;2). No apparent clustering was detected based on \u003cem\u003epath_MMR\u003c/em\u003e genes (Fig.\u0026nbsp;2). To study specific differences between groups carrying each of the \u003cem\u003epath_MMR\u003c/em\u003e genes we excluded \u003cem\u003ePSM2\u003c/em\u003e, since we only had one carrier in the cohort. When comparing \u003cem\u003eMLH1\u003c/em\u003e, \u003cem\u003eMSH2\u003c/em\u003e and \u003cem\u003eMSH6\u003c/em\u003e carrier groups PCoA and PERMANOVA analyses did not indicate notable differences between groups (p-value\u0026thinsp;=\u0026thinsp;0.319)(supplement Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.). The significant difference between the groups' variances (PERMDISP p-value\u0026thinsp;=\u0026thinsp;0.006** and ANOSIM p-value\u0026thinsp;=\u0026thinsp;0.01**) is likely due to large differences in group sizes (see Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eCirculating metabolites-specific results\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eLipoprotein- and lipid-related alterations in LS and CRC cohorts compared to controls.\u003c/em\u003e ANCOVA or GLiM analysis was employed, with covariates (age, sex, BMI), to examine 65 key metabolites (Fig.\u0026nbsp;3, supplemental table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Analyses revealed distinct metabolic alterations within the LS cohort compared to the control cohort, particularly in relation to lipoprotein particles and their lipid content (Fig.\u0026nbsp;3). Notably, the mean total cholesterol in the LS cohort was 6.5% higher compared to the control cohort. Similarly, cholesterol bound to low-density lipoprotein (LDL) were elevated in LS compared to control, while no significant differences were observed in cholesterol bound to high-density lipoprotein (HDL) particles (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003eApolipoprotein A1 (ApoA1), a key constituent of HDL particles, displayed higher levels in the LS relative to the control. Related to this, LS cohort had higher amounts of total HDL particles but when particle sizes were inspected separately, only the amount of small and medium size HDL particles differed compared to controls (Fig.\u0026nbsp;3). Total LDL particle consentration was not altered, but small and medium size LDL particle levels were elevated in the LS cohort compared to control. Very low-density lipoprotein (VLDL) displayed an enlargement in average particle size in the LS relative to the control cohort. Furthermore, the LS compared to the control cohort exhibited heightened levels of triglycerides specifically localized within VLDL particles (Fig.\u0026nbsp;3). Elevated concentrations of total cholines, phosphatidylcholines, and phosphoglycerides were detected in the LS cohort when compared to the control group (Fig.\u0026nbsp;3). In contrast, the CRC cohort did not exhibit any significant alterations in lipoprotein and lipid metabolism-related metabolites when compared to the control cohort (Fig.\u0026nbsp;3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLipoprotein and lipid- levels vary between different path_MMR carriers\u003c/em\u003e. ANCOVA and GLiM analyses, with covariates (age, sex, BMI) were used to determine whether different \u003cem\u003epath_MMR\u003c/em\u003e carriers express different levels of 65 selected non-redundant key metabolites (supplemental table 3). A finding was that MLH1 carriers had the highest circulating cholesterol levels (mean of total cholesterol, \u003cem\u003eMLH1\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.44 mmol/l, \u003cem\u003eMSH2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.81 mmol/l and \u003cem\u003eMSH6\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.72 mmol/l) and \u003cem\u003eMLH1\u003c/em\u003e carriers had significantly higher cholesterol levels than \u003cem\u003eMSH6\u003c/em\u003e carriers (Fig.\u0026nbsp;4E). Of the cholesterol transportation particles, the amounts of very low-density lipoprotein (VLDL) (Fig.\u0026nbsp;4B) and LDL (Fig.\u0026nbsp;4C) and the concentration of ApoB, main lipoprotein in these particles (Fig.\u0026nbsp;4A), were highest in \u003cem\u003eMLH1\u003c/em\u003e-cohort, and significantly lower in \u003cem\u003eMSH6\u003c/em\u003e-cohort when compared to \u003cem\u003eMLH1\u003c/em\u003e-cohort (Fig.\u0026nbsp;4A, B, C). VLDL and LDL-bound cholesterol levels were also highest in the \u003cem\u003eMLH1\u003c/em\u003e cohort (Fig.\u0026nbsp;4F, G). Additionally, fatty acids (Fig.\u0026nbsp;4I), LDL-bound triglycerides (Fig.\u0026nbsp;4K) and phospholipids were upregulated in the \u003cem\u003eMLH1\u003c/em\u003e cohort (Fig.\u0026nbsp;4D, H, L). In conclusion, the levels of most circulating metabolites exhibited similarity among different \u003cem\u003epath-MMR\u003c/em\u003e carriers (supplement table 3). However, \u003cem\u003eMLH1\u003c/em\u003e carriers demonstrated higher mean levels of lipoprotein and lipid-related metabolites when compared to \u003cem\u003eMSH6\u003c/em\u003e carriers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCirculating amino acids, ketone bodies and GlycA show similarity between LS and CRC cohorts\u003c/em\u003e. In the LS and CRC cohort, glutamine levels were elevated, whereas all other studied amino acids; alanine, histidine, isoleucine, phenylalanine, tyrosine, valine, and total branced-chain amino acids (BCAAs) were curtailed compared to the control group (Fig.\u0026nbsp;3). GlycA levels were higher in LS and CRC cohorts when compared with the control group (Fig.\u0026nbsp;3). When examining ketogenesis products, both CRC and LS cohorts had altered ketone body expression levels compared to the control cohort (Fig.\u0026nbsp;3). In summary, these results revealed that the LS cohort shows similarity with CRC cohort regarding circulating amino acids, ketone bodies and inflammation marker GlycA signatures. There was no significant difference in these metabolite levels when comparing different \u003cem\u003epath_MMR\u003c/em\u003e carrier cohorts to each other (supplementary table 3).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the circulating metabolome signature of 80 cancer-free carriers of LS and compared it to two distinct groups: a cancer-free non-carrier control cohort and a cohort of individuals with sporadic CRC. Our findings showed that the metabolomic signatures of LS carriers more closely resembled those in the CRC cohort than in the control cohort. No significant omics-level differences were found within LS carriers based on different \u003cem\u003epath_MMR\u003c/em\u003e gene variants. However, our individual metabolite level inspections revealed notably higher total cholesterol levels and other significant alterations related to lipoprotein \u0026ndash; and lipid metabolism in LS carriers compared to control, which was not evident in the CRC-control comparison. Furthermore, we also identified within LS cohort differences; \u003cem\u003epath_MLH1\u003c/em\u003e carriers showing the highest levels of specific lipid and lipoprotein metabolite. Similar alterations in lipoprotein- and lipid metabolism were not detected in individuals with sporadic CRC. Additionally, both LS and CRC cohorts exhibited distinct yet parallel alterations in the circulating amino acids, ketone bodies and GlycA levels.\u003c/p\u003e \u003cp\u003eThe circulating metabolome is associated with cancer risk\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Germline mutations in DNA repair genes elevate cancer risk by imposing a high mutation load on fast-proliferating epithelial tissues. However, there is a limited understanding of the interaction between germline mutations in the DNA repair system and systemic metabolomics. DNA repair gene \u003cem\u003eBRCA1\u003c/em\u003e has been shown to impact cellular metabolism\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Additionally, women with this breast cancer predisposition gene exhibit an altered circulating metabolome signature\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In the context of CRC, \u003cem\u003eMLH1\u003c/em\u003e deficiency in the CRC cell model has been found to disrupt mitochondrial metabolism\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our findings revealed that LS carriers had a significantly altered circulating metabolome signature compared to the control cohort. Interestingly, this signature closely resembled the circulating metabolome signature observed in sporadic CRC patients. These results, together with previous findings related to \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003epath_MMR\u003c/em\u003e, suggest that these cancer-predisposing germline mutations not only increase the mutation load in epithelial cells but also impact systemic metabolomic status.\u003c/p\u003e \u003cp\u003eThe association between cancer risk and lipoprotein and lipid levels has been extensively studied, but the results remain controversial. A recent systemic meta-analysis showed that triglycerides and total cholesterol positively correlated with CRC incident rate, while high levels of HDL cholesterol negatively correlated with CRC incidences \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This analysis did not show an association between LDL cholesterol and CRC risk. However, some studies indicate a U-shaped association, suggesting that intermediate LDL cholesterol levels are related to the lowest cancer risk\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In the LS carriers with type two diabetes, triglyceride level was not, but cholesterol level was associated with higher CRC risk\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. While there is no clear consensus on whether lipoprotein and lipid metabolism associates with CRC risk or not, it is evident that lipoprotein and lipids play a critical role as functional molecules in various carcinogenesis-related processes. Dysregulation of lipid metabolism represents an important metabolic alteration in cancer. Lipoproteins and lipids act as energy producers, signaling molecules and source material for the biogenesis of cell membranes\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Cholesterol is a key component of cell membrane lipid rafts, which play a vital role in cancer signaling. It can directly activate oncogenic signaling pathways\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Moreover, cholesterol and lipoproteins are essential in triggering immune responses\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our findings revealed that in comparison to the control cohort cancer-free carriers of LS exhibited higher cholesterol levels and alterations in the distribution of cholesterol-transporting lipoprotein particles. \u003cem\u003eMLH1\u003c/em\u003e carriers with the highest cancer risk had the highest cholesterol levels. The elevated lipoprotein and lipid levels in LS carriers could be the response to the high levels of immune activity known to be present in LS. It is possible that increased lipoprotein and lipid levels support immune cell functions, aiding in the elimination of premalignant cells. On the other hand, elevated levels might also provide growth advantages to malignant cells by boosting oncogenic signaling and overall cell proliferation. The exact role of elevated lipoprotein and lipid levels in LS carcinogenesis, whether protective or oncogenic, remains to be thoroughly investigated in future studies.\u003c/p\u003e \u003cp\u003eAmino acids and ketone bodies have links to cancer progression. Amino acids are necessary building blocks for cancer cell protein synthesis, and the cancer cell ketone body\u0026rsquo;s metabolism has been shown to be disrupted\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Interestingly, the circulating amino acid histidine has been found to have an inverse association with CRC risk\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Alterations in circulating amino acid levels have been reported in many cancers. A decrease in circulating amino acids is often suggested to be caused by cachexia. However, this may not be the sole reason, as similar decreases have been observed in cancer patients without weight loss or cachexia\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The reduction in amino acids could potentially be attributed to the high demand for amino acids by cancer cells\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Our results showed that LS carriers compared to the control group exhibited a similar decrease in circulating amino acids. This suggests that amino acids might be consumed by non-tumorous cells, for example, immune cells, in LS carriers.\u003c/p\u003e \u003cp\u003eKetone bodies serve as an energy source and can be involved in various metabolic pathways. Produced in the liver, ketone bodies are transported to other tissues when needed. Due to impaired ketone metabolism in cancer cells, most of cancer cells cannot utilize ketone bodies as an energy source, and ketone bodies can even impose reactive oxygen species production, inhibiting cancer cell growth\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Thus, ketone bodies possess anti-cancer properties\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. However, the reasons behind LS carriers expressing a similar circulating ketone bodies\u0026rsquo; profile as CRC patients remain unclear.\u003c/p\u003e \u003cp\u003eInflammation and its biomarkers have been strongly associated with cancer risk, progression, and survival \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. GlycA, a novel inflammation marker has been linked to CRC incidence and mortality \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Elevated GlycA levels indicate both acute and chronic inflammation, serving as predictive markers for overall mortality. These levels remain persistently elevated and stable for an extended period, spanning up to a decade\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our results showed increased GlycA levels in the CRC cohort, and in addition to that LS carriers displayed a modest yet statistically significant increase in GlycA levels. This rise in GlycA level could be attributed to heightened immune activity in LS.\u003c/p\u003e \u003cp\u003eOur study provides valuable insights into the circulating metabolome of LS. It is important to acknowledge certain limitations attached to our study. For instance, sample size, although comprehensive, is still relatively small, which may have limited detecting subtle differences in certain metabolomic parameters. More in-depth discussions of limitations and strengths of this study are presented in the supplemental file.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results demonstrate that the oncogenic stress imposed by \u003cem\u003epath_MMR\u003c/em\u003e genes is reflected at the systemic metabolomic level. These constitutional alterations in energy metabolism may play a significant role in the etiology of LS-related cancers. The findings of our study raise several intriguing questions regarding the interaction between systemic metabolism and pre-carcinogenic processes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eLynch Syndrome (LS), Colorectal cancer (CRC), DNA mismatch repair gene (MMR), Pathogenic DNA mismatch repair gene (path_MMR), global N-acetyl glycosylation (GlycA), very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), high-density lipoprotein (HDL), Principal coordinate analysis (PCoA), generalized linear model (GLiM).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by: T.J.-the EU Marie Skłodowska-Curie Actions [Grant agreement ID: 101026706], S.W.- the Finnish Cultural Foundation (grant #00211177), T.T.S.-research grants from Jane and Aatos Erkko Foundation, Sigrid Juselius Foundation, Finnish Medical Foundation, Emil Aaltonen Foundation, Cancer Foundation Finland, Relander Foundation, and state research funding. E.K.L Academy of Finland #335249 and #330281 and E.K.L: University of Jyv\u0026auml;skyl\u0026auml;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.T.S. reports a consultation fee from Amgen Finland and is a co-owner and CEO of Healthfund Finland Ltd, and the Clinical Advisory Board of LS Cancer Diag Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Central Finland Health Care District (KSSHP 3/2016).\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants, and ethical approval of sample collections was from: the Ethics committees of the Helsinki and Uusimaa Health Care District HUS/155/2021), Central Finland Health Care District the University of Jyv\u0026auml;skyl\u0026auml; (KSSHP D# 1U/2018, 1/2019 and KSSHP 3/2016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.J. and E.K.L. contributed to the conception and design of the work. T.T.S. organized clinical sample collection. S.W. and E.K.L. organized the JYU control sample collection. T.T.S. and J-P.M. offered medical expertise and guidance. J.K. and M.K. took part in the data analysis and visualization. T.J. did the main part of the data analysis and drafted the manuscript. All authors critically revised the manuscript. All approved final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all study participants from LS, control and CRC cohorts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWin AK, Dowty JG, Reece JC, Lee G, Templeton AS, Plazzer JP \u003cem\u003eet al.\u003c/em\u003e Variation in the risk of colorectal cancer in families with Lynch syndrome: a retrospective cohort study. \u003cem\u003eLancet Oncol\u003c/em\u003e 2021; \u003cstrong\u003e22\u003c/strong\u003e: 1014\u0026ndash;1022.\u003c/li\u003e\n\u003cli\u003eDominguez-Valentin M, Sampson JR, Sepp\u0026auml;l\u0026auml; TT, ten Broeke SW, Plazzer J-P, Nakken S \u003cem\u003eet al.\u003c/em\u003e Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database. \u003cem\u003eGenetics in Medicine\u003c/em\u003e 2020; \u003cstrong\u003e22\u003c/strong\u003e: 15\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eSiev\u0026auml;nen T, T\u0026ouml;rm\u0026auml;kangas T, Laakkonen EK, Mecklin JP, Pylv\u0026auml;n\u0026auml;inen K, Sepp\u0026auml;l\u0026auml; TT \u003cem\u003eet al.\u003c/em\u003e Body weight, physical activity, and risk of cancer in lynch syndrome. \u003cem\u003eCancers (Basel)\u003c/em\u003e 2021; \u003cstrong\u003e13\u003c/strong\u003e. doi:10.3390/cancers13081849.\u003c/li\u003e\n\u003cli\u003eKarppinen JE, T\u0026ouml;rm\u0026auml;kangas T, Kujala UM, Sipil\u0026auml; S, Laukkanen J, Aukee P \u003cem\u003eet al.\u003c/em\u003e Menopause modulates the circulating metabolome: evidence from a prospective cohort study . \u003cem\u003eEur J Prev Cardiol\u003c/em\u003e 2022; \u003cstrong\u003e29\u003c/strong\u003e: 1448\u0026ndash;1459.\u003c/li\u003e\n\u003cli\u003eChu X, Jaeger M, Beumer J, Bakker OB, Aguirre-Gamboa R, Oosting M \u003cem\u003eet al.\u003c/em\u003e Integration of metabolomics, genomics, and immune phenotypes reveals the causal roles of metabolites in disease. \u003cem\u003eGenome Biol\u003c/em\u003e 2021; \u003cstrong\u003e22\u003c/strong\u003e: 198.\u003c/li\u003e\n\u003cli\u003eYao X, Tian Z. 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Genetic background increases cancer risk in individuals with Lynch syndrome; however, not all carriers develop cancer. Various lifestyle factors can influence Lynch syndrome cancer risk, and lifestyle choices actively shape systemic metabolism, with circulating metabolites potentially serving as the mechanical link between lifestyle and cancer risk. This study aims to characterize the circulating metabolome of Lynch syndrome carriers, shedding light on the energy metabolism status in this cancer predisposition syndrome.\u003c/p\u003e \u003cp\u003eThis study consists of a three-group cross-sectional analysis to compare the circulating metabolome of cancer-free Lynch syndrome carriers, sporadic colorectal cancer (CRC) patients, and healthy non-carrier controls. We detected elevated levels of circulating cholesterol, lipids, and lipoproteins in LS carriers. Furthermore, we unveiled that Lynch syndrome carriers and CRC patients displayed similar alterations compared to healthy non-carriers in circulating amino acid and ketone body profiles. Both groups exhibited increased systemic inflammation based on higher levels of global N-acetyl glycosylation (GlycA). Overall, a remarkable similarity between the circulating metabolome of healthy Lynch syndrome carriers and CRC patients suggests shared metabolic perturbations that may contribute to Lynch syndrome cancer susceptibility.\u003c/p\u003e \u003cp\u003eThis study provides valuable insights into systemic metabolic landscape of Lynch syndrome individuals. The findings hint at shared metabolic patterns between cancer-free Lynch syndrome carriers and CRC patients.\u003c/p\u003e","manuscriptTitle":"Circulating metabolome landscape in Lynch Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-08 15:59:22","doi":"10.21203/rs.3.rs-3561844/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2023-12-07T08:30:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-12-01T13:24:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5bc1f8d2-2731-4891-84c5-659e91d9ea8f","date":"2023-11-30T15:48:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3d4b9898-4e66-4cb4-a891-3d59c7510c71","date":"2023-11-30T10:10:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-11-27T09:50:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-06T13:13:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-06T13:13:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer \u0026 Metabolism","date":"2023-11-05T10:16:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmet","sideBox":"Learn more about [Cancer \u0026 Metabolism](http://cancerandmetabolism.biomedcentral.com/)","snPcode":"40170","submissionUrl":"https://submission.nature.com/new-submission/40170/3","title":"Cancer \u0026 Metabolism","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4649f8b0-bfb1-48d2-ae7f-a20196aa7ec0","owner":[],"postedDate":"November 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-02-12T15:04:56+00:00","versionOfRecord":{"articleIdentity":"rs-3561844","link":"https://doi.org/10.1186/s40170-024-00331-9","journal":{"identity":"cancer-and-metabolism","isVorOnly":false,"title":"Cancer \u0026 Metabolism"},"publishedOn":"2024-02-05 15:01:16","publishedOnDateReadable":"February 5th, 2024"},"versionCreatedAt":"2023-11-08 15:59:22","video":"","vorDoi":"10.1186/s40170-024-00331-9","vorDoiUrl":"https://doi.org/10.1186/s40170-024-00331-9","workflowStages":[]},"version":"v1","identity":"rs-3561844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3561844","identity":"rs-3561844","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}