Comorbidities confound metabolomics studies of human disease

Comorbidities confound metabolomics studies of human disease | 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 Comorbidities confound metabolomics studies of human disease Madis Jaagura, Jaanika Kronberg, Anu Reigo, Oliver Aasmets, Tiit Nikopensius, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4419599/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC-MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the most prevalent chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (96%, 226/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions. Health sciences/Biomarkers/Predictive markers Health sciences/Risk factors Biological sciences/Microbiology/Communities/Microbiome Health sciences/Diseases/Cardiovascular diseases Biological sciences/Biochemistry/Metabolomics comorbidities metabolomics chronic disease risk factors electronic health records biobank Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The onset and progression of chronic diseases are influenced by a combination of factors, including genetics, environment, lifestyle, and the microbiome[ 1 – 3 ]. The etiology of chronic diseases extends beyond isolated conditions, as many chronic conditions share a well-established set of common clinical and lifestyle risk factors[ 4 , 5 ]. Perturbation of common pathways suggests a significant level of connectivity, and understanding how these associations relate to coexistence of chronic conditions is paramount. One out of three patients suffer from two or more chronic conditions, termed multimorbidity[ 6 , 7 ]. This is exemplified in individuals with gout, where 74% experience hypertension, and 71% exhibit stage 2 chronic kidney disease[ 8 ]. Therefore, it is essential to distinguish disease-specific biomarkers from those which reflect the progression of other concurrent diseases, termed comorbidities[ 9 ]. For accurate profiling of disease patterns, in-depth health information encompassing each patients' medical history is needed. Recent decades have introduced an era of systematic data collection by the establishment of large population-based biobanks, which have been fundamental for the identification of risk factors for chronic diseases[ 10 ]. These biobanks actively recruit participants from the general population and accumulate large sample collections, characterized by comprehensive health data from questionnaires and/or electronic health records (EHR), along with multiple omics data layers. The rich health data facilitates the identification of individuals with diverse disease conditions along with the history of comorbidities and hence becomes indispensable for disentangling risk factors of chronic diseases[ 11 ]. Biobanks also serve as invaluable resources for epidemiological and clinical studies, wherein stored blood samples can be retrospectively analyzed using advanced analytics methods, including high-throughput metabolomics. Nevertheless, the limited availability of such large cohorts with available follow-up data has resulted in only a few studies aimed at identifying metabolic risk factors across common chronic conditions[ 12 – 15 ]. While these studies have revealed similarities in association signatures across diseases with distinct pathophysiologies, a comprehensive understanding of disease-specific and shared risk factors remains elusive. Notably, among the mentioned studies, only one has incorporated high-sensitivity mass spectrometry (MS) to determine metabolite profiles for risk estimation[ 12 ]. MS presents a more valuable option as it facilitates the measurement of gut-derived and modulated biomolecules, including bile acids, short-chain fatty acids, branched-chain amino acids, methylamines, tryptophan, and indole derivatives[ 16 , 17 ]. These biomolecules are modified in metabolic disorders and may serve as significant risk factors for the onset of chronic conditions[ 12 , 18 ]. In this study, we investigated the onset of 14 non-communicable diseases (NCD) in 991 Estonian Biobank (EstBB) participants and their associations with baseline levels of 1,375 plasma metabolites measured with untargeted MS profiling. The primary aim of this study was to identify and distinguish disease-specific and common metabolites which are contributing to the risk of chronic diseases. For this, we used the EHR information to uncover comorbidity profiles of all cases and specific disease-naive controls selected from a population cohort. Results Study overview and data description We studied a well-phenotyped cohort of 991 individuals from the Estonian Biobank with available untargeted plasma metabolite data generated by Metabolon HD4 platform. Following the exclusion of individuals with missing data (see Methods), the analysis comprised 919 participants (63.1% females), with an average follow-up time of 11.0 years (SD 4.4 years). To evaluate the effect of the metabolite levels on the risk of developing chronic diseases, 14 common conditions with more than 40 incident cases were evaluated (Fig. 1 , Supplementary Table S1 ). The mean age and body mass index at sampling was 46.7 years (SD 16.8) and 26.8 kg/m2 (SD 5.7), respectively. Other characteristics of the study population are listed in Supplementary Table S2 . Metabolomics analysis was performed on plasma samples collected between 2002 and 2018. Subsequently, following quality checks and the exclusion of infrequent and drug-related metabolites (see Methods), association analysis was conducted on 1375 metabolites (Supplementary Table S3 ). To evaluate the impact of comorbidities on risk estimates, we performed a secondary analysis by adjusting for comorbidities (Fig. 1 a). Additionally, all accessible metabolites were cross-referenced with recent literature data to determine their association with the gut microbiome. Plasma metabolites predict the onset of various NCDs To investigate the role of plasma metabolites in the incidence of 14 NCDs, Cox proportional hazards models adjusted for age, sex, body mass index (BMI), and smoking status were constructed. In total, we detected 254 significant associations (FDR < 0.1) with 13 incident diseases (Fig. 2 a, Supplementary Fig. S1 ). A total of 17% (235/1375) metabolites were significantly (FDR < 0.1) linked to at least one disease (Fig. 2 b, Supplementary Fig. S1 , Supplementary Table S4 ). The largest proportion of the associated metabolites were linked with risk of developing gout (n = 118). A substantial number of metabolites showed an association with lipidemias (n = 43) and type 2 diabetes (T2D, n = 31). At the same time, primary hypertension (n = 14) and several cardiac conditions showed a lower number of associations: atrial fibrillation and flutter (AFF, n = 10), heart failure (n = 9), chronic ischemic heart disease (CIHD, n = 7), other cardiac arrhythmias (n = 5), hypertensive heart disease with heart failure (HHD with HF, n = 3). Similarly, only a few associations were detected for iron deficiency anemia (n = 8), other anxiety disorders (n = 3), depressive episode (n = 2), and asthma (n = 1). The significant predictors predominantly comprised lipids (n = 92), amino acids (n = 55), and unidentified metabolites (n = 39) (Fig. 2 b. Supplementary Table S4 online). Consistent with previous studies, we observed significant associations between uric acid and the increased risk of gout (hazard ratio, HR 7), as well as cholesterol and the increased risk of lipidemias (HR 2.1) (Fig. 2 d). Elevated uric acid levels, indicative of hyperuricemia, are a known risk factor of gout, suggesting that both metabolites may mirror metabolic changes in the pre-disease state[ 19 ]. The five strongest associations with each incident condition are depicted in Supplementary Fig. S2 online. Notably, among the best predictors of gout and lipidemias, strong correlations were observed within incident cases of respective conditions (Supplementary Fig. S2 , Supplementary Table S5 online). On the contrary, the top predictors for T2D exhibited relatively low levels of correlation indicating potentially higher heterogeneity among these metabolites and related pathways. The majority of identified interactions indicated an increased risk (Fig. 2 c, Supplementary Fig. S1 online). Among the top ten associations with highest HR, nine were specifically associated with the development of incident gout (Fig. 2 d). In contrast, when examining associations with negative HR indicating diminished risk with higher metabolite values, the situation was more variable - five out of ten of the most significant predictors were identified in relation to incident T2D. NCDs have partially overlapping metabolic predictors We investigated the extent of unique and shared metabolic predictors for diseases. Most of the reported predictors (92%, 217/235) were uniquely associated with the risk of a single disease (Suppl Fig. S3 online). Shared associations were detected mainly between two chronic diseases (17 predictors) and no common predictors were seen between more than 3 diseases (Fig. 3 ).The highest number of shared associations appeared between gout and T2D (n = 6), gout and AFF (n = 5), and gout and lipidemias (n = 3). For example, higher level of mannonate was associated with increased risk of incident gout (HR 1.6), T2D (HR 2), and HHD with HF (HR 1.6), corroborating a previous study, which demonstrated association of higher mannonate levels with severe insulin-deficiency[ 20 ]. An unidentified metabolite X-24588 was associated with incident gout (HR 1.9) and T2D (HR 1.8). This metabolite has been previously associated with hepatic triglyceride content[ 21 ]. Disease-specific associations with NCDs are more robust to adjusting for comorbidities While comorbidities are commonly overlooked during control selection, we next aimed to integrate them into our analysis. To achieve this, we employed principal component analysis based on Hamming distance between comorbidity presence/absence profiles of the study subjects. In the secondary analysis, we additionally adjusted Cox proportional hazards models by the first two principal components. This adjustment aimed to address the differences in the disease burden between incident cases and controls, thereby strengthening the reliability of our findings. For instance, 56% of incident cases of AFF in the EstBB cohort were already diagnosed with concurrent primary hypertension, whereas only 22% of randomly selected participants without incident AFF exhibited primary hypertension at the time of sampling. In the sensitivity analysis, a lower total number of significant associations (198 vs 254, 73% overlap) and unique predictors (188 vs 235, 75% overlap) were observed compared to analysis conducted without adjusting for comorbidities (Fig. 4 a, Supplementary Fig. S4 and S5, Supplementary Table S4 online). Reduction in the number of associations was more evident in the case of gout (84 vs 118), lipidemias (36 vs 43), and T2D (22 vs 31) (Fig. 4 b). The incident cases for these diseases were inflated by comorbid prevalent diagnoses, potentially explaining the loss of signal in the secondary analysis (Supplementary Table S6 online). Overall, employing comorbidities as covariates led to reduction in both the total number of disease-specific (178 vs 217) and shared risk predictors (10 vs 18) (Fig. 4 c). The more pronounced decrease among common predictors could suggest that these associations were confounded by the presence of comorbid conditions (Supplementary Fig. S6 , Supplementary Table S6 online). In total, 166 disease-specific associations were consistently identified, resulting in a 76% overlap with previously identified metabolites from the primary analysis. This suggests the existence of a more robust set of predictors exclusively associated with each specific medical condition, as opposed to a limited number of widespread incident risk signals shared among multiple chronic conditions. A substantial portion of risk factors for NCDs are linked with microbiota The growing evidence of the microbial activity on metabolites prompted us to pay attention to disease-associated metabolites with pre-established significant associations to the gut microbiome. We extracted and aggregated the data from four recent publications which reported microbiota-explained variance of individual serum or plasma metabolites[ 22 – 25 ]. This analysis revealed notable microbiome contributions for 96% (226/235) of significant and 85% (970/1140) of non-significant predictors (see Supplementary Table S4 online). Within the prominent microbiome-associated metabolites (with any reported R2 value exceeding 0.1) significant associations were shown for T2D, lipidemias, primary hypertension, HHD with HF, AFF, and gout (Fig. 5 , Supplementary Fig. S7 online). These associations were predominantly exclusive to a single disease and included a high number of unidentified metabolites. Notably, 3-phenylpropionate (hydrocinnamate) and hyocholate were associated with reduced risk of AFF and gout, respectively, while well-established cardiometabolic markers 1,5-anhydroglucitol (1,5-AG) and metabolonic lactone sulfate were linked to decreased and increased risk of incident T2D, respectively[ 26 – 28 ]. Among microbial metabolites originating from amino acids, indolepropionate displayed a reduced risk of incident lipidemias, while 3-indoxyl sulfate and 6-hydroxyindole sulfate demonstrated a lower risk of incident AFF. However, no associations were found between trimethylamine N-oxide (TMAO), phenylacetylglutamine, or cinnamoylglycine and disease risk, contrasting previous findings[ 29 – 32 ]. Further discussion is available in Supplementary Discussion section. Discussion In this study, we conducted an untargeted metabolomics analysis of plasma to identify both disease-specific and shared risk factors across 14 chronic conditions in the EstBB subcohort of 991 individuals. We demonstrated the value of well-phenotyped population-based biobank data for identifying predictive metabolic markers. Notably, we observed predominantly disease-specific signals rather than a widespread commonality among multiple chronic diseases. Nevertheless, in terms of common signals between studied chronic diseases, risk factors for gout, shared with T2D, AFF, and lipidemias stood out prominently, suggesting potential metabolic interactions between these conditions. We observed a decrease in the shared predictors when adjusting for prevalent comorbidities. Additionally, we showed that a high proportion of identified predictors had prior association with gut microbial composition. Importantly, our findings imply that comorbidities may contribute to the shared incident risk signature observed across chronic conditions. The highest number of incident metabolic risk associations was identified for gout, potentially due to its high comorbidity rate and its role as a risk factor for other conditions. Gout is an arthritic condition induced by hyperuricemia leading to urate deposits in the tissues. Previous studies on gout have shown associations with metabolic syndrome and chronic kidney disease[ 33 , 34 ]. We are not aware of any untargeted metabolomics studies investigating risk factors of gout. Nevertheless, previous research on prevalent gout has established connections with altered amino acid levels, perturbations in purine, glycerophospholipid, sphingolipid, and carbohydrate metabolism[ 35 ]. These findings were also partially replicated in our study. For instance, a substantial number of N -acetylated amino acids (n = 15) were unequivocally associated with an increased risk of gout. These protein degradation products have been linked to various incident chronic diseases[ 12 ]. Notably, among these amino acids, N -acetylalanine showed the highest risk for incident gout and has previously been associated with an elevated risk of renal disease, heart failure, and mortality, as well as reduced glomerular performance[ 12 , 36 ]. The direct effect of N -acetylalanine on renal disease, heart failure, and mortality has been shown to be fully and partially mitigated by creatinine and uric acid, respectively[ 12 ]. Moreover, within incident gout cases, N -acetylated amino acids were highly correlated with levels of creatinine - a well-known marker of kidney function. Among carbohydrate predictors, Pietzner et al., demonstrated a similar mitigation effect by creatinine and uric acid for N -acetylneuraminate, N -acetylglucosamine/ N -acetylgalactosamine, arabitol/xylitol, and erythronate, all of which were uniquely associated with an increased risk of gout in our study. This suggests connectivity between loss of kidney function and the development of gout. While our analysis accounted for the prevalence of 13 other conditions for each incident condition, further adjustments for factors such as markers of renal function (e.g., eGFR) might be necessary for a more detailed understanding of chronic disease risk in a multimorbidity setup. Within the studied conditions, we observed a limited (8%) concurrence of metabolite incident risk factors. In contrast, Pietzner et al., reported a 65.5% overlap for metabolite predictors among 27 noncommunicable diseases, including, ten cancer types when data was sourced from hospitalization and cancer registry data[ 12 ]. This represents a crucial distinction from our study, as we not only obtained data from the aforementioned registries but also integrated EHR data from primary care and other relevant registries. For example, MacRae et al., suggested using EHRs from various registries for classification of clinical data as they reported higher age of onset of multimorbidity within the identical patient cohort when relying on information derived from hospitalizations compared to data obtained from primary care sources[ 37 ]. This suggests that relying solely on hospitalization data might result in inaccurate estimation of comorbidities, likely influencing findings of disease risks and reported interconnectivity among chronic diseases. We noticed that, for most of the studied conditions, the prevalence of comorbidities was substantially higher in incident cases compared to controls. In response, we aimed to enhance the analysis by including baseline comorbidities information as additional covariates. This aligns with a recent study emphasizing the need for distinguishing disease-specific changes from confounders from pre- and comorbidities[ 9 ]. More specifically, Fromentin et al., employed a design that incorporated not only healthy and clinically ill individuals but also subjects with dysmetabolic morbidities, enabling the comparison of metabolic signatures across various disease states and clinical stages. Similarly, our approach aimed to disentangle condition-specific effects from the multimorbidity signal. Adjustment for comorbidities resulted in a reduction in both disease-specific and shared predictors, with the most pronounced impact observed in conditions that initially exhibited the highest number of associations, namely, gout, lipidemias, and T2D. Therefore, associations initially thought to be common might be attributed to the presence of shared comorbid conditions rather than being independent associations across various diagnoses. For future studies, a more thorough consideration of distinct comorbidity profiles could enhance the detection of risk factors[ 38 ]. We also propose that utilizing registry-based electronic health record (EHR) data could potentially be expanded to specifically select subjects at various stages of disease progression, each with their respective comorbidity profiles, and corresponding selection of appropriate controls. We also demonstrated predominantly disease-specific associations among metabolites linked to the microbiome in the external studies we reviewed. For example, indolepropionate and 3-phenylpropionate were exclusively associated with reduced risk of lipidemias and AFF, respectively. Both metabolites have been associated with reduced chronic disease risk[ 12 ]. Also, in the same study by Pietzner et al., levels of these metabolites were not significantly mediated by any of the available routine clinical parameters, including renal markers. Dekkers et al., showed that several Eubacteriales sp. and more specifically, Faecalibacterium prausnitzii species are positively associated with aforementioned metabolites[ 25 ]. In addition, multiple associations of microbially produced indole-derived metabolites and reduced risk of chronic diseases were observed. Contrastingly, previous studies have linked indoxyl sulfate with further progression of chronic kidney disease and cardiovascular disease[ 39 ]. Therefore, it could be hypothesized that in individuals with normal renal function, maintaining optimal levels of uremic toxins could protect against cardiovascular issues. Previous research indicates that unidentified compounds contribute significantly to the variability in gut microbial profiles among individuals. Importantly, also in our study, a noteworthy proportion of these metabolites demonstrated associations with an increased or decreased risk of chronic diseases. It is worth noting that our results may warrant reevaluation, once these unknown metabolites have been identified and their role elucidated. It is also important to consider that the assessment of the status of microbiome-metabolite associations was based on previous research which analyzed the explained variance of blood metabolites. However, the variance explained of the fecal metabolome is much higher than that of the blood metabolome[ 40 ]. Furthermore, a recent study reported markedly superior accuracy in predicting the levels of fecal metabolites from the taxonomic profiles compared to predicting blood metabolites[ 41 ]. Consequently, the low levels of explained variance of specific blood metabolites should not be interpreted as an absence of a connection to gut microbiota. This study also emphasized that, compared to blood, there are more robust associations between fecal metabolites and prevalent cardiometabolic diseases, suggesting that fecal levels could potentially serve as a better proxy for identifying risk factors for chronic diseases modulated by the gut microbiota. Despite this, our results indicate that plasma levels of microbiome-derived metabolites serve as a proxy for incident chronic disease. Our study is set apart from prior research on the simultaneous investigation of metabolite incident disease risk factors by multiple aspects. First, utilization of extensive registry data distinguishes our approach from studies that depend on self-reported or single registry-based data, enhancing the robustness of our findings, and by providing less biased and more objective/standardized diagnosis status of multiple chronic conditions. Second, the inclusion of a wide range of frequently occurring chronic disorders, from cardiac and metabolic conditions to mood disorders, contributes to a comprehensive exploration of risk factors, identifying significant associations for all investigated conditions. Third, alongside the conventional analysis, we adjust risk predictions for comorbidities. This results in a more nuanced evaluation of risk factors specific to diseases, as well as those shared among them. Notably, the potential confounding effects arising from comorbidities might not have been comprehensively addressed in previous studies[ 12 ]. Last, we demonstrate a high number of microbially-associated metabolites among the significant incident disease predictors. This was achieved by integrating recently published data on the explained variance of metabolite levels attributed to the gut microbiome. Through this approach, we were able to link previously established microbiome-metabolite associations to a large proportion of the significant predictors. This investigation has certain limitations that warrant consideration. Crucially, our study encounters constraints in terms of statistical power due to small sample size in the condition-specific incident case groups. Moreover, the absence of a validation cohort could affect the generalizability of our findings. However, the specific selection of diverse conditions would require extensive collaboration with partner institutions, potentially limited by the wide scope of this study. In addition, we opted not to employ any additional inclusion or exclusion criteria specific to any particular disease. While a single ICD-10-specific definition of chronic conditions might impose limitations, it does provide a standardized and plain approach that facilitates ease of assessment and replication for broad selection of diseases. Finally, we did not account for the confounding effects of treatment or medication intakes. Conclusion In conclusion, our study presents a unique contribution to the field by utilizing extensive registry data for exploration of a diverse array of chronic diseases. While acknowledging certain limitations, our research provides valuable insights into understanding the microbial connection and specificity of metabolic predictors for incident chronic diseases. Methods Sample description In this study, we utilized retrospective and prospective data from well-characterized individuals in the Estonian Biobank (EstBB). Established in 2000, the EstBB encompasses over 210,000 adults (aged 18–93) across Estonia and maintains updated electronic health records (EHR) through regular linkages to primary care, hospital databases, and national registries, including the Cancer Registry and Causes of Death Registry, in addition to the national health insurance fund[ 11 , 42 ]. Disease and condition records were coded using International Classification of Diseases, 10th revision (ICD-10). The study cohort comprised 991 individuals who joined the EstBB between 2002 and 2019. During recruitment, participants provided venous blood samples and completed extensive questionnaires covering health-related topics such as lifestyle, diet, and pre-existing ICD-10 coded clinical diagnoses[ 11 ]. 919 individuals with complete covariate information (age, sex, BMI, smoking status) were included in the subsequent analyses. Chronic conditions were identified and aggregated based on the first three characters of ICD-10 codes from EHR data, enabling the tracking of participants’ health over time and the analysis of both prevalent and incident diseases. For this study, we selected 14 chronic diseases with a minimum of 40 incident cases (Fig. 1 b, Supplementary Table 1). The date of incidence for specific disease-naive individuals was defined as the first diagnosis event after sample enrollment in EstBB, with positive disease status assigned to cases having at least two unique data entries on different dates during the follow-up period. Controls for each condition were selected by excluding individuals with incident or prevalent status or those with a single positive data entry during the follow-up period. Supplementary Table S6 provides data on incident and prevalent comorbidities within study groups. Metabolomics data Untargeted metabolomics profiling on EDTA plasma samples stored in -80C was conducted in 2021 using an ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (HD4, Metabolon Inc., Durham, USA)[ 12 , 43 ]. Raw data were subjected to Metabolon standard quality control and processing, including imputation and batch-normalization of peak area data. Metabolite identification was conducted against purified standards or recurrent unknown entities. Subsequently, log-transformed values were derived and scaled by mean-centering and dividing by the standard deviation. The employed metabolomics pipeline encompasses well-established gut microbiota-derived metabolites, such as choline metabolites, tryptophan metabolites in kynurenine and indole pathways, and bile acids[ 18 ]. Metabolites with fewer than 10 measurements or designated as medications were eliminated. Metadata for all analyzed metabolites can be found in Supplementary Table S3 . Statistical analysis Time-dependent Cox proportional hazards regression models were used to assess hazard ratio (HR) of incidence events compared to control group. All models were adjusted for age, sex, BMI, and by current/previous smoking status. In order to address any potential imbalance in comorbidity burden between cases and controls, a secondary analysis supported by Principal Component Analysis (PCA) was employed. Briefly, pairwise Hamming distances were calculated from binarized comorbid disease status information for each selected disease and then subjected to PCA. Subsequently, all models were further adjusted by incorporating the first two principal components representing the comorbidity load. P values were corrected for multiple testing by application of Benjamini-Hochberg (B-H) procedure. Significance was determined by FDR < 0.1. Literature analysis for metabolite and gut microbiome associations In this study, we conducted a comprehensive analysis of metabolites, integrating data from existing literature to provide an additional layer of information on microbiome-metabolite associations. It's essential to clarify that the microbiome composition analysis for the respective cases was beyond the scope of this study. We focused on large-scale studies (N > 300) that utilized MS metabolomics and metagenomic sequencing to explore the microbial capacity to predict serum or plasma metabolite levels[ 22 – 25 ]. Three of these studies (excluding Chen et al., 2022) utilized an untargeted MS platform by Metabolon. Specifically, we extracted explained variance data of metabolites from the supplementary data of the aforementioned publications. Additionally, when reported, we considered interactions with FDR < 0.05 as significant microbiome associations, resulting in 1132 significant associations out of the 1375 studied metabolites. To establish links with metabolites from these studies, we first used Metabolon ID, and when unavailable, we employed Metabolon Chemical Name, and finally Human Metabolome Database (HMDB) ID. For consistency, all HMDB IDs were transformed to 7-digit format, as required, from their original 5- and 6-digit accession numbers. Ethics statement and Consent for publication Estonian biobank conducts all data collection and research activities according to the Estonian Human Genes Research Act (HGRA). Ethical approval was obtained from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs; approval No. 1.1–12/624) and for the data release from EstBB (T06 6–7/GI/8175). Subjects signed a broad consent form during recruitment, and to ensure privacy protection, no personally identifiable information was used in the analyses. Data availability The datasets generated and analyzed during the current study contain sensitive information from healthcare registers and are therefore not publicly available. However, they can be obtained from the corresponding author upon reasonable request. The procedure for accessing the data from the Estonian Biobank is available at https://genomics.ut.ee/en/content/estonian-biobank . The datasets generated and analyzed during the current study are not publicly available since the data access to the Estonian Biobank must follow the informed consent regulations of the Estonian Committee on Bioethics and Human Research, which are clearly described in the Data Access section at https://genomics.ut.ee/en/content/estonian-biobank . Rights of Estonian Biobank's participants are regulated by Human Genes Research Act (HGRA) § 9 – Voluntary nature of gene donation ( https://www.riigiteataja.ee/en/eli/ee/531102013003/consolide/current ). All data access to the Estonian Biobank's data must adhere to the informed consent regulations established by the Estonian Committee on Bioethics and Human Research. To initiate a request for phenotype data, it is necessary to submit a preliminary request to [email protected] . Declarations Acknowledgements The authors would like to thank Mari-Liis Tammesoo, Marili Palover, Neeme Tõnisson, Liis Leitsalu, and Esta Pintsaar for participating in the sample collection process of the EstBB cohort. We also thank Krista Fischer for contributing to the experimental design of this study and acknowledge the Estonian Biobank research team members Andres Metspalu, Tõnu Esko, Mari Nelis, Georgi Hudjashov, and Lili Milani. EstBB Metabolon assays used in this study were funded by Biomarin Pharmaceutical. This work was written at writing retreats and writing days organized by the Institute of Genomics, University of Tartu. Funding Current work was funded by Estonian Research Council grants (PRG1414 to E.O.) and an EMBO Installation grant (No. 3573 to E.O.). The work of J.K, U.V. and T.E. was supported by the Estonian Research Council grant PRG1291. Part of the analysis was performed on the HPC servers of the University of Tartu. Author contributions M.J., O.A., E.O., J.K. formulated overall objectives and study design. T.N., U.V., T.E. organized the collection and analysis of the samples. M.J. organized the phenotype and health data from questionnaires and electronic health records. M.J. performed the data analysis. M.J. interpreted the data and prepared the figures. M.J. wrote the first version of the paper. E.O., O.A., A.R., J.K, L.B., K.E., A.W. contributed to the revision of the paper. Estonian Biobank Research Team collected and provided EstBB data. All authors read and approved the final version of the paper. Competing interests During the drafting of the manuscript, L.B. is an employee of BioMarin. References Stanaway, J.D.; Afshin, A.; Gakidou, E.; Lim, S.S.; Abate, D.; Abate, K.H.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; et al. 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Supplementary Files SupplementaryFigureS1.png SupplementaryFigureS2.png SupplementaryFigureS3.png SupplementaryFigureS4.png SupplementaryFigureS5.png SupplementaryFigureS6.png SupplementaryFigureS7.png SupplementaryFigureS8.png SupplementaryTablesS162024.4.17.xlsx Cite Share Download PDF Status: Published Journal Publication published 22 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Jul, 2024 Reviews received at journal 22 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 11 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers invited by journal 24 May, 2024 Editor assigned by journal 24 May, 2024 Editor invited by journal 20 May, 2024 Submission checks completed at journal 20 May, 2024 First submitted to journal 14 May, 2024 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. 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Analysis plan. b - Counts of controls (blue), incident cases (red), prevalent cases (grey, excluded from further analysis) for selected diseases. 14 chronic conditions with more than 40 incident cases were studied. Cox proportional hazard models were adjusted for age, sex, bmi, smoking status in the primary analysis. In the secondary analysis, Cox models were additionally adjusted by the first two principal components (PC) of Hamming distance between comorbidity presence/absence profiles of the study subjects. AFF - atrial fibrillation and flutter, HHD with HF - hypertensive heart disease with heart failure, CIHD - chronic ischemic heart disease, T2D - type 2 diabetes, LC-MS - liquid chromatography mass spectrometry.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/da4320300c30065aad49d2ae.jpg"},{"id":57728419,"identity":"886a19e0-8f3a-4d97-a0b9-51260c163c7d","added_by":"auto","created_at":"2024-06-04 21:45:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between plasma metabolome and risk of 14 chronic diseases using random controls (FDR \u0026lt; 0.1)\u003c/strong\u003e. \u003cstrong\u003ea\u003c/strong\u003e Total number of significant associations with incident diseases. \u003cstrong\u003eb\u003c/strong\u003e Total number of significant predictors divided into biochemical groups. \u003cstrong\u003ec\u003c/strong\u003e Volcano plot of the hazard ratios (HR) and FDR values of incident risk factors for chronic conditions. \u003cstrong\u003ed\u003c/strong\u003e Top 10 associations with both increased and decreased risk of incident diseases. AFF - atrial fibrillation and flutter, HHD with HF - hypertensive heart disease with heart failure, CIHD - chronic ischemic heart disease, T2D - type 2 diabetes. Cox models were adjusted for age, body mass index, sex, and smoking status. Error bars show the 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/fccae826383bea6aac60a4ab.png"},{"id":57728416,"identity":"a090e26b-19d5-47c9-a5b7-a8da15bbc2db","added_by":"auto","created_at":"2024-06-04 21:45:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommon risk factors of chronic conditions.\u003c/strong\u003e Heatmap illustrates hazard ratios of metabolites shared between at least two conditions (FDR \u0026lt; 0.1 significant values are encased in black frames; green highlight - nominally significant reduced risk; red highlight - nominally significant increased risk). AFF - atrial fibrillation and flutter, HHD with HF - hypertensive heart disease with heart failure, CIHD - chronic ischemic heart disease, T2D - type 2 diabetes. Cox models were adjusted for age, body mass index, sex, and smoking status.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/583759d5d2feb5492d76e707.png"},{"id":57728412,"identity":"4f421968-337a-4409-9f5c-d5bc0d523b83","added_by":"auto","created_at":"2024-06-04 21:45:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of results from primary and comorbidity-adjusted analyses.\u003c/strong\u003e A comparison of results of primary analysis (before adjusting for comorbidities, black) and secondary analysis (after adjusting for comorbidities, dark gray). Light gray highlights the overlap of significant associations/predictors between the two approaches. \u003cstrong\u003ea\u003c/strong\u003e Total number of associations. \u003cstrong\u003eb\u003c/strong\u003e Number of predictors across evaluated conditions. \u003cstrong\u003ec \u003c/strong\u003eNumber of condition-specific (Ndiagnoses = 1) and shared predictors (Ndiagnoses\u0026gt; 1). AFF - atrial fibrillation and flutter, HHD with HF - hypertensive heart disease with heart failure, CIHD - chronic ischemic heart disease, T2D - type 2 diabetes. Cox models for primary analysis were adjusted for age, body mass index, sex, smoking status. Cox models for sensitivity analysis were further adjusted by the first two principal components calculated from Hamming distances between comorbidity profiles.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/b636682355c2f8801df2cb97.png"},{"id":57728417,"identity":"a71074e7-4229-46e2-badf-8fbbebfc2df8","added_by":"auto","created_at":"2024-06-04 21:45:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":273242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop microbiome-related incident risk factors of chronic conditions\u003c/strong\u003e. Left - heatmap illustrates hazard ratio values of the 15 foremost microbiome-related identified and unidentified metabolites with at least 1 significant association (FDR \u0026lt; 0.1 significant values are encased in black frames, nominally significant with green or red highlights). Right - heatmap shows metabolite variance explained by the gut microbiota from the analyzed literature[22–25]. AFF - atrial fibrillation and flutter, HHD with HF - hypertensive heart disease with heart failure, CIHD - chronic ischemic heart disease, T2D - type 2 diabetes. Cox models were adjusted for age, body mass index, sex, and smoking status.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/6699dc7c004704503228892e.png"},{"id":67682671,"identity":"faad2065-f453-4d63-8591-cb7ef50ad1ac","added_by":"auto","created_at":"2024-10-28 16:14:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1925861,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/34a0bf45-e286-4562-801c-025ab542bbde.pdf"},{"id":57729531,"identity":"523e950d-6938-4f2b-9005-39cf101f1c92","added_by":"auto","created_at":"2024-06-04 21:53:29","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":93372,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/c3949d216d8b9888e00aa71f.png"},{"id":57728422,"identity":"8b8df34f-c023-483e-a0eb-a9a24b9c7e12","added_by":"auto","created_at":"2024-06-04 21:45:29","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":378664,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/ec933fb862b92c4c0ed219a4.png"},{"id":57728420,"identity":"18ad8c62-ecc7-4501-863d-5c6d9555f112","added_by":"auto","created_at":"2024-06-04 21:45:28","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":60445,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/a47584dc02cbe7136b0994d8.png"},{"id":57728410,"identity":"ff39a2d2-da49-4eb9-882b-e0cb874b5260","added_by":"auto","created_at":"2024-06-04 21:45:27","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":87518,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/6a9bbf10d46015d73cbc4df5.png"},{"id":57728409,"identity":"773e9344-3ad4-45cc-9e19-ef44b85646e8","added_by":"auto","created_at":"2024-06-04 21:45:26","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":55420,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/97664013425795bc08e9a820.png"},{"id":57729512,"identity":"f8291cb1-ba0d-4a2e-95e0-1bcc9f4478b9","added_by":"auto","created_at":"2024-06-04 21:53:27","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":55997,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS6.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/a0715ced13b3078045e6ca2f.png"},{"id":57728415,"identity":"103aa7d7-535d-48f1-9393-e9b30f13a3e7","added_by":"auto","created_at":"2024-06-04 21:45:27","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":822049,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS7.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/cdad8165f1f6a3255402697a.png"},{"id":57728413,"identity":"2d82d62f-e56e-47e5-8fb5-2c32d0e36a95","added_by":"auto","created_at":"2024-06-04 21:45:27","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":143173,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS8.png","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/c5dec8d4d9926e31fc89062b.png"},{"id":57728418,"identity":"eec2aa55-e4ce-4ea7-948c-6d2864973890","added_by":"auto","created_at":"2024-06-04 21:45:28","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":3515287,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS162024.4.17.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4419599/v1/b3d5e31abc7e25a259a8a3e6.xlsx"}],"financialInterests":"Competing interest reported. During the drafting of the manuscript, L.B. is an employee of BioMarin.","formattedTitle":"Comorbidities confound metabolomics studies of human disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe onset and progression of chronic diseases are influenced by a combination of factors, including genetics, environment, lifestyle, and the microbiome[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The etiology of chronic diseases extends beyond isolated conditions, as many chronic conditions share a well-established set of common clinical and lifestyle risk factors[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Perturbation of common pathways suggests a significant level of connectivity, and understanding how these associations relate to coexistence of chronic conditions is paramount. One out of three patients suffer from two or more chronic conditions, termed multimorbidity[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This is exemplified in individuals with gout, where 74% experience hypertension, and 71% exhibit stage 2 chronic kidney disease[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, it is essential to distinguish disease-specific biomarkers from those which reflect the progression of other concurrent diseases, termed comorbidities[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For accurate profiling of disease patterns, in-depth health information encompassing each patients' medical history is needed.\u003c/p\u003e \u003cp\u003eRecent decades have introduced an era of systematic data collection by the establishment of large population-based biobanks, which have been fundamental for the identification of risk factors for chronic diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These biobanks actively recruit participants from the general population and accumulate large sample collections, characterized by comprehensive health data from questionnaires and/or electronic health records (EHR), along with multiple omics data layers. The rich health data facilitates the identification of individuals with diverse disease conditions along with the history of comorbidities and hence becomes indispensable for disentangling risk factors of chronic diseases[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Biobanks also serve as invaluable resources for epidemiological and clinical studies, wherein stored blood samples can be retrospectively analyzed using advanced analytics methods, including high-throughput metabolomics.\u003c/p\u003e \u003cp\u003eNevertheless, the limited availability of such large cohorts with available follow-up data has resulted in only a few studies aimed at identifying metabolic risk factors across common chronic conditions[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While these studies have revealed similarities in association signatures across diseases with distinct pathophysiologies, a comprehensive understanding of disease-specific and shared risk factors remains elusive. Notably, among the mentioned studies, only one has incorporated high-sensitivity mass spectrometry (MS) to determine metabolite profiles for risk estimation[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. MS presents a more valuable option as it facilitates the measurement of gut-derived and modulated biomolecules, including bile acids, short-chain fatty acids, branched-chain amino acids, methylamines, tryptophan, and indole derivatives[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These biomolecules are modified in metabolic disorders and may serve as significant risk factors for the onset of chronic conditions[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we investigated the onset of 14 non-communicable diseases (NCD) in 991 Estonian Biobank (EstBB) participants and their associations with baseline levels of 1,375 plasma metabolites measured with untargeted MS profiling. The primary aim of this study was to identify and distinguish disease-specific and common metabolites which are contributing to the risk of chronic diseases. For this, we used the EHR information to uncover comorbidity profiles of all cases and specific disease-naive controls selected from a population cohort.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy overview and data description\u003c/h2\u003e \u003cp\u003eWe studied a well-phenotyped cohort of 991 individuals from the Estonian Biobank with available untargeted plasma metabolite data generated by Metabolon HD4 platform. Following the exclusion of individuals with missing data (see Methods), the analysis comprised 919 participants (63.1% females), with an average follow-up time of 11.0 years (SD 4.4 years). To evaluate the effect of the metabolite levels on the risk of developing chronic diseases, 14 common conditions with more than 40 incident cases were evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The mean age and body mass index at sampling was 46.7 years (SD 16.8) and 26.8 kg/m2 (SD 5.7), respectively. Other characteristics of the study population are listed in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eMetabolomics analysis was performed on plasma samples collected between 2002 and 2018. Subsequently, following quality checks and the exclusion of infrequent and drug-related metabolites (see Methods), association analysis was conducted on 1375 metabolites (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). To evaluate the impact of comorbidities on risk estimates, we performed a secondary analysis by adjusting for comorbidities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Additionally, all accessible metabolites were cross-referenced with recent literature data to determine their association with the gut microbiome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlasma metabolites predict the onset of various NCDs\u003c/h2\u003e \u003cp\u003eTo investigate the role of plasma metabolites in the incidence of 14 NCDs, Cox proportional hazards models adjusted for age, sex, body mass index (BMI), and smoking status were constructed. In total, we detected 254 significant associations (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) with 13 incident diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 17% (235/1375) metabolites were significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1) linked to at least one disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The largest proportion of the associated metabolites were linked with risk of developing gout (n\u0026thinsp;=\u0026thinsp;118). A substantial number of metabolites showed an association with lipidemias (n\u0026thinsp;=\u0026thinsp;43) and type 2 diabetes (T2D, n\u0026thinsp;=\u0026thinsp;31). At the same time, primary hypertension (n\u0026thinsp;=\u0026thinsp;14) and several cardiac conditions showed a lower number of associations: atrial fibrillation and flutter (AFF, n\u0026thinsp;=\u0026thinsp;10), heart failure (n\u0026thinsp;=\u0026thinsp;9), chronic ischemic heart disease (CIHD, n\u0026thinsp;=\u0026thinsp;7), other cardiac arrhythmias (n\u0026thinsp;=\u0026thinsp;5), hypertensive heart disease with heart failure (HHD with HF, n\u0026thinsp;=\u0026thinsp;3). Similarly, only a few associations were detected for iron deficiency anemia (n\u0026thinsp;=\u0026thinsp;8), other anxiety disorders (n\u0026thinsp;=\u0026thinsp;3), depressive episode (n\u0026thinsp;=\u0026thinsp;2), and asthma (n\u0026thinsp;=\u0026thinsp;1). The significant predictors predominantly comprised lipids (n\u0026thinsp;=\u0026thinsp;92), amino acids (n\u0026thinsp;=\u0026thinsp;55), and unidentified metabolites (n\u0026thinsp;=\u0026thinsp;39) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e online). Consistent with previous studies, we observed significant associations between uric acid and the increased risk of gout (hazard ratio, HR 7), as well as cholesterol and the increased risk of lipidemias (HR 2.1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Elevated uric acid levels, indicative of hyperuricemia, are a known risk factor of gout, suggesting that both metabolites may mirror metabolic changes in the pre-disease state[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe five strongest associations with each incident condition are depicted in Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e online. Notably, among the best predictors of gout and lipidemias, strong correlations were observed within incident cases of respective conditions (Supplementary Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e online). On the contrary, the top predictors for T2D exhibited relatively low levels of correlation indicating potentially higher heterogeneity among these metabolites and related pathways.\u003c/p\u003e \u003cp\u003eThe majority of identified interactions indicated an increased risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e online). Among the top ten associations with highest HR, nine were specifically associated with the development of incident gout (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). In contrast, when examining associations with negative HR indicating diminished risk with higher metabolite values, the situation was more variable - five out of ten of the most significant predictors were identified in relation to incident T2D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNCDs have partially overlapping metabolic predictors\u003c/h2\u003e \u003cp\u003eWe investigated the extent of unique and shared metabolic predictors for diseases. Most of the reported predictors (92%, 217/235) were uniquely associated with the risk of a single disease (Suppl Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e online). Shared associations were detected mainly between two chronic diseases (17 predictors) and no common predictors were seen between more than 3 diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).The highest number of shared associations appeared between gout and T2D (n\u0026thinsp;=\u0026thinsp;6), gout and AFF (n\u0026thinsp;=\u0026thinsp;5), and gout and lipidemias (n\u0026thinsp;=\u0026thinsp;3). For example, higher level of mannonate was associated with increased risk of incident gout (HR 1.6), T2D (HR 2), and HHD with HF (HR 1.6), corroborating a previous study, which demonstrated association of higher mannonate levels with severe insulin-deficiency[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. An unidentified metabolite X-24588 was associated with incident gout (HR 1.9) and T2D (HR 1.8). This metabolite has been previously associated with hepatic triglyceride content[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDisease-specific associations with NCDs are more robust to adjusting for comorbidities\u003c/h2\u003e \u003cp\u003eWhile comorbidities are commonly overlooked during control selection, we next aimed to integrate them into our analysis. To achieve this, we employed principal component analysis based on Hamming distance between comorbidity presence/absence profiles of the study subjects. In the secondary analysis, we additionally adjusted Cox proportional hazards models by the first two principal components. This adjustment aimed to address the differences in the disease burden between incident cases and controls, thereby strengthening the reliability of our findings. For instance, 56% of incident cases of AFF in the EstBB cohort were already diagnosed with concurrent primary hypertension, whereas only 22% of randomly selected participants without incident AFF exhibited primary hypertension at the time of sampling.\u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, a lower total number of significant associations (198 vs 254, 73% overlap) and unique predictors (188 vs 235, 75% overlap) were observed compared to analysis conducted without adjusting for comorbidities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Supplementary Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e and S5, Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e online). Reduction in the number of associations was more evident in the case of gout (84 vs 118), lipidemias (36 vs 43), and T2D (22 vs 31) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The incident cases for these diseases were inflated by comorbid prevalent diagnoses, potentially explaining the loss of signal in the secondary analysis (Supplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e online).\u003c/p\u003e \u003cp\u003eOverall, employing comorbidities as covariates led to reduction in both the total number of disease-specific (178 vs 217) and shared risk predictors (10 vs 18) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The more pronounced decrease among common predictors could suggest that these associations were confounded by the presence of comorbid conditions (Supplementary Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e online).\u003c/p\u003e \u003cp\u003eIn total, 166 disease-specific associations were consistently identified, resulting in a 76% overlap with previously identified metabolites from the primary analysis. This suggests the existence of a more robust set of predictors exclusively associated with each specific medical condition, as opposed to a limited number of widespread incident risk signals shared among multiple chronic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eA substantial portion of risk factors for NCDs are linked with microbiota\u003c/h2\u003e \u003cp\u003eThe growing evidence of the microbial activity on metabolites prompted us to pay attention to disease-associated metabolites with pre-established significant associations to the gut microbiome. We extracted and aggregated the data from four recent publications which reported microbiota-explained variance of individual serum or plasma metabolites[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This analysis revealed notable microbiome contributions for 96% (226/235) of significant and 85% (970/1140) of non-significant predictors (see Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e online). Within the prominent microbiome-associated metabolites (with any reported R2 value exceeding 0.1) significant associations were shown for T2D, lipidemias, primary hypertension, HHD with HF, AFF, and gout (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplementary Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e online). These associations were predominantly exclusive to a single disease and included a high number of unidentified metabolites. Notably, 3-phenylpropionate (hydrocinnamate) and hyocholate were associated with reduced risk of AFF and gout, respectively, while well-established cardiometabolic markers 1,5-anhydroglucitol (1,5-AG) and metabolonic lactone sulfate were linked to decreased and increased risk of incident T2D, respectively[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Among microbial metabolites originating from amino acids, indolepropionate displayed a reduced risk of incident lipidemias, while 3-indoxyl sulfate and 6-hydroxyindole sulfate demonstrated a lower risk of incident AFF. However, no associations were found between trimethylamine N-oxide (TMAO), phenylacetylglutamine, or cinnamoylglycine and disease risk, contrasting previous findings[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Further discussion is available in Supplementary Discussion section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted an untargeted metabolomics analysis of plasma to identify both disease-specific and shared risk factors across 14 chronic conditions in the EstBB subcohort of 991 individuals. We demonstrated the value of well-phenotyped population-based biobank data for identifying predictive metabolic markers. Notably, we observed predominantly disease-specific signals rather than a widespread commonality among multiple chronic diseases. Nevertheless, in terms of common signals between studied chronic diseases, risk factors for gout, shared with T2D, AFF, and lipidemias stood out prominently, suggesting potential metabolic interactions between these conditions. We observed a decrease in the shared predictors when adjusting for prevalent comorbidities. Additionally, we showed that a high proportion of identified predictors had prior association with gut microbial composition. Importantly, our findings imply that comorbidities may contribute to the shared incident risk signature observed across chronic conditions.\u003c/p\u003e \u003cp\u003eThe highest number of incident metabolic risk associations was identified for gout, potentially due to its high comorbidity rate and its role as a risk factor for other conditions. Gout is an arthritic condition induced by hyperuricemia leading to urate deposits in the tissues. Previous studies on gout have shown associations with metabolic syndrome and chronic kidney disease[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We are not aware of any untargeted metabolomics studies investigating risk factors of gout. Nevertheless, previous research on prevalent gout has established connections with altered amino acid levels, perturbations in purine, glycerophospholipid, sphingolipid, and carbohydrate metabolism[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These findings were also partially replicated in our study. For instance, a substantial number of \u003cem\u003eN\u003c/em\u003e-acetylated amino acids (n\u0026thinsp;=\u0026thinsp;15) were unequivocally associated with an increased risk of gout. These protein degradation products have been linked to various incident chronic diseases[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Notably, among these amino acids, \u003cem\u003eN\u003c/em\u003e-acetylalanine showed the highest risk for incident gout and has previously been associated with an elevated risk of renal disease, heart failure, and mortality, as well as reduced glomerular performance[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The direct effect of \u003cem\u003eN\u003c/em\u003e-acetylalanine on renal disease, heart failure, and mortality has been shown to be fully and partially mitigated by creatinine and uric acid, respectively[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, within incident gout cases, \u003cem\u003eN\u003c/em\u003e-acetylated amino acids were highly correlated with levels of creatinine - a well-known marker of kidney function. Among carbohydrate predictors, Pietzner et al., demonstrated a similar mitigation effect by creatinine and uric acid for \u003cem\u003eN\u003c/em\u003e-acetylneuraminate, \u003cem\u003eN\u003c/em\u003e-acetylglucosamine/\u003cem\u003eN\u003c/em\u003e-acetylgalactosamine, arabitol/xylitol, and erythronate, all of which were uniquely associated with an increased risk of gout in our study. This suggests connectivity between loss of kidney function and the development of gout. While our analysis accounted for the prevalence of 13 other conditions for each incident condition, further adjustments for factors such as markers of renal function (e.g., eGFR) might be necessary for a more detailed understanding of chronic disease risk in a multimorbidity setup.\u003c/p\u003e \u003cp\u003eWithin the studied conditions, we observed a limited (8%) concurrence of metabolite incident risk factors. In contrast, Pietzner et al., reported a 65.5% overlap for metabolite predictors among 27 noncommunicable diseases, including, ten cancer types when data was sourced from hospitalization and cancer registry data[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This represents a crucial distinction from our study, as we not only obtained data from the aforementioned registries but also integrated EHR data from primary care and other relevant registries. For example, MacRae et al., suggested using EHRs from various registries for classification of clinical data as they reported higher age of onset of multimorbidity within the identical patient cohort when relying on information derived from hospitalizations compared to data obtained from primary care sources[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This suggests that relying solely on hospitalization data might result in inaccurate estimation of comorbidities, likely influencing findings of disease risks and reported interconnectivity among chronic diseases.\u003c/p\u003e \u003cp\u003eWe noticed that, for most of the studied conditions, the prevalence of comorbidities was substantially higher in incident cases compared to controls. In response, we aimed to enhance the analysis by including baseline comorbidities information as additional covariates. This aligns with a recent study emphasizing the need for distinguishing disease-specific changes from confounders from pre- and comorbidities[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. More specifically, Fromentin et al., employed a design that incorporated not only healthy and clinically ill individuals but also subjects with dysmetabolic morbidities, enabling the comparison of metabolic signatures across various disease states and clinical stages. Similarly, our approach aimed to disentangle condition-specific effects from the multimorbidity signal. Adjustment for comorbidities resulted in a reduction in both disease-specific and shared predictors, with the most pronounced impact observed in conditions that initially exhibited the highest number of associations, namely, gout, lipidemias, and T2D. Therefore, associations initially thought to be common might be attributed to the presence of shared comorbid conditions rather than being independent associations across various diagnoses. For future studies, a more thorough consideration of distinct comorbidity profiles could enhance the detection of risk factors[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. We also propose that utilizing registry-based electronic health record (EHR) data could potentially be expanded to specifically select subjects at various stages of disease progression, each with their respective comorbidity profiles, and corresponding selection of appropriate controls.\u003c/p\u003e \u003cp\u003e We also demonstrated predominantly disease-specific associations among metabolites linked to the microbiome in the external studies we reviewed. For example, indolepropionate and 3-phenylpropionate were exclusively associated with reduced risk of lipidemias and AFF, respectively. Both metabolites have been associated with reduced chronic disease risk[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Also, in the same study by Pietzner et al., levels of these metabolites were not significantly mediated by any of the available routine clinical parameters, including renal markers. Dekkers et al., showed that several Eubacteriales sp. and more specifically, Faecalibacterium prausnitzii species are positively associated with aforementioned metabolites[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, multiple associations of microbially produced indole-derived metabolites and reduced risk of chronic diseases were observed. Contrastingly, previous studies have linked indoxyl sulfate with further progression of chronic kidney disease and cardiovascular disease[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, it could be hypothesized that in individuals with normal renal function, maintaining optimal levels of uremic toxins could protect against cardiovascular issues.\u003c/p\u003e \u003cp\u003ePrevious research indicates that unidentified compounds contribute significantly to the variability in gut microbial profiles among individuals. Importantly, also in our study, a noteworthy proportion of these metabolites demonstrated associations with an increased or decreased risk of chronic diseases. It is worth noting that our results may warrant reevaluation, once these unknown metabolites have been identified and their role elucidated. It is also important to consider that the assessment of the status of microbiome-metabolite associations was based on previous research which analyzed the explained variance of blood metabolites. However, the variance explained of the fecal metabolome is much higher than that of the blood metabolome[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, a recent study reported markedly superior accuracy in predicting the levels of fecal metabolites from the taxonomic profiles compared to predicting blood metabolites[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Consequently, the low levels of explained variance of specific blood metabolites should not be interpreted as an absence of a connection to gut microbiota. This study also emphasized that, compared to blood, there are more robust associations between fecal metabolites and prevalent cardiometabolic diseases, suggesting that fecal levels could potentially serve as a better proxy for identifying risk factors for chronic diseases modulated by the gut microbiota. Despite this, our results indicate that plasma levels of microbiome-derived metabolites serve as a proxy for incident chronic disease.\u003c/p\u003e \u003cp\u003eOur study is set apart from prior research on the simultaneous investigation of metabolite incident disease risk factors by multiple aspects. First, utilization of extensive registry data distinguishes our approach from studies that depend on self-reported or single registry-based data, enhancing the robustness of our findings, and by providing less biased and more objective/standardized diagnosis status of multiple chronic conditions. Second, the inclusion of a wide range of frequently occurring chronic disorders, from cardiac and metabolic conditions to mood disorders, contributes to a comprehensive exploration of risk factors, identifying significant associations for all investigated conditions. Third, alongside the conventional analysis, we adjust risk predictions for comorbidities. This results in a more nuanced evaluation of risk factors specific to diseases, as well as those shared among them. Notably, the potential confounding effects arising from comorbidities might not have been comprehensively addressed in previous studies[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Last, we demonstrate a high number of microbially-associated metabolites among the significant incident disease predictors. This was achieved by integrating recently published data on the explained variance of metabolite levels attributed to the gut microbiome. Through this approach, we were able to link previously established microbiome-metabolite associations to a large proportion of the significant predictors.\u003c/p\u003e \u003cp\u003eThis investigation has certain limitations that warrant consideration. Crucially, our study encounters constraints in terms of statistical power due to small sample size in the condition-specific incident case groups. Moreover, the absence of a validation cohort could affect the generalizability of our findings. However, the specific selection of diverse conditions would require extensive collaboration with partner institutions, potentially limited by the wide scope of this study. In addition, we opted not to employ any additional inclusion or exclusion criteria specific to any particular disease. While a single ICD-10-specific definition of chronic conditions might impose limitations, it does provide a standardized and plain approach that facilitates ease of assessment and replication for broad selection of diseases. Finally, we did not account for the confounding effects of treatment or medication intakes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study presents a unique contribution to the field by utilizing extensive registry data for exploration of a diverse array of chronic diseases. While acknowledging certain limitations, our research provides valuable insights into understanding the microbial connection and specificity of metabolic predictors for incident chronic diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSample description\u003c/h2\u003e \u003cp\u003eIn this study, we utilized retrospective and prospective data from well-characterized individuals in the Estonian Biobank (EstBB). Established in 2000, the EstBB encompasses over 210,000 adults (aged 18\u0026ndash;93) across Estonia and maintains updated electronic health records (EHR) through regular linkages to primary care, hospital databases, and national registries, including the Cancer Registry and Causes of Death Registry, in addition to the national health insurance fund[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Disease and condition records were coded using International Classification of Diseases, 10th revision (ICD-10).\u003c/p\u003e \u003cp\u003eThe study cohort comprised 991 individuals who joined the EstBB between 2002 and 2019. During recruitment, participants provided venous blood samples and completed extensive questionnaires covering health-related topics such as lifestyle, diet, and pre-existing ICD-10 coded clinical diagnoses[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 919 individuals with complete covariate information (age, sex, BMI, smoking status) were included in the subsequent analyses.\u003c/p\u003e \u003cp\u003eChronic conditions were identified and aggregated based on the first three characters of ICD-10 codes from EHR data, enabling the tracking of participants\u0026rsquo; health over time and the analysis of both prevalent and incident diseases. For this study, we selected 14 chronic diseases with a minimum of 40 incident cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Supplementary Table\u0026nbsp;1). The date of incidence for specific disease-naive individuals was defined as the first diagnosis event after sample enrollment in EstBB, with positive disease status assigned to cases having at least two unique data entries on different dates during the follow-up period. Controls for each condition were selected by excluding individuals with incident or prevalent status or those with a single positive data entry during the follow-up period. Supplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e provides data on incident and prevalent comorbidities within study groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics data\u003c/h2\u003e \u003cp\u003eUntargeted metabolomics profiling on EDTA plasma samples stored in -80C was conducted in 2021 using an ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (HD4, Metabolon Inc., Durham, USA)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Raw data were subjected to Metabolon standard quality control and processing, including imputation and batch-normalization of peak area data. Metabolite identification was conducted against purified standards or recurrent unknown entities. Subsequently, log-transformed values were derived and scaled by mean-centering and dividing by the standard deviation.\u003c/p\u003e \u003cp\u003eThe employed metabolomics pipeline encompasses well-established gut microbiota-derived metabolites, such as choline metabolites, tryptophan metabolites in kynurenine and indole pathways, and bile acids[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Metabolites with fewer than 10 measurements or designated as medications were eliminated. Metadata for all analyzed metabolites can be found in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTime-dependent Cox proportional hazards regression models were used to assess hazard ratio (HR) of incidence events compared to control group. All models were adjusted for age, sex, BMI, and by current/previous smoking status. In order to address any potential imbalance in comorbidity burden between cases and controls, a secondary analysis supported by Principal Component Analysis (PCA) was employed. Briefly, pairwise Hamming distances were calculated from binarized comorbid disease status information for each selected disease and then subjected to PCA. Subsequently, all models were further adjusted by incorporating the first two principal components representing the comorbidity load. P values were corrected for multiple testing by application of Benjamini-Hochberg (B-H) procedure. Significance was determined by FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLiterature analysis for metabolite and gut microbiome associations\u003c/h2\u003e \u003cp\u003eIn this study, we conducted a comprehensive analysis of metabolites, integrating data from existing literature to provide an additional layer of information on microbiome-metabolite associations. It's essential to clarify that the microbiome composition analysis for the respective cases was beyond the scope of this study. We focused on large-scale studies (N\u0026thinsp;\u0026gt;\u0026thinsp;300) that utilized MS metabolomics and metagenomic sequencing to explore the microbial capacity to predict serum or plasma metabolite levels[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Three of these studies (excluding Chen et al., 2022) utilized an untargeted MS platform by Metabolon. Specifically, we extracted explained variance data of metabolites from the supplementary data of the aforementioned publications. Additionally, when reported, we considered interactions with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as significant microbiome associations, resulting in 1132 significant associations out of the 1375 studied metabolites. To establish links with metabolites from these studies, we first used Metabolon ID, and when unavailable, we employed Metabolon Chemical Name, and finally Human Metabolome Database (HMDB) ID. For consistency, all HMDB IDs were transformed to 7-digit format, as required, from their original 5- and 6-digit accession numbers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement and Consent for publication\u003c/h2\u003e \u003cp\u003eEstonian biobank conducts all data collection and research activities according to the Estonian Human Genes Research Act (HGRA). Ethical approval was obtained from the Estonian Committee on Bioethics and Human Research (Estonian Ministry of Social Affairs; approval No. 1.1\u0026ndash;12/624) and for the data release from EstBB (T06 6\u0026ndash;7/GI/8175). Subjects signed a broad consent form during recruitment, and to ensure privacy protection, no personally identifiable information was used in the analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study contain sensitive information from healthcare registers and are therefore not publicly available. However, they can be obtained from the corresponding author upon reasonable request. The procedure for accessing the data from the Estonian Biobank is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genomics.ut.ee/en/content/estonian-biobank\u003c/span\u003e\u003cspan address=\"https://genomics.ut.ee/en/content/estonian-biobank\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The datasets generated and analyzed during the current study are not publicly available since the data access to the Estonian Biobank must follow the informed consent regulations of the Estonian Committee on Bioethics and Human Research, which are clearly described in the Data Access section at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genomics.ut.ee/en/content/estonian-biobank\u003c/span\u003e\u003cspan address=\"https://genomics.ut.ee/en/content/estonian-biobank\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Rights of Estonian Biobank's participants are regulated by Human Genes Research Act (HGRA) \u0026sect;\u0026nbsp;9 \u0026ndash; Voluntary nature of gene donation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.riigiteataja.ee/en/eli/ee/531102013003/consolide/current\u003c/span\u003e\u003cspan address=\"https://www.riigiteataja.ee/en/eli/ee/531102013003/consolide/current\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All data access to the Estonian Biobank's data must adhere to the informed consent regulations established by the Estonian Committee on Bioethics and Human Research. To initiate a request for phenotype data, it is necessary to submit a preliminary request to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\[email protected]\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Mari-Liis Tammesoo, Marili Palover, Neeme T\u0026otilde;nisson, Liis Leitsalu, and Esta Pintsaar for participating in the sample collection process of the EstBB cohort. We also thank Krista Fischer for contributing to the experimental design of this study and acknowledge the Estonian Biobank research team members Andres Metspalu, T\u0026otilde;nu Esko, Mari Nelis, Georgi Hudjashov, and Lili Milani. EstBB Metabolon assays used in this study were funded by Biomarin Pharmaceutical. This work was written at writing retreats and writing days organized by the Institute of Genomics, University of Tartu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCurrent work was funded by Estonian Research Council grants (PRG1414 to E.O.) and an EMBO Installation grant (No. 3573 to E.O.). The work of J.K, U.V. and T.E. was supported by the Estonian Research Council grant PRG1291. Part of the analysis was performed on the HPC servers of the University of Tartu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.J., O.A., E.O., J.K. formulated overall objectives and study design. T.N., U.V., T.E. organized the collection and analysis of the samples. M.J. organized the phenotype and health data from questionnaires and electronic health records. M.J. performed the data analysis. M.J. interpreted the data and prepared the figures. M.J. wrote the first version of the paper. E.O., O.A., A.R., J.K, L.B., K.E., A.W. contributed to the revision of the paper. Estonian Biobank Research Team collected and provided EstBB data. All authors read and approved the final version of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the drafting of the manuscript, L.B. is an employee of BioMarin.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStanaway, J.D.; Afshin, A.; Gakidou, E.; Lim, S.S.; Abate, D.; Abate, K.H.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; et al. Global, Regional, and National Comparative Risk Assessment of 84 Behavioural, Environmental and Occupational, and Metabolic Risks or Clusters of Risks for 195 Countries and Territories, 1990\u0026ndash;2017: A Systematic Analysis for the Global Burden of Disease Study 2017. 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BMC Musculoskelet Disord 2013, \u003cem\u003e14\u003c/em\u003e, 1\u0026ndash;5, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2474-14-363/TABLES/4\u003c/span\u003e\u003cspan address=\"10.1186/1471-2474-14-363/TABLES/4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"comorbidities, metabolomics, chronic disease, risk factors, electronic health records, biobank","lastPublishedDoi":"10.21203/rs.3.rs-4419599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4419599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The co-occurrence of multiple chronic conditions, termed multimorbidity, presents an expanding global health challenge, demanding effective diagnostics and treatment strategies. Chronic ailments such as obesity, diabetes, and cardiovascular diseases have been linked to metabolites interacting between the host and microbiota. In this study, we investigated the impact of co-existing conditions on risk estimations for 1375 plasma metabolites in 919 individuals from population-based Estonian Biobank cohort using liquid chromatography mass spectrometry (LC-MS) method. We leveraged annually linked national electronic health records (EHRs) data to delineate comorbidities in incident cases and controls for the most prevalent chronic conditions. Among the 254 associations observed across 13 chronic conditions, we primarily identified disease-specific risk factors (92%, 217/235), with most predictors (96%, 226/235) found to be related to the gut microbiome upon cross-referencing recent literature data. Accounting for comorbidities led to a reduction of common metabolite predictors across various conditions. In conclusion, our study underscores the potential of utilizing biobank-linked retrospective and prospective EHRs for the disease-specific profiling of diverse multifactorial chronic conditions.","manuscriptTitle":"Comorbidities confound metabolomics studies of human disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 21:45:18","doi":"10.21203/rs.3.rs-4419599/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-23T19:54:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T15:11:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-16T18:28:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171546436048428130734437426145107465408","date":"2024-07-11T15:58:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319370639864122632743145701239899662174","date":"2024-07-02T04:45:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-24T06:44:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-24T06:41:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-20T08:15:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-20T08:12:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-14T13:35:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e400504c-2747-4f66-8c6c-1f5a20283d32","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32538294,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":32538295,"name":"Health sciences/Risk factors"},{"id":32538296,"name":"Biological sciences/Microbiology/Communities/Microbiome"},{"id":32538297,"name":"Health sciences/Diseases/Cardiovascular diseases"},{"id":32538298,"name":"Biological sciences/Biochemistry/Metabolomics"}],"tags":[],"updatedAt":"2024-10-28T16:12:18+00:00","versionOfRecord":{"articleIdentity":"rs-4419599","link":"https://doi.org/10.1038/s41598-024-75556-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-22 15:58:09","publishedOnDateReadable":"October 22nd, 2024"},"versionCreatedAt":"2024-06-04 21:45:18","video":"","vorDoi":"10.1038/s41598-024-75556-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-75556-1","workflowStages":[]},"version":"v1","identity":"rs-4419599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4419599","identity":"rs-4419599","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}