Identifying possible biomarkers of lower urinary tract symptoms using metabolomics and partial least square regression

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We recorded the following variables: International prostatic symptom score (IPSS), prostate volume, comorbidities, PSA, height, weight, triglycerides, glycemia, HDL cholesterol, and blood pressure. The study of 41 plasma metabolites was done using the nuclear magnetic resonance spectroscopy technique. First, the correlations between the metabolites and the IPSS were done using Pearson. Second, significant biomarkers of LUTS from metabolites were further analysed using a multiple linear regression model. Finally, we validated the findings using partial least square regression (PLS). Results Small to moderate correlations were found between IPSS and methionine (-0.301), threonine (-0.320), lactic acid (0.294), pyruvic acid (0.207) and 2-aminobutyric-acid (0.229). The multiple linear regression model revealed that only threonine (p = 0.022) was significantly associated with IPSS, whereas methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847) and 2-aminobutyric-acid (p = 0.244) lost their significance. However, all metabolites lost their significance in the PLS model. Conclusion When using the robust PLS-regression method, none of the metabolites in our analysis had a significant association with lower urinary tract symptoms. This highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology. LUTS metabolomics BPH biomarker PLS Introduction Benign prostatic hyperplasia (BPH) is a prominent urological issue among elderly men in various nations, serving as a major etiology for lower urinary tract symptoms (LUTS) in this population. Descriptive epidemiological studies have reported varying prevalence rates of BPH, ranging from 12–42%(Lee, Chan, & Lai, 2017), with one study estimating a lifetime risk of 36.6%(Wang, Guo, Zhang, Tian, & Zhang, 2015). Globally, the number of prevalent BPH cases was estimated at 940 million in 2019, compared to 511 million in 2000. The substantial increase of 70.5% in the global prevalence of BPH can predominantly be ascribed to population ageing. This emerging trend, coupled with the escalating life expectancy worldwide, is projected to further augment the overall burden of BPH. The disease has been linked to increased healthcare costs and diminished quality of life (GBD, 2022). It is associated with various serious complications, including a higher risk of falls, depression, and reduced health-related quality of life (HRQoL) indicators such as sleep, psychological well-being, daily activities, and sexual health. The impact of BPH extends beyond the patient to their family and society.(Speakman, Kirby, Doyle, & Ioannou, 2015) Consequently, there is a pressing need for proactive monitoring and planning to address the potential strain on healthcare systems. Additionally, this situation underscores the necessity for improved patient stratification, considering the most effective treatment strategies, and adopting personalized medicine approaches (Feinstein L., 2018) Biomarkers for BPH can potentially distinguish between different BPH-related conditions. Currently, these conditions are primarily identified based on symptoms. Biomarker research aims to identify the risk of disease progression and guide the development of earlier, personalized, efficient, and cost-effective medical approaches for managing BPH-related LUTS. Furthermore, these efforts in biomarker discovery could provide a new understanding of the molecular causes of histological BPH and the clinical presentations of BPH, which have remained unclear despite ongoing research(Mullins et al., 2008) The serum prostate-specific antigen (PSA) is the only available biomarker in clinical practice (Cornu J.N., 2023). Pooled analyses of randomized controlled trials (RCTs) involving men with LUTS and presumed benign prostatic obstruction (BPO) have demonstrated that PSA exhibits good predictive value for assessing prostate volume, prostate growth, changes in symptoms, quality of life (QoL)/bother and clinical progression to urinary retention (Kozminski, Wei, Nelson, & Kent, 2015; Patel et al., 2018; Roehrborn, 2008) In a recent meta-analysis, the collective evidence regarding microRNAs (miRNAs) involved in the pathogenesis of BPH was reviewed. The analysis highlighted miR-221 as a notably associated miRNA with BPH, indicating its potential as a biomarker and therapeutic target for early detection and management of the condition.(Greco et al., 2019) The term "metabolome" was initially introduced by Oliver et al. in 1998 to describe the complete collection of low molecular weight compounds found within a cell, which are necessary for its maintenance, growth, and normal function. The metabolome also contributes to the metabolic reactions occurring within a cell during specific physiological or developmental stages(Oliver, Winson, Kell, & Baganz, 1998). Metabolomics, an analytical chemistry method, aims to measure a portion or the entirety of the metabolome. It can potentially discover new biomarkers that can predict the incidence, severity, and progression of diseases and identify underlying pathophysiological abnormalities (Newgard, 2017). Initially, metabolomics primarily focused on biomarker discovery (Johnson, Ivanisevic, & Siuzdak, 2016). Consequently, plasma trimethylamine N-oxide (TMAO) and urinary taurine have been identified as markers for cardiovascular disease (Koeth et al., 2013). A link has been established between dysregulated metabolism of branched-chain amino acids (BCAAs) and various cardiometabolic diseases (Newgard, 2017). In 2018, Mitsui et al. conducted the only metabolomics study to investigate male LUTS. They postulated that the abnormal glucose metabolism observed in metabolic syndrome may also be present in LUTS patients. If confirmed, these hypotheses could open new avenues for developing novel LUTS treatments by elucidating modifications in the amino acid profiles of plasma (Mitsui et al., 2018). The primary objective of this study was to validate and replicate the findings of the previously published metabolomics approach study that focused on male LUTS secondary to BPH. Furthermore, our study employed a distinct statistical method, partial least squares (PLS) regression, widely recognized as the standard statistical method in metabolomics research. Material and method A cross-sectional study was conducted at the Urologic Outpatient Clinic of Førde Central Hospital, with an inclusion period spanning from November 20, 2018, to February 17, 2021. Patients were referred by their general practitioners (GPs). They were selected by the first author based on strict inclusion and exclusion criteria. Our research group previously published the study protocol (Hopland-Nechita, Andersen, & Beisland, 2022). At the time of inclusion, none of the enrolled patients were undergoing medical treatment for LUTS. The control group consisted of patients referred to the Surgical Outpatient Clinic of Førde Central Hospital for conditions other than LUTS, who consented to participate in the study. Detailed medical histories and lists of current medications were obtained, and the comorbidities were quantified using the Charlson index and ASA (American Society of Anesthesiology) scores. Blood samples were collected from fasting patients in the morning, between 08:00 and 09:00 a.m. The immediate analysis included regular blood tests such as PSA, cholesterol, triglycerides, and creatinine. Serum was obtained following a standardized protocol: (i) Blood plasma was collected in 5 ml tubes containing gel (Vakuette® Serum Gel with activator). (ii) The tubes were gently inverted five times and placed vertically for coagulation. (iii) After 30 minutes, the sample was centrifuged at 2000xg for 10 minutes. Visual inspection of the serum was conducted for any residues, and if present, the centrifugation process was repeated. (iv) The serum tube was refrigerated at 4°C until 0.5 ml aliquots were pipetted into cryotubes. (v) Finally, the cryotubes were stored at -80°C. Method for metabolomics analysis Serum metabolites were analyzed using proton NMR (nuclear magnetic resonance) spectroscopy (600 MHz instrument, Bruker Biospin) according to the procedure described in the literature (Jiménez et al., 2018) (Dona et al., 2014). Advanced data analytical tools using latent variable methods, described in detail in Rajalahti et al.(Rajalahti et al., 2009; Rajalahti et al., 2010), are employed for analyzing the resulting spectra. These tools enable the detection of biomarker signatures from complex spectral profiles with higher reliability and make interpreting the results more accessible. Statistical analysis: Patient characteristic data were analyzed using the Student t-test. Pearson correlation was employed to assess the association between metabolites and the International Prostate Symptom Score (IPSS). Metabolites were also compared between the LUTS and the control groups using a t-test without adjustment. Logistic regression analysis was then performed on the metabolites that showed statistic significant differences (p < 0.05) between LUTS and controll groups, adjusting for age and comorbidities quantified by the Charlson index and the ASA score. Two-tailed P-values and 95% confidence intervals were reported. For investigating the relationship between the IPSS and the explanatory variables, partial least squares (PLS) regression was employed, utilizing the approach described by Rajalahti and Kvalheim (Rajalahti & Kvalheim, 2011). The PLS regression method decomposes the explanatory variables into PLS components, which are linear combinations of the original variables maximizing the covariance with the outcome variable. This technique is particularly suitable for handling multicollinear variables. To validate the models, Monte Carlo resampling was performed with 100 repetitions, randomly selecting 50% of the observations as an external validation set for each repetition. Target projection (TP) was applied to the obtained PLS models to calculate selectivity ratios (SR) for each explanatory variable, following the methodology outlined by Rajalahti et al. (Rajalahti et al., 2009) The statistical software IBM SPSS Statistics Version 26 was used for the standard statistical analyses, while Sirius 11.5 (Pattern Recognition Systems AS) was utilized for PLS analysis. The project received approval from the Norwegian South-East Regional Ethics Committee (REC reference number: 2018/114). In accordance with the approved protocol, all participating patients provided informed consent before their inclusion in the study. Results Out of the 169 patients who met the inclusion criteria, 91 (53%) provided informed consent to participate in the study by signing the required forms. Eight patients were subsequently excluded from the analysis, including five with a diagnosis of prostate cancer, one with a diagnosis of acute leukaemia, one with a diagnosis of bladder cancer, and one who withdrew their informed consent within one month of the inclusion date. Consequently, a total of 83 patients were included in the statistical analysis. The general characteristics of these patients are presented in Table 1. Table 1 Patient characteristics Variables Control Group (IPSS <8) n = 20 LUTS Group (IPSS ≥8) n = 63 P-value Age (years) 63.4 (7.1) 64.4 (6.3) 0.546 IPSS, mean (SD) 4.3 (2.5) 18.3 (5.5) <0.001 ASA Classification, n (%) 1: 14 (70%) 2: 5 (25%) 3: 1 (5%) 1: 21 (33.3%) 2: 39 (61.9%) 3: 3 (4.8%) 0.013 Charlson Index, n (%) 0: 13 (65%) 1: 6 (30%) 2: 1 (5%) 0: 43 (68.3%) 1: 13 (20.6%) 2: 7 (11.1%) 0.807 Prostate Volume (cm 3 ), mean (SD) 56.7 (28.4) 52.1 (22.6) 0.466 Residual Urine (ml), mean (SD) 81.6 (36.2) 101.7 (131.8) 0.573 Q-max (ml/sec), mean (SD) 17.7 (11.4) 15.8 (8.8) 0.475 PSA (µg/L). mean (SD) 3.5 (3.4) 3.1 (3) 0.608 BMI (kg/m 2 ), mean (SD) 26.9 (3.2) 27.3 (4) 0.710 Note: IPSS: International Prostatic Symptom Score, LUTS: Lower urinary tract symptoms, ASA: American Society of Anestesiology, PSA: Prostatic Specific Antigen, BMI: Body Mass Index The patient cohort included in this study is representative of individuals referred to urologists for LUTS examinations. Conversely, the control group serves as a typical representation of men aged 50 to 80 years in Norway. Following NMR analysis, 41 metabolites were identified and considered for further statistical analysis. The analysis revealed significant Pearson correlations between the IPSS and several metabolites. Specifically, there was a negative correlation between IPSS and methionine (r = -0.301, p = 0.006) and threonine (r = -0.32, p = 0.003). On the other hand, a positive correlation was observed between IPSS and lactic acid (r = 0.294, p = 0.007) and 2-aminobutyric acid (r = 0.229, p = 0.038). However, the correlation between IPSS and pyruvic acid was not statistically significant (r = 0.207, p = 0.062). Table 2 Pearson correlations between metabolites and the IPSS IPSS Methionine [mmol/L] Threonine [mmol/L] Lactic acid [mmol/L] Pyruvic acid [mmol/L] 2-Aminobutyric acid [mmol/L] Pearson correlation -0.301 -0.32 0.294 0.207 0.229 P-value 0.006 0.003 0.007 0.062 0.038 Note: IPSS: International Prostatic Symptom Score The results of the t-test indicate significant differences between the Control Group and LUTS Group for the variables methionine (p = 0.002), threonine (p = 0.038), lactic acid (p = 0.031), and 2-aminobutyric acid (p = 0.043). However, no statistically significant difference was observed for pyruvic acid (p = 0.194). Table 3 Metabolites in LUTS versus control group Variables, mean (SD) Control Group (IPSS <8) n = 20 LUTS Group (IPSS ≥8) n = 62 P-value Methionine [mmol/L] 0.04 (0.02) 0.02 (0.02) 0.002 Threonine [mmol/L] 0.15 (0.1) 0.09 (0.09) 0.038 Lactic acid [mmol/L] 1.63 (0.36) 1.87 (0.44) 0.031 Pyruvic acid [mmol/L] 0.05 (0.02) 0.05 (0.03) 0.194 2-Aminobutyric acid [mmol/L] 0.01 (0.02) 0.03 (0.02) 0.043 Note: IPSS: International Prostatic Symptom Score; LUTS: Lower urinary tract symptoms Only threonine (p = 0.022) demonstrated a significant association with the IPSS in the multiple linear regression model. However, the variables methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847), and 2-aminobutyric acid (p = 0.244) did not retain their statistical significance with the IPSS (Table 4). Table 4 Multiple linear regression model with the IPSS as the dependent variable Variables B (95% CI) Beta p-value Age -0.01 (-0.3 – 0.26) -0.01 0.898 Charlson index -1.91 (-4.65 – 0.83) -0.17 0.168 ASA 3.14 (-0.43 – 7.72) 0.23 0.084 Methionine [mmol/L] -74.31 (-163.94 – 15.32) -0.18 0.103 Threonine [mmol/L] -20.91 (-38.66 – -3.17) -0.26 0.022 Lactic acid [mmol/L] 4.55 (-0.77 – 9.88) 0.25 0.093 Pyruvic acid [mmol/L] -7.87 (-88.76 – 73) -0.02 0.847 2-Aminobutyric acid [mmol/L] 46.74 (-32.51 – 125.99) 0.12 0.244 Note: Adjusted R 2 = 0.192. IPSS: International Prostatic Symptom Score The cross-validated PLS regression analysis revealed no valid PLS components associated with IPSS. Therefore, we could not report reliable explained variance or selectivity ratios with 95% CIs for the individual explanatory variables. Discussion This study aimed to investigate potential novel biomarkers for moderate to severe LUTS utilizing a metabolomics-based approach and employing statistical methods with distinct features from previous studies. The findings revealed moderate correlations between the IPSS and several metabolites, including methionine, threonine, lactic acid, pyruvic acid, and 2-aminobutyric acid. However, in the multiple linear regression model, only threonine exhibited a significant association with IPSS. Interestingly, none of the analyzed metabolites demonstrated a significant association with LUTS when utilising the robust PLS-regression method. The small to moderate correlations between IPSS and some metabolites, including methionine, threonine, lactic acid, pyruvic acid, and 2-aminobutyric acid, suggest that these metabolites may play a role in the pathophysiology of LUTS. However, the lack of significance in the PLS model highlights the need for more extensive studies using larger sample sizes and a broader range of metabolites to confirm or refute these findings. Additionally, the lack of significance in the PLS model emphasizes the importance of using appropriate statistical methods when exploring new biomarkers in urology. The significant association between lower threonine serum levels and IPSS in the multiple linear regression model suggests that threonine may be a potential biomarker for LUTS. Threonine is an essential amino acid involved in protein synthesis, immune function, energy metabolism and collagen production(Tang, Tan, Ma, & Ma, 2021). It is also a precursor for glycine, known to relax smooth muscles, including the urinary bladder(Hong, Son, Kim, Oh, & Choi, 2005). We can extrapolate that threonine may play a role in the pathophysiology of LUTS by affecting the contractile properties of the urinary bladder. Threonine also helps maintain the health of the mucous membranes in the urinary tract by promoting mucus production, which helps protect the tissues from damage caused by acidic urine and other harmful substances(Tang et al., 2021). In addition, threonine has been shown to have anti-inflammatory properties, which can help reduce inflammation in the urinary tract caused by infections or other conditions(Manosalva et al., 2021). Threonine also plays a role in glucose metabolism and insulin signaling. Some evidence suggests that a low threonine level may be associated with insulin resistance and other features of metabolic syndrome. However, the evidence is not consistent(Guo, 2014). Threonine has been found to inhibit fat mass and improve lipid metabolism in already obese mice(Ma et al., 2020). On the other hand, a study in obese and overweight adults found that threonine supplementation did not improve insulin sensitivity, glucose metabolism, or other markers of metabolic health(Rigamonti et al., 2020) Validating biomarkers may present challenges, particularly in measuring subtle differences in metabolite concentrations between target and control groups, the absence of targeted metabolomic experiments for follow-up, and the influence of inter-individual variation due to genetic and environmental factors (Johnson et al., 2016). To address confounding factors and identify metabolites correlated with biological processes, it is crucial to establish an appropriate experimental design and statistical power, employ questionnaires with population stratification, and utilize suitable statistical tools (Ellis et al., 2012). The analysis of metabolomics data poses significant challenges, primarily due to high data dimensionality (with numerous variables and limited samples) and the risk of overfitting the model (where the selected statistical approach overfits the training data but performs poorly on subsequent samples) (Gromski et al., 2015). Partial least squares regression is a commonly employed method for multivariate discrimination between sample classes in metabolomics analysis (Szymańska, Saccenti, Smilde, & Westerhuis, 2012). In 2018, Mitsui et al. published the only metabolomics study to investigate male LUTS (Mitsui et al., 2018). Using the Mann-Whitney U test and multivariate logistic regression as statistical tools, they identified nine metabolites that exhibited differences between the control and LUTS groups. Specifically, they found that increased levels of glutamate and decreased levels of arginine, asparagine, citrulline, and glutamine were associated with LUTS in males. The authors postulated that abnormal glucose metabolism can be triggered as a response to starvation leading to a deceleration of the citric acid cycle. Enhanced amino acid metabolism may occur in patients with LUTS due to accelerated gluconeogenesis, supplying substances to the citric acid cycle. The urea cycle, correlated with the citric acid cycle, may also slow down in synchronization. Furthermore, reductions in arginine levels could potentially impact nitric oxide synthase. Therefore, alterations in plasma amino acid profiles may be associated with the onset of LUTS. However, it is important to acknowledge the limitations of this study, as acknowledged by the authors themselves. These limitations include the small sample size, lack of rigorous inclusion/exclusion criteria (as some patients were already on LUTS medication, which could impact the metabolomics results), and the control group primarily comprising urological patients, potentially not representing the average male in the target age group. Additionally, the study lacked appropriate statistical methods for biomarker validation, as discussed earlier. Our study possesses several strengths. Firstly, it employed a metabolomics-based approach, enabling a comprehensive and unbiased assessment of the metabolic profile associated with lower urinary tract symptoms (LUTS). Additionally, the study implemented strict inclusion and exclusion criteria, ensuring participants were selected from the average male within the specified age group. Moreover, appropriate statistical methods, such as multiple linear regression and partial least squares (PLS), were employed to identify potential biomarkers for LUTS. However, the study is not without limitations. Firstly, the sample size was relatively small, which may restrict the generalizability of the findings. Additionally, the study acknowledges the inherent limitations of metabolite sample preparation, which may introduce fluctuations in serum metabolite levels that are not necessarily representative of their actual variations. Considering the limitations of our own study and previously published research, specific patterns suggest a potential link between LUTS and disturbances in amino acid metabolism. Specifically, disruptions in gluconeogenesis from amino acids appear to be implicated. However, these findings remain inconclusive and necessitate validation in future studies with larger sample sizes and targeted metabolomics approaches. Suppose a consistent serum amino acid profile associated with LUTS can be identified. In that case, it holds promise for the development of therapeutic interventions. In conclusion, this study provides preliminary evidence for the potential use of metabolomics in identifying novel biomarkers for LUTS. Threonine was identified as a potential biomarker for LUTS using multiple linear regression but not by using the partial least square regression. Further studies are required to confirm this finding. The study highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology and the need for more extensive studies using larger sample sizes and targeted metabolomics to identify novel biomarkers for LUTS. Declarations Ethical Approval The project is approved by the Norwegian South-East Regional Ethics Committee (REC reference number: 2018/114). In accordance with the approval, all participating patients signed an informed consent form prior to inclusion Competing interests None of the authors report conflicts of interest Authors' contributions F.H-N was primarily responsible for the design of the study, data collection, data analysis, and statistical analysis. Additionally, F.H-N wrote the main manuscript text and prepared the tables. J.R.A provided assistance in the design of the study, statistical analysis, particularly in the application of partial least squares (PLS) regression and supervision. T.R.K contributed significantly to the metabolomics analysis and provided support in the application of PLS regression. C.B played a crucial role in the design of the study, statistical analysis and the scientific content of the article, and supervision, ensuring adherence to the highest scientific standards. All authors actively participated in reviewing and critically evaluating the manuscript, making substantial contributions to its refinement. All authors have approved the submitted version of the manuscript Funding The project is part of the PhD of the first author F.V H-N. The Førde Central Hospital supported the project through a PhD grant, project number: F-12894-D10602-01-12-01 Availability of data and materials All the raw data, both the database and the metabolomics raw data is available upon request. References Cornu J.N., M. G., H. Hashim, T.R.W. Herrmann, S. Malde, C. Netsch, M. Rieken, V. Sakalis, M. Tutolo, M. Baboudjian, N. Bhatt, M. Creta, M. Karavitakis, L. Moris. (2023). EAU Guideline on Non-Neurogenic Male Lower Urinary Tract Symptoms (LUTS), incl. Benign Prostatic Obstruction (BPO). Dona, A. C., Jiménez, B., Schäfer, H., Humpfer, E., Spraul, M., Lewis, M. R., . . . Nicholson, J. K. (2014). 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J Proteome Res, 9 (7), 3608-3620. doi:10.1021/pr100142m Rajalahti, T., & Kvalheim, O. M. (2011). Multivariate data analysis in pharmaceutics: a tutorial review. Int J Pharm, 417 (1-2), 280-290. doi:10.1016/j.ijpharm.2011.02.019 Rigamonti, A. E., Leoncini, R., De Col, A., Tamini, S., Cicolini, S., Abbruzzese, L., . . . Sartorio, A. (2020). The Appetite-Suppressant and GLP-1-Stimulating Effects of Whey Proteins in Obese Subjects are Associated with Increased Circulating Levels of Specific Amino Acids. Nutrients, 12 (3). doi:10.3390/nu12030775 Roehrborn, C. G. (2008). BPH progression: concept and key learning from MTOPS, ALTESS, COMBAT, and ALF-ONE. BJU Int, 101 Suppl 3 , 17-21. doi:10.1111/j.1464-410X.2008.07497.x Speakman, M., Kirby, R., Doyle, S., & Ioannou, C. (2015). Burden of male lower urinary tract symptoms (LUTS) suggestive of benign prostatic hyperplasia (BPH) - focus on the UK. BJU Int, 115 (4), 508-519. doi:10.1111/bju.12745 Szymańska, E., Saccenti, E., Smilde, A. K., & Westerhuis, J. A. (2012). Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics, 8 (Suppl 1), 3-16. doi:10.1007/s11306-011-0330-3 Tang, Q., Tan, P., Ma, N., & Ma, X. (2021). Physiological Functions of Threonine in Animals: Beyond Nutrition Metabolism. Nutrients, 13 (8). doi:10.3390/nu13082592 Wang, W., Guo, Y., Zhang, D., Tian, Y., & Zhang, X. (2015). The prevalence of benign prostatic hyperplasia in mainland China: evidence from epidemiological surveys. Sci Rep, 5 , 13546. doi:10.1038/srep13546 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Sep, 2023 Read the published version in Metabolomics → Version 1 posted Editorial decision: Major revision 01 Aug, 2023 Reviews received at journal 01 Aug, 2023 Reviews received at journal 24 Jul, 2023 Reviewers agreed at journal 23 Jul, 2023 Reviewers agreed at journal 20 Jul, 2023 Reviewers invited by journal 19 Jun, 2023 Editor assigned by journal 19 Jun, 2023 Submission checks completed at journal 19 Jun, 2023 First submitted to journal 17 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3074710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":211156516,"identity":"e1467cf8-b693-4e93-8703-4e2fe11a7e96","order_by":0,"name":"Florin V Hopland-Nechita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBAC9nYgkQBm8jAcYGCw4WFgB7NxA57DyFoOMKTxMDATowXKZABaA+QS1MLMfOzDwx0MefzsZw8e/lBzXoa/mfmYxBsGOzndBlxa2JJnJJ5hKJbsyUs4cODYbR6Jw2xpknMYko3NDmDXYs/MY8yQ2MaQuOEGj8GBA2y3eQyYecykgf5K3IZDCw9My36wln/nSNCyQQKo5WDbAWK0sCUDtUgkzjiTY3DgbF8yyC/JlnMMcPuFh735MOPPNpvE/vYzxh8qvtnZ87c3H7zxpsJODpcWKJBAFzDAq3wUjIJRMApGAQEAAJvxUpqaTIyuAAAAAElFTkSuQmCC","orcid":"","institution":"Førde Central Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Florin","middleName":"V","lastName":"Hopland-Nechita","suffix":""},{"id":211156519,"identity":"d2bdedce-7898-43b5-a76c-7bc31db87719","order_by":1,"name":"John R Andersen","email":"","orcid":"","institution":"Western Norway University of Applied Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"John","middleName":"R","lastName":"Andersen","suffix":""},{"id":211156521,"identity":"5461e74f-fb5c-49bb-b132-4c024f44f590","order_by":2,"name":"Tarja Rajalahti Kvalheim","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tarja","middleName":"Rajalahti","lastName":"Kvalheim","suffix":""},{"id":211156523,"identity":"72a05aed-a8d6-4055-bb5b-6ba9e8970571","order_by":3,"name":"Christian Beisland","email":"","orcid":"","institution":"University of Bergen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Beisland","suffix":""}],"badges":[],"createdAt":"2023-06-17 05:29:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3074710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3074710/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11306-023-02046-2","type":"published","date":"2023-09-12T15:01:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43301128,"identity":"c0a30a44-ab98-472c-a544-a5eea460f52c","added_by":"auto","created_at":"2023-09-18 15:08:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":236180,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3074710/v1/202f4180-4777-429e-a192-d0ef5bc2a4fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying possible biomarkers of lower urinary tract symptoms using metabolomics and partial least square regression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBenign prostatic hyperplasia (BPH) is a prominent urological issue among elderly men in various nations, serving as a major etiology for lower urinary tract symptoms (LUTS) in this population. Descriptive epidemiological studies have reported varying prevalence rates of BPH, ranging from 12\u0026ndash;42%(Lee, Chan, \u0026amp; Lai, 2017), with one study estimating a lifetime risk of 36.6%(Wang, Guo, Zhang, Tian, \u0026amp; Zhang, 2015). Globally, the number of prevalent BPH cases was estimated at 940\u0026nbsp;million in 2019, compared to 511\u0026nbsp;million in 2000. The substantial increase of 70.5% in the global prevalence of BPH can predominantly be ascribed to population ageing. This emerging trend, coupled with the escalating life expectancy worldwide, is projected to further augment the overall burden of BPH. The disease has been linked to increased healthcare costs and diminished quality of life (GBD, 2022). It is associated with various serious complications, including a higher risk of falls, depression, and reduced health-related quality of life (HRQoL) indicators such as sleep, psychological well-being, daily activities, and sexual health. The impact of BPH extends beyond the patient to their family and society.(Speakman, Kirby, Doyle, \u0026amp; Ioannou, 2015) Consequently, there is a pressing need for proactive monitoring and planning to address the potential strain on healthcare systems. Additionally, this situation underscores the necessity for improved patient stratification, considering the most effective treatment strategies, and adopting personalized medicine approaches (Feinstein L., 2018)\u003c/p\u003e \u003cp\u003eBiomarkers for BPH can potentially distinguish between different BPH-related conditions. Currently, these conditions are primarily identified based on symptoms. Biomarker research aims to identify the risk of disease progression and guide the development of earlier, personalized, efficient, and cost-effective medical approaches for managing BPH-related LUTS. Furthermore, these efforts in biomarker discovery could provide a new understanding of the molecular causes of histological BPH and the clinical presentations of BPH, which have remained unclear despite ongoing research(Mullins et al., 2008)\u003c/p\u003e \u003cp\u003eThe serum prostate-specific antigen (PSA) is the only available biomarker in clinical practice (Cornu J.N., 2023). Pooled analyses of randomized controlled trials (RCTs) involving men with LUTS and presumed benign prostatic obstruction (BPO) have demonstrated that PSA exhibits good predictive value for assessing prostate volume, prostate growth, changes in symptoms, quality of life (QoL)/bother and clinical progression to urinary retention (Kozminski, Wei, Nelson, \u0026amp; Kent, 2015; Patel et al., 2018; Roehrborn, 2008)\u003c/p\u003e \u003cp\u003eIn a recent meta-analysis, the collective evidence regarding microRNAs (miRNAs) involved in the pathogenesis of BPH was reviewed. The analysis highlighted miR-221 as a notably associated miRNA with BPH, indicating its potential as a biomarker and therapeutic target for early detection and management of the condition.(Greco et al., 2019)\u003c/p\u003e \u003cp\u003eThe term \"metabolome\" was initially introduced by Oliver et al. in 1998 to describe the complete collection of low molecular weight compounds found within a cell, which are necessary for its maintenance, growth, and normal function. The metabolome also contributes to the metabolic reactions occurring within a cell during specific physiological or developmental stages(Oliver, Winson, Kell, \u0026amp; Baganz, 1998). Metabolomics, an analytical chemistry method, aims to measure a portion or the entirety of the metabolome. It can potentially discover new biomarkers that can predict the incidence, severity, and progression of diseases and identify underlying pathophysiological abnormalities (Newgard, 2017). Initially, metabolomics primarily focused on biomarker discovery (Johnson, Ivanisevic, \u0026amp; Siuzdak, 2016). Consequently, plasma trimethylamine N-oxide (TMAO) and urinary taurine have been identified as markers for cardiovascular disease (Koeth et al., 2013). A link has been established between dysregulated metabolism of branched-chain amino acids (BCAAs) and various cardiometabolic diseases (Newgard, 2017). In 2018, Mitsui et al. conducted the only metabolomics study to investigate male LUTS. They postulated that the abnormal glucose metabolism observed in metabolic syndrome may also be present in LUTS patients. If confirmed, these hypotheses could open new avenues for developing novel LUTS treatments by elucidating modifications in the amino acid profiles of plasma (Mitsui et al., 2018).\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to validate and replicate the findings of the previously published metabolomics approach study that focused on male LUTS secondary to BPH. Furthermore, our study employed a distinct statistical method, partial least squares (PLS) regression, widely recognized as the standard statistical method in metabolomics research.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cp\u003eA cross-sectional study was conducted at the Urologic Outpatient Clinic of F\u0026oslash;rde Central Hospital, with an inclusion period spanning from November 20, 2018, to February 17, 2021. Patients were referred by their general practitioners (GPs). They were selected by the first author based on strict inclusion and exclusion criteria. Our research group previously published the study protocol (Hopland-Nechita, Andersen, \u0026amp; Beisland, 2022). At the time of inclusion, none of the enrolled patients were undergoing medical treatment for LUTS. The control group consisted of patients referred to the Surgical Outpatient Clinic of F\u0026oslash;rde Central Hospital for conditions other than LUTS, who consented to participate in the study. Detailed medical histories and lists of current medications were obtained, and the comorbidities were quantified using the Charlson index and ASA (American Society of Anesthesiology) scores.\u003c/p\u003e \u003cp\u003e \u003cem\u003eBlood samples\u003c/em\u003e were collected from fasting patients in the morning, between 08:00 and 09:00 a.m. The immediate analysis included regular blood tests such as PSA, cholesterol, triglycerides, and creatinine. Serum was obtained following a standardized protocol: (i) Blood plasma was collected in 5 ml tubes containing gel (Vakuette\u0026reg; Serum Gel with activator). (ii) The tubes were gently inverted five times and placed vertically for coagulation. (iii) After 30 minutes, the sample was centrifuged at 2000xg for 10 minutes. Visual inspection of the serum was conducted for any residues, and if present, the centrifugation process was repeated. (iv) The serum tube was refrigerated at 4\u0026deg;C until 0.5 ml aliquots were pipetted into cryotubes. (v) Finally, the cryotubes were stored at -80\u0026deg;C.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMethod for metabolomics analysis\u003c/h2\u003e \u003cp\u003eSerum metabolites were analyzed using proton NMR (nuclear magnetic resonance) spectroscopy (600 MHz instrument, Bruker Biospin) according to the procedure described in the literature (Jim\u0026eacute;nez et al., 2018) (Dona et al., 2014). Advanced data analytical tools using latent variable methods, described in detail in Rajalahti et al.(Rajalahti et al., 2009; Rajalahti et al., 2010), are employed for analyzing the resulting spectra. These tools enable the detection of biomarker signatures from complex spectral profiles with higher reliability and make interpreting the results more accessible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003ePatient characteristic data were analyzed using the Student t-test. Pearson correlation was employed to assess the association between metabolites and the International Prostate Symptom Score (IPSS). Metabolites were also compared between the LUTS and the control groups using a t-test without adjustment. Logistic regression analysis was then performed on the metabolites that showed statistic significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between LUTS and controll groups, adjusting for age and comorbidities quantified by the Charlson index and the ASA score. Two-tailed P-values and 95% confidence intervals were reported.\u003c/p\u003e \u003cp\u003eFor investigating the relationship between the IPSS and the explanatory variables, partial least squares (PLS) regression was employed, utilizing the approach described by Rajalahti and Kvalheim (Rajalahti \u0026amp; Kvalheim, 2011). The PLS regression method decomposes the explanatory variables into PLS components, which are linear combinations of the original variables maximizing the covariance with the outcome variable. This technique is particularly suitable for handling multicollinear variables. To validate the models, Monte Carlo resampling was performed with 100 repetitions, randomly selecting 50% of the observations as an external validation set for each repetition. Target projection (TP) was applied to the obtained PLS models to calculate selectivity ratios (SR) for each explanatory variable, following the methodology outlined by Rajalahti et al. (Rajalahti et al., 2009)\u003c/p\u003e \u003cp\u003eThe statistical software IBM SPSS Statistics Version 26 was used for the standard statistical analyses, while Sirius 11.5 (Pattern Recognition Systems AS) was utilized for PLS analysis.\u003c/p\u003e \u003cp\u003e The project received approval from the Norwegian South-East Regional Ethics Committee (REC reference number: 2018/114). In accordance with the approved protocol, all participating patients provided informed consent before their inclusion in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOut of the 169 patients who met the inclusion criteria, 91 (53%) provided informed consent to participate in the study by signing the required forms. Eight patients were subsequently excluded from the analysis, including five with a diagnosis of prostate cancer, one with a diagnosis of acute leukaemia, one with a diagnosis of bladder cancer, and one who withdrew their informed consent within one month of the inclusion date. Consequently, a total of 83 patients were included in the statistical analysis. The general characteristics of these patients are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Patient characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003eControl Group\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(IPSS \u0026lt;8) n = 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003eLUTS Group\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(IPSS\u0026nbsp;\u0026ge;8) n = 63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e63.4 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e64.4 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eIPSS, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e4.3 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e18.3 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eASA Classification, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e1: 14 (70%)\u003c/p\u003e\n \u003cp\u003e2: 5 (25%)\u003c/p\u003e\n \u003cp\u003e3: 1 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e1: 21 (33.3%)\u003c/p\u003e\n \u003cp\u003e2: 39 (61.9%)\u003c/p\u003e\n \u003cp\u003e3: 3 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eCharlson Index, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e0: 13 (65%)\u003c/p\u003e\n \u003cp\u003e1: 6 (30%)\u003c/p\u003e\n \u003cp\u003e2: 1 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e0: 43 (68.3%)\u003c/p\u003e\n \u003cp\u003e1: 13 (20.6%)\u003c/p\u003e\n \u003cp\u003e2: 7 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eProstate Volume (cm\u003csup\u003e3\u003c/sup\u003e), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e56.7 (28.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e52.1 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eResidual Urine (ml), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e81.6 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e101.7 (131.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eQ-max (ml/sec), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e17.7 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e15.8 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003ePSA (\u0026micro;g/L). mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e3.5 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e3.1 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.161993769470406%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.4797507788162%\" valign=\"top\"\u003e\n \u003cp\u003e26.9 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.5202492211838%\" valign=\"top\"\u003e\n \u003cp\u003e27.3 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.838006230529595%\" valign=\"top\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Note: IPSS: International Prostatic Symptom Score, LUTS: Lower urinary tract symptoms, ASA: American Society of Anestesiology, PSA: Prostatic Specific Antigen, BMI: Body Mass Index \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe patient cohort included in this study is representative of individuals referred to urologists for LUTS examinations. Conversely, the control group serves as a typical representation of men aged 50 to 80 years in Norway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing NMR analysis, 41 metabolites were identified and considered for further statistical analysis. The analysis revealed significant Pearson correlations between the IPSS and several metabolites. Specifically, there was a negative correlation between IPSS and methionine (r = -0.301, p = 0.006) and threonine (r = -0.32, p = 0.003). On the other hand, a positive correlation was observed between IPSS and lactic acid (r = 0.294, p = 0.007) and 2-aminobutyric acid (r = 0.229, p = 0.038). However, the correlation between IPSS and pyruvic acid was not statistically significant (r = 0.207, p = 0.062).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Pearson correlations between metabolites and the IPSS\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003eIPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37190082644628%\" valign=\"top\"\u003e\n \u003cp\u003eMethionine [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.570247933884298%\" valign=\"top\"\u003e\n \u003cp\u003eThreonine\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.066115702479339%\" valign=\"top\"\u003e\n \u003cp\u003eLactic acid\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.057851239669422%\" valign=\"top\"\u003e\n \u003cp\u003ePyruvic acid\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;[mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.47107438016529%\" valign=\"top\"\u003e\n \u003cp\u003e2-Aminobutyric acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003ePearson correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e-0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.570247933884298%\" valign=\"top\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.066115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.057851239669422%\" valign=\"top\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.47107438016529%\" valign=\"top\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.462809917355372%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.37190082644628%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.570247933884298%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.066115702479339%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.057851239669422%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.47107438016529%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: IPSS: International Prostatic Symptom Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the t-test indicate significant differences between the Control Group and LUTS Group for the variables methionine (p = 0.002), threonine (p = 0.038), lactic acid (p = 0.031), and 2-aminobutyric acid (p = 0.043). However, no statistically significant difference was observed for pyruvic acid (p = 0.194).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Metabolites in LUTS versus control group\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.048465266558967%\" valign=\"top\"\u003e\n \u003cp\u003eVariables, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.33279483037157%\" valign=\"top\"\u003e\n \u003cp\u003eControl Group (IPSS \u0026lt;8)\u003c/p\u003e\n \u003cp\u003en = 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.394184168012924%\" valign=\"top\"\u003e\n \u003cp\u003eLUTS Group (IPSS\u0026nbsp;\u0026ge;8)\u003c/p\u003e\n \u003cp\u003en = 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.224555735056544%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.048465266558967%\" valign=\"top\"\u003e\n \u003cp\u003eMethionine [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.33279483037157%\" valign=\"top\"\u003e\n \u003cp\u003e0.04 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.394184168012924%\" valign=\"top\"\u003e\n \u003cp\u003e0.02 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.224555735056544%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.048465266558967%\" valign=\"top\"\u003e\n \u003cp\u003eThreonine [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.33279483037157%\" valign=\"top\"\u003e\n \u003cp\u003e0.15 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.394184168012924%\" valign=\"top\"\u003e\n \u003cp\u003e0.09 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.224555735056544%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.048465266558967%\" valign=\"top\"\u003e\n \u003cp\u003eLactic acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.33279483037157%\" valign=\"top\"\u003e\n \u003cp\u003e1.63 (0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.394184168012924%\" valign=\"top\"\u003e\n \u003cp\u003e1.87 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.224555735056544%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.048465266558967%\" valign=\"top\"\u003e\n \u003cp\u003ePyruvic acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.33279483037157%\" valign=\"top\"\u003e\n \u003cp\u003e0.05 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.394184168012924%\" valign=\"top\"\u003e\n \u003cp\u003e0.05 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.224555735056544%\" valign=\"top\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.048465266558967%\" valign=\"top\"\u003e\n \u003cp\u003e2-Aminobutyric acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.33279483037157%\" valign=\"top\"\u003e\n \u003cp\u003e0.01 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.394184168012924%\" valign=\"top\"\u003e\n \u003cp\u003e0.03 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.224555735056544%\" valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: IPSS: International Prostatic Symptom Score; LUTS: Lower urinary tract symptoms\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnly threonine (p = 0.022) demonstrated a significant association with the IPSS in the multiple linear regression model. However, the variables methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847), and 2-aminobutyric acid (p = 0.244) did not retain their statistical significance with the IPSS (Table 4). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Multiple linear regression model with the IPSS as the dependent variable\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e-0.01 (-0.3 \u0026ndash; 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eCharlson index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e-1.91 (-4.65 \u0026ndash; 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eASA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e3.14 (-0.43 \u0026ndash; 7.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eMethionine [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e-74.31 (-163.94 \u0026ndash; 15.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eThreonine [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e-20.91 (-38.66 \u0026ndash; -3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003eLactic acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e4.55 (-0.77 \u0026ndash; 9.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003ePyruvic acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e-7.87 (-88.76 \u0026ndash; 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.11340206185567%\" valign=\"top\"\u003e\n \u003cp\u003e2-Aminobutyric acid [mmol/L]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.95876288659794%\" valign=\"top\"\u003e\n \u003cp\u003e46.74 (-32.51 \u0026ndash; 125.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"top\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;Note: Adjusted R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 0.192. IPSS: International Prostatic Symptom Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cross-validated PLS regression analysis revealed no valid PLS components associated with IPSS. Therefore, we could not report reliable explained variance or selectivity ratios with 95% CIs for the individual explanatory variables.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to investigate potential novel biomarkers for moderate to severe LUTS utilizing a metabolomics-based approach and employing statistical methods with distinct features from previous studies. The findings revealed moderate correlations between the IPSS and several metabolites, including methionine, threonine, lactic acid, pyruvic acid, and 2-aminobutyric acid. However, in the multiple linear regression model, only threonine exhibited a significant association with IPSS. Interestingly, none of the analyzed metabolites demonstrated a significant association with LUTS when utilising the robust PLS-regression method.\u003c/p\u003e \u003cp\u003eThe small to moderate correlations between IPSS and some metabolites, including methionine, threonine, lactic acid, pyruvic acid, and 2-aminobutyric acid, suggest that these metabolites may play a role in the pathophysiology of LUTS. However, the lack of significance in the PLS model highlights the need for more extensive studies using larger sample sizes and a broader range of metabolites to confirm or refute these findings. Additionally, the lack of significance in the PLS model emphasizes the importance of using appropriate statistical methods when exploring new biomarkers in urology.\u003c/p\u003e \u003cp\u003eThe significant association between lower threonine serum levels and IPSS in the multiple linear regression model suggests that threonine may be a potential biomarker for LUTS. Threonine is an essential amino acid involved in protein synthesis, immune function, energy metabolism and collagen production(Tang, Tan, Ma, \u0026amp; Ma, 2021). It is also a precursor for glycine, known to relax smooth muscles, including the urinary bladder(Hong, Son, Kim, Oh, \u0026amp; Choi, 2005). We can extrapolate that threonine may play a role in the pathophysiology of LUTS by affecting the contractile properties of the urinary bladder. Threonine also helps maintain the health of the mucous membranes in the urinary tract by promoting mucus production, which helps protect the tissues from damage caused by acidic urine and other harmful substances(Tang et al., 2021). In addition, threonine has been shown to have anti-inflammatory properties, which can help reduce inflammation in the urinary tract caused by infections or other conditions(Manosalva et al., 2021). Threonine also plays a role in glucose metabolism and insulin signaling. Some evidence suggests that a low threonine level may be associated with insulin resistance and other features of metabolic syndrome. However, the evidence is not consistent(Guo, 2014). Threonine has been found to inhibit fat mass and improve lipid metabolism in already obese mice(Ma et al., 2020). On the other hand, a study in obese and overweight adults found that threonine supplementation did not improve insulin sensitivity, glucose metabolism, or other markers of metabolic health(Rigamonti et al., 2020)\u003c/p\u003e \u003cp\u003eValidating biomarkers may present challenges, particularly in measuring subtle differences in metabolite concentrations between target and control groups, the absence of targeted metabolomic experiments for follow-up, and the influence of inter-individual variation due to genetic and environmental factors (Johnson et al., 2016). To address confounding factors and identify metabolites correlated with biological processes, it is crucial to establish an appropriate experimental design and statistical power, employ questionnaires with population stratification, and utilize suitable statistical tools (Ellis et al., 2012). The analysis of metabolomics data poses significant challenges, primarily due to high data dimensionality (with numerous variables and limited samples) and the risk of overfitting the model (where the selected statistical approach overfits the training data but performs poorly on subsequent samples) (Gromski et al., 2015). Partial least squares regression is a commonly employed method for multivariate discrimination between sample classes in metabolomics analysis (Szymańska, Saccenti, Smilde, \u0026amp; Westerhuis, 2012).\u003c/p\u003e \u003cp\u003eIn 2018, Mitsui et al. published the only metabolomics study to investigate male LUTS (Mitsui et al., 2018). Using the Mann-Whitney U test and multivariate logistic regression as statistical tools, they identified nine metabolites that exhibited differences between the control and LUTS groups. Specifically, they found that increased levels of glutamate and decreased levels of arginine, asparagine, citrulline, and glutamine were associated with LUTS in males. The authors postulated that abnormal glucose metabolism can be triggered as a response to starvation leading to a deceleration of the citric acid cycle. Enhanced amino acid metabolism may occur in patients with LUTS due to accelerated gluconeogenesis, supplying substances to the citric acid cycle. The urea cycle, correlated with the citric acid cycle, may also slow down in synchronization. Furthermore, reductions in arginine levels could potentially impact nitric oxide synthase. Therefore, alterations in plasma amino acid profiles may be associated with the onset of LUTS. However, it is important to acknowledge the limitations of this study, as acknowledged by the authors themselves. These limitations include the small sample size, lack of rigorous inclusion/exclusion criteria (as some patients were already on LUTS medication, which could impact the metabolomics results), and the control group primarily comprising urological patients, potentially not representing the average male in the target age group. Additionally, the study lacked appropriate statistical methods for biomarker validation, as discussed earlier.\u003c/p\u003e \u003cp\u003eOur study possesses several strengths. Firstly, it employed a metabolomics-based approach, enabling a comprehensive and unbiased assessment of the metabolic profile associated with lower urinary tract symptoms (LUTS). Additionally, the study implemented strict inclusion and exclusion criteria, ensuring participants were selected from the average male within the specified age group. Moreover, appropriate statistical methods, such as multiple linear regression and partial least squares (PLS), were employed to identify potential biomarkers for LUTS.\u003c/p\u003e \u003cp\u003eHowever, the study is not without limitations. Firstly, the sample size was relatively small, which may restrict the generalizability of the findings. Additionally, the study acknowledges the inherent limitations of metabolite sample preparation, which may introduce fluctuations in serum metabolite levels that are not necessarily representative of their actual variations.\u003c/p\u003e \u003cp\u003eConsidering the limitations of our own study and previously published research, specific patterns suggest a potential link between LUTS and disturbances in amino acid metabolism. Specifically, disruptions in gluconeogenesis from amino acids appear to be implicated. However, these findings remain inconclusive and necessitate validation in future studies with larger sample sizes and targeted metabolomics approaches. Suppose a consistent serum amino acid profile associated with LUTS can be identified. In that case, it holds promise for the development of therapeutic interventions.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides preliminary evidence for the potential use of metabolomics in identifying novel biomarkers for LUTS. Threonine was identified as a potential biomarker for LUTS using multiple linear regression but not by using the partial least square regression. Further studies are required to confirm this finding. The study highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology and the need for more extensive studies using larger sample sizes and targeted metabolomics to identify novel biomarkers for LUTS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project is approved by the Norwegian South-East Regional Ethics Committee (REC reference number: 2018/114). In accordance with the approval, all participating patients signed an informed consent form prior to inclusion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors report conflicts of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.H-N was primarily responsible for the design of the study, data collection, data analysis, and statistical analysis. Additionally, F.H-N wrote the main manuscript text and prepared the tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJ.R.A provided assistance in the design of the study, statistical analysis, particularly in the application of partial least squares (PLS) regression and supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.R.K contributed significantly to the metabolomics analysis and provided support in the application of PLS regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eC.B played a crucial role in the design of the study, statistical analysis and the scientific content of the article, and supervision, ensuring adherence to the highest scientific standards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors actively participated in reviewing and critically evaluating the manuscript, making substantial contributions to its refinement. All authors have approved the submitted version of the manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project is part of the PhD of the first author F.V H-N. The F\u0026oslash;rde Central Hospital supported the project through a PhD grant, project number: F-12894-D10602-01-12-01\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the raw data, both the database and the metabolomics raw data is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCornu J.N., M. G., H. Hashim, T.R.W. Herrmann, S. Malde, C. Netsch, M. Rieken, V. Sakalis, M. Tutolo, M. Baboudjian, N. Bhatt, M. Creta, M. Karavitakis, L. Moris. (2023). EAU Guideline on Non-Neurogenic Male Lower Urinary Tract Symptoms (LUTS), incl. 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BPH progression: concept and key learning from MTOPS, ALTESS, COMBAT, and ALF-ONE. \u003cem\u003eBJU Int, 101 Suppl 3\u003c/em\u003e, 17-21. doi:10.1111/j.1464-410X.2008.07497.x\u003c/li\u003e\n \u003cli\u003eSpeakman, M., Kirby, R., Doyle, S., \u0026amp; Ioannou, C. (2015). Burden of male lower urinary tract symptoms (LUTS) suggestive of benign prostatic hyperplasia (BPH) - focus on the UK. \u003cem\u003eBJU Int, 115\u003c/em\u003e(4), 508-519. doi:10.1111/bju.12745\u003c/li\u003e\n \u003cli\u003eSzymańska, E., Saccenti, E., Smilde, A. K., \u0026amp; Westerhuis, J. A. (2012). Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies. \u003cem\u003eMetabolomics, 8\u003c/em\u003e(Suppl 1), 3-16. doi:10.1007/s11306-011-0330-3\u003c/li\u003e\n \u003cli\u003eTang, Q., Tan, P., Ma, N., \u0026amp; Ma, X. (2021). Physiological Functions of Threonine in Animals: Beyond Nutrition Metabolism. \u003cem\u003eNutrients, 13\u003c/em\u003e(8). doi:10.3390/nu13082592\u003c/li\u003e\n \u003cli\u003eWang, W., Guo, Y., Zhang, D., Tian, Y., \u0026amp; Zhang, X. (2015). The prevalence of benign prostatic hyperplasia in mainland China: evidence from epidemiological surveys. \u003cem\u003eSci Rep, 5\u003c/em\u003e, 13546. doi:10.1038/srep13546\u0026nbsp;\u003c/li\u003e\n\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":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LUTS, metabolomics, BPH, biomarker, PLS","lastPublishedDoi":"10.21203/rs.3.rs-3074710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3074710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe \u003cem\u003eobjective\u003c/em\u003e of this study was to explore potential novel biomarkers for moderate to severe lower urinary tract symptoms (LUTS) using a metabolomics-based approach, and statistical methods with significant different features than previous reported\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients and the controls were selected to participate in the study according to inclusion/exclusion criteria (n = 82). We recorded the following variables: International prostatic symptom score (IPSS), prostate volume, comorbidities, PSA, height, weight, triglycerides, glycemia, HDL cholesterol, and blood pressure. The study of 41 plasma metabolites was done using the nuclear magnetic resonance spectroscopy technique. First, the correlations between the metabolites and the IPSS were done using Pearson. Second, significant biomarkers of LUTS from metabolites were further analysed using a multiple linear regression model. Finally, we validated the findings using partial least square regression (PLS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmall to moderate correlations were found between IPSS and methionine (-0.301), threonine (-0.320), lactic acid (0.294), pyruvic acid (0.207) and 2-aminobutyric-acid (0.229). The multiple linear regression model revealed that only threonine (p = 0.022) was significantly associated with IPSS, whereas methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847) and 2-aminobutyric-acid (p = 0.244) lost their significance. However, all metabolites lost their significance in the PLS model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen using the robust PLS-regression method, none of the metabolites in our analysis had a significant association with lower urinary tract symptoms. This highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology.\u003c/p\u003e","manuscriptTitle":"Identifying possible biomarkers of lower urinary tract symptoms using metabolomics and partial least square regression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-22 04:44:16","doi":"10.21203/rs.3.rs-3074710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-01T12:57:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-01T12:46:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-24T14:31:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169798e2-0adc-4b79-a090-ca6870123b1d","date":"2023-07-23T14:56:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cabeda42-c770-4391-ab19-9b06d3cdec83","date":"2023-07-20T13:55:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-19T12:41:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-19T10:38:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-19T10:38:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2023-06-17T05:14:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"287e7c93-44e8-48f5-9169-6ba966c350f2","owner":[],"postedDate":"June 22nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-18T15:04:58+00:00","versionOfRecord":{"articleIdentity":"rs-3074710","link":"https://doi.org/10.1007/s11306-023-02046-2","journal":{"identity":"metabolomics","isVorOnly":false,"title":"Metabolomics"},"publishedOn":"2023-09-12 15:01:04","publishedOnDateReadable":"September 12th, 2023"},"versionCreatedAt":"2023-06-22 04:44:16","video":"","vorDoi":"10.1007/s11306-023-02046-2","vorDoiUrl":"https://doi.org/10.1007/s11306-023-02046-2","workflowStages":[]},"version":"v1","identity":"rs-3074710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3074710","identity":"rs-3074710","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}