BACKGROUND: Lower urinary tract symptoms (LUTS), including urinary incontinence, urgency and nocturia, affect approximately half of women worldwide. Despite their prevalence, the underlying mechanisms causal of LUTS are poorly understood, which is likely a reflection of a multifactorial aetiology. Current diagnostic methods for LUTS are invasive and costly, while available treatments are limited by side effects leading to poor patient compliance. As a result, much recent research has been directed toward identifying novel, non-invasive biomarkers that have strong diagnostic and prognostic value. However, the majority of those identified lack specificity or have minimal prognostic benefit. Metabonomics is the quantitative assessment of metabolites and small molecule changes involved in biochemical processes thereby offering a direct assessment of system and local metabolic status. AIM: In this study, we aimed to identify urine metabolic signatures associated with LUTS using proton nuclear magnetic resonance (1H-NMR) spectroscopy and to assess whether profiling and/ or subsequent multivariate analysis would be useful to characterise metabolic perturbations associated with individual lower urinary tract symptoms and overactive bladder. METHODS: Women attending urogynaecology and gynaecology clinics completed the validated International Consultation on Incontinence – Female Lower Urinary Tract Symptoms Questionnaire. Asymptomatic control patients were defined as those with normal scores for bladder pain and all storage and incontinence symptom items. Participants provided midstream urine samples and metabolic profiles were acquired using 1H-NMR. Resulting spectral data were assessed using a combination of unsupervised and supervised multivariatemodelling techniques with discriminatory metabolites identified using 1D- and 2Ddimensional NMR based approaches. RESULTS: A total of 214 urine samples were collected from women attending tertiary urogynaecology clinics (cases; n=176) and healthy control women attending general gynaecology clinics (n=36). High variation in the urine metabolome was observed across the cohort however analysis of the data using the KODAMA (knowledge discovery by accuracy maximization) algorithm identified associations between urine metabolic profiles and BMI, parity, overactive bladder syndrome, frequency, straining and bladder storage. Partition around menoids clustering of the KODAMA data identified four urinary metabotypes, one of which was associated with increased urinary frequency and decreased BMI. CONCLUSION: Our study suggests that metabolic profiling of urine samples from LUTS patients offers the potential to identify differences in underlying aetiology, which may permit stratification of patient populations and the design of more personalized treatment strategies.