METHOD

Abstract

The invention provides a method of diagnosing overactive bladder disorder (OAB), the method comprising: measuring the concentrations of one or more of adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO) and interleukin 5 (IL-5) in a sample obtained from a subject; normalising the concentrations to the concentration of creatinine (Cr) in the sample; range standardising the normalised concentrations and subject's age to the following values: Age to 120 years old; ATP/Cr to 0.000001; ACh/Cr to 0.1; NO to 20000; IL-5/Cr to 100; wherein the likelihood of having OAB (p.sub.OAB)=1/1+e.sup.−x, where X=one or more of the following: (a) (−2.688±1.050)+5.472±2.098×subject's age+1.356±0.559×Gender (Female=1, Male=0)+(−7.998±40.273)×[IL-5/Cr]; (b) (−2.141±0.966)+4.506±1.902×subject's age+1.034±0.519×Gender (Female=1, Male=0)+(−5294.063±9075.456)×[ACh/Cr]; (c) (−2.825±1.072)+5.964±2.167×subject's age+1.312±0.562×Gender (Female=1, Male=0)+17.790±58.762×[IL-5/Cr]+(−9180.821±12700.057)×[ACh/Cr]; (d) (−2.993±1.197)+5.580±2.309×subject's age+1.724±0.719×Gender (Female=1, Male=0)+63.571±73.444×[IL-5/Cr]+(−0908.523±13606.752)×[ACh/Cr]+(−566.991±636.589)×[ATP/Cr]; (e) (−3.090±1.200)+5.393±2.256×subject's age+1.797±0.717×Gender (Female=1, Male=0)+34.767±56.331×[IL-5/Cr]+(−562.743±629.316)×[ATP/Cr]; or (f) (−2.650±1.067)+5.516±2.120×subject's age+1.389±0.583×Gender (Female=1, Male=0)+(−4.060±45.238)×[IL-5/Cr]+(−1.456±6.833)×[NO/Cr]; and wherein a pOAB above a threshold indicates that the subject has a high likelihood of having or developing OAB and a pOAB below a threshold indicates that the subject does not have OAB.

Claims

1. A method of diagnosing overactive bladder disorder (OAB), the method comprising: measuring the concentrations of one or more of adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO) and interleukin 5 (IL-5) in a sample obtained from a subject; normalising the concentrations to the concentration of creatinine (Cr) in the sample; range standardising the normalised concentrations and subject's age to the following values: Age to 120 years old; ATP/Cr to 0.000001; ACh/Cr to 0.1; NO to 20000; IL-5/Cr to 100; wherein the likelihood of having OAB (pOAB)=1/1+e−x, where X=one or more of the following: (a) (−2.688±1.050)+5.472±2.098×subject's age+1.356±0.559×Gender (Female=1, Male=0)+(−7.998±40.273)×[IL-5/Cr]; (b) (−2.141±0.966)+4.506±1.902×subject's age+1.034±0.519×Gender (Female=1, Male=0)+(−5294.063±9075.456)×[ACh/Cr]; (c) (−2.825±1.072)+5.964±2.167×subject's age+1.312±0.562×Gender (Female=1, Male=0)+17.790±58.762×[IL-5/Cr]+(−9180.821±12700.057)×[ACh/Cr]; (d) (−2.993±1.197)+5.580±2.309×subject's age+1.724±0.719×Gender (Female=1, Male=0)+63.571±73.444×[IL-5/Cr]+(−10908.523±13606.752)×[ACh/Cr]+(−566.991±636.589)×[ATP/Cr]; (e) (−3.090±1.200)+5.393±2.256×subject's age+1.797±0.717×Gender (Female=1, Male=0)+34.767±56.331×[IL-5/Cr]+(−562.743±629.316)×[ATP/Cr]; or (f) (−2.650±1.067)+5.516±2.120×subject's age+1.389±0.583×Gender (Female=1, Male=0)+(−4.060±45.238)×[IL-5/Cr]+(−1.456±6.833)×[NO/Cr]; and wherein a pOAB above a threshold indicates that the subject has a high likelihood of having or developing OAB and a pOAB below a threshold indicates that the subject does not have OAB.

2. The method of claim 1, wherein the pOAB threshold is 0.5.

3. The method of claim 1, wherein the sample is a urine sample.

4. The method of claim 1, wherein the concentrations of any of ATP, ACh, NO, IL-5 or Cr are measured using an antibody-based platform or an RNA aptamer-based platform or a combination thereof.

5. The method of claim 1, to wherein the method further comprises administering a therapeutic agent to a subject diagnosed as having OAB.

6. A method of treating OAB, the method comprising diagnosing OAB in a patient using the method of claim 1, and administering a therapeutic agent to the patient.

7. The method of claim 5, wherein the therapeutic agent is an antimuscarinic drug or a β3 adrenergic receptor agonist.

8. The method claim 7, wherein the antimuscarinic drug is selected from one or more of darifenacin, oxybutynin, tolterodine, solifenacin, trospium, flavoxate, propiverine or fesoterodine.

9. The method of claim 7, wherein the β3 adrenergic receptor agonist is mirabegron.

10. A method of monitoring the progression of OAB, the method comprising measuring first and second POAB values according to the method of claim 1, wherein the first and second POAB values are obtained from first and second samples obtained from a subject having or suspected of having OAB.

11. The method of claim 10, wherein the first and second samples are obtained at an interval of at least two weeks.

12. A computer system comprising processing means/a processor configured to execute instructions for: receiving measured concentrations of one or more of adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO), interleukin 5 (IL-5) and creatinine (Cr); normalising the concentrations of ATP, ACh, NO and IL-5 to the concentration of Cr; range standardising the normalised concentrations and subject's age to the following values: Age to 120 years old; ATP/Cr to 0.000001; ACh/Cr to 0.1; NO to 20000; IL-5/Cr to 100; calculating X according to one or more of the following formulae: (a) X=(−2.688±1.050)+5.472±2.098×subject's age+1.356±0.559×Gender (Female=1, Male=0)+(−7.998±40.273)×[IL-5/Cr]; (b) X=(−2.141±0.966)+4.506±1.902×subject's age+1.034±0.519×Gender (Female=1, Male=0)+(−5294.063±9075.456)×[ACh/Cr]; (c) X=(−2.825±1.072)+5.964±2.167×subject's age+1.312±0.562×Gender (Female=1, Male=0)+17.790±58.762×[IL-5/Cr]+(−9180.821±12700.057)×[ACh/Cr]; (d) X=(−2.993±1.197)+5.580±2.309×subject's age+1.724±0.719×Gender (Female=1, Male=0)+63.571±73.444×[IL-5/Cr]+(−10908.523±13606.752)×[ACh/Cr]+(−566.991±636.589)×[ATP/Cr]; (e) X=(−3.090±1.200)+5.393±2.256×subject's age+1.797±0.717×Gender (Female=1, Male=0)+34.767±56.331×[IL-5/Cr]+(−562.743±629.316)×[ATP/Cr]; or (f) X=(−2.650±1.067)+5.516±2.120×subject's age+1.389±0.583×Gender (Female=1, Male=0)+(−4.060±45.238)×[IL-5/Cr]+(−1.456±6.833)×[NO/Cr]; and using X to calculate the likelihood of the subject having OAB (pOAB) using the formula pOAB=1/1+e−x.

13. A computer program comprising instructions which, when executed by a processor/processing means cause the processor/processing means to: receive measured concentrations of one or more of adenosine triphosphate (ATP), acetylcholine (ACh), nitric oxide (NO), interleukin 5 (IL-5) and creatinine (Cr); normalise the concentrations of ATP, ACh, NO and IL-5 to the concentration of Cr; range standardise the normalised concentrations and subject's age to the following values: Age to 120 years old; ATP/Cr to 0.000001; ACh/Cr to 0.1; NO to 20000; IL-5/Cr to 100; and calculate X according to one or more of the following formulae: (a) X=(−2.688±1.050)+5.472±2.098×subject's age+1.356±0.559×Gender (Female=1, Male=0)+(−7.998±40.273)×[IL-5/Cr]; (b) X=(−2.141±0.966)+4.506±1.902×subject's age+1.034±0.519×Gender (Female=1, Male=0)+(−5294.063±9075.456)×[ACh/Cr]; (c) X=(−2.825±1.072)+5.964±2.167×subject's age+1.312±0.562×Gender (Female=1, Male=0)+17.790±58.762×[IL-5/Cr]+(−9180.821±12700.057)×[ACh/Cr]; (d) X=(−2.993±1.197)+5.580±2.309×subject's age+1.724±0.719×Gender (Female=1, Male=0)+63.571±73.444×[IL-5/Cr]+(−10908.523±13606.752)×[ACh/Cr]+(−566.991±636.589)×[ATP/Cr]; (e) X=(−3.090±1.200)+5.393±2.256×subject's age+1.797±0.717×Gender (Female=1, Male=0)+34.767±56.331×[IL-5/Cr]+(−562.743±629.316)×[ATP/Cr]; or (f) X=(−2.650±1.067)+5.516±2.120×subject's age+1.389±0.583×Gender (Female=1, Male=0)+(−4.060±45.238)×[IL-5/Cr]+(−1.456±6.833)×[NO/Cr]; and use X to calculate the likelihood of the subject having OAB (pOAB) using the formula pOAB=1/1+e−x.

14. A computer readable medium comprising the computer program according to claim 13.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The invention will now be described in detail, by way of example only, with reference to the figures.

[0061] FIG. 1 shows a flow diagram of participants' selection. ICIQ-OAB=International consultation on incontinence questionnaire−overactive bladder.

[0062] FIG. 2 shows frequency distributions of ICIQ-OAB urinary symptom scores and urinary symptoms' bothersome scores of eligible participants. (A-E) Frequency distributions (n=95) of ICIQ-OAB urinary symptom scores of eligible participants. (F-J) Frequency distributions (n=81) of ICIQ-OAB urinary symptoms plus bothersome score of eligible participants, 14 participants left one or some of the symptom associated bothersome questions blank. ICIQ-OAB=International consultation on incontinence questionnaire−overactive bladder.

[0063] FIG. 3 shows the correlation between the urinary concentrations of candidate biomarkers and participants' total ICIQ-OAB urinary symptom scores. Cr=creatinine, all the urinary biomarker values were normalised to urinary creatinine concentrations; Grey circle=participants from OAB symptomatic cluster; Black circle=participants from OAB symptomatic cluster; r=Spearman r; p=p-value; **=significant p=value of 0.01≤p≥0.001; Number format of y-axis=log.

[0064] FIG. 4 shows receiver-operating characteristic curves (ROCs) of diagnostically reliable OAB prediction models. (A) Combination 10=Age+Gender+IL-5. (B) Combination 12=Age+Gender+ACh. (C) Combination 14=Age+Gender+IL-5+ACh. (D) Combination 15—Age+Gender+IL-5+ACh+ATP. (E) Combination 17=Age+Gender+IL-5+ATP. (F) Combination 18=Age+Gender+IL-5+NO. AUC=Area under the ROC curve; Black diagonal solid line=prediction model curve; Grey diagonal dashed line=chance line.

[0065] FIG. 5 shows a schematic illustration of a computer system configured to execute a computer program in line with the present invention. A computer [outer box] is shown. The computer is configured to execute a computer program to carry out the diagnostic method as described herein. The computer contains a processor, a memory, a display and an input/output for receiving data and/or outputting data.

EXAMPLES

[0066] Materials and Methods

[0067] Recruitment of Participants

[0068] This study and all its procedures were approved by the National Research Ethics Service (NRES) Committee South Central Berkshire (REC reference: 13/SC/0501). A total of 113 volunteer participants were recruited via volunteer sampling from the staff and students of the University of Portsmouth (UoP) and from the residents of The Briars Greensleeves Homes Trust, Isle of Wight; The National Federation of Women's Institutes; Portsmouth and Portsmouth Pensioners' Association. Participants were asked to complete International consultation on incontinence questionnaire-overactive bladder (ICIQ-OAB) questionnaire and to provide a fresh midstream urine sample. Collected samples and data were made anonymous using an ID code system.

[0069] Inclusion Criteria

[0070] Male or female participants aged and able to give informed consent for participation in the study.

[0071] Exclusion Criteria

[0072] Male or female participants aged 18; unable to give informed consent; diagnosed with neurologic disease (stroke, MS, Parkinson's disease, spinal cord injury); have history of uterine, cervical, vaginal or urethral cancer; history of cyclophosphamide use or any type of chemical cystitis; history of benign or malignant bladder tumours; have had Botulinum toxin injections, neuromodulation or augmentation cystoplasty.

[0073] Urine Pathology Tests

[0074] Pathology tests including microscopic, chromogenic UTI medium and dipstick urinalysis tests were immediately performed on a small proportion of each collected urine sample. Any positive test meant that a sample was considered ‘unhealthy’ and excluded from the study. The remainder of the urine sample was centrifuged (at 4000 rpm, 10 mins, at 4° C.) to separate into cell pellet and supernatant and stored separately at −80° C.

[0075] Biomarker Assays

[0076] The urinary (cell-free) concentrations of the candidate biomarkers were measured using ENLITEN® ATP Assay System Bioluminescence Detection Kit (FF2000, Promega, UK); Amplex® Red Acetylcholine/Acetylcholinesterase assay (Invitrogen™ Molecular Probes™, A12217, UK); Sievers Nitric Oxide Analyser (NOA™ 280i, Analytix, UK); BD OptEIA™ human MCP-1 enzyme-linked immunosorbent assay (ELISA) (559017, BD biosciences, UK); Quantikine® human IL-5 ELISA Kit (R&D Systems®, D5000B, UK) and the OptEIA™ Human IL-5 ELISA Set (555202, BD biosciences, UK) according to the manufacturers' instructions.

[0077] Creatinine Assay

[0078] All urinary biomarker values were normalized to urinary creatinine (Cr) concentrations. Creatinine was measured using the Cayman Creatinine (urinary) Colourimetric Assay Kit (CAY500701, Cambridge Bioscience, UK), following the manufacturer's instructions.

[0079] Statistical Analysis

[0080] Cluster Analysis

[0081] TwoStep cluster analysis was performed using IBM SPSS statistics 22.0 on the data obtained from ICIQ-OAB questionnaire (data not shown). The distribution of responses to each question (symptom/bothersome measure) was range standardized on a 0 to 1 scale. The software was programmed to automatically identify a maximum number of 15 clusters. Cluster analysis was run on different aspects of data obtained from the ICIQ-OAB questionnaire in order to identify the best combination of urinary characteristic scores that could be used to classify OAB patients. This included classification based on data acquired from urinary symptom scores only (USSO), urinary bothersome scores only (UBSO) or urinary symptom scores plus associated bothersome scores (USCPABS).

[0082] Correlation Analysis

[0083] Spearman's rank correlation coefficient (GraphPad Prism 6 software) was used to assess the relationship between the candidate urinary biomarkers and participants' total urinary symptoms scores. In addition, correlation test (IBM SPSS statistics 22.0) was used to assess strength of the potential relationship of the independent variables with dependent variable (outcome/OAB symptomatic); and to assess the multicollinearity between independent variables (any two independent variables with an r value above 0.80 are considered inter-correlated).

[0084] Binary Logistic Regression

[0085] The ability of the candidate biomarkers, individually or in combination (in different combination settings and with confounders (age, gender, collected urine volume), in predicting the probability of someone being OAB symptomatic was studied using binary logistic regression test (IBM SPSS statistics 22.0). In this case, instead of standardising each variable to its highest available measured value in this study, each variable was range standardised to the highest possible number that could be measured for any one human (and even for some biomarkers the considered value was much higher) i.e. age was range standardized to 120 years old; volume was range standardized to 1000 ml of urine; and candidate biomarkers were range standardised to the following values: ATP/Cr to 0.000001, ACh/Cr to 0.1, NO/Cr to 20000, Nitrite to 200, MCP-1/Cr to 100 and IL-5/Cr to 100. In this way, any measured value in the future could be range standardized to the same values used in this study and consequently could be placed in the generated logit formulae to estimate the probability of the presence of OAB.

[0086] Receiver Operating Characteristic (ROC), Positive Predictive Value (PPV) and Negative Predictive Value (NPV) Analyses

[0087] ROC curve analysis (IBM SPSS statistics 22.0) was used in order to evaluate the discriminatory power of the generated OAB prediction models using predicted probability (PRE) values generated by logistic regression analyses. A predictive model with area under the ROC curve (AUC≥0.7) was considered to have clinically reliable diagnostic power. The optimal cut-off value of the predicted probability (pOAB) for each prediction equation was determined as the value with the maximum Youden Index (J=sensitivity+specificity−1). PPV and NPV of each OAB predictive equation was calculated based on the sensitivity and specificity at optimal cut-off value and based on an estimate of OAB prevalence of 20% [Irwin et al. 2011].

[0088] Results

[0089] Participants

[0090] Participants representing urinary symptoms similar to those of OAB were excluded from the analyses based on exclusion criteria and urinalysis tests. Out of 113 recruited participants, a total of 95 participants were eligible to be involved in the further analyses (FIG. 1).

[0091] Distribution of Urinary ICIQ-OAB Characteristic Scores

[0092] The frequency distributions of urinary ICIQ-OAB characteristic scores of the eligible participants are shown in FIG. 2. The distributions of urinary symptom scores or urinary symptom scores plus associated bothersome scores were right-skewed and no apparent bimodal distribution, suggestive of two groups (OAB symptomatic and asymptomatic) was observed. Hence, the conventional total-score based diagnosis would not be appropriate for early-stage OAB diagnosis or grouping of the participants in this study.

[0093] Cluster Analysis

[0094] Two-step cluster analyses were performed on the participants' ICIQ-OAB urinary scores in order to identify any natural groupings (clusters). Two natural clusters were identified amongst participants based on the ICIQ-OAB questionnaire data (data not shown). All of the 95 eligible participants were included in the USSO based cluster analysis, whereas eighty one participants were included in the UBSO or USCPABS based analyses as 14 participants left one or some of the bothersome questions blank. Consequently, clusters formed based on USSO have higher statistical power to determine natural groupings compared to UBSO or USCPABS based analyses. Furthermore, cluster analysis identified urgency—key OAB symptom [Abrams et al. 2012]—as the main cluster predictor component in USSO based analysis. Therefore, the two clusters formed based on USSO were selected for biomarker profile assessments. Amongst 95 participants, 36 and 59 participants were assigned to clusters 1 and 2, respectively (Table 1). The distribution of urinary symptom scores amongst the two identified clusters are shown in Table 1. Participants in cluster 2 had statistically significant higher urinary symptom scores and were older compared to those in cluster 1 (Table 1). Therefore, cluster 1 was designated as ‘OAB asymptomatic’ and cluster 2 was designated as ‘OAB symptomatic’ for further analyses.

TABLE-US-00001 TABLE 1 Characterisation of clusters identified using two-step cluster analysis. Participant Cluster Cluster P characteristic 1 2 value N 36 59 Gender (F/M) 20/16 41/18 ns.sup.a Age (yrs)    54 (45.50-58.00).sup.b   59 (49.00-69.50) 0.0079.sup.c U, median (IQR)    0 (0.00-0.00) 0.25 (0.25-0.37) ≤0.0001.sup.c I, median (IQR)  0.0 (0.00-0.00) 0.20 (0.00-0.20) ≤0.0001.sup.c F, median (IQR) 0.33 (0.00-0.33) 0.33 (0.33-0.67) 0.0048.sup.c N, median (IQR) 0.00 (0.00-0.25) 0.25 (0.00-0.25) 0.0115.sup.c n = number of participants in each cluster; ns = not significant; U = range standardised urgency symptom score (range: 0-1); I = range standardised incontinence symptom (range: 0-1); F = range standardised frequency symptom score (range: 0-1); N = range standardised nocturia symptom score (range: 0-1); IQR = Interquartile range, 1.sup.st quartile-3.sup.rd quartile. .sup.a= Z-test was used for comparison. .sup.b= one missing age value, n = 35 for cluster 1 .sup.c= Mann-Whitney test

[0095] Correlation Analysis

[0096] FIG. 3 shows the relationship between the concentrations of the candidate urinary biomarkers (ATP, ACh, NO, Nitrite, MCP-1 and IL-5) and participants' total urinary symptoms scores. Positive trends were observed between the candidate biomarkers and participants' urinary symptom scores, and reached a statistically significant level with urinary ATP concentration (FIG. 3). The overlap of the concentrations of the candidate biomarkers between the asymptomatic and symptomatic groups further elucidated the inadequacy of a single biomarker as a diagnostic measure in those at early stages of OAB development (FIG. 3).

[0097] The correlation between the dependent variable (outcome i.e. OAB symptomatic) and the independent variables (i.e. participants' age, gender, total collected urine volume and urinary concentrations of ATP, ACh, NO, Nitrite, MCP-1 and IL-5) were summarised (data not shown). The correlation was only statistically significant with age (p value=0.008), which suggests that age may be a strong individual predictor of the outcome when subjected to logistic regression test. No multicollinearity (i.e. r value>0.80) was observed between the predictor variables, hence, all deemed to be suitable to be used simultaneously in logistic regression analyses.

[0098] Logistic Regression Analysis

[0099] Logistic regression analysis was used to assess whether a reliable OAB prediction equation could be developed by incorporating the candidate urinary biomarkers and participants' confounders. Initially, the power of each predicting parameter was assessed individually (data not shown). Amongst all the individual predicting parameters, age, as expected, was the only one that showed a statistically significant OAB prediction power over its null prediction model (Omnibus test p value=0.041, Table 2). In order to assess whether the addition of other predicting parameters would increase prediction ability of age, 20 combination models were developed by incorporating candidate urinary biomarkers and other confounders (data not shown). Amongst all the developed prediction models, seven (combinations 1, 10, 12, 14, 15, 17 & 18) were also shown to have statistically significant OAB prediction powers over their associated null models and were shown to have good fit (Omnibus test p values 0.05, HL test p value 0.05, Table 2).

TABLE-US-00002 TABLE 2 Prediction abilities of candidate biomarkers and participants' confounders assessed individually and in combination, using binary regression. Logistic Regression parameters Pr Null PR New O test HL test model model (p (p Predictive model n e % (%) value) value) Age 94 2 62.80 61.70 0.041 0.060 Combination 1 94 2 62.80 67.00 0.020 0.238 Age, Gender Combination 10 81 15 60.50 66.70 0.011 0.677 Age, Gender, Il-5 Combination 12 82 14 58.50 64.60 0.039 0.473 Age, Gender, ACh Combination 14 79 17 59.50 65.80 0.015 0.281 Age, Gender, Il-5, ACh Combination 15 62 34 59.70 66.10 0.045 0.849 Age, Gender, Il-5, ACh, ATP Combination 17 63 33 60.30 65.50 0.026 0.726 Age, Gender, Il-5, ATP Combination 18 81 15 60.50 66.70 0.024 0.550 Age, Gender, Il-5, NO n = number participants included in analysis e = number participants excluded in analysis due to missing data; Null model = model with no predicting variable(s), just the intercept; Pr Null model (%) = percentage of cases for which the dependent variable was correctly predicted given the null model; New model = model with predicting variable(s); Pr New model (%) = percentage of cases for which the dependent variable was correctly predicted given the new model; O test-Omnibus Test of Model Coefficients assess whether the inclusion of predicting variable(s) will statistically improve the predicting ability of the new model over the null model, p ≤ 0.05 suggest (bold) statistically significant improvement in predicting ability of the new model null model. HL test-The Hosmer-Lemeshow goodness of fit test, a statistical test to assess goodness of fit for logistic regression models, ≥ 0.05 (bold) suggests model is a good fit. * All the urinary biomarker values were normalised to urinary creatinine concentrations.

[0100] ROC Analysis and OAB Prediction Equations

[0101] Discriminatory abilities of the eight prediction models with statistically significant OAB prediction abilities (significant Omnibus test p values, Table 2), were assessed by ROC analysis. Six prediction models (i.e. combinations 10, 12, 14, 15, 17 and 18) were shown to have clinically reliable diagnostic powers (Table 3, AUC≥0.7, ROC plots are shown in FIG. 4). Therefore, equations predicting likelihood of OAB were constructed for these six combinations (Table 4), whereby measuring urinary levels of the relevant biomarker(s) in each equation and entering the value(s) in the formula, the probability of someone having OAB (pOAB) could be calculated.

[0102] The optimal cut-off value of the predicted probability (pOAB) for each prediction equation was determined as the value with the maximum Youden Index (J) (Table 5). The sensitivity and specificity of each prediction equation at its optimal cut-off value is shown in Table 5. Subsequently, positive predictive value (PPV) and negative predictive value (NPV) of each OAB predictive equation was calculated based on the sensitivity and specificity at optimal cut-off value and based on an estimate of OAB prevalence of 20% [Irwin et al. 2011] (Table 5). Combination 17 predictive equation deemed to be the more reliable equation, considering both PPV (41%) and NPV (90%) values, compared to the other predictive equations (Table 5). In other words, by measuring the urinary concentrations of IL-5 and ATP and entering the Cr-normalised and standardised values in the combination 17 predictive equation alongside patient's age and gender, the pOAB can be calculated, and if pOAB>0.56 (pOAB cut-off for combination 17, Table 5) then a patient could be considered as having high risk of having/developing OAB. Considering the OAB prevalence of 20%, in real-world combination 17 predictive equation is capable of diagnosing 41% of patients with OAB and ruling out 90% of those without OAB, correctly. In general all the predictive equations had high NPV values (89-92%), meaning developed equations would be more suitable to rule out OAB than positively diagnosing it. Nevertheless, all the developed predictive equations showed to have higher PPV and NPV values compared to the current gold standard invasive tool that relies on the presence of DO for diagnosing those with OAB (Table 5, ΔPPV and ΔNPV).

TABLE-US-00003 TABLE 3 Discriminatory abilities of the selected prediction models were assessed by Receiver Operating Characteristic (ROC) analysis. Predictive p model AUC SE value Age 0.633 0.056 0.008 Combination 1 0.673 0.057 0.005 Age, Gender Combination 10 0.726 0.056 0.001 Age, Gender, IL-5 Combination 12 0.704 0.058 0.002 Age, Gender, ACh Combination 14 0.719 0.057 0.001 Age, Gender, IL-5, ACh Combination 15 0.719 0.064 0.004 Age, Gender, I1-5, ACh, ATP Combination 17 0.727 0.063 0.002 Age, Gender, IL-5, ATP Combination 18 0.731 0.056 0.000 Age, Gender, IL-5, NO AUC = area ROC curve; Bold value = AUC ≥ 0.70, meaning predictive model has clinically sufficient discriminatory power; SE = Standard error.

TABLE-US-00004 TABLE 4 OAB predictive equations. OAB prediction equation.sup.a Predictive Probability of having OAB (p.sub.OAB) = model 1/1 + e.sup.−x, where X = Combination 10 X = (−2.688 ± 1.050) + 5.472 ± 2.098 × subject's age + 1.356 ± 0.559 × Gender (Female = 1, Male = 0) + (−7.998 ± 40.273) × [IL-5/Cr] Combination 12 X = (−2.141 ± 0.966) + 4.506 ± 1.902 × subject's age + 1.034 ± 0.519 × Gender (Female = 1, Male = 0) + (−5294.063 ± 9075.456) × [ACh/Cr] Combination 14 X = (−2.825 ± 1.072) + 5.964 ± 2.167 × subject's age + 1.034 ± 0.519 × Gender (Female = 1, Male = 0) + 17.790 ± 58.762 × [IL-5/Cr] + (−9180.821 ± 12700.057) × [ACh/Cr] Combination 15 X = (−2.993 ± 1.197) + 5.580 ± 2.309 × subject's age + 1.724 ± 0.719 × Gender (Female = 1, Male = 0) + 63.571 ± 58.76273.444 × [IL-5/Cr] + (−10908.523 ± 13606.752) × [ACh/Cr] + (−566.991 ± 636.589) × [ATp/Cr) Combination 17 X = (−3.090 ± 1.200) + 5.393 ± 2.256 × subject's age + 1.797 ± 0.717 × Gender (Female = 1, Male = 0) + 34.767 ± 56.331 × [IL-5/Cr] + (−562.743 ± 629.316) × [ATP/Cr] Combination 18 X = (−2.650 ± 1.067) + 5.516 ± 2.120 × subject's age + 1.389 ± 0.583 × Gender (Female = 1, Male = 0) + (−4.060 ± 45.238) × [IL-5/Cr] + (−1.456 ± 6.833) × [NO/Cr] p.sub.OAB = probability of having OAB; e = exponential-e; Cr—Creatinine, urinary biomarker value needs to be normalised to creatinine concentrations before being entered in the equation. .sup.a= values need to be range standardised to the reported value in Methods and Materials section before being entered in equation.

TABLE-US-00005 TABLE 5 Diagnostic characteristics of the constructed OAB predictive models. Based on maximum Youden Index Based on 20% (J) prevalence of OAB Predictive pOAB cut- Sensitivity Specificity PPV NPV model off (%) (%) (%) (%) Combination 10 0.51 67 69 35 89 5 3 Combination 12 0.46 81 53 30 92 0 6 Combination 14 0.46 74 63 33 91 3 5 Combination 15 0.56 65 76 40 90 10 4 Combination 17 0.56 66 76 41 90 11 4 Combination 18 0.51 67 69 35 89 5 3 Presence of DO.sup.a 54 68 30 86 p.sub.OAB = probability of having OAB; PPV Positive predictive value; NPV = Negative predictive value; ΔPPV = (PPV of predictive model) − (PPV of Urodynamic); ΔNPV = (NPV of predictive model) − (NPV of Urodynamic). .sup.aSensitivity and specificity values for urodynamic test was obtained from Digesu etal. 2003 study where the presence of DO was use as a marker for diagnosing those presenting with OAB symptoms.

CONCLUSION

[0103] This analysis elucidated six combinations, with clinically reliable diagnostic power to distinguish participants with or without early-stage OAB (Table 2 and FIG. 4). Predictive equations for all six combinations had high NPV values (89-92%) but relatively lower PPV values (30-41%); meaning that they are better suited to rule out OAB. The performance of these combinations is similar to other urine-based tests for other disease e.g. bladder carcinoma [Valenberg et al. 2018] or prostate cancer [Lughezzani et al. 2018]. This means that by measuring the urinary levels of the associated biomarker(s) in each equation and entering the value(s) in the formula, these novel equations would enable healthcare professionals to predict the probability of someone having OAB at much earlier stage in its development and more accurately than the current invasive means of urodynamically-observed DO (Table 5), but more importantly would enable them to accurately and non-invasively exclude OAB in those patients with aetiologically distinct diseases which have overlapping symptoms with OAB.

REFERENCES

[0104] Abrams P, Chapple C R, Jünemann K P, Sharpe S. Urinary urgency: a review of its assessment as the key symptom of the overactive bladder syndrome. World J Urol. 2012 June; 30(3):385-92. [0105] Abrams P, Cardozo L, Wagg A, Wein A. (Eds) Incontinence 6th Edition (2017). ICI-ICS. International Continence Society, Bristol UK, ISBN: 978-0956960733. [0106] Digesu G A, Khullar V, Cardozo L, Salvatore S. Overactive bladder symptoms: do we need urodynamics? Neurourology and Urodynamics. 2003; 22:105-8. [0107] Hashim H, Abrams P. Is the bladder a reliable witness for predicting detrusor overactivity? The Journal of Urology. 2006; 175:191-4. [0108] Irwin D E, Kopp Z S, Agatep B, Milsom I, Abrams P. Worldwide prevalence estimates of lower urinary tract symptoms, overactive bladder, urinary incontinence and bladder outlet obstruction. BJU Int. 2011; 108:1132-8. [0109] Lughezzani G, Saita A, Lazzeri M et al (in press). Comparison of the Diagnostic Accuracy of Micro-ultrasound and Magnetic Resonance Imaging/Ultrasound Fusion Targeted Biopsies for the Diagnosis of Clinically Significant Prostate Cancer. Eur Urol. (in press). [0110] Valenberg F J P V, Hiar A M, Wallace E, et al. Prospective Validation of an mRNA-based Urine Test for Surveillance of Patients with Bladder Cancer. Eur Urol. (in press).