Advertisement
You have accessJournal of UrologyReview Article1 Nov 2019

Biomarkers Implicated in Lower Urinary Tract Symptoms: Systematic Review and Pathway Analyses

    View All Author Information

    Abstract

    Purpose:

    Lower urinary tract symptoms are prevalent and burdensome, yet methods to enhance diagnosis and appropriately guide therapies are lacking. We systematically reviewed the literature for human studies of biomarkers associated with lower urinary tract symptoms.

    Materials and Methods:

    PubMed®, EMBASE® and Web of Science® were searched from inception to February 13, 2018. Articles were included if they were in English, performed in benign urological populations without neurological disorders or interstitial cystitis/bladder pain syndrome, and assessed a biomarker’s association with or ability to predict specific lower urinary tract symptoms or urological conditions. Bioinformatic pathway analyses were conducted to determine whether individual biomarkers associated with symptoms are present in unifying pathways.

    Results:

    Of 6,150 citations identified 125 met the inclusion criteria. Most studies (93.6%) assessed biomarkers at 1 time point and were cross-sectional in nature. Few studies adjusted for potentially confounding clinical variables or assessed biomarkers in an individual over time. No individual biomarkers are currently validated as diagnostic tools for lower urinary tract symptoms. Compared to controls, pathway analyses identified multiple immune response pathways that were enriched in overactive bladder syndrome and cell migration/cytoskeleton remodeling pathways that were enriched in female stress incontinence.

    Conclusions:

    Major deficiencies in the existing biomarker literature include poor reproducibility of laboratory data, unclear classification of patients with lower urinary tract symptoms and lack of adjustment for clinical covariates. Despite these limitations we identified multiple putative pathways in which panels of biological markers need further research.

    Abbreviations and Acronyms

    BMI

    body mass index

    CRP

    C-reactive protein

    ELISA

    enzyme-linked immunosorbent assay

    FDR

    false discovery rate

    GO

    Gene Ontology

    IC/BPS

    interstitial cystitis/bladder pain syndrome

    IL

    interleukin

    LUTS

    lower urinary tract symptoms

    NGF

    nerve growth factor

    OAB

    overactive bladder

    SUI

    stress urinary incontinence

    UUI

    urgency urinary incontinence

    Lower urinary tract symptoms encompass a broad range of symptoms in men and women. These include storage symptoms like overactive bladder and UUI; nocturia; stress urinary incontinence; and obstructive voiding symptoms like incomplete bladder emptying, hesitancy or post-void symptoms. LUTS are highly prevalent1 and impact quality of life and emotional well-being,2 but we still have a poor understanding of lower urinary tract symptom pathophysiology. New information linking LUTS to other inflammatory and metabolic disorders may provide a deeper understanding of LUTS and improve our ability to diagnose and treat these symptoms.3–5

    The Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) was established with the goal of identifying and explaining clinically relevant subtypes of LUTS cases.6 We systematically reviewed the literature for biomarkers that are associated with LUTS, primarily storage and voiding symptoms in the absence of bladder pain or neurological diagnoses. We also used bioinformatic pathway analyses to combine biomarker data from individual studies to assess if certain biological pathways are enhanced in specific LUTS subtypes.

    Materials and Methods

    Data Sources

    An experienced reference librarian searched PubMed/MEDLINE, Embase and Web of Science databases between inception and February 13, 2018 using a combination of medical subheadings (MeSH), key words, text words related to LUTS (including voiding and storage symptoms) and biomarkers. The search was limited to English language original research publications in humans. The full search strategy is detailed in supplementary table 1 (https://www.jurology.com).

    Eligibility and Study Selection

    We defined eligibility before literature searches using the PICOS (participants, interventions, comparators, outcomes and study design) framework.7 As detailed in the Appendix, we limited our review to those studies performed in populations with benign urological conditions without neurological disorders, chronic prostatitis/chronic pelvic pain syndrome or IC/BPS. In some studies those with cancer or IC/BPS were compared to groups of patients with LUTS. In these instances only the biomarker information associated with LUTS and control groups was included for this review. We excluded articles that examined associations between prostate specific antigen and prostate cancer since this literature review was not intended to examine oncologic biomarkers. We also excluded articles that only reported on LUTS as a whole or used total AUA-SI (AUA symptom index) if data were not available for us to examine the specific symptom (eg urgency, voiding symptom etc) that contributed to the score. After the initial search we identified those articles in which “prostatic neoplasm” was identified as a major subheading. Since oncologic studies could have theoretically included benign LUTS cases for comparison groups, one reviewer screened these articles based on title alone and retained any potentially relevant studies for further review. Next 2 independent reviewers screened all remaining articles based on title and abstract, and further excluded duplicates, reviews and conference abstracts without full publications. To resolve discrepancies 2 separate reviewers further assessed any citations where there was disagreement.

    Data Abstraction and Analysis

    Full text articles were reviewed using a standardized data abstraction form. We collected study characteristics, patient characteristics, intervention (type of biomarker and comparator groups), outcome definitions and study results. Data were abstracted by one reviewer and a second reviewer confirmed accuracy. Discrepancies were resolved by discussion or by referral to a third reviewer. For each article the risk of bias was determined based on the study design and methodological characteristics. All symptoms were collated under the symptom categories of 1) OAB, 2) nocturia, 3) SUI and 4) voiding symptoms.

    Pathway Analysis

    The gene identifiers from biomarker, proteomic and gene expression studies associated with specific LUTS were imported into MetaCore™ (Clarivate Analytics, https://portal.genego.com). Due to limitations in combining gene expression and metabolite data in the MetaCore platform, we could not incorporate metabolite data with other biomarkers. Within established biological pathways enrichment analyses were conducted to test whether specific biomarkers tended to show up-regulation or down-regulation in participants with LUTS relative to controls. We extracted the mean protein or expression abundances in LUTS participants and controls from relevant studies. The LUTS participant/control ratio of mean abundances provides an estimate of up-regulation or down-regulation. Next, we imported these data into MetaCore to examine the intersection between the affected gene products (ie proteins and/or transcripts) and existing Gene Ontology networks. The enrichment analysis algorithm uses statistics (such as the hypergeometric mean) to take into account the number of affected proteins in a data set, the total number of proteins submitted in a data set and the total number of proteins in the intersecting network. This analysis returns a p value that specifies the likelihood that the intersection between the list of affected proteins and a particular network is obtained purely by chance. Furthermore, since we were testing multiple objects at once, we adjusted for multiple testing using FDR adjusted p values.

    We decided a priori that meta-analyses would only be performed if at least 3 or more studies assessed the same biomarkers with sufficiently similar outcome definitions and with minimal risk of bias. All data are reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement.8

    Results

    After excluding duplicates we identified 6,150 citations through initial database searches. Of these citations 207 were selected for full text review and 125 met inclusion criteria for this systematic review (fig. 1, plus full reference list in supplementary tables, https://www.jurology.com). In general, studies were cross-sectional and only indicated associations with urological symptoms. Eight studies (6.4%) included repeat sampling on a cohort over time. Furthermore, few studies used regression analysis or modeling to adjust for potential confounders such as age, BMI or other inflammatory conditions. Meta-analyses already exist for 1 biomarker (nerve growth factor). Due to the overall poor quality of the literature, additional meta-analyses were not performed. Details about individual biomarkers evaluated in each study and biomarkers positively and negatively associated with individual symptoms are listed in supplementary tables 2 through 6 (https://www.jurology.com).

    Figure 1.Study selection

    Figure 1. Study selection

    Biomarkers of Overactive Bladder

    OAB is typically defined as urgency, with or without UUI, usually with frequency and nocturia.9 For this portion of the review we considered biomarkers associated with OAB in general, UUI (“wet-OAB” in some studies) or urinary urgency/frequency (“dry-OAB” in some studies). Since there could be slightly different pathophysiology, studies that only assessed nighttime storage symptoms were considered in the nocturia studies.

    Multiple types of biomarkers were assessed in association with OAB (table 1, and supplementary tables 2 and 6, https://www.jurology.com). The largest body of literature exists for NGF, where 40 studies evaluated associations between NGF and OAB. These included 37 studies evaluating urinary NGF, 3 studies evaluating serum NGF and 2 evaluating bladder urothelial tissue. One prior systematic review and 2 meta-analyses collectively summarized much of the published literature through 2016.10–12 These publications included 23 studies that were also included in this review. A majority of studies assessing urinary NGF (32 of 37) showed that urinary NGF is elevated in OAB compared to controls (supplementary table 6, A1, https://www.jurology.com). However, 31 of 37 studies used an enzyme-linked immunosorbent assay from Promega Corporation and the technical validity of this test antibody has been challenged due to nonspecific binding.13–15 This Promega ELISA kit was withdrawn from the market in 2014. Furthermore, most of these studies evaluated NGF at 1 point and looked for associations with crude clinical phenotypes without controlling for potential clinical confounders.

    Table 1. Summary of biomarker studies for overactive bladder

    Biomarker No. Studies Summary of Effect
    NGF 40 Multiple poorer quality studies showing associations between urinary NGF and OAB; higher quality studies do not show differences between NGF and controls when controlling for covariates, especially age. Limited data showing that serum NGF may be elevated in subset of pts with OAB and metabolic syndrome, but not in controls or other types of OAB. Two studies in bladder tissue fail to demonstrate differences in urothelial NGF between OAB and controls.
    Brain derived neurotrophic factor 7 Multiple poorer quality studies showing associations between brain derived neurotrophic factor and OAB, higher quality studies do not show differences after controlling for potentially confounding factors.
    Prostaglandin E2 7 Mixture of effect, 4 poor quality studies show association while 3 poor quality studies do not.
    Adenosine triphosphate 6 Variable results in 3 studies, 3 additional studies performed using urodynamic fluid (not solely in urine).
    CRP 13 Conflicting results are noted in multiple analyses using regression to control for potentially confounding variables.
    Other inflammatory 17 Overall poor quality studies that do not control for confounders and often do not adjust for multiple testing, variable associations noted (supplementary tables 2 and 5, https://www.jurology.com).
    Receptors/channels 11 Multiple studies showing higher M3 muscarinic receptor expression/density and lower β-3 adrenergic receptor expression in OAB, no differences in M2 receptors between OAB and controls. More TRPV1 receptors in OAB, variable results with regard to purinergic receptors and gene for β-3 adrenoreceptor (ADR3B).
    Gene expression/proteomic 2 Small sample sizes, lack of adjustment for confounders, and lack of adjustment for false-discovery rate limit conclusive results.
    Metabolomic 2 Metabolites may be useful in phenotyping (understanding subtypes of OAB).
    Microbiome 9 Mostly association studies, require further investigation with longitudinal sampling and studies that control for potentially confounding factors.

    Individual study details are listed in supplementary table 2 (https://www.jurology.com).

    Recently more rigorous studies were performed using a different ELISA and also incorporated multivariable analyses. After controlling for age, BMI and other relevant covariates, no differences in urinary NGF remained between OAB and control participants in 3 studies.14,16,17 Only data from Pennycuff et al were included in prior meta-analyses.16 A separate study looking at serum markers, also not included in prior meta-analyses, shows further evidence of the importance of potential confounders. Investigators found that individuals with OAB and metabolic syndrome had higher serum NGF compared to those with OAB without metabolic syndrome and also compared to healthy controls.18 However, the relationships between systemic NGF and urinary NGF are not entirely clear. In a prior study performed with the Promega ELISA kit Liu et al demonstrated that serum and urine NGF are highly correlated and are both elevated in patients with OAB compared to controls.19 The same group studied urine vs urothelium and found that, despite higher urinary NGF in OAB participants over controls, there were no differences in NGF in urothelial tissue in the same subjects.20 These findings corroborated those of Birder et al, who also did not identify differences in NGF between OAB and controls in urothelial tissue biopsies.21 Taken together these data suggest that if NGF is indeed elevated in urine, it may be filtered from blood and excreted, or alternatively that urinary NGF originates separately from serum NGF and is actively trafficked to the urine without a reservoir in the urothelium.

    In multiple studies urinary NGF decreased after successful treatment of OAB. However, these studies were also performed using the Promega ELISA kit with nonspecific binding and were also conducted without addressing clinical confounders. Thus, it is not entirely clear if NGF is responding to changes in OAB status or other physiological indicators. NGF is a nonspecific biomarker that also appears to change in other bladder conditions like IC/BPS and bladder outlet obstruction. Therefore, we can conclude that higher urinary and serum NGF may indeed be associated with OAB, but these associations appear to be mechanistically complex and could be due to a confounding factor.

    The literature regarding brain derived neurotrophic factor and prostaglandin E2 parallels the findings concerning NGF. Although studies report associations, they fail to control for potential confounders, or when investigators adjust for potentially confounding variables they fail to confirm the initially proposed associations. Adenosine trisphosphate has been studied in urine and voided urodynamic fluid with conflicting results. Purinergic (P2X) receptors in bladder tissue also show conflicting results.

    C-reactive protein has been assessed in multiple studies that include regression analyses, also with conflicting results. This includes 4 studies in 4,019 participants where there were no differences in serum CRP,22–25 and 4 separate studies in 5,926 men where CRP remained associated with OAB symptoms after adjusting for confounding factors.26–29 Using participants from the Boston Area Community Health study Kupelian et al initially did not see differences in CRP among those with OAB compared to controls.30 However, they repeated their analyses after redefining OAB symptoms and later found that higher CRP was associated with urinary urgency and frequency in men and women.31 Multivariable regression modeling was used in both of these studies.

    Finally, there has been a significant increase in the study of the urinary microbiome and OAB. The microbiome appears to differ in certain clinical scenarios, one of which is OAB. Although there are differences in the abundance of sequence identified microbes, we currently lack longitudinal data, and many existing studies do not control for potentially confounding variables such as age, hormonal status, body mass index and presence of diabetes. Therefore, the implications of these initial association studies and how they relate to symptom development require further investigation.

    Biomarkers of Nocturia

    Eight studies assessed biomarkers specifically associated with nocturia, defined as 2 or more voids per night (supplementary tables 3 and 6, https://www.jurology.com). The 5 largest studies used multivariable regression modeling to control for potentially confounding factors and showed that higher serum beta natriuretic peptide, lower urinary 6-sulfaoxymelatonin (urinary metabolite of melatonin) and higher serum CRP were associated with nocturia. In a study that performed serial blood sampling over 48 hours older adults with nocturia had higher nighttime neurotransmitters associated with hypertension (noradrenaline and dopamine), higher beta natriuretic peptide, lower melatonin and lower vasopressin compared to control groups. Detailed references are included in the supplementary tables (https://www.jurology.com).

    Biomarkers of Stress Urinary Incontinence

    A total of 23 studies assessed biomarkers associated with female SUI (supplementary tables 4 and 6, https://www.jurology.com). Of these studies 2 queried biomarkers predictive of favorable outcomes after surgery while controlling for covariates with regression analyses. Participants in the ValUE (Value of Urodynamic Evaluation) trial underwent biomarker assessment before and after mid urethral sling surgery.32 A year after surgery urinary IL-12p70 decreased, while urinary NGF increased in a manner that was independent of any covariates, including urgency symptoms. Investigators also found that higher baseline N-telopeptide of crosslinked type I collagen (NTx) in urine was predictive of surgical failure 1 year later. NTx is a marker of bone resorption and remodeling, so the mechanistic implications of this association are not clear. Urine samples from ValUE trial participants were also subjected to urinary microbiome analysis. There were no associations between the urinary microbiome and SUI symptoms. Rather, microbial diversity was associated with concomitant UUI, hormone status and BMI.

    A number of investigators studied tissue from periurethral vaginal wall biopsies in women with SUI compared to controls. Associations between sex hormones and SUI were identified. Lower serum estradiol and fewer estrogen and progesterone receptors were found in premenopausal women with SUI. However, protein levels and potential confounders were not thoroughly assessed and there are risks of bias in these studies. It was suggested by weak evidence from other studies that collagen breakdown and decreased collagen turnover are associated with SUI. Although there are suggestions of hormonal influences on collagen turnover in periurethral tissue, these associations have not been rigorously studied in humans.

    Among studies comparing the extracellular matrix of women with SUI and controls there were conflicting results. It is unclear whether confounding or bias contributed to these differences, as investigators did not systematically control for age, menopausal status, phase of menstrual cycle and/or the presence of concomitant prolapse. There was stronger evidence that calpain-2, a proteolytic enzyme involved in cellular function, degradation of myofibrils and myoblast cell fusion, is implicated in SUI.33,34

    Biomarkers of Voiding Symptoms

    Voiding symptoms include straining, intermittent or weak urinary stream, incomplete bladder emptying and post-void symptoms. We identified 11 studies that specifically assessed biomarkers in the context of voiding symptoms (supplementary tables 5 and 6, https://www.jurology.com). Individual voiding symptoms in these studies were generally not well specified.

    The highest level of evidence comes from a population based cohort study of 730 men in Australia. These men underwent sampling twice during a 5-year period and investigators did not find any serum inflammatory biomarkers associated with incident voiding symptoms.29

    Multiple population based, cross-sectional studies have been performed in other populations from the United States, China and Korea. Two studies including approximately 2,300 men revealed inconsistent associations, while 5 other studies in more than 8,700 men did not find associations between voiding symptoms and CRP. One study included approximately 1,850 women, and demonstrated that incomplete emptying and weak stream were associated with higher CRP in women only.

    Integrated Pathway Analyses

    Biomarkers positively and negatively associated with each of the 4 symptom categories were incorporated into pathway enrichment analyses (supplementary table 6, https://www.jurology.com). There were no significantly enriched pathways regarding biomarkers for nocturia or voiding symptoms.

    For OAB multiple enriched pathways were identified. The top 10 pathways based on FDR adjusted significance are displayed in table 2. The majority of these affected pathways involve immune responses that are typically seen with an allergen, chemical irritant or microbe. Enriched pathways are those involved in immune cell migration, adhesion and inflammatory responses. Detailed maps of the top 3 enriched pathways with up-regulated and down-regulated signals are displayed in supplementary figure 1 (https://www.jurology.com). The GO cellular processes that correspond with the enriched pathways are depicted in figure 2.

    Table 2. OAB pathway enrichment analysis

    No. Pathway Total Proteins in Pathway Proteins from Data Set p Value FDR p Value Affected Proteins from Data Set
    1 Maturation and migration of dendritic cells in skin sensitization 41 12 7.143E-15 6.895E-12 MHC class II beta chain, MHC class II, IL-1 beta, IL-6, IL-8, E-cadherin, CD83, HLA-DRB4, HLA-DRB, TNF-alpha, MHC class II alpha chain, IL-12 beta
    2 NF-kB, AP-1 and MAPK mediated proinflammatory cytokine production by eosinophils in asthma 43 12 1.363E-14 6.895E-12 IL-1 beta, IL-6, IL-4, GRO-1, IL-8, GM-CSF, CCL17, MIP-1-alpha, IL-5, ENA-78, TNF-alpha, MGF
    3 PDE4 regulation of cyto/chemokine expression in arthritis 49 12 7.771E-14 2.621E-11 IL-1 beta, IL-6, IL-10, IL-8, GM-CSF, MIP-1-alpha, IP10, ENA-78, PKA-reg (cAMP-dependent), TNF-alpha, IL-12 beta, PI3K cat class IA
    4 PDE4 regulation of cyto/chemokine expression in inflammatory skin diseases 50 11 3.197E-12 8.088E-10 IL-1 beta, IL-13, IL-6, IL-4, G-protein alpha-i family, IL-10, IL-8, IP10, PKA-reg (cAMP-dependent), TNF-alpha, IL-12 beta
    5 TNF-alpha induced inflammatory signaling in normal and asthmatic airway epithelium 38 10 4.577E-12 9.263E-10 IL-13, IL-6, IL-4, GRO-1, IL-8, GM-CSF, CCL17, IP10, IL-5, TNF-alpha
    6 Intercellular relations in chronic obstructive pulmonary disease (general schema) 30 9 1.499E-11 2.529E-09 IL-1 beta, IL-6, GRO-1, Granzyme B, IL-8, GM-CSF, IP10, TNF-alpha, EGF
    7 Proinflammatory cytokine release from eosinophils in asthma 34 9 5.305E-11 7.669E-09 IL-1 beta, IL-13, IL-6, NGF, IL-4, IL-8, GM-CSF, IL-5, TNF-alpha
    8 Immune response_T cell subsets: secreted signals 25 8 1.185E-10 1.498E-08 IL-13, IL-6, IL-4, IL-10, GM-CSF, MIP-1-alpha, IL-5, TNF-alpha
    9 Proinflammatory cytokine production by Th17 cells in asthma 53 10 1.652E-10 1.857E-08 MHC class II, IL-1 beta, IL-13, IL-6, C3aR, IL-4, IL-10, IL-8, IL-5, TNF-alpha
    10 Glomerular injury in lupus nephritis 92 12 2.085E-10 2.110E-08 IL-1 beta, IL-6, GRO-1, OX40L(TNFSF4), IFI56, IL-8, GM-CSF, GRO-2, MIP-1-alpha, IP10, NGAL, TNF-alpha

    The top 10 enriched molecular pathways that mapped to the data set of biomarkers associated with OAB, with enriched pathways identified using MetaCore.

    P value specifies the likelihood that the intersection between the list of affected proteins and a particular network is obtained purely by chance.

    Figure 2.GO cellular processes enriched in biomarkers for OAB

    Figure 2. GO cellular processes enriched in biomarkers for OAB

    For female SUI multiple enriched pathways were also identified (table 3). The most enriched pathway was the Slit-ROBO cell-signaling pathway. This pathway is often a repulsive cue for axon guidance but has also been implicated in angiogenesis and cell migration.35 The second most enriched pathway was the cytoskeleton remodeling (keratin filaments) pathway that is involved in cell shape control and provides intracellular mechanical strength.36,37 Detailed maps of the top 3 enriched pathways with up-regulated and down-regulated signals are displayed in supplementary figure 2 (https://www.jurology.com). The GO cellular processes that correspond with the enriched pathways are depicted in figure 3.

    Table 3. SUI pathway enrichment analysis

    No. Pathway Total Proteins in Pathway Proteins from Data Set p Value FDR p Value Affected Proteins from Data Set
    1 Development_Slit-Robo signaling 30 4 3.782E-05 0.010 ROBO4, Actin cytoskeletal, Actin, ACTB
    2 Cytoskeleton remodeling_Keratin filaments 36 4 7.896E-05 0.010 Keratin 1, Epiplakin, Actin cytoskeletal, GRB2
    3 Immune response_Oncostatin M signaling via MAPK 37 4 8.810E-05 0.010 MMP-13, TIMP1, GRB2, MMP-1
    4 Blood coagulation_Blood coagulation 39 4 1.087E-04 0.010 Coagulation factor IX, Protein C inhibitor, Coagulation factor X, CPB2
    5 Immune response_Oncostatin M signaling via JAK-Stat 22 3 3.594E-04 0.026 TIMP1, STAT3, MMP-1
    6 Cell adhesion_extracellular matrix remodeling 55 4 4.171E-04 0.026 MMP-13, Actin cytoskeletal, TIMP1, MMP-1
    7 Development_Leptin signaling via JAK/STAT and MAPK cascades 25 3 5.295E-04 0.029 TIMP1, GRB2, STAT3
    8 Signal transduction_mTORC1 downstream signaling 61 4 6.201E-04 0.029 p70 S6 kinases, p70 S6 kinase2, Rictor, STAT3
    9 Role of tissue factor-induced thrombin signaling in cancerogenesis 65 4 7.892E-04 0.033 MMP-13, MRLC, Actin cytoskeletal, Coagulation factor X
    10 Development_S1P1 receptor signaling via beta-arrestin 34 3 1.322E-03 0.045 p70 S6 kinases, p70 S6 kinase2, GRB2

    The top 10 enriched molecular pathways that mapped to the data set of biomarkers associated with female SUI, with enriched pathways identified using MetaCore.

    P value specifies the likelihood that the intersection between the list of affected proteins and a particular network is obtained purely by chance.

    Figure 3.GO cellular processes enriched in biomarkers for female SUI

    Figure 3. GO cellular processes enriched in biomarkers for female SUI

    Discussion

    We have performed a comprehensive systematic review and pathway analyses of biomarkers associated with LUTS in humans. Unfortunately no individual biomarkers are strongly associated with specific LUTS. However, by integrating individual studies into pathway analyses we have identified multiple candidate biological pathways that could be considered for future research.

    This study is strengthened by the coordinated search strategy, predefined review criteria and strict methodology. Using these methods we reviewed and summarized a large body of seemingly disparate literature. By incorporating pathway analyses we were able to integrate this literature to identify potential targets for future studies. However, our study is limited in that much of the data that we entered into pathway analyses comes from individual studies where there was a moderate risk of bias. Most studies were cross-sectional, with biomarkers only being assessed at 1 time point, which limits causal inferences. Many studies lacked adjustment for confounding factors. Our pathway analyses indicate that for OAB in particular, multiple inflammatory and immune response pathways could be important. Immune response pathways can also be influenced by other covariates such as age and obesity, which are frequently encountered in patients with OAB but were not always adjusted for in primary studies. Therefore, our results should be interpreted with caution. An additional challenge is that due to technological limitations with available software, metabolite data could not be combined with proteomic data for pathway analyses.

    Our review demonstrates that multiple investigators have assessed biomarkers to better understand LUTS, but there are ongoing issues with how investigators define and classify LUTS. For example, there are many instances where investigators publish biomarkers associated with LUTS as a whole or biomarkers associated with a total score on a multifactorial symptom index. This may occur partially because patients rarely present with only 1 or very few LUTS.38,39 However, in these instances imprecise classification of LUTS limits the conclusions that we can reach, particularly when it comes to pathophysiology. Future studies assessing whether biomarkers are associated with “symptom clusters” rather than traditional diagnostic groupings may be useful.40

    Our search identified a number of individual biomarkers that have been studied. However, thus far, single biomarkers have not proven sufficient for classifying and phenotyping individuals. This is partially due to a lack of consistency but also because many individual biomarkers fail to discriminate between LUTS and other bladder conditions. Some researchers have proposed that future research should focus on “fingerprints” or panels of multiple molecules to better understand, diagnose and treat LUTS.41,42 Our pathway analyses provide some direction for this type of future research. For OAB in particular our results indicate multiple immune response pathways that were highly significantly enriched even after FDR adjustment. On the surface pathway names like “Maturation and migration of dendritic cells in skin sensitization” may not carry much meaning. However, evidence suggests that this pathway involves cellular signaling in response to a “danger signal.”43 This signaling response is heavily dependent on inflammasomes carrying multiple cytokines. Inflammasomes have recently been reported as important mediators of bladder pathology and may prove to be a useful target for future biomarker based research.44 Similarly, the most enriched pathway in our SUI pathway analysis, the “Development_Slit-robo signaling” pathway, prevents axons from migrating to inappropriate locations. An interesting feature is that in reproductive tissues this pathway is further regulated by sex hormones,35 which have also been implicated in SUI. Careful study of these and other proposed pathways using panels of biomarkers may lead to breakthroughs in understanding pathophysiological mechanisms behind bladder symptoms.

    Conclusions

    Without understanding the pathophysiology behind ill-defined symptomatic benign urological conditions, precision medicine and improvements in patient care remain elusive. To advance the field and gain meaningful information about biomarkers and LUTS, we recommend that future studies use clear definitions of specific LUTS. Furthermore, studies that sample multiple time points and use rigorous adjustment for potentially confounding variables are needed. Ideally individual biomarkers should also be explored in the context of biomarker panels that can better distinguish different LUTS based on proposed pathophysiological mechanisms. These studies will likely require collaboration between clinician scientists who understand clinical phenotyping and potentially confounding clinical variables, basic scientists who understand the strengths and pitfalls of different molecular biology and biochemical techniques, and quantitative scientists who can incorporate clinical variables, metadata and other relevant variables into robust statistical models.

    Acknowledgments

    The following individuals were instrumental in the planning and conduct of this study at each of the participating institutions: Duke University, Durham, NC (DK097780): PIs: Cindy Amundsen, MD, Kevin Weinfurt, PhD; Co-Is: Kathryn Flynn, PhD, Matthew O. Fraser, PhD, Todd Harshbarger, PhD, Eric Jelovsek, MD, Aaron Lentz, MD, Drew Peterson, MD, Nazema Siddiqui, MD, Alison Weidner, MD; Study Coordinators: Carrie Dombeck, MA, Robin Gilliam, MSW, Akira Hayes, Shantae McLean, MPH. University of Iowa, Iowa City, IA (DK097772): PIs: Karl Kreder, MD, MBA, Catherine S. Bradley, MD, MSCE, Co-Is: Bradley A. Erickson, MD, MS, Susan K. Lutgendorf, PhD, Vince Magnotta, PhD, Michael A. O’Donnell, MD, Vivian Sung, MD; Study Coordinator: Ahmad Alzubaidi. Northwestern University, Chicago, IL (DK097779): PIs: David Cella, Brian Helfand, MD, PhD; Co-Is: James W. Griffith, PhD, Kimberly Kenton, MD, MS, Christina Lewicky-Gaupp, MD, Todd Parrish, PhD, Jennie Yufen Chen, PhD, Margaret Mueller, MD; Study Coordinators: Sarah Buono, Maria Corona, Beatriz Menendez, Alexis Siurek, Meera Tavathia, Veronica Venezuela, Azra Muftic, Pooja Talaty, Jasmine Nero. Dr. Helfand, Ms. Talaty and Ms. Nero are at NorthShore University HealthSystem. University of Michigan Health System, Ann Arbor, MI (DK099932): PI: J. Quentin Clemens, MD, FACS, MSCI; Co-Is: Mitch Berger, MD, PhD, John DeLancey, MD, Dee Fenner, MD, Rick Harris, MD, Steve Harte, PhD, Anne P. Cameron, MD, John Wei, MD; Study Coordinators: Morgen Barroso, Linda Drnek, Greg Mowatt, Julie Tumbarello. University of Washington, Seattle, WA (DK100011): PI: Claire Yang, MD; Co-I: John L. Gore, MD, MS; Study Coordinators: Alice Liu, MPH, Brenda Vicars, RN. Washington University in St. Louis, St. Louis, MO (DK100017): PIs: Gerald L. Andriole, MD, H. Henry Lai, MD; Co-I: Joshua Shimony, MD, PhD; Study Coordinators: Susan Mueller, RN, BSN, Heather Wilson, LPN, Deborah Ksiazek, BS, Aleksandra Klim, RN, MHS, CCRC. National Institute of Diabetes and Digestive and Kidney Diseases, Division of Kidney, Urology, and Hematology, Bethesda, MD: Project Scientist: Ziya Kirkali, MD; Project Officer: John Kusek, PhD; NIH Personnel: Tamara Bavendam, MD, Robert Star, MD, Jenna Norton. Arbor Research Collaborative for Health, Data Coordinating Center (DK097776 and DK099879): PI: Robert Merion, MD, FACS; Co-Is: Victor Andreev, PhD, DSc, Brenda Gillespie, PhD, Gang Liu, PhD, Abigail Smith, PhD; Project Manager: Melissa Fava, MPA, PMP; Clinical Study Process Manager: Peg Hill-Callahan, BS, LSW; Clinical Monitor: Timothy Buck, BS, CCRP; Research Analysts: Margaret Helmuth, MA, Jon Wiseman, MS; Project Associate: Julieanne Lock, MLitt. Robin Gilliam, MSW, Duke University, research coordinator, provided assistance with obtaining full text references. Megan von Isenburg, MSLS, Duke University, reference librarian, provided assistance with literature searches. Jennifer McCready-Maynes, from Arbor Research Collaborative for Health, provided editorial assistance.

    Appendix.

    PICOS Criteria for Studies Included in Systematic Review

    Population of interest Inclusions: Adult men and women with lower urinary tract symptoms. Exclusions: Studies in animals, children, adults with neurologic diagnoses, genitourinary malignancy, post-surgical LUTS, chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), or interstitial cystitis/bladder pain syndrome (IC/BPS)
    Intervention of interest Diagnostic biomarkers that differentiate specific urinary symptoms (ie overactive bladder, nocturia, stress urinary incontinence, or voiding symptoms)
    Comparison Any comparison group including control group, historical control, and pre-post designs with subject as own control
    Outcomes Inclusions: Blood, urine, tissue, or salivary biochemical markers associated with specific urinary symptoms. Exclusions: Studies where biochemical markers are studied in context with “all LUTS” without differentiation of specific symptoms
    Study design Cross-sectional, observational, and randomized trial designs

    References

    • 1. : Population-based survey of urinary incontinence, overactive bladder, and other lower urinary tract symptoms in five countries: results of the EPIC study. Eur Urol 2006; 50: 1306. Google Scholar
    • 2. : The impact of overactive bladder, incontinence and other lower urinary tract symptoms on quality of life, work productivity, sexuality and emotional well-being in men and women: results from the EPIC study. BJU Int 2008; 101: 1388. Google Scholar
    • 3. : Is there a link between overactive bladder and the metabolic syndrome in women? A systematic review of observational studies. Int J Clin Pract 2015; 69: 199. Google Scholar
    • 4. : Current status of the relationship between metabolic syndrome and lower urinary tract symptoms. Eur Urol Focus 2018; 4: 25. Google Scholar
    • 5. : Lower urinary tract symptoms and metabolic disorders: ICI-RS 2014. Neurourol Urodyn 2016; 35: 278. Google Scholar
    • 6. : Symptoms of lower urinary tract dysfunction research network. J Urol 2016; 196: 146. LinkGoogle Scholar
    • 7. : Association between framing of the research question using the PICOT format and reporting quality of randomized controlled trials. BMC Med Res Methodol 2010; 10: 11. Google Scholar
    • 8. : PRISMA statement. Epidemiology 2011; 22: 128. Google Scholar
    • 9. : The standardisation of terminology of lower urinary tract function: report from the Standardisation Sub-committee of the International Continence Society. Neurourol Urodyn 2002; 21: 167. Google Scholar
    • 10. : Urinary nerve growth factor levels could be a biomarker for overactive bladder symptom: a meta-analysis. Genet Mol Res 2014; 13: 8609. Google Scholar
    • 11. : Nerve growth factor (NGF): a potential urinary biomarker for overactive bladder syndrome (OAB)?BJU Int 2013; 111: 372. Google Scholar
    • 12. : Could urinary nerve growth factor be a biomarker for overactive bladder? A meta-analysis. Neurourol Urodyn 2017; 36: 1703. Google Scholar
    • 13. : NGF and proNGF reciprocal interference in immunoassays: open questions, criticalities, and ways forward. Front Mol Neurosci 2016; 9: 63. Google Scholar
    • 14. : Urinary biomarkers in women with refractory urgency urinary incontinence randomized to sacral neuromodulation versus onabotulinumtoxinA compared to controls. J Urol 2017; 197: 1487. LinkGoogle Scholar
    • 15. : Have we been led astray by the NGF biomarker data?Neurourol Urodyn 2017; 36: 203. Google Scholar
    • 16. : Urinary neurotrophic peptides in postmenopausal women with and without overactive bladder. Neurourol Urodyn 2017; 36: 740. Google Scholar
    • 17. : Elevated CXC chemokines in urine noninvasively discriminate OAB from UTI. Am J Physiol Renal Physiol 2016; 311: F548. Google Scholar
    • 18. : Association between metabolic syndrome and serum nerve growth factor levels in women with overactive bladder. Gynecol Obstet Invest 2018; 83: 140. Google Scholar
    • 19. : Increased serum nerve growth factor levels in patients with overactive bladder syndrome refractory to antimuscarinic therapy. Neurourol Urodyn 2011; 30: 1525. Google Scholar
    • 20. : Nerve growth factor levels are increased in urine but not urothelium in patients with detrusor overactivity. Tzu Chi Med J 2010; 22: 165. Google Scholar
    • 21. : Role of urothelial nerve growth factor in human bladder function. Neurourol Urodyn 2007; 26: 405. Google Scholar
    • 22. : Is high-sensitivity C-reactive protein associated with lower urinary tract symptoms in aging men? Results from the Hallym Aging Study. Korean J Urol 2012; 53: 335. Google Scholar
    • 23. : The role of serum C-reactive protein in women with lower urinary tract symptoms. Int Urogynecol J 2012; 23: 935. Google Scholar
    • 24. : Prevalence of overactive bladder and associated risk factors in 1359 patients with type 2 diabetes. Urology 2011; 78: 1040. Google Scholar
    • 25. : Associations between C-reactive protein and benign prostatic hyperplasia/lower urinary tract symptom outcomes in a population-based cohort. Am J Epidemiol 2009; 169: 1281. Google Scholar
    • 26. : Association between high-sensitivity C-reactive protein and lower urinary tract symptoms in healthy Korean populations. Urology 2015; 86: 139. Google Scholar
    • 27. : Serum C-reactive protein levels are associated with residual urgency symptoms in patients with benign prostatic hyperplasia after medical treatment. Urology 2011; 78: 1373. Google Scholar
    • 28. : Increased high-sensitivity C-reactive protein predicts a high risk of lower urinary tract symptoms in Chinese male: results from the Fangchenggang Area Male Health and Examination Survey. Prostate 2012; 72: 193. Google Scholar
    • 29. : Lower urinary tract symptoms, depression, anxiety and systemic inflammatory factors in men: a population-based cohort study. PLoS One 2015; 10: e0137903. Google Scholar
    • 30. : Association of C-reactive protein and lower urinary tract symptoms in men and women: results from Boston Area Community Health survey. Urology 2009; 73: 950. Google Scholar
    • 31. : Association of overactive bladder and C-reactive protein levels. Results from the Boston Area Community Health (BACH) Survey. BJU Int 2012; 110: 401. Google Scholar
    • 32. : A randomized trial of urodynamic testing before stress-incontinence surgery. N Engl J Med 2012; 366: 1987. Google Scholar
    • 33. : The role of calpain-calpastatin system in the development of stress urinary incontinence. Int Urogynecol J 2010; 21: 63. Google Scholar
    • 34. : miR-93-mediated collagen expression in stress urinary incontinence via calpain-2. Mol Med Rep 2018; 17: 624. Google Scholar
    • 35. : The SLIT-ROBO pathway: a regulator of cell function with implications for the reproductive system. Reproduction 2010; 139: 697. Google Scholar
    • 36. : Characterization of human epiplakin: RNAi-mediated epiplakin depletion leads to the disruption of keratin and vimentin IF networks. J Cell Sci 2005; 118: 781. Google Scholar
    • 37. : Plakins: a family of versatile cytolinker proteins. Trends Cell Biol 2002; 12: 37. Google Scholar
    • 38. : Baseline lower urinary tract symptoms in patients enrolled in LURN: a prospective, observational cohort study. J Urol 2018; 199: 1023. LinkGoogle Scholar
    • 39. : Prevalence and characteristics of urinary incontinence in a treatment seeking male prospective cohort: results from the LURN study. J Urol 2018; 200: 397. LinkGoogle Scholar
    • 40. : Symptom based clustering of women in the LURN Observational Cohort Study. J Urol 2018; 200: 1323. LinkGoogle Scholar
    • 41. : Current concepts in urinary biomarkers for overactive bladder: what is the evidence?Curr Bladder Dysfunct Rep 2017; 12: 260. Google Scholar
    • 42. : Novel biomarkers of overactive bladder syndrome. Ginekol Pol 2017; 88: 568. Google Scholar
    • 43. : Danger, intracellular signaling, and the orchestration of dendritic cell function in skin sensitization. J Immunotoxicol 2013; 10: 223. Google Scholar
    • 44. : The emerging role of inflammasomes as central mediators in inflammatory bladder pathology. Curr Urol 2018; 11: 57. Google Scholar

    The corresponding author certifies that, when applicable, a statement(s) has been included in the manuscript documenting institutional review board, ethics committee or ethical review board study approval; principles of Helsinki Declaration were followed in lieu of formal ethics committee approval; institutional animal care and use committee approval; all human subjects provided written informed consent with guarantees of confidentiality; IRB approved protocol number; animal approved project number.

    This is publication number 17 of the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN).

    Supported by the National Institute of Diabetes and Digestive and Kidney Diseases through cooperative agreements (Grants DK097780, DK097772, DK097779, DK099932, DK100011, DK100017, DK097776, DK099879).

    Research at Northwestern University was supported by the National Institutes of Health National Center for Advancing Translational Sciences, Grant Number UL1TR001422. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

    No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article.

    Advertisement