Predictors of Symptom Severity and Predictive Performance of Immune- Inflammation Indices Levels in Patients with Benign Prostatic Hyperplasia attending Dessie Comprehensive Specialized Hospital

Predictors of Symptom Severity and Predictive Performance of Immune- Inflammation Indices Levels in Patients with Benign Prostatic Hyperplasia attending Dessie Comprehensive Specialized Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictors of Symptom Severity and Predictive Performance of Immune- Inflammation Indices Levels in Patients with Benign Prostatic Hyperplasia attending Dessie Comprehensive Specialized Hospital Altaseb Beyene Kassaw, Said Mohammed Abdu, Gashaw Abebe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6494156/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background : Although the relatively unchanging consequences of age and genetic factors significantly contribute to the development and progression of Benign Prostatic Hyperplasia (BPH), numerous modifiable factors play a role in preventing progression or alleviating symptoms. Identifying predictors of symptom severity and objective biomarkers is beneficial for effective management. This study aimed to determine the predictors of BPH symptom severity and assess the predictive performance of hematologically derived inflammatory markers in patients attending Dessie Comprehensive Specialized Hospital in Ethiopia. Methods and materials : A cross-sectional study was conducted from August to October 2024. Data was collected through face-to-face interviews performed by well-trained health professionals using the KoboCollect application. STATA version 17 was utilized for statistical data analysis. Bivariable and multivariable logistic regression analyses were employed to identify predictors of BPH severity, with a p-value of < 0.05 considered statistically significant in the multivariable logistic regression. The predictive performance of immune-inflammatory index levels for the severity of BPH was evaluated using receiver operating characteristic (ROC) curve analysis. Ethical clearance was obtained from the Ethics Review Committee of Wollo University. Results : A total of 232 BPH patients were included in this study, of whom 84 (36.21%) presented with severe symptoms. Age ≥65 years (AOR = 2.55, 95% CI: 1.19, 5.45), low physical activity (AOR = 2.56, 95% CI: 1.22 - 5.35), central obesity (AOR = 2.79, 95% CI: 1.07 - 7.25), and elevated immune-inflammatory markers [SII > 564.92 x 10³ (AOR = 2.97, 95% CI: 1.10 - 7.98), PII > 273.3 x 10⁶ (AOR = 2.46, 95% CI: 1.04 - 5.86), and NLR > 1.368 (AOR = 2.87, 95% CI: 1.01 - 8.14)] were significantly associated with severe BPH symptoms (p-value < 0.05). In ROC analysis, SII exhibited the highest predictive performance for severe symptoms with an Area Under the Curve (AUC) of 0.736 (95% CI: 0.67, 0.801); optimal cut-off: 564.92 × 10³; specificity - 72%, sensitivity - 69%; PII demonstrated similar performance with an AUC of 0.729 (95% CI: 0.66, 0.796); optimal cut-off: 273.3 × 10⁶; specificity and sensitivity of 69%. LMR displayed poor predictive ability (AUC = 0.403; 95% CI: 0.328, 0.477). Conclusion : Advanced age, adverse lifestyle factors, and elevated systemic inflammatory markers (SII, PII, NLR) were significant predictors of BPH severity. SII and PII, derived from routine blood tests, showed moderate predictive value for identifying patients with severe BPH and may serve as accessible, non-invasive adjunct biomarkers. LMR was not identified as a useful predictor in this context. These findings highlighted the role of inflammation in BPH severity and suggest markers for clinical assessment. Further studies are recommended to validate these findings and explore their implications in therapeutic decision-making. Health sciences/Urology/Prostate Health sciences/Urology/Urological manifestations Benign prostatic hyperplasia lower urinary tract symptoms immune inflammation Figures Figure 1 Figure 2 Figure 3 Background Benign prostatic hyperplasia (BPH) is an enlargement of the prostate gland from the progressive hyperplasia of stromal and glandular prostatic cells (1, 2). It is a histologic diagnosis, and the proliferation is in smooth muscle and epithelial cells, more within the transition zone (3). The prostatic transition zone comprises approximately 5% of the prostate and surrounds the proximal urethra. This zone is the site of continual growth throughout life (4). Even though there are differences in the reported prevalence of BPH among countries, it has been stated that more than 20% of men between the ages of 30 and 79 years, equivalent to 15 million, and approximately 80% of men by 70 years of age would be estimated to have the problem (5). The disease is clinically manifested in the form of lower urinary tract symptoms (LUTS) associated with benign prostatic enlargement (6). The LUTS encompass a range of symptoms, broadly categorized into bladder storage issues like urinary frequency, urgency (7), and voiding difficulties such as dysuria, hesitancy, intermittent stream, poor flow, post-micturition dribbling, and incomplete voiding (5, 7). Although prostatic tissue proliferation is classified as benign and may not be life-threatening, if left untreated, it can lead to LUTS and significantly decrease the quality of life of those affected (8, 9), as well as an increased risk of acute urinary retention (9). The number of men diagnosed with BPH and LUTS has steadily increased over the last decade, driven by longer lifespans, greater disease awareness, and improved diagnoses (7, 9). The prevalence of LUTS associated with BPH parallels that of pathologic BPH; more than 50% of men over 50 are believed to experience LUTS secondary to an enlarged prostate gland (10). While age and genetics are well-established risk factors, recent studies highlight the crucial role of modifiable lifestyle and metabolic factors in the progression of LUTS in BPH patients. Obesity has been strongly linked to BPH-related LUTS, with higher body mass index (BMI) correlating with greater prostate volume and more severe urinary symptoms (11, 12). Behavioral factors like physical inactivity, smoking also contribute significantly, as sedentary individuals and smokers show a higher prevalence of LUTS (13, 14), and poor dietary habits, particularly inadequate fruit and vegetable intake, increase LUTS risk (15). Metabolic disorders, particularly diabetes mellitus, have been implicated in the pathophysiology of LUTS, with diabetic individuals being at significantly higher risk of developing severe symptoms. In addition, chronic inflammation has been implicated in the pathogenesis and symptom progression of BPH, as inflammatory processes within the prostate foster tissue remodeling, stromal expansion, and symptom development (16). The presence of inflammatory infiltrates in prostate tissue has been associated with an increased risk of acute urinary retention and worsening LUTS (17). Furthermore, a number of hematological-derived biomarkers that are indicative of systemic immune-inflammatory responses have been confirmed in recent decades for their correlation with the diagnosis and progression of BPH. Markers such as the platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) have been evaluated for their diagnostic and prognostic use for BPH (18, 19). Additionally, these inflammatory markers have demonstrated powerful diagnostic and prognostic value in various metabolic risks related to BPH and various tumors, including prostate cancer (20, 21). Elevated levels of these markers correlate with increased disease severity, poorer treatment response, and a higher likelihood of requiring invasive intervention (22, 23). Specifically, higher SII values have been associated with the progression of LUTS in men with BPH (19). However, the value of those hematological-derived inflammatory markers in predicting symptom severity of BPH remains obscure. On top of that, controversial results have been conveyed in several studies regarding other predictors of severe BPH (24-26). For example, some studies (26, 27) found a lower prevalence of severe BPH among alcohol consumers, while other studies (28) reported a positive correlation. Similar contradictions exist for smoking and physical activity, with some studies (26, 28) finding no association, while others suggest smokers and ex-smokers face higher BPH risk. Likewise, while some studies (26, 27) have shown no relationship between dyslipidemia, diabetes, and BPH, others (29-31) have found strong links. Most importantly, to the best of our knowledge, no documented study has assessed the severity of BPH symptoms in Ethiopia, particularly in the study area. Therefore, this study aimed to identify predictors of symptom severity and evaluate the predictive performance of immune-inflammation indices in BPH patients at Dessie Comprehensive Specialized Hospital. This would aid in implementing appropriate interventions or targeted approaches. Since most risk factors are modifiable, prevention and control strategies would be proposed to alleviate symptoms, prevent disease progression, and preserve quality of life. Furthermore, this study will encourage additional research into biomarkers that could help stratify patients based on their risk of developing severe symptoms of BPH. Methods and materials Study Setting and Period The study was conducted at Dessie Comprehensive Specialized Hospital in Northeast Ethiopia from August to October 2024. The hospital is situated in Dessie town, part of the Amhara Regional State, which is 401 km northeast of the capital, Addis Ababa. It serves as a referral center for South Wollo and surrounding zones, catering to approximately 7 million people, including those from neighboring regions. The hospital includes units such as internal medicine, surgery, gynecology and obstetrics, pediatrics, oncology, psychiatry, laboratory, orthopedics, pharmacy, and neurology. The surgery unit has both inpatient and outpatient services, catering to new patients as well as follow-up cases. According to hospital data, an average of 170 to 200 patients with BPH visited the hospital per month between February and June 2024. A hospital-based cross-sectional study took place from August to October 2024. Study population and eligibility criteria The study targeted BPH patients attending Dessie Comprehensive Specialized Hospital. The source population comprised all BPH patients visiting the hospital, while the study population included those who attended during the data collection period. Eligible participants were male patients aged 40 years and above with a confirmed physician diagnosis of BPH. Patients were excluded if they were critically ill, had severe physical disabilities, or presented with symptomatic co-infections, sepsis, or uropathologic conditions such as urinary stones and urogenital cancer. Those with a history of prostate or urethral surgery were also excluded. Additionally, individuals taking medications that could alter or control bladder symptoms, including anticholinergics, 5α-reductase inhibitors, phosphodiesterase-5 inhibitors, and hormone replacement therapy, were not eligible for the study. Sample size and sampling procedures The sample size was determined using OpenEpi (Version 3.01) based on the odds ratio (OR) method using the following assumptions: Considering 95% CI, 80% power. The sample size was obtained by taking hypertension as a risk factor from a previous study, where the proportion of hypertension among non-severe prostatic symptoms was 19.4 %, and an odds ratio of 2.5 (29). Accordingly, the total sample size becomes 2014. When the 10% non-response rate was added, the final sample size became 235. Each BPH patient who fulfilled the inclusion criteria was selected as a study participant through a consecutive sampling technique. When the subject did not meet the inclusion criteria, the next client was approached until the recommended sample size was reached. The participants were assigned based on the IPSS urological scores (30). According to the criteria, participants were categorized as mildly to moderately symptomatic (IPSS: 0-19) or as severely symptomatic (IPSS: 20-35). Study Variables The outcome variable in this study was BPH severity, categorized as severely symptomatic (IPSS: 20–35) and asymptomatic to moderately symptomatic (IPSS: 0–19). Independent variables included sociodemographic factors such as age, occupation, educational status, and residence. Comorbidities, including a history of coronary heart disease, diabetes, and hypertension, were also considered. Physical measurements such as blood pressure (BP), body mass index (BMI), and waist circumference (WC) were included. Behavioral factors such as alcohol consumption, cigarette smoking, khat chewing, physical exercise, and fruit and vegetable intake were also assessed. Additionally, fasting blood sugar levels and immune-inflammatory indices, including the systemic immune-inflammation index (SII), pan immuno-inflammatory index (PII), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR), were analyzed. Prostate-related factors, such as prostate size and the duration since BPH diagnosis, were also evaluated. Operational definitions Alcohol drinking status: Alcohol drinker :-defined as the intake of any type of alcoholic beverage, such as beer, wine or locally prepared alcoholic beverages, more than once per week in the past one year, regardless of the amount, non-drinkers :-those who drink less than once per week for the last one year or never drink alcoholic products (31) Diabetes : was considered if a diagnosis is recorded by a physician, or the participant is under treatment, or FBS ≥126 mg/ dl during the time of data collection (32). Hypertension : was considered if a diagnosis is recorded by a physician, or the patient is under treatment, or has resting BP measurements of SBP ≥130 mmHg and/ or DBP≥85 mmHg (33). Khat chewer: Participants who have been chewing khat in any amount during the past year, otherwise non-chewers (34). Fruit and vegetable intake : For raw green leafy vegetables, 1 serving = one cup; for cooked or chopped vegetables, 1 serving = ½ cup; for fruit (apple, banana, orange etc…), 1 serving = 1 medium size piece; for chopped, cooked and canned fruit, 1 serving = ½ cup; and for juice from fruit, 1 serving = ½ cup (35). Low-level physical activity : A person not meeting any of the criteria for the moderate- or high-level categories (36). Moderate-physical activity : any activity that causes a small increase in breathing or heart rate (brisk walking or carrying light loads) that continued for at least 30 min with a minimum of 3 days per week or 5 or more days of these activities for at least 20 min per day or ≥3 days of vigorous-intensity activity per week of at least 20 min per day (36). Severity of BPH: The severity of BPH was quantified by the American Urological Association or International Prostate Symptom Scoring (IPSS) system, recommended by the World Health Organization (WHO, 1991) as the preferred instrument to measure BPH symptoms. It consists of seven symptoms: intermittency, urgency, weak stream, incomplete emptying, straining, frequency, and nocturia. Each participant was asked to rate the severity of their symptoms on a six-point sub-scale, from a minimum score of 0 to a maximum score of 5, with the lowest score reflecting ‘no symptoms. The sum of all the answers made up an IPSS total score (score range 0–35 points), which was grouped into asymptomatic to moderately symptomatic( 20) (30). Smoking status: Smoker :-those who have a cigarette smoking practice within the last one year, irrespective of the amount, Non-smokers:- who never smoked in their lifetime or smokers before one year (34). High-level physical activity : any activity that causes a large increase in breathing or heart rate if continued for at least 30 minutes (e.g., running, carrying or lifting heavy loads, digging or construction work) for a minimum of three days per week (36). Weight classifications by BMI: underweight (BMI< 18.5) , normal weight (BMI 18.5-24.9), overweight (BMI 25-29.9),andobese (BMI≥30) (36). Immune-inflammatory indices SII was calculated as platelet × neutrophil/lymphocyte (37). PII was calculated as neutrophil count× platelet count (× monocyte count/lymphocyte count (38). NLR was calculated as the ratio of neutrophils to lymphocytes in peripheral blood (39). PLR: will be calculated as the ratio of platelets to lymphocytes in peripheral blood (39). Data and blood sample collection procedure Trained data collectors conducted interviews using an Amharic version questionnaire to gather relevant information. Blood samples (approximately 5 mL) were collected using a sterile technique after participants had undergone overnight fasting or a minimum of 8 hours of fasting. Informed consent was obtained before collecting samples for fasting blood sugar (FBS) and complete blood count (CBC) analysis. A well-trained laboratory technician performed the tests using a calibrated chemistry analyzer, adhering strictly to standard operating procedures. Participants with abnormal test results were referred to the follow-up unit for further evaluation. The data collection process, including anthropometric, clinical, and biochemical measurements, was supervised by the principal investigator and a designated supervisor to ensure accuracy and quality control. Questionnaire tool Data was collected using a structured questionnaire via the KoboCollect application on Android phones. The questionnaire was designed and uploaded into the KoboCollect platform, which allowed for efficient data entry and real-time data monitoring. Behavioral factors such as smoking, khat chewing, alcohol consumption, physical activity, and fruit and vegetable intake were also assessed. The International Prostate Symptom Score (IPSS) was applied to evaluate BPH symptom severity(30). Data collection was conducted through face-to-face interviews. Physical measurements, including weight, height, waist circumference, and blood pressure, were taken using the WHO stepwise approach (36), with calibrated equipment operated by trained health professionals. Height was measured with participants standing upright, and weight was recorded without shoes or heavy items. Waist circumference was measured midway between the lower ribs and iliac crest using a non-stretch tape. Height and weight were approximated to the nearest 0.1 cm and 0.1 kg, respectively, and BMI was calculated as weight (kg) divided by height squared (m²). A physician performed a digital rectal examination to estimate prostate size. Laboratory analysis Determination of hematological parameters/or immuno-inflammatory indices A complete blood count (CBC) was performed using an automated hematology analyzer. The machine automatically measured white blood cells (e.g., lymphocytes, neutrophils, monocytes) and platelets. Additional immune-inflammatory indices were calculated, including the SII, PII, PLR, and NLR. Determination of fasting serum glucose Fasting serum glucose was measured using the enzymatic glucose oxidase method with a commercial reagent kit(40). As a principle, the glucose in the sample could be oxidized to D-gluconic acid and hydrogen peroxide in the presence of glucose oxidase (GOD). Hydrogen peroxide then reacted with phenol and 4-aminoantipyrine, catalyzed by peroxidase (POD), forming a pink-colored quinoneimine compound. The intensity of the color, measured at 520 nm, is directly proportional to the glucose concentration in the sample. Data quality control Data quality was ensured through the proper design and pretesting of the questionnaire. The questionnaire was initially prepared in English, translated into Amharic, and then back-translated into English. A pretest was conducted on 10% of the total sample (22 participants) at Boru Meda General Hospital to assess the reliability of the items, and necessary modifications were made accordingly. Before data collection, training was provided to data collectors on the study objectives, methodology, relevance, and on using the KoboCollect platform. Data quality was further ensured by strictly adhering to the manufacturers' instructions and standard operating procedures during the laboratory testing processes. Special attention was given to biochemical analyses, and reagent expiration dates were checked. After data collection, each questionnaire was reviewed daily for completeness, clarity, and consistency. Data processing and analysis The data collected using the KoboCollect application on Android phones were imported into STATA 17 for processing and analysis. After data collection, the responses were synchronized from the mobile devices and exported in a format compatible with STATA for further analysis. The data were first cleaned to ensure accuracy, completeness, and consistency. Any inconsistencies, missing values, or outliers were checked. Descriptive statistics were used to summarize the sociodemographic, behavioral, and clinical characteristics of the participants. For analysis, categorical variables were coded numerically, and continuous variables were checked for normality. The associations between the outcome variable and potential predictors were investigated using the bivariable and multivariable binary logistic regression model. Variables with a p-value < 0.25 in the bivariable logistic regression were fitted into the multivariable logistic regression model for final analysis. Adjusted odds ratios with 95% confidence intervals (95% CI) were used for the interpretation of the strength of prediction of the independent variables to the outcome. A P-value of < 0.05 at 95% in multivariable logistic regression was considered as statistically significant. The goodness of fit of the model was tested by the Hosmer-Lemeshow test. The predictive performance of immune-inflammation indices levels for the symptom severity of BPH was evaluated using a receiver operating characteristic (ROC) curve analysis. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the immune-inflammation indices test were calculated using a cut-off value that will be selected from the ROC curve, and the Youden Index was used to identify the optimal cut-off values. Ethics approval and participants' consent This study was conducted in accordance with the ethical standards of the institutional research committee and the principles outlined in the Declaration of Helsinki. Before the study began, ethical clearance was obtained from the ethical review committee of the College of Medicine and Health Science, Wollo University [Ref. No- CMHS1119/20/2024]. Written informed consent was obtained from all participants before the interview. Privacy, anonymity, and confidentiality were ensured throughout the study process. Participants were informed that refusing consent or withdrawing from the study would not negatively affect their access to healthcare. Result Background characteristics of the study participants A total of 232 patients diagnosed with benign prostatic hyperplasia (BPH) were included in the study, with a response rate of 98.72%. The mean age of the participants was 66.92 years (range: 40 to 90 years). The majority of participants, 157 (67.67%), were from rural areas, and 106 (45.7%) did not receive formal education. The most common comorbid condition observed was hypertension (21.98%). Additionally, 131 (56.47%) and 148 (63.79%) of the participants were non-drinkers of alcohol and non-chewers of khat, respectively (Table 1 ). Table 1 Sociodemographic characteristics of the study participants Variable Frequency(N) Percent (%) Age categories 40–64 114 49.1 > 65 118 50.9 Occupation Farmer 119 51.3 Merchant 70 30.2 Employee 21 9.1 Daily labor 12 5.2 Other 10 4.3 Residence Urban 75 32.33 Rural 157 67.67 Educational status No formal education 106 45.7 Primary 55 23.7 Secondary 21 9.1 Diploma and above 50 21.6 Marital status Married 213 91.8 Other 19 8.2 Physical activity Low 108 46.6 Adequate 124 53.4 Days of FEV intake per week >= 5 41 17.7 3–5 158 68.1 <3 33 14.2 Number of FEV intake per day < 5 servings 167 72.0 ≥ 5 servings 65 28.0 Khat chewing Non-chewer 150 64.7 Khat chewer 82 35.3 Smoking Non-smoker 198 85.3 Smoker 34 14.7 Alcohol consumption Non-drinker 131 56.5 Alcohol drinker 101 43.5 Diabetes No 196 84.5 Yes 36 15.5 Hypertensive No 169 72.84 Yes 63 27.16 Co-morbid heart disease No 204 87.93 Yes 28 12.07 Duration since BPH diagnosis (mean ± SD) - 4.63 ± 2.21 Anthropometric and laboratory findings The study revealed that the median systolic blood pressure (SBP) was 130 mmHg, with an interquartile range (IQR) of 120 to 140 mmHg. Central obesity was noted among 45 participants, representing 19.4% of the sample. The average prostate size was 52.29 ± 33.17 cubic centimeters. The median fasting blood sugar (FBS) level was 98 mg/dL, with an IQR of 89.25 to 108 mg/dL. The mean lymphocyte count was 1.71 x 10³ cells/µL, with a range from 1.37 to 2.43 x 10³ cells/µL. Furthermore, the Systemic Inflammation Index (SII) had a median value of 474.12 x10 3 , with an IQR ranging from 215.85 to 949.59 x 10 3 (Table 2 ). Table 2 Anthropometric and laboratory findings of the study participants. Variables Mean + SD, Median (IQR), N (%) Systolic BP, mmHg 130 (120–140) Diastolic BP, mmHg 82 (76–90 Central Obesity No 187 (80.6%) Yes 45(19.4%) BMI Underweight 15 (6.5%) Normal weight 206 (88.8%) Overweight /obese 11(4.7%) Prostate size, cc 52.2930 + 33.17 Hematological parameters FBS, mg/dl 98.0 (89.25–108) Lymphocyte, x 10 3 /µL 1.71 (1.37–2.43) Neutrophil, x 10 3 /µL 3. 23(2.22–4.94) Monocyte, x 10 3 /µL 0. 43 (0.28–0.67) Platelet, x 10 3 /µL 246. 50 (194.75–343.75) Derived Hematological Parameters SII 479. 059 (215.846– 949.59) x10 3 PII 237.175 (76.866–470.329) x10 6 NLR 1.84 (1.02–3.06) PLR 135.85 (94.65- 213.34 LMR 3.87 (2.32–7.07) Abbreviations: BP- Blood pressure, BMI- Body Mass Index, FBS- Fasting Blood Sugar, SII- systemic immune-inflammation index, PII- pan immuno-inflammatory index, NLR- neutrophil-lymphocyte ratio, PLR- platelet to lymphocyte ratio. Symptom scores of benign prostatic hyperplasia The analysis of the prostatic symptom score of the participants revealed varying levels in both storage and voiding symptoms. Among storage symptoms, nocturia had a mean score of 2.09 ± 1.695, while urgency was reported with a higher mean score of 2.76 ± 1.721. Voiding symptoms were also notable, with weak stream averaging 2.23 ± 1.651 and straining at 2.21 ± 1.601. Regarding overall BPH symptom severity, as measured by the International Prostate Symptom Score (IPSS), the majority of participants (148, 63.79%) experienced mild to moderate symptoms (Table 3). Further stratification by age revealed that patients over 65 years of age exhibited a higher prevalence of severe symptoms (42.5%), Fig. 1. Table 3. Prostatic symptom scores of the study participants Items Mean + SD, N (%) Storage symptoms Nocturia 2.09 + 1.695 Frequency 1.81 + 1.211 Urgency 2.76 + 1.721 Voiding symptoms Weak stream 2.23 + 1.651 Straining 2.21 + 1.601 Intermittency 1.87 + 1.236 Incomplete emptying 2.13 + 1.395 BPH symptom severity (IPSS score) 20 84 (36.21%) Predictive value of immune-inflammatory indices in predicting severe symptoms of benign prostatic hyperplasia The predictive value and optimal cut-off values of hematological-derived immune-inflammatory indices, SII, PII, NLR, and LMR, were evaluated in assessing severe symptoms of BPH. Accordingly, the SII exhibited the highest area under the curve (AUC) at 0.736 (95% CI: 0.67–0.80), with an optimal cut-off value of 564.92 × 10³, achieving 72% specificity and 69% sensitivity. Similarly, the Platelet-to-Lymphocyte Index (PII) had an AUC of 0.729 (95% CI: 0.66–0.796), with a cut-off of 273.3x 10 6, specificity of 69%, and sensitivity of 69% (Fig. 2), (Table 4). Conversely, the Lymphocyte-to-Monocyte Ratio (LMR) had the lowest predictive performance, with an AUC of 0.403 (95% CI: 0.328–0.477), indicating limited discriminative ability (Fig. 3) Table 4. Predictive value of immune-inflammatory indices in predicting severe symptoms of BPH Parameters AUC (95%CI) Specificity Sensitivity Optimal cut of values SII 0.736(0.67, 0.801) 72% 69% 564.92x 10 3 PII 0.729 (0.66, 0.796) 69% 69% 273.3x 10 6 NLR 0.67(0.60, 0.74) 48% 89% 1.366 PLR 0.67(0.62, 0.76) 66% 68% 143.981 LMR 0.403 (0.328 0.477) 30% 76% 179.207 Symptom severity predictors of Benign prostatic hyperplasia The associations between the severity of benign prostatic hyperplasia (BPH) symptoms and potential predictors were analyzed using bivariable and multivariable binary logistic regression models. In the bivariable analysis, age, residence, physical activity, number of servings of FEV int6ake, khat chewing, duration of BPH, prostate size, BMI, central obesity, DM, hypertension, SII, PII, and NLR, had a p-value < 0.25 and were fitted into the multivariable binary logistic regression model for final analysis. Findings of multivariable analysis indicated that age, level of physical activity, central obesity, SII, PII, and NLR were significantly associated with the symptom severity of BPH. The odds of experiencing severe BPH symptoms among individuals aged 65 years and older were 2.55 times higher compared to those aged <65 (AOR = 2.55, 95% CI: 1.19, 5.45, p = 0.016), assuming that the effect of other covariates is constant. Similarly, individuals with low physical activity had 2.56 times higher odds of severe symptoms compared to those with adequate physical activity (AOR = 2.56, 95% CI: 1.22 - 5.35, p-value = 0.013). Furthermore, the odds of experiencing severe BPH symptoms among individuals with central obesity were 2.79 times higher than those without central obesity (AOR = 2.79, 95% CI: 1.07- 7.25, P = 0.035), assuming that the effect of other covariates is constant. Regarding inflammatory markers, individuals with elevated SII values (≥ 564.92 × 10 3 ) had 2.97 times greater odds of severe BPH symptoms than those with lower SII values (AOR = 2.97, 95% CI: 1.10 - 7.98, p-value = 0.031). Likewise, individuals with PII values ≥ 273.3x 10 6 were 2.46 times more likely to experience severe symptoms than those with lower PII values (AOR = 2.46, 95% CI: 1.04 - 5.86, p-value = 0.042), assuming other factors remain constant. Moreover, for patients with NLR values ≥ 1.368, the odds of severe BPH symptoms were 2.87 times higher compared to those with lower NLR values (AOR = 2.87, 95% CI: 1.01- 8.14, p-value = 0.048), provided that the effect of other covariates is controlled (Table 5). Table 5. Multivariable binary logistic regression analysis of factors associated with severe symptoms of BPH at Dessie Comprehensive Specialized Hospital, 2025 (n=232). Variables BPH symptom severity AOR (95% CI) P- value Mild-moderate (N=148) Sever (N=84) Age categories 40-64 86 (58.1%) 28(33.3%) 1 - > 65 62 (41.9%) 56 (66.7%) 2.55 (1.19 5.45) 0.016* Residence Urban 55 (37.2%) 20(23.8%) 1 - Rural 93 (62.8%) 64 (76.2%) 1.17 (0.53, 2.57) 0.700 Physical activity Low 57 (38.5%) 51 (60.7%) 2.56 (1.22, 5.35) 0.013* Adequate 91 (61.5%) 33 (39.3%) 1.00 - Number of servings of FEV intakes <5 servings 102 (68.9%) 65 (77.4%) 0.91 (0.37, 2.23) 0.836 ≥5 servings 46 (31.1%) 19 (22.6%) 1 - Khat chewing Non-chewer 100 (67.6%) 50 (59.5%) 1 - Khat chewer 48 (32.4%) 34 (40.5%) 2.05 (0.92, 4.57) 0.078 BPH duration - - - 1.03 (0.86,1.23) 0.732 Prostate Size - - - 0.99 (0.98, 1.01) 0.510 BMI Underweight 7 (4.7%) 8 (9.5%) 3.49 (14.00) 0.078 Normal weight 134 (90.5%) 72 (85.7%) 1 - Overweight/obese 7 (4.7%) 4 (4.8%) 0.41 (2.19) 0.296 Central Obesity No 129(87.2%) 57 (67.9%) 1 - Yes 19 (12.8%) 27 (32.1%) 2.79 (1.07, 7.25) 0.035* Diabetes No 129 (87.2%) 67 (79.8%) 1 - Yes 19 (12.8%) 17 (20.2%) 1.12 (0.43, 2.94) 0.820 Hypertension =140 36 (24.3%) 27 (32.1%) 1.42 (0.60, 3.37) 0.430 SII Value =564.92x 10 3 40 (27.0%) 61 (72.6%) 2.97 (1.10, 7.98) 0.031* PII Value =273.3x 10 6 41(27.7%) 61 (72.6%) 2.46 (1.04, 5.86) 0.042* NLR Value = 1.368 75 (50.7%) 76 (90.5%) 2.87 (1.01, 8.14) 0.048* PLR Value = 143.791 51 (34.5%) 51 (34.5%) 57 (67.9%) 0.388 Abbreviations : AOR- Adjusted Odds ratio, FEV- Fruit and vegetable intake, SII- systemic immune-inflammation index, PII- pan immuno-inflammatory index, NLR- neutrophil-lymphocyte ratio, PLR- platelet to lymphocyte ratio. Discussion Benign prostatic hyperplasia (BPH) is a common age-related condition that significantly impacts the quality of life of older men (2). Characterized by the non-malignant enlargement of the prostate gland, BPH leads to lower urinary tract symptoms (LUTS), including frequency, urgency, nocturia, and weak urinary stream (7). The severity of these symptoms varies widely, and identifying predictors of severe BPH is crucial for timely intervention and management. This study investigated the predictors of symptom severity and the predictive performance of immune-inflammatory markers in BPH patients attending Dessie Comprehensive Specialized Hospital. The findings revealed that 84 (36.21%) of the study population exhibited severe BPH symptoms, indicating the significant clinical burden of this condition within the studied setting. Furthermore, several factors, including age ≥ 65 years, low physical activity, central obesity, elevated Prostate Inflammation Index (PII), increased Neutrophil-to-Lymphocyte Ratio (NLR), and elevated Platelet-to-Lymphocyte Ratio (PLR), demonstrate a statistically significant association with the severity of BPH symptoms. The observation that advanced age (≥ 65 years) is significantly associated with severe BPH symptoms aligns with established literature (7, 14, 41). Age-related hormonal changes, particularly the decline in testosterone and the relative increase in estrogen, are believed to play a pivotal role in the pathogenesis of BPH (42). Moreover, the progressive structural and functional changes in the prostate gland with advancing age contribute to the development and exacerbation of lower urinary tract symptoms (LUTS) (43). The present study's finding reinforces the understanding that age remains a crucial independent predictor of BPH, hence, regular screening and early intervention in older individuals are essential to prevent disease progression. Low physical activity was also significantly associated with severe BPH symptoms. This finding aligns with growing evidence highlighting the protective role of exercise against BPH progression and its severity (14, 44). Reduced physical activity can lead to metabolic disturbances, increased systemic inflammation, and hormonal imbalances, all of which may promote prostatic hyperplasia and worsen symptom severity (45). Encouraging lifestyle modifications, including increased physical activity, could be a crucial strategy for modifying BPH severity. The observed association between central obesity and severe BPH symptoms agrees with previous studies (46-48), where waist circumference and metabolic syndrome components were linked to increased BPH severity. The possible explanation is that visceral fat contributes to systemic inflammation, insulin resistance, and altered hormone metabolism, which can accelerate prostatic growth and worsen urinary symptoms (49). Another main finding was the association between derived immune-inflammatory markers and severe BPH symptoms. The results demonstrated that higher levels of SII, PII, and NLR were independent predictors of severe symptoms, reinforcing the role of systemic inflammation in BPH progression. Patients with elevated SII values ≥ 564.92 × 10 3 had a higher likelihood of experiencing severe symptoms compared to those with lower SII values. SII reflects the systemic inflammatory response, and while primarily studied in the context of cancer prognosis, emerging evidence suggests its association with BPH and LUTS progression. Other studies have also explored the association of SII in the context of LUTS and found that elevated SII levels were significantly correlated with the clinical progression of LUTS in older men (22). Similarly, individuals with a PII value of ≥ 273.3 × 10 6 also face greater odds of severe symptoms. These findings align with previous research indicating that inflammatory pathways, particularly neutrophil activation and platelet aggregation, play a role in prostate tissue remodeling and disease severity (50). This also supports the growing evidence highlighting chronic inflammation as a critical driver of BPH progression (51). The observed association of NLR with severe BPH symptoms also supports existing research that highlights the involvement of neutrophils and lymphocytes in prostatic inflammatory processes (52) . NLR, a widely recognized marker of systemic inflammation, reflects the dynamic balance between pro-inflammatory (neutrophils) and anti-inflammatory (lymphocytes) components of the immune response. This association between high NLR and severe BPH symptoms is in line with previous studies, which found that increased NLR levels were linked to worse prostatic symptoms(53). Some studies have consistently demonstrated that elevated NLR levels were significantly associated with an increased risk of LUTS and symptom progression in BPH (54). Further supporting this association, other studies proposed that higher NLR levels could serve as an indicator of severe LUTS and BPH progression (18, 55). Beyond identifying associations, this research evaluated the capacity of readily available, hematology-derived inflammatory indices to predict the presence of severe BPH symptoms. The results indicate that SII exhibited the highest area under the curve (AUC) at 0.736 with an optimal cut-off value of 564.92 × 10³, achieving 72% specificity and 69% sensitivity. This suggests that a fair-to-moderate ability of SII to discriminate between patients with and without severe BPH symptoms (56). SII is a composite marker that reflects the balance between systemic inflammation and immune response. While this study identified an optimal cut-off value of 564.92 x 103, it is important to note that other studies may have identified different optimal cut-off values for SII in the context of BPH. For example, one study reported a very high AUC of 1.0 for SII in diagnosing BPH and its progression (19). The PII showed similar predictive performance, with an AUC of 0.729. Its optimal cut-off was determined to be 273.3 × 10 6 , achieving a specificity of 68% and a sensitivity of 67%. The comparable performance of SII and PII suggests that integrating platelet counts (as done in both SII and PII) alongside neutrophil and lymphocyte counts may provide valuable information for predicting BPH severity in this study. Both markers represent composite indices reflecting systemic inflammation and immune cell balance, potentially capturing different facets of the inflammatory situation contributing to severe symptoms (54). While NLR was identified as a significant predictor in the association analysis, its specific predictive performance (AUC) was seemingly lower than SII and PII. However, their established association may support their relevance in the context of BPH inflammation. Strengths and limitations of the study This study highlights significant factors associated with severe BPH symptoms, contributing to the understanding of BPH severity risk factors. It also provides valuable insights into the predictive performance of easily accessible immune-inflammatory markers in assessing BPH symptom severity, which can aid in early symptom stratification. However, this study has some limitations. First, it was conducted at a single center, which may limit the generalizability of the results to broader populations. Second, unmeasured confounders that may influence both inflammation and BPH severity were not extensively explored. Third, the absence of prostatic tissue analysis means we could not directly assess local inflammatory processes within the prostate. Finally, the optimal cut-off values for the inflammatory markers, derived from this specific population, require external validation before wider clinical application. Therefore, while this study provides valuable preliminary insights, future research should focus on prospective, multicenter studies with detailed assessments to confirm these findings and explore the underlying mechanisms linking systemic inflammation to BPH symptom severity. Implications and future directions The findings suggest that easily derivable immuno-inflammatory indices, particularly SII and PII, reflect underlying inflammatory processes related to BPH severity and hold moderate potential as predictive markers. Clinicians in similar settings could consider these markers, calculated from routine complete blood counts, as supplementary information to standard clinical assessment tools for patient evaluation and stratification. High SII or PII values might indicate a greater likelihood of severe symptoms, potentially prompting closer monitoring or consideration of anti-inflammatory therapeutic strategies; however, further research is necessary to confirm clinical utility . Future research should focus on validating these findings in larger, prospective, multi-center studies to affirm the predictive performance of SII and PII and their respective cut-off values across diverse populations. Longitudinal studies are essential to establish causality and understand how changes in these markers over time correlate with disease progression or response to treatment. Investigating the specific inflammatory pathways linking elevated SII, PII, and NLR to prostatic inflammation and LUTS development would also be valuable. Furthermore, exploring the combined predictive value of these markers with established clinical parameters could lead to more robust prediction models for severe BPH. Conclusion In conclusion, this study identified that age, central obesity, physical inactivity, and high inflammatory markers (SII, PII, NL) were found to be independent predictors of severe BPH symptoms. Additionally, the predictive performance of hematological-derived immune-inflammatory indices was evaluated with severe BPH symptoms, and SII and PII demonstrated the highest predictive capabilities. These findings underscore the role of systemic inflammation in BPH progression and suggest that hematological inflammatory indices may serve as practical, cost-effective tools for early identification and management of patients at risk for symptom stratification. Future research should focus on validating these markers across diverse populations and exploring targeted interventions to mitigate inflammation-related BPH progression. Abbreviations BPH: Benign prostatic hyperplasia BOO: Bladder Outlet Obstruction BP: Blood Pressure BPE: Benign Prostatic Enlargement CVDs: Cardiovascular Diseases CBC: Complete Blood Count FBS: Fasting Blood Sugar IPSS: International Prostate Symptom Score LUTS: Lower Urinary Tract Symptoms NLR: Neutrophil-Lymphocyte Ratio PII: Pan Immuno-Inflammatory Index PL: Platelet–Lymphocyte Ratio SII: Systemic Immune-Inflammation Index WBC: White Blood Cells WC: Waist Circumference Declarations Acknowledgment We would like to express our sincere gratitude to Wollo University for providing the necessary materials and support that made this research possible. Our heartfelt thanks also go to the study participants, whose willingness to contribute their time and insights was invaluable. We are equally grateful to the data collectors for their dedication and effort in ensuring the accuracy and completeness of the data. Consent for publication Not applicable Competing interests The authors declare no competing interests Funding This study did not receive any specific grant from funding agencies in the public, commercial, or for-profit sectors. Availability of data and materials The datasets used or analyzed in this study are available from the corresponding author upon reasonable request. Authors’ contributions All authors contributed to the study conception and design. GA led conceptualization, methodology, and project administration. ABK was responsible for data curation and formal analysis. ABK and SM contributed to the writing, original draft preparation, as well as reviewing and editing the manuscript. All authors read and approved of the final manuscript. References Chughtai, B., Lee, R., Te, A. & Kaplan, S. Role of inflammation in benign prostatic hyperplasia. Rev. Urol. 13 (3), 147 (2011). Untergasser, G., Madersbacher, S. & Berger, P. 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15:39:32","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172592,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6494156/v1/d1add08aee256d6320aa0188.html"},{"id":92010236,"identity":"6624a07d-2b65-4eea-a8cc-3326cb90312f","added_by":"auto","created_at":"2025-09-23 15:39:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":323456,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of BPH symptom severity by age categories\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6494156/v1/7be759b4f10b643cdad649c2.png"},{"id":92011609,"identity":"f017d1d5-aa44-4615-a001-7ab4ff3bc4ba","added_by":"auto","created_at":"2025-09-23 15:47:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":754487,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of hematological-derived immune-inflammatory indices to predict symptom severity of BPH.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6494156/v1/e663061cc2f6aec5ae9bd1de.png"},{"id":92010248,"identity":"f0c1f70f-3dd4-464a-8256-a456ebd7f019","added_by":"auto","created_at":"2025-09-23 15:39:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208608,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of LMR to predict symptom severity of BPH.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6494156/v1/9bbdaf20a04cc050a26d60c2.png"},{"id":97723826,"identity":"d685ea24-6d8b-4d79-8ab8-e4738a3b2177","added_by":"auto","created_at":"2025-12-08 16:08:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2925787,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6494156/v1/cfe4e7a6-970b-417d-99b7-7686c27b70aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of Symptom Severity and Predictive Performance of Immune- Inflammation Indices Levels in Patients with Benign Prostatic Hyperplasia attending Dessie Comprehensive Specialized Hospital","fulltext":[{"header":"Background","content":"\u003cp\u003eBenign prostatic hyperplasia (BPH) is an enlargement of the prostate gland from the progressive hyperplasia of stromal and glandular prostatic cells (1, 2). It is a histologic diagnosis, and the proliferation is in smooth muscle and epithelial cells, more within the transition zone (3). The prostatic transition zone comprises approximately 5% of the prostate and surrounds the proximal urethra. This zone is the site of continual growth throughout life (4). Even though there are differences in the reported prevalence of \u0026nbsp;BPH among countries, it has been stated that more than 20% of men between the ages of 30 and 79 years, equivalent to 15 million, and approximately 80% of men by 70 years of age would be estimated to have the problem (5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe disease is clinically manifested in the form of lower urinary tract symptoms (LUTS)\u0026nbsp;associated with benign prostatic enlargement (6). The LUTS encompass a range of symptoms, broadly categorized into bladder storage issues like urinary frequency, urgency (7), and voiding difficulties such as dysuria, hesitancy, intermittent stream, poor flow, post-micturition dribbling, and incomplete voiding (5, 7). Although prostatic tissue proliferation is classified as benign and may not be life-threatening, if left untreated, it can lead to LUTS and significantly decrease the quality of life of those affected (8, 9), as well as an increased risk of acute urinary retention (9). The number of men diagnosed with BPH and LUTS has steadily increased over the last decade, driven by longer lifespans, greater disease awareness, and improved diagnoses (7, 9). The prevalence of LUTS associated with BPH parallels that of pathologic BPH; more than 50% of men over 50 are believed to experience LUTS secondary to an enlarged prostate gland (10).\u003c/p\u003e\n\u003cp\u003eWhile age and genetics are well-established risk factors, recent studies highlight the crucial role of modifiable lifestyle and metabolic factors in the progression of LUTS in BPH patients. Obesity has been strongly linked to BPH-related LUTS, with higher body mass index (BMI) correlating with greater prostate volume and more severe urinary symptoms (11, 12). Behavioral factors like physical inactivity, smoking also contribute significantly, as sedentary individuals and smokers show a higher prevalence of LUTS (13, 14), and poor dietary habits, particularly inadequate fruit and vegetable intake, increase LUTS risk (15). Metabolic disorders, particularly diabetes mellitus, have been implicated in the pathophysiology of LUTS, with diabetic individuals being at significantly higher risk of developing severe symptoms.\u003c/p\u003e\n\u003cp\u003eIn addition, chronic inflammation has been implicated in the pathogenesis and symptom progression of BPH, as inflammatory processes within the prostate foster tissue remodeling, stromal expansion, and symptom development (16). The presence of inflammatory infiltrates in prostate tissue has been associated with an increased risk of acute urinary retention and worsening LUTS (17). Furthermore, a number of hematological-derived biomarkers that are indicative of systemic immune-inflammatory responses have been confirmed in recent decades for their correlation with the diagnosis and progression of BPH. Markers such as the platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) have been evaluated for their diagnostic and prognostic use for BPH (18, 19). Additionally, these inflammatory markers have demonstrated powerful diagnostic and prognostic value in various metabolic risks related to BPH and various tumors, including prostate cancer (20, 21). Elevated levels of these markers correlate with increased disease severity, poorer treatment response, and a higher likelihood of requiring invasive intervention (22, 23). Specifically, higher SII values have been associated with the progression of LUTS in men with BPH (19).\u003c/p\u003e\n\u003cp\u003eHowever, the value of those hematological-derived inflammatory markers in predicting symptom severity of BPH remains obscure. On top of that, controversial results have been conveyed in several studies regarding other predictors of severe BPH (24-26). For example, \u0026nbsp;some studies (26, 27) found a lower prevalence of severe BPH among alcohol consumers, while other studies (28) reported a positive correlation. Similar contradictions exist for smoking and physical activity, with some studies (26, 28) finding no association, while others suggest smokers and ex-smokers face higher BPH risk. Likewise, while some studies (26, 27) have shown no relationship between dyslipidemia, diabetes, and BPH, others (29-31) have found strong links. Most importantly, to the best of our knowledge, no documented study has assessed the severity of BPH symptoms in Ethiopia, particularly in the study area. Therefore, this study aimed to identify predictors of symptom severity and evaluate the predictive performance of immune-inflammation indices in BPH patients at Dessie Comprehensive Specialized Hospital. This would aid in implementing appropriate interventions or targeted approaches. Since most risk factors are modifiable, prevention and control strategies would be proposed to alleviate symptoms, prevent disease progression, and preserve quality of life. Furthermore, this study will encourage additional research into biomarkers that could help stratify patients based on their risk of developing severe symptoms of BPH.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003ch2\u003eStudy Setting and Period\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe study was conducted at Dessie Comprehensive Specialized Hospital in Northeast Ethiopia from August to October 2024. The hospital is situated in Dessie town, part of the Amhara Regional State, which is 401 km northeast of the capital, Addis Ababa. It serves as a referral center for South Wollo and surrounding zones, catering to approximately 7 million people, including those from neighboring regions. The hospital includes units such as internal medicine, surgery, gynecology and obstetrics, pediatrics, oncology, psychiatry, laboratory, orthopedics, pharmacy, and neurology. The surgery unit has both inpatient and outpatient services, catering to new patients as well as follow-up cases. According to hospital data, an average of 170 to 200 patients with BPH visited the hospital per month between February and June 2024. A hospital-based cross-sectional study took place from August to October 2024.\u003c/p\u003e\n\u003ch3\u003eStudy population and eligibility criteria\u003c/h3\u003e\n\u003cp\u003eThe study targeted BPH patients attending Dessie Comprehensive Specialized Hospital. The source population comprised all BPH patients visiting the hospital, while the study population included those who attended during the data collection period.\u003c/p\u003e\n\u003cp\u003eEligible participants were male patients aged 40 years and above with a confirmed physician diagnosis of BPH. Patients were excluded if they were critically ill, had severe physical disabilities, or presented with symptomatic co-infections, sepsis, or uropathologic conditions such as urinary stones and urogenital cancer. Those with a history of prostate or urethral surgery were also excluded. Additionally, individuals taking medications that could alter or control bladder symptoms, including anticholinergics, 5α-reductase inhibitors, phosphodiesterase-5 inhibitors, and hormone replacement therapy, were not eligible for the study.\u003c/p\u003e\n\u003ch2 id=\"_Toc119267387\"\u003eSample size and sampling procedures\u003c/h2\u003e\n\u003cp\u003eThe sample size was determined using OpenEpi (Version 3.01) based on the odds ratio (OR) method using the following assumptions: Considering 95% CI, 80% power. The sample size was obtained by taking hypertension as a risk factor from a previous study, where the proportion of hypertension among non-severe prostatic symptoms was 19.4 %, and an odds ratio of 2.5 (29). Accordingly,\u0026nbsp;the total sample size becomes 2014. When the 10% non-response rate was added, the final sample size became 235.\u003c/p\u003e\n\u003cp\u003eEach BPH patient who fulfilled the inclusion criteria was selected as a study participant through a consecutive sampling technique. When the subject did not meet the inclusion criteria, the next client was approached until the recommended sample size was reached. The participants were assigned based on the IPSS urological scores (30). According to the criteria, participants were categorized as mildly to moderately symptomatic (IPSS: 0-19) or as severely symptomatic (IPSS: 20-35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Variables\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome variable in this study was BPH severity, categorized as severely symptomatic (IPSS: 20–35) and asymptomatic to moderately symptomatic (IPSS: 0–19). Independent variables included sociodemographic factors such as age, occupation, educational status, and residence. Comorbidities, including a history of coronary heart disease, diabetes, and hypertension, were also considered. Physical measurements such as blood pressure (BP), body mass index (BMI), and waist circumference (WC) were included. Behavioral factors such as alcohol consumption, cigarette smoking, khat chewing, physical exercise, and fruit and vegetable intake were also assessed. Additionally, fasting blood sugar levels and immune-inflammatory indices, including the systemic immune-inflammation index (SII), pan immuno-inflammatory index (PII), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR), were analyzed. Prostate-related factors, such as prostate size and the duration since BPH diagnosis, were also evaluated.\u003c/p\u003e\n\u003ch2\u003eOperational definitions\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eAlcohol drinking status: Alcohol drinker\u003c/strong\u003e:-defined as the intake of any type of alcoholic beverage, such as beer, wine or locally prepared alcoholic beverages, more than once per week in the past one year, regardless of the amount,\u003cstrong\u003enon-drinkers\u003c/strong\u003e:-those who drink less than once per week for the last one year or never drink alcoholic products (31)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e: was considered if a diagnosis is recorded by a physician, or the participant is under treatment, or FBS ≥126 mg/ dl during the time of data collection (32).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e: was \u0026nbsp;considered if a diagnosis is recorded by a physician, or the patient is under treatment, or has resting BP measurements of SBP ≥130 mmHg and/ or \u0026nbsp; DBP≥85 mmHg (33).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKhat chewer:\u003c/strong\u003e Participants who have been chewing khat in any amount during the past year, otherwise \u003cstrong\u003enon-chewers\u003c/strong\u003e (34).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFruit and vegetable intake\u003c/strong\u003e: For raw green leafy vegetables, 1 serving = one cup; for cooked or chopped vegetables, 1 serving = ½ cup; for fruit (apple, banana, orange etc…), 1 serving = 1 medium size piece; for chopped, cooked and canned fruit, 1 serving = ½ cup; and for juice from fruit, 1 serving = ½ cup (35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLow-level physical activity\u003c/strong\u003e: A person not meeting any of \u0026nbsp;the criteria for the moderate- or high-level categories (36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerate-physical activity\u003c/strong\u003e: any activity that causes a small increase in breathing or heart rate (brisk walking or carrying light loads) that continued for at least 30 min with a minimum of 3 days per week or 5 or more days of these activities for at least 20 min per day or ≥3 days of vigorous-intensity activity per week of at least 20 min per day (36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeverity of BPH:\u0026nbsp;\u003c/strong\u003eThe severity of BPH was quantified by the American Urological Association or International Prostate Symptom Scoring (IPSS) system, recommended by the World Health Organization (WHO, 1991) as the preferred instrument to measure BPH symptoms. It consists of seven symptoms: intermittency, urgency, weak stream, incomplete emptying, straining, frequency, and nocturia. Each participant was asked to rate the severity of their symptoms on a six-point sub-scale, from a minimum score of 0 to a maximum score of 5, with the lowest score reflecting ‘no symptoms. The sum of all the answers made up an IPSS total score (score range 0–35 points), which was grouped into asymptomatic to moderately symptomatic( \u0026lt;19) and severe (\u003cu\u003e\u0026gt;\u003c/u\u003e20) (30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking status: Smoker\u003c/strong\u003e:-those who have a cigarette smoking practice within the last one year, irrespective of the amount, Non-smokers:- who never smoked in their lifetime or smokers before one year (34).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-level physical activity\u003c/strong\u003e: any activity that causes a large increase in breathing or heart rate if continued for at least 30 minutes (e.g., running, carrying or lifting heavy loads, digging or construction work) for a minimum of three days per week (36).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeight classifications by BMI:\u003c/strong\u003eunderweight (BMI\u0026lt; 18.5)\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003enormal weight (BMI 18.5-24.9), overweight (BMI 25-29.9),andobese (BMI≥30) (36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune-inflammatory indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;SII was calculated as \u0026nbsp;platelet × neutrophil/lymphocyte (37).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;PII was calculated as \u0026nbsp;neutrophil count× platelet count (× monocyte count/lymphocyte count (38).\u003c/p\u003e\n\u003cp\u003eNLR was \u0026nbsp;calculated as the ratio of neutrophils to lymphocytes in peripheral blood (39).\u003c/p\u003e\n\u003cp\u003ePLR: will be calculated as the ratio of platelets to lymphocytes in peripheral blood (39).\u003c/p\u003e\n\u003ch2 id=\"_Toc119267394\"\u003eData and blood sample collection procedure\u003c/h2\u003e\n\u003cp\u003eTrained data collectors conducted interviews using an Amharic version questionnaire to gather relevant information. Blood samples (approximately 5 mL) were collected using a sterile technique after participants had undergone overnight fasting or a minimum of 8 hours of fasting. Informed consent was obtained before collecting samples for fasting blood sugar (FBS) and complete blood count (CBC) analysis. A well-trained laboratory technician performed the tests using a calibrated chemistry analyzer, adhering strictly to standard operating procedures. Participants with abnormal test results were referred to the follow-up unit for further evaluation. The data collection process, including anthropometric, clinical, and biochemical measurements, was supervised by the principal investigator and a designated supervisor to ensure accuracy and quality control.\u003c/p\u003e\n\u003ch3 id=\"_Toc119267395\"\u003eQuestionnaire tool\u003c/h3\u003e\n\u003cp\u003eData was collected using a structured questionnaire via the KoboCollect application on Android phones. The questionnaire was designed and uploaded into the KoboCollect platform, which allowed for efficient data entry and real-time data monitoring. Behavioral factors such as smoking, khat chewing, alcohol consumption, physical activity, and fruit and vegetable intake were also assessed. The International Prostate Symptom Score (IPSS) was applied to evaluate BPH symptom severity(30). Data collection was conducted through face-to-face interviews.\u003c/p\u003e\n\u003cp\u003ePhysical measurements, including weight, height, waist circumference, and blood pressure, were taken using the WHO stepwise approach (36), \u0026nbsp;with calibrated equipment operated by trained health professionals. Height was measured with participants standing upright, and weight was recorded without shoes or heavy items. Waist circumference was measured midway between the lower ribs and iliac crest using a non-stretch tape. Height and weight were approximated to the nearest 0.1 cm and 0.1 kg, respectively, and BMI was calculated as weight (kg) divided by height squared (m²). A physician performed a digital rectal examination to estimate prostate size.\u003c/p\u003e\n\u003ch3 id=\"_Toc119267397\"\u003e\u0026nbsp;Laboratory analysis\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of hematological parameters/or immuno-inflammatory indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA complete blood count (CBC) was performed using an automated hematology analyzer. The machine automatically measured white blood cells (e.g., lymphocytes, neutrophils, monocytes) and platelets. Additional immune-inflammatory indices were calculated, including the SII, PII, PLR, and NLR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of fasting serum glucose\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting serum glucose was measured using the enzymatic glucose oxidase method with a commercial reagent kit(40). As a principle, the glucose in the sample could be oxidized to D-gluconic acid and hydrogen peroxide in the presence of glucose oxidase (GOD). Hydrogen peroxide then reacted with phenol and 4-aminoantipyrine, catalyzed by peroxidase (POD), forming a pink-colored quinoneimine compound. The intensity of the color, measured at 520 nm, is directly proportional to the glucose concentration in the sample.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc119267398\"\u003e\u0026nbsp;Data quality control\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/h2\u003e\n\u003cp id=\"_Toc119267399\"\u003eData quality was ensured through the proper design and pretesting of the questionnaire. The questionnaire was initially prepared in English, translated into Amharic, and then back-translated into English. A pretest was conducted on 10% of the total sample (22 participants) at Boru Meda General Hospital to assess the reliability of the items, and necessary modifications were made accordingly.\u003c/p\u003e\n\u003cp\u003eBefore data collection, training was provided to data collectors on the study objectives, methodology, relevance, and on using the KoboCollect platform. Data quality was further ensured by strictly adhering to the manufacturers' instructions and standard operating procedures during the laboratory testing processes. Special attention was given to biochemical analyses, and reagent expiration dates were checked. After data collection, each questionnaire was reviewed daily for completeness, clarity, and consistency.\u003c/p\u003e\n\u003ch2\u003eData processing and analysis\u003c/h2\u003e\n\u003cp\u003eThe data collected using the KoboCollect application on Android phones were imported into STATA 17 for processing and analysis. After data collection, the responses were synchronized from the mobile devices and exported in a format compatible with STATA for further analysis. The data were first cleaned to ensure accuracy, completeness, and consistency. Any inconsistencies, missing values, or outliers were checked. Descriptive statistics were used to summarize the sociodemographic, behavioral, and clinical characteristics of the participants. For analysis, categorical variables were coded numerically, and continuous variables were checked for normality. \u0026nbsp;The associations between the outcome variable and potential predictors were investigated using the bivariable and multivariable binary logistic regression model. Variables with a p-value \u0026lt; 0.25 in the bivariable logistic regression were fitted into the multivariable logistic regression model for final analysis. Adjusted odds ratios with 95% confidence intervals (95% CI) were used for the interpretation of the strength of prediction of the independent variables to the outcome. A\u0026nbsp;P-value of \u0026lt; 0.05 at 95% in multivariable logistic regression was considered as statistically significant. The goodness of fit of the model was tested by the Hosmer-Lemeshow test. The predictive performance of immune-inflammation indices levels for the symptom severity of BPH was evaluated using a receiver operating characteristic (ROC) curve analysis. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the immune-inflammation indices test were calculated using a cut-off value that will be selected from the ROC curve, and the Youden Index was used to identify the optimal cut-off values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and participants' consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the institutional research committee and the principles outlined in the Declaration of Helsinki. Before the study began, ethical clearance was obtained from the ethical review committee of the College of Medicine and Health Science, Wollo University [Ref. No- CMHS1119/20/2024]. Written informed consent was obtained from all participants before the interview. Privacy, anonymity, and confidentiality were ensured throughout the study process. Participants were informed that refusing consent or withdrawing from the study would not negatively affect their access to healthcare.\u003c/p\u003e"},{"header":"Result","content":"\u003ch3\u003eBackground characteristics of the study participants\u003c/h3\u003e\n\u003cp\u003eA total of 232 patients diagnosed with benign prostatic hyperplasia (BPH) were included in the study, with a response rate of 98.72%. The mean age of the participants was 66.92 years (range: 40 to 90 years). The majority of participants, 157 (67.67%), were from rural areas, and 106 (45.7%) did not receive formal education. The most common comorbid condition observed was hypertension (21.98%). Additionally, 131 (56.47%) and 148 (63.79%) of the participants were non-drinkers of alcohol and non-chewers of khat, respectively (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic characteristics of the study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency(N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAge categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMerchant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eEducational status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiploma and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDays of FEV intake per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;= 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNumber of FEV intake per day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5 servings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5 servings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKhat chewing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-chewer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKhat chewer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAlcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCo-morbid heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuration since BPH diagnosis (mean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eAnthropometric and laboratory findings\u003c/h3\u003e\n\u003cp\u003eThe study revealed that the median systolic blood pressure (SBP) was 130 mmHg, with an interquartile range (IQR) of 120 to 140 mmHg. Central obesity was noted among 45 participants, representing 19.4% of the sample. The average prostate size was 52.29\u0026thinsp;\u0026plusmn;\u0026thinsp;33.17 cubic centimeters. The median fasting blood sugar (FBS) level was 98 mg/dL, with an IQR of 89.25 to 108 mg/dL. The mean lymphocyte count was 1.71 x 10\u0026sup3; cells/\u0026micro;L, with a range from 1.37 to 2.43 x 10\u0026sup3; cells/\u0026micro;L. Furthermore, the Systemic Inflammation Index (SII) had a median value of 474.12 x10\u003csup\u003e3\u003c/sup\u003e, with an IQR ranging from 215.85 to 949.59 x 10\u003csup\u003e3\u003c/sup\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnthropometric and laboratory findings of the study participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;+\u0026thinsp;SD, Median (IQR), N (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (120\u0026ndash;140)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (76\u0026ndash;90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCentral Obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187 (80.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206 (88.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight /obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eProstate size, cc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.2930\u0026thinsp;+\u0026thinsp;33.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHematological parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFBS, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.0 (89.25\u0026ndash;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLymphocyte, x 10\u003csup\u003e3\u003c/sup\u003e /\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71 (1.37\u0026ndash;2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNeutrophil, x 10\u003csup\u003e3\u003c/sup\u003e /\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. 23(2.22\u0026ndash;4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMonocyte, x 10\u003csup\u003e3\u003c/sup\u003e /\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0. 43 (0.28\u0026ndash;0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePlatelet, x 10\u003csup\u003e3\u003c/sup\u003e /\u0026micro;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246. 50 (194.75\u0026ndash;343.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDerived Hematological Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e479. 059 (215.846\u0026ndash; 949.59) x10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.175 (76.866\u0026ndash;470.329) x10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84 (1.02\u0026ndash;3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.85 (94.65- 213.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.87 (2.32\u0026ndash;7.07)\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\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eBP- Blood pressure, BMI- Body Mass Index, FBS- Fasting Blood Sugar, SII- systemic immune-inflammation index, PII- pan immuno-inflammatory index, NLR- neutrophil-lymphocyte ratio, PLR- platelet to lymphocyte ratio.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSymptom scores of benign prostatic hyperplasia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of the prostatic symptom score of the participants revealed varying levels in both storage and voiding symptoms. Among storage symptoms, nocturia had a mean score of 2.09 \u0026plusmn; 1.695, while urgency was reported with a higher mean score of 2.76 \u0026plusmn; 1.721. Voiding symptoms were also notable, with weak stream averaging 2.23 \u0026plusmn; 1.651 and straining at 2.21 \u0026plusmn; 1.601. Regarding overall BPH symptom severity, as measured by the International Prostate Symptom Score (IPSS), the majority of participants (148, 63.79%) experienced mild to moderate symptoms (Table 3). Further stratification by age revealed that patients over 65 years of age exhibited a higher prevalence of severe symptoms (42.5%), Fig. 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Prostatic symptom scores of the study participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 450px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u003cu\u003e+\u003c/u\u003e SD, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStorage symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eNocturia \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2.09 \u003cu\u003e+\u003c/u\u003e 1.695\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eFrequency \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1.81 \u003cu\u003e+\u003c/u\u003e 1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eUrgency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2.76 \u003cu\u003e+\u003c/u\u003e\u0026nbsp; \u0026nbsp;1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Voiding symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eWeak stream \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2.23 + 1.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eStraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2.21 \u003cu\u003e+\u003c/u\u003e 1.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eIntermittency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e1.87 + 1.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003eIncomplete emptying \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e2.13 \u003cu\u003e+\u003c/u\u003e\u0026nbsp; \u0026nbsp;1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 250px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPH symptom severity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(IPSS score)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026lt;\u003c/u\u003e 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e148 (63.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u0026nbsp;\u003c/u\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e84 (36.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive value of immune-inflammatory indices in predicting severe symptoms of benign prostatic hyperplasia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictive value and optimal cut-off values of hematological-derived immune-inflammatory indices, SII, PII, NLR, and LMR, were evaluated in assessing severe symptoms of BPH. \u0026nbsp;Accordingly, the SII exhibited the highest area under the curve (AUC) at 0.736 (95% CI: 0.67\u0026ndash;0.80), with an optimal cut-off value of 564.92 \u0026times; 10\u0026sup3;, achieving 72% specificity and 69% sensitivity. \u0026nbsp;Similarly, the Platelet-to-Lymphocyte Index (PII) had an AUC of 0.729 (95% CI: 0.66\u0026ndash;0.796), with a cut-off of 273.3x 10\u003csup\u003e6,\u003c/sup\u003e specificity of 69%, and sensitivity of 69% (Fig. 2), (Table 4). \u0026nbsp;Conversely, the Lymphocyte-to-Monocyte Ratio (LMR) had the lowest predictive performance, with an AUC of 0.403 (95% CI: 0.328\u0026ndash;0.477), indicating limited discriminative ability (Fig. 3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. \u0026nbsp;Predictive value of immune-inflammatory indices in predicting severe symptoms of BPH\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal cut of values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.736(0.67, 0.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;564.92x 10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePII\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0.729 (0.66, \u0026nbsp; \u0026nbsp;0.796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e273.3x 10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0.67(0.60, 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0.67(0.62, 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e143.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0.403 (0.328 0.477)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e179.207\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSymptom severity predictors of Benign prostatic hyperplasia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe associations between the severity of benign prostatic hyperplasia (BPH) symptoms and potential predictors were analyzed using bivariable and multivariable binary logistic regression models. In the bivariable analysis, age, residence, physical activity, number of servings of FEV int6ake, khat chewing, duration of BPH, prostate size, BMI, central obesity, DM, hypertension, SII, PII, and NLR, had a p-value \u0026lt; 0.25 and were fitted into the multivariable binary logistic regression model for final analysis. Findings of multivariable analysis indicated that age, level of physical activity, central obesity, SII, PII, and NLR were significantly associated with the symptom severity of BPH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe odds of experiencing severe BPH symptoms among individuals aged 65 years and older were 2.55 times higher compared to those aged \u0026lt;65 (AOR = 2.55, 95% CI: 1.19, 5.45, p = 0.016), assuming that the effect of other covariates is constant. Similarly, individuals with low physical activity had 2.56 times higher odds of severe symptoms compared to those with adequate physical activity (AOR = 2.56, 95% CI: 1.22 - 5.35, p-value = 0.013). \u0026nbsp;Furthermore, the odds of experiencing severe BPH symptoms among individuals with central obesity were 2.79 times higher than those without central obesity (AOR = 2.79, 95% CI: 1.07- 7.25, P = 0.035), assuming that the effect of other covariates is constant. Regarding inflammatory markers, individuals with elevated SII values (\u0026ge; 564.92 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e) had 2.97 times greater odds of severe BPH symptoms than those with lower SII values (AOR = 2.97, 95% CI: 1.10 - 7.98, p-value = 0.031). Likewise, individuals with PII values \u0026ge; 273.3x 10\u003csup\u003e6\u003c/sup\u003e were 2.46 times more likely to experience severe symptoms than those with lower PII values (AOR = 2.46, 95% CI: 1.04 - 5.86, p-value = 0.042), assuming other factors remain constant. Moreover, for patients with NLR values \u0026ge; 1.368, the odds of severe BPH symptoms were 2.87 times higher compared to those with lower NLR values (AOR = 2.87, 95% CI: 1.01- 8.14, p-value = 0.048), provided that the effect of other covariates is controlled (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Multivariable binary logistic regression analysis of factors associated with severe symptoms of BPH at Dessie Comprehensive Specialized Hospital, 2025 (n=232).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"670\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPH symptom severity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP- value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild-moderate (N=148)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSever (N=84)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e40-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e86 (58.1%) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e28(33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026gt;\u003c/u\u003e 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e62 (41.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.55 (1.19 5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e55 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20(23.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e93 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e64 (76.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.17 (0.53, 2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e57 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e51 (60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.56 (1.22, 5.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eAdequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e91 (61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e33 (39.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of servings of FEV intakes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt;5 servings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e102 (68.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e65 (77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.91 (0.37, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026ge;5 servings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e46 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e19 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKhat chewing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eNon-chewer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e100 (67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e50 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eKhat chewer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e48 (32.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e34 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.05 (0.92, 4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBPH duration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.03 (0.86,1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProstate Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.99 (0.98, 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e3.49 (14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e134 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e72 (85.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eOverweight/obese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e7 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4 (4.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.41 (2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCentral Obesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e129(87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e57 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e19 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e27 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.79 (1.07, 7.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e129 (87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e67 (79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e19 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e17 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.12 (0.43, 2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt; 140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e112 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e57 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026gt;=140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e36 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e27 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.42 (0.60, 3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSII Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt;564.92x 10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e108 (73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026gt;=564.92x 10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e40 (27.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e61 (72.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.97 (1.10, 7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePII Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt;273.3x 10\u003csup\u003e6\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e107(72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23(27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026gt;=273.3x 10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e41(27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e61 (72.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.46 (1.04, 5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt; 1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e73 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026gt;= 1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e75 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e76 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e2.87 (1.01, 8.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026lt; 143.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e97 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e97 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e27 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026gt;= 143.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e51 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e51 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e57 (67.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: AOR- Adjusted Odds ratio, FEV- Fruit and vegetable intake, SII- systemic immune-inflammation index, PII- pan immuno-inflammatory index, NLR- neutrophil-lymphocyte ratio, PLR- platelet to lymphocyte ratio.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBenign prostatic hyperplasia (BPH) is a common age-related condition that significantly impacts the quality of life of older men (2). Characterized by the non-malignant enlargement of the prostate gland, BPH leads to lower urinary tract symptoms (LUTS), including frequency, urgency, nocturia, and weak urinary stream (7). The severity of these symptoms varies widely, and identifying predictors of severe BPH is crucial for timely intervention and management. This study investigated the predictors of symptom severity and the predictive performance of immune-inflammatory markers in BPH patients attending Dessie Comprehensive Specialized Hospital. The findings revealed that 84 (36.21%) of the study population exhibited severe BPH symptoms, indicating the significant clinical burden of this condition within the studied setting. Furthermore, several factors, including age ≥ 65 years, low physical activity, central obesity, elevated Prostate Inflammation Index (PII), increased Neutrophil-to-Lymphocyte Ratio (NLR), and elevated Platelet-to-Lymphocyte Ratio (PLR), demonstrate a statistically significant association with the severity of BPH symptoms.\u003c/p\u003e\n\u003cp\u003eThe observation that advanced age (≥ 65 years) is significantly associated with severe BPH symptoms aligns with established literature (7, 14, 41). Age-related hormonal changes, particularly the decline in testosterone and the relative increase in estrogen, are believed to play a pivotal role in the pathogenesis of BPH (42). Moreover, the progressive structural and functional changes in the prostate gland with advancing age contribute to the development and exacerbation of lower urinary tract symptoms (LUTS) (43). The present study's finding reinforces the understanding that age remains a crucial independent predictor of BPH, hence, regular screening and early intervention in older individuals are essential to prevent disease progression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLow physical activity was also significantly associated with severe BPH symptoms. This finding aligns with growing evidence highlighting the protective role of exercise against BPH progression and its severity (14, 44). Reduced physical activity can lead to metabolic disturbances, increased systemic inflammation, and hormonal imbalances, all of which may promote prostatic hyperplasia and worsen symptom severity (45). Encouraging lifestyle modifications, including increased physical activity, could be a crucial strategy for modifying BPH severity. The observed association between central obesity and severe BPH symptoms agrees with previous studies \u0026nbsp;(46-48), where waist circumference and metabolic syndrome components were linked to increased BPH severity. The possible explanation is that visceral fat contributes to systemic inflammation, insulin resistance, and altered hormone metabolism, which can accelerate prostatic growth and worsen urinary symptoms (49).\u003c/p\u003e\n\u003cp\u003eAnother main finding was the association between derived immune-inflammatory markers and severe BPH symptoms. The results demonstrated that higher levels of SII, PII, and NLR were independent predictors of severe symptoms, reinforcing the role of systemic inflammation in BPH progression. Patients with elevated SII values ≥ 564.92 × 10\u003csup\u003e3\u003c/sup\u003e had a higher likelihood of experiencing severe symptoms compared to those with lower SII values. \u0026nbsp;SII reflects the systemic inflammatory response, and while primarily studied in the context of cancer prognosis, emerging evidence suggests its association with BPH and LUTS progression. Other studies have also explored the association of SII in the context of LUTS and found that elevated SII levels were significantly correlated with the clinical progression of LUTS in older men (22). Similarly, individuals with a PII value of ≥ 273.3 × 10\u003csup\u003e6\u003c/sup\u003e also face greater odds of severe symptoms. These findings align with previous research indicating that inflammatory pathways, particularly neutrophil activation and platelet aggregation, play a role in prostate tissue remodeling and disease severity (50). This also supports the growing evidence highlighting chronic inflammation as a critical driver of BPH progression (51).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe observed association of NLR with severe BPH symptoms \u0026nbsp;also supports existing research that highlights the involvement of neutrophils and lymphocytes in prostatic inflammatory processes \u0026nbsp;(52) . NLR, a widely recognized marker of systemic inflammation, reflects the dynamic balance between pro-inflammatory (neutrophils) and anti-inflammatory (lymphocytes) components of the immune response. This association between high NLR and severe BPH symptoms is in line with previous studies, which found that increased NLR \u0026nbsp;levels were linked to worse prostatic symptoms(53). Some studies have consistently demonstrated that elevated NLR levels were significantly associated with an increased risk of \u0026nbsp;LUTS and symptom progression in BPH (54). Further supporting this association, other studies proposed that higher NLR levels could serve as an indicator of severe LUTS and BPH progression (18, 55).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond identifying associations, this research evaluated the capacity of readily available, hematology-derived inflammatory indices to predict the presence of severe BPH symptoms. The results indicate that SII exhibited the highest area under the curve (AUC) at 0.736 with an optimal cut-off value of 564.92 × 10³, achieving 72% specificity and 69% sensitivity. This suggests that a fair-to-moderate ability of SII to discriminate between patients with and without severe BPH symptoms (56). SII is a composite marker that reflects the balance between systemic inflammation and immune response. While this study identified an optimal cut-off value of 564.92 x 103, it is important to note that other studies may have identified different optimal cut-off values for SII in the context of BPH. For example, one study reported a very high AUC of 1.0 for SII in diagnosing BPH and its progression (19).\u003c/p\u003e\n\u003cp\u003eThe PII showed similar predictive performance, with an AUC of 0.729. Its optimal cut-off was determined to be 273.3 × 10\u003csup\u003e6\u003c/sup\u003e, achieving a specificity of 68% and a sensitivity of 67%. The comparable performance of SII and PII suggests that integrating platelet counts (as done in both SII and PII) alongside neutrophil and lymphocyte counts may provide valuable information for predicting BPH severity in this study. Both markers represent composite indices reflecting systemic inflammation and immune cell balance, potentially capturing different facets of the inflammatory situation contributing to severe symptoms (54). While NLR was identified as a significant predictor in the association analysis, its specific predictive performance (AUC) was seemingly lower than SII and PII. However, their established association may support their relevance in the context of BPH inflammation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights significant factors associated with severe BPH symptoms, contributing to the understanding of BPH severity risk factors. It also provides valuable insights into the predictive performance of easily accessible immune-inflammatory markers in assessing BPH symptom severity, which can aid in early symptom stratification. However, this study has some limitations. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;First, it was conducted at a single center, which may limit the generalizability of the results to broader populations. Second, unmeasured confounders that may influence both inflammation and BPH severity were not extensively explored. Third, the absence of prostatic tissue analysis means we could not directly assess local inflammatory processes within the prostate. \u0026nbsp;Finally, the optimal cut-off values for the inflammatory markers, derived from this specific population, require external validation before wider clinical application. Therefore, while this study provides valuable preliminary insights, future research should focus on prospective, multicenter studies with detailed assessments to confirm these findings and explore the underlying mechanisms linking systemic inflammation to BPH symptom severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications and future directions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings suggest that easily derivable immuno-inflammatory indices, particularly SII and PII, reflect underlying inflammatory processes related to BPH severity and hold moderate potential as predictive markers. Clinicians in similar settings could consider these markers, calculated from routine complete blood counts, as supplementary information to standard clinical assessment tools for patient evaluation and stratification. High SII or PII values might indicate a greater likelihood of severe symptoms, potentially prompting closer monitoring or consideration of anti-inflammatory therapeutic strategies; however, further research is necessary to confirm clinical utility\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture research should focus on validating these findings in larger, prospective, multi-center studies to affirm the predictive performance of SII and PII and their respective cut-off values across diverse populations. Longitudinal studies are essential to establish causality and understand how changes in these markers over time correlate with disease progression or response to treatment. Investigating the specific inflammatory pathways linking elevated SII, PII, and NLR to prostatic inflammation and LUTS development would also be valuable. Furthermore, exploring the combined predictive value of these markers with established clinical parameters could lead to more robust prediction models for severe BPH.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study identified that age, central obesity, physical inactivity, and high inflammatory markers (SII, PII, NL) were found to be independent predictors of severe BPH symptoms. Additionally, the predictive performance of hematological-derived immune-inflammatory indices was evaluated with severe BPH symptoms, and SII and PII demonstrated the highest predictive capabilities. These findings underscore the role of systemic inflammation in BPH progression and suggest that hematological inflammatory indices may serve as practical, cost-effective tools for early identification and management of patients at risk for symptom stratification. Future research should focus on validating these markers across diverse populations and exploring targeted interventions to mitigate inflammation-related BPH progression.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBPH: Benign prostatic hyperplasia\u003c/p\u003e\n\u003cp\u003eBOO: Bladder Outlet Obstruction\u003c/p\u003e\n\u003cp\u003eBP: Blood Pressure\u003c/p\u003e\n\u003cp\u003eBPE: Benign Prostatic Enlargement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVDs: Cardiovascular Diseases\u003c/p\u003e\n\u003cp\u003eCBC: Complete Blood Count\u003c/p\u003e\n\u003cp\u003eFBS: Fasting Blood Sugar\u003c/p\u003e\n\u003cp\u003eIPSS: International Prostate Symptom Score\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLUTS: Lower Urinary Tract Symptoms\u003c/p\u003e\n\u003cp\u003eNLR: Neutrophil-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003ePII: Pan Immuno-Inflammatory Index\u003c/p\u003e\n\u003cp\u003ePL: Platelet–Lymphocyte Ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSII: Systemic Immune-Inflammation Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWBC: White Blood Cells\u003c/p\u003e\n\u003cp\u003eWC: Waist Circumference\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Wollo University for providing the necessary materials and support that made this research possible. Our heartfelt thanks also go to the study participants, whose willingness to contribute their time and insights was invaluable. We are equally grateful to the data collectors for their dedication and effort in ensuring the accuracy and completeness of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used or analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. GA led conceptualization, methodology, and project administration. ABK was responsible for data curation and formal analysis. ABK and SM contributed to the writing, original draft preparation, as well as reviewing and editing the manuscript. All authors read and approved of the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChughtai, B., Lee, R., Te, A. \u0026amp; Kaplan, S. Role of inflammation in benign prostatic hyperplasia. \u003cem\u003eRev. Urol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (3), 147 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUntergasser, G., Madersbacher, S. \u0026amp; Berger, P. Benign prostatic hyperplasia: age-related tissue-remodeling. \u003cem\u003eExp. Gerontol.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e (3), 121\u0026ndash;128 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, E. H., Larson, J. A. \u0026amp; Andriole, G. L. 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Association Study Lung Cancer\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (9), 1315\u0026ndash;1316 (2010).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Benign prostatic hyperplasia, lower urinary tract symptoms, immune inflammation","lastPublishedDoi":"10.21203/rs.3.rs-6494156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6494156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Although the relatively unchanging consequences of age and genetic factors significantly contribute to the development and progression of Benign Prostatic Hyperplasia (BPH), numerous modifiable factors play a role in preventing progression or alleviating symptoms. Identifying predictors of symptom severity and objective biomarkers is beneficial for effective management. This study aimed to determine the predictors of BPH symptom severity and assess the predictive performance of hematologically derived inflammatory markers in patients attending Dessie Comprehensive Specialized Hospital in Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and materials\u003c/strong\u003e: A cross-sectional study was conducted from August to October 2024. Data was collected through face-to-face interviews performed by well-trained health professionals using the KoboCollect application. STATA version 17 was utilized for statistical data analysis. Bivariable and multivariable logistic regression analyses were employed to identify predictors of BPH severity, with a p-value of \u0026lt; 0.05 considered statistically significant in the multivariable logistic regression. The predictive performance of immune-inflammatory index levels for the severity of BPH was evaluated using receiver operating characteristic (ROC) curve analysis. Ethical clearance was obtained from the Ethics Review Committee of Wollo University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 232 BPH patients were included in this study, of whom 84 (36.21%) presented with severe symptoms. Age ≥65 years (AOR = 2.55, 95% CI: 1.19, 5.45), low physical activity (AOR = 2.56, 95% CI: 1.22 - 5.35), central obesity (AOR = 2.79, 95% CI: 1.07 - 7.25), and elevated immune-inflammatory markers [SII \u0026gt; 564.92 x 10³ (AOR = 2.97, 95% CI: 1.10 - 7.98), PII \u0026gt; 273.3 x 10⁶ (AOR = 2.46, 95% CI: 1.04 - 5.86), and NLR \u003cu\u003e\u0026gt;\u003c/u\u003e 1.368 (AOR = 2.87, 95% CI: 1.01 - 8.14)] were significantly associated with severe BPH symptoms (p-value \u0026lt; 0.05). In ROC analysis, SII exhibited the highest predictive performance for severe symptoms with an Area Under the Curve (AUC) of 0.736 (95% CI: 0.67, 0.801); optimal cut-off: 564.92 × 10³; specificity - 72%, sensitivity - 69%; PII demonstrated similar performance with an AUC of 0.729 (95% CI: 0.66, 0.796); optimal cut-off: 273.3 × 10⁶; specificity and sensitivity of 69%. LMR displayed poor predictive ability (AUC = 0.403; 95% CI: 0.328, 0.477).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Advanced age, adverse lifestyle factors, and elevated systemic inflammatory markers (SII, PII, NLR) were significant predictors of BPH severity. SII and PII, derived from routine blood tests, showed moderate predictive value for identifying patients with severe BPH and may serve as accessible, non-invasive adjunct biomarkers. LMR was not identified as a useful predictor in this context. These findings highlighted the role of inflammation in BPH severity and suggest markers for clinical assessment. Further studies are recommended to validate these findings and explore their implications in therapeutic decision-making.\u003c/p\u003e","manuscriptTitle":"Predictors of Symptom Severity and Predictive Performance of Immune- Inflammation Indices Levels in Patients with Benign Prostatic Hyperplasia attending Dessie Comprehensive Specialized Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 15:39:27","doi":"10.21203/rs.3.rs-6494156/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T02:38:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-07T09:11:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339883166939235208904923388501995921038","date":"2025-10-07T07:16:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T18:38:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6822596945559420384929502359283005588","date":"2025-09-20T14:44:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-15T13:33:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-01T09:09:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T20:40:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T12:32:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-21T08:31:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c002c786-4ff8-4a28-a54a-08cc011f2181","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55057270,"name":"Health sciences/Urology/Prostate"},{"id":55057271,"name":"Health sciences/Urology/Urological manifestations"}],"tags":[],"updatedAt":"2025-12-08T16:00:50+00:00","versionOfRecord":{"articleIdentity":"rs-6494156","link":"https://doi.org/10.1038/s41598-025-30765-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-05 15:57:29","publishedOnDateReadable":"December 5th, 2025"},"versionCreatedAt":"2025-09-23 15:39:27","video":"","vorDoi":"10.1038/s41598-025-30765-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30765-0","workflowStages":[]},"version":"v1","identity":"rs-6494156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6494156","identity":"rs-6494156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}