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Brazilian Journal of Pharmaceutical Sciences

On-line version ISSN 2175-9790

Braz. J. Pharm. Sci. vol.54 no.3 São Paulo  2018  Epub Nov 29, 2018

http://dx.doi.org/10.1590/s2175-97902018000317552 

Article

Demographic, clinical and lifestyle predictors for severity of erectile dysfunction and biomarkers level in Malaysian patients

Raymond Mark1 

Hasniza Zaman Huri1  2  * 

Azad Hassan Abdul Razack3 

1 Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia,

2 Clinical Investigation Centre, University Malaya Medical Centre, Lembah Pantai Kuala Lumpur, Malaysia,

3 Departments of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia


ABSTRACT

The incidence of erectile dysfunction (ED) is rising worldwide and its prevalence is one of the main health concerns that affect overall men well-being in Malaysia. The cluster of demographic, clinical and lifestyle factors may have contributed to the severity of ED and changes in biomarkers level; nevertheless these have not been studied extensively. This cross sectional study involved a total of 276 patients with 138 was diagnosed with ED. The demographic, clinical, lifestyle factors and severity of ED were assessed using a set of questionnaire and the International Index of Erectile Function (IIEF-5). Meanwhile, Total Testosterone (TT) and Asymmetric dimethylarginine (ADMA) levels were determined using the enzyme-linked immunosorbant assay (ELISA). Binary logistic regression test was used to demonstrate the predictors of severity of ED, TT and ADMA levels. Significant predictors for worsening of severity of ED are self-employed [10.55 (0.43 - 257.06), p=0.004], pensioner [8.07 (0.19 - 352.45), p=0.026], non-government employee [1.16 (0.05 - 26.26), p=0.04] and TT [0.41 (0.25 - 0.69), p=0.001]. Nevertheless, pensioner [0.08 (0.01 - 0.87), p=0.038] and unemployed [0.04 (0.01 - 0.42), p=0.007], were the predictors that may influence the changes of TT levels. On the other hand, academic qualification (secondary) [4.51 (0.48 - 42.83), p=0.014] and intensity of physical activities (< 1 hour/day) [2.61 (0.65 - 10.48), p=0.008] were the predictors which were more likely to influence the changes of ADMA levels in ED patients. TT and ADMA levels were influenced by demographic and lifestyle factors whilst severity of ED was predicted by demographic and clinical factors in Malaysian ED population. These predictors may provide new knowledge on risk factors of severity of ED and help in management of ED. Thus, the predictive models could serve as a primary guidance to physicians to ensure ED being managed and treated more effectively.

Keywords: Erectile dysfunction/biomarker/clinical; Demography; Lifestyle; Men’s Health; Malaysia

Introduction

According to National Institutes of Health (NIH) consensus panel, erectile dysfunction (ED) is defined as the inability of a man to obtain and/or maintain a penile erection that is sufficient for satisfactory sexual performance (NIH Consensus Conference, 1993; Shamloul, Ghanem, 2013). A recent study by Rahman showed that 69.5 % of Malaysians men with aged 40 to 79 years old reported had ED (Ab Rahman, Al-Sadat, Yun Low, 2011). Since it is not life threatening, men give less attention and left it untreated. Currently more than 150 million men worldwide are affected by this sexual disorder and this number could rise to 320 million by 2025 (Meller et al., 2013; Nunes, Labazi, Webb, 2012). Erectile dysfunction which was previously known as impotence is common among men aged 40 and above (Pastuszak, 2014). Few epidemiology studies reported that the prevalence of erectile dysfunction varies according to age range, countries and ethnic groups. In Malaysia, Ab Rahman et al. (2011) and Fadzil et al. (2014) reported 69.5% and 62% of Malaysian aged 40 years and above have ED respectively and the incidence varies among the three main ethnic groups living in this country (Ab Rahman, Al-Sadat, Yun Low, 2011; Fadzil et al., 2014).

ED was considered as organic and psychogenic origin. Increasing knowledge on erectile function postulates that demographic, clinical and lifestyle factors could play important roles in causing severe ED in an individual. However, these factors have not been well studied in Malaysian ED patients. The association of demographic factors with severity of ED can be seen with ethnicity (Momtaz et al., 2014; Tan et al., 2011) and current marital status (Oyekanmi et al., 2012). Besides, the socioeconomic states such as occupation or employment status of an individual (Al Mogbel, 2014), academic qualification (Momtaz et al., 2014) and household income (Martin et al., 2014) have been found to have indirect association with severity of ED (Ab Rahman, Al-Sadat, Yun Low, 2011; Safarinejad, Hosseini, 2004). Apart from these, clinical factors such as biomarkers and type of comorbidities have been hypothesized to be strongly associated with severity of ED. Evidences signified that TT and ADMA were found to be the most prominent biomarkers associated with severity of ED. Studies have also shown that decreased levels of TT (Brooke et al., 2014) and increased ADMA levels (Aktoz et al., 2010) play a part in the expression of nitric oxide (NO) synthesis substantially leading to progression of severe ED. There are many convincing studies evidences that comorbidities such as type 2 diabetes mellitus (T2DM) (Tamler, 2009), coronary artery disease (CAD) (Grossmann, Gianatti, Zajac, 2010), hyperlipidaemia (Musicki et al., 2010; Nikoobakht, Nasseh, Pourkasmaee, 2005), hypertension (Nunes, Labazi, Webb, 2012) and lower urinary tract symptoms (LUTS)/ benign prostatic hyperplasia (BPH) (Dutkiewicz et al., 2012) as risk factors of ED and have been strongly associated with severity of ED. Practising good quality lifestyles are known to reduce the risk of severe ED. Besides that, lack of physical activity (Kałka et al., 2013) has been well documented and proven to be associated with severity of ED (La Vignera et al., 2013). In addition, studies have also shown that active and healthy lifestyles without smoking (Cao et al., 2013) and alcohol (Lee et al., 2010) significantly delay the progression of severe ED.

A biological marker or biomarker is defined as biochemical alterations that are measurable in human cells or fluids and can be used as an indicator for a biological process (Micheel, Nass, Omenn, 2012). Biomarkers are used in clinical settings to determine the status of a disease and also as an indicator of treatment response (Strimbu, Tavel, 2010). Amongst biomarkers that have been prominently associated with erectile dysfunction are ADMA, NO, advanced glycation end product (AGE), TT, oxytocin, interleukin 6, sphingosine-1-phosphate and more. Out of these, TT and ADMA which have an essential role in mechanism of erection were selected for this study. TT activates and increases the synthesis of cyclic guanosine monophosphate (cGMP) by stimulating endothelial nitric oxide synthase (eNOS) synthase and increasing the expression of NO. NO is a crucial enzymes for erection. In addition, TT helps in the vasodilation of the penile arterioles around the corpora cavernosa and allows more blood inflow to maintain an erection (Isidori et al., 2014). Meanwhile, the role of ADMA as an endogenous inhibitor of NO synthase decreases the bioavailability of NO, which eventually inhibits the relaxation of smooth muscle in penile tissues and leads to ED (Javaroni, Neves, 2012). Recent study shows that elevated ADMA level in serum is strongly associated with high prevalence of ED (Aktoz et al., 2010), nevertheless ED is less prevalent in patients with high serum concentration of TT (Brooke et al., 2014). Studies have been conducted on association of demographic, clinical and lifestyle factors with biomarker levels (TT and ADMA), however the potential predictors associated with these biomarkers levels in ED patients have yet to be identified, particularly in the Malaysian population.

There is scarcity of studies on severity of ED and biomarkers level in the Malaysian ED population. Most of the available studies on association of demographic, clinical and lifestyle factors with severity of ED and biomarkers cannot be generalized to the Malaysian population. Besides, the demographic, clinical and lifestyle factors have been studied as individual factors and linked to severity of ED. Nevertheless, to the best knowledge of the authors, no Malaysian studies have investigated the impact of combination of these factors with severity of ED and biomarkers level. It is essential to study these factors and their associations in order to identify the risk factors which influence the severity of ED and biomarker levels. Therefore, the present study was designed to assess the association of demographic, clinical and lifestyle factors with severity of ED and biomarker levels in Malaysian ED patients. This study also aimed to determine the predominant predictors that influenced the severity of ED and biomarker levels in Malaysian ED patients.

Material and methods

Study design and setting

An unmatched case-control study was conducted in patients who attended ED, surgery, eye and medical clinics in a university hospital, University Malaya Medical Centre (UMMC) at Kuala Lumpur, Malaysia from September 2013 until April 2015. The study was conducted in accordance with the Declaration of Helsinki and was approved by the medical ethics committee of the UMMC (MEC reference number 1010.18). Sample size was calculated from the published guidelines for testing individual predictors (VanVoorhis, Morgan, 2007). Based on the study, regression sample size for testing individual predictors is determined using formula, N > 104 + m, (where m is the number of predictors or independent variables). Convenience sampling was applied to select the subjects for this study (Huri et al., 2016). A random selection of patients from the non-ED pool was included in the study (controls). Patients aged 40 years old and above (Pastuszak, 2014) who met the inclusion criteria were approached and briefed about the purpose of this study. Those who fulfilled the inclusion-exclusion criteria were selected and patient information sheet (PIS) was then given to them. Patients who voluntarily agreed to participate in this study were given a consent form to acknowledge the agreement to participate in this study. A face-to-face interview was conducted to complete a study questionnaire. An overview of the study methodology is illustrated in Figure 1.

FIGURE 1: Overview of study methodology. 

Inclusion and Exclusion Criteria

  • a. Inclusion Criteria (case)

  • Adult male patients ≥ 40 years old (Pastuszak, 2014)

  • Patients who have IIEF-5 score ≤ 21 (Pastuszak, 2014)

  • b. Inclusion Criteria (control)

  • Adult male patients ≥ 40 years old

  • Patients who have IIEF-5 score > 21

  • c. Exclusion Criteria (case)

  • Patients that are critically ill who required medical treatment,

  • Patients who are unwilling to participate in the survey,

  • Patients who are diagnosed with psychiatry illness or mentally challenged,

  • Patients who underwent surgical treatment for ED,

  • Patients who are admitted and required intervention in hospital,

  • Patients who received non pharmacological local therapy (herbal medications),

  • Patients who received hormone therapy for ED (hypogonadism),

  • Immunocompromised patients (HIV, AIDS and cancer patients).

  • d. Exclusion Criteria (control)

  • Patients that are critically ill requiring medical treatment

  • Patients who are unwilling to participate in the survey

  • Patients who are diagnosed with psychiatry illness or mentally challenged.

  • Patients who are admitted and required intervention in hospital

  • Immunocompromised patients (HIV, AIDS and cancer patients)

Questionnaire

Participants who gave their consent were provided with a study questionnaire which was adapted from few previous studies (Ab Rahman, Al-Sadat, Yun Low, 2011; Khoo, Tan, Low, 2008; Tan et al., 2007). The questionnaire was divided into three parts. The first part (Part A) of the questionnaire assessed the socio-demographic characteristics such as age, ethnicity, marital status, occupation, academic qualification and monthly income. The second part of the questionnaire measured life style factors of patients which comprised of factors such as physical activities (intensity and duration of physical activities), smoking history (intensity and duration of smoking) and alcohol consumption. Part C analysed detailed medical history (history of comorbidities) and sexual health (IIEF-5) of participants. The outline of the study questionnaire is as follow.

Part A: assessment of demographic factors

Socio-demographic characteristics were assessed in this first part of the questionnaire. Patient’s age was calculated using year of birth as stated in hospital information system. Ethnic is classified into three major populations of Malaysia; Malays, Chinese and Indians. Marital status is categorized into married and others (never married or single, divorced, and widower). Occupation is classified into 5 categories; government employee, non-government employee, self-employed, pensioner and unemployed. Respondents’ academic qualification is measured based on Malaysian education system which includes no formal schooling, primary education, secondary educations (PMR, SPM, STPM, A-Level, certificate) and tertiary/higher educations (Diploma, Bachelor degree and Postgraduate degree) (Ab Rahman, Al-Sadat, Yun Low, 2011; Momtaz et al., 2014). Meanwhile monthly income is defined as low (< RM1000), medium (RM 1000 - RM 2999) and high (RM ≥ 3000) based on patients’ salary in Malaysian Ringgit (MYR) (Ab Rahman, Al-Sadat, Yun Low, 2011; Momtaz et al., 2014).

Part B: assessment of life style factors

This part of questionnaire evaluates life style factors of patients including items on physical activity, cigarette smoking history, and alcohol consumption. Physical activity was assessed on how often patients involved in activities which took hard physical efforts that made them breather harder than normal in the past 3 years.(Ab Rahman, Al-Sadat, Yun Low, 2011) Among the other questions asked to assess physical activities are:

  • Over the last 3 years, how often did you do physical activities? For example heavy lifting, aerobic, carrying light loads, bicycling at a regular pace, or playing badminton.

  • How many days per week were usually spent on doing these physical activities?

  • How much total time did you usually spend on doing physical activities on one of those days?

The smoking history of patients was assessed. A patient was considered a smoker if he had smoked more than one cigarette per day for at least one year; an ex-smoker if he had stopped smoking more than one year from the day of interview and a non-smoker if he had never smoked cigarette in his whole life (Chrysohoou et al., 2013). Smokers were asked additional questions on how many cigarettes they smoked per day and the number of years they have been smoking. Based on the questionnaire responses, the respondents’ status is classified as current smoker, non-smoker, and ex-smoker with varying intensity of smoking (number of cigarettes per day) and duration of smoking (years of smoking) (Gades et al., 2005). Patients were evaluated based on consumption of drinks such as beer, wine, spirit, samsu, tuak, sake or others which contain alcohol (Ab Rahman, Al-Sadat, Yun Low, 2011). Response categories ranged from no (never drank alcohol in his life) to alcoholic (with an average of daily or weekly alcohol consumption) and ex-alcohol consumer (had stopped drinking alcohol more than one year from the day of interview) (Ab Rahman, Al-Sadat, Yun Low, 2011; Bacon et al., 2006).

Part C: assessment of clinical factors and sexual health

Self-reported health conditions of patients on history of comorbidities were questioned in this questionnaire. The information on comorbidities was obtained from patients’ medical records. Meanwhile, the list of PDE-5 inhibitors prescribed for ED patients was obtained from Pharmacy Information System, UMMC using patients’ RN number. The severity of ED was measured using the International Index of Erectile Function-5 (IIEF-5) which is a self-evaluation questionnaire used for the assessment of male sexual function including screening and diagnosis of severity of ED (Pastuszak, 2014). Participants were asked to rate their ability to achieve and maintain an erection that is good enough for a satisfactory sexual intercourse without any treatment for the past 6 months. Response options ranged from very low, low, moderate, high and very high. Among the questions asked were as follow:

  • How do you rate your confidence that you could get and keep an erection?

  • When you had erections with sexual stimulation, how often were your erections hard enough for penetration?

  • During sexual intercourse, how often were you able to maintain your erection after you had penetrated (entered) your partner?

  • During sexual intercourse, how difficult was it to maintain your erection to completion of intercourse?

  • When you attempted sexual intercourse, how often was it satisfactory for you?

Presence of ED is denoted by IIEF scores of equal or less than 21. Severity of ED is further categorised based on the IIEF scores: severe (5-7), moderate (8-11), mild to moderate (12-16), mild (17-21), and no ED (22-25). For analysis purposes, a binary variable was created based on IIEF-5 where scores from 12-21 is considered “less severe (Mild to Mild/Moderate)” and 5-11 is considered as “moderate to severe” ED. The IIEF-5 assessments were completed by consultant urologists in UMMC with a specialisation in ED.

Blood sampling and biomarkers analysis

Ten (10.0) ml of blood samples were collected in two SST tubes (BD vacutainer) and were immediately centrifuged at 4000 rpm for 15 min (Önder et al., 2014). The supernatant (serum) was decanted into clean eppendorf centrifuge tubes and kept frozen in the -20°c freezer (Remi scientific, Malaysia, model: DW-YL270) until analysis. ADMA and TT levels were determined by using a commercially available enzyme-linked immunosorbant assay (ELISA) kit according to manufacturers’ instructions (Uscn Life Science Inc, Wuhan, China). (Ali et al., 2012; Griffeth, Carretero, Burks, 2013).

Statistical analysis

The data was analysed using Statistical Package for Social Sciences (SPSS) version 19.0 (IBM Corp. Released 2010, IBM SPSS Statistics for Windows, version 19.0. Armonk, NY: IBM Corp, USA). Descriptive statistics were used to summarize patients’ demographic, clinical and lifestyle characteristics. Discrete variables were expressed as frequencies and percentages (N, %). On the other hand, continuous data was expressed as mean ± standard deviation. Chi-square test was used to analyse discrete variables, meanwhile one-way ANOVA was used to compare the means of continuous variables. To build the predictor model, all variables that showed significant association with both primary analysis (case & control group) and secondary analysis (severity of ED, TT and ADMA) were subjected to a binary logistic regression test (enter method) to determine their odds ratio (OR) and confidence intervals. The dependent variable of predictor models for severity of ED is categorised as scale 1: participants with IIEF scores of 12-21 and 2: participants with IIEF scores of 5-11. Plot receiver-operating characteristic (ROC) curve was used to determine the cut-off point for biomarkers levels (TT and ADMA). The ROC analysis indicated cut-off point of TT is 9.55 nmol/L. The dependent variable for this modelling is TT with scale 1: high serum concentration level of TT, ≥ 9.55 nmol/L and 2: low serum concentration level of TT < 9.55 nmol/L. Meanwhile, an ADMA cut-off point of 0.665µmol/L yielded from ROC analysis. The dependent variable for this modelling is ADMA with scale 1: high concentration of serum ADMA, ≥ 0.665 µmol/L and 2: low concentration of serum ADMA, < 0.665 µmol/L. Hosmer and Lemeshow test with p > 0.05 indicates that the entered data is suitable with the tested model. Nagelkerke R square (R2) value shows the percentage of predictor’s in influencing dependent variable. The ranking of predictors in the model were determined by the odds ratio (OR) values. Results with p < 0.05 were considered statistically significant.

Results

Demographic, clinical and lifestyle characteristics of study population

A total of 337 patients were approached. Out of these, 42 respondents were excluded as they do not fulfil the inclusion criteria while 19 respondents disagreed to participate in this study. As a result, 276 patients who met the inclusion-exclusion criteria has participated in this study whereas 138 patients were reported having ED (IIEF scores ≤ 21) and categorised as the case group. The balance 138 patients who have IIEF > 21 were categorised as the control group. The association of demographic, clinical, biomarkers and lifestyle factors with erectile dysfunction; case and control (primary analysis) and ED severity subgroup (secondary analysis) were presented in Table I. Meanwhile, the association of demographic, clinical and lifestyle factors with biomarkers level (TT and ADMA) were shown in Table II.

TABLE I: Association of Demographic, Clinical, Biomarkers and Lifestyle factors with Erectile Dysfunction and Severity of ED 

Primary Analysis (Case and Control) Secondary Analysis (Cases only)
Case n=138 (%) / Mean ± SD Control n=138 (%) / Mean ± SD p Value Mild to Mild/Moderate n=105 (%) / Mean ± SD Moderate to Severe n=33 (%) / Mean ± SD p Value
Demographic factors
Age Ethnic Malay Chinese Indian 58.64 ± 9.23 68 (49.3) 39 (28.3) 31 (22.5) 54.80 ± 7.62 64 (46.4) 41 (29.7) 33 (23.9) < 0.001d* 0.890a 54.99 ± 6.11 52 (49.5) 29 (27.6) 24 (22.9) 70.27 ± 7.76 16 (48.5) 10 (30.3) 7 (21.2) < 0.001d* 0.951a
Marital Status Married Others 133 (96.4) 5 (3.6) 138 (100) 0 (0.00) 0.06 b 105 (100.0) 0 (0.0) 28 (84.8) 5 (15.2) 0.001b *
Occupation Government Employee Non-government employee Self Employed Pensioner Unemployed 36 (26.1) 29 (21.0) 21 (15.2) 30 (21.7) 22 (15.9) 65 (47.1) 11 (8.0) 7 (5.1) 34 (24.6) 21 (15.2) < 0.001a * 33 (31.4) 26 (24.8) 17 (16.2) 16 (15.2) 13 (12.4) 3 (9.1) 3 (9.1) 4 (12.1) 14 (42.4) 9 (27.3) < 0.001b *
Academic Qualification Primary Secondary Tertiary 11 (8.0) 111 (80.4) 16 (11.6) 9 (6.5) 92 (66.7) 37 (26.8) 0.006 a * 4 (3.8) 88 (83.8) 13 (12.4) 7 (21.2) 23 (69.7) 3 (9.1) 0.006 b *
Monthly Household Income Low Medium High 30 (21.7) 65 (47.1) 43 (31.2) 30 (21.7) 24 (17.4) 84 (60.9) < 0.001a 13 (12.4) 51 (48.6) 41 (39.0) 17 (51.5) 14 (42.4) 2 (6.1) < 0.001b
Clinical factors
Type 2 Diabetes Mellitus Present Absent 70 (50.7) 68 (49.3) 73 (52.9) 65 (47.1) 0.810c 51 (48.6) 54 (51.4) 19 (57.6) 14 (42.4) 0.482c
Hypertension Present Absent 85 (61.6) 53 (38.4) 77 (55.8) 61 (44.2) 0.392c 66 (62.9) 39 (37.1) 19 (57.6) 14 (42.4) 0.735c
Hyperlipidemia Present Absent 65 (47.1) 73 (52.9) 56 (40.6) 82 (59.4) 0.332c 46 (43.8) 59 (56.2) 19 (57.6) 14 (42.4) 0.237c
Coronary Artery Disease Present Absent Lower Urinary Tract Symptoms/ Benign prostatic hyperplasia Present Absent 80 (58.0) 58 (42.0) 42 (30.4) 96 (69.6) 72 (52.2) 66 (47.8) 35 (25.4) 103 (74.6) 0.397c 0.421c 58 (55.2) 47 (44.8) 26 (24.8) 79 (75.2) 22 (66.7) 11 (33.3) 16 (48.5) 17 (51.5) 0.338c 0.076c
Biomarkers
Total Testosterone (TT) (nmol/L) Asymmetric dimethylarginine (ADMA) (µmol/L) 11.58 ± 2.53 0.65 ± 0.16 20.94 ± 2.89 0.50 ± 0.08 < 0.001d* < 0.001d * 12.49 ± 1.96 0.61 ± 0.14 8.70 ± 1.95 0.80 ± 0.12 < 0.001d* < 0.001d *
Life style Factors
Physical activities Yes No 48 (34.8) 90 (65.2) 83 (60.1) 55 (39.9) < 0.001c * 42 (40.0) 63 (60.0) 6 (18.2) 27 (81.8) 0.037 c *
Life style Factors
Duration of physical activities No ≤ 2 days/week > 2 days/week 90 (65.2) 31 (22.5) 17 (12.3) 55 (39.9) 25 (18.1) 58 (42.0) < 0.001a * 63 (60.0) 28 (26.7) 14 (13.3) 27 (81.8) 3 (9.1) 3 (9.1) 0.059 b
Intensity of physical activities No < 1 Hour/day ≥ 1 Hour/day 90 (65.2) 15 (10.9) 33 (23.9) 55 (39.9) 41 (29.7) 42 (30.4) < 0.001a * 63 (60.0) 11 (10.5) 31 (29.5) 27 (81.8) 4 (12.1) 2 (6.1) 0.022 b *
Smoking Yes No 81 (58.7) 57 (41.3) 57 (41.3) 81 (58.7) 0.006 c * 62 (59.0) 43 (41.0) 19 (57.6) 14 (42.4) 0.881 c
Duration of smoking No smoking 1 - 20 years 21 - 40 years > 40 years 57 (41.3) 8 (5.8) 42 (30.4) 31 (22.5) 81 (58.7) 29 (21.0) 18 (13.0) 10 (7.2) < 0.001a * 43 (41.0) 4 (3.8) 37 (35.2) 21 (20.0) 14 (42.4) 4 (12.1) 5 (15.2) 10 (30.3) 0.057 b
Intensity of smoking No Smoking 1 - 2 Cigarette(s)/day 3 - 5 Cigarettes/day ≥ 6 Cigarettes/day 57 (41.3) 3 (2.2) 68 (49.3) 10 (7.2) 81 (58.7) 23 (16.7) 25 (18.1) 9 (6.5) < 0.001b * 43 (41.0) 2 (1.9) 51 (48.6) 9 (8.6) 14 (42.4) 1 (3.0) 17 (51.5) 1 (3.0) 0.737 b
Alcohol consumption Yes No 41 (29.7) 97 (70.3) 29 (21.0) 109 (79.0) 0.128 c 32 (30.5) 73 (69.5) 9 (27.3) 24 (72.7) 0.894 c

Notes: Nominal data are reported as number (percentage), meanwhile interval data are expressed as mean±SD.

a Computed using Pearson Chi Square.

b Computed using Fisher’s Exact Test.

c Computed using Continuity Correction.

d Computed using one way ANOVA test.

*statistically significant (p<0.05).

TABLE II: Association of Demographic, Clinical, and Lifestyle factors with Biomarkers level (TT and ADMA), n= 138 

Total Testosterone ≤ 9.55 nmol/L n=33 (%) Total Testosterone > 9.55 nmol/L n=105 (%) p Value Asymmetric dimethylarginine ≤ 0.665 µmol/L n=67 (%) Asymmetric dimethylarginine > 0.665 µmol/L n=71 (%) p Value
Demographic factors
Ethnic Malay Chinese Indian 15 (45.5) 11 (33.3) 7 (21.2) 53 (50.4) 28 (26.7) 24 (22.9) 0.758 a 31 (46.3) 17 (25.4) 19 (28.4) 37 (52.1) 22 (31.0) 12 (16.9) 0.267 a
Marital Status Married Others 28 (84.8) 5 (15.2) 105 (100.0) 0 (0.00) 0.001b 67 (100.0) 0 (0.0) 66 (93.0) 5 (7.0) 0.059 b
Demographic factors
Occupation Government Employee Non-government employee Self Employed Pensioner Unemployed 3 (9.1) 2 (6.1) 5 (15.2) 15 (45.5) 8 (24.2) 33 (31.4) 27 (25.7) 16 (15.2) 15 (14.3) 14 (13.3) < 0.001b 21 (31.3) 20 (29.9) 11 (16.4) 9 (13.4) 6 (9.0) 15 (21.1) 9 (12.7) 10 (14.1) 21 (29.6) 16 (22.5) 0.006 a*
Academic Qualification Primary Secondary Tertiary 7 (21.2) 25 (75.8) 1 (3.0) 4 (3.8) 86 (81.9) 15 (14.3) 0.002 b 2 (3.0) 53 (79.1) 12 (17.9) 9 (12.7) 58 (81.7) 4 (5.6) 0.014 b*
Monthly Household Income Low Medium High 16 (48.5) 14 (42.4) 3 (9.1) 14 (13.3) 51 (48.6) 40 (38.1) < 0.001 b 5 (7.5) 33 (49.3) 29 (43.3) 25 (35.2) 32 (45.1) 14 (19.7) <0.001a*
Clinical factors
Type 2 Diabetes Mellitus Present Absent 20 (60.6) 13 (39.4) 50 (47.6) 55 (52.4) 0.270 c 31 (46.3) 36 (53.7) 39 (54.9) 32 (45.1) 0.397 c
Hypertension Present Absent 17 (51.5) 16 (48.5) 68 (64.8) 37 (35.2) 0.246 c 45 (67.2) 22 (32.8) 40 (56.3) 31 (43.7) 0.258 c
Hyperlipidemia Present Absent 18 (54.5) 15 (45.5) 47 (44.8) 58 (55.2) 0.434 c 29 (43.3) 38 (56.7) 36 (50.7) 35 (49.3) 0.483 c
Coronary Artery Disease Present Absent Lower Urinary Tract Symptoms/ Benign prostatic hyperplasia Present Absent 21 (63.6) 12 (36.4) 14 (42.4) 19 (57.6) 59 (56.2) 46 (43.8) 28 (26.7) 77 (73.8) 0.580 c 0.134 c 35 (52.2) 32 (47.8) 16 (23.9) 51 (76.1) 45 (63.4) 26 (36.6) 26 (36.6) 45 (63.4) 0.249 c 0.150 c
Sildenafil 100mg Yes No Sildenafil 50mg Yes No Tadalafil 20mg Yes No Vardenafil 10mg Yes No Vardenafil 20mg Yes No 3 (9.1) 30 (90.9) 19 (57.6) 14 (42.4) 3 (9.1) 30 (90.9) 5 (15.2) 28 (84.8) 3 (9.1) 30 (90.9) 31 (29.5) 74 (70.5) 43 (41.0) 62 (59.0) 19 (18.1) 86 (81.9) 4 (3.8) 101 (96.2) 8 (7.6) 97 (92.4) 0.080 b 0.140 c 0.282 b 0.058 b 0.724 b 20 (29.9) 47 (70.1) 29 (43.3) 38 (56.7) 9 (13.4) 58 (86.6) 4 (6.0) 63 (94.0) 5 (7.5) 62 (92.5) 14 (19.7) 57 (80.3) 33 (46.5) 38 (53.5) 13 (18.3) 58 (81.7) 5 (7.0) 66 (93.0) 6 (8.5) 65 (91.5) 0.237 c 0.837 c 0.583 c 0.799 b 0.830 c
Life style Factors
Physical activities Yes No 2 (6.1) 31 (93.9) 46 (43.8) 59 (56.2) < 0.001b* 30 (44.8) 37 (55.2) 18 (25.4) 53 (74.6) 0.027 c*
Duration of physical activities No ≤ 2 days/week > 2 days/week 31 (93.9) 1 (3.0) 1 (3.0) 59 (56.2) 30 (28.6) 16 (15.2) < 0.001b* 37 (55.2) 19 (28.4) 11 (16.4) 53 (74.6) 12 (16.9) 6 (8.5) 0.055 a
Intensity of physical activities No < 1 Hour/day ≥ 1 Hour/day 31 (93.9) 1 (3.0) 1 (3.0) 59 (56.2) 14 (13.3) 32 (30.5) < 0.001b* 37 (55.2) 6 (9.0) 24 (35.8) 53 (74.6) 9 (12.7) 9 (12.7) 0.006 a*
Smoking Yes No 21 (63.6) 12 (36.4) 60 (57.1) 45 (42.9) 0.647 c 39 (58.2) 28 (41.8) 42 (59.2) 29 (40.8) 0.910 c
Duration of smoking No smoking 1 - 20 years 21 - 40 years > 40 years 12 (36.4) 3 (9.1) 5 (15.2) 13 (39.4) 45 (42.9) 5 (4.8) 37 (35.2) 18 (17.1) 0.019 b* 28 (41.8) 5 (7.5) 24 (35.8) 10 (14.9) 29 (40.8) 3 (4.2) 18 (25.4) 21 (29.6) 0.160 b
Intensity of smoking No Smoking 1 - 2 Cigarette(s)/day 3 - 5 Cigarettes/day ≥ 6 Cigarettes/day 12 (36.4) 0 (0.0) 21 (63.6) 0 (0.0) 45 (42.9) 3 (2.9) 47 (44.7) 10 (9.5) 0.104 b 28 (41.8) 1 (1.5) 30 (44.8) 8 (11.9) 29 (40.8) 2 (2.8) 38 (53.5) 2 (2.8) 0.189 b
Alcohol consumption Yes No 11 (33.3) 22 (66.7) 30 (28.6) 75 (71.4) 0.761c 21 (31.3) 46 (68.7) 20 (28.2) 51 (71.8) 0.825 c

Notes: Nominal data are reported as number (percentage), meanwhile interval data are expressed as mean±SD.

a Computed using Pearson Chi Square.

b Computed using Fisher’s Exact Test.

c Computed using Continuity Correction.

*statistically significant (p<0.05)

Predictors of severity of ED, TT and ADMA level

Variables which gave significant result in both case & control (primary analysis) and severity group (secondary analysis) were subjected to this binary logistics regression analysis. The significant predictors for severity of ED in this study population were TT (p = 0.001) and occupation (p = 0.03). Based on the OR value, self-employed was ranked as first predictor which was 10.55 times most likely to increase severity of ED followed by pensioner (OR = 8.07) and non-government employee (OR = 1.16). Meanwhile, TT levels were 0.41 times more likely to worse severity of ED in Malaysian population. However in Malaysia, ADMA, academic qualification, monthly income, physical activity and intensity of physical activity were reported as insignificant predictors for severity of ED (Table III).

TABLE III: Predictors of Severity of ED (Binary Logistic Regression - Factors Associated with Severity of ED) 

Predictors OR 95% CI p-value
Low High
Occupation a Government Non-government Self employed Pensioner Unemployed 1.16 10.55 8.07 4.04 0.05 0.43 0.19 0.31 26.26 257.06 352.45 52.25 0.03* 0.04* 0.004* 0.026* 0.071
TT 0.41 0.25 0.69 0.001*
ADMA 320.75 0.77 133030.84 0.06
Academic Qualification b Primary Secondary Tertiary 0.02 0.01 0.00019 0.00031 1.18 0.68 0.10 0.06 0.07
Monthly Income c Low Medium High 36.86 10.73 0.60 0.22 2269.33 521.76 0.21 0.09 0.23
Physical activity d Yes No 1.57 0.16 15.45 0.408 0.701
Intensity of Physical activity e No < 1 Hour/day ≥ 1 Hour/day 1.34 1.07 0.12 0.88 15.30 1.26 0.82 0.82 0.86

Notes: Binary logistics regression test was done to obtain the OR and p value;

*statistically significant (p<0.05);

a Reference category: Government;

b Reference category: Primary Education;

c Reference category: Low Monthly Income;

d Reference category: Yes (Actively involved in physical activity);

e Reference category: No (Physical activity - Inactive).

The only significant predictor which influenced TT level in Malaysian ED patients was occupation (p = 0.023). Specifically, pensioner (0.038) and unemployed (0.007) decreased the TT level by 0.08 and 0.04 times respectively (Table IV). On the other hand, intensity of physical activity (p = 0.018) and academic qualification (p = 0.047) were reported as the significant predictors which influenced ADMA level in Malaysian ED population. Spending less hours (inactive) for physical activities are 2.61 times most likely increasing ADMA level than active men (OR = 0.87). Meanwhile, men with secondary academic qualification are 4.51 times most likely to increase ADMA level than men with tertiary academic qualification level (OR = 1.88) (Table V).

TABLE IV: Predictors of Total Testosterone Level (Binary Logistic Regression - Factors Associated with TT level) 

Predictors OR 95% CI p-value
Low High
Occupation a Government Non-government Self employed Pensioner Unemployed 0.50 0.46 0.08 0.04 0.06 0.03 0.01 0.01 4.56 6.62 0.87 0.42 0.023* 0.542 0.571 0.038* 0.007*
Academic Qualification b Primary Secondary Tertiary 0.06 0.33 0.01 0.02 1.71 6.10 0.108 0.099 0.458
Monthly Income c Low Medium High 0.08 1.26 0.01 0.15 0.84 10.82 0.102 0.335 0.835
Physical activity d No Yes 7.03 0.25 195.13 0.999 0.250
Duration Physical activity d No ≤ 2 days/week > 2 days/week 2.16 2.65 0.05 0.95 91.51 50.51 0.755 0.686 0.686
Intensity Physical activity d No < 1 Hour/day ≥ 1 Hour/day 0.87 0.48 0.04 0.04 20.13 52.39 0.932 0.932 0.827
Duration of smoking e No smoking 1 - 20 years 21 - 40 years > 40 years 1.40 0.24 1.46 0.39 0.01 0.31 4.99 4.13 7.00 0.628 0.608 0.324 0.635

Notes: Binary logistics regression test was done to obtain the OR and p value;

*statistically significant (p<0.05);

a Reference category: Government;

b Reference category: Primary Education;

c Reference category: Low Monthly Income;

d Reference category: No (Physical activity - Inactive);

e Reference category: No Smoking

TABLE V: Predictors of Asymmetric Dimethylarginine (ADMA) level (Binary Logistic Regression - Factors Associated with ADMA level) 

Predictors OR 95% CI p-value
Low High
Occupation a Government Non-government Self employed Pensioner Unemployed 5.40 1.89 6.23 7.15 0.45 0.16 0.51 0.64 64.15 22.73 76.70 80.36 0.109 0.181 0.615 0.153 0.111
Academic Qualification b Primary Secondary Tertiary 4.51 1.88 0.48 0.48 42.83 7.40 0.047* 0.014* 0.341
Monthly Income c Low Medium High 20.99 1.85 1.87 0.52 235.25 6.60 0.418 0.190 0.367
Physical activity d Yes No 0.52 0.19 1.41 0.198
Intensity Physical activity e No < 1 Hour/day ≥ 1 Hour/day 2.61 0.87 0.65 0.18 10.48 4.21 0.018* 0.008* 0.060

Notes: Binary logistics regression test was done to obtain the OR and p value;

*statistically significant (p<0.05);

a Reference category: Government;

b Reference category: Primary Education;

c Reference category: Low Monthly Income;

d Reference category: Yes (Actively involved in physical activity);

e Reference category: No (Physical activity - Inactive).

Discussion

Severity of ED was increased as the age increased. Mean IIEF-5 scores was higher in control group compared to case (ED patients). This finding was consistent with a survey conducted in Malaysia (Khoo et al., 2008). Beside, half of the patients who participated in this study were reported having ED (n = 138). This prevalence is in accordance with two studies conducted in Malaysia previously which reported nearly two third of the study population were suffering from ED (Ab Rahman, Al-Sadat, Yun Low, 2011; Fadzil et al., 2014). Occupation (p = 0.03) and TT levels (p = 0.001) are the significant predictors for worsening of severity level of ED in Malaysian population. Meanwhile, occupation (p = 0.023) is the only significant predictor which influencing TT level in Malaysian ED patients specifically, pensioner (0.038) and unemployed (0.007) decreasing the TT level by 0.08 and 0.04 times respectively. On the other hand, intensity of physical activity (p = 0.018) and academic qualification (p = 0.047) were reported as the significant predictors which influencing ADMA level in Malaysian ED population.

Factors associated with severity of ED

Our study showed that occupation is significantly associated with the severity of ED and this finding is supported by few studies conducted previously in Malaysia and globally (AlMogbel, 2014; Aytaç et al., 2000). This study envisions that patients' ED management is prone to be affected by occupation status. Particularly, self-employed men were 10.55 times more likely to have severe ED as compared to government employees. Besides that, pensioners (OR = 8.07) and non-government employees (OR = 1.16) were also more prone to have severe ED than government employees. A study conducted in 26 diabetes clinics in Israel involving 900 ED patients with diabetes mellitus showed severity of ED was significantly increased in unemployed (p < 0.0001) and blue-collar profession patients (p = 0.0044) (Kalter-Leibovici et al., 2005). The term "blue-collar" and "white-collar" are occupational classifications that differentiate workers who perform manual labour and professional jobs respectively. Similar report was obtained from a study conducted by Aytaç et al. (2000) that revealed a significant association between occupation or employment status with severity of ED. The result demonstrated that men in blue-collar professions were 1.55 times more likely to acquire severe ED when compared to men in white-collar occupation (O.R.=1.55, 95% C.I.=1.06-2.28) (Aytaç et al., 2000). Furthermore, another study involving 312 Saudi diabetic patients reported that unemployed men were more likely to develop severe ED as compared to employees, and significant association was obtained between occupation and severity of ED (X2 = 29.069, df = 3, p = 0.001) (AlMogbel, 2014). In our study, IIEF scores were reported to be higher among patients with better employment status with less workload and work stress. This explains that men with stable employment, who are also generously compensated have a greater sense of control over their wellbeing (Momtaz et al., 2014). Besides that, ED was severe among patients with unstable employment as this could be due to financial constraints in treating ED. The costs associated with ED treatment can be a financial burden. Therefore, the ED treatment is unaffordable for some patients who have unsecured job coupled with high living expenses (Hackett, 2005). This situation explains the huge difference in severity of ED among patients in different categories of occupation. High workload and unemployment for a longer period of time can impact our health as well. Stress due to unemployment or work burden eventually affects erection and impedes men’s sexual life. A significant negative impact on sexual health was reported in a psychological study inspecting the association between sexual dysfunction and occupational stress (Štulhofer, Træen, Carvalheira, 2013). The study revealed that working longer hours and stress in accomplishing a work within a targeted time eventually affect sexual performance. This study also showed that men with a higher workload are 1.8 times more likely to have issues in sexual relationship than men with less work strain (Štulhofer, Traeen, Carvalheira, 2013). In addition, financial crisis faced by unemployed men causes their ED to be untreated over a long time. This could also lead to the risk of increasing ED severity. Approaching a therapist or a doctor is the best solution to alleviate severe ED. Besides that, discussing the problem with partners by expressing their feeling and expectations could help affected men to feel better.

This finding emphasized that there was a significant association between TT level and the severity of ED. From this study, it can be observed that TT (p < 0.001) was significantly associated with severity of ED. Furthermore, binary regression analysis indicated low TT level as a significant predictor which was more likely to cause severe ED, (OR=0.41). High level of TT might not heavily affect erectile function, however low TT level can have significant impact on the severity level of ED (Mikhail, 2006). This was proven in a few studies which explored significant effect of low TT level on the severity of ED (Brooke et al., 2014; Liao et al., 2012; Martínez-Jabaloyas et al., 2006). TT plays a crucial role in the sexual function of men and several studies have investigated the relationship between TT levels with male sexual behaviours especially ED (Liao et al., 2012; Tsujimura, 2013). According to a systematic study by Mikhail, low level of TT may have a major impact on erectile function (Mikhail, 2006). Study conducted on 1,776 Chinese men aged 20 to 77 years old showed that TT is significantly associated with severity of ED with an odds ratio of 1.02 after adjusting the confounding variable; age (OR: 1.02; 95% CI; 1.00-1.04) (Liao et al., 2012). This finding was consistent with the evidence from a study by Brooke et al. (2014) involving 126 ED patients with type II diabetes which reported a significant positive correlation between IIEF-5 scores and TT (r = 0.546, p = 0.001), where the severity of ED decreases as TT level increases (Brooke et al., 2014). Parallel to this finding, a prospective study by Kang et al. (2011) involving 134 men who visited urology clinics with the complaint of ED revealed a significant positive correlation between serum TT level and IIEF scores (r=0.124, p=0.034). This study also disclosed that TT levels below the normal range significantly increased the severity of ED (Kang et al., 2011). TT acts as a vasodilator for the penile arterioles. The vasodilation of the arteries around the corpora cavernosa allows more blood to flow in to help maintain the erection. Additionally, TT activates the NO synthase and increases the expression of NO and phosphodiesterase type 5, which are essential enzymes for erection (Blute et al., 2008). Besides that, testosterone restores structural, biochemical and physiological changes and induces new DNA synthesis in the smooth muscle cells and blood vessels. In addition, testosterone also contributes to venous occlusion and helps men to maintain erection (Shabsigh, 2005). Investigation on TT level should be done in men who were having symptoms of ED. Undeniably, testosterone therapy can be considered as a recommended and effective treatment for men with severe ED.

Factors associated with Total Testosterone (TT) levels in Malaysian ED patients

The effect of demographic, clinical and lifestyle factors cause significant changes on binary regression model of TT level in ED patients. After all the significant variables of demographic, clinical and lifestyle factors are included into binary regression model, occupation (p = 0.023) remained as significant predictor. Findings revealed that pensioners (OR = 0.08, 95% CI = 0.01-0.87, p = 0.038) and unemployed men (OR = 0.04, 95% CI = 0.01-0.42, p = 0.07) are most likely to have low TT level as compared to government employees.

To the best of the author’s knowledge, very few studies have been done on predictors of demographic factors on TT level in ED patients. However, some association studies have been conducted based on low TT level with occupation and found that lower social status is significantly associated with low TT level (Zitzmann, Nieschlag, 2001). Men with better employment status have higher TT level as compared to unemployed men and pensioners. This result was in accordance with multiple studies that had been conducted earlier, studying the significant impact of occupation on TT level. Men who have work stress are likely to have lower TT level. On the contrary, higher TT level helps in lowering stress and in giving more strength and inspiration to perform better at work (Greene et al., 2014). It is a known fact that stress may have physiological effects on body and cause infertility by decreasing TT level. Besides that, high stress and low TT level result in common physical symptoms including erectile dysfunction. There are few studies evidencing that the stress hormone cortisol and TT level work against each other and a high stress level is strongly associated with low TT level. Increasing stress level due to heavy workload could affect gonadal hormones secretion which results in low TT levels (Nargund, 2015). A meta-analysis study reported that cortisol level may have a significant effect on TT level (Geniole et al., 2016). The notable psychological association between TT level and stress is not well known. It is most likely that the chemicals secreted in response to stress may regulate the TT production and control its level. This indicates that socioeconomic statuses as well as psychological stress influence men’s endocrine system and in turn influences the TT level.

Factors associated with Asymmetric Dimethylarginine (ADMA) level in Malaysian ED patients

Academic qualification is presented as predictor which influences ADMA level in ED patients. The link between education and ADMA level is complex and is tied closely to income. Although no studies were established on the association of academic qualification with ADMA level yet, there were few theories and hypotheses which can illustrate how education can be associated with ADMA level in term of patients’ health. Education can increase an individual’s knowledge levels and sense of control over their life, which in turn would encourage them to embrace a healthier lifestyle. Educated men value their health highly. They constantly look for the guidance of a physician or an experienced health care provider about their health and these men are ready to communicate effectively with them. However, less educated people might be unaware of their health due to lack of knowledge on diseases and its symptoms, which compromises on their health. In addition, poor quality of doctor-patient communication contributes to the high percentage of health issue among this group (Verlinde et al., 2012). Low morbidity rate was reported among educated men and they are less likely to have poor health. In contrast, the opposite result was observed for less educated men (Sallis, Carlson, 2015; Zimmerman, Woolf, 2014). Besides that, a few studies suggest that educated people have healthier behaviours. Those with higher education level were less likely to smoke, drink heavily, use illegal drugs or be overweight (Cutler, Lleras-Muney, 2010). In this study, the knowledge on chemical nature of biomarker (especially ADMA) is essential as it will help them to predict how it is produced and the mechanism of action in our body. This knowledge will help an individual to be aware of the biomarker level changes and to seek medical help in order to overcome the problem.

Meanwhile, increasing intensity of physical activities could help to decrease ADMA concentration level in ED patients. This finding is consistent with a study conducted by Mittermayer et al. (2005), which reported that constant physical activity significantly decreases ADMA concentration (p < 0.001) (Mittermayer et al., 2005). Even though the above mentioned study was conducted on non-ED population, the finding concluded that there is an association between intensity of physical activities and ADMA levels. Recently, Kevin (2011) showed that after a moderate to intense exercise, ADMA level decreased in patients from the initial level before they began the exercise (0.42 ± 0.02 and 0.43 ± 0.03 micromol/l). The ADMA level started to increase again after they discontinued their exercise (Kevin, 2011). A decrease in ADMA level was even recorded in an animal study after the rats were exposed to exercise training, although the result was statistically insignificant (Esfahani et al., 2013). These studies concluded that ADMA level can be lowered by consistent physical activities. The mechanism to decrease ADMA level associated with physical exercise is crucial. ADMA is produced in substantial amount through methylation of protein-bound L-arginine by protein methyltransferase (PRMT) enzyme and the decomposition of these ADMA by DDAH activity appears to determine the ADMA level in our body (Teplan et al., 2014). Intensive physical activities may affect antioxidant level through increased expression of the superoxide dismutase. The DDAH activity was reduced by the increased concentration of oxidants level and oxidative stress. Hence, the enhancement of antioxidant status through regular exercise might be related to the decrease in ADMA level in physically active individuals (Mittermayer et al., 2005; Teplan et al., 2014). Besides that, constant physical activity can initiate the endothelial NO-synthase (eNOS) expression through increased endothelial shear stress and arterial blood flow. As we discussed earlier, ADMA inhibits NO synthesis. However, increasing intensity of physical exercise elevate NO bioavailability. This explains that ADMA level is suppressed upon significant improvement in physical activities which resulted in increased concentration level of NO (Schlager et al., 2011). Furthermore, ADMA could trigger the development of atherosclerosis by inhibiting NO production from amino acid l-arginine. Hence, decreased ADMA levels may enhance endothelial function in ED patients who were physically active (Böger, 2006). Since high ADMA level was related to ED (Paroni et al., 2012), our findings demonstrate that physical activity does not only decrease ADMA level but could also contribute to improvement in erectile function.

In addition, food intake and diets may be associated with ED. It is noted that cleaning up our diet is the more natural ways to improve the erectile function. Foods as well as the environmental pollutants and chemicals that can cause ED should be avoided for a better men’s sexual health (Esposito et al., 2010). Research has demonstrated that similar eating patterns that can cause cardiovascular diseases due to limited blood flow in the coronary arteries also block blood flow to the arteries around corpora cavernosa (La Favor et al., 2013). Diets that lack of fruits and vegetables alongside with plenty of fatty, fried and processed foods can lead to decreased blood circulation throughout the body. According to Esposito, ED is disproportion to clean diet, which includes fruits, vegetables, whole grains, fish, heart-healthy fats including nuts and olive oil (Esposito et al., 2010). Animal fat and cholesterol is particularly bad for our heart. In fact, many men do not realize that ED is one of the early signs of cardiovascular diseases whereas a good blood circulation is needed to achieve and maintain an erection.

Besides, consuming foods packed in cans or plastic would increase the possibility having severe ED. Bisphenol-A (BPA) is a chemical found in plastic products as well as on the linings in canned foods which can inhibit sex hormones (Vandenberg et al., 2007; Rudel et al., 2011). Few studies have found links associating BPA with ED and decreased sexual desire, as well as infertility (Li et al., 2009; Li et al., 2010). Other than this, soy and processed foods which were high in carbohydrates and excessive unhealthy fats have an estrogenic effect and can affect men’s hormones as well. It contains substances that mimic estrogen, a female hormone that men have in smaller amounts. It is important to maintain the right balance between estrogen and testosterone to preserve the sexual health especially erectile function (Siepmenn et al., 2011).

There are evidences that chronic heavy drinking can cause liver and nerve damage which can interfering with the normal balance of male sex hormone levels that can lead to ED. Alcohol may alter the endocrine system and has an inhibitory effect on hypothalamic pituitary testicular axis which has direct effect on testis. Alteration in gonadotropin releasing hormone receptor by alcohol may result in decrease of LH synthesis. Moreover, increased β-endorphin levels in heavy alcoholics may lead to testicular damage and diminish testosterone production (La Vignera et al., 2013).

In conclusion, the purpose of this study was to examine the combination of demographic, clinical and lifestyle factors that could be associated with the severity of ED and biomarker levels in Malaysian ED patients. The binary regression result concluded occupation and TT as the significant predictors of severity of ED. Demographic and biomarker dominate clinical and lifestyle factors in determining predictors of severity of ED in Malaysian ED patients. It shows that Malaysian patients generally live a healthy lifestyle practising good ethics in term of physical activeness, smoking and alcohol consumption. Meanwhile, demographic and lifestyle factors predominate clinical factors in influencing TT and ADMA levels. As per the present results, there is a reasonable need to investigate the TT level and occupational background of patients when they present with severe ED. Urologists or ED experts should examine patients’ TT levels and add the necessary improvements in treating ED patients. Besides, urologists also need to educate ED patients and give priorities for their health issues regardless of their occupation background and help them to live a quality life. Identification of these predictors influencing severity of ED, TT and ADMA level may help to increase our knowledge on risk factors of ED. Thus, these predictors may serve as new indicators in providing primarily guidance to urologists or physician and ensuring ED can be managed and treated more effectively.

Acknowledgement

The authors would like to thank University of Malaya, Malaysia (RP024A-14HTM) for financial and technical supports.

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Received: October 16, 2017; Accepted: January 29, 2018

*Correspondence: H. Z. Huri. Department of Pharmacy, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur. Phone: 603 79676659 / Fax: 603 79541904. hasnizazh@um.edu.my

Conflict Of Interest

The authors declare that there is no conflict of interest.

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