Introduction
The prostate is a male reproductive organ. It is
approximately the dimension of a walnut and can be found near the
base of the bladder. Prostatitis, benign prostate hyperplasia (BPH)
and prostate cancer (PCa) are the three most frequent types of
prostate diseases. Of note, ~25% of men aged ≥55 years have a
prostate-related issues; the prevalence of such issues increases to
>50% by the age of 70 years (1). The early stages of prostate disease
may not exhibit any symptoms (1).
Prostatitis is an inflammation of the prostate gland
caused by exposure to potentially harmful microorganisms. Chronic
infection is considered to play a crucial role in the formation and
development of BPH or PCa via oxidative stress and the generation
of reactive oxygen species that cause mutations, or by inducing
epigenetic modifications that promoting the transformation of
neoplastic cells (2-4).
PCa is the second most common type of cancer in terms of occurrence
and fifth in terms of mortality globally, and it is notable for its
geographical variability (5).
Prostatitis and PCa are both major health concerns and a strong
association has been demonstrated between the two conditions
(6). Nicotine from cigarettes
exacerbates prostate diseases and significantly increases the
chance of developing urinary tract infections and enhances the
dysfunction of the urinary system. Terminating smoking can reduce
chronic prostatic inflammation and dysfunctional storage (7). There are several potential mechanisms
of cigarette smoking for maximizing the prevalence and incidence
rates of prostate diseases. Cigarette smoking alters the hormonal
equilibrium, increases bioavailable testosterone and decreases
bioavailable estradiol levels (8).
Cigarettes have significant quantities of cadmium, which is
considered an effective carcinogen related to cancer progression
(9,10).
Furthermore, diabetes markedly increases the
incidence of BPH and PCa, indicating that glucose metabolism is
critical in the evolution of cancer of the prostate. Individuals
with type 2 diabetes have been found to have larger prostate
glands. Insulin and related factors stimulate prostate growth,
changes in the expression of sexual hormones, the initiation of
systemic inflammatory processes and oxidative stress (11,12).
A large family of low-molecular-weight proteins,
known as cytokines, mediates cell-to-cell communications. They
display complex roles in inflammation, tumor immunobiology, host
defense, immunity and tumor pathogenesis (13). Interleukins (ILs), interferons,
colony-stimulating factors, chemokines and tumor necrosis elements
are among the primary subgroups of cytokines, and they are produced
as membrane-bound or released proteins (14). Several recent discoveries support
the essential role that IL-6 plays in the genesis of PCa. IL-6 has
the ability to stimulate both pro- and anti-inflammatory responses,
interact with a range of cell types, exert both autocrine and
paracrine actions in the prostate, and trigger intracellular
signals that mediate several biological responses in PCa (15). IL-6 mediates numerous crucial
physiological processes, including controlling the acute-phase
response at the onset of acute inflammation, regulating B-cell and
T-cell differentiation and activation, and promoting cell
proliferation and survival (16).
Neutrophils, and endothelial and epithelial cells release the
pro-inflammatory chemokine, IL-8, which is expressed in cancer
cells and functions as both a chemoattractant and activator for
granulocytes, thereby promoting cell proliferation, invasion,
survival and chemoresistance. Its levels are elevated in the serum
of men with PCa and has been related to adverse effects (17). IL-37 exerts both anti-inflammatory
and immunomodulatory effects. According to previous research, it
can reduce the inflammatory response in several disorders (18). IL-37 is a protein that is produced
by a variety of tumor cells, as well as tissues and cells that are
healthy in the body. It may reduce a host's immune response to
malignancy and accelerate disease progression. However, several
studies have uncovered its anticancer and host-protective
properties; this cytokine may serve as both a crucial prognostic
biomarker and a cutting-edge treatment option for a range of
malignancies (19).
Members of the transforming growth factor (TGF)-β
family play vital roles in regulating the differentiation, growth
arrest and apoptosis of healthy epithelial cells. However, they can
also promote cancer, depending on the stage and type of tumor. The
signaling mechanism of TGF in cancer can serve as either a
carcinogenic or tumor-suppressive mechanism. In the initial stages,
it attenuates cell development as a tumor suppressor. However, in
the later stages, it promotes invasion and metastasis. TGF-β
signaling promotes bone metastasis in PCa (20,21).
The aim of the present study was to analyze critical
clinical risk factors and blood levels of routine diagnostic
markers [prostate-specific antigen (PSA), erythrocyte sedimentation
rate (ESR), C-reactive protein (CRP), zinc, vitamin D3 and renal
function] and inflammatory cytokines (IL-6, IL-8, IL-37 and TGF-β)
in Iraqi male patients with various prostate disorders.
Subjects and methods
Study subjects
The present case-control study included 180 male
subjects aged from 45 to 85 years. The patient group (n=135)
included patients with prostate conditions. It was further
classified into three groups with an equal number of patients, as
follows: PCa (n=45), BPH (n=45) and prostatitis (n=45). For
comparison, a healthy control group (n=45) was included which
matched the patient groups in age range. A standardized demographic
and clinical data collection sheet was utilized. The sheet captured
data about patient identification code, age, medical history,
smoking history, diabetes mellitus status, the use of antidiabetic
medications, such as metformin, and other pertinent clinical
details. The data collection process involved direct patient
interviews and a comprehensive review of medical records at
Al-Yarmouk Teaching Hospital, Baghdad, Iraq and from the period
between January, 2024 to June, 2024.
Inclusion and exclusion criteria
The inclusion criteria defined participants aged
between 45-85 years with a urologist-confirmed diagnosis of
prostatitis and/or BPH/lower urinary tract symptoms (LUTS)/PCa. The
exclusion criteria included those with any other types of cancer,
autoimmune disease, any renal or thyroid disease, congenital heart
disease, incomplete clinical and/or laboratory records, and the
absence of confirmed prostate disease. The controls were men within
the same age range, with no history or clinical evidence of any
prostate disease, with low/normal PSA levels and no diabetes
mellitus.
Diagnostic criteria for prostate
diseases
Urology specialists at Al-Yarmouk Teaching Hospital
made the patient diagnoses. Confirmed PCa diagnoses were made via
the official guidelines on the diagnosis of PCa that detail the
need for prostate biopsies to undergo histopathological assessments
(22). Prostate enlargement due to
BPH was assumed based on LUTS along the American Urological
Association (AUA) recommendations for the assessment of LUTS/BPH,
which involves taking a patient history, performing a digital
rectal examination (DRE), obtaining a prostate ultrasound and a PSA
assessment (23). Prostatitis was
diagnosed based on the symptoms, a urine culture (if applicable),
and the presence of inflammation along the European Association of
Urology (EAU) guidelines on urological infections and male
accessory gland infections (24).
The healthy controls had no history of prostate diseases and/or had
no symptoms of prostate disease, and had low and/or normal levels
of PSA. Diabetes mellitus was treated as a clinical risk factor and
diagnosed based on a fasting blood glucose level of at least 126
mg/dl (or) the current use of antidiabetic medications. Due to the
effect of inflammation and metabolism, the use of metformin was
documented as a treatment variable and analyses were carried out
separately.
Sample size calculation
The sample size was determined using G*Power
(version 3.1). For a one-way ANOVA with four groups, assuming a
medium effect size (f=0.25), an alpha error probability of 0.05,
and a statistical power of 80%, the estimated minimum total sample
size was 180 participants, or ~45 per group.
Ethical approval
The study protocol was approved by the Research
Ethics Committee of the College of Science, Mustansiriyah
University, Baghdad, Iraq (Approval no. BCSMU/3024/00081M; January,
2024). In accordance with the approved protocol and within the
framework of academic collaboration with Mustansiriyah University,
blood samples and clinical data were collected from Al-Yarmouk
Teaching Hospital in Baghdad. The treating medical team and
hospital administration provided the records and clinical samples
after obtaining the necessary ethical approvals (Approval no. 9274;
December, 2023) and written informed consent from all
participants.
Sample collection and preparation
The collection of blood samples from all individuals
was divided into two parts: A total of 2 ml was added into EDTA
tube for hematological tests [ESR, complete blood count (CBC) and
CRP], and 3 ml were used to obtain serum, which was used for the
estimation of serum level of diagnostic biochemical and
immunological parameters (the serum was stored at -20˚C until
use).
Laboratory measurements
As part of the routine clinical work-up of the
patients at Al-Yarmouk Teaching Hospital, hematological and
biochemical parameters [ESR, CBC, CRP, PSA, fasting blood sugar
(FBS), hemoglobin A1c (HbA1c), ferritin, urea, creatinine, serum
zinc and vitamin D3 levels] were collected in accordance with the
data-collection permit of the hospital and are available
retrospectively as clinical data. The research team then measured
the serum cytokine levels of IL-6 (ID: SL1001Hu), IL-8 (ID:
SL1004Hu), IL-37(ID: SL2231Hu), growth differentiation factor 15
(GDF-15) (ID: EL0270Hu) and TGF-β (ID: EL0003Mt) using
enzyme-linked immunosorbent assay (ELISA) kits in the laboratories
of the Department of Biology, College of Science, Mustansiriyah
University. Kits were sold and shipped from Sunglong Biotech and,
as per the manufacturer's instructions, measurements were taken
using a microplate reader (BioRad Laboratories, Inc.) that detects
absorbance.
Statistical analysis
IBM SPSS Statistics Version 27 was used for
statistical analysis. Data are expressed as the mean ± standard
error (SE). Data distribution was evaluated using the Shapiro-Wilk
test for normality. One-way analysis of variance (ANOVA) was used
to evaluate differences, and pairwise comparisons were conducted
using Tukey's honestly significant difference (HSD) test.
Categorical variables were evaluated using Pearson's Chi-squared
test. Cytokine and clinical biomarker correlation/s within the
disease groups of PCa, BPH and prostatitis were evaluated using
Spearman's correlation analysis. The discriminatory performance of
biomarkers within the present study cohort was evaluated using
receiver operating characteristic (ROC) analysis. It was not
intended to assess the clinical diagnostic validity of the
biomarkers. A P-value ≤0.05 was considered to indicate a
statistically significant difference. The Benjamini-Hochberg false
discovery rate (FDR) correction was applied to the omnibus
biomarker p-values. In this study, type I error was regarded as
P≤0.05(25). The effect size was
calculated to provide better interpretation of the results. For
one-way ANOVA, effect size was reported as eta squared (η²), and
for multinomial logistic regression, adjusted odds ratios were
reported along with 95% confidence intervals (CIs). For ROC
analysis, an area under the curve (AUC) along with 95% CI values
are reported. When relevant, 95% CI values for group means were
calculated.
Results
Patient demographic characteristics
and risk factors
The demographic profile of the patients in the
present study is presented in Table
I. Each group included 45 participants and was matched within
the same age range (45-85 years). Smoking and diabetes mellitus
were significantly more frequent among patients with prostate
diseases than among the healthy controls (P<0.001 for both). The
highest frequency of smoking was detected in the BPH group (43/45,
95.6%) followed by prostatitis (41/45, 91.1%) and PCa (29/45,
64.4%) groups. Diabetes mellitus was most prevalent in the PCa
group (41/45, 91.1%), followed by the BPH (37/45, 82.2%) and
prostatitis (31/45, 68.9%) groups, whereas no cases of diabetes
were recorded in the healthy control group. A multivariable
multinomial logistic regression model was performed using the
control group as the reference category. The overall model was
statistically significant (likelihood ratio χ²=70.38, P<0.001;
R²=0.140). Following adjustment for age, smoking and diabetes,
smoking was independently associated with BPH [odds ratio (OR),
4.02; 95% CI, 1.10-14.66; P=0.035] and PCa (OR, 6.65; 95% CI,
1.75-25.26; P=0.005), while the association with prostatitis was
borderline (OR, 3.40; 95% CI, 0.96-12.00; P=0.058). Diabetes was
independently associated with prostatitis (OR, 5.71; 95% CI,
2.24-14.55; P<0.001), BPH (OR, 10.75; 95% CI, 3.76-30.70;
P<0.001) and prostate cancer (OR, 76.48; 95% CI, 9.31-628.47;
P<0.001). Age was not significantly associated with any disease
group following adjustment (Fig. 1
and Table SI). These findings
suggested that smoking and diabetes were associated with the
prostate disease groups in the present study, particularly BPH and
PCa; however, causality cannot be inferred from the case-control
design.
 | Table IDemographic characteristics of the
study subjects in the different groups. |
Table I
Demographic characteristics of the
study subjects in the different groups.
| Characteristic | Control (n=45) | Prostatitis
(n=45) | BPH (n=45) | PCa (n=45) | P-value |
|---|
| Age range,
years | 65.30±0.98 | 68.04±1.08 | 66.17±0.55 | 64.93±1.06 | 0.086 |
| Smokers, n (%) | 1 (2.2) | 41 (91.1) | 43 (95.6) | 29 (64.4) | <0.001 |
| Non-smokers, n
(%) | 44 (97.8) | 4 (8.9) | 2 (4.4) | 16 (35.6) | |
| Diabetic, n
(%) | 0 (0.0) | 31 (68.9) | 37 (82.2) | 41 (91.1) | <0.001 |
| Non-diabetic, n
(%) | 45 (100.0) | 14 (31.1) | 8 (17.8) | 4 (8.9) | |
Clinical and biochemical parameters
among the study groups
The differences detected in the examined groups from
various diagnostic parameters yielded a considerable variation
within the groups concerning the majority of the studied biomarkers
(Table II). The commonly accepted
reference ranges for selected biomarkers are as follows: PSA (<4
ng/ml), ESR (0-20 mm/h), CRP (<5 mg/l), vitamin D3 (20-50
ng/ml), zinc (70-120 mcg/dl) and ferritin (30-400 ng/ml) in adult
males). In the present study, among the study groups, patients with
PCa had the greatest mean values of the aforementioned inflammatory
and tumor-associated markers, such as ESR, white blood cell count
(WBC) and CRP, in addition to PSA and ferritin; Tukey's HSD
pairwise significance varied by marker, as detailed in Tables II and SII. The mean PSA level in patients with
PCa was 51.75±4.33 ng/ml. This was significantly higher than that
of patients with BPH (8.69±0.64 ng/ml), prostatitis (3.05±0.18
ng/ml) and the healthy controls (1.98±0.10 ng/ml). ESR and WBC were
also markedly elevated in patients with PCa (52.95±4.02 mm/h and
15.80±0.67 x109/l, respectively). All the examined
inflammatory biomarkers were lowest among the healthy participants.
Both vitamin D3 and zinc levels were higher in the healthy controls
(31.76±3.04 ng/ml and 97.86±2.16 mcg/dl, respectively) compared to
the participants with prostate disease, who had significantly lower
(P<0.001) levels. se findings suggest that the inflammatory and
biochemical disturbance levels for PCa in the present cohort were
higher compared to the other study groups. However, it is important
to note that the markers in question are not specific to any one
disease.
| Table IISerum levels of diagnostic parameters
of prostate disease among the study groups. |
Table II
Serum levels of diagnostic parameters
of prostate disease among the study groups.
| Groups | ESR (mm/h) | WBC
(x109/l) | CRP (mg/l) | PSA (ng/ml) | Vitamin D3
(ng/ml) | Zinc (mcg/dl) | Ferritin
(ng/ml) | Urea (mg/dl) | Creatinine
(mg/dl) |
|---|
| PCa |
52.95±4.02a |
15.80±0.67a |
30.09±14.13a |
51.75±4.33a |
12.10±1.06c |
15.59±0.58d |
597.93±58.11a |
47.03±2.30a |
1.21±0.06a |
| BPH |
28.13±1.77b |
11.61±0.57b |
6.36±0.34a,b |
8.69±0.64b |
23.39±1.13b |
33.48±1.07c |
421.59±36.97b |
44.14±1.67a |
1.16±0.05a,b |
| Prostatitis |
19.76±1.09c |
11.47±0.34b |
11.52±1.07a,b |
3.05±0.18b |
13.35±1.99c |
67.94±3.26b |
325.75±13.55b,c |
37.76±1.32b |
1.01±0.04b |
| Control |
10.07±0.53d |
6.40±0.24c |
2.67±0.09b |
1.98±0.10b |
31.76±3.04a |
97.86±2.16a |
206.61±9.57c |
34.60±0.64b |
0.84±0.02c |
| P-value | <0.001 | <0.001 | 0.025 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Glycemic data of the study groups
As displayed in Table
III, there are notable differences in FBS and HbA1c levels
across the study groups (P<0.001). Patients with PCa had the
highest levels of glucose (FBS, 174.58±9.53 mg/dl; and HbA1c,
7.14±0.15%), followed by patients with prostatitis and BPH, and the
healthy controls had the lowest levels (FBS, 121.55±2.92 mg/dl; and
HbA1c, 5.61±0.05%). This pattern may be associated with prostate
disease due to altered glucose metabolism.
| Table IIISerum levels of FBS and HbA1c of the
investigated groups. |
Table III
Serum levels of FBS and HbA1c of the
investigated groups.
| Groups | FBS (mg/dl) | HbA1c (%) |
|---|
| PCa |
174.58±9.53a |
7.14±0.15a |
| BPH |
139.67±4.63b,c |
6.71±0.17a |
| Prostatitis |
144.56±4.92b |
6.05±0.14b |
| Healthy
control |
121.55±2.92c |
5.61±0.05b |
| P-value | <0.001 | <0.001 |
Cytokine levels in the study
groups
The differences in the levels of cytokines across
the study groups are displayed in Table IV, while the 95% CI values are
presented in Table SIII. Patients
with PCa had the highest mean concentration levels of TGF-β, IL-6
and IL-8, with values of 107.33±4.98, 72.59±2.04 and 156.43±18.52
pg/ml, respectively. Tukey's HSD analysis revealed that there were
no significant differences in IL-6 or IL-8 levels in those with PCa
and BPH, while the remaining adjusted pairwise comparisons are
detailed in Tables IV and
SII. The IL-37 levels were also
found to be higher than those in the healthy controls (11.43±0.50
pg/ml) in both PCa (15.38±0.33 pg/ml) and prostatitis (15.93±1.02
pg/ml) groups (P<0.001). GDF-15 levels also differed
significantly among the study groups (P=0.040). The highest mean
serum level of GDF-15 was detected in the PCa group (202.25±3.18
pg/ml), followed by the BPH (179.59±21.93 pg/ml) and prostatitis
(174.41±9.19 pg/ml) groups, whereas the control group exhibited the
lowest level (150.65±2.80 pg/ml) (Table IV). Tukey's HSD test revealed a
significant difference only between the PCa and the control group,
as shown in Tables IV and
SII. For instance, the FDR test
was applied across the analyzed biomarkers, and all differences
remained statistically significant, as shown in Table SIV. Effect size analysis revealed
that large differences recorded between group, for TGF-β
(η²=0.633), IL-37 (η²=0.159), IL-6 (η²=0.848), IL-8 (η²=0.478) and
GDF-15 (η²=0.045); these finding are supported by the data
presented in Table SV. These
findings suggested that IL-6, TGF-β and IL-8 strongly contributed
to the differentiation among the study groups. These inflammatory
cytokines activation may be related to PCa and may contribute to
disease activity.
| Table IVLevels of TGF-β, IL-37, IL-6 and IL-8
in the patients and healthy subjects. |
Table IV
Levels of TGF-β, IL-37, IL-6 and IL-8
in the patients and healthy subjects.
| Groups | TGF-β (pg/ml) | IL-37 (pg/ml) | IL-6 (pg/ml) | IL-8 (pg/ml) | GDF-15 (pg/ml) |
|---|
| PCa |
107.33±4.98a |
15.38±0.33a |
72.59±2.04a |
156.43±18.52a |
202.25±3.18a |
| BPH |
85.98±1.22b |
12.51±0.34b |
68.54±0.65a |
147.87±6.38a |
179.59±21.93a,b |
| Prostatitis |
46.11±3.36c |
15.93±1.02a |
63.32±1.38b |
108.16±1.17b |
174.41±9.19a,b |
| Control |
38.06±0.75c |
11.43±0.50b |
4.76±0.34c |
35.95±0.70c |
150.65±2.80b |
| P-value | <0.01 | <0.001 | <0.01 | <0.01 | 0.040 |
Impact of metformin treatment
All metformin-related evaluations were made
comparing the metformin-treated participants with
metformin-untreated participants within each group. The treated and
untreated participants included the following: Prostatitis (4
treated; 41 untreated); BPH (9 treated; 36 untreated); PCa (21
treated; 24 untreated). The metformin-untreated participants of the
prostatitis group had higher GDF-15 levels compared to the treated
participants (147.00±0.71 vs.176.80±9.92 pg/ml; P=0.004). In the
BPH group, IL-6 levels were lower among the participants treated
with metformin (66.97±0.75) compared to those untreated
(69.22±0.68; P=0.036). For PCa group, IL-37, TGF-β and IL-8 had no
significant metformin-related differences. No significant
differences in the levels of cytokines and GDF-15 were found in the
PCa group. Particularly the metformin-treated prostatitis subgroup
had a small sample size, and therefore were largely
hypothesis-generating rather than confirmatory. The data supporting
these findings are presented in Table
SVI and Fig. 2.
Correlation analysis
Spearman's correlation analyses were performed
within each disease group (PCa, BPH and prostatitis) to assess the
correlations between cytokines and clinical biomarkers. Among the
cytokines and clinical biomarkers, Spearman's correlation analysis
revealed multiple statistically significant correlations, as shown
in Table V. In the PCa group, CRP
negatively correlated with IL-6 (ρ=-0.316, P<0.05) and with
GDF-15 (ρ=0.312, P<0.05). In the BPH group, there was a negative
correlation between IL-37 and PSA (ρ=-0.337, P<0.05), a positive
correlation between CRP and IL-37 (ρ=0.301, P<0.05), and a
negative correlation between IL-37 and TGF-β (ρ=-0.305, P<0.05).
In the prostatitis group, there were positive correlations between
PSA and CRP (ρ=0.311, P<0.05), as well as between IL-37 and PSA
(ρ=0.358 and P<0.05). The correlation heatmaps of all
significant correlations analyzed in the PCa, BPH and prostatitis
groups are presented in Fig.
3.
| Table VCorrelation between cytokines and
studied biomarkers in the PCa, BPH and prostatitis groups. |
Table V
Correlation between cytokines and
studied biomarkers in the PCa, BPH and prostatitis groups.
| A, PCa group |
|---|
| |
Cytokine/biomarker |
|---|
|
Cytokine/biomarker | PSA | CRP | IL-6 | IL-8 | IL-37 | TGF-β | GDF-15 |
|---|
| PSA | 1 | 0.030 | -0.045 | -0.113 | -0.226 | -0.036 | -0.260 |
| CRP | 0.030 | 1 | -0.316a | -0.151 | -0.225 | 0.183 | 0.312a |
| IL-6 | -0.045 | -0.316a | 1 | 0.063 | -0.001 | -0.009 | -0.091 |
| IL-8 | -0.113 | -0.151 | 0.063 | 1 | 0.026 | 0.088 | -0.030 |
| IL-37 | -0.226 | -0.225 | -0.001 | 0.026 | 1 | -0.115 | 0.123 |
| TGF-β | -0.036 | 0.183 | -0.009 | 0.088 | -0.115 | 1 | -0.066 |
| GDF-15 | -0.260 | 0.312a | -0.091 | -0.030 | 0.123 | -0.066 | 1 |
| B, BPH group |
| |
Cytokine/biomarker |
|
Cytokine/biomarker | PSA | CRP | IL-6 | IL-8 | IL-37 | TGF-β | GDF-15 |
| PSA | 1 | -0.213 | -0.120 | -0.034 | -0.337a | 0.213 | 0.257 |
| CRP | -0.213 | 1 | 0.167 | 0.240 | 0.301a | -0.024 | -0.160 |
| IL-6 | -0.120 | 0.167 | 1 | -0.064 | 0.163 | -0.184 | 0.103 |
| IL-8 | -0.034 | 0.240 | -0.064 | 1 | 0.144 | 0.198 | 0.086 |
| IL-37 | -0.337a | 0.301a | 0.163 | 0.144 | 1 | -0.305a | -0.066 |
| TGF-β | 0.213 | -0.024 | -0.184 | 0.198 | -0.305a | 1 | -0.036 |
| GDF-15 | 0.257 | -0.160 | 0.103 | 0.086 | -0.066 | -0.036 | 1 |
| C, Prostatitis
group |
| |
Cytokine/biomarker |
|
Cytokine/biomarker | PSA | CRP | IL-6 | IL-8 | IL-37 | TGF-β | GDF-15 |
| PSA | 1 | 0.311a | -0.020 | -0.151 | 0.358a | -0.085 | -0.253 |
| CRP | 0.311a | 1 | -0.020 | -0.151 | 0.166 | 0.132 | -0.029 |
| IL-6 | -0.020 | -0.020 | 1 | 0.002 | 0.019 | 0.207 | -0.097 |
| IL-8 | -0.151 | -0.151 | 0.002 | 1 | -0.155 | -0.061 | -0.011 |
| IL-37 | 0.358a | 0.166 | 0.019 | -0.155 | 1 | 0.109 | -0.230 |
| TGF-β | -0.085 | 0.132 | 0.207 | -0.061 | 0.109 | 1 | 0.094 |
| GDF-15 | -0.253 | -0.029 | -0.097 | -0.011 | -0.230 | 0.094 | 1 |
Analysis of ROC curves
ROC curves were calculated for measuring their
discriminative performance with respect to inflammatory and
cytokine markers. In the prostatitis cohort, the highest AUC value
for IL-8 was 1.000, with IL-6 (AUC, 0.969) and CRP (AUC, 0.918)
following. The AUC values for IL-37 and PSA were 0.763 and 0.764,
respectively. In the BPH cohort, the AUC values for both IL-8 and
PSA were 1.000, and the participants exhibited a large separation
from the control group for TGF-β (AUC, 0.986), IL-6 (AUC, 0.978)
and CRP (AUC, 0.967). In the PCa cohort, IL-6 and CRP, along with
TGF-β (AUC, 0.983), IL-8 (AUC, 0.907), IL-37 (AUC, 0.811) and
GDF-15 (AUC, 0.922), exhibited separation from the control group.
Although the presented AUC values are data-driven exploratory
measures, they require independent cohort replication to prove
diagnostic feasibility. These findings are presented in Fig. 4 and Table VI.
| Table VIROC statistics for each disease group
against the control group. |
Table VI
ROC statistics for each disease group
against the control group.
| A, Prostatitis
group |
|---|
| Marker | AUC | 95% CI | P-value | Cut-off | Sensitivity, % | Specificity, % |
|---|
| PSA | 0.763 | 0.667-0.860 | <0.001 | 2.99 | 54.0 | 97.7 |
| CRP | 0.918 | 0.849-0.987 | <0.001 | 4.20 | 84.0 | 100.0 |
| IL-37 | 0.764 | 0.666-0.863 | <0.001 | 11.80 | 82.0 | 65.9 |
| GDF-15 | 0.626 | 0.512-0.741 | 0.031 | 159.40 | 52.0 | 79.5 |
| TGF-β | 0.656 | 0.532-0.781 | 0.014 | 46.84 | 60.0 | 93.2 |
| IL-6 | 0.969 | 0.924-1.000 | <0.001 | 45.22 | 100.0 | 95.5 |
| IL-8 | 1.000 | 1.000-1.000 | <0.001 | 91.53 | 100.0 | 100.0 |
| B, BPH group |
| Marker | AUC | 95% CI | P-value | Cut-off | Sensitivity, % | Specificity, % |
| PSA | 1.000 | 1.000-1.000 | <0.001 | 3.36 | 100.0 | 100.0 |
| CRP | 0.967 | 0.930-1.000 | <0.001 | 4.20 | 87.0 | 100.0 |
| IL-37 | 0.642 | 0.521-0.763 | 0.021 | 11.06 | 82.6 | 56.8 |
| GDF-15 | 0.578 | 0.456-0.699 | 0.212 | 168.90 | 37.0 | 88.6 |
| TGF-β | 0.986 | 0.959-1.000 | <0.001 | 68.46 | 100.0 | 97.7 |
| IL-6 | 0.978 | 0.935-1.000 | <0.001 | 62.21 | 97.8 | 97.7 |
| IL-8 | 1.000 | 1.000-1.000 | <0.001 | 66.89 | 100.0 | 100.0 |
| C, PCa group |
| Marker | AUC | 95% CI | P-value | Cut-off | Sensitivity, % | Specificity, % |
| PSA | 1.000 | 1.000-1.000 | <0.001 | 4.20 | 100.0 | 100.0 |
| CRP | 0.978 | 0.942-1.000 | <0.001 | 4.50 | 95.3 | 100.0 |
| IL-37 | 0.811 | 0.710-0.912 | <0.001 | 12.50 | 95.3 | 65.9 |
| GDF-15 | 0.922 | 0.849-0.996 | <0.001 | 186.80 | 93.0 | 95.5 |
| TGF-β | 0.983 | 0.962-1.000 | <0.001 | 50.20 | 97.7 | 95.5 |
| IL-6 | 0.980 | 0.949-1.000 | <0.001 | 40.96 | 100.0 | 95.5 |
| IL-8 | 0.907 | 0.819-0.995 | <0.001 | 61.36 | 90.7 | 100.0 |
Discussion
The present case-control study aimed to develop
region-specific data on some of the clinical risks, as well as
patterns of the inflammatory/metabolic biomarkers in Iraqi men
diagnosed with PCa, BPH and prostatitis. The association of smoking
and diabetes with the disease categories was maintained following
the application of multivariable analysis; nonetheless, the authors
of the present study recommend that the readers interpret the
association they describe as an association and not as a causal
association. There were no diabetic individuals in the control
group, and the wide confidence interval for the comparison of
diabetes in PCa indicates that this result may be unstable, and,
therefore, the authors suggest interpreting the finding with
caution.
The smoking variable was reported as yes/no only;
thus, the authors were not able to report any smoking dose-response
associations. These limitations should be considered as smoking,
metabolic disease and chronic inflammation may be associated with
aging, healthcare-seeking behavior and other diseases/conditions
not included in the present study.
The higher PSA levels reported in PCa cases aligns
with the role of PSA in the assessment of PCa; however, PSA is not
specific and may be influenced by BPH, inflammation and other
clinical factors. The assessment of PCa via PSA and DRE, along with
imaging and a targeted tissue biopsy, is a stepwise process that
incorporates a risk-based assessment, and a definitive diagnosis is
rendered on histopathological assessment of the prostate biopsy
(22). Thus, the elevated PSA in
the present study supports the clinical categorization of the PCa
group, but should not stand alone in evidenced-based prostate
cancer diagnosis. Likewise, the ESR, CRP, WBC, ferritin, urea and
creatinine levels should not be considered validated markers of
prostate disease, as they may be markers of inflammation and a
burden of disease, as well as a potential obstructive uropathy and
other comorbidities.
The decreased vitamin D3 and zinc levels in the
patient groups may indicate the effects of nutrition and metabolism
or the effects of inflammation and other diseases. Further studies
are required to understand the possible interrelationships and
determine whether the observed vitamin D3 and zinc levels are in a
cause or consequence association with other factors.
The observed cytokine levels are also plausible;
however, they remain exploratory. Several studies have linked the
inflammatory pathways and the cytokines of the prostate
microenvironment with tumor growth, the innate and adaptive immune
responses, and the inflammation of prostate disorders (25-31).
The context-dependent role of TGF-β levels in cancer explains the
increased levels of TGF-β in PCa. In the early stages of other
types of cancer, TGF-β may function as a suppressor; in the later
stages, TGF-β may function as a facilitator of cancer (32). Concern should be warranted with the
increased levels of IL-37. These may indicate a compensatory
response of inflammation and leaves the interpretation of these
levels open due to the anti-inflammatory and immunoregulatory role
of IL-37, which varies in cancer of different types, stages and
microenvironments (33,34). In the present study, the increased
levels of IL-37 may be a compensatory response and should not be
interpreted as a direct marker of disease severity.
The ROC results depicted the separation of defined
cases and defined controls in the present dataset and should not be
viewed as a validation of clinical diagnosis. The data presented in
Table VI should not be
interpreted as a substitutive threshold for screening or prognosis
of clinical evaluation of PSA levels. Among other evaluated
clinical pathways of PSA, DRE, imaging and histopathology, these
unexplored biomarkers may serve as adjunctive candidates. These
cut-offs would require to be validated in larger, prospective,
independent cohorts with clinically relevant comparator groups,
pre-specified outcome definitions, and adequate power before any
use in screening, risk stratification, monitoring, or
prognosis.
As a whole, the data of the present study are in
agreement with additional literature (35-37)
regarding the associations of inflammatory and metabolic pathways
with the biology of prostate disease. Nevertheless, the results
diverge from the clinically documented biomarker literature, as the
present study was a single-center investigation with a case-control
design and small and unproportionate subgroups. Thus, the results
should be viewed primarily as hypothesis-generating regional data
for an Iraqi population. Future studies are warranted to
incorporate larger multicenter efforts, with the staging and
grading of PCa and BPH, stratified classification of prostatitis,
the classification of medications, the intensity of smoking, and
the degree of follow-up, in order to seek and identify any
biomarker patterns which would be relevant to clinical practice and
have potential for independent predictive power beyond current
practice.
The present study had certain limitations which
should be mentioned. Some limitations in design, data and methods
constrain the interpretation and generalizability of the findings
of the present study. First, due to the case-control design, all
connections regarding risk factors, biomarkers and groups of
prostate diseases were only associative observations. Second,
cancer staging and Gleason/ISUP grading were lacking in the PCa
cohort; thus, the extent of biomarkers and the grade of cancer were
constrained. Third, BPH data lacked prostate volume and the
International Prostate Symptom Score, constraining the
interpretation of the severity of BPH. Fourth, smoking status was a
yes/no variable only, and consequently, no analysis of the smoking
dose-response was possible. Fifth, a lack of detail on other
medications and comorbidities may have resulted in residual
confounding and bias, particularly in the case of metformin. Sixth,
the present single-center study, recruited only in Baghdad, limits
generalizability to only that region of Iraq. Finally, with a
rather small sample size of 45 participants per group, these
findings need to be replicated in larger, multicenter, longitudinal
studies with external validation of biomarker cut-offs.
In conclusion, in the present case-control study,
smoking, diabetes, inflammatory markers, some cytokines, and lower
levels of vitamin D3 and zinc among Iraqi men were found to be
associated with some prostate diseases. The greatest elevations in
markers of inflammation and a slow ESR, as well as in CRP, IL-6,
IL-8 and TGF-β levels among the study population occurred in the
patients with PCa. These findings suggest that the inflammation and
metabolism of Iraqi patients with PCa warrants further attention.
However, these findings fail to establish the value of these
markers in diagnosis and prognosis. The markers assessed in the
present study are candidate exploratory markers, and the ROC
cut-off values derived from the present study are exploratory
cut-off values. Further studies using larger populations are
warranted in order to define these markers of inflammation, as well
as make therapeutic and prognostic adjustments related to these
markers, before they could be considered for assessment,
monitoring, or risk stratification. These studies should include
independent external validation, detailed clinical assessment and
staging, medication adjustments, and analysis of the clinical
course of the patients.
Supplementary Material
Multivariable multinomial logistic
regression for disease groups relative to controls.
Tukey’s HSD adjusted pairwise P-values
for continuous variables presented in Tables I, II, III and IV.
Cytokine and GDF-15 mean values with
95% confidence intervals.
Raw and FDR-adjusted P-values for
between-group biomarker comparisons.
Effect sizes (η2) for
cytokine and GDF-15 comparisons across the study groups.
Effect of metformin treatment on
inflammatory and related biomarkers within the disease groups.
Acknowledgements
The authors are grateful to the College of Science
at Mustansiriyah University, Baghdad, Iraq for providing the
facilities for the present study.
Funding
Funding: No funding was received.
Availability of data and materials
The data generated in the present study may be
requested from the corresponding author.
Authors' contributions
MMAA conceptualized the study. In addition to
drafting, revising and editing the manuscript, MMAA and OAJ
contributed to the study methodology. All authors (MMAA, OAJ and
IAA) were involved in data validation, investigation, and in the
writing and preparation of the original draft of the manuscript.
IAA contributed to the formal analysis, data curation and figure
preparation. OAJ provided laboratory facilities, reagents,
instruments and technical support. IAA and MMA supervised the study
and involved in project administration. The published version of
the manuscript has been read and approved by all authors. All
authors (MMAA, OAJ and IAA) confirm the authenticity of all the raw
data.
Ethics approval and consent to
participate
The study protocol was approved by the Research
Ethics Committee of the College of Science, Mustansiriyah
University, Baghdad, Iraq (Approval no. BCSMU/3024/00081M; January,
2024). In accordance with the approved protocol and within the
framework of academic collaboration with Mustansiriyah University,
blood samples and clinical data were collected from Al-Yarmouk
Teaching Hospital in Baghdad. The treating medical team and
hospital administration provided the records and clinical samples
after obtaining the necessary ethical approvals (Approval no. 9274;
December, 2023) and written informed consent from all
participants.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
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![Comparing metformin-treated
participants with metformin-untreated participants within the three
patient groups [those with (A) prostatitis, (B) BPH and (C) PCa]
according to diagnostic laboratory parameters of prostate disease.
*P<0.05 and **P<0.01. ns, not
significant; BPH, benign prostatic hyperplasia; PCa, prostate
cancer; CRP, C-reactive protein; PSA, prostate-specific antigen;
IL, interleukin; GDF-15, growth differentiation factor 15; TGF,
transforming growth factor.](/article_images/wasj/8/5/wasj-08-05-00500-g01.jpg)
![ROC analysis for inflammatory and
cytokine marker levels in the patient groups [those with (A)
prostatitis, (B) BPH and (C) PCa]. ROC, receiver operating
characteristic; BPH, benign prostatic hyperplasia; PCa, prostate
cancer; CRP, C-reactive protein; PSA, prostate-specific antigen;
IL, interleukin; GDF-15, growth differentiation factor 15; TGF,
transforming growth factor.](/article_images/wasj/8/5/wasj-08-05-00500-g03.jpg)
