Serum prolactin level in male type 2 diabetes mellitus patients at the University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia, 2024

Abstract

The anterior pituitary gland secretes the peptide hormone prolactin, which is important for lactation, reproductive health, and metabolic regulation. Its secretion is primarily regulated by dopamine and also its elevated level is known as hyperprolactinemia. The aim of this study was to assess serum prolactin level in T2DM patients at the University of Gondar comprehensive specialized hospital. A comparative cross-sectional study was conducted from June 27 to August 20, 2024 involving 330 study participants selected using systematic random sampling. Of these, 165 were male patients with T2DM and 165 were apparently healthy controls. Sociodemographic, behavioral, and clinical data were measured by structured questionnaires. Hormonal parameters, Fasting blood sugar and lipid profiles were measured by chemiluminescent immunoassay and spectrophotometric principles respectively. Data were analyzed using SPSS version 25. Descriptive statistics, independent t-tests, and binary logistic regression were performed with significance set at p < 0.05. The prevalence of hyperprolactinemia was 9.1% in T2DM patients and 6.1% in the control group. A statistically significant difference in mean serum prolactin level was observed between T2DM and control group (7.79 ± 2.9 vs. 6.81 ± 3.1, p-value = 0.004) respectively. Hyperprolactinemia was more likely to occur in study individuals with high dietary diversity (AOR = 3.213, 95% CI: 1.253–8.239, P-value = 0.015). The incidence of hyperprolactinemia increases by 1.009 (95% CI: 1.001–1.018; P-value = 0.035) for every unit rise in FBS (1 mg/dl). In conclusion, the mean serum prolactin level was significantly higher among T2DM patients compared to healthy control groups. In addition, the prevalence of hyperprolactinemia was higher in T2DM patients than the control groups. Dietary diversity and elevated FBS were a significant predictors of hyperprolactinemia. So, the healthcare providers should consider routine screening of serum prolactin level in patients diagnosed with T2DM.

Introduction

Prolactin (PRL) is a multifunctional pituitary hormone necessary for several bodily physiological processes. In addition to being crucial for the initiation and maintenance of lactation, it also appears to play a role in immunological control, growth and development, osmoregulation, brain function, behavior, metabolism, an adipokine to regulate adipogenesis, and reproduction^1–3. Because the PRL receptors are expressed in several tissues and cells, including lymphoid cells, the endometrium, the prostate, and adipocytes these various roles of PRL be carried out^1–3.

Understanding the regulatory mechanisms of PRL secretion is crucial, as disruptions in this system may influence metabolic health. Dopamine released within the median eminence from the terminals of tuberoinfundibular dopaminergic (TIDA) neurons tonically inhibits the spontaneous production of PRL by the anterior pituitary lactotrophs. PRL directly interacts with TIDA neurons to enhance their rate of activity and consequently dopamine output maintaining low circulating levels of PRL in both males and non-pregnant females through a “short-loop” negative feedback^4–6.

Dopamine agonists boost dopaminergic neurotransmission, which can reset the hypothalamus and correct high PRL levels^7,8. B-cell hypoplasia, a decreased quantity of pancreatic insulin mRNA, a dulled insulin secretory response to glucose, and mild glucose intolerance are all associated with PRL deletion or PRL receptor insufficiency^9,10.

The physiological hyperprolactinemia a condition where the level of prolactin is increased naturally to support metabolic and reproductive functions without causing long term harm. Whereas, pathological hyperprolactinemia including prolactinoma is caused by systemic and pituitary diseases with long term consequences¹¹. Weight gain, subclinical atherosclerosis, insulin resistance, decreased glucose tolerance, hyperinsulinemia, atherogenic dyslipidemia, and endothelial dysfunction are all complications of long-term PRL excess(pathological hyperprolactinemia)^12,13.

In the past, PRL was thought to be a factor that causes diabetes because pathological hyperprolactinemia caused insulin resistance and impaired pancreatic β-cell activity¹⁴. However, experimental evidences showed that PRL has played a significant role in controlling glucose metabolism; by activating pancreatic β-cell PRL receptors, increases insulin secretion and lowers the glucose threshold by insulin production^15–20. In addition, PRL promotes adipocyte development and suppresses lipolysis by activating peroxisome proliferative-activated receptor gamma (PPAR-γ)^21,22.

Prolactin level in patients with T2DM may be impacted by diabetic medication. Different scholars suggests that, patients who are taking glyburide raises PRL while metformin decreases it because of fluctuations in hypothalamic dopaminergic activity, which is crucial for controlling PRL from the anterior pituitary^23–25.

It is well established that hyperprolactinemia reduces gonadal hormone production and male fertility by either blocking the release of GnRH via prolactin receptors on hypothalamic dopaminergic neurons or by causing an increase in the secretion of adrenal corticoids²⁶. Studies showed that a slight increase in circulating prolactin led to a significant suppression of luteinizing hormone (LH) and follicle stimulating hormone (FSH) in rat²⁷. Several types of male infertility, such as azoospermia and oligozoospermia, were linked to elevated PRL levels, which may be related to a disturbance in regular gonadal function²⁸.

Several studies also revealed that, dyslipidemia and other metabolic abnormalities are correlated with both high and low levels of prolactin, which is linked to metabolic syndrome. The hormone’s influence on lipid profiles is significant, as it can affect body weight and insulin sensitivity, thereby impacting overall metabolic health^22,29.

Even though, many scholars have tried to study the association between prolactin and T2DM, to the best of our knowledge this study is the first study in Sub-Saharan countries. The aim of this study was to assess serum prolactin level in T2DM patients at the University of Gondar Comprehensive Specialized Hospital (UoGCSH).

Method and materials

Study area

The study was conducted at the UoGCSH in Gondar town, Amhara, Ethiopia. The UoGCSH, a major teaching hospital with 977 beds, 29 wards, and emergency rooms, serves nearly 13 million people in its surrounding area and neighboring regions. It provides a wide range of medical services, including internal medicine, surgery, obstetrics and gynecology, pediatrics, laboratory tests, eye care, physiotherapy, dental care, cervical health, psychiatry, dermatology, and drug supply. The hospital also offers various social services and has specialized units for tuberculosis, kala-azar, cancer treatment, fistula surgery, psychiatric and psychological treatment, palliative and rehabilitation services, and both adult and pediatric intensive care units, as well as cataract surgery. Gondar town is located 750 km northwest of Addis Ababa and has an estimated population of about 487,224 in 2023³⁰.

Study design and period

A comparative cross-sectional study design was used, with the data collection period from June 27 to August 20, 2024.

Study populations

There were a total of 330 volunteers, divided into two groups. The first group consisted of patients with T2DM, and the second group comprised age-matched, apparently healthy controls. Patients from the UoGCSH chronic patient care clinic and health professionals at the UoGCSH were used to create the case and control groups, respectively. Data were gathered from all participants who fulfilled the inclusion criteria from June 27 to August 20, 2024.

Eligibility criteria

Inclusion criteria

The study comprised adult male patients who attended the UoGCSH chronic patient care clinic, had a verified diagnosis of T2DM, and provided their voluntarily informed consent. Enrollment also included apparently healthy adult males who were found to be normoglycemic through screening tests and who also gave their voluntary consent to participate.

Exclusion criteria

The study excluded participants with a proven history of end-stage renal disease, hypothyroidism, T1DM, mixed pituitary tumors, prolactinoma, or chronic liver disease based on a review of their medical records. Additional exclusion criteria included the use of certain drugs known to affect PRL level, such as bromocriptine, metoclopramide, antipsychotic medications, and risperidone. Furthermore, individuals who might be unable to provide accurate information due to mental health conditions or hearing difficulties were also excluded.

Sampling technique

Study participants were chosen from the diabetic follow-up cohort at the chronic care clinic of the UoGCSH using systematic random sampling. According to clinic records, an estimated 12,000 patients were seen each year for diabetes treatment, with an additional 2,000 anticipated during the two-month data collection period. The sampling interval (k) was calculated by dividing the estimated population (N) by the necessary sample size (n): k = N/n. Thus, k = 2000/330 ≈ 6.06 rounded to 6. Consequently, every sixth patient who came into the clinic was enlisted. To ensure randomness at the beginning of the sampling procedure, the first participant was chosen using a simple random lottery method among the first six eligible patients. For the control groups, we used a convenience sampling method, recruiting health professionals from the hospital who volunteered and met the study’s criteria.

Study variables

Dependent variable

Hyperprolactinemia.

Independent variables

Socio-demographic variables: age, marital status, educational level, residence, occupation.

Clinical and anthropometric variables; waist circumference, BMI, treatment duration, type of medication, HTN, dyslipidemia, diabetic complications.

Biochemical variables: FSH, LH, TG, TC, HDL-c, LDL-c, FBS.

Operational and standard definitions

Hyperprolactinemia: based on the upper normal limit established by the manufacturer of Beckman Coulter Inc., Danaher Corporation, and Brea, CA, USA, we defined hyperprolactinemia as a serum PRL level exceeding 13.13 ng/ml¹⁰.

Body mass index is weight in kilogram divided by height in meter square and the diabetic patients will be classified as underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m ²), overweight (25.0–29.9 kg/m² ), and obese (≥ 30 kg/m ²)based on World Health Organization (WHO) guideline category³¹.

Hyperglycemia: a person whose FBS level greater than 126 mg/dl, Normal FBS; a person whose FBS level between 70 and 126 mg/dl. Hypoglycemia; a person whose FBS level below 70 mg/dl³².

Hypertension: a person whose SBP ≥ 140 mm Hg and/or their DBP is ≥ 90 mm Hg following repeated examination³³.

Diet: can be assessed by dietary diversity score, a qualitative measure of food consumption that reflects household access to a variety of foods which is categorized as low dietary diversity (≤ 5 food groups), and high dietary diversity (≥ 6 food groups)³⁴.

Dyslipidemia: a person whose plasma lipoprotein function and levels are disrupted, an individual whose TC > 200 mg/dl, TG > 150 mg/dl, LDL-c > 130 mg/dl, and HDL-c³⁵.

Normal FSH lies between 0.51 and 10.5 IU/L, High FSH > 10.5 IU/L³⁶.

Normal LH lies between 0.81 and 9.05 IU/L, High LH > 9.05 IU/L³⁶.

Data collection procedure

For the study, a standardized data collection method was developed, which included a structured questionnaire and checklist. The questionnaire was initially created in English and then translated into Amharic, the local language. It covered behavioral, clinical, and socio-demographic aspects across three major areas. 5% of the sample at Maraki Health Center participated in a pretest to assess the tool’s reliability, consistency, and clarity.

Using predetermined inclusion criteria, eligible volunteers were found and given comprehensive information about the study’s goals. All participants gave their signed, informed consent before participating. Prior to data collection, screening procedures of FBS testing were carried out for the control group to assess their diabetes status and validate eligibility. Participants who satisfied the eligibility requirements were then questioned using a structured questionnaire. A stadiometer and a bioelectrical impedance analyzer (Seca, Germany) were used to get anthropometric measures, such as height and weight. In order to determine their BMI, participants had to take off their shoes, hats, and bulky clothing. They were then classified as underweight, normal weight, or obese based on WHO criteria³¹. The average of two measurements obtained after a 10-minute rest period was used to measure blood pressure using a mercury sphygmomanometer.

A standard checklist was used to retrieve clinical information from medical records, such as diabetic complications, duration of diabetes, and type of medicine. A standardized questionnaire was used to evaluate behavioral traits like diet, physical activity, and alcohol. Physical activity was assessed using the WHO guidelines, physically active was defined adults should do at least 150–300 min of moderate-intensity aerobic physical activity, or at least 75–150 min of vigorous-intensity aerobic physical activity per week³⁷.

Sample collection and processing

Standardized procedures for sample collection were closely followed by skilled laboratory technologists. They draw 8–12 h fasting 5 mL of venous blood from each study participant using 21-gauge disposable syringe after cleaning the puncture site with 70% alcohol. The samples were then transferred into serum separator tubes. After the blood was clotted for 30-minutes, serum was separated by centrifuging the sample for 5 min at 3,500 revolutions per minute.

Laboratory procedure

The Beckman Coulter DxI 800 AU automated analyzer (Beckman Coulter Inc., Danaher Corporation, Brea, CA, USA) was used with a principle of chemiluminescent immunoassay to measure the serum levels of gonadal hormones, such as prolactin, LH, and FSH. Regular quality control procedures were implemented throughout the study to ensure data integrity and reliability. We also used control samples alongside study samples to monitor assay performance and ensure the accuracy and precision of laboratory results.

The Access PRL (ng/ml) assay is a simultaneous one-step immunoenzymatic (“sandwich”) assay. A sample is added to a reaction vessel along with polyclonal goat anti-PRL alkaline phosphatase conjugate, and paramagnetic particles coated with mouse monoclonal anti-PRL antibody. The serum or plasma (heparin) PRL binds to the monoclonal anti-PRL on the solid phase, while the goat anti-PRL-alkaline phosphatase conjugate reacts with a different antigenic site on the serum PRL. After incubation, materials bound to the solid phase are held in a magnetic field while unbound materials are washed away. Then, the chemiluminescent substrate is added to the vessel and light generated by the reaction is measured with a luminometer. The light production is directly proportional to the concentration of analyte in the sample. Analyte concentration is automatically determined from a stored calibration¹⁰.

Using two-step sandwich immunoenzymatic assays, which show a direct correlation between light intensity and analyte concentration, LH and FSH were measured. Multi-point calibration curves were used in all experiments, and the analyzer’s built-in luminometer was used to interpret the results³⁶.

Data quality assurance and management

Data collectors and laboratory staff received thorough training about the study’s objective, ethical issues, interviewing methods, laboratory procedures, and quality control techniques. Under the guidance of the lead investigator, skilled laboratory technologists collected venous blood, while data collectors gathered comprehensive socio-demographic, clinical, and behavioral data. The lead investigator ensured the quality of the collected data through regular oversight, immediate feedback, and confirmation of accuracy and consistency. Traceability was maintained by cross-referencing specimen labels with participants’ unique identifiers, and all data were systematically examined for accuracy and clarity.

Data analysis and interpretation

Statistical Package for Social Sciences version 25 (SPSS Inc., Chicago, IL, USA) was utilized for data entry, cleaning, coding, and analysis. The Kolmogorov-Smirnov test and histograms were used to determine whether continuous variables were normal; a p-value of ≥ 0.05 was regarded as suggestive of a normal distribution. The data were compiled using descriptive statistics, such as means, medians, frequencies, and percentages, and the results were displayed in tables and figures.

Independent t-tests were used to compare groups for continuous variables. After controlling for relevant confounders, binary logistic regression analysis was used to find independent predictors of hyperprolactinemia. The multivariable logistic model contained variables that had a p-value of less than 0.25 in bivariable analysis. The variables’ chi-square test results were examined before regression analysis began, and those satisfying the chi-square test assumptions were added to the binary logistic regression. Variance inflation factor (VIF) was also used to evaluate multicollinearity, and all included variables had VIF < 10.

Ethical consideration

The ethical clearance was obtained from the ethical review committee of the School of Biomedical and Laboratory Sciences with the reference number SBMLS/762/2024. The study was conducted in accordance with the Declaration of Helsinki³⁸. A permission letter was gained from UOGCSH. The study’s advantages and disadvantages were explained to the participants, and they were free to withdraw at any moment.

Result

Socio-demographic characteristics of the study participants

This study comprised 330 individuals in total. Of whom, 165 were patients with T2DM, while the remaining 165 were control groups that appeared to be in a healthy state. Majority, 49.1% and 37% of T2DM patients and apparently healthy control groups respectively were able read and write. Majority, 40% and 43.6% of T2DM patients and apparently healthy control groups were with the age group of 55–64 respectively (Table 1).

Table 1.

Socio-demographic characteristics of the study participants attending at UOG CMHS, Gondar 2024 (n = 330).

Variable	Categories	Case (n = 165)	Control (n = 165)	Total (n = 330)	Chi- square (p-value)
		N (%)	N (%)	N (%)
Age	35–44	19(11.5%)	24(14.5%)	43(13.0%)	0.170
	45–54	39(23.6%)	44(26.7%)	83(25.2%)
	55–64	66(40.0%)	72(43.6%)	138(41.8%)
	> 65	41(24.8%)	25(15.2%)	66(20%)
Marital status	Married	153(92.7%)	152(92.1%)	305(92.4%)	0.935
	Single	3(1.8%)	4(2.4%)	7(2.1%)
	Divorced	6(3.6%)	7(4.2%)	13(3.9%)
	Widowed	3(1.8%)	2(1.2%)	5(1.5%)
Residence	Urban	154(93.3%)	162(98.2%)	316(95.8%)	0.052
	Rural	11(6.7%)	3(1.8%)	14(4.2%)
Occupation	Merchant	33(20.0%)	40(24.2%)	73(22.1%)	0.000
	Farmer	17(10.3%)	3(1.8%)	20(6.1%)
	Daily labor	7(4.2%)	4(2.4%)	11(3.3%)
	Gov’t employee	36(21.8%)	80(48.5%)	116(35.2%)
	Retired	58(35.2%)	33(20.0%)	91(27.6%)
	Others	14(8.5%)	5(3.0%)	19(5.7%)
Educational level	Unable to read and write	12(7.3%)	9(5.5%)	21(6.4%)	0.077
	Read and write	81(49.1%)	61(37.0%)	142(43.0%)
	Primary school	38(23.0%)	55(33.3%)	93(28.2%)
	Secondary school	34(20.6%)	40(24.2%)	74(22.4%)

Others* driver, NGO servant, private servant.

Anthropometric, Behavioral, and Clinical characteristics of the study participants

The mean BMI level of the T2DM and apparently healthy control groups were 25 ± 3.2 and 25 ± 2.9 kg/m², respectively. The mean duration of diabetes in T2DM patients was 5.3 ± 5.5 years. Among the T2DM patients, 62.4% were taking metformin, while 37.6% were using insulin as an anti-hyperglycemic agent (Table 2).

Table 2.

Anthropometric, Behavioral, and clinical characteristics of study participants attending at UOG CMHS, Gondar 2024 (n = 330).

Variables	Category	Case (N = 165)	Control (N = 165)	Total (N = 330)	Chi-square p-value)
History of HTN	No	70(42.4%)		70(21.2%)	NA
	Yes	95(57.6%)		95(28.8%)
SBP	< 140 mm/Hg	101(61.2%)	163(98.8%)	264(80%)	0.000*
	≥ 140 mm/Hg	64(38.8%)	2(1.2%)	66(20%)
DBP	< 90 mm/Hg	154(93.3%)	162(98.2%)	316(95.8%)	0.052
	≥ 90 mm/Hg	11(6.7%)	3(1.8%)	14(4.2%)
BMI	Normal weight	77(46.7%)	84(50.9%)	161(48.8%)	0.625
	Over weight	72(43.6%)	69(41.8%)	141(42.7%)
	Obese	16(9.7%)	12(7.3%)	28(8.5%)
WC	< 94 cm	55(33.3%	52(31.5%)	107(32.4%)	0.073
	> 94 cm	110(66.7%)	113(68.5%)	223(67.6%)
Dietary Diversity	High	92(55.8%)	39(23.6%)	131(39.7%)	0.000*
	Low	73(44.2%)	126(76.4%)	199(60.3%)
Physical Activity	Physically active	96(58.2%)	88(53.3%)	184(55.8%)	0.438
	Physically Inactive	69(41.8%)	77(46.7%)	146(44.2%)
Duration of DM	< 11 year	111(67.3%)		111(33.6%)	NA
	> 11 year	54(32.7%)		54(16.4%)
Type of medication	Metformin	103(62.4%)		103(31.2%)	NA
	Insulin	62(37.6%)		62(18.8%)
DM complication	Nephropathy	2(1.2%)	-	2(0.6%)	NA
	Retinopathy	10(6.1%)	-	10(3.1%)
	Neuropathy	4(2.4%)	-	4(1.2%)
	No complication	149(90.3%)	-	149(45.1%)

NA: Not Applicable, DM: Diabetes mellitus, SBP: Systolic Blood Pressure, DBP: Diastolic Blood Pressure, HTN: Hypertension, BMI: Body Mass Index, WC: Waist Circumference.

Biochemical profile of the study participants

The mean levels of LDL-cholesterol for the T2DM and apparently healthy control groups were 82.9 ± 29.47 and 89.7 ± 31.8, respectively. 40% (40%) of T2DM patients were hyperglycemic, while none of the apparently healthy control group exhibited hyperglycemia (Table 3).

Table 3.

Biochemical profile of the study participants attending at UOG CMHS, Gondar 2024 (n = 330).

Variable	Categories	Case (n = 165)	Control (n = 165)	Total (n = 330)	Chi-square (p-value)
		N (%)	N (%)	N (%)
PRL(ng/ml)	Normal	150(90.9%)	155(93.9%)	305(92.4%)	0.406
	High	15(9.1%)	10(6.1%)	25(7.6%)
FSH(IU/L)	Normal	136(82.4%)	148(89.7%)	284(86.1%)	0.080
	High	29(17.6%)	17(10.3%)	46(13.9%)
LH(IU/L)	Normal	118(71.5%)	116(70.3%)	234(70.9%)	0.904
	High	47(28.5%)	49(29.7%)	96(29.1%)
FBS(mg/dl)	Normoglycemic	66(40%)	165(100%)	231(70.0%)	NA
	Hyperglycemic	99(60%)	-----	99(30.0%)
TC(mg/dl)	Normal	155(93.9%)	148(89.7%)	303(91.8%)	0.228
	High	10(6.1%)	17(10.3%)	27(8.2%)
TG(mg/dl)	Normal	113(68.5%)	118(71.5%)	231(70%)	0.631
	High	52(31.5%)	47(28.5%)	99(30%)
HDL_c(mg/dl)	low	143(86.7%)	128(77.6%)	271(82.1%)	0.044
	Normal	22(13.3%)	37(22.4%)	59(17.9%)
LDL_c(mg/dl)	Normal	153(92.7%)	148(89.7%)	301(91.2%)	0.437
	High	12(7.3%)	17(10.3%)	29(8.8%)

*Statistically significant FSH = follicle stimulating hormone LH = luteinizing hormone FBS = fasting blood sugar TC = total cholesterol TG = triglyceride HDL-c = high density lipoprotein cholesterol LDL = low density lipoprotein.

Mean level of serum prolactin among T2DM and control group

There was a statistically significant difference in mean serum PRL level between patients with T2DM and the healthy control group (p-value = 0.004) (Table 4).

Table 4.

Independent T test for the comparison of serum prolactin mean difference between T2DM patients and control groups in UOG CSH, Gondar 2024(n = 330).

Variable	T2DM	Control	Mean difference	p-value
	X ± SD	X ± SD
PRL(ng/ml)	7.79 ± 2.9	6.81 ± 3.1	0.98097	0.004*

*Statistically significant PRL = Prolactin X = mean SD = Standard deviation.

Prevalence of hyperprolactinemia

The prevalence of hyperprolactinemia among T2DM and age matched apparently healthy controls was 9.1% [95% CI: 5.2%, 14.6%] and 6.1% [5.0%, 11.0%] respectively (Fig. 1).

Fig. 1.

Prevalence of hyperprolactinemia among T2DM patients and apparently healthy control group from UOG CSH Gondar 2024.

Factors associated with hyperprolactinemia among the study participants

Variables with a p-value of less than 0.25 in the bivariable logistic regression analysis and those fulfilling the chi-square test assumption were further analyzed in the multivariable logistic regression model. Prior to multivariable analysis, multicollinearity among the independent variables was assessed using VIF diagnostics. VIF values for every variable were less than 10, indicating no significant multicollinearity. Multivariable logistic regression revealed that dietary diversity and FBS level were independent factors that were substantially associated with hyperprolactinemia across the whole study group. Hyperprolactinemia was more likely to occur in individuals with high dietary diversity (AOR = 3.213, 95% CI: 1.253–8.239, P-value = 0.015) (Table 5).

Table 5.

Bivariable and multivariable binary logistic regression analysis result for factors associated with hyperprolactinemia among study participants (n = 330).

Variable	Category	Hyperprolactinemia		COR (95%CI)	AOR (95% CI)	P-value
		Yes	No
Stress	Low level	21	278	Ref	ref	Ref
	High level	4	27	1.96(0.627–6.132)	1.76(0.538-5.756)	0.350
Presence of DM	No	10	155	Ref	Ref	Ref
	Yes	15	150	1.55(0.675–3.558)	0.632(0.215- 1.859)	0.404
Diet category	Low	7	192	Ref	ref	Ref
	High	18	113	3.56(1.489–8.513)	3.213(1.253–8.239)	0.015*
Continues independent variable
Variable			COR (95% CI)		AOR (95% CI)	P-value
Fasting blood sugar			1.009(1.002–1.015)		1.009(1.001–1.017)	0.029*
Follicle stimulating hormone			0.954(0.889–1.025)		0.953(0.884- 1.026)	0.201

*= statistically significant, COR = crude odds ratio, AOR = adjusted odds ratio, CI: confidence interval, Ref = reference.

Factors associated with hyperprolactinemia among T2DM patients

Predictor variables with a p-value of less than 0.25 in the bivariable logistic regression model-such as the duration of diabetes, type of treatment, FBS, and FSH were included in the multivariable logistic regression model. As a result, FBS showed a significant association with hyperprolactinemia among T2DM patients. The incidence of hyperprolactinemia increased by 1.009 (95% CI: 1.001–1.018; p-value = 0.035) for every unit rise in FBS (1 mg/dl) (Table 6).

Table 6.

Bivariable and multivariable binary logistic regression analysis result for factors associated with hyperprolactinemia among type 2 diabetes mellitus patients (n = 165).

Variable	Category	Hyperprolactinemia		COR (95%CI)	AOR (95% CI)	P-value
		Yes	No
Duration of DM	< 11 year	7	104	Ref	ref	Ref
	> 11 year	8	46	2.584(0.884–7.549)	2.006(0.621-6.486)	0.245
Type of medication	Metformin	5	102	0.265(0.086–0.817)	0.330(0.099-1.103)	0.072
	Insulin	10	63	Ref	ref	ref
Continues independent variable
Variable			COR (95% CI)		AOR (95% CI)	P-value
Fasting blood sugar			1.009(1.002–1.017)		1.009(1.001–1.018)	0.035*
Follicle stimulating hormone			0.922(0.831–1.022)		0.915(0.822-1.017)	0.100

*= statistically significant, COR = crude odds ratio, AOR = adjusted odds ratio, CI: confidence interval, Ref = reference.

Discussion

The purpose of this study was to evaluate serum PRL level and the factors contributing to hyperprolactinemia in male T2DM patients compared to age-matched, apparently healthy control groups. The mean serum PRL level in T2DM patients was significantly higher than that of the apparently healthy control groups (7.79 ± 2.9 vs. 6.81 ± 3.1; p-value = 0.004). Factors such as fasting blood sugar (FBS) and dietary diversity were found to be significantly associated with hyperprolactinemia in the multivariable analysis.

The mean serum PRL level in T2DM patients was higher than apparently healthy control groups (7.79 ± 2.9 vs. 6.81 ± 3.1 p-value = 0.004). This finding was similar with the study conducted in Baghdad, Iraq³⁹. Another similar study conducted at Al-Mustansiriyia University Baghdad, Iraq in 2017⁴⁰, also showed similar finding with our study. The possible reason for the mean difference between the two groups might be the compensatory response of PRL to insulin resistance among T2DM. According to evidences, PRL has played a significant role in controlling glucose metabolism; by activating pancreatic β-cell PRL receptors, which then increases insulin secretion and lowers the glucose threshold by insulin production¹⁵. The other possible reason might be due to diminished dopaminergic activity because obese had a lower dopamine receptor⁴¹. On the other hand, the finding of this study was inconsistent with the study that investigate the association between serum PRL level and T2DM in Isfahan, Iran ((5.32 ± 0.36 vs. 18.38 ± 2.3) T2DM and the control groups respectively⁴².

In this study, FBS was significantly associated with hyperprolactinemia both in the study participants and T2DM patients. It was found that a unit increase in FBS level among study participants and T2DM patients increases the risk of hyperprolactinemia by 0.9% (AOR = 1.009, 95% CI: 1.001, 1.017, p-value = 0.029) and 1.009 (AOR = 1.009, 95% CI: 1.001–1.018, p-value = 0.035) respectively. This finding was supported by the study done by Macotela Y, et al.⁴³. Another studies done in China and West Pomerania concluded that higher serum PRL level was associated with lower prevalence of diabetes^20,44. Several possible reasons might be explained for this association. First, the body may produce more PRL as a compensatory method to help control glucose metabolism and improve insulin sensitivity in response to high FBS⁴⁵. Studies suggested that physiological levels of PRL enhance pancreatic β-cell proliferation, insulin secretion, and glucose homeostasis³.

Second, Prolactin’s effect on insulin resistance and glucose metabolism is actually dependent on its level in the blood. Even though, pathological levels of PRL, worsen hepatic and whole-body insulin resistance and reduce the ability of the body to secrete insulin in diabetic mice^14,46. Physiologically high PRL level causes glucose level to rise normally and adaptively. Increased hepatic insulin sensitivity and b-cell bulk, which in turn boosted insulin production^46,47. Moreover, PRL can promotes adipocyte development and suppresses lipolysis by activating peroxisome proliferative-activated receptor gamma (PPAR-γ)²¹. So, the inhibition of lipolysis can decrease the risk of diabetes. Health care providers should consider this association when developing treatment plans for those patients with elevated FBS. Further longitudinal and mechanistic studies are recommended to clarify these mechanisms and understand the complexities of prolactin’s role in metabolic disease settings.

Diet was one of a variable that significantly associated with hyperprolactinemia among the study participants. Individuals with high dietary diversity were 3.2 odds more likely to develop hyperprolactinemia as compared to those individuals with low dietary diversity (AOR = 3.213 [95% CI: 1.253–8.239, P-value = 0.015]). The possible reason might be those individuals with high dietary diversity consume wider varieties of food. Studies suggested that food enriched in protein, and saturated fats can stimulate PRL secretion and cause hyperprolactinemia⁴⁸. Another study supported that, acute stimulation of human PRL secretion occurs when mixed meals including protein are consumed⁴⁹. To investigate the underlying mechanisms and consequences of this association, more research with follow up study is required. To strengthen our findings, we suggest that future studies could explore the specific food groups contributing to this association and evaluate the underlying biological mechanisms.

In the current study, the prevalence of hyperprolactinemia among T2DM patients was 9.1%( 95% CI: 5.20%, 14.6%) which was consistent with a study done in Tunis (10%)⁵⁰. However, our finding was higher than the prevalence of hyperprolactinemia and clinically apparent prolactinoma in men undergoing fertility evaluation (2.1%)⁵¹. This discrepancy may suggest hyperprolactinemia is more prevalent in T2DM patients. A complex interaction of hormonal abnormalities, such as insulin resistance and altered dopamine signaling, is linked to T2DM. Reduced dopamine activity in T2DM may make the normal dopamine regulation of PRL release better⁵².

This study does not show any significant association between hyperprolactinemia with treatment of diabetes and diabetes complications in contrast to other study. Studies on the pharmacotherapy of diabetes showed that drugs such as glyburide and metformin can affect PRL level, indicating a possible relationship between diabetes drugs and hormonal regulation³⁹. Metformin lowers the chance of developing macro-vascular disease and other long-term consequences of diabetes. Additionally, it was discovered to slow the development of prediabetes into diabetes^53,54. Similar to the study investigating the role of PRL as a cardiovascular risk in T2DM patients in Egypt⁵⁵, this study doesn’t show any significant association between all lipid profiles with PRL and hyperprolactinemia. Our study also didn’t show any significant association between FSH and LH with hyperprolactinemia. However, the study done in Tehran, Iran showed that higher PRL correlated positively with the LH/FSH ratio⁵⁶.

Conclusion and recommendation

This study concluded that the mean serum PRL level is higher in T2DM patients compared to apparently healthy control groups. Additionally, the prevalence of hyperprolactinemia among T2DM patients is higher than the control groups. Predictor variables such as FBS and diet are significantly associated with hyperprolactinemia. Health care providers should consider routine screening of serum PRL level in patients diagnosed with T2DM. They should also encourage dietary modifications that may help to regulate PRL level, particularly for T2DM patients. Further longitudinal studies is necessary to investigate the underlying mechanisms connecting diet and FBS level to hyperprolactinemia.

Strength and limitation of the study

One of the strengths of this study was the ability to measure first morning PRL level that may minimizes diurnal variation and allows for a more accurate assessment of hyperprolactinemia. The inclusion of age-matched, apparently healthy controls further strengthen the findings. This study also provides evidence on the relationship between PRL level and male T2DM in a local context where data are limited.

The cross-sectional nature of the study precludes causal inferences regarding the relationship between hyperprolactinemia and T2DM. Being a single-center study, the findings may not be generalizable to broader populations. Key hormonal regulators of PRL, such as thyroid hormones and dopamine, were not assessed. Additionally, dietary evaluation based on food group count may not accurately reflect overall nutritional quality. The influence of medications other than anti-hyperglycemic agents, such as antipsychotics or antidepressants, was not investigated. Due to reagent constraints, only a single PRL and FBS levels were measured to rule-out hyperprolactinemia and assess the diabetes status of the control group, respectively.

Abbreviations

PRL

prolactin

TIDA

Tuberoinfundibular dopaminergic neurons

PPAR ¥

Peroxisome proliferative activated receptor-gamma

T1DM

Type 1 diabetes mellitus

T2DM

Type 2 diabetes mellitus

FBS

Fasting blood sugar

BMI

Body mass index

WC

Waist circumference

LDL-c

Low density lipoprotein-cholesterol

HDL-c

High density lipoprotein cholesterol

TC

Total cholesterol

TG

Triglyceride

VIF

Variance inflation factor

CI

Confidence interval

COR

Crude odds ratio

AOR

Adjusted odds ratio

RPM

Revolution per minute

SPSS

Statistical package for social science

Author contributions

Conceptualization: Arega ZenawData Collection: Arega Zenaw, Sintayehu Admas, Nigusie Alemu, Mitkie TigabieFormal analysis **:** Arega Zenaw, Sintayehu Admass, Nigusie Alemu, Mitkie TigabieInvestigation: Arega ZenawMethodology **:** Arega ZenawSupervision **:** Arega Zenaw, Abebaw Worede, Elias Chanie, Belete Biadgo, Getnet FeteneWriting – original draft **:** Arega ZenawWriting – review & editing: Arega Zenaw, Abebaw Worede, Belete Biadgo, and Elias Chanie. All authors read and approved the final manuscript.

Data availability

The data will be available on acceptance from the corresponding author.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The study was approved by the ethical clearance committee of Biomedical and laboratory Science of the University of Gondar. Each participant was informed in detail and his/her consent was obtained before the data collection.

Consent for publication

All participants provided written informed consent to publish this study.