Lipid Profile Differences in Monomeric Hyperprolactinemia, Macroprolactinemia, and Healthy Controls: A Comparative Analysis

Lipid Profile Differences in Monomeric Hyperprolactinemia, Macroprolactinemia, and Healthy Controls: A Comparative Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lipid Profile Differences in Monomeric Hyperprolactinemia, Macroprolactinemia, and Healthy Controls: A Comparative Analysis Sami Bahçebaşı, Ferhat Gökay, Yasin Şimşek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6101984/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2025 Read the published version in Neuroendocrinology → Version 1 posted You are reading this latest preprint version Abstract Objective : This study aims to investigate lipid profile differences among patients with monomeric hyperprolactinemia, macroprolactinemia, and healthy controls. Additionally, lipid parameters across various etiological subgroups of monomeric hyperprolactinemia were evaluated . Methods : A total of 181 participants were included in this retrospective study. They were divided into three groups based on macroprolactin recovery rates: macroprolactinemia (n = 35) , gray zone (n = 16), and monomeric hyperprolactinemia (n = 95). The monomeric hyperprolactinemia group was subdivided into six etiological subgroups: prolactinoma (n = 49), idiopathic hyperprolactinemia (n = 31), drug-induced hyperprolactinemia (n = 4), CKD-related hyperprolactinemia (n = 3), PCOS-related hyperprolactinemia (n = 6), and empty sella syndrome (n = 2). A healthy control group (n = 35) was included. Lipid profiles, including total cholesterol, LDL, HDL, triglycerides, and non-HDL cholesterol, were compared across groups. Results : Prolactinoma patients had significantly higher total cholesterol, LDL, triglycerides, and non-HDL levels compared to the macroprolactinemia group and healthy controls ( p < 0.001 ). Idiopathic hyperprolactinemia also showed elevated lipid levels, but to a lesser extent . Other subgroups exhibited no significant lipid abnormalities. Macroprolactinemia patients had lipid profiles similar to healthy controls. Conclusion : Prolactinomais associated with significant dyslipidemia, characterized by elevated cholesterol, LDL, triglycerides, and non-HDL. Differentiating between monomeric hyperprolactinemia subgroups and macroprolactinemia is essential for effective management of metabolic risk. Prolactin hyperprolactinemia macroprolactinemia prolactinoma lipid profile dyslipidemia cholesterol Introduction Prolactin is a hormone synthesized and secreted by the anterior pituitary gland, primarily known for its essential role in stimulating breast development and lactation. Secretion of prolactin follows a circadian rhythm, with levels peaking during sleep and reaching their lowest levels during waking hours. Most circulating prolactin exists in its monomeric form (23 kDa), but larger molecular forms, collectively referred to as macroprolactin (150–170 kDa), also circulate. Macroprolactin forms when prolactin binds to immunoglobulins (IgG or IgA) or undergoes glycosylation, leading to the creation of polymeric aggregates. These larger forms are biologically inactive; however, they interfere with prolactin measurements by binding to anti-prolactin antibodies, complicating the clinical assessment of prolactin levels [ 1 , 2 ]. The condition known as macroprolactinemia occurs when macroprolactin predominates in circulation and is found in 4–40% of hyperprolactinemic patients [ 3 ]. This can lead to inaccurate prolactin assay results, as the presence of macroprolactin distorts standard testing methods. To classify hyperprolactinemia, the recovery rates of macroprolactin are often used, where a recovery rate above 60% suggests monomeric hyperprolactinemia, while a recovery rate below 40% indicates macroprolactinemia. A recovery between 40–60% is considered to be in the "gray zone" [4,5]. Despite the significance of these distinctions, their impact on lipid metabolism has been less thoroughly examined. Beyond its role in lactation, prolactin has been implicated in a variety of metabolic processes, including lipid metabolism. Prolactin influences adipose tissue by modulating lipid profiles, particularly by increasing triglyceride (TG) levels through the inhibition of lipoprotein lipase activity in adipocytes [ 6 ]. Furthermore, hyperprolactinemia is often associated with endocrine imbalances such as decreased estrogen levels, which contribute to lipid dysregulation. This manifests as increased total cholesterol and low-density lipoprotein cholesterol (LDL) levels and decreased high-density lipoprotein cholesterol (HDL) levels [ 7 ]. Elevated prolactin levels are frequently observed in overweight and obese individuals, potentially due to the relationship between adipocyte insulin resistance and prolactin secretion [ 8 ]. The association between prolactin levels and lipid metabolism has been studied extensively, particularly in patients with prolactinomas. Elevated LDL levels are commonly found in these patients, while HDL levels tend to be lower [ 9 ]. Moreover, treatment with prolactin-lowering agents, such as cabergoline, has been shown to reduce total cholesterol and LDL levels, suggesting a direct role of prolactin in lipid modulation [ 10 , 11 ]. Triglyceride levels are typically elevated in hyperprolactinemic individuals but often normalize after prolactin-lowering treatment [ 12 ]. However, inconsistencies remain in the literature, as some studies have not identified a clear correlation between prolactin levels and lipid profiles [ 13 ]. Despite these findings, the specific effects of macroprolactinemia on lipid metabolism remain underexplored. Most research has focused on monomeric hyperprolactinemia or prolactinomas, with less emphasis on macroprolactinemia and its potential role in lipid dysregulation. Additionally, there is a gap in understanding lipid profiles across different etiological subgroups of monomeric hyperprolactinemia. These subgroups include prolactinoma, idiopathic hyperprolactinemia, drug-induced hyperprolactinemia, chronic kidney disease (CKD)-related hyperprolactinemia, polycystic ovary syndrome (PCOS)-related hyperprolactinemia, and empty sella syndrome. The aim of this study is to investigate the relationship between macroprolactinemia and lipid profiles, focusing on the six etiological subgroups of monomeric hyperprolactinemia. Specifically, we seek to examine potential differences in lipid parameters—such as total cholesterol, LDL, HDL, triglycerides, and non-HDL—among patients with macroprolactinemia, prolactinoma, idiopathic hyperprolactinemia, drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, PCOS-related hyperprolactinemia, and empty sella syndrom e. By including a healthy control group for comparison, we aim to provide a comprehensive analysis of the effects of prolactin dysregulation on lipid metabolism. Materials and Methods The records of 2,200 patients who underwent prolactin testing were retrospectively reviewed, and among them, 1,040 patients also had macroprolactin testing. Inclusion criteria : Adults aged 18 years or older. Patients with confirmed hyperprolactinemia. Available lipid profile data. Thyroid function test results were available for all participants. For healthy control, individuals who did not have any complaints during their routine check-ups, whose prolactin and lipid tests, liver, kidney and thyroid function tests were normal and who did not use medication were included. Exclusion criteria : Use of lipid-lowering medications (patients were screened for medication use, and those receiving lipid-lowering treatment were excluded). Pregnancy or lactation. Severe comorbidities affecting lipid metabolism (e.g., diabetes, liver disease). Patients with abnormal thyroid function tests (were excluded from the study because it may affect lipoprotein levels.) Following inclusion and exclusion criteria, 181 participants were selected for the study. The study was conducted at Kayseri City Hospital’s Internal Medicine and Endocrinology outpatient clinics between January 2020 and July 2023. Ethical approval was obtained from the Kayseri City Hospital Ethics Committee (approval number: 871, 11.07.2023). The participants were categorized into three groups based on their macroprolactin recovery levels: Group 1 : Macroprolactinemia (macroprolactin recovery 60%, 95 patients). Additionally, the monomeric hyperprolactinemia group was subdivided into six etiological subgroups: prolactinoma (n = 49), idiopathic hyperprolactinemia (n = 31), drug-induced hyperprolactinemia (n = 4), CKD-related hyperprolactinemia (n = 3), PCOS-related hyperprolactinemia (n = 6), and empty sella syndrome (n = 2). A healthy control group (n = 35) was included for comparison. Data analysis was performed using IBM SPSS 22 (Statistical Package for Social Sciences). Normality of the data was assessed using Kolmogorov-Smirnov and Shapiro-Wilk tests. Descriptive statistics are presented as means and standard deviations for continuous variables. ANOVA tests were used for parametric data, and Kruskal-Wallis tests were employed for non-parametric data. Chi-square tests were used for categorical variables. Spearman correlation analysis was conducted to assess correlations. A p-value of < 0.05 was considered statistically significant. Results A total of 181 participants were included in the study, representing various subgroups: macroprolactinemia (n = 35), gray zone (n = 16), and monomeric hyperprolactinemia (n = 95). The monomeric hyperprolactinemia group was further subdivided into six etiological subgroups: prolactinoma (n = 49), idiopathic hyperprolactinemia (n = 31), drug-induced hyperprolactinemia (n = 4), chronic kidney disease (CKD)-related hyperprolactinemia (n = 3), polycystic ovary syndrome (PCOS)-related hyperprolactinemia (n = 6), and empty sella syndrome (n = 2). Additionally, a healthy control group (n = 35) was included for comparison. Drugs used by patients with drug-related hyperprolactinemia: levocetirizine, paliperidone, risperidone, tegretol-nervium were detected. Demographics (Table 1 ): Table 1 Clinical and Biochemical Features of Elevated Prolactin Levels Groups and Healthy Controls Parameters Macro-prolactinemia n (35) Gray zone n (16) Monomeric hyperprolactinemia n (95) Healty Control n (35) P value Prolactinoma n (49) Idiopathic n (31) Drug-Induced n (4) CKD-Related n (3) PCOS-Related n (6) Empty Sella n (2) Age (mean, min-max) 29 (18–58) 24 (18–41) 39 (13–75) 36 (21–57) 26 (20–40) 40 (31–50) 23 (19–25) 50 (45–55) 31 (18–50) < 0.001 Gender (mean, min-max) Male 3 (8.6%) 1 (6.3%) 8 (16.3%) 1 (3.2%) 0 1 (33.3%) 0 1 (2.9%) 0.250 Female 32 (91.4%) 15 (93.7%) 41 (83.7%) 30 (96.8%) 4 2 (66.7%) 6 2 34 (97.1%) Glucose (mean, min-max) 89 (68–130) 87 (73–105) 95 (77–194) 85 (67–136) 88 (74–110) 97 (81–99) 91 (87–94) 102 (99–104) 85 (69–106) 0.002 TC (mean ± SD) 165 ± 3.88 183 ± 10 201 ± 5.2 186 ± 7.34 156 ± 8.11 274 ± 27.39 160 ± 10.32 166 ± 41 162 ± 3.7 < 0,001 LDL (mean ± SD) 102 ± 3.65 118 ± 9.30 131 ± 4.95 118 ± 5.72 90.8 ± 8.18 186 ± 29 94 ± 8.18 93 ± 13 94 ± 2.62 < 0,001 HDL (mean ± SD) 52 ± 2.28 53 ± 4.32 52 ± 1.93 54 ± 1.87 51 ± 9.43 47 ± 4.26 52 ± 3.77 66 ± 14.5 59 ± 2.31 0.295 TG (mean, min-max) 92 (45–245) 97 (35–168) 119 (33–283) 100 (45–231) 99 (75–121) 200 (137–325) 111 (62–172) 158 (59–256) 79 (27–135) 0.007 Non-HDL (mean ± SD) 114 ± 3.8 130 ± 11.6 149 ± 5.7 132 ± 7.2 105 ± 11.8 227 ± 25.5 108 ± 12 101 ± 55.5 103 ± 3.25 < 0,001 Oligoamenorrhea (n, %) 18 (%43.8) 6 (40%) 20 (47.6%) 12 (40%) 1 (25%) 1 (33.3%) 6 (100%) 0 0 Galactorrhea (n, %) 3 (8.6%) 6 (37.5%) 13 (26.5%) 7 (22.6) 1 (25%) 0 0 0 0 Erectile dysfunction (n, %) 0 1 (100%) 4 (50%) 1 (100%) - 0 - - 0 Infertility (n, %) 4 (11.4%) 1 (6.3%) 4 (8.2%) 1 (3.2%) 0 0 0 0 0 Headache (n, %) 0 1 (6.3%) 0 1 (3.2%) 0 0 0 0 0 TC (Total Cholesterol), LDL (Low-Density Lipoprotein), HDL (High-Density Lipoprotein), TG (Triglycerides), Non-HDL: Cholesterol levels excluding HDL Significant differences in age were observed across the groups. The macroprolactinemia group had a mean age of 29 years (range: 18–58), which was significantly younger than the monomeric hyperprolactinemia group (mean age 39 years, range: 13–75) and the prolactinoma subgroup (mean age 36 years, range: 21–57). The gray zone group had the youngest mean age (24 years, range: 18–41), which was statistically significantly lower than that of the prolactinoma group (p < 0.001). Chronic kidney disease (CKD)-related hyperprolactinemia was the oldest group, with a mean age of 50 years (range: 45–55). Healthy controls had a mean age of 31 years (range: 18–50). Glucose Levels (Table 1 ): No significant differences in glucose levels were found between macroprolactinemia and the gray zone group (p = 1), nor between macroprolactinemia and idiopathic hyperprolactinemia (p = 1). However, significant differences were observed between prolactinoma and idiopathic hyperprolactinemia (p = 0.008), with prolactinoma patients showing significantly lower glucose levels. Prolactinoma also exhibited a significant difference compared to healthy controls (p = 0.006). Lipid Profiles (Table 1 ): Total Cholesterol (TC) : Prolactinoma patients had significantly higher total cholesterol compared to the macroprolactinemia group (p < 0.001) and the healthy control group (p < 0.001). No significant difference was found between the macroprolactinemia group and healthy controls (p = 1). Non-HDL Cholesterol (Non-HDL) : Prolactinoma patients showed significantly higher non-HDL levels compared the macroprolactinemia group (p < 0.001) and the healthy control group (p < 0.001), but there was no significant difference between the macroprolactinemia group and healthy controls (p = 0.977). HDL Cholesterol (HDL) : No significant differences in HDL levels were observed between macroprolactinemia and any other group (p > 0.05). LDL Cholesterol (LDL) : Prolactinoma patients showed significantly higher LDL levels compared to both macroprolactinemia (p < 0.001) and healthy controls (p < 0.001). There were no significant differences in LDL levels between macroprolactinemia and healthy controls (p = 0.977). Triglycerides (TG) : Prolactinoma patients had significantly higher triglyceride levels compared to macroprolactinemia (p < 0.001). Significant differences in TG levels were found between prolactinoma and healthy controls (p = 0.011). However, no significant differences were found between macroprolactinemia and healthy controls (p = 1). The comparison between healthy controls and patients with CKD-related hyperprolactinemia revealed a significant difference, with triglyceride levels being higher in the patient group (test statistic: 3.261, p-value: 0.040). However, no significant differences were observed in cholesterol (TC), LDL, HDL, or non-HDL cholesterol levels between the CKD-related hyperprolactinemia group and the other groups (drug-induced hyperprolactinemia, PCOS-related hyperprolactinemia, empty sella syndrome) when compared to the healthy control group. Significant differences in triglyceride levels were found only between the CKD-related hyperprolactinemia group and the healthy control group. Clinical Features (Table 1 ): Oligoamenorrhea : The highest frequency of oligoamenorrhea was observed in the monomeric hyperprolactinemia group, particularly in the prolactinoma (40%) and idiopathic hyperprolactinemia (47.6%) subgroups. The gray zone group had a lower prevalence of oligoamenorrhea (40%). Galactorrhea : Galactorrhea was most common in the prolactinoma (22.6%) and idiopathic hyperprolactinemia (26.5%) subgroups. The gray zone group exhibited a higher rate of galactorrhea (37.5%) compared to macroprolactinemia (8.6%). Erectile Dysfunction : In the gray zone group, 100% of males reported erectile dysfunction, whereas 50% of males in the monomeric hyperprolactinemia group reported the condition. Infertility : No significant differences were found between the groups for infertility, although the prolactinoma and idiopathic hyperprolactinemia groups reported cases of infertility. Comparisons Between Groups (Table 2 ): Table 2 Post-Hoc Analysis of Age, Glucose, and Lipid Parameters Across Elevated Prolactin Levels Groups and Healthy Controls Group 1 Group 2 Age Test Statistic P Value Glucose Test Statistic P Value TC Diff. SE P Value Non-HDL Diff. SE P Value HDL Diff. SE P Value LDL Diff. SE P Value TG Test Statistic P Value Macroprolactinemia Gray Zone 1,153 1 -0,365 1 -18.19 10.72 0.982 -16.24 12.19 1 -1.95 4.89 1 -16.4 9.98 0.988 -0,761 1 Macroprolactinemia Prolactinoma 4,411 < 0,001 -2,642 0,296 -35.68 6.49 < 0,001 -35.37 6.82 < 0,001 -0.31 2.99 1 -29.46 6.15 < 0,001 -2,098 1 Macroprolactinemia Idiopathic Hyperprolactinemia -1,982 1 1.057 1 1,057 1 -20.52 8.30 0.464 -18.43 8.14 0.644 -2.095 2.95 1 -16.56 6.79 0.482 -0,550 1 Macroprolactinemia Healthy Control -1,237 1 1,033 1 3.2 5.35 1 10.6 5 0.750 -7.4 3.25 0.613 7.51 4.49 0.977 1,393 1 Gray Zone Prolactinoma -4,599 < 0,001 1,668 1 -17.49 11.27 0.994 -19.13 12.89 0.997 1.64 4.73 1 -13.06 10.52 1 -0,815 1 Gray Zone Idiopathic Hyperprolactinemia -2.72 0.237 -2,718 0,237 1.21 1 1,213 1 -2.33 12.4 1 -2.19 13.63 1 -0.143 4.71 1 -0.16 10.91 1 0,305 1 Gray Zone Healthy Control -2,133 1 1,191 1 21.39 10.66 0.888 26.84 12 0.762 -5.45 4.9 1 23.91 9.65 0.579 1,864 1 Prolactinoma Idiopathic Hyperprolactinemia 2,125 1 3,683 0,008 15.15 9 0.975 16.94 9.16 0.924 -1.79 2.68 1 12.9 7.56 0.970 1,432 1 Prolactinoma Healthy Control 3,075 0,076 3,784 0,006 38.88 6.38 < 0,001 45.97 6.53 < 0,001 -7.09 3.01 0.539 36.98 5.6 < 0,001 3,602 0,011 Idiopathic Hyperprolactinemia Healthy Control -0,783 1 0,065 1 -23.72 8.22 0.194 -29.03 7.89 0.024 5.31 2.97 0.949 -24.08 6.29 0.015 -1,900 1 Macroprolactinemia vs Prolactinoma : Statistically significant differences were observed across several parameters. Prolactinoma had significantly higher total cholesterol, LDL, and triglyceride levels, as well as a significantly lower glucose level compared to macroprolactinemia (p < 0.001 for TC, LDL, and TG). No significant difference was observed in HDL levels (p = 1). Macroprolactinemia vs Healthy Control : Macroprolactinemia showed no significant differences in total cholesterol and LDL levels compared to the healthy control group (p = 1). There were also no significant differences in HDL and triglycerides between these two groups (p = 1 for all comparisons). Gray Zone vs Prolactinoma : The prolactinoma group had significantly higher total cholesterol, LDL, and triglyceride levels compared to the gray zone group (p < 0.001 for TC and LDL), while HDL levels did not differ significantly (p = 1). Gray Zone vs Healthy Control : The gray zone group showed no significant differences in total cholesterol, LDL, or triglyceride levels compared to healthy controls (p = 1 for TC, LDL, and TG). HDL levels were also similar between the gray zone group and healthy controls (p = 1). Gray Zone vs Macroprolactinemia : No significant differences were observed between the gray zone and macroprolactinemia groups for total cholesterol, LDL, non-HDL, or triglyceride levels (p = 1 for all comparisons). HDL levels were also comparable between the two groups (p = 1). Prolactinoma vs Healthy Control : Prolactinoma exhibited significantly higher total cholesterol, non-HDL, LDL, and triglyceride levels compared to healthy controls (p < 0.001 for TC, non-HDL, and LDL, p = 0.011 for TG). No significant difference in HDL was found (p = 0.539). Idiopathic Hyperprolactinemia vs Prolactinoma : There was a significant difference in glucose levels between the idiopathic hyperprolactinemia and prolactinoma groups (p = 0.008), with prolactinoma patients having significantly lower glucose levels. Additionally, the prolactinoma group exhibited higher cholesterol and triglyceride levels compared to the idiopathic hyperprolactinemia group. However, HDL levels did not show significant differences between the two groups (p = 1). Idiopathic Hyperprolactinemia vs Healthy Control : Idiopathic hyperprolactinemia showed significantly higher non-HDL cholesterol (p = 0.024) and LDL cholesterol (p = 0.015) levels compared to healthy controls. No significant differences were found in total cholesterol, HDL, or triglyceride levels between the two groups (p = 1 for all comparisons). Pituitary Adenoma Presence Among Groups (Table 3) Table 3 Presence of pituitary adenoma among groups Pituitary adenoma (Taken from MRI) Macroprolactinemia n (15) Gray zone n (13) Monomeric hyperprolactinemia Healty control n (2) Prolactinoma n (49) Idiopathic n (31) Drug-induced n (1) CKD-Related n (2) PCOS-Related n (3) Empty Sella n (2) Microadenoma 4 (26.7%) 0 45 (91.8%) 0 0 0 0 0 1 (Insidentaloma) Macroadenoma 1 (6.7%) 0 4 (8.2%) 0 0 0 0 0 0 No adenoma 10 (66.7%) 13 0 31 1 2 3 2 1 The presence of pituitary adenomas, identified through MRI, varied among the groups. The results are summarized as follows: Macroprolactinemia : 4 patients (26.7%) had microadenomas, and 1 patient (6.7%) had a macroadenoma. Prolactinoma : 45 patients (91.8%) had microadenomas, and 4 patients (8.2%) had macroadenomas. Healthy Control : 1 patient had a microadenoma (incidentaloma). Other groups (Gray zone, Monomeric hyperprolactinemia, Idiopathic hyperprolactinemia, Drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, PCOS-related hyperprolactinemia, Empty Sella Syndrome): No pituitary adenomas were observed in these groups. This analysis highlights that pituitary adenomas, particularly microadenomas, were predominantly found in the prolactinoma group, followed by the macroprolactinemia group. The healthy control group showed a single incidental microadenoma. Correlations Between Prolactin Levels, Macroprolactinemia Recovery, and Lipid Parameters (Table 4) Table 4 Correlations Between Prolactin Levels, Macroprolactinemia Recovery, and Lipid Parameters Correlations TC HDL Age LDL TG Non-HDL Spearman's rho Prolactine Correlation Coefficient 0.274 -0.183 0.126 0.356 0.253 0.388 P value < 0.001 0.014 0.092 < 0.001 0.001 < 0.001 Macroprolactin recovery Correlation Coefficient 0.243 0.028 0.299 0.230 0.143 0.247 P value 0.003 0.734 < 0.001 0.005 0.082 0.002 Prolactin levels : A significant positive correlation was found between prolactin levels and total cholesterol (TC) (r = 0.274, p < 0.001), LDL (r = 0.356, p < 0.001), triglycerides (TG) (r = 0.253, p = 0.001), and non-HDL (r = 0.388, p < 0.001). A significant negative correlation was observed between prolactin levels and HDL (r = -0.183, p = 0.014). There was no significant correlation between prolactin levels and age (r = 0.126, p = 0.092). Macroprolactinemia recovery : Macroprolactinemia recovery showed a significant positive correlation with age (r = 0.299, p < 0.001), total cholesterol (TC) (r = 0.243, p = 0.003), and LDL (r = 0.230, p = 0.005). There was no significant correlation between macroprolactinemia recovery and HDL (r = 0.028, p = 0.734), triglycerides (TG) (r = 0.143, p = 0.082), or non-HDL (r = 0.247, p = 0.002). This analysis indicates significant correlations between prolactin levels and lipid parameters, particularly total cholesterol, LDL, triglycerides, and non-HDL, whereas macroprolactinemia recovery was more strongly associated with age, TC, and LDL. Summary of Key Findings Prolactinoma exhibited the highest levels of total cholesterol, LDL, non-HDL, and triglycerides compared to other groups and showed a significant decrease in glucose levels compared to macroprolactinemia. Macroprolactinemia had significantly lower total cholesterol and LDL levels compared to prolactinoma, as well as significantly lower triglycerides than prolactinoma. The gray zone and macroprolactinemia groups had similar lipid levels to healthy controls, with no significant differences noted in most of the parameters. Pituitary adenomas were predominantly observed in the prolactinoma group, with 91.8% of patients having microadenomas, and 8.2% having macroadenomas. In macroprolactinemia, 26.7% had microadenomas, and 6.7% had macroadenomas. A single incidental microadenoma was detected in the healthy control group. Prolactin levels showed significant positive correlations with total cholesterol, LDL, triglycerides, and non-HDL, while there was a negative correlation with HDL. There was no significant correlation between prolactin levels and age. Macroprolactinemia recovery showed significant positive correlations with age, total cholesterol, and LDL, while no significant correlation was found with HDL, triglycerides, or non-HDL. Discussion In our study, we observed distinct lipid profile differences across the various prolactin-related conditions. Patients with prolactinoma exhibited significantly higher levels of total cholesterol (TC), LDL, non-HDL cholesterol, and triglycerides compared to patients with macroprolactinemia. However, no significant differences in HDL levels were noted among these groups. Patients with macroprolactinemia and the gray zone group showed no significant differences in lipid profiles compared to healthy controls, with similar levels of total cholesterol, LDL, triglycerides, and HDL. Prolactin may influence lipid metabolism through various mechanisms. It has been shown that prolactin can directly affect adipose tissue by inhibiting lipoprotein lipase activity, which leads to higher triglyceride levels [ 4 ]. Hyperprolactinemia also lowers estrogen levels, which may contribute to the elevation of total cholesterol and LDL while simultaneously decreasing HDL[5]. These mechanisms are unlikely to be active in macroprolactinemia, as macroprolactin, being biologically inactive due to its large size, does not significantly impact estrogen levels or lipoprotein lipase activity in adipose tissue[ 2 , 3 ]. This may explain the lipid profile differences we observed between monomeric hyperprolactinemia and macroprolactinemia. In a study by Krysiak et al., no significant differences in cholesterol, LDL, HDL, or triglyceride levels were found between patients with macroprolactinemia and healthy controls. However, patients with monomeric hyperprolactinemia exhibited higher triglyceride levels and lower HDL levels [ 14 ]. Our results differed from these findings, as we observed significantly higher levels of total cholesterol, non-HDL cholesterol, LDL, and triglycerides in patients with monomeric hyperprolactinemia, particularly in those with prolactinoma, compared to the macroprolactinemia group. In contrast, no significant differences were found in HDL levels. Additionally, patients with macroprolactinemia and those in the gray zone displayed lipid profiles similar to those of healthy controls, suggesting that the biologically inactive nature of macroprolactin may have a minimal impact on lipid metabolism. This contrasts with Krysiak et al.'s study, which did not investigate the subgroups within hyperprolactinemia, but rather focused on isolated macroprolactinemia and monomeric hyperprolactinemia. Our study expands on these findings by specifically examining the lipid profiles across these distinct hyperprolactinemia subgroups. Prolactinoma patients in our study exhibited significantly higher levels of total cholesterol, LDL, and triglycerides than both the macroprolactinemia group and healthy controls, consistent with findings from other studies [ 7 , 8 ]. In addition, prolactinoma patients had significantly lower glucose levels compared to the macroprolactinemia group. The differences observed in lipid profiles may be attributed to the direct effects of prolactinoma-induced hyperprolactinemia on lipid metabolism. Non-HDL cholesterol levels, which have not always been consistently reported in previous research, were significantly higher in patients with prolactinoma compared to healthy controls. This could reflect inhibition of lipoprotein lipase, a reduced release of fatty acids from VLDL, failure to form triglycerides, and an increase in VLDL particles in prolactinoma patients. Interestingly, the gray zone and macroprolactinemia groups exhibited similar lipid profiles to healthy controls, with no significant differences in total cholesterol, LDL, triglyceride, or HDL levels. This suggests that macroprolactinemia, due to the biologically inert nature of macroprolactin, does not have a significant effect on lipid metabolism. Similarly, the gray zone group, which contains patients with low or undetectable levels of prolactin, showed no major lipid abnormalities. Our study found significant correlations between prolactin levels and lipid parameters, with elevated prolactin associated with higher total cholesterol, LDL, triglycerides, and non-HDL cholesterol, as well as lower HDL cholesterol. These results are consistent with Schwetz et al. (2017), who demonstrated that prolactinomas lead to dyslipidemia, particularly elevated cholesterol and LDL levels, and that treatment to reduce prolactin levels improves these lipid parameters. Our findings align with this, suggesting that elevated prolactin may contribute to adverse lipid profiles, potentially increasing cardiovascular risk [ 12 ]. In the prolactinoma group, pituitary adenomas, particularly microadenomas, were found in the vast majority of patients (91.8%), with a small number having macroadenomas (8.2%). In comparison, macroprolactinemia patients had fewer adenomas (26.7% microadenomas, 6.7% macroadenomas). These findings emphasize that pituitary adenomas, especially microadenomas, are more frequently associated with prolactinoma than with macroprolactinemia. In our study, we observed a significant increase in triglyceride (TG) levels in patients with chronic kidney disease (CKD)-related hyperprolactinemia compared to healthy controls (p = 0.040). However, no significant differences were found in LDL, total cholesterol, or non-HDL levels. This could be due to the small sample size (only 3 patients), which may limit the ability to detect differences in these lipid markers. CKD itself is known to elevate TG levels, as impaired kidney function disrupts lipid metabolism through mechanisms such as reduced clearance of lipoproteins, altered lipoprotein lipase activity, and increased lipoprotein synthesis[ 15 , 16 ]. The accumulation of triglycerides and cholesterol in CKD is linked to reduced renal lipid clearance, contributing to increased cardiovascular risk[ 17 ]. Additionally, CKD-related dyslipidemia is associated with chronic inflammation, oxidative stress, and hormonal changes, including elevated prolactin. Hyperprolactinemia may further exacerbate lipid abnormalities by affecting lipoprotein lipase activity[ 18 ]. Although only TG levels were significantly altered in our study, the findings underscore the complex relationship between CKD, hyperprolactinemia, and lipid metabolism. The role of prolactin in lipid regulation requires further investigation, and monitoring lipid levels in CKD patients with hyperprolactinemia is crucial for managing cardiovascular risk. In our study, no significant differences were found in lipoprotein parameters, including total cholesterol (TC), LDL, HDL, or non-HDL cholesterol levels, between the empty sella syndrome group and healthy controls. It is worth noting, however, that the empty sella group in our study was small, with only two patients included. While there are studies indicating that lipid levels may be elevated in empty sella syndrome, particularly in cases of secondary empty sella due to hypopituitarism [ 19 ], our findings did not show a clear association with lipid changes. Additionally, although some literature suggests that prolactin levels may be elevated in patients with empty sella syndrome, especially in the context of hypopituitarism or microadenomas [ 20 ], no study has yet thoroughly investigated the direct relationship between prolactin levels and lipid parameters in these patients. The lack of significant findings in our study may be attributed to the small sample size of the empty sella group and the absence of clear, consistent patterns in the literature regarding lipid profile alterations specifically related to prolactin levels in this condition. Our study found that, similar to previous studies on drug-induced hyperprolactinemia and PCOS-related hyperprolactinemia, there were no significant changes in lipid profiles in patients with elevated prolactin levels due to macroprolactinemia or in those with prolactin levels in the macroprolactinemia gray zone. Specifically, studies have shown that drug-induced hyperprolactinemia and PCOS-related hyperprolactinemia do not result in significant alterations in lipid parameters, despite elevated prolactin levels in these conditions due to medication or hormonal imbalances [ 21 , 22 ]. Our findings support this notion, as patients with macroprolactinemia or those with prolactin levels in the gray zone exhibited stable lipid profiles, which may be explained by the fact that macroprolactin, the inactive form of prolactin, does not significantly influence metabolic processes. Consequently, prolactin levels in these patients may not reach clinically relevant elevations that could cause lipid abnormalities. However, it is important to acknowledge that we specifically examined the macroprolactinemia gray zone, and there is limited data in the literature addressing its impact on lipid metabolism, indicating a need for further research to better understand this relationship. Additionally, when comparing idiopathic hyperprolactinemia with prolactinoma, we observed significant differences in glucose levels (p = 0.008), with prolactinoma patients having significantly lower glucose levels. This finding is in line with previous studies suggesting that glucose metabolism may be altered in prolactinoma patients, potentially due to prolactin's effects on insulin sensitivity and secretion [ 23 ]. Interestingly, in our study, glucose levels were also found to be lower in prolactinoma patients compared to healthy controls, which further supports the potential influence of prolactin on glucose metabolism in these individuals. However, when comparing idiopathic hyperprolactinemia with healthy controls, no significant difference in glucose levels was observed. It is important to note that there is limited research specifically comparing glucose levels in patients with idiopathic hyperprolactinemia to healthy controls. The lack of significant differences in our study could suggest that idiopathic hyperprolactinemia does not lead to significant alterations in glucose metabolism, unlike prolactinoma. One potential explanation could be that prolactin levels in idiopathic hyperprolactinemia may not reach the same elevated thresholds as seen in prolactinoma, and thus may not exert a significant impact on insulin sensitivity or secretion. Further research specifically comparing idiopathic hyperprolactinemia with healthy controls is needed to better understand any potential effects of prolactin on glucose metabolism in these patients. Moreover, prolactinoma patients had higher total cholesterol and triglyceride levels compared to idiopathic hyperprolactinemia patients. This supports findings from other studies suggesting that prolactin excess, particularly in prolactinoma, can be associated with dyslipidemia, likely due to the effects of elevated prolactin on lipid metabolism [ 24 ]. Prolactinoma is characterized by a tumor in the pituitary gland, leading to significantly higher prolactin levels, which can have a more pronounced effect on lipid metabolism. In contrast, idiopathic hyperprolactinemia refers to elevated prolactin levels without an identifiable cause. Although prolactin levels are elevated in both conditions, the levels in idiopathic hyperprolactinemia are typically not as markedly high as in prolactinoma. In our study, while prolactin levels in prolactinoma were higher than in idiopathic hyperprolactinemia, this difference was not statistically significant (p = 0.083). Nevertheless, prolactin levels in prolactinoma were still higher, which might explain the more pronounced lipid abnormalities observed in this group. The elevated cholesterol and triglyceride levels seen in prolactinoma may be related to altered hormonal regulation, which could influence hepatic lipid production and storage. These differences in prolactin levels and the underlying causes of hyperprolactinemia might explain the distinct lipid profiles observed between these two conditions. On the other hand, idiopathic hyperprolactinemia showed significantly higher non-HDL cholesterol (p = 0.024) and LDL cholesterol (p = 0.015) compared to healthy controls. This finding is somewhat less established in the literature. While prolactin is known to affect lipid metabolism, the exact mechanisms through which idiopathic hyperprolactinemia influences lipid profiles are still under investigation. Some studies suggest that the link between prolactin and lipid metabolism could be related to its interaction with estrogen and other hormones that influence cholesterol homeostasis [ 25 ]. In our study, the higher non-HDL and LDL levels in idiopathic hyperprolactinemia compared to healthy controls could indicate a more complex metabolic alteration in this group, potentially due to subtle disruptions in endocrine feedback mechanisms, despite the absence of a clear underlying cause like prolactinoma or other pathologies. In contrast, HDL levels did not show significant differences between idiopathic hyperprolactinemia and healthy controls (p = 1). However, other studies have found lower HDL levels in patients with hyperprolactinemia, suggesting that prolactin elevation may affect HDL levels differently depending on the specific form of hyperprolactinemia [ 26 ]. Finally, our findings regarding the lack of significant differences in triglyceride, total cholesterol, and HDL levels between idiopathic hyperprolactinemia and healthy controls (p = 1) are consistent with the limited available literature. Notably, lipid metabolism in idiopathic hyperprolactinemia has not been extensively studied, and the only relevant study we found, conducted by Koca et al. (2021), did not specifically address lipid profiles but rather investigated cardiovascular risk predictability through arterial stiffness measurements in patients with idiopathic hyperprolactinemia. Their results showed no significant relationship between prolactin levels and arterial stiffness or blood pressure, indicating that mild hyperprolactinemia may not have immediate cardiovascular implications. However, the study did not evaluate LDL or non-HDL cholesterol levels in detail [ 27 ]. In contrast to the study's findings on cardiovascular risk, our results indicate that LDL and non-HDL cholesterol levels were significantly higher in patients with idiopathic hyperprolactinemia compared to healthy controls, suggesting that prolactin’s primary effect on lipid metabolism may involve these specific lipid fractions rather than triglycerides. Further research is required to clarify the long-term cardiovascular risks associated with idiopathic hyperprolactinemia. In summary, patients with prolactinoma exhibited a significantly more adverse lipid profile, characterized by elevated levels of total cholesterol, LDL, non-HDL, and triglycerides, compared to patients with macroprolactinemia, those in the gray zone, and healthy controls. These lipid abnormalities are consistent with the effects of hyperprolactinemia on lipid and lipoprotein levels, where total cholesterol, LDL, and triglycerides are increased, while HDL shows no significant change or a decrease, as reported in previous studies [ 28 ]. In contrast, macroprolactinemia and gray zone patients exhibited lipid profiles similar to those of healthy controls, suggesting that the biologically inactive nature of macroprolactin may not significantly affect lipid metabolism. Furthermore, prolactin levels were positively correlated with adverse lipid parameters, such as total cholesterol, LDL, non-HDL, and triglycerides, while a negative correlation was noted with HDL levels. These findings support the hypothesis that elevated prolactin levels contribute to dyslipidemia, as prolactin has been shown to influence lipoprotein metabolism. On the other hand, recovery from macroprolactinemia was positively correlated with age, total cholesterol, and LDL levels but showed no significant association with HDL or triglycerides, further highlighting the distinct metabolic effects of different prolactin-related conditions. Conclusion This study demonstrates that prolactinoma is associated with significant dyslipidemia, including elevated total cholesterol, LDL, and triglyceride levels, compared to macroprolactinemia and healthy controls. The biologically inactive nature of macroprolactin likely explains the lack of significant lipid abnormalities in the macroprolactinemia group. In terms of subgroups within the monomeric hyperprolactinemia category, prolactinoma patients showed the most prominent lipid alterations, particularly with significantly higher total cholesterol, LDL, and triglyceride levels. Idiopathic hyperprolactinemia also showed some elevation in lipid levels, but less severe than prolactinoma. Other subgroups, such as drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, PCOS-related hyperprolactinemia, and empty sella syndrome, showed no significant lipid abnormalities, likely due to their smaller sample sizes. Overall, prolactinoma presents the most concerning metabolic profile, highlighting the need for close lipid monitoring and management in these patients. Differentiating between monomeric hyperprolactinemia subgroups and macroprolactinemia is essential for guiding appropriate clinical management and reducing cardiovascular risk. Study Limitations This study has several limitations that should be acknowledged: Small Sample Size in Certain Subgroups : Although the study included a total of 181 participants, some subgroups within the monomeric hyperprolactinemia group, such as drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, and empty sella syndrome, had very small sample sizes. This limits the generalizability of the findings for these specific subgroups and may have affected the statistical power to detect significant differences in lipid profiles. Retrospective Study Design : As this was a retrospective analysis, the study relied on previously recorded medical data. This limits the control over the consistency of data collection and potential confounding factors that could not be accounted for. Prospective studies with standardized data collection would provide more robust insights. Lack of Longitudinal Data : The study did not evaluate changes in lipid profiles over time or the impact of treatment (e.g., prolactin-lowering therapies) on lipid levels. Future research should include longitudinal data to assess the long-term effects of prolactin dysregulation and its treatment on lipid metabolism. Single-Center Study : The study was conducted in a single medical center, which may limit the generalizability of the findings to other populations with different demographic or genetic characteristics. Larger, multi-center studies would be needed to confirm these results across diverse populations. Abbreviations CKD Chronic Kidney Disease HDL High-Density Lipoprotein Cholesterol LDL Low-Density Lipoprotein Cholesterol PCOS Polycystic Ovary Syndrome TG Triglycerides TC Total Cholesterol VLDL Very Low-Density Lipoprotein Non-HDL Non-High-Density Lipoprotein Cholesterol MRI Magnetic Resonance Imaging PRL Prolactin LPL Lipoprotein Lipase CVD Cardiovascular Disease FSH Follicle-Stimulating Hormone LH Luteinizing Hormone IR Insulin Resistance Declarations Compliance with ethical standards: Conflict of Interest: The authors have no conflicts of interest to declare. Ethical Approval: Ethical approval was waived by the local Ethics Committee of Kayseri City Hospital (approval number: 871, 11.07.2023) in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. Informed Consent: Because our study was a retrospective archive scan, informed consent was not obtained. Written informed consent from participants was not required in accordance with local/national guidelines. Funding Sources This study was not supported by any sponsor or funder. Author Contributions Author 1 and author 3 designed the experiments and analyzed the data. Author 1 and author 2 analyzed the data and prepared the manuscript. All authors approved the final manuscript. Data Availability Statement The data that support the findings of this study are available on request from Author 1 OR the corresponding author. 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J Cell Mol Med 28(23):e70067. 10.1111/jcmm.70067 PMID: 39663784; PMCID: PMC11635126 Koca AO, Dagdeviren M, Akkan T, Keskin M, Pamuk N, Altay M (2021) Is idiopathic mild hyperprolactinemia a cardiovascular risk factor? Niger J Clin Pract. ;24(2):213–219. 10.4103/njcp.njcp_178_20 . PMID: 33605911 Feingold KR The Effect of Endocrine Disorders on Lipids and Lipoproteins. [Updated 2023 Apr 6]. In: Feingold KR, Anawalt B, Blackman MR, editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK409608/ Cite Share Download PDF Status: Published Journal Publication published 26 Aug, 2025 Read the published version in Neuroendocrinology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6101984","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427714967,"identity":"9c0e8df7-3635-45aa-be9b-d525a6b22d64","order_by":0,"name":"Sami Bahçebaşı","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCA2xQBjPzAQbGBuK0QNUxsyWQqoWBx4A4LXzH29IffNxTJ6/bzvNN4ucOGzkG9sNHN+DTInnm2MHGGc8OG247zLtNsvdMmjEDT1raDXxaDG6kNzbzHDjACNIiwdt2OLFBgseMGC119tsO8zyT/EuclrSDQC3MiUAtbNJE2QL0S+LMGQcOJ287zGZsLduWZsxGyC/AEDP48OFAne2284cf3nzbZiPHz374GF4tyIBFAkSyEVKGDJg/kKJ6FIyCUTAKRg4AALIxUmdMpdMsAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-0909-7024","institution":"Kayseri City Hospital: Kayseri Sehir Egitim ve Arastirma Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Sami","middleName":"","lastName":"Bahçebaşı","suffix":""},{"id":427714968,"identity":"1fcc997d-219f-4bda-a216-e348bacb5e2a","order_by":1,"name":"Ferhat Gökay","email":"","orcid":"","institution":"Kayseri City Hospital: Kayseri Sehir Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Ferhat","middleName":"","lastName":"Gökay","suffix":""},{"id":427714969,"identity":"4d206440-818c-4fa2-bccc-d8aba856e9cb","order_by":2,"name":"Yasin Şimşek","email":"","orcid":"","institution":"Kayseri City Hospital: Kayseri Sehir Egitim ve Arastirma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Yasin","middleName":"","lastName":"Şimşek","suffix":""}],"badges":[],"createdAt":"2025-02-25 06:28:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6101984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6101984/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1159/000547540","type":"published","date":"2025-08-27T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90533075,"identity":"bfed2ad9-f3dd-4c8e-930c-daa421a49cba","added_by":"auto","created_at":"2025-09-03 18:56:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772216,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6101984/v1/0bbaab22-a5c3-45c1-b0b1-d5f6523677cd.pdf"}],"financialInterests":"","formattedTitle":"Lipid Profile Differences in Monomeric Hyperprolactinemia, Macroprolactinemia, and Healthy Controls: A Comparative Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProlactin is a hormone synthesized and secreted by the anterior pituitary gland, primarily known for its essential role in stimulating breast development and lactation. Secretion of prolactin follows a circadian rhythm, with levels peaking during sleep and reaching their lowest levels during waking hours. Most circulating prolactin exists in its monomeric form (23 kDa), but larger molecular forms, collectively referred to as macroprolactin (150\u0026ndash;170 kDa), also circulate. Macroprolactin forms when prolactin binds to immunoglobulins (IgG or IgA) or undergoes glycosylation, leading to the creation of polymeric aggregates. These larger forms are biologically inactive; however, they interfere with prolactin measurements by binding to anti-prolactin antibodies, complicating the clinical assessment of prolactin levels [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe condition known as macroprolactinemia occurs when macroprolactin predominates in circulation and is found in 4\u0026ndash;40% of hyperprolactinemic patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This can lead to inaccurate prolactin assay results, as the presence of macroprolactin distorts standard testing methods. To classify hyperprolactinemia, the recovery rates of macroprolactin are often used, where a recovery rate above 60% suggests monomeric hyperprolactinemia, while a recovery rate below 40% indicates macroprolactinemia. A recovery between 40\u0026ndash;60% is considered to be in the \"gray zone\" [4,5]. Despite the significance of these distinctions, their impact on lipid metabolism has been less thoroughly examined.\u003c/p\u003e \u003cp\u003eBeyond its role in lactation, prolactin has been implicated in a variety of metabolic processes, including lipid metabolism. Prolactin influences adipose tissue by modulating lipid profiles, particularly by increasing triglyceride (TG) levels through the inhibition of lipoprotein lipase activity in adipocytes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, hyperprolactinemia is often associated with endocrine imbalances such as decreased estrogen levels, which contribute to lipid dysregulation. This manifests as increased total cholesterol and low-density lipoprotein cholesterol (LDL) levels and decreased high-density lipoprotein cholesterol (HDL) levels [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Elevated prolactin levels are frequently observed in overweight and obese individuals, potentially due to the relationship between adipocyte insulin resistance and prolactin secretion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between prolactin levels and lipid metabolism has been studied extensively, particularly in patients with prolactinomas. Elevated LDL levels are commonly found in these patients, while HDL levels tend to be lower [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, treatment with prolactin-lowering agents, such as cabergoline, has been shown to reduce total cholesterol and LDL levels, suggesting a direct role of prolactin in lipid modulation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Triglyceride levels are typically elevated in hyperprolactinemic individuals but often normalize after prolactin-lowering treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, inconsistencies remain in the literature, as some studies have not identified a clear correlation between prolactin levels and lipid profiles [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these findings, the specific effects of macroprolactinemia on lipid metabolism remain underexplored. Most research has focused on monomeric hyperprolactinemia or prolactinomas, with less emphasis on macroprolactinemia and its potential role in lipid dysregulation. Additionally, there is a gap in understanding lipid profiles across different etiological subgroups of monomeric hyperprolactinemia. These subgroups include prolactinoma, idiopathic hyperprolactinemia, drug-induced hyperprolactinemia, chronic kidney disease (CKD)-related hyperprolactinemia, polycystic ovary syndrome (PCOS)-related hyperprolactinemia, and empty sella syndrome.\u003c/p\u003e \u003cp\u003eThe aim of this study is to investigate the relationship between macroprolactinemia and lipid profiles, focusing on the six etiological subgroups of monomeric hyperprolactinemia. Specifically, we seek to examine potential differences in lipid parameters\u0026mdash;such as total cholesterol, LDL, HDL, triglycerides, and non-HDL\u0026mdash;among patients with macroprolactinemia, prolactinoma, idiopathic hyperprolactinemia, drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, PCOS-related hyperprolactinemia, and empty sella syndrom\u003cb\u003ee.\u003c/b\u003e By including a healthy control group for comparison, we aim to provide a comprehensive analysis of the effects of prolactin dysregulation on lipid metabolism.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe records of 2,200 patients who underwent prolactin testing were retrospectively reviewed, and among them, 1,040 patients also had macroprolactin testing.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e:\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAdults aged 18 years or older.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePatients with confirmed hyperprolactinemia.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAvailable lipid profile data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThyroid function test results were available for all participants.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFor healthy control, individuals who did not have any complaints during their routine check-ups, whose prolactin and lipid tests, liver, kidney and thyroid function tests were normal and who did not use medication were included.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e:\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eUse of lipid-lowering medications (patients were screened for medication use, and those receiving lipid-lowering treatment were excluded).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePregnancy or lactation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSevere comorbidities affecting lipid metabolism (e.g., diabetes, liver disease).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePatients with abnormal thyroid function tests (were excluded from the study because it may affect lipoprotein levels.)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFollowing inclusion and exclusion criteria, 181 participants were selected for the study. The study was conducted at Kayseri City Hospital\u0026rsquo;s Internal Medicine and Endocrinology outpatient clinics between January 2020 and July 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewas obtained from the Kayseri City Hospital Ethics Committee (approval number: 871, 11.07.2023).\u003c/p\u003e\n\u003cp\u003eThe participants were categorized into three groups based on their macroprolactin recovery levels:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGroup 1\u003c/strong\u003e: Macroprolactinemia (macroprolactin recovery\u0026thinsp;\u0026lt;\u0026thinsp;40%, 35 patients).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGroup 2\u003c/strong\u003e: Gray zone (macroprolactin recovery 40\u0026ndash;60%, 16 patients).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eGroup 3\u003c/strong\u003e: Monomeric hyperprolactinemia (macroprolactin recovery\u0026thinsp;\u0026gt;\u0026thinsp;60%, 95 patients).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAdditionally, the monomeric hyperprolactinemia group was subdivided into six etiological subgroups: prolactinoma (n\u0026thinsp;=\u0026thinsp;49), idiopathic hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;31), drug-induced hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;4), CKD-related hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;3), PCOS-related hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;6), and empty sella syndrome (n\u0026thinsp;=\u0026thinsp;2). A healthy control group (n\u0026thinsp;=\u0026thinsp;35) was included for comparison.\u003c/p\u003e\n\u003cp\u003eData analysis was performed using IBM SPSS 22 (Statistical Package for Social Sciences). Normality of the data was assessed using Kolmogorov-Smirnov and Shapiro-Wilk tests. Descriptive statistics are presented as means and standard deviations for continuous variables. ANOVA tests were used for parametric data, and Kruskal-Wallis tests were employed for non-parametric data. Chi-square tests were used for categorical variables. Spearman correlation analysis was conducted to assess correlations. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 181 participants were included in the study, representing various subgroups: macroprolactinemia (n\u0026thinsp;=\u0026thinsp;35), gray zone (n\u0026thinsp;=\u0026thinsp;16), and monomeric hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;95). The monomeric hyperprolactinemia group was further subdivided into six etiological subgroups: prolactinoma (n\u0026thinsp;=\u0026thinsp;49), idiopathic hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;31), drug-induced hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;4), chronic kidney disease (CKD)-related hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;3), polycystic ovary syndrome (PCOS)-related hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;6), and empty sella syndrome (n\u0026thinsp;=\u0026thinsp;2). Additionally, a healthy control group (n\u0026thinsp;=\u0026thinsp;35) was included for comparison.\u003c/p\u003e\n\u003cp\u003eDrugs used by patients with drug-related hyperprolactinemia: levocetirizine, paliperidone, risperidone, tegretol-nervium were detected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical and Biochemical Features of Elevated Prolactin Levels Groups and Healthy Controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMacro-prolactinemia\u003c/p\u003e\n \u003cp\u003en (35)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGray zone\u003c/p\u003e\n \u003cp\u003en (16)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eMonomeric hyperprolactinemia\u003c/p\u003e\n \u003cp\u003en (95)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHealty Control\u003c/p\u003e\n \u003cp\u003en (35)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProlactinoma\u003c/p\u003e\n \u003cp\u003en (49)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIdiopathic\u003c/p\u003e\n \u003cp\u003en (31)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug-Induced\u003c/p\u003e\n \u003cp\u003en (4)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCKD-Related\u003c/p\u003e\n \u003cp\u003en (3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCOS-Related\u003c/p\u003e\n \u003cp\u003en (6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmpty Sella\u003c/p\u003e\n \u003cp\u003en (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (mean, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (18\u0026ndash;58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e(18\u0026ndash;41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (13\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (21\u0026ndash;57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (20\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (31\u0026ndash;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (19\u0026ndash;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (45\u0026ndash;55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (18\u0026ndash;50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (93.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (96.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (97.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose (mean, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (68\u0026ndash;130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (73\u0026ndash;105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (77\u0026ndash;194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (67\u0026ndash;136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (74\u0026ndash;110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (81\u0026ndash;99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (87\u0026ndash;94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (99\u0026ndash;104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (69\u0026ndash;106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165\u0026thinsp;\u0026plusmn;\u0026thinsp;3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u0026thinsp;\u0026plusmn;\u0026thinsp;7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274\u0026thinsp;\u0026plusmn;\u0026thinsp;27.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166\u0026thinsp;\u0026plusmn;\u0026thinsp;41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u0026thinsp;\u0026plusmn;\u0026thinsp;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u0026thinsp;\u0026plusmn;\u0026thinsp;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (45\u0026ndash;245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (35\u0026ndash;168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (33\u0026ndash;283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (45\u0026ndash;231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (75\u0026ndash;121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (137\u0026ndash;325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (62\u0026ndash;172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158 (59\u0026ndash;256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (27\u0026ndash;135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HDL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOligoamenorrhea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (%43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (47.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGalactorrhea\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eErectile dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfertility\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeadache\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eTC (Total Cholesterol), LDL (Low-Density Lipoprotein), HDL (High-Density Lipoprotein), TG (Triglycerides), Non-HDL: Cholesterol levels excluding HDL\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSignificant differences in age were observed across the groups. The macroprolactinemia group had a mean age of 29 years (range: 18\u0026ndash;58), which was significantly younger than the monomeric hyperprolactinemia group (mean age 39 years, range: 13\u0026ndash;75) and the prolactinoma subgroup (mean age 36 years, range: 21\u0026ndash;57). The gray zone group had the youngest mean age (24 years, range: 18\u0026ndash;41), which was statistically significantly lower than that of the prolactinoma group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chronic kidney disease (CKD)-related hyperprolactinemia was the oldest group, with a mean age of 50 years (range: 45\u0026ndash;55). Healthy controls had a mean age of 31 years (range: 18\u0026ndash;50).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlucose Levels\u003c/strong\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cp\u003eNo significant differences in glucose levels were found between macroprolactinemia and the gray zone group (p\u0026thinsp;=\u0026thinsp;1), nor between macroprolactinemia and idiopathic hyperprolactinemia (p\u0026thinsp;=\u0026thinsp;1). However, significant differences were observed between prolactinoma and idiopathic hyperprolactinemia (p\u0026thinsp;=\u0026thinsp;0.008), with prolactinoma patients showing significantly lower glucose levels. Prolactinoma also exhibited a significant difference compared to healthy controls (p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipid Profiles\u003c/strong\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cholesterol (TC)\u003c/strong\u003e: Prolactinoma patients had significantly higher total cholesterol compared to the macroprolactinemia group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the healthy control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference was found between the macroprolactinemia group and healthy controls (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HDL Cholesterol (Non-HDL)\u003c/strong\u003e: Prolactinoma patients showed significantly higher non-HDL levels compared the macroprolactinemia group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the healthy control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but there was no significant difference between the macroprolactinemia group and healthy controls (p\u0026thinsp;=\u0026thinsp;0.977).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHDL Cholesterol (HDL)\u003c/strong\u003e: No significant differences in HDL levels were observed between macroprolactinemia and any other group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eLDL Cholesterol (LDL)\u003c/strong\u003e: Prolactinoma patients showed significantly higher LDL levels compared to both macroprolactinemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and healthy controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences in LDL levels between macroprolactinemia and healthy controls (p\u0026thinsp;=\u0026thinsp;0.977).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides (TG)\u003c/strong\u003e: Prolactinoma patients had significantly higher triglyceride levels compared to macroprolactinemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Significant differences in TG levels were found between prolactinoma and healthy controls (p\u0026thinsp;=\u0026thinsp;0.011). However, no significant differences were found between macroprolactinemia and healthy controls (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe comparison between healthy controls and patients with CKD-related hyperprolactinemia revealed a significant difference, with triglyceride levels being higher in the patient group (test statistic: 3.261, p-value: 0.040). However, no significant differences were observed in cholesterol (TC), LDL, HDL, or non-HDL cholesterol levels between the CKD-related hyperprolactinemia group and the other groups (drug-induced hyperprolactinemia, PCOS-related hyperprolactinemia, empty sella syndrome) when compared to the healthy control group. Significant differences in triglyceride levels were found only between the CKD-related hyperprolactinemia group and the healthy control group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Features\u003c/strong\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eOligoamenorrhea\u003c/strong\u003e: The highest frequency of oligoamenorrhea was observed in the monomeric hyperprolactinemia group, particularly in the prolactinoma (40%) and idiopathic hyperprolactinemia (47.6%) subgroups. The gray zone group had a lower prevalence of oligoamenorrhea (40%).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGalactorrhea\u003c/strong\u003e: Galactorrhea was most common in the prolactinoma (22.6%) and idiopathic hyperprolactinemia (26.5%) subgroups. The gray zone group exhibited a higher rate of galactorrhea (37.5%) compared to macroprolactinemia (8.6%).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eErectile Dysfunction\u003c/strong\u003e: In the gray zone group, 100% of males reported erectile dysfunction, whereas 50% of males in the monomeric hyperprolactinemia group reported the condition.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eInfertility\u003c/strong\u003e: No significant differences were found between the groups for infertility, although the prolactinoma and idiopathic hyperprolactinemia groups reported cases of infertility.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eComparisons Between Groups\u003c/strong\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePost-Hoc Analysis of Age, Glucose, and Lipid Parameters Across Elevated Prolactin Levels Groups and Healthy Controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003cp\u003eDiff.\u003c/p\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-HDL Diff.\u003c/p\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHDL Diff.\u003c/p\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLDL Diff.\u003c/p\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003cp\u003eTest Statistic\u003c/p\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,153\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,365\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.19\u003c/p\u003e\n \u003cp\u003e10.72\u003c/p\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.24\u003c/p\u003e\n \u003cp\u003e12.19\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.95\u003c/p\u003e\n \u003cp\u003e4.89\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.4\u003c/p\u003e\n \u003cp\u003e9.98\u003c/p\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,761\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,411\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,642\u003c/p\u003e\n \u003cp\u003e0,296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35.68\u003c/p\u003e\n \u003cp\u003e6.49\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35.37\u003c/p\u003e\n \u003cp\u003e6.82\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29.46\u003c/p\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,098\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdiopathic Hyperprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,982\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.057\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1,057\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.52\u003c/p\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.43\u003c/p\u003e\n \u003cp\u003e8.14\u003c/p\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.095\u003c/p\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.56\u003c/p\u003e\n \u003cp\u003e6.79\u003c/p\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,550\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,237\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,033\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003cp\u003e5.35\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.4\u003c/p\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003cp\u003e4.49\u003c/p\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,393\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4,599\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,668\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.49\u003c/p\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.13\u003c/p\u003e\n \u003cp\u003e12.89\u003c/p\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.06\u003c/p\u003e\n \u003cp\u003e10.52\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,815\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdiopathic Hyperprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.72\u003c/p\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003cp\u003e-2,718\u003c/p\u003e\n \u003cp\u003e0,237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e1,213\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.33\u003c/p\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.19\u003c/p\u003e\n \u003cp\u003e13.63\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.143\u003c/p\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003cp\u003e10.91\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,305\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,133\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,191\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.39\u003c/p\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.84\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.45\u003c/p\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.91\u003c/p\u003e\n \u003cp\u003e9.65\u003c/p\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,864\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdiopathic Hyperprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,125\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,683\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0,008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.15\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.94\u003c/p\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.79\u003c/p\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,432\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,075\u003c/p\u003e\n \u003cp\u003e0,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,784\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0,006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.88\u003c/p\u003e\n \u003cp\u003e6.38\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.97\u003c/p\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.09\u003c/p\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.98\u003c/p\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,602\u003c/p\u003e\n \u003cp\u003e0,011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIdiopathic Hyperprolactinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,783\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,065\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.72\u003c/p\u003e\n \u003cp\u003e8.22\u003c/p\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-29.03\u003c/p\u003e\n \u003cp\u003e7.89\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-24.08\u003c/p\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,900\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia vs Prolactinoma\u003c/strong\u003e: Statistically significant differences were observed across several parameters. Prolactinoma had significantly higher total cholesterol, LDL, and triglyceride levels, as well as a significantly lower glucose level compared to macroprolactinemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for TC, LDL, and TG). No significant difference was observed in HDL levels (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia vs Healthy Control\u003c/strong\u003e: Macroprolactinemia showed no significant differences in total cholesterol and LDL levels compared to the healthy control group (p\u0026thinsp;=\u0026thinsp;1). There were also no significant differences in HDL and triglycerides between these two groups (p\u0026thinsp;=\u0026thinsp;1 for all comparisons).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone vs Prolactinoma\u003c/strong\u003e: The prolactinoma group had significantly higher total cholesterol, LDL, and triglyceride levels compared to the gray zone group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for TC and LDL), while HDL levels did not differ significantly (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone vs Healthy Control\u003c/strong\u003e: The gray zone group showed no significant differences in total cholesterol, LDL, or triglyceride levels compared to healthy controls (p\u0026thinsp;=\u0026thinsp;1 for TC, LDL, and TG). HDL levels were also similar between the gray zone group and healthy controls (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGray Zone vs Macroprolactinemia\u003c/strong\u003e: No significant differences were observed between the gray zone and macroprolactinemia groups for total cholesterol, LDL, non-HDL, or triglyceride levels (p\u0026thinsp;=\u0026thinsp;1 for all comparisons). HDL levels were also comparable between the two groups (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eProlactinoma vs Healthy Control\u003c/strong\u003e: Prolactinoma exhibited significantly higher total cholesterol, non-HDL, LDL, and triglyceride levels compared to healthy controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for TC, non-HDL, and LDL, p\u0026thinsp;=\u0026thinsp;0.011 for TG). No significant difference in HDL was found (p\u0026thinsp;=\u0026thinsp;0.539).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eIdiopathic Hyperprolactinemia vs Prolactinoma\u003c/strong\u003e: There was a significant difference in glucose levels between the idiopathic hyperprolactinemia and prolactinoma groups (p\u0026thinsp;=\u0026thinsp;0.008), with prolactinoma patients having significantly lower glucose levels. Additionally, the prolactinoma group exhibited higher cholesterol and triglyceride levels compared to the idiopathic hyperprolactinemia group. However, HDL levels did not show significant differences between the two groups (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eIdiopathic Hyperprolactinemia vs Healthy Control\u003c/strong\u003e: Idiopathic hyperprolactinemia showed significantly higher non-HDL cholesterol (p\u0026thinsp;=\u0026thinsp;0.024) and LDL cholesterol (p\u0026thinsp;=\u0026thinsp;0.015) levels compared to healthy controls. No significant differences were found in total cholesterol, HDL, or triglyceride levels between the two groups (p\u0026thinsp;=\u0026thinsp;1 for all comparisons).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003ePituitary Adenoma Presence Among Groups (Table 3)\u003c/h3\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePresence of pituitary adenoma among groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePituitary adenoma\u003c/p\u003e\n \u003cp\u003e(Taken from MRI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMacroprolactinemia\u003c/p\u003e\n \u003cp\u003en (15)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGray zone\u003c/p\u003e\n \u003cp\u003en (13)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"6\" align=\"left\"\u003e\n \u003cp\u003eMonomeric hyperprolactinemia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHealty control\u003c/p\u003e\n \u003cp\u003en (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProlactinoma\u003c/p\u003e\n \u003cp\u003en (49)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIdiopathic\u003c/p\u003e\n \u003cp\u003en (31)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug-induced\u003c/p\u003e\n \u003cp\u003en (1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCKD-Related\u003c/p\u003e\n \u003cp\u003en (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCOS-Related\u003c/p\u003e\n \u003cp\u003en (3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmpty Sella\u003c/p\u003e\n \u003cp\u003en (2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicroadenoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (91.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(Insidentaloma)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroadenoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo adenoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe presence of pituitary adenomas, identified through MRI, varied among the groups. The results are summarized as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactinemia\u003c/strong\u003e: 4 patients (26.7%) had microadenomas, and 1 patient (6.7%) had a macroadenoma.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eProlactinoma\u003c/strong\u003e: 45 patients (91.8%) had microadenomas, and 4 patients (8.2%) had macroadenomas.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Control\u003c/strong\u003e: 1 patient had a microadenoma (incidentaloma).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eOther groups\u003c/strong\u003e (Gray zone, Monomeric hyperprolactinemia, Idiopathic hyperprolactinemia, Drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, PCOS-related hyperprolactinemia, Empty Sella Syndrome): No pituitary adenomas were observed in these groups.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis analysis highlights that pituitary adenomas, particularly microadenomas, were predominantly found in the prolactinoma group, followed by the macroprolactinemia group. The healthy control group showed a single incidental microadenoma.\u003c/p\u003e\n\u003ch3\u003eCorrelations Between Prolactin Levels, Macroprolactinemia Recovery, and Lipid Parameters (Table 4)\u003c/h3\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations Between Prolactin Levels, Macroprolactinemia Recovery, and Lipid Parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"9\" align=\"left\"\u003e\n \u003cp\u003eCorrelations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-HDL\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eSpearman\u0026apos;s rho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProlactine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacroprolactin recovery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrelation Coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003eProlactin levels\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eA significant positive correlation was found between prolactin levels and total cholesterol (TC) (r\u0026thinsp;=\u0026thinsp;0.274, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), LDL (r\u0026thinsp;=\u0026thinsp;0.356, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), triglycerides (TG) (r\u0026thinsp;=\u0026thinsp;0.253, p\u0026thinsp;=\u0026thinsp;0.001), and non-HDL (r\u0026thinsp;=\u0026thinsp;0.388, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eA significant negative correlation was observed between prolactin levels and HDL (r = -0.183, p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThere was no significant correlation between prolactin levels and age (r\u0026thinsp;=\u0026thinsp;0.126, p\u0026thinsp;=\u0026thinsp;0.092).\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cstrong\u003eMacroprolactinemia recovery\u003c/strong\u003e:\u003c/div\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eMacroprolactinemia recovery showed a significant positive correlation with age (r\u0026thinsp;=\u0026thinsp;0.299, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total cholesterol (TC) (r\u0026thinsp;=\u0026thinsp;0.243, p\u0026thinsp;=\u0026thinsp;0.003), and LDL (r\u0026thinsp;=\u0026thinsp;0.230, p\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThere was no significant correlation between macroprolactinemia recovery and HDL (r\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;=\u0026thinsp;0.734), triglycerides (TG) (r\u0026thinsp;=\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.082), or non-HDL (r\u0026thinsp;=\u0026thinsp;0.247, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis analysis indicates significant correlations between prolactin levels and lipid parameters, particularly total cholesterol, LDL, triglycerides, and non-HDL, whereas macroprolactinemia recovery was more strongly associated with age, TC, and LDL.\u003c/p\u003e\n\u003ch3\u003eSummary of Key Findings\u003c/h3\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eProlactinoma exhibited the highest levels of total cholesterol, LDL, non-HDL, and triglycerides compared to other groups and showed a significant decrease in glucose levels compared to macroprolactinemia.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMacroprolactinemia had significantly lower total cholesterol and LDL levels compared to prolactinoma, as well as significantly lower triglycerides than prolactinoma.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe gray zone and macroprolactinemia groups had similar lipid levels to healthy controls, with no significant differences noted in most of the parameters.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePituitary adenomas were predominantly observed in the prolactinoma group, with 91.8% of patients having microadenomas, and 8.2% having macroadenomas. In macroprolactinemia, 26.7% had microadenomas, and 6.7% had macroadenomas. A single incidental microadenoma was detected in the healthy control group.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eProlactin levels showed significant positive correlations with total cholesterol, LDL, triglycerides, and non-HDL, while there was a negative correlation with HDL. There was no significant correlation between prolactin levels and age.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMacroprolactinemia recovery showed significant positive correlations with age, total cholesterol, and LDL, while no significant correlation was found with HDL, triglycerides, or non-HDL.\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we observed distinct lipid profile differences across the various prolactin-related conditions. Patients with prolactinoma exhibited significantly higher levels of total cholesterol (TC), LDL, non-HDL cholesterol, and triglycerides compared to patients with macroprolactinemia. However, no significant differences in HDL levels were noted among these groups. Patients with macroprolactinemia and the gray zone group showed no significant differences in lipid profiles compared to healthy controls, with similar levels of total cholesterol, LDL, triglycerides, and HDL.\u003c/p\u003e \u003cp\u003eProlactin may influence lipid metabolism through various mechanisms. It has been shown that prolactin can directly affect adipose tissue by inhibiting lipoprotein lipase activity, which leads to higher triglyceride levels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Hyperprolactinemia also lowers estrogen levels, which may contribute to the elevation of total cholesterol and LDL while simultaneously decreasing HDL[5]. These mechanisms are unlikely to be active in macroprolactinemia, as macroprolactin, being biologically inactive due to its large size, does not significantly impact estrogen levels or lipoprotein lipase activity in adipose tissue[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This may explain the lipid profile differences we observed between monomeric hyperprolactinemia and macroprolactinemia.\u003c/p\u003e \u003cp\u003eIn a study by Krysiak et al., no significant differences in cholesterol, LDL, HDL, or triglyceride levels were found between patients with macroprolactinemia and healthy controls. However, patients with monomeric hyperprolactinemia exhibited higher triglyceride levels and lower HDL levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our results differed from these findings, as we observed significantly higher levels of total cholesterol, non-HDL cholesterol, LDL, and triglycerides in patients with monomeric hyperprolactinemia, particularly in those with prolactinoma, compared to the macroprolactinemia group. In contrast, no significant differences were found in HDL levels. Additionally, patients with macroprolactinemia and those in the gray zone displayed lipid profiles similar to those of healthy controls, suggesting that the biologically inactive nature of macroprolactin may have a minimal impact on lipid metabolism. This contrasts with Krysiak et al.'s study, which did not investigate the subgroups within hyperprolactinemia, but rather focused on isolated macroprolactinemia and monomeric hyperprolactinemia. Our study expands on these findings by specifically examining the lipid profiles across these distinct hyperprolactinemia subgroups.\u003c/p\u003e \u003cp\u003eProlactinoma patients in our study exhibited significantly higher levels of total cholesterol, LDL, and triglycerides than both the macroprolactinemia group and healthy controls, consistent with findings from other studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition, prolactinoma patients had significantly lower glucose levels compared to the macroprolactinemia group. The differences observed in lipid profiles may be attributed to the direct effects of prolactinoma-induced hyperprolactinemia on lipid metabolism. Non-HDL cholesterol levels, which have not always been consistently reported in previous research, were significantly higher in patients with prolactinoma compared to healthy controls. This could reflect inhibition of lipoprotein lipase, a reduced release of fatty acids from VLDL, failure to form triglycerides, and an increase in VLDL particles in prolactinoma patients.\u003c/p\u003e \u003cp\u003eInterestingly, the gray zone and macroprolactinemia groups exhibited similar lipid profiles to healthy controls, with no significant differences in total cholesterol, LDL, triglyceride, or HDL levels. This suggests that macroprolactinemia, due to the biologically inert nature of macroprolactin, does not have a significant effect on lipid metabolism. Similarly, the gray zone group, which contains patients with low or undetectable levels of prolactin, showed no major lipid abnormalities.\u003c/p\u003e \u003cp\u003eOur study found significant correlations between prolactin levels and lipid parameters, with elevated prolactin associated with higher total cholesterol, LDL, triglycerides, and non-HDL cholesterol, as well as lower HDL cholesterol. These results are consistent with Schwetz et al. (2017), who demonstrated that prolactinomas lead to dyslipidemia, particularly elevated cholesterol and LDL levels, and that treatment to reduce prolactin levels improves these lipid parameters. Our findings align with this, suggesting that elevated prolactin may contribute to adverse lipid profiles, potentially increasing cardiovascular risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the prolactinoma group, pituitary adenomas, particularly microadenomas, were found in the vast majority of patients (91.8%), with a small number having macroadenomas (8.2%). In comparison, macroprolactinemia patients had fewer adenomas (26.7% microadenomas, 6.7% macroadenomas). These findings emphasize that pituitary adenomas, especially microadenomas, are more frequently associated with prolactinoma than with macroprolactinemia.\u003c/p\u003e \u003cp\u003eIn our study, we observed a significant increase in triglyceride (TG) levels in patients with chronic kidney disease (CKD)-related hyperprolactinemia compared to healthy controls (p\u0026thinsp;=\u0026thinsp;0.040). However, no significant differences were found in LDL, total cholesterol, or non-HDL levels. This could be due to the small sample size (only 3 patients), which may limit the ability to detect differences in these lipid markers. CKD itself is known to elevate TG levels, as impaired kidney function disrupts lipid metabolism through mechanisms such as reduced clearance of lipoproteins, altered lipoprotein lipase activity, and increased lipoprotein synthesis[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The accumulation of triglycerides and cholesterol in CKD is linked to reduced renal lipid clearance, contributing to increased cardiovascular risk[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, CKD-related dyslipidemia is associated with chronic inflammation, oxidative stress, and hormonal changes, including elevated prolactin. Hyperprolactinemia may further exacerbate lipid abnormalities by affecting lipoprotein lipase activity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although only TG levels were significantly altered in our study, the findings underscore the complex relationship between CKD, hyperprolactinemia, and lipid metabolism. The role of prolactin in lipid regulation requires further investigation, and monitoring lipid levels in CKD patients with hyperprolactinemia is crucial for managing cardiovascular risk.\u003c/p\u003e \u003cp\u003eIn our study, no significant differences were found in lipoprotein parameters, including total cholesterol (TC), LDL, HDL, or non-HDL cholesterol levels, between the empty sella syndrome group and healthy controls. It is worth noting, however, that the empty sella group in our study was small, with only two patients included. While there are studies indicating that lipid levels may be elevated in empty sella syndrome, particularly in cases of secondary empty sella due to hypopituitarism [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e], our findings did not show a clear association with lipid changes. Additionally, although some literature suggests that prolactin levels may be elevated in patients with empty sella syndrome, especially in the context of hypopituitarism or microadenomas [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e], no study has yet thoroughly investigated the direct relationship between prolactin levels and lipid parameters in these patients. The lack of significant findings in our study may be attributed to the small sample size of the empty sella group and the absence of clear, consistent patterns in the literature regarding lipid profile alterations specifically related to prolactin levels in this condition.\u003c/p\u003e \u003cp\u003eOur study found that, similar to previous studies on drug-induced hyperprolactinemia and PCOS-related hyperprolactinemia, there were no significant changes in lipid profiles in patients with elevated prolactin levels due to macroprolactinemia or in those with prolactin levels in the macroprolactinemia gray zone. Specifically, studies have shown that drug-induced hyperprolactinemia and PCOS-related hyperprolactinemia do not result in significant alterations in lipid parameters, despite elevated prolactin levels in these conditions due to medication or hormonal imbalances [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our findings support this notion, as patients with macroprolactinemia or those with prolactin levels in the gray zone exhibited stable lipid profiles, which may be explained by the fact that macroprolactin, the inactive form of prolactin, does not significantly influence metabolic processes. Consequently, prolactin levels in these patients may not reach clinically relevant elevations that could cause lipid abnormalities. However, it is important to acknowledge that we specifically examined the macroprolactinemia gray zone, and there is limited data in the literature addressing its impact on lipid metabolism, indicating a need for further research to better understand this relationship.\u003c/p\u003e \u003cp\u003eAdditionally, when comparing idiopathic hyperprolactinemia with prolactinoma, we observed significant differences in glucose levels (p\u0026thinsp;=\u0026thinsp;0.008), with prolactinoma patients having significantly lower glucose levels. This finding is in line with previous studies suggesting that glucose metabolism may be altered in prolactinoma patients, potentially due to prolactin's effects on insulin sensitivity and secretion [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Interestingly, in our study, glucose levels were also found to be lower in prolactinoma patients compared to healthy controls, which further supports the potential influence of prolactin on glucose metabolism in these individuals.\u003c/p\u003e \u003cp\u003eHowever, when comparing idiopathic hyperprolactinemia with healthy controls, no significant difference in glucose levels was observed. It is important to note that there is limited research specifically comparing glucose levels in patients with idiopathic hyperprolactinemia to healthy controls. The lack of significant differences in our study could suggest that idiopathic hyperprolactinemia does not lead to significant alterations in glucose metabolism, unlike prolactinoma. One potential explanation could be that prolactin levels in idiopathic hyperprolactinemia may not reach the same elevated thresholds as seen in prolactinoma, and thus may not exert a significant impact on insulin sensitivity or secretion. Further research specifically comparing idiopathic hyperprolactinemia with healthy controls is needed to better understand any potential effects of prolactin on glucose metabolism in these patients.\u003c/p\u003e \u003cp\u003eMoreover, prolactinoma patients had higher total cholesterol and triglyceride levels compared to idiopathic hyperprolactinemia patients. This supports findings from other studies suggesting that prolactin excess, particularly in prolactinoma, can be associated with dyslipidemia, likely due to the effects of elevated prolactin on lipid metabolism [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Prolactinoma is characterized by a tumor in the pituitary gland, leading to significantly higher prolactin levels, which can have a more pronounced effect on lipid metabolism. In contrast, idiopathic hyperprolactinemia refers to elevated prolactin levels without an identifiable cause. Although prolactin levels are elevated in both conditions, the levels in idiopathic hyperprolactinemia are typically not as markedly high as in prolactinoma. In our study, while prolactin levels in prolactinoma were higher than in idiopathic hyperprolactinemia, this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.083). Nevertheless, prolactin levels in prolactinoma were still higher, which might explain the more pronounced lipid abnormalities observed in this group. The elevated cholesterol and triglyceride levels seen in prolactinoma may be related to altered hormonal regulation, which could influence hepatic lipid production and storage. These differences in prolactin levels and the underlying causes of hyperprolactinemia might explain the distinct lipid profiles observed between these two conditions.\u003c/p\u003e \u003cp\u003eOn the other hand, idiopathic hyperprolactinemia showed significantly higher non-HDL cholesterol (p\u0026thinsp;=\u0026thinsp;0.024) and LDL cholesterol (p\u0026thinsp;=\u0026thinsp;0.015) compared to healthy controls. This finding is somewhat less established in the literature. While prolactin is known to affect lipid metabolism, the exact mechanisms through which idiopathic hyperprolactinemia influences lipid profiles are still under investigation. Some studies suggest that the link between prolactin and lipid metabolism could be related to its interaction with estrogen and other hormones that influence cholesterol homeostasis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our study, the higher non-HDL and LDL levels in idiopathic hyperprolactinemia compared to healthy controls could indicate a more complex metabolic alteration in this group, potentially due to subtle disruptions in endocrine feedback mechanisms, despite the absence of a clear underlying cause like prolactinoma or other pathologies.\u003c/p\u003e \u003cp\u003eIn contrast, HDL levels did not show significant differences between idiopathic hyperprolactinemia and healthy controls (p\u0026thinsp;=\u0026thinsp;1). However, other studies have found lower HDL levels in patients with hyperprolactinemia, suggesting that prolactin elevation may affect HDL levels differently depending on the specific form of hyperprolactinemia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, our findings regarding the lack of significant differences in triglyceride, total cholesterol, and HDL levels between idiopathic hyperprolactinemia and healthy controls (p\u0026thinsp;=\u0026thinsp;1) are consistent with the limited available literature. Notably, lipid metabolism in idiopathic hyperprolactinemia has not been extensively studied, and the only relevant study we found, conducted by Koca et al. (2021), did not specifically address lipid profiles but rather investigated cardiovascular risk predictability through arterial stiffness measurements in patients with idiopathic hyperprolactinemia. Their results showed no significant relationship between prolactin levels and arterial stiffness or blood pressure, indicating that mild hyperprolactinemia may not have immediate cardiovascular implications. However, the study did not evaluate LDL or non-HDL cholesterol levels in detail [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In contrast to the study's findings on cardiovascular risk, our results indicate that LDL and non-HDL cholesterol levels were significantly higher in patients with idiopathic hyperprolactinemia compared to healthy controls, suggesting that prolactin\u0026rsquo;s primary effect on lipid metabolism may involve these specific lipid fractions rather than triglycerides. Further research is required to clarify the long-term cardiovascular risks associated with idiopathic hyperprolactinemia.\u003c/p\u003e \u003cp\u003eIn summary, patients with prolactinoma exhibited a significantly more adverse lipid profile, characterized by elevated levels of total cholesterol, LDL, non-HDL, and triglycerides, compared to patients with macroprolactinemia, those in the gray zone, and healthy controls. These lipid abnormalities are consistent with the effects of hyperprolactinemia on lipid and lipoprotein levels, where total cholesterol, LDL, and triglycerides are increased, while HDL shows no significant change or a decrease, as reported in previous studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, macroprolactinemia and gray zone patients exhibited lipid profiles similar to those of healthy controls, suggesting that the biologically inactive nature of macroprolactin may not significantly affect lipid metabolism.\u003c/p\u003e \u003cp\u003eFurthermore, prolactin levels were positively correlated with adverse lipid parameters, such as total cholesterol, LDL, non-HDL, and triglycerides, while a negative correlation was noted with HDL levels. These findings support the hypothesis that elevated prolactin levels contribute to dyslipidemia, as prolactin has been shown to influence lipoprotein metabolism. On the other hand, recovery from macroprolactinemia was positively correlated with age, total cholesterol, and LDL levels but showed no significant association with HDL or triglycerides, further highlighting the distinct metabolic effects of different prolactin-related conditions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that prolactinoma is associated with significant dyslipidemia, including elevated total cholesterol, LDL, and triglyceride levels, compared to macroprolactinemia and healthy controls. The biologically inactive nature of macroprolactin likely explains the lack of significant lipid abnormalities in the macroprolactinemia group.\u003c/p\u003e\n\u003cp\u003eIn terms of subgroups within the monomeric hyperprolactinemia category, prolactinoma patients showed the most prominent lipid alterations, particularly with significantly higher total cholesterol, LDL, and triglyceride levels. Idiopathic hyperprolactinemia also showed some elevation in lipid levels, but less severe than prolactinoma. Other subgroups, such as drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, PCOS-related hyperprolactinemia, and empty sella syndrome, showed no significant lipid abnormalities, likely due to their smaller sample sizes.\u003c/p\u003e\n\u003cp\u003eOverall, prolactinoma presents the most concerning metabolic profile, highlighting the need for close lipid monitoring and management in these patients. Differentiating between monomeric hyperprolactinemia subgroups and macroprolactinemia is essential for guiding appropriate clinical management and reducing cardiovascular risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be acknowledged:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eSmall Sample Size in Certain Subgroups\u003c/strong\u003e:\u003cbr\u003eAlthough the study included a total of 181 participants, some subgroups within the monomeric hyperprolactinemia group, such as drug-induced hyperprolactinemia, CKD-related hyperprolactinemia, and empty sella syndrome, had very small sample sizes. This limits the generalizability of the findings for these specific subgroups and may have affected the statistical power to detect significant differences in lipid profiles.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRetrospective Study Design\u003c/strong\u003e:\u003cbr\u003eAs this was a retrospective analysis, the study relied on previously recorded medical data. This limits the control over the consistency of data collection and potential confounding factors that could not be accounted for. Prospective studies with standardized data collection would provide more robust insights.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLack of Longitudinal Data\u003c/strong\u003e:\u003cbr\u003eThe study did not evaluate changes in lipid profiles over time or the impact of treatment (e.g., prolactin-lowering therapies) on lipid levels. Future research should include longitudinal data to assess the long-term effects of prolactin dysregulation and its treatment on lipid metabolism.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSingle-Center Study\u003c/strong\u003e:\u003cbr\u003eThe study was conducted in a single medical center, which may limit the generalizability of the findings to other populations with different demographic or genetic characteristics. Larger, multi-center studies would be needed to confirm these results across diverse populations.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolycystic Ovary Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eVLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVery Low-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNon-HDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-High-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMRI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePRL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProlactin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLPL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLipoprotein Lipase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCVD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFSH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFollicle-Stimulating Hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLuteinizing Hormone\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eEthical approval was waived by the local Ethics Committee of Kayseri City Hospital (approval number: 871, 11.07.2023) in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc472330566\"\u003eBecause our study was a retrospective archive scan, informed consent was not obtained. Written informed consent from participants was not required in accordance with local/national guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc472330568\"\u003eThis study was not supported by any sponsor or funder.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor 1 and author 3 designed the experiments and analyzed the data. Author 1 and author 2 analyzed the data and prepared the manuscript. All authors approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from Author 1 OR the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSamperi I, Lithgow K, Karavitaki N, Hyperprolactinaemia (2019) J Clin Med. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm8122203\u003c/span\u003e\u003cspan address=\"10.3390/jcm8122203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 31847209; PMCID: PMC6947286 8[12]:2203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimatsu A, Hattori N (2012) Macroprolactinemia: diagnostic, clinical, and pathogenic significance. 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PMID: 29894997\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofbauer S, Horka L, Seidenberg S, Da Mutten R, Regli L, Serra C, Beuschlein F, Erlic Z (2024) Metabolic and inflammatory parameters in relation to baseline characterization and treatment outcome in patients with prolactinoma: insights from a retrospective cohort study at a single tertiary center. Front Endocrinol (Lausanne) 15:1363939. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2024.1363939\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2024.1363939\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 38645431; PMCID: PMC11026551\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlezer A, Santana MR, Bronstein MD, Donato J Jr, Jallad RS (2023) The interplay between prolactin and cardiovascular disease. 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J Cell Mol Med 28(23):e70067. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcmm.70067\u003c/span\u003e\u003cspan address=\"10.1111/jcmm.70067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 39663784; PMCID: PMC11635126\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoca AO, Dagdeviren M, Akkan T, Keskin M, Pamuk N, Altay M (2021) Is idiopathic mild hyperprolactinemia a cardiovascular risk factor? Niger J Clin Pract. ;24(2):213\u0026ndash;219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/njcp.njcp_178_20\u003c/span\u003e\u003cspan address=\"10.4103/njcp.njcp_178_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 33605911\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeingold KR The Effect of Endocrine Disorders on Lipids and Lipoproteins. [Updated 2023 Apr 6]. In: Feingold KR, Anawalt B, Blackman MR, editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK409608/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK409608/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prolactin, hyperprolactinemia, macroprolactinemia, prolactinoma, lipid profile, dyslipidemia, cholesterol","lastPublishedDoi":"10.21203/rs.3.rs-6101984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6101984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aims to investigate lipid profile differences among patients with monomeric hyperprolactinemia, macroprolactinemia, and healthy controls. Additionally, lipid parameters across various etiological subgroups of monomeric hyperprolactinemia were evaluated\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 181 participants were included in this retrospective study. They were divided into three groups based on macroprolactin recovery rates: macroprolactinemia (n = 35)\u003cstrong\u003e, \u003c/strong\u003egray zone (n = 16), and monomeric hyperprolactinemia (n = 95). The monomeric hyperprolactinemia group was subdivided into six etiological subgroups: prolactinoma (n = 49), idiopathic hyperprolactinemia (n = 31), drug-induced hyperprolactinemia (n = 4), CKD-related hyperprolactinemia (n = 3), PCOS-related hyperprolactinemia (n = 6), and empty sella syndrome (n = 2). A healthy control group (n = 35) was included.\u003cstrong\u003e \u003c/strong\u003eLipid profiles, including total cholesterol, LDL, HDL, triglycerides, and non-HDL cholesterol, were compared across groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Prolactinoma patients had significantly higher total cholesterol, LDL, triglycerides, and non-HDL levels compared to the macroprolactinemia group and healthy controls \u003cstrong\u003e(\u003c/strong\u003ep \u0026lt; 0.001\u003cstrong\u003e). \u003c/strong\u003eIdiopathic hyperprolactinemia also showed elevated lipid levels, but to a lesser extent\u003cstrong\u003e.\u003c/strong\u003e Other subgroups exhibited no significant lipid abnormalities. Macroprolactinemia patients had lipid profiles similar to healthy controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Prolactinomais associated with significant dyslipidemia, characterized by elevated cholesterol, LDL, triglycerides, and non-HDL. Differentiating between monomeric hyperprolactinemia subgroups and macroprolactinemia is essential for effective management of metabolic risk.\u003c/p\u003e","manuscriptTitle":"Lipid Profile Differences in Monomeric Hyperprolactinemia, Macroprolactinemia, and Healthy Controls: A Comparative Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-14 04:24:50","doi":"10.21203/rs.3.rs-6101984/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91982c3b-ea04-4924-956d-a6f35d0c3ce0","owner":[],"postedDate":"March 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T18:56:37+00:00","versionOfRecord":{"articleIdentity":"rs-6101984","link":"https://doi.org/10.1159/000547540","journal":{"identity":"neuroendocrinology","isVorOnly":true,"title":"Neuroendocrinology"},"publishedOn":"2025-08-27 00:00:00","publishedOnDateReadable":"August 27th, 2025"},"versionCreatedAt":"2025-03-14 04:24:50","video":"","vorDoi":"10.1159/000547540","vorDoiUrl":"https://doi.org/10.1159/000547540","workflowStages":[]},"version":"v1","identity":"rs-6101984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6101984","identity":"rs-6101984","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}