Pituitary adenomas are frequent intracranial tumors following only meningiomas and gliomas in their incidence (1). Prolactinomas (PRLs) are the most common clinical subtype among pituitary adenomas (2). Their prevalence and incidence is about 50 per 10.000 and 3-5 new cases per 100.000 population per year, according to newer epidemiological studies (3).

The clinical signs and symptoms of PRL are mainly related to hyperprolactinemia, which is the hallmark of these tumors (4). In general, prolactin levels correlate well with pituitary adenoma size (5–7). Rarely, these tumors are symptomatic due to the mass effect causing compression of the nearby structures, resulting in primarily visual loss or pituitary insufficiency. The most common and known clinical presentation of hyperprolactinemia is hypogonadism, related to the inhibition of the gonadotropin secretion and action (8). However, there is increasing evidence of metabolic alterations in patients with hyperprolactinemia, which might be related to the increased cardiovascular morbidity observed in patients with high prolactin levels (9). Changes in lipid and glucose metabolism and weight gain has been described (10–18). Whilst some of the effects are related to the concomitant hypogonadism (19), others might be directly evoked by the prolactin hypersecretion itself or other unknown mechanism [reviewed by (20, 21)].

Medical treatment with dopamine agonists (DA) is the therapy of choice for PRL with humoral response, defined as normoprolactinemia in 68% of cases, tumor shrinkage in 62% of cases and relieving infertility or other symptoms in 53%, respectively (22, 23). Recurrence of hyperprolactinemia after withdrawal of DAs varies widely among different studies between 2- 80%, depending of the DA-type, treatment duration and initial tumor size (24, 25). For patients who are intolerant or resistant to DA, surgery is the best option. With recent advances in neurosurgical strategies, treatment related morbidity and mortality has decreased significantly, and it is considered by some specialists to be a valid first-line therapeutic alternative (26, 27), since the surgical cure is seen in up to 67% of patients (27).

As response to medical treatment varies considerably between patients, identifying new markers for diagnostic stratification and prognosis would aid in identifying patients in need of more aggressive medical treatments or even surgery as a first option. Both metabolic as well as inflammatory markers have been applied successfully as diagnostic and prognostic markers in tumor patients, including patients with endocrine tumors (28–32). In these conditions (e.g. primary aldosteronism, catecholamine excess) metabolic comorbidities have been described as well (33, 34) and metabolic markers have shown potential for diagnostic purposes (30–32).

Several inflammatory markers have been studied in tumor patients. The Neutrophile-to-Lymphocyte-Ratio (NLR) as an inflammatory marker reflects an ineffective immune response to the tumor and invasiveness with poor outcomes. The Platelet-to-Lymphozyte-Ratio (PLR) is also associated with poor cancer outcomes. The Glasgow Prognostic Score (GPS) is reflecting malnutrition and systemic inflammation. The Systemic Immune Inflammation Index (SII) is an important prognostic factor associated with lower postoperative survival in several types of cancer (35). A poor cancer prognosis is often associated with a reduced Prognostic Nutrition Index (PNI) (36). The Neutrophil-Platelet Score (NPS) have a prognostic value in different tumor diseases (37). Whilst metabolic changes in patients with Cushing syndrome and acromegaly are part of the syndrome description, increased inflammation is not a well-acknowledged component (38, 39). For decades, we have known that there is an increased inflammation in patients with Cushing syndrome and that this might contribute to cardiovascular morbidities in patients with Cushing syndrome (40). Similarly, in patients with acromegaly, proinflammatory processes have been described which influence the cardiovascular risk profile of these patients before and after treatment (41). Therefore is of no surprise, that recent studies evidenced increased inflammatory markers in patients with pituitary adenomas, in specific patients with Cushing disease, and with much less extent in acromegaly and PRL patients compared to non-functioning adenoma (28, 29) To our knowledge, only one study focused on this topic in patients with PRL, who found some differences in the hemostatic parameters in comparison to healthy controls (42).

Consequently, our study aimed to investigate the potential of metabolic and inflammatory changes in patients with PRL, both for characterizing their condition and as a prognostic tool.

Out of the 90 PRL patients in the NeoExNET registry, 31 were excluded from the study. Twenty-five were excluded due to incomplete baseline laboratory levels, one for undergoing surgical treatment, and five because they did not meet the distinct criteria for differentiating between non-functioning pituitary adenoma and PRL. Additionally, patients with primary hypothyroidism, whether or not they were undergoing levothyroxine substitution (a total of 4 patients), and those on oral contraceptive pills or testosterone substitution (a total of 2 patients) at the time of PRL diagnosis were also excluded. An exception was made for one patient who had received a single dose of testosterone enanthate two weeks before the baseline laboratory tests; this patient was included in the study.

Baseline characteristics of the remaining 59 patients are detailed in Table 1A . This group included 12 patients with microprolactinoma (33% women, average age 36 years) and 47 patients with macroprolactinoma (49% women, average age 34 years). There were no significant differences in age and sex between microprolactinoma and macroprolactinoma patients. Hyperprolactinemia was present in all patients, with 47 patients (89%) exhibiting hypogonadism at presentation, more commonly in those with macroprolactinoma. Other forms of pituitary insufficiency were found in 12 (27%) of the macroprolactinoma patients (corticotrop 7%, thyreotrop 20%) but not in any microprolactinoma patients. Symptoms of mass effect, particularly visual disturbances, were noted in six macroprolactinoma patients but in none with microprolactinoma.

At baseline, macroprolactinoma patients had lower testosterone levels (p=0.005) and higher prolactin levels (p<0.001) compared to those with microprolactinoma. No other differences in clinical, endocrine, metabolic, and inflammatory parameters were observed. A negative correlation was identified between adenoma size, volume, and prolactin level with testosterone levels ( Supplementary Table 3 ). The absence of correlation with estradiol levels might be due to non-standardized sample collection relative to the menstrual cycle. However, a significant negative correlation with fT4 was noted for all three PRL parameters. This association was confirmed after adjusting for age and sex in the regression analysis. Additionally, a positive correlation was found between adenoma size and BMI and HbA1c, adenoma volume and BMI, as well as prolactin with heart rate, BMI, and HbA1c ( Supplementary Table 3 ). Of these correlations, only the association between prolactin and HbA1c was confirmed in the regression analysis ( Table 1B ).

In this study, we present clinical and laboratory data from a retrospective cohort of 59 patients, with additional follow-up data for 46 patients, all from a single tertiary center. We found no correlation or association between inflammatory markers and the clinical/metabolic presentation at baseline or follow-up, as well as with the predefined outcome prediction. However, our PRL cohort displayed several metabolic peculiarities, which have been previously reported in the literature with varying results.

For instance, the prevalence of obesity (31%), overweight/obesity (58.6%), diabetes mellitus (8.5%), and dyslipidemia (20.3%) in our cohort appears to be higher, whilst arterial hypertension (11.9%) lower compared to the general population. According to the Federal Statistical Office in Switzerland, the prevalences in 2022 were 12% for obesity, 43% for overweight/obesity, 5% for diabetes mellitus (excluding prediabetes), 15% for dyslipidemia and 20% for arterial hypertension (45–47). In our analysis, we observed a correlation between increased BMI and tumor size, volume, or prolactin levels, as well as rising HbA1c levels in relation to tumor size and prolactin levels at baseline. Although the regression analysis did not confirm an association between BMI and PRL parameters at baseline, literature supports increased BMI and/or body fat in patients with PRL (48–51), which correlates with baseline prolactin levels (50, 52) and may be independent of hypogonadism (53). Chronic prolactin excess, as seen in patients with PRL, is postulated to directly affect the appetite regulation, leading to increased food intake, contributing to weight gain and even overt obesity in animal models (54). Weight loss post-prolactinoma treatment is documented in several studies but not universally observed [reviewed by (20)]. This effect may be independent of dopamine agonist treatment, as seen in a cohort of surgically treated patients (52). In our cohort, we did not document significant BMI changes, potentially due to the non-standardized timing of follow-up measurements, which may have been too brief to observe such effects.

The impact of prolactin levels on glucose metabolism, indicated by HbA1c levels in our study, was confirmed in our regression analysis. While not statistically significant (p=0.06), we noted a trend towards lower HbA1c levels under treatment. Abnormal glucose homeostasis and higher insulin resistance in patients with PRL, improving after treatment, have been previously described [reviewed by (20, 21)]. The primary effect appears to be due to normalization of prolactin levels, as observed in surgically treated patients (52), but a pharmacological effect is also plausible, as cabergoline treatment improves glucose homeostasis in patients without PRL (55).

Another significant finding in our study is the positive impact of PRL treatment on lipid profiles, similar to the published literature [reviewed by (20)]. We observed no baseline association between PRL parameters and lipids, but a significant reduction in LDL levels post-treatment was noted. This reduction did not correlate with changes in PRL parameters but did with changes in HbA1c. The pathogenesis of this observation is unclear but might relate to changes in BMI and fat distribution as well as improvement of the glucose homeostasis post-treatment, or could stem directly from medical intervention [reviewed by (21)].

However, it remains unclear whether these differences are attributable to prolactin itself or concurrent hypogonadism. It has been hypothesized that the impact of hyperprolactinemia on glucose homeostasis may be direct, through its effects on pancreatic beta cells. This is supported by the discovery of prolactin receptor expression on insulin-secreting cell lines, with chronic hyperprolactinemia also being linked to impaired insulin secretion (21).

High prolactin levels have been shown to directly reduce adiponectin levels in cell and animal model studies. This reduction in adiponectin leads to decreased insulin-mediated inhibition of hepatic gluconeogenesis, resulting in lower glucose uptake and reduced fatty acid oxidation by fat and muscle cells (18, 56).

Within the subgroup of patients with comorbidities, there was a significantly higher proportion of hypogonadism among those with overweight/obesity (94.1% versus 58.3%, p=0.002). The prevalence of hypogonadism in patients with obesity (94.4% versus 72.5%) and dyslipidemia (100% versus 74.5%) was higher, though it did not reach statistical significance (data not shown), likely due to the low total number of cases with the respective comorbidity. However, there might be a direct effect of hyperprolactinemia and its resolution on the lipid profile. Studies in rodents and human adipose tissue cell lines have shown that prolactin directly reduces lipoprotein lipase activity, thereby increasing triglyceride levels (21, 57).

It is crucial to recognize that abnormalities in glucose and lipid homeostasis, as well as fat distribution and BMI, have been described in male patients with hypogonadism (58). Hypogonadism, a major endocrine complication of hyperprolactinemia that usually resolves or at least improves after successful treatment, might play a significant role in the observed metabolic abnormalities and their improvements after successful treatment in patients with PRL. For example, in our cohort, all male patients with hypogonadism had normal gonadal function at follow-up, or at least significantly improved to the extent that no replacement therapy was necessary. Notably, in our cohort, we did not identify any correlation between changes in testosterone levels in males and the metabolic parameters studied. Also in the literature, some studies suggest this beneficial effect after treatment is independent of gonadal function normalization (59, 60).

Other forms of pituitary insufficiency, which were not actively studied in our cohort (for example, we did not perform dynamic testing for growth hormone deficiency or assess for partial corticotrope insufficiency), might also contribute to the metabolic abnormalities in these patients, which might improve or resolve after treatment (61, 62). Interestingly, although we did not observe a significant difference in cortisol levels after treatment, there was a significant correlation between increasing cortisol levels and the reduction of lipid levels (total and LDL-cholesterol) after treatment in our cohort.

However, our study, like others, lacks sufficient patient numbers to fully explore all variables influencing lipid profiles and other metabolic changes in these patients, considering the known potential for multiple pathogenetic mechanisms in PRL patients.

An intriguing observation in our study was the association of fT4 with prolactin parameters at baseline, and a significant increase in fT4 under treatment. The change in fT4 correlated with changes in prolactin and adenoma volume. While no changes in fT4 were documented in the studied literature (20, 21, 48–53, 55, 59, 60), one study reported an increase in fT3 post-treatment with lower baseline fT3 levels compared to controls (51). We found no correlation between fT4 and the metabolic abnormalities observed, beside a negative correlation with the change of BMI after treatment (which was not significant within our cohort as discussed above). Few studies have conducted specific analyses to determine if metabolic changes (BMI, lipid profile) are related to these slight alterations in thyroid hormone production (14, 15, 59). It is known that hypothyroidism leads to increased total and LDL cholesterol, as thyroid hormones regulate the LDL receptor in the liver, reducing LDL clearance in hypothyroidism. Therefore, the improvement in cholesterol levels might also be related to changes in thyroid function.

From the various clinical, endocrine, metabolic, and inflammatory markers tested, we found a positive association between adenoma size/volume and a negative association with fT4 values concerning the time needed for prolactin normalization. We believe that the main reason for the increase in fT4 is related to the decrease in prolactinoma size after treatment. It has been postulated that the mass effect of the prolactinomas causes the partial thyrotrope deficiency observed in these cases, either directly (63) or through an indirect effect on the intrasellar blood flow (64). Due to the small patient cohort, we were not able to definitively assess whether the effect of fT4 was independent of adenoma size and volume, although there was a significant baseline association. Literature suggests male gender is associated with more rapid prolactin normalization (65), but no other parameters have been identified. Similar to one published study [33], we did not find a role for inflammatory markers in the context of outcome prediction.

The major limitations of our study are its retrospective design and the lack of a standardized follow-up protocol, mainly driven by the treating physician’s clinical evaluation and expertise. Additionally, the number of patients included at baseline and with follow-up data is insufficient to identify minor changes or allow for multiple statistical adjustments for factors implicated in the pathogenesis of potential metabolic and inflammatory parameters. Nevertheless, the number of patients in our cohort does not differ significantly from the numbers reported in previously published data (20, 21), which might be the main reason that the differences were not as pronounced in PRL patients compared to those with Cushing disease or acromegaly, where the impact on metabolic and inflammatory traits is more evident (40, 41). However, we believe that our cohort is representative of PRL patients, due to the similarities in our findings related to the metabolic changes also observed in other published studies. We also believe that the lack of findings regarding inflammatory markers might be related to the small sample size in both our study and others.

Another limitation of our study is that we were unable to assess the impact of treatment modality (medical treatment with dopamine agonists versus surgery) on metabolic/inflammatory/endocrine outcomes after prolactin normalization, due to the very low number of surgically treated patients in our registry, which were then excluded from this study. In summary, our study adds further evidence to the metabolic significance of PRL as a disease and negates the role of inflammatory markers in patient stratification and outcome prediction.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

The studies involving humans were approved by Die kantonale Ethikkomission, Zürich. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

SH: Data curation, Formal analysis, Writing – original draft, Writing – review & editing. LH: Writing – review & editing. SS: Writing – review & editing. RD: Data curation, Formal analysis, Writing – review & editing. LR: Writing – review & editing. CS: Writing – review & editing. FB: Writing – review & editing. ZE: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing.