Association between Prolactinoma and Incident Type 2 Diabetes Mellitus: Evidence from a Korean Nationwide Cohort
Article information
Abstract
Background
The effect of prolactinoma on the development of type 2 diabetes mellitus (T2DM) has not been evaluated at the population level. We investigated the association between prolactinoma and incident T2DM in a nationwide cohort.
Methods
We analyzed data from the Korean National Health Insurance Service–National Sample Cohort, which includes 2% of the total national population selected by random sampling, covering the period from 2002 to 2019. A total of 335 patients with newly diagnosed prolactinoma and 1,562 age- and sex-matched controls were included. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for T2DM using Cox proportional hazards regression. Time-dependent HRs were also estimated.
Results
The proportion of prolactinoma patients who developed T2DM was significantly higher than that of controls (10.1% vs. 5.5%; relative risk, 1.84; 95% CI, 1.26 to 2.69). After adjustment for age, sex, income, and comorbidities, the risk of T2DM remained significantly higher in the prolactinoma group (HR, 1.56; 95% CI, 1.01 to 2.41). Subgroup analysis showed a markedly increased risk among individuals aged ≥50 years (HR, 4.47; 95% CI, 1.66 to 12.08). The positive association between prolactinoma and T2DM decreased over time but remained significant for up to 3 years after prolactinoma diagnosis (HR, 9.28 [95% CI, 3.45 to 24.97]; HR, 3.15 [95% CI, 1.55 to 6.38]; and HR, 0.82 [95% CI, 0.44 to 1.53] for ≤1, 1–3, and >3 years, respectively).
Conclusion
Newly diagnosed prolactinoma was independently associated with a higher risk of T2DM, with the association diminishing over 3 years. These findings highlight the importance of monitoring for T2DM during the early management of prolactinoma, particularly in middle-aged patients who may have been previously overlooked.
INTRODUCTION
Prolactinomas, which are most commonly benign prolactin-secreting adenomas originating from lactotroph cells, account for approximately 50% of all pituitary adenomas in both women and men [1]. Prolactinoma can be effectively managed with dopamine agonists, which normalize serum prolactin levels, reduce adenoma mass, and restore gonadal function [1]. Most symptoms are controlled with bromocriptine or cabergoline. Even in patients with gigantic prolactinomas, dopamine agonists remain highly effective; 97% of patients show improved visual fields, 60% achieve normalized prolactin levels, and 74% experience reduced adenoma volume [2–4]. Therefore, prolactinoma has long been regarded as a relatively benign clinical condition [1].
However, recent research has demonstrated that prolactin contributes to the pathophysiology of several chronic illnesses, including cardiovascular disease, liver cirrhosis, and kidney disorders [5–7]. These observations suggest that hyperprolactinemia may play a role in the development of chronic metabolic disturbances rather than functioning solely as a hormonal anomaly. Prior clinical research has shown that hyperglycemia and insulin resistance are more frequently associated with hyperprolactinemia caused by pituitary prolactinoma [8,9]. In addition, prolactin has been implicated in worsening lipid profiles, raising the possibility of a relationship between prolactin and adverse cardiovascular outcomes [10]. Although several studies have examined the association between hyperprolactinemia and metabolic syndrome or insulin resistance, relatively few have explored the relationship between pituitary prolactinoma and the subsequent incidence of diabetes mellitus (DM) using a large nationwide cohort. In this retrospective cohort study, we investigated the incidence of type 2 diabetes mellitus (T2DM) among patients with prolactin-secreting pituitary adenomas using a large, nationally representative sample derived from the Korean National Health Insurance Database.
METHODS
Data source
This retrospective cohort study used data from the Korean National Health Insurance Service–National Sample Cohort (NHIS-NSC) version 2.2. Established in 2022, this database was constructed from administrative claims data for one million individuals, representing approximately 2% of the entire Korean population. The NHIS-NSC is a population-based cohort created through random sampling with proportional allocation across age, sex, income level, and insurance eligibility, thereby ensuring representativeness of the national population. The cohort has been validated to closely reflect the demographic and healthcare characteristics of the Korean population [11]. The database includes individuals insured by the NHIS or eligible for medical care. The sample was drawn as of 2006, providing prospective medical record information from 2006 to 2019. The eligible population was stratified with proportional allocation according to age, sex, region, health insurance type, and household income. The cohort includes information on socioeconomic status, diagnoses and comorbidities, demographic characteristics, prescriptions, and medical costs from all clinics and hospitals [11]. The study adheres to the ethical principles of the Declaration of Helsinki and was approved by the Institutional Ethics Committee of the Asan Medical Center (number S2022-2509-0001). Because the National Sample Cohort consists of de-identified data, informed consent was waived.
Definitions of prolactinoma, study participants, and study outcomes
In South Korea, the Ministry of Health and Welfare designates diseases with fewer than 20,000 patients, or where the exact number of patients is uncertain due to diagnostic difficulty, as ‘rare and incurable diseases’ [12]. Through the NHIS, the government has provided financial support for individuals with rare and incurable diseases since 2009 [12]. To be registered, patients must meet predefined diagnostic criteria set by the Ministry of Health and Welfare for each condition [12]. The code for benign neoplasm of the pituitary gland (V162) is assigned by physicians based on compatible imaging, such as brain magnetic resonance imaging, with or without biochemical evidence. In this study, prolactinoma was operationally defined using a combination of International Classification of Diseases, 10th Revision (ICD-10) codes for pituitary adenoma (D35.2) and hyperprolactinemia (E22.1), the coverage code for pituitary neoplasms (V162), and at least two prescriptions for dopamine agonists (bromocriptine or cabergoline) between 2006 and 2019 (Supplemental Table S1). Prolactinoma was identified if at least one of the following criteria was met: (1) D35.2 and E22.1; (2) V162 and E22.1; (3) D35.2 and ≥2 dopamine agonist prescriptions; or (4) V162 and ≥2 dopamine agonist prescriptions. This definition was adapted from previous literature to ensure comprehensive identification of prolactinoma cases [13]. The index date was defined as the date of the first observed medical claim related to prolactinoma. For each prolactinoma patient, an individual without pituitary adenoma was randomly selected from the control group at a 5:1 ratio after exact matching by age and sex. Individuals diagnosed with T2DM prior to study enrollment were excluded. The outcome was newly diagnosed T2DM, defined as at least one claim based on a primary or secondary diagnosis using ICD-10 codes E11–E14. The cohort was followed from the index date until the onset of T2DM, death, or December 31, 2019, whichever occurred first. In total, 335 patients with prolactinoma and 1,562 controls were included.
Definition of comorbidities
We identified comorbidities to account for metabolic risk factors potentially associated with the primary outcome. Hypertension was defined as at least one claim with a primary or secondary diagnosis according to ICD-10 codes I10–I13 and I15 before the index date. Dyslipidemia was defined as at least one claim with a primary or secondary diagnosis according to ICD-10 code E78 before the index date.
Statistical analysis
Baseline characteristics were analyzed using descriptive statistics. We compared baseline characteristics at the time of prolactinoma diagnosis and at the time of age- and sex-matching for controls. Categorical variables were presented as frequency and percentage. Continuous variables were described as mean and standard deviation for normally distributed data. Between-group comparisons of continuous variables and categorical variables were conducted using the independent sample t test and chi-squared test, respectively. We estimated the relative risk (RR) as the ratio of the proportion of subjects who developed T2DM in the prolactinoma group to that in the control group. Incidence curves were generated using the Kaplan-Meier method, and the log-rank test was performed. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression analysis [14]. The proportional hazards assumption was evaluated using a log-minus-log plot. When the assumption was violated, we determined appropriate intervals for varying HRs by examining the plot for significant divergence or intersection points. Based on these intervals, time-dependent HRs and their 95% CIs were estimated using time-dependent hazard regression analysis [15]. Age, sex, income, and the presence of hypertension and dyslipidemia were included as covariates in all models to produce adjusted HRs (aHRs). Subgroup analyses were conducted according to age, sex, and the presence of hypertension and dyslipidemia. To explore results after adjusting for baseline body mass index (BMI) as an additional covariate, we performed analyses among patients with available baseline BMI data. To address missing BMI values, we used a last observation carried forward approach, using the most recent BMI recorded within 4 years prior to the index date.
We also conducted an analysis stratifying prolactinoma patients by treatment status. Patients were classified as treated if they received dopamine agonists, surgery, or both, and as untreated if they did not receive any treatment. We calculated the RR of T2DM for treated and untreated prolactinoma patients compared with controls, along with the corresponding 95% CIs. Statistical significance was defined as P<0.05. All analyses were conducted using R statistical software version 3.3.3 (R Foundation for Statistical Computing, Vienna, Austria) and SAS Enterprise Guide software version 8.1 (SAS Institute Inc., Cary, NC, USA).
RESULTS
Baseline characteristics of participants
Baseline characteristics of patients with prolactinoma and controls are presented in Table 1. The mean age of patients with prolactinoma and the control group was 34.64±10.51 and 35.46±11.28 years, respectively. The proportion of women in both groups was 89%. Income levels were comparable, as indicated by similar proportions across each decile of the total population’s household income. There was no significant difference in BMI between patients diagnosed with prolactinoma and the control group. The prevalence of baseline hypertension and dyslipidemia was significantly higher in patients with prolactinoma than in controls. Among patients with prolactinoma, 70.1% received treatment and 29.9% did not receive any treatment. Of those treated, 87.5% received a dopamine agonist only, 3.2% underwent surgery only, and 9.2% received both treatments.
Baseline Characteristics in Patients with Prolactinoma and Age- and Sex-Matched Controls
Prolactinoma and risk of T2DM
The proportion of individuals who developed T2DM was significantly higher among patients with prolactinoma than among controls (10.1% vs. 5.5%; RR, 1.84; 95% CI, 1.26 to 2.69) (Table 2). In the crude model, patients with prolactinoma had a 1.92-fold increased risk of developing T2DM compared with those without prolactinoma (95% CI, 1.29 to 2.86; P=0.001). After adjustment for age, sex, income, and the presence of hypertension and dyslipidemia, a 56% higher risk of incident T2DM remained in patients with prolactinoma (95% CI, 1.01 to 2.41; P=0.046). The cumulative incidence of T2DM over 12 years was also significantly higher among patients with prolactinoma compared with controls (P=0.0013, log-rank test) (Fig. 1).
Hazard Ratios for Type 2 Diabetes Mellitus in Patients with Prolactinoma and Controls
Kaplan-Meier plot for the time to onset of type 2 diabetes mellitus. The blue line represents the control group, while the red line denotes the prolactinoma group. Shaded areas around each line indicate 95% confidence intervals. The P value was calculated using the log-rank test. The table below the graph displays the number at risk.
Time-dependent association of prolactinoma with T2DM
The longitudinal association between prolactinoma and the development of T2DM was evaluated. Overall, the increased risk of incident T2DM associated with prolactinoma diminished with longer follow-up, with transitions occurring at approximately 12 and 36 months after diagnosis (Supplemental Fig. S1). In the adjusted analysis, the risk of T2DM was 9.28 times higher in patients with prolactinoma compared with controls within 12 months of diagnosis (95% CI, 3.45 to 24.97; P<0.001), and 3.15 times higher during the 12- to 36-month period (95% CI, 1.55 to 6.38; P=0.002) (Table 3). After 36 months, the risk was not significantly different between the two groups.
Time-Dependent Hazard Ratio for Type 2 Diabetes Mellitus in Patients with Prolactinoma Compared with Controls
Subgroup analysis for risk of T2DM
Among individuals aged 50 years and older, the risk of developing T2DM was 4.47 times higher in patients with prolactinoma than in controls (95% CI, 1.66 to 12.08; P=0.003) (Supplemental Table S2). However, among individuals younger than 50, there was no significant difference in risk between the groups. Women with prolactinoma, who accounted for 89% of the prolactinoma cohort, showed a trend toward a 58% increased risk of T2DM (95% CI, 0.98 to 2.54; P=0.059). A diagnosis of prolactinoma was associated with a 2.78-fold increased risk of T2DM among hypertensive patients (95% CI, 1.08 to 7.15; P=0.033).
Sensitivity analysis for participants with BMI data
Given the important role of BMI in T2DM pathophysiology and the substantial proportion of participants with missing BMI data, a sensitivity analysis incorporating baseline BMI as a covariate was performed in participants with available BMI measurements. The risk of T2DM was higher in patients with prolactinoma than in controls in the unadjusted analysis (HR, 1.79; 95% CI, 1.01 to 3.04; P=0.031) (Supplemental Table S3). However, this difference was not maintained in the covariate-adjusted model (aHR, 1.49; 95% CI, 0.85 to 2.59; P=0.162). In the time-dependent hazard regression analysis, the increased risk of T2DM in patients with prolactinoma showed a similar pattern of attenuation over time (Supplemental Table S4). Before 12 months and during the 12- to 36-month period, the aHRs were 12.18 and 3.46, respectively (95% CI, 3.34 to 43.17; and 95% CI, 1.56 to 7.66). After 36 months, the risk was not significantly different between the two groups (aHR, 0.45; 95% CI, 0.16 to 1.27; P=0.134).
Risk of T2DM by treatment status in prolactinoma patients
Among patients with prolactinoma, those who received treatment had a significantly higher risk of developing T2DM compared with controls (12.3% vs. 5.6%; RR, 2.19; 95% CI, 1.47 to 3.26; P<0.001) (Supplemental Table S5). In contrast, there was no significant difference in risk between untreated prolactinoma patients and controls (5.0% vs. 5.6%; RR, 0.88; 95% CI, 0.37 to 2.14; P=0.789).
DISCUSSION
This population-based cohort study demonstrated that prolactinoma is significantly associated with an increased risk of incident T2DM. After adjusting for confounders, the risk of developing T2DM was 56% higher in patients with newly diagnosed prolactinoma compared with age- and sex-matched controls without prolactinoma. The increased risk of T2DM associated with prolactinoma was particularly pronounced in participants aged ≥50 years, with an aHR of 4.47. The positive association between prolactinoma and T2DM varied over time, with a 9.28- and 3.15-fold higher risk in the prolactinoma group during the first and 1–3 years of follow-up, respectively, but a similar risk after three years. A sensitivity analysis adjusting for BMI also showed a time-dependent association during the first 3 years. To our knowledge, this is the first nationwide study to examine the longitudinal effect of prolactinoma on T2DM development compared with controls without pituitary adenoma. These findings offer new insight into the potential risk of T2DM in patients with newly diagnosed prolactinoma, especially in those ≥ 50 years of age, who may have received less attention in clinical management.
The association between hyperprolactinemia and insulin resistance or DM has been described in previous studies [8,9,16–18]. A cross-sectional study of premenopausal women showed that those with prolactinoma or idiopathic hyperprolactinemia demonstrated greater insulin resistance, based on the Homeostatic Model Assessment Index and fasting glucose, than age-matched controls [9]. In men participating in the Framingham Heart Study, the risk of incident DM increased 1.7-fold with each 5 mg/dL increase in prolactin levels [18]. Similarly, during a follow-up of more than 4 years, both transsphenoidal surgery and medical therapy with dopamine agonists significantly reduced fasting glucose, supporting the beneficial effect of prolactin normalization on glucose metabolism regardless of treatment modality [19]. Although most of these studies were small, they support a role for hyperprolactinemia in exacerbating hyperglycemia [8,9,16–18]. Consistent with this evidence, our study showed a significantly higher incidence of T2DM in patients with prolactinoma than in healthy controls. Subgroup and BMI-based sensitivity analyses also indicated a trend toward increased risk, although statistical significance was not consistently maintained in covariate-adjusted models due to the limited number of events. Beyond the traditional therapeutic focus on restoring prolactin levels, gonadal function, and bone health [1], our findings emphasize the need for attention to DM risk in patients with prolactinoma.
Conversely, a meta-analysis of prolactin levels within the physiological range showed a 50% lower risk of prevalent T2DM in individuals in the highest quartile compared with those in the lowest quartile [20]. One trial included in the analysis was a prospective study of 2,377 Chinese participants, which found a 52% reduced risk of DM in women in the fourth quartile of prolactin, with a median level of 12.97 ng/mL [21]. A cohort study of 8,615 United States women also showed an inverse relationship between prolactin levels and T2DM risk over 9–10 years of follow-up, with a cutoff of 15.8 ng/mL in the highest quartile [22]. Moreover, overtreatment with cabergoline to achieve subphysiologic prolactin levels (<5 ng/mL) in women with hyperprolactinemia resulted in worsening hyperglycemia and insulin resistance compared with treatment within the reference range (5 to 25 ng/mL) or no treatment in normoprolactinemic controls [23]. These findings contrast with those of our study because prior research analyzed prolactin’s effects at physiological or subphysiological levels, whereas our study focused on pathologically elevated prolactin. Taken together, both excessive and deficient prolactin appear to adversely affect glucose regulation. Determining the optimal prolactin level for minimizing DM risk remains an important future research question.
Consistent with clinical evidence, in vitro and in vivo studies have shown heterogeneity in glucose metabolism according to prolactin levels. Prolactin increases insulin production and sensitivity by promoting β-cell proliferation and survival, upregulating insulin gene transcription and adiponectin, and suppressing fatty acid synthase [24–27]. In contrast, hyperprolactinemia induces insulin resistance and compensatory hyperinsulinemia [28]. In diabetic rats, both low- and high-dose prolactin administration for 4 weeks increased β-cell mass, yet glucose tolerance improved only in the low-dose group and worsened in the high-dose group [28]. Low-dose prolactin induced β-cell hyperplasia through neogenesis and proliferation, whereas high-dose exposure caused β-cell hypertrophy with increased apoptosis [28]. Hyperprolactinemia also exacerbates insulin resistance by suppressing insulin receptor binding, adiponectin expression, and lipoprotein lipase activity [17,29,30]. These mechanisms support a link between prolactinoma, insulin resistance, and T2DM development.
The present study also demonstrated time-dependent attenuation of the association between newly diagnosed prolactinoma and incident T2DM. Whether this reflects true variation in metabolic effects over time or the influence of treatment requires further investigation. It is possible that the risk of T2DM decreased following treatment for prolactinoma, as approximately two-thirds of patients in our cohort received therapy. Clinical trials have shown that dopamine agonists or surgery improve hyperglycemia and insulin resistance in patients with hyperprolactinemia [19,31,32]. Although definitive conclusions cannot be drawn from this retrospective cohort, our findings suggest that surveillance for incident T2DM may be most important during the first 3 years after prolactinoma diagnosis.
In our stratified analysis of prolactinoma patients by treatment status, those who underwent therapy exhibited a significantly higher risk of developing T2DM compared with the control group, whereas untreated patients showed no such increase. This pattern may reflect confounding by treatment indication, as patients selected for treatment likely had more severe disease, elevated prolactin levels, or other baseline risk factors that prompted intervention. The Korean NHIS-NSC database we used lacks detailed clinical parameters, such as serum prolactin measurements, which limited our ability to adjust fully for these potential confounders. Future investigations should incorporate data sources with richer clinical and laboratory information to clarify the contributions of disease severity and treatment to diabetes risk and to facilitate more precise risk stratification.
Current guidelines for the management of prolactinoma do not recommend normalizing serum prolactin levels in post-menopausal women unless other clinical indications exist [1]. The average age of natural menopause in Korea is 49.3 years [33]; thus, many patients with prolactinoma who are older than 50 years discontinue treatment and undergo less intensive follow- up. Interestingly, our study demonstrated that prolactinoma was associated with a higher risk of T2DM in individuals ≥50 years compared with younger age groups. Age groups with a high baseline incidence of T2DM may be especially vulnerable to the additional risk associated with prolactinoma, given that the mean age of T2DM diagnosis in Korea is 50 to 55 years [34]. Additionally, prolactinoma in postmenopausal women is more often diagnosed as macroprolactinoma, which is typically accompanied by mass effects or markedly elevated prolactin levels [35]. Although this study did not evaluate the degree of hyperprolactinemia or tumor size, participants aged ≥50 years may have had more advanced disease, contributing to their substantially increased T2DM risk. Therefore, in contrast to the conventional palliative approach for prolactinoma in middle-aged women who do not require treatment to preserve fertility or menstrual regularity, ongoing monitoring for glucose intolerance and T2DM during the first 3 years after diagnosis may still be warranted.
A major strength of this study is its use of a nationally representative population-based cohort to investigate the association between prolactinoma and incident T2DM. The risk of developing T2DM in patients with prolactinoma was evaluated in comparison with age- and sex-matched controls. Additionally, subgroup analyses by age, sex, and comorbidities, as well as a sensitivity analysis adjusting for BMI, were performed. However, several limitations should be noted. First, because the NHIS-NSC database includes only 2% of the total Korean population, estimations of incidence carry inherent limitations. Nevertheless, nationwide studies covering the entire Korean population have reported annual prolactinoma incidence rates of 15–23.5 cases per million [13,36], and our estimate of approximately 23.9 per million annually aligns with these figures, supporting the validity of our results. Moreover, the NHIS-NSC database is nationally representative because participants are selected using random sampling proportional to key demographic and socioeconomic strata [11]. Further validation using data from the entire national population is still warranted. Second, causal relationships between prolactinoma and T2DM cannot be established in this observational study. Third, defining prolactinoma based on claims data may limit diagnostic accuracy. To reduce this concern, prolactinoma was identified using a combination of diagnostic codes, prescription records, and registry-based verification, all of which primarily involve expert pituitary clinicians. Likewise, defining T2DM, hypertension, and dyslipidemia solely using ICD-10 codes, without laboratory values or medication records, may have introduced misclassification, potentially underestimating early or undiagnosed disease. Fourth, because claims data do not include serum prolactin measurements, we could not stratify T2DM risk according to actual prolactin levels. Prospective studies with detailed baseline and follow- up prolactin measurements, along with treatment status, would help clarify dose–response relationships. Fifth, as with other claims-based studies, residual confounding is possible because we could not adjust for all T2DM-related risk factors. Sixth, although obesity is a well-known risk factor for T2DM, matching or adjustment for BMI in the primary analyses was not feasible due to substantial missing BMI data, largely attributable to the younger age distribution of the cohort and lower participation in health screening. This may have resulted in residual confounding by adiposity; however, sensitivity analyses among participants with BMI data yielded results largely consistent with the primary findings. Additionally, subgroup analyses suggested that the T2DM risk associated with prolactinoma may differ according to characteristics such as age and sex. However, certain subgroups, such as women aged ≥50 years, contained relatively few participants, limiting statistical power for interaction testing. Larger and more diverse cohorts will be needed to determine whether the observed associations vary meaningfully across subpopulations and to clarify the influence of demographic and clinical factors. Finally, because this study was conducted in a Korean population, the findings may not be generalizable to other ethnic groups. Validation in diverse populations is needed.
In conclusion, this nationwide cohort study showed that patients with newly diagnosed prolactinoma had a significantly higher risk of incident T2DM compared with healthy controls, with those aged ≥50 years experiencing more than a fourfold increased risk. The association between prolactinoma and T2DM decreased over time, with a significantly elevated risk persisted for up to 3 years after diagnosis. These results support broadening the management goals for prolactinoma to include early monitoring for glucose intolerance and T2DM, regardless of patient age.
Supplementary Material
Supplemental Fig. S1.
Log-minus-log plot for time to onset of type 2 diabetes mellitus. The dotted and solid lines correspond to the control and prolactinoma groups, respectively. The x-axis represents the follow-up duration on a logarithmic scale.
Supplemental Table S1.
Definition of Prolactinoma
Supplemental Table S2.
Subgroup Analysis for Risk of Type 2 Diabetes Mellitus in Patients with Prolactinoma Compared with Controls
Supplemental Table S3.
Hazard Ratio for Type 2 Diabetes Mellitus in Participants with BMI Data
Supplemental Table S4.
Time-Dependent Hazard Ratio for Type 2 Diabetes Mellitus in Patients with Prolactinoma Compared with Controls in Participants with BMI Data
Supplemental Table S5.
Relative Risk of Type 2 Diabetes Mellitus in Treated and Untreated Prolactinoma Patients Compared with Controls
Notes
CONFLICTS OF INTEREST
No potential conflict of interest relevant to this article was reported.
ACKNOWLEDGMENTS
This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00241523) and Ministry of Science and ICT of Korea (RS-2024-00393728, RS-2024-00438349), and Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea (2023IP0101).
AUTHOR CONTRIBUTIONS
Conception or design: S.H.M. Acquisition, analysis, or interpretation of data: B.R.Y., J.H.Y. Drafting the work or revising: H.N.J., M.S.K., S.H.M. Final approval of the manuscript: S. H.M.