European Journal of Cardiovascular Medicine

Tuberculosis (TB) remains a global public health concern and is particularly burdensome in high-incidence countries like India. According to the World Health Organization (WHO) Global Tuberculosis Report 2023, India accounted for nearly 28% of the global TB burden, highlighting the persistent challenge faced by the national TB elimination program. Despite significant advancements in diagnostics, drug regimens, and control strategies, TB continues to cause significant morbidity and mortality, especially in socioeconomically vulnerable populations.

While TB can manifest in both pulmonary and extrapulmonary forms, the clinical spectrum of disease severity is wide—ranging from mild, localized illness to extensive, systemic involvement. The ability to objectively quantify TB severity at the time of diagnosis is vital for patient triage, treatment tailoring, and prognostication. However, current tools for assessing severity, such as chest radiography, sputum smear grading, or symptom scoring, are often semi-quantitative, subjective, and can vary widely depending on host factors and comorbidities. This creates a pressing need for reliable, quantifiable biomarkers that reflect disease severity and progression.

Biomarkers such as C-reactive protein (CRP), interferon gamma release assays (IGRAs), and procalcitonin have been studied in TB, but their utility in assessing severity and monitoring therapeutic response remains inconsistent. A biomarker that not only reflects the disease burden at baseline but also dynamically responds to treatment could serve as a useful clinical tool.

Prolactin is a peptide hormone predominantly secreted by the anterior pituitary gland. While traditionally associated with lactation and reproductive functions, prolactin also plays a significant role in immune regulation. It acts on lymphocytes, macrophages, and dendritic cells by binding to prolactin receptors expressed on these immune cells, influencing cytokine production and cellular proliferation. Elevated prolactin levels have been reported in autoimmune and infectious diseases, and it is considered a stress-responsive hormone. Experimental evidence has indicated its role in modulating Th1-type immune responses, which are central to the host defense against

Mycobacterium tuberculosis.

Despite its known immunomodulatory properties, the relationship between prolactin levels and TB severity has not been systematically studied. Whether prolactin levels can serve as a surrogate marker for bacillary load or immune activation in TB patients remains an area of active exploration. Moreover, its potential role as a prognostic marker—indicating response to anti-tubercular therapy (ATT)—could offer added clinical utility.

In this context, we conducted a prospective observational study at a tertiary care hospital in Central India to evaluate serum prolactin levels in patients with newly diagnosed TB, analyze their correlation with disease severity, and assess trends during and after completion of therapy. Additionally, we aimed to determine the diagnostic performance of serum prolactin as a biomarker for severe TB using Receiver Operating Characteristic (ROC) analysis.

Objectives

1. To evaluate serum prolactin levels in newly diagnosed TB patients.
2. To assess the correlation between serum prolactin and TB severity.
3. To analyze changes in prolactin levels following anti-tubercular therapy.
4. To determine the diagnostic utility of prolactin via ROC analysis.

Study Design and Setting- This was a hospital-based prospective observational study conducted from January 2024 to December 2024 at a tertiary care hospital in Central India.

Study Population

Inclusion Criteria:

• Adult patients (18–65 years) with newly diagnosed pulmonary or extrapulmonary TB confirmed by microbiological or radiological criteria.

Exclusion Criteria:

• Pregnant or lactating women
• Patients with pituitary disorders or endocrine diseases
• Chronic liver or renal disease
• Patients on dopamine agonists or antipsychotic drugs

Data Collection

• Clinical history, symptom score (scale 0–10), BMI
• Sputum AFB grading and chest radiography
• Serum prolactin measured using Chemiluminescent Immunoassay (CLIA) at baseline, 2 months, and 6 months

Disease Severity Classification- Patients were categorized as mild, moderate, or severe based on symptomatology, radiographic extent, and sputum bacillary load.

Statistical Analysis- SPSS v26.0 was used. ANOVA and Pearson correlation were employed for group comparisons. ROC analysis identified prolactin cut-off values. Repeated measures ANOVA was used to assess follow-up trends.

The study included a total of 120 newly diagnosed tuberculosis patients, stratified into mild (n = 40), moderate (n = 40), and severe (n = 40) disease categories based on clinical and radiological parameters. The results below summarize the demographic distribution, serum prolactin levels across severity groups, ROC analysis for prolactin cut-off values, and post-treatment trends over a six-month follow-up period.

Table 1: Demographic and Clinical Profile (n = 100)

Data are presented as number (%) for categorical variables and mean ± standard deviation (SD) for continuous variables. The distribution of demographic characteristics was compared using the chi-square test for categorical variables and ANOVA for continuous variables. p-values < 0.05 were considered statistically significant.

Table 2: Serum Prolactin by TB Severity

Serum prolactin levels are presented as mean ± SD. The differences in prolactin levels between the severity groups (mild, moderate, and severe TB) were analyzed using one-way ANOVA. Post-hoc analysis was performed with Tukey’s test for pairwise comparisons. p-values < 0.05 were considered statistically significant.

Table 3: Correlation of Prolactin with Disease Parameters

The Receiver Operating Characteristic (ROC) curve analysis was performed to determine the optimal prolactin cut-off value for predicting severe TB. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. An AUC > 0.80 was considered indicative of good diagnostic accuracy.

Table 4: ROC Analysis for Prolactin as a Marker of Severe TB

Data are presented as mean ± SD. Changes in serum prolactin levels at baseline and during follow-up (after 3 and 6 months of treatment) were analyzed using paired t-tests. p-values < 0.05 were considered statistically significant.

The bar graph representing the ROC analysis metrics for prolactin as a marker of severe TB

Table 5: Prolactin Trend Post-Treatment (n = 80)

The correlation between serum prolactin levels and TB severity was assessed using Pearson’s correlation coefficient for continuous variables. The association between prolactin levels and treatment response was analyzed using paired t-tests for pre- and post-treatment values. p-values < 0.05 were considered statistically significant

The present study provides compelling evidence supporting serum prolactin as a potential biomarker for tuberculosis (TB) severity. Our findings demonstrated that prolactin levels were significantly elevated in patients with severe TB compared to those with mild or moderate disease, and levels decreased progressively following antitubercular therapy (ATT). These trends suggest that prolactin not only correlates with disease severity but also reflects disease activity and treatment response, indicating its potential utility in both diagnosis and longitudinal monitoring.

Previous investigations have explored prolactin's role in infectious diseases, although studies specifically focusing on TB remain limited. Banu Rekha et al. (2006) reported higher prolactin levels in patients with active pulmonary TB compared to those with latent TB and healthy controls, implying prolactin’s involvement in the host immune response. However, their study did not stratify patients by disease severity or assess longitudinal trends. Our study addresses this gap by prospectively evaluating patients across severity grades and tracking prolactin levels during treatment, thereby offering more granular insights.

From an immunological perspective, prolactin is increasingly recognized as a cytokine-like hormone with the ability to modulate immune cell behavior. It promotes T-cell proliferation, activates macrophages, and enhances the secretion of key Th1 cytokines like interferon-gamma (IFN-γ), which are crucial in the host defense against Mycobacterium tuberculosis (Jones et al. 2001; Matera et al. 2010). This immunostimulatory profile aligns with our observation of higher prolactin levels in severe TB cases, suggesting that prolactin may serve as a surrogate marker of heightened immune activation in response to increased bacillary burden.

In contrast, studies in chronic infections like HIV and hepatitis have sometimes reported suppressed prolactin levels due to cytokine-mediated hypothalamic-pituitary axis dysfunction (Yilmaz et al. 2004). These differing responses may reflect the distinct immune pathophysiology of TB, characterized by a sustained cell-mediated immune response rather than immunosuppression, particularly in drug-sensitive pulmonary TB. Notably, prolactin has also been implicated in autoimmune conditions such as systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), where elevated levels correlate with disease activity and flare-ups (Orbach and Shoenfeld 2007), further underscoring its role in immune-driven inflammatory states.

The diagnostic utility of prolactin in our cohort was evaluated using Receiver Operating Characteristic (ROC) analysis, which identified a cut-off of 29.5 ng/mL for predicting severe TB. The area under the curve (AUC) was 0.88 (95% CI: 0.81–0.94), indicating good sensitivity and specificity. This performance is comparable to commonly used inflammatory biomarkers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), which have shown AUC values ranging from 0.80 to 0.85 in previous studies evaluating TB severity (Rajagopalan et al. 2019). However, unlike these nonspecific markers, prolactin may offer added clinical value by reflecting both immune activation and neuroendocrine stress responses.

Our longitudinal data also revealed a significant decline in prolactin levels over six months of ATT, supporting its potential role as a biomarker for treatment response. These findings are consistent with Aggarwal et al. (2017), who observed normalization of stress-related hormones, including prolactin, in TB patients responding to treatment. While their study focused broadly on endocrine changes during TB therapy, our study correlates these changes specifically with clinical improvement and severity classification. Importantly, two patients with persistently elevated prolactin levels at the end of treatment either relapsed or showed incomplete resolution, suggesting a possible prognostic role for prolactin that merits further investigation.

Nevertheless, our study has several limitations. It was conducted at a single tertiary care center with a relatively small sample size, limiting the generalizability of the findings. Factors such as psychological stress, medications, and subclinical endocrine disorders, which can influence prolactin levels, could not be fully controlled. Furthermore, the lack of a healthy control group and comparator groups with other respiratory infections limits the specificity analysis of prolactin as a TB-specific marker.

Despite these limitations, our study offers several strengths, including a prospective design, clear stratification of TB severity, and serial prolactin assessments, which collectively enhance the robustness of the findings. The observed correlation between prolactin levels and disease severity, supported by comparative literature, suggests a pathophysiological link worthy of deeper exploration. Given its ease of measurement, affordability, and biological plausibility, prolactin could be incorporated as an adjunct biomarker in TB care, particularly in resource-limited settings where conventional diagnostic tools may be unavailable or insufficient.

In inference, this study adds to the growing body of evidence supporting the role of prolactin as an immune biomarker in infectious diseases. It not only reflects baseline TB severity but also responds to therapy, highlighting its dual diagnostic and prognostic potential. Further multicenter studies with larger sample sizes and broader inclusion criteria, including extrapulmonary and drug-resistant TB cases, are warranted to validate and extend these findings modulation.

Serum prolactin shows promise as a biomarker for assessing TB severity and monitoring response to treatment. A cut-off of 29.5 ng/mL may help identify patients with severe disease. Further multicentric longitudinal studies are warranted to confirm its clinical utility.

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