Published online Jul 28, 2026. doi: 10.3748/wjg.119442
Revised: March 19, 2026
Accepted: April 2, 2026
Published online: July 28, 2026
Processing time: 168 Days and 0.9 Hours
Hypopituitarism is a high-risk factor for rapidly progressive metabolic dys
To evaluate FIB-9 performance and compare it with traditional indices and mar
This retrospective study of 35 patients (19 cirrhosis, 16 non-cirrhosis) evaluated the diagnostic accuracy of the FIB-9 index. Comparisons were made with the FIB-4 index, aspartate aminotransferase-to-platelet ratio index (APRI), direct serum fibrosis markers [hyaluronic acid (HA), procollagen III N-terminal peptide, type IV collagen, and laminin], and liver stiffness measurement (LSM). Performance was assessed using the area under the receiver operating characteristic curve (AUROC) method.
FIB-9 was significantly higher in the cirrhosis group than in controls [3.75 (2.04, 3.99) vs 0.49 (0.04, 1.59), P < 0.001]. FIB-9 demonstrated the highest diagnostic accuracy (AUROC = 0.941; 95%CI: 0.856-1.000), outperforming APRI (AUROC = 0.903), FIB-4 (AUROC = 0.872), and direct markers like HA (AUROC = 0.878). At an optimal cutoff of 1.748, FIB-9 yielded 89.5% sensitivity and 100.0% specificity. Notably, individual parameters such as alanine aminotransferase (P = 0.417) and albumin (P = 0.109) failed to distinguish between groups. LSM was significantly elevated in the cirrhosis group [23.2 (15.8, 31.9) kPa vs 5.7 (3.9, 6.8) kPa, P < 0.001].
The FIB-9 index is superior to traditional indices and direct markers for identifying cirrhosis in hypopituitarism, serving as a highly reliable rule-in tool for clinical triage.
Core Tip: Patients with hypopituitarism are at high risk for rapidly progressive metabolic liver disease, yet traditional non-invasive tests often lack accuracy in this specific endocrine-mediated context. This study provides the first clinical validation of the fibrosis-9 (FIB-9) index in hypopituitary patients. Our findings reveal that FIB-9 (area under the receiver operating characteristic curve = 0.941) significantly outperforms aspartate aminotransferase-to-platelet ratio index, fibrosis-4, and conventional serum markers in identifying cirrhosis. Using an optimal cutoff of 1.748, FIB-9 serves as a highly reliable triage tool, enabling clinicians to accurately identify high-risk patients for advanced liver assessment while sparing others from unnecessary testing.
- Citation: Li T, Wang X, Nie M, Han Q, Wu XY, Mao JF. Fibrosis index nine superiority for cirrhosis in hypopituitarism: Clinical validation of endocrine liver triage tool. World J Gastroenterol 2026; 32(28): 119442
- URL: https://www.wjgnet.com/1007-9327/full/v32/i28/119442.htm
- DOI: https://dx.doi.org/10.3748/wjg.119442
Hypopituitarism is increasingly recognized as a risk factor for metabolic liver disease and cryptogenic cirrhosis. It most commonly results from craniopharyngioma (CP), germ cell tumors, or pituitary stalk interruption syndrome (PSIS)[1]. Long-term hormone deficiencies, particularly growth hormone (GH) and cortisol deficiencies, contribute to hypothalamic obesity (HO), insulin resistance, and progressive hepatic fibrosis, even in young patients[2-5]. Recent studies have reported a striking prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD; 62%-71%) and significant fibrosis (≥ F2) in up to one-third of hypopituitary survivors of sellar tumors, with some progressing to cirrhosis by early adulthood[6,7].
Although liver biopsy remains the reference standard for fibrosis staging, its invasiveness limits its routine use. Consequently, non-invasive tests (NITs), such as the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index, are widely used to estimate fibrosis severity. However, their performance in endocrine-mediated liver injury remains uncertain, as conventional liver enzyme levels may be misleading in this setting[8].
Recently, a next-generation NIT, fibrosis-9 (FIB-9), was developed to improve diagnostic accuracy by integrating nine routinely available parameters: Age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin (Alb), total bilirubin (TBil), gamma-glutamyl transferase (GGT), platelet count, creatinine, and glucose. In a multicenter cohort of patients with MASLD, FIB-9 outperformed FIB-4 in detecting advanced fibrosis [area under the receiver operating characteristic (ROC) curve: 0.863 vs 0.757] and demonstrated accuracy comparable to that of proprietary tests[9].
To date, the utility of FIB-9 has not been evaluated in patients with hypopituitarism, who are at high risk of rapid fibrosis progression yet remain underrepresented in existing validation studies. Unlike classic MASLD, which is primarily driven by obesity and insulin resistance, liver disease in hypopituitarism results from multiple hormonal deficiencies that directly disrupt hepatic lipid metabolism and fibrogenesis[6,7]. This distinct pathophysiology may render conventional fibrosis scores suboptimal. Therefore, this study aimed to evaluate the diagnostic performance of FIB-9 for liver fibrosis in patients with hypopituitarism and to compare it with APRI, FIB-4, direct serum fibrosis markers [hyaluronic acid (HA), procollagen III N-terminal peptide (PIIINP), type IV collagen (CIV), and laminin (LN)], and liver stiffness measurement (LSM).
Clinical data from 35 patients with hypopituitarism were retrospectively collected from outpatient records between September 2022 and September 2025. The cohort comprised individuals with hypopituitarism secondary to CP resection, germ cell tumors treated with chemotherapy and/or radiotherapy, or PSIS, which represented the predominant congenital etiology with complete data in our cohort; overall, CP was the most common etiology. Patients were stratified into two groups according to cirrhosis status: A cirrhosis group and a non-cirrhosis group.
Hypopituitarism was diagnosed on the basis of clinical manifestations of pituitary hormone deficiency and confirmed by dynamic endocrine testing[1]. GH deficiency (GHD) and central adrenal insufficiency were assessed simultaneously using either the insulin tolerance test (ITT) or the glucagon stimulation test (GST). For the diagnosis of GHD, a peak GH level < 3 μg/L was required during ITT, whereas for GST, a peak GH level < 3 μg/L [< 1 μg/L if body mass index (BMI) ≥ 30 kg/m2] was applied in accordance with AACE/ACE guidelines[10]. A peak cortisol level < 18 μg/dL (< 500 nmol/L) during these tests was considered diagnostic of central adrenal insufficiency. The adrenocorticotropic hormone (ACTH) stimulation test was used only to exclude primary adrenal insufficiency or when ITT or GST was contraindicated. Central hypothyroidism was identified by low free thyroxine (FT4) with low or normal thyroid-stimulating hormone (TSH) levels; thyrotropin-releasing hormone testing was added when clinically indicated. Hypothalamic-pituitary-gonadal axis status was assessed by basal luteinizing hormone, follicle-stimulating hormone, testosterone in males, or estradiol in females, together with compatible clinical features.
Cirrhosis was diagnosed by the treating physician using a composite non-invasive approach consistent with current AASLD and EASL guidelines[8,11] and the Baveno VII consensus[12], which recognize that in patients with overt clinical or imaging signs of portal hypertension, cirrhosis can be diagnosed with high confidence without mandatory liver biopsy or elastography. In cases in which LSM by transient elastography (FibroScan®) was available, either the M or XL probe was selected according to body habitus to ensure reliable assessment, and cirrhosis was defined as an LSM ≥ 12.5 kPa, a threshold widely validated for F4 fibrosis[11,12]. In patients without LSM data, the diagnosis relied on highly specific markers of advanced liver disease endorsed by these international guidelines[11,12], specifically requiring typical morphologic signs on abdominal imaging (e.g., a nodular liver surface, splenomegaly, or portal vein dilatation) and/or endoscopic evidence of gastroesophageal varices in the setting of chronic liver disease. The non-cirrhosis group comprised individuals with no clinical, imaging, or elastographic evidence of cirrhosis (F4). Importantly, this group included patients with a normal liver or mild-to-moderate fibrosis (F0-F3); when LSM was available, values < 7.0 kPa served as confirmatory evidence for the absence of advanced fibrosis, whereas values between 7.0 kPa and 12.5 kPa were classified as non-cirrhotic provided that no signs of portal hypertension were present.
All participants met the following criteria: (1) Confirmed hypopituitarism due to CP resection, germ cell tumor (after chemotherapy and/or radiotherapy), or PSIS, as verified by brain magnetic resonance imaging; (2) Regular follow-up at the endocrinology outpatient clinic during the study period; and (3) Availability of comprehensive clinical and laboratory data.
Demographic and laboratory parameters were retrospectively compared between the cirrhosis and non-cirrhosis groups. These included hepatocellular injury markers (AST and ALT), cholestatic enzymes [GGT and alkaline phosphatase (ALP)], synthetic function indices [Alb, TBil, and international normalized ratio (INR)], platelet count, direct serum fibrosis biomarkers including HA, PIIINP, CIV, and LN, as well as LSM, when available.
Three non-invasive fibrosis scores were calculated: APRI, FIB-4 index, and FIB-9 index. All laboratory parameters required for score calculation were available for all participants. Laboratory tests and clinical assessments were per
These indices were compared to evaluate their diagnostic performance for detecting advanced liver disease and to identify the most accurate tool for assessing liver fibrosis in patients with hypopituitarism.
Data analysis: Statistical analyses were performed using GraphPad Prism (version 9.5.1; GraphPad Software, San Diego, CA, United States) and R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were assessed for normality using the Shapiro-Wilk test and for homogeneity of variance using Levene’s test. Normally distributed data are presented as mean ± SD, whereas non-normally distributed data are presented as median (interquartile range). According to data distribution, baseline comparisons between the cirrhosis and non-cirrhosis groups were performed using the independent-samples t test for normally distributed variables or the Mann-Whitney U test for non-normally distributed variables. Categorical variables are presented as n (%) and were compared using the χ2 test or Fisher’s exact test, as appropriate. A two-sided P value < 0.05 was considered statistically significant.
Data visualization: ROC analysis was performed for APRI, FIB-4, FIB-9, and serum fibrosis markers (HA, PIIINP, CIV, and LN) to evaluate diagnostic performance. Areas under the ROC curve (AUCs) with 95%CIs were calculated using the pROC package. The optimal cutoff value for FIB-9, determined by the maximum Youden index, was marked on the ROC curve. All figures were formatted for publication using ggpubr.
A total of 35 patients with confirmed hypopituitarism were included, comprising 19 patients in the cirrhosis group and 16 in the non-cirrhosis group. In both groups, CP was the most common underlying etiology, followed by PSIS and germ cell tumors. The distribution of etiologies did not differ significantly between the two groups (P = 0.537).
Among baseline demographic characteristics, patients in the cirrhosis group were significantly older than those in the non-cirrhosis group [28 (20, 34) years vs 19.5 (16, 23.5) years, P = 0.024]. No significant between-group differences were observed in sex distribution, height, weight, or BMI (all P > 0.05).
Baseline endocrine assessment showed comparable pituitary hormone deficiency profiles in the two groups. FT4, TSH, peak cortisol, insulin-like growth factor 1 (IGF-1), testosterone in males, and estradiol in females were not significantly different between groups (all P > 0.05). Hormone replacement therapy was also similarly distributed, including thyroid hormone replacement (100% in both groups), glucocorticoid replacement (94.7% vs 87.5%, P = 0.742), GH replacement (63.2% vs 50.0%, P = 0.547), and gonadal hormone replacement (47.4% vs 62.5%, P = 0.486).
Overall, except for age, baseline demographic, etiological, endocrine, and treatment characteristics were comparable between the cirrhosis and non-cirrhosis groups, supporting the comparability of the two groups for subsequent analyses. Detailed baseline characteristics are summarized in Table 1.
| Variable | Cirrhosis group (n = 19) | Non-cirrhosis group (n = 16) | P value |
| Female sex | 8 (42.1) | 5 (31.3) | 0.727 |
| Age (year) | 28 (20, 34) | 19.5 (16, 23.5) | 0.024a |
| Height (m) | 1.78 (1.58, 1.81) | 1.71 (1.60, 1.78) | 0.233 |
| Weight (kg) | 87 (62, 101) | 69.48 (62.13, 88.75) | 0.304 |
| BMI (kg/m2) | 27.76 (23.86, 30.84) | 24.71 (22.08, 28.89) | 0.849 |
| Etiology | 0.537 | ||
| CP | 8 (42.1) | 10 (62.5) | |
| PSIS | 7 (36.8) | 4 (25) | |
| Germ cell tumor | 4 (21.1) | 2 (12.5) | |
| TH replacement | 19 (100) | 16 (100) | 1 |
| FT4 (ng/dL) | 1.09 (0.96, 1.23) | 1.33 (1.10, 1.44) | 0.444 |
| TSH (mIU/L) | 0.032 (0.011, 0.143) | 0.01 (0.008, 0.042) | 0.939 |
| Glucocorticoid replacement | 18 (94.7) | 14 (87.5) | 0.742 |
| Peak Cortisol (μg/dL) | 0.5 (0.5, 0.6) | 1.65 (0.5, 7.55) | 0.080 |
| GH replacement | 12 (63.2) | 8 (50) | 0.547 |
| IGF-1 (ng/mL) | 72.5 (24.5, 110.5) | 55 (41, 123) | 0.757 |
| Gonadal hormone replacement | 9 (47.4) | 10 (62.5) | 0.486 |
| T (ng/mL)1 | 2.79 (0.1, 3.33; n = 11) | 1.45 (0.1, 2.54; n = 11) | 0.270 |
| E2 (pg/mL)2 | < 15 (n = 8) | 15 (15, 79.5; n = 5) | 0.097 |
Compared with the non-cirrhosis group, patients in the cirrhosis group had significantly higher AST and GGT levels (P = 0.033 and P < 0.0001, respectively). In contrast, ALT, ALP, Alb, and TBil levels did not differ significantly between the two groups (all P > 0.05).
The INR was significantly higher in the cirrhosis group than in the non-cirrhosis group (P = 0.027). In addition, platelet count was markedly lower in the cirrhosis group (P < 0.0001). Detailed liver-related parameters are summarized in Table 2.
| Variable | Cirrhosis group (n = 19) | Non-cirrhosis group (n = 16) | P value |
| Diabetes | 7 (36.8) | 0 (0) | 0.003b |
| AST (IU/L) | 53 (35.5, 63) | 32 (26.75, 50) | 0.033a |
| ALT (IU/L) | 36 (23, 63) | 27.5 (15, 55.25) | 0.417 |
| GGT (IU/L) | 85 (58, 176) | 26 (16, 44.25) | < 0.0001c |
| ALP (IU/L) | 108 (82, 205) | 121 (70, 205) | 0.938 |
| Alb (g/L) | 46 (40, 48) | 47.5 (46, 49) | 0.109 |
| INR | 1.09 (1.05, 1.16) | 1.02 (0.99, 1.07) | 0.027a |
| Bilirubin (μmol/L) | 13.6 (10.6, 25) | 10.9 (9.33, 17.68) | 0.066 |
| Platelet (G/L) | 102 (58, 183) | 255 (217.8, 320) | < 0.0001c |
| HA (ng/mL) | 157.5 (92, 247.8; n = 18) | 63 (40.5, 87.75; n = 14) | 0.0002c |
| PIIINP (ng/mL) | 16 (9.3, 26.25; n = 18) | 16 (9, 23; n = 15) | 0.800 |
| CIV (ng/mL) | 136 (103.8, 176.8; n = 18) | 81 (50, 106; n = 15) | 0.0003c |
| LN (ng/mL) | 163.5 (105, 196.8) | 122 (100, 156; n = 15) | 0.100 |
| FIB-4 | 2.15 (1.20, 4.05) | 0.48 (0.34, 0.75) | < 0.0001c |
| APRI | 1.05 (0.59, 2.17) | 0.34 (0.27, 0.4) | < 0.0001c |
| FIB-9 | 3.75 (1.89, 3.99) | 0.49 (0.02, 1.61) | < 0.0001c |
| LSM (kPa) | 23.2 (15.8, 31.9; n = 15) | 5.7 (3.9, 6.8; n = 5) | 0.0001c |
As shown in Table 2, four direct serum fibrosis biomarkers were compared between the cirrhosis and non-cirrhosis groups. HA and CIV levels were significantly higher in the cirrhosis group than in the non-cirrhosis group (P = 0.0002 and P = 0.0003, respectively). In contrast, LN and PIIINP did not differ significantly between the two groups (P = 0.100 and P = 0.800, respectively).
LSM was also significantly higher in the cirrhosis group than in the non-cirrhosis group [23.2 (15.8, 31.9) kPa vs 5.7 (3.9, 6.8) kPa, P = 0.0001], as summarized in Table 2.
The NITs FIB-4, APRI, and FIB-9 were all significantly higher in the cirrhosis group than in the non-cirrhosis group (all P < 0.0001; Table 2 and Figure 1).
ROC analysis showed that FIB-9 had the highest AUC for identifying cirrhosis (AUC = 0.941, 95%CI: 0.856-1.000), followed by APRI (AUC = 0.903, 95%CI: 0.787-1.000) and FIB-4 (AUC = 0.872, 95%CI: 0.750-0.994; Figure 2A). Among the serum fibrosis biomarkers, HA (AUC = 0.878, 95%CI: 0.759-0.996) and CIV (AUC = 0.852, 95%CI: 0.711-0.993) also showed good diagnostic performance, whereas LN (AUC = 0.670, 95%CI: 0.481-0.860) and PIIINP (AUC = 0.528, 95%CI: 0.322-0.734) showed limited discriminatory ability.
Based on the maximum Youden index, the optimal cutoff value for FIB-9 was 1.748, yielding a sensitivity of 89.5% and a specificity of 100% for detecting cirrhosis. As shown in Figure 3, FIB-9 values were significantly higher in the cirrhosis group than in the non-cirrhosis group (P < 0.0001), with minimal overlap between the two distributions. Patients with cirrhosis (n = 19) had higher FIB-9 values, with a median of 3.75 (1.89, 3.99), whereas patients without cirrhosis (n = 16) had lower values, with a median of 0.49 (0.02, 1.61). This distribution pattern visually supports the discriminatory ability of FIB-9 for differentiating cirrhosis from non-cirrhosis. The ROC curve for FIB-9 is shown in Figure 2B.
In this study, we evaluated the diagnostic performance of three NITs, FIB-4, APRI, and FIB-9, for detecting cirrhosis in patients with hypopituitarism, most of whom had undergone CP resection. Our results demonstrate that FIB-9 exhibited better accuracy in distinguishing cirrhotic from non-cirrhotic individuals. In contrast, conventional serum fibrosis biomarkers—including HA, CIV, LN, and PIIINP—showed limited diagnostic utility for identifying advanced liver fibrosis in this setting.
Accumulating evidence supports a close association between hypopituitarism, particularly GHD, and metabolic dysregulation, including central obesity, insulin resistance, and dyslipidemia[13,14]. In our cohort, all 35 patients with confirmed hypopituitarism were overweight or obese overall, and IGF-1 levels were low in both groups, consistent with the metabolic vulnerability of this population (Table 1). These abnormalities represent key components of metabolic syndrome and are well-established drivers of MASLD, the updated nomenclature for what was formerly termed non-alcoholic fatty liver disease[15,16]. Notably, individuals with GHD have a significantly increased risk of MASLD, with a pooled odds ratio of 4.27 (95%CI: 1.33-13.68; P = 0.015) compared with GH-replete controls[17].
Beyond GHD, dysfunction of other pituitary axes may also contribute to MASLD pathogenesis. Central hypothy
Given that MASLD may progress silently to advanced fibrosis, cirrhosis, and hepatocellular carcinoma[22], early identification of high-risk individuals is essential. This need is reinforced by the global burden of cirrhosis, which ranks among the leading causes of death worldwide and accounts for approximately 2%-4% of all global mortality[23,24]. In Europe, cirrhosis is also a major cause of years of working life lost[24]. In this context, simple and accessible non-invasive tools for identifying advanced fibrosis are of particular clinical value, especially in vulnerable populations such as patients with chronic hypopituitarism.
Of note, HO is a severe and treatment-refractory form of weight gain caused by damage to hypothalamic satiety centers, often after extensive CP resection[25]. In our cohort, CP resection was the predominant etiology, and overall BMI was increased, suggesting that HO may have been present in a substantial proportion of patients. Although HO was not formally assessed using dedicated criteria, its potential influence should be considered. HO is characterized by profound insulin resistance, hyperleptinemia, and marked metabolic dysregulation[5], all of which may aggravate MASLD and potentially affect fibrosis-related biomarkers. Nevertheless, because both cirrhotic and non-cirrhotic groups had similar etiological backgrounds, this potential effect was likely balanced between groups.
In our cohort, conventional non-invasive approaches showed limited ability to identify compensated cirrhosis. This included widely used fibrosis indices such as APRI and FIB-4, as well as direct serum fibrosis biomarkers including HA, CIV, LN, and PIIINP. Routine liver enzymes such as ALT, AST, and GGT may remain normal or only mildly elevated in early fibrosis, particularly in metabolically vulnerable individuals[26], and can be influenced by obesity, alcohol intake, and medications. Similarly, synthetic markers such as Alb and bilirubin often remain within the reference range until decompensation develops[27]. Consistent with this, we observed no significant differences in ALT, ALP, Alb, or TBil between cirrhotic and non-cirrhotic patients, despite higher AST and GGT levels in the cirrhosis group.
Direct serum fibrosis markers also showed variable and generally limited performance in this endocrine population. In our cohort, PIIINP and LN did not significantly distinguish cirrhosis from non-cirrhosis. Several factors may explain this finding. First, both markers reflect extracellular matrix turnover, but their circulating levels may be influenced by the complex endocrine and metabolic disturbances present in hypopituitarism. Second, assay-related variability and imperfect standardization across platforms may reduce reproducibility. Third, patients with hypopituitarism often exhibit altered protein metabolism and complex endocrine-metabolic disturbances, which may influence circulating fibrosis marker levels independently of true fibrosis stage[28,29]. Although direct evidence in endocrine-deficient populations remains limited, these considerations provide a plausible framework for interpreting the suboptimal performance of conventional serum biomarkers in our study.
These findings underscore an important limitation of traditional tools: Advanced fibrosis may be missed during the clinically silent compensated stage, especially in the context of hormonal and metabolic dysregulation[30,31]. Although FIB-4 and APRI are widely recommended as first-line, low-cost tools in routine practice, their performance in metabolically high-risk populations is often modest[8]. In a multinational study involving more than 5000 participants, Graupera et al[31] reported that FIB-4 missed 43% of cases with liver stiffness ≥ 8 kPa (≥ F3 fibrosis), with the missed-diagnosis rate increasing to 63% among patients with diabetes. Its positive predictive value was low (7%-17%), and the AUC ranged from 0.57 to 0.68. Given that patients with hypopituitarism share important metabolic features with these high-risk populations, the limited performance of conventional NITs in our cohort is not unexpected. Together, these data highlight the need for more accurate and pathophysiologically relevant indices to facilitate timely detection of cirrhosis in this vulnerable population.
FIB-9 is a machine learning-derived index developed specifically for MASLD[9]. Its reported diagnostic performance is comparable to that of proprietary panels such as FibroTest, while remaining freely accessible through an open-source calculator (https://gilles-hunault.leria-info.univ-angers.fr/wstat/FIB-9.php), which enhances its feasibility in both specialized and resource-limited settings. In our cohort of patients with hypopituitarism, FIB-9 showed the best discriminatory ability for cirrhosis among the tested NITs and serum fibrosis biomarkers. At the optimal cutoff of 1.748 derived from ROC analysis, FIB-9 achieved a specificity of 100%, effectively eliminating false-positive results in this dataset. However, this high specificity came at the expense of sensitivity: 2 of 19 patients with cirrhosis (10.5%) had FIB-9 values below this threshold and would have been missed. Accordingly, FIB-9 should not be interpreted as a rule-out test; rather, it appears particularly useful as a rule-in tool in this clinical context.
From a practical perspective, a FIB-9 value ≥ 1.748 may serve as a strong indication for further liver assessment, such as LSM or specialist referral. In contrast, patients with lower values—especially those with persistent metabolic risk factors—may still require periodic monitoring, given the non-negligible false-negative rate. This high specificity may be especially useful in screening pathways, where unnecessary referrals for elastography or biopsy may increase logistical, financial, and psychological burdens. Importantly, FIB-9 is calculated from routine laboratory data, making it readily applicable in outpatient endocrinology and primary care settings.
It is also important to clarify why an independent ROC analysis for LSM was not performed. In this study, LSM ≥ 12.5 kPa constituted one component of the composite reference standard used to define cirrhosis. Therefore, evaluating the diagnostic accuracy of LSM against the same reference standard would have introduced methodological circularity and artificially inflated its apparent performance. Rather than serving as a competitor to elastography, FIB-9 may complement LSM in clinical practice. While LSM remains a key non-invasive modality when available and technically feasible, FIB-9 offers an accessible and inexpensive alternative, particularly in settings where elastography is unavailable or difficult to perform, such as in patients with marked obesity or limited access to specialized equipment.
Our findings are broadly consistent with the original validation study by Calès et al[9], in which FIB-9 showed strong diagnostic performance in general MASLD populations. The present study extends that observation to a distinct endocrine cohort and suggests that FIB-9 may retain clinical usefulness in patients with chronic pituitary hormone deficiencies. Notably, in our cohort, FIB-9 showed the highest AUC among the evaluated non-invasive indices and achieved complete specificity at the identified cutoff. This performance profile supports its potential utility as a rule-in test for cirrhosis in patients with hypopituitarism. Patients with FIB-9 values above the threshold may be prioritized for advanced liver evaluation, whereas those below the threshold may undergo follow-up surveillance according to their broader metabolic and clinical risk profile. A pragmatic clinical algorithm is illustrated in Figure 4.
FIB-9 may also have potential as a dynamic monitoring tool. Interventions such as GH replacement[32], weight reduction[33], and lipid control[34] may influence hepatic fibrogenesis, raising the possibility that serial FIB-9 measurements could reflect changes in liver risk over time. In our study, hormone replacement status, including GH, thyroid hormone, glucocorticoid, and gonadal hormone replacement, was systematically documented and was comparable between groups (all P > 0.05), suggesting that treatment distribution did not introduce major between-group imbalance. Nevertheless, hormone replacement may still affect metabolic status and liver-related indices longitudinally. Future prospective studies should therefore assess whether changes in endocrine status or replacement therapy are associated with longitudinal changes in FIB-9.
Several limitations should be acknowledged. First, this was a single-center study with a modest sample size, which limits statistical precision and generalizability. Given the small sample (n = 35), the identified cutoff and AUC estimates should be considered exploratory and hypothesis-generating. Second, liver biopsy, the histological gold standard, was not performed. Instead, cirrhosis was defined using a guideline-based composite reference standard incorporating LSM and high-specificity clinical or imaging features according to AASLD, EASL, and Baveno VII recommendations[8,11,12]. Although this approach is clinically reasonable, the use of mixed diagnostic modalities may still introduce some heterogeneity in staging. Third, this retrospective cohort lacked uniformly available data on the duration of hormone replacement therapy and the timing of prior surgery or radiotherapy. In addition, because many patients developed hypopituitarism after CP resection, hypothalamic injury and HO may have contributed residual confounding that could not be formally quantified. Finally, the etiological spectrum mainly included CP, germinoma, and PSIS, and did not capture rarer causes of hypopituitarism. Therefore, external validation in larger multicenter cohorts is needed before these findings can be generalized more broadly. To address these limitations, we are currently designing a prospective multicenter study to: (1) Validate the identified FIB-9 cutoff across diverse endocrine centers; (2) Assess the longitudinal performance of FIB-9 in monitoring liver-related changes during hormone replacement therapy; and (3) Explore its integration into routine clinical decision-making. Such efforts may help clarify the clinical utility of FIB-9 and support its further development as a non-invasive tool for endocrine-related liver disease.
In conclusion, our findings support FIB-9 as a practical and accessible tool for cirrhosis identification in patients with hypopituitarism. To our knowledge, this is the first study to evaluate a next-generation fibrosis score in an endocrine-specific liver disease population. Although our findings require external validation, they suggest that FIB-9 may help extend non-invasive fibrosis assessment to a high-risk group that is often underrecognized in routine hepatology screening.
In this cohort of patients with hypopituitarism, FIB-9, which is freely available through an open-access calculator, showed the highest diagnostic accuracy for identifying cirrhosis among the non-invasive indices evaluated. Using the maximum Youden index, an optimal cutoff value of 1.748 yielded high specificity for cirrhosis, supporting the potential value of FIB-9 as a practical rule-in and triage tool in this high-risk population.
We would like to thank Peking Union Medical College Hospital for providing the clinical environment and facilities necessary to conduct this study. Our sincere thanks also go to the colleagues who provided administrative and technical assistance, ensuring the successful completion of this project.
| 1. | Fleseriu M, Christ-Crain M, Langlois F, Gadelha M, Melmed S. Hypopituitarism. Lancet. 2024;403:2632-2648. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 23] [Cited by in RCA: 68] [Article Influence: 34.0] [Reference Citation Analysis (0)] |
| 2. | He H, Li DM. One Case of Pituitary Stalk Interruption Syndrome Associated with Liver Cirrhosis. Endocr Metab Immune Disord Drug Targets. 2023;23:1229-1234. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 3] [Cited by in RCA: 3] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
| 3. | Saaybi SR, Shiau H, Lee G, Orandi BJ, Gutierrez Sanchez LH. Treatment of rapid recurrence of severe steatosis with combined glucagon-like peptide-1 agonist and growth hormone therapy in a pediatric patient transplanted for metabolic dysfunction-associated steatohepatitis cirrhosis in the setting of hypopituitarism. Am J Transplant. 2025;25:1123-1126. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 2] [Reference Citation Analysis (0)] |
| 4. | Hazlehurst JM, Tomlinson JW. Non-alcoholic fatty liver disease in common endocrine disorders. Eur J Endocrinol. 2013;169:R27-R37. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 67] [Cited by in RCA: 70] [Article Influence: 5.4] [Reference Citation Analysis (4)] |
| 5. | Argente J, Farooqi IS, Chowen JA, Kühnen P, López M, Morselli E, Gan HW, Spoudeas HA, Wabitsch M, Tena-Sempere M. Hypothalamic obesity: from basic mechanisms to clinical perspectives. Lancet Diabetes Endocrinol. 2025;13:57-68. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 22] [Reference Citation Analysis (4)] |
| 6. | Kang SJ, Kwon A, Jung MK, Chae HW, Kim S, Koh H, Shin HJ, Kim HS. High Prevalence of Nonalcoholic Fatty Liver Disease Among Adolescents and Young Adults With Hypopituitarism due to Growth Hormone Deficiency. Endocr Pract. 2021;27:1149-1155. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 4] [Cited by in RCA: 18] [Article Influence: 3.6] [Reference Citation Analysis (1)] |
| 7. | Nishizawa H, Iguchi G, Murawaki A, Fukuoka H, Hayashi Y, Kaji H, Yamamoto M, Suda K, Takahashi M, Seo Y, Yano Y, Kitazawa R, Kitazawa S, Koga M, Okimura Y, Chihara K, Takahashi Y. Nonalcoholic fatty liver disease in adult hypopituitary patients with GH deficiency and the impact of GH replacement therapy. Eur J Endocrinol. 2012;167:67-74. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 159] [Cited by in RCA: 141] [Article Influence: 10.1] [Reference Citation Analysis (1)] |
| 8. | European Association for the Study of the Liver. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis - 2021 update. J Hepatol. 2021;75:659-689. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1546] [Cited by in RCA: 1437] [Article Influence: 287.4] [Reference Citation Analysis (10)] |
| 9. | Calès P, Canivet CM, Costentin C, Lannes A, Oberti F, Fouchard I, Hunault G, de Lédinghen V, Boursier J. A new generation of non-invasive tests of liver fibrosis with improved accuracy in MASLD. J Hepatol. 2025;82:794-804. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 27] [Cited by in RCA: 26] [Article Influence: 26.0] [Reference Citation Analysis (1)] |
| 10. | Yuen KCJ, Biller BMK, Radovick S, Carmichael JD, Jasim S, Pantalone KM, Hoffman AR. American association of clinical endocrinologists and American college of endocrinology guidelines for management of growth hormone deficiency in adults and patients transitioning from pediatric to adult care. Endocr Pract. 2019;25:1191-1232. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 84] [Cited by in RCA: 230] [Article Influence: 32.9] [Reference Citation Analysis (0)] |
| 11. | Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, Kleiner DE, Loomba R. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 2023;77:1797-1835. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 1980] [Cited by in RCA: 1873] [Article Influence: 624.3] [Reference Citation Analysis (8)] |
| 12. | de Franchis R, Bosch J, Garcia-Tsao G, Reiberger T, Ripoll C; Baveno VII Faculty. Baveno VII - Renewing consensus in portal hypertension. J Hepatol. 2022;76:959-974. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2244] [Cited by in RCA: 2142] [Article Influence: 535.5] [Reference Citation Analysis (21)] |
| 13. | Garmes HM. Special features on insulin resistance, metabolic syndrome and vascular complications in hypopituitary patients. Rev Endocr Metab Disord. 2024;25:489-504. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1] [Cited by in RCA: 11] [Article Influence: 5.5] [Reference Citation Analysis (0)] |
| 14. | Johannsson G, Ragnarsson O. Growth hormone deficiency in adults with hypopituitarism-What are the risks and can they be eliminated by therapy? J Intern Med. 2021;290:1180-1193. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 6] [Cited by in RCA: 26] [Article Influence: 5.2] [Reference Citation Analysis (0)] |
| 15. | Yeo YH, Zhu Y, Gao J, Liu S, Ni W, Rui F, Bai X, Geng N, Jin R, Speliotes EK, Wu C, Shi J, Qi X, Chen VL, Newsome PN, Li J. Anthropometric Measures and Mortality Risk in Individuals With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A Population-Based Cohort Study. Aliment Pharmacol Ther. 2025;62:168-179. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 20] [Cited by in RCA: 19] [Article Influence: 19.0] [Reference Citation Analysis (0)] |
| 16. | Targher G, Byrne CD, Tilg H. MASLD: a systemic metabolic disorder with cardiovascular and malignant complications. Gut. 2024;73:691-702. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 420] [Cited by in RCA: 448] [Article Influence: 224.0] [Reference Citation Analysis (1)] |
| 17. | Kong T, Gu Y, Sun L, Zhou R, Li J, Shi J. Association of nonalcoholic fatty liver disease and growth hormone deficiency: a systematic review and meta-analysis. Endocr J. 2023;70:959-967. [RCA] [PubMed] [DOI] [Full Text] [Cited by in RCA: 11] [Reference Citation Analysis (0)] |
| 18. | Pu S, Zhao B, Jiang Y, Cui X. Hypothyroidism/subclinical hypothyroidism and metabolic dysfunction-associated steatotic liver disease: advances in mechanism and treatment. Lipids Health Dis. 2025;24:75. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 11] [Article Influence: 11.0] [Reference Citation Analysis (0)] |
| 19. | Ratziu V, Scanlan TS, Bruinstroop E. Thyroid hormone receptor-β analogues for the treatment of metabolic dysfunction-associated steatohepatitis (MASH). J Hepatol. 2025;82:375-387. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 52] [Cited by in RCA: 58] [Article Influence: 58.0] [Reference Citation Analysis (1)] |
| 20. | Vargas-Beltran AM, Armendariz-Pineda SM, Martínez-Sánchez FD, Martinez-Perez C, Torre A, Cordova-Gallardo J. Interplay between endocrine disorders and liver dysfunction: Mechanisms of damage and therapeutic approaches. World J Gastroenterol. 2025;31:108827. [PubMed] [DOI] [Full Text] |
| 21. | Guarnotta V, Mineo MI, Radellini S, Pizzolanti G, Giordano C. Dual-release hydrocortisone improves hepatic steatosis in patients with secondary adrenal insufficiency: a real-life study. Ther Adv Endocrinol Metab. 2019;10:2042018819871169. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 6] [Cited by in RCA: 13] [Article Influence: 1.9] [Reference Citation Analysis (0)] |
| 22. | Horn P, Tacke F. Metabolic reprogramming in liver fibrosis. Cell Metab. 2024;36:1439-1455. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 268] [Cited by in RCA: 287] [Article Influence: 143.5] [Reference Citation Analysis (0)] |
| 23. | Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023;79:516-537. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 1738] [Cited by in RCA: 1490] [Article Influence: 496.7] [Reference Citation Analysis (6)] |
| 24. | Huang DQ, Terrault NA, Tacke F, Gluud LL, Arrese M, Bugianesi E, Loomba R. Global epidemiology of cirrhosis - aetiology, trends and predictions. Nat Rev Gastroenterol Hepatol. 2023;20:388-398. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 666] [Cited by in RCA: 592] [Article Influence: 197.3] [Reference Citation Analysis (5)] |
| 25. | Olsson DS, Andersson E, Bryngelsson IL, Nilsson AG, Johannsson G. Excess mortality and morbidity in patients with craniopharyngioma, especially in patients with childhood onset: a population-based study in Sweden. J Clin Endocrinol Metab. 2015;100:467-474. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 119] [Cited by in RCA: 148] [Article Influence: 13.5] [Reference Citation Analysis (0)] |
| 26. | Woreta TA, Alqahtani SA. Evaluation of abnormal liver tests. Med Clin North Am. 2014;98:1-16. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 97] [Cited by in RCA: 131] [Article Influence: 10.9] [Reference Citation Analysis (1)] |
| 27. | Aragon G, Younossi ZM. When and how to evaluate mildly elevated liver enzymes in apparently healthy patients. Cleve Clin J Med. 2010;77:195-204. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 71] [Cited by in RCA: 69] [Article Influence: 4.3] [Reference Citation Analysis (1)] |
| 28. | Nielsen MJ, Leeming DJ, Goodman Z, Friedman S, Frederiksen P, Rasmussen DGK, Vig P, Seyedkazemi S, Fischer L, Torstenson R, Karsdal MA, Lefebvre E, Sanyal AJ, Ratziu V. Comparison of ADAPT, FIB-4 and APRI as non-invasive predictors of liver fibrosis and NASH within the CENTAUR screening population. J Hepatol. 2021;75:1292-1300. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 59] [Cited by in RCA: 54] [Article Influence: 10.8] [Reference Citation Analysis (0)] |
| 29. | Ramakrishnan A, Velmurugan G, Somasundaram A, Mohanraj S, Vasudevan D, Vijayaragavan P, Nightingale P, Swaminathan K, Neuberger J. Prevalence of abnormal liver tests and liver fibrosis among rural adults in low and middle-income country: A cross-sectional study. EClinicalMedicine. 2022;51:101553. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 5] [Cited by in RCA: 10] [Article Influence: 2.5] [Reference Citation Analysis (0)] |
| 30. | Loomba R, Adams LA. Advances in non-invasive assessment of hepatic fibrosis. Gut. 2020;69:1343-1352. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 281] [Cited by in RCA: 273] [Article Influence: 45.5] [Reference Citation Analysis (2)] |
| 31. | Graupera I, Thiele M, Serra-Burriel M, Caballeria L, Roulot D, Wong GL, Fabrellas N, Guha IN, Arslanow A, Expósito C, Hernández R, Aithal GP, Galle PR, Pera G, Wong VW, Lammert F, Ginès P, Castera L, Krag A; Investigators of the LiverScreen Consortium. Low Accuracy of FIB-4 and NAFLD Fibrosis Scores for Screening for Liver Fibrosis in the Population. Clin Gastroenterol Hepatol. 2022;20:2567-2576.e6. [RCA] [PubMed] [DOI] [Full Text] [Cited by in Crossref: 240] [Cited by in RCA: 220] [Article Influence: 55.0] [Reference Citation Analysis (1)] |
| 32. | Betlejewska J, Hubska J, Roszkowska Z, Maciejczyk A, Bachurska D, Domański J, Miarka M, Raszeja-Wyszomirska J, Bobrowicz M, Ambroziak U. Endocrine Disorders and Metabolic Dysfunction-Associated Steatotic Liver Disease: A Narrative Review. Biomedicines. 2025;13:2500. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in RCA: 4] [Reference Citation Analysis (0)] |
| 33. | Petta S, Kim K, Targher G, Romeo S, Sookoian S, Zheng MH, Aghemo A, Valenti L. Focus on Semaglutide 2.4 mg/week for the Treatment of Metabolic Dysfunction-Associated Steatohepatitis. Liver Int. 2025;45:e70407. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 20] [Cited by in RCA: 13] [Article Influence: 13.0] [Reference Citation Analysis (0)] |
| 34. | Ma S, Yang M, Qiang W, Zhou F, Chen Z, Gao Y, Zhang L. Regulatory effects of miR-30c knockout on hepatic lipid metabolism and progression of liver fibrosis. BMC Gastroenterol. 2025;25:777. [RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)] [Cited by in Crossref: 2] [Cited by in RCA: 2] [Article Influence: 2.0] [Reference Citation Analysis (0)] |