Diagnostic evaluation of central precocious puberty in girls

[Value of single-phase gonadotropin-releasing hormone stimulation test in diagnosis of central precocious puberty in girls with different levels of body mass index]

OBJECTIVES

To investigate the value of single-phase gonadotropin releasing hormone (GnRH) stimulation test in the diagnosis of central precocious puberty (CPP) in girls with different levels of body mass index (BMI).

METHODS

A retrospective analysis was performed for the data of 760 girls with breast development before 7.5 years of age who attended the Third Affiliated Hospital of Zhengzhou University from January 2017 to August 2023. According to the results of GnRH stimulation test and clinical manifestations, they were divided into a CPP group (297 girls) and a non-CPP group (463 girls). According to the values of BMI, the girls were divided into a normal weight group (540 girls), an overweight group (116 girls), and an obese group (104 girls). The receiver operating characteristic (ROC) curve was used to investigate the value of single-phase GnRH stimulation test in the diagnosis of CPP in girls with different levels of BMI.

RESULTS

Luteinizing hormone (LH)/follicle-stimulating hormone at 30 minutes after GnRH stimulation had an area under the curve (AUC) of 0.985 in the diagnosis of CPP, which was higher than the AUC at 0, 60, and 90 minutes (P<0.05). LH at 30 minutes had a similar diagnostic value to LH at 60 minutes (P>0.05). LH at 30 minutes was negatively correlated with BMI and BMI-Z value (P<0.05).The AUC for diagnosing CPP in normal weight, overweight, and obese girls at 30 minutes LH was 0.952, 0.965, and 0.954, respectively (P<0.05).

CONCLUSIONS

The 30-minute GnRH stimulation test has a good value in the diagnosis of CPP in girls with different levels of BMI and is expected to replace the traditional GnRH stimulation test, but the influence of BMI on LH level should be taken seriously.

MRI Radiomics Features of Adenohypophysis Determine the Activation of Hypothalamic-Pituitary-Gonadal Axis in Peri-Puberty Children

BACKGROUND

The status of the hypothalamic-pituitary-gonadal (HPG) axis is important for assessing the onset of physiological or pathological puberty. The reference standard gonadotropin-releasing hormone (GnRH) stimulation test requires hospital admission and repeated blood samples. A simple noninvasive method would be beneficial.

OBJECTIVES

To explore a noninvasive method for evaluating HPG axis activation in children using an MRI radiomics model.

STUDY TYPE

Retrospective.

POPULATION

Two hundred thirty-nine children (83 male; 3.6-14.6 years) with hypophysial MRI and GnRH stimulation tests, randomly divided a training set (168 children) and a test set (71 children).

FIELD STRENGTH/SEQUENCE

3.0 T, 3D isotropic fast spin echo (CUBE) T1-weighted imaging (T1WI) sequences.

ASSESSMENT

Radiomics features were extracted from sagittal 3D CUBE T1WI, and imaging signatures were generated using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Diagnostic performance for differential diagnosis of HPG status was compared between a radiomics model and MRI features (adenohypophyseal height [aPH] and volume [aPV]).

STATISTICAL TESTS

Receiver operating characteristic (ROC) and decision curve analysis (DCA). A P value <0.05 was considered statistically significant.

RESULTS

Eight hundred fifty-one radiomics features were extracted and reduced to 10 by the LASSO method in the training cohort. The radiomics model based on CUBE T1WI showed good performance in assessment of HPG axis activation with an area under the ROC curve (AUC) of 0.81 (95% CI: 0.71, 0.91) in the test set. The AUC of the radiomics model was significantly higher than that of aPH (0.81 vs. 0.65) but there was no significant difference compared to aPV (0.81 vs. 0.78, P = 0.58). In DCA analysis, the radiomics signature showed higher net benefit over the aPV and aPH models.

DATA CONCLUSIONS

The MRI radiomics model has potential to assess HPG axis activation status noninvasively, potentially providing valuable information in the diagnosis of patients with pathological puberty onset.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Basal gonadotropin levels combine with pelvic ultrasound and pituitary volume: a machine learning diagnostic model of idiopathic central precocious puberty

OBJECTIVE

The current diagnosis of central precocious puberty (CPP) relies on the gonadotropin-releasing hormone analogue (GnRHa) stimulation test, which requires multiple invasive blood sampling procedures. The aim of this study was to construct machine learning models incorporating basal pubertal hormone levels, pituitary magnetic resonance imaging (MRI), and pelvic ultrasound parameters to predict the response of precocious girls to GnRHa stimulation test.

METHODS

This retrospective study included 455 girls diagnosed with precocious puberty who underwent transabdominal pelvic ultrasound, brain MRI examinations and GnRHa stimulation testing were retrospectively reviewed. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Four machine learning classifiers were developed to identify girls with CPP, including logistic regression, random forest, light gradient boosting (LightGBM), and eXtreme gradient boosting (XGBoost). The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic (AUC) and F1 score of the models were measured.

RESULTS

The participates were divided into an idiopathic CPP group (n = 263) and a non-CPP group (n = 192). All machine learning classifiers used achieved good performance in distinguishing CPP group and non-CPP group, with the area under the curve (AUC) ranging from 0.72 to 0.81 in validation set. XGBoost had the highest diagnostic efficacy, with sensitivity of 0.81, specificity of 0.72, and F1 score of 0.80. Basal pubertal hormone levels (including luteinizing hormone, follicle-stimulating hormone, and estradiol), averaged ovarian volume, and several uterine parameters were predictors in the model.

CONCLUSION

The machine learning prediction model we developed has good efficacy for predicting response to GnRHa stimulation tests which could help in the diagnosis of CPP.

Clinical significance of serum MBD3 detection in girls with central precocious puberty

Diagnosis of central precocious puberty (CPP) in girls remains a huge challenge. The current study was to measure the serum expression of methyl-DNA bind protein 3 (MBD3) in CPP girls and assess its diagnostic efficacy. To begin with, we enrolled 109 CPP girls and 74 healthy pre-puberty girls. Then, MBD3 expression in their serum samples was measured via reverse transcription-quantitative polymerase chain reaction, and its diagnostic efficacy on CPP was assessed via the receiver operating characteristic (ROC) curve, followed by correlation analysis between serum MBD3 and patient age, gender, bone age, weight, height, body mass index, basal luteinizing hormone (LH), peak LH, basal follicle-stimulating hormone (FSH), peak FSH, and ovarian size using bivariate correlations method. Finally, independent predictors of MBD3 expression were confirmed using multivariate linear regression analysis. MBD3 was highly expressed in sera of CPP patients. The area under the ROC curve of MBD3 diagnosing CCP was 0.9309, with 1.475 cut-off value (92.66% sensitivity and 86.49% specificity). MBD3 expression positively correlated with basal LH, peak LH, basal FSH, and ovarian size, among which basal LH was considered the strongest independent predictor of MBD3, followed by basal FSH and peak LH. In summary, serum MBD3 could act as a biomarker in aiding CPP diagnosis.

Auxological and Metabolic Parameters of Children Undergoing the Gonadotropin-Releasing Hormone Stimulation Test: Correlations with the Final Diagnosis of Central Precocious Puberty in a Single-Center Study

Background-Central precocious puberty (CPP) is characterized by clinical, biochemical, and radiological features similar to those of normal puberty, but CPP occurs before the age of eight in girls and before the age of nine in boys, subsequently leading to a reduction in the final body height in adulthood due to premature fusion of growth plates. The diagnosis of CPP is confirmed with a gonadotropin-releasing hormone (GnRH) stimulation test, which can lead to different interpretations because the diagnostic peak levels of luteinizing hormone (LH) can vary. Patients and methods-This was a single-center, retrospective observational study investigating the possible correlation between gonadotropin peaks on the GnRH test and auxological, metabolic, and radiological parameters of patients evaluated for CPP. We collected and analyzed data from the medical records of children with suspected CPP over a period from January 2019 to July 2022 who underwent a GnRH test at the Fondazione Policlinico Universitario Agostino Gemelli in Rome, Italy. Results-Our correlation analysis revealed no statistically significant differences in any auxological and radiological parameters. Among laboratory parameters, baseline levels of LH, follicle-stimulating hormone, sex hormone-binding globulin, and 17-beta estradiol were higher in children with a definitive diagnosis of CPP than in those with a negative GnRH test. In particular, the levels of LH at baseline and after the GnRH test were statistically significant in the group of CPP patients, consistent with the interpretation of the test. In the multivariate analysis, using a cut-off value of 4.1 IU/L, LH peaks showed both very high sensitivity (94%) and very high specificity (95%); all other variables showed high specificity (90%) but unsatisfactory sensitivity. Conclusion-Basal hormone dosages and, especially, basal levels of LH should be considered before performing a GnRH test as they might anticipate the final diagnosis of CPP.

Machine Learning Approach for Prediction of the Test Results of Gonadotropin-Releasing Hormone Stimulation: Model Building and Implementation

Precocious puberty in girls is defined as the onset of pubertal changes before 8 years of age, and gonadotropin-releasing hormone (GnRH) agonist treatment is available for central precocious puberty (CPP). The gold standard for diagnosing CPP is the GnRH stimulation test. However, the GnRH stimulation test is time-consuming, costly, and requires repeated blood sampling. We aimed to develop an artificial intelligence (AI) prediction model to assist pediatric endocrinologists in decision making regarding the optimal timing to perform the GnRH stimulation test. We reviewed the medical charts of 161 girls who received the GnRH stimulation test from 1 August 2010 to 31 August 2021, and we selected 15 clinically relevant features for machine learning modeling. We chose the models with the highest area under the receiver operating characteristic curve (AUC) to integrate into our computerized physician order entry (CPOE) system. The AUC values for the CPP diagnosis prediction model (LH ≥ 5 IU/L) were 0.884 with logistic regression, 0.912 with random forest, 0.942 with LightGBM, and 0.942 with XGBoost. For the Taiwan National Health Insurance treatment coverage prediction model (LH ≥ 10 IU/L), the AUC values were 0.909, 0.941, 0.934, and 0.881, respectively. In conclusion, our AI predictive system can assist pediatric endocrinologists when they are deciding whether a girl with suspected CPP should receive a GnRH stimulation test. With proper use, this prediction model may possibly avoid unnecessary invasive blood sampling for GnRH stimulation tests.

Differential diagnosis of precocious puberty in girls during the COVID-19 pandemic: a pilot study

BACKGROUND

To investigate the differential diagnosis of girls aged 6 to 8 years with idiopathic premature thelarche (IPT) and central precocious puberty (CPP) during the COVID-19 pandemic. We explored predicted adult height (PAH) discrepancy to guide appropriate diagnosis and treatment.

METHODS

From January 2020 to December 2021, Chinese girls aged 6 to 8 years with precocious puberty were recruited. They were divided into IPT and CPP groups. Clinical characteristics, including height, weight, body mass index (BMI), basal luteinizing hormone (LH), oestradiol, uterine length and volume, follicle numbers (d > 4 mm) and bone age (BA) were recorded. We analysed differential diagnosis and PAH discrepancy in both groups. Binary logistic regression analysis was used to explore risk factors for CPP, and receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic value of related indexes.

RESULTS

Sixty patients, including 40 girls with IPT and 20 girls with CPP, were recruited. The prevalence of overweight and obesity in the entire cohort was 25% (15/60) and was significantly higher in IPT than CPP, 32.5% (13/40) vs. 10% (2/20), respectively (P=0.045). There were significant differences in LH, uterine volume, follicle numbers and BA (P<0.05). The impaired PAH of IPT and CPP was 0.01 ± 1.19 SD and 0.62 ± 0.94 SD with significant differences (P=0.047). Logistic regression analysis showed that LH and follicle numbers were independent risk factors for CPP. The ROC curve showed that the area under the curve (AUC) of LH and follicle numbers were 0.823 and 0.697. The sensitivity and specificity of LH with a cut off of 0.285 IU/L were 78.9% and 77.8%. The sensitivity and specificity of follicle numbers with a cut off of 3.5 were 89.5% and 52.8%.

CONCLUSION

The prevalence of overweight and obesity in 6- to 8-year-old girls with precocious puberty was high. Auxological data should not be used in the differential diagnosis of IPT and CPP. Basal LH above 0.285 IU/L and follicle numbers greater than 4 were important features suggestive of CPP. PAH was impaired in individuals with CPP, but it was not impaired in individuals with IPT.

[Value of basal luteinizing hormone level combined with uterine volume measurement in the early diagnosis of central precocious puberty in girls with different Tanner stages]

OBJECTIVES

To study the value of basal luteinizing hormone (LH) level combined with uterine volume measurement in the early diagnosis of central precocious puberty (CPP) in girls with different Tanner stages.

METHODS

A retrospective analysis was performed on the girls who presented with breast development before the age of 8 years and attended the Third Affiliated Hospital of Zhengzhou University from January 2017 to September 2022. According to the results of gonadotropin-releasing hormone (GnRH) agonist test, the girls with peak LH ≥5.0 IU/L and peak LH/follicle stimulating hormone ≥0.6 were enrolled as the positive group, and the other girls were enrolled as the negative group. The two groups were compared in terms of the basal LH level and uterine volume. The receiver operating characteristic (ROC) curve was used to analyze their value in the early diagnosis of CPP.

RESULTS

For the girls with Tanner B2 and B3 stages, the positive group had significantly higher basal LH level and uterine volume than the negative group (P<0.05). The basal LH level had an optimal cut-off value of 0.325 IU/L and 0.505 IU/L respectively in the diagnosis of Tanner stage B2/B3 CPP, while uterine volume had an optimal cut-off value of 1.639 mL and 2.158 mL respectively. Basal LH level combined with uterine volume measurement had a significantly larger area under the ROC curve than uterine volume measurement alone (P<0.001), but with no significant difference compared with that of basal LH level measurement alone (P>0.05).

CONCLUSIONS

Basal LH level combined with uterine volume measurement is valuable in the early diagnosis of CPP in girls with different Tanner stages, which provides a basis and guiding significance for clinical diagnosis of CPP.

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

BACKGROUND

A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer science, we aimed to develop a machine learning-based diagnostic model that processed baseline CPP-related variables and a brief GnRHa stimulation test for CPP diagnosis.

METHODS

We recruited girls suspected of precocious puberty and underwent a GnRHa stimulation test at Children Hospital 2, Vietnam, and Cathay General Hospital, Taiwan. Clinical data, bone age measurement, and 30-min post-stimulation blood test were used to build up the predictive model. The candidate model was developed by different machine learning algorithms that were mainly evaluated by sensitivity, specificity, the area under the receiver operator characteristic curve (AUC), and F1-score in internal and external validation data to classify girls as CPP and non-CPP at different time-points (0-min, 30-min, 60-min, and 120-min post-stimulation).

RESULTS

Among the 614 girls diagnosed with PP, 524 (85.3%) had CPP. The random forest algorithm yielded the highest value of F1-score (0.976), specificity (0.893), positive predicted value (0.987), and relatively high value of AUC (0.972) that contributed to high probability to identify CPP. The performance metrics of the 30-min post-stimulation diagnostic model including sensitivity and specificity surpassed those of the 0-minute model (0-min) and were equivalent to those of the model obtained 60-min and 120-min post-stimulation. Hence, our machine learning-based model helps shorten the stimulation test to 30 minutes after GnRHa injection, in general, it requires 120 minutes for a completed GnRHa stimulation test.

CONCLUSIONS

We developed a diagnostic model based on clinical features and a single sample 30-minute post-stimulation to identify CPP in girls that can reduce distress for children caused by multiple blood samplings.

Precocious puberty and microbiota: The role of the sex hormone-gut microbiome axis

Puberty is a critical phase of life associated with physiological changes related to sexual maturation, and represents a complex process regulated by multiple endocrine and genetic controls. Puberty is driven by hormones, and it can impact the gut microbiome (GM). GM differences between sex emerge at puberty onset, confirming a relationship between microbiota and sex hormones. In this narrative review, we present an overview of precocious pubertal development and the changes in the GM in precocious puberty (PP) in order to consider the role of the sex hormone-gut microbiome axis from the perspective of pediatric endocrinology. Bidirectional interactions between the GM and sex hormones have been proposed in different studies. Although the evidence on the interaction between microbiota and sex hormones remains limited in pediatric patients, the evidence that GM alterations may occur in girls with central precocious puberty (CPP) represents an interesting finding for the prediction and prevention of PP. Deepening the understanding of the connection between the sex hormones and the role of microbiota changes can lead to the implementation of microbiota-targeted therapies in pubertal disorders by offering a pediatric endocrinology perspective.

The Role of Pediatric Nutrition as a Modifiable Risk Factor for Precocious Puberty

Puberty is a critical phase of growth and development characterized by a complex process regulated by the neuroendocrine system. Precocious puberty (PP) is defined as the appearance of physical and hormonal signs of pubertal development at an earlier age than is considered normal. The timing of puberty has important public health, clinical, and social implications. In fact, it is crucial in psychological and physical development and can impact future health. Nutritional status is considered as one of the most important factors modulating pubertal development. This narrative review presents an overview on the role of nutritional factors as determinants of the timing of sexual maturation, focusing on early-life and childhood nutrition. As reported, breast milk seems to have an important protective role against early puberty onset, mainly due to its positive influence on infant growth rate and childhood overweight prevention. The energy imbalance, macro/micronutrient food content, and dietary patterns may modulate the premature activation of the hypothalamic-pituitary-gonadal axis, inducing precocious activation of puberty. An increase in knowledge on the mechanism whereby nutrients may influence puberty will be useful in providing adequate nutritional recommendations to prevent PP and related complications.