Advanced bone age as an indicator facilitates the diagnosis of precocious puberty

Percent Final Height Is a Novel Method That Identifies Differences Between the Rate of Development in American Children of Different Races

INTRODUCTION

The Greulich & Pyle (G&P) Radiographic Atlas of Skeletal Development uses hand x-rays obtained between 1926 and 1942 on children of Caucasian ancestry. Our study uses modern Caucasian, Black, Hispanic, and Asian children to investigate patterns of development as a function of percent final height (PFH).

METHODS

A retrospective review, at a single institution, was conducted using children who received a hand x-ray, a height measurement taken within 60 days of that x-ray, and a final height. BA and CA were compared between races. PFH was calculated by dividing height at the time of the x-ray by their final height. To further evaluate differences between races in CA or BA, PFH was then modeled as a function of CA or BA using a fifth-degree polynomial regression, and mean ages at the 85th PFH were compared. Patients were then divided into Sanders stages 1, 2-4, and 5-8 and the mean PFH, CA, and BA of the Asian, Black, and Hispanic children were compared with the White children using Student t test. P values less than 0.05 were considered significant.

RESULTS

We studied 498 patients, including 53 Asian, 83 Black, 190 Hispanic, and 172 White patients. Mean BA was significantly greater than CA in Black males (1.27 y) and females (1.36 y), Hispanic males (1.12 y) and females (1.29 y), and White females (0.74 y). Hispanic and Black patients were significantly more advanced in BA than White patients ( P <0.001). At the 85th PFH, White and Hispanic males were older than Black males by at least 7 months ( P <0.001), and White females were significantly older than Hispanic females by 6.4 months ( P <0.001). At 85th PFH for males, Hispanic and Black males had greater BA than White males by at least 5 months ( P <0.001), and Asian females had a greater BA than Black females by at least 5 months ( P <0.001). Compared with White children, Hispanic children were significantly younger at Sanders 2-4 than White children, and Black children were skeletally older at Sanders 5-8.

CONCLUSIONS

BA was greater than CA by ≥1 year in Black and Hispanic children, and that these children had a significantly greater BA than their White counterparts. Black males and Hispanic females reached their 85th PFH at younger ages, and Hispanic males and Asian females were the most skeletally mature at their 85th PFH. Our results suggest that BA and CA may vary as a function of race, and further studies evaluating growth via the 85th PFH may be necessary.

LEVEL OF EVIDENCE

Therapeutic Study - Level IV.

Screening for central precocious puberty by single basal Luteinizing Hormone levels

PURPOSE

To identify cut-off for basal LH levels and for pelvic ultrasound uterine and ovarian parameters indicating an Hypotalamic-Pituitary-Gonadal (HPG) axis activation as diagnostic of Central Precocious Puberty (CPP).

METHODS

248 girls referred for suspected precocious/early puberty who had undergone a GnRH stimulation test were enrolled and divided into three groups: Premature Idiopathic Thelarche (PIT), CPP, and Early Puberty (EA). For every patient basal serum Luteinising Hormone (LH) and Follicle Stimulating Hormone (FSH), basal LH/FSH ratio and pelvic ultrasonographic parameters were also collected. Through the use of Receiver Operating Curves (ROCs) the sensitivity (Se) and specificity (Sp) of basal LH, FSH, LH/FSH ratio and ultrasonographic parameters were evaluated at each level and Area Under the Curve (AUC) was measured.

RESULTS

Basal LH model ≥0.14 mIU/mL reached the highest predictability (90.6% and 78.2%, Se and Sp, respectively). Basal LH/FSH ratio ≥0.1 showed a sensitivity of 85.90% and a specificity of 78.14%, while basal FSH cut-off (≥2.36 mIU/mL) had the lowest predictability, with a less favourable sensitivity (71%) and specificity (70.5%). Cut-off point for uterine length as 35 mm, (83.5% and 42.9% of Se and Sp, respectively) was calculated. For ovarian volumes, ROC curves showed very low sensitivity and specificity.

CONCLUSION

A single basal LH measurement under the cut-off limit may be adequate to exclude an HPG axis activation as CPP.

Clinical Management and Therapy of Precocious Puberty in the Sapienza University Pediatrics Hospital of Rome, Italy

Puberty identifies the transition from childhood to adulthood. Precocious puberty is the onset of signs of pubertal development before age eight in girls and before age nine in boys, it has an incidence of 1/5000-1/10,000 with an F:M ratio ranging from 3:1 to 20:1. Precocious puberty can be divided into central, also known as gonadotropin-dependent precocious puberty or true precocious puberty, and peripheral, also recognized as gonadotropin-independent precocious puberty or precocious pseudopuberty. Thus, the main aim of this narrative report is to describe the standard clinical management and therapy of precocious puberty according to the experience and expertise of pediatricians and pediatric endocrinologists at Policlinico Umberto I, Sapienza University of Rome, Italy. In the suspicion of early sexual maturation, it is important to collect information regarding the age of onset, the speed of maturation of secondary sexual features, exposure to exogenous sex steroids and the presence of neurological symptoms. The objective examination, in addition to the evaluation of secondary sexual characteristics, must also include the evaluation of auxological parameters. Initial laboratory investigations should include serum gonadotropin levels (LH and FSH) and serum levels of the sex steroids. Brain MRI should be performed as indicated by the 2009 Consensus Statement in all boys regardless of chronological age and in all girls with onset of pubertal signs before 6 years of age. The gold standard in the treatment of central precocious puberty is represented by GnRH analogs, whereas, as far as peripheral forms are concerned, the triggering cause must be identified and treated. At the moment there are no reliable data establishing the criteria for discontinuation of GnRH analog therapy. However, numerous pieces of evidence suggest that the therapy should be suspended at the physiological age at which puberty occurs.

2022 Clinical practice guidelines for central precocious puberty of Korean children and adolescents

The Committee of Central Precocious Puberty of Korean Pediatrics and Adolescents of the Korean Society of Pediatric Endocrinology has newly developed evidence-based 2022 clinical practice guidelines for central precocious puberty in Korean children and adolescents. These guidelines provide the grade of recommendations, which includes both the strength of recommendations and the level of evidence. In the absence of sufficient evidence, recommendations are based on expert opinion. These guidelines have been revised and supplement the previous guidelines "Clinical Guidelines for Precocious Puberty 2011," and are drawn from a comprehensive review of the latest domestic and international research and the grade of recommendation appropriate to the domestic situation. This review summarizes the newly revised guidelines into 8 key questions and 27 recommendations and consists of 4 sections: screening, diagnosis, treatment, and long-term outcome of central precocious puberty.

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.

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.

[Clinical guidelines «Precocious puberty»]

The precocious puberty is an urgent problem of pediatric endocrinology characterized by clinical and pathogenetic heterogeneity. The appearance of secondary sex characteristics before the age of 8 years in girls and 9 years in boys requires timely diagnosis and the appointment of pathogenetically justified treatment in order to achieve the target indicators of final growth and prevent social deprivation. The developed clinical guidelines are the main working tool of the practitioner. They briefly and structurally present the main information about the epidemiology and modern classification of рrecocious puberty, methods of its diagnosis and treatment based on the principles of evidence-based medicine.

DNA methylation profile is a quantitative measure of biological aging in children

DNA methylation changes within the genome can be used to predict human age. However, the existing biological age prediction models based on DNA methylation are predominantly adult-oriented. We established a methylation-based age prediction model for children (9-212 months old) using data from 716 blood samples in 11 DNA methylation datasets. Our elastic net model includes 111 CpG sites, mostly in genes associated with development and aging. The model performed well and exhibited high precision, yielding a 98% correlation between the DNA methylation age and the chronological age, with an error of only 6.7 months. When we used the model to assess age acceleration in children based on their methylation data, we observed the following: first, the aging rate appears to be fastest in mid-childhood, and this acceleration is more pronounced in autistic children; second, lead exposure early in life increases the aging rate in boys, but not in girls; third, short-term recombinant human growth hormone treatment has little effect on the aging rate of children. Our child-specific methylation-based age prediction model can effectively detect epigenetic changes and health imbalances early in life. This may thus be a useful model for future studies of epigenetic interventions for age-related diseases.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study

Background

Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling.

Objective

We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test.

Methods

In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models.

Results

Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability.

Conclusions

The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.