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Frontiers in Endocrinology 2022The average age at thelarche has trended downwards worldwide since 1970s; however, the onset age of "precocious puberty", defined as the lower percentiles of thelarche...
BACKGROUND
The average age at thelarche has trended downwards worldwide since 1970s; however, the onset age of "precocious puberty", defined as the lower percentiles of thelarche age, has been rarely reported. This systematic review aims to evaluate secular trends in age at thelarche among Chinese girls.
METHODS
This systematic review on the age at thelarche during puberty among Chinese girls was conducted systematic search of both Chinese (Chinese National Knowledge Infrastructure, WanFang Database, and the Chinese Scientific Journals Database) and English (PubMed, Cochrane Library, and Embase) databases. Data were analyzed using the GraphPad Prism v9.0.
RESULTS
A total of 16 studies involving 177,886 Chinese girls were synthesized. The QualSyst scores of these studies were high at an average of 21.25. The timing of Tanner breast stage 2 (B2) occurred earlier over time at the P, P, and median ages. Weighted analyses revealed that the overall onset age of B2 tended to be younger at P, P, and P. The age of B2 varied across regions and areas. For example, P, P, and median age of B2 in years were younger in southern regions than that in northern regions of China (P: 5.94 . 7.3; P: 6.6 . 7.9; median age: 8.26 . 9.5), and median age of B2 in urban areas (8.26 years) was earlier than that in rural areas (10.29 years). In addition, median age of B2 from 12 single-center studies was earlier than that from 4 multicenter studies (8.26 . 9.18 years).
CONCLUSIONS
The current findings indicated that pubertal breast development age among Chinese girls presented an advanced trend over the past 20 years, which urges the necessity to revisit and redefine "precocious puberty" and provides useful recommendations for clinical practice.
Topics: Female; Humans; Child, Preschool; Child; Young Adult; Adult; East Asian People; Puberty; Puberty, Precocious; Breast; China
PubMed: 36506059
DOI: 10.3389/fendo.2022.1042122 -
Frontiers in Endocrinology 2024Central precocious puberty (CPP) is a common endocrine disorder in children, and its diagnosis primarily relies on the gonadotropin-releasing hormone (GnRH) stimulation... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Central precocious puberty (CPP) is a common endocrine disorder in children, and its diagnosis primarily relies on the gonadotropin-releasing hormone (GnRH) stimulation test, which is expensive and time-consuming. With the widespread application of artificial intelligence in medicine, some studies have utilized clinical, hormonal (laboratory) and imaging data-based machine learning (ML) models to identify CPP. However, the results of these studies varied widely and were challenging to directly compare, mainly due to diverse ML methods. Therefore, the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for CPP remains elusive. The aim of this study was to investigate the diagnostic value of ML models based on clinical, hormonal (laboratory) and imaging data for CPP through a meta-analysis of existing studies.
METHODS
We conducted a comprehensive search for relevant English articles on clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP, covering the period from the database creation date to December 2023. Pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) were calculated to assess the diagnostic value of clinical, hormonal (laboratory) and imaging data-based ML models for diagnosing CPP. The I test was employed to evaluate heterogeneity, and the source of heterogeneity was investigated through meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test.
RESULTS
Six studies met the eligibility criteria. The pooled sensitivity and specificity were 0.82 (95% confidence interval (CI) 0.62-0.93) and 0.85 (95% CI 0.80-0.90), respectively. The LR+ was 6.00, and the LR- was 0.21, indicating that clinical, hormonal (laboratory) and imaging data-based ML models exhibited an excellent ability to confirm or exclude CPP. Additionally, the SROC curve showed that the AUC of the clinical, hormonal (laboratory) and imaging data-based ML models in the diagnosis of CPP was 0.90 (95% CI 0.87-0.92), demonstrating good diagnostic value for CPP.
CONCLUSION
Based on the outcomes of our meta-analysis, clinical and imaging data-based ML models are excellent diagnostic tools with high sensitivity, specificity, and AUC in the diagnosis of CPP. Despite the geographical limitations of the study findings, future research endeavors will strive to address these issues to enhance their applicability and reliability, providing more precise guidance for the differentiation and treatment of CPP.
Topics: Child; Humans; Artificial Intelligence; Machine Learning; Puberty, Precocious; Reproducibility of Results; Sensitivity and Specificity
PubMed: 38590824
DOI: 10.3389/fendo.2024.1353023 -
Pituitary Oct 2021POU1F1 mutations are prevalent in Indian CPHD cohorts. Genotype-phenotype correlation is not well-studied.
CONTEXT
POU1F1 mutations are prevalent in Indian CPHD cohorts. Genotype-phenotype correlation is not well-studied.
AIM
To describe phenotypic and genotypic spectrum of POU1F1 mutations in our CPHD cohort and present systematic review as well as genotype-phenotype analysis of all mutation-positive cases reported in world literature.
METHODS
Retrospective study of POU1F1 mutation-positive patients from a western-Indian center. PRISMA guidelines based pubmed search of published literature of all mutation-positive patients.
RESULTS
Our cohort had 15 POU1F1 mutation-positive patients (9 index, 6 relatives). All had severe GH, TSH and prolactin deficiencies (GHD, TSHD and PD). TSHD was diagnosed earliest followed by GHD (median ages: TSHD-6 months, GHD-3 years), while PD was more variable. Two sisters had central precocious puberty at 7 years of age. Pubic hair was deficient in all post-pubertal patients (females: P1-P2, males: P3-P4). Splice-site/intronic/frameshift mutations were most common, while missense/nonsense mutations were less frequent (33%). Review of world literature yielded 114 patients (82 index patients) from 58 studies. GHD was present in all patients. TSHD was spared in 12.5% and PD in 4.4% patients. Missense/nonsense mutations accounted for 75% of spectrum. Phenotype-genotype analysis revealed higher mean peak-GH levels (1.1 vs 0.2 ng/ml, p = 0.008) and lower prevalence of anterior-pituitary hypoplasia (63.6% vs 86.3%, p = 0.03) in patients with heterozygous than homozygous and compound heterozygous mutations.
CONCLUSIONS
We present largest series of POU1F1 mutation-positive patients. Precocious puberty and defective pubarche are lesser-appreciated phenotypic features. Our mutation spectrum is different from that of world literature. Patients with heterozygous mutations have milder phenotype.
Topics: Female; Humans; Hypopituitarism; Male; Mutation; Retrospective Studies; Transcription Factor Pit-1; Transcription Factors
PubMed: 33742319
DOI: 10.1007/s11102-021-01140-9