Development of a disease diagnostic model to predict the occurrence of central precocious puberty of female

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Methods We established a retrospective cohort of girls with early breast development aged 6–9 years who visited the outpatient clinic of Beijing Children's Hospital from January 2017 to October 2022. Based on their breast development outcomes, the patients were divided into a pubertal development(PD) group and a premature thelarche (PT) group. Anthropometry, clinical, laboratory, and imaging variables ascertained were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a disease diagnostic model. Accuracy of the model was measured by the area under the receiver operating characteristic curve (AUC). Results The development cohort included 1001 girls aged 6–9 years. The mean (SD) age of patients was 7.86 (0.54) years, 36.4% of patients were finally diagnosed with PD, the other 63.6% were diagnosed with PT. From 14 potential predictors, 4 variables (bone age (BA)/chronological age (CA), basal luteinizing hormone (LH) level, uterine diameter and ovarian volume) were independent predictive factors. Body mass index (BMI) were considered to have some clinical significance. So the 5 variables included in the disease diagnostic model. BA/CA (OR, 2.04; 95% CI, 0.80–4.56; P < 0.001), basal LH level (OR, 8.08; 95% CI, 3.63–11.03; P < 0.001), uterine diameter (OR, 0.59; 95% CI, 0.34–1.22; P = .0006), ovarian volume (OR, 0.41; 95% CI, 0.03–1.09; P = 0.07), BMI (OR, 0.06; 95% CI, -0.06-0.15; P = 0.27), The mean AUC in the development cohort was 0.97 (95% CI, 0.88–1.05) and the AUC in the validation cohort was 0.94 (95% CI, 0.79–1.08). Conclusions : In this study, a disease diagnostic model was developed that may help predict a girl’s risk of diagnosing central precocious puberty. female central precocious puberty disease diagnostic model Figures Figure 1 Figure 2 Abstract Image Introduction Female precocious puberty is defined as the development of secondary sexual characteristics before the age of 7.5 years[ 1 ]. It is divided into central precocious puberty (CPP), peripheral precocious puberty (PPP), and partial forms of precocious puberty. CPP is initiated by the hypothalamic-pituitary-gonadal (HPG) axis, whereas PPP is not. Idiopathic central precocious puberty (ICPP) is the most common cause of sexual precocious puberty in girls. Female eraly puberty (EP) is defined as the development of secondary sexual characteristics before the age of 9 years[ 2 , 3 ]. ICPP and EP, collectively we call pubertal development (PD). Premature thelarche (PT) refers to isolated breast development before age 8 in girls, without any other signs of sexual maturation[ 4 ]. It is also known as variant precocious puberty and is benign, requiring no intervention. Premature CPP or rapid pubertal progression leads to premature menarche in girls, who may not achieve full height potential or have psychosocial problems and need prompt diagnosis and treatment. And girls with central precocious puberty also with breast development as the first symptom. Therefore, prompt recognition of true precocious puberty is important. At present, there is an increasing emphasis on basal sex hormones for the diagnosis of central precocious puberty, but GnRH provocation test is still required when there is a high index of suspicion. The nonphysiological nature of the GnRH provocation test, which does not truly reflect the level of gonadal development and requires multiple blood sampling, causes some distress to the affected children. So clinicians devoted to find more reliable, feasible and convenient indicators for diagnosing CPP. We aimed to construct a disease diagnostic model based on the breast development outcomes in chinese girls to identify girls with true precocious puberty in a timely manner without overdiagnosis. Materials and methods Subjects and group stratification This is a retrospective study. In all, 1102 female children aged 6-9years who visited the outpatient clinic of Beijing Children's Hospital from January 2017 to October 2022 with "breast development" as the chief complaint were enrolled in this study, and clinical data and follow-up progress were assessed. Girls with "breast development" need to have well-established breast ultrasound, pelvic ultrasound, bone age, and related blood tests such as sex hormones and thyroid hormones. Based on the follow-up outcomes, the cohort were categorized into an PD group and a PT group. The outcome groups diagnostic criteria were as follows. The study protocol was conformed to the ethical guidelines of the declaration of helsinki. The study was approved by the ethics committee of Beijing Children’s Hospital, Capital Medical University. Diagnostic criteria The PD diagnostic criteria[ 1 ] (2022 Chinese guidelines) were as follows: (1) presence of secondary sexual characteristics before 9 years of age in girls; (2) linear growth acceleration (> 6 cm/yr); (3) advanced bone age (BA) (≥ 1 year); (4) uterine length diameter exceeding 3.4 cm, ovarian volume > 1 ml, and multiple follicles > 4 mm in diameter; and (5) gonadal axis function initiation: basal levels of luteinizing hormone (LH) > 0.83IU/L or a positive GnRH provocation test (peak LH ≥ 5IU/L and peak LH / peak follicle stimulating hormone (FSH) ≥ 0.6). The criteria for PT were as follows: the girl showed isolated breast development before the age of 8 years, without other signs of pubertal development during follow-up, such as linear growth acceleration and advanced BA; uterine length was less than 3.4 cm, and the ovarian volume was less than 1 ml; and unelevated basal levels of LH (less than 0.83IU/L) or a negative GnRH provocation test. Observations All the subjects were interviewed in detail about their medical history and then underwent a physical examination that included measurements of height, weight, and calculation of body mass index (BMI). Body weight and height were measured with the patient barefoot and wearing light clothes. BMI was calculated using the formula [BMI (kg/m 2 ) = weight (kg)/height 2 (m 2 )]. Breast development was determined according to Tanner staging and monitored every 3 months up to age 9 years. Serum sex hormone levels, including those of FSH, LH, estradiol (E2), and progesterone, were measured by immunochemiluminescence every 3 months. Pelvic ultrasound was performed every 3 months by professionally trained endocrine sonographer. BA was assessed using the G-P method every 6 months by the same pediatric endocrinologist in all times. Magnetic resonance imaging (MRI) of the pituitary or sellar region was performed in some patients. Those who did not undergo MRI had no signs or abnormal neurological findings indicating CNS tumors, during follow-up. Potential Predictive Variables Potential predictive variables included the following patient characteristics: anthropometry, clinical signs and symptoms, laboratory findings, and imaging results. Anthropometry variables included chronological age (CA), height, height SDS, weight, weight SDS, BMI and BMI SDS. Clinical signs and symptoms: Tanner staging of breast development. Laboratory findings included basal FSH, LH, and estradiol (E2). Imaging results included BA, uterine diameter, ovarian volume. Variable Selection and Model Construction All 1102 patients with breast development in the development cohort were included for variable selection and model development. As described herein, 14 variables were entered into the selection process. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to minimize the potential collinearity of variables measured from the same patient and over-fitting of variables. Imputation for missing variables was considered if missing values were less than 20%. We used predictive mean matching to impute numeric features, logistic regression to impute binary variables, and Bayesian polytomous regression to impute factor features. We used L1-penalized least absolute shrinkage and selection regression for multivariable analyses, augmented with 5-fold cross validation for internal validation. This is a logistic regression model that penalizes the absolute size of the coefficients of a regression model based on the value of λ. With larger penalties, the estimates of weaker factors shrink toward zero, so that only the strongest predictors remain in the model. The most predictive covariates were selected by the minimum (λ min). The R package “glmnet” statistical software (R Foundation) was used to perform the LASSO regression. Subsequently, variables identified by LASSO regression analysis were entered into logistic regression models and those that were consistently statistically significant were used to construct the model. Assessment of Accuracy The accuracy of model was assessed using the area under the receiver-operator characteristic curve (AUC). Results A total of 1102 girls aged 6–9 years were included at baseline, 90 were lost to follow-up, and 11 were excluded because of the following diseases: 4 with congenital adrenal hyperplasia (CAH) and 7 with ovarian cysts. Thus, 1001 participants with follow-up data were analyzed. In regards to outcome, they were categorized to either PD, if they fulfilled the criteria at any time point during follow-up, or simple PT. Three hundred and sixty four (36.4%) were finally diagnosed with PD; the other 637 (63.6%) were diagnosed with PT. The five diagnostic criteria were simultaneously met for the diagnosis of PD. A flowchart of participant screening and enrollment is shown in Fig. 1 . Characteristics of the Development Cohort Overall, the mean (SD) age of patients in the cohort was 7.86 (0.54) years. The comparative analysis of the physical status of the children in the 2 groups revealed that the PD group had significantly higher height, weight, and BMI as compared with the PT group. Children in the PD group had advanced BA, sex hormone levels, and pelvic ultrasound parameters. Their anthropometrics and laboratory values are listed in Table 1 . Table 1 Anthropometry and clinical characteristics among patients in the development cohort Characteristic PT PD P No 637 364 Age (y) 7.8 ± 0.52 7.9 ± 0.69 0.352 Height (cm) 130.7 ± 5.75 124.1 ± 6.62 <0.01 Height SDS 0.53 ± 0.99 1.16 ± 0.99 0.026 Weight (kg) 28.2 ± 5.04 30.7 ± 5.82 <0.01 Weight SDS 0.80 ± 1.21 1.34 ± 1.27 0.028 BMI (kg/m 2 ) 16.4 ± 2.62 17.0 ± 2.24 0.032 BMI SDS 0.49 ± 1.44 0.85 ± 1.13 <0.01 BA (y) 8.29 ± 1.28 9.37 ± 1.11 <0.01 BA/CA 1.06 ± 0.14 1.19 ± 0.13 <0.01 Basal LH (IU/L) 0.12 ± 0.19 1.04 ± 1.15 <0.01 Basal FSH (IU/L) 2.69 ± 1.30 4.80 ± 2.44 <0.01 E2 (pg/ml) 22.0 ± 14.6 37.5 ± 21.4 <0.01 Uterine diameter (cm) 3.53 ± 0.52 4.12 ± 0.63 <0.01 Ovarian volume (ml) 1.29 ± 0.48 1.58 ± 0.48 <0.01 PT, premature thelarche; PD, pubertal development; BMI, body mass index; BA, bone age; CA, chronological age; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol Predictor Selection 14 variables measured (Table 1 ) were included in the LASSO regression. After LASSO regression selection, 11 variables remained significant predictors of CPP, including height, height SDS, weight, weight SDS, BMI, BMI-SDS, BA/CA, LH, E2, uterine diameter, ovarian volume. Inclusion of these 11 variables in a logistic regression model resulted in 4 variables that were independently statistically significant predictors of critical illness and were included in model. These variables included basal BA/CA, LH level, uterine diameter and ovarian volume. Frisch[ 5 ] proposed the “critical weight hypothesis” as early as the 1970s, which stated that a certain body fat depot seems to be required for the process of initiating normal reproductive function. And our group[ 6 ] also found that when CPP was diagnosed in children, their mean BMI was 17.0 kg/m 2 (equivalent to a normally developing girl aged 10.5 years). Due to the large population base in China, increasing BMI in the disease diagnosis model can reduce the rate of misdiagnosis to some extent. So, the 5 variables were included in the disease diagnostic model: BA/CA (OR, 2.04; 95% CI, 0.80–4.56; P < 0.001), basal LH level (OR, 8.08; 95% CI, 3.63–11.03; P < 0.001), uterine diameter (OR, 0.59; 95% CI, 0.34–1.22; P = .0006), ovarian volume (OR, 0.41; 95% CI, 0.03–1.09; P = 0.07), BMI (OR, 0.06; 95% CI, -0.06-0.15; P = 0.27) (Table 2 ). Table 2 Multivariable logistic regression model for diagnosing central precocious puberty in girls with breast development in China Variable Odds ratio 95% CI Basal LH (IU/L) 8.08 (3.6280319, 11.0357372) Uterine diameter (cm) 0.59 (0.3395130, 1.2219208) BA/CA 2.04 (0.7998785, 4.5673873) ovarian volume(ml) 0.41 (0.0283984, 1.0958552) BMI (kg/m 2 ) 0.06 (-0.0616142, 0.1479303) LH, luteinizing hormone; BA, bone age; CA, chronological age; BMI, body mass index. Construction of the disease diagnostic model The ICPP disease diagnostic model was constructed based on the coefficients from the logistic model. We used the following formulas for the logistic model to calculate the probability. $$probability=\frac{{exp}^{-6.48+2.04\ast BA/CA+8.08\ast basal LH +0.59\ast uterine diameter+0.41\ast ovarian volume+0.06\ast BMI}}{1+{exp}^{-6.48+2.04\ast BA/CA+8.08\ast basal LH +0.59\ast uterine diameter+0.41\ast ovarian volume+0.06\ast BMI}}$$ The Performance of CPP disease diagnostic model By internal bootstrap validation, the mean AUC in the development cohort was 0.97 (95% CI, 0.88–1.05) and the AUC in the validation cohort was 0.94 (95% CI, 0.79–1.08). (Fig. 2 ) Validation of the CPP disease diagnostic model We collected an external validation cohort consisting of 226 female children aged 6–9 who visited Luhe hospitalthe or Traditional Chinese Medicine Department of Beijing Children's Hospital. Among them, there were 149 cases of isolated breast development (66%) and 77 cases of puberty development (34%). The characteristics of each variable are shown in Table 3 . The accuracy of the CPP disease diagnostic model in the validation cohort was similar to that of the development cohort with an AUC in the validation cohort of 0.95(95%CI,0.84–0.93) Table 3 Demographics and clinical characteristics of patients in validation cohorts. Characteristic PT PD No 149 77 Age (y) 7.3 ± 0.86 7.8 ± 0.76 Height(cm) 127.6 ± 7.31 132.6 ± 6.88 Height SDS 0.81 ± 1.07 1.06 ± 1.00 Weight (kg) 27.1 ± 4.95 30.7 ± 6.71 Weight SDS 1.05 ± 1.21 1.43 ± 1.40 BMI (kg/m 2) 16.6 ± 1.89 17.2 ± 2.56 BMI SDS 0.59 ± 0.97 0.87 ± 1.27 BA (y) 8.2 ± 1.29 9.1 ± 1.34 BA/CA 1.11 ± 0.12 1.17 ± 0.12 Basal LH (IU/L) 0.12 ± 0.23 0.72 ± 0.78 Basal FSH (IU/L) 3.07 ± 1.56 4.12 ± 2.07 E2 (pg/ml) 18.1 ± 10.5 35.4 ± 26.5 Uterine diameter (cm) 3.52 ± 0.69 4.00 ± 0.66 Ovarian volume (ml) 1.33 ± 0.50 1.61 ± 0.46 PT, premature thelarche; PD, pubertal development; BMI, body mass index; BA, bone age; CA, chronological age; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol. Discussion To date, there is an increasing emphasis on basal sex hormone levels for diagnosing CPP because of the insurmountable drawbacks of the nonphysiological GnRH stimulation test, which does not truly reflect the level of gonadal development and requires multiple blood samples. Huynh[ 7 ] simplified the procedure of the GnRH stimulation test, reducing the duration and number of blood collections, however, it still used the method of stimulation test. Given that the GnRH stimulation test is cumbersome to perform and that overdiagnosis of CPP due to false positives after the GnRH stimulation test has been documented in the infant population[ 8 ], the possibility of replacing this test with a simplified evaluation panel including basal laboratory hormonal values, such as LH, and pelvic ultrasonography, which is noninvasive and relatively easy to perform, has been continuously reviewed over the years[ 9 – 23 ]. Pelvic ultrasound indices overlap more in children with PT and precocious puberty and therefore do not have high value for predicting CPP[ 9 – 20 ]. It has been mentioned in the literature that basal gonadotropin levels are useful, but different studies have different cutoff values for diagnosing CPP, so the ideal cutoff value for diagnosing CPP is difficult to determine[ 21 – 23 ]. Zou P et al[ 24 ] used pituitary MRI as an indicator for the diagnosis of precocious puberty. They determined the activation status of the HPG axis based on pituitary size and volume, which has some theoretical significance. However, due to the small size of the pituitary gland itself, and its division into the anterior lobe, posterior lobe, and stalk, it is difficult to assess subtle changes in different regions for the evaluation of sexual development. This increases the workload for radiologists and clinicians. Additionally, the guideline states that pituitary MRI is not necessary for girls with early breast development after the age of 6, which can lead to unnecessary examinations and economic burden. Jingyu You et al[ 25 ] used the age of onset of sexual development (breast development) as a predictive indicator, however, the age of sexual development initiation is often difficult for many parents and patients to recall accurately, leading to recall bias. Bo Yuan et al[ 26 ] used the initial diagnosis age, baseline gonadotropin levels, and pelvic ultrasound indices to predict the diagnosis of CPP, without using bone age indicators, however, bone age is of great significance in the process of sexual precocity. Using more comprehensive indicators for evaluation will lead to better predictive results. As early as the 1970s, Frisch[ 5 ] proposed the “critical weight hypothesis”, which stated that a certain body fat depot seems to be required for the process of initiating normal reproductive function. Several studies and epidemiological reports have noted that obesity in girls is a risk factor for early pubertal development[ 27 – 30 ]. Research in females suggests that obesity is more likely to lead to precocious puberty[ 31 , 32 ]. We similarly found in a previous study[ 6 ], the physical development, including height, weight and BMI measurements, of girls with early breast development, compared with that of normally developing girls, was significantly advanced corresponding to the mean values for girls older by 1–2 year, finding consistent with reports of other studies[ 32 – 36 ]. Girls in the PD group had higher weight, height, and BMI measurements than those in the PT group. We focused on physical signs and a combination of biomarkers for diagnosing CPP. In this study, we developed a disease diagnostic model to predict a patient’s risk of diagnosing central precocious puberty. The performance of this model was satisfactory with accuracy based on AUCs in both the development and validation cohorts. The model can be used by clinicians to estimate an girl with breast development, whose risk of developing CPP. The 5 variables required for calculation of the risk of developing CPP are generally readily available during outpatient visits. If the patient’s estimated risk for CPP is low, the clinician may choose to monitor, whereas high-risk estimates might support aggressive treatment or monitor. Limitations Potential limitations of this study include a modest sample size for constructing the disease diagnostic model and a relatively small sample for validation.The data for model development and validation are entirely from China, which could potentially limit the generalizability of the model in other areas of the world. Additional validation studies of the CPP diagnostic model from areas outside China should be completed. Conclusions In this study, we developed a disease diagnostic model to estimate the risk of diagnosing CPP among girls with breast development based on 5 variables commonly measured during outpatient visits. Estimating the risk of diagnosing CPP could help identify patients who are and are not likely CPP, thus supporting appropriate treatment and avoiding overdiagnosis and overtreatment. Declarations Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Acknowledgments The study was supported by the patients and the cooperation of their families and the help of doctors and nurses in the Department of Endocrinology. A uthor contributions All the authors helped to perform the research; Manman Zhao and Yannan Zheng collected clinical samples and wrote the manuscript; Guoshuang Feng performed statistical analysis; Bingyan Cao contributed to the project management; Chunxiu Gong conceived and designed the project and revised the manuscript. All the listed authors revised the paper critically and approved the final version of the submitted manuscript. Conflicts of interest statement The authors declare that they have no competing interests. References Chinese Medical Association Pediatrics endocrine genetic metabolomics group. Consensus on diagnosis and treatment of central precocious puberty(2015). Chin J Pediatr. 53: 412-418(2015). doi:10.3760/cma.j.issn.0578-1310.2015.06.004 Lebrethon MC, Bourguignon JP. Management of central isosexual precocity: diagnosis, treatment, outcome. Curr Opin Pediatr. 12:394-399 (2000). doi: 10.1097/00008480-200008000-00020 Mul D, Oostdijk W, Drop SL. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4133586","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282261847,"identity":"26fb73d1-e1fd-4208-ae8c-b31efe252918","order_by":0,"name":"Manman Zhao","email":"","orcid":"","institution":"Capital Medical University, National Center for Children’s Health","correspondingAuthor":false,"prefix":"","firstName":"Manman","middleName":"","lastName":"Zhao","suffix":""},{"id":282261848,"identity":"332cf827-91e2-4026-a39a-5b21832836b3","order_by":1,"name":"Guoshuang Feng","email":"","orcid":"","institution":"Capital Medical University, National Center for Children’s Health","correspondingAuthor":false,"prefix":"","firstName":"Guoshuang","middleName":"","lastName":"Feng","suffix":""},{"id":282261849,"identity":"5148606b-7121-4395-9033-73add564da48","order_by":2,"name":"Bingyan Cao","email":"","orcid":"","institution":"Capital Medical University, National Center for Children’s Health","correspondingAuthor":false,"prefix":"","firstName":"Bingyan","middleName":"","lastName":"Cao","suffix":""},{"id":282261850,"identity":"24d2648a-c8b7-423d-9075-aed9f220bd1c","order_by":3,"name":"Yannan Zheng","email":"","orcid":"","institution":"Capital Medical University, National Center for Children’s Health","correspondingAuthor":false,"prefix":"","firstName":"Yannan","middleName":"","lastName":"Zheng","suffix":""},{"id":282261851,"identity":"d5f39cd7-abf9-44ff-9548-af4ce09c7483","order_by":4,"name":"Chunxiu Gong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACCRBhAGYyPoAyiNfCbECCFghgk8CtDAnwz+4xe8xTcNiuX7r9WuWPgjvyDOyHj27Aa8mdM+aGMwwOJ8+cc6bsNo/BM8MGnrS0G/i0GEjkmEl8AGoxuJGTdpvB4DBjgwSPGWEtCVAthT8MDtsTpwVoi53BjfRjDDwGhxMJapG4kVYmOcMgPUFyRg6zNFBLchshv/DPSN4mzfPH2p5fIv3hxx9/Dtv2sx8+hlcLDCQ2AF0FZrERoxwE7BkY2B8Qq3gUjIJRMApGGAAAXLVIOEpfOJYAAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University, National Center for Children’s Health","correspondingAuthor":true,"prefix":"","firstName":"Chunxiu","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2024-03-20 02:18:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4133586/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4133586/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53255693,"identity":"eae2fc77-95b0-40bb-9cfe-e2077b962fa4","added_by":"auto","created_at":"2024-03-22 13:29:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135287,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant screening and enrollment.\u003c/p\u003e\n\u003cp\u003eCAH, congenital adrenal hyperplasia; ICPP, idiopathic central precocious puberty; EP, early puberty; PD, pubertal development; PT, premature thelarche\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4133586/v1/671cd641aaf0fcfb6b9c0a2c.png"},{"id":53255695,"identity":"8ed77cd9-16bd-4aa4-84e6-d44c3b71b7cb","added_by":"auto","created_at":"2024-03-22 13:29:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47038,"visible":true,"origin":"","legend":"\u003cp\u003eThe area under the receiver-operator characteristic (ROC) curve (AUC) of diagnosing CPP among girls with breast development.\u003c/p\u003e\n\u003cp\u003eCPP, central precocious puberty; ROC, receiver-operator characteristic; AUC, area under the receiver-operator characteristic curve\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4133586/v1/b8d89b05dcd8875d33efd13d.png"},{"id":55265177,"identity":"26c48326-a1f8-4e1c-8180-3bf6e2634fd7","added_by":"auto","created_at":"2024-04-25 01:57:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":690870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4133586/v1/ab2814d4-b604-4269-8b81-6b52dea8c831.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a disease diagnostic model to predict the occurrence of central precocious puberty of female","fulltext":[{"header":"Abstract Image","content":"\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1711089973.png\"\u003e\u003c/p\u003e\n"},{"header":"Introduction","content":"\u003cp\u003eFemale precocious puberty is defined as the development of secondary sexual characteristics before the age of 7.5 years[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is divided into central precocious puberty (CPP), peripheral precocious puberty (PPP), and partial forms of precocious puberty. CPP is initiated by the hypothalamic-pituitary-gonadal (HPG) axis, whereas PPP is not. Idiopathic central precocious puberty (ICPP) is the most common cause of sexual precocious puberty in girls. Female eraly puberty (EP) is defined as the development of secondary sexual characteristics before the age of 9 years[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. ICPP and EP, collectively we call pubertal development (PD). Premature thelarche (PT) refers to isolated breast development before age 8 in girls, without any other signs of sexual maturation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is also known as variant precocious puberty and is benign, requiring no intervention. Premature CPP or rapid pubertal progression leads to premature menarche in girls, who may not achieve full height potential or have psychosocial problems and need prompt diagnosis and treatment. And girls with central precocious puberty also with breast development as the first symptom. Therefore, prompt recognition of true precocious puberty is important.\u003c/p\u003e \u003cp\u003eAt present, there is an increasing emphasis on basal sex hormones for the diagnosis of central precocious puberty, but GnRH provocation test is still required when there is a high index of suspicion. The nonphysiological nature of the GnRH provocation test, which does not truly reflect the level of gonadal development and requires multiple blood sampling, causes some distress to the affected children. So clinicians devoted to find more reliable, feasible and convenient indicators for diagnosing CPP.\u003c/p\u003e \u003cp\u003eWe aimed to construct a disease diagnostic model based on the breast development outcomes in chinese girls to identify girls with true precocious puberty in a timely manner without overdiagnosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and group stratification\u003c/h2\u003e \u003cp\u003eThis is a retrospective study. In all, 1102 female children aged 6-9years who visited the outpatient clinic of Beijing Children's Hospital from January 2017 to October 2022 with \"breast development\" as the chief complaint were enrolled in this study, and clinical data and follow-up progress were assessed. Girls with \"breast development\" need to have well-established breast ultrasound, pelvic ultrasound, bone age, and related blood tests such as sex hormones and thyroid hormones. Based on the follow-up outcomes, the cohort were categorized into an PD group and a PT group. The outcome groups diagnostic criteria were as follows.\u003c/p\u003e \u003cp\u003e The study protocol was conformed to the ethical guidelines of the declaration of helsinki. The study was approved by the ethics committee of Beijing Children\u0026rsquo;s Hospital, Capital Medical University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic criteria\u003c/h2\u003e \u003cp\u003eThe PD diagnostic criteria[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (2022 Chinese guidelines) were as follows: (1) presence of secondary sexual characteristics before 9 years of age in girls; (2) linear growth acceleration (\u0026gt;\u0026thinsp;6 cm/yr); (3) advanced bone age (BA) (\u0026ge;\u0026thinsp;1 year); (4) uterine length diameter exceeding 3.4 cm, ovarian volume\u0026thinsp;\u0026gt;\u0026thinsp;1 ml, and multiple follicles\u0026thinsp;\u0026gt;\u0026thinsp;4 mm in diameter; and (5) gonadal axis function initiation: basal levels of luteinizing hormone (LH)\u0026thinsp;\u0026gt;\u0026thinsp;0.83IU/L or a positive GnRH provocation test (peak LH\u0026thinsp;\u0026ge;\u0026thinsp;5IU/L and peak LH / peak follicle stimulating hormone (FSH)\u0026thinsp;\u0026ge;\u0026thinsp;0.6).\u003c/p\u003e \u003cp\u003eThe criteria for PT were as follows: the girl showed isolated breast development before the age of 8 years, without other signs of pubertal development during follow-up, such as linear growth acceleration and advanced BA; uterine length was less than 3.4 cm, and the ovarian volume was less than 1 ml; and unelevated basal levels of LH (less than 0.83IU/L) or a negative GnRH provocation test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eObservations\u003c/h2\u003e \u003cp\u003eAll the subjects were interviewed in detail about their medical history and then underwent a physical examination that included measurements of height, weight, and calculation of body mass index (BMI). Body weight and height were measured with the patient barefoot and wearing light clothes. BMI was calculated using the formula [BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;weight (kg)/height\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e)]. Breast development was determined according to Tanner staging and monitored every 3 months up to age 9 years. Serum sex hormone levels, including those of FSH, LH, estradiol (E2), and progesterone, were measured by immunochemiluminescence every 3 months. Pelvic ultrasound was performed every 3 months by professionally trained endocrine sonographer. BA was assessed using the G-P method every 6 months by the same pediatric endocrinologist in all times. Magnetic resonance imaging (MRI) of the pituitary or sellar region was performed in some patients. Those who did not undergo MRI had no signs or abnormal neurological findings indicating CNS tumors, during follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePotential Predictive Variables\u003c/h2\u003e \u003cp\u003ePotential predictive variables included the following patient characteristics: anthropometry, clinical signs and symptoms, laboratory findings, and imaging results. Anthropometry variables included chronological age (CA), height, height SDS, weight, weight SDS, BMI and BMI SDS. Clinical signs and symptoms: Tanner staging of breast development. Laboratory findings included basal FSH, LH, and estradiol (E2). Imaging results included BA, uterine diameter, ovarian volume.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eVariable Selection and Model Construction\u003c/h2\u003e \u003cp\u003eAll 1102 patients with breast development in the development cohort were included for variable selection and model development. As described herein, 14 variables were entered into the selection process. Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to minimize the potential collinearity of variables measured from the same patient and over-fitting of variables. Imputation for missing variables was considered if missing values were less than 20%. We used predictive mean matching to impute numeric features, logistic regression to impute binary variables, and Bayesian polytomous regression to impute factor features. We used L1-penalized least absolute shrinkage and selection regression for multivariable analyses, augmented with 5-fold cross validation for internal validation. This is a logistic regression model that penalizes the absolute size of the coefficients of a regression model based on the value of λ. With larger penalties, the estimates of weaker factors shrink toward zero, so that only the strongest predictors remain in the model. The most predictive covariates were selected by the minimum (λ min). The R package \u0026ldquo;glmnet\u0026rdquo; statistical software (R Foundation) was used to perform the LASSO regression. Subsequently, variables identified by LASSO regression analysis were entered into logistic regression models and those that were consistently statistically significant were used to construct the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Accuracy\u003c/h2\u003e \u003cp\u003eThe accuracy of model was assessed using the area under the receiver-operator characteristic curve (AUC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1102 girls aged 6\u0026ndash;9 years were included at baseline, 90 were lost to follow-up, and 11 were excluded because of the following diseases: 4 with congenital adrenal hyperplasia (CAH) and 7 with ovarian cysts. Thus, 1001 participants with follow-up data were analyzed. In regards to outcome, they were categorized to either PD, if they fulfilled the criteria at any time point during follow-up, or simple PT. Three hundred and sixty four (36.4%) were finally diagnosed with PD; the other 637 (63.6%) were diagnosed with PT. The five diagnostic criteria were simultaneously met for the diagnosis of PD. A flowchart of participant screening and enrollment is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Development Cohort\u003c/h2\u003e \u003cp\u003eOverall, the mean (SD) age of patients in the cohort was 7.86 (0.54) years. The comparative analysis of the physical status of the children in the 2 groups revealed that the PD group had significantly higher height, weight, and BMI as compared with the PT group. Children in the PD group had advanced BA, sex hormone levels, and pelvic ultrasound parameters. Their anthropometrics and laboratory values are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometry and clinical characteristics among patients in the development cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBA (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBA/CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal LH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal FSH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian volume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePT, premature thelarche; PD, pubertal development; BMI, body mass index; BA, bone age; CA, chronological age; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePredictor Selection\u003c/h2\u003e \u003cp\u003e14 variables measured (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were included in the LASSO regression. After LASSO regression selection, 11 variables remained significant predictors of CPP, including height, height SDS, weight, weight SDS, BMI, BMI-SDS, BA/CA, LH, E2, uterine diameter, ovarian volume. Inclusion of these 11 variables in a logistic regression model resulted in 4 variables that were independently statistically significant predictors of critical illness and were included in model. These variables included basal BA/CA, LH level, uterine diameter and ovarian volume.\u003c/p\u003e \u003cp\u003eFrisch[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] proposed the \u0026ldquo;critical weight hypothesis\u0026rdquo; as early as the 1970s, which stated that a certain body fat depot seems to be required for the process of initiating normal reproductive function. And our group[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] also found that when CPP was diagnosed in children, their mean BMI was 17.0 kg/m\u003csup\u003e2\u003c/sup\u003e (equivalent to a normally developing girl aged 10.5 years). Due to the large population base in China, increasing BMI in the disease diagnosis model can reduce the rate of misdiagnosis to some extent.\u003c/p\u003e \u003cp\u003eSo, the 5 variables were included in the disease diagnostic model: BA/CA (OR, 2.04; 95% CI, 0.80\u0026ndash;4.56; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), basal LH level (OR, 8.08; 95% CI, 3.63\u0026ndash;11.03; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uterine diameter (OR, 0.59; 95% CI, 0.34\u0026ndash;1.22; P\u0026thinsp;=\u0026thinsp;.0006), ovarian volume (OR, 0.41; 95% CI, 0.03\u0026ndash;1.09; P\u0026thinsp;=\u0026thinsp;0.07), BMI (OR, 0.06; 95% CI, -0.06-0.15; P\u0026thinsp;=\u0026thinsp;0.27) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression model for diagnosing central precocious puberty in girls with breast development in China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal LH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(3.6280319, 11.0357372)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.3395130, 1.2219208)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBA/CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.7998785, 4.5673873)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eovarian volume(ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.0283984, 1.0958552)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.0616142, 0.1479303)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eLH, luteinizing hormone; BA, bone age; CA, chronological age; BMI, body mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the disease diagnostic model\u003c/h2\u003e \u003cp\u003eThe ICPP disease diagnostic model was constructed based on the coefficients from the logistic model. We used the following formulas for the logistic model to calculate the probability.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$probability=\\frac{{exp}^{-6.48+2.04\\ast BA/CA+8.08\\ast basal LH +0.59\\ast uterine diameter+0.41\\ast ovarian volume+0.06\\ast BMI}}{1+{exp}^{-6.48+2.04\\ast BA/CA+8.08\\ast basal LH +0.59\\ast uterine diameter+0.41\\ast ovarian volume+0.06\\ast BMI}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eThe Performance of CPP disease diagnostic model\u003c/h2\u003e \u003cp\u003eBy internal bootstrap validation, the mean AUC in the development cohort was 0.97 (95% CI, 0.88\u0026ndash;1.05) and the AUC in the validation cohort was 0.94 (95% CI, 0.79\u0026ndash;1.08). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the CPP disease diagnostic model\u003c/h2\u003e \u003cp\u003eWe collected an external validation cohort consisting of 226 female children aged 6\u0026ndash;9 who visited Luhe hospitalthe or Traditional Chinese Medicine Department of Beijing Children's Hospital. Among them, there were 149 cases of isolated breast development (66%) and 77 cases of puberty development (34%). The characteristics of each variable are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The accuracy of the CPP disease diagnostic model in the validation cohort was similar to that of the development cohort with an AUC in the validation cohort of 0.95(95%CI,0.84\u0026ndash;0.93)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical characteristics of patients in validation cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBA (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBA/CA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal LH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal FSH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian volume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ePT, premature thelarche; PD, pubertal development; BMI, body mass index; BA, bone age; CA, chronological age; LH, luteinizing hormone; FSH, follicle stimulating hormone; E2, estradiol.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo date, there is an increasing emphasis on basal sex hormone levels for diagnosing CPP because of the insurmountable drawbacks of the nonphysiological GnRH stimulation test, which does not truly reflect the level of gonadal development and requires multiple blood samples. Huynh[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] simplified the procedure of the GnRH stimulation test, reducing the duration and number of blood collections, however, it still used the method of stimulation test. Given that the GnRH stimulation test is cumbersome to perform and that overdiagnosis of CPP due to false positives after the GnRH stimulation test has been documented in the infant population[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the possibility of replacing this test with a simplified evaluation panel including basal laboratory hormonal values, such as LH, and pelvic ultrasonography, which is noninvasive and relatively easy to perform, has been continuously reviewed over the years[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePelvic ultrasound indices overlap more in children with PT and precocious puberty and therefore do not have high value for predicting CPP[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It has been mentioned in the literature that basal gonadotropin levels are useful, but different studies have different cutoff values for diagnosing CPP, so the ideal cutoff value for diagnosing CPP is difficult to determine[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Zou P et al[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] used pituitary MRI as an indicator for the diagnosis of precocious puberty. They determined the activation status of the HPG axis based on pituitary size and volume, which has some theoretical significance. However, due to the small size of the pituitary gland itself, and its division into the anterior lobe, posterior lobe, and stalk, it is difficult to assess subtle changes in different regions for the evaluation of sexual development. This increases the workload for radiologists and clinicians. Additionally, the guideline states that pituitary MRI is not necessary for girls with early breast development after the age of 6, which can lead to unnecessary examinations and economic burden. Jingyu You et al[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] used the age of onset of sexual development (breast development) as a predictive indicator, however, the age of sexual development initiation is often difficult for many parents and patients to recall accurately, leading to recall bias. Bo Yuan et al[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] used the initial diagnosis age, baseline gonadotropin levels, and pelvic ultrasound indices to predict the diagnosis of CPP, without using bone age indicators, however, bone age is of great significance in the process of sexual precocity. Using more comprehensive indicators for evaluation will lead to better predictive results.\u003c/p\u003e \u003cp\u003eAs early as the 1970s, Frisch[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] proposed the \u0026ldquo;critical weight hypothesis\u0026rdquo;, which stated that a certain body fat depot seems to be required for the process of initiating normal reproductive function. Several studies and epidemiological reports have noted that obesity in girls is a risk factor for early pubertal development[\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Research in females suggests that obesity is more likely to lead to precocious puberty[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We similarly found in a previous study[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the physical development, including height, weight and BMI measurements, of girls with early breast development, compared with that of normally developing girls, was significantly advanced corresponding to the mean values for girls older by 1\u0026ndash;2 year, finding consistent with reports of other studies[\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Girls in the PD group had higher weight, height, and BMI measurements than those in the PT group.\u003c/p\u003e \u003cp\u003eWe focused on physical signs and a combination of biomarkers for diagnosing CPP. In this study, we developed a disease diagnostic model to predict a patient\u0026rsquo;s risk of diagnosing central precocious puberty. The performance of this model was satisfactory with accuracy based on AUCs in both the development and validation cohorts. The model can be used by clinicians to estimate an girl with breast development, whose risk of developing CPP. The 5 variables required for calculation of the risk of developing CPP are generally readily available during outpatient visits. If the patient\u0026rsquo;s estimated risk for CPP is low, the clinician may choose to monitor, whereas high-risk estimates might support aggressive treatment or monitor.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003ePotential limitations of this study include a modest sample size for constructing the disease diagnostic model and a relatively small sample for validation.The data for model development and validation are entirely from China, which could potentially limit the generalizability of the model in other areas of the world. Additional validation studies of the CPP diagnostic model from areas outside China should be completed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we developed a disease diagnostic model to estimate the risk of diagnosing CPP among girls with breast development based on 5 variables commonly measured during outpatient visits. Estimating the risk of diagnosing CPP could help identify patients who are and are not likely CPP, thus supporting appropriate treatment and avoiding overdiagnosis and overtreatment.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the patients and the cooperation of their families and the help of doctors and nurses in the Department of Endocrinology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003euthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll\u0026nbsp;the\u0026nbsp;authors helped to perform the research; Manman Zhao\u0026nbsp;and Yannan Zheng\u0026nbsp;collected clinical samples and wrote the manuscript;\u0026nbsp;Guoshuang Feng performed statistical analysis;\u0026nbsp;Bingyan Cao\u0026nbsp;contributed to the project management; Chunxiu Gong conceived and designed the project and revised the manuscript. All the listed authors revised the paper critically and approved the final version of the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflicts of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChinese Medical Association Pediatrics endocrine genetic metabolomics group. Consensus on diagnosis and treatment of central precocious puberty(2015). Chin J Pediatr. 53: 412-418(2015). doi:10.3760/cma.j.issn.0578-1310.2015.06.004\u003c/li\u003e\n\u003cli\u003eLebrethon MC, Bourguignon JP. Management of central isosexual precocity: diagnosis, treatment, outcome. Curr Opin Pediatr. 12:394-399 (2000). doi: 10.1097/00008480-200008000-00020\u003c/li\u003e\n\u003cli\u003eMul D, Oostdijk W, Drop SL. Early puberty in girls. 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J Pediatr Endocrinol Meta. 31:323-329(2018) . doi:10.1515/jpem-2017-0124\u003c/li\u003e\n\u003cli\u003eLee HS, Park HK, Ko JH, Kim YJ, Hwang JS. Utility of basal luteinizing hormone levels for detecting central precocious puberty in girls. Horm Metab Res. 4: 851-854 (2012). doi:10.1055/s-0032-1321905\u003c/li\u003e\n\u003cli\u003eLi X, Ou Y, Fan Y. Diagnostic value of baseline serum luteinizing hormone level for central precocious puberty in girls. Chin J Contemp. Pediatr. 19: 729-733(2017) . doi:10.7499/j.issn.1008-8830.2017.07.001\u003c/li\u003e\n\u003cli\u003eZou PF, Zhang LF, Zhang RF, Wang CY, Lin XT, Lai C, et al. Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter-Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls. J Magn Reson Imaging. 58(6):1977-1987(2023). doi: 10.1002/jmri.28709\u003c/li\u003e\n\u003cli\u003eYou JY, Cheng XY, Li XJ, Li MQ, Yao L, Luo FH, et al. Clinical risk score for central precocious puberty among girls with precocious pubertal development: a cross sectional study. BMC Endocr Disord. 21(1):75-86(2021). doi: 10.1186/s12902-021-00740-7.\u003c/li\u003e\n\u003cli\u003eYuan B, Pi YL, Zhang YN, Xing P, Chong HM, et al. A diagnostic model of idiopathic central precocious puberty based on transrectal pelvic ultrasound and basal gonadotropin levels. J Int Med Res. 48(8):1-7(2020). doi: 10.1177/0300060520935278.\u003c/li\u003e\n\u003cli\u003eDavison KK, Susman EJ, Birch LL. Precent body fat at age 5 predicts earlier pubertal development among girls at age 9. Prediatrics. 111:815-821 (2003). doi:10.1542/peds.111.4.815\u003c/li\u003e\n\u003cli\u003eLee JM, Appugliese D, Kaciroti N, Corwyn RF, Bradley RH, Lumeng JC. Weight status in young girls and the onset of puberty. Pediatrics. 119: 624-630 (2007) . doi:10.1542/peds.2006-2188\u003c/li\u003e\n\u003cli\u003eCurrie C, Ahluwalia N, Godeau E, Gabhainn SN, Due P, Currie DB. Is Obesity at Individual and National Level Associated With Lower Age at Menarche? Evidence From 34 Countries in the Health Behaviour in School-aged Children Study. Journal of Adolescent Healt.50:621\u0026ndash;626 (2012) . doi:10.1016/j. jadohealth. 2011.10.254\u003c/li\u003e\n\u003cli\u003eLazzeri G, Tosti C, Pammolli A, Troiano G, Alessio Vieno A, Natale Canale, et al. Overweight and lower age at menarche: evidence from the Italian HBSC cross-sectional survey. BMC Women\u0026apos;s Health.18:168-175(2018). doi:10.1186/s12905-018-0659-0\u003c/li\u003e\n\u003cli\u003eDeng Y, Liang J, Zong Y, Yu P, Xie RS, Guo YF, et al. Timing of spermarche and menarche among urban students in Guangzhou, China: Trends from 2005 to 2012 and association with Obesity. Sci Rep. 8:263-270 (2018). doi:10.1038/s41598-017-18423-6\u003c/li\u003e\n\u003cli\u003eCrocker MK, Stern EA, Sedaka NM, Shomaker LB, Brady SM, Ali AH, et al. Sexual dimorphisms in the associations of BMI and body fat with indices of pubertal development in girls and boys. J Clin Endocrinol Metab. 99: E1519\u0026ndash;E1529 (2014). doi:10.1210/jc.2014-1384\u003c/li\u003e\n\u003cli\u003eChen C, Zhang Y, Sun W, Chen Y, Jiang Y, Song Y, et al. Investigating the relationship between precocious puberty and obesity: A cross-sectional study in Shanghai, China. BMJ Open. 7: e014004 (2017). doi:10.1136/bmjopen-2016-014004\u003c/li\u003e\n\u003cli\u003eBenedet J, da Silva Lopes A, Adami F, de Fragas Hinnig P, de Assis Guedes de Vasconcelos F. Association of sexual maturation with excess body weight and height in children and adolescents. BMC Pediatr. 14:72-78 (2014) . doi:10.1186/1471-2431-14-72\u003c/li\u003e\n\u003cli\u003eAris IM, Rifas-Shiman SL, Zhang X, Yang S, Switkowski K, Fleisch AF, et al. Association of BMI with Linear Growth and Pubertal Development. Obesity. 27:1661-1670 (2019) . doi:10.1002/oby.22592\u003c/li\u003e\n\u003cli\u003eLai X, Fu SM, Lin JF, Huang SZ, Yu TG, Li XQ, et al, Association of Obesity and Body Fat Percentage with Pubertal State in Six- to Nine-Year-Old Chinese Females. Childood Obesity. 17: 525-533 (2021). doi:10.1089/chi.2020.0247.\u003cstrong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"female, central precocious puberty, disease diagnostic model","lastPublishedDoi":"10.21203/rs.3.rs-4133586/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4133586/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo develop a clinical model for predicting the occurrence of Central Precocious Puberty based on the breast development outcomes in chinese girls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established a retrospective cohort of girls with early breast development aged 6–9 years who visited the outpatient clinic of Beijing Children's Hospital from January 2017 to October 2022. Based on their breast development outcomes, the patients were divided into a pubertal development(PD) group and a premature thelarche (PT) group. Anthropometry, clinical, laboratory, and imaging variables ascertained were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a disease diagnostic model. Accuracy of the model was measured by the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe development cohort included 1001 girls aged 6–9 years. The mean (SD) age of patients was 7.86 (0.54) years, 36.4% of patients were finally diagnosed with PD, the other 63.6% were diagnosed with PT. From 14 potential predictors, 4 variables (bone age (BA)/chronological age (CA), basal luteinizing hormone (LH) level, uterine diameter and ovarian volume) were independent predictive factors. Body mass index (BMI) were considered to have some clinical significance. So the 5 variables included in the disease diagnostic model. BA/CA (OR, 2.04; 95% CI, 0.80–4.56; P \u0026lt; 0.001), basal LH level (OR, 8.08; 95% CI, 3.63–11.03; P \u0026lt; 0.001), uterine diameter (OR, 0.59; 95% CI, 0.34–1.22; P = .0006), ovarian volume (OR, 0.41; 95% CI, 0.03–1.09; P = 0.07), BMI (OR, 0.06; 95% CI, -0.06-0.15; P = 0.27), The mean AUC in the development cohort was 0.97 (95% CI, 0.88–1.05) and the AUC in the validation cohort was 0.94 (95% CI, 0.79–1.08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions :\u003c/strong\u003e In this study, a disease diagnostic model was developed that may help predict a girl’s risk of diagnosing central precocious puberty.\u003c/p\u003e","manuscriptTitle":"Development of a disease diagnostic model to predict the occurrence of central precocious puberty of female","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-22 13:29:21","doi":"10.21203/rs.3.rs-4133586/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1cea2165-255e-4b9f-a02e-4ad6d84ca1f4","owner":[],"postedDate":"March 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-23T09:00:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-22 13:29:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4133586","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4133586","identity":"rs-4133586","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}