The present study focused on bone metabolism indicators in patients of different age and gender using HIS data from one hospital, providing mathematical modeling to assist the future study of age and gender in disease research. Although multiple studies have been published that provide a reference for differences in children and adolescents [2-8], those studies did not provide a precise breakdown of age and gender, namely at what patient age does parameters require precise matching and which age does they not. Researchers believe that age matching should be performed on all subjects in disease control research, but strict age-matching substantially increases the cost and difficulty of conducting the study. The present research found that patients aged more older than 20 years demonstrate that P1NP, β-CTx and 25(OH)D in patient research do not require age matching. This will greatly help researchers reduce the cost and difficulty of their research.
The biological data for young individuals is very complicated, and so we established mathematical models to correlate data for people under the age of 20. We noted that P1NP and β-CTx levels were especially correlated with age. The concentrations of P1NP in infants and young children were higher although their values gradually decreased with age, but not in a simple linear relationship. Firstly, it significantly declined from 0-5 years, followed by a period of equilibrium and a small escalation phase between the ages of 6 and 15. Over 15, P1NP levels again rapidly declined by the age of 20 to values close to those of adults. Many studies have demonstrated that P1NP and β-CTx are associated with age, but none have analyzed the relationship in detail or curve fitted the relationship, as conducted here. According to statistical analysis, the relationship with P1NP and age is a complex third‐degree polynomial function, and the curve fits well using a cubic relationship. There are two peaks in bone growth in childhood. The first peak appears in infancy and the second in early adolescence. The present study found that bone metabolic indicators grew at their highest rate during puberty, the fastest rate for bone minerals over the age 12-13. As an indicator related to rapid growth rate in healthy individuals, it is not surprising that serum ALP and PINP declines after puberty. β-CTx declined before the age of 4, then increased over the ages 4-14 years, gradually decreasing after the age of 14. In the present study, because R2 was less than 0.3, the trend was judged to not be significant, possibly due to the limited sample size. A positive result might have been possible had the sample size been larger. The data obtained from the HIS in the present study included a large number of records of infants and children which allowed us to construct a mathmatical model. Prior to this study, almost no bone metabolic data had been published regarding Chinese children or adolescents, especially babies. In one large study, the minimum age was 15-19 years[9]. Another study that researched a large cohort of healthy adults in China, focused on healthy individuals older than 20 years of age [10]. Thus, studies on children in China, both healthy and sick, are scarce[11, 12]. Studies of pediatric-specific diseases are valuable as reference data. However, due to the sample size and narrow-specificity of the diseases in question, the results are often generally only applicable in a limited fashion[13-15]. Nevertheless, the curve model of young patients obtained in this study provides a good basis for future research.
Bone metabolism is closely related to gender, an association which has been described in multiple studies[9, 16-19]. In the present research, we found significant differences in P1NP, β-CTx, 25(OH)D and ALP concentrations between genders at all ages. However, if study subjects are stratified by age, the differences in gender are not apparent. This suggests that differences in gender depend on the ages of individuals, indicating that gender does not need to be strictly matched if age has been matched. Conversely, if age is not strictly matched, it would be necessary to strictly match by gender. Clearly, in clinical research, it is easier to match gender than age, so matching gender only would be advantageous for future research studies.