[Research progress on influencing factors of early adiposity rebound in children and the effect on adolescent development in girls]

With the increasing rate of overweight and obesity in children worldwide, adiposity rebound(AR)closely related to obesity has become the spotlight, and early AR phase has a broad impact on pubertal development in girls, but the specific mechanism of action isn't very clear.This paper is review of the prevalence of early AR at home and abroad, and its influencing factors, the impact of AR on the adolescent development of girls and related mechanisms, to identify high-risk individuals with early AR, early AR to identify early adolescent development, and take early intervention measures to promote children's health.

[Independent and combined effects of pre-pregnancy BMI and gestational diabetes on early adiposity rebound timing in children]

Objective: To examine the independent and combined effects of pre-pregnancy BMI and gestational diabetes (GDM) on early adiposity rebound (AR) timing in children. Methods: Based on the "Ma'anshan Birth Cohort Study", 2 896 eligible maternal and infant pairs were recruited. In the cohort, we collected pre-pregnancy height, weight, 24 to 28 weeks GDM diagnosis, follow-up at 42 days, three months, six months, nine months of age, and every six months after one year of age, and continuously followed up to 6 years old, and obtained the child's length/height, weight, and other data. The intensity of the association between pre-pregnancy BMI, GDM, and early AR timing was analyzed by the multivariate logistic regression model. Multiplication and additive models were used to analyze how pre-pregnancy BMI and GDM influenced early AR timing in children. Results: The prevalence of underweight, average weight, overweight, and obesity before pregnancy were 23.2% (672), 66.4% (1 923), 8.7% (251), and 1.7% (50). The prevalence of GDM was 12.4%. We found that 39.3% of children had AR, and the average age at AR was (4.38±1.08). The results of multifactorial logistic regression analysis showed that pre-pregnancy overweight (OR=1.67,95%CI:1.27-2.19), pre-pregnancy obesity (OR=3.05,95%CI:1.66-5.56), and maternal GDM (OR=1.40,95%CI:1.11-1.76) were risk factors for early AR timing in children. In contrast, pre-pregnancy underweight (OR=0.60,95%CI:0.49-0.73) was a protective factor for early AR timing in children. Compared with the different effects of pre-pregnancy overweight/obesity and maternal GDM alone, the combined effect caused a higher risk of early AR timing in children, with OR values (95%CI) were 2.03 (1.20-3.44), 3.43 (1.06-11.12), respectively. The multiplication and additive models showed no interaction between pre-pregnancy BMI and GDM-influenced early AR timing in children. Conclusion: Higher pre-pregnancy BMI and maternal GDM are the independent risk factors for the early AR timing in children, and the co-occurrence of the two is higher risks, but there was no statistical interaction.

[Accuracy and clinical outcome of a real-time surgical navigation system for the placement of quad zygomatic implants]

Objective: To evalute the accuracy and clinical outcome of a real-time navigation system for the placement of quad zygomatic implants. Methods: Twenty-four patients [9 males and 15 females, mean age was (50.8±14.7) years old], from January 2015 to December 2019, with 96 zygomatic implants placed under a real-time navigation system in Department of Second Dental Center and Department of Oral Implantology of Ninth People's Hospital, Shanghai Jiaotong University School of Medicine were included in the study. The preoperative and the postoperative multislice CT or cone-beam CT were fused to measure and record the entry, exit and angle deviation between the planned and placed implants. The implants were divided into groups according to implant insertion approach (real-time navigation and free-hand), implant length (<47.5 mm and ≥47.5 mm) and implant position (proximal and distal implant). And the differences of implant accuracy were analyzed. The intraoperative and postoperative complications were also recorded. The implant survival rate was evaluated after 6 months follow-up. A P value<0.05 indicates statistical significance. Results: The mean entry, exit and angle deviation of zygomatic implants were (1.49±0.64) mm, [2.03(1.58, 2.40)] mm and (2.49°±1.12°), respectively. The average entry, exit and angle deviation of the navigation guided implant insertion group were (1.45±0.60) mm, (1.96±0.44) mm and (2.66±1.13°) respectively, while those of the free-hand group were (1.50±0.64) mm, (2.04±0.79) mm and (2.50°±1.13°) respectively. There was no significant difference between the two groups (P>0.05). The average entry, exit and angle deviation of the group with length<47.5 mm were (1.42±0.60) mm, (2.13±0.60) mm and (2.61°±1.08°) respectively and those of the group with length ≥ 47.5 mm were (1.52±0.65) mm, (1.98±0.82) mm and (2.43°±1.14°) respectively. No significant difference was found between the two groups (P>0.05). In proximal implant group, the average entry, exit and angle deviation were (1.55±0.69) mm, (2.05±0.92) mm and (2.48°±1.16 °) respectively while those of distal implant group were (1.43±0.57) mm, (2.01±0.57) mm and (2.49°±1.10°), respectively. No significant difference was detected between the two groups (P>0.05). All zygomatic implants were placed uneventfully. There were no intra-operative complications, and post-operative reversible complications developed in 3 patients. Two zygomatic implants were lost and the overall zygomatic implant survival rate was 97.9% (94/96) within a follow-up of 6 months. Conclusions: Quad zygomatic implant placement can be achieved with high accuracy and predictable clinical outcome under guidance of a real-time navigation system.