Background: A previous 2014 meta-analysis reported a positive association between obesity and periodontitis. It was considered necessary to update the recently published papers and to analyse subgroups on important clinical variables that could affect the association between obesity and periodontitis. Therefore, we updated the latest studies and attempted to derive more refined results.Methods: All observational studies were eligible for inclusion. The Newcastle–Ottawa scale was used to qualitatively evaluate the risk of bias. Subgroup analyses were conducted for patients aged 18–34, 35–54, and 55+ years and the countries (European countries, USA, Brazil, Japan, Korea, and other Asian countries).Results: Thirty-seven full-text articles were included. Obesity conferred increased odds of periodontal disease with an odds ratio (1.35, 95% CI: 1.05–1.75). In the subgroup analysis by age, the odds ratio was the highest in the 18–34 years group (2.21, 95% CI: 1.26–3.89). In the subgroup analysis by country, European countries had the highest odds ratio (2.46, 95% CI: 1.11–5.46).Conclusion: Despite the differences in degree, a positive association between obesity and periodontitis was found regardless of country or age. Therefore, medical professionals should try to prevent periodontitis by controlling patient weights, and more studies should be conducted to determine the association between obesity and oral health.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022301343.

Introduction: Several studies have reported on the maternal age‐associated risks of congenital anomalies. However, there is a paucity of studies with comprehensive review of anomalies. We aimed to quantify the risk of birth defects in children born to middle‐aged mothers compared with that in children born to young or older mothers.Material and methods: We classified maternal ages into three groups: young (<20 years old), middle (20–34 years old) and older age (≥35 years old). Observational studies that met our age criteria were eligible for inclusion. The articles searched using the Embase and MEDLINE databases were those published from 1989 to January 21, 2021. The Newcastle–Ottawa scale was used to assess the risk of bias. If heterogeneity exceeded 50%, the random effect method was used; otherwise, the fixed‐effect method was used. Prospero registration number: CRD42021235229.Results: We included 15 cohort, 14 case–control and 36 cross‐sectional studies. The pooled unadjusted odds ratio (95% CI) of any congenital anomaly was 1.64 (1.40–1.92) and 1.05 (0.95–1.15) in the older and young age groups, respectively (very low quality of evidence). The pooled unadjusted odds ratio of chromosomal anomaly was 5.64 (5.13–6.20) and 0.69 (0.54–0.88) in the older and young age groups, respectively. The pooled unadjusted odds ratio of non‐chromosomal anomaly was 1.09 (1.01–1.17) and 1.10 (1.01–1.21) in the older and young age groups, respectively (very low quality of evidence). The incidence of abdominal wall defects was increased in children of women in the young maternal age group.Conclusions: We identified that very low quality evidence suggests that women in the older maternal age group had increased odds of having children with congenital anomalies compared with those in the 20–34 year age group. There was no increase in odds of children with congenital anomalies in women of <20 year age group except for abdominal defects compared with those in the 20–34 year age group. The results stem from very low quality evidence with no adjustment of confounders.

Several studies have demonstrated an association between the risk asthma/allergic rhinitis and the environment. However, to date, no systematic review or meta-analysis has investigated these factors. We conducted a systematic review and meta-analysis to assess the association between urban/rural living and the risk of asthma and allergic rhinitis. We searched the Embase and Medline databases for relevant articles and included only cohort studies to observe the effects of time-lapse geographical differences.Papers containing information on rural/urban residence and respiratory allergic diseases were eligible for inclusion. We calculated the relative risk (RR) and 95% confidence interval (CI) using a 2 × 2 contingency table and used random effects to pool data. Our database search yielded 8388 records, of which 14 studies involving 50,100,913 participants were finally included. The risk of asthma was higher in urban areas compared to rural areas (RR, 1.27; 95% CI, 1.12–1.44,p< 0.001), but not for the risk of allergic rhinitis (RR, 1.17; 95% CI, 0.87–1.59,p= 0.30). The risk of asthma in urban areas compared to rural areas was higher in the 0–6 years and 0–18 years age groups, with RRs of 1.21 (95% CI, 1.01–1.46,p= 0.04) and 1.35 (95% CI, 1.12–1.63,p= 0.002), respectively. However, there was no significant difference in the risk of asthma between urban and rural areas for children aged 0–2 years, with a RR of 3.10 (95% CI, 0.44–21.56,p= 0.25). Our study provides epidemiological evidence for an association between allergic respiratory diseases, especially asthma, and urban/rural living. Future research should focus on identifying the factors associated with asthma in children living in urban areas. The review was registered in PROSPERO (CRD42021249578).

Several studies have recently reported on the association between smartphone usage and self-reported sleep quality. However, no systematic review or meta-analysis has yet been performed. We aimed to analyze the association between smartphone usage time and self-reported sleep quality. We searched for articles published up to January 13, 2022, using the Embase and Medline databases. All observational studies were eligible for inclusion. The Newcastle-Ottawa scale was used to evaluate the risk of bias within studies. We used restricted cubic spline analysis to perform a dose-response analysis. Seventeen studies with a total of 36,485 participants were included. The pooled odds ratio was 2.28 (confidence interval [CI]: 1.81-2.89; P < .001) and heterogeneity was 80%. In the dose-response analysis conducted on 5 studies, the regression coefficient between daily smartphone usage time (hours/day) and poor sleep quality was 1.042 (1.027-1.058), which confirmed a significantly positive association. Smartphone overuse was closely associated with poor self-reported sleep quality, sleep deprivation, and sleep latency prolongation. Further studies using a more structured method and high-quality evidence (cohort or case-control) should be conducted. CRD42022303371. Chu Y, Oh Y, Gwon M, et al. Dose-response analysis of smartphone usage and self-reported sleep quality: a systematic review and meta-analysis of observational studies. J Clin Sleep Med. 2023;19(3):621-630.