소아비만

A meta-analysis of cross-sectional studies found that shorter-time sleep was correlated with increased risk of obesity in children. However, findings from prospective cohort studies were inconsistent. PubMed and other data resources were searched through May 2015. Twenty-five eligible studies were identified including 56,584 children and adolescents with an average 3.4-year follow-up. Compared with children having the longest sleep duration (~12.2 hours), kids with the shortest sleep duration (~10.0 hours) were 76% more likely to be overweight/obese (pooled odds ratio [OR]: 1.76; 95% confidence interval [CI]: 1.39, 2.23); and had relatively larger annual BMI gain (pooled β coefficient: 0.13; 95% CI: 0.01, 0.25 kg/m2). With every 1 hour/day increment in sleep duration, the risk of overweight/obesity was reduced by 21% (OR: 0.79; 95% CI: 0.70, 0.89); and the annual BMI gain declined by 0.05 kg/m2 (β = −0.05; 95% CI: −0.09, −0.01). The observed associations were not appreciably modified by region, baseline age or the length of follow-up. Accumulated literature indicates a modest inverse association between sleep duration and the risk of childhood overweight/obesity. Further research is needed to determine the age and gender specified optimal hours of sleep and ideal sleep pattern with respect to obesity prevention in children.

Literature search: As shown in Figure 1, 168 relevant articles were retrieved from PubMed. Of them, 145 were excluded for one of the following reasons: 1) not in English (n = 5); 2) not original studies (e.g., letter to editor) (n = 22); 3) not prospective cohort studies (n = 26); 4) not in children/adolescents (n = 26); 5) not on sleep duration or no outcome of interest (n = 72); 6) no sufficient information on measures of the association of interest (n = 1). In addition, 10 studies were identified from EMBASE, Google Scholar, or the reference lists of the relevant articles, for a total of 25 eligible studies in the meta-analysis.

Characteristics of included studies: Table 1 presents characteristics of the 25 included studies. Eleven studies were conducted in the USA, four in Australia, two in Canada, two in Denmark, and one in each of the following countries: UK, Germany, New Zealand, Portugal, Korea, and Belgium. Some studies reported results separately based on two or three different age groups, or separately by genders. We treated them as independent cohorts. In the final dataset, 56,584 children and adolescents from 25 studies (32 independent cohorts), with an average 3.4-year follow-up, were included.

Information on sleep duration and the risk of overweight/obesity were available in 11 studies (14 cohorts), including 31,185 participants and 4,473 cases with a follow-up range from 21 months to 5 years. Data on sleep duration and annual BMI gain were available in six studies (8 cohorts) including 5,341 participants with a follow-up range from 2 to 5 years.

Sleep duration and risk of overweight/obesity: Figure 2 shows the association between sleep duration and the risk of overweight/obesity. By combining data from seven studies (10 independent cohorts), the pooled OR (95% CIs) was 1.76 (95% CI: 1.39, 2.23) for participants in the shortest sleep duration group as compared with those who were in the longest sleep duration group. The heterogeneity was high among studies (I2 = 70.5%, P < 0.01). No strong evidence of publication bias was observed (Egger’s test, P = 0.10).

For the dose-response relationship, after pooling available data from 8 studies (9 independent cohorts), the risk of overweight/obesity was 21% lower with every 1 hour/day sleep duration increment (combined OR: 0.79; 95% CI: 0.70, 0.89). A high heterogeneity among studies was observed (I2 = 81.2%, P < 0.01). Because Egger’s test indicated a significant publication bias (P = 0.01), the “trim and fill” method was used to adjust for the publication bias. With this adjustment, the pooled linear association was somewhat attenuated and became statistically non-significant (pooled OR: 0.92; 95% CI: 0.81, 1.05).

Sleep duration and annual BMI gain: Figure 3 demonstrates the relation between sleep duration and annual BMI gain. Four studies (5 independent cohorts) provided data on the multivariable-adjusted β coefficient (95% CIs) comparing children with the shortest to the longest sleep duration. The pooled results indicated that participants with the shortest sleep duration had significantly more annual BMI gain (β coefficient: 0.13; 95% CI: 0.01, 0.25). High heterogeneity among studies existed (I2 = 67.5%, P = 0.02). The test for publication bias approached significance (Egger’s test, P = 0.054).

For the dose-response relationship, 4 studies (6 independent cohorts) had data available. Every 1 hour/day sleep duration increment would decrease annual BMI gain by 0.05 kg/m2 (β = −0.05; 95% CI: −0.09, −0.01). A moderate heterogeneity was observed (I2 = 42.7, P = 0.12). There was no evidence indicating publication bias (Egger’s test, P = 0.70).

Subgroup and sensitivity analysis: According to the available data, we examined potential effect modification by study region (USA vs. Non-USA), age (<3, 3 < 5, ≥5 year), or follow-up duration (above or below the median). None of these factors materially modified the observed associations (Table 2). A few studies examined gender difference between sleep duration and BMI z-score or PBF. Although the number of studies is not sufficient for us to pool the results, none of these studies found a significant gender difference.

In addition, the results were not appreciably changed when replacing random-effects models with fixed-effects models (data not shown). No single study substantially influenced the pooled results on sleep duration and the risk of overweight/obesity when omitting one study each time in the meta-analysis (Supplemental Table). However, the association between sleep duration and annual BMI gain was substantially attenuated when removing one of three independent cohorts (Silva, 2011, Lee, 2012, or O’Dea, 2012) at a time (Supplemental Table).

Sleep duration and secondary outcomes: Table 3 shows the association between sleep duration and secondary outcomes. Sleep duration was inversely associated with BMI, BMI z-score and WC, and positively related to FMI.

Background: Anxiety and depression are more common in children with obesity than in children of normal weight, but it is unclear whether this association is independent of other known risk factors. Interpretation of results from previous studies is hampered by methodological limitations, including self-reported assessment of anxiety, depression, and anthropometry. The aim of this study was to investigate whether obesity increases the risk of anxiety or depression independently of other risk factors in a large cohort of children and adolescents, using robust measures with regard to exposure and outcome.

Methods: Children aged 6–17 years in the Swedish Childhood Obesity Treatment Register (BORIS, 2005–2015) were included (n = 12,507) and compared with a matched group (sex, year of birth, and area of residence) from the general population (n = 60,063). The main outcome was a diagnosis of anxiety or depression identified through ICD codes or dispensed prescribed medication within 3 years after the end of obesity treatment. Hazard ratios (HRs) with 95% confidence intervals (CIs) from Cox proportional models were adjusted for several known confounders.

Results: Obesity remained a significant risk factor for anxiety and depression in children and adolescents after adjusting for Nordic background, neuropsychiatric disorders, family history of anxiety/depression, and socioeconomic status. Girls in the obesity cohort had a 43% higher risk of anxiety and depression compared to girls in the general population (adjusted HR 1.43, 95% CI 1.31–1.57; p < 0.0001). The risk in boys with obesity was similar (adjusted HR 1.33, 95% CI 1.20–1.48; p < 0.0001). In sensitivity analyses, excluding subjects with neuropsychiatric disorders and a family history of anxiety/depression, the estimated risks in individuals with obesity were even higher compared with results from the main analyses (adjusted HR [95% CI]: girls = 1.56 [1.31–1.87], boys = 2.04 [1.64–2.54]).

Conclusions: Results from this study support the hypothesis that obesity per se is associated with risk of both anxiety and depression in children and adolescents.

A total of 12,507 individuals with obesity (46.9% girls) and 60,063 individuals in the comparison group (47.1% girls) were included in the study. In children with obesity, 9.7% suffered from anxiety or depression compared with 5.0% of individuals in the comparison group (p < 0.0001). Overall, anxiety and depression were more common in girls than in boys (7.0% vs. 4.8%; p < 0.0001), although boys were roughly 8 months younger than girls at first diagnosis or dispensed prescribed medication (p < 0.0001). Of all children with anxiety and depression, 56% had at least one parent with anxiety/depression (p < 0.0001). Common risk factors for anxiety and depression, e.g. neuropsychiatric disorders and low SES, were more prevalent among children and adolescents with obesity compared to the comparison group. Characteristics of the study cohort stratified by sex are shown in Table 1.

Risk factors for anxiety and depression: Obesity was a strong risk factor for anxiety and depression in both sexes: adjusted HR [95% CI] for girls was 1.43, [1.31–1.57], p < 0.0001, and for boys 1.33, [1.20–1.48], p < 0.0001 (Additional file 2). History of maternal and paternal anxiety/depression was of equal importance for the risk of anxiety and depression in children both with and without obesity (Fig. 2).

Sensitivity and interaction analyses: In sensitivity analyses, excluding children with neuropsychiatric disorders and a family history of anxiety/depression, the risks for anxiety and depression were even higher in individuals with obesity compared with the adjusted risk estimates from the main analyses (Table 2). Interaction analyses were performed to explore whether the association between childhood obesity and the risk of anxiety and depression was modified by co-existing neuropsychiatric diagnoses. Adjusted analyses showed that the association between obesity in childhood and the risk of anxiety and depression was attenuated by neuropsychiatric disorders in both sexes (test for interaction p < 0.001, data not shown).

Risk factors for anxiety and depression in children and adolescents with obesity: Descriptive statistics for children and adolescents with obesity are shown in Additional file 3. At the last clinical visit, 37% of individuals who had undergone obesity treatment had morbid obesity, 48% obesity, 14% overweight, and 1% normal weight.

In both boys and girls, the risk of anxiety and/or depressive disorders increased with increasing age at the start of obesity treatment (Table 3). Significant risk factors for anxiety and depression in children and adolescents with obesity included poor treatment response, dropping out of obesity treatment, Nordic origin, neuropsychiatric disorders, and a family history of anxiety/depression (Table 3).

Background: Although increasing evidence has revealed the efficacy of acupuncture in obesity/overweight, actual improvement in metabolism in children and adolescents is unclear. Therefore, we conducted a meta-analysis to evaluate this correlation.

Methods: A comprehensive search was conducted using multiple databases, including Medline, Cochrane, Embase, Web of Science, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Chinese Scientific Journal Database, and Wan-fang Data, to identify relevant randomized controlled trials published before February 1, 2023. General information and data for the descriptive and quantitative analyses were extracted.

Results: Fifteen randomized controlled trials of 1288 obese/overweight children and teenagers were included. All the trials were conducted in China and South Korea. Regarding quality assessment, no other significant risk of bias was found. The acupuncture groups were more likely to have improved metabolic indicators of obesity/overweight than the control groups, in terms of body mass index (standardized mean difference [SMD] = −0.45, 95% confidence interval [CI]: −0.69 to −0.21, I2 = 71.4%), body weight (SMD = −0.48, 95% CI: −0.92 to −0.05, I2 = 84.9%), and serum leptin (SMD = −0.34, 95% CI: −0.58 to −0.10, I2 = 91.8%). The subgroup analysis showed that for body mass index, the results were consistent regardless of the intervention duration, body acupuncture or auricular acupuncture combined with other interventions.

Conclusion: Our results suggest that acupuncture is effective in improving metabolic outcomes of obese/overweight children and adolescents. Owing to the limited number of trials included in this study, the results should be interpreted with caution.

3.1. Literature screening results and characteristics of included trials: A total of 8770 records were retrieved from the initial target database, of which 1689 were deleted because of duplication. The manual review identified 8 trials that met the inclusion criteria. In this case, a total of 15 unique trials were eventually included in our study (shown in Fig. 1).

All 15 trials were conducted in China and South Korea and published before February 1, 2023. Altogether, 698 and 590 participants were randomized to the EGs and CGs, respectively. The proportion of boys (N = 279) was significantly higher than that of girls (N = 211) (5 studies did not report these data). Different acupuncture approaches were used to intervene in obesity/overweight children and teenagers for 20 days to 3 months (shown in Table 1).

3.2. Quality of included trials: Random assignment was adequately described in all 15 studies; however, none mentioned allocation concealment. The blinding of operators and subjects was mentioned in only 2 studies and the remaining studies did not use blinding or mention it. All 15 trials fully reported the relevant outcomes; therefore, the risk of attrition bias was low (shown in Supplementary Figs. 1 and 2, http://links.lww.com/MD/J680, http://links.lww.com/MD/J681).

3.3. Primary outcomes / 3.3.1. BMI.: Twelve trials investigated the efficacy of EGs and CGs in improving BMI. The results showed that EGs were more effective than CGs in improving BMI, with an SMD value of −0.45 (95% CI: −0.69 to −0.21, I2 = 71.4%) (shown in Table 2A). The funnel plot suggested publication bias, but the quantitative Egger test (P =.235) showed no publication bias (shown in Supplementary Fig. 3A, http://links.lww.com/MD/J682).

3.3.2. WC.: Six trials investigated the efficacy of EGs and CGs in improving WC, but the results showed no statistical significance (shown in Table 2A). The funnel plot suggested no publication bias, and the quantitative Egger test (P =.085) showed the same indication (shown in Supplementary Fig. 3B, http://links.lww.com/MD/J682).

3.3.3. BW.: Nine trials investigated the efficacy of EGs and CGs in weight loss, and the results showed that the efficacy of EGs was better than that of CGs in weight loss, with an SMD value of −0.56 (95% CI: −1.01 to −0.10, I2 = 85.7%) (shown in Table 2A). The funnel plot suggested no publication bias, and the quantitative Egger test (P =.337) showed the same indication (shown in Supplementary Fig. 3C, http://links.lww.com/MD/J682).

3.4. Metabolic outcomes / 3.4.1. Serum leptin.: Four trials investigated the efficacy of EGs and CGs in improving serum leptin, and the results showed that EGs were better than CGs in improving serum leptin, with an SMD value of −0.34 (95% CI: −0.58 to −0.10, I2 = 91.8%) (shown in Table 2A). The funnel plot suggested no publication bias, and the quantitative Egger test (P =.095) showed the same indication (shown in Supplementary Fig. 3D, http://links.lww.com/MD/J682).

3.4.2. Other metabolic indicators.: Two trials investigated the efficacy of EGs and CGs in lowering TC, TG, and LDL, and increasing HDL. It was found that EGs were superior to CGs in lowering TC and LDL. The SMD of TC was −0.74 (95% CI: −2.17 to −0.70, I2 = 91.8%) and the SMD of LDL was −0.45 (95% CI: −0.87 to −0.03, I2 = 5.7%), however, the results of TG and HDL were not statistically different (shown in Table 2A). The corresponding funnel plots all suggested that there was no publication bias (shown in Supplementary Fig. 3E–H, http://links.lww.com/MD/J682).

3.5. Subgroup analyses: Based on the primary outcomes, the pre-specified subgroup analyses were divided into 10 categories to explore possible sources of heterogeneity, some of which revealed sources of heterogeneity. In terms of BMI, when regions were considered, the backward region (SMD = −0.68, 95% CI: −0.97, −0.39, I2 = 19.3%) had significantly lower heterogeneity than the develop region (SMD = −0.34, 95% CI: −0.62, −0.05, I2 = 73.9%). When taking publication year into account, year ≤2015 (SMD = −0.60, 95% CI: −0.84, −0.36, I2 = 0.0%) had significantly lower heterogeneity than year >2015 (SMD = −0.38, 95% CI: −0.70, −0.06, I2 = 78.3%). In terms of BW, when the types of acupuncture were considered, body acupuncture (SMD = −0.59, 95% CI: −0.89, −0.28, I2 = 0.0%) had significantly lower heterogeneity than auricular acupuncture (SMD = −0.92, 95% CI: −1.81, −0.04, I2 = 91.2%) (shown in Table 2B–E).