Adverse Childhood Experiences and Risk of Abnormal Body Mass Index: A Global Systematic Review and Meta-Analysis.
Sohrab AmiriNailah MahmoodRahemeen YusufNadirah GhenimiSyed Fahad JavaidMoien A B KhanPublished in: Children (Basel, Switzerland) (2024)
(1) Objectives: The impact of abnormal body mass index (BMI) on health is extensive, and various risk factors contribute to its effects. This study aimed to examine the association between adverse childhood experiences (ACEs) and BMI categories, including underweight, overweight, obesity, severe obesity, and morbid obesity; (2) Methods: Three databases were searched: Web of Science, PubMed, and Scopus. Manual searches were conducted using Google Scholar and ResearchGate. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the association between ACEs and BMI. A random-effects model was used to combine the ORs and CIs across studies; (3) Results: This meta-analysis included 71 studies. The pooled ORs for the relationship between ACEs and obesity was 1.42 (95% CI: 1.24-1.63, Z = 4.96, p < 0.001), indicating a significant association. ACEs showed a positive association with overweight (OR = 1.16, 95% CI: 1.06-1.27, Z = 3.24, p = 0.001). Specifically, ACEs ≥ 4 were strongly associated with obesity (OR = 2.06, 95% CI: 1.27-3.36, Z = 2.90, p = 0.004). Sexual abuse was also found to be significantly associated with obesity (OR = 1.46, 95% CI: 1.29-1.65, Z = 5.98, p < 0.001); (4) Conclusion: This study finds that individuals who have experienced ACEs are more likely to have a higher BMI in adulthood. Therefore, ACEs should be considered a factor associated with abnormal BMI.
Keyphrases
- weight gain
- body mass index
- weight loss
- insulin resistance
- metabolic syndrome
- high fat diet induced
- type diabetes
- systematic review
- bariatric surgery
- physical activity
- risk factors
- healthcare
- public health
- adipose tissue
- randomized controlled trial
- case control
- depressive symptoms
- emergency department
- early life
- early onset
- climate change
- risk assessment
- deep learning
- artificial intelligence