5-HTTLPR polymorphism in bulimia nervosa: a multiple-model meta-analysis
Abstract
Several lines of research have found that genes in the serotonergic system may cause susceptibility to eating disorders (EDs). In particular, functional polymorphisms of the serotonin transporter gene (5-HTT) have been suspected to play a role in the pathogenesis of eating disorders. Several studies have examined the association between the 5-HTTLPR polymorphism and bulimia nervosa (BN). The results of these investigations have been unclear. The aims of this meta-analysis were to clarify the association between BN and 5-HTTLPR using statistical models not used by previous meta-analyses, and extend upon previous meta-analyses by including new samples. PsychINFO, ISI, and PubMed databases were searched for studies published up to May 2011. Ultimately, six case-control samples were included. Data were pooled using dominant and additive models. Both models showed a nonsignificant association between the 5-HTTLPR polymorphism and BN. However, this does not detract from recent research suggesting that the 5-HTTLPR polymorphism may be responsible for the phenotypic variability in the psychopathological symptoms observed in patients with BN. Future research should examine the association of BN with 5-HTTLPR using the recently proposed triallelic model.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.