The first genome-wide association study of internet addiction; Revealed substantial shared risk factors with neurodevelopmental psychiatric disorders
Bibliographic record
Abstract
BACKGROUND: Internet addiction disorder (IAD) is listed as a disorder requiring further studies in the diagnostic and statistical manual of mental disorders (DSM-V). Psychological studies showed significant co-morbidity of IAD with depression, alcohol abuse, and anxiety disorder. Etiology and genetic bases of IAD are unclear. AIMS: Present study aimed to investigate the genetic, psychological, and cognitive bases of a tendency to internet addiction. METHODS AND PROCEDURES: DNA was extracted from blood samples of IADs (N = 16,520) and 18,000 matched non-psychiatric subjects. Genotyping for the subjects was performed using SNP Array. Psychological, neuropsychological, and neurological characteristics were conducted. OUTCOMES AND RESULTS: Seventy-two SNPs in 24 genes have been detected significantly associated with IAD. Most of these SNPs were risk factors for psychiatric disorders. Most similarity detected with autism spectrum disorder, bipolar disorder and schizophrenia. Higher anxiety, stress, and neuroticism and deficits in working memory, attention, planning, and processing speed were detected in IADs. CONCLUSIONS: This study is the first genome-wide association study of IAD that showed strong shared genetic bases with neurodevelopmental disabilities and psychiatric disorders. IMPLICATIONS: Genetic risk factors in IADs may cause several cognitive and neurodevelopmental brain function abnormalities, which lead to excessive Internet usage. It may suggest that IAD could be a marker for vulnerability to developmental psychiatric disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".