Food Addiction and its Contribution to Metabolic Syndrome: A Review of Biological Pathways and Epidemiological Evidence
Bibliographic record
Abstract
Metabolic syndrome (MetS), a significant obstacle to public health, characterized by a constellation of metabolic abnormalities such as insulin resistance, hypertension, dyslipidemia, and abdominal obesity. High economic burden to the health service of metabolic syndrome was reported. As a contributing factor to metabolic syndrome, the concept of food addiction is receiving more attention. Food addiction is linked to changes in reward system and dopamine signaling, especially within the mesolimbic reward circuit. Highly palatable foods that are high in sugar, fat, and sodium stimulate dopamine receptors similarly to addictive drugs, which reinforces compulsive eating habits. Prolonged exposure to the mentioned foods causes neuro-adaptive changes that reduce satiety signals and encourage overeating, leading to central obesity and ultimately metabolic syndrome. Although excessive caloric intake could be a behavioral problem, food addiction shares neurobiological pathways with substance use disorders. In addition to body weight, factors influencing by food addiction such as reward pathways, hormonal imbalances, dysbiosis, systemic inflammation and oxidative stress, and genetic factors play a role in the relationship between food addiction and metabolic syndrome. This narrative review explores the mechanisms connecting food addiction to metabolic syndrome and addresses epidemiological patterns that highlight its importance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".