Multifaceted Effects of Intermittent Fasting on the Treatment and Preventionof Diabetes, Cancer, Obesity or Other Chronic Diseases
Bibliographic record
Abstract
BACKGROUND: Obesity and diabetes are global epidemics resulting in a range of comorbidities. Both have been linked to an increased risk of hormonal imbalance, cancer, and other significant disorders, which are a concerning trend for cancer rates in the backdrop of rising obesity and diabetes rates worldwide. Around 1 in 10 persons in the United States and Canada have serious illnesses correlated to type 2 diabetes and early death. It is believed that the US economy alone spends $245 billion annually due to this health burden. Lifestyle modification with intermittent fasting protocol and proper diet helps lower blood glucose level, maintain the body mass index, and reduce inflammation, which is the main cause of all chronic diseases. METHODS: We searched case series and clinical trials on type 2 diabetes, insulin resistance, cancer, thyroid, cardiovascular disease, or other inflammatory diseases in response to intermittent fasting in the PubMed, MEDLINE, and Google Scholar databases. OBJECTIVE: In this review, we have focused on intermittent fasting-based approaches that are becoming more widely accepted for improving health and reducing unwanted effects in patients with type 2 diabetes, cancer, cardiovascular disease, neurodegenerative disease, obesity, thyroid, and hormonal imbalance; it is also contemplated whether intermittent fasting can be considered as a non-medicinal therapeutic option for persons suffering from chronic diseases. CONCLUSION: Intermittent fasting successfully reversed diabetes, thyroid, and high blood pressure, elevated lipid levels, and maintained the body mass index; also, studies have shown that it has been instructed to be followed for the treatment and prevention of cancer and neurodegenerative diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".