The Metabolic Syndrome Diseases – Interventions Using Micronutrients
Bibliographic record
Abstract
OpinionMetabolic Syndrome is defined as a cluster of interrelated conditions such as central obesity, dyslipidemia, impaired glucose metabolism and hypertension [1].In addition to genetic predisposition as in South Asians, a sedentary lifestyle and a high caloric intake contribute to the development of this cluster of metabolic conditions.The worldwide increase in the incidence of this condition has made it a global epidemic.The recent Finnish study [2] attests to the decrease in incidence of Type 1 diabetes following fortification of dietary milk products with cholecalciferol.Insulin resistance and central obesity standout as the common feature of this cluster of conditions.The binding of insulin-to-insulin receptor (IR) results in the dimerization of the alpha and beta subunits and auto phosphorylation of the beta subunit leading to the activation of the Ras -MAPK and PI3K -Akt signaling cascades.The activation of PI3K associated with insulin receptors IRS1 and IRS2 results in the phosphorylation Akt-Foxo1 and is central to the control of nutrient homeostasis.The inactivation of Akt -Foxo1 pathway with the resulting activation of the forkhead/winged helix family transcription factor through suppression of IRS1 and IRS2 in organs following hyperinsulinemia, are suggested to be key mechanisms in the development of the metabolic syndrome.Hence, targeting the IRS -Akt -Foxo1 signaling cascade will provide a therapeutic approach for the treatment of the metabolic syndrome cluster of conditions.The activation of Akt affects cell survival and energy homeostasis by increasing glycogen synthetase activity, decreasing gluconeogenesis, promoting hepatic lipogenesis and cardiac cell survival.These phosphorylation-mediated cell events are the result of insulin signaling in various cells and tissues.Insulin is at the centre of adaptive metabolic transition in insulin-responsive tissues.Hyperinsulinemia inhibits the acute action of insulin on Foxo1 phosphorylation as well as transcription of IRS2 gene in insulin-responsive tissues.Insulin resistance in adipose tissue has effects on the endocrine system in addition to its effect on metabolism.Under conditions of obesity pro-inflammatory factors -TNF alpha, interleukin and leptin -are increased and anti-inflammatory factors such as adiponectin are decreased resulting in an inflammatory condition.No single therapeutic pathway has been identified as a "therapeutic target" for treatment of the metabolic syndrome.Aggressive lifestyle changes have been shown to help in the amelioration of this condition.Where this is not adequate, pharmacotherapy including a combination of nutraceuticals affords an option.Recent studies confirm that several vitamins and their metabolites participate in various physiological processes as hormones, antioxidants and regulators of tissue growth and differentiation, lowering the risk associated with many chronic and degenerative diseases [3,4].These protective effects are achieved at levels of the vitamin intake far higher than the "recommended dietary allowance".They have profound impact on neurological, endocrine and immune systems.Vitamins or their metabolites interact with specific protein entities 535 Interventions Obes Diabetes
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".