Assessing the Impact of Serum Calcium, 25-Hydroxy Vitamin D, Ferritin, and Uric Acid Levels on Colorectal Cancer Risk
Bibliographic record
Abstract
Background: The aim of this study is to investigate whether vitamin D, calcium, ferritin, and uric acids play a beneficial biomarker role in the prevention of colorectal cancer (CRC) risk. Methods: The case-control design was employed, including 650 CRC cases and 650 controls aged 35 to 70 years, comprising both men and women. The study encompasses sociodemographic data, clinical information, radiological diagnoses, and biochemical measurements. Results: Statistically significant differences were observed between CRC and controls in terms of age, diagnostic radiology, tomography, positron emission tomography/computed tomography (PET/CT), colonoscopy, CRC awareness, risk factors, age, genetics, exposure to chemicals, inadequate nutrition, smoking, hookah and alcohol use. Significant differences were also identified in intestinal inflammations, obesity, processed foods (P < 0.001), abdominal pain and cramps, diarrhea, constipation, blood in stool, bloating (gas), irritable bowel, nausea/vomiting, anemia, stress, fatigue, weakness, and weight loss. Regarding biochemical parameters, statistically significant differences were found between CRC and controls in terms of hemoglobin, glycated hemoglobin (HbA1c), fasting blood glucose (FBG), vitamin D, neutrophil level, red blood cell (RBC), white blood cell (WBC), platelet level, platelet count, hematocrit, potassium, sodium (Na), calcium, creatinine, cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), bilirubin, uric acid, iron (Fe), ferritin, C-reactive protein (CRP), total protein, systolic blood pressure (SBP), and diastolic blood pressure (DBP) parameters (P < 0.001). Multivariate stepwise regression analysis was performed to find the best risk factors for the diagnosis of CRC as the dependent variable. As a result of the analysis, intestinal inflammation (P < 0.001), nausea/vomiting (P < 0.001), stomach pain (P = 0.003), hookah-smoking (P = 0.034), uric acid (P < 0.001), bilirubin (P < 0.001), cigarette smoke exposure (P = 0.033), processed food consumption (P = 0.002), calcium levels (P = 0.029), vitamin D deficiency (P < 0.001), and ferritin (P < 0.001) levels were identified as significant determinants for CRC. Conclusions: The current study demonstrated that vitamin D, calcium, ferritin, and uric acids play a beneficial biomarker role in reducing the risk of CRC prevention. The increase in CRC rates may be associated with lifestyle, environmental and hereditary factors, nutrition, alcohol consumption, hookah use, and cigarette smoking.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".