Functional annotation of lactase gene and its distal enhancer MCM6 for prediction of metabolically unhealthy obesity
Bibliographic record
Abstract
Introduction Lactose maldigestion associated with single nucleotide variants (SNV) of the genes for lactase (LCT) and its enhancer minichromosome maintenance complex component 6 (MCM6) is one of the key triggers that initiate meta-inflammation in metabolically unhealthy obesity (MUO). The aim is to study the contribution of LCT and MCM6 gene SNV to the development of MUO in children. Material and methods 152 obese children aged 6–18 years were genotyped for the LCT/MCM6 genes (RT-PCR, Synevo, Ukraine). The main group (n = 77) according to the IDEFICS 2014 recommendations was represented by children with MUO. The control group (n = 75) consisted of children with metabolically healthy obesity. Whole genome sequencing (NGS, CeGat, Germany) was performed in 27 children of the main and 15 children of the control group. To verify the results, bioinformatics analysis, analysis of nominal data, calculation of Cramer’s criterion (V), Pearson’s randomness factor (C), and the normalized value of Pearson’s coefficient (C’) were used. Results mong obese children 20 SNV LCT and 11 SNV MCM6 were revealed. Odds ratio (OR) for MUO to detect SNV LCT A/G rs3213891 – 1.75 (95% CI 0.17–18.4); G/A rs3213890 –2.5 (95% CI 0.65–10.06); C/T rs3754689 – 3.4 (95% CI 1–13.6). SNV MCM6 G/A rs1057031 – OR = 2.6 (95% CI 0.65–10). There is a direct correlation between MUO and SNV LCT with genotypes: A/G rs3213891 (V = 0.073; C = 0.072; C’ = 0.102); G/A rs3213890 (V = 0.284; C = 0.273; C’ = 0.386); C/T rs3754689 (V = 0.278; C = 0.268; C’ = 0.379) and SNV MCM6 G/A rs1057031 (V = 0.143; C = 0.142; C’ = 0.201), p < 0.05. Conclusions The greatest contribution to the development of MUO in children out of 20 SNV of the LCT gene identified by us in obesity was found for the three genotypes A/G rs3213891, G/A rs3213890, C/T rs3754689, and SNV MCM6 G/A rs1057031 out of 11 SNV MCM6 diagnosed by us.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.036 |
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".