Remnant cholesterol, clinical characteristics and mortality in 41.767 patients from LIPIDOGRAM 2004-2015 studies - the factor analysis for mixed data cluster analysis
Bibliographic record
Abstract
Abstract Introduction Remnant cholesterol (remnant-C) contributes to residual cardiovascular risk and is produced by metabolism of triglyceride rich proteins. Aim To assess association between clinical characteristics of patients with elevated remnant-C in a large cohort of patients under the care of primary care physicians. Methods The LIPIDOGRAM studies were carried out in the primary care in Poland in 2004, 2006 and 2015. Patients (n=47,398) recruited in all 16 administrative regions in Poland and physicians were proportionally distributed to the number of inhabitants in each administrative region. Each patient was asked to fill the questionnaire on risk factors, chronic diseases, treatment and lifestyle. In the present analysis we included patients with body mass index (BMI )>18.5 kg/m2 aged 18-75 years. We set the cut-off point of fasting remnant cholesterol at >30 mg/dL (>0.77 mmol/L) that differentiated subjects at high risk of cardiovascular events. We carried out Factor Analysis for Mixed Data (FAMD) cluster analysis to discern groups of patients with similar clinical profiles. Results 41,767 patients, for which we had all relevant data, were included in the analysis. Follow-up rate was 97.8%. Three FAMD-derived clinical characteristics patterns accounted for 47.8% of the total variance and were retained for further analysis. The first pattern was associated with higher prevalence of metabolic syndrome (MetS), higher waist circumference, higher levels of non-HDL-C and remnant-C levels. The second pattern was distinguished by lipid parameters, particularly higher HDL-C levels, and was not significantly associated with comorbid conditions. The third pattern was linked to male sex, previous myocardial infarction, smoking, age, lower remnant-C levels, and reduced prevalence of obesity. All patterns were associated with 5-year mortality. The hazard ratio (HR) for mortality per 1 standard deviation (SD) increase in the first pattern score was 1.18 (95% CI: 1.16-1.22, p<0.001). Patients with clinical characteristics corresponding to second pattern had more favorable outcome HR (per 1SD score increase) – 0.75, 95%CI (0.73-0.78, p<0.001). Patients in third cluster, similarly to patients in cluster 1 had increased mortality HR (per 1SD score) – 1.10, (95%CI:1.06-1.15, p <0.001). Higher (>0.77 mmol/l / 30 mg/dl) remnant-C cholesterol significantly contributed to classification of patients into first and third clusters. It was positively associated with first pattern (r=0.64, p<0.001), but negatively with the third pattern (r=0.39, p<0.001). Conclusions Elevated Remnant-C was associated with clinical pattern typical for metabolic syndrome. Interestingly, lower remnant-C levels in patients with comorbidities and more advanced age may not automatically be indicative of a better prognosis – this requires further investigation.Figure 1.Figure 2.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".