Clinical staging to guide management of metabolic disorders and their sequelae: a European Atherosclerosis Society consensus statement
Bibliographic record
Abstract
Obesity rates have surged since 1990 worldwide. This rise is paralleled by increases in pathological processes affecting organs such as the heart, liver, and kidneys, here termed systemic metabolic disorders (SMDs). For clinical management of SMD, the European Atherosclerosis Society proposes a pathophysiology-based system comprising three stages: Stage 1, where metabolic abnormalities such as dysfunctional adiposity and dyslipidaemia occur without detectable organ damage; Stage 2, which involves early organ damage manifested as Type 2 diabetes, asymptomatic diastolic dysfunction, metabolic-associated steatohepatitis (MASH), and chronic kidney disease (CKD); and Stage 3, characterized by more advanced organ damage affecting multiple organs. Various forms of high-risk obesity, driven by maintained positive energy balance, are the most common cause of SMD, leading to ectopic lipid accumulation and insulin resistance. This progression affects various organs, promoting comorbidities such as hypertension and atherogenic dyslipidaemia. Genetic factors influence SMD susceptibility, and ethnic disparities in SMD are attributable to genetic and socioeconomic factors. Key SMD features include insulin resistance, inflammation, pre-diabetes, Type 2 diabetes, MASH, hypertension, CKD, atherogenic dyslipidaemia, and heart failure. Management strategies involve lifestyle changes, pharmacotherapy, and metabolic surgery in severe cases, with emerging treatments focusing on genetic approaches. The staging system provides a structured approach to understanding and addressing the multi-faceted nature of SMD, which is crucial for improving health outcomes. Categorization of SMD abnormalities by presence and progression is aimed to improve awareness of a multi-system trait and encourage a tailored and global approach to treatment, ultimately aiming to reduce the burden of obesity-related comorbidities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".