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Lean metabolic syndrome is associated with similar long-term prognosis as metabolic syndrome in overweight/obese patients. Analysis 47,399 Patients from nationwide LIPIDOGRAM 2004–2015 cohort studies

2022· article· en· W4306254338 on OpenAlexfundno aff
Tadeusz Osadnik, Maciej Banach, Marek Gierlotka, K Nalewajko, D Nowak, Z Zak, Łukasz Skowron, Kamila Osadnik, Jacek Jerzy Jozwiak

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
FundersValeant Pharmaceuticals International
KeywordsMedicineMetabolic syndromeOverweightObesityBlood pressureInternal medicineCohortPopulationEndocrinologyEnvironmental health

Abstract

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Abstract Background Obesity was once though as an indispensable component of metabolic syndrome. Therefore, there a lot of definitions of metabolic syndrome with only few not including obesity as a criterion. The necessity of the obesity criterion in the metabolic health assessment is being questioned, as similar metabolic disturbances can occur in normal weight individuals. This relatively new concept is often referred as lean metabolic syndrome. Data on long term prognosis is scarce. Purpose To evaluate association between metabolic health and long-term prognosis. Methods Lipidogram studies were carried out in Poland in 2004, 2006 and 2015 in the population representative for patients in primary care setting. Patients were recruited in all 16 administrative regions in Poland and physicians were proportionally to the number of inhabitants in a given administrative region. Each patient was given a questionnaire on chronic diseases, treatment and lifestyle. Questionnaire was administered by physician. The diagnosis of metabolic syndrome was based on the presence of at least two of the following: 1) systolic blood pressure (SBP) ≥130 mmHg or diastolic blood pressure (DBP) ≥85 mmHg, 2) triglycerides (TG) >150 mg/dl, 3) high-density lipoprotein cholesterol (HDL-C) <40 mg/dl men and <50 mg/dl in women, 4) total cholesterol (TC) >200 mg/dl, and 5) fasting glucose (FBG) >100 mg/dl. Basing on those criteria and BMI with a cut off value of 25 kg/m2, patients were divided into four categories: healthy slim, metabolically healthy overweight/obese, lean metabolic syndrome and overweight/obese metabolic syndrome. The median follow up was 5570. this analysis data were censored at 3650 days. Results The median age of the study participants was 56.4 years. 7901 (16.7%) fell into category healthy slim, 14607 (30.8%) were classified as metabolically healthy overweight/obese, 3827 (8.1%) fulfilled criteria for lean metabolic syndrome whilst the remaining 21063 (44.4%) patients were diagnosed with overweight/obese metabolic syndrome. There were 4065 deaths during 10 years follow-up. Patients with lean metabolic syndrome had similar risk as patients with overweight/obese metabolic syndrome (HR=1.33, 95% CI: 1.17–1.52, p<0.001 and HR=1.32, 95% CI: 1.20–1.45, p<0.001). Metabolically heathy overweight/obese patients had only slightly higher risk of dying than healthy slim patients but this difference was less pronounced (HR=1.11, 95% CI: 1.08–1.23, p=0.03). Conclusions Lean metabolic syndrome confers similar risk as metabolic syndrome in overweigh/obese patients. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): The present study was funded by an unrestricted educational grant from Valeant. As a supporter of the study, Valeant played no role in the study design, data analysis, data interpretation, or writing of the report. The present study was also supported by Silesian Analytical Laboratories (SLA, Katowice, Poland).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.274
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2022
Admission routes1
Has abstractyes

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