Identifying Sex-Specific Differences in Young-Onset Metabolic Syndrome Using Primary Care Electronic Medical Records
Bibliographic record
Abstract
Objectives:To apply a case definition to a Northern Alberta-based primary care practice population and to assess the sex-specific characteristics of young-onset metabolic syndrome (MetS). Design:We carried out a cross-sectional study to identify and estimate the prevalence of MetS using electronic medical record (EMR) data and perform descriptive comparative analyses of demographic and clinical characteristics between males and females. Setting:Northern Alberta Primary Care Research Network (NAPCReN) consists of EMR patient data from 77 physicians among 18 clinics. Participants:Patients with one or more clinic visit between 2015 and 2018, between 18 and 40 years old, residing in Northern Alberta. Main Outcome Measures:Comparison of prevalence in MetS between sexes as well as sex-specific distribution of MetS characteristics [body mass index (BMI), fasting blood glucose, glycated hemoglobin, triglycerides, and high-density lipoprotein cholesterol (HDL-C), presence of hypertension, and presence of diabetes]. Results:Of 15,766 patients, 4.4% (n = 700) had young-onset MetS based on recorded data, prevalence was nearly twice as high in males (6.1%, n = 354) compared with females (3.5%, n = 346). The most prevalent risk factor for MetS consisted of having an elevated BMI for both females (90.9%) and males (91.5%). In the presence of MetS, more females had lower HDL-C [68.2% females (F) vs. 52.5% males (M)], and higher prevalence of diabetes (21.4% F vs. 9.0% M), whereas more males had hypertriglyceridemia (60.4% F vs. 79.7% M) and hypertension (12.4% F vs. 15.8% M). Females also had consistently higher percentages of absent laboratory data compared with males when identified as having MetS and BMI ≥25 kg/m2. Conclusions:Males have nearly twice the prevalence of young-onset MetS compared with females, with notable sex-specific differences in the manifestation of MetS, although we suspect that this is partially due to underreporting where the absence of anthropomorphic and laboratory investigations point to a lack of testing. Sex-specific screening for MetS, especially among young females of childbearing years, is important for downstream prevention.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".