The Cluster of Abnormalities Related to Metabolic Syndrome Is Associated With Reduced Glomerular Filtration Rate and Raised Albuminuria in Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: As association of metabolic syndrome (MS) with chronic kidney disease (CKD) has not been extensively studied in patients with type 2 diabetes, we addressed these issues. Methods: Intrapersonal means of 12 measurements of waist circumference, blood pressure and high-density lipoprotein (HDL) cholesterol and those of six measurements of fasting triglycerides during 12 months were calculated in a cohort of 168 previously reported Japanese patients with type 2 diabetes. Based on these means, MS was diagnosed according to the modified National Cholesterol Education Program Adult Treatment Panel III criteria with the Asian definition of abdominal obesity. CKD was defined as the presence of low estimated glomerular filtration rate (eGFR < 60 mL/min/1.73 m 2 ), albuminuria (urinary albumin/creatinine ratio (ACR) >= 30 mg/g) or both. Results: Of 168 patients, 77 patients (46 %) had MS and 67 (40 %) had CKD. As the number of MS components increased from 1 through 5, the prevalence of albuminuria (9%, 38%, 30%, 41%, and 50%, P < 0.001), low eGFR (0%, 10%, 24%, 22%, and 50%, P < 0.001) and consequently, CKD increased (9%, 41%, 48%, 52%, and 75%, P < 0.001). Urinary ACR increased and eGFR decreased as a function of the number of MS components. As compared to patients without MS, prevalence of low eGFR (26% vs. 7%, P = 0.001) and CKD (52% vs. 30%, P = 0.005) was higher in patients with MS but prevalence of albuminuria did not differ (36% vs. 27%, P = 0.2). Conclusion: In Japanese patients with type 2 diabetes, the cluster of abnormalities related to MS was associated not only with higher prevalence of albuminuria, reduced kidney function and hence the increase in CKD but also with corresponding changes in urinary ACR and eGFR. J Clin Med Res. 2017;9(9):759-764 doi: https://doi.org/10.14740/jocmr3097w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".