Prevalence of Metabolic Syndrome Among the End-Stage Renal Disease Patients on Hemodialysis
Bibliographic record
Abstract
BACKGROUND: Patients with metabolic syndrome (MetS) have a 2.6-fold greater risk of incident chronic kidney disease (CKD). The primary goal of this study was to assess the prevalence of MetS in patients with end-stage renal disease (ESRD) who are on hemodialysis (HD) and the impact of MetS presence on HD-related outcomes. METHODS: This is a cross-sectional study conducted in the Dialysis Center, King Abdulaziz Specialist Hospital, Taif, Saudi Arabia. It was conducted among ESRD patients that attended the Dialysis Center between August 2013 and September 2016. We excluded patients on peritoneal dialysis and those < 18 years old. We used the International Diabetes Federation (IDF) criteria to identify patients with MetS. RESULTS: , and mean waist circumference (WC) of 92.0 (SD 23.5) cm. The mean duration of the HD was 69.3 (SD 65.6) months with arteriovenous fistula (AVF) as the most common access for HD. Of the patients, 38.2% had MetS. Compared to those without MetS, those with MetS were more likely to be older (P < 0.001), be female (P < 0.001), be married (P < 0.001), have higher BMI (P < 0.001), have larger WC (P < 0.001), have T2D and hypertension (HTN) (P < 0.001), have shorter HD duration (P < 0.001), have a longer duration since the AVF was placed (P = 0.026), and have high post-HD creatinine levels (P = 0.010) and were less likely to have adequate HD (P = 0.004) and have parathyroid hormone (PTH) at goal (P = 0.046). CONCLUSION: MetS is common among ESRD and MetS was associated with more comorbidity, worse anthropometric measures at baseline, and worse HD-related outcomes. The limitations were small sample size and single center.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".