SP131WEIGHT GAIN AND OFFICE BLOOD PRESSURE IN LIVING KIDNEY DONORS: A FIVE YEAR FOLLOW UP STUDY
Bibliographic record
Abstract
INTRODUCTION: Weight gain and body fat are known to increase with advancing age in the general population.(1) Changes in weight and body composition are related to increases in blood pressure, risk of diabetes and chronic renal disease.(1-3) Recent evidence suggests weight gain in living kidney donor’s (LKD) after nephrectomy increases the risk of hypertension and diabetes.(4) We aimed to identify if LKD gained more weight over time when compared to healthy controls. METHODS: Seated office blood pressure, weight and height were recorded at baseline for both healthy controls (eligible to donate a kidney) and potential LKD. Both groups were followed up 5 years after nephrectomy or original baseline visit for controls. RESULTS: Eighty-six patients were studied (39 healthy controls and 47 LKD); mean age at baseline for donors and controls were 48yrs and 45yrs respectively. There were no differences in baseline demographics between LKD and controls. Living kidney donors had a significant increase in weight over the 5 year follow-up period (73.74 ± 14.11 to 75.54 ± 15.38 kg, p=0.037), which was not seen in controls (73.76 ± 11.17 to 74.74 ±11.64 kg, p=0.29). Furthermore, a change in weight was not associated with age. There was no significant difference in office systolic blood pressure in LKD (123.90 ± 11.86 to 122.52 ±11.86 mmHg, p=0.506) or controls (122.86 ±17.82 to 122.18 ±16.40 mmHg, p=0.793). Office diastolic blood pressure however, increased in LKD (74.80 ±8.83 80.54 ± 9.34 mmHg, p<0.001). This was not significant in controls (75.69 ±13.03 to 79.21 ± 9.22 mmHg, p=0.053). An increase in weight was positively correlated with a rise in diastolic blood pressure in LKD (r=0.305, p=0.039), but this relationship was not seen in controls. CONCLUSIONS: Weight gain may be an important confounder for studies of LKD. Increases in weight relative to that observed in controls over a 5 year follow-up period are likely to reflect long-standing changes in lifestyle. It may also suggest LKD actively lost weight in order to fulfil criteria to donate and consequently returned to their baseline weight. In addition to lifestyle counselling pre-operatively, this study highlights the importance of weight and body mass index surveillance in LKD. 1. Turcato E, Armellini F, Bergamo-Andreis IA, Zamboni M, Bosello O, Micciolo R, et al. Effects of age on body fat distribution and cardiovascular risk factors in women. The American Journal of Clinical Nutrition. 1997;66(1):111-5. 2. Sabaka P, Dukat A, Gajdosik J, Bendzala M, Caprnda M, Simko F. The effects of body weight loss and gain on arterial hypertension control: an observational prospective study. European journal of medical research. 2017;22(1):43-. 3. Kovesdy CP, Furth SL, Zoccali C, World Kidney Day Steering C. Obesity and Kidney Disease: Hidden Consequences of the Epidemic. Canadian journal of kidney health and disease. 2017;4:2054358117698669. 4. Issa N, Sánchez OA, Kukla A, Riad SM, Berglund DM, Ibrahim HN, et al. Weight gain after kidney donation: Association with increased risks of type 2 diabetes and hypertension. Clinical Transplantation. 2018;32(9):e13360.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".