Association of Three Different Dietary Approaches to Stop Hypertension Diet Indices with Renal Function in Renal Transplant Recipients.
I-Hsin LinTuyen Van DuongYi-Chun ChenShih-Wei NienI-Hsin TsengYi-Ming WuYang-Jen ChiangHsu-Han WangChia-Yu ChiangChia-Hui ChiuMing-Hsu WangChia-Tzu ChangNien-Chieh YangYing-Tsen LinTe-Chih WongPublished in: Nutrients (2023)
Several dietary indices assess the impacts of the Dietary Approaches to Stop Hypertension (DASH) diet on health outcomes. We explored DASH adherence and renal function among 85 Taiwanese renal transplant recipients (RTRs) in a cross-sectional study. Data collection included demographics, routine laboratory data, and 3-day dietary records. Three separate DASH indices, that defined by Camões (based on nine nutrients), that defined by Fung (using seven food groups and sodium), and that modified by Fung (as above but separated for men and women) were used. Renal function was ascertained through the estimated glomerular filtration rate (eGFR) from patients' medical records. Participants' mean age was 49.7 ± 12.6 years and eGFR was 54.71 ± 21.48 mL/min/1.73 m 2 . The three established DASH diet indices displayed significant correlations (r = 0.50-0.91) and indicated the nutritional adequacy of the diet. Multiple linear regressions indicated a significant positive association between higher DASH scores for each index and increased eGFR. In addition, RTRs in the highest DASH score tertile had higher eGFR rates than those in the lowest tertile, regardless of confounding variables. Adherence to a DASH-style diet correlated with better renal function among RTRs. Educating RTRs about the DASH diet may prevent graft function deterioration.
Keyphrases
- physical activity
- small cell lung cancer
- weight loss
- epidermal growth factor receptor
- tyrosine kinase
- blood pressure
- healthcare
- end stage renal disease
- ejection fraction
- type diabetes
- electronic health record
- big data
- newly diagnosed
- heavy metals
- prognostic factors
- machine learning
- peritoneal dialysis
- risk factors
- clinical practice
- arterial hypertension
- neural network