Samenvatting
The long-term effects of higher dietary protein intake on cardiovascular and renal outcomes in the general population are not clear. We analyzed data from 8461 individuals who did not have renal disease and participated in two or three subsequent screenings (6.4-yr follow-up) in a prospective, community-based cohort study (Prevention of Renal and Vascular ENd-stage Disease [PREVEND]). We calculated daily protein intake from 24-h urinary urea excretion (Maroni formula) and used Cox proportional hazard models to analyze the associations between protein intake, cardiovascular events, and mortality. We used mixed-effects models to investigate the association between protein intake and change in renal function over time. The mean +/- SD daily protein intake was 1.20 +/- 0.27 g/kg. Protein intake was significantly associated with cardiovascular events during follow-up. The associations seemed U-shaped; compared with intermediate protein intake, individuals with either higher or lower protein intake had higher event rates. All-cause mortality and noncardiovascular mortality also were significantly associated with protein intake; individuals with low protein intake had the highest event rates. We found no association between baseline protein intake and rate of renal function decline during follow-up. In summary, in the general population, high protein intake does not promote accelerated decline of renal function but does associate with an increased risk for cardiovascular events.
Originele taal-2 | English |
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Pagina's (van-tot) | 1797-1804 |
Aantal pagina's | 8 |
Tijdschrift | Journal of the American Society of Nephrology |
Volume | 20 |
Nummer van het tijdschrift | 8 |
DOI's | |
Status | Published - aug.-2009 |
Vingerafdruk
Duik in de onderzoeksthema's van 'High Protein Intake Associates with Cardiovascular Events but not with Loss of Renal Function'. Samen vormen ze een unieke vingerafdruk.Datasets
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Prevention of Renal and Vascular End-stage Disease (PREVEND)
Gansevoort, R. T. (Creator), University of Groningen, 2017
Dataset