Hyon K Choi1, Natalie McCormick2, Na Lu3, Sharan K Rai4, Chio Yokose1, Yuqing Zhang1. 1. Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 2. Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, and Arthritis Research Canada, Richmond, British Columbia, Canada. 3. Arthritis Research Canada, Richmond, British Columbia, Canada. 4. Massachusetts General Hospital, Harvard Medical School, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, and Arthritis Research Canada, Richmond, British Columbia, Canada.
Abstract
OBJECTIVE: To examine modifiable risk factors in relation to the presence of hyperuricemia and to estimate the proportion of hyperuricemia cases in the general population that could be prevented by risk factor modification, along with estimates of the variance explained. METHODS: Using data obtained from 14,624 adults representative of the US civilian noninstitutionalized population, we calculated adjusted prevalence ratios for hyperuricemia, population attributable risks (PARs), and the variance explained according to the following 4 factors: body mass index (BMI; ≥25 kg/m2 ), alcohol intake, nonadherence to a Dietary Approaches to Stop Hypertension (DASH) diet, and diuretic use. RESULTS: BMI, alcohol intake, adherence to a DASH-style diet, and diuretic use were all associated with serum urate levels and the presence of hyperuricemia in a dose-dependent manner. The corresponding PARs of hyperuricemia cases for overweight/obesity (prevalence 60%), nonadherence to a DASH-style diet (prevalence 82%), alcohol use (prevalence 48%), and diuretic use (prevalence 8%) were 44% (95% confidence interval [95% CI] 41%, 48%), 9% (95% CI 3%, 16%), 8% (95% CI 5%, 11%), and 12% (95% CI 11%, 14%), respectively, whereas the corresponding variances explained were 8.9%, 0.1%, 0.5%, and 5.0%. Our simulation study showed the variance nearing 0% as exposure prevalence neared 100%. CONCLUSION: In this nationally representative study, 4 modifiable risk factors (BMI, the DASH diet, alcohol use, and diuretic use) could be used to individually account for a notable proportion of hyperuricemia cases. However, the corresponding serum urate variance explained by these risk factors was very small and paradoxically masked their high prevalences, providing real-life empirical evidence for its limitations in assessing common risk factors.
OBJECTIVE: To examine modifiable risk factors in relation to the presence of hyperuricemia and to estimate the proportion of hyperuricemia cases in the general population that could be prevented by risk factor modification, along with estimates of the variance explained. METHODS: Using data obtained from 14,624 adults representative of the US civilian noninstitutionalized population, we calculated adjusted prevalence ratios for hyperuricemia, population attributable risks (PARs), and the variance explained according to the following 4 factors: body mass index (BMI; ≥25 kg/m2 ), alcohol intake, nonadherence to a Dietary Approaches to Stop Hypertension (DASH) diet, and diuretic use. RESULTS: BMI, alcohol intake, adherence to a DASH-style diet, and diuretic use were all associated with serum urate levels and the presence of hyperuricemia in a dose-dependent manner. The corresponding PARs of hyperuricemia cases for overweight/obesity (prevalence 60%), nonadherence to a DASH-style diet (prevalence 82%), alcohol use (prevalence 48%), and diuretic use (prevalence 8%) were 44% (95% confidence interval [95% CI] 41%, 48%), 9% (95% CI 3%, 16%), 8% (95% CI 5%, 11%), and 12% (95% CI 11%, 14%), respectively, whereas the corresponding variances explained were 8.9%, 0.1%, 0.5%, and 5.0%. Our simulation study showed the variance nearing 0% as exposure prevalence neared 100%. CONCLUSION: In this nationally representative study, 4 modifiable risk factors (BMI, the DASH diet, alcohol use, and diuretic use) could be used to individually account for a notable proportion of hyperuricemia cases. However, the corresponding serum urate variance explained by these risk factors was very small and paradoxically masked their high prevalences, providing real-life empirical evidence for its limitations in assessing common risk factors.
Authors: Nicola Dalbeth; Meaghan E House; Gregory D Gamble; Anne Horne; Bregina Pool; Lauren Purvis; Angela Stewart; Marilyn Merriman; Murray Cadzow; Amanda Phipps-Green; Tony R Merriman Journal: Ann Rheum Dis Date: 2013-01-24 Impact factor: 19.103
Authors: Nicola Dalbeth; Jordyn Allan; Gregory D Gamble; Anne Horne; Owen M Woodward; Lisa K Stamp; Tony R Merriman Journal: Arthritis Res Ther Date: 2020-11-04 Impact factor: 5.156