Nutrition, metabolism, and cardiovascular diseases : NMCD · 2018
Serum uric acid potentially links metabolic health to measures of fuel use in lean and obese individuals.
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Mele C, Tagliaferri M A, Saraceno G, Mai S, Vietti R, Zavattaro M, Aimaretti G, Scacchi M, Marzullo P
Аннотация
BACKGROUND AND AIMS: Uric acid (UA) is a byproduct of the high-energy purine metabolism and is conventionally regarded as a marker of cardio-metabolic impairment. Its potential relationship with energy homeostasis is unknown to date.
METHODS AND RESULTS: In a cross-sectional study on 121 otherwise healthy obese and 99 sex- and-age-matched lean subjects, UA levels were analyzed in relation to metabolic health, inflammatory markers, respiratory quotient (RQ) and resting energy expenditure (REE) as assessed by indirect calorimetry, fat mass (%FM) and fat-free mass (FFM) as determined by bioimpedance analysis. As expected, obese and lean subjects differed in BMI, glucolipid homeostasis, leptin and insulin levels, inflammatory markers, %FM and FFM (p < 0.001 for all). Likewise, UA levels (p < 0.001) and rates of hyperuricaemia (40.5% vs 3.0%, p < 0.0001) were also higher in obese than lean controls. Further, indirect calorimetry confirmed that obesity increased REE and decreased RQ significantly (p < 0.001). Beyond the expected metabolic correlates, in individual and merged groups UA levels were associated negatively with RQ and positively with REE (p < 0.0001 for both). In multivariable regression analysis, significant independent predictors of UA were BMI and sex. When BMI was replaced by measures of body composition, %FM and FFM emerged as significant predictors of serum UA (p < 0.0001).
CONCLUSIONS: A potential link relates serum UA to measures of resting energy expenditure and their determinants.
METHODS AND RESULTS: In a cross-sectional study on 121 otherwise healthy obese and 99 sex- and-age-matched lean subjects, UA levels were analyzed in relation to metabolic health, inflammatory markers, respiratory quotient (RQ) and resting energy expenditure (REE) as assessed by indirect calorimetry, fat mass (%FM) and fat-free mass (FFM) as determined by bioimpedance analysis. As expected, obese and lean subjects differed in BMI, glucolipid homeostasis, leptin and insulin levels, inflammatory markers, %FM and FFM (p < 0.001 for all). Likewise, UA levels (p < 0.001) and rates of hyperuricaemia (40.5% vs 3.0%, p < 0.0001) were also higher in obese than lean controls. Further, indirect calorimetry confirmed that obesity increased REE and decreased RQ significantly (p < 0.001). Beyond the expected metabolic correlates, in individual and merged groups UA levels were associated negatively with RQ and positively with REE (p < 0.0001 for both). In multivariable regression analysis, significant independent predictors of UA were BMI and sex. When BMI was replaced by measures of body composition, %FM and FFM emerged as significant predictors of serum UA (p < 0.0001).
CONCLUSIONS: A potential link relates serum UA to measures of resting energy expenditure and their determinants.