台灣飲食模式與尿酸間的相關性研究

Abstract

研究背景和目的：根據台灣國民營養現況之調查，尿酸高(男性：≧7.7mg/dl;女性：≧6.6mg/dl)或是服用降尿酸藥物者，男性盛行率約為26%，女性約為19%。尿酸為痛風、胰島素阻抗及代謝症候群之危險因子。造成尿酸升高的原因有三，一為過度生成尿酸，二是不易排除尿酸，最後則為合併兩種情況。過去研究肉類、海鮮食品、酒精和非酒精的糖類飲品，會增加血清中的尿酸值，而乳製品和咖啡可明顯降低尿酸值。營養素方面，膳食纖維對於高尿酸血症具有保護作用。飲食模式為考慮整體飲食習慣對於疾病之影響，相較於觀察單一食物的影響，飲食模式更貼近真實的生活習慣。但至目前為止，台灣尚無飲食模式與尿酸間相關性的研究，因此本研究之目的為觀察飲食模式與尿酸之相關性及比較不同獲取飲食模式之統計方法其差異性。 材料與方法：去除熱量不在正常範圍(女性: 500<總熱量<2842; 男性: 800<總熱量<3245及大於或小於3個標準差)內之人數和尿酸或是臨床變項中缺失值者，最後共有266人進入分析，飲食模式取得方法為主成分分析、偏最小平方法及探索性因素分析，每個飲食模式皆可獲得因素分數，用此因素分數觀察與尿酸間的相關性，並利用判別分析決定飲食模式對於尿酸之預測能力。由因素分析中可得三個飲食模式，模式適配度之p值大於0.05。 結果：尿酸傾向飲食模式(uric acid-prone pattern)包含海鮮食品、肉類、內臟飲料、飲料、蛋、油炸物及主食，魚類飲食模式(fish pattern)與僅含魚類，蔬菜及水果飲食模式(vegetable and fruit pattern)則是含有豆類製品、水果、深色蔬菜和淺色蔬菜。將三個飲食模式分數作四分位分層之趨勢檢定皆未達顯著水準。判別分析之結果顯示，於模式中加入尿酸傾向飲食模式，特異度為80%，敏感度下降至52%。 結論：本研究透過因素分析取得三個飲食模式，其與尿酸間之相關性，在經調整干擾因子後，並無顯著之結果，但身體質量指數與性別與尿酸具有顯著相關性存在。總熱量於本研究中僅在模式中進行調整，以避免減少食物之變異程度。

Background and objectives: Hyperuricemia is the risk factor for gout and insulin resistance. In Taiwan the prevalence of adult hyperuricemia are 26% and 17% in men and women respectively. Previous studies showed single food or nutrient affected serum uric acid, however, single food or nutrient are not consumed in isolation, in fact, in numerous different combinations that generated complex synergistic effects. The combined effects of nutrients or foods were observed through dietary patterns and the results from dietary patterns analysis are more helpful in disseminating diet-related information rather than related to single food or nutrient. No data showed the relationship between dietary patterns and serum level of uric acid among Chinese. We conducted the data-driven methods to explore the association between dietary patterns and hyperuricemia and compared to different methods for derived dietary patterns. Methods and materials: We recruited adults who age older 35 years. The participants without uric acid and confounding factors data or energy intake beyond normal range (women: 500 kcal < total energy < 2842 kcal; men: 800 kcal < total energy < 3245 kcal) or energy intake beyond 3 standard deviation we were excluded. All participants had signed the informed consents. Derived dietary pattern methods are principle component analysis, partial least square and factor analysis, and then the factor score for each pattern is derived and used in regression analysis to test relationship between patterns and uric acid. In our study, we use discriminant analysis to cumulate the sensitivity and specificity of dietary patterns. Three dietary patterns are chose from factor analysis, and p value of the model fitness test is great 0.05. Results: The relationship between three patterns and food, the contents of uric acid-prone pattern is seafood, meat, viscus, beverage, egg, fried food, and staple; fish pattern is only fish; Soy products, fruit, dark vegetable and white vegetable are included in vegetable and fruit pattern. Test the trend for uric acid by quartile of factor score, p for trend are not significance. Discriminant analysis results shows to add uric acid-prone pattern into model, specificity is 80%, and sensitivity is 52%. Conclusion: We derived three dietary patterns from factor analysis. The association between dietary patterns and uric acid are no significance after adjusted confounding factors. However, BMI and gender are significant effects for uric acid. In our study, total energy is adjusted by modeling to avoid reduction of food variations.