Study design and participants
This study was based on case-control design. Participants were recruited from Wenzhou hospital of traditional Chinese medicine affiliated to Zhejiang Chinese medicine university in a period from January 2017 to December 2021. Participants diagnosed with hyperuricemia were included in case group and should also meet the following criteria: (1) with an age > 18 years; (2) with basic communication capability and memory; (3) without cancer, neuropsychological diseases, diabetes and gastrointestinal diseases, as these diseases have large impact on food pattern; (4) the food pattern was not largely changed in last 12 months. Almost the same number of participants without hyperuricemia were included in the control group. Male proportion and age distribution in the control group were similar to the case group, while other eligible criteria for the control group were also in accord with the case group. This study was performed in line with the principles of the Declaration of Helsinki, and approved by the Ethical Committee of Wenzhou hospital of traditional Chinese medicine affiliated to Zhejiang Chinese medicine university. Informed consent was obtained from all individual participants included in the study.
Anthropological data and health-related information were derived from electronic patient record. Anthropological data included age, gender, weight (kg), height (cm) and body mass index (BMI). Health-related information included diastolic blood pressure (mmHg), systolic blood pressure (mmHg), heart rate (beats/minutes), serum uric acid (μmol/L), smoking, drinking and chronic diseases. Smoking statues were divided into current smoking, ever smoking and never smoking. Ever smoking was defined as those who quit smoking for more than 12 months. Alcohol drinking statues were classified as current drinking, ever drinking and never drinking. Ever drinking was defined as those who quit drinking for more than 12 months. The list of chronic diseases was as follows: hypertension, coronary heart disease, osteoporosis, chronic obstructive pulmonary disease， chronic kidney disease and hyperlipidemia. These chronic diseases required a clear clinical diagnosis, and self-reporting was not considered in this study as the lack of uniform standards of diseases assessment from different participants.
Definition of hyperuricemia
We used a Chinese general criterion to diagnose hyperuricemia in this study. Hyperuricemia for men was defined as fasting serum uric acid > 420 μmol/L in two separate days, and for women was > 360 μmol/L.  In addition, individuals diagnosed as gout or underwent hyperuricemia treatment were also classified as hyperuricemia.
Food consumption survey by food frequency questionnaire
A validated food frequency questionnaire (FFQ) was applied for food consumption survey. Briefly, FFQ in this study was adapted from China Health and Nutrition Survey which is an ongoing open cohort and designed to examine the effects of the health, nutrition status and other factors on the study population . Given the differences in local diet pattern, 12 representative food groups in Zhejiang province were set in the FFQ, inclusive of grains, potato, vegetable, fruit, soy, fish, red meat, poultry, dairy, egg, beverages and edible oil. A small-scale pre-survey was also conducted to verify FFQ validity. Food consumption survey was performed though face-to-face interview by well-trained investigators. The participants were asked to answer diet-related information in a period of last 12 months. Eating amount (g/time or ml/time) and eating frequency (times/day, times/week, times/month or times/year) were recorded. Daily consumption of the 12 food groups were calculated, respectively.
Food consumption and dietary acid load
In order to compute daily nutrient intake, the related data from the Chinese food composition table (2012 version) were used. Energy intake and three macro-nutrients including carbohydrate, fat and protein were calculated. In order to evaluate dietary acid load (PRAL and NEAP), additional nutrients including sodium, magnesium, phosphorus, magnesium and calcium were also computed. According to previously studies , PRAL and NEAP were calculated by the following formulas, respectively:
PRAL (mEq/d) =0.4888 × protein intake (g/d) + 0.0366 × phosphorus (mg/d) - 0.0205 × potassium (mg/d) - 0.0125 × calcium (mg/d) - 0.0263 × magnesium (mg/d);
NEAP (mEq/d) = (54.5 × protein intake (g/d) ÷ potassium intake (mEq/d))-10.2
Variables on discrete distribution were as number and proportion, while if variables on continuous distribution were in accord with normal distribution than they were exhibited as mean and standard deviation (SD), otherwise were exhibited as median and range interquartile (IQR). Variable difference was evaluated by χ2 test, student’s test or Wilcoxon rank sum test. Logistic regression model was applied to assess the association between dietary acid load (PRAL and NEAP) and the odds of hyperuricemia. Dietary acid load was divided into four level: the first quartile (Q1), the second quartile (Q2), the third quartile (Q3), and the fourth quartile (Q4), in which each group has an approximately same number of participants. Q1 was considered as a reference group and odd ratios (ORs) and the correspondent confidence intervals (CIs) of Q2, Q3 and Q4 were computed. Additionally, possible risk factors, such as age and sex, as well as multiply variables (age, sex, BMI, diastolic blood pressure, systolic blood pressure, smoking, drinking and chronic disease) were put in the Logistic regression model to obtain adjusted effects. Statistical analysis was conducted in R language (version 3.5.1, R Core Development Team). Forest plots were produced based on R language software package named FORESTPLOT. In this study, a p-value < 0.05 was perceived as statistical significance.