Association between serum uric acid and spirometric pulmonary function in Korean adults: The 2016 Korea National Health and Nutrition Examination Survey.

BACKGROUND: A limited number of epidemiological studies have investigated the association between serum uric acid and pulmonary function in the general population. However, the results have been inconclusive.
OBJECTIVES: This study was performed to investigate the association between serum uric acid and spirometric pulmonary function in general population.
METHODS: Among the 8,150 participants who participated in the 2016 Korea National Health and Nutrition Examination Survey, 2,901 participants were analyzed in this study. Subjects were divided into four groups according to forced vital capacity (FVC)% predicted or forced expiratory volume in 1 second (FEV1) % predicted quartiles. Participants in the lowest quartile of FVC % predicted and FEV1% predicted were compared to those in the remaining quartiles according to age, education level, household income, smoking status, alcohol consumption, aerobic exercise, obesity, hypertension, diabetes, renal impairment, serum uric acid, and hyperuricemia. Multivariable logistic regression analyses were used to calculate the odds ratio (OR) of hyperuricemia for participants in the lowest quartile of FVC% and FEV1 predicted, with above covariates.
RESULTS: In women, hyperuricemia was associated with lowest quartile of FVC% predicted (OR 1.71, 95% CI 1.06-2.75, p = 0.027) and FEV1 predicted (OR 1.70, 95% CI 1.06-2.74, p = 0.028) respectively, serving as above confounding variables. In men, hyperuricemia (OR 1.54, 95% CI 1.07-2.22, p = 0.021) was associated with the lowest quartile of FEV1% predicted, not FVC% predicted. According to median age, in women, age ≥ 56 years old with hyperuricemia was associated with lowest quartile of FVC% predicted (OR 1.85, 95% CI 1.04-3.28, p = 0.037) and FEV1% predicted (OR 1.99, 95% CI 1.11-3.75, p = 0.021), respectively. In men, age ≥ 56 years old with hyperuricemia was associated with lowest quartile of FEV1% predicted (OR 1.75, 95% CI 1.05-2.94, p = 0.033), not FCV% predicted.
CONCLUSIONS: Hyperuricemia was associated with lowest quartile of FEV1% or FVC% predicted in Korean general population. This correlation between hyperuricemia and low pulmonary function was more pronounced in women and older age.

Introduction

Uric acid is primarily synthesized in the liver, intestines, muscles, kidneys and vascular endothelium, as an end product of the action of xanthine oxidase on exogenous purines [1]. Hyperuricemia is well known to be associated with increased risk of incident and recurrent gout [2]. However, recent research in uric acid has focused primarily on its potential as a mediator of human diseases other than gout.

Most epidemiologic studies have suggested that elevated serum uric acid levels are associated with cardiovascular diseases, including coronary heart disease, stroke, congestive heart failure, and hypertension [3-6]. Furthermore, traditional cardiovascular risk factors, including metabolic syndrome, insulin resistance, obesity, non-alcoholic fatty liver disease, and chronic kidney disease, are also related to elevated serum uric acid levels [7-11]. Several experimental and clinical studies support a role of uric acid as a causal factor in these conditions, via its contributions to systemic inflammation, endothelial dysfunction, and oxidative stress [1, 8].

In this context, increased levels of uric acid have also been observed in a variety of respiratory disorders, including obstructive sleep apnea, pulmonary hypertension and chronic obstructive pulmonary disease (COPD) [12-14]. A limited number of epidemiological studies have investigated the association between serum uric acid levels and pulmonary function in the general population [15, 16]. However, the results have been inconclusive.

In this study, we investigated the association between serum uric acid and pulmonary function in Korean population using data from the 2016 Korea National Health and Nutrition Examination Survey (KNHANES).

Methods

Study population and data collection

This study was based on data from the 2016 KNHANES, a cross-sectional and nationally representative survey conducted by the Korean Center for Disease Control for Health Statistics. The KNHANES has been conducted periodically since 1998 to assess the health and nutritional status of the noninstitutionalized civilian population of Korea. Participants were selected using a systemic proportional allocation sampling method with multistage stratification. A standardized interview was conducted in the homes of all participants to collect information on demographic variables, family history, medical history, medications used, and a variety of other health-related variables. The health interview was based on an established questionnaire to determine the demographic and socioeconomic characteristics of the subjects including age, education level, occupation, income, marital status, smoking status, alcohol consumption, aerobic exercise, previous and current diseases, and family disease history.

As part of the interview, subjects were asked whether they exercised with an intensity that caused a slight increase in breathing rate and sweating; those who exercised regularly at moderate intensity were asked about the frequency with which they exercised per week, and the length of time per exercise session. Participants who performed aerobic exercise were defined as those who spent at least 150 min performing moderate physical activity, or 75 min performing high-intensity physical activity, per week. Alcohol consumption was assessed based on responses to questions regarding drinking behavior, including average alcohol consumption and drinking frequency, during the month prior to the interview. Diabetes was defined as a fasting plasma glucose (FPG) concentration ≥ 126 mg/dL (7.0 mmol/L), current use of anti-diabetes medication, or a previous diagnosis of diabetes by a physician. Obesity was defined as a body mass index (BMI) ≥25 kg/m2 according to the Asia-Pacific obesity classification [17]. Height and weight were obtained using standardized techniques and equipment. Height was measured to the nearest 0.1 cm using a portable stadiometer (Seriter, Bismarck, ND, USA). Weight was measured to the nearest 0.1 kg using a Giant-150N calibrated balance-beam scale (Hana, Seoul, Korea). BMI was calculated by dividing the weight by the height squared (kg/m2). Systolic and diastolic blood pressure were measured by standard methods using a standard mercury sphygmomanometer (Baumanometer, WA Baum Co. Inc., Copiague, NY, USA), while the patient was seated. Three measurements were obtained for all subjects at 5-min intervals, and the average of the second and third measurements was used in the analysis. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, or diastolic blood pressure ≥ 90 mmHg, or use of antihypertensive medications irrespective of blood pressure.

Of the 8,150 participants in the 2016 KNHANES, the number of individuals aged ≥ 19 years were 6,382. Among these subjects, 3,341 were assessed for both serum uric acid levels and pulmonary function. Pulmonary function tests were performed only in individuals aged ≥ 40 years.

A total of 282 subjects were excluded from the analysis due to pre-existing diseases, including liver cirrhosis (n = 13), renal failure (n = 9), lung cancer (n = 9), asthma (n = 94), heart failure (n = 117), and cerebrovascular accident (n = 60). A total of 158 examinees who had baseline FEV1 or FVC quality attribute of “D (questionable results, use with caution)” or “F”(results not valid)” were also exclude [18, 19]. Finally, 2,901 participants were analyzed in this study (Fig 1).

Fig 1

Participants disposition to be included in this study.

Pulmonary function test

Well-trained technicians measured forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) using a spirometer (Vyntus Spiro; Care Fusion, San Diego, CA, USA) and SentrySuite (Care Fusion) according to the American Thoracic Society criteria [20]. All spirometry values were prebronchodilator results.

The FVC% predicted and FEV1% predicted were calculated by dividing the FVC and FEV1 by the predicted FVC and FEV1, respectively. The predicted FEV1 and FVC for each subject were calculated using published equations [21]. Subjects were divided into four groups according to FVC% predicted or FEV1% predicted quartiles, as follows:

FVC% predicted:

Men: Q1 <82.4%; Q2 82.–89.9%; Q3 90.0–97.2%; Q4 ≥ 97.3%

Women: Q1, <84.1%; Q2 84.2–91.8%; Q3 91.9–100.0%; Q4 ≥ 100.1%

FEV1% predicted:

Men: Q1 <81.8%; Q2 81.9–89.9%; Q3 90.0–97.6%; Q4 ≥ 97.7%

Women: Q1 <84.7%; Q2 84.8–92.7%; Q3 92.8–100.0%; Q4 ≥ 100.1%

Obstructive pattern was defined as FEV1/FVC<0.70, and restrictive pattern was defined as FEV1/FVC≥0.7 and FVC <80%, While normal lung function was defined as FEV1/FVC≥0.70 and FVC ≥80% [22, 23].

Laboratory methods

Serum uric acid levels were measured using a uricase colorimetry method with an autoanalyzer (7600‐210; Hitachi, Tokyo, Japan), with serum uric acid levels > 7.0 mg/dL in men and > 6.0 mg/dL in women defined as hyperuricemia.

The estimated glomerular filtration rate (eGFR) was calculated based on serum creatinine levels using the Chronic Kidney Disease Epidemiology Collaboration equation. Renal impairment was defined as an eGFR < 60 mL/min/1.73 m2 [24].

Ethics statement

This study was approved by the institutional review board of Ilsan Paik Hospital, Republic of Korea (IRB 2019-10-012). After obtaining approval for the study protocol, the KNHANES dataset was made available at the request of the investigator. Because the dataset did not include any personal information and participants’ consent had already been given for the KNHANES, our study was exempt from the requirement for informed consent.

Statistical analyses

Statistical analyses were performed using SPSS for Windows software (ver. 21.0; SPSS Inc., Chicago, IL, USA). Demographic and clinical characteristics, including gender, age, education level (elementary school/junior high school/senior high school/college graduated), smoking status (non-/ex-/current smoker), alcohol consumption (none/1-3/week/≥4/week), aerobic exercise, obesity, hypertension, diabetes, and hyperuricemia were analyzed using chi-squared tests. Household income, FVC% predicted, FEV1% predicted, and serum uric acid were analyzed by independent samples t-tests. Age, education level, household income, smoking status, alcohol consumption, aerobic exercise, obesity, hypertension, diabetes, renal impairment, serum uric acid, and hyperuricemia of participants in the lowest quartile of FEV1 or FVC% predicted (Q1 group) were compared to those in the remaining quartiles (Q2–4 group) using independent samples t-tests or chi-squared tests. Comparisons of serum uric acid and the prevalence of hyperuricemia between Q1 group and Q2–4 group were done after adjusting for confounding factors in model 1 (Age) and 2 (all variables) (ANCOVA test). Serum uric acid and the prevalence of hyperuricemia were adjusted for confounding factors in model 1 (Age) and 2 (all variables). Spearman’s correlation was used in order to determine the association between serum uric acid level and FVC% predicted, FEV1% predicted or FEV1/FVC. Age was adjusted in Model 1. In Model 2, age, education, household income (KRW), smoking (non-, ex-, and current smoker), alcohol consumption (none, 1-3/week, and ≥ 4/week), aerobic exercise, BMI (kg/m2), systolic BP (mmHg), fasting plasma glucose (mg/dL), and eGFR (mL/min/1.73 m2) were adjusted.

Multivariable logistic regression analyses were used to calculate the odds ratio (OR) of hyperuricemia for participants in the lowest quartile of FVC% predicted and FEV1% predicted, with age, education, household income, alcohol consumption, smoking, aerobic exercise, obesity, hypertension, diabetes, and renal impairment serving as covariates. All tests were two sided, with p < 0.05 considered indicative of statistical significance.

Results

Clinical characteristics of the study population

The demographic and clinical characteristics of the study population according to gender are shown in Table 1. The median age was 57 years (range: 40–80 years), and 57% of the participants were female. The mean FVC% predicted and FEV1% predicted in the general population were 91.1 ± 0.2 and 91.2 ± 0.2, respectively. The mean serum uric acid level was 4.9 ± 0.1 mg/dL, with an overall prevalence of hyperuricemia of 9.7%.

Table 1

Clinical characteristics of study population.

	Men (n = 1,266)	Women (n = 1,635)	p	Total (n = 2,901)
Age (years)	56 (40–80)	57 (40–80)	0.870	57 (40–80)
Education (%)			<0.001
Elementary school graduated	20.3	32.2		27.0
Junior high school graduated	12.1	14.0		13.2
Senior high school graduated	30.6	31.3		31.0
College graduated	37.0	22.5		28.8
Household Income (x10,000 KRW)	423.0 ± 8.8	392.3 ± 7.9	0.009	405.7 ± 5.9
Smoking (%)			<0.001
Non-smoker	19.1	92.3		60.4
Ex- smoker	49.0	3.0		23.1
Current smoker	31.9	4.7		16.6
Alcohol consumption (%)			<0.001
None	19.7	40.0		31.2
1-3/week	65.1	57.7		60.9
≥ 4/week	15.2	2.3		7.9
Aerobic exercise (%)	45.7	40.4	0.005	42.7
Obesity (%)	41.4	36.8	0.011	38.8
Hypertension (%)	43.6	35.4	<0.001	39.0
Diabetes (%)	17.5	12.7	<0.001	14.8
Renal impairment (%)	4.3	3.5	0.245	3.9
FVC% predicted	89.6 ± 0.3	92.1 ± 0.3	<0.001	91.1 ± 0.2
FEV1% predicted	89.0 ± 0.4	92.9 ± 0.3	<0.001	91.2 ± 0.2
Serum uric acid (mg/dL)	5.7 ± 0.1	4.4 ± 0.1	<0.001	4.9 ± 0.1
Hyperuricemia (%)	14.8	5.7	<0.001	9.7

Data are expressed as mean ± SEM or %, except age. Age are expressed as median (minimum-maximum).

FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second.

Men were found to have higher levels of education and household income, and were also more likely to smoke and engage in heavy alcohol consumption compared to women. The rates of aerobic exercise, obesity, hypertension, and diabetes were also higher in men than women. The mean serum uric acid level was 5.7 ± 0.1 mg/dL in men and 4.4 ± 0.1 mg/dL in women. The overall prevalence of hyperuricemia was 14.8% in men and 5.7% in women. Both the mean FVC% predicted and mean FEV1% predicted were higher in women than in men (89.6 ± 0.3 vs. 92.1 ± 0.3; p < 0.001, and 89.0 ± 0.4 vs. 92.9 ± 0.3; p < 0.001, respectively).

Serum uric acid and the prevalence of hyperuricemia according to the FEV1/FVC 0.7

Overall, 74.7% of participants showed normal spirometric values. 12.1% and 13.2% of subjects have restrictive pattern and obstructive pattern in pulmonary function test, respectively. We compared the values of serum uric acid and prevalence of hyperuricemia in those with FEV1/FVC ≥ 0.7 to those with < 0.7. The prevalence of hyperuricemia in those with FEV1/FVC ≥ 0.7 was 14.2% in men and 5.6% in women. The prevalence of hyperuricemia in those with FEV1/FVC < 0.7 was 17.2% in men and 8.6% in women. There is no significant difference in the prevalence of hyperuricemia according to the obstructive pattern in pulmonary function test, using FEV1/FVC 0.7 criteria. The mean serum uric acid level in those with FEV1/FVC ≥ 0.7 was 5.7 ± 0.1 mg/dL in men and 4.4 ± 0.1 mg/dL in women. The mean serum uric acid level in those with FEV1/FVC < 0.7 was 5.8 ± 0.1 mg/dL in men and 4.4 ± 0.1 mg/dL in women. There is also no significant difference in the mean serum uric acid levels according to the obstructive pattern in pulmonary function test.

Clinical characteristics according to gender and FVC % predicted

We compared clinical characteristics between the participants in the lowest FVC% predicted quartile (Q1 group) and those in the remaining quartiles (Q2–4 group) (Table 2). In men, the median FVC% predicted of the Q1 and Q2–4 groups were 77.1 and 93.4, respectively. The Q2–4 group was younger, and had a higher education level and higher household income, compared to the Q1 group. The rates of obesity, hypertension, diabetes, and renal impairment were higher in the Q1 group. Serum uric acid levels, and the prevalence of hyperuricemia, were not statistically different between groups.

Table 2

Clinical characteristics according to gender and FVC% predicted.

	Men (n = 1,.266)			Women (n = 1,635)
	FVC% predicted Q1 (n = 313)	FVC% predicted Q2-4 (n = 953)	p	FVC% predicted Q1 (n = 406)	FVC% predicted Q2-4 (n = 1,229)	p
FVC% predicted	77.1 (33.1–82.3)	93.4 (82.5–121.6)	<0.001	79.4 (48.2–84.1)	95.8 (84.1–138.6)	<0.001
Age (years)	64 (40–80)	55 (40–80)	<0.001	61 (40–80)	55 (40–80)	<0.001
Education (%)			0.001			<0.001
Elementary school graduated	26.5	18.3		40.6	29.5
Junior high school graduated	14.4	11.3		14.5	13.8
Senior high school graduated	29.1	31.2		28.6	32.1
College graduated	30.0	39.2		16.3	24.6
Household Income (x10,000 KRW)	353.2 ± 17.8	446.0 ± 10.0	<0.001	348.2 ± 15.1	406.7 ± 9.2	0.001
Smoking (%)			0.165			0.523
Non-smoker	19.2	19.1		93.1	92.0
Ex- smoker	53.0	47.6		3.2	2.9
Current smoker	27.8	33.3		3.7	5.0
Alcohol consumption (%)			0.003			0.004
None	26.2	17.6		46.8	37.8
1-3/week	58.1	67.4		50.7	60.0
≥ 4/week	15.7	15.0		2.5	2.2
Aerobic exercise (%)	44.1	46.3	0.514	35.0	42.2	0.010
Obesity (%)	52.1	37.9	<0.001	50.7	32.1	<0.001
Hypertension (%)	56.5	39.3	<0.001	48.3	31.2	<0.001
Diabetes (%)	28.4	13.9	<0.001	20.2	10.3	<0.001
Renal impairment (%)	7.0	3.5	0.010	6.2	2.6	0.002
Serum uric acid (mg/dL)
Unadjusted	5.7 (5.6–5.9)	5.7 (5.6–5.8)	0.645	4.6 (4.5–4.7)	4.3 (4.2–4.3)	<0.001
Model 1	5.8 (5.6–5.9)	5.7 (5.6–5.8)	0.175	4.5 (4.5–4.6)	4.3 (4.2–4.4)	<0.001
Model 2	5.8 (5.6–5.9)	5.7 (5.6–5.8)	0.159	4.5 (4.4–4.6)	4.3 (4.3–4.4)	0.006
Hyperuricemia (%)
Unadjusted	16.9	14.2	0.233	9.9	4.4	<0.001
Model 1	18.0 (14.0–22.1)	13.8 (11.5–16.2)	0.077	9.1 (6.8–11.4)	4.6 (3.4–5.9)	0.001
Model 2	17.6 (13.5–21.6)	14.0 (11.7–16.2)	0.136	7.9 (5.7–10.1)	5.0 (3.8–6.2)	0.025

Data are expressed as mean ± SEM or %, except FVC% predicted, age, serum uric acid, and hyperuricemia. FVC% predicted and age are expressed as median (minimum-maximum). Serum uric acid, and hyperuricemia are expressed as mean (95% CI). FVC, forced vital capacity.

Model 1: adjusted for age Model 2: adjusted for all variables

In women, the median FVC% predicted of the Q1 and Q2–4 groups were 79.4 and 95.8, respectively. The Q2–4 group was younger, had a higher education level and household income, and was more likely to engage in aerobic exercise compared to the Q1 group. The rates of frequent alcohol consumption, obesity, hypertension, diabetes, and renal impairment were all higher in the Q1 group. Serum uric acid levels (4.6 mg/dL vs. 4.3 mg/dL) and the prevalence of hyperuricemia (9.9% vs. 4.4%) were higher in the Q1 group compared to the Q2–4 group. These differences remained statistically significant after adjusting for all variables (model 1 and 2).

Clinical characteristics according to gender and FEV1% predicted

Next, we compared clinical characteristics between participants in the lowest quartile of FEV1% predicted (Q1 group) and those in the remaining quartiles (Q2–4 group) (Table 3).

Table 3

Clinical characteristics according to gender and FEV1% predicted.

	Men (n = 1,.266)			Women (n = 1,635)
	FEV1% predicted Q1 (n = 315)	FEV1% predicted Q2-4 (n = 951)	p	FEV1% predicted Q1 (n = 411)	FEV1% predicted Q2-4 (n = 1,224)	p
FEV1% predicted	74.9 (28.1–81.7)	93.5 (81.8–129.8)	<0.001	78.9 (47.0–84.7)	96.9 (84.7–158.0)	<0.001
Age (years)	62 (40–80)	55 (40–80)	<0.001	57 (40–80)	56 (40–80)	0.044
Education (%)			<0.001			0.626
Elementary school graduated	27.9	17.8		33.1	31.9
Junior high school graduated	16.8	10.5		12.4	14.5
Senior high school graduated	27.0	31.9		32.8	30.7
College graduated	28.3	39.9		21.7	22.8
Household Income (x10,000 KRW)	375.8 ± 18.1	438.7 ± 10.0	0.002	386.2 ± 15.3	394.3 ± 9.2	0.651
Smoking (%)			0.103			0.734
Non- smoker	15.2	20.4		93.2	92.0
Ex-smoker	49.8	48.7		2.7	3.1
Current smoker	34.9	30.9		4.1	4.9
Alcohol consumption (%)			<0.001			0.019
None	24.4	18.2		45.7	38.1
1-3/week	53.7	68.9		51.8	59.7
≥ 4/week	21.9	12.9		2.4	2.2
Aerobic exercise (%)	42.2	46.9	0.152	41.1	40.2	0.772
Obesity (%)	41.0	41.5	0.895	36.0	37.0	0.723
Hypertension (%)	51.1	41.1	0.002	37.7	34.6	0.283
Diabetes (%)	24.8	15.0	<0.001	14.8	12.0	0.146
Renal impairment (%)	5.4	4.0	0.298	4.6	3.1	0.162
Serum uric acid (mg/dL)
Unadjusted	5.7 (5.6–5.9)	5.7 (5.6–5.8)	0.508	4.4 (4.3–4.5)	4.3 (4.3–4.4)	0.095
Model 1	5.8 (5.6–5.9)	5.7 (5.6–5.8)	0.212	4.4 (4.3–4.5)	4.3 (4.3–4.4)	0.158
Model 2	5.8 (5.7–6.0)	5.7 (5.6–5.8)	0.105	4.4 (4.3–4.5)	4.3 (4.3–4.4)	0.226
Hyperuricemia (%)
Unadjusted	18.4	13.7	0.040	8.3	4.9	0.011
Model 1	19.1 (15.2–23.1)	13.4 (11.2–15.7)	0.015	8.1 (5.8–10.3)	5.0 (3.7–6.3)	0.020
Model 2	19.2 (15.3–23.2)	13.4 (11.2–15.7)	0.014	7.8 (5.6–10.0)	5.0 (3.7–6.3)	0.034

Data are expressed as mean ± SEM or %, except FEV1% predicted, age, serum uric acid, and hyperuricemia. FEV1% predicted and age are expressed as median (minimum-maximum). Serum uric acid, and hyperuricemia are expressed as mean (95% CI). FEV1, forced expiratory volume in 1 second.

Model 1: adjusted for age Model 2: adjusted for age, education, household income, smoking, alcohol consumption, hypertension, and diabetes

In men, the median FEV1% predicted of the Q1 and Q2–4 groups were 74.9 and 93.5, respectively. The Q2–4 group was younger age, and had higher education household income levels compared to the Q1 group. The rates of frequent alcohol consumption, hypertension, and diabetes were all higher in the Q1 group. Serum uric acid levels were not statistically different between groups (5.7 mg/dL vs. 5.7 mg/dL for the Q1 and Q2–4 groups, respectively). However, the prevalence of hyperuricemia was significantly higher in the Q1 group, even after adjusting for age, education, household income, smoking, alcohol consumption, hypertension, and diabetes (model 1 and 2).

In women, the median FEV1% predicted of the Q1 and Q2–4 groups were 78.9 and 96.9, respectively. The Q2–4 group was younger and consumed less alcohol compared to the Q1 group. Serum uric acid levels were not significantly different between the groups (4.4 mg/dL vs. 4.3 mg/dL for the Q1 and Q2–4 groups, respectively). The prevalence of hyperuricemia (8.3% vs. 4.9%) was significantly higher in the Q1 group, even after adjusting for above variables (model 1 and 2).

Correlations of serum uric acid with FVC% predicted, FEV1% predicted, and FEV1/FVC

No association was observed between serum uric acid levels and spirometric pulmonary function in men (Table 4). In contrast, unadjusted or adjusted FVC% predicted was negatively correlated with serum uric acid levels in women (Spearman correlation coefficient -0.145, p < 0.001). Unadjusted or adjusted FEV1% predicted was also negatively associated with serum uric acid levels (Spearman correlation coefficient -0.058, p = 0.019) in women, irrespective of age. There is no significant association between serum uric acid levels and FEV1/FVC (Table 4).

Table 4

Correlation between serum uric acid and FVC% predicted or FEV1% predicted or FEV1/FVC.

Men
	r	p
FVC% predicted	-0.028	0.323
FVC% predicted model 1	-0.053	0.059
FVC% predicted model 2
Total	-0.059	0.037
Age < 56 years old	-0.059	0.153
Age ≥ 56 years old	-0.058	0.140
FEV1% predicted	-0.022	0.424
FEV1% predicted model 1	-0.032	0.259
FEV1% predicted model 2
Total	-0.053	0.063
Age < 56 years old	-0.036	0.387
Age ≥ 56 years old	-0.067	0.087
FEV1/FVC	-0.029	0.306
FEV1/FVC model 1	-0.019	0.510
FEV1/FVC model 2
Total	-0.009	0.740
Age < 56 years old	-0.034	0.407
Age ≥ 56 years old	-0.030	0.445
Women
	r	p
FVC% predicted	-0.145	<0.001
FVC% predicted model 1	-0.122	<0.001
FVC% predicted model 2
Total	-0.095	<0.001
Age < 56 years old	-0.100	0.006
Age ≥ 56 years old	-0.087	0.011
FEV1% predicted	-0.058	0.019
FEV1% predicted model 1	-0.064	0.010
FEV1% predicted model 2
Total	-0.092	<0.001
Age < 56 years old	-0.078	0.032
Age ≥ 56 years old	-0.099	0.004
FEV1/FVC	-0.018	0.476
FEV1/FVC model 1	-0.029	0.240
FEV1/FVC model 2
Total	-0.015	0.556
Age < 56 years old	-0.026	0.481
Age ≥ 56 years old	-0.047	0.171

r, Spearman correlation coefficient. FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second.

Model 1: adjusted for age

Model 2: adjusted for age, education, household income (KRW), smoking (non-, ex-, and current smoker), alcohol consumption (none, 1-3/week, and ≥ 4/week), aerobic exercise, BMI (kg/m2), systolic BP (mmHg), fasting plasma glucose (mg/dL), and eGFR (mL/min/1.73 m2)

Multivariable logistic regression analysis of the lowest quartile of FVC% predicted

Logistic regression analyses were performed to identify factors independently associated with the lowest quartile of FVC% predicted. Age, education level, household income, alcohol consumption, smoking, aerobic exercise, obesity, hypertension, diabetes, renal impairment, and hyperuricemia were included as potential confounders (Table 5).

Table 5

Multivariable logistic regression analyses for lowest quartile of FVC% predicted.

	Men		Women
	Odds ratio (95% CI)	p	Odds ratio (95% CI)	p
Age (years)  19–44	Reference		Reference
45–64	1.98 (1.19–3.32)	0.009	1.29 (0.86–1.96)	0.228
65-	4.71 (2.67–8.33)	<0.001	2.70 (1.63–4.46)	<0.001
Education		0.907		0.704
Elementary school graduated	Reference		Reference
Junior high school graduated	1.04 (0.65–1.57)	0.857	1.10 (0.75–1.60)	0.640
Senior high school graduated	0.98 (0.66–1.45)	0.908	1.19 (0.85–1.68)	0.314
College graduated	1.03 (0.67–1.57)	0.904	1.07 (0.71–1.61)	0.744
Household income		0.061
Quartile 1 (the lowest quartile)	Reference		Reference
Quartile 2	0.71 (0.47–1.06)	0.093	1.31 (0.93–1.86)	0.128
Quartile 3	0.62 (0.40–0.95)	0.028	1.48 (1.02–2.14)	0.041
Quartile 4 (the highest quartile)	0.63 (0.40–0.98)	0.042	1.14 (0.76–1.69)	0.526
Alcohol consumption		0.392
None	Reference		Reference
1-3/week	0.81 (0.57–1.15)	0.238	0.87 (0.68–1.12)	0.283
≥ 4/week	0.74 (0.47–1.18)	0.201	1.05 (0.48–2.27)	0.910
Smoking
Non-smoker	Reference		Reference
Ex-smoker	1.03 (0.71–1.50)	0.879	1.09 (0.55–2.14)	0.805
Current smoker	1.08 (0.71–1.64)	0.722	0.77 (0.42–1.40)	0.385
Aerobic exercise	1.03 (0.78–1.36)	0.843	0.82 (0.64–1.05)	0.124
Obesity	1.94 (1.46–2.57)	<0.001	1.74 (1.36–2.22)	<0.001
Hypertension	1.42 (1.07–1.89)	0.016	1.29 (0.99–1.68)	0.065
Diabetes	1.75 (1.25–2.45)	0.001	1.58 (1.14–2.21)	0.007
Renal impairment	0.84 (0.45–1.58)	0.589	0.95 (0.52–1.75)	0.867
Hyperuricemia	1.32 (0.90–1.93)	0.151	1.71 (1.06–2.75)	0.027

FVC, forced vital capacity.

Older age, obesity, and diabetes were associated with the lowest quartile of FVC% predicted for both men and women. In only women, hyperuricemia (OR 1.71, 95% CI 1.06–2.75, p = 0.027) was associated with lowest quartile of FVC% predicted.

Multivariable logistic regression analyses of lowest quartile of FEV1% predicted

Logistic regression analyses were performed to identify factors independently associated with the lowest quartile of FEV1% predicted. Age, education level, household income, alcohol consumption, smoking, aerobic exercise, obesity, hypertension, diabetes, renal impairment, and hyperuricemia were included as potential confounders (Table 6). In men, age, current smoker, diabetes, and hyperuricemia (OR 1.54, 95% CI 1.07–2.22, p = 0.021) were associated with the lowest quartile of FEV1% predicted. In women, age and hyperuricemia (OR 1.70, 95% CI 1.06–2.74, p = 0.028) were associated with the lowest quartile of FEV1% predicted.

Table 6

Multivariable logistic regression analyses for lowest quartile of FEV1% predicted.

	Men		Women
	Odds ratio (95% CI)	p	Odds ratio (95% CI)	p
Age (years)  19–44	Reference		Reference
45–64	1.32 (0.83–2.09)	0.240	1.50 (1.02–2.21)	0.039
65-	2.26 (1.33–3.83)	0.002	1.74 (1.07–2.83)	0.025
Education
Elementary school graduated	Reference		Reference
Junior high school graduated	1.10 (0.71–1.71)	0.670	0.90 (0.61–1.32)	0.584
Senior high school graduated	0.68 (0.46–0.99)	0.046	1.22 (0.87–1.70)	0.249
College graduated	0.70 (0.46–1.05)	0.087	1.15 (0.78–1.69)	0.486
Household income
Quartile 1 (the lowest quartile)	Reference		Reference
Quartile 2	0.72 (0.48–1.07)	0.099	1.09 (0.76–1.54)	0.650
Quartile 3	0.70 (0.46–1.07)	0.100	1.23 (0.85–1.78)	0.270
Quartile 4 (the highest quartile)	0.79 (0.51–1.23)	0.298	1.14 (0.78–1.67)	0.510
Alcohol consumption
None	Reference		Reference
1-3/week	0.71 (0.50–1.00)	0.051	0.74 (0.58–0.94)	0.014
≥ 4/week	1.23 (0.80–1.89)	0.353	0.98 (0.46–2.08)	0.958
Smoking
Non-smoker	Reference		Reference
Ex-smoker	1.35 (0.92–1.98)	0.127	0.96 (0.48–1.92)	0.909
Current smoker	1.80 (1.19–2.72)	0.006	0.90 (0.51–1.60)	0.728
Aerobic exercise	0.95 (0.72–1.24)	0.680	1.04 (0.82–1.31)	0.742
Obesity	1.00 (0.76–1.32)	0.984	0.89 (0.69–1.14)	0.346
Hypertension	1.14 (0.86–1.51)	0.356	1.02 (0.78–1.33)	0.886
Diabetes	1.50 (1.07–2.10)	0.018	1.22 (0.87–1.72)	0.250
Renal impairment	0.67 (0.351.28)	0.222	1.04 (0.55–1.96)	0.907
Hyperuricemia	1.54 (1.07–2.22)	0.021	1.70 (1.06–2.74)	0.028

FEV1, forced expiratory volume in 1 second.

Odds ratio of hyperuricemia for the lowest quartile of FCV% predicted and the lowest quartile of FEV1% predicted according to age ≥ 56 years old and < 56 years old

Additionally, we showed odds ratios of hyperuricemia for the lowest quartile of FCV% predicted and the lowest quartile of FEV1% predicted according to age ≥ 56 years old and < 56 years old (median age) in Table 7. In women, age ≥ 56 years old with hyperuricemia was associated with lowest quartile of FVC% predicted (OR 1.85, 95% CI 1.04–3.28, p = 0.037) and FEV1% predicted (OR 1.99, 95% CI 1.11–3.75, p = 0.021), respectively. In men, age ≥ 56 years old with hyperuricemia was associated with lowest quartile of FEV1% predicted (OR 1.75, 95% CI 1.05–2.94, p = 0.033), not FCV% predicted. Neither man nor woman younger than 56 years old was associated with lowest quartile of FCV% predicted or FEV1% predicted.

Table 7

Odds ratio of hyperuricemia for the lowest quartile of FCV% predicted and FEV1% predicted according to age ≥ 56 years old and < 56 years old.

	Men (n = 1266)		Women (n = 1635)
	Odds ratio (95% CI)	p	Odds ratio (95% CI)	p
for lowest quartile of FCV% predicted
< 56 years old	0.36 (0.77–2.43) (n = 606)	0.291	1.54 (0.63–3.76) (n = 761)	0.347
≥ 56 years old	1.32 (0.79–2.22) (n = 660)	0.293	1.85 (1.04–3.28) (n = 874)	0.037
for lowest quartile of FEV1% predicted
< 56 years old	1.36 (0.79–2.33)	0.269	1.20 (0.49–2.96)	0.692
≥ 56 years old	1.75 (1.05–2.94)	0.033	1.99 (1.11–3.57)	0.021

Covariates: age, education level, household income, smoking status, alcohol consumption, aerobic exercise, obesity, hypertension, diabetes, and renal impairment.

FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second.

Discussion

Using data from the 2016 KNHANES, the prevalence of hyperuricemia in the representative sample of Korean adults included in this study was 9.7% (14.8% in men, 5.7% in women). We found that hyperuricemia was inversely associated with spirometric pulmonary function in Korean population. This reverse correlation between hyperuricemia and pulmonary function was more pronounced in women and older age (≥56 years old).

Previous epidemiological studies have investigated the association between serum uric acid and pulmonary function in the general population, however, the results have been inconclusive. A recent Japanese study showed similar results to those presented here, including significant inverse correlations of serum uric acid levels with spirometric parameters in female subjects who participated in an annual health check [15].

In contrast, a second study found a significant positive correlation between pulmonary function and serum uric acid levels, in both males and females recruited from Kangbuk Samsung Hospital Health Screening Center, Seoul, Korea [16]. While that study was similar to the analysis presented herein, in that it used a cross-sectional design, there are some significant differences between the two studies. First, the subjects who participated in the Kangbuk Samsung Health Study were significantly younger than those included in our study (mean age were 40 and 56 years, respectively). Because age might be a moderator for the reverse association between pulmonary function and serum uric acid, we performed sub-analysis about odds ratio of hyperuricemia for the lowest quartile of FCV% predicted and the lowest quartile of FEV1% predicted according to age ≥ 56 years old and < 56 years old. Actually, correlation between lowest quartile of FCV% and/or FEV1% predicted and hyperuricemia was apparent only in women ≥ 56 years old, not in women <56 years old. There was also significant association between hyperuricemia and lowest quartile of FEV1% predicted in men ≥ 56 years old, not in men <56 years old. Furthermore, the Kangbuk Samsung Health Study was performed in Seoul, where the higher average socioeconomic status of that region could have constituted a sampling bias. Using a definition of hyperuricemia of > 7.0 mg/dL uric acid in men and > 6.0 mg/dL in women, the prevalence rates of hyperuricemia in the Kangbuk Samsung Health study (25.5% in men and 8.5% in women) were much higher than those in both our study (14.8% in men and 5.7% in women) and a previous study of the Korean population (14.3% in men and 2.2% in women) [25]. The analytical methods were also different between the studies. We compared serum uric acid level and the prevalence of hyperuricemia between participants in the lowest quartile of pulmonary function and those in the other quartiles, while the Kangbuk Samsung Health Study compared pulmonary function between hyperuricemia and normouricemia groups. In this study, we included age, education level, household income, smoking status, alcohol consumption, aerobic exercise, obesity, hypertension, and diabetes as covariates; only some of these variables overlapped with those adjusted for in the Kangbuk Samsung Health Study, which included age, BMI, smoking status, alcohol consumption, liver and renal function, lipid profiles, C-reactive protein level, mean blood pressure, and glycosylated hemoglobin (HbA1c) level. Therefore, we believe that differences in study populations, potential confounders including age and sex, and analytical methods may be responsible for the discrepancies in results seen among otherwise similar cross-sectional studies.

In this study, there was sex-difference in the association between serum uric acid and low pulmonary function as well as age. No association was observed between serum uric acid levels and spirometric pulmonary function in men. In contrast, unadjusted or adjusted FVC% predicted and FEV1% predicted were negatively correlated with serum uric acid levels in women, irrespective of age. Regarding to hyperuricemia, in women, hyperuricemia was associated with both lowest quartile of FVC% predicted and FEV1 predicted. However, in men, hyperuricemia was associated with only the lowest quartile of FEV1% predicted, not FVC% predicted. These results suggested that reverse correlation between serum uric acid and pulmonary function was more pronounced in women. However, the mechanism of the age or sex-specific difference between them is still unclear. Previously, although several studies showed that sex differences in the association between hyperuricemia and cardiometabolic risk, there has been no studies about sex differences in the relation between hyperuricemia and lung function [26-29].

One possibility is that there is an inherent difference in uric acid metabolism between the sexes, including a higher fractional urate excretion and lower serum urate concentration in women compared to men [26, 30]. We speculated that estrogen change might play a role in mediation of the association between serum uric acid and lung function. Another possibility is that other cofactors influencing pulmonary function not considered in this study may also be affected by gender.

Oxidative stress is thought to play a mediating role in the association between uric acid level and impaired lung function. The final reaction necessary for uric acid production involves the conversion of xanthine to uric acid, which is catalyzed by the enzyme xanthine oxidoreductase (XOR) [31]. During this process, XOR uses molecular oxygen as an electron acceptor, producing superoxide anions and other ROS [32]. The lungs could be a major target organ for exogenous oxidants and endogenous ROS generated by inflammatory cells [33], with evidence of higher uric acid concentrations within the epithelial lining fluid of the airways [33, 34]. ROS and cellular injury have also been implicated in a variety of pulmonary diseases, including asthma, COPD, acute respiratory distress syndrome (ARDS), and cystic fibrosis [33].

However, because high level of uric acid could be viewed as a ‘double-edge sword’; it may have beneficial antioxidant effects, paradoxically, any potential association between hyperuricemia and lung function in a cross-sectional study must be interpreted carefully, putting confounding factors aside [35-37].

Other mechanisms, such as hypoxia and systemic inflammation, may also modulate the association between hyperuricemia and poor pulmonary function [38]. However, this study excluded subjects with overt clinical disease, such as asthma, lung cancer, or heart failure diagnosed by a doctor.

The major strength of this study was the inclusion of a large, representative sample of adult Koreans. To the best of our knowledge, this is the large scale study to demonstrate an inverse association between hyperuricemia and pulmonary function in general population using nationally representative data. However, our study also had some limitations. First, we did not take into account the use of uric acid-lowering drugs, which reduce serum uric acid levels. Second, we used a single measurement of uric acid. Third, although we adjusted for many confounding factors, there may be other potentially important variables that were not accounted for, similar to other cross-sectional studies. Finally, we could not establish a causal relationship between hyperuricemia and poor pulmonary function due to the inherent limitations of the retrospective design.

Lastly, we could not identify a definitive mechanism underlying the age- and sex-specific association between pulmonary function and hyperuricemia.

In conclusion, hyperuricemia was associated with lowest quartile of FEV1% or FVC% predicted using data taken from the 2016 KNHANES. This correlation between hyperuricemia and low pulmonary function was more pronounced in women and older age. Further epidemiologic studies will be required to confirm this reverse association between serum uric acid and pulmonary function.

7 May 2020

PONE-D-19-34352

Association between Uric Acid and Pulmonary Function in Korean Adults: The 2016 Korea National Health and Nutrition Examination Survey

PLOS ONE

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Reviewer #1: Dear Editor:

The authors made a cross-sectional study for an associaiton of serum uric acid and FVC predicted and FEV1 predicted in the general population in Korea. They found a sex difference in the association where hyperuricemia was associated poor FVC and FEV1 predicted in women only.

In general the paper is well writtern; however some major issues should be addressed.

1. In title, the authors used "pulmonary function" to represent FVC and FEV1 predicted seemed too broad covering. In addition, it is suggested to add serum preceding uric acid.

2. In the background of the abstract, it is no correct that there has been some papers regarding the assocaition between SUA and FVC and FEV1 predicted.

3. In methods: pulmonary function test, the authors should mention what the prevalence of COPD and compare the values of SUA and prevalence of hyperuricemia in those with FEV1/FVC <70% to those those >=70%

4. In statistical analyses, smoking status should be added in the logistic regression analysis in men and women. In addtion the authors did not describe the method for table 4. I also suggest to take a look on FEV1/FVC using the similar analyses.

5. The authors cited the ref 19 using a Korean cohort and suggested that age might be a moderator for the association. The authors should make a subcohort study for those <=56 years (mean age) and those > 56 years to compre the result of ref 19.

6. There were no discussions for the sex difference in the association between SUA and FVC and FEV1 predicted. Some papers have done for the sex difference for the SUA and metabolic diseases (e.g. Lin YK, et al. Sex-specific association of hyperuricemia with cardiometabolic abnormalities in a military cohort: The CHIEF study. Medicine (Baltimore). 2020 Mar;99(12):e19535.; and Lin JW, et al. Sex-Specific Association Between Serum Uric Acid and Elevated Alanine Aminotransferase in a Military Cohort: The CHIEF Study. Endocr Metab Immune Disord Drug Targets. 2019;19(3):333-340).

Reviewer #2: This manuscript aims to investigate the association between uric acid levels and pulmonary function in general Korean population who participated in the 2016 Korea National Health and Nutrition Examination Survey (KNHANES). The cross-sectional and nationally representative survey showed that hyperuricemia was associated with low pulmonary function in Korean women.

However, some questions need to be considered.

1. Overall, the design of this study does not allow determination of causality. Please be sure to eliminate any implication of causality in the manuscript.

2. Abstract: It is better to limit the number of words up to 300 in the abstract.

(1) The methods were too simple; the study outcomes should be described in the method section.

(2) The full name of the abbreviation should be presented for the first time to show in the manuscript. For example, the full name of FVC% predicted and FEV1% predicted.

(2) Result: The content of the result should be more brief and clear.

3. Introduction:

(1) Please provide the references of the previous studies about the association between serum uric acid levels and pulmonary function.

4. Method:

(1) Please add the reference of “obesity”.

(2) What sphygmomanometer was used should be described.

(3) Whether the final participants included participants with pulmonary disease (such as COPD, etc) should be described in detail.

(4) Please provide the flow chart of study participants.

(5) Why using the GFR rather than eGFR?

(6) Why not include smoking status in the logistic regression model? And why the analyses for FCV% or FEV1% was included different covariates in the model? How were the covariates of the adjusted model selected?

(7) Did other covariates (such as biochemical indicator) collected in the present study?

5. Result:

(1) Clinical characteristics of the study population: Paragraph 2, “compared to” may be better than “relative to”.

(2) The detail value of odds ratio and its 95% confidence interval of “1.68-fold” should be presented.

(3) In the Table 4, why not provide model 1, 2, 3 in the men?

(4) Univariate or multivariate logistic regression was used in the Table 5 and Table 6?

(5) The association between serum uric acid and lowest quartile of FVC% predicted or lowest quartile of FEV1% predicted should be analyzed.

(6) How about the stratified analyses of the association between uric acid and the FVC% predicted and FEV1% predicted?

6. Discussion:

(1) Paragraph 3: “mean age is” might better than “mean age =”.

(2) Paragraph 7: The description, “the first study” is inappropriate.

7. Overall, the quality of date analysis, and the paper writing should be further improved.

Reviewer #3: The authors proposed the association between hyperuricemia and low pulmonary function in women not in men. The idea is interesting. However, many detailed information was lacking, for example, about 40% people included in the final analysis, making it difficult to judge the validity of the study findings. My comments are:

1. In the current study, authors defined smoking category as non-smoker, ex-smoker and current smoker. Have authors considered to categorize as active smoker (defined as participants who reported smoking ≥100 cigarettes in their lifetime and who currently smoked) and Passive smokers (defined as those who did not smoke but had been exposed to tobacco smoke either in the workplace or at home during the last week)? If it is possible, it is strongly recommended to change current definition to refine their analysis. Authors also need to explain their definition of “non-smoker” and “ex-smoker” in current study. The “non-smokers” can be defined as participants who smoked <100 cigarettes during their lifetime, had not smoked currently, and had not been exposed to second hand tobacco smoke. The “ex-smokers” can be defined as participants who smoked >100 cigarettes during their lifetime, had not smoked currently, and had not been exposed to second hand tobacco smoke”. There are many references available that authors can refer it to their finding. Considering that smoking may have a long-term effect on SUA/hyperuricemia, the finding may be biased from low smoking rate in women.

2. Since gender is a strong factor for SUA/hyperuricemia, the authors need to explain their finding only in women. By doing so, the readers could get much clear picture of distribution of potential confounders.

3. By adjusting eGFR in model 2, the association became stronger. Authors need to explain this phenomenon with plausible mechanism in discussion.

4. Both alcohol consumption and BMI were strong risk factors for SUA/hyperuricemia. In the current analysis, residual confounding by these two factors may be one potential explanation for the current findings. Authors also need to address these issues with further analysis.

5. Authors need to address the method of multiple imputation in current analysis and how many percentages of covariates were imputed. If not, authors need to address how to deal with missing values.

**********

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Reviewer #1: Yes: Gen-Min Lin

Reviewer #2: No

Reviewer #3: No

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30 Jul 2020

Reviewer #1: Dear Editor:

The authors made a cross-sectional study for an associaiton of serum uric acid and FVC predicted and FEV1 predicted in the general population in Korea. They found a sex difference in the association where hyperuricemia was associated poor FVC and FEV1 predicted in women only.

In general the paper is well writtern; however some major issues should be addressed.

1. In title, the authors used "pulmonary function" to represent FVC and FEV1 predicted seemed too broad covering. In addition, it is suggested to add serum preceding uric acid.

Thank you for your comments.

We changed the title to “Association between Serum Uric Acid and Spirometric Pulmonary Function in Korean Adults: The 2016 Korea National Health and Nutrition Examination Survey’.

2. In the background of the abstract, it is no correct that there has been some papers regarding the assocaition between SUA and FVC and FEV1 predicted.

We modified the background of the abstract, as follows

A limited number of epidemiological studies have investigated the association between serum uric acid levels and pulmonary function in the general population, however, the results have been inconclusive.

3. In methods: pulmonary function test, the authors should mention what the prevalence of COPD and compare the values of SUA and prevalence of hyperuricemia in those with FEV1/FVC <70% to those those >=70%

Spirometric values

Normal PFT Restrictive pattern Obstructive pattern

men 65.3% 12.6% 22.0%

women 81.9% 11.7% 6.4%

total 74.7% 12.1% 13.2%

Serum uric acid and the prevalence of hyperuricemia according to the FEV1/FVC 0.7

FEV1/FVC ≥ 0.7 FEV1/FVC < 0.7 p

Hyperuricemia(%) men 14.2 17.2 0.216

women 5.6 8.6 0.193

Serum uric acid (mg/dl) men 5.7 ± 0.1 5.8 ± 0.1 0.196

women 4.4 ± 0.1 4.4 ± 0.1 0.817

Mean ± SEM

Because all spirometry values were prebronchodilator results, we could not make an accurate diagnosis of COPD, directly. However, as you suggested, we described the prevalence of obstructive pattern and the values of serum uric acid and prevalence of hyperuricemia in those with FEV1/FVC ≥ 0.7 to those with < 0.7, in the method and result section, as follows,

Method section> Pulmonary function test

Obstructive pattern was defined as FEV1/FVC <0.70, and restrictive pattern was defined as FEV1/FVC≥ 0.7 and FVC <80%, While normal lung function was defined as FEV1/FVC≥0.70 and FVC ≥80% [22, 23].

Result section> serum uric acid and the prevalence of hyperuricemia according to the FEV1/FVC

Overall, 74.7% of participants showed normal spirometric values. 12.1 % and 13.2 % of subjects have restrictive pattern and obstructive pattern in pulmonary function test, respectively. We compared the values of serum uric acid and prevalence of hyperuricemia in those with FEV1/FVC ≥ 0.7 to those with < 0.7. The prevalence of hyperuricemia in those with FEV1/FVC ≥ 0.7 was 14.2% in men and 5.6% in women. The prevalence of hyperuricemia in those with FEV1/FVC <0.7 was 17.2% in men and 8.6% in women. There is no significant difference in the prevalence of hyperuricemia according to the obstructive pattern in pulmonary function test, using FEV1/FVC 0.7 criteria. The mean serum uric acid level in those with FEV1/FVC ≥ 0.7 was 5.7 ± 0.1 mg/dL in men and 4.4 ± 0.1 mg/dL in women. The mean serum uric acid level in those with FEV1/FVC < 0.7 was 5.8 ± 0.1 mg/dL in men and 4.4 ± 0.1 mg/dL in women. There is also no significant difference in the mean serum uric acid levels according to the obstructive pattern in pulmonary function test.

4. In statistical analyses, smoking status should be added in the logistic regression analysis in men and women. In addtion the authors did not describe the method for table 4. I also suggest to take a look on FEV1/FVC using the similar analyses.

Thank you for your comments. As you pointed out, we added smoking status in logistic regression analysis and modified Table 5 and 6. Additionally, as your suggestion, we examined the correlation between serum uric acid and FEV1/FVC and modified Table 4.

5. The authors cited the ref 19 using a Korean cohort and suggested that age might be a moderator for the association. The authors should make a subcohort study for those <=56 years (mean age) and those > 56 years to compre the result of ref 19.

We performed these sub-cohort study according to age and modified Table 4 and added Table 7.

We also added this content in Result section, as follows,

Result section>

“Correlations of serum uric acid with FVC% predicted, FEV1% predicted, and FEV1/FVC

No association was observed between serum uric acid levels and spirometric pulmonary function in men (Table 4). In contrast, unadjusted or adjusted FVC% predicted was negatively correlated with serum uric acid levels in women (Spearman correlation coefficient -0.145, p < 0.001). Unadjusted or adjusted FEV1% predicted was also negatively associated with serum uric acid levels (Spearman correlation coefficient -0.058, p = 0.019) in women, irrespective of age. There is no significant association between serum uric acid levels and FEV1/FVC (Table 4).”

“Odds ratio of hyperuricemia for the lowest quartile of FCV% predicted and the lowest quartile of FEV1% predicted according to age ≥ 56 years old and < 56 years old.

Additionally, we showed odds ratios of hyperuricemia for the lowest quartile of FCV% predicted and the lowest quartile of FEV1% predicted according to age ≥ 56 years old and < 56 years old (median age) in Table 7. In women, age ≥ 56 years old with hyperuricemia was associated with lowest quartile of FVC% predicted (OR 1.85, 95% CI 1.04-3.28, p = 0.037) and FEV1 % predicted (OR 1.99, 95% CI 1.11–3.75, p = 0.021), respectively. In men, age ≥ 56 years old with hyperuricemia was associated with lowest quartile of FEV1 % predicted (OR 1.75, 95% CI 1.05–2.94, p = 0.033), not FCV% predicted. Neither man nor woman younger than 56 years old was associated with lowest quartile of FCV% predicted or FEV1% predicted.”

We added this content in Discussion section, as follows,

“We found that hyperuricemia was inversely associated with spirometric pulmonary function in Korean population. This reverse correlation between hyperuricemia and pulmonary function was more pronounced in women and older age (≥56 years old).”

“Because age might be a moderator for the reverse association between pulmonary function and serum uric acid, we performed sub-analysis about odds ratio of hyperuricemia for the lowest quartile of FCV% predicted and the lowest quartile of FEV1% predicted according to age ≥ 56 years old and < 56 years old (median age). Actually, correlation between lowest quartile of FCV% and/or FEV1% predicted and hyperuricemia was apparent only in women ≥ 56 years old, not in women <56 years old. There was also significant association between hyperuricemia and lowest quartile of FEV1 % predicted in men ≥ 56 years old, not in men <56 years old.”

6. There were no discussions for the sex difference in the association between SUA and FVC and FEV1 predicted. Some papers have done for the sex difference for the SUA and metabolic diseases (e.g. Lin YK, et al. Sex-specific association of hyperuricemia with cardiometabolic abnormalities in a military cohort: The CHIEF study. Medicine (Baltimore). 2020 Mar;99(12):e19535.; and Lin JW, et al. Sex-Specific Association Between Serum Uric Acid and Elevated Alanine Aminotransferase in a Military Cohort: The CHIEF Study. Endocr Metab Immune Disord Drug Targets. 2019;19(3):333-340).

Thank you for your comments. We added discussion about the sex difference in the association between serum uric acid and low pulmonary function, as follows.

“In this study, there was sex-difference in the association between serum uric acid and low pulmonary function as well as age. No association was observed between serum uric acid levels and spirometric pulmonary function in men. In contrast, unadjusted or adjusted FVC% predicted and FEV1% predicted were negatively correlated with serum uric acid levels in women, irrespective of age. Regarding to hyperuricemia, in women, hyperuricemia was associated with both lowest quartile of FVC% predicted and FEV1 predicted. However, in men, hyperuricemia was associated with only the lowest quartile of FEV1% predicted, not FVC% predicted. These results suggested that reverse correlation between serum uric acid and pulmonary function was more pronounced in women.

One possibility is that there is an inherent difference in uric acid metabolism between the sexes, including a higher fractional urate excretion and lower serum urate concentration in women compared to men [24, 25]. Previously, although several studies showed that sex differences in the association between hyperuricemia and cardiometabolic risk, there has been no studies about sex differences in the relation between hyperuricemia and lung function [25-27]. Women may have greater xanthium oxidase activity and reactive oxygen species (ROS) production, resulting in severe vascular inflammation, arterial stiffness, and elevated blood pressure as compared with men. We speculated that estrogen change might play a role in mediation of the association between serum uric acid and lung function. Another possibility is that other cofactors influencing pulmonary function not considered in this study may also be affected by gender.

Reviewer #2: This manuscript aims to investigate the association between uric acid levels and pulmonary function in general Korean population who participated in the 2016 Korea National Health and Nutrition Examination Survey (KNHANES). The cross-sectional and nationally representative survey showed that hyperuricemia was associated with low pulmonary function in Korean women.”

However, some questions need to be considered.

1. Overall, the design of this study does not allow determination of causality. Please be sure to eliminate any implication of causality in the manuscript.

Thank you for your comments. We eliminated all the “-fold higher risk” sentences in multivariable logistic regression analyses.

2. Abstract: It is better to limit the number of words up to 300 in the abstract.

(1) The methods were too simple; the study outcomes should be described in the method section.

Thank you for your comments. We modified the methods section in Abstract, as follows,

“Among the 8,150 participants who participated in the 2016 Korea National Health and Nutrition Examination Survey, 2,901 participants were analyzed in this study. Subjects were divided into four groups according to forced vital capacity (FVC)% predicted or forced expiratory volume in 1 second (FEV1) % predicted quartiles. Participants in the lowest quartile of FVC % predicted and FEV1% predicted were compared to those in the remaining quartiles according to age, education level, household income, smoking status, alcohol consumption, aerobic exercise, obesity, hypertension, diabetes, renal impairment, serum uric acid, and hyperuricemia. Multivariable logistic regression analyses were used to calculate the odds ratio of hyperuricemia for participants in the lowest quartile of FVC% and FEV1 predicted, with above covariates.”

(2) The full name of the abbreviation should be presented for the first time to show in the manuscript.

For example, the full name of FVC% predicted and FEV1% predicted.

Thank you for your comments.

We added the full name of the abbreviation in abstract, as follows,

forced vital capacity (FVC)

forced expiratory volume in 1 second (FEV1)

odds ratio (OR)

(2) Result: The content of the result should be more brief and clear.

As you suggested, we modified the result section in abstract, as follows,

In women, hyperuricemia was associated with lowest quartile of FVC% predicted (OR 1.71, 95% CI 1.06–2.75, p = 0.027) and FEV1 predicted (OR 1.70, 95% CI 1.06–2.74, p = 0.028) respectively, serving as above confounding variables. In men, hyperuricemia (OR 1.54, 95% CI 1.07–2.22, p = 0.021) was associated with the lowest quartile of FEV1% predicted, not FVC% predicted.

According to median age, in women, age≥ 56 years old with hyperuricemia was associated with lowest quartile of FVC% predicted (OR 1.85, 95% CI 1.04-3.28, p = 0.037) and FEV1 % predicted (OR 1.99, 95% CI 1.11–3.75, p = 0.021), respectively. In men, age≥ 56 years old with hyperuricemia was associated with lowest quartile of FEV1 % predicted (OR 1.75, 95% CI 1.05–2.94, p = 0.033), not FCV% predicted.

3. Introduction:

(1) Please provide the references of the previous studies about the association between serum uric acid levels and pulmonary function.

Thank you for your comments.

We added references” A limited number of epidemiological studies have investigated the association between serum uric acid levels and pulmonary function in the general population, however, the results have been inconclusive [15,16].

4. Method:

(1) Please add the reference of “obesity”.

We added the reference “Obesity was defined as a body mass index (BMI) ≥25 kg/m2 according to the Asia-Pacific obesity classification.”

REFERENCE> Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157-63. Epub 2004/01/17. doi: 10.1016/s0140-6736(03)15268-3. PubMed PMID: 14726171.

(2) What sphygmomanometer was used should be described.

Systolic and diastolic blood pressure were measured by standard methods using a standard mercury sphygmomanometer (Baumanometer, WA Baum Co. Inc., Copiague, NY, USA).

(3) Whether the final participants included participants with pulmonary disease (such as COPD, etc) should be described in detail.

In the health interview about medical history, there was no survey item on the presence of “COPD”, but “asthma” on questionnaire.

A total of 282 subjects were excluded from the analysis due to pre-existing diseases, including liver cirrhosis (n = 13), renal failure (n = 9), lung cancer (n = 9), asthma (n = 94), heart failure (n = 117), and cerebrovascular accident (n = 60), for a final total of 3,059 subjects.

(4) Please provide the flow chart of study participants.

Thank you for your comment.

As you suggested, we added flow chart of study participants in Figure1.

(5) Why using the GFR rather than eGFR?

We used estimated GFR using CKD-EPI.

“The estimated glomerular filtration rate (eGFR) was calculated based on serum creatinine levels using the Chronic Kidney Disease Epidemiology Collaboration equation.”

(6) Why not include smoking status in the logistic regression model? And why the analyses for FCV% or FEV1% was included different covariates in the model? How were the covariates of the adjusted model selected?

Thank you for your comments.

We included all covariates including smoking status and modified table 5 and 6.

(7) Did other covariates (such as biochemical indicator) collected in the present study?

Unfortunately, other biochemical indicators were not collected in this study.

5. Result:

(1) Clinical characteristics of the study population: Paragraph 2, “compared to” may be better than “relative to”.

We changed “relative to” �” compared to” in all sentences.

(2) The detail value of odds ratio and its 95% confidence interval of “1.68-fold” should be presented.

AS you pointed out, we presented 95% CI and p-value.

(3) In the Table 4, why not provide model 1, 2, 3 in the men?

We added model 1 and 2 in the men in table 4.

(4) Univariate or multivariate logistic regression was used in the Table 5 and Table 6?

Table 5 and 6 showed multivariable logistic regression analyses. We modified the titles of the table 5 and 6.

(5) The association between serum uric acid and lowest quartile of FVC% predicted or lowest quartile of FEV1% predicted should be analyzed.

Regarding to FVC% predicted, only in women, serum uric acid level and the prevalence of hyperuricemia were higher in the Q1 group compared to the Q2–4 group.

Regarding to FEV1% predicted, both in men and women, serum uric acid levels were not significantly different between the Q1 and Q2–4 groups. However, the prevalence of hyperuricemia was higher in Q1 than Q2-4 groups, in both men and women.

We described the association between serum uric acid and lowest quartile of FVC% predicted and lowest quartile of FEV1% predicted in result section and presented Table 2 and 3.

(6) How about the stratified analyses of the association between uric acid and the FVC% predicted and FEV1% predicted?

Unadjusted analysis

men Quintile 1 Quintile 2 Quintile 3 hyperuricemia p

Uric acid (mg/dl) (-5.0) (5.1-5.9) (6.0-7.0) >7

FVC % predicted 89.6± 0.6 89.2 ± 0.7 90.7 ± 0.6 88.2± 0.8 0.114

FEV1 % predicted 88.9 ± 0.7 88.9 ± 0.7 90.1 ± 0.7 87.3± 0.9 0.143

women Quintile 1 Quintile 2 Quintile 3 hyperuricemia p

Uric acid (mg/dl) (-3.9) (4.0-4.6) (4.7-6.0) >6

FVC % predicted 93.3 ± 0.5 93.3 ± 0.5 90.9 ± 0.5 86.5 ± 1.3 <0.001

FEV1 % predicted 93.1 ± 0.5 93.8 ± 0.6 92.2 ± 0.5 90.1 ± 1.6 0.039

In men, there is no association between serum uric acid level and FVC% predicted or FEV1% predicted. However, in women, serum uric acid was inversely associated with FVC% predicted and FEV1% predicted.

Theses finding were similar to the results from the comparison of serum uric acid levels between Q1 and Q2-4 FVC% predicted or FEV1% predicted. To avoid redundancy and confusion, we did not add this content additionally.

6. Discussion:

(1) Paragraph 3: “mean age is” might better than “mean age =”.

We changed to “mean age is”

(2) Paragraph 7: The description, “the first study” is inappropriate.

We changed “first study” to “large scale study”

7. Overall, the quality of date analysis, and the paper writing should be further improved.

As your suggestion, we modified the data analyses and revised the manuscript on the whole in light of the reviewer’s comments.

Reviewer #3: The authors proposed the association between hyperuricemia and low pulmonary function in women not in men. The idea is interesting. However, many detailed information was lacking, for example, about 40% people included in the final analysis, making it difficult to judge the validity of the study findings. My comments are:

1. In the current study, authors defined smoking category as non-smoker, ex-smoker and current smoker. Have authors considered to categorize as active smoker (defined as participants who reported smoking ≥100 cigarettes in their lifetime and who currently smoked) and Passive smokers (defined as those who did not smoke but had been exposed to tobacco smoke either in the workplace or at home during the last week)? If it is possible, it is strongly recommended to change current definition to refine their analysis. Authors also need to explain their definition of “non-smoker” and “ex-smoker” in current study. The “non-smokers” can be defined as participants who smoked <100 cigarettes during their lifetime, had not smoked currently, and had not been exposed to second hand tobacco smoke. The “ex-smokers” can be defined as participants who smoked >100 cigarettes during their lifetime, had not smoked currently, and had not been exposed to second hand tobacco smoke”. There are many references available that authors can refer it to their finding. Considering that smoking may have a long-term effect on SUA/hyperuricemia, the finding may be biased from low smoking rate in women.

We could not change the definition of smoking status, since we used data from KNHNES, which defined smoking category as follows,

Current smoker: smoking ≥100 cigarettes in their lifetime and who currently smoked

Ex-smoker: smoking ≥100 cigarettes during their lifetime, had not smoked currently

Non-smoker: smoking <100 cigarettes during their lifetime, had not smoked currently

2. Since gender is a strong factor for SUA/hyperuricemia, the authors need to explain their finding only in women. By doing so, the readers could get much clear picture of distribution of potential confounders.

Thank you for your comments. We added discussion about the sex difference in the association between serum uric acid and low pulmonary function, as follows.

“In this study, there was sex-difference in the association between serum uric acid and low pulmonary function as well as age. No association was observed between serum uric acid levels and spirometric pulmonary function in men. In contrast, unadjusted or adjusted FVC% predicted and FEV1% predicted were negatively correlated with serum uric acid levels in women, irrespective of age. Regarding to hyperuricemia, in women, hyperuricemia was associated with both lowest quartile of FVC% predicted and FEV1 predicted. However, in men, hyperuricemia was associated with only the lowest quartile of FEV1% predicted, not FVC% predicted. These results suggested that reverse correlation between serum uric acid and pulmonary function was more pronounced in women. However, the mechanism of the age or sex-specific difference between them is still unclear. Previously, although several studies showed that sex differences in the association between hyperuricemia and cardiometabolic risk, there has been no studies about sex differences in the relation between hyperuricemia and lung function [26-29].

One possibility is that there is an inherent difference in uric acid metabolism between the sexes, including a higher fractional urate excretion and lower serum urate concentration in women compared to men [26, 30]. Women may have greater xanthium oxidase activity and reactive oxygen species (ROS) production, resulting in severe vascular inflammation, arterial stiffness, and elevated blood pressure as compared with men. We speculated that estrogen change might play a role in mediation of the association between serum uric acid and lung function. Another possibility is that other cofactors influencing pulmonary function not considered in this study may also be affected by gender.”

3.By adjusting eGFR in model 2, the association became stronger. Authors need to explain this phenomenon with plausible mechanism in discussion.

Spearman’s correlation was used in order to determine the association between serum uric acid level and FVC% predicted, FEV1% predicted or FEV1/FVC. Age was adjusted in Model 1. In Model 2, age, education, household income (KRW), smoking (non-, ex-, and current smoker), alcohol consumption (none, 1-3/week, and � 4/week), aerobic exercise, BMI (kg/m2), systolic BP (mmHg), fasting plasma glucose (mg/dL), and eGFR (mL/min/1.73 m2) were adjusted.

Spearman correlation coefficient were likely to be similar in model 1 and 2.

4. Both alcohol consumption and BMI were strong risk factors for SUA/hyperuricemia. In the current analysis, residual confounding by these two factors may be one potential explanation for the current findings. Authors also need to address these issues with further analysis.

Alcohol intake none 1-3/week � ≥ 4/week p

BMI men 24.5 ± 0.2 24.6 ± 0.1 24.7± 0.2 0.787

Women 24.4 ± 0.1 24.1 ± 0.1 24.4± 0.6 0.204

Unfortunately, we could not find significant association between alcohol intake and BMI in this study. So we could not explain the current finding with residual confounding by these two factors

5. Authors need to address the method of multiple imputation in current analysis and how many percentages of covariates were imputed. If not, authors need to address how to deal with missing values.

Since we excluded subjects who did not undergo uric acid levels and pulmonary function tests from KNHNES which did not include missing values in other demographic and clinical factors as covariates, we didn’t have to deal with missing values.

Submitted filename: reply to reviewers HJW 20200727.docx

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29 Sep 2020

PONE-D-19-34352R1

Association between Serum Uric Acid and Spirometric Pulmonary Function in Korean Adults: The 2016 Korea National Health and Nutrition Examination Survey

PLOS ONE

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Reviewer #3: The sentence of below can be misled readers without references. I strongly recommend to update references. If not, it is needed to be deleted.

"Women may have greater xanthium oxidase activity and reactive

oxygen species (ROS) production, resulting in severe vascular inflammation, arterial stiffness,

and elevated blood pressure as compared with men."

**********

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Reviewer #1: Yes: Gen-Min Lin

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4 Oct 2020

Reply to Reviewers

Reviewer #3: The sentence of below can be misled readers without references. I strongly recommend to update references. If not, it is needed to be deleted.

"Women may have greater xanthium oxidase activity and reactive

oxygen species (ROS) production, resulting in severe vascular inflammation, arterial stiffness,

and elevated blood pressure as compared with men."

--> Thank you for your comments. As your suggestion, we deleted those sentences to avoid unnecessary misunderstanding.

Submitted filename: reply to reviewer 20201005.docx

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7 Oct 2020

Association between Serum Uric Acid and Spirometric Pulmonary Function in Korean Adults: The 2016 Korea National Health and Nutrition Examination Survey

PONE-D-19-34352R2

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12 Oct 2020

PONE-D-19-34352R2

Association between Serum Uric Acid and Spirometric Pulmonary Function in Korean Adults: The 2016 Korea National Health and Nutrition Examination Survey

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