Eur J Epidemiol (2014) 29:937–940 DOI 10.1007/s10654-014-9970-8

LETTER TO THE EDITOR

Urinary sodium excretion is associated with short sleep duration In Cheol Hwang • Doosup Shin

Received: 15 September 2014 / Accepted: 27 October 2014 / Published online: 2 November 2014 Ó Springer Science+Business Media Dordrecht 2014

A growing body of evidence demonstrates that sleep is not a waste of time; short sleep duration is associated with increased risk for many harmful outcomes. Given that sleep deprivation leads to metabolic derangement, short sleep duration is considered a risk factor for obesity [1]. Indeed, interrupted sleep was also associated with less healthy dietary habits [2]: a recent investigation suggested that sodium intake is associated with sleep impairment [3]. Sodium intake is positively associated with cardiovascular (CV) events [4], primarily via elevated blood pressure (BP). Traditionally, dietary sodium has been estimated from recall questionnaires or via 24-h urinary excretion. However, dietary surveys suffer from potential recall bias and require calculations by a nutritionist. It is also difficult to achieve sufficient reliability using 24-h urine collection due to poor compliance and errors associated with incomplete collection. Accordingly, an alternative method of dietary sodium assessment using spot urine samples has been suggested [5]: the spot urinary sodium to creatinine ratio (U[Na?]/Cr) has been demonstrated as a useful indicator for predicting BP and hypertension [6]. There are few data on the association between sodium intake and sleep duration: short sleepers were more likely to be on a low sodium diet [7]. Thus far, studies on this subject have been conducted in experimental environments or have used self-report dietary questionnaires. In this large-scale, nationwide study, we sought to determine I. C. Hwang (&) Department of Family Medicine, Gachon University Gil Medical Center, Incheon 405-760, Republic of Korea e-mail: [email protected] D. Shin Department of Education and Research, Seoul National University Hospital, Seoul, Republic of Korea

whether an association between sodium intake and sleep duration was present using a quantitative measure of sodium intake, U[Na?]/Cr. This study was based on data of the Korean National Health and Nutrition Examination Survey (KNHANES) 2008-2011 [6]. We identified 19,722 subjects aged 40 years and older. Of these, we excluded 1,608 with chronic disease (i.e., stroke, myocardial infarction, any malignancy, and renal or liver disease). We hypothesized that these subjects were likely to participate in lifestyle modification programs. Next, we excluded individuals with factors (elevated creatinine and taking antihypertensive medication) that could influence urinary excretion of sodium [6] and those with no available data on urinary sodium levels or sleep duration. After these exclusions, a total of 8,929 participants were included in the final analysis. This study was approved by the relevant IRB. Data on the following demographic and health behavior variables were extracted from health interviews: age, sex, monthly household income, educational level, marital status, physical activity, smoking history, alcohol consumption, and sleep duration. Blood and spot urine samples were collected in the morning on the same day of the interview after individuals had fasted for at least 8 h. Fasting plasma glucose (FPG), serum creatinine, serum lipid, and the first morning urine levels of sodium and creatinine were measured using a Hitachi Automatic Analyzer (Hitachi, Tokyo, Japan). Subsequently, U[Na?]/Cr (mmol/mmol) was calculated. Sleep duration was self-reported and assessed by responses to the single question: ‘‘How much on average do you sleep each day?’’ Sleep duration was categorized as ‘‘short sleep (\6 h),’’ ‘‘optimal sleep (6–8 h)’’ or ‘‘long sleep ([8 h)’’. In terms of physical activity, individuals who performed a moderate-intensity activity more than 5 days a week or a vigorous activity more than 3 days a

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week were classified as getting ‘‘regular physical activity’’. ‘‘Problem drinkers’’ were defined as individuals who drank more than seven drinks (male) or five drinks (female) at a time, more than 2 days per week. Metabolic parameters were assessed according to the AHA/NHLBI’s Asia criteria. All statistical analyses were performed using STATA SE 9.2 (STATA Corp., Texas, USA). We used independent t tests or v2 tests to assess whether participants’ characteristics varied according to sleep duration. To assess the association of U[Na?]/Cr with sleep duration, U[Na?]/Cr was divided into quartiles. Odds ratios (ORs) for short and long sleep duration and their 95 % confidence intervals were calculated for U[Na?]/Cr quartiles using both unadjusted

and adjusted logistic regression models. These associations were also assessed using multiple logistic regression models that included log2-transformed U[Na?]/Cr as a continuous variable. All statistical tests were two-tailed, and statistical significance was set at P \ 0.05. The average sleep duration of the current sample was 6.73 ± 1.35 h/day and Table 1 presents the participants’ characteristics stratified by sleep duration. Short or long sleepers were significantly older than optimal sleepers. The proportions of female and unmarried subjects were much greater in short sleepers than optimal sleepers. Optimal sleepers had higher economic status and educational levels than non-optimal sleepers. Interestingly, the prevalence of current smokers was higher in optimal sleepers than in

Table 1 Demographic and clinical characteristics of sample, stratified by sleep duration Optimal sleepers (6–8 h) (n = 6,907)

Short sleeper (\6 h) (n = 1,431)

Pa (Optimal vs. short)

Long sleepers ([8 h) (n = 591)

Pa (Optimal vs. long)

Age (years)

53.2 ± 0.1

59.1 ± 0.3

\0.001

57.3 ± 0.5

\0.001

Sex Male

45.8

36.8

\0.001

48.6

54.2

63.2

Demographic variables

Female

0.193

54.2

Economic status High

58.9

42.1

Low

41.1

57.8

CHigh school

59.9

37.3

BMiddle school

40.1

63.0

\0.001

45.6

\0.001

54.4

Educational level \0.001

38.8

\0.001

61.2

Marital status Married

86.2

74.9

Unmarried

13.8

25.1

\0.001

83.6

0.080

16.4

Health behaviors Current smoker

22.1

19.0

0.010

24.9

0.116

Problem drinker

5.4

6.2

0.193

8.5

0.002

Recent depressive mood Regular physical activity

12.7 24.4

22.9 24.1

\0.001 0.817

14.4 20.8

0.245 0.050

U[Na?]/Cr, mmol/mmol

14.1 ± 0.1

16.4 ± 0.3

\0.001

15.0 ± 0.4

0.016

Taking lipid-lowering agents

2.7

3.0

0.471

1.9

0.239

Taking glucose-lowering agents

4.3

5.9

0.006

6.1

0.041

Waist circumference (cm)

81.3 ± 0.1

81.7 ± 0.2

0.198

81.4 ± 0.4

0.908

Systolic BP (mmHg)

120.0 ± 0.2

123.0 ± 0.5

Metabolic syndrome components

\0.001

123.6 ± 0.8

\0.001

Diastolic BP (mmHg)

78.0 ± 0.1

77.8 ± 0.3

0.415

78.0 ± 0.4

0.893

Fasting glucose (mg/dL)

97.9 ± 0.3

98.2 ± 0.6

0.575

100.9 ± 1.2

0.002

Triglycerides (mg/dL)

137.9 ± 1.3

135.3 ± 2.7

0.402

147.9 ± 5.0

0.036

HDL-C (mg/dL)

52.6 ± 0.2

52.8 ± 0.3

0.570

51.4 ± 0.5

0.026

U[Na?]/Cr urinary sodium to creatinine ratio, BP blood pressure, HDL-C high-density lipoprotein cholesterol Data are presented as mean ± SEs or percentages a

Extracted from t tests or v2 tests

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Sodium intake and short sleep

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Table 2 Odds ratios and 95 % CIs for short and long sleepers by U[Na?]/Cr quartile (reference group = 6–8 h sleep) Short sleepers (\6 h)

Per doubling of U[Na?]/Cr

Long sleepers ([8 h)

Unadjusted

Model 1

Model 2

Unadjusted

Model 1

Model 2

1.35 (1.27–1.45)***

1.11 (1.04–1.19)**

1.09 (1.01–1.17)*

1.07 (0.98–1.18)

0.97 (0.88–1.08)

0.95 (0.86–1.05)

U[Na?]/Cr quartiles, mmol/mmol Q 1 (B8.2) Q 2 (8.2–12.5)

1 1.19 (1.00–1.42)

1 1.03 (0.86–1.24)

1 1.03 (0.86–1.25)

1 0.79 (0.62–1.01)

1 0.73 (0.57–0.93)*

1 0.75 (0.58–0.96)*

Q 3 (12.5–18.7)

1.21 (1.12–1.33)***

1.07 (0.97–1.17)

1.06 (0.96–1.16)

1.04 (0.93–1.17)

0.98 (0.87–1.10)

0.97 (0.85–1.09)

Q 4 ([18.7)

1.28 (1.21–1.35)***

1.11 (1.04–1.18)**

1.09 (1.02–1.16)**

1.02 (0.94–1.10)

0.93 (0.85–1.02)

0.92 (0.84–1.01)

Test for trend

1.28 (1.21–1.35)***

1.10 (1.04–1.17)**

1.08 (1.02–1.15)**

1.05 (0.97–1.13)

0.97 (0.89–1.05)

0.95 (0.87–1.03)

U[Na?]/Cr = urinary sodium to creatinine ratio Model 1, adjusted for age and sex; Model 2, adjusted for the variables in Model 1 plus clinical variables (current smoking status, problem drinking status, regular exercise status, depressive mood, and presence of metabolic syndrome components based on AHA/NHLBI diagnostic criteria) and other demographic variables (education level, economic status, and marital status) * P \ 0.05 ** P \ 0.01 *** P \ 0.001

short sleepers, whereas problem drinkers were much more common among long sleepers than optimal sleepers. The prevalence of subjects reporting recent feelings of depression was greater among short sleepers than optimal sleepers. Similarly, a greater proportion of non-optimal sleepers took anti-diabetic agents. Non-optimal sleepers had higher mean systolic BP than optimal sleepers. In terms of FPG and serum lipid levels, long sleepers exhibited more metabolic derangement than optimal sleepers. Finally, mean levels of U[Na?]/Cr were much higher in non-optimal sleepers than optimal sleepers. Table 2 shows ORs for nonoptimal sleepers by U[Na?]/Cr quartile. The OR for short sleepers associated with a doubling of U[Na?]/Cr values was 1.09, after adjusting for potential demographic and clinical confounding factors. Relative to the lowest quartile of U[Na?]/Cr, the OR for short sleepers in the highest quartile was 1.09, after adjusting for the same covariates. Although some experimental studies have presented preliminary data on the association between sleep duration and sodium intake, there has been little population-based research on this subject. One recent nationwide study [7], however, revealed that reports of a low-salt diet were more common in short sleepers than optimal sleepers. Similarly, short sleepers reported more exercise. Because short sleepers are likely to suffer from health problems, they are also more likely to be enrolled in health interventions, making them appear healthier than optimal sleepers; thus, comorbid conditions may confound investigation of the association between sleep duration and health behaviors. We designed this study to avoid problems present in earlier research. First, to avoid confounding factors, we limited our study to participants who did not suffer from chronic

diseases or hypertension. We note that this restriction was more likely to attenuate associations between sleep duration and metabolic risk. This is why long sleepers had more metabolic components than short sleepers in our study. A greater proportion of short sleepers, who were more likely to be at risk for metabolic components, were excluded from this study: 7.9 % of optimal, 10.8 % of short, and 6.1 % of long sleepers were excluded due to chronic disease; similarly, 24.5 % of optimal, 34.2 % of short, and 17.9 % of long sleepers were excluded due to hypertension (data not shown). Second, whereas previous studies extracted dietary information from questionnaires based on 24-h recall, we assessed sodium intake using biomarkers. Several lines of evidence indicate that high sodium intake has the capacity to impact sleep. High sodium intake is a well-known risk factor for elevated BP. Renal excretion of sodium, or ‘‘pressure natriuresis,’’ is one protective mechanism against elevated BP. Under conditions that exceed the capacity of this buffering system, quality of sleep may deteriorate due to fluid retention. Indeed, a very recent population-based study demonstrated that dietary sodium was independently associated with sleep difficulties [3]. Given the current finding that sodium intake is not related to long sleep duration, our results further strengthen the hypothesis that it disturbs one’s sleep. There is also evidence supporting the idea that sleep restriction may increase sodium intake, however, raising the possibility of reverse causality. Sleep restriction leads to changes in eating patterns and food choice. Indeed, recurrent sleep restriction under free-living conditions increases calorie intake from snacks [8]. Reward-driven feeding behavior may play a role in this phenomenon; advanced brain imaging demonstrates

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food-related reward activation after sleep curtailment [9]. Impaired sleep is associated with lower reactivity of rewardrelated brain systems, suggesting stronger stimulation is needed to reach the same level of neural activation [10], and salt has a strong propensity for addiction just as do many drugs. This study has several limitations. First, the cross-sectional design does not allow us to infer the direction of causality between sodium intake and short sleep duration. To determine causality, a randomized trial restricting sodium or altering sleep duration would be required. Second, we relied on self-reports of sleep duration. These might be influenced by behavioral or mental health variables. Third, the short sleep group may be heterogeneous. Some may naturally require less sleeper or to be more resilient to sleep loss. Unfortunately, we did not have information regarding the causes of short sleep duration, such as lack of sleepiness, lack of time or voluntary sleep restriction. Despite these limitations, this study’s findings are highly informative. Our results underscore a significant association between short sleep duration and sodium intake, two factors that may influence each other. Our findings provide further evidence that improving sleep duration or reducing sodium intake may aid individuals break a vicious cycle. Further studies should assess whether this association is primarily driven by a preference for salty foods or by sleep curtailment as well as whether dietary sodium has physiologic effects on sleep quality. Acknowledgments This study was supported by the Senior-friendly Product R&D program funded by the Ministry of Health & Welfare thorough the Korea Health Industry Development Institute (HI14C1435).

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I. C. Hwang, D. Shin Conflict of interest

We have no conflict of interest.

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