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Association between night work and dyslipidemia in South Korean men and women: a cross-sectional study

  • Jae Hong Joo
  • Doo Woong Lee
  • Dong-Woo Choi
  • Eun-Cheol ParkEmail author
Open Access
Research

Abstract

Background

Previous studies have reported that an irregular work schedule, particularly nighttime work, is associated with an altered lipid profile. Additionally, a mismatch in circadian rhythm can affect sleeping and eating habits, leading to poor health. This study aimed to examine the association between night work and dyslipidemia among South Korean adults aged ≥30 years.

Methods

For this study, the data of 5813 participants in the 2013–2016 Korea National Health and Nutrition Examination Survey were analyzed. Diagnoses of dyslipidemia were based on blood sampling tests of total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol, and triglyceride levels. Night work was defined as that conducted during evening (6 P.M.–12 A.M.) and overnight hours (12 A.M.–8 A.M.). The association between night work hours and dyslipidemia in South Korean men and women was investigated using a logistic regression analysis.

Results

After adjusting for sociodemographic, economic, health-related, and nutritional factors, an association of night work with dyslipidemia was observed in male participants (odds ratio = 1.53, 95% confidence interval: 1.05–2.24). In subset analyses of male participants, night workers who skipped meals were more likely to have dyslipidemia than their day-working counterparts. Among men who slept < 7 h, night workers had a higher probability of dyslipidemia than day workers. In contrast, no statistically significant association between night work and dyslipidemia was observed in female participants, although the probability of dyslipidemia appeared to increase with advancing age. Furthermore, when women with dyslipidemia were subdivided by occupational categories, night workers in white collar positions were more likely to have dyslipidemia than their day-working counterparts.

Conclusion

Our study observed an association of night work with dyslipidemia, particularly in men. Although these findings may support interventions for South Korean night workers, further studies are needed for validation.

Keywords

Dyslipidemia Night work Eating habit Sleep duration White-collar 

Abbreviations

CVD

Cardiovascular disease

HDL

High-density lipoprotein

KNHANES

Korea National Health and Nutrition Examination Survey

LDL

Low-density lipoprotein

Background

The concept of shift work arose from industrial growth and the increase of 24-h workplaces, which required continuous staffing and irregular work schedules [1, 2]. In South Korea, the prevalence of night and shift work is highest in the field of manufacturing, followed by wholesale and retail businesses [3]. Although no consensus has been reached regarding the definition of shift work, this term is often used in reference to work hours outside of the conventional daytime period.

The major difficulties associated with shift work mainly involve work conducted during evening or overnight hours, due to its effects on circadian rhythm. Changes in circadian rhythms can disrupt homeostasis and lead to the desynchronization of enzymatic activity and metabolic function [4]. For example, evidence suggests a correlation between an altered distribution of food intake due to a mismatch in circadian rhythm (e.g., nighttime food ingestion) and increased cholesterol levels [5]. Circadian rhythm disturbances have also been identified as a significant factor related to cardiovascular disease (CVD). For example, an inability of the circadian rhythm governing oxygen supply to adapt promptly to the changing demands of night work will likely lead to myocardial infarction [4]. Furthermore, night workers are more likely to experience fatigue due to a lack of sleep [6]. Although this relationship is poorly understood, sleep deprivation has been identified as a potential risk factor for CVD [7].

CVD is the cause of substantial societal burdens worldwide and is the leading cause of death in South Korea, where the CVD-associated mortality rate has been increasing gradually in recent years. In 2017, diseases of the circulatory system accounted for 21.5% of all deaths in South Korea, second only to neoplasms (28.1%) [8]. The prevalence of dyslipidemia, a major risk factor for CVD [9], is also increasing in South Korea [10], with reported rates ranging from 30 to 60% [10]. Although age, hypertension, and obesity are commonly known risk factors for dyslipidemia, these factors are better controlled and moderated today than in previous periods [11]. Therefore, the increased prevalence of dyslipidemia in South Korea is likely attributable to lifestyle factors.

Previous studies have reported associations between irregular work schedules, particularly night work, and altered lipid profiles [12, 13]. Therefore, preventive measures are needed to mitigate lipid disorders and ensure the well-being of workers during non-standard working hours. Night work appears to serve as barrier to a healthy lifestyle and a threat to well-being, as a circadian rhythm mismatch can disrupt adequate sleeping and eating habits, leading to poor health [14]. We hypothesize that in night workers, insufficient amounts of sleep and irregular eating habits may contribute to the onset of dyslipidemia. In this study, therefore, we aimed to investigate and elucidate the association of dyslipidemia with night work.

Methods

Study participants

We collected data from the 2013 to 2016 Korea National Health and Nutrition Examination Survey (KNHANES), which was conducted by the Korea Centers for Disease Control and Prevention (KCDCP). The KNHANES is a self-reported, nationally representative survey of South Koreans of all ages and is designed to gather annual national data on sociodemographic, economic, and health-related conditions and behaviors. Since 2007, the collected data have been subjected to an annual review and approval by the KCDCP Research Ethics Review Committee. The KNHANES 2013–2016 included 31,908 participants. We excluded 25,285 of these participants for various reasons (Fig. 1). First, participants with a previous clinical diagnosis of dyslipidemia were excluded, as this may have influence the reliability of the outcome (n = 3328). Second, in this study, dyslipidemia was diagnosed via a blood samples collected during the KNHANES. Therefore, people younger than 30 years were excluded because they did not undergo blood testing as part of the survey (n = 9656). Third, our study aimed to examine specific relationships with dyslipidemia. As dyslipidemia and metabolic syndrome share a few diagnostic components, including high-density lipoprotein (HDL) cholesterol and triglycerides levels, participants who met the modified National Cholesterol Education Program Expert Panel Adult Treatment Panel III (NCEP-ATP III) diagnostic criteria for metabolic syndrome with a lower waist circumference were excluded to increase the validity of our study (n = 4163) [15, 16]. Fourth, people deemed ineligible because they were unemployed or were not representative of covariates considered in the study (failure to answer the questionnaires or lack of applicability) were also excluded (n = 8138). Finally, the analyzed sample comprised 5813 participants (men: 2821 and women: 2992).
Fig. 1

Flow diagram of subject inclusion and exclusion

Variables

Dyslipidemia, the dependent variable in this study, was diagnosed based on the levels of total, high density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol, and triglycerides in blood samples collected after 9–12 h of fasting. According to the 2015 Korean Guidelines for the Management of Dyslipidemia, one of the following four criteria was required: (a) total cholesterol ≥240 mg/dL, (b) HDL cholesterol ≤40 mg/dL, (c) LDL cholesterol ≥160 mg/dL, or (d) triglycerides ≥200 mg/dL [11].

The main independent variable was the work pattern, which included three categories: day, night, and other shifts. The day shift was defined as between 6 A.M. and 6 P.M., while the night shift merged both evening (6 P.M.–12 A.M.) and overnight work (12 A.M.–8 A.M.). Other shifts included various types of working patterns, such as alternating shifts (e.g., day-night-day), 24-h shifts (a full 24-h shift followed by a day(s) off), and split shifts (≥2 shifts within a day).

Socio-demographic, economic, health-related, and nutritional factors were also assessed. Socio-demographic factors included age (30–39, 40–49, 50–59, and ≥ 60 years), region (metropolitan or rural), educational level (high school or less or college and/or beyond), and marital status (married or unmarried). Economic factors included the household income (low, mid-low, mid-high, or high) and occupational category (white-, pink-, or blue-collar employment). Health-related factors included eating habits (regular consumption of breakfast, lunch, and dinner or skipping meals), physical activity/week (active: ≥150 min of moderate activity, ≥ 75 min of vigorous activity, or a mixture of both for ≥150 min; inactive: < 150 min of moderate activity, < 75 min of vigorous activity, or a mixture of both for < 150 min), sleep duration (0–6 or ≥ 7 h per night), smoking status (current smoker, ex-smoker, or non-smoker), alcohol consumption status (≥2 times/month or never), body mass index (BMI) defined obesity status (in reference to the Korean guidelines for overweight and obesity; underweight/normal: < 23, overweight: 23–24.9, and obese: ≥25) [17], hypertension (in reference to the Korean guideline for normal BP, < 120/80 mmHg; normal: 90–199 mmHg systolic or 60–79 mmHg diastolic; prehypertension: 120–139 mmHg systolic or 80–89 mmHg diastolic; hypertension: ≥140 mmHg systolic or ≥ 90 mmHg diastolic) [18], and menopausal status (yes or no). Nutritional factors included macronutrient intake (total kcal, protein, fat, and carbohydrate). For the continuous variables (macronutrient intakes), the OR was calculated for every 100-kcal increase in calorie intake and every 10-g increase in protein, fat, and carbohydrate intake.

Statistical analysis

All statistical analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC, USA). The chi-square (χ [2]) test was used to evaluate the general characteristics of the study population. For continuous variables (macronutrient intake), a t-test was used to calculate the means and standard deviations. A multiple logistic regression analysis was used to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) in three different models. Model 1 yielded a crude OR, model 2 was adjusted for socio-demographic and economic factors, and model 3 was adjusted for all socio-demographic, economic, health-related, and nutritional factors. Multiple logistic regression analyses of subgroups were also performed to examine the association between night work and dyslipidemia according to occupational category, eating habits, and sleep duration. A general linear model analysis was also used to calculate the mean levels of the four diagnostic determinants (total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides), and the distributions and percentages of each were calculated. The stratified, clustering, and weight variables developed by the KNHANES were applied to all analyses to improve the representativeness of the sample and account for the limited proportion of participants retained in the final analysis [19]. The significance level was set at p value < 0.05.

Results

Table 1 summarizes the general characteristics of the study population, which included 2821 men and 2992 women. A total of 816 (28.9%) men and 469 (15.7%) women had dyslipidemia. Of the 196 male participants who reported working at night, 76 (38.8%) had dyslipidemia, and the prevalence of dyslipidemia was greater among these night workers compared to those who worked at other times (day: 684/2404, 28.5%; other shifts: 56/221, 25.3%). A similar trend was observed among the female participants, as 70 of the 379 women (18.5%) who reported working at night had dyslipidemia (day: 382/2512, 15.2%; other shifts: 17/101, 16.8%).
Table 1

General characteristics of the study population

Variables

Dyslipidemia

Male

 

Female

TOTAL

Yes

No

P-value

TOTAL

Yes

No

P-value

N

%

N

%

N

%

N

%

N

%

N

%

Work pattern

      

0.004

      

0.252

 Day

2404

85.2

684

28.5

1720

71.5

 

2512

84.0

382

15.2

2130

84.8

 

 Night

196

6.9

76

38.8

120

61.2

 

379

12.7

70

18.5

309

81.5

 

 Other shifts

221

7.8

56

25.3

165

74.7

 

101

3.4

17

16.8

84

83.2

 

Age(years)

      

0.011

      

<.0001

 30~39

741

26.3

210

28.3

531

71.7

 

830

27.7

85

10.2

745

89.8

 

 40~49

738

26.2

236

32.0

502

68.0

 

997

33.3

119

11.9

878

88.1

 

 50~59

634

22.5

196

30.9

438

69.1

 

742

24.8

163

22.0

579

78.0

 

  ≥ 60

708

25.1

174

24.6

534

75.4

 

423

14.1

102

24.1

321

75.9

 

Region

      

0.586

      

0.314

 Metropolitan

1696

60.1

497

29.3

1199

70.7

 

1861

62.2

282

15.2

1579

84.8

 

 Rural

1125

39.9

319

28.4

806

71.6

 

1131

37.8

187

16.5

944

83.5

 

Educational level

      

0.348

      

<.0001

  ≤ Highschool

1541

54.6

457

29.7

1084

70.3

 

1812

60.6

343

18.9

1469

81.1

 

  ≥ College

1280

45.4

359

28.0

921

72.0

 

1180

39.4

126

10.7

1054

89.3

 

Occupational categoriesª

      

0.156

      

<.0001

 White

1085

38.5

336

31.0

749

69.0

 

1275

42.6

144

11.3

1131

88.7

 

 Pink

355

12.6

101

28.5

254

71.5

 

859

28.7

157

18.3

702

81.7

 

 Blue

1381

49.0

379

27.4

1002

72.6

 

858

28.7

168

19.6

690

80.4

 

Household income

      

0.179

      

<.0001

 Low

272

9.6

79

29.0

193

71.0

 

295

9.9

75

25.4

220

74.6

 

 Mid-low

679

24.1

184

27.1

495

72.9

 

701

23.4

115

16.4

586

83.6

 

 Mid-high

910

32.3

251

27.6

659

72.4

 

908

30.3

121

13.3

787

86.7

 

 High

960

34.0

302

31.5

658

68.5

 

1088

36.4

158

14.5

930

85.5

 

Marital status

      

0.898

      

0.087

 Living w/ spouse

2465

87.4

712

28.9

1753

71.1

 

2384

79.7

360

15.1

2024

84.9

 

 Living w/o spouse

356

12.6

104

29.2

252

70.8

 

608

20.3

109

17.9

499

82.1

 

Eating habit(daily)

      

0.014

      

0.564

 Regularly eat breakfast, lunch, and dinner

1772

62.8

484

27.3

1288

72.7

 

1625

54.3

249

15.3

1376

84.7

 

 Skip meal(s)

1049

37.2

332

31.6

717

68.4

 

1367

45.7

220

16.1

1147

83.9

 

Physical activity

      

0.356

      

0.145

 Active

1549

54.9

437

28.2

1112

71.8

 

1528

51.1

254

16.6

1274

83.4

 

 Inactive

1272

45.1

379

29.8

893

70.2

 

1464

48.9

215

14.7

1249

85.3

 

Sleep duration(hours)

      

0.077

      

0.833

 0~6

1460

51.8

401

27.5

1059

72.5

 

1570

52.5

244

15.5

1326

84.5

 

  ≥ 7

1361

48.2

415

30.5

946

69.5

 

1422

47.5

225

15.8

1197

84.2

 

Smoking status

      

<.0001

      

0.346

 Current smoker

1046

37.1

352

33.7

694

66.3

 

135

4.5

17

12.6

118

87.4

 

 Ex-smoker

1149

40.7

310

27.0

839

73.0

 

131

4.4

25

19.1

106

80.9

 

 Non-smoker

626

22.2

154

24.6

472

75.4

 

2726

91.1

427

15.7

2299

84.3

 

Drinking status

      

0.097

      

0.001

  ≥ 2 times / month

2063

73.1

579

28.1

1484

71.9

 

1420

47.5

189

13.3

1231

86.7

 

 Never

758

26.9

237

31.3

521

68.7

 

1572

52.5

280

17.8

1292

82.2

 

BMIb

      

<.0001

      

<  0.0001

 Obese(≥25)

809

28.7

257

31.8

552

68.2

 

564

18.9

129

22.9

435

77.1

 

 Overweight(23~24.9)

855

30.3

291

34.0

564

66.0

 

667

22.3

138

20.7

529

79.3

 

 Normal+underweight(< 23)

1157

41.0

268

23.2

889

76.8

 

1761

58.9

202

11.5

1559

88.5

 

Hypertension

      

0.102

      

<  0.0001

 Hypertension

627

22.2

160

25.5

467

74.5

 

375

12.5

82

21.9

293

78.1

 

 Pre-hypertension

897

31.8

269

30.0

628

70.0

 

601

20.1

111

18.5

490

81.5

 

 Normal

1297

46.0

387

29.8

910

70.2

 

2016

67.4

276

13.7

1740

86.3

 

Diabetes

      

0.006

      

0.102

 Diabetes mellitus

176

6.2

49

27.8

127

72.2

 

78

2.6

19

24.4

59

75.6

 

 Impaired fasting glucose

641

22.7

154

24.0

487

76.0

 

400

13.4

62

15.5

338

84.5

 

 Normal

2004

71.0

613

30.6

1391

69.4

 

2514

84.0

388

15.4

2126

84.6

 

Menopause

             

<  0.0001

 Yes

       

834

27.9

193

23.1

641

76.9

 

 No

       

2158

72.1

276

12.8

1882

87.2

 

Year

      

0.604

      

0.014

 2013

747

26.5

216

28.9

531

71.1

 

764

25.5

119

15.6

645

84.4

 

 2014

704

25.0

200

28.4

504

71.6

 

722

24.1

90

12.5

632

87.5

 

 2015

668

23.7

206

30.8

462

69.2

 

689

23.0

129

18.7

560

81.3

 

 2016

702

24.9

194

27.6

508

72.4

 

817

27.3

131

16.0

686

84.0

 

Calorie intake(Kcal)c

2468.3

±1007.7

2466.7

±1106.9

2468.9

±964.3

0.958

1806.7

±712.2

1803.5

±753.0

1807.3

±704.2

0.914

Protein intake(g)c

86.3

±60.3

88.3

±87.4

85.5

±44.8

0.259

62.8

±31.8

62.0

±30.8

62.9

±32.0

0.567

Fat intake(g)c

53.1

±40.6

52.8

±46.5

53.2

±37.9

0.788

40.4

±28.6

37.2

±28.1

40.9

±28.7

0.010

Carbohydrate intake(g)c

361.9

±132.9

358.9

±128.3

363.2

±134.8

0.439

286.8

±115.9

294.0

±128.4

285.4

±113.4

0.141

Total

2821

100.0

816

28.9

2005

71.1

 

2992

100.0

469

15.7

2523

84.3

 

BMI body mass index

aThree groups based on International Standard Classification Occupations codes

bObesity status defined by BMI based on 2014 Clinical Practice Guidelines for Overweight and Obesity in Korea

cMean and Standard deviation (SD) of the continuous independent variables in this study

Table 2 summarizes the results from the multiple logistic analysis of the association between night work and dyslipidemia. In all three models, the association between night work and dyslipidemia remained statistically significant in male participants (model 1: OR = 1.58, 95% CI: 1.12–2.21; model 2: OR = 1.61, 95% CI: 1.13–2.29; model 3: OR = 1.53, 95% CI: 1.05–2.24). By contrast, however, no statistically significant association of night work with dyslipidemia was observed in female participants. However, women aged 50 years or older were more likely to have dyslipidemia, compared to their younger counterparts (50–59 years: OR = 1.61, 95% CI: 1.04–2.50; ≥60 years: OR = 1.66, 95% CI: 0.92–3.01).
Table 2

Odds ratio for dyslipidemia

Variables

Dyslipidemia

Model 1

Model 2

Model 3

Male

Female

Male

Female

Male

Female

OR

95% CI

OR

95% CI

OR

95% CI

OR

95% CI

OR

95% CI

OR

95% CI

Work pattern

 Day

1.00

   

1.00

   

1.00

   

1.00

   

1.00

   

1.00

   

 Night

1.58

(1.12

2.21)*

1.16

(0.82

1.64)

1.61

(1.13

2.29)*

1.19

(0.82

1.72)

1.53

(1.05

2.24)*

1.12

(0.76

1.66)

 Other shifts

0.78

(0.55

1.11)

0.90

(0.50

1.62)

0.84

(0.58

1.21)

0.95

(0.53

1.72)

0.84

(0.58

1.21)

0.94

(0.50

1.74)

Age(years)

 30~39

        

1.00

   

1.00

   

1.00

   

1.00

   

 40~49

        

1.20

(0.93

1.56)

1.22

(0.85

1.74)

1.34

(1.03

1.75)*

1.19

(0.82

1.73)

 50~59

        

1.07

(0.80

1.42)

1.98

(1.39

2.81)*

1.31

(0.96

1.77)

1.61

(1.04

2.50)*

  ≥ 60

        

0.82

(0.60

1.13)

2.02

(1.29

3.16)*

1.13

(0.79

1.62)

1.66

(0.92

3.01)

Region

 Metropolitan

        

1.00

   

1.00

   

1.00

   

1.00

   

 Rural

        

1.00

(0.83

1.22)

1.07

(0.83

1.37)

1.03

(0.84

1.26)

1.08

(0.83

1.39)

Educational level

  ≤ Highschool

        

1.39

(1.09

1.77)*

1.19

(0.82

1.73)

1.34

(1.04

1.71)*

1.15

(0.78

1.69)

  ≥ College

        

1.00

   

1.00

   

1.00

   

1.00

   

Occupational categoriesª

 White

        

1.00

   

1.00

   

1.00

   

1.00

   

 Pink

        

0.73

(0.53

1.01)

1.29

(0.88

1.90)

0.68

(0.49

0.96)

1.24

(0.84

1.83)

 Blue

        

0.78

(0.60

1.03)

1.33

(0.89

1.99)

0.76

(0.58

1.01)

1.27

(0.83

1.95)

Household income

 Low

        

0.93

(0.64

1.37)

1.35

(0.86

2.12)

0.93

(0.63

1.38)

1.33

(0.82

2.14)

 Mid-low

        

0.75

(0.57

0.98)

0.90

(0.66

1.24)

0.72

(0.55

0.95)

0.87

(0.63

1.21)

 Mid-high

        

0.82

(0.65

1.04)

0.79

(0.59

1.06)

0.81

(0.64

1.03)

0.76

(0.57

1.03)

 High

        

1.00

   

1.00

   

1.00

   

1.00

   

Marital status

 Living w/ spouse

        

1.00

   

1.00

   

1.00

   

1.00

   

 Living w/o spouse

        

1.05

(0.79

1.40)

1.19

(0.90

1.56)

1.05

(0.79

1.41)

1.14

(0.87

1.51)

Eating habit(daily)

 Regularly eat breakfast, lunch, and dinner

                

1.00

   

1.00

   

 Skip meal(s)

                

1.19

(0.96

1.47)

1.42

(1.09

1.85)*

Physical activity

 Active

                

1.00

   

1.00

   

 Inactive

                

0.99

(0.82

1.20)

0.82

(0.65

1.03)

Sleep duration(hours)

 0~6

                

0.84

(0.69

1.02)

0.83

(0.66

1.05)

  ≥ 7 ≥ 7

                

1.00

   

1.00

   

Smoking status

 Current smoker

                

1.70

(1.29

2.24)*

0.87

(0.45

1.67)

 Ex-smoker

                

1.24

(0.95

1.61)

1.55

(0.95

2.54)

 Non-smoker

                

1.00

   

1.00

   

Drinking status

  ≥ 2 times / month

                

0.82

(0.66

1.01)

0.79

(0.62

1.00)

 Never

                

1.00

   

1.00

   

BMIb

 Obese(≥25)

                

1.74

(1.36

2.23)*

1.92

(1.43

2.58)*

 Overweight(23~24.9)

                

1.86

(1.48

2.33)*

1.67

(1.24

2.26)*

 Normal+underweight(< 23)

                

1.00

   

1.00

   

Hypertension

 Hypertension

                

0.79

(0.60

1.03)

1.00

(0.68

1.45)

 Pre-hypertension

                

0.91

(0.74

1.12)

1.08

(0.79

1.48)

 Normal

                

1.00

   

1.00

   

Diabetes

 Diabetes mellitus

                

0.93

(0.59

1.44)

1.39

(0.73

2.66)

 Impaired fasting glucose

                

0.65

(0.51

0.83)

0.80

(0.57

1.15)

 Normal

                

1.00

   

1.00

   

Menopause

 Yes

                    

1.43

(0.99

2.08)

 No

                    

1.00

   

Year

 2013

                

1.04

(0.79

1.37)

1.06

(0.71

1.59)

 2014

                

1.01

(0.77

1.33)

0.65

(0.45

0.94)

 2015

                

1.15

(0.86

1.54)

1.09

(0.78

1.53)

 2016

                

1.00

   

1.00

   

Calorie intake(Kcal)c

                

1.00

(0.98

1.03)

1.03

(0.94

1.12)

Protein intake(g)d

                

1.03

(0.99

1.05)

1.00

(0.94

1.07)

Fat intake(g)d

                

0.96

(0.92

1.00)

0.89

(0.82

0.98)

Carbohydrate intake(g)d

                

1.00

(0.99

1.01)

1.00

(0.97

1.04)

Model 1: unadjusted; Model 2: adjusted for age, region, educational level, occupational categories, household income, marital status; Model 3: adjusted for age, region, educational level, occupational categories, household income, marital status, eating habit, physical activity, sleep duration, smoking status, drinking status, BMI, hypertension, diabetes, menopause, micronutrients, and year

BMI body mass index

aThree groups based on International Standard Classification Occupations codes

bObesity status defined by BMI based on 2014 Clinical Practice Guidelines for Overweight and Obesity in Korea

cPer 100 (Kcal) increase

dPer 10 (g) increase

*P < 0.05

Table 3 summarizes the results from subgroup analyses stratified by occupational categories, eating habits, and sleep duration. Male night workers who reported skipping meals were more likely to have dyslipidemia, compared to their day working counterparts (OR = 1.63, 95% CI: 1.00–2.67). Similarly, male night workers who slept for 0–6 h were more likely to have dyslipidemia, compared to their day working counterparts. Among female participants, a strong significant association was observed between the occupational category and dyslipidemia, as female night workers with white collar jobs had a nearly three-fold risk of dyslipidemia, compared to their day working counterparts (OR = 2.95, 95% CI: 1.68–5.16).
Table 3

The results of subgroup analysis of dyslipidemia to work pattern stratified by occupational categories, eating habit, and sleep duration

Variables

Dyslipidemia

Day

Night

Other shifts

ORa

ORa

95% CI

ORa

95% CI

Male

 Occupational categoriesb

  White

1.00

1.75

(0.95

3.24)

1.56

(0.60

4.04)

  Pink

1.00

1.14

(0.50

2.61)

0.86

(0.39

1.89)

  Blue

1.00

1.70

(0.99

2.94)

0.72

(0.44

1.18)

 Eating habit(daily)

  Regularly eat breakfast, lunch, and dinner

1.00

1.56

(0.85

2.86)

0.72

(0.44

1.16)

  Skip meal(s)

1.00

1.63

(1.00

2.67)*

1.16

(0.64

2.10)

 Sleep duration(hours)

  0~6

1.00

1.75

(1.04

2.93)*

0.91

(0.54

1.54)

   ≥ 7 ≥ 7

1.00

1.34

(0.78

2.31)

0.79

(0.46

1.36)

Female

 Occupational categoriesb

  White

1.00

2.95

(1.68

5.16)*

0.23

(0.03

1.76)

  Pink

1.00

0.85

(0.49

1.45)

1.65

(0.67

4.09)

  Blue

1.00

0.48

(0.20

1.14)

1.06

(0.37

2.99)

 Eating habit(daily)

  Regularly eat breakfast, lunch, and dinner

1.00

1.76

(0.99

3.01)

0.68

(0.21

2.18)

  Skip meal(s)

1.00

0.80

(0.49

1.30)

1.01

(0.46

2.22)

 Sleep duration(hours)

  0~6

1.00

1.28

(0.77

2.12)

1.00

(0.50

2.04)

   ≥ 7

1.00

1.03

(0.60

1.75)

0.88

(0.28

2.74)

aOR adjusted for all sociodemographic, economic, health-related, and nutritional factors considered in the study

bThree groups based on International Standard Classification Occupations codes

*P < 0.05

Table 4 individually summarizes the mean values of the four dyslipidemia diagnostic parameters: (a) total cholesterol, (b) HDL cholesterol, LDL, (c) cholesterol, and (d) triglycerides, as well as the related distributions and percentages of the study sample. Among male subjects, night workers were generally more likely to present with dyslipidemia, compared to their counterparts with other work shift patterns, with 8.2, 16.3, 3.6, and 15.3% meeting the respective criteria of ≥240 mg/dL total cholesterol, ≤40 mg/dL HDL cholesterol, ≥160 mg/dL LDL cholesterol, and ≥ 200 mg/dL triglycerides.
Table 4

Mean values of total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides

Variable

Dyslipidemia

 

Yesa

 

Noa

 

Total

N

%

Mean ± SD

P-value

N

%

Mean ± SD

P-value

Male (n = 2821)

 Total cholesterol

 

≥240 ≥ 240 ≥ 240

0.044

< 240 < 240 < 240

0.475

  Day

2404

170

7.1

254.852941

±

14.6

 

2234

92.9

185.707623

±

27.5

 

  Night

196

16

8.2

247.125

±

5.4

 

180

91.8

183.117978

±

29.3

 

  Other shifts

221

10

4.5

247.9

±

12.4

 

211

95.5

185.149038

±

26.8

 

 HDL cholesterol

 

≤40 ≤ 40 ≤ 40

0.532

> 40

0.455

  Day

2404

346

14.4

34.8849827

±

3.5

 

2058

85.6

52.1910633

±

9.8

 

  Night

196

32

16.3

35.6163125

±

3.5

 

164

83.7

51.7899506

±

9.9

 

  Other shifts

221

31

14.0

34.9723226

±

3.4

 

190

86.0

51.3105455

±

9.0

 

 LDL cholesterol

 

≥160

0.049

< 160

0.904

  Day

2404

71

3.0

175.915493

±

14.4

 

2333

97.0

112.69986

±

24.7

 

  Night

196

7

3.6

168.428571

±

8.6

 

189

96.4

114.042254

±

27.0

 

  Other shifts

221

5

2.3

162

±

3.9

 

216

97.7

112.460317

±

22.8

 

 Triglycerides

 

≥200

0.552

< 200

0.765

  Day

2404

285

11.9

296.74386

±

142.1

 

2119

88.1

104.792435

±

38.6

 

  Night

196

30

15.3

310.733333

±

167.1

 

166

84.7

106.871951

±

37.8

 

  Other shifts

221

21

9.5

267.47619

±

72.4

 

200

90.5

105.822335

±

39.6

 

Female (n = 2992)

 Total cholesterol

 

≥240 ≥ 240

0.288

< 240 < 240

0.481

  Day

2512

190

7.6

260.110526

±

17.5

 

2322

92.4

185.358222

±

26.7

 

  Night

379

33

8.7

256.0

±

12.6

 

346

91.3

183.956268

±

28.3

 

  Other shifts

101

10

9.9

254.7

±

14.2

 

91

90.1

182.835165

±

30.4

 

 HDL cholesterol

 

≤40

0.848

> 40

0.003

  Day

2512

140

5.6

35.6

±

3.5

 

2372

94.4

57.6918124

±

11.1

 

  Night

379

21

5.5

35.6

±

3.7

 

358

94.5

59.1713183

±

11.8

 

  Other shifts

101

6

5.9

36.5

±

2.5

 

95

94.1

60.7348632

±

11.2

 

 LDL cholesterol

 

≥160

0.746

< 160

0.551

  Day

2512

56

2.2

174.303571

±

13.0

 

2456

97.8

110.192771

±

25.0

 

  Night

379

8

2.1

177.5

±

14.8

 

371

97.9

110.836735

±

24.1

 

  Other shifts

101

4

4.0

172.0

±

7.4

 

97

96.0

104.608696

±

31.2

 

 Triglycerides

 

≥200

0.871

< 200

0.009

  Day

2512

82

3.3

269.743902

±

88.5

 

2430

96.7

87.3859794

±

36.0

 

  Night

379

24

6.3

280.0

±

84.5

 

355

93.7

85.0284091

±

36.9

 

  Other shifts

101

2

2.0

262.5

±

70.0

 

99

98.0

76.5555556

±

33.9

 

HDL high density lipoprotein, LDL low density lipoprotein

aCut-offs according to the 2015 Korean Guidelines for the Management of Dylipidemia

Discussion

After controlling for socio-demographic, economic, health-related, and nutritional factors, we found that night work increased the risk of dyslipidemia in the male participants. Physiological activities, such as eating patterns, lipid/carbohydrate/glucose metabolism, and sleep, all operate on day/night rhythms [20] that are controlled by the circadian biological clock [20]. Accordingly, work schedules that extend beyond the standard 9 A.M.–5 P.M. period impair the circadian rhythm [21]. Night work-related disruptions of the biological clock are likely to result in obesity, impaired insulin secretion, and aberrant glucose homeostasis [20, 22]. Notably, overlap has been observed between the mechanisms associated with insulin resistance and atherosclerosis (a consequence of dyslipidemia), including elevated levels of glucose and free acids that cause oxidant stress, the activation of proinflammatory pathways, low levels of HDL, and high levels of triglycerides [23, 24]. The circadian clock is a key regulator of lipid metabolism and therefore, the lipid profile [25, 26], and periodic disruption of circadian rhythm negatively affects lipid metabolism [26, 27]. Accordingly, night work is more strongly associated with dyslipidemia, compared to day or other shift work.

Meal skipping is a common practice in modern society. Commonly, constant changes in the daily routines of night workers lead to irregular meal times. In our subgroup analysis, we observed a significant positive association of night work with dyslipidemia among male participants who reported skipping meals. Several previous studies reported that these workers tend to skip meals and snack more frequently during the night shift [28, 29, 30]. Additionally, compared with regular eaters, meal skippers have higher average values of mean weight, BMI, and triglycerides, which have all been identified as risk factors for dyslipidemia [31].

.Sleep deprivation negatively affects metabolism and impairs the homeostatic control of energy intake (i.e., protein, fat, and carbohydrate) [28, 32], while also promoting the development of an atherogenic lipid profile [33]. These effects explain the significant association between sleep duration and dyslipidemia in this study. Specifically, night workers who slept for < 7 h per night faced a higher risk of dyslipidemia, compared to their counterparts who reported more sleep. The National Sleep Foundation recommends that adults sleep for 7 h per night [34]. According to previous studies, permanent night workers receive less sleep than day workers [35, 36]. Night workers who sleep during the day will inevitably be exposed to light, which hinders the duration and quality of sleep [37]. Specifically, light is the main environmental regulator of circadian rhythm. As the human brain tends to wake when the environment transitions from darkness to light [38], night workers find it difficult to sleep during the day.

Previous studies have reported higher rates of physical inactivity and obesity among white-collar workers, particularly female workers, than those in other occupations [39, 40]. Furthermore, physical inactivity during working hours negatively affects the health of white-collar workers [41]. Both obesity and physical inactivity have been recognized as risk factors for dyslipidemia. These findings seem relevant to our findings, as our subgroup analysis showed a significant association between night work and dyslipidemia among female white-collar workers. Notably, age also correlated directly with the prevalence of dyslipidemia in women, particularly among menopausal women older than 50 years of age. This may be attributable to lipoprotein changes associated with menopause [42], which include increased levels of total and LDL cholesterol [42, 43].

This study had several limitations. First, the cross-sectional design rendered us unable to determine a causal relationship between night work and dyslipidemia. Second, the durations of day, night, and other shift work could not be determined because of limitations of the KNHANES questionnaire. Finally, the key covariates considered in this study, including the sleep duration and eating habits, were self-reported and may have been subject to recall bias. Despite these limitations, this study also featured strengths. This study involved a large, well-validated dataset collected from a nationally representative sample of the South Korean population. Therefore, the findings will likely support the development of interventions and health policies aimed at the increasing problem of dyslipidemia in this population. The study thus makes a relevant contribution to the fields of cardiovascular medicine and epidemiology. Additionally, the KNHANES questionnaires are updated annually to incorporate changes in the real-life health circumstances of South Koreans. Therefore, KNHANES data have been used widely in health-related studies and have provided meaningful insights to inform health policy development in South Korea.

Conclusions

The findings of previous studies suggest an association of an irregular work schedule, particularly nighttime work, with an altered lipid profile. Accordingly, in this study, we examine the association between night work and dyslipidemia in a nationally representative sample of South Korean adults aged ≥30 years who participated in the KNHANES 2013–2016. In the overall analysis, we found a significant association of night work with dyslipidemia only among male workers. Additionally, subgroup analyses of male workers who reported skipping meals or receiving < 7 h of sleep per night revealed associations of night work with dyslipidemia. Among female participants, a subgroup analysis of white-collar workers found that those who worked at night faced higher risk of dyslipidemia, compared to their day working counterparts.

However, our study was unable to determine a causal relationship between the onset of dyslipidemia and night work, and further investigations are needed to validate the findings of our study. Given the increasing prevalence of dyslipidemia in South Korea and the association of this condition with cardiovascular disease, we also suggest the development of future interventions intended to alleviate dyslipidemia among night workers and ease the burden of CVD in South Korea.

Notes

Acknowledgements

KNHANES is an ongoing surveillance system that assesses the health and nutritional status of residents of the Republic of Korea, monitors trends in health risk factors and the prevalence of major chronic diseases, and provides data for the development and evaluation of national health policies and programs.

Funding

No funding was received for this study.

Availability of data and materials

All data generated or analyzed in this study are included in this article.

Authors’ contributions

JHJ. and ECP. designed the study, collected the data, performed the statistical analysis, and wrote the manuscript. DWL. and DWC. contributed to the discussion and reviewed and edited the manuscript. ECP. is the guarantor of this work and as such, had full access to all of the data. `ECP. assumes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study analyzed existing data and therefore did not require approval from an ethics review board. This study used data from the Korea National Health and Nutrition Examination Survey (KNHANES), which has been subject to an annual review and approval by the KCDC Research Ethics Review Committee since 2007.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflicts of interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Copyright information

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of Public Health, Graduate SchoolYonsei UniversitySeoulRepublic of Korea
  2. 2.Institute of Health Services ResearchYonsei UniversitySeoulRepublic of Korea
  3. 3.Department of Preventive MedicineYonsei University College of MedicineSeoulRepublic of Korea

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