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. 2022 Oct 9;10(10):1977. doi: 10.3390/healthcare10101977

Association between Nighttime Work and HbA1c Levels in South Korea

Yeon-Suk Lee 1, Jae Hong Joo 2,3,*,†, Eun-Cheol Park 3,4,*,†
Editors: Helena Martynowicz, Alberto Modenese
PMCID: PMC9601925  PMID: 36292424

Abstract

Background: As the world has become a 24 h society, people’s demands have generated various work schedules, leading to an increase in workers’ health problems. The study aimed to investigate the association between nighttime work and HbA1c levels among South Korean adults over the age of 30. Methods: Participants were selected from the 2016–2019 Korea National Health and Nutrition Survey; those diagnosed with diabetes were excluded. The dependent variable was the HbA1c level reported in the KNHANES health examination report. The main independent variable was the participant’s work schedule. Work schedules were classified into three categories based on the participant’s report: (1) day; (2) night and overnight, and (3) other. Generalized multiple linear regression was used, and the significance level was defined as p < 0.05. Results: The participants comprised 4773 men and 4818 women. Those engaged in the “day” schedule served as the reference group. Among the male participants, the “night and overnight” group had significantly larger HbA1c (%) levels than the “day” group (β = 0.061, p = 0.0085). Among these nighttime male workers, HbA1c (%) levels were particularly higher in the people who were physically inactive (β = 0.094, p = 0.0031), slept less than 7 h (β = 0.108, p = 0.0009), and skipped meals (β = 0.064, p = 0.0401). Conclusion: Our results revealed an association of nighttime work with increased HbA1c levels in male participants. High-risk groups for HbA1c levels require careful observation of physical activity, sleeping time, and eating habits.

Keywords: shift work, work schedule, nighttime, HbA1c

1. Introduction

Shift work has become an essential part of the modern society with the emergence of occupational conditions requiring 24 h labor in the fields of healthcare, manufacturing, wholesale and retail businesses [1]. The term “shift work” often refers to the employment practice designed to provide service across the entire day including nighttime and early morning. Recently, there has been a growing number of publications suggesting the association between shift work and negative health outcomes. In particular, work hours outside of the conventional daytime is likely to cause disruption of circadian rhythms, which could potentially lead to the development of chronic diseases, including metabolic disorders, cardiovascular disease, and cancer [2,3,4,5].

Diabetes is one of the most prevalent metabolic disorders worldwide. According to the International Diabetes Federation (IDF), 463 million people (9.3%) were suffering from it in 2019 [6,7]. Given the increased life expectancy due to the advances in medicine, population aged 65 years and older will account for 20% of the global population within the next few years [6]. Hence, this implies that the growing population of elderly people corresponds to the epidemiologic concept of increased prevalence of diabetes. The current trend estimates that a sharp increase in the prevalence of diabetes will result in as many as 700 million patients by 2045 [7]. In South Korea, diabetes is a substantial contributor to public health concerns, affecting nearly 10% of the national population, and has been reported as one of the leading causes of death among both sexes [6,7,8,9]. Thus, it is anticipated that diabetes will increase its burden on the healthcare system in the future.

Potential health-related risk factors associated with shift work, such as irregular living habits, work stress, and sleep complaints, can act as part of a diabetes outbreak [10,11,12]. It has been suggested that shift workers are more likely to experience the destruction of their biorhythms around the clock [13,14,15,16]. Of the various work schedules, those who conduct labor during the nighttime have reported lower scores in perceived health. Hemoglobin A1c (HbA1c) is a central biomarker for the presence and severity of hyperglycemia that is used as a predictive biomarker for diabetes [17]. Thus, this study aimed to investigate the potential effect of nighttime (i.e., night and overnight) work on HbA1c in comparison to the conventional daytime work.

2. Methods

2.1. Study Participants

We collected data from the 2016–2019 Korea National Health and Nutrition Examination Survey (KNHANES). The KNHANES is a self-reported nationally representative survey of South Koreans of all ages 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 comprises secondary data and is publicly available to researchers [18].

There were 32,379 participants in the 2016–2019 KNHANES. A total of 2435 people who had already been diagnosed with diabetes at the time of the survey were excluded to ensure the reliability of the results. Additionally, 9541 participants under the age of 30 were excluded because the majority of them did not undergo blood testing as part of the survey. We also excluded 10,812 participants with no response/other missing values from each variable’s survey items. Finally, a total of 9591 participants (4773 men and 4818 women) were selected for analysis.

2.2. Variables

HbA1c level, the dependent variable in this study, was measured in the blood samples collected after 8 h of fasting. HbA1c was considered as a continuous variable. Blood samples are among the key components of health examination reported in the KNHANES which was conducted at mobile examination centers that travel to the survey location [18].

The main independent variable was the work schedule. Work schedules were classified into three categories based on the survey report as follows: (1) day; (2) night and overnight; and (3) other (rotational, flexible, split, or irregular working hours/schedules).

Demographic, socioeconomic, health, and disease-related factors were also assessed to account for the confounding. The demographic factors included age (30~39, 40~49, 50~59, 60~69, and ≥70 years). The socioeconomic factors included educational level (≤elementary, middle, high, ≥college), occupational status (white, pink, blue), household income (low, middle, high), and household composition (one person, one-generation household, ≥two-generation household). The health-related factors included physical activity (active, inactive), smoking status (current smoker, former smoker, non-smoker), drinking status (2~4 times/week, 2~4 times/month, never or occasionally), sleeping hours (<7 h, ≥7 h), eating habits (eating three meals regularly, skipping meals), total energy intake (proteins, fats, carbohydrates; quintile 1, quintile 2, quintile 3, quintile 4, quintile 5), and body mass index (BMI)-defined obesity status (obese (≥ 25), normal (18.5~24.9), underweight (<18.5)). The disease-related factors included hypertension (hypertension (SBP ≥ 140 mm Hg), prehypertension (120 ≤ SBP ≤ 139 mm Hg), normal (SBP < 120 mm Hg)) and fasting glucose level (impaired fasting glucose (100~125 mg/dL), normal (<100 mg/dL)).

2.3. Statistical Analysis

The mean HbA1c level was calculated for each of the categorized variables included in the study. Analysis of variance (ANOVA) was performed to compare the mean HbA1c levels within each categorized variable to assess for significant differences. A generalized linear regression model (GLM) adjusted for confounding variables was used to assess the HbA1c level according to the reported work schedule. Those who reported the “day” schedule served as the reference group. The stratified, clustering, and weight variables developed by the KNHANES were applied to all the analyses to improve the representativeness of the sample and account for the limited proportion of participants retained in the final analysis. All the statistical analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC, USA).

3. Results

Table 1 summarizes the general characteristics of the study population (male: 4773; female: 4818). Among the male participants, 4103 (86.0%) reported “day”, 302 (6.3%) reported “night and overnight”, and 368 (7.7%) reported “other” as their ordinary work schedule. Of the male workers, those who reported “night and overnight” had the highest mean HbA1c level (5.64) with the lowest standard deviation. The p-value for ANOVA within the work schedule was 0.0071.

Table 1.

General characteristics of the study subjects and analysis of variance for the HbA1c levels.

Variables HbA1c (%)
Total Male Female
n % n % Mean ± SD p-Value n % Mean ± SD p-Value
Total 9591 100.0 4773 49.8 5.60 ± 0.39 4818 50.2 5.55 ± 0.34
Work schedule 0.0071 0.2566
Day 8180 85.3 4103 86.0 5.59 ± 0.43 4077 84.6 5.55 ± 0.42
Night and overnight 883 9.2 302 6.3 5.64 ± 0.46 581 12.1 5.56 ± 0.42
Other 528 5.5 368 7.7 5.64 ± 0.49 160 3.3 5.60 ± 0.44
Age <0.0001 <0.0001
30–39 2107 22.0 1135 23.8 5.43 ± 0.31 972 20.2 5.31 ± 0.30
40–49 2623 27.3 1253 26.3 5.53 ± 0.42 1370 28.4 5.42 ± 0.34
50–59 2441 25.5 1138 23.8 5.65 ± 0.41 1303 27.0 5.64 ± 0.37
60–69 1615 16.8 821 17.2 5.73 ± 0.47 794 16.5 5.78 ± 0.45
≥70 805 8.4 426 8.9 5.83 ± 0.58 379 7.9 5.90 ± 0.51
Educational level 0.8381 0.8638
≤Elementary 1404 14.6 515 10.8 5.74 ± 0.49 889 18.5 5.79 ± 0.50
Middle 919 9.6 446 9.3 5.72 ± 0.48 473 9.8 5.68 ± 0.41
High 2982 31.1 1418 29.7 5.62 ± 0.46 1564 32.5 5.54 ± 0.40
≥College 4286 44.7 2394 50.2 5.53 ± 0.39 1892 39.3 5.43 ± 0.34
Occupational status 0.0719 0.0607
White 3936 41.0 1952 40.9 5.52 ± 0.39 1984 41.2 5.43 ± 0.35
Pink 1886 19.7 565 11.8 5.61 ± 0.44 1321 27.4 5.60 ± 0.45
Blue 3769 39.3 2256 47.3 5.66 ± 0.47 1513 31.4 5.68 ± 0.44
Household income 0.451 0.3665
Low 1022 10.7 386 8.1 5.72 ± 0.58 636 13.2 5.74 ± 0.50
Middle 5143 53.6 2,621 54.9 5.60 ± 0.42 2522 52.3 5.56 ± 0.42
High 3426 35.7 1,766 37.0 5.56 ± 0.43 1660 34.5 5.48 ± 0.37
Household composition 0.5193 0.3746
One-person household 936 9.8 412 8.6 5.56 ± 0.47 524 10.9 5.70 ± 0.53
One-generation household 2280 23.8 1238 25.9 5.68 ± 0.49 1042 21.6 5.66 ± 0.44
≥Two-generation household 6375 66.5 3123 65.4 5.57 ± 0.41 3252 67.5 5.50 ± 0.38
Physical activity 0.7231 0.9600
Active 4086 42.6 2161 45.3 5.57 ± 0.43 1,925 40.0 5.54 ± 0.41
Inactive 5505 57.4 2612 54.7 5.62 ± 0.45 2,893 60.0 5.56 ± 0.43
Smoking status <0.0001 0.4196
Current smoker 1864 19.4 1648 34.5 5.61 ± 0.45 216 4.5 5.47 ± 0.37
Former smoker 2381 24.8 2100 44.0 5.62 ± 0.45 281 5.8 5.43 ± 0.45
Non-smoker 5346 55.7 1025 21.5 5.54 ± 0.40 4,321 89.7 5.57 ± 0.42
Drinking status <0.0001 <0.0001
2–4 times/week 2471 25.8 1824 38.2 5.57 ± 0.42 647 13.4 5.41 ± 0.38
2–4 times/month 2289 23.9 1264 26.5 5.58 ± 0.45 1025 21.3 5.47 ± 0.40
Never or occasionally 4831 50.4 1685 35.3 5.64 ± 0.44 3146 65.3 5.61 ± 0.43
Hours of sleep 0.0426 0.2398
<7 h 3779 39.4 1932 40.5 5.62 ± 0.46 1847 38.3 5.59 ± 0.41
≥7 h 5812 60.6 2841 59.5 5.58 ± 0.42 2971 61.7 5.53 ± 0.43
Eating habits 0.0975 <0.0001
Eating three meals regularly 5438 56.7 2773 58.1 5.64 ± 0.46 2665 55.3 5.63 ± 0.43
Skip meal (s) 4153 43.3 2000 41.9 5.54 ± 0.41 2153 44.7 5.46 ± 0.39
Total energy intake (kcal) a 0.4732 0.8141
Quintile 1 1919 20.0 956 20.0 5.64 ± 0.48 963 20.0 5.56 ± 0.46
Quintile 2 1918 20.0 954 20.0 5.62 ± 0.49 964 20.0 5.57 ± 0.40
Quintile 3 1919 20.0 955 20.0 5.58 ± 0.41 964 20.0 5.56 ± 0.43
Quintile 4 1918 20.0 954 20.0 5.60 ± 0.44 964 20.0 5.55 ± 0.43
Quintile 5 1917 20.0 954 20.0 5.54 ± 0.35 963 20.0 5.53 ± 0.39
BMI (kg/m2) b <0.0001 <0.0001
Obese (≥25) 3340 34.8 2015 42.2 5.66 ± 0.44 1325 27.5 5.71 ± 0.46
Normal (18.5~24.9) 5964 62.2 2669 55.9 5.55 ± 0.43 3295 68.4 5.51 ± 0.39
Underweight (<18.5) 287 3.0 89 1.9 5.51 ± 0.35 198 4.1 5.33 ± 0.34
Hypertension (mm Hg) 0.0017 <0.0001
Hypertension (≥140) 2758 28.8 1582 33.1 5.71 ± 0.47 1176 24.4 5.78 ± 0.48
Prehypertension (120~139) 2605 27.2 1520 31.8 5.58 ± 0.43 1085 22.5 5.59 ± 0.39
Normal (<120) 4228 44.1 1671 35.0 5.51 ± 0.39 2557 53.1 5.44 ± 0.36
Fasting glucose level (mg/dL) <0.0001 <0.0001
Impaired fasting glucose (100~125) 3206 33.4 1930 40.4 5.79 ± 0.49 1276 26.5 5.85 ± 0.48
Normal (<100) 6385 66.6 2843 59.6 5.47 ± 0.35 3542 73.5 5.45 ± 0.34
Year <0.0001 <0.0001
2016 2258 23.5 1144 24.0 5.57 ± 0.45 1114 23.1 5.50 ± 0.40
2017 2382 24.8 1181 24.7 5.55 ± 0.40 1201 24.9 5.53 ± 0.41
2018 2492 26.0 1220 25.6 5.58 ± 0.41 1272 26.4 5.55 ± 0.43
2019 2459 25.6 1228 25.7 5.68 ± 0.49 1231 25.6 5.64 ± 0.43
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a Total energy intake = (carbohydrates (g) × 4 kcal/g) + (proteins (g) × 4 kcal/g) + (fats (g) × 9 kcal/g). b Obesity status defined by body mass index (BMI) based on the 2014 Clinical Practice Guidelines for Overweight and Obesity in Korea.

Among the 4818 female participants, 4077 (84.6%) reported “day”, 581 (12.1%) reported “night and overnight”, and 160 (3.3%) reported “other” as their ordinary work schedule. Of the male workers, those who reported “night and overnight” had the highest mean HbA1c level (5.64) with the lowest standard deviation. The p-value for ANOVA was not statistically significant.

Table 2 summarizes the results of the generalized multiple regression analysis for HbA1c levels according to the types of work schedule. The HbA1c levels are represented as adjusted beta (β) coefficient in relation to “day” work schedules. Among the male workers, the “night and overnight” workers had the highest HbA1c levels (β = 0.061), and that was statistically significant (p = 0.0085). On the other hand, there was no significant difference among the female workers according to their reported work schedules.

Table 2.

Generalized multiple linear regression for HbA1c levels according to work schedule.

Variable HbA1c (%)
Male Female
β SE p-Value β SE p-Value
Work schedule
Day Ref. Ref.
Night and overnight 0.061 0.023 0.0085 0.019 0.017 0.2687
Other 0.008 0.025 0.7557 0.049 0.032 0.1273
Age
30–39 Ref. Ref.
40–49 0.056 0.015 0.0002 0.062 0.013 <0.0001
50–59 0.135 0.017 <0.0001 0.200 0.017 <0.0001
60–69 0.207 0.022 <0.0001 0.272 0.024 <0.0001
≥70 0.296 0.039 <0.0001 0.326 0.038 <0.0001
Educational level
≤Elementary −0.017 0.028 0.5471 0.009 0.026 0.7357
Middle −0.020 0.026 0.4381 0.014 0.024 0.5605
High −0.012 0.015 0.4321 −0.003 0.013 0.8335
≥College Ref. Ref.
Occupational status
White Ref. Ref.
Pink 0.040 0.020 0.0460 0.014 0.014 0.3233
Blue 0.036 0.015 0.0166 −0.010 0.017 0.5349
Household income
Low 0.005 0.031 0.8590 −0.001 0.022 0.9491
Middle 0.013 0.013 0.3162 0.013 0.011 0.2480
High Ref. Ref.
Household composition
One-person household −0.040 0.021 0.0522 0.032 0.021 0.1370
One-generation household −0.002 0.016 0.9219 0.010 0.015 0.4869
≥Two-generation household Ref. Ref.
Physical activity
Active Ref. Ref.
Inactive 0.006 0.012 0.5834 −0.002 0.011 0.8164
Smoking status
Current smoker 0.080 0.016 < 0.0001 −0.010 0.023 0.6642
Former smoker 0.015 0.015 0.3137 −0.027 0.023 0.2503
Non-smoker Ref. Ref.
Drinking status
2–4 times/week −0.113 0.014 < 0.0001 −0.118 0.016 <0.0001
2–4 times/month −0.046 0.015 0.0020 −0.035 0.013 0.0077
Never or occasionally Ref. Ref.
Hours of sleep
<7 h 0.021 0.012 0.0727 0.023 0.011 0.0393
≥7 h Ref. Ref.
Eating habits
Eating three meals regularly Ref. Ref.
Skipping meals −0.020 0.013 0.1198 −0.045 0.011 <0.0001
Total energy intake (kcal) a
Quintile 1 0.004 0.018 0.8165 −0.014 0.018 0.4402
Quintile 2 0.022 0.019 0.2519 0.000 0.016 0.9795
Quintile 3 Ref. Ref.
Quintile 4 0.018 0.018 0.3109 −0.007 0.016 0.6509
Quintile 5 0.000 0.017 0.9834 0.007 0.016 0.6596
BMI (kg/m2) b
Obese (≥25) 0.071 0.012 <0.0001 0.080 0.013 <0.0001
Normal (18.5~24.9) Ref. Ref.
Underweight (<18.5) −0.031 0.031 0.3094 −0.067 0.025 0.0079
Hypertension (mm Hg)
Hypertension (≥140) 0.064 0.016 <0.0001 0.079 0.018 <0.0001
Prehypertension (120~139) 0.022 0.014 0.1189 0.030 0.014 0.0272
Normal (<120) Ref. Ref.
Fasting glucose level (mg/dL)
Impaired fasting glucose (100~125) 0.267 0.013 <0.0001 0.297 0.015 <0.0001
Normal (<100) Ref. Ref.
Year
2016 Ref. Ref.
2017 −0.021 0.016 0.1844 0.033 0.015 0.0321
2018 −0.003 0.017 0.8475 0.048 0.015 0.0018
2019 0.095 0.018 <0.0001 0.120 0.015 <0.0001
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BMI—body mass index. a Total energy intake = (carbohydrates (g) × 4 kcal/g) + (proteins (g) × 4 kcal/g) + (fats (g) × 9 kcal/g). b Obesity status defined by BMI based on the 2014 Clinical Practice Guidelines for Overweight and Obesity in Korea.

Table 3 summarizes the subgroup results of generalized multiple linear regression for HbA1c levels stratified by physical activity, hours of sleep, and daily eating. Among the 302 male “night and overnight” workers, the average HbA1c level significantly increased to 0.108 (p = 0.009) for those who reported having less than 7 h of sleep daily, to 0.064 (p = 0.0401) for those who reported skipping meals, and to 0.108 (p = 0.0579) for those who intook the least amount of energy. Those who reported being physically inactive also showed an increased level of HbA1c (β = 0.094; p = 0.0031).

Table 3.

Generalized multiple linear regression for HbA1c levels stratified by physical activity, hours of sleep, and daily eating.

Variable HbA1c (%)
Day Male (n = 4773) Female (n = 4818)
Night and Overnight (n = 302) Other (n = 368) Night and Overnight (n = 581) Other (n = 160)
Β β SE p-Value β SE p-Value β SE p-Value β SE p-Value
Physical activity
Active Ref. 0.023 0.034 0.4995 0.014 0.035 0.6842 −0.011 0.027 0.6791 −0.015 0.038 0.7016
Inactive Ref. 0.094 0.032 0.0031 −0.002 0.037 0.9605 0.037 0.022 0.0899 0.092 0.048 0.0561
Hours of sleep
<7 h Ref. 0.108 0.033 0.0009 −0.049 0.038 0.2011 0.024 0.026 0.3685 0.053 0.048 0.2747
≥7 h Ref. 0.034 0.032 0.2894 0.065 0.032 0.0434 0.014 0.022 0.5105 0.042 0.041 0.3095
Eating habits
Eating three meals regularly Ref. 0.053 0.033 0.1095 −0.008 0.035 0.8227 −0.006 0.025 0.8017 0.056 0.050 0.2661
Skipping meals Ref. 0.064 0.031 0.0401 0.028 0.036 0.4494 0.035 0.022 0.1206 0.055 0.041 0.1852
Total energy intake (kcal) a
Quintile 1 Ref. 0.108 0.057 0.0579 0.067 0.077 0.3870 0.025 0.038 0.5129 0.132 0.110 0.2336
Quintile 2 Ref. 0.059 0.056 0.2922 −0.042 0.045 0.3539 0.008 0.038 0.8297 0.079 0.074 0.2863
Quintile 3 Ref. 0.057 0.041 0.1679 −0.075 0.052 0.1555 0.010 0.044 0.8155 0.036 0.052 0.4823
Quintile 4 Ref. −0.021 0.050 0.6658 0.021 0.057 0.7097 0.021 0.036 0.5720 −0.011 0.057 0.8546
Quintile 5 Ref. 0.045 0.046 0.3293 0.070 0.053 0.1913 0.066 0.030 0.0298 0.070 0.047 0.1352
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a Total energy intake = (carbohydrates (g) × 4 kcal/g) + (proteins (g) × 4 kcal/g) + (fats (g) × 9 kcal/g).

Figure 1 demonstrates the results of generalized multiple linear regression for HbA1c levels according to the specific work schedules. In the male participants, HbA1c levels increased the greatest among those who worked overnight (β = 0.092; p = 0.0247), followed by those who worked during the night (β = 0.500; p = 0.0441).

Figure 1.

Figure 1

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Generalized multiple linear regression for HbA1c levels according to the specific types of work schedules; * p-value < 0.05.

4. Discussion

After controlling for confounding variables such as demographic, socioeconomic, health, and disease-related factors, the male participants who worked during the night and overnight showed increased levels of HbA1c in comparison to those who worked during the conventional daytime. On the other hand, there was no significant difference in HbA1c levels among the female participants according to their ordinary work schedules. Sex differences in glycated hemoglobin levels have not been widely discussed [19]. Although no consensus has been reached regarding sex effects on HbA1c, hormonal changes during the menstrual cycle may account for the differences in HbA1c levels in male and female participants [19,20,21]. Previous literature suggested that women have a shorter red blood cell survival in comparison to men, and this may lower HbA1c levels. Other empirical evidence showed that estrogen plays a role in suppressing erythropoiesis in vitro and in vivo, which is implicated in lowering HbA1c levels in women [21].

Metabolism operates on circadian rhythms around the clock. Work schedules outside the standard daytime period may impair physiological activities and are likely to result in aberrant glucose homeostasis [22,23]. The findings of our study add empirical evidence to support the existing phenomenon that workers who work during the nighttime have increased HbA1c levels [23,24,25,26,27,28]. In particular, the study showed that HbA1c levels increased in the nighttime workers who were physically inactive, those who slept less than 7 h, and those who had the least amount of energy intake.

Exercise promotes glucose metabolism, which leads to elevated insulin sensitivity and lower blood sugar. Furthermore, lack of exercise can increase the risk of diabetes. Prior research publications showed that people with impaired fasting glucose disorder showed a statistically significant difference in the relative risk for non-exercising groups (1.375 for men and 1.124 for women), confirming the benefits of exercise [29]. Studies of diabetic and prediabetic men also showed that one to two physical activities a week were more effective in controlling blood sugar than inactivity [29,30].

HbA1c levels of people working during the nighttime with less than seven hours of sleep were significantly higher. Adequate sleeping time can help the body recover from fatigue and maintain biorhythms. According to the recommended sleeping time for each age group provided in the National Sleep Foundation (NSF) guidelines for 2015, the optimal sleeping time for adults is more than seven hours [31]. Structural changes in modern society are affecting the amount and quality of sleep as shifts increase and various types of working hours arise [32]. In addition, the amount and quality of this poor sleep are consistent with previous studies that linked them with the risk of type 2 diabetes [33,34].

Regarding eating habits, higher HbA1c levels are found in people who skip more than one meal a day compared to those who regularly eat three meals a day while working evenings and nights. Dining patterns have changed to Westernized eating habits since industrialization, which has also affected the development of diabetes, closely related to insulin secretion [30,31,32,33,34]. Carlson et al. assessed the effect of glucose metabolism on healthy men and women of normal weight without decreasing energy consumption [35,36]. They found that the morning fasting blood sugar level of the meal skipping group was higher than that of the group who regularly ate three meals a day. A prior study that investigated the relationship between the regularity of meals and impaired blood glucose disorder in adult non-diabetes groups also showed aligned results, with a 1.27 times higher incidence of impaired fasting glucose disorder in the groups that skipped meals [36,37].

In this study, HbA1c levels did not reach the prediabetes level. However, previous studies reported higher rates of the incidence of diabetes for five years according to glycated hemoglobin levels. Notably, HbA1c levels increased to less than 9% if they previously were 5.0–5.5%, to 9–25% if they previously were 6.0–6.5%, and to 25–50% if they previously were 6.0–6.5% [2]. Another previous study tracked HbA1c levels for six years, excluding those with a history of diabetes. It showed the highest sensitivity (59%) and specificity (77%) when the HbA1c level was initially 5.6%, with a 2.4 times increase in men and 3.1 times increase in women [38].

This study has several limitations. First, the cross-sectional design rendered us unable to determine a causal relationship between nighttime work and HbA1c levels. Second, the key covariates considered in this study, including physical activity, sleeping time, and eating habits were self-reported, possibly subject to recall bias. Third, the duration in working years of the current shift work was not considered. Lastly, the types of shift work were not specified in the survey data, and the workers were only specified by “collar colors”. Despite these limitations, this study also has certain strengths. First, it was the first study to analyze the association between shift work and HbA1c for non-diabetes groups in South Korea. Second, the data used in this study came from a national health statistics survey that calculated more than 500 health indicators such as eating habits and chronic diseases. The National Health and Nutrition Survey comprised a representative sample of the South Korean population, representing the actual health status of Koreans, and an updated questionnaire every year can identify changes in health status. Therefore, it can contribute to improving the health of the people by reflecting on health policies.

5. Conclusions

This study investigated the association between nighttime work and HbA1c levels among Korean adults aged 30 or older. We found a significant association between working night shifts and HbA1c levels in the men. Lack of exercise, sleeping less than 7 h, and skipping more than one meal a day were especially highly associated with increased HbA1c levels. High-risk groups with increased HbA1c levels require careful monitoring of physical activity, sleeping time, eating habits, and BMI. However, our study could not determine a causal relationship between HbA1c and night work, and further studies are needed for validation.

Acknowledgments

We thank the Korea Centers for Disease Control (KCDC) which produced and provided the national-level survey data. We also thank those colleagues from the Institute of Health Services Research at Yonsei University who gave advice regarding important intellectual content.

Author Contributions

Y.-S.L. and E.-C.P. designed the study, collected the data, performed the statistical analysis, and wrote the manuscript. J.H.J. contributed to the discussion and reviewed and edited the manuscript. E.-C.P. is the guarantor of this work and as such had full access to all of the data. J.H.J. and E.-C.P. assume responsibility for the integrity of the data and the accuracy of the data analysis, and they are the corresponding authors of the manuscript. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This study was an analysis of the existing data; thus, it did not require approval by an ethics review board. The data used in this study came from the KNHANES, and it has been annually reviewed and approved by the Korea Centers for Disease Control (KCDC) Research Ethics Review Committee since 2007.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. The data used in the study is a secondary data with encrypted personal information.

Data Availability Statement

The data is publicly accessible on the website of KNHANES administered by the Korea Disease Control and Prevention Agency (https://knhanes.cdc.go.kr/knhanes/index.do, accessed on 4 October 2022).

Conflicts of Interest

The authors declare that they have no competing interest.

Funding Statement

This research received no external funding.

Footnotes

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data is publicly accessible on the website of KNHANES administered by the Korea Disease Control and Prevention Agency (https://knhanes.cdc.go.kr/knhanes/index.do, accessed on 4 October 2022).

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