Relationship between Marriage and Prediabetes among Healthcare Workers: Mediating Effect of Triglycerides
Abstract
:1. Introduction
2. Materials and Methods
2.1. Burnout Measurement
- How often do you feel tired?
- How often are you physically exhausted?
- How often are you emotionally exhausted?
- How often do you think “I can’t take it anymore?”
- How often do you feel worn out?
- How often do you feel weak and susceptible to illness?
2.2. Musculoskeletal Pain Measurement
2.3. Demographic/Living/Work Data
2.4. Health Check Data
2.5. Statistic Methods
- where s_a and s_b are the standard deviations of a and b, respectively. Z_m values exceeding |1.96|, |2.57|, and |3.90| (for a two-tailed test) are significant at α = 0.05, 0.01, and 0.0001, respectively.
3. Results
3.1. Description of Survey Variables and Prediabetes for Participants
3.2. A Logistic Regression Model of Marital Status and IFG
3.3. Mediation Models of Marriage State, TG, and IFG
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Atlas, I. IDF Diabetes Atlas, 9th ed.; International Diabetes Federation: Brussels, Belgium, 2019. [Google Scholar]
- Tabák, A.G.; Herder, C.; Rathmann, W.; Brunner, E.J.; Kivimäki, M. Prediabetes: A high-risk state for diabetes development. Lancet 2012, 379, 2279–2290. [Google Scholar] [CrossRef]
- Phillips, L.S.; Ratner, R.E.; Buse, J.B.; Kahn, S.E. We can change the natural history of type 2 diabetes. Diabetes Care 2014, 37, 2668–2676. [Google Scholar] [CrossRef]
- Aldossari, K.K.; Shubair, M.M.; Al-Zahrani, J.; Alduraywish, A.A.; AlAhmary, K.; Bahkali, S.; Aloudah, S.M.; Almustanyir, S.; Al-Rizqi, L.; El-Zahaby, S.A.; et al. Association between chronic pain and diabetes/prediabetes: A population-based cross-sectional survey in Saudi Arabia. Pain Res. Manag. 2020, 2020, 8239474. [Google Scholar] [CrossRef] [PubMed]
- Schnurr, T.M.; Jakupović, H.; Carrasquilla, G.D.; Ängquist, L.; Grarup, N.; Sørensen, T.I.A.; Tjønneland, A.; Overvad, K.; Pedersen, O.; Hansen, T.; et al. Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: A case-cohort study. Diabetologia 2020, 63, 1324–1332. [Google Scholar] [CrossRef]
- Andes, L.J.; Cheng, Y.J.; Rolka, D.B.; Gregg, E.W.; Imperatore, G. Prevalence of prediabetes among adolescents and young adults in the United States, 2005–2016. JAMA Pediatr. 2020, 174, e194498. [Google Scholar] [CrossRef] [PubMed]
- Seo, E.; Lee, Y.; Mun, E.; Kim, D.H.; Jeong, Y.; Lee, J.; Jeong, J.; Lee, W. The effect of long working hours on developing type 2 diabetes in adults with prediabetes: The Kangbuk Samsung cohort study. Ann. Occupa. Environ. Med. 2022, 34, e4. [Google Scholar] [CrossRef] [PubMed]
- Strikwerda, M.; Beulens, J.W.; Remmelzwaal, S.M.; Schoonmade, L.J.M.; van Straten, A.; Schram, M.T.; Elders, P.J.; Rutters, F. The association of burnout and vital exhaustion with type 2 diabetes: A systematic review and meta-analysis. Psychosom. Med. 2021, 83, 1013–1030. [Google Scholar] [CrossRef]
- Gao, Y.; Gan, T.; Jiang, L.; Yu, L.; Tang, D.; Wang, Y.; Li, X.; Ding, G. Association between shift work and risk of type 2 diabetes mellitus: A systematic review and dose-response meta-analysis of observational studies. Chronobiol. Int. 2020, 37, 29–46. [Google Scholar] [CrossRef]
- Amanat, S.; Ghahri, S.; Dianatinasab, A.; Fararouei, M.; Dianatinasab, M. Exercise and Type 2 Diabetes. In Physical Exercise for Human Health; Advances in Experimental Medicine and Biology; Xiao, J., Ed.; Springer: Singapore, 2020; Volume 1228. [Google Scholar] [CrossRef]
- Magkos, F.; Hjorth, M.F.; Astrup, A. Diet and exercise in the prevention and treatment of type 2 diabetes mellitus. Nat. Rev. Endocrinol. 2020, 16, 545–555. [Google Scholar] [CrossRef]
- Krishnappa, M.; Patil, K.; Parmar, K.; Trivedi, P.; Mody, N.; Shah, C.; Faldu, K.; Maroo, S.; Parmar, D. Effect of saroglitazar 2 mg and 4 mg on glycemic control, lipid profile and cardiovascular disease risk in patients with type 2 diabetes mellitus: A 56-week, randomized, double blind, phase 3 study (PRESS XII study). Cardiovas. Diabetol. 2020, 19, 93. [Google Scholar] [CrossRef]
- Koball, H.L.; Moiduddin, E.; Henderson, J.; Goesling, B.; Besculides, M. What Do We Know About the Link Between Marriage and Health? J. Fam. Issues 2010, 31, 1019–1040. [Google Scholar] [CrossRef]
- Brown, J.L.; Miller, D. Couples’ gender role preferences and management of family food preferences. J. Nutri. Edu. Behav. 2002, 34, 215–223. [Google Scholar] [CrossRef]
- Bove, C.F.; Sobal, J.; Rauschenbach, B.S. Food choices among newly married couples: Convergence, conflict, individualism, and projects. Appetite 2003, 40, 25–41. [Google Scholar] [CrossRef]
- Appiah, D.; Schreiner, P.J.; Selvin, E.; Demerath, E.W.; Pankow, J.S. Spousal diabetes status as a risk factor for incident type 2 diabetes: A prospective cohort study and meta-analysis. Acta Diabetol. 2019, 56, 619–629. [Google Scholar] [CrossRef]
- Ford, K.J.; Robitaille, A. How sweet is your love? Disentangling the role of marital status and quality on average glycemic levels among adults 50 years and older in the English Longitudinal Study of Ageing. BMJ Open Diabetes Res. Care 2023, 11, e003080. [Google Scholar] [CrossRef] [PubMed]
- Ramezankhani, A.; Azizi, F.; Hadaegh, F. Associations of marital status with diabetes, hypertension, cardiovascular disease and all-cause mortality: A long term follow-up study. PLoS ONE 2019, 14, e0215593. [Google Scholar] [CrossRef] [PubMed]
- Al-Zahrani, J.M.; Aldiab, A.; Aldossari, K.K.; Al-Ghamdi, S.; Batais, M.A.; Javad, S.; Nooruddin, S.; Zahid, N.; Razzak, H.A.; El-Metwally, A. Prevalence of prediabetes, diabetes and its predictors among females in AlKharj, Saudi Arabia: A cross-sectional study. Ann. Global Health 2019, 85, 109. [Google Scholar] [CrossRef]
- Zhao, J.; Zhang, Y.; Wei, F.; Song, J.; Cao, Z.; Chen, C.; Zhang, K.; Feng, S.; Wang, Y.; Li, W.-D. Triglyceride is an independent predictor of type 2 diabetes among middle-aged and older adults: A prospective study with 8-year follow-ups in two cohorts. J. Transl. Med. 2019, 17, 403. [Google Scholar] [CrossRef]
- Cox, R.A.; García-Palmieri, M.R. Cholesterol, Triglycerides, and Associated Lipoproteins. In Clinical Methods: The History, Physical, and Laboratory Examinations, 3rd ed.; Butterworths: Boston, MA, USA, 1990. [Google Scholar]
- Liu, Z.; Rochfort, S.; Cocks, B. Milk lipidomics: What we know and what we don’t. J. Transl. Med. 2018, 71, 70–85. [Google Scholar] [CrossRef] [PubMed]
- Alves-Bezerra, M.; Cohen, D.E. Triglyceride metabolism in the liver. Comprehen. Physio. 2017, 8, 1–8. [Google Scholar] [CrossRef]
- Iqbal, J.; Hussain, M.M. Intestinal lipid absorption. Am. J. Physio. Endocri. Meta. 2009, 296, E1183–E1194. [Google Scholar] [CrossRef] [PubMed]
- Gordon, T.; Fisher, M.; Ernst, N.; Rifkind, B.M. Relation of diet to LDL cholesterol, VLDL cholesterol, and plasma total cholesterol and triglycerides in white adults. The Lipid Research Clinics prevalence study. Arteriosclerosis 1982, 2, 502–512. [Google Scholar] [CrossRef]
- Kristensen, T.S.; Borritz, M.; Villadsen, E.; Christensen, K.B. The Copenhagen burnout inventory: A new tool for the assessment of burnout. Work Stress 2005, 19, 192–207. [Google Scholar] [CrossRef]
- Aulia, C. Validity and reliability test of the Nordic musculoskeletal questionnaire with formal and informal sector workers. In Proceedings of the 7th International Conference on Public Health, Solo, Indonesia, 18–19 November 2020; pp. 100–106. [Google Scholar]
- Palmer, K.; Smith, G.; Kellingray, S.; Cooper, C. Repeatability and validity of an upper limb and neck discomfort questionnaire: The utility of the standardized Nordic questionnaire. Occupa. Med. 1999, 49, 171–175. [Google Scholar] [CrossRef] [PubMed]
- Dahl, A.G.; Havang, S.; Hagen, K. Reliability of a self-administrated musculoskeletal questionnaire: The fourth Trøndelag health study. Musculoskelet. Sci. Pract. 2022, 57, 102496. [Google Scholar] [CrossRef]
- Hair, J.F.; Anderson, R.E.; Tatham, R.L.; Black, W.C. Multivariate Data Analysis with Readings, 4th ed.; Prentice Hall, Inc.: Upper Saddle River, NJ, USA, 1995. [Google Scholar]
- Genuth, S.; Alberti, K.G.; Bennett, P.; Buse, J.; Defronzo, R.; Kahn, R.; Kitzmiller, J.; Knowler, W.C.; Lebovitz, H.; Lernmark, A.; et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003, 26, 3160–3167. [Google Scholar] [CrossRef]
- Baron, R.M.; Kenny, D.A. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal Social Psycho. 1986, 51, 1173–1182. [Google Scholar] [CrossRef]
- Iacobucci, D. Mediation analysis and categorical variables: The final frontier. J. Consum. Psycho. 2012, 22, 582–594. [Google Scholar] [CrossRef]
- Echouffo-Tcheugui, J.B.; Selvin, E. Prediabetes and what it means: The epidemiological evidence. Ann. Rev. Public Health 2021, 42, 59–77. [Google Scholar] [CrossRef]
- National Nutrition and Health Status Change Survey Results Report 2017–2020, Nutrition and Health Survey in Taiwan. 2022. Available online: https://www.hpa.gov.tw/Pages/List.aspx?nodeid=3998 (accessed on 20 August 2024).
- Mainous, A.G., 3rd; Tanner, R.J.; Baker, R.; Zayas, C.E.; Harle, C.A. Prevalence of prediabetes in England from 2003 to 2011: Population-based, cross-sectional study. BMJ. Open 2014, 4, e005002. [Google Scholar] [CrossRef]
- Vatcheva, K.P.; Fisher-Hoch, S.P.; Reininger, B.M.; McCormick, J.B. Sex and age differences in prevalence and risk factors for prediabetes in Mexican-Americans. Diabetes Res. Clin. Pract. 2020, 159, 107950. [Google Scholar] [CrossRef]
- Brož, J.; Malinovská, J.; Nunes, M.A.; Kučera, K.; Rožeková, K.; Žejglicová, K.; Urbanová, J.; Jenšovský, M.; Brabec, M.; Lustigová, M. Prevalence of diabetes and prediabetes and its risk factors in adults aged 25–64 in the Czech Republic: A cross-sectional study. Diabetes Res. Clin. Pract. 2020, 170, 108470. [Google Scholar] [CrossRef] [PubMed]
- Mäntyselkä, P.; Miettola, J.; Niskanen, L.; Kumpusalo, E. Chronic pain, impaired glucose tolerance and diabetes: A community-based study. Pain 2008, 137, 34–40. [Google Scholar] [CrossRef] [PubMed]
- Mäntyselkä, P.; Miettola, J.; Niskanen, L.; Kumpusalo, E. Persistent pain at multiple sites—Connection to glucose derangement. Diabetes Res. Clin. Pract. 2009, 84, e30–e32. [Google Scholar] [CrossRef]
- Cichosz, S.L.; Fleischer, J.; Hoeyem, P.; Laugesen, E.; Poulsen, P.L.; Christiansen, J.S.; Ejskjær, N.; Hansen, T.K. Objective measurements of activity patterns in people with newly diagnosed type 2 diabetes demonstrate a sedentary lifestyle. Diabetic Med. 2013, 30, 1063–1066. [Google Scholar] [CrossRef] [PubMed]
- Park, K.S.; Hwang, S.Y. Lifestyle-related predictors affecting prediabetes and diabetes in 20–30-year-old young Korean adults. Epidemiol. Health 2020, 42, e2020014. [Google Scholar] [CrossRef] [PubMed]
- Mc Donald Posso, A.J.; Bradshaw Meza, R.A.; Mendoza Morales, E.A.; Jaen, Y.; Cumbrera Ortega, A.; Mendoza Posada, E.J. Diabetes in panama: Epidemiology, risk factors, and clinical management. Ann. Global Health 2015, 81, 754–764. [Google Scholar] [CrossRef]
- Suvitaival, T.; Bondia-Pons, I.; Yetukuri, L.; Pöhö, P.; Nolan, J.J.; Hyötyläinen, T.; Kuusisto, J.; Orešič, M. Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men. Metabolism 2018, 78, 1–12. [Google Scholar] [CrossRef]
- Wang, Y.-L.; Koh, W.-P.; Talaei, M.; Yuan, J.-M.; Pan, A. Association between the ratio of triglyceride to high-density lipoprotein cholesterol and incident type 2 diabetes in Singapore Chinese men and women. J. Diabetes 2017, 9, 689–698. [Google Scholar] [CrossRef]
- Seghieri, M.; Tricò, D.; Natali, A. The impact of triglycerides on glucose tolerance: Lipotoxicity revisited. Diabetes Metab. 2017, 43, 314–322. [Google Scholar] [CrossRef]
- Pfeiler, T.M.; Egloff, B. Personality and eating habits revisited: Associations between the big five, food choices, and body mass index in a representative Australian sample. Appetite 2020, 149, 104607. [Google Scholar] [CrossRef] [PubMed]
- Cohn, J.S.; McNamara, J.R.; Cohn, S.D.; Ordovas, J.M.; Schaefer, E.J. Postprandial plasma lipoprotein changes in human subjects of different ages. J. Lipid Res. 1988, 29, 469–479. [Google Scholar] [CrossRef] [PubMed]
- Cassader, M.; Gambino, R.; Ruiu, G.; Marena, S.; Bodoni, P.; Pagano, G. Postprandial triglyceride-rich lipoprotein changes in elderly and young subjects. Aging 1996, 8, 421–428. [Google Scholar] [CrossRef]
- Vinagre, C.G.; Freitas, F.R.; de Mesquita, C.H.; Vinagre, J.C.; Mariani, A.C.; Kalil-Filho, R.; Maranhão, R.C. Removal of chylomicron remnants from the bloodstream is delayed in aged subjects. Aging Dis. 2018, 9, 748–754. [Google Scholar] [CrossRef] [PubMed]
- Jeon, J.Y.; Ko, S.-H.; Kwon, H.-S.; Kim, N.H.; Kim, J.H.; Kim, C.S.; Song, K.-H.; Won, J.C.; Lim, S.; Choi, S.H.; et al. Prevalence of diabetes and prediabetes according to fasting plasma glucose and HbA1c. Diabetes Metab. J. 2013, 37, 349–357. [Google Scholar] [CrossRef] [PubMed]
- Kam-On Chung, J.; Xue, H.; Wing-Hang Pang, E.; Chuen-Chu Tam, D. Accuracy of fasting plasma glucose and hemoglobin A1c testing for the early detection of diabetes: A pilot study. Front. Lab. Med. 2017, 1, 76–81. [Google Scholar] [CrossRef]
- Khaled Fahim, N.; Negida, A. Sample Size Calculation Guide—Part 1: How to Calculate the Sample Size Based on the Prevalence Rate. Adv. J. Emerg. Med. 2018, 2, e50. [Google Scholar] [CrossRef] [PubMed]
- Coelho, P.; Duarte, M.; Cabral, E.T.; Mateus, S.; Rodrigues, F. Childhood and Adolescent Obesity in a School in Interior Portugal—A Teen without Risk Study. Obesities 2024, 4, 281–291. [Google Scholar] [CrossRef]
Surveyed Variable | N (%) | IFG (Individuals = 220; 21.17%) | |||||||
---|---|---|---|---|---|---|---|---|---|
No age Stratification | Aged 20–37 Years | Aged > 38 Years | |||||||
n (%)/m ± SD | p | N’ | n (%)/m ± SD | p | N’ | n (%)/m ± SD | p | ||
Sex | |||||||||
Female | 885 (85.18) | 173 (19.55) | 0.004 a | 452 | 61 (13.05) | 0.028 a | 433 | 112 (25.87) | 0.034 a |
Male | 154 (14.82) | 47 (30.52) | 81 | 19 (23.46) | 73 | 28 (38.36) | |||
Age | |||||||||
20–37 years | 533 (51.30) | 80 (15.01) | <0.001 a | - | - | - | - | ||
>38 years | 506 (48.70) | 140 (27.76) | - | - | - | - | |||
Married | |||||||||
Yes | 453 (43.60) | 128 (28.26) | <0.001 a | 112 | 27 (24.11) | 0.004 a | 341 | 101 (29.62) | 0.169 a |
Other | 586 (56.40) | 92 (15.70) | 421 | 53 (12.59) | 165 | 39 (23.64) | |||
Raising children | |||||||||
Yes | 395 (38.02) | 108 (27.34) | <0.001 a | 82 | 21 (25.61) | 0.007 a | 313 | 87 (27.80) | 1.00 a |
No | 644 (61.98) | 112 (17.39) | 451 | 59 (13.08) | 193 | 53 (27.46) | |||
Fostering parents | |||||||||
Yes | 537 (51.68) | 123 (22.91) | 0.172 b | 244 | 38 (15.57) | 0.808 a | 293 | 85 (29.01) | 0.481 a |
No | 502 (48.32) | 97 (19.32) | 289 | 42 (14.53) | 213 | 55 (25.82) | |||
Drinking coffee habits | |||||||||
Never | 169 (16.27) | 31 (18.34) | 0.111 b | 127 | 19 (14.96) | 0.392 b | 42 | 12 (28.57) | 0.972 b |
Occasionally | 456 (43.89) | 85 (18.64) | 263 | 33 (12.55) | 193 | 52 (26.94) | |||
One cup per day | 355 (34.17) | 87 (24.51) | 126 | 24 (19.05) | 229 | 63 (27.51) | |||
Two cups per day | 45 (4.33) | 12 (26.67) | 14 | 3 (21.43) | 31 | 9 (29.03) | |||
At least two cups per day | 14 (1.35) | 5 (35.71) | 3 | 1 (33.33) | 11 | 4 (36.36) | |||
Alcohol use in past month | |||||||||
Never | 653 (62.85) | 137 (20.98) | 0.864 b | 319 | 43 (13.48) | 0.381 b | 334 | 94 (28.14) | 0.246 b |
Occasionally | 383 (36.86) | 82 (21.41) | 212 | 37 (17.45) | 171 | 45 (26.32) | |||
Drinking every day | 3 (0.29) | 1 (33.33) | 2 | 0 (0) | 1 | 1 (100) | |||
Sleeping time every day | |||||||||
<5 h | 47 (4.52) | 9 (19.15) | 0.440 b | 26 | 4 (15.38) | 0.307 b | 21 | 5 (23.81) | 0.568 b |
5–6 h | 377 (36.28) | 85 (22.55) | 209 | 40 (19.14) | 168 | 45 (26.79) | |||
6–7 h | 444 (42.73) | 98 (22.07) | 199 | 24 (12.06) | 245 | 74 (30.20) | |||
7–8 h | 143 (13.76) | 25 (17.48) | 81 | 10 (12.35) | 62 | 15 (24.19) | |||
>8 h | 28 (2.69) | 3 (10.71) | 18 | 2 (11.11) | 10 | 1 (10.00) | |||
Exercise habits | |||||||||
Never | 65 (6.26) | 9 (13.85) | 0.348 b | 29 | 4 (13.79) | 0.924 b | 36 | 5 (13.89) | 0.195 b |
Less than once monthly | 249 (23.97) | 51 (20.48) | 147 | 25 (17.01) | 102 | 26 (25.49) | |||
At least once monthly | 191 (18.38) | 37 (19.37) | 108 | 17 (15.74) | 83 | 20 (24.10) | |||
At least once weekly | 464 (44.66) | 104 (22.41) | 228 | 31 (13.60) | 236 | 73 (30.93) | |||
At least once daily | 70 (6.74) | 19 (27.14) | 21 | 3 (14.29) | 49 | 16 (32.65) | |||
Body weight | |||||||||
Underweight | 88 (8.47) | 7 (7.95) | <0.001 b | 69 | 6 (8.70) | <0.001 b | 19 | 1 (5.26) | <0.001 b |
Healthy weight | 616 (59.29) | 97 (15.75) | 319 | 38 (11.91) | 297 | 59 (19.87) | |||
Overweight | 192 (18.48) | 55 (28.65) | 70 | 11 (15.71) | 122 | 44 (36.07) | |||
Obesity | 143 (13.76) | 61 (42.66) | 75 | 25 (33.33) | 68 | 36 (52.94) | |||
Chronic diseases (excluding diabetes) | |||||||||
Yes | 389 (37.44) | 99 (25.45) | 0.009 a | 164 | 31 (18.90) | 0.114 a | 225 | 68 (30.22) | 0.272 a |
No | 650 (62.56) | 121 (18.62) | 369 | 49 (13.28) | 281 | 72 (25.62) | |||
Profession fields | |||||||||
Physician | 121 (11.65) | 30 (24.79) | 0.005 b | 61 | 13 (21.31) | 0.294 b | 60 | 17 (28.33) | 0.043 b |
Nurses | 475 (45.72) | 81 (17.05) | 272 | 34 (12.50) | 203 | 47 (23.15) | |||
Technical staff | 102 (9.82) | 18 (17.65) | 47 | 7 (14.89) | 55 | 11 (20.00) | |||
Administration staff or others | 341 (32.82) | 91 (26.69) | 153 | 26 (16.99) | 188 | 65 (34.57) | |||
Work time | |||||||||
Work time daily (hrs.) | 1039 | 8.52 (0.88) | - | 533 | 8.56 (0.83) | - | 506 | 8.47 (0.92) | - |
Shift work | |||||||||
Irregular shift | 158 (15.21) | 27 (17.09) | 0.008 b | 104 | 12 (11.54) | 0.139 b | 54 | 15 (27.78) | 0.830 b |
Regular shift | 132 (12.70) | 18 (13.64) | 97 | 10 (10.31) | 35 | 8 (22.86) | |||
Night shift | 133 (12.80) | 23 (17.29) | 87 | 12 (13.79) | 46 | 11 (23.91) | |||
Day shift | 616 (59.29) | 152 (24.68) | 245 | 46 (18.78) | 371 | 106 (28.57) | |||
Education degree | |||||||||
PhD. | 9 (0.87) | 4 (44.44) | 0.013 b | 1 | 0 (0) | 0.004 b | 8 | 4 (50) | 0.487 b |
Master | 137 (13.19) | 33 (24.09) | 49 | 9 (18.37) | 88 | 24 (27.27) | |||
Bachelor | 856 (82.39) | 169 (19.74) | 477 | 67 (14.05) | 379 | 102 (26.91) | |||
Others | 37 (3.56) | 14 (37.84) | 6 | 4 (66.67) | 31 | 10 (32.26) | |||
Burnout | |||||||||
Personal burnout | 1039 | 39.26 ± 19.75 | - | 533 | 40.87 ± 19.90 | - | 506 | 37.56 ± 19.47 | - |
The frequency on Musculoskeletal pain | |||||||||
Neck and shoulder pain | 1039 | 0.003 ± 0.93 | - | 533 | −0.027 ± 0.84 | - | 506 | 0.029 ± 1.00 | - |
Pain in both ankles | 1039 | 0.003 ± 0.81 | - | 533 | −0.056 ± 0.44 | - | 506 | 0.059 ± 1.06 | - |
Health check | |||||||||
FBG (mg/dL) | 1039 | 93.49 ± 8.60 | - | 533 | 91.89 ± 7.62 | - | 506 | 95.18 ± 9.25 | - |
TG (mg/dL) | 1039 | 74.03 ± 53.63 | - | 533 | 65.82 ± 48.36 | - | 506 | 82.68 ± 57.47 | - |
OR (95% CI) | ||||||
---|---|---|---|---|---|---|
No age Stratification | 20–37 Years | >38 Years | ||||
Surveyed Variable | M0 | M1 | M0 | M1 | M0 | M1 |
Main effect | ||||||
Married 3 | 2.12 (1.56, 2.86) | 1.65 (1.14, 2.38) | 2.21 (1.31, 3.71) | 1.89 (1.08, 3.33) | 1.36 (0.89, 2.09) | 1.43 (0.91, 2.26) |
TG | 1.01 (1.006, 1.011) | 1.004 (1.00, 1.01) | 1.01 (1.005, 1.015) | 1.01 (1.00, 1.01) | 1.01 (1.003, 1.01) | 1.003 (1.00, 1.01) |
Confounders | ||||||
Female 1 | 0.55 (0.38, 0.81) | 0.81 (0.52, 1.28) | 0.51 (0.29, 0.91) | 0.84 (0.43, 1.62) | 0.56 (0.33, 0.94) | 0.91 (0.50, 1.64) |
>38 years 2 | 2.17 (1.59, 2.95) | 1.37 (0.94, 1.99) | - | |||
Raising children 4 | 1.79 (1.32, 2.42) | - | 2.29 (1.30, 4.03) | - | 1.02 (0.68, 1.52) | - |
Fostering parents 5 | 1.24 (0.92, 1.67) | - | 1.09 (0.67, 1.75) | - | 1.17 (0.79, 1.75) | - |
Drinking coffee every day 6 | 1.47 (1.09, 1.99) | 1.11 (0.79, 1.56) | 1.58 (0.95, 2.62) | - | 1.04 (0.70, 1.54) | - |
Ever alcohol use in a month 7 | 1.03 (0.76, 1.40) | - | 1.34 (0.83, 2.17) | - | 0.93 (0.62, 1.41) | - |
Sleeping time less than 6 h 8 | 1.11 (0.82, 1.49) | - | 1.68 (1.04, 2.71) | 1.47 (0.88, 2.44) | 0.91 (0.61, 1.36) | - |
Exercise at least once weekly 9 | 1.26 (0.93, 1.70) | - | 0.82 (0.51, 1.32) | - | 1.51 (1.01, 2.26) | 1.47 (0.95, 2.28) |
Underweight 10 | 0.46 (0.21, 1.03) | 0.70 (0.31, 1.59) | 0.70 (0.29, 1.74) | 0.91 (0.36, 2.29) | 0.22 (0.03, 1.71) | 0.31 (0.04, 2.39) |
Overweight 10 | 2.15 (1.47, 3.14) | 1.66 (1.10, 2.50) | 1.38 (0.67, 2.85) | 1.14 (0.53, 2.45) | 2.28 (1.43, 3.63) | 2.08 (1.27, 3.43) |
Obesity 10 | 3.98 (2.68, 5.91) | 3.45 (2.22, 5.37) | 3.70 (2.06, 6.65) | 2.95 (1.49, 5.83) | 4.54 (2.61, 7.91) | 4.30 (2.38, 7.79) |
Chronic diseases (excluded diabetes) 11 | 1.49 (1.10, 2.02) | 1.35 (0.97, 1.88) | 1.52 (0.93, 2.49) | - | 1.26 (0.85, 1.86) | - |
Physician 12 | 0.91 (0.56, 1.46) | 0.83 (0.49, 1.40) | 1.32 (0.63, 2.78) | - | 0.75 (0.40, 1.41) | 0.69 (0.35, 1.36) |
Nurse 12 | 0.57 (0.40, 0.79) | 0.74 (0.50, 1.10) | 0.70 (0.40, 1.22) | - | 0.57 (0.37, 0.89) | 0.62 (0.38, 1.01) |
Technical staff 12 | 0.59 (0.34, 1.03) | 0.58 (0.32, 1.06) | 0.86 (0.35, 2.12) | - | 0.47 (0.23, 0.98) | 0.46 (0.22, 1.00) |
Work time every day | 0.97 (0.82, 1.16) | - | 1.10 (0.83, 1.45) | - | 0.94 (0.75, 1.17) | - |
Irregular shift work 12 | 0.63 (0.40, 0.99) | 0.84 (0.51, 1.40) | 0.56 (0.29, 1.12) | - | 0.96 (0.51, 1.82) | - |
Regular shift work 13 | 0.48 (0.28, 0.82) | 0.56 (0.31, 1.01) | 0.50 (0.24, 1.03) | - | 0.74 (0.33, 1.68) | - |
Night shift work 13 | 0.64 (0.39, 1.04) | 0.94 (0.54, 1.64) | 0.69 (0.35, 1.38) | - | 0.79 (0.39, 1.60) | - |
Master or PhD. 14 | 1.32 (0.88, 1.98) | - | 1.27 (0.59, 2.74) | 1.10 (0.67, 1.79) | - | |
Personal burnout | 1.00 (0.99, 1.01) | - | 1.01 (0.99, 1.02) | - | 1.00 (0.99, 1.01) | - |
Neck and shoulder pain | 1.23 (1.07, 1.43) | 1.19 (1.02, 1.40) | 1.30 (1.03, 1.65) | 1.31 (1.01, 1.69) | 1.18 (0.98, 1.43) | - |
Pain in both | 1.11 (0.95, 1.31) | - | 0.59 (0.25, 1.38) | - | 1.12 (0.95, 1.33) | - |
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© 2024 by the authors. Published by MDPI on behalf of the Lithuanian University of Health Sciences. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Chen, Y.-H.; Lin, J.-J.; Tang, H.-M.; Yang, C.-W.; Jong, G.-P.; Yang, Y.-S. Relationship between Marriage and Prediabetes among Healthcare Workers: Mediating Effect of Triglycerides. Medicina 2024, 60, 1418. https://doi.org/10.3390/medicina60091418
Chen Y-H, Lin J-J, Tang H-M, Yang C-W, Jong G-P, Yang Y-S. Relationship between Marriage and Prediabetes among Healthcare Workers: Mediating Effect of Triglycerides. Medicina. 2024; 60(9):1418. https://doi.org/10.3390/medicina60091418
Chicago/Turabian StyleChen, Yong-Hsin, Jia-June Lin, Hsiu-Mei Tang, Ching-Wen Yang, Gwo-Ping Jong, and Yi-Sun Yang. 2024. "Relationship between Marriage and Prediabetes among Healthcare Workers: Mediating Effect of Triglycerides" Medicina 60, no. 9: 1418. https://doi.org/10.3390/medicina60091418