The Influencing Factors of Serum Lipids among Middle-aged Women in Northeast China

  • Xinyao ZHANG Dept. of Social Medicine and Health Management, School of Public Health, Jilin University, Changchun, Jilin, China
  • Li SHEN Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Yanjun WANG Dept. of Nursing, Second Hospital of Jilin University, Changchun, Jilin, China
  • Xin GUO Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Jing DOU Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Yaogai LV Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Zhiqiang XUE Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Anning ZHANG Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Lina JIN Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
  • Yan YAO Dept. of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin, China
Keywords: Dyslipidemia, Influencing factors, Serum lipids, Quantile regression

Abstract

Abstract Background: Dyslipidemia is a common and serious health problem, especially in middle-aged women. We aimed to reveal quantile-specific associations of serum lipids [triglycerides (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-c) and high density lipoprotein cholesterol (HDL-c)] with influencing factors in middle-aged women. Methods: A sample of 5635 participants were enrolled from Jilin, China, in 2012. Quantile regression (QR) model was performed to identify factors which influenced serum lipids in different quantiles. Results: The influencing factors of TG, TC, LDL-c and HDL-c were different. Waist circumference (WC), menopause, smoking, diabetes and hypertension were positively associated with TG in almost all quantiles; Menopause and age were positively associated with TC in almost all quantiles. WC, living in urban areas and alcohol consumption were positively associated with TC in low and middle quantiles, diabetes was positively associated with TC from P50 to P95. The result of LDL-c was similar to TC; BMI was negatively associated with HDL-c from P50 to P90. WC and diabetes were negatively associated with HDL-c from P5 to P90. Conclusion: Among middle-aged women, menopause, diabetes and WC were the main factors affecting the serum lipids. Postmenopausal women would get more risk in increasing the level of serum lipids.    

References

1. Bayram F, Kocer D, Gundogan K et al (2014). Prevalence of dyslipidemia and associated risk factors in Turkish adults. J Clin Lipidol, 8(2):206-16.
2. He H, Yu YQ, Li Y et al (2014). Dyslipidem-ia awareness, treatment, control and in-fluence fac-tors among adults in the Jilin province in China: a cross-sectional study. Lipids Health Dis,13:122.
3. Joint committee issued Chinese guideline for the management of dyslipidemia in adults. [2016 Chinese guideline for the management of dyslipidemia in adults]. Zhonghua Xin Xue Guan Bing Za Zhi,44(10):833-53.
4. Qi L, Ding X, Tang W et al (2015). Preva-lence and Risk Factors Associated with Dyslipidemia in Chongqing, China. Int J Environ Res Public Health,12(10):13455-65.
5. Sharma U, Kishore J, Garg A et al (2013). Dyslipidemia and associated risk factors in a re-settlement colony of Delhi. J Clin Lipid-ol,7(6):653-60.
6. Krauss RM (2004). Lipids and lipoproteins in pa-tients with type 2 diabetes. Diabetes Care, 27(6):1496-504.
7. Pisciotta L, Bertolini S, Pende A (2015). Lip-opro-teins, stroke and statins. Curr Vasc Pharmacol, 13(2):202-8.
8. Pan L, Yang Z, Wu Y et al (2016). The prev-alence, awareness, treatment and control of dyslipidemia among adults in China. Atherosclerosis, 248:2-9.
9. Di Angelantonio E, Sarwar N, Perry P et al (2009). Major lipids, apolipoproteins, and risk of vascular disease. JAMA, 302(18):1993-2000.
10. Libby P, Lichtman AH, Hansson GK (2013). Immune effector mechanisms implicated in atherosclerosis: from mice to humans. Immunity, 38(6):1092-104.
11. Rodriguez CJ, Daviglus ML, Swett K et al (2014). Dyslipidemia patterns among Hispanics/Latinos of diverse back-ground in the United States. Am J Med, 127(12):1186-94.
12. Shen X, Li K, Chen P et al (2015). Associa-tions of blood pressure with common factors among left-behind farmers in ru-ral China: a cross-sectional study using quantile regression analysis. Medicine (Bal-timore), 94(2):e142.
13. Lin CY, Bondell H, Zhang HH, Zou H (2013). Variable Selection for Nonpara-metric Quantile Regression via Smooth-ing Spline AN OVA. Stat, 2(1):255-68.
14. Ye J, Li Z, Lv Y et al (2017). Associations of Blood Pressure with the Factors among Adults in Jilin Province: A Cross-Sectional Study Using Quan-tile Regres-sion Analysis. Sci Rep, 7(1):13613.
15. Wennerholm C, Bromley C, Johansson A et al (2017). Two tales of cardiovascular risks-middle-aged women living in Swe-den and Scotland: a cross-sectional com-parative study. BMJ Open, 7(8):e16527.
16. Moreira MA, Zunzunegui MV, Vafaei A et al (2016). Sarcopenic obesity and physical perfor-mance in middle aged women: a cross-sectional study in Northeast Brazil. BMC Public Health, 16:43.
17. Yu J, Ma Y, Yang S et al (2015). Risk Factors for Cardiovascular Disease and Their Clustering among Adults in Jilin (China). Int J Environ Res Public Health,13(1):h13010070.
18. Yu J, Tao Y, Tao Y et al (2016). Optimal cut-off of obesity indices to predict car-diovascular disease risk factors and met-abolic syndrome among adults in North-east China. BMC Public Health, 16(1):1079.
19. Guo X, Shen L, Dou J et al(2017). Associa-tions of Fasting Blood Glucose with In-fluencing Factors in Northeast China: A Quantile Regression Analysis. Int J Environ Res Public Health, 14(11): pii: E1368.
20. Mauvais-Jarvis F, Clegg DJ, Hevener AL (2013). The role of estrogens in control of energy bal-ance and glucose homeo-stasis. Endocr Rev, 34(3):309-38.
21. Fonseca M, Da SI, Ferreira S (2017). Impact of menopause and diabetes on athero-genic lipid profile: is it worth to analyse lipoprotein subfrac-tions to assess cardi-ovascular risk in women? Diabetol Metab Syndr, 9:22.
22. Sai AJ, Gallagher JC, Fang X (2011). Effect of hormone therapy and calcitriol on se-rum lipid profile in postmenopausal older women: asso-ciation with estrogen recep-tor-alpha genotypes. Menopause, 18(10):1101-12.
23. Rettberg JR, Yao J, Brinton RD (2014). Es-trogen: a master regulator of bioenergetic systems in the brain and body. Front Neu-roendocrinol, 35(1):8-30.
24. Zaman HH, Chai LL (2013). Drug-related prob-lems in type 2 diabetes mellitus pa-tients with dyslipidemia. BMC Public Health,13:1192.
25. Digenio A, Dunbar RL, Alexander VJ et al(2016).
Antisense-Mediated Lowering of Plasma Apolipoprotein C-III by Volanesorsen Im-proves Dyslipidemia and Insulin Sen-sitivity in Type 2 Diabetes. Diabetes Care, 39(8):1408-15.
26. Srikanth S, Deedwania P (2016). Manage-ment of Dyslipidemia in Patients with Hypertension, Di-abetes, and Metabolic Syndrome. Curr Hyper-tens Rep, 18(10):76.
27. Rios-Gonzalez BE, Ibarra-Cortes B, Ramirez-Lopez G, Sanchez-Corona J, Magana-Torres MT (2014). Association of polymorphisms of genes involved in lipid metabolism with blood pressure and lipid values in mexican hypertensive indi-viduals. Dis Markers,2014:150358.
28. Zheng R, Mao Y (2017). Triglyceride and glucose (TyG) index as a predictor of in-cident hypertension: a 9-year longitudinal population based study. Lipids Health Dis,16(1):175.
Published
2018-11-01
How to Cite
1.
ZHANG X, SHEN L, WANG Y, GUO X, DOU J, LV Y, XUE Z, ZHANG A, JIN L, YAO Y. The Influencing Factors of Serum Lipids among Middle-aged Women in Northeast China. IJPH. 47(11):1660-6.
Section
Original Article(s)