Associação entre jet lag social e controle metabólico em pacientes com doenças crônicas não transmissíveis

• Ano de defesa: 2017
• Autor(a) principal: Mota, Maria Carliana
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Ciências da Saúde
• Palavras-chave em Português: Jet lag social; Doenças crônicas não transmissíveis; Ritmos circadianos; Desalinhamento circadiano; Social jetlag; Non-communicable chronic diseases; Circadian rhythms; Circadian misalignment; Ciências médicas; Síndrome do jet lag; Doenças crônicas; Ritmo Circadiano; CNPQ::CIENCIAS DA SAUDE
• Link de acesso: https://repositorio.ufu.br/handle/123456789/22511; http://dx.doi.org/10.14393/ufu.te.2018.486

Introduction: Recent studies point to social jetlag (SJL) - which reflects the circadian misalignment of sleep times during weekdays and weekends - as a new risk factor for excess weight and altered metabolic parameters. Objective: To evaluate the association between SJL and control of clinical and biochemical markers of individuals with non-comunicable chronic diseases (NCCDs). Material and methods: Patients with NCCDs [obesity, systemic arterial hypertension (SHA), type 2 diabetes mellitus (TD2) or dyslipidaemia] attended at public health service in the city of Uberlândia-MG answered a questionnaire involving demographic data, use of medications, physical activity and habitual sleep pattern. The anthropometric parameters were measured: height, weight, and waist circumference; and a 24-hour food recall was applied. From the medical records of the volunteer, blood pressure (BP) values and glycemic and lipid profile data were collected. SLJ was calculated based on the absolute difference between mid-sleep time at weekends and on weekdays. The data were analyzed in two study design: crosssectional and retrospective longitudinal. The cross-sectional study compared the sociodemographic, anthropometric and circadian parameters between the different obesity status, as well as investigated the association between JLS and metabolic and BP parameters. Thus, obesity status was classified in three levels: non-obese: BMI<30 kg/m2; healthy obese: BMI ≥ 30 kg/m2 and less than three high-risk biomarkers for metabolic syndrome; and unhealthy obese: BMI ≥ 30 kg/m2 and high-risk values on three or more biomarkers for metabolic syndrome. In the longitudinal retrospective study, two metabolic and BP parameters with an interval of one year were compared to each other, in patients with and without JLS. Multiple linear regression, logistic regression and generalized estimation equations (GEE) adjusted for confounding factors were performed to examine the association between SJL, metabolic parameters and BP. Results: The cross-sectional study included 792 patients [581 women (73%), age: 55.9 + 12.4 years]. Patients with SJL (>1 h) presented a significant odds ratio (OR) of being overweight (BMI>25kg/m2) (OR=2.0, CI=1.2–3.6, p=0.006) and unhealthy obese (OR=1.8, CI=1.1–2.8, p=0.01) when compared to individuals without SJL. In the longitudinal retrospective study, 654 patients were included [492 women (75%), age: 56.0 + 12.0 years]. Multiple linear regression analysis adjusted for confounding variables showed that SJL was positively associated with: delta (difference) in fasting glucose levels (β = 0.09, p = 0.04) and triglycerides (β = 0.09; p = 0.03) in the interval of one year; and negatively associated with delta levels of high-density lipoprotein (HDL-c). GEE analysis showed a worse fasting glucose profile over the course of one year among individuals with SJL (> 1h) when compared to subjects without SJL (p = 0.03). Conclusion: SJL is associated with a greater risk for overweight and unhealthy obesity. In addition, JLS may negatively influence the control of metabolic markers related to NCCDs, especially fasting glucose, triglyceride and HDL-c levels.