Cardiometabolic Morbidity and Mortality with Smoking Cessation, Review of Recommendations for People with Diabetes and Obesity.
Katarina KosPublished in: Current diabetes reports (2020)
Nicotine is a key player in modulating energy balance by influencing lipid storage in adipose tissue by affecting lipolysis, energy input by modulating appetite and energy output by increasing sympathetic drive and thermogenesis. It also increases insulin resistance and promotes abdominal obesity. The CVD risk and mortality associated with cigarette smoking potentiate the CVD risks in patients with diabetes. Evidence supports the benefit of quitting cigarette smoking regardless of any subsequent weight gain. Data suggests that the cardiometabolic risk is limited to the first few years and that cardiovascular health and mortality benefit of smoking cessation outweighs the harm related to weight gain. This weight gain can be limited by nicotine replacement of which e-cigarettes (vaping) are increasingly popular if it is not an alternative to cigarette smoking. However, long-term health data on e-cigarettes is needed prior to formal recommendation for its use in smoking cessation. The recommendation for cessation of cigarette smoking is justified for those at high risk of weight gain and diabetes. However, for most benefit, consideration should be given for personalized weight management to limit weight gain. Awareness of a 'lean paradox' by which lower weight is associated with increased CVD risk may help to improve motivation and insight into the bias of smoking, health and body composition otherwise known to epidemiologists as the 'obesity paradox'.
Keyphrases
- weight gain
- smoking cessation
- adipose tissue
- body mass index
- insulin resistance
- birth weight
- body composition
- weight loss
- replacement therapy
- type diabetes
- healthcare
- public health
- cardiovascular disease
- glycemic control
- metabolic syndrome
- high fat diet
- mental health
- bone mineral density
- signaling pathway
- electronic health record
- cardiovascular events
- resistance training
- big data
- polycystic ovary syndrome
- clinical practice
- high intensity
- deep learning