"For instance, in this same cohort, we previously demonstrated that despite weight gain, smoking cessation leads to improvements in lipids, lipoproteins, and endothelial function, each of which are established markers of CVD risk , . Recently, longitudinal analyses from the Framingham Heart Study and Women's Health Initiative showed that weight gain after a quit attempt did not significantly attenuate the CVD risk reduction after quitting smoking , . However, the participants described in those reports had lower body-mass indexes than those in our study. "
[Show abstract][Hide abstract] ABSTRACT: Weight gain after smoking cessation may increase diabetes mellitus and impaired fasting glucose (IFG) risk. This study evaluated associations between smoking cessation and continued smoking with incident diabetes and IFG three years after a quit attempt. The 1504 smokers (58% female) were mean (standard deviation) 44.7 (11.1) years old and smoked 21.4 (8.9) cigarettes/day. Of 914 participants with year 3 data, the 238 abstainers had greater weight gain, increase in waist circumference, and increase in fasting glucose levels than the 676 continuing smokers (p≤0.008). In univariate analyses, Year 3 abstinence was associated with incident diabetes (OR = 2.60, 95% CI 1.44–4.67, p = .002; 4.3% absolute excess) and IFG (OR = 2.43, 95% CI 1.74–3.41, p<0.0001; 15.6% absolute excess). In multivariate analyses, incident diabetes was associated independently with older age (p = 0.0002), higher baseline body weight (p = 0.021), weight gain (p = 0.023), baseline smoking rate (p = 0.008), baseline IFG (p<0.0001), and baseline hemoglobin A1C (all p<0.0001). Smoking more at baseline predicted incident diabetes among eventual abstainers (p<0.0001); weighing more at baseline predicted incident diabetes among continuing smokers (p = 0.0004). Quitting smoking is associated with increased diabetes and IFG risk. Independent risk factors include older age, baseline body weight, baseline glycemic status, and heavier pre-quit smoking. These findings may help target smokers for interventions to prevent dysglycemia.
PLoS ONE 06/2014; 9(6):e98278. DOI:10.1371/journal.pone.0098278 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Changes in cancer therapy, in addition to changes in obesity prevalence, suggest the need for a current assessment of weight gain patterns following breast cancer diagnosis. The aim of this study was to evaluate factors associated with weight gain among breast cancer survivors prior to enrolling into a behavioral weight loss intervention.
Anthropometric measures and data on weight-related factors were collected at baseline on 665 breast cancer survivors. Postdiagnosis weight gain was determined between entry into the trial and previous diagnosis up to 5 years. Multivariate logistic regression analyses were used to evaluate the association between weight gain and influencing factors.
The mean weight gain was 4.5 % body weight (standard deviation = 10.6); 44 % of women experienced ≥5 % body weight gain. The risk of weight gain was inversely associated with age (adjusted odds ratio (ORadj) = 0.97, 95 % confidence interval (95 % CI) 0.95-0.99), Hispanic ethnicity (ORadj = 0.30, 95 % CI 0.13-0.68), and overweight (ORadj = 0.11, 95 % CI 0.05-0.23) or obese (ORadj = 0.03, 95 % CI 0.02-0.07) status at diagnosis and positively associated with time elapsed since diagnosis (ORadj = 1.19/year, 95 % CI 1.04-1.36). Women prescribed aromatase inhibitors were 46 % less likely to gain weight compared to women prescribed selective estrogen-receptor modulators (ORadj = 0.54, 95 % CI 0.31-0.93). The risk of weight gain was positively associated with smoking at diagnosis (ORadj = 2.69, 95 % CI 1.12-6.49) although this was attributable to women who subsequently quit smoking.
Postdiagnosis weight gain is common and complex and influenced by age, ethnicity, weight, smoking status, time elapsed since diagnosis, and endocrine-modulating therapy.
Weight gain continues to be a concern following a diagnosis of breast cancer. Factors influencing this weight gain include age, ethnicity, weight, smoking status, time elapsed since diagnosis, and endocrine-modulating therapy. Effective weight management strategies are needed for this population of women.
Journal of Cancer Survivorship 03/2014; DOI:10.1007/s11764-014-0351-9 · 3.29 Impact Factor
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