Changes in Weight, cardiovascular risk factors and estimated risk of coronary heart disease following smoking cessation in Japanese male workers: HIPOP-OHP study

The effect of smoking on open carpal tunnel release recovery

INTRODUCTION

This study examines the relationship between smoking status and surgical outcomes in open carpal tunnel release. Smoking status has previously been correlated with orthopedic surgical complications unless smoking cessation occurs at least 4 weeks prior to surgery; however, the effect of smoking on open carpal tunnel release has not been specifically examined.

METHODS

This study is a retrospective review of patient charts over the last 5 years (n = 131). Smoking status was determined at the time of carpal tunnel release from the patients' charts. Patients were dichotomized as smokers (n = 58) or non-smokers (n = 73). Data comprised preoperative pain, postoperative pain at 2 and 6 weeks, postoperative wrist stiffness, wound healing time, and infection status. Data were compared on chi square, Fisher exact, and one-sided Fisher exact tests.

RESULTS

Infection rates were significantly higher in smokers. Postoperative numbness and wrist stiffness were also significantly higher in smokers. Smokers reported higher postoperative pain scores at 2 and 6 weeks. Non-smokers were also significantly more likely to report complete pain resolution at 6 weeks.

DISCUSSION

In the present study smoking was associated with surgical outcome complications in open carpal tunnel release. This could be attributed to immune system suppression or to poor wound healing, both of which are known side-effects of smoking. Pain scores 6 weeks post-surgery were significantly higher in smokers than in non-smokers. Smokers undergoing open carpal tunnel release need to understand the risk of surgical complications and should consider smoking cessation to optimize their chances of successful surgery.

CONCLUSION

Patients who were active smokers showed poorer recovery from open carpal tunnel surgery than non-smoking patients.

Changes in body weight and cardiovascular risk factors in a Chinese population with type 2 diabetes mellitus: a longitudinal study

Introduction

The primary care management of blood glucose, blood pressure, lipid profiles, and body weight is important among patients with type 2 diabetes mellitus (T2DM) to prevent disease progression. Information on how weight changes would improve or deteriorate cardiovascular (CV) risk factors is warranted for making primary care recommendations. We aimed to investigate the changes in body weight and CV risk factors and to analyse their association in a Chinese population with T2DM.

Methods

We retrieved longitudinal data between 2020 and 2021 from 1,758 adult primary care patients enrolled in a diabetic retinopathy (DR) screening programme. Linear associations of changes in body weight with CV risk factors were explored. Multivariable logistic regression analysis was performed to examine associations between different weight change categories and the worsening of CV risk factors.

Results

The mean age of all the participants was 63.71 years, and over half of participants were females. During a one-year follow-up period, 24.7% of patients had a weight loss of ≥3%, while 22.2% of patients had a weight gain of ≥3%. Patients who had a weight loss of ≥3% were more likely to prevent the worsening of haemoglobin A1c (HbA1c) and triglycerides, while those who had a weight gain of ≥3% tended to have worsened HbA1c, lipid profiles, and blood pressure.

Conclusion

Results from this real-world investigation suggested the concurrent need for weight loss intervention among patients who are overweight or obese and weight gain prevention among patients whose body weight falls within the normal range in the context of community-based diabetes management.

Association of Change in Smoking Status and Subsequent Weight Change with Risk of Nonalcoholic Fatty Liver Disease

Background/Aims

Smoking is considered a risk factor for the development of nonalcoholic fatty liver disease (NAFLD). However, the association of a weight change after a change in smoking status and the risk of NAFLD remains undetermined.

Methods

This study used the Korean National Health Insurance Service-National Sample Cohort. Based on the first (2009 to 2010) and second (2011 to 2012) health examination periods, 139,180 adults aged at least 40 years were divided into nonsmoking, smoking cessation, smoking relapse, and sustained smoking groups. NAFLD was operationally defined using the fatty liver index. The adjusted odds ratio (aOR) and 95% confidence interval (CI) were calculated using multivariable-adjusted logistic regression.

Results

Compared to nonsmoking with no body mass index (BMI) change, the risk of NAFLD was significantly increased among subjects with BMI gain and nonsmoking (aOR, 4.07; 95% CI, 3.77 to 4.39), smoking cessation (aOR, 5.52; 95% CI, 4.12 to 7.40), smoking relapse (aOR, 7.51; 95% CI, 4.81 to 11.72), and sustained smoking (aOR, 6.65; 95% CI, 5.33 to 8.29), whereas the risk of NAFLD was reduced among participants with BMI loss in all smoking status groups. In addition, smoking cessation (aOR, 1.76; 95% CI, 1.35 to 2.29) and sustained smoking (aOR, 1.64; 95% CI, 1.39 to 1.94) were associated with higher risk of NAFLD among participants with no BMI change. The liver enzyme levels were higher among participants with smoking cessation and BMI gain.

Conclusions

Monitoring and management of weight change after a change in smoking status may be a promising approach to reducing NAFLD.

Meta-analysis of the effects of smoking and smoking cessation on triglyceride levels

Smoking increases lipid levels, including triglycerides, leading to increased cardiovascular disease risk. We performed a meta-analysis to quantify the effects of smoking and smoking cessation on triglyceride levels. The PubMed and Scopus databases were searched to identify studies reporting either triglyceride levels in smokers and non-smokers or the effects of smoking cessation on triglyceride levels. Fixed- and random-effects models were used to perform the analyses when three or more studies/comparisons were available. We identified 169 and 21 studies evaluating the effects of smoking and smoking cessation, respectively, on triglyceride levels. Triglyceride levels were 0.50 mmol/L (95% confidence interval: 0.49-0.50 mmol/L) higher in smokers than non-smokers, but the effect differed widely across studies. No statistically significant effect was observed on triglyceride levels between baseline and 6 weeks (mean difference [MD] = 0.02 [-0.09, 0.12] mmol/L), 2 months (MD = 0.03 [-0.21, 0.27] mmol/L), 3 months (MD = 0.08 [-0.03, 0.21] mmol/L), or 1 year (MD = 0.04 [-0.06, 0.14] mmol/L) after quitting. However, a slightly significant decrease in triglyceride levels was observed at 1 month after cessation (MD = -0.15 [-0.15, -0.01] mmol/L). The results of this meta-analysis provide a basis for understanding the effects of smoking and smoking cessation on triglyceride levels, which could have important implications for public health.

The Effect of Smoking Cessation on Body Weight and Other Metabolic Parameters with Focus on People with Type 2 Diabetes Mellitus

Smokers with diabetes mellitus substantially lower their risks of microvascular and macrovascular diabetic complications, in particular cardiovascular disease, by quitting smoking. However, subsequent post-smoking-cessation weight gain may attenuate some of the beneficial effects of smoking cessation and discourage attempts to quit. Weight gain can temporarily exacerbate diabetes and deteriorate glycemic control and metabolic profile. The molecular mechanisms by which quitting smoking leads to weight gain are largely associated with the removal of nicotine's effects on the central nervous system. This review addresses mechanisms of post-smoking-cessation weight gain, by reviewing the effects of nicotine on appetite, food intake, eating behaviour, energy expenditure, fat oxidation and appetite-regulating peptides. We also highlight correlations between post-cessation weight gain and risk of type 2 diabetes, consequences of weight gain in people with type 2 diabetes and the role of pharmacotherapies, which combine treatment of nicotine addiction and promotion of weight control.

Lifelong smoking status, weight gain, and subsequent risk of major adverse cardiovascular events: Long-term follow-up of a middle-aged Chinese population

OBJECTIVE

The aim of this study was to explore the association of lifelong smoking status with risk of major adverse cardiovascular events (MACE) accounting for weight change in a Chinese cohort.

METHODS

The cohort of the People's Republic of China-United States of America (PRC-USA) Collaborative Study of Cardiovascular and Cardiopulmonary Epidemiology was established in 1983 to 1984, resurveyed during 1987 to 1988 and 1993 to 1994, and followed up to 2005. A total of 5,849 participants who survived in 1993 to 1994 were classified into never smokers, long-term quitters, short-term quitters, short-term relapsers and new smokers, long-term relapsers and new smokers, and persistent smokers according to the information on lifelong smoking status collected in all three surveys. The associations of lifelong smoking status with MACE in the subsequent 10 years were explored with Cox proportional hazards models.

RESULTS

During a median follow-up of 10.2 years, 694 participants had MACE. Compared with persistent smokers, the multivariable-adjusted hazard ratio of developing MACE was 0.83 (95% CI: 0.61-1.12) for short-term quitters, 0.75 (95% CI: 0.54-1.02) for long-term quitters, and 0.68 (95% CI: 0.54-0.85) for never smokers (p_trend = 0.001). In comparison, the hazard ratio was 1.03 (95% CI: 0.77-1.35) for long-term relapsers and new smokers and 0.78 (95% CI: 0.46-1.22) for short-term relapsers and new smokers (p_trend = 0.018). These associations were not significantly altered by further adjusting for weight change in the past 10 years.

CONCLUSIONS

Lifelong smoking status is significantly associated with risk of MACE. As time duration increased, health benefit to quitters would become close to that of never smokers, and harms to relapsers and new smokers would become close to that of persistent smokers.

Changes in Body Weight and Concurrent Changes in Cardiovascular Risk Profiles in Community Residents in Japan: the Hisayama Study

AIM

We investigated the influence of weight change on concurrent changes in predicted cardiovascular disease (CVD) risk and individual CVD risk factors over time.

METHODS

A total of 2,140 community-dwellers aged 40-74 years participated in both 2002 and 2007 health examinations. Obesity was defined as body mass index ≥ 25 kg/m2. Weight trajectories were classified as: "stable obese" (obese at both examinations), "obese to nonobese" (obese in 2002 but nonobese in 2007), "nonobese to obese" (nonobese in 2002 but obese in 2007), or "stable nonobese" (nonobese at both examinations). We compared changes in the model-predicted risk for CVD and individual CVD risk factors across weight-change categories.

RESULTS

The predicted risk for CVD increased during 5 years in all groups; the increment in the predicted risk for CVD was smallest in the obese to nonobese participants and steepest in the nonobese to obese subjects. Compared with the stable obese participants, the obese to nonobese participants had greater favorable changes in waist circumferences, blood pressure, fasting plasma glucose, serum high-density lipoprotein cholesterol, serum triglycerides, and liver enzymes. For all these parameters, opposite trends were observed when comparing the nonobese to obese participants with the stable nonobese group.

CONCLUSIONS

We demonstrated the favorable association of losing weight in obese people and avoiding excessive weight gain in nonobese people with global risk of future CVD and individual CVD risk factors in a real-world setting. The findings could improve behavioral lifestyle interventions that provide information on the health consequences of weight change at health checkups.

Dose-Dependent Effect of Smoking on Risk of Diabetes Remains after Smoking Cessation: A Nationwide Population-Based Cohort Study in Korea

BACKGROUND

This study aimed to evaluate the dose-dependent effects of smoking on risk of diabetes among those quitting smoking.

METHODS

We analyzed clinical data from a total of 5,198,792 individuals age 20 years or older who received health care check-up arranged by the national insurance program of Korea between 2009 and 2016 using the Korean National Health Insurance Service database. Cumulative smoking was estimated by pack-years. Smokers were classified into four categories according to the amount of smoking: light smokers (0.025 to 5 smoking pack-years), medium smokers (5 to 14 smoking pack-years), heavy smokers (14 to 26 smoking pack-years), and extreme smokers (more than 26 smoking pack-years).

RESULTS

During the study period, 164,335 individuals (3.2% of the total population) developed diabetes. Compared to sustained smokers, the risk of diabetes was significantly reduced in both quitters (hazard ratio [HR], 0.858; 95% confidence interval [CI], 0.838 to 0.878) and nonsmokers (HR, 0.616; 95% CI, 0.606 to 0.625) after adjustment for multiple risk factors. The risk of diabetes gradually increased with amount of smoking in both quitters and current smokers. The risk of diabetes in heavy (HR, 1.119; 95% CI, 1.057 to 1.185) and extreme smokers (HR, 1.348; 95% CI, 1.275 to 1.425) among quitters was much higher compared to light smokers among current smokers.

CONCLUSION

Smoking cessation was effective in reducing the risk of diabetes regardless of weight change. However, there was a potential dose-dependent association between smoking amount and the development of diabetes. Diabetes risk still remained in heavy and extreme smokers even after smoking cessation.

Smoking cessation, weight gain and risk of cardiovascular disease

OBJECTIVE

To examine whether the relationship between smoking cessation and risk of cardiovascular disease (CVD) was modified by weight gain.

METHODS

A total of 69 910 participants (29 650 men and 46 260 women) aged 45-74 years were grouped into six groups by smoking status in the first and 5-year surveys: sustained smokers, recent quitters according to postcessation weight gain (no weight gain, 0.1-5.0 kg, >5.0 kg), long-term quitters and never smokers. Quitting smoking within and longer than 5 years were defined as recent and long-term quitters, respectively. We used Cox proportional hazard models to estimate the HR for incident CVD, coronary heart disease (CHD) and stroke.

RESULTS

We identified 4023 CVDs (889 CHDs and 3217 strokes) during a median of 14.8 years of follow-up. Compared with sustained smokers, the multivariable HR (95% CI) for CVD was 0.66 (0.52 to 0.83) for recent quitters without weight gain, 0.71 (0.55 to 0.90) for recent quitters with weight gain of 0.1-5.0 kg, 0.70 (0.44 to 1.10) for recent quitters with weight gain of >5.0 kg, 0.56 (0.49 to 0.64) for long-term quitters, and 0.60 (0.55 to 0.66) for never smokers. The analysis restricted to men showed a similar association. Prespecified analysis by age suggested that recent quitters overall had a lower HR for CVD among those aged <60 years vs ≥60 years. Similar patterns of association were observed in CHD and stroke.

CONCLUSIONS

Postcessation weight gain did not attenuate the protective association between smoking cessation and risk of CVD.

Is the ongoing obesity epidemic partly explained by concurrent decline in cigarette smoking? Insights from a longitudinal population study. The Tromsø Study 1994-2016

The increase of obesity coincides with a substantial decrease in cigarette smoking. We assessed post-cessation weight change and its contribution to the obesity epidemic in a general population in Norway. A total of 14,453 participants (52.6% women), aged 25-54 years in 1994, who attended at least two of four surveys in the Tromsø Study between 1994 and 2016, were included in the analysis. Hereof 77% participated in both the first and the last survey. Temporal trends in mean body mass index (BMI), prevalence of obesity (BMI ≥ 30 kg/m²) and daily smoking were estimated with generalized estimation equations. We assessed BMI change by smoking status (ex-smoker, quitter, never smoker, daily smoker), and also under a scenario where none quit smoking. In total, the prevalence of daily smoking was reduced over the 21 years between Tromsø 4 (1994-1995) and Tromsø 7 (2015-2016) by 22 percentage points. Prevalence of obesity increased from 5 - 12% in 1994-1995 to 21-26% in 2015-2016, where obesity in the youngest (age 25-44 in 1994) increased more than in the oldest (p < 0.0001). Those who quit smoking had a larger BMI gain compared to the other three smoking subgroups over the 21 years (p < 0.0001). The scenario where none quit smoking would imply a 13% reduction in BMI gain in the population, though substantial age-related differences were noted. We conclude that smoking cessation contributed to the increase in obesity in the population, but was probably not the most important factor. Public health interventions should continue to target smoking cessation, and also target obesity prevention.

Smoking cessation, weight gain, cardiovascular risk, and all-cause mortality: a meta-analysis

OBJECTIVE

Quitting smoking usually results in weight gain, rising the concern that weight gain after smoking cessation may attenuate the benefits of smoking quitting on cardiovascular health. We aimed to examine the risk of cardiovascular disease (CVD) among smoking quitters compared with smokers, stratified by post-cessation weight change.

METHODS

We searched PubMed, Scopus, and Web of Science for eligible studies published before September 2020. Cohort studies examining the relative risk (RR) of CVD among smoking quitters stratified by post-cessation weight change, with smokers being treated as the reference, were included. Two investigators extracted the key characteristics of each included study using a standard electronic form.

RESULTS

We identified 9 studies for this meta-analysis. Compared with smokers, the pooled RRs (95% confidence interval) of CVD among quitters with weight gain and among those without were 0.74 [0.66, 0.83]) and 0.86 [0.80, 0.92]), with no evidence of heterogeneity between studies being observed. Moreover, the associations appeared to be significantly stronger among quitters with weight gain than those without, particularly in the analysis of coronary heart disease and stroke (pooled RRs = 0.65 [0.59, 0.71] vs. 0.79 [0.71, 0.88] for coronary heart disease and 0.67 [0.62, 0.73] vs. 0.76 [0.72, 0.81] for stroke, respectively; P for interaction < 0.05).

CONCLUSIONS

Smoking cessation was associated with a significantly lower risk of CVD and all-cause mortality, regardless of post-cessation weight gain. A greater risk reduction among quitters with weight gain than those without merits further investigations.

Smoking Cessation, Weight Gain, and the Trajectory of Estimated Risk of Coronary Heart Disease: 8-Year Follow-up From a Prospective Cohort Study

Introduction

The effect of weight gain following smoking cessation on cardiovascular risks is unclear. We aimed to prospectively investigate the association of weight gain following smoking cessation with the trajectory of estimated risks of coronary heart disease (CHD).

Methods

In a cohort of 18 562 Japanese male employees aged 30–64 years and initially free of cardiovascular diseases, participants were exclusively grouped into sustained smokers, quitters with weight gain (body weight increase ≥5%), quitters without weight gain (body weight increase <5% or weight loss), and never smokers. Global 10-year CHD risk was annually estimated by using a well-validated prediction model for the Japanese population. Linear mixed models and piecewise linear mixed models were used to compare changes in the estimated 10-year CHD risk by smoking status and weight change following smoking cessation.

Results

During a maximum of 8-year follow-up, both quitters with and without weight gain had a substantially decreased level of estimated 10-year CHD risk after quitting smoking, compared with sustained smokers (all ps for mean differences < .001). The estimated 10-year CHD risk within the first year after cessation decreased more rapidly in quitters without weight gain than in quitters with weight gain (change rate [95% confidence interval, CI] −0.90 [−1.04 to −0.75] vs. −0.40 [−0.60 to −0.19] % per year, p < .0001). Thereafter, the estimated 10-year CHD risk in both groups increased at similar rates (change rate [95% CI] −0.07 [−0.21 to 0.07] vs. 0.11 [−0.09 to 0.30] % per year, p = .16, from year 1 to year 2; and 0.10 [0.05 to 0.15] vs. 0.11 [0.04 to 0.18] % per year, p = .80, from year 2 to year 8).

Conclusions

In this population of middle-aged, Japanese male workers, smoking cessation greatly reduces the estimated 10-year risk of CHD. However, weight gain weakens the beneficial effect of quitting smoking in a temporary and limited fashion.

Implications

To the best of our knowledge, this study is the first to examine the effect of weight gain following smoking cessation on the trajectory of the absolute risk of CHD. Our data imply that the benefits of cessation for reducing the absolute risk of CHD outweigh the potential risk increase due to weight gain, and suggest that in order to maximize the beneficial effects of quitting smoking, interventions to control post-cessation weight gain might be warranted.

Protective effect of smoking cessation on subsequent myocardial infarction and ischemic stroke independent of weight gain: A nationwide cohort study

Smoking cessation reduces the cardiovascular risk but increases body weight. We investigated the risk of subsequent myocardial infarction and ischemic stroke according to weight gain after smoking cessation, using a nationwide population based cohort. We enrolled 3,797,572 Korean adults aged over 40 years who participated in national health screenings between 2009 and 2010. Subjects who quit smoking were classified into three subgroups according to the weight change between baseline and 4 years prior. Myocardial infarctions and ischemic strokes were followed until the end of 2015. We compared the hazard ratios among smoking cessation subgroups, non-smokers, and current smokers. The mean changes in weight (1.5 ± 3.9 kg) of the smoking cessation group were higher than those of the other groups (p < 0.0001). A total of 31,277 and 46,811 subjects were newly diagnosed with myocardial infarction and ischemic stroke, respectively. Regardless of weight change, all subgroups of smoking cessation had significantly less risk than current smokers. The subgroup of smoking cessation with weight gain over 4kg showed the lowest risk for myocardial infarctions (hazard ratio 0.646, 95% confidence interval 0.583-0.714, p < 0.0001) and ischemic strokes (hazard ratio 0.648, 95% confidence interval 0.591-0.71, p < 0.0001) after multivariable adjustment. In conclusion, weight gain after smoking cessation did not adversely affect the cardiovascular protective effect.

Cancer risk among young men with weight gain after smoking cessation: A population-based cohort study

BACKGROUND

Smoking cessation may help the current smokers to reduce cancer risk. However, weight gain following smoking cessation may attenuate the protective association of cessation with cancer.

PATIENTS AND METHODS

Our study included 1,278,794 men who were aged 20-39 years and underwent two consecutive health examinations by the National Health Insurance Service, without previous diagnosis of cancer. Participants were categorized into continual smokers, quitters with different degree of body weight change, and never smokers based on the biennial national health screening program (2002-2003 and 2004-2005) and were followed from January 1, 2006 to December 31, 2015. Cox proportional hazard models and restricted cubic spline model was used to evaluate the association of post-cessation weight change and cancer risk after adjustment for potential confounders.

RESULTS

During the 10 years of follow-up, the analyses included 1,278,794 men with 21,494 cancer incidences. Compared to continual smokers, quitters without weight gain of 2.0 kg had significantly lower risk of obesity-related cancer (hazard ratio [HR], 0.88; 95% confidence interval [CI], 0.79-0.97), smoking-related cancer (HR, 0.90; 95% CI, 0.83 to 0.98), and gastrointestinal cancer (HR, 89; 95% CI, 0.80 to 0.98). Weight gain among quitters attenuated the risk reduction of cancer compared to continual smoking. Among quitters, weight gain up to 5.0 kg with smoking cessation showed protective association with cancer risk among quitters without weight gain.

CONCLUSION

Excessive weight gain with smoking cessation among quitters was not associated with reduced risk of several cancer types. This association should be taken into account when recommending smoking cessation to prevent cancer.

Weight change after smoking cessation and incident metabolic syndrome in middle-aged Korean men: an observational study

We aimed to examine the effect of weight change attributable to cessation of cigarette smoking on newly diagnosed metabolic syndrome (MetS). We prospectively followed 5,809 men aged between 40 to 69 years without MetS at baseline in the Health Examinees-Gem (HEXA-G) study up to 4 years. The participants were grouped into continual smokers, quitters with weight gain, quitters without weight change, quitters with weight loss, and never smokers. We constructed multivariable logistic regression models adjusted for sociodemographic factors, health status, and health conditions to estimate the odds of newly diagnosed MetS. During the follow-up, there were 609 cases of newly diagnosed MetS in 5,809 men of the HEXA-G study. After adjustment for potential confounders, the odd ratios (OR) and 95% confidence intervals (95% CI) for MetS were 1.90 (95% CI: 1.43–2.52) in quitters with weight gain, 0.77 (95% CI: 0.60–1.00) in quitters without weight change, and 0.40 (95% CI: 0.28–0.57) in quitters with weight loss compared with continual smokers. Never smokers also had lower odds of MetS (OR = 0.54; 95% CI: 0.42–0.71) compared to continual smokers. Weight management program following smoking cessation may be necessary in clinical practice to reduce worsening of cardiometabolic risk factors related to post-cessation weight gain.

Weight gain after smoking cessation: more data to refute concerns

Take home figure.

Weight gain after smoking cessation does not modify its protective effect on myocardial infarction and stroke: evidence from a cohort study of men

Aims

This study aimed to investigate the association between smoking cessation, post-cessation body mass index (BMI) change and risk of myocardial infarction (MI) and stroke in men.

Methods and results

A prospective cohort study using the National Health Insurance Service (NHIS) data set collected from 2002 to 2013 was implemented. Based on the first (2002-03) and second (2004-05) NHIS health check-up periods, 108 242 men aged over 40 years without previous diagnoses of MI or stroke were grouped into sustained smokers, quitters with BMI gain, quitters without BMI change, quitters with BMI loss, and non-smokers. Body mass index change was defined as the difference of more than 1.0 kg/m2 between the two health check-up periods. The participants were followed-up from 1 January 2006 to 31 December 2013. Hazard ratios (HRs) and 95% confidence intervals (HR, 95% CI) were computed using Cox proportional hazard models adjusted for sociodemographic, health status, and family health history. Compared to the sustained smokers, the risk of MI and stroke was significantly reduced in both quitters with BMI gain (HR 0.33; 95% CI 0.16-0.70 for MI and HR 0.75; 95% CI 0.57-1.00 for stroke) and without BMI change (HR 0.55; 95% CI 0.37-0.83 for MI and HR 0.75; 95% CI 0.62-0.92 for stroke), but no significant association was found in quitters with BMI loss (HR 0.91; 95% CI 0.43-1.91 for MI and HR 0.86; 95% CI 0.57-1.31 for stroke), respectively. Non-smokers had lower risk of MI (HR 0.37; 95% CI 0.32-0.43) and stroke (HR 0.68; 95% CI 0.64-0.73) compared to the sustained smokers.

Conclusion

Post-cessation BMI change did not significantly modify the protective association of smoking cessation with MI and stroke.

Effects of smoking habit change on all-cause mortality and cardiovascular diseases among patients with newly diagnosed diabetes in Korea

This study aimed to investigate the effects of smoking habit change on the risks of all-cause mortality and cardiovascular diseases (CVDs) among patients with newly diagnosed diabetes using the Korean National Sample Cohort data. Survival regression analyses for the risks of all-cause mortality and CVDs were performed. Quitters without body mass index (BMI) change (adjusted hazard ratio [aHR], 0.68; 95% confidence interval [CI], 0.46-1.00) and quitters with BMI loss (aHR, 1.76; 95% CI, 1.13-2.73) showed significantly reduced and substantially the increased risk of all-cause mortality, respectively, compared with sustained smokers. Smoking reduction after diabetes diagnosis may have potential positive effects. However, definite benefits on the health outcomes were not identified in this study. Participants who started smoking after diabetes diagnosis had higher risks of all-cause mortality and CVDs than those who were never smokers or ex-smokers, although not statistically significant. In conclusion, smoking cessation after diabetes diagnosis could reduce the risks of all-cause mortality and cardiovascular events among patients with newly diagnosed diabetes when accompanied by proper weight management. Therefore, physicians should advice patients with newly diagnosed type 2 diabetes on the importance of smoking cessation in combination with long-term weight management to maximize the benefits of smoking cessation.

Smoking Cessation without Educational Instruction could Promote the Development of Metabolic Syndrome

AIM

Smoking cessation is particularly important for maintaining health; however, the subsequent risk of an increased body weight is of major concern. The present study investigated the influence of smoking cessation on the incidence of metabolic syndrome and its components in the Japanese general population.

METHODS

This study enrolled individuals without metabolic syndrome or a history of smoking via our annual health checkup program (n=5,702, 55.2±11.5 years). Participants were divided into three groups mentioned below and followed up with the endpoint being the development of metabolic syndrome: (1) subjects who had never smoked and did not smoke during the observation period (non-smoker); (2) those who continued smoking during the observation period (continuous smoker); and (3) those who ceased smoking during the observation period (smoking cessation).

RESULTS

During the observation period (median 1,089 days), 520 subjects developed metabolic syndrome, and Kaplan-Meier analysis showed a higher incidence of metabolic syndrome in the smoking cessation group than in the other groups. Smoking cessation was confirmed as an independent predictor of the new onset of metabolic syndrome by multivariate Cox proportional hazard analysis after adjustment. Subjects only from the smoking cessation group showed a significant deterioration in metabolic factors during the study in correlation with an increased waist circumference after smoking cessation.

CONCLUSIONS

Smoking cessation without instruction could be followed by the development of metabolic syndrome, and the incidence of metabolic syndrome might reduce the benefit obtained from smoking cessation. Therefore, further educational outreach is needed to prevent the progression of metabolic syndrome during the course of smoking cessation.

[Influence of modifiable cardiovascular risk factors on antihypertensive therapy efficiency escape]

AIM

To evaluate the influence of cardiovascular risk factors on antihypertensive therapy (AHT) efficiency escape (EE).

SUBJECTS AND METHODS

Data on 59 patients with grades 1-3 hypertension (Stages I-II) were analyzed. During chosen AHT, 24-hour blood pressure monitoring was done at baseline, 1 and 3 months after beginning the observation to identify/rule out the AHT EE phenomenon.

RESULTS

The AHT EE group (Group 1) as compared with the group that needed no therapy correction within 3 months (Group 2) was observed to have the following: elevated fasting blood glucose levels (FBGL) (5.8±0.8 vs 5.3±0.7 mmol/l; p=0.008) and higher impaired glucose tolerance (IGT) rates (8 (27.6%) vs 4 (13.3%) cases (p=0.03)); a more number of smoking patients (8 (27.6%) vs 3 (10%) cases; p=0.02); a larger number of patients with a compromised family history of cardiovascular diseases (17 (58.6%) vs 11 (36%); p=0.02). Furthermore, in Group 1 baseline average systolic blood pressure during 24 hours (SBP-24) proved to be higher than that in Group 2 (127.4±4.2 vs 122.4±6.8 mm Hg; p=0.002). Odds ratio (OR) for developing the EE phenomenon increased by 60% with a rise of 0.5 mmol in FBGL (OR, 1.60; 95% confidence interval (CI), 1.06 to 2.4; p=0.02) and by 18% with an increase of 1 mm Hg in baseline SBP-24 (OR, 1.18; 95% CI, 1.05 to 1.33; p=0.004). Multivariate analysis indicated that the independent predictors of AHT EE were a compromised family history (OR, 3.7; 95% CI, 1.1 to 12.1; p=0.03) and IGT (OR, 4.1; 95% CI, 1.02 to 16.4; p=0.04).

CONCLUSION

AHT EE was influenced by FBGL, IGT, smoking, a compromised family history, and baseline SBP-24 level.

Association between Weight Changes after Smoking Cessation and Cardiovascular Disease among the Korean Population

Background

Cigarette smoking is a risk factor for cardiovascular disease (CVD) and has both beneficial and harmful effects in CVD. We hypothesized that weight gain following smoking cessation does not attenuate the CVD mortality of smoking cessation in the general Korean population.

Methods

Study subjects comprised 2.2% randomly selected patients from the Korean National Health Insurance Corporation, between 2002 and 2013. We identified 61,055 subjects who were classified as current smokers in 2003–2004. After excluding 21,956 subjects for missing data, we studied 30,004 subjects. We divided the 9,095 ex-smokers into two groups: those who gained over 2 kg (2,714), and those who did not gain over 2 kg (6,381, including weight loss), after smoking cessation. Cox proportional hazards regression models were used to estimate the association between weight gain following smoking cessation and CVD mortality.

Results

In the primary analysis, the hazard ratios of all-cause deaths and CVD deaths were assessed in the three groups. The CVD risk factors and Charlson comorbidity index adjusted hazard ratios (aHRs) for CVD deaths were 0.80 (95% confidence interval [CI], 0.37 to 1.75) for ex-smokers with weight gain and 0.80 (95% CI, 0.50 to 1.27) for ex-smokers with no weight gain, compared to one for sustained smokers. The associations were stronger for events other than mortality. The aHRs for CVD events were 0.69 (95% CI, 0.54 to 0.88) and 0.81 (95% CI, 0.70 to 0.94) for the ex-smokers with and without weight gain, respectively.

Conclusion

Although smoking cessation leads to weight gain, it does not increase the risk of CVD death.

Smoking cessation and long-term weight gain in the Framingham Heart Study: an application of the parametric g-formula for a continuous outcome

Weight gain after smoking cessation can lessen the health benefits of, and reduce the incentives for, quitting smoking. Randomized clinical trials of smoking cessation have estimated this weight gain only over short periods of follow-up. We provide an estimate of long-term post-cessation weight gain in the Framingham Heart Study, a prospective observational study. We identified 2001 smokers free of diabetes, cancer, and cardiovascular disease in 1952. Using the parametric g-formula we estimated mean weight in 1972 if all smokers had quit at baseline versus if all had continued smoking. Our estimates were adjusted for demographic, socio-economic, and clinical factors at baseline and during follow-up. The estimated mean weight (95 % CI) at 20 years if all smokers had quit smoking was 75.2 kg (73.5, 76.6), compared with 70.2 kg (68.7, 71.8) if they had smoked 20 cigarettes/day and 73.4 kg (71.9, 74.6) if they had smoked 5 cigarettes/day (i.e., an estimated mean weight gain of 5.1 kg (3.1, 6.6) and 1.8 kg (0.8, 2.8), respectively). Smokers who were overweight or obese at baseline had a greater post-cessation weight gain on average. Our estimates suggest that smoking cessation can result in increases in body weight over 20 years. While the benefits of smoking cessation outweigh the risks due to post-cessation weight gain, our results highlight the need for long-term weight management interventions in combination with smoking cessation.

The effect of tobacco cessation on weight gain, obesity, and diabetes risk

OBJECTIVE

Most smokers gain weight after quitting, and some develop new onset obesity and type 2 diabetes. The purpose of this paper is to synthesize the current science investigating the consequences of tobacco cessation on body weight and diabetes, as well as intervention strategies that minimize or prevent weight gain while still allowing for successful tobacco cessation.

METHODS

Systematic reviews and relevant studies that were published since prior reviews were selected.

RESULTS

Smoking cessation can cause excessive weight gain in some individuals and can be associated with clinically significant outcomes such as diabetes or obesity onset. Interventions that combine smoking cessation and weight control can be effective for improving cessation and minimizing weight gain but need to be tested in specific populations.

CONCLUSIONS

Despite the health benefits of quitting tobacco, post-cessation weight gain and new onset obesity and diabetes are a significant concern. Promising interventions may need to be more widely applied to reduce the consequences of both obesity and tobacco use.

Tobacco Use and its Relationship with Health Complaints Among Employees of Kermanshah Province, Iran

Background:

Identifying the pattern of tobacco use and its related factors in employees is crucial. This study aimed to investigate the pattern of tobacco use and its related factors in employees of Kermanshah Province, Iran.

Methods:

In 2012, 7129 employees were investigated in a cross-sectional study using the census method. Data on tobacco use and on several chronic diseases obtained using a standardized questionnaire on noncommunicable diseases risk factors of the World Health Organization through face-to-face interviews. Statistical analysis was performed based on the Chi-square test and multivariate logistic regression.

Results:

In general, the prevalence of tobacco use, smoking cigarettes, and smoking waterpipe was 9.9%, 8.9%, and 1.2% among the employees, respectively. Tobacco use was significantly higher in the age group over 40 (14.0%), in male gender (13.3%), in married individuals (10.8%) and in those with diploma and lower degree (16.4%), (P < 0.001). At the individual level, the odds ratio of tobacco use was 1.5 (95% confidence interval [CI]: 1.2–1.8) in hypertensive, 1.8 (95% CI: 1.2–2.6) in diabetic employees and 1.7 (95% CI: 1.3–2.3) in those with heart diseases, compared to healthy individuals. After adjusting for age, gender, marital status and educational level, there was not any significant relationship between tobacco use and health complaints and only the demographic variables remained significant.

Conclusions:

Preventive public health policies are mandatory especially in younger ages and male employees to promote their knowledge on disadvantages of tobacco use.

Smoking is associated with more abdominal fat in morbidly obese patients

INTRODUCTION

While the association between cigarette smoking and abdominal fat has been well studied in normal and overweight patients, data regarding the influence of tobacco use in patients with morbid obesity remain scarce. The aim of this study is to evaluate body fat distribution in morbidly obese smokers.

METHODS

We employed a cross-sectional study and grouped severely obese patients (body mass index [BMI] >40 kg/m2 or >35 kg/m2 with comorbidities) according to their smoking habits (smokers or non-smokers). We next compared the anthropometrical measurements and body composition data (measured by electric bioimpedance) of both groups. We analyzed the effect of smoking on body composition variables using univariate and multiple linear regression (MLR); differences are presented as regression coefficients (b) and their respective 95% confidence intervals.

RESULTS

We included 536 morbidly obese individuals, 453 (84.5%) non-smokers and 83 (15.5%) smokers. Male smokers had a higher BMI (b=3.28 kg/m2, p=0.036), larger waist circumference (b=6.07 cm, p=0.041) and higher percentage of body fat (b=2.33%, p=0.050) than non-smokers. These differences remained significant even after controlling for confounding factors. For females, the only significant finding in MLR was a greater muscle mass among smokers (b=1.34kg, p=0.028). No associations were found between tobacco load measured in pack-years and anthropometric measures or body composition.

DISCUSSION

Positive associations between smoking and BMI, and waist circumference and percentage of body fat, were found among male morbidly obese patients, but not among females. To the best of our knowledge, this study is the first investigation of these aspects in morbidly obese subjects. We speculate that our findings may indicate that the coexistence of morbid obesity and smoking helps to explain the more serious medical conditions, particularly cardiovascular diseases and neoplasms, seen in these patients.

Prevalence of dyslipidemia and its association with insomnia in a community based population in China

Background

Studies of the prevalence of dyslipidemia and its association with insomnia are scarce in China. This study investigated the prevalence of dyslipidemia and its association with insomnia in a community based Chinese population.

Methods

We conducted a cross-sectional survey in Beijing and recruited 10054 participants aged ≥18 years. The association between self-reported insomnia and dyslipidemia was determined by multiple logistic regression models. Age, gender, education, obesity, body mass index, physical activity, current smoking, current drinking, diabetes, and hypertension were adjusted as confounders. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were reported as effect measurements.

Results

The prevalence of dyslipidemia in those with no insomnia, occasional insomnia, and frequent insomnia were 53.3%, 54.3%, and 54.5% in men and 52.0%, 54.8%, and 61.2% in women. Compared with subjects with no insomnia, the multivariate adjusted ORs and 95% CIs for those with occasional insomnia and frequent insomnia were 1.07(0.86 ~ 1.34) and 1.19(0.89 ~ 1.60) for men, and 1.00(0.86 ~ 1.14) and 1.23(1.03 ~ 1.47) for women.

Conclusions

These observations indicate that frequent insomnia was associated with a higher prevalence of dyslipidemia in women. This association was not significant in men.

Association of short sleep duration with impaired glucose tolerance or diabetes mellitus

Aims/Introduction:  To examine the cross‐sectional relationship between sleep duration and impaired glucose tolerance (IGT), including diabetes mellitus (DM), we analyzed a large‐scale healthy workers database in Japan.

Materials and Methods:  We examined the baseline database of 4143 participants (3415 men and 728 women) aged 19–69 years. Sleep duration of participants was categorized into four groups: <6, 6 to <7, 7 to <8 and ≥8 h. The physical activity of each participant was classified according to the International Physical Activity Questionnaire (IPAQ). We defined IGT including DM (IGT/DM) in the present study according to previous studies as follows: fasting blood sugar level ≥110 mg/dL, or if <8 h after meals ≥140 mg/dL, or on medication for diabetes mellitus, or those diagnosed as having DM. Logistic regression was applied to estimate the odds ratio (OR) to examine the relationship between IGT/DM, sleep duration and other related factors.

Results:  The number of participants with IGT/DM was 402 (9.7%). The factors that significantly associated with IGT/DM were age (OR 1.08, 95% confidence interval [CI] 1.07–1.10, P < 0.001), high blood pressure (OR 1.94, CI 1.52–2.47, P < 0.001), and <6 h of sleep duration in comparison with 6 to <7 h sleep (OR 2.32, CI 1.18–4.55, P = 0.015). The associations of difficulty in sleep initiation, IPAQ classification, current smoking and alcohol intake with IGT/DM were not statistically significant.

Conclusions:  Our results showed that shorter sleep duration (<6 h of sleep duration per night) was associated with a risk of IGT/DM independent of other lifestyle habits and metabolic risk factors. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2011.00114.x, 2011)

Relationship between shift work and clustering of the metabolic syndrome diagnostic components

AIM

To examine the relationship between the type of work and the number of metabolic syndrome diagnostic components(MetS-DC), as well as the risk of MetS, with adjustment for lifestyle habits in Japanese workers.

METHODS

We examined the baseline data from 4,427 participants(81.4% male) aged 19 to 69 years old. The physical activity of each participant was classified according to the International Physical Activity Questionnaire(IPAQ). We defined the four MetS-DC in this study as follows: 1) high blood pressure(BP): systolic BP ≥130 mmHg, or diastolic BP ≥85 mmHg, or the use of antihypertensive drugs; 2) dyslipidemia: high-density lipoprotein-cholesterol concentration ＜40 mg/dl or triglyceride concentration ≥150 mg/dl, or on medication for dyslipidemia; 3) dysglycemia: fasting blood sugar level ≥110 mg/dl, or if less than eight hours after meals ≥140mg/dl, or on medication for diabetes mellitus; 4) overweight: a body mass index ≥25kg/m(2). We defined MetS as overweight plus two or more of the MetS-DC.

RESULTS

There were 3,094 subjects in the daytime work group, 73 in the fixed nighttime work group, 1,017 in the shift work group and 243 in the day-to-night work group. The Poisson regression analysis revealed that fixed nighttime (regression coefficient [b]=-0.233, P=0.028) and shift work (b=0.098, P=0.034) independently contributed to the number of MetS-DC, compared to daytime work. The multivariate logistic analysis not including sleep hours in the model showed that shift work was positively related to MetS (odd ratio=1.47, P＜0.01).

CONCLUSION

Shift work were significantly associated with the number of MetS-DC, and was related to risk of MetS compared to daytime work.

The effect of quitting smoking on HDL-cholesterol - a review based on within-subject changes

A higher concentration of high density lipoprotein cholesterol (HDL-C) in ex-smokers than smokers has consistently been observed. Better evidence of quitting effects comes from within-subject changes. We extend an earlier meta-analysis to quantify the reduction, and investigate variation by time quit and other factors. We conducted Medline and Cochrane searches for studies measuring HDL-C in subjects while still smoking and later having quit. Using unweighted and inverse-variance weighted regression analysis, we related changes (in mmol/l) to intra-measurement period, and estimated time quit, and to study type, location and start year, age, sex, product smoked, validation of quitting, baseline HDL-C, baseline and change in weight/BMI, and any study constraints on diet or exercise. Forty-five studies were identified (17 Europe, 16 North America, 11 Asia, 1 Australia). Thirteen were observational, giving changes over at least 12 months, with most involving >1000 subjects. Others were smoking cessation trials, 12 randomized and 20 non-randomized. These were often small (18 of <100 subjects) and short (14 of <10 weeks, the longest a year). Thirty studies provided results for only one time interval. From 94 estimates of HDL-C change, the unweighted mean was 0.107 (95% CI 0.085-0.128). The weighted mean 0.060 (0.044 to 0.075) was lower, due to smaller estimates in longer term studies. Weighted means varied by time quit (0.083, 0.112, 0.111, 0.072, 0.058 and 0.040 for <3, 3 to <6, 6 to <13, 13 to <27, 27 to <52 and 52+ weeks, p=0.006). After adjustment for time quit, estimates varied by study constraint on diet/exercise (p=0.003), being higher in studies requiring subjects to maintain their pre-quitting habits, but no other clear differences were seen, with significant (p<0.05) increases following quitting being evident in all subgroups studied, except where data were very limited. For both continuing and never smokers, the data are (except for two large studies atypically showing significant HDL-C declines in both groups, and a smaller decline in quitters) consistent with no change, and contrast markedly with the data for quitters. We conclude that quitting smoking increases HDL-C, and that this increase occurs rapidly after quitting, with no clear pattern of change thereafter.

Comparison of cardiovascular risk factors for peripheral artery disease and coronary artery disease in the korean population

Background and Objectives

The objective of this study was to analyze and compare risk factors for peripheral artery disease (PAD) and coronary artery disease (CAD).

Subjects and Methods

The sample included 7936 Korean patients aged ≥20 years who were hospitalized from 1994 to 2004. Of the 7936 subjects, PAD (n=415), CAD (n=3686), and normal controls (Control) (n=3835) were examined at the Health Promotion Center, Samsung Medical Center.

Results

The mean age (years) of PAD subjects was 64.4 (±9.3), while CAD subjects was 61.2 (±9.9), and Control subjects was 59.9 (±9.1) (p<0.01). The proportion of males was 90.6% for PAD, 71.4% for CAD, and 75.5% for Control subjects (p<0.01). The adjusted odds ratios (ORs) for hypertension, diabetes mellitus, hypercholesterolemia, smoking, metabolic syndrome and chronic kidney disease were significantly higher in subjects with PAD or CAD compared to those in Control. However, the ORs for high density lipoprotein, being overweight, and being obese were significantly lower in PAD subjects compared to those in Control.

Conclusion

We found that cardiovascular risk factors were in fact risk factors for both PAD and CAD.

Association of smoking cessation and weight change with cardiovascular disease among adults with and without diabetes

IMPORTANCE

Smoking cessation reduces the risks of cardiovascular disease (CVD), but weight gain that follows quitting smoking may weaken the CVD benefit of quitting.

OBJECTIVE

To test the hypothesis that weight gain following smoking cessation does not attenuate the benefits of smoking cessation among adults with and without diabetes.

DESIGN, SETTING, AND PARTICIPANTS

Prospective community-based cohort study using data from the Framingham Offspring Study collected from 1984 through 2011. At each 4-year examination, self-reported smoking status was assessed and categorized as smoker, recent quitter (≤ 4 years), long-term quitter (>4 years), and nonsmoker. Pooled Cox proportional hazards models were used to estimate the association between quitting smoking and 6-year CVD events and to test whether 4-year change in weight following smoking cessation modified the association between smoking cessation and CVD events.

MAIN OUTCOME MEASURE

Incidence over 6 years of total CVD events, comprising coronary heart disease, cerebrovascular events, peripheral artery disease, and congestive heart failure.

RESULTS

After a mean follow-up of 25 (SD, 9.6) years, 631 CVD events occurred among 3251 participants. Median 4-year weight gain was greater for recent quitters without diabetes (2.7 kg [interquartile range {IQR}, -0.5 to 6.4]) and with diabetes (3.6 kg [IQR, -1.4 to 8.2]) than for long-term quitters (0.9 kg [IQR, -1.4 to 3.2] and 0.0 kg [IQR, -3.2 to 3.2], respectively, P < .001). Among participants without diabetes, age- and sex-adjusted incidence rate of CVD was 5.9 per 100 person-examinations (95% CI, 4.9-7.1) in smokers, 3.2 per 100 person-examinations (95% CI, 2.1-4.5) in recent quitters, 3.1 per 100 person-examinations (95% CI, 2.6-3.7) in long-term quitters, and 2.4 per 100 person-examinations (95% CI, 2.0-3.0) in nonsmokers. After adjustment for CVD risk factors, compared with smokers, recent quitters had a hazard ratio (HR) for CVD of 0.47 (95% CI, 0.23-0.94) and long-term quitters had an HR of 0.46 (95% CI, 0.34-0.63); these associations had only a minimal change after further adjustment for weight change. Among participants with diabetes, there were similar point estimates that did not reach statistical significance.

CONCLUSIONS AND RELEVANCE

In this community-based cohort, smoking cessation was associated with a lower risk of CVD events among participants without diabetes, and weight gain that occurred following smoking cessation did not modify this association. This supports a net cardiovascular benefit of smoking cessation, despite subsequent weight gain.

Smoking was a Possible Negative Predictor of Incident Hypertension After a Five-Year Follow-up Among a General Japanese Population

BACKGROUNDS

The association between cigarette smoking and hypertension is controversial. The aim of this study is to investigate the association between smoking and incident hypertension.

METHODS

This is a post-hoc five-year follow-up study in a general Japanese population. Logistic regressions were performed using incident hypertension as an outcome and smoking status as an independent predictor adjusting for sex, age, body mass index (BMI), total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, fasting plasma glucose (FPG), drinking status, and diabetes in 1,297 subjects without hypertension at baseline.

RESULTS

The incidence of hypertension was 16.9% vs. 27.6% (smokers vs. nonsmokers, P = 0.01) in men and 0.0% vs. 16.9% (smokers vs. nonsmokers, P = 0.03) in women. The odds ratio (OR) (95% confidence interval (CI)) of incident hypertension was 0.38 (0.19 - 0.76) (P = 0.006) for smokers at baseline, 0.33 (0.16 - 0.68) (P = 0.003) for continuing smokers, and 2.11 (0.33 - 13.45) (P = 0.4) for ex-smokers. Age (OR = 1.52, P < 0.0001), BMI (OR = 1.46, P < 0.0001), and FPG (OR = 1.23, P = 0.007) were other independent predictors of incident hypertension.

CONCLUSIONS

Smoking was a possible significant negative predictor of incident hypertension in a general Japanese population.

Relationship between Dietary and Other Lifestyle Habits and Cardiometabolic Risk Factors in Men

BACKGROUND

Prevalence of men with cardiometabolic risk factors (CMRF) is increasing in Japan. Few studies have comprehensively examined the relation between lifestyles and CMRF.

METHODS

We examined the baseline data from 3,498 male workers ages 19 to 69 years who participated in the high-risk and population strategy for occupational health promotion (HIPOP-OHP) study at 12 large-scale companies throughout Japan. The physical activity of each participant was classified according to the International Physical Activity Questionnaire (IPAQ). Dietary intake was surveyed by a semi-quantitative Food Frequency Questionnaire. We defined four CMRF in this study as follows: 1) high blood pressure (BP): systolic BP ≥ 130 mmHg, or diastolic BP ≥ 85 mmHg, or the use of antihypertensive drugs; 2) dyslipidemia: high-density lipoprotein-cholesterol concentration < 40 mg/dl, or triglycerides concentration ≥ 150 mg/dl, or on medication for dyslipidemia; 3) impaired glucose tolerance: fasting blood sugar concentration ≥110 mg/dl; 4) obese: a body mass index ≥ 25 kg/m2.

RESULTS

Those who had 0 to 4 CMRF accounted for 1,597 (45.7%), 1,032 (29.5%), 587 (16.8%), 236 (6.7%), and 44 (1.3%) participants, respectively, in the Poisson distribution. Poisson regression analysis revealed that independent factors that contributed to the number of CMRF were age (b = 0.020, P < 0.01), IPAQ (b = -0.091, P < 0.01), alcohol intake (ml/day) (b = 0.001, P = 0.03), percentage of protein intake (b = 0.059, P = 0.01), and total energy intake (kcal)(b = 0.0001, P < 0.01). Furthermore, alcohol intake and its frequency had differential effects.

CONCLUSIONS

Alcohol intake, percent protein and total energy intake were positively associated, whereas drinking frequency and IPAQ were inversely associated, with the number of CMRF.

Age at quitting smoking as a predictor of risk of cardiovascular disease incidence independent of smoking status, time since quitting and pack-years

Background

Risk prediction for CVD events has been shown to vary according to current smoking status, pack-years smoked over a lifetime, time since quitting and age at quitting. The latter two are closely and inversely related. It is not known whether the age at which one quits smoking is an additional important predictor of CVD events. The aim of this study was to determine whether the risk of CVD events varied according to age at quitting after taking into account current smoking status, lifetime pack-years smoked and time since quitting.

Findings

We used the Cox proportional hazards model to evaluate the risk of developing a first CVD event for a cohort of participants in the Framingham Offspring Heart Study who attended the fourth examination between ages 30 and 74 years and were free of CVD. Those who quit before the median age of 37 years had a risk of CVD incidence similar to those who were never smokers. The incorporation of age at quitting in the smoking variable resulted in better prediction than the model which had a simple current smoker/non-smoker measure and the one that incorporated both time since quitting and pack-years. These models demonstrated good discrimination, calibration and global fit. The risk among those quitting more than 5 years prior to the baseline exam and those whose age at quitting was prior to 44 years was similar to the risk among never smokers. However, the risk among those quitting less than 5 years prior to the baseline exam and those who continued to smoke until 44 years of age (or beyond) was two and a half times higher than that of never smokers.

Conclusions

Age at quitting improves the prediction of risk of CVD incidence even after other smoking measures are taken into account. The clinical benefit of adding age at quitting to the model with other smoking measures may be greater than the associated costs. Thus, age at quitting should be considered in addition to smoking status, time since quitting and pack-years when counselling individuals about their cardiovascular risk.

Healthy lifestyle factors associated with reduced cardiometabolic risk

Minimal data are available regarding the cumulative effects of healthy lifestyle behaviours on cardiometabolic risk. The objective of the present study was to examine a combination of healthy lifestyle behaviours associated with cardiometabolic risk reduction. The analysis was based on a cross-sectional study of 1454 participants from the population-based Lipid Research Clinic's Princeton Follow-up Study. The healthy lifestyle factors included fruit and vegetable intake ≥ 5 servings/d, meat intake ≤ 2 servings/d, never smoking, consuming 2-6 alcoholic drinks/week, television (TV) viewing time ≤ 2 h/d and moderate to vigorous physical activity ≥ 4 h/week. The combination of healthy lifestyle behaviours was strongly and negatively associated with the presence of cardiometabolic risk, as well as with a composite cardiometabolic risk score after adjustment for race, age, generation and sex. With each additional healthy lifestyle factor, cardiometabolic risk decreased by 31 % (OR 0·69; 95 % CI 0·61, 0·78). A higher healthy lifestyle score was associated with a lower prevalence of cardiometabolic risk (P for trend < 0·001). Compared with individuals having 0-1 healthy lifestyle behaviours, those with 5 or 6 healthy lifestyle behaviours had a 70 % lower prevalence of cardiometabolic risk (OR 0·30; 95 % CI 0·13, 0·67). Healthy lifestyle behaviours including sufficient fruit and vegetable intake, less meat intake, less TV viewing time, abstinence from smoking, modest alcohol intake and regular exercise are associated with reduced cardiometabolic risk.

Longitudinal study on white blood cell count and the incidence of metabolic syndrome

OBJECTIVE

Many studies have revealed that white blood cell count (WBC) is related to insulin resistance which is a central mechanism of metabolic syndrome (MetS). However, few cohort studies have examined the role of WBC in the development of MetS. We hypothesized that WBC is associated with the future development of MetS, and investigated the longitudinal incidence of MetS in healthy workers.

METHODS

WBC was measured in 5,073 workers (mean age 42.5 years) without MetS at baseline. The incidence of MetS was monitored over 7 years of follow-up, in relation to quartiles of WBC. During the follow-up, 925 participants were diagnosed as MetS.

RESULTS

Incidence of MetS was increased in participants with higher WBC: the rates of incidence of MetS were 22.6, 32.9, 42.9, and 57.5 per 1,000 person-years of follow-up in the 1st, 2nd, 3rd, and 4th quartiles of WBC, respectively. After adjustments for confounding factors, the adjusted hazards ratio (95% confidence interval) for MetS was 1.00 (reference), 1.22 (0.98 to 1.51), 1.52 (1.24 to 1.87), and 1.66 (1.35 to 2.04) through the quartiles of WBC, respectively, (p <0.001). This relationship was consistent among current smokers and never smokers, and among male and female genders, respectively.

CONCLUSION

WBC is useful in predicting the future development of MetS which leads to atherosclerotic diseases.

Relationship between sleep duration and clustering of metabolic syndrome diagnostic components

OBJECTIVE

To examine the relation between sleep duration and metabolic syndrome (MetS).

METHODS

We examined the baseline data from 4356 healthy workers (3556 men and 800 women) aged 19-69 years. The physical activity of each participant was classified according to the International Physical Activity Questionnaire (IPAQ). We defined four components of MetS diagnostic components in this study as follows: 1) high blood pressure (BP) systolic BP [SBP] ≥ 130 mmHg, or diastolic BP [DBP] ≥ 85 mmHg, or on medication; 2) dyslipidemia (high-density lipoprotein-cholesterol concentration <40 mg/dL, or triglycerides concentration ≥150 mg/dL, or on medication; 3) impaired glucose tolerance (fasting blood sugar concentration ≥ 110 mg/dL, or if less than 8 hours after meals ≥ 140 mg/dL), or on medication; and 4) overweight (body mass index [BMI] ≥ 25 kg/m(2)), or obesity (BMI ≥ 30 kg/m(2)). There were 680 participants in the group, with sleep duration <6 hours (15.6%).

RESULTS

Those who had 0-4 MetS diagnostic components, including overweight, accounted for 2159, 1222, 674, 255, and 46 participants, respectively, in the Poisson distribution. Poisson regression analysis revealed that independent factors that contributed to the number of MetS diagnostic components were being male (regression coefficient b = 0.752, P < 0.001), age (b = 0.026, P < 0.001), IPAQ classification (b = -0.238, P = 0.034), and alcohol intake (mL/day) (b = 0.018, P < 0.001). Short sleep duration (<6 hours) was also related to the number of MetS (b = 0.162, P < 0.001). The results of analyses with obesity component showed a similar association.

CONCLUSION

Short sleep duration was positively associated with the number of MetS diagnostic components independent of other lifestyle habits.