Smoking is associated with increased resting energy expenditure in the general population: The NEO study

Blauw, Lisanne L.; Boon, Mariëtte R.; Rosendaal, Frits R.; Mutsert, Renée de; Gast, Karin B.; Dijk, Ko Willems van; Rensen, Patrick C.N.; Dekkers, Olaf M.

doi:10.1016/j.metabol.2015.08.008

Cited by 25 publications

(15 citation statements)

References 28 publications

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“…All measurements were performed early in the morning after a fasting period of 8–10 h according to standardized conditions, i.e., abstention from alcohol, smoking, and vigorous physical activity for 24 h prior to the assessment. According to the protocol, smoking was not allowed for occasional and current smokers on the morning of the test, until the end of measurements; however, current smokers were asked to maintain their smoking habits on the day before [22]. Data were excluded from analysis if the respiratory quotient was outside the expected range (0.71–0.90), when measured REE was ±3 standard deviations outside the mean REE, or if the presence of overt peripheral edema was detected.…”

Section: Methodsmentioning

confidence: 99%

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

et al. 2019

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This study aimed to develop and validate new predictive equations for resting energy expenditure (REE) in a large sample of subjects with obesity also considering raw variables from bioimpedance-analysis (BIA). A total of 2225 consecutive obese outpatients were recruited and randomly assigned to calibration (n = 1680) and validation (n = 545) groups. Subjects were also split into three subgroups according to their body mass index (BMI). The new predictive equations were generated using two models: Model 1 with age, weight, height, and BMI as predictors, and Model 2 in which raw BIA variables (bioimpedance-index and phase angle) were added. Our results showed that REE was directly correlated with all anthropometric and raw-BIA variables, while the correlation with age was inverse. All the new predictive equations were effective in estimating REE in both sexes and in the different BMI subgroups. Accuracy at the individual level was high for specific group-equation especially in subjects with BMI > 50 kg/m2. Therefore, new equations based on raw-BIA variables were as accurate as those based on anthropometry. Equations developed for BMI categories did not substantially improve REE prediction, except for subjects with a BMI > 50 kg/m2. Further studies are required to verify the application of those formulas and the role of raw-BIA variables for predicting REE.

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Section: Methodsmentioning

confidence: 99%

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

et al. 2019

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“…According to the protocol, smoking was not allowed for occasional smokers on the day of the test; however, to avoid any confounding factors, current smokers asked to keep their current smoking habits. 11 …”

Section: Methodsmentioning

confidence: 99%

Prediction and evaluation of resting energy expenditure in a large group of obese outpatients

Marra¹,

Cioffi²,

Sammarco³

et al. 2017

Int J Obes

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Background/Objectives:The aim of this study was to compare resting energy expenditure (REE) measured (MREE) by indirect calorimetry (IC) and REE predicted (PREE) from established predictive equations in a large sample of obese Caucasian adults.Subjects/Methods:We evaluated 1851 obese patients (body mass index (BMI)>30 kg m−2) aged between 18a and 65 years. Data were obtained by comparing MREE with PREE, derived from different equations, within and between normal weight and obese groups. The mean differences between PREE and MREE as well as the accuracy prediction within ±10% level were investigated in the whole sample and in three subgroups, classified by BMI (Group 1=30–39.9 kg m−2; Group 2=40–49.9 kg m−2; Group 3>50 kg m−2).Results:We observed that FAO, Henry and Muller3 (body composition (BC)) equations provided good mean PREE–MREE (bias −0.7, −0.3 and 0.9% root mean standard error (RMSE) 273, 263 and 269 kcal per day, respectively); HB and Henry equations were more accurate individually (57 and 56.9%). Only the Muller1 (BC) equation gave the lowest PREE–MREE difference (bias −1.7% RMSE 228 kcal per day) in females, while Johnstone and De Lorenzo equations were the most accurate (55.1 and 54.8%). When the sample was split into three subgroups according to BMI, no differences were found in males; however, the majority of the equations included in this study failed to estimate REE in severely obese females (BMI>40 kg m−2). Overall, prediction accuracy was low (~55%) for all predictive equations, regardless of BMI.Conclusions:Different established equations can be used for estimating REE at the population level in both sexes. However, the accuracy was very low for all predictive equations used, particularly among females and when BMI was high, limiting their use in clinical practice. Our findings suggest that the validation of new predictive equations would improve the accuracy of REE prediction, especially for severely obese subjects (BMI>40 kg m−2).

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“…Several studies reported a higher incidence of metabolic syndrome in smokers [17], with a greater risk of developing type 2 diabetes [18] and cardiovascular diseases [19]. Smoking is generally associated with lower body weight, possibly by enhancing total energy expenditure [20]. However, active smokers who smoke more intensively tend to weigh more than light smokers.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Impact of Chronic Cigarette Smoke Exposure in Obese Mice: Metabolic, Pulmonary, Intestinal, and Cardiac Issues

et al. 2020

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Unhealthy lifestyle choices, such as bad eating behaviors and cigarette smoking, have major detrimental impacts on health. However, the inter-relations between obesity and smoking are still not fully understood. We thus developed an experimental model of high-fat diet-fed obese C57BL/6 male mice chronically exposed to cigarette smoke. Our study evaluated for the first time the resulting effects of the combined exposure to unhealthy diet and cigarette smoke on several metabolic, pulmonary, intestinal, and cardiac parameters. We showed that the chronic exposure to cigarette smoke modified the pattern of body fat distribution in favor of the visceral depots in obese mice, impaired the respiratory function, triggered pulmonary inflammation and emphysema, and was associated with gut microbiota dysbiosis, cardiac hypertrophy and myocardial fibrosis.

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Smoking is associated with increased resting energy expenditure in the general population: The NEO study

Cited by 25 publications

References 28 publications

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

Prediction and evaluation of resting energy expenditure in a large group of obese outpatients

Modelling the Impact of Chronic Cigarette Smoke Exposure in Obese Mice: Metabolic, Pulmonary, Intestinal, and Cardiac Issues

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