Smoking Is Associated with More Abdominal Fat in Morbidly Obese Patients

<div><p>Introduction</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Discussion</p><p>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.</p></div

Depression Dimensions: Integrating Clinical Signs and Symptoms from the Perspectives of Clinicians and Patients

<div><p>Background</p><p>Several studies have recognized that depression is a multidimensional construct, although the scales that are currently available have been shown to be limited in terms of the ability to investigate the multidimensionality of depression. The objective of this study is to integrate information from instruments that measure depression from different perspectives–a self-report symptomatic scale, a clinician-rated scale, and a clinician-rated scale of depressive signs–in order to investigate the multiple dimensions underlying the depressive construct.</p><p>Methods</p><p>A sample of 399 patients from a mood disorders outpatient unit was investigated with the Beck Depression Inventory (BDI), the Hamilton Depression Rating Scale (HDRS), and the Core Assessment of Psychomotor Change (CORE). Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were used to investigate underlying dimensions of depression, including item level analysis with factor loadings and item thresholds.</p><p>Results</p><p>A solution of six depression dimensions has shown good-fit to the data, with no cross-loading items, and good interpretability. Item-level analysis revealed that the multidimensional depressive construct might be organized into a continuum of severity in the following ascending order: sexual, cognitive, insomnia, appetite, non-interactiveness/motor retardation, and agitation.</p><p>Conclusion</p><p>An integration of both signs and symptoms, as well as the perspectives of clinicians and patients, might be a good clinical and research alternative for the investigation of multidimensional issues within the depressive syndrome. As predicted by theoretical models of depression, the melancholic aspects of depression (non-interactiveness/motor retardation and agitation) lie at the severe end of the depressive continuum.</p></div

Exploratory Factor Analysis (EFA) <i>eigenvalues</i>.

<p>Legend: Horizontal axis: number of factors; vertical axis: factor <i>eigenvalues</i>. The six-factor solution provided the most parsimonious and interpretable description.</p

Exploratory Factor Analysis six-factor solution.

<p>Legend: HAM, 17-item Hamilton Depression Rating Scale; BDI, 21-item Beck Depression Inventory; CORE, Core Assessment of Psychomotor change. The items that did not enter the models by Uher and Parker are coded in the “none” item dimension category.</p><p>Exploratory Factor Analysis six-factor solution.</p

Clinical and demographic profile of the sample stratified by gender and according to smoking status.

<p>Note: Results expressed as mean (± standard deviation): sex, age, weight, BMI (body mass index), WC (waist circumference), HC (hip circumference), WHR (waist to hip ratio), SMM (skeletal muscle mass); body fat, percentage of body fat, MBR (basal metabolic rate), NpacY (number of pack-years), number of cigarettes/day, age of starting, smoking period; Results expressed in number (percentage): Diabetes, HOMA (homeostasis model assessment insulin resistant), dyslipidemia, hypertension, hypothyroidism, alcohol abuse, picky eater.</p><p>Discrete variables analyzed by Pearson or Fisher chi-square test; continuous variables analyzed by Student t test and ANOVA test with Bonferroni correction (for multiple comparisons).</p><p>Values in bold = statistic significant (p<0.05).</p><p>Clinical and demographic profile of the sample stratified by gender and according to smoking status.</p

Socio-demographic and clinical characteristics of the sample (n = 399).

<p>Legend: BDI, 21-item Beck Depression Inventory; HDRS, 17-item Hamilton Depression Rating Scale; CORE, Core Assessment of Psychomotor change.</p><p>Socio-demographic and clinical characteristics of the sample (n = 399).</p

Clinical and demographic profile of the sample stratified by smoking status.

<p>Note: Results expressed as mean ±SD: sex, age, weight, BMI (body mass index), WC (waist circumference), HC (hip circumference), WHR (waist to hip ratio), SMM (skeletal muscle mass); body fat, body fat, MBR (basal metabolic rate); Results expressed in number (percentage): diabetes, HOMA (homeostasis model assessment insulin resistant), dyslipidemia, hypertension, hypothyroidism, alcohol abuse, picky eater; Results expressed as median (25/75 percentile): NpacY (number of pack-years), N cigarettes/day (number of cigarettes/day) age of onset, smoking period.</p><p>Discrete variables analyzed by Pearson or Fisher chi-square test; continuous variables analyzed by Student t test and ANOVA test with Bonferroni correction (for multiple comparisons).</p><p>Values in bold = statistic significant (p<0.05).</p><p>Clinical and demographic profile of the sample stratified by smoking status.</p

Confirmatory Factor Analysis with factor loadings and response option thresholds.

<p>Legend: HAM, 17-item Hamilton Depression Rating Scale; BDI, 21-item Beck Depression Inventory; CORE, Core Assessment of Psychomotor Change; Loc, items locations; R2, squared factor loading (proportion of variance in that indicator variable explained by the factor).</p><p>Confirmatory Factor Analysis with factor loadings and response option thresholds.</p