Obesity and Quality of Life: Mediating Effects of Pain and Comorbidities

Objective: To estimate the association between body mass index (BMI) and health‐related quality of life (HRQL) and examine whether joint pain and obesity‐related comorbidities mediate the BMI‐HRQL association. Research Methods and Procedures: Population‐based survey data from the 1999 Behavioral Risk Factor Surveillance Survey. Adults (N = 155, 989) were classified according to BMI as underweight (<18.5 kg/m²), desirable weight (18.5 to 24.9 kg/m²), overweight (25 to 29.9 kg/m²), obese class I (30 to 34.9 kg/m²), obese class II (35 to 39.9 kg/m²), and obese class III (≥40 kg/m²). Data including general health status, unhealthy days in the past 30 caused by physical problems and mental problems, and total unhealthy days in the past 30 were collected. Results: After adjusting for age, sex, race, smoking, education, and income, we observed J‐shaped associations between BMI and HRQL. Compared with desirable weight adults, underweight, overweight, and obesity classes I, II, and III adults [odds ratio (OR) = 1.57, 1.19, 1.95, 2.72, and 4.36, respectively] were significantly (p < 0.001) more likely to report fair/poor general health status. For unhealthy days caused by physical problems, the corresponding ORs were 1.51, 1.15, 1.66, 2.27, and 3.61 (p < 0.001). For unhealthy days caused by mental problems, the ORs were 1.35, 1.14 1.43, 1.57, and 2.25 (p < 0.001). For total unhealthy days, the corresponding ORs were 1.27, 1.09, 1.37, 1.73, and 2.46 (p < 0.01). Adding joint pain and obesity‐related comorbidities into models attenuated BMI‐HRQL associations. Discussion: Associations between BMI and HRQL indices were J‐shaped. Joint pain and comorbidities may mediate BMI‐HRQL associations.     

Self‐Reported Body Mass Index and Health‐Related Quality of Life: Findings from the Behavioral Risk Factor Surveillance System

Objective: To examine the relationship between self‐reported body mass index (BMI) and health‐related quality of life in the general adult population in the United States. Research Methods and Procedures: Using data from 109,076 respondents in the 1996 Behavioral Risk Factor Surveillance System, we examined how self‐reported BMI is associated with five health‐related quality of life measures developed by the Centers for Disease Control and Prevention for population health surveillance. Results: After adjusting for age, gender, race or ethnicity, educational attainment, employment status, smoking status, and physical activity status, participants with a self‐reported BMI of <18.5 kg/m² and participants with a self‐reported BMI of ≥30 kg/m² reported impaired quality of life. Compared with persons with a self‐reported BMI of 18.5 to <25 kg/m², odds ratios (ORs) of poor or fair self‐rated health increased among persons with self‐reported BMIs of <18.5 (1.57, 95% confidence interval [CI]: 1.31 to 1.89), 25 to <30 kg/m² (1.12, 95% CI: 1.04 to 1.20), 30 to <35 kg/m² (1.65, 95% CI: 1.50 to 1.81), 35 to <40 kg/m² (2.58, 95% CI: 2.21 to 3.00), and ≥40 kg/m² (3.23, 95% CI: 2.63 to 3.95); ORs for reporting ≥14 days of poor physical health during the previous 30 days were 1.44 (95% CI: 1.21 to 1.72), 1.04 (95% CI: 0.96 to 1.14), 1.32 (95% CI: 1.19 to 1.47), 1.80 (95% CI: 1.52 to 2.13), and 2.37 (95% CI: 1.90 to 2.94), respectively; ORs for having ≥14 days of poor mental health during the previous 30 days were 1.18 (95% CI: 0.97 to 1.42), 1.02 (95% CI: 0.95 to 1.11), 1.22 (95% CI: 1.10 to 1.36), 1.68 (95% CI: 1.42 to 1.98), and 1.66 (95% CI: 1.32 to 2.09), respectively. Discussion: In the largest study to date, low and increased self‐reported BMI significantly impaired health‐related quality of life. Particularly, deviations from normal BMI affected physical functioning more strongly than mental functioning.     

Relationship between Obesity and Health-Related Quality of Life in Men

Objective: Few studies examining the relationship between obesity and health-related quality of life (HRQOL) have used a medical outpatient population or demonstrated a relationship in men. Furthermore, most studies have not adequately considered comorbid illness. The goal of this study was to examine the relationship between body mass index (BMI) and HRQOL in male outpatients while considering comorbid illness. Research Methods and Procedures: This cross-sectional study examined 1168 male outpatients from Durham Veterans’ Affairs Medical Center. Multiple linear regression was used to examine the relationship of BMI with each subscale from the Medical Outcomes Study Short Form 36 while adjusting for age, race, comorbid illness, depression, and physical activity. Results: Participants had a mean age of 54.7 ± 5.6 years; 69% were white and 29% were African American. The distribution for BMI was as follows: 18.5 to <25 kg/m² (21%), 25 to <30 kg/m² (43%), 30 to <35 kg/m² (25%), 35 to <40 kg/m² (8%), and ≥40 kg/m² (3%). Mean Short Form 36 subscale scores were lower than U.S. norms by an average of 27%. Individuals with BMI ≥40 kg/m² had significantly lower scores compared with normal weight individuals on the Role-Physical and Vitality subscales. On the Physical Functioning and Physical Component subscales, lower scores were observed at BMI ≥35 kg/m². On the Bodily Pain subscale, lower scores were observed at BMI ≥25 kg/m². Discussion: An inverse relationship between BMI and physical aspects of HRQOL exists in a population of male outpatients. Increased BMI was most prominently associated with bodily pain; this relationship should receive more attention in clinical care and research.     

Obesity Increases the Risk of Spontaneous Abortion during Infertility Treatment

Objective: This study examines the relationship between body mass and the risk of spontaneous abortion in a large cohort of patients who received infertility treatment. Research Methods and Procedures: This is a retrospective study using data on pregnancies (n = 2349) achieved after treatment in a tertiary medical center from 1987 to 1999. One pregnancy per subject was included, and the subjects were stratified into five body mass groups based on body mass index (BMI): underweight, <18.5 kg/m²; normal, 18.5 to 24.9 kg/m²; overweight, 25 to 29.9 kg/m²; obese, 30 to 34.9 kg/m²; and very obese, ≥35 kg/m². Logistical regression analysis was used. Results: The overall incidence of spontaneous abortion was 20% (476 of 2349). The effect of BMI on the risk of spontaneous abortion was significant after adjusting for several independent risk factors. Compared with the reference group (BMI 18.5 to 24.9 kg/m²), underweight women had a similar risk of spontaneous abortion, whereas there was progressive increase of risk in overweight, obese, and very obese groups (p < 0.05, p < 0.01, and p < 0.001, respectively). Discussion: Of all known risk factors for spontaneous abortion, the control of obesity has great significance because it is noninvasive, potentially modifiable, possibly amenable to low cost, and self‐manageable by patients. This study established a positive relationship between BMI and the risk of spontaneous abortion in women who became pregnant after assisted reproductive technology treatment.     

Sex,Race/Ethnicity,Socioeconomic Status,and BMI in Relation to Self‐Perception of Overweight

Objective: To compare the self‐perception of overweight in the study population according to sex, race/ethnicity, and socioeconomic status and to compare the self‐perception of overweight among individuals classified as normal weight, overweight, and obese. Research Methods and Procedures: Data from 5440 adults who participated in the 1994 to 1996 Continuing Survey of Food Intakes by Individuals and the Diet and Health Knowledge Survey conducted by the U.S. Department of Agriculture were analyzed. Data for analysis included self‐perceived weight status, self‐reported weight and height, and demographic and socioeconomic data. Underweight individuals, defined as those with a body mass index <18.5 kg/m², were excluded from the analysis. Results: Self‐perception of overweight was more common in women compared with men and in whites compared with blacks or Hispanics. Both the correct and incorrect perception of overweight was more common in normal weight and overweight white women compared with black women. More overweight and obese white men correctly perceived their overweight status compared with black men. Multiple logistic regression showed that the odds ratio of perceived overweight was significantly higher in women, whites, and individuals with higher body mass index, higher income, and higher education. Discussion: Self‐perceived overweight varied by sex, race/ethnicity, and socioeconomic status. Erroneous perception of body weight may have important health and behavioral implications. In particular, a considerable proportion of overweight men may be at risk of obesity if they continue to perceive themselves as having normal weight.     

Correcting biases in estimates of mortality attributable to obesity

Objective: To assess whether a recent study that found a relatively small number of excess deaths attributable to obesity may have underestimated by not correcting for statistical biases. Research Methods and Procedures: This prospective cohort study used data from the First National Health and Nutrition Examination Survey Epidemiologic Follow‐Up Study. Survival analyses were conducted using 9690 individuals 32 to 87 years of age and 1886 all‐cause deaths during a 9.1‐year follow‐up. Corrections were made for the reputed regression‐dilution bias by using the average BMI during the decade before follow‐up as predictor. Corrections for the reputed reverse‐causation bias were made by excluding participants with a history of serious illness. Attributable fractions were calculated and used to estimate excess deaths. Results: The uncorrected estimate of excess deaths attributable to obesity (BMI ≥30) was 41.9, using 18.5 to 25 kg/m² as ideal‐weight category. Using average BMI as predictor increased the estimate to 93.3. Correcting for reverse‐causation effects increased the estimate further to 131.1 (range, 93.3 to 169.0). The uncorrected hazard ratio, 1.25, was increased to 1.41 by using average BMI as predictor, and then to 2.40 by correcting for reverse causation. Using BMI 21 to 25 kg/m² and 23 to 25 kg/m² as ideal‐weight categories increased the corrected estimates to 144.6 (range, 80.5 to 177.2) and 164.1 (range, 103.8 to 194.9), respectively. Larger increases were found for overweight and Grade 2 to 4 obesity (BMI ≥35 kg/m²). For overweight, the uncorrected estimate using 18.5 to 25 kg/m² as ideal‐weight category was ?88.3 and the corrected estimate using 23 to 25 kg/m² as ideal‐weight category was 205.4 (range, 114.5 to 296.3). Discussion: Correcting for statistical biases and using higher ideal‐weight categories increased the estimate of excess deaths attributable to obesity by ～400% and changed the negative estimate for overweight to a large positive estimate.     

Body mass index related electrocardiographic findings in healthy young individuals with a normal body mass index

Introduction

An increased body mass index (BMI) (>25 kg/m²) is associated with a wide range of electrocardiographic changes. However, the association between electrocardiographic changes and BMI in healthy young individuals with a normal BMI (18.5–25 kg/m²) is unknown. The aim of this study was to evaluate the association between BMI and electrocardiographic parameters.

Methods

Data from 1,290 volunteers aged 18 to 30 years collected at our centre were analysed. Only subjects considered healthy by a physician after review of collected data with a normal BMI and in sinus rhythm were included in the analysis. Subjects with a normal BMI (18.5–25 kg/m²) were divided into BMI quartiles analysis and a backward multivariate regression analysis with a normal BMI as a continuous variable was performed.

Results

Mean age was 22.7 ± 3.0 years, mean BMI was 22.0, and 73.4% were male. There were significant differences between the BMI quartiles in terms of maximum P-wave duration, P-wave balance, total P-wave area in lead V1, PR-interval duration, and heart axis. In the multivariate model maximum P-wave duration (standardised coefficient (SC) = +0.112, P < 0.001), P-wave balance in lead V1 (SC = +0.072, P < 0.001), heart axis (SC = −0.164, P < 0.001), and Sokolow-Lyon voltage (SC = −0.097, P < 0.001) were independently associated with BMI.

Conclusion

Increased BMI was related with discrete electrocardiographic alterations including an increased P-wave duration, increased P-wave balance, a leftward shift of the heart axis, and decreased Sokolow-Lyon voltage on a standard twelve lead electrocardiogram in healthy young individuals with a normal BMI.

     

Obesity in South Africa: The South African Demographic and Health Survey

Objectives: To ascertain the anthropometric profile and determinants of obesity in South Africans who participated in the Demographic and Health Survey in 1998. Research Methods and Procedures: A sample of 13,089 men and women (age, ≥15 years) were randomly selected and then stratified by province and urban and nonurban areas. Height, weight, mid-upper arm circumference, and waist and hip circumference were measured. Body mass index (BMI) was used as an indicator of obesity, and the waist/hip ratio (WHR) was used as an indicator of abdominal obesity. Multivariate regression identified sociodemographic predictors of BMI and waist circumference in the data. Results: Mean BMI values for men and women were 22.9 kg/m² and 27.1 kg/m², respectively. For men, 29.2% were overweight or obese (≥25 kg/m²) and 9.2% had abdominal obesity (WHR ≥1.0), whereas 56.6% of women were overweight or obese and 42% had abdominal obesity (WHR >0.85). Underweight (BMI <18.5 kg/m²) was found in 12.2% of men and 5.6% of women. For men, 19% of the variation of BMI and 34% of the variation in waist circumference could be explained by age, level of education, population group, and area of residence. For women, these variables explained 16% of the variation of BMI and 24% of the variation in waist circumference. Obesity increased with age, and higher levels of obesity were found in urban African women. Discussion: Overnutrition is prevalent among adult South Africans, particularly women. Determinants of overnutrition include age, level of education, ethnicity, and area of residence.     

Obesity among People with and without Mental Retardation across Adulthood

Objective: This study was designed to explore obesity during adulthood and the likelihood of moving out of obesity among 1809 adults without disability and 680 adults with mental retardation who received care at the same primary care practices during the period of 1990 to 2003. Research Method and Procedures: A retrospective observational design using medical records first identified patients with mental retardation (MR) and age‐matched controls without disabilities. Data on BMI collected during each primary care visit allowed exploration of obesity at three levels. Moving out of obesity was defined as having a BMI <25 kg/m². We also abstracted data on age, sex, race, and other medical conditions. Results: For adults 20 to 29 years of age, 33.1% of patients without disability and 21% of patients with MR had a BMI >30 kg/m². Between the ages of 50 and 59 years, 40.5% of the patients without disability and 35.2% of the patients with MR had a BMI >30 kg/m². Patients with mild MR had similar prevalence rates of obesity and patients with severe MR had significantly lower prevalence of obesity compared with the patients without disability through 50 years of age. Throughout the period from 20 to 60 years of age, between 15% and 40% of individuals with and without MR, who were previously obese, were not currently obese. Discussion: Throughout the adult years, an increasing proportion of individuals with and without MR are obese. However, obesity is not a chronic state; many people transition back to a normal body weight.     

Midlife obesity and long-term risk of nursing home admission

Objective: Obesity is a growing problem among middle‐aged individuals. We investigated whether obesity in middle‐aged individuals influences the need for future nursing home care and whether the risk of nursing home admission associated with obesity is greater in whites than in blacks. Research Methods and Procedures: The study population (N = 8804) consisted of long‐term members of the Kaiser Permanente Medical Care Plan ages 75 to 85 years in 1995 who had completed a standardized, multiphasic health checkup while in their 50s. The multiphasic health checkup examinations were performed as part of routine medical care between the years 1964 and 1973 and included standardized measurements of BMI. We used health plan records to assess incident nursing home admissions from 1995 to 2002. The risk of nursing home admission associated with standard categories of midlife BMI was estimated using Cox proportional hazard analysis. Results: During an average follow‐up of 5.1 years, the nursing home admission rate was 6.8 per 100 person‐years of observation. After adjustment for comorbidities, midlife obesity predicted incident nursing home admission ～25 years later [hazard ratio (HR), 1.30; 95% confidence interval (CI), 1.15 to 1.46; p < 0.001]. Overweight BMI at midlife was not associated with future nursing home admission (HR, 1.05; 95% CI, 0.97 to 1.14; p = 0.23). The risk of nursing home admission associated with midlife obesity was higher in whites (HR, 1.34; 95% CI, 1.17 to 1.54; p < 0.001) than in blacks (HR 1.15; 95% CI, 0.87 to 1.52; p = 0.32), but the difference between races was not significant (p for interaction = 0.65). Discussion: Obesity among middle‐aged individuals is associated with an increased risk of nursing home admission in late life and may be an important target for reducing the future societal burden of nursing home care.     

Weight Change and Risk of Developing Type 2 Diabetes

Objective: To examine the association between weight change and risk of type 2 diabetes and whether initial weight modifies the association. Research Methods and Procedures: This is a prospective cohort study of 20, 187 alumni from Harvard University and the University of Pennsylvania. At baseline in 1962 or 1966, men (mean age, 45.9 years) reported their weight, height, and other risk factors. They also had had their weight and height measured at university entry (mean age, 18.5 years). Participants were followed from baseline to 1998 for type 2 diabetes. Results: During follow‐up, 1223 men developed type 2 diabetes. Weight gain significantly increased the risk of this disease. The multivariate relative risks associated with BMI change from university entry to baseline of <?0.5, ±0.5, >0.5 to 1.0, >1.0 to 1.5, >1.5 to 2.0, >2.0 to 3.0, and >3.0 kg/m² per decade were 0.88, 1.00 (referent), 1.29, 2.09, 2.69, 4.67, and 7.02, respectively (p for trend < 0.0001). Even among men with a low initial BMI < 21 kg/m², weight gain significantly increased risk; the corresponding relative risks were (no cases), 1.00 (referent), 1.00, 1.93, 2.47, 4.82, and 7.68, respectively (p for trend < 0.0001). Discussion: A low initial BMI does not ameliorate the increase in risk of type 2 diabetes with weight gain. Avoidance of weight gain, even among lean individuals, is important to reduce the risk of this disease.     

Body Mass Index and Chronic Energy Deficiency Among Urban Bengalee Male Slum Dwellers of Kolkata, India: Relationship with Family Income

A cross-sectional study of 191 adult (>18 years) Bengalee male slum dwellers of Kolkata, India, was undertaken to study the relationships of family income with body mass index (BMI) and chronic energy deficiency (CED). Results revealed that the mean height, weight, and BMI of the subjects were 162.2 cm, 54.0 kg, and 20.5 kg/m², respectively. The overall frequency of CED (BMI < 18.5 kg/m²) was 33.5%. Based on the World Health Organization classification, the prevalence of CED among this population was high (20–39%) and thus the situation is serious. Overall, monthly family income (MFI) was significantly positively correlated (r = 0.18, p < 0.05) with BMI. Linear regression analyses showed that MFI had significant impact (p < 0.05) on BMI. The percent variation in BMI explained by MFI was 2.6%. Subjects belonging to the lowest family income group (FIG) had the lowest mean BMI (19.5 kg/m²) and the highest rate of CED (46.6%) while those in the highest FIG had the largest mean BMI (21.4 kg/m²) and lowest rate of CED (23.1%). There was a significant FIG difference (F = 2.965, p < 0.05) in mean BMI. Moreover, there existed FIG differences (χ ² = 7.54, p < 0.06) in CED rates. In conclusion, this study provided strong evidence that FIG was significantly associated with BMI and the presence of CED. The rate of CED was high, indicating a serious situation. These findings may have severe public health implications. It is recommended that immediate nutritional intervention programs be initiated among this population along with serious efforts to increase their family income.     

Prevalence of Pre‐obesity and Obesity in Urban Adult Mexicans in Comparison with Other Large Surveys

Objective: 1. To estimate the prevalence of pre‐obesity and obesity in a 1992 to 1993 national survey of the Mexican urban adult population. 2. To compare our findings with other national surveys and with data for Mexican Americans. Research Methods and Procedures: The national representative sample of the Mexican urban adult population included 8462 women and 5929 men aged 20 to 69 years from 417 towns of >2500 people. Body mass index (BMI), calculated from measured weight and height, was classified using the World Health Organization categories of underweight (BMI < 18.5 kg/m²), normal weight (BMI 18.5 to 24.9 kg/m²), pre‐obesity (PreOB = BMI 25 to 29.9 kg/m²) and obesity (OB = BMI 30+ kg/m²). Estimates for Mexican Americans were calculated from U.S. survey data. Results: Overall, 38% of the Mexican urban adult population were classified as pre‐obese and 21% as obese. Men had a higher prevalence of pre‐obesity than women did at all ages, but women had higher values of obesity. Both pre‐obesity and obesity increased with age up to the age range brackets of 40 to 49 or 50 to 59 years for both men and women. Both pre‐obesity and obesity prevalence estimates were remarkably similar to data for Mexican Americans from 1982 through 1984. Comparison with other large surveys showed that countries differed more in the prevalence of obesity than of pre‐obesity, leading to differences in the PreOB/OB ratio, and that countries also differed in the gender ratio (female/male) for both pre‐obesity and obesity. Discussion: Pre‐obesity and obesity were high in our population and increased with age. Our approach of characterizing large surveys by PreOB/OB and gender ratios appeared promising.     

Vitamin D in Overweight/Obese Women and Its Relationship With Dietetic and Anthropometric Variables

Overweight/obese persons usually have an inadequate vitamin D status, a situation commonly made worse by an inadequate intake of this vitamin. For this reason, the aim of this study was to analyze dietetic and anthropometric differences in a group of young, overweight/obese Spanish women with respect to their vitamin D status. The study subjects were 66 white Spanish women (aged 20–35 years) with a BMI of 24–35 kg/m². Dietetic, anthropometric, and biochemical data were collected. Women were divided into two groups depending on their serum vitamin D concentrations: LD (women with <90 nmol/l 25(OH)D) and HD (women with ≥90 nmol/l 25(OH)D). The intakes of vitamin D, calcium, and supplements were similar in both groups. The body weight, BMI, and waist circumference of the HD subjects were smaller than those recorded for the LD subjects (68.6 ± 4.2 kg, 26.0 ± 1.3 kg/m², and 79.4 ± 3.4 cm compared to 76.2 ± 9.8, 28.6 ± 3.2 kg/m², and 86.2 ± 9.3 cm, respectively; P < 0.05). The hip circumference and the waist/hip ratio were similar in both groups. A BMI of <27.7 kg/m² (P50) was associated with serum vitamin D concentrations of ≥90 nmol/l (odds ratio = 0.1313; confidence interval: 0.0149–1.1599; P < 0.05). Overweight/obese women are at higher risk of vitamin D deficiency, largely due to excess adiposity rather than inadequate intake.     

Obesity and C-reactive protein levels among white, black, and hispanic US adults

Objective: Studies suggest that obesity's adverse impact on cardiovascular mortality may be reduced in African Americans relative to white Americans. We examined whether obesity's association with novel cardiovascular risk factors such as C‐reactive protein (CRP) also varies by race and ethnicity. Methods and Procedures: We analyzed data from 10,492 white, African‐American, and Hispanic‐American participants of the 1999–2004 National Health and Nutrition Examination Survey, who were aged 20 years and older, with a BMI ≥18.5 kg/m² and CRP ≤10 mg/l. We fit sex‐specific multivariable models of the association of BMI or waist circumference with log CRP levels and tested for interactions of BMI or waist circumference with race/ethnicity. Results: Higher BMI was significantly associated with higher CRP in all racial/ethnic groups for both men and women (P > 0.05 for BMI–race/ethnicity interaction) before and after adjustment for age, education, and health behaviors. Larger waist circumference was also associated with higher CRP levels in all racial/ethnic groups before and after adjustment; among women, the relationship was strongest for Mexican Hispanics (P < 0.01 for waist circumference–race/ethnicity interaction). Results were similar after additional adjustment for medications that might affect CRP levels. Discussion: The association between obesity and CRP is at least as strong in African Americans and Hispanic Americans as in white Americans. Racial differences in the relationship between obesity and cardiovascular mortality are unlikely to be due to racial differences in obesity's impact on CRP.     

Changes in Body Weight and Waist Circumference Affect Incident Hypercholesterolemia During 7 Years of Follow‐up

Objective: To assess whether changes in total and regional adiposity affect the odds for becoming hypercholesterolemic. Methods and Procedures: Changes in BMI and waist circumference were compared to self‐reported physician‐diagnosed hypercholesterolemia in 24,397 men and 10,023 women followed prospectively in the National Runners' Health Study. Results: Incident hypercholesterolemia were reported by 3,054 men and 519 women during (mean ± s.d.) 7.8 ± 1.8 and 7.5 ± 2.0 years of follow‐up, respectively. Despite being active, men's BMI increased by 1.15 ± 1.71 kg/m² and women's BMI increased by 0.96 ± 1.89 kg/m². The odds for developing hypercholesterolemia increased significantly in association with gains in BMI and waist circumferences in both sexes. A gain in BMI ≥2.4 kg/m² significantly (P < 0.0001) increased the odds for hypercholesterolemia by 94% in men and 129% in women compared to those whose BMI declined (40 and 76%, respectively, adjusted for average of the baseline and follow‐up BMI, P < 0.0001). A gain of ≥6 cm in waist circumference increased men's odds for hypercholesterolemia by 74% (P < 0.0001) and women's odds by 70% (P < 0.0001) relative to those whose circumference declined (odds increased 40% at P < 0.0001 and 49% at P < 0.01, respectively adjusted for average circumference). BMI and waist circumference at the end of follow‐up were significantly associated (P < 0.0001) with the log odds for hypercholesterolemia in both men (e.g., coefficient ± s.e.: 0.115 ± 0.011 per kg/m²) and women (e.g., 0.119 ± 0.019 per kg/m²) when adjusted for baseline values, whereas baseline BMI and circumferences were unrelated to the log odds when adjusted for follow‐up values. Discussion: These observations are consistent with the hypothesis that weight gain acutely increases the risk for hypercholesterolemia.     

The Incidence Rate of Seizures in Relation to BMI in UK Adults

A retrospective cohort study using the data from The Health Improvement Network (THIN) database in the United Kingdom was conducted to examine the incidence rates of seizures across different BMI levels in the adult population aged ≥18 years. Poisson regression was used to examine the relationship between BMI and seizures. The overall incidence rate of seizures was found to be 31.2 cases per 100,000 person‐years. The incidence rate of seizures (cases per 100,000 person‐years) in obese patients (BMI ≥ 30 kg/m²) was 34.8 (95% confidence interval (CI), 23.1, 46.4), comparable to that in patients with normal weight (BMI between 18.5 and 24.9 kg/m²) (35.8, 95%CI (26.6, 44.9)). In contrast, underweight patients (<18.5 kg/m²) or extremely obese (≥40 kg/m²) patients tended to have higher incidence rates than those with normal weight. After adjustment for age, gender, and smoking status, compared to patients with normal weight, those who were underweight or extremely obese had a rate ratio (RR) for seizures of 1.6 (95%CI (0.7, 3.8)) and 1.7 (95%CI (0.7, 3.9)), respectively. To date, we have not found any study that examines the associations between BMI or obesity and seizures. In this study, the incidence rates of seizures in the extremely obese and underweight patients tended to be higher than that in the normal‐weight patients.     

Is There a Role for Gastric Accommodation and Satiety in Asymptomatic Obese People?

Objective: The relationships of gastric accommodation and satiety in moderately obese individuals are unclear. We hypothesized that obese people had increased gastric accommodation and reduced postprandial satiety. The objective of this study was to compare gastric accommodation and satiety between obese and non‐obese asymptomatic subjects. Research Methods and Procedures: In 13 obese (body mass index [BMI] ≥ 30 kg/m²; mean BMI, 37.0 ± 4.9 kg/m²) and 19 non‐obese control subjects (BMI < 30 kg/m²; mean BMI, 26.2 ± 2.9 kg/m²), we used single photon emission computed tomography to measure fasting and postprandial gastric volumes and expressed the accommodation response as the ratio of postprandial/fasting volumes. The satiety test measured maximum tolerable volume of ingestion of liquid nutrient meal (Ensure) and symptoms 30 minutes after cessation of ingestion. Results: Total fasting and postprandial gastric volumes and the ratio of postprandial/fasting gastric volume were not different between asymptomatic obese and control subjects. However, the fasting volume of the distal stomach was greater in obese than in control subjects. Maximum tolerable volume of ingested Ensure and aggregate symptom score 30 minutes later were also not different between obese and control subjects. Discussion: Asymptomatic obese individuals (within the BMI range of 32.6 to 48 kg/m²) did not show either increased postprandial gastric accommodation or reduced satiety. These datasuggest that gastric accommodation is unlikely to provide an important contribution to development of moderate obesity.     

Clinical Profile and Management of Patients With Hypertension and Chronic Ischemic Heart Disease According to BMI

Obesity is associated with numerous risk factors and comorbidities such as hypertension, metabolic syndrome, type 2 diabetes, and cardiovascular diseases. However, numerous studies have reported an obesity paradox; the overweight and obese patients with established cardiovascular disease have better prognosis than those with a BMI <25 kg/m². This study was designed to assess potential differences in the clinical profile and management of hypertensive outpatients with chronic ischemic heart disease in obese and lean patients that could explain these two apparently contradictory points. Overweight and obesity were defined as a BMI 25–29.9 kg/m² and ≥30 kg/m², respectively. Cardiovascular risk factors goals were considered according to European Society of Hypertension‐European Society of Cardiology 2003, National Cholesterol Education Program Adult Treatment Panel III and American Diabetes Association 2005 guidelines. A sample of 2,024 patients (66.8 ± 10.1 years; 31.7% women) was included. Of these, 0.1% had a BMI <20 kg/m²; 17.1% BMI 20–24.9 kg/m²; 53.7% BMI 25–29.9 kg/m²; 23.7% BMI 30–34.9 kg/m²; 4.3% BMI 35–39.9 kg/m²; and 1.1% BMI ≥40 kg/m². The subgroup of patients with BMI ≥30 kg/m² had a higher proportion of women, diastolic dysfunction, diabetes, dyslipidemia, left ventricular hypertrophy, and heart failure. There was an inverse relationship between risk factors control rates and BMI (all comparisons BMI 20–24.9 kg/m² vs. 25–29.9 kg/m² vs. ≥30 kg/m²): blood pressure (BP) control (51.7% vs. 42.4% vs. 29.2%, P < 0.001); low‐density lipoprotein cholesterol (LDL‐C) control (35.2% vs. 30.5% vs. 27.9%, P = 0.03) and diabetes control (38.6% vs. 27.6% vs. 22.2%, P = 0.023). In conclusion, in patients with hypertension and chronic ischemic heart disease, as BMI increases, the clinical profile worsens as well as risk factors control rates.     

Work‐Related Physical Activity Is Not Associated with Body Mass Index and Obesity

Objective: To analyze the association of work‐related physical activity (WRPA) and leisure‐time physical activity (LTPA) with body mass index (BMI) and obesity in the Spanish adult population aged 20 to 60 years. Research Methods and Procedures: The data were taken from the 1993 Spanish National Health Survey. We analyzed a sample of 12,044 men and women representative of the Spanish population aged 20 to 60 years. BMI and frequency of obesity (BMI ≥ 30 kg/m²) were obtained from self‐reported weight and height. Multiple linear regression and logistic regression models were constructed, adjusting for the main confounding factors. WRPA and LTPA were measured by two questions to classify subjects into four categories of physical activity. Results: Neither mean BMI nor percentage of obesity varied significantly (p > 0.05) by WRPA. Mean BMI was significantly higher (p < 0.01) in those who were inactive in their leisure time (25.90 kg/m² in men and 24.43 kg/m² in women) than in those who reported vigorous activity (24.42 kg/m² and 22.97 kg/m² in men and women, respectively). The odds ration (OR) for obesity decreased with increasing level of LTPA in both men (OR of 0.64 for vigorous activity) and women (OR = 0.68), showing a statistically significant dose‐response relation in both men (for linear trend, p = 0.0021) and women (p = 0.0245). Discussion: These results raise questions about the association between WRPA and obesity and suggest the need to reexamine models of the obesity epidemic that point to automation of the workplace as one of the major explanatory factors.