Relation between body mass index, physical inactivity and use of prescription drugs: the Doetinchem Cohort Study.

BACKGROUND: Obesity and physical inactivity are associated with several diseases such as diabetes, cardiovascular diseases, musculoskeletal complaints, osteoporosis, certain types of cancer and depression. However, few data are available on the specific types of medication associated with obesity and physical inactivity. OBJECTIVE: The aim of this study was to determine the independent association of body mass index (BMI) and physical inactivity with use of specific classes of prescription drugs, and the interaction between BMI and physical inactivity. METHODS: The Doetinchem Cohort Study is a population-based longitudinal study. We analyzed cross-sectional data of 1703 men and 1841 women, examined between 1998 and 2002, for whom drug-dispending data were available from the PHARMO database. Drugs were coded according to the WHO Anatomical Therapeutic Chemical (ATC) classification system. Body weight was measured during the physical examination. Physical activity was assessed using an extensive questionnaire. Persons were defined as a user of a certain drug class if they filed at least one prescription in the year around (+/-6 months) the examination. RESULTS: Compared with normal weight persons (BMI 18.5-25 kg m(-2)), obese persons (BMI>30 kg m(-2)) had a higher use of prescription drugs of several drug classes, especially cardiovascular drugs (OR (95% CI): 3.83 (2.61-5.64) in men and 2.80 (2.03-3.86) in women) and diabetes drugs (OR (95% CI): 5.72 (2.32-14.14) in men and 3.92 (1.80-8.54) in women). In women, physical inactivity was also associated with higher use of certain drug classes, such as drugs for blood and blood-forming organs (OR (95% CI): 2.11 (1.22-3.65)) and musculoskeletal drugs (OR (95% CI): 2.07 (1.45-2.97)), whereas in men this was not the case. We found no interaction between BMI and physical inactivity with respect to use of prescription drugs. CONCLUSION: In both men and women, obesity was associated with a higher use of several types of prescription drugs, whereas physical inactivity was only associated with a higher use of certain drug classes in women.

Modeling predicted that tobacco control policies targeted at lower educated will reduce the differences in life expectancy.

BACKGROUND AND OBJECTIVE: To estimate the effects of reducing the prevalence of smoking in lower educated groups on educational differences in life expectancy. METHODS: A dynamic Markov-type multistate transition model estimated the effects on life expectancy of two scenarios. A "maximum scenario" where educational differences in prevalence of smoking disappear immediately, and a "policy target-scenario" where difference in prevalence of smoking is halved over a 20-year period. The two scenarios were compared to a reference scenario, where smoking prevalences do not change. Five Dutch cohort studies, involving over 67,000 participants aged 20 to 90 years, provided relative mortality risks by educational level, and smoking habits were assessed using national data of more than 120,000 persons. RESULTS: In the reference scenario, the difference in life expectancy at age 40 between highest and lowest educated groups was 5.1 years for men and 2.7 years for women. In the "maximum scenario" these differences were reduced to 3.6 years for men and 1.7 years for women (reduction approximately 30%), and in the "policy target-scenario" differences were 4.7 years for men and 2.4 years for women (reduction approximately 10%). CONCLUSION: Theoretically, educational differences in life expectancy would be reduced by 30% at maximum, if variations in smoking prevalence were eliminated completely. In practice, tobacco control policies that are targeted at the lower educated may reduce the differences in life expectancy by approximately 10%.

Health care costs attributable to overweight calculated in a standardized way for three European countries.

This article presents a tool to calculate health care costs attributable to overweight in a comparable and standardized way. The purpose is to describe the methodological principles of the tool and to put it into use by calculating and comparing the costs attributable to overweight for The Netherlands, Germany and Czech Republic. The tool uses a top-down and prevalence-based approach, consisting of five steps. Step one identifies overweight-related diseases and age- and gender-specific relative risks. Included diseases are ischemic heart disease, stroke, hypertension, type 2 diabetes mellitus, colorectal cancer, postmenopausal breast cancer, endometrial cancer, kidney cancer and osteoarthritis. Step two consists of collecting data on the age- and gender-specific prevalence of these diseases. Step three uses the population-attributable prevalence to determine the part of the prevalence of these diseases that is attributable to overweight. Step four calculates the health care costs associated with these diseases. Step five calculates the costs of these diseases that are attributable to overweight. Overweight is responsible for 20-26% of the direct costs of included diseases, with sensitivity analyses varying this percentage between 15-31%. Percentage of costs attributable to obesity and preobesity is about the same. Diseases with the highest percentage of costs due to overweight are diabetes, endometrial cancer and osteoarthritis. Disease costs attributable to overweight as a percentage of total health care expenditures range from 2 to 4%. Data are consistent for all three countries, resulting in roughly a quarter of costs of included diseases being attributable to overweight.

Increased alpha-linolenic acid intake lowers C-reactive protein, but has no effect on markers of atherosclerosis.

OBJECTIVE: To investigate the effects of increased alpha-linolenic acid (ALA)-intake on intima-media thickness (IMT), oxidized low-density lipoprotein (LDL) antibodies, soluble intercellular adhesion molecule-1 (sICAM-1), C-reactive protein (CRP), and interleukins 6 and 10. DESIGN: Randomized double-blind placebo-controlled trial. SUBJECTS: Moderately hypercholesterolaemic men and women (55 +/- 10 y) with two other cardiovascular risk factors (n = 103). INTERVENTION: Participants were assigned to a margarine enriched with ALA (fatty acid composition 46% LA, 15% ALA) or linoleic acid (LA) (58% LA, 0.3% ALA) for 2 y. RESULTS: Dietary ALA intake was 2.3 en% among ALA users, and 0.4 en% among LA users. The 2-y progression rate of the mean carotid IMT (ALA and LA: +0.05 mm) and femoral IMT (ALA:+0.05 mm; LA:+0.04 mm) was similar, when adjusted for confounding variables. After 1 and 2 y, ALA users had a lower CRP level than LA users (net differences -0.53 and -0.56 mg/l, respectively, P < 0.05). No significant effects were observed in oxidized LDL antibodies, and levels of sICAM-1, interleukins 6 and 10. CONCLUSIONS: A six-fold increased ALA intake lowers CRP, when compared to a control diet high in LA. The present study found no effects on markers for atherosclerosis. SPONSORSHIP: The Dutch 'Praeventiefonds'.

The impact of body mass index in old age on cause-specific mortality.

OBJECTIVES: To assess the association between Body Mass Index (BMI) and cause-specific mortality in older adults and to assess which BMI was associated with lowest mortality. DESIGN: Prospective study. SETTING: European towns. PARTICIPANTS: 1,980 older adults, aged 70-75 years from the SENECA (Survey in Europe on Nutrition and the Elderly: a concerted action) study. MEASUREMENTS: BMI, examined in 1988/1989, and mortality rates and causes of death during 10 years of follow-up. RESULTS: Cox proportional hazards model including both BMI and BMI², accounting for sex, smoking status, educational level and age at baseline showed that BMI was associated with all-cause mortality (p<0.01), cardiovascular mortality (p<0.01) and mortality from other causes (p<0.01), but not with cancer or respiratory mortality (p>0.3). The lowest all-cause mortality risk was found at 27.1 (95%CI 24.1, 29.3) kg/m², and this risk was increased with statistical significance when higher than 31.4 kg/m² and lower than 21.1 kg/m². The lowest cardiovascular mortality risk was found at 25.6 (95%CI 17.1, 28.4) kg/m², and was increased with statistical significance when higher than 30.9 kg/m². CONCLUSION: In this study, BMI was associated with all-cause mortality risk in older people. This risk was mostly driven by an increased cardiovascular mortality risk, as no association was found for mortality risk from cancer or respiratory disease. Our results indicate that the WHO cut-off point of 25 kg/m² for overweight might be too low in old age, but more studies are needed to define specific cut-off points.

Maintenance of weight loss after lifestyle interventions for overweight and obesity, a systematic review.

Lifestyle interventions can reduce body weight, but weight regain is common and may particularly occur with higher initial weight loss. If so, one may argue whether the 10% weight loss in clinical guidelines is preferable above a lower weight loss. This systematic review explores the relation between weight loss during an intervention and weight maintenance after at least 1 year of unsupervised follow-up. Twenty-two interventions (during at least 1 month) in healthy overweight Caucasians were selected and the mean percentages of weight loss and maintenance were calculated in a standardized way. In addition, within four intervention groups (n > 80) maintenance was calculated stratified by initial weight loss (0-5%, 5-10%, >10%). Overall, mean percentage maintenance was 54%. Weight loss during the intervention was not significantly associated with percentage maintenance (r = -0.26; P = 0.13). Percentage maintenance also not differed significantly between interventions with a weight loss of 5-10% vs. >10%. Consequently, net weight loss after follow-up differed between these categories (3.7 vs. 7.0%, respectively; P < 0.01). The analyses within the four interventions confirmed these findings. In conclusion, percentage maintenance does not clearly depend on initial weight loss. From this perspective, 10% or more weight loss can indeed be encouraged and favoured above lower weight loss goals.

Relationship between costs of lifestyle interventions and weight loss in overweight adults.

Lifestyle interventions in a healthcare setting are effective for weight loss, but it is unclear whether more expensive interventions result in more weight loss. Our objective was to explore the relationship between intervention costs and effectiveness in a systematic review of randomized trials. Intervention studies were selected from 14 reviews and from a systematic MEDLINE-search. Studies had to contain a dietary and a physical activity component and report data on measured weight loss in healthy Caucasian overweight adults. Intervention costs were calculated in a standardized way. The association between costs and percentage weight loss after 1 year was assessed using regression analysis. Nineteen original studies describing 31 interventions were selected. The relationship between weight loss and intervention costs was best described by an asymptotic regression model, which explained 47% of the variance in weight loss. A clinically relevant weight loss of 5% was already observed in interventions of approximately euro110. Results were similar in an intention-to-treat analysis. In conclusion, lifestyle interventions in health care for overweight adults are relatively cheap and higher intervention costs are associated with more weight loss, although the effect of costs on weight loss levels off with growing costs.

The impact of school-based prevention of overweight on psychosocial well-being of children.

This review aimed to gain insight in the extent to which psychosocial effects of obesity prevention programmes have been studied, to give an overview of the methods used to measure the particular psychosocial aspects and - if possible - to quantify the effects found. Intervention studies (n = 267) covering the period 1990-October 2005 were derived from seven reviews about childhood obesity interventions. An additional search identified 2754 studies covering the period January 2005-February 2008. In total, 2901 papers (excluding 120 duplicates) were screened for inclusion. Sixty-nine papers covering 53 interventions were included and screened on measuring psychosocial variables. All original authors were contacted. Seven of the selected interventions measured psychosocial variables, five of which evaluated a net intervention effect as compared with a control condition. Only two interventions reported a statistically significant net intervention effect (a decrease in use of purging or diet pills and a decrease in peer ratings of aggression and observed verbal aggression). We conclude that a minority of childhood obesity interventions investigate the effects of their programmes on psychosocial well-being of children and adolescents. It is recommended that in the future, these programmes will be evaluated in a uniform way on a broad range of psychosocial aspects.

Intra-national variation in trends in overweight and leisure time physical activities in The Netherlands since 1980: stratification according to sex, age and urbanisation degree.

OBJECTIVE: To investigate time trends in overweight and Leisure Time Physical Activities (LTPA) in The Netherlands since 1980. Intra-national differences were examined stratified for sex, age and urbanisation degree. SUBJECTS AND METHODS: We used a random sample of about 140,000 respondents aged 20-69 years from the Health Interview Survey (Nethhis) and subsequent Permanent Survey on Living Conditions (POLS). Self-reported data on weight and height and demographic characteristics were gathered through interviews (every year) and data on LTPA were collected by self-administered questionnaires (1990-1997, 2001-2004). Linear regression analysis was performed for trend analyses. RESULTS: During 1981-2004, mean body mass index (BMI) increased significantly by 1.0 kg/m(2) (average per year=0.05 kg/m(2)). Trends were similar across sex and different degrees of urbanisation, but varied across age groups. In 20-to 39-year-old women, mean BMI increased by 1.7 kg/m(2), which was more than in older age groups (P