Anna Peeters

Obesity in adulthood and its consequences for life expectancy: a life-table analysis.

Context Middle-aged adults who are overweight or obese may have shorter life expectancies than normal-weight adults, but how much shorter? Contribution This analysis of data from the Framingham Heart Study from 1948 to 1990 showed that, on average, adults who were obese (body mass index [BMI] 30 kg/m2) at age 40 years lived 6 to 7 years less than their normal-weight counterparts. Adults who were overweight (BMI, 25 to 29.9 kg/m2) and did not smoke lived about 3 years less than normal-weight nonsmokers. Adults who were obese and smoked lived 13 to 14 years less than normal-weight nonsmokers. Cautions Descriptions of lost life expectancy do not necessarily predict length of life that could be gained from obesity prevention or treatment programs. The Editors The increasing prevalence of overweight and obesity, coupled with their associations with death, disability, and disease, has led to their identification as a major, potentially preventable cause of premature morbidity and death (1-9). However, it is difficult to estimate the public health impact of overweight and obesity because of complex interactions with age; smoking; and obesity-related risk factors, such as diabetes, hypertension, and lipid disorders (8, 10-12). The observed relationship between body mass index (BMI) and mortality has been described as J-shaped; mortality increases as a result of underweight, overweight, and obesity. However, preexisting illness and inadequate control of smoking may cause at least part of the increased mortality at very low weight (8). Consequently, there have been no robust estimates of life expectancy lost as a result of obesity. A primary reason is the lack of understanding of probable, healthy, or unhealthy weight trajectories over the life course. Conclusions regarding appropriate weight trajectories between adulthood and older age are complicated by uncertainties about age-appropriate measurements of obesity and the effects of smoking, obesity-associated risk factors for cardiovascular disease, and unintended weight loss (13, 14). We provide an estimate of the effect of obesity and overweight in adulthood on life expectancy, implicitly taking into account the various possible weight trajectories throughout the life course. We take advantage of the cohort follow-up made available by the Framingham Heart Study to analyze the differences in life course for various BMI groups. We make no assumptions about the relationship between BMI and mortality at older ages. Our primary objective was to analyze the reductions in life expectancy associated with overweight and obesity at 40 years of age. Methods Data Source The Framingham Heart Study is a longitudinal study with excellent follow-up on mortality. The original study cohort involved 5209 adults, age 28 through 62 years, residing in Framingham, Massachusetts, between 1948 and 1951 (15). To examine the effect of overweight and obesity in adulthood, we used the data from more than 40 years of follow-up (examinations 1 through 21) on age at death for persons 30 through 49 years of age at baseline (n = 3607). Height and weight were measured at baseline (7, 15). Smoking status at baseline was defined categorically as self-reported current smoker or nonsmoker. No information was available on smoking status before study entry. Information on all three variables was available for 3582 participants (99%). Because the relationship between weight and mortality is affected by underlying disease (8, 14, 16), we excluded participants who had cardiovascular disease (17) at baseline or died within 4 years of follow-up (63 participants, including 50 deaths). Because our analysis focused on the risk for death associated with overweight and obesity, we also excluded 62 underweight persons (BMI < 18.5 kg/m2). The final analyses involved 3457 participants (1550 men and 1907 women). We analyzed the effect of potential confounders on the relationship between obesity and mortality (6, 8, 18). Of the 3457 participants examined, hypertension and diabetes status was available for all participants, and physical activity level was available for 2893 (84%) participants. Total serum cholesterol level was available for 2127 (62%) participants and was therefore not taken into account. We defined hypertension at baseline as either systolic blood pressure of 160 mm Hg or greater or diastolic blood pressure of 95 mm Hg or greater in two repeated measurements. Physical activity (a continuous index derived from hours of activity and rest) was not available until examination 4 (approximately 8 years after baseline). Level of education at baseline was available for 3350 (97%) participants. Potential confounders were analyzed by using only complete cases. BMI Group Classification Body mass index at baseline was calculated as weight in kg/height in m2. We defined three BMI categories based on World Health Organization guidelines (2): group I (normal weight), BMI of 18.5 to 24.9 kg/m2; group II (overweight), BMI of 25 to 29.9 kg/m2; and group III (obese), BMI greater than or equal to 30 kg/m2 (including 19 people with BMI > 40 kg/m2). Survival Analysis We used S-Plus 2000 (MathSoft, Inc., Seattle, Washington) for all statistical analyses. Survival curves for each BMI group were compared by using KaplanMeier plots. We assessed the association between BMI group at baseline and mortality over the 40 years of follow-up by using Cox proportional-hazards analysis, with age as the time scale. The effect of BMI was analyzed separately within strata defined by sex and smoking status at baseline. We tested the proportionality of hazards assumption by analysis of the Schoenfeld residuals (19, 20). Statistical significance was set at the 5% level. Life Course Analysis Within each stratum, we estimated age-specific mortality rates for each BMI group by using Poisson regression analysis; age at follow-up and BMI group at baseline were categorical variables. Although the hazard ratios estimated for BMI group from this analysis are equivalent to those estimated from the Cox analyses, Poisson regression also optimizes the hazard associated with each age at follow-up. Life tables were derived for each BMI group, representing populations that were 40 years of age and free of cardiovascular disease at study entry. Conversions between mortality rates and probabilities assumed that within each single age interval, the hazard is constant. The life expectancy at 90 years of age was assumed to be a constant 4.53 for men and 5.05 for women for each BMI group (based on life expectancies of the total Framingham Study sample [21]). The main outcome measure, life expectancy at 40 years of age, was calculated as the mean age at death within a life-table population. Confidence intervals for the life table measures were calculated by using a bootstrap procedure, based on 10 000 replicates. We report the bootstrap biascorrect, adjusted 95% CIs (using the bias-corrected accelerated percentile interval algorithm) (22). Although computationally demanding, the bootstrap procedure is easier than an analytical alternative that includes both the variance of the Poisson model and the variance of the life table. Role of the Funding Source The Framingham Heart Study was conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the Framingham Heart Study Investigators. The NHLBI reviewed this article for scientific content and consistency of data interpretation with previous Framingham Heart Study publications; significant comments were incorporated into the text before submission for publication. The NHLBI had no role in the design, conduct, analyses, and reporting of the study or in the decision to submit the manuscript for publication. The Netherlands Heart Foundation and the Netherlands Organization for Scientific Research funded our study. Neither had any role in the design, conduct, analyses and reporting of the study or in the decision to submit the manuscript for publication. Results The characteristics at baseline within the Framingham Study cohort were generally as expected: The probability of death increased with each higher category of BMI group, the relationship between the prevalence of smoking and BMI group was inverse (Table 1), and age generally increased with each higher category of BMI group (7, 8, 23). Although male nonsmokers were a small group and may represent an unusual cohort for that time, they were analyzed in the same way as the other groups. We did this because male nonsmokers had BMI-related risks similar to those of female nonsmokers and to findings in previous studies examining the relationship between BMI and mortality. Table 1. Characteristics of Original Framingham Heart Study Participants, Age 30 to 49 Years at Baseline (19481951) BMI and Survival With participants categorized by BMI at baseline, we used Cox proportional-hazards analysis to determine the relative rate of death over the 40 years of follow-up. We found that sex did not significantly modify the effect of BMI but that smoking status at baseline did, as has been previously described (10, 12, 24, 25). Additional analyses were performed separately for strata defined by sex and smoking status at baseline. The Figure illustrates the empirical survival curves for each BMI group within each of the four strata: female nonsmokers, female smokers, male nonsmokers, and male smokers. The survival disadvantage associated with BMI group II compared with BMI group I is apparently smaller in smokers than in nonsmokers. The hazard ratios for mortality associated with BMI group were generally consistent between strata, although neither male nor female smokers in BMI group II showed an increased mortality risk (Table 2). The proportional hazards assumption seemed appropriate for BMI, both by analysis of the Schoenfeld residuals and by comparison of the Cox- derived hazard ratios for two distinct follow-up periods with approximately

The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies.

The objectives of this meta-analysis were to examine the magnitude of the relative risk (RR) of developing type 2 diabetes for overweight and obese populations, compared to those with normal weight, and to determine causes of the variation in RR between various cohort studies. The magnitude of the RR was analyzed by combining 18 prospective cohort studies that matched defined criteria. The variance in RR between studies was explored. The overall RR of diabetes for obese persons compared to those with normal weight was 7.19, 95% CI: 5.74, 9.00 and for overweight was 2.99, 95% CI: 2.42, 3.72. The variation in RR among studies was explored and it was found that the effect of heterogeneity was highly related with sample size, method of assessment of body mass index (BMI) and method of ascertainment of type 2 diabetes. By combining only cohort studies with more than 400 cases of incident diabetes (>median), adjusted by at least three main confounding variables (age, family history of type 2 diabetes, physical activity), measured BMI, and diabetes determined by clinical diagnosis, the RR was 7.28, 95% CI: 6.47, 8.28 for obesity and 2.92, 95% CI: 2.57, 3.32 for overweight.

Blood Pressure in Adulthood and Life Expectancy With Cardiovascular Disease in Men and Women: Life Course Analysis

Limited information exists about the consequences of hypertension during adulthood on residual life expectancy with cardiovascular disease. We aimed to analyze the life course of people with high blood pressure levels at age 50 in terms of total life expectancy and life expectancy with and without cardiovascular disease compared with normotensives. We constructed multistate life tables for cardiovascular disease, myocardial infarction, and stroke using data from 3128 participants of the Framingham Heart Study who had their 50th birthday while enrolled in the study. For the life table calculations, we used hazard ratios for 3 transitions (healthy to death, healthy to disease, and disease to death) by categories of blood pressure level and adjusted by age, sex, and confounders. Irrespective of sex, 50-year-old hypertensives compared with normotensives had a shorter life expectancy, a shorter life expectancy free of cardiovascular disease, myocardial infarction, and stroke, and a longer life expectancy lived with these diseases. Normotensive men (22% of men) survived 7.2 years (95% confidence interval, 5.6 to 9.0) longer without cardiovascular disease compared with hypertensives and spent 2.1 (0.9 to 3.4) fewer years of life with cardiovascular disease. Similar differences were observed in women. Compared with hypertensives, total life expectancy was 5.1 and 4.9 years longer for normotensive men and women, respectively. Increased blood pressure in adulthood is associated with large reductions in life expectancy and more years lived with cardiovascular disease. This effect is larger than estimated previously and affects both sexes similarly. Our findings underline the tremendous importance of preventing high blood pressure and its consequences in the population.

The number of years lived with obesity and the risk of all-cause and cause-specific mortality

BACKGROUND The role of the duration of obesity as an independent risk factor for mortality has not been investigated. The aim of this study was to analyse the association between the duration of obesity and the risk of mortality. METHODS A total of 5036 participants (aged 28-62 years) of the Framingham Cohort Study were followed up every 2 years from 1948 for up to 48 years. The association between obesity duration and all-cause and cause-specific mortality was analysed using time-dependent Cox models adjusted for body mass index. The role of biological intermediates and chronic diseases was also explored. RESULTS The adjusted hazard ratio (HR) for mortality increased as the number of years lived with obesity increased. For those who were obese for 1-4.9, 5-14.9, 15-24.9 and ≥ 25 years of the study follow-up period, adjusted HRs for all-cause mortality were 1.51 [95% confidence interval (CI) 1.27-1.79], 1.94 (95% CI 1.71-2.20), 2.25 (95% CI 1.89-2.67) and 2.52 (95% CI 2.08-3.06), respectively, compared with those who were never obese. A dose-response relation between years of duration of obesity was also clear for all-cause, cardiovascular, cancer and other-cause mortality. For every additional 2 years of obesity, the HRs for all-cause, cardiovascular disease, cancer and other-cause mortality were 1.06 (95% CI 1.05-1.07), 1.07 (95% CI 1.05-1.08), 1.03 (95% CI 1.01-1.05) and 1.07 (95% CI 1.05-1.11), respectively. CONCLUSIONS The number of years lived with obesity is directly associated with the risk of mortality. This needs to be taken into account when estimating its burden on mortality.

Substantial Intentional Weight Loss and Mortality in the Severely Obese

Objective:To compare all-cause mortality in a surgical weight loss cohort with a similarly aged, obese population-based cohort. Summary Background Data:Significant weight loss following bariatric surgery improves the comorbidities associated with obesity. Improved survival as a result of surgical weight loss has yet to be clearly demonstrated using clinical data. Methods:The surgical weight loss cohort was a series of consecutive patients treated with a laparoscopic adjustable gastric band in Melbourne between June 1994 and April 2005. The Melbourne Collaborative Cohort Study (MCCS) provided a community control cohort, recruited between 1992 and 1994 and followed to June 2005 to determine vital status. Height and weight were recorded at baseline in both studies. Subjects between 37 and 70 years and with a body mass index (BMI) of ≥35 were included. Vital status was determined by follow-up and searching of death registries. Survival time was compared using Kaplan-Meier estimates, and hazard of death was determined using Cox regression, adjusting for sex, age at baseline, and BMI at baseline. Results:Of 966 weight loss patients (mean age 47 years, mean BMI 45 kg/m2), the median follow-up time was 4 years. Mean weight loss after 2 years was 22.8% ± 9% (58% of excess weight). The MCCS cohort included 2119 severely obese members (mean age, 55 years; mean BMI, 38 kg/m2; median follow-up time, 12 years). There were 4 deaths in the weight loss cohort and 225 deaths in the MCCS cohort. Weight loss patients had 72% lower hazard of death than the community control cohort (hazard ratio, 0.28; 95% confidence interval, 0.10–0.85). Conclusions:Substantial surgical weight loss in a morbidly obese population was associated with a significant survival advantage.

The Polymeal: a more natural, safer, and probably tastier (than the Polypill) strategy to reduce cardiovascular disease by more than 75%

Abstract Objective Although the Polypill concept (proposed in 2003) is promising in terms of benefits for cardiovascular risk management, the potential costs and adverse effects are its main pitfalls. The objective of this study was to identify a tastier and safer alternative to the Polypill: the Polymeal. Methods Data on the ingredients of the Polymeal were taken from the literature. The evidence based recipe included wine, fish, dark chocolate, fruits, vegetables, garlic, and almonds. Data from the Framingham heart study and the Framingham offspring study were used to build life tables to model the benefits of the Polymeal in the general population from age 50, assuming multiplicative correlations. Results Combining the ingredients of the Polymeal would reduce cardiovascular disease events by 76%. For men, taking the Polymeal daily represented an increase in total life expectancy of 6.6 years, an increase in life expectancy free from cardiovascular disease of 9.0 years, and a decrease in life expectancy with cardiovascular disease of 2.4 years. The corresponding differences for women were 4.8, 8.1, and 3.3 years. Conclusion The Polymeal promises to be an effective, non-pharmacological, safe, cheap, and tasty alternative to reduce cardiovascular morbidity and increase life expectancy in the general population.

Cost-Effectiveness of Surgically Induced Weight Loss for the Management of Type 2 Diabetes: Modeled Lifetime Analysis

OBJECTIVE To estimate the cost-effectiveness of surgically induced weight loss relative to conventional therapy for the management of recently diagnosed type 2 diabetes in class I/II obese patients. RESEARCH DESIGN AND METHODS This study builds on a within-trial cost-efficacy analysis. The analysis compares the lifetime costs and quality-adjusted life-years (QALYs) between the two intervention groups. Intervention costs were extrapolated based on observed resource utilization during the trial. The proportion of patients in each intervention group with remission of diabetes at 2 years was the same as that observed in the trial. Health care costs for patients with type 2 diabetes and outcome variables required to derive estimates of QALYs were sourced from published literature. A health care system perspective was adopted. Costs and outcomes were discounted annually at 3%. Costs are presented in 2006 Australian dollars (AUD) (currency exchange: 1 AUD = 0.74 USD). RESULTS The mean number of years in diabetes remission over a lifetime was 11.4 for surgical therapy patients and 2.1 for conventional therapy patients. Over the remainder of their lifetime, surgical and conventional therapy patients lived 15.7 and 14.5 discounted QALYs, respectively. The mean discounted lifetime costs were 98,900 AUD per surgical therapy patient and 101,400 AUD per conventional therapy patient. Relative to conventional therapy, surgically induced weight loss was associated with a mean health care saving of 2,400 AUD and 1.2 additional QALYs per patient. CONCLUSIONS Surgically induced weight loss is a dominant intervention (it both saves health care costs and generates health benefits) for managing recently diagnosed type 2 diabetes in class I/II obese patients in Australia.

Diabetes and risk of physical disability in adults: a systematic review and meta-analysis

BACKGROUND According to previous reports, the risk of disability as a result of diabetes varies from none to double. Disability is an important measure of health and an estimate of the risk of disability as a result of diabetes is crucial in view of the global diabetes epidemic. We did a systematic review and meta-analysis to estimate this risk. METHODS We searched Ovid, Medline, Embase, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature up to Aug 8, 2012. We included studies of adults that compared the risk of disability-as measured by activities of daily living (ADL), instrumental activities of daily living (IADL), or mobility-in people with and without any type of diabetes. We excluded studies of subpopulations with specific illnesses or of people in nursing homes. From the studies, we recorded population characteristics, how diabetes was diagnosed (by doctor or self-reported), domain and definition of disability, and risk estimates for disability. We calculated pooled estimates by disability type and type of risk estimate (odds ratio [OR] or risk ratio [RR]). RESULTS Our systematic review returned 3224 results, from which 26 studies were included in our meta-analyses. Diabetes increased the risk of mobility disability (15 studies; OR 1.71, 95% CI 1.53-1.91; RR 1.51, 95% CI 1.38-1.64), of IADL disability (ten studies; OR 1.65, 95% CI 1.55-1.74), and of ADL disability (16 studies; OR 1.82, 95% CI 1.63-2.04; RR 1.82, 95% CI 1.40-2.36). INTERPRETATION Diabetes is associated with a strong increase in the risk of physical disability. Efforts to promote healthy ageing should account for this risk through prevention and management of diabetes. FUNDING Monash University, Baker IDI Bright Sparks Foundation, Australian Postgraduate Award, VicHealth, National Health and Medical Research Council, Australian Research Council, Victorian Government.

A Comparison of Adiposity Measures as Predictors of All‐cause Mortality: The Melbourne Collaborative Cohort Study

Objective: Our goal was to examine five different measures of adiposity as predictors of all‐cause mortality.

Cancer Risk Among People With Type 1 and Type 2 Diabetes: Disentangling True Associations, Detection Bias, and Reverse Causation

OBJECTIVE Evidence indicates an increased risk of certain cancers among people with type 2 diabetes. Evidence for rarer cancers and for type 1 diabetes is limited. We explored the excess risk of site-specific cancer incidence and mortality among people with type 1 and type 2 diabetes, compared with the general Australian population. RESEARCH DESIGN AND METHODS Registrants of a national diabetes registry (953,382) between 1997 and 2008 were linked to national death and cancer registries. Standardized incidence and mortality ratios (SIRs/SMRs) are reported. RESULTS For type 1 diabetes, significant elevated SIRs were observed for pancreas, liver, esophagus, colon and rectum (females only [F]), stomach (F), thyroid (F), brain (F), lung (F), endometrium, and ovary, and decreased SIRs were observed for prostate in males. Significantly increased SMRs were observed for pancreas, liver, and kidney (males only), non-Hodgkin’s lymphoma, brain (F), and endometrium. For type 2 diabetes, significant SIRs were observed for almost all site-specific cancers, with highest SIRs observed for liver and pancreas, and decreased risks for prostate and melanoma. Significant SMRs were observed for liver, pancreas, kidney, Hodgkin’s lymphoma, gallbladder (F), stomach (F), and non-Hodgkin’s lymphoma (F). Cancer risk was significantly elevated throughout follow-up time but was higher in the first 3 months postregistration, suggesting the presence of detection bias and/or reverse causation. CONCLUSIONS Type 1 and type 2 diabetes are associated with an excess risk of incidence and mortality for overall and a number of site-specific cancers, and this is only partially explained by bias. We suggest that screening for cancers in diabetic patients is important.