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1.  Association of Lifecourse Socioeconomic Status with Chronic Inflammation and Type 2 Diabetes Risk: The Whitehall II Prospective Cohort Study 
PLoS Medicine  2013;10(7):e1001479.
Silvia Stringhini and colleagues followed a group of British civil servants over 18 years to look for links between socioeconomic status and health.
Please see later in the article for the Editors' Summary
Background
Socioeconomic adversity in early life has been hypothesized to “program” a vulnerable phenotype with exaggerated inflammatory responses, so increasing the risk of developing type 2 diabetes in adulthood. The aim of this study is to test this hypothesis by assessing the extent to which the association between lifecourse socioeconomic status and type 2 diabetes incidence is explained by chronic inflammation.
Methods and Findings
We use data from the British Whitehall II study, a prospective occupational cohort of adults established in 1985. The inflammatory markers C-reactive protein and interleukin-6 were measured repeatedly and type 2 diabetes incidence (new cases) was monitored over an 18-year follow-up (from 1991–1993 until 2007–2009). Our analytical sample consisted of 6,387 non-diabetic participants (1,818 women), of whom 731 (207 women) developed type 2 diabetes over the follow-up. Cumulative exposure to low socioeconomic status from childhood to middle age was associated with an increased risk of developing type 2 diabetes in adulthood (hazard ratio [HR] = 1.96, 95% confidence interval: 1.48–2.58 for low cumulative lifecourse socioeconomic score and HR = 1.55, 95% confidence interval: 1.26–1.91 for low-low socioeconomic trajectory). 25% of the excess risk associated with cumulative socioeconomic adversity across the lifecourse and 32% of the excess risk associated with low-low socioeconomic trajectory was attributable to chronically elevated inflammation (95% confidence intervals 16%–58%).
Conclusions
In the present study, chronic inflammation explained a substantial part of the association between lifecourse socioeconomic disadvantage and type 2 diabetes. Further studies should be performed to confirm these findings in population-based samples, as the Whitehall II cohort is not representative of the general population, and to examine the extent to which social inequalities attributable to chronic inflammation are reversible.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, a metabolic disorder characterized by high amounts of glucose (sugar) in the blood. Blood sugar levels are normally controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes) blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes, which was previously called adult-onset diabetes, can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. However, as the disease progresses, the pancreatic beta cells, which make insulin, become impaired and patients may eventually need insulin injections. Long-term complications, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes.
Why Was This Study Done?
Socioeconomic adversity in childhood seems to increase the risk of developing type 2 diabetes but why? One possibility is that chronic inflammation mediates the association between socioeconomic adversity and type 2 diabetes. Inflammation, which is the body's normal response to injury and disease, affects insulin signaling and increases beta-cell death, and markers of inflammation such as raised blood levels of C-reactive protein and interleukin 6 are associated with future diabetes risk. Notably, socioeconomic adversity in early life leads to exaggerated inflammatory responses later in life and people exposed to social adversity in adulthood show greater levels of inflammation than people with a higher socioeconomic status. In this prospective cohort study (an investigation that records the baseline characteristics of a group of people and then follows them to see who develops specific conditions), the researchers test the hypothesis that chronically increased inflammatory activity in individuals exposed to socioeconomic adversity over their lifetime may partly mediate the association between socioeconomic status over the lifecourse and future type 2 diabetes risk.
What Did the Researchers Do and Find?
To assess the extent to which chronic inflammation explains the association between lifecourse socioeconomic status and type 2 diabetes incidence (new cases), the researchers used data from the Whitehall II study, a prospective occupational cohort study initiated in 1985 to investigate the mechanisms underlying previously observed socioeconomic inequalities in disease. Whitehall II enrolled more than 10,000 London-based government employees ranging from clerical/support staff to administrative officials and monitored inflammatory marker levels and type 2 diabetes incidence in the study participants from 1991–1993 until 2007–2009. Of 6,387 participants who were not diabetic in 1991–1993, 731 developed diabetes during the 18-year follow-up. Compared to participants with the highest cumulative lifecourse socioeconomic score (calculated using information on father's occupational position and the participant's educational attainment and occupational position), participants with the lowest score had almost double the risk of developing diabetes during follow-up. Low lifetime socioeconomic status trajectories (being socially downwardly mobile or starting and ending with a low socioeconomic status) were also associated with an increased risk of developing diabetes in adulthood. A quarter of the excess risk associated with cumulative socioeconomic adversity and nearly a third of the excess risk associated with low socioeconomic trajectory was attributable to chronically increased inflammation.
What Do These Findings Mean?
These findings show a robust association between adverse socioeconomic circumstances over the lifecourse of the Whitehall II study participants and the risk of type 2 diabetes and suggest that chronic inflammation explains up to a third of this association. The accuracy of these findings may be affected by the measures of socioeconomic status used in the study. Moreover, because the study participants were from an occupational cohort, these findings need to be confirmed in a general population. Studies are also needed to examine the extent to which social inequalities in diabetes risk that are attributable to chronic inflammation are reversible. Importantly, if future studies confirm and extend the findings reported here, it might be possible to reduce the social inequalities in type 2 diabetes by promoting interventions designed to reduce inflammation, including weight management, physical activity, and smoking cessation programs and the use of anti-inflammatory drugs, among socially disadvantaged groups.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001479.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals, and the general public, including information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes; it includes peoples stories about diabetes
The nonprofit Diabetes UK also provides detailed information about diabetes for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the nonprofit Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
Information about the Whitehall II study is available
doi:10.1371/journal.pmed.1001479
PMCID: PMC3699448  PMID: 23843750
2.  Rate of weight gain predicts change in physical activity levels: a longitudinal analysis of the EPIC-Norfolk cohort 
Objective
To investigate the relationship of body weight and its changes over time with physical activity.
Design
Population-based prospective cohort study (Norfolk cohort of the European Prospective Investigation into Cancer and Nutrition, EPIC-Norfolk, United Kingdom)
Subjects
25639 men and women aged 39-79 years at baseline. Physical activity was self-reported. Weight and height were measured by standard clinical procedures at baseline and self-reported at 18-month and 10-y follow-ups (calibrated against clinical measures). Main outcome measure was physical activity at the 10-y follow-up
Results
Body weight and physical activity were inversely associated in cross-sectional analyses. In longitudinal analyses, an increase in weight was associated with higher risk of being inactive 10 years later, after adjusting for baseline activity, 18-month activity, sex, baseline age, prevalent diseases, socioeconomic status, education, smoking, total daily energy intake, and alcohol intake. Compared with stable weight, a gain in weight of >2 kg/y during short-, medium- and long-term was consistently and significantly associated with greater likelihood of physical inactivity after 10 y, with the most pronounced effect for long-term weight gain, OR=1.89 (95% CI: 1.30-2.70) in fully adjusted analysis. Weight gain of 0.5-2 kg/y over long term was substantially associated with physical inactivity after full adjustment, OR=1.26 (95% CI: 1.11-1.41).
Conclusion
Weight gain (during short-, medium- and long-term) is a significant determinant of future physical inactivity independent of baseline weight and activity. Compared with maintaining weight, moderate (0.5-2 kg/y) and large weight gain (>2 kg/y) significantly predict future inactivity; a potentially vicious cycle including further weight gain, obesity and complications associated with a sedentary lifestyle. Based on current predictions of obesity trends, we estimate that the prevalence of inactivity in England would exceed 60% in year 2020.
doi:10.1038/ijo.2012.58
PMCID: PMC3635037  PMID: 22531093
physical activity; obesity; weight gain; cohort study; epidemiology
3.  Patterns of Obesity Development before the Diagnosis of Type 2 Diabetes: The Whitehall II Cohort Study 
PLoS Medicine  2014;11(2):e1001602.
Examining patterns of change in body mass index (BMI) and other cardiometabolic risk factors in individuals during the years before they were diagnosed with diabetes, Kristine Færch and colleagues report that few of them experienced dramatic BMI changes.
Please see later in the article for the Editors' Summary
Background
Patients with type 2 diabetes vary greatly with respect to degree of obesity at time of diagnosis. To address the heterogeneity of type 2 diabetes, we characterised patterns of change in body mass index (BMI) and other cardiometabolic risk factors before type 2 diabetes diagnosis.
Methods and Findings
We studied 6,705 participants from the Whitehall II study, an observational prospective cohort study of civil servants based in London. White men and women, initially free of diabetes, were followed with 5-yearly clinical examinations from 1991–2009 for a median of 14.1 years (interquartile range [IQR]: 8.7–16.2 years). Type 2 diabetes developed in 645 (1,209 person-examinations) and 6,060 remained free of diabetes during follow-up (14,060 person-examinations). Latent class trajectory analysis of incident diabetes cases was used to identify patterns of pre-disease BMI. Associated trajectories of cardiometabolic risk factors were studied using adjusted mixed-effects models. Three patterns of BMI changes were identified. Most participants belonged to the “stable overweight” group (n = 604, 94%) with a relatively constant BMI level within the overweight category throughout follow-up. They experienced slightly worsening of beta cell function and insulin sensitivity from 5 years prior to diagnosis. A small group of “progressive weight gainers” (n = 15) exhibited a pattern of consistent weight gain before diagnosis. Linear increases in blood pressure and an exponential increase in insulin resistance a few years before diagnosis accompanied the weight gain. The “persistently obese” (n = 26) were severely obese throughout the whole 18 years before diabetes diagnosis. They experienced an initial beta cell compensation followed by loss of beta cell function, whereas insulin sensitivity was relatively stable. Since the generalizability of these findings is limited, the results need confirmation in other study populations.
Conclusions
Three patterns of obesity changes prior to diabetes diagnosis were accompanied by distinct trajectories of insulin resistance and other cardiometabolic risk factors in a white, British population. While these results should be verified independently, the great majority of patients had modest weight gain prior to diagnosis. These results suggest that strategies focusing on small weight reductions for the entire population may be more beneficial than predominantly focusing on weight loss for high-risk individuals.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 350 million people have diabetes, a metabolic disorder characterized by high amounts of glucose (sugar) in the blood. Blood sugar levels are normally controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest form of diabetes) blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing sugar from the blood become insulin resistant. Type 2 diabetes, which was previously called adult-onset diabetes, can be controlled with diet and exercise, and with drugs that help the pancreas make more insulin or that make cells more sensitive to insulin. Long-term complications, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about 10 years compared to people without diabetes. The number of people with diabetes is expected to increase dramatically over the next decades, coinciding with rising obesity rates in many countries. To better understand diabetes development, to identify people at risk, and to find ways to prevent the disease are urgent public health goals.
Why Was This Study Done?
It is known that people who are overweight or obese have a higher risk of developing diabetes. Because of this association, a common assumption is that people who experienced recent weight gain are more likely to be diagnosed with diabetes. In this prospective cohort study (an investigation that records the baseline characteristics of a group of people and then follows them to see who develops specific conditions), the researchers tested the hypothesis that substantial weight gain precedes a diagnosis of diabetes and explored more generally the patterns of body weight and composition in the years before people develop diabetes. They then examined whether changes in body weight corresponded with changes in other risk factors for diabetes (such as insulin resistance), lipid profiles and blood pressure.
What Did the Researchers Do and Find?
The researchers studied participants from the Whitehall II study, a prospective cohort study initiated in 1985 to investigate the socioeconomic inequalities in disease. Whitehall II enrolled more than 10,000 London-based government employees. Participants underwent regular health checks during which their weight and height were measured, blood tests were done, and they filled out questionnaires for other relevant information. From 1991 onwards, participants were tested every five years for diabetes. The 6,705 participants included in this study were initially free of diabetes, and most of them were followed for at least 14 years. During the follow-up, 645 participants developed diabetes, while 6,060 remained free of the disease.
The researchers used a statistical tool called “latent class trajectory analysis” to study patterns of changes in body mass index (BMI) in the years before people developed diabetes. BMI is a measure of human obesity based on a person's weight and height. Latent class trajectory analysis is an unbiased way to subdivide a number of people into groups that differ based on specified parameters. In this case, the researchers wanted to identify several groups among all the people who eventually developed diabetes each with a distinct pattern of BMI development. Having identified such groups, they also examined how a variety of tests associated with diabetes risk, and risks for heart disease and stroke changed in the identified groups over time.
They identified three different patterns of BMI changes in the 645 participants who developed diabetes. The vast majority (606 individuals, or 94%) belonged to a group they called “stable-overweight.” These people showed no dramatic change in their BMI in the years before they were diagnosed. They were overweight when they first entered the study and gained or lost little weight during the follow-up years. They showed only minor signs of insulin-resistance, starting five years before they developed diabetes. A second, much smaller group of 15 people gained weight consistently in the years before diagnosis. As they were gaining weight, these people also had raises in blood pressure and substantial gains in insulin resistance. The 26 remaining participants who formed the third group were persistently obese for the entire time they participated in the study, in some cases up to 18 years before they were diagnosed with diabetes. They had some signs of insulin resistance in the years before diagnosis, but not the substantial gain often seen as the hallmark of “pre-diabetes.”
What Do These Findings Mean?
These results suggest that diabetes development is a complicated process, and one that differs between individuals who end up with the disease. They call into question the common notion that most people who develop diabetes have recently gained a lot of weight or are obese. A substantial rise in insulin resistance, another established risk factor for diabetes, was only seen in the smallest of the groups, namely the people who gained weight consistently for years before they were diagnosed. When the scientists applied a commonly used predictor of diabetes called the “Framingham diabetes risk score” to their largest “stably overweight” group, they found that these people were not classified as having a particularly high risk, and that their risk scores actually declined in the last five years before their diabetes diagnosis. This suggests that predicting diabetes in this group might be difficult.
The researchers applied their methodology only to this one cohort of white civil servants in England. Before drawing more firm conclusions on the process of diabetes development, it will be important to test whether similar results are seen in other cohorts and among more diverse individuals. If the three groups identified here are found in other cohorts, another question is whether they are as unequal in size as in this example. And if they are, can the large group of stably overweight people be further subdivided in ways that suggest specific mechanisms of disease development? Even without knowing how generalizable the provocative findings of this study are, they should stimulate debate on how to identify people at risk for diabetes and how to prevent the disease or delay its onset.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001602.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals, and the general public, including information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes; it includes people's stories about diabetes
The charity Diabetes UK also provides detailed information about diabetes for patients and carers, including information on healthy lifestyles for people with diabetes, and has a further selection of stories from people with diabetes; the charity Healthtalkonline has interviews with people about their experiences of diabetes
MedlinePlus provides links to further resources and advice about diabetes (in English and Spanish)
More information about the Whitehall II study is available
doi:10.1371/journal.pmed.1001602
PMCID: PMC3921118  PMID: 24523667
4.  Comparison of weight in middle age, weight at 18 years, and weight change between, in predicting subsequent 14 year mortality and coronary events: Caerphilly Prospective Study 
OBJECTIVE—The prevalence of obesity is increasing in many European countries and in the United States. This report examines the mortality and morbidity associated with being overweight and obese in the Caerphilly Prospective Study and the relative effects of weight in middle age and self reported weight at 18 years.
DESIGN—All men aged 45 to 59 years from the town of Caerphilly, South Wales and outlying villages were identified and 2512 men were examined for the first time between 1979 and 1983. Men were asked to recall their weight at 18 years of age (when the majority had been examined for National Service) so that weight then, weight at screening, and the difference could be related to their 14 year follow up from screening. A total of 2335 men could recall their weight at 18 years. By 14 years of follow up from screening 465 men had died and 382 had had coronary events.
RESULTS—Mean body mass index in men who reported their weight at 18 years was 22.3 (SD 2.8) kg/m2 and only 41 of these men (1.8%) were classified as obese (index ⩾ 30 kg/m2). The index did not predict all cause mortality when examined by quintile. For major ischaemic heart disease (non-fatal or fatal ischaemic heart disease) the relative odds was 1.73 (95% CI 1.21, 2.48) in the top fifth of the distribution (body mass index ⩾ 24.2 kg/m2) compared with the bottom fifth (body mass index <20.1 kg/m2). In men with an index ⩾ 30 kg/m2 however, the relative odds were 2.03 (95% CI, 1.03, 4.01) for all cause mortality and 2.17 (95% CI, 1.08, 4.34) for major ischaemic heart disease, adjusted for age, smoking habit and social class. When men were recruited to the study, from 1979 to 1983; the mean body mass index had increased to 26.2 (SD 3.6), a mean increase of 3.9 kg/m2 or 11.2 kg; 299 men (12.1%) were classified as obese and showed significantly increased relative odds of both all cause mortality (1.53 (95% CI 1.14, 2.06) and major ischaemic heart disease (1.55 (95% CI 1.13, 2.11)), adjusted for age, smoking habit and social class relative to the non-obese men. The effect of gain in weight from 18 years to recruitment was also examined; all cause mortality showed highest mortality in the fifth of the distribution who experienced weight loss or minimal weight gain. For major ischaemic heart disease an inconsistent, weak trend was shown, the relative odds rising to a maximum of 1.26 (0.89, 1.80) in the top fifth of weight gain compared with the bottom fifth. Weight gain showed strong associations with potential cardiovascular risk factors measured at recruitment; insulin, triglyceride, glucose, diastolic and systolic blood pressure and high density lipoprotein-cholesterol.
CONCLUSIONS—Body mass at 18 years of age of 30 kg/m2 or more conferred increased risk for all cause mortality and major ischaemic heart disease during 14 years of follow up of men aged 45 to 59 years. By the baseline examination the prevalence of obesity (body mass index ⩾30) had increased from 1.8% to 12.1%; obese men also showed an excess risk of major ischaemic heart disease and overall mortality, but these risks were lower than those predicted from 18 years of age. Weight gain was strongly associated with smoking habit, the greatest weight gain being among ex-smokers and the least among light smokers. Weight gain from 18 years of age to baseline examination showed little relation with subsequent mortality and risk of major ischaemic heart disease when adjusted for age, smoking habit and social class. The lowest mortality rate occurred in the "fifth" of men who gained a mean weight of 16.1 kg. Weight gain is closely associated with some adverse cardiovascular risk factors; in particular with insulin, triglyceride, glucose and diastolic blood pressure.


Keywords: obesity; prospective study; ischaemic heart disease
doi:10.1136/jech.54.5.344
PMCID: PMC1731668  PMID: 10814654
5.  Combined Impact of Health Behaviours and Mortality in Men and Women: The EPIC-Norfolk Prospective Population Study 
PLoS Medicine  2008;5(1):e12.
Background
There is overwhelming evidence that behavioural factors influence health, but their combined impact on the general population is less well documented. We aimed to quantify the potential combined impact of four health behaviours on mortality in men and women living in the general community.
Methods and Findings
We examined the prospective relationship between lifestyle and mortality in a prospective population study of 20,244 men and women aged 45–79 y with no known cardiovascular disease or cancer at baseline survey in 1993–1997, living in the general community in the United Kingdom, and followed up to 2006. Participants scored one point for each health behaviour: current non-smoking, not physically inactive, moderate alcohol intake (1–14 units a week) and plasma vitamin C >50 mmol/l indicating fruit and vegetable intake of at least five servings a day, for a total score ranging from zero to four. After an average 11 y follow-up, the age-, sex-, body mass–, and social class–adjusted relative risks (95% confidence intervals) for all-cause mortality(1,987 deaths) for men and women who had three, two, one, and zero compared to four health behaviours were respectively, 1.39 (1.21–1.60), 1.95 (1.70–-2.25), 2.52 (2.13–3.00), and 4.04 (2.95–5.54) p < 0.001 trend. The relationships were consistent in subgroups stratified by sex, age, body mass index, and social class, and after excluding deaths within 2 y. The trends were strongest for cardiovascular causes. The mortality risk for those with four compared to zero health behaviours was equivalent to being 14 y younger in chronological age.
Conclusions
Four health behaviours combined predict a 4-fold difference in total mortality in men and women, with an estimated impact equivalent to 14 y in chronological age.
From a large prospective population study, Kay-Tee Khaw and colleagues estimate the combined impact of four behaviors--not smoking, not being physically inactive, moderate alcohol intake, and at least five vegetable servings a day--amounts to 14 additional years of life.
Editors' Summary
Background.
Every day, or so it seems, new research shows that some aspect of lifestyle—physical activity, diet, alcohol consumption, and so on—affects health and longevity. For the person in the street, all this information is confusing. What is a healthy diet, for example? Although there are some common themes such as the benefit of eating plenty of fruit and vegetables, the details often differ between studies. And exactly how much physical activity is needed to improve health? Is a gentle daily walk sufficient or simply a stepping stone to doing enough exercise to make a real difference? The situation with alcohol consumption is equally confusing. Small amounts of alcohol apparently improve health but large amounts are harmful. As a result, it can be hard for public-health officials to find effective ways to encourage the behavioral changes that the scientific evidence suggests might influence the health of populations.
Why Was This Study Done?
There is another factor that is hindering official attempts to provide healthy lifestyle advice to the public. Although there is overwhelming evidence that individual behavioral factors influence health, there is very little information about their combined impact. If the combination of several small differences in lifestyle could be shown to have a marked effect on the health of populations, it might be easier to persuade people to make behavioral changes to improve their health, particularly if those changes were simple and relatively easy to achieve. In this study, which forms part of the European Prospective Investigation into Cancer and Nutrition (EPIC), the researchers have examined the relationship between lifestyle and the risk of dying using a health behavior score based on four simply defined behaviors—smoking, physical activity, alcohol drinking, and fruit and vegetable intake.
What Did the Researchers Do and Find?
Between 1993 and 1997, about 20,000 men and women aged 45–79 living in Norfolk UK, none of whom had cancer or cardiovascular disease (heart or circulation problems), completed a health and lifestyle questionnaire, had a health examination, and had their blood vitamin C level measured as part of the EPIC-Norfolk study. A health behavior score of between 0 and 4 was calculated for each participant by giving one point for each of the following healthy behaviors: current non-smoking, not physically inactive (physical inactivity was defined as having a sedentary job and doing no recreational exercise), moderate alcohol intake (1–14 units a week; a unit of alcohol is half a pint of beer, a glass of wine, or a shot of spirit), and a blood vitamin C level consistent with a fruit and vegetable intake of at least five servings a day. Deaths among the participants were then recorded until 2006. After allowing for other factors that might have affected their likelihood of dying (for example, age), people with a health behavior score of 0 were four times as likely to have died (in particular, from cardiovascular disease) than those with a score of 4. People with a score of 2 were twice as likely to have died.
What Do These Findings Mean?
These findings indicate that the combination of four simply defined health behaviors predicts a 4-fold difference in the risk of dying over an average period of 11 years for middle-aged and older people. They also show that the risk of death (particularly from cardiovascular disease) decreases as the number of positive health behaviors increase. Finally, they can be used to calculate that a person with a health score of 0 has the same risk of dying as a person with a health score of 4 who is 14 years older. These findings need to be confirmed in other populations and extended to an analysis of how these combined health behaviors affect the quality of life as well as the risk of death. Nevertheless, they strongly suggest that modest and achievable lifestyle changes could have a marked effect on the health of populations. Armed with this information, public-health officials should now be in a better position to encourage behavior changes likely to improve the health of middle-aged and older people.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050012.
The MedlinePlus encyclopedia contains a page on healthy living (in English and Spanish)
The MedlinePlus page on seniors' health contains links to many sites dealing with healthy lifestyles and longevity (in English and Spanish)
The European Prospective Investigation into Cancer and Nutrition (EPIC) study is investigating the relationship between nutrition and lifestyle and the development of cancer and other chronic diseases; information about the EPIC-Norfolk study is also available
The US Centers for Disease Control and Prevention provides information on healthy aging for older adults, including information on health-related behaviors (in English and Spanish)
The UK charity Age Concerns provides a fact sheet about staying healthy in later life
The London Health Observatory, which provides information for policy makers and practitioners about improving health and health care, has a section on how lifestyle and behavior affect health
doi:10.1371/journal.pmed.0050012
PMCID: PMC2174962  PMID: 18184033
6.  Health Behaviours, Socioeconomic Status, and Mortality: Further Analyses of the British Whitehall II and the French GAZEL Prospective Cohorts 
PLoS Medicine  2011;8(2):e1000419.
Further analysis of data from two prospective cohorts reveals differences in the extent to which health behaviors attenuate associations between socioeconomic position and mortality outcomes.
Background
Differences in morbidity and mortality between socioeconomic groups constitute one of the most consistent findings of epidemiologic research. However, research on social inequalities in health has yet to provide a comprehensive understanding of the mechanisms underlying this association. In recent analysis, we showed health behaviours, assessed longitudinally over the follow-up, to explain a major proportion of the association of socioeconomic status (SES) with mortality in the British Whitehall II study. However, whether health behaviours are equally important mediators of the SES-mortality association in different cultural settings remains unknown. In the present paper, we examine this issue in Whitehall II and another prospective European cohort, the French GAZEL study.
Methods and Findings
We included 9,771 participants from the Whitehall II study and 17,760 from the GAZEL study. Over the follow-up (mean 19.5 y in Whitehall II and 16.5 y in GAZEL), health behaviours (smoking, alcohol consumption, diet, and physical activity), were assessed longitudinally. Occupation (in the main analysis), education, and income (supplementary analysis) were the markers of SES. The socioeconomic gradient in smoking was greater (p<0.001) in Whitehall II (odds ratio [OR]  = 3.68, 95% confidence interval [CI] 3.11–4.36) than in GAZEL (OR  = 1.33, 95% CI 1.18–1.49); this was also true for unhealthy diet (OR  = 7.42, 95% CI 5.19–10.60 in Whitehall II and OR  = 1.31, 95% CI 1.15–1.49 in GAZEL, p<0.001). Socioeconomic differences in mortality were similar in the two cohorts, a hazard ratio of 1.62 (95% CI 1.28–2.05) in Whitehall II and 1.94 in GAZEL (95% CI 1.58–2.39) for lowest versus highest occupational position. Health behaviours attenuated the association of SES with mortality by 75% (95% CI 44%–149%) in Whitehall II but only by 19% (95% CI 13%–29%) in GAZEL. Analysis using education and income yielded similar results.
Conclusions
Health behaviours were strong predictors of mortality in both cohorts but their association with SES was remarkably different. Thus, health behaviours are likely to be major contributors of socioeconomic differences in health only in contexts with a marked social characterisation of health behaviours.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The influence of the socioeconomic environment on the health of individuals and populations is well known, giving rise to the so-called social determinants of health. The social determinants of health are the conditions in which people are born, grow, live, work, and age, including the health system. These circumstances are shaped by the distribution of money, power, and resources at global, national, and local levels, which are themselves influenced by policy choices. The social determinants of health are mostly responsible for health inequities—the unfair and avoidable differences in health status seen within and between countries. In addition, health-damaging behaviors are often strongly socially patterned. For example, material constraints, lack of knowledge, and limited opportunities to follow health promoting messages often act as barriers that prevent those from lower socioeconomic groups to adopt a healthy lifestyle. Yet the extent to which health behaviors explain social inequalities in health remains unclear and can range from 12% to 72% according to some studies.
Why Was This Study Done?
In a recently published paper using data from the British Whitehall II cohort, the researchers showed that longitudinal assessment of health behaviors accounted for socioeconomic differences in mortality better than a single baseline assessment as used in most previous studies. (The Whitehall II study started in 1985 to examine the socioeconomic gradient in health among 10,308 London-based civil servants [6,895 men and 3,413 women] aged 35–55).
However, it is not clear whether health behaviors are equally important mediators of the socioeconomic-health association in different cultural settings. In this study, the researchers examine this issue by comparing their recent findings of the Whitehall II study with another European cohort, the French GAZEL study. (The GAZEL study started in 1989 among employees of the French national gas and electricity company totaling 20,625 employees [15,011 men and 5,614 women], aged 35–50.) The Whitehall II study and the GAZEL study have comparable designs in the way both assess socioeconomic status, health behaviors, and mortality and have a similar age range and follow-up period.
What Did the Researchers Do and Find?
The researchers included 9,771 participants from the Whitehall II study and 17,760 from the GAZEL study—mean follow up for Whitehall II was 19.5 years and for GAZEL was 16.5 years. The researchers used occupation as the main marker of socioeconomic status, and education and income as supplementary markers of socioeconomic status. Apart from a few exceptions, the researchers analyzed each cohort separately and used statistical techniques to calculate: the mortality rates per 1000 person-years for each socioeconomic group; the age- and sex-adjusted prevalence rates of smoking, heavy alcohol consumption, unhealthy diet, and physical inactivity, at the first and the last follow-up of the study for each socioeconomic group; and the differences in health behaviors prevalence between lowest and highest occupational position. Then the researchers used a statistical model to deduce the contribution of all health behaviors.
The researchers found that the socioeconomic gradient in smoking, unhealthy diet, and physical inactivity was greater in Whitehall II than in GAZEL. Socioeconomic differences in mortality were similar in the two cohorts, a hazard ratio of 1.62 in Whitehall II and 1.94 in GAZEL for lowest versus highest occupational position. Health behaviors weakened the association between socioeconomic status and mortality by 75% in Whitehall II but only by 19% in GAZEL. The supplementary analysis the researchers conducted using education and income as socioeconomic markers gave similar results.
What Do These Findings Mean?
These results suggest that the social patterning of unhealthy behaviors differs between countries. Although in both cohorts socioeconomic status and health behaviors were strong predictors of mortality, major differences in the social patterning of unhealthy behaviors in the two cohorts meant that the causal chains leading from socioeconomic status to health behaviors to mortality were different. Therefore it may be that health behaviors are likely to only be major contributors of socioeconomic differences in health in contexts with a marked social characterization of those behaviors. In order to identify the common and unique determinants of social inequalities in health in different populations, there needs to be further comparative research on the relative importance of different pathways linking socioeconomic status to health.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000419.
WHO provides information on social determinants of health
University College London provides information on the Whitehall study
The GAZEL study is available in an online open access format
doi:10.1371/journal.pmed.1000419
PMCID: PMC3043001  PMID: 21364974
7.  Change in the Body Mass Index Distribution for Women: Analysis of Surveys from 37 Low- and Middle-Income Countries 
PLoS Medicine  2013;10(1):e1001367.
Using cross-sectional surveys, Fahad Razak and colleagues investigate how the BMI (body mass index) distribution is changing for women in low- and middle-income countries.
Background
There are well-documented global increases in mean body mass index (BMI) and prevalence of overweight (BMI≥25.0 kg/m2) and obese (BMI≥30.0 kg/m2). Previous analyses, however, have failed to report whether this weight gain is shared equally across the population. We examined the change in BMI across all segments of the BMI distribution in a wide range of countries, and assessed whether the BMI distribution is changing between cross-sectional surveys conducted at different time points.
Methods and Findings
We used nationally representative surveys of women between 1991–2008, in 37 low- and middle-income countries from the Demographic Health Surveys ([DHS] n = 732,784). There were a total of 96 country-survey cycles, and the number of survey cycles per country varied between two (21/37) and five (1/37). Using multilevel regression models, between countries and within countries over survey cycles, the change in mean BMI was used to predict the standard deviation of BMI, the prevalence of underweight, overweight, and obese. Changes in median BMI were used to predict the 5th and 95th percentile of the BMI distribution. Quantile-quantile plots were used to examine the change in the BMI distribution between surveys conducted at different times within countries. At the population level, increasing mean BMI is related to increasing standard deviation of BMI, with the BMI at the 95th percentile rising at approximately 2.5 times the rate of the 5th percentile. Similarly, there is an approximately 60% excess increase in prevalence of overweight and 40% excess in obese, relative to the decline in prevalence of underweight. Quantile-quantile plots demonstrate a consistent pattern of unequal weight gain across percentiles of the BMI distribution as mean BMI increases, with increased weight gain at high percentiles of the BMI distribution and little change at low percentiles. Major limitations of these results are that repeated population surveys cannot examine weight gain within an individual over time, most of the countries only had data from two surveys and the study sample only contains women in low- and middle-income countries, potentially limiting generalizability of findings.
Conclusions
Mean changes in BMI, or in single parameters such as percent overweight, do not capture the divergence in the degree of weight gain occurring between BMI at low and high percentiles. Population weight gain is occurring disproportionately among groups with already high baseline BMI levels. Studies that characterize population change should examine patterns of change across the entire distribution and not just average trends or single parameters.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The number of obese people (individuals who have an excessive amount of body fat) is rapidly increasing in many countries. Globally, there were about 200 million obese adults in 1995; by 2010, 475 million adults were obese and another billion were classified as overweight. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30.0 kg/m2. Compared to people with a healthy weight (a BMI between 18.5 and 24.9 kg/m2), obese individuals and overweight individuals (who have a BMI between 25.0 and 29.9 kg/m2) have an increased risk of developing diabetes, heart disease and stroke, and tend to die younger. At the same time in many developing countries substantial numbers of people are underweight (BMI <18.5 kg/m2) or have chronic energy deficiency (BMI <16.0 kg/m2) and are at risk of increased risk of dying due to infectious disease or respiratory problems.
Why Was This Study Done?
The global obesity epidemic is usually described in terms of increases in the average BMI or in the prevalence of obesity (the proportion of the population whose BMI is above 30.0 kg/m2). Such descriptions assume that the BMIs of fat and thin people are increasing at the same rate and that the shape of the population's BMI distribution curve remains constant. However, as average BMI and the prevalence of obesity can increase it is unclear how the prevalence of underweight changes. This is potentially important for the health of the population because underweight individuals, like obese individuals, tend to die younger than healthy weight individuals, particularly in low-income countries. In this study, the researchers use repeated cross-sectional survey data collected from low- and middle-income countries in the Demographic and Health Surveys (DHS) to examine changes in BMI in women across the BMI distribution between 1991 and 2008. Repeated cross-sectional surveys collect data from a population at multiple time points from different individuals drawn from the same population, DHS are a data collection and surveillance project that help developing countries track health and population trends.
What Did the Researchers Do and Find?
The researchers used statistical models to analyze data from DHS surveys of more than 730,000 women living in 37 low- and middle-income countries (two to five surveys per country). Increasing average BMI was associated with an increase in the standard deviation of BMI (a measure of the dispersion of BMI in the population) both across and within countries over time. With increasing average BMI, the BMI at both the 5th and 95th percentile increased; 90% of the BMIs in a population lie between these percentiles so these BMI values indicate the spread of the BMI distribution. However, the BMI at the 95th percentile increased about 2.5 times faster than the BMI at the 5th percentile. Moreover, with increasing average BMI, the prevalence of overweight and obesity increased faster than the decline in the prevalence of underweight. Finally, quantile-quantile plots for each country (a graphical method that compares two distributions) revealed a consistent pattern of unequal weight gain across the BMI distribution as average BMI increased, with pronounced weight gains at the obese end of the distribution and little change at the underweight end.
What Do These Findings Mean?
These findings show that increases in average BMI are associated with an increased spread of BMI across and within populations. Consequently, changes in average BMI or single measurements such as the prevalence of overweight do not capture the divergence in the degree of weight gain occurring between that part of the population that has a low BMI and that part that has a high BMI. In other words, at least for the low- and middle-income countries included in this study, population weight gain is occurring disproportionately among groups with high baseline BMI levels. The researchers suggest, therefore, that the characterization of the BMI of populations over time should examine the patterns of change across the whole BMI distribution. Moreover, rather than a single broad population strategy for weight control, optimum health outcomes, they suggest, might be achieved by a strategy that includes targeted interventions to reduce weight in high BMI segments of the population and to increase weight in low BMI segments.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001367.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on obesity (in several languages); Malri's story describes the health risks faced by an obese child
The UK National Health Service Choices website also provides detailed information about obesity and a link to a personal story about losing weight
The International Obesity Taskforce provides information about the global obesity epidemic
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
MedlinePlus has links to further information about obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001367
PMCID: PMC3545870  PMID: 23335861
8.  Multiple socioeconomic determinants of weight gain: the Helsinki Health Study 
BMC Public Health  2013;13:259.
Background
Socioeconomic differences in weight gain have been found, but several socioeconomic determinants have not been simultaneously studied using a longitudinal design. The aim of this study was to examine multiple socioeconomic determinants of weight gain.
Methods
Mail surveys were conducted in 2000–2002 among 40 to 60-year old employees of the City of Helsinki, Finland (n = 8 960, response rate 67%). A follow-up survey was conducted among the baseline respondents in 2007 with a mean follow-up of 5 to 7 years (n = 7 332, response rate 83%). The outcome measure was weight gain of 5 kg or more over the follow-up. Socioeconomic position was measured by parental education, childhood economic difficulties, own education, occupational class, household income, home ownership and current economic difficulties. Multivariable logistic regression models were fitted adjusting simultaneously for all covariates in the final model.
Results
Of women 27% and of men 24% gained 5 kg or more in weight over the follow-up. Among women, after adjusting for age, baseline weight and all socioeconomic determinants, those with basic (OR 1.40 95% CI 1.11-1.76) or intermediate education (OR 1.43 95% CI 1.08-1.90), renters (OR 1.18 95% CI 1.03-1.36) and those with occasional (OR 1.19 95% CI 1.03-1.38) or frequent (OR 1.50 95% CI 1.26-1.79) economic difficulties had increased risk of weight gain. Among men, after full adjustment, having current frequent economic difficulties (OR 1.70 95% CI 1.15-2.49) remained associated with weight gain.
Conclusions
Current economic difficulties among both women and men, and among women low education and renting, were associated with weight gain. Prevention of weight gain among ageing people would benefit from focusing in particular on those with economic difficulties.
doi:10.1186/1471-2458-13-259
PMCID: PMC3608219  PMID: 23517457
Socioeconomic position; Weight gain; Follow-up; Adulthood; Childhood; Cohort
9.  Rotating Night Shift Work and Risk of Type 2 Diabetes: Two Prospective Cohort Studies in Women 
PLoS Medicine  2011;8(12):e1001141.
An Pan and colleagues examined data from two Nurses' Health Studies and found that extended periods of rotating night shift work were associated with a modestly increased risk of type 2 diabetes, partly mediated through body weight.
Background
Rotating night shift work disrupts circadian rhythms and has been associated with obesity, metabolic syndrome, and glucose dysregulation. However, its association with type 2 diabetes remains unclear. Therefore, we aimed to evaluate this association in two cohorts of US women.
Methods and Findings
We followed 69,269 women aged 42–67 in Nurses' Health Study I (NHS I, 1988–2008), and 107,915 women aged 25–42 in NHS II (1989–2007) without diabetes, cardiovascular disease, and cancer at baseline. Participants were asked how long they had worked rotating night shifts (defined as at least three nights/month in addition to days and evenings in that month) at baseline. This information was updated every 2–4 years in NHS II. Self-reported type 2 diabetes was confirmed by a validated supplementary questionnaire. We documented 6,165 (NHS I) and 3,961 (NHS II) incident type 2 diabetes cases during the 18–20 years of follow-up. In the Cox proportional models adjusted for diabetes risk factors, duration of shift work was monotonically associated with an increased risk of type 2 diabetes in both cohorts. Compared with women who reported no shift work, the pooled hazard ratios (95% confidence intervals) for participants with 1–2, 3–9, 10–19, and ≥20 years of shift work were 1.05 (1.00–1.11), 1.20 (1.14–1.26), 1.40 (1.30–1.51), and 1.58 (1.43–1.74, p-value for trend <0.001), respectively. Further adjustment for updated body mass index attenuated the association, and the pooled hazard ratios were 1.03 (0.98–1.08), 1.06 (1.01–1.11), 1.10 (1.02–1.18), and 1.24 (1.13–1.37, p-value for trend <0.001).
Conclusions
Our results suggest that an extended period of rotating night shift work is associated with a modestly increased risk of type 2 diabetes in women, which appears to be partly mediated through body weight. Proper screening and intervention strategies in rotating night shift workers are needed for prevention of diabetes.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Around 346 million people worldwide have diabetes—a chronic disease affecting blood glucose levels, which over time may lead to serious damage in many body systems. In 2004, an estimated 3.4 million people died from consequences of high blood sugar, with more than 80% of deaths occurring in low-and middle-income countries. Type 2 diabetes accounts for 90% of people with diabetes and is largely the result of excess body weight and physical inactivity, which causes the body to use insulin ineffectively. One strategy in the public health response to the increasing prevalence and incidence of type 2 diabetes is to focus on the prevention and management of obesity by targeting risk factors of obesity.
Previous studies have suggested that rotating night shift work, which is common and becoming increasingly prevalent in countries worldwide, is associated with an increased risk of obesity and metabolic syndrome, conditions closely related to type 2 diabetes.
Why Was This Study Done?
Some studies have investigated the association between rotating night shift work and type 2 diabetes but have experienced methodological problems (such as minimal information on the rotating shift work, small sample sizes, and limited study populations), which make interpretation of the results difficult. In this study, the researchers attempted to overcome these methodological issues by prospectively examining the relationship between duration of rotating night shift work and risk of incident type 2 diabetes and, also if the duration of shift work was associated with greater weight gain, in two large cohorts of women in the United States.
What Did the Researchers Do and Find?
The researchers used data from the Nurses' Health Study I (NHS I, established in 1976 and included 121,704 women) and the Nurses' Health Study II (NHS II, established in 1989 and included 116,677 women), in which participating women completed regular questionnaires about their lifestyle practices and the development of chronic diseases. In both studies, the women also gave information about how long they had done rotating night shifts work (defined as at least three nights/month in addition to 19 days and evenings in that month), and this information was updated at regular intervals over the study follow-up period (18 years). The comparison group was women who did not report a history of rotating night shift work.
To assess the incidence of diabetes in both cohorts, the researchers sent a supplementary questionnaire to women who reported a diagnosis of diabetes, which asked about the symptoms, diagnostic tests, and medical management: if at least one of the National Diabetes Data Group criteria was reported, the researchers considered confirmed a diagnosis of type 2 diabetes. The researchers then used statistical methods (time-dependent Cox proportional hazards models) to estimate the hazard ratios of the chance of women working rotating shifts developing type 2 diabetes as a ratio of the chance of women not working rotating shifts developing diabetes.
The researchers found that in NHS I, 6,165 women developed type 2 diabetes and in NHS II 3,961 women developed type 2 diabetes. Using their statistical models, the researchers found that the duration of rotating night shift work was strongly associated with an increased risk of type 2 diabetes in both cohorts. The researchers found that in both cohorts, compared with women who reported no rotating night shift work, the HR of women developing type 2 diabetes, increased with the numbers of years working rotating shifts (the HRs of working rotating shifts for 1–2, 3–9, 10–19, and ≥20 years were 0.99, 1.17, 1.42, and 1.64, respectively, in NHS I, and in NHS II, 1.13, 1.34, 1.76, and 2.50, respectively). However, these associations were slightly weaker after the authors took other factors into consideration, except for body mass index (BMI).
What Do These Findings Mean?
These findings show that in these women, there is a positive association between rotating night shift work and the risk of developing type 2 diabetes. Furthermore, long duration of shift work may also be associated with greater weight gain. Although these findings need to be confirmed in men and other ethnic groups, because a large proportion of the working population is involved in some kind of permanent night and rotating night shift work, these findings are of potential public health significance. Additional preventative strategies in rotating night shift workers should therefore be considered.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001141.
This study is further discussed in a PLoS Medicine Perspective by Mika Kivimki and colleagues
Wikipedia has information about the Nurses’ Health study (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
Detailed information about the Nurses’ Health Study is available
The World Health Organization provides comprehensive information about all kinds of diabetes
For more information about diabetes that is useful for patients see Diabetes UK
doi:10.1371/journal.pmed.1001141
PMCID: PMC3232220  PMID: 22162955
10.  When does cardiovascular risk start? Past and present socioeconomic circumstances and risk factors in adulthood 
STUDY OBJECTIVES: To compare associations of childhood and adult socioeconomic position with cardiovascular risk factors measured in adulthood. To estimate the effects of adult socioeconomic position after adjustment for childhood circumstances. DESIGN: Cross sectional survey, using the relative index of inequality method to compare socioeconomic differences at different life stages. SETTING: The Whitehall II longitudinal study of men and women employed in London offices of the Civil Service at study baseline in 1985-88. PARTICIPANTS: 4774 men and 2206 women born in the period 1930-53 who were administered questions on early socioeconomic circumstances. MAIN RESULTS: Adult occupational position (employment grade) was inversely associated (high status-low risk) with current smoking and leisure time physical inactivity, with waist/height, and with metabolic risk factors HDL cholesterol, triglycerides, post-load glucose and fibrinogen. Associations of these variables with childhood socioeconomic position (father's Registrar General Social Class) were weaker or absent, with the exception of smoking in women. Childhood social position was associated with adult weight in both sexes and with current smoking, waist/height, HDL cholesterol and fibrinogen in women. Height, a measure of health capital or constitution, was weakly linked with father's social class and more strongly linked with own employment grade. The combination of childhood disadvantage (low father's class) together with a low status clerical occupation in men was particularly associated with higher body mass index as an adult (interaction test p < 0.001). Adjustment for earlier socioeconomic position--using father's class and own education level simultaneously--did not weaken the effects of adult socioeconomic position, except in the case of smoking in women, when the grade effect was reduced by 59 per cent. CONCLUSIONS: Cardiovascular risk factors in adulthood were in general more strongly related to adult than to childhood socioeconomic position. Among women but not men there was a strong but unexplained link between father's class and adult smoking habit. In both sexes degree of obesity was associated with both childhood and adulthood social position. These findings suggest that the socially patterned accumulation of health capital and cardiovascular risk begins in childhood and continues, according to socioeconomic position, during adulthood.
 
PMCID: PMC1756821  PMID: 10656084
11.  Body weight dissatisfaction by socioeconomic status among obese, preobese and normal weight women and men: results of the cross-sectional KORA Augsburg S4 population survey 
BMC Public Health  2012;12:342.
Background
Body weight dissatisfaction is an important factor in preventing weight gain and promoting weight loss or maintenance. This study focuses on differences in the rates of body weight dissatisfaction among obese, preobese and normal weight women and men by socioeconomic status within a general adult population in Germany.
Methods
Data were analyzed from 4186 adults aged 25 to 74 who participated in a cross-sectional, representative population-based health survey (KORA S4, 1999–2001, Augsburg region/Germany). Body mass was measured anthropometrically and indexed following international standards. Among the 2123 women participating in the survey, 40.3% had a normal weight, 34.9% were preobese, and 24.8% were obese (compared to 25.9%, 51.4% and 22.6% among men, respectively). Body weight dissatisfaction, educational level, household income and occupational status were assessed by computer-aided personal interviewing. An index for socioeconomic status was calculated and categorized into quintiles. Multiple logistic regressions were performed to test for differences in the odds of body weight dissatisfaction across socioeconomic strata in normal weight, preobese and obese groups. Body mass index, age, family status, place of residence and health behaviors were adjusted for.
Results
Overall, being dissatisfied with one’s body weight was more prevalent in women (48.3%) than in men (33.2%). In the normal weight group, no significant differences in the odds of being dissatisfied were found across socioeconomic groups among women or men. Among preobese men, compared to the lowest socioeconomic stratum, increased odds of being dissatisfied with one’s body weight were associated with the highest socioeconomic index group (OR = 2.3, 95% CI: 1.4–3.8), middle and high educational level (OR = 1.6, 95% CI: 1.1–2.3, and OR = 1.9, 95% CI: 1.3–3.7), high income (OR = 1.8, 95% CI: 1.2–2.7), and middle and high occupational status (both OR = 1.8, 95% CI: 1.2–2.6). Among preobese women, the odds of being dissatisfied were only significantly elevated in those with a middle educational level (OR = 1.6, 95% CI: 1.1–2.3). Among obese men, elevated odds were found in the highest socioeconomic index group (OR = 3.7, 95% CI: 1.8–7.5) and in those with a high educational level (OR = 2.3, 95% CI: 1.3–4.1), high income (OR = 2.6, 95% CI: 1.4–4.7), and middle and high occupational status (both OR = 2.2, 95% CI: 1.3–3.6). The odds of dissatisfaction among obese women were not associated with socioeconomic status as a whole, but were associated with a high educational level, albeit with a comparatively large confidence interval (OR = 3.6, 95% CI: 1.0–12.8).
Conclusions
In Germany, body weight dissatisfaction is more prevalent among obese and preobese men in high socioeconomic status groups, a pattern not found in women. The exception to this is a greater prevalence of dissatisfaction among obese and preobese women with a high educational level (albeit inconsistently). Moreover, there is a social gradient in body weight dissatisfaction, especially in obese men, which may partly explain why obesity is more prevalent in men with low socioeconomic status. It also suggests that they are a target group for obesity care in which body weight satisfaction is an important topic.
doi:10.1186/1471-2458-12-342
PMCID: PMC3533751  PMID: 22571239
Obesity; Preobesity; Normal weight; Body weight dissatisfaction; Socioeconomic status; Gender
12.  Mid-term Body Mass Index increase among obese and non-obese individuals in middle life and deprivation status: A cohort study 
BMC Public Health  2005;5:32.
Background
In the UK, obesity is associated with a clear socioeconomic gradient, with individuals of lower socioeconomic status being more likely to be obese. Several previous studies, using individual measures of soecioeconomic status, have shown a more rapid increase in Body Mass Index (BMI) over time among adults of lower socioeconomic status. We conducted a study to further examine whether ecologically defined deprivation status influences within-individual BMI change during middle life, as the answer to this question can help determine optimal preventive strategies both for obesity per se, and its' associated socioeconomic disparities.
Methods
Anonymised records of participants to the Stockport population-based cardiovascular disease risk factor screening programme were analysed. Individuals aged 35–55 who had a first screening episode between 1989 and 1993, and a subsequent screening episode were included in the study. Deprivation status was defined using quintiles of the Townsend score. Mean annual BMI change by deprivation group was calculated using linear regression. Subsequently, deprivation group was included in the model as an ordinal variable, to test for trend. The modelling was repeated separately for individuals who were obese (BMI < 30) and non-obese at the time of first screening. In supplementary analysis, regression models were also adjusted for baseline BMI.
Results
Of 21,976 women and 19,158 men initially screened, final analysis included just over half of all individuals [11,158 (50.8%) women and 9,831 (51.3%) men], due to the combined effect of loss to follow-up and incomplete BMI ascertainment. In both sexes BMI increased by 0.19 kg/m2 annually (95% Confidence Intervals 0.15–0.24 for women and 0.16–0.23 for men). All deprivation groups had similar mean annual change, and there was no evidence of a significant deprivation trend (p = 0.801, women and 0.892, men). Restricting the analysis to individuals who were non-obese at baseline did not alter the results in relation to the lack of a deprivation effect. When restricting the analysis to individuals who were obese at baseline however, the findings were suggestive of an association of BMI increase with higher deprivation group, which was further supported by a significant association when adjusting for baseline BMI.
Conclusion
In the study setting, the BMI of non-obese individuals aged 35–55 was increasing over time independently of deprivation status; among obese individuals a positive association with higher deprivation was found. The findings support that socioeconomic differences in mean BMI and obesity status are principally attained prior to 35 years of age. Efforts to tackle inequalities in mean BMI and obesity status should principally concentrate in earlier life periods, although there may still be scope for focusing inequality reduction efforts on obese individuals even in middle life.
doi:10.1186/1471-2458-5-32
PMCID: PMC1090593  PMID: 15811178
13.  Plasma Phospholipid Fatty Acid Concentration and Incident Coronary Heart Disease in Men and Women: The EPIC-Norfolk Prospective Study 
PLoS Medicine  2012;9(7):e1001255.
Kay-Tee Khaw and colleagues analyze data from a prospective cohort study and show associations between plasma concentrations of saturated phospholipid fatty acids and risk of coronary heart disease, and an inverse association between omega-6 polyunsaturated phospholipid fatty acids and risk of coronary heart disease.
Background
The lack of association found in several cohort studies between dietary saturated fat and coronary heart disease (CHD) risk has renewed debate over the link between dietary fats and CHD.
Methods and Findings
We assessed the relationship between plasma phospholipid fatty acid (PFA) concentration and incident CHD using a nested case control design within a prospective study (EPIC-Norfolk) of 25,639 individuals aged 40–79 years examined in 1993–1997 and followed up to 2009. Plasma PFA concentrations were measured by gas chromatography in baseline samples retrieved from frozen storage. In 2,424 men and women with incident CHD compared with 4,930 controls alive and free of cardiovascular disease, mean follow-up 13 years, saturated PFA (14:0, 16:0,18:0) plasma concentrations were significantly associated with increased CHD risk (odds ratio [OR] 1.75, 95% CI 1.27–2.41, p<0.0001), in top compared to bottom quartiles (Q), and omega-6 polyunsaturated PFA concentrations were inversely related (OR 0.77, 0.60–0.99, p<0.05) after adjusting for age, sex, body mass index, blood pressure, smoking, alcohol intake, plasma vitamin C, social class, education, and other PFAs. Monounsaturated PFA, omega-3 PFA, and trans PFA concentrations were not significantly associated with CHD. Odd chain PFA (15:0, 17:0) concentrations were significantly inversely associated with CHD (OR 0.73, 0.59–0.91, p<0.001, Q4 versus Q1). Within families of saturated PFA or polyunsaturated PFA, significantly heterogeneous relationships with CHD were observed for individual fatty acids.
Conclusions
In this study, plasma concentrations of even chain saturated PFA were found to be positively and omega-6 polyunsaturated PFA inversely related to subsequent coronary heart disease risk. These findings are consistent with accumulating evidence suggesting a protective role of omega-6 fats substituting for saturated fats for CHD prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Coronary heart disease (CHD) is a condition caused by a build-up of fatty deposits on the inner walls of the blood vessels that supply the heart, causing the affected person to experience pain, usually on exertion (angina). A complete occlusion of the vessel by deposits causes a heart attack (myocardial infarction). Lifestyle factors, such as diet (particularly one high in fat), contribute to causing CHD. There are different types of fat, some of which are thought to increase risk of CHD, such as saturated fat, typically found in meat and dairy foods. However, others, such as unsaturated fats (polyunsaturated and monounsaturated fats) found in foods such as vegetable oils, fish, and nuts, may actually help prevent this condition.
Why Was This Study Done?
Although there have been many studies investigating the role of different types of dietary fat in coronary heart disease, it is still not clear whether coronary heart disease can be prevented by changing the type of dietary fat consumed from saturated to unsaturated fats or by lowering all types of dietary fat. Furthermore, many of these studies have relied on participants recalling their dietary intake in questionnaires, which is an unreliable method for different fats. So in this study, the researchers used an established UK cohort to measure the levels of different types of fatty acids in blood to investigate whether a diet high in saturated fatty acids and low in unsaturated fatty acids increases CHD risk.
What Did the Researchers Do and Find?
The researchers used a selection of 10,000 participants (all men and women aged 40–79 years) from the prospective European Prospective Investigation into Cancer (EPIC)-Norfolk cohort. Blood samples from the selected participants taken at the start of the study in 1993–1997 were analyzed to determine levels of specific fatty acids. Participants were followed up till 2011. The researchers identified 2,424 participants who were subsequently diagnosed with CHD using death certificates and hospital discharge data and matched these with 4,930 controls who were still alive and free of known coronary disease. The researchers grouped the type of blood fatty acids identified in the blood samples into six families (even chain saturated fatty acid, odd chain saturated fatty acid, omega-6 polyunsaturated fatty acid, omega-3 polyunsaturated fatty acid, monounsaturated fatty acid, and trans-fatty acid), which represented saturated and unsaturated fatty acids. Using statistical methods, the researchers then compared the risks of developing CHD between cases and controls by the concentration of fatty acid families after adjusting for age and sex and other factors, such as body mass index, physical activity, and smoking. Using these methods, the researchers found that there was no overall significant relationship between total blood fatty acid concentration and CHD but there was a positive association with increasing blood saturated fatty acid concentration after adjusting for other fatty acid concentrations, with an odds ratio of 1.83 comparing higher versus lower concentrations. This risk was attenuated after adjusting for cholesterol levels, indicating that much of the association between saturated fatty acid and CHD is likely to be mediated through blood cholesterol levels. In contrast, blood omega-6 poly-unsaturated fatty acid concentrations were associated with lower CHD risk. Blood monounsaturated fatty acids, omega-3 poly-unsaturated fatty acids, and trans-fatty acids were not consistently associated with CHD risk. The authors also noted that within families of fatty acids, individual fatty acids related differently to CHD risk.
What Do These Findings Mean?
These findings suggest that plasma concentrations of saturated fatty acids are associated with increased risk of CHD and that concentrations of omega-6 poly-unsaturated fatty acids are associated with decreased risk of CHD. These findings are consistent with other studies and with current dietary advice for preventing CHD, which encourages substituting foods high in saturated fat with n-6 polyunsaturated fats. The results also suggest that different fatty acids may relate differently to CHD risk and that the overall balance between different fatty acids is important. However, there are limitations to this study, such as that factors other than diet (genetic differences in metabolism, for example) may cause changes to blood fatty acid levels so a major question is to identify what factors influence blood fatty acid concentrations. Nevertheless, these findings suggest that individual fatty acids play a role in increasing or decreasing risks of CHD.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001255.
Information about the EPIC-Norfolk study is available
The American Heart Foundation provides patient-friendly information about different dietary fats as does Medline
The British Heart Foundation also provides patient-friendly information on heart conditions
doi:10.1371/journal.pmed.1001255
PMCID: PMC3389034  PMID: 22802735
14.  Job strain among blue-collar and white-collar employees as a determinant of total mortality: a 28-year population-based follow-up 
BMJ Open  2012;2(2):e000860.
Objectives
To investigate the effect of job demand, job control and job strain on total mortality among white-collar and blue-collar employees working in the public sector.
Design
28-year prospective population-based follow-up.
Setting
Several municipals in Finland.
Participants
5731 public sector employees from the Finnish Longitudinal Study on Municipal Employees Study aged 44–58 years at baseline.
Outcomes
Total mortality from 1981 to 2009 among individuals with complete data on job strain in midlife, categorised according to job demand and job control: high job strain (high job demands and low job control), active job (high job demand and high job control), passive job (low job demand and low job control) and low job strain (low job demand and high job control).
Results
1836 persons died during the follow-up. Low job control among men increased (age-adjusted HR 1.26, 95% CI 1.12 to 1.42) and high job demand among women decreased the risk for total mortality HR 0.82 (95% CI 0.71 to 0.95). Adjustment for occupational group, lifestyle and health factors attenuated the association for men. In the analyses stratified by occupational group, high job strain increased the risk of mortality among white-collar men (HR 1.52, 95% CI 1.09 to 2.13) and passive job among blue-collar men (HR 1.28, 95% CI 1.05 to 1.47) compared with men with low job strain. Adjustment for lifestyle and health factors attenuated the risks. Among white-collar women having an active job decreased the risk for mortality (HR 0.78, 95% CI 0.60 to 1.00).
Conclusion
The impact of job strain on mortality was different according to gender and occupational group among middle-aged public sector employees.
Article summary
Article focus
High job strain and its components, high job demand and low job control, predict cardiovascular and total mortality.
Although lower socioeconomic position is a risk factor for premature total mortality, few studies have explored the effect of job strain on mortality within socioeconomic groups and the ones that exist, report conflicting findings.
Key messages
In a population-based cohort of middle-aged public sector employees, low job control among men increased and high job demand among women decreased the risk of mortality during a 28-year follow-up.
High job strain increased the risk of mortality among white-collar men and passive job among blue-collar men compared with men with low job strain.
Active job among white-collar women decreased the risk for mortality compared with those with low job strain.
Strengths and limitations of this study
A major strength was the representative large sample of public sector employees working both in white-collar and blue-collar professions and the long follow-up time on mortality collected from the national mortality register.
A limitation is the self-reported job strain, however, high correlations between subjective and expert ratings on work conditions have been reported. The assessment of job strain was measured at a single time point in midlife which might imperfectly reflect long-term job strain, however, the municipal employees in our cohort had stable work histories indicating stability probably also for job strain during their earlier working life.
doi:10.1136/bmjopen-2012-000860
PMCID: PMC3307125  PMID: 22422919
15.  Education and occupational social class: which is the more important indicator of mortality risk? 
STUDY OBJECTIVES: In the UK, studies of socioeconomic differentials in mortality have generally relied upon occupational social class as the index of socioeconomic position, while in the US, measures based upon education have been widely used. These two measures have different characteristics; for example, social class can change throughout adult life, while education is unlikely to alter after early adulthood. Therefore different interpretations can be given to the mortality differentials that are seen. The objective of this analysis is to demonstrate the profile of mortality differentials, and the factors underlying these differentials, which are associated with the two socioeconomic measures. DESIGN: Prospective observational study. SETTING: 27 work places in the west of Scotland. PARTICIPANTS: 5749 men aged 35-64 who completed questionnaires and were examined between 1970 and 1973. FINDINGS: At baseline, similar gradients between socioeconomic position and blood pressure, height, lung function, and smoking behaviour were seen, regardless of whether the education or social class measure was used. Manual social class and early termination of full time education were associated with higher blood pressure, shorter height, poorer lung function, and a higher prevalence of smoking. Within education strata, the graded association between smoking and social class remains strong, whereas within social class groups the relation between education and smoking is attenuated. Over 21 years of follow up, 1639 of the men died. Mortality from all causes and from three broad cause of death groups (cardiovascular disease, malignant disease, and other causes) showed similar associations with social class and education. For all cause of death groups, men in manual social classes and men who terminated full time education at an early age had higher death rates. Cardiovascular disease was the cause of death group most strongly associated with education, while the non- cardiovascular non-cancer category was the cause of death group most strongly associated with adulthood social class. The graded association between social class and all cause mortality remains strong and significant within education strata, whereas within social class strata the relation between education and mortality is less clear. CONCLUSIONS: As a single indicator of socioeconomic position occupational social class in adulthood is a better discriminator of socioeconomic differentials in mortality and smoking behaviour than is education. This argues against interpretations that see cultural-- rather than material--resources as being the key determinants of socioeconomic differentials in health. The stronger association of education with death from cardiovascular causes than with other causes of death may reflect the function of education as an index of socioeconomic circumstances in early life, which appear to have a particular influence on the risk of cardiovascular disease.
 
PMCID: PMC1756692  PMID: 9616419
16.  Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: the Renfrew and Paisley Study 
OBJECTIVE: To investigate the associations of individual and area-based socioeconomic indicators with cardiovascular disease risk factors and mortality. DESIGN: Prospective study. SETTING: The towns of Renfrew and Paisley in the west of Scotland. PARTICIPANTS: 6961 men and 7991 women included in a population-based cardiovascular disease screening study between 1972 and 1976. MAIN OUTCOME MEASURES: Cardiovascular disease risk factors and cardiorespiratory morbidity at the time of screening: 15 year mortality from all causes and cardiovascular disease. RESULTS: Both the area-based deprivation indicator and individual social class were associated with generally less favourable profiles of cardiovascular disease risk factors at the time of the baseline screening examinations. The exception was plasma cholesterol concentration, which was lower for men and women in manual social class groups. Independent contributions of area-based deprivation and individual social class were generally seen with respect to risk factors and morbidity. All cause and cardiovascular disease mortality rates were both inversely associated with socioeconomic position whether indexed by area-based deprivation or social class. The area- based and individual socioeconomic indicators made independent contributions to mortality risk. CONCLUSIONS: Individually assigned and area-based socioeconomic indicators make independent contributions to several important health outcomes. The degree of inequalities in health that exist will not be demonstrated in studies using only one category of indicator. Similarly, adjustment for confounding by socioeconomic position in aetiological epidemiological studies will be inadequate if only one level of indicator is used. Policies aimed at reducing socioeconomic differentials in health should pay attention to the characteristics of the areas in which people live as well as the characteristics of the people who live in these areas.
 
PMCID: PMC1756721  PMID: 9764262
17.  Physical Activity Attenuates the Genetic Predisposition to Obesity in 20,000 Men and Women from EPIC-Norfolk Prospective Population Study 
PLoS Medicine  2010;7(8):e1000332.
Shengxu Li and colleagues use data from a large prospective observational cohort to examine the extent to which a genetic predisposition toward obesity may be modified by living a physically active lifestyle.
Background
We have previously shown that multiple genetic loci identified by genome-wide association studies (GWAS) increase the susceptibility to obesity in a cumulative manner. It is, however, not known whether and to what extent this genetic susceptibility may be attenuated by a physically active lifestyle. We aimed to assess the influence of a physically active lifestyle on the genetic predisposition to obesity in a large population-based study.
Methods and Findings
We genotyped 12 SNPs in obesity-susceptibility loci in a population-based sample of 20,430 individuals (aged 39–79 y) from the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort with an average follow-up period of 3.6 y. A genetic predisposition score was calculated for each individual by adding the body mass index (BMI)-increasing alleles across the 12 SNPs. Physical activity was assessed using a self-administered questionnaire. Linear and logistic regression models were used to examine main effects of the genetic predisposition score and its interaction with physical activity on BMI/obesity risk and BMI change over time, assuming an additive effect for each additional BMI-increasing allele carried. Each additional BMI-increasing allele was associated with 0.154 (standard error [SE] 0.012) kg/m2 (p = 6.73×10−37) increase in BMI (equivalent to 445 g in body weight for a person 1.70 m tall). This association was significantly (pinteraction = 0.005) more pronounced in inactive people (0.205 [SE 0.024] kg/m2 [p = 3.62×10−18; 592 g in weight]) than in active people (0.131 [SE 0.014] kg/m2 [p = 7.97×10−21; 379 g in weight]). Similarly, each additional BMI-increasing allele increased the risk of obesity 1.116-fold (95% confidence interval [CI] 1.093–1.139, p = 3.37×10−26) in the whole population, but significantly (pinteraction = 0.015) more in inactive individuals (odds ratio [OR] = 1.158 [95% CI 1.118–1.199; p = 1.93×10−16]) than in active individuals (OR = 1.095 (95% CI 1.068–1.123; p = 1.15×10−12]). Consistent with the cross-sectional observations, physical activity modified the association between the genetic predisposition score and change in BMI during follow-up (pinteraction = 0.028).
Conclusions
Our study shows that living a physically active lifestyle is associated with a 40% reduction in the genetic predisposition to common obesity, as estimated by the number of risk alleles carried for any of the 12 recently GWAS-identified loci.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
In the past few decades, the global incidence of obesity—defined as a body mass index (BMI, a simple index of weight-for-height that uses the weight in kilograms divided by the square of the height in meters) of 30 and over, has increased so much that this growing public health concern is now commonly referred to as the “obesity epidemic.” Once considered prevalent only in high-income countries, obesity is an increasing health problem in low- and middle-income countries, particularly in urban settings. In 2005, at least 400 million adults world-wide were obese, and the projected figure for 2015 is a substantial increase of 300 million to around 700 million. Childhood obesity is also a growing concern. Contributing factors to the obesity epidemic are a shift in diet to an increased intake of energy-dense foods that are high in fat and sugars and a trend towards decreased physical activity due to increasingly sedentary lifestyles.
However, genetics are also thought to play a critical role as genetically predisposed individuals may be more prone to obesity if they live in an environment that has abundant access to energy-dense food and labor-saving devices.
Why Was This Study Done?
Although recent genetic studies (genome-wide association studies) have identified 12 alleles (a DNA variant that is located at a specific position on a specific chromosome) associated with increased BMI, there has been no convincing evidence of the interaction between genetics and lifestyle. In this study the researchers examined the possibility of such an interaction by assessing whether individuals with a genetic predisposition to increased obesity risk could modify this risk by increasing their daily physical activity.
What Did the Researchers Do and Find?
The researchers used a population-based cohort study of 25,631 people living in Norwich, UK (The EPIC-Norfolk study) and identified individuals who were 39 to 79 years old during a health check between 1993 and 1997. The researchers invited these people to a second health examination. In total, 20,430 individuals had baseline data available, of which 11,936 had BMI data at the second health check. The researchers used genotyping methods and then calculated a genetic predisposition score for each individual and their occupational and leisure-time physical activities were assessed by using a validated self-administered questionnaire. Then, the researchers used modeling techniques to examine the main effects of the genetic predisposition score and its interaction with physical activity on BMI/obesity risk and BMI change over time. The researchers found that each additional BMI-increasing allele was associated with an increase in BMI equivalent to 445 g in body weight for a person 1.70 m tall and that the size of this effect was greater in inactive people than in active people. In individuals who have a physically active lifestyle, this increase was only 379 g/allele, or 36% lower than in physically inactive individuals in whom the increase was 592 g/allele. Furthermore, in the total sample each additional obesity-susceptibility allele increased the odds of obesity by 1.116-fold. However, the increased odds per allele for obesity risk were 40% lower in physically active individuals (1.095 odds/allele) compared to physically inactive individuals (1.158 odds/allele).
What Do These Findings Mean?
The findings of this study indicate that the genetic predisposition to obesity can be reduced by approximately 40% by having a physically active lifestyle. The findings of this study suggest that, while the whole population benefits from increased physical activity levels, individuals who are genetically predisposed to obesity would benefit more than genetically protected individuals. Furthermore, these findings challenge the deterministic view of the genetic predisposition to obesity that is often held by the public, as they show that even the most genetically predisposed individuals will benefit from adopting a healthy lifestyle. The results are limited by participants self-reporting their physical activity levels, which is less accurate than objective measures of physical activity.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000332.
This study relies on the results of previous genome-wide association studies The National Human Genome Research Institute provides an easy-to-follow guide to understanding such studies
The International Association for the Study of Obesity aims to improve global health by promoting the understanding of obesity and weight-related diseases through scientific research and dialogue
The International Obesity Taskforce is the research-led think tank and advocacy arm of the International Association for the Study of Obesity
The Global Alliance for the Prevention of Obesity and Related Chronic Disease is a global action program that addresses the issues surrounding the prevention of obesity
The National Institutes of Health has its own obesity task force, which includes 26 institutes
doi:10.1371/journal.pmed.1000332
PMCID: PMC2930873  PMID: 20824172
18.  Body weight changes and outpatient medical care utilisation: Results of the MONICA/KORA cohorts S3/F3 and S4/F4 
Objectives: To test the effects of body weight maintenance, gain, and loss on health care utilisation in terms of outpatient visits to different kinds of physicians in the general adult population.
Methods: Self-reported utilisation data were collected within two population-based cohorts (baseline surveys: MONICA-S3 1994/95 and KORA-S4 1999/2001; follow-ups: KORA-F3 2004/05 and KORA-F4 2006/08) in the region of Augsburg, Germany, and were pooled for present purposes. N=5,147 adults (complete cases) aged 25 to 64 years at baseline participated. Number of visits to general practitioners (GPs), internists, and other specialists as well as the total number of physician visits at follow-up were compared across 10 groups defined by body mass index (BMI) category maintenance or change. Body weight and height were measured anthropometrically. Hierarchical generalized linear regression analyses with negative binomial distribution adjusted for sex, age, socioeconomic status (SES), survey, and the need factors incident diabetes and first cancer between baseline and follow-up were conducted.
Results: In fully adjusted models, compared to the group of participants that maintained normal weight from baseline to follow-up, the following groups had significantly higher GP utilisation rates: weight gain from normal weight (+36%), weight loss from preobesity (+39%), maintained preobesity (+34%), weight gain after preobesity (+43%), maintained moderate obesity (+48%), weight gain from moderate obesity (+107%), weight loss from severe obesity (+114%), and maintained severe obesity (+83%). Regarding internists, those maintaining moderate obesity reported +107% more visits; those with weight gain from moderate obesity reported +91%. The latter group also had +41% more consultations with other physicians. Across all physicians, mean number of visits were estimated at 7.8 per year for maintained normal weight, 9 for maintained preobesity, 11 for maintained moderate obesity, and 12 for maintained severe obesity. Among those with weight loss, the mean number of visits were 8.7, 10.6 and 10.8 for baseline preobesity, moderate obesity, and severe obesity, respectively. Finally, those with weight gain from normal weight and preobesity reported 9.4 and 9.3 visits, respectively, and those with baseline moderate and follow-up severe obesity reported 13.1 visits (the most overall). Women reported higher GP and other physician utilisation. While all utilisation rates increased with age, GP utilisation was lower in middle to high SES groups.
Conclusion: Compared to maintained normal weight over a 7- to 10-year period, maintained overweight, weight gain and weight loss are associated with higher outpatient physician utilisation in adults, especially after baseline obesity. These effects only partly became insignificant after inclusion of incident diabetes or first cancer into the model. Future research should further elucidate the associations between weight development and health care utilisation by BMI status and the mechanisms underlying these associations.
doi:10.3205/psm000087
PMCID: PMC3488805  PMID: 23133503
outpatient physician utilisation; obesity; body mass index; cohort studies; body weight maintenance, gain, and loss
19.  Parental socioeconomic position and development of overweight in adolescence: longitudinal study of Danish adolescents 
BMC Public Health  2010;10:520.
Background
An inverse social gradient in overweight among adolescents has been shown in developed countries, but few studies have examined whether weight gain and the development of overweight differs among adolescents from different socioeconomic groups in a longitudinal study. The objective was to identify the possible association between parental socioeconomic position, weight change and the risk of developing overweight among adolescents between the ages 15 to 21.
Methods
Prospective cohort study conducted in Denmark with baseline examination in 1996 and follow-up questionnaire in 2003 with a mean follow-up time of 6.4 years. A sample of 1,656 adolescents participated in both baseline (mean age 14.8) and follow-up (mean age 21.3). Of these, 1,402 had a body mass index (BMI = weight/height2kg/m2) corresponding to a value below 25 at baseline when adjusted for age and gender according to guidelines from International Obesity Taskforce, and were at risk of developing overweight during the study period. The exposure was parental occupational status. The main outcome measures were change in BMI and development of overweight (from BMI < 25 to BMI > = 25).
Results
Average BMI increased from 21.3 to 22.7 for girls and from 20.6 to 23.6 in boys during follow-up. An inverse social gradient in overweight was seen for girls at baseline and follow-up and for boys at follow-up. In the full population there was a tendency to an inverse social gradient in the overall increase in BMI for girls, but not for boys. A total of 13.4% developed overweight during the follow-up period. Girls of lower parental socioeconomic position had a higher risk of developing overweight (OR's between 4.72; CI 1.31 to 17.04 and 2.03; CI 1.10-3.74) when compared to girls of high parental socioeconomic position. A tendency for an inverse social gradient in the development of overweight for boys was seen, but it did not meet the significance criteria
Conclusions
The levels of overweight and obesity among adolescents are high and continue to rise. Results from this study suggest that the inverse social gradient in overweight becomes steeper for girls and emerges for boys in late adolescence (age span 15 to 21 years). Late adolescence seems to be an important window of opportunity in reducing the social inequality in overweight among Danish adolescents.
doi:10.1186/1471-2458-10-520
PMCID: PMC2940915  PMID: 20799987
20.  The association between self-rated health and mortality in different socioeconomic groups in the GAZEL cohort study 
Objectives
Self-rated-health (SRH) is considered a valid measure of health status as it has been shown to predict mortality in several studies. We examine whether SRH predicts mortality equally well in different socioeconomic groups.
Methods
Data (14879 men and 5525 women) are drawn from GAZEL, a prospective cohort study of French public utility workers. Data on SRH and the socioeconomic measures (education, occupational position and income) were taken from the baseline questionnaire (1989), when the average age of individuals was 44.2 years (SD = 3.5). Mortality follow-up was available for a mean of 17.2 years and analysed over the first 10 years and over the entire follow-up period. Associations between SRH and mortality were assessed using Cox regression models using the Relative Index of Inequality (RII) to summarize associations.
Results
The RII for the association between SRH and mortality over the first 10 years was 6.78 (95% confidence interval (CI)=3.33–13.81) in the lowest occupational group and 2.10 (95% CI = 0.97–4.54) in the highest. For income, the RIIs were 8.82 (95% CI=4.70–16.54) for the lowest and 1.80 (95% CI=0.86–3.80) for the highest groups respectively. Findings over the full follow-up period were similar. The association between SRH and mortality was weaker in the high occupation and income groups, both in the short and the long term. The results for education were similar but generally weaker than for the other socioeconomic measures.
Conclusions
The predictive ability of SRH for mortality weakens with increasing socioeconomic advantage among middle-aged individuals. Thus SRH appears not to measure “true” health status in a similar way across socioeconomic categories.
doi:10.1093/ije/dym170
PMCID: PMC2610258  PMID: 18025034
Adult; Employment; Female; Follow-Up Studies; Forecasting; France; epidemiology; Health Status; Health Surveys; Humans; Male; Mortality; trends; Proportional Hazards Models; Self Concept; Social Class; socio-economic factors; occupation; income; education
21.  Maintaining a High Physical Activity Level Over 20 Years and Weight Gain 
JAMA : the journal of the American Medical Association  2010;304(23):10.1001/jama.2010.1843.
Context
Data supporting physical activity guidelines to prevent long-term weight gain are sparse, particularly during the period when the highest risk of weight gain occurs.
Objective
To evaluate the relationship between habitual activity levels and changes in body mass index (BMI) and waist circumference over 20 years.
Design, Setting, and Participants
The Coronary Artery Risk Development in Young Adults (CARDIA) study is a prospective longitudinal study with 20 years of follow-up, 1985-86 to 2005-06. Habitual activity was defined as maintaining high, moderate, and low activity levels based on sex-specific tertiles of activity scores at baseline. Participants comprised a population-based multi-center cohort (Chicago, Illinois; Birmingham, Alabama; Minneapolis, Minnesota; and Oakland, California) of 3554 men and women aged 18 to 30 years at baseline.
Main Outcome Measures
Average annual changes in BMI and waist circumference
Results
Over 20 years, maintaining high levels of activity was associated with smaller gains in BMI and waist circumference compared with low activity levels after adjustment for race, baseline BMI, age, education, cigarette smoking status, alcohol use, and energy intake. Men maintaining high activity gained 2.6 fewer kilograms (+ 0.15 BMI units per year; 95 % confidence interval [CI] 0.11-0.18 vs +0.20 in the lower activity group; 95% CI, 0.17-0.23) and women maintaining higher activity gained 6.1 fewer kilograms (+0.17 BMI units per year; 95 % CI, 0.12-0.21 vs. +0.30 in the lower activity group; 95 % CI, 0.25-0.34). Men maintaining high activity gained 3.1 fewer centimeters in waist circumference (+0.52 cm per year; 95 % CI, 0.43-0.61 cm vs 0.67 cm in the lower activity group; 95 % CI, 0.60-0.75) and women maintaining higher activity gained 3.8 fewer centimeters (+0.49 cm per year; 95 % CI, 0.39-0.58 vs 0.67 cm in the lower activity group; 95 % CI, 0.60-0.75).
Conclusion
Maintaining high activity levels through young adulthood may lessen weight gain as young adults transition to middle age, particularly in women.
doi:10.1001/jama.2010.1843
PMCID: PMC3864556  PMID: 21156948
22.  Prospective weight change and colon cancer risk in male US health professionals 
Epidemiological studies are remarkably consistent, especially among men, in showing that overweight and obesity [body mass index (BMI) >25] are associated with increased risk of colon cancer. However, no prospective studies address the influence of weight change in adulthood on subsequent colon cancer risk. In this study, we investigated whether weight change influences colon cancer risk utilizing prospectively collected weight data. We included 46,349 men aged 40–75 participating in the Health Professionals Follow-Up Study. Questionnaires including items on weight were completed every second year during follow-up from 1986 to 2004. Updated weight change between consecutive questionnaires during follow-up and recalled weight gain since age 21 was evaluated. All eligible men were cancer-free at baseline. Proportional hazard and restricted spline regression models were implemented. Over an 18-year period, we documented 765 cases of colon cancer. Cumulative mean BMI >22.5 was associated with significantly increased risk of colon cancer. The short-term weight change in the prior 2 to 4 years was positively and significantly associated with risk [HR = 1.14 (95% confidence interval, 1.00–1.29) for 4.54 kg (10 pounds) increment, p = 0.04 for overall trend]. Weight gain per 10 years since age 21 was associated with significantly increased risk [HR = 1.33 (1.12–1.58) for 4.54 kg increase per 10 years, p = 0.001]. We estimated that 29.5% of all colon cancer cases was attributable to BMI above 22.5. Our results add support that overweight and obesity are modifiable risk factors for colon cancer among men and suggest that weight has an important influence on colon cancer risk even in later life.
doi:10.1002/ijc.23612
PMCID: PMC3965300  PMID: 18546286
colonic neoplasms/epidemiology; weight gain; weight loss
23.  Association between Class III Obesity (BMI of 40–59 kg/m2) and Mortality: A Pooled Analysis of 20 Prospective Studies 
PLoS Medicine  2014;11(7):e1001673.
In a pooled analysis of 20 prospective studies, Cari Kitahara and colleagues find that class III obesity (BMI of 40–59) is associated with excess rates of total mortality, particularly due to heart disease, cancer, and diabetes.
Please see later in the article for the Editors' Summary
Background
The prevalence of class III obesity (body mass index [BMI]≥40 kg/m2) has increased dramatically in several countries and currently affects 6% of adults in the US, with uncertain impact on the risks of illness and death. Using data from a large pooled study, we evaluated the risk of death, overall and due to a wide range of causes, and years of life expectancy lost associated with class III obesity.
Methods and Findings
In a pooled analysis of 20 prospective studies from the United States, Sweden, and Australia, we estimated sex- and age-adjusted total and cause-specific mortality rates (deaths per 100,000 persons per year) and multivariable-adjusted hazard ratios for adults, aged 19–83 y at baseline, classified as obese class III (BMI 40.0–59.9 kg/m2) compared with those classified as normal weight (BMI 18.5–24.9 kg/m2). Participants reporting ever smoking cigarettes or a history of chronic disease (heart disease, cancer, stroke, or emphysema) on baseline questionnaires were excluded. Among 9,564 class III obesity participants, mortality rates were 856.0 in men and 663.0 in women during the study period (1976–2009). Among 304,011 normal-weight participants, rates were 346.7 and 280.5 in men and women, respectively. Deaths from heart disease contributed largely to the excess rates in the class III obesity group (rate differences = 238.9 and 132.8 in men and women, respectively), followed by deaths from cancer (rate differences = 36.7 and 62.3 in men and women, respectively) and diabetes (rate differences = 51.2 and 29.2 in men and women, respectively). Within the class III obesity range, multivariable-adjusted hazard ratios for total deaths and deaths due to heart disease, cancer, diabetes, nephritis/nephrotic syndrome/nephrosis, chronic lower respiratory disease, and influenza/pneumonia increased with increasing BMI. Compared with normal-weight BMI, a BMI of 40–44.9, 45–49.9, 50–54.9, and 55–59.9 kg/m2 was associated with an estimated 6.5 (95% CI: 5.7–7.3), 8.9 (95% CI: 7.4–10.4), 9.8 (95% CI: 7.4–12.2), and 13.7 (95% CI: 10.5–16.9) y of life lost. A limitation was that BMI was mainly ascertained by self-report.
Conclusions
Class III obesity is associated with substantially elevated rates of total mortality, with most of the excess deaths due to heart disease, cancer, and diabetes, and major reductions in life expectancy compared with normal weight.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
The number of obese people (individuals with an excessive amount of body fat) is increasing rapidly in many countries. Worldwide, according to the Global Burden of Disease Study 2013, more than a third of all adults are now overweight or obese. Obesity is defined as having a body mass index (BMI, an indicator of body fat calculated by dividing a person's weight in kilograms by their height in meters squared) of more than 30 kg/m2 (a 183-cm [6-ft] tall man who weighs more than 100 kg [221 lbs] is obese). Compared to people with a healthy weight (a BMI between 18.5 and 24.9 kg/m2), overweight and obese individuals (who have a BMI between 25.0 and 29.9 kg/m2 and a BMI of 30 kg/m2 or more, respectively) have an increased risk of developing diabetes, heart disease, stroke, and some cancers, and tend to die younger. Because people become unhealthily fat by consuming food and drink that contains more energy (kilocalories) than they need for their daily activities, obesity can be prevented or treated by eating less food and by increasing physical activity.
Why Was This Study Done?
Class III obesity (extreme, or morbid, obesity), which is defined as a BMI of more than 40 kg/m2, is emerging as a major public health problem in several high-income countries. In the US, for example, 6% of adults are now morbidly obese. Because extreme obesity used to be relatively uncommon, little is known about the burden of disease, including total and cause-specific mortality (death) rates, among individuals with class III obesity. Before we can prevent and treat class III obesity effectively, we need a better understanding of the health risks associated with this condition. In this pooled analysis of prospective cohort studies, the researchers evaluate the risk of total and cause-specific death and the years of life lost associated with class III obesity. A pooled analysis analyzes the data from several studies as if the data came from one large study; prospective cohort studies record the characteristics of a group of participants at baseline and follow them to see which individuals develop a specific condition.
What Did the Researchers Do and Find?
The researchers included 20 prospective (mainly US) cohort studies from the National Cancer Institute Cohort Consortium (a partnership that studies cancer by undertaking large-scale collaborations) in their pooled analysis. After excluding individuals who had ever smoked and people with a history of chronic disease, the analysis included 9,564 adults who were classified as class III obese based on self-reported height and weight at baseline and 304,011 normal-weight adults. Among the participants with class III obesity, mortality rates (deaths per 100,000 persons per year) during the 30-year study period were 856.0 and 663.0 in men and women, respectively, whereas the mortality rates among normal-weight men and women were 346.7 and 280.5, respectively. Heart disease was the major contributor to the excess death rate among individuals with class III obesity, followed by cancer and diabetes. Statistical analyses of the pooled data indicate that the risk of all-cause death and death due to heart disease, cancer, diabetes, and several other diseases increased with increasing BMI. Finally, compared with having a normal weight, having a BMI between 40 and 59 kg/m2 resulted in an estimated loss of 6.5 to 13.7 years of life.
What Do These Findings Mean?
These findings indicate that class III obesity is associated with a substantially increased rate of death. Notably, this death rate increase is similar to the increase associated with smoking among normal-weight people. The findings also suggest that heart disease, cancer, and diabetes are responsible for most of the excess deaths among people with class III obesity and that having class III obesity results in major reductions in life expectancy. Importantly, the number of years of life lost continues to increase for BMI values above 50 kg/m2, and beyond this point, the loss of life expectancy exceeds that associated with smoking among normal-weight people. The accuracy of these findings is limited by the use of self-reported height and weight measurements to calculate BMI and by the use of BMI as the sole measure of obesity. Moreover, these findings may not be generalizable to all populations. Nevertheless, these findings highlight the need to develop more effective interventions to combat the growing public health problem of class III obesity.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001673.
The US Centers for Disease Control and Prevention provides information on all aspects of overweight and obesity (in English and Spanish)
The World Health Organization provides information on obesity (in several languages); Malri's story describes the health risks faced by an obese child
The UK National Health Service Choices website provides information about obesity, including a personal story about losing weight
The Global Burden of Disease Study website provides the latest details about global obesity trends
The US Department of Agriculture's ChooseMyPlate.gov website provides a personal healthy eating plan; the Weight-Control Information Network is an information service provided for the general public and health professionals by the US National Institute of Diabetes and Digestive and Kidney Diseases (in English and Spanish)
MedlinePlus provides links to other sources of information on obesity (in English and Spanish)
doi:10.1371/journal.pmed.1001673
PMCID: PMC4087039  PMID: 25003901
24.  Lifetime socioeconomic position and mortality: prospective observational study. 
BMJ : British Medical Journal  1997;314(7080):547-552.
OBJECTIVES: To assess the influence of socioeconomic position over a lifetime on risk factors for cardiovascular disease, on morbidity, and on mortality from various causes. DESIGN: Prospective observational study with 21 years of follow up. Social class was determined as manual or non-manual at three stages of participants' lives: from the social class of their father's job, the social class of their first job, and the social class of their job at the time of screening. A cumulative social class indicator was constructed, ranging from non-manual social class at all three stages of life to manual social class at all three stages. SETTING: 27 workplaces in the west of Scotland. PARTICIPANTS: 5766 men aged 35-64 at the time of examination. MAIN OUTCOME MEASURES: Prevalence and level of risk factors for cardiovascular disease; morbidity; and mortality from broad causes of death. RESULTS: From non-manual social class locations at all three life stages to manual at all stages there were strong positive trends for blood pressure, body mass index, current cigarette smoking, angina, and bronchitis. Inverse trends were seen for height, cholesterol concentration, lung function, and being an ex-smoker. 1580 men died during follow up. Age adjusted relative death rates in comparison with the men of non-manual social class locations at all three stages of life were 1.29 (95% confidence interval 1.08 to 1.56) in men of two non-manual and one manual social class; 1.45 (1.21 to 1.73) in men of two manual and one non-manual social class; and 1.71 (1.46 to 2.01) in men of manual social class at all three stages. Mortality from cardiovascular disease showed a similar graded association with cumulative social class. Mortality from cancer was mainly raised among men of manual social class at all three stages. Adjustment for a wide range of risk factors caused little attenuation in the association of cumulative social class with mortality from all causes and from cardiovascular disease; greater attenuation was seen in the association with mortality from non-cardiovascular, non-cancer disease. Fathers having a manual [corrected] occupation was strongly associated with mortality from cardiovascular disease: relative rate 1.41 (1.15 to 1.72). Participants' social class at the time of screening was more strongly associated than the other social class indicators with mortality from cancer and from non-cardiovascular, non-cancer causes. CONCLUSIONS: Socioeconomic factors acting over the lifetime affect health and risk of premature death. The relative importance of influences at different stages varies for the cause of death. Studies with data on socioeconomic circumstances at only one stage of life are inadequate for fully elucidating the contribution of socioeconomic factors to health and mortality risk.
PMCID: PMC2126019  PMID: 9055712
25.  Socioeconomic Position in Childhood and Adulthood and Weight Gain over 34 Years: The Alameda County Study 
Annals of epidemiology  2007;17(8):608-614.
PURPOSE
Socioeconomic position (SEP) has been shown to be related to obesity and weight gain, especially among women. It is unclear how different measures of socioeconomic position may impact weight gain over long periods of time, and whether the effect of different measures vary by gender and age group. We examined the effect of childhood socioeconomic position, education, occupation, and log household income on a measure of weight gain using individual growth mixed regression models and Alameda County Study data collected over thirty four years(1965–1999).
METHODS
Analyses were performed in four groups stratified by gender and age at baseline: women, 17–30 years (n = 945) and 31–40 years (n = 712); men, 17–30 years (n = 766) and 31–40 years (n = 608).
RESULTS
Low childhood SEP was associated with increased weight gain among women 17–30 (0.13 kg/year, p < 0.001). Low educational status was associated with increased weight gain among women 17–30 (0.14 kg/year, p = 0.030), 31–40 (0.14 kg/year, p = 0.014), and men 17–30 (0.20 kg/year, p = 0.001).
CONCLUSION
Log household income was inversely associated with weight gain among men 31–40 (−0.10 kg/yr, p = 0.16). Long-term weight gain in adulthood is associated with childhood SEP and education in women and education and income in men.
doi:10.1016/j.annepidem.2007.03.007
PMCID: PMC3196359  PMID: 17521922

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