Optimum BMI?

A place to get your questions answered from McDougall staff dietitian, Jeff Novick, MS, RDN.

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Re: Optimum BMI?

Postby Summer » Wed Feb 29, 2012 12:54 pm

Thank you so much for your response and feedback, Jeff! I'm just a little stressed reading that study for the sake of my 17yo's future lifelong health given recently her bmi has been right around 25 which she was expressing some concern over being defined as 'overweight'. My own inclination is that given she passes the 'plate test', has a waist/hip ratio of .59, and is physically fit and active it's a nonissue (and not something I even want her to think about, honestly), but at the same time I don't want to be leading her astray especially after reading that study specific to people her age and it's lifelong implications! I've definitely been serving up more calorie dense meals lately, not realizing (since it was all low fat starch based plant foods) that it would be an issue, so I guess it wouldn't hurt to go back to more soups and salads again. Again, I really appreciate your insight here into these issues towards better health.
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Re: Optimum BMI?

Postby JeffN » Tue Dec 25, 2012 8:37 pm

More benefit to BMI

In Health
Jeff

Research Letters | Dec 10/24, 2012
Body Mass Index vs Cholesterol in Cardiovascular Disease Risk Prediction Models
David Faeh; Julia Braun; Matthias Bopp
Arch Intern Med. 2012;172(22):1766-1768.
http://archinte.jamanetwork.com/article ... ID=1391006

Article

Traditional modifiable risk factors for cardiovascular disease (CVD) are smoking, high blood pressure, and unfavorable blood lipid concentrations. Models combining these factors predict CVD more accurately than models considering CVD risk factors in an isolated manner.1- 3 Combined risk prediction models include the Framingham Risk Score or, from Europe, the SCORE (Systematic Coronary Risk Evaluation).1- 2 One disadvantage of these assessments is that they require blood sampling for lipid measurements. This precludes the estimation of the 10-year risk of a CVD event, eg, from self-reports. In electronic health records, the lack of information on cholesterol was the most common reason why CVD risk could not be calculated.4 In contrast, body height and weight are available in virtually all health data sets. On the basis of the SCORE method and using a population sample from Switzerland, we aimed at comparing the traditional prediction model using total cholesterol with a version in which we replaced cholesterol with body mass index (BMI).1


European guidelines on cardiovascular disease prevention in clinical practice (version 2012) : the fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts).
Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, Verschuren WM, Albus C, Benlian P, Boysen G, Cifkova R, Deaton C, Ebrahim S, Fisher M, Germano G, Hobbs R, Hoes A, Karadeniz S, Mezzani A, Prescott E, Ryden L, Scherer M, Syvänne M, Op Reimer WJ, Vrints C, Wood D, Zamorano JL, Zannad F; Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR).
Int J Behav Med. 2012 Dec;19(4):403-88. doi: 10.1007/s12529-012-9242-5. No abstract available.
PMID:23093473
http://www.escardio.org/guidelines-surv ... ention.pdf


Body Mass Index vs Cholesterol in Cardiovascular Disease Risk Prediction Models
David Faeh, MD, MPH; Julia Braun, MSc; Matthias Bopp, PhD, MPH
Arch Intern Med. 2012;172(22):1766-1768.

Traditional modifiable risk factors for cardiovascular disease (CVD) are smoking, high blood pressure, and unfavorable blood lipid concentrations. Models combining these factors predict CVD more accurately than models considering CVD risk factors in an isolated manner.1- 3 Combined risk prediction models include the Framingham Risk Score or, from Europe, the SCORE (Systematic Coronary Risk Evaluation).1- 2 One disadvantage of these assessments is that they require blood sampling for lipid measurements. This precludes the estimation of the 10-year risk of a CVD event, eg, from self-reports. In electronic health records, the lack of information on cholesterol was the most common reason why CVD risk could not be calculated.4 In contrast, body height and weight are available in virtually all health data sets. On the basis of the SCORE method and using a population sample from Switzerland, we aimed at comparing the traditional prediction model using total cholesterol with a version in which we replaced cholesterol with body mass index (BMI).1

Methods

Risk factor data stem from 17 791 men and women older than 16 years who participated in either of 2 CVD studies: the National Research Program 1A (NRP1A), a community health promotion initiative focused on CVD prevention, and the Swiss MONICA (Monitoring of Trends and Determinants in Cardiovascular Disease) population survey, an international project of the World Health Organization. We obtained mortality follow-up by anonymously linking the data from the CVD studies with the Swiss National Cohort (SNC), which encompasses all residents of Switzerland enumerated in the national 1990 or 2000 censuses as well as data from death and emigration registries until the end of 2008. Linkage success was 94% (NRP1A) and 97% (MONICA). The 95th percentile of follow-up was 31.2 years, during which 2170 men and 1761 women died (749 and 630 from CVD, respectively).5- 6

Methods

Blood sampling and cholesterol measurement were described.5- 6 Body mass index was calculated from measured (without shoes) height and weight (calculated as weight in kilograms divided by height in meters squared). We defined smoking as smoking 1 cigarette or more per day. Nonsmokers include former and never smokers. Systolic blood pressure was recorded as the mean of 2 measurements. Fatal CVD events were defined according to the Eighth Revision International Classification of Diseases codes 390 to 458 (until 1994) and International Statistical Classification of Diseases, 10th Revision codes I00 to I99.

Methods

Risk models were calculated with Weibull proportional hazards regression as previously described.1 To compare the prediction abilities of the cholesterol and BMI model, we calculated the mean cross-validated (leave-one-out) Brier score,7 which measures the mean squared difference between the risk score and the actual outcome. The lower the difference, the better the respective risk prediction model. The Brier score covers both calibration and sharpness of a prediction model.7

RESULTS.

Compared with cholesterol (eFigure), the BMI model (Figure) showed higher risks at all ages and could better discriminate persons at high and low CVD risk. Moreover, the synergistic effects in combination with smoking and in particular with blood pressure were stronger than with cholesterol. Body mass index, but not cholesterol, was significantly associated with mortality. The prediction ability of BMI was better based on the lower Brier score (eTable 1). Because explanatory variables (age, sex, smoking, and blood pressure) other than BMI or cholesterol remained the same in the 2 models, the difference between the Brier scores was small. In a common model with cholesterol, BMI remained significant, while cholesterol did not (eTable 2). Thus, cholesterol did not contribute to the explanation of the association between risk factors and mortality when BMI was included in the same model.

Figure. Absolute 10-year risk of fatal cardiovascular disease (CVD) based on the model using body mass index (BMI). Each risk percentage is calculated using a combination of given risk factor values (eg, a man aged 60 years, who is a smoker and has a systolic blood pressure of 180 and a BMI of 35 [calculated as weight in kilograms divided by height in meters squared], has an absolute risk for fatal CVD of 4%). NRP1A indicates National Research Program 1A; MONICA, Monitoring of Trends and Determinants in Cardiovascular Disease.

COMMENT.

Using BMI instead of cholesterol in CVD risk prediction models may provide more accurate estimates. Traditional models such as Framingham or SCORE include cholesterol or total to high-density lipoprotein cholesterol ratio but do not consider BMI in their equation.1- 2 In line with our results, Green et al4 found that using BMI instead of cholesterol allowed at least equivalent CVD risk estimation based on electronic health records and that the use of BMI could reduce unnecessary laboratory testing. The fact that BMI renders blood sampling unnecessary leads to a substantial increase of population-based samples available for CVD risk estimation. The use of BMI may not only ease CVD risk assessment but could have further advantages. Compared with dyslipidemia screening, screening for obesity has a stronger scientific foundation and is unconditionally recommended.4 Furthermore, lifestyle changes (diet and physical activity) promoting weight loss or preventing weight gain may improve health more strongly than lipid-lowering treatment. In contrast, knowledge of cholesterol may not lead to behavioral changes, and there are also doubts concerning the effectiveness and safety of statin treatment for primary prevention of CVD.4,8

In conclusion, our results suggest that BMI may be a valuable alternative to cholesterol in CVD risk prediction models. This finding needs to be validated in other populations.
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Re: Optimum BMI?

Postby JeffN » Tue Dec 03, 2013 7:13 pm

"These findings suggest that overweight and obesity are risk factors for MI and IHD regardless of the presence or absence of metabolic syndrome and that metabolic syndrome is no more valuable than BMI in identifying individuals at risk."

http://archinte.jamanetwork.com/article ... ultClick=3

Myocardial Infarction and Ischemic Heart Disease in Overweight and Obesity With and Without Metabolic Syndrome
JAMA Intern Med. Published online November 11, 2013. doi:10.1001/jamainternmed.2013.10522

ABSTRACT

Importance Overweight and obesity likely cause myocardial infarction (MI) and ischemic heart disease (IHD); however, whether coexisting metabolic syndrome is a necessary condition is unknown.

Objective To test the hypothesis that overweight and obesity with and without metabolic syndrome are associated with increased risk of MI and IHD.

Design, Setting, and Participants We examined 71 527 individuals from the Copenhagen General Population Study and categorized them according to body mass index (BMI) as normal weight, overweight, or obese and according to absence or presence of metabolic syndrome.

Main Outcomes and Measures Hazard ratios for incident MI and IHD according to combinations of BMI category and absence or presence of metabolic syndrome.

Results During a median of 3.6 years’ follow-up, we recorded 634 incident MI and 1781 incident IHD events. For MI, multivariable adjusted hazard ratios vs normal weight individuals without metabolic syndrome were 1.26 (95% CI, 1.00-1.61) in overweight and 1.88 (95% CI, 1.34-2.63) in obese individuals without metabolic syndrome and 1.39 (95% CI, 0.96-2.02) in normal weight, 1.70 (95% CI, 1.35-2.15) in overweight, and 2.33 (95% CI, 1.81-3.00) in obese individuals with metabolic syndrome. For IHD, results were similar but attenuated. Normal weight vs overweight vs obesity and presence vs absence of metabolic syndrome did not interact on risk of MI or IHD (P = .90 and P = .44). Among individuals both with and without metabolic syndrome there were increasing cumulative incidences of MI and IHD from normal weight through overweight to obese individuals (log-rank trend P = .006 to P  < .001). Although the multivariable adjusted hazard ratio for MI in individuals with vs without metabolic syndrome was 1.54 (95% CI, 1.32-1.81) across all BMI categories, addition of metabolic syndrome to a multivariable model including BMI and other clinical characteristics improved the Harell C-statistic only slightly for risk of MI (comparison P = .03) but not for IHD (P = .41).

Conclusions and Relevance These findings suggest that overweight and obesity are risk factors for MI and IHD regardless of the presence or absence of metabolic syndrome and that metabolic syndrome is no more valuable than BMI in identifying individuals at risk.


Lay Press Article
http://www.healthline.com/health-news/h ... ome-111113

Study: 'Healthy Obese' Still at Increased Risk of Heart Attack
Written by Shawn Radcliffe | Published on November 11, 2013

Even metabolically “healthy” overweight and obese people are at an increased risk of heart attack and heart disease, according to a new study.

Maintaining a healthy body weight is essential for reducing the risk of heart disease, according to a new study from Denmark. This is true even for people who don’t have metabolic syndrome (MetS), a group of risk factors for heart disease, diabetes, and stroke.

“We documented that overweight and obese individuals have an increased risk of heart attack and disease even in the absence of metabolic syndrome,” says Dr. Børge Nordestgaard, co-author of the new study, published today in JAMA Internal Medicine. “In other words, even metabolically healthy overweight and obese people are at an increased risk of heart problems.”
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Re: Optimum BMI?

Postby JeffN » Tue Dec 03, 2013 7:14 pm

Obesity and mortality risk: new findings from body mass index trajectories.
Zheng H, Tumin D, Qian Z.
Am J Epidemiol. 2013 Dec 1;178(11):1591-9. doi: 10.1093/aje/kwt179. Epub 2013 Sep 7.
PMID:24013201

Abstract

Little research has addressed the heterogeneity and mortality risk in body mass index (BMI) trajectories among older populations.

Applying latent class trajectory models to 9,538 adults aged 51 to 77 years from the US Health and Retirement Study (1992–2008), we defined 6 latent BMI trajectories: normal weight downward, normal weight upward, overweight stable, overweight obesity, class I obese upward, and class II/III obese upward. Using survival analysis,

we found that people in the overweight stable trajectory had the highest survival rate, followed by those in the overweight obesity, normal weight upward, class I obese upward, normal weight downward, and class II/III obese upward trajectories. The results were robust after controlling for baseline demographic and socioeconomic characteristics, smoking status, limitations in activities of daily living, a wide range of chronic illnesses, and self-rated health. Further analysis suggested that BMI trajectories were more predictive of mortality risk than was static BMI status. Using attributable risk analysis, we found that approximately 7.2% of deaths after 51 years of age among the 1931–1941 birth cohort were due to class I and class II/III obese upward trajectories.

This suggests that trajectories of increasing obesity past 51 years of age pose a substantive threat to future gains in life expectancy.
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Re: Optimum BMI?

Postby JeffN » Tue Dec 03, 2013 7:17 pm

Annals of Internal Medicine tip sheet for Dec. 3, 2013 -- 'Overweight and healthy' is a myth

1. Evidence suggests that "healthy and overweight" is a myth

A systematic review and meta-analysis of observational studies published from 1950 until 2013 suggests that there is no such thing as being healthy and overweight, according to an article published in Annals of Internal Medicine. Persons in the same BMI category can have varied metabolic features, such as lipid profile, glucose tolerance, blood pressure, and waist circumference. Some obese individuals have normal metabolic features, despite their increased body fat. This profile has been described as "benign obesity" or "metabolically healthy obesity." Similarly, some normal-weight individuals may have adverse metabolic features, despite having a healthy BMI. Researchers reviewed published research to assess the association between metabolic status and all-cause mortality and cardiovascular events in normal-weight (BMI, 18.5 to 25 kg/m2), overweight (BMI, 25 to 30 kg/m2), and obese adults. The research showed that metabolically healthy obese individuals were at increased risk for death and cardiovascular events over the long term compared with metabolically healthy normal-weight persons, suggesting that increased BMI without metabolic abnormalities is not a benign condition. The research also showed that regardless of BMI category, metabolically unhealthy individuals had increased risk for events compared with healthy normal-weight individuals. The researchers conclude that both BMI and metabolic status should be considered when evaluating an individual's health risks. The authors of an accompanying editorial write that recognizing that there is no level of healthy obesity is the first step. Next, physicians need to focus on treating obesity as any other chronic disease that requires long-term attention.



Editorials | 3 December 2013
The Myth of Healthy Obesity
James O. Hill, PhD; and Holly R. Wyatt, MD

Journal pre-amble: In this issue, Kramer and colleagues' meta-analysis provides strong evidence that “healthy obesity” is a myth. The editorialists discuss the meta-analysis and conclude that no level of obesity is healthy. They believe that recognizing this is an important step toward developing and implementing strategies to combat the obesity epidemic.

In this issue, Kramer and colleagues’ meta-analysis provides strong evidence that “healthy obesity” is a myth (1). This evidence fuels the debate about the existence of a subset of obese persons who are unlikely to have long-term, negative health effects and should not be targeted for treatment (2). The review identified 8 studies that included a total of 61 386 persons followed long enough to investigate the associations of body mass index (BMI) and metabolic status with total mortality and cardiovascular events.

Not surprisingly, the evidence showed that metabolically healthy nonobese persons (the reference group) had the lowest risk for these outcomes and that being metabolically unhealthy, regardless of BMI, increased risk. The most interesting finding was that the metabolically healthy obese group was also at increased risk. However, this risk was observed only in studies with more than 10 years of follow-up. Metabolically healthy overweight persons had a risk similar to that of the reference group.

.Kramer and colleagues conclude that being metabolically unhealthy at any weight confers health risks and that normal weight does not indicate cardiometabolic health. These findings cast doubt on the existence of metabolically healthy obesity. The authors speculate that persons who are metabolically healthy but obese probably have subclinical levels of risk factors that worsen over time. If so, the question is whether this change in risk is an inevitable consequence of obesity or is due to subsequent weight gain or behaviors. For obese persons to be truly healthy, must they have and maintain a healthy lifestyle?

.Also of interest is that the metabolically unhealthy overweight group had an increased risk for total mortality and cardiovascular events over time, whereas the metabolically healthy overweight group did not. Controversy exists over the effect of overweight on total mortality, with some reports suggesting that overweight may be protective (3). It is essential to consider metabolic risk factors when examining the effect of overweight on mortality.

.The meta-analysis has limitations. Most studies had inadequate information on participants’ health behaviors, did not present data about weight gain, focused only on total mortality and cardiovascular events, and did not include older participants. By uncovering the limitations of the current evidence, this review will hopefully stimulate research to more thoroughly understand the interactions among weight status, metabolic status, and health outcomes. The results are consistent with the notion that obesity is a disease. In light of these findings, we consider common misperceptions about obesity.

.First, the review casts doubt that any obese persons have no long-term risk for cardiometabolic disease. Obesity affects almost all aspects of human function and physiology. Although Kramer and colleagues focused on total mortality and cardiovascular events, obesity also increases risk for type 2 diabetes, kidney disease, and some types of cancer (4). It is linked to orthopedic problems, reproductive problems, depression, asthma, sleep apnea, renal disease, back pain, skin infections, and cognitive decline (4). Obesity produces social stigma and overall reduced quality of life (5). It would be a mistake to label obese persons as healthy on the basis of only the presence or absence of risk factors for cardiometabolic disease.

.A second common misperception is that we cannot afford to treat everyone with obesity, so we have to prioritize those with cardiometabolic risk. However, doing so would deny treatment to those who may later develop cardiometabolic disease. Although many health care providers argue that avoiding diabetes and cardiovascular disease is the most important reason to tackle obesity, many patients would probably prioritize other outcomes. We believe that there are many good reasons to lose weight. If we assume that we cannot afford to treat all obesity, denying treatment on the basis of cardiometabolic risk will be extremely difficult to justify.

.A third misperception is that effective treatment for obesity is unavailable. Although we lack a simple algorithm or medication to eradicate this condition, clinically significant weight loss can be achieved with behavioral treatment, pharmacologic agents, and bariatric surgery (6). However, treatment of obesity brings real challenges.

.Obesity is not cured even when the excessive body fat is successfully reduced. Patient adherence and long-term sustainability are just as challenging in treatment of obesity as they are in long-term treatment of any disease. Losing weight and maintaining a reduced body weight are different physiologic processes and therefore require different treatment strategies for maximum success (7). Just as in treatment of other chronic conditions, treatment of obesity needs to be evaluated and adjusted over time to maximize success. Health care providers may not have eagerly stepped up to tackle obesity partly because many practicing physicians today have had no formal training in treating this condition and do not feel confident that they have the tools, skills, and time needed to be successful.

.A fourth misperception is that weight loss and reducing cardiometabolic risk are the highest-priority goals in obesity treatment. Yet, is a person who has lost enough weight to achieve normal metabolic measures but who has sleep problems, orthopedic issues, or difficulty managing stress really “healthy”? Perhaps we need a more comprehensive measurement of well-being to measure success. For example, decades of work from Blair and associates (8) has consistently shown that cardiorespiratory fitness is a very strong predictor of total and cardiovascular mortality independent of BMI. We must develop a means of assessing success in obesity treatment that considers overall well-being and includes but is not limited to BMI and decreasing metabolic risk.

.Fifth, some believe that overweight is not as much of a priority for intervention as obesity. As Kramer and colleagues found (1), metabolically unhealthy overweight persons have increased risk for cardiovascular events and total mortality and are candidates for obesity treatment. Metabolically healthy overweight persons are at risk for gaining more weight and becoming obese. The priority for them might involve prevention of weight gain. This is an important distinction because, although large behavioral changes are needed to produce and maintain weight loss, prevention of weight gain can be accomplished with much smaller, and perhaps more feasible, behavior changes (9).

.Obesity is taking a toll on the health and well-being of Americans. Accepting that no level of obesity is healthy is an important step toward deciding how best to use our resources and our political will to develop and implement strategies to combat the obesity epidemic.





Reviews | 3 December 2013
Are Metabolically Healthy Overweight and Obesity Benign Conditions?: A Systematic Review and Meta-analysis
Caroline K. Kramer, MD, PhD; Bernard Zinman, CM, MD; and Ravi Retnakaran, MD

Abstract

Background: Recent interest has focused on a unique subgroup of overweight and obese individuals who have normal metabolic features despite increased adiposity. Normal-weight individuals with adverse metabolic status have also been described. However, it remains unclear whether metabolic phenotype modifies the morbidity and mortality associated with higher body mass index (BMI).

Purpose: To determine the effect of metabolic status on all-cause mortality and cardiovascular events in normal-weight, overweight, and obese persons.

Data Sources: Studies were identified from electronic databases.

Study Selection: Included studies evaluated all-cause mortality or cardiovascular events (or both) and clinical characteristics of 6 patient groups defined by BMI category (normal weight/overweight/obesity) and metabolic status (healthy/unhealthy), as defined by the presence or absence of components of the metabolic syndrome by Adult Treatment Panel III or International Diabetes Federation criteria.

Data Extraction: Two independent reviewers extracted the data. Metabolically healthy people of normal weight made up the reference group.

Data Synthesis: Eight studies (n = 61 386; 3988 events) evaluated participants for all-cause mortality and/or cardiovascular events. Metabolically healthy obese individuals (relative risk [RR], 1.24; 95% CI, 1.02 to 1.55) had increased risk for events compared with metabolically healthy normal-weight individuals when only studies with 10 or more years of follow-up were considered. All metabolically unhealthy groups had a similarly elevated risk: normal weight (RR, 3.14; CI, 2.36 to 3.93), overweight (RR, 2.70; CI, 2.08 to 3.30), and obese (RR, 2.65; CI, 2.18 to 3.12).

Limitation: Duration of exposure to the metabolic–BMI phenotypes was not described in the studies and could partially affect the estimates.

Conclusion: Compared with metabolically healthy normal-weight individuals, obese persons are at increased risk for adverse long-term outcomes even in the absence of metabolic abnormalities, suggesting that there is no healthy pattern of increased weight.
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Re: Optimum BMI?

Postby JeffN » Sat Jan 18, 2014 10:51 am

Obesity and late-age survival without major disease or disability in older women.
JAMA Intern Med. 2014 Jan 1;174(1):98-106. doi: 10.1001/jamainternmed.2013.12051.
PMID:24217806

ABSTRACT

Importance

The effect of obesity on late-age survival in women without disease or disability is unknown.

Objective

To investigate whether higher baseline body mass index and waist circumference affect women’s survival to 85 years of age without major chronic disease (coronary disease, stroke, cancer, diabetes mellitus, or hip fracture) and mobility disability.

Design, Setting, and Participants

Examination of 36 611 women from the Women’s Health Initiative observational study and clinical trial programs who could have reached 85 years or older if they survived to the last outcomes evaluation on September 17, 2012. Recruitment was from 40 US clinical centers from October 1993 through December 1998. Multinomial logistic regression models were used to estimate odds ratios and 95% CIs for the association of baseline body mass index and waist circumference with the outcomes, adjusting for demographic, behavioral, and health characteristics.

Main Outcomes and Measures

Mutually exclusive classifications: (1) survived without major chronic disease and without mobility disability (healthy); (2) survived with 1 or more major chronic disease at baseline but without new disease or disability (prevalent diseased); (3) survived and developed 1 or more major chronic disease but not disability during study follow-up (incident diseased); (4) survived and developed mobility disability with or without disease (disabled); and (5) did not survive (died).

Results

Mean (SD) baseline age was 72.4 (3.0) years (range, 66-81 years). The distribution of women classified as healthy, prevalent diseased, incident diseased, disabled, and died was 19.0%, 14.7%, 23.2%, 18.3%, and 24.8%, respectively. Compared with healthy-weight women, underweight and obese women were more likely to die before 85 years of age. Overweight and obese women had higher risks of incident disease and mobility disability. Disability risks were striking. Relative to healthy-weight women, adjusted odds ratios (95% CIs) of mobility disability were 1.6 (1.5-1.8) for overweight women and 3.2 (2.9-3.6), 6.6 (5.4-8.1), and 6.7 (4.8-9.2) for class I, II, and III obesity, respectively. Waist circumference greater than 88 cm was also associated with higher risk of earlier death, incident disease, and mobility disability.

Conclusions and Relevance

Overall and abdominal obesity were important and potentially modifiable factors associated with dying or developing mobility disability and major chronic disease before 85 years of age in older women.

..The number of women 85 years and older in the United States is increasing rapidly, with 11.6 million projected by 2050.1 Aging without affliction of a major chronic disease or disability is a desired goal for individuals and could ease disability-related health care costs, which was approximately 27% of US health care expenditures in 2006.2

..Obesity prevalence in older US women is also increasing. In 2007-2010, 40% of women aged 65 to 74 years and 29% of women 75 years and older were obese—up by 4% and 5%, respectively, from 2003-2006.3 Obesity is a modifiable risk factor for physical disability4,5 and for many diseases that are highly prevalent in older women, including cardiovascular disease, diabetes mellitus, and some cancers.6- 8 Whether obesity affects women’s capacity to reach late adulthood without major disease or disability is unknown. Characteristics associated with healthy survival in older men have been explored in the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS),9,10 which found greater likelihood of late-age survival without disease and disability among men who were leaner in midlife. However, studies in older women, who live longer and whose rates of obesity, disease, and disability differ from men, are lacking. Using an ethnically diverse population of Women’s Health Initiative (WHI) participants who could be followed up to 85 years of age or death, we investigated whether obesity in older women decreased survival to 85 years of age without major disease or disability and determined whether any risks conferred varied by race/ethnicity and baseline smoking behavior.


(From the Methods)

Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Using standard World Health Organization (WHO) cut points,6 BMI was categorized as follows: underweight (<18.5), healthy weight (18.5 to <25), overweight (25 to <30), obese I (30 to <35), obese II (35 to <40), and obese III (=/>40). Asian/Pacific Islander women were evaluated using WHO cut points for Asian populations15: underweight (<18.5), healthy weight (18.5 to <23), overweight (23 to <27.5), and obese (=/>27.5). Waist circumference (WC) was measured during expiration at the narrowest section of the torso and dichotomized at a cut point of 88 cm.6



.DISCUSSION.

This study of older women with a baseline age range of 66 to 81 years and nearly 19 years of follow-up found that obesity and higher WC were associated with a higher risk of death, major chronic disease, and mobility disability before reaching 85 years of age. These associations persisted after adjustment for behavioral and socioeconomic risk factors, including physical activity, smoking status, and educational level.

..Women in our study demonstrated prolonged longevity, with 75.2% surviving to 85 years of age and nearly one-fifth doing so without mobility disability or a major age-related morbidity defined as a diagnosis of coronary disease, stroke, cancer, diabetes, or hip fracture. In the Cardiovascular Health Study All Stars study,19 63% of 1677 men and women 77 to 102 years of age had no physical impairment. The Framingham Heart Study20 of 2531 older adults who could survive to 85 years of age reported 36% overall survival and 22% survival without major morbidities, including cardiovascular disease, stroke, cancer, and dementia. In the HHP/HAAS,10 42% of 5820 Japanese American men survived to 85 years of age, 11% without disease and disability.

..In our study, women with a BMI of 35 or higher had a more than 6-fold higher risk of mobility disability by 85 years of age compared with women with a BMI ranging from 18.5 to 24.9. Remaining mobile substantially affects quality of life, functional independence, long-term care, and risk of institutionalization.21- 23 Persons with disability use more health care services and incur an economic burden to society.24,25 In 2006, nearly 27% of total US health care costs were spent on disability-related health care expenditures.2 The population of older women is expected to increase, and the prevalence of obesity in this age group continues to rise. Thus, preventing or reducing obesity in older postmenopausal women has important individual, public health, and economic implications on later-life morbidity. There is evidence of successful interventions for weight loss in older obese women, which also demonstrated improvements in cardiometabolic risk factors.26 The risks and benefits of weight reduction strategies should continue to be researched.

..Our finding of an association of BMI-defined obesity and increased risk of disability was stronger than in previous studies of older populations.5,27- 30 The differences might have occurred because our sample was older and comprised only postmenopausal women and our disability measure primarily focused on impaired mobility. Reuser et al29 reported a hazard ratio (95% CI) for disability of 2.8 (2.2-3.6) for women with a BMI of 35 or higher relative to women with a BMI of 18.5 to 24.9. Most published literature, including the study by Reuser et al, characterized disability using measures that reflect severe self-care limitations rather than measures predominantly related to impaired mobility. Obesity negatively affects the musculoskeletal system and is an important risk factor for conditions that affect mobility, such as arthritis.31 Indeed, a study32 of 282 older adults found a 7-fold higher risk of poor overall lower-extremity performance, which included walking and balancing, among persons with a BMI of 35 or higher relative to those with a BMI less than 24.9. Our definition of disability as a measure of mobility impairment may better reflect obesity-related associations.

..We also reported a higher risk of mobility disability among overweight women and those with a higher WC. The association between overweight and later-life disability in the literature is mixed. Some reported an increased risk,5,29,30 whereas others found no association.4,27 Diehr et al28 reported that overweight persons spent more years without an activity-of-daily-living difficulty than healthy-weight persons, although findings were less stable in older women than men. Studies33,34 consistently reported that higher WC was a strong predictor of future mobility disability in older women.

..Obese and overweight women and those with a higher WC also had increased risk of developing coronary disease, stroke, cancer, diabetes, and/or hip fracture before 85 years of age. Overall and abdominal obesity are well-established risk factors for many of these age-related chronic diseases.7,8 However, studies in exclusively older populations suggest that the magnitude of the risk might not be as strong as in younger populations. The Cardiovascular Health Study35 described no differences in the risk of myocardial infarction, stroke, and cancer among 2752 overweight, obese, and healthy-weight women 65 years or older. In another study of 70-year-old women, obesity was not associated with incident coronary disease36 or stroke.37 Folsom et al38 identified a weak increased risk of incident cancer in obese women only but a protective dose-response relationship for BMI and WC with hip fracture incidence. In contrast, studies35,38- 40 consistently reported a strong association between higher BMI and WC and increased diabetes risk. To complicate matters, many older people have multimorbidities.41 Our results suggested overweight and obesity were associated with an increased risk of developing chronic disease in late life. However, we found a weak association between BMI and survival to 85 years of age among women with major chronic disease at baseline who did not develop any new morbidity during follow-up. These women were demographically similar to the healthy survival group. This finding could be explained by self-selection. Women who entered the study with prevalent disease may be less affected by their disease history than women with these diseases who elected not to join the study. In this study, we did not delineate type, duration, severity, or number of diseases, and we did not examine characteristics associated with disease management, which are important considerations but not within the scope of this study. Of note, women characterized as “healthy” in our study were not necessarily disease free and might have had health conditions that did not cause mobility disability but were not considered, such as eye diseases.

..Our results revealed a J-shaped association between BMI and mortality. This observation is consistent with the published literature27,42- 45 and suggests further consideration of healthy-weight ranges and appropriate weight reduction goals for overweight older people.35,46 Debate persists about the appropriate definition of obesity for older adults.46- 49 Waist circumferences may better represent excess abdominal fat in older people.49,50 Yet, literature on WC and mortality in older populations is mixed. In a study of women 55 years and older, only those in the highest quintile of WC (>96 cm) had an increased all-cause mortality risk, although their estimations did not adjust for BMI.38 However, women 50 years and older with a WC greater than 75 cm were found to have a higher BMI-adjusted risk of all-cause mortality, with risks increasing further as WC increased.51 We observed an increased risk of death before 85 years of age in women with a WC greater than 88 cm that was independent of BMI, suggesting that both BMI and WC may be important determinants of mortality in older women.

..Underweight women in our study were at increased risk of death before 85 years of age but only represented 1.2% of our population. Hypotheses regarding the increased mortality risk in underweight women include malnutrition, frailty, and underlying disease or disability.43 Studies27,43,44,49 often exclude data from the first few follow-up visits to account for this possible confounding, but sensitivity analyses that excluded earlier deaths did not change our results.

..Compared with healthy-weight women of the same race/ethnicity, black/African American women who were overweight and Hispanic/Latina women who were obese at baseline had higher risks of developing a major chronic disease by 85 years of age than white women. In addition, black/African American women with a higher WC relative to those with a lower WC had a higher risk of incident disease before 85 years of age than white women. Research that examines differences by race/ethnicity in the association of BMI to risk of late-life disease in older women is limited. However, studies consistently report that these minority groups have disproportionately higher rates of overweight and obesity3,52,53 and higher rates of major chronic diseases, including diabetes,54,55 cardiovascular disease,56- 58 and cancer.59- 61 Debate continues about whether the standard WHO definitions for overweight and obesity are appropriate for Asian populations.15,62 Reanalysis using WHO cut points for Asian populations15 suggested that overweight Asian/Pacific Islander women also had a higher risk of incident disease by late age compared with overweight white women. Confirmation in larger samples is needed, but standard WHO cut points may underestimate disease risk for Asian/Pacific Islander women.

..This study has limitations. The WHI participants may have been healthier at baseline than their age counterparts in the general population. However, strong associations of body size with the outcomes were still observed. We did not include all forms of disability, such as sensory or cognitive impairment. Yet, our focus on mobility disability acknowledged the importance of maintaining the ability to walk in healthy aging, and a strong link to obesity was described. Finally, we did not consider body size changes over time, which increases during the midlife but is variable in older ages.63,64 These anthropometric changes are likely to affect health and survival in later ages, but the temporality of these changes is complex and beyond the scope of this study.

..The large, diverse sample of older women, high retention and outcome ascertainment rates, and the availability of adjudicated outcomes for major diseases were study strengths. Body size measurements were clinically measured and included WC. Our analyses included nearly 19 years of follow-up data from a prospective cohort study that included women who died before 85 years of age, which cannot be captured in de novo studies of long-lived persons.

..Having a healthy BMI or WC was associated with a higher likelihood of surviving to older ages without a major disease or mobility disability. In contrast, higher BMI and WC was associated with an increased risk of death before reaching 85 years of age and with late-age survival with incident disease and mobility disability. Obese women, in particular, had an increased risk of developing a mobility disability by 85 years of age. Successful strategies aimed at maintaining healthy body weight, minimizing abdominal fat accretion, and guiding safe, intentional weight loss for those who are already obese should be further investigated and disseminated.
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Re: Optimum BMI?

Postby JeffN » Sat Jan 18, 2014 12:25 pm

JeffN wrote:"These findings suggest that overweight and obesity are risk factors for MI and IHD regardless of the presence or absence of metabolic syndrome and that metabolic syndrome is no more valuable than BMI in identifying individuals at risk."

http://archinte.jamanetwork.com/article ... ultClick=3

Myocardial Infarction and Ischemic Heart Disease in Overweight and Obesity With and Without Metabolic Syndrome
JAMA Intern Med. Published online November 11, 2013. doi:10.1001/jamainternmed.2013.10522

ABSTRACT

Importance Overweight and obesity likely cause myocardial infarction (MI) and ischemic heart disease (IHD); however, whether coexisting metabolic syndrome is a necessary condition is unknown.

Objective To test the hypothesis that overweight and obesity with and without metabolic syndrome are associated with increased risk of MI and IHD.

Design, Setting, and Participants We examined 71 527 individuals from the Copenhagen General Population Study and categorized them according to body mass index (BMI) as normal weight, overweight, or obese and according to absence or presence of metabolic syndrome.

Main Outcomes and Measures Hazard ratios for incident MI and IHD according to combinations of BMI category and absence or presence of metabolic syndrome.

Results During a median of 3.6 years’ follow-up, we recorded 634 incident MI and 1781 incident IHD events. For MI, multivariable adjusted hazard ratios vs normal weight individuals without metabolic syndrome were 1.26 (95% CI, 1.00-1.61) in overweight and 1.88 (95% CI, 1.34-2.63) in obese individuals without metabolic syndrome and 1.39 (95% CI, 0.96-2.02) in normal weight, 1.70 (95% CI, 1.35-2.15) in overweight, and 2.33 (95% CI, 1.81-3.00) in obese individuals with metabolic syndrome. For IHD, results were similar but attenuated. Normal weight vs overweight vs obesity and presence vs absence of metabolic syndrome did not interact on risk of MI or IHD (P = .90 and P = .44). Among individuals both with and without metabolic syndrome there were increasing cumulative incidences of MI and IHD from normal weight through overweight to obese individuals (log-rank trend P = .006 to P  < .001). Although the multivariable adjusted hazard ratio for MI in individuals with vs without metabolic syndrome was 1.54 (95% CI, 1.32-1.81) across all BMI categories, addition of metabolic syndrome to a multivariable model including BMI and other clinical characteristics improved the Harell C-statistic only slightly for risk of MI (comparison P = .03) but not for IHD (P = .41).

Conclusions and Relevance These findings suggest that overweight and obesity are risk factors for MI and IHD regardless of the presence or absence of metabolic syndrome and that metabolic syndrome is no more valuable than BMI in identifying individuals at risk.




Editorial on the above...


.Maintaining a healthy body weight is paramount
Jackson CL, Stampfer MJ.
JAMA Intern Med. 2014 Jan 1;174(1):23-4. doi: 10.1001/jamainternmed.2013.8298. No abstract available.
PMID:24217562

Comments .

In this well-designed and well-executed prospective analysis among 71 519 participants in the Copenhagen General Population Study, Thomsen and Nordestgaard1 categorized individuals at baseline as normal weight, overweight, or obese on the basis of their body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) and according to the absence or presence of metabolic syndrome (MetS), and observed them for occurrence of ischemic heart disease (IHD) to address a question of major clinical and public health importance: Are overweight persons without MetS at increased risk for IHD? With a median of 3.6 years of follow-up and 1781 IHD events overall, the authors showed a clear increase in risk in this group. Among those without MetS, the hazard ratios were 1.26 (95% CI, 1.00-1.61) in overweight and 1.88 (95% CI, 1.34-2.63) in obese participants, compared with normal weight participants without MetS. Using the same comparison group, the hazard ratios among those with MetS were 1.39 (95% CI, 0.96-2.02) in normal weight, 1.70 (95% CI, 1.35-2.15) in overweight, and 2.33 (95% CI, 1.81-3.00) in obese participants. The components that compose MetS are established consequences of excess adiposity, as well as established cardiovascular disease risk factors, so these results seem unexpected. Indeed, much of the impact of obesity is mediated through components of MetS. Hence, one might expect overweight individuals without MetS not to be at increased risk. However, in this and other studies, MetS is defined as a dichotomy, but physiologically it should be thought of as continuous, as are all of its components. Given the continuum, it is plausible that the “metabolically healthy overweight or obese” phenotype may be transient and that those with this phenotype are likely eventually to develop the risk factors that make up MetS. Thus, the overweight or obese individuals with diagnosed MetS seem to include individuals at more advanced stages along the continuum of the pathological process. For example, in this study, the overweight and obese individuals who did not meet the criteria for MetS had worse levels of component parameters and were closer to the higher end within BMI categories, which implies that they were closer to MetS on the continuum than their leaner counterparts. Supporting this concept, a recent prospective cohort study of 4056 adults in Australia showed that a substantial portion of metabolically healthy obese individuals developed MetS (especially those with a relatively high baseline waist circumference) during the 5.5 to 10.3 years of follow-up.2 The authors concluded that one-third of the healthy obese participants were in a transient state and that waist circumference can add to the prediction of future risk of MetS in individuals with this phenotype. In the study by Thomsen and Nordestgaard,1 normal weight individuals with MetS, considered to have the normal weight and metabolically unhealthy phenotype, had an increased risk of IHD, although it was not statistically significant, perhaps because of the relatively small sample size in this category; it seems likely that such individuals do carry an increased risk.

..These findings call into question the role of the construct of MetS in research and clinical practice. Clinicians tend to dichotomize conditions determined by continuous clinical measures (eg, hypertension with blood pressure and diabetes mellitus with fasting serum glucose level) mainly as a tool for making treatment decisions, and these decision points are dependent in part on the risks and benefits of the treatment options. However, clinicians do not per se treat MetS. Instead, they treat the individual components that have a well-established dose-response relationship with IHD. To be useful in clinical practice or for research, MetS would have to define a synergistic effect beyond the independent associations of its component risk factors for IHD. Perhaps its main utility is as a heuristic guide to considering the underlying physiologic processes and common pathways, since different risk factors stem from obesity, especially abdominal obesity, rather than as a summary risk score or a trigger for pharmacologic therapy.

..Besides questions related to how much added value there is to assessing MetS (beyond its component elements), the findings from this study have important implications and clearly corroborate the clinical and public health message that adiposity is not benign and that achieving and maintaining a healthy body weight (typically, BMI, >18.5 to <25.0 kg/m2) is of paramount importance. Particular focus should be placed on the study findings in the overweight group, where the prior evidence is much less clear than for the obese. It is already well established that BMIs of at least 35 kg/m2 are associated with increased risk of morbidity and mortality; however, despite decades of research and irrefutable evidence, some argue that lesser levels of overweight are benign or even preferable for optimal health. The finding of Thomsen and Nordestgaard1 that even overweight individuals without common metabolic risk factors are still at increased risk for myocardial infarction and IHD compared with normal weight persons is entirely consistent with a wealth of data demonstrating a clear relation of overweight with incidence of diabetes mellitus, hypertension, and IHD as described in a comprehensive review by Hu.3

..Innumerable studies also consistently show a wide range of serious adverse health effects of overweight, not only for IHD, but also for stroke, postmenopausal breast cancer, colorectal cancer, and many other important causes of death.3 Why then do some studies report that overweight individuals tend to have lower mortality rates than normal weight individuals4? The explanation is unlikely to be a mysterious unknown tonic effect of adiposity that could counteract the adverse effect on so many key causes of death. Instead, the explanation is that in those studies of mortality, the lean and normal weight group is a mix of those who are healthy (and maintain a healthy weight through a balance of diet and activity) and those who are affected by smoking and chronic illnesses. In studies of mortality that remove or mitigate these influences by excluding smokers and those with chronic disease, the nadir for mortality rates falls in the normal BMI range, as one would expect.5,6 Studies of disease incidence, such as that by Thomsen and Nordestgaard,1 typically and appropriately exclude from baseline individuals who already have received a diagnosis of the disease of interest, here IHD. This mitigates the potential impact of clinical or subclinical disease on BMI, known as reverse causation, permitting a direct examination of the impact of being overweight on IHD risk, regardless of MetS status.

..The findings of Thomsen and Nordestgaard1 add important new evidence to counter the common belief in the scientific and lay communities that the adverse health effects of overweight are generally inconsequential as long as the individual is metabolically healthy. In contrast, this study adds further evidence for the increased risks associated with overweight, even among those who might be considered metabolically healthy. These results also underscore the importance of focusing on weight gain prevention due to the difficulty in achieving and maintaining weight loss to reverse being overweight or obese.
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Re: Optimum BMI?

Postby JeffN » Sun Feb 02, 2014 1:05 pm

From Dr Fontana...

Energy efficiency as a unifying principle for human, environmental, and global health

Fontana L, Atella V, Kammen DM. (2013) Energy efficiency as a unifying principle for human, environmental, and global health [v1; ref status: indexed, http://f1000r.es/y8] F1000Research 2013, 2:101 (doi: 10.12688/f1000research.2-101.v1)

http://f1000research.com/articles/2-101/v1

"Accordingly, our (unpublished) data, derived from a very large dataset of Italian patients seen by general practitioners through the National Health Search Network, show that excessive body weight is associated with a striking increase in health-care costs that could very likely lead to the bankruptcy of the health care system (Figure 1). Clearly, Italy is not an exception in the industrialized world."

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Re: Optimum BMI?

Postby JeffN » Mon Mar 31, 2014 12:03 pm

From Dr Luigi Fontana and Dr Frank Hu.

Fontana, L. and Hu, F. B. (2014), Optimal body weight for health and longevity: bridging basic, clinical, and population research. Aging Cell. doi: 10.1111/acel.12207

http://onlinelibrary.wiley.com/doi/10.1 ... 12207/full
http://onlinelibrary.wiley.com/enhanced ... cel.12207/

Summary

Excess body weight and adiposity cause insulin resistance, inflammation, and numerous other alterations in metabolic and hormonal factors that promote atherosclerosis, tumorigenesis, neurodegeneration, and aging. Studies in both animals and humans have demonstrated a beneficial role of dietary restriction and leanness in promoting health and longevity. Epidemiological studies have found strong direct associations between increasing body mass index (BMI) and risks of developing type 2 diabetes, cardiovascular disease, and several types of cancer, beginning from BMI of 20–21 kg m−2. Although a recent meta-analysis suggests that overweight individuals have significantly lower overall mortality than normal-weight individuals, these data are likely to be an artifact produced by serious methodological problems, especially confounding by smoking, reverse causation due to existing chronic disease, and nonspecific loss of lean mass and function in the frail elderly. From a clinical and public health point of view, maintaining a healthy weight through diet and physical activity should remain the cornerstone in the prevention of chronic diseases and the promotion of healthy aging.

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Re: Optimum BMI?

Postby JeffN » Wed Sep 03, 2014 5:58 pm

Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults
The Lancet, Volume 384, Issue 9945, Pages 755 - 765, 30 August 2014
doi:10.1016/S0140-6736(14)60892-8
Published Online: 14 August 2014

http://www.thelancet.com/journals/lance ... 40-6736(14)60892-8/fulltext

Summary

Background
High body-mass index (BMI) predisposes to several site-specific cancers, but a large-scale systematic and detailed characterisation of patterns of risk across all common cancers adjusted for potential confounders has not previously been undertaken. We aimed to investigate the links between BMI and the most common site-specific cancers.

Methods
With primary care data from individuals in the Clinical Practice Research Datalink with BMI data, we fitted Cox models to investigate associations between BMI and 22 of the most common cancers, adjusting for potential confounders. We fitted linear then non-linear (spline) models; investigated effect modification by sex, menopausal status, smoking, and age; and calculated population effects.

Findings
5·24 million individuals were included; 166 955 developed cancers of interest. BMI was associated with 17 of 22 cancers, but effects varied substantially by site. Each 5 kg/m2 increase in BMI was roughly linearly associated with cancers of the uterus (hazard ratio [HR] 1·62, 99% CI 1·56—1·69; p<0·0001), gallbladder (1·31, 1·12—1·52; p<0·0001), kidney (1·25, 1·17—1·33; p<0·0001), cervix (1·10, 1·03—1·17; p=0·00035), thyroid (1·09, 1·00—1·19; p=0·0088), and leukaemia (1·09, 1·05—1·13; p≤0·0001). BMI was positively associated with liver (1·19, 1·12—1·27), colon (1·10, 1·07—1·13), ovarian (1·09, 1.04—1.14), and postmenopausal breast cancers (1·05, 1·03—1·07) overall (all p<0·0001), but these effects varied by underlying BMI or individual-level characteristics. We estimated inverse associations with prostate and premenopausal breast cancer risk, both overall (prostate 0·98, 0·95—1·00; premenopausal breast cancer 0·89, 0·86—0·92) and in never-smokers (prostate 0·96, 0·93—0·99; premenopausal breast cancer 0·89, 0·85—0·94). By contrast, for lung and oral cavity cancer, we observed no association in never smokers (lung 0·99, 0·93—1·05; oral cavity 1·07, 0·91—1·26): inverse associations overall were driven by current smokers and ex-smokers, probably because of residual confounding by smoking amount. Assuming causality, 41% of uterine and 10% or more of gallbladder, kidney, liver, and colon cancers could be attributable to excess weight. We estimated that a 1 kg/m2 population-wide increase in BMI would result in 3790 additional annual UK patients developing one of the ten cancers positively associated with BMI.

Interpretation
BMI is associated with cancer risk, with substantial population-level effects. The heterogeneity in the effects suggests that different mechanisms are associated with different cancer sites and different patient subgroups.

Funding
National Institute for Health Research, Wellcome Trust, and Medical Research Council.
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Re: Optimum BMI?

Postby JeffN » Wed Oct 01, 2014 7:39 pm

More on overweight/obesity & cancer

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http://edition.cnn.com/2014/10/01/healt ... ?hpt=he_c2

The link between fat and cancer
By Jacque Wilson , CNN
October 1, 2014 -- Updated 1116 GMT (1916 HKT) CNN.com

CNN(CNN) -- You likely know that being overweight increases your risk for cardiovascular disease and diabetes. But did you know it also increases your risk for cancer?

If you didn't, you're not alone. While around 90% of Americans know that smoking is linked to higher rates of cancer, Dr. Clifford Hudis says, the inverse is true for obesity and cancer; less than 10% of us realize how fat is related to this chronic disease.

"Obesity is a major, under-recognized contributor to the nation's cancer toll and is quickly overtaking tobacco as the leading preventable cause of cancer," Hudis and his colleagues at the American Society of Clinical Oncology write in a new position paper.

In fact, as many as 84,000 cancer diagnoses each year are linked to obesity, according to the National Cancer Institute. Excess fat also affects how cancer treatments work and may increase a cancer patient's risk of death, either from cancer or from other related causes.

The key word, Hudis says, is preventable. While we can't change the fact that we're all getting older (incidence rates for most cancers increase as patients age), we can change our weight through diet, exercise, sleep and stress management.

The link

In 2003, the New England Journal of Medicine published the results of a study that included more than 900,000 American adults. Researchers followed the healthy study participants for 16 years, and found the heaviest participants were more likely to develop and die from cancer than participants who were at a healthy weight.

After their analysis, the study authors concluded that excess fat "could account for 14% of all deaths from cancer in men and 20% of those in women."

Since then, research has simply strengthened the link between obesity and cancer. Studies have found a relationship between weight and the risk of as many as 12 cancers, says Dr. Otis Brawley, chief medical officer for the American Cancer Society, including endometrial, colorectal, esophageal, kidney and pancreatic cancers.

A recent report published in the American Association for Cancer Research's journal predicted the top cancer killers in the United States by 2030 will be lung, pancreas and liver -- in part because of rising obesity rates.

The science behind it

"It's not enough to say there's an association between obesity and cancer. We need to know why," Hudis says. "With the why, we can do something about it."

Scientists are exploring several hypotheses on how excess fat increases a person's risk for cancer. The answer may be slightly different for each type of cancer, but the encompassing explanation seems to be that obesity triggers changes in how the body operates, which can cause harmful cell growth and cell division.

Many of these changes may be linked to inflammation. In general, inflammation occurs when your body is reacting to something out of the norm -- say a virus or a splinter in your foot. Obesity seems to cause chronic inflammation, which in turn may promote cancer development.

Take for example, Hudis says, hormone-sensitive breast cancers. Chemicals in the body meant to regulate inflammation also increase production of the hormone estrogen. And studies have shown excess estrogen can cause breast cancer tumors.

Fat tissue also produces hormones called adipokines, which can stimulate or inhibit cell growth, according to a fact sheet from the oncology society. If these hormones are out of balance, the body may not be able to properly fight cell damage.

Treatment and mortality

Obesity can affect a cancer patient's outcome from diagnosis to remission, Hudis says.

Obesity-related pain or unbalanced hormone levels may distract patients from the early warning signs of some cancers. Fatty tissue can also make it difficult for doctors to see tumors on imaging scans. And a late diagnosis often means a lower chance for survival.

The relationship between cancer and obesity also matters after diagnosis. Cancer treatments, such as radiation or chemotherapy, may be hindered by a patient's size. If the patient needs surgery, studies show excess fat puts them at a higher risk of complications, infections and death.

Tumor Paint: Changing the way surgeons fight cancer

A recent study of 80,000 breast cancer patients found that pre-menopausal women with a BMI over 30 had a 21.5% chance of dying, compared to women with an average BMI who had a 16.6% chance of death.

Remaining obese as a survivor can also increase your risk of developing what's called a secondary cancer, the authors of this new position paper say.

What you can do to reduce your risk

In general, "people should be aware that overweight and obesity, as common as they are in our population, have serious consequences," Hudis says. "Cancer is really just another one."

Start reducing your risk now: Stay active. Eat nutritious foods that are low in calories. Get seven to eight hours of sleep a night. Manage your stress levels. All these behaviors will help you reach a healthy weight.

If you or someone you know is a cancer survivor, talk to your oncologist. He or she should be aware of the link between cancer and obesity, Hudis says, and able to help you find resources in your community.

The American Society of Clinical Oncology is recommending more research be done on weight loss in the cancer survivor population to determine the best intervention method -- and whether losing weight after a diagnosis improves patient outcomes. The results of these future studies could help persuade insurance providers to reimburse patients for weight management programs.
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Re: Optimum BMI?

Postby JeffN » Sat Dec 13, 2014 7:10 am

Metabolic Signatures of Adiposity in Young Adults: Mendelian Randomization Analysis and Effects of Weight Change mPublished: December 09, 2014. DOI: 10.1371/journal.pmed.1001765

http://www.plosmedicine.org/article/inf ... ed.1001765

Abstract

Background

Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood.

Methods and Findings

We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16–39 y; 51% women; mean ± standard deviation BMI 24±4 kg/m2). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%–183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87%±3%; R2 = 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160%±2%; R2 = 0.92).

Conclusions

Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood.


Editors' Summary

Background

Adiposity—having excessive body fat—is a growing global threat to public health. Body mass index (BMI, calculated by dividing a person's weight in kilograms by their height in meters squared) is a coarse indicator of excess body weight, but the measure is useful in large population studies. Compared to people with a lean body weight (a BMI of 18.5–24.9 kg/m2), individuals with higher BMI have an elevated risk of developing life-shortening cardiometabolic diseases—cardiovascular diseases that affect the heart and/or the blood vessels (for example, heart failure and stroke) and metabolic diseases that affect the cellular chemical reactions that sustain life (for example, diabetes). People become unhealthily fat by consuming food and drink that contains more energy (calories) than they need for their daily activities. So adiposity can be prevented and reversed by eating less and exercising more.

Why Was This Study Done?

Epidemiological studies, which record the patterns of risk factors and disease in populations, suggest that the illness and death associated with excess body weight is partly attributable to abnormalities in how individuals with high adiposity metabolize carbohydrates and fats, leading to higher blood sugar and cholesterol levels. Further, adiposity is also associated with many other deviations in the metabolic profile than these commonly measured risk factors. However, epidemiological studies cannot prove that adiposity causes specific changes in a person's systemic (overall) metabolic profile because individuals with high BMI may share other characteristics (confounding factors) that are the actual causes of both adiposity and metabolic abnormalities. Moreover, having a change in some aspect of metabolism could also lead to adiposity, rather than vice versa (reverse causation). Importantly, if there is a causal effect of adiposity on cardiometabolic risk factor levels, it might be possible to prevent the progression towards cardiometabolic diseases by weight loss. Here, the researchers use “Mendelian randomization” to examine whether increased BMI within the normal and overweight range is causally influencing the metabolic risk factors from many biological pathways during early adulthood. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. Several gene variants are known to lead to modestly increased BMI. Thus, an investigation of the associations between these gene variants and risk factors across the systemic metabolite profile in a population of healthy individuals can indicate whether higher BMI is causally related to known and novel metabolic risk factors and higher cardiometabolic disease risk.

What Did the Researchers Do and Find?

The researchers measured the BMI of 12,664 adolescents and young adults (average BMI 24.7 kg/m2) living in Finland and the blood levels of 82 metabolites in these young individuals at a single time point. Statistical analysis of these data indicated that elevated BMI was adversely associated with numerous cardiometabolic risk factors. For example, elevated BMI was associated with raised levels of low-density lipoprotein, “bad” cholesterol that increases cardiovascular disease risk. Next, the researchers used a gene score for predisposition to increased BMI, composed of 32 gene variants correlated with increased BMI, as an “instrumental variable” to assess whether adiposity causes metabolite abnormalities. The effects on the systemic metabolite profile of a 1-kg/m2 increment in BMI due to genetic predisposition closely matched the effects of an observed 1-kg/m2 increment in adulthood BMI on the metabolic profile. That is, higher levels of adiposity had causal effects on the levels of numerous blood-based metabolic risk factors, including higher levels of low-density lipoprotein cholesterol and triglyceride-carrying lipoproteins, protein markers of chronic inflammation and adverse liver function, impaired insulin sensitivity, and elevated concentrations of several amino acids that have recently been linked with the risk for developing diabetes. Elevated BMI also causally led to lower levels of certain high-density lipoprotein lipids in the blood, a marker for the risk of future cardiovascular disease. Finally, an examination of the metabolic changes associated with changes in BMI in 1,488 young adults after a period of six years showed that those metabolic measures that were most strongly associated with BMI at a single time point likewise displayed the highest responsiveness to weight change over time.

What Do These Findings Mean?

These findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults beyond the effects on cholesterol and blood sugar. Like all Mendelian randomization studies, the reliability of the causal association reported here depends on several assumptions made by the researchers. Nevertheless, these findings suggest that increased adiposity has causal adverse effects on multiple cardiometabolic risk markers in non-obese young adults. Importantly, the results of both the causal effect analyses and the longitudinal study suggest that there is no threshold below which a BMI increase does not adversely affect the metabolic profile, and that a systemic metabolic profile linked with high cardiometabolic disease risk that becomes established during early adulthood can be reversed. Overall, these findings therefore highlight the importance of weight reduction as a key target for metabolic risk factor control among young adults.



NYTimes Article

NYTimes: Weight Gain Carries Risks, No Matter Your Weight

http://well.blogs.nytimes.com/2014/12/1 ... hone-share

Even in young adults of normal weight, increases in body mass index also lead to increased risk of heart disease and other problems.


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JeffN
 
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Re: Optimum BMI?

Postby JeffN » Fri Dec 26, 2014 4:00 pm

http://www.the-scientist.com//?articles ... rter-Life/

The Scientist » The Nutshell
Obesity Linked to Shorter Life

Excess body weight can decrease one’s life expectancy by nearly 10 years, according to a new modeling study.
By Jef Akst | December 5, 2014

Modeling risk of developing heart disease and type 2 diabetes against obesity, researchers at McGill University in Montreal have found that being excessively overweight can reduce human life by up to nine years, with younger people significantly more affected, according to a study published yesterday (December 4) in The Lancet Diabetes & Endocrinology. Healthy life, free from diabetes or cardiovascular disease, can be shrunk by up to 19 years.

“The pattern is clear,” the authors wrote: “the more an individual weighs and the younger their age, the greater the effect on their health.”

The team, led by McGill’s Steven Grover, used data from the US National Health and Nutrition Examination Survey. The model revealed that excess weight led to just a year or two of lost life in older men and women to up to nine years in men aged 20 to 39 years and seven-and-a-half years in women of the same age. “Healthy life-years lost were two to four times higher than total years of life lost for all age groups and bodyweight categories,” the researchers wrote.

“Meaningful metrics are needed for education, counselling, and health promotion,” Edward Gregg, Chief of the Epidemiology and Statistics Branch in the Division of Diabetes Translation at the US Centers for Disease Control and Prevention wrote in an accompanying commentary. “[This] might place a higher premium on decision-making methods that can simultaneously take a lifecourse perspective, incorporate interventions, and consider individual differences so that clinicians and public health leaders alike can effectively tackle the next phases of the obesity and diabetes epidemics.”


http://www.bbc.com/news/health-30327777

5 December 2014
Obese lose up to eight years of life
By James Gallagher
Health editor, BBC News website

Being severely obese can knock up to eight years off your life and cause decades of ill health, a report says.

The analysis showed being obese at a young age was more damaging to health and life expectancy.

The team, at McGill University in Canada, said heart problems and type 2 diabetes were major sources of disability and death.

Experts said people were frequently "ignorant" of the consequences of obesity.

The health problems caused by obesity are well known.

The report, in the Lancet Diabetes and Endocrinology, used a computer model to take those risks and calculate the impact of weight on life expectancy throughout life.

In comparison with 20 to 39-year-olds with a healthy weight, severely obese men of the same age lost 8.4 years of life and women lost 6.1.

Men also spent 18.8 more years living in poor health while women spent 19.1 in that state.

Moving up an age group to those in the forties and fifties, men lost 3.7 years and women 5.3 years to obesity.

Men and women in their sixties and seventies lost just one year of life to obesity, but still faced seven years in ill health.

'Clear pattern'

Prof Steven Grover said: "Our computer modelling study shows that obesity is associated with an increased risk of developing cardiovascular disease, including heart disease and stroke, and diabetes that will, on average, dramatically reduce an individual's life expectancy.

"The pattern is clear. The more an individual weighs and the younger their age, the greater the effect on their health, as they have many years ahead of them during which the increased health risks associated with obesity can negatively impact their lives."

Responding to the findings, Barbara Dinsdale, lifestyle manager for the charity Heart Research UK, said: "How many more wake-up calls do we need?

"This research study yet again supports the clear message that by becoming obese you not only take years off your life, but also life off your years in terms of experiencing more years in poor health rather than enjoying a happy, active and productive life."

"Whatever size you are, small, manageable but sustainable changes are the way forward for a happier, healthier and longer life, and reduced risk of heart disease and type 2 diabetes."

Tam Fry, of the National Obesity Forum, said: "People persist in thinking that fat is just fat and appear ignorant of the many diseases that a high body mass index triggers.

"If they were told that they could lose a leg or go blind from diabetes or develop life-threatening complications from other similar diseases, I am sure they would think hard and twice before piling on the pounds."


Obesity, diabetes, and the moving targets of healthy-years estimation.
Gregg E.
Lancet Diabetes Endocrinol. 2014 Dec 4. pii: S2213-8587(14)70242-6. doi: 10.1016/S2213-8587(14)70242-6. [Epub ahead of print] No abstract available.
PMID:25483221

Many studies have attempted to quantify the effect of obesity on death, fueling a sustained controversy about which levels of bodyweight can harm health. 1 However, many investigators have argued that life expectancy does not capture the essence of the damage that obesity causes across a lifetime and that better long-term metrics are needed to convey risk, judge interventions, and motivate behaviour. 2 In The Lancet Diabetes & Endocrinology , Steven Grover and colleagues 3 model the effect of diabetes and ...


Years of life lost and healthy life-years lost from diabetes and cardiovascular disease in overweight and obese people: a modelling study.
Grover SA, Kaouache M, Rempel P, Joseph L, Dawes M, Lau DC, Lowensteyn I.
Lancet Diabetes Endocrinol. 2014 Dec 4. pii: S2213-8587(14)70229-3. doi: 10.1016/S2213-8587(14)70229-3. [Epub ahead of print]
PMID:25483220

Summary

Background

Despite the increased risk of cardiovascular disease and type 2 diabetes associated with excess bodyweight, development of a clinically meaningful metric for health professionals remains a challenge. We estimated the years of life lost and the life-years lost from diabetes and cardiovascular disease associated with excess bodyweight.

Methods

We developed a disease-simulation model to estimate the annual risk of diabetes, cardiovascular disease, and mortality for people with BMI of 25—<30 kg/m2 (overweight), 30—<35 kg/m2 (obese), or 35 kg/m2 and higher (very obese), compared with an ideal BMI of 18·5—<25 kg/m2. We used data from 3992 non-Hispanic white participants in the National Nutrition and Examination Survey (2003—10) for whom complete risk factor data and fasting glucose concentrations were available. After validation of the model projections, we estimated the years of life lost and healthy life-years lost associated with each bodyweight category.

Findings

Excess bodyweight was positively associated with risk factors for cardiovascular disease and type 2 diabetes. The effect of excess weight on years of life lost was greatest for young individuals and decreased with increasing age. The years of life lost for obese men ranged from 0·8 years (95% CI 0·2—1·4) in those aged 60—79 years to 5·9 years (4·4—7·4) in those aged 20—39 years, and years lost for very obese men ranged from 0·9 (0—1·8) years in those aged 60—79 years to 8·4 (7·0—9·8) years in those aged 20—39 years, but losses were smaller and sometimes negligible for men who were only overweight. Similar results were noted for women (eg, 6·1 years [4·6—7·6] lost for very obese women aged 20—39 years; 0·9 years [0·1—1·7] lost for very obese women aged 60—79 years). Healthy life-years lost were two to four times higher than total years of life lost for all age groups and bodyweight categories.

Interpretation

Our estimations for both healthy life-years and total years of life lost show the effect of excess bodyweight on cardiovascular disease and diabetes, and might provide a useful health measure for discussions between health professionals and their patients.
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Re: Optimum BMI?

Postby JeffN » Thu Feb 05, 2015 11:06 am

The Truth Behind Healthy Obesity

http://www.laboratoryequipment.com/news ... id=4400385

Q: What was the most surprising thing you found in your research?

A: These results were not overly surprising, as we already know that healthy obese adults have a greater risk for developing type 2 diabetes and cardiovascular disease than healthy normal-weight adults. A few previous studies using shorter follow-up times showed about one-third of healthy obese adults progress to unhealthy obesity. Our study, with at least 10 years longer follow-up, indicates that this tendency gets stronger with time, with about half making this transition after 20 years. It was striking how much more likely healthy obese adults were to become unhealthy obese than healthy or unhealthy non-obese adults, indicating that healthy obesity is often just a phase.

Q: What is the take home message of your research and results?

A: There is a clear tendency for healthy obese adults to progress to ill-health over time, indicating that healthy obesity is often just a phase. A long-term view of healthy obesity is needed in order to avoid inappropriate public health messages.
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Re: Optimum BMI?

Postby JeffN » Wed Mar 11, 2015 7:28 pm

"[Metabolically healthy abdominal obese] is a relatively unstable condition and a considerable portion of these individuals lose their metabolic health at longer follow-ups."


Natural course of metabolically healthy abdominal obese adults after 10 years of follow-up: the Tehran Lipid and Glucose Study.
Eshtiaghi R, Keihani S, Hosseinpanah F, Barzin M, Azizi F.
Int J Obes (Lond). 2015 Mar;39(3):514-9. doi: 10.1038/ijo.2014.176. Epub 2014 Oct 7.
PMID:25287753

Abstract

Objective: This study aims to assess the natural course of metabolically healthy abdominal obese (MHAO) phenotype and determine the predictors of change in the metabolic status in this population over 10 years of follow-up.

Methods: A total of 916 MHAO subjects from the Tehran Lipid and Glucose Study were followed for changes in their metabolic health status. Anthropometric and metabolic indices were measured at baseline and were compared between subjects with healthy and unhealthy metabolic conditions at the end of follow-up. Predictors of change in metabolic health were assessed in logistic regression models. National waist circumference cutoffs were used for definition of abdominal obesity. Metabolic health was defined as 1 metabolic components of metabolic syndrome according to the Joint Interim Statement criteria.

Results: At the end of the follow-up, nearly half of the MHAO subjects lost their metabolic health and 42.1% developed metabolic syndrome by definition. Low high-density lipoprotein cholesterol, hypertriglyceridemia and homeostasis model assessment-insulin resistance at baseline were significant predictors of change in metabolic health condition.

Conclusion: MHAO is a relatively unstable condition and a considerable percentage of these individuals will lose their metabolic health as time passes. Baseline metabolic characteristics may be useful predictors of this change and should be considered in the care of these individuals.
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