JeffN wrote:There have been several studies recently looking at the Obesity Paradox, which basically says, you can be obese, and have good numbers and not be at a higher risk. What all these new studies have been showing is that in most cases, they are at higher risk. So, being obese (which is BMI over 30) is a risk. Interesting, what they also find and we have always known, is that you can be at a healthy weight and still be at a high risk. So BMI and weight alone is not the end all. Health is.
From the recent article...
Metabolically Healthy' Obesity? Not So Much, Study Finds
Nearly 40% increase in CV events, even without diabetes or biomarker risk factors
https://www.medpagetoday.com/primarycare/obesity/73173"In addition, a substantial majority of metabolically healthy obese women (84%) became metabolically unhealthy during the course of the study. Surprisingly, so did more than two-thirds (68%) of metabolically healthy normal-weight women, Eckel's group reported online in The Lancet Diabetes & Endocrinology.
Furthermore, the study found that cardiovascular disease risk was highest in metabolically unhealthy women regardless of their weight. Compared with healthy women of normal weight, the risk was significantly higher in metabolically unhealthy normal-weight women (HR 2.43; 95% CI 2.19-2.68), overweight women (HR 2.61; 95% CI 2.36-2.89), and obese women (HR 3.15; 95% CI 2.83-3.50)."References
Eckel N, et al "Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90,257 women (the Nurses' Health Study): 30-year follow-up from a prospective cohort study" Lancet Diabetes Endocrinol 2018; DOI: 10.1016/S2213-8587(18)30137-2.
Lavie CJ et al "Obesity is rarely healthy" Lancet Diabetes Endocrinol 2018; DOI: 10.1016/S2213-8587(18)30143-8.
Here is a group of studies from the last few years on the topic.
"MHAO [Metabolically healthy abdominal obese] is a relatively unstable condition and a considerable portion of these individuals lose their metabolic health at longer follow-ups."2016
"These findings show that metabolically healthy obesity is not a harmless condition and that the obese phenotype, regardless of metabolic abnormalities, can adversely affect renal function."
Metabolically Healthy Obesity and Development of Chronic Kidney Disease: A Cohort Study.
Chang Y, Ryu S, Choi Y, Zhang Y, Cho J, Kwon MJ, Hyun YY, Lee KB, Kim H, Jung HS, Yun KE, Ahn J, Rampal S, Zhao D, Suh BS, Chung EC, Shin H, Pastor-Barriuso R, Guallar E.
Ann Intern Med. 2016 Mar 1;164(5):305-12. doi: 10.7326/M15-1323. Epub 2016 Feb 9.
PMID: 26857595
Abstract
Background: The risk for chronic kidney disease (CKD) among obese persons without obesity-related metabolic abnormalities, called metabolically healthy obesity, is largely unexplored.
Objective: To investigate the risk for incident CKD across categories of body mass index in a large cohort of metabolically healthy men and women.
Design: Prospective cohort study.
Setting: Kangbuk Samsung Health Study, Kangbuk Samsung Hospital, Seoul, South Korea.
Participants: 62 249 metabolically healthy, young and middle-aged men and women without CKD or proteinuria at baseline.
Measurements: Metabolic health was defined as a homeostasis model assessment of insulin resistance less than 2.5 and absence of any component of the metabolic syndrome. Underweight, normal weight, overweight, and obesity were defined as a body mass index less than 18.5 kg/m2, 18.5 to 22.9 kg/m2, 23 to 24.9 kg/m2, and 25 kg/m2 or greater, respectively. The outcome was incident CKD, defined as an estimated glomerular filtration rate less than 60 mL/min/1.73 m2.
Results: During 369 088 person-years of follow-up, 906 incident CKD cases were identified. The multivariable-adjusted differences in 5-year cumulative incidence of CKD in underweight, overweight, and obese participants compared with normal-weight participants were -4.0 (95% CI, -7.8 to -0.3), 3.5 (CI, 0.9 to 6.1), and 6.7 (CI, 3.0 to 10.4) cases per 1000 persons, respectively. These associations were consistently seen in all clinically relevant subgroups.
Limitation: Chronic kidney disease was identified by a single measurement at each visit.
Conclusion: Overweight and obesity are associated with an increased incidence of CKD in metabolically healthy young and middle-aged participants. These findings show that metabolically healthy obesity is not a harmless condition and that the obese phenotype, regardless of metabolic abnormalities, can adversely affect renal function.
"Weight management is needed for all individuals since weight change has a significant effect on metabolic health without considering the impact of weight change according to weight status "
Natural Course of Metabolically Healthy Overweight/Obese Subjects and the Impact of Weight Change.
Zheng R, Liu C, Wang C, Zhou B, Liu Y, Pan F, Zhang R, Zhu Y.
Nutrients. 2016 Jul 15;8(7). pii: E430.
PMID: 27428997
http://www.mdpi.com/2072-6643/8/7/430/htm Abstract
Few studies have described the characteristics of metabolically healthy individuals with excess fat in the Chinese population. This study aimed to prospectively investigate the natural course of metabolically healthy overweight/obese (MH-OW/OB) adults, and to assess the impact of weight change on developing metabolic abnormalities. During 2009-2010, 525 subjects without any metabolic abnormalities or other obesity-related diseases were evaluated and reevaluated after 5 years. The subjects were categorized into two groups of overweight/obese and normal weight based on the criteria of BMI by 24.0 at baseline. At follow-up, the MH-OW/OB subjects had a significantly increased risk of developing metabolically abnormalities compared with metabolically healthy normal-weight (MH-NW) individuals (risk ratio: 1.35, 95% confidence interval: 1.17-1.49, p value < 0.001). In the groups of weight gain and weight maintenance, the MH-OW/OB subjects was associated with a larger increase in fasting glucose, triglycerides, systolic blood pressure, diastolic blood pressure and decrease in high-density lipoprotein cholesterol comparing with MH-NW subjects. In the weight loss group, no significant difference of changes of metabolic parameters was observed between MH-OW/OB and MH-NW adults. This study verifies that MH-OW/OB are different from MH-NW subjects. Weight management is needed for all individuals since weight change has a significant effect on metabolic health without considering the impact of weight change according to weight status.
Body mass index and mortality: understanding the patterns and paradoxes.
Wild SH, Byrne CD.
BMJ. 2016 May 4;353:i2433. doi: 10.1136/bmj.i2433. No abstract available.
PMID: 27146663
http://www.bmj.com/content/353/bmj.i2433People who are lean for life have the lowest mortality
The optimal body mass index (BMI) associated with lowest risk of all cause mortality is not known. As excess adiposity increases risk of conditions such as diabetes that reduce life expectancy, one might expect increasing BMI to be associated with increasing mortality. However, compared with normal weight, underweight is associated with increased mortality and modestly elevated BMI is associated with lower mortality. The former pattern is only partly explained by confounding by smoking or comorbidity, and the second observation has been called the obesity paradox.1 In addition, the influence on mortality of different patterns of weight change throughout the life course is poorly understood. Two linked papers attempt to shed light on these important subjects.2 3
Aune and colleagues (doi:10.1136/bmj.i1256) report a meta-analysis of 230 prospective studies with more than 3.74 million deaths among more than 30.3 million participants, providing further evidence that adiposity (measured by BMI) increases the risk of premature death.2 Some increase in risk was observed in lower weight participants, and in the analysis of all participants the lowest mortality was observed with a BMI of around 25. However, the lowest mortality was observed in the BMI range 23-24 among never smokers, in the BMI range 22-23 among healthy never smokers, and in the BMI range 20-22 among studies of never smokers with longer durations of follow-up (=/>20 and =/>25 years). The findings show the importance of smoking and comorbidity in confounding the association between BMI and mortality and contributing to the apparent paradox of a U shaped association.
The attenuation of the observed J shaped relation in analyses confined to never smokers with longer follow-up and the finding of the lowest mortality in the BMI range 20-22 in this group suggest that any increased mortality among never smokers with low BMI is probably a result of residual confounding from unidentified comorbidity. However, the authors were unable to investigate how changes in weight over time might influence their findings.
In a second paper (doi:10.1136/bmj.i2195), Song and colleagues used an interesting strategy to try to find out how weight trajectories from age 5 to 50 years influence all cause and cause specific mortality among adults over 60 years of age.3 Having validated an approach in an earlier study in which people were asked to identify their body shape from outline drawings of different body shapes (somatotypes),4 the investigators studied associations between changes in somatotypes over time and mortality outcomes, using data from two large US prospective cohort studies.
They identified five common patterns or weight trajectories: lean-stable, lean-moderate increase, lean-marked increase, medium-stable/increase, and heavy stable/increase. Unsurprisingly, the authors found that people who reported remaining lean throughout life had the lowest mortality and that those who reported being heavy as children and who remained heavy or gained further weight had the highest mortality. Gaining weight from childhood to age 50 was associated with increased mortality compared with people who reported remaining lean. Weight gain was more strongly associated with cardiovascular than all cause mortality, and the effect was more pronounced among never smokers than ever smokers.
The association between weight gain and cancer mortality was also stronger among never smokers than ever smokers, presumably owing to the higher proportion of obesity related cancers among non-smokers. The stronger association between weight gain and mortality among people with diabetes suggests that diabetes may act as marker of metabolically unhealthy obesity within strata of BMI with the adverse effects of hypertension, dyslipidaemia, and insulin resistance added to the adverse effects of hyperglycaemia. Such findings are a reminder that BMI by itself is an imperfect measure of adiposity.
Recall of body shape is also an imperfect measure. Correlation coefficients between objective and subjective levels of adiposity varied between r=0.36 and r=0.66 in different age groups of men and women. Misclassification bias seems likely, although what effect this might have on the study’s findings is unclear. Interestingly, the authors did not identify repeated loss and regain of weight (weight cycling) as a separate trajectory. This pattern of weight change is thought to increase risk of diabetes, but limited evidence exists to support an effect on mortality.5 6
In conclusion, the study by Aune and colleagues suggests an optimal BMI for lowest mortality likely to apply to European and North American populations. Optimal BMI can be expected to vary by age, ethnicity, and the proportion of people with comorbidity in different populations, and secular declines in mortality may have been even more marked if the prevalence of obesity had not increased. The study by Song and colleagues is an important step forward in furthering our understanding of how weight gain over the life course, particularly in mid-life, is likely to influence health and mortality. Major challenges remain in finding effective ways to prevent weight gain, support weight loss, and prevent weight re-gain, in both individuals and populations.
2015
"MHAO [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.
Letters | January 2015
The Natural Course of Healthy Obesity Over 20 Years
Joshua A. Bell, MSc; Mark Hamer, PhD; Séverine Sabia, PhD; Archana Singh-Manoux, PhD; G. David Batty, PhD; Mika Kivimaki, PhD
J Am Coll Cardiol. 2015;65(1):101-102. doi:10.1016/j.jacc.2014.09.077
Intense interest surrounds the “healthy” obese phenotype, which is defined as obesity in the absence of metabolic risk factor clustering (1). Efforts to understand the cardiovascular consequences of healthy obesity are ongoing (2); however, its conceptual validity and clinical value rest on the assumption that it is a stable physiological state, rather than a transient phase of obesity-associated metabolic deterioration. Therefore, a fundamental question is whether healthy obese adults maintain this metabolically healthy profile over the long term or naturally transition into unhealthy obesity over time. Few studies have examined this; in those that have, durations of follow-up have been modest, with none exceeding 10 years (3,4). Accordingly, we aimed to describe the natural course of healthy obesity over 2 decades in a large population-based study.
http://www.sciencedaily.com/releases/20 ... 170012.htm"Independent of metrics, however, the health message regarding weight is still unanimous: exercise and healthy dietary choices benefit everyone. “At a certain point, despite all the so-called fit-fat people, the demographics say that there’s a huge risk of diabetes and heart disease at very high BMI,” notes Lazar. “We can’t assume we’ll be one of the lucky ones who will have a BMI in the obese category but will still be protected from heart disease.”
The Scientist » Magazine » Notebook
A Weighty Anomaly
Why do some obese people actually experience health benefits?
By Jyoti Madhusoodanan |
November 1, 2015
http://www.the-scientist.com//?articles ... y-Anomaly/THE ENDOCRINE THEORY: Some researchers have posited that fat cells may secrete molecules that affect glucose homeostasis in muscle or liver tissue.
COURTESY OF MITCHELL LAZAR
In the early 19th century, Belgian mathematician Adolphe Quetelet was obsessed with a shape: the bell curve. While helping with a population census, Quetelet proposed that the spread of human traits such as height and weight followed this trend, also known as a Gaussian or normal distribution. On a quest to define a “normal man,” he showed that human height and weight data fell along his beloved bell curves, and in 1823 devised the “Quetelet Index”—more familiar to us today as the BMI, or body mass index, a ratio of weight to height.
Nearly two centuries later, clinicians, researchers, and fitness instructors continue to rely on this metric to pigeonhole people into categories: underweight, healthy, overweight, or obese. But Quetelet never intended the metric to serve as a way to define obesity. And now, a growing body of evidence suggests these categories fail to accurately reflect the health risks—or benefits—of being overweight.
Although there is considerable debate surrounding the prevalence of metabolically healthy obesity, when obesity is defined in terms of BMI (a BMI of 30 or higher), estimates suggest that about 10 percent of adults in the U.S. are obese yet metabolically healthy, while as many as 80 percent of those with a normal BMI may be metabolically unhealthy, with signs of insulin resistance and poor circulating lipid levels, even if they suffer no obvious ill effects. “If all we know about a person is that they have a certain body weight at a certain height, that’s not enough information to know their health risks from obesity,” says health-science researcher Paul McAuley of Winston-Salem State University. “We need better indicators of metabolic health.”
If all we know about a person is that they have a certain body weight at a certain height, that’s not enough information to know their health risks from obesity. We need better indicators of metabolic health.—Paul McAuley,
Winston-Salem State University
The dangers of being overweight, such as a higher risk of heart disease, type 2 diabetes, and other complications, are well known. But some obese individuals—dubbed the “fat fit”—appear to fare better on many measures of health when they’re heavier. Studies have found lower mortality rates, better response to hemodialysis in chronic kidney disease, and lower incidence of dementia in such people. Mortality, it’s been found, correlates with obesity in a U-shaped curve (J Sports Sci, 29:773-82, 2011). So does extra heft help or hurt?
To answer that question, researchers are trying to elucidate the metabolic reasons for this obesity paradox.
In a recent study, Harvard University epidemiologist Goodarz Danaei and his colleagues analyzed data from nine studies involving a total of more than 58,000 participants to tease apart how obesity and other well-known metabolic risk factors influence the risk of coronary heart disease. Controlling these other risk factors, such as hypertension or high cholesterol, with medication is simpler than curbing obesity itself, Danaei explains. “If you control a person’s obesity you get rid of some health risks, but if you control hypertension or diabetes, that also reduces health risks, and you can do the latter much more easily right now.”
Danaei’s team assessed BMI and metabolic markers such as systolic blood pressure, total serum cholesterol, and fasting blood glucose. The three metabolic markers only explained half of the increased risk of heart disease across all study participants. In obese individuals, the other half appeared to be mediated by fat itself, perhaps via inflammatory markers or other indirect mechanisms (Epidemiology, 26:153-62, 2015). While Danaei’s study was aimed at understanding how obesity hurts health, the results also uncovered unknown mechanisms by which excess adipose tissue might exert its effects. This particular study revealed obesity’s negative effects, but might these unknown mechanisms hold clues that explain the obesity paradox?
Other researchers have suggested additional possibilities—for example, that inflammatory markers such as TNF-a help combat conditions such as chronic kidney disease, or that obesity makes a body more capable of making changes to, and tolerating changes in, blood flow depending on systemic needs (Am J Clin Nutr, 81:543-54, 2005).
According to endocrinologist Mitchell Lazar at the University of Pennsylvania, the key to explaining the obesity paradox may be two nonexclusive ways fat tissue is hypothesized to function. One mechanism, termed the endocrine theory, suggests that fat cells secrete, or don’t secrete enough of, certain molecules that influence glucose homeostasis in other tissues, such as muscle or liver. The first such hormone to be discovered was leptin; later studies reported several other adipocyte-secreted factors, including adiponectin, resistin, and various cytokines.
The other hypothesis, dubbed the spillover theory, suggests that storing lipids in fat cells has some pluses. Adipose tissue might sequester fat-soluble endotoxins, and produce lipoproteins that can bind to and clear harmful lipids from circulation. When fat cells fill up, however, these endotoxins are stashed in the liver, pancreas, or other organs—and that’s when trouble begins. In “fat fit” people, problems typically linked to obesity such as high cholesterol or diabetes may be avoided simply because their adipocytes mop up more endotoxins.
“In this model, one could imagine that if you could store even more fat in fat cells, you could be even more obese, but you might be protected from problems [associated with] obesity because you’re protecting the other tissues from filling up with lipids that cause problems,” says Lazar. “This may be the most popular current model to explain the fat fit.”
Although obesity greatly increases the risk of type 2 diabetes—up to 93-fold in postmenopausal women, for example—not all obese people suffer from the condition. Similarly, a certain subtype of individuals with “normal” BMIs are at greater risk of developing insulin resistance and type 2 diabetes than others with BMIs in the same range. Precisely what distinguishes these two cohorts is still unclear. “Just as important as explaining why some obese people don’t get diabetes is to explain why other subgroups—normal-weight people or those with lipodystrophy—sometimes get it,” Lazar says. “If there are multiple subtypes of obesity and diabetes, can we figure out genetic aspects or biomarkers that cause one of these phenotypes and not the other?”
To Lazar, McAuley, and other researchers, it’s increasingly evident that BMI may not be that metric. Finding better ways to assess a healthy weight, however, has proven challenging. Researchers have tested measures, such as the body shape index (ABSI) or the waist-hip ratio, which attempt to gauge visceral fat—considered to be more metabolically harmful than fat in other body locations. However, these metrics have yet to be implemented widely in clinics, and few are as simple to understand as the BMI (Science, 341:856-58, 2013).
Independent of metrics, however, the health message regarding weight is still unanimous: exercise and healthy dietary choices benefit everyone. “At a certain point, despite all the so-called fit-fat people, the demographics say that there’s a huge risk of diabetes and heart disease at very high BMI,” notes Lazar. “We can’t assume we’ll be one of the lucky ones who will have a BMI in the obese category but will still be protected from heart disease.”
2014
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.
2013
Weight Loss Diet Intervention Has a Similar Beneficial Effect on Both Metabolically Abnormal Obese and Metabolically Healthy but Obese Premenopausal Women.
Ruiz JR, Ortega FB, Labayen I.
Ann Nutr Metab. 2013 Apr 5;62(3):221-228. [Epub ahead of print]
PMID: 23571719
Abstract
Background/Aims: We studied the effect of a 12-week energy-restricted diet intervention on cardiometabolic risk in two groups of nonmorbid obese premenopausal Caucasian women, i.e. a metabolically abnormal obese (MAO) and a metabolically healthy but obese (MHO) group.
Methods: The participants were 53 MAO and 25 MHO women (age range 19-49 years; body mass index inclusion criterion: 30-39.9). We assessed changes in body weight and composition, blood lipids, insulin resistance, hepatic enzymes, inflammatory markers and adipocytokines.
Results: Overall, many of the study outcomes improved with the intervention in both MAO and MHO participants, but there was no difference in the magnitude of change between the groups. Body weight, waist circumference and total fat mass decreased significantly in response to the intervention in both MAO and MHO women (all p < 0.001). Fasting insulin, insulin resistance (homeostasis model assessment), hepatic enzymes (alanine aminotransferase and gamma-glutamyltransferase), fatty liver index and leptin levels also decreased in both groups after the intervention (all p < 0.001), whereas total cholesterol, triglycerides and C-reactive protein decreased significantly only in MAO women (all p < 0.001).
Conclusion: These findings reinforce the idea that MHO women would also benefit from a lifestyle weight reduction intervention.
2012
Prognostic implications for insulin-sensitive and insulin-resistant normal-weight and obese individuals from a population-based cohort.
Bo S, Musso G, Gambino R, Villois P, Gentile L, Durazzo M, Cavallo-Perin P, Cassader M.
Am J Clin Nutr. 2012 Oct 3. [Epub ahead of print]
PMID: 23034958
Abstract
BACKGROUND: There are few prospective data on the prognosis of insulin-sensitive and insulin-resistant normal-weight (NW) or obese individuals.
OBJECTIVES: The estimated liver fat content, incidences of hyperglycemia and cardiovascular disease, and all-cause and cardiovascular mortality rates were investigated in a population-based cohort of 1658 individuals who were categorized according to BMI and insulin resistance as defined by HOMA-IR values =/>2.5 and the presence of metabolic syndrome.
DESIGN: This was a prospective cohort study with a 9-y follow-up. Anthropometric values, blood pressure, and blood metabolic variables were measured, and information on vital status was collected from demographic files at follow-up.
RESULTS: A total of 137 of 677 NW individuals (20%) were classified as insulin resistant and normal weight (IR-NW), and 72 of 330 obese individuals (22%) were classified as insulin sensitive and obese (IS-obese). Incidences of diabetes, impaired fasting glucose, and cardiovascular events were 0.4%, 6.3%, and 3.3%, respectively, in insulin-sensitive and normal-weight (IS-NW) individuals (reference category); 5.8%, 10.2%, and 6.6%, respectively, in IR-NW individuals; and 5.6%, 8.3%, 8.3%, respectively, in IS-obese individuals. In a multiple logistic regression model, risks of incident hyperglycemia and cardiovascular events were increased in both groups compared with in the reference category [HR (95% CI): 2.54 (1.42, 4.55) and 1.98 (0.86, 4.54) in IR-NW subjects; 2.16 (1.01, 4.63) and 2.76 (1.05, 7.28) in IS-obese subjects]. The estimated liver fat content significantly increased during follow-up only in the IR-NW group in the same model. Cardiovascular mortality was 2-3-fold higher in IR-NW and IS-obese than in IS-NW individuals in a Cox regression model.
CONCLUSIONS: Our data refute the existence of healthy obese phenotypes because IS-obese individuals showed increased cardiometabolic risk. The existence of unhealthy NW phenotypes is supported by their increased risk of incident hyperglycemia, fatty liver, cardiovascular events, and death.
And A few more recent ones on the health consequence of obesity
Body mass index and mortality: understanding the patterns and paradoxes.
Wild SH, Byrne CD.
BMJ. 2016 May 4;353:i2433. doi: 10.1136/bmj.i2433. No abstract available.
PMID: 27146663
http://www.bmj.com/content/353/bmj.i2433People who are lean for life have the lowest mortality
The optimal body mass index (BMI) associated with lowest risk of all cause mortality is not known. As excess adiposity increases risk of conditions such as diabetes that reduce life expectancy, one might expect increasing BMI to be associated with increasing mortality. However, compared with normal weight, underweight is associated with increased mortality and modestly elevated BMI is associated with lower mortality. The former pattern is only partly explained by confounding by smoking or comorbidity, and the second observation has been called the obesity paradox.1 In addition, the influence on mortality of different patterns of weight change throughout the life course is poorly understood. Two linked papers attempt to shed light on these important subjects.2 3
Aune and colleagues (doi:10.1136/bmj.i1256) report a meta-analysis of 230 prospective studies with more than 3.74 million deaths among more than 30.3 million participants, providing further evidence that adiposity (measured by BMI) increases the risk of premature death.2 Some increase in risk was observed in lower weight participants, and in the analysis of all participants the lowest mortality was observed with a BMI of around 25. However, the lowest mortality was observed in the BMI range 23-24 among never smokers, in the BMI range 22-23 among healthy never smokers, and in the BMI range 20-22 among studies of never smokers with longer durations of follow-up (=/>20 and =/>25 years). The findings show the importance of smoking and comorbidity in confounding the association between BMI and mortality and contributing to the apparent paradox of a U shaped association.
The attenuation of the observed J shaped relation in analyses confined to never smokers with longer follow-up and the finding of the lowest mortality in the BMI range 20-22 in this group suggest that any increased mortality among never smokers with low BMI is probably a result of residual confounding from unidentified comorbidity. However, the authors were unable to investigate how changes in weight over time might influence their findings.
In a second paper (doi:10.1136/bmj.i2195), Song and colleagues used an interesting strategy to try to find out how weight trajectories from age 5 to 50 years influence all cause and cause specific mortality among adults over 60 years of age.3 Having validated an approach in an earlier study in which people were asked to identify their body shape from outline drawings of different body shapes (somatotypes),4 the investigators studied associations between changes in somatotypes over time and mortality outcomes, using data from two large US prospective cohort studies.
They identified five common patterns or weight trajectories: lean-stable, lean-moderate increase, lean-marked increase, medium-stable/increase, and heavy stable/increase. Unsurprisingly, the authors found that people who reported remaining lean throughout life had the lowest mortality and that those who reported being heavy as children and who remained heavy or gained further weight had the highest mortality. Gaining weight from childhood to age 50 was associated with increased mortality compared with people who reported remaining lean. Weight gain was more strongly associated with cardiovascular than all cause mortality, and the effect was more pronounced among never smokers than ever smokers.
The association between weight gain and cancer mortality was also stronger among never smokers than ever smokers, presumably owing to the higher proportion of obesity related cancers among non-smokers. The stronger association between weight gain and mortality among people with diabetes suggests that diabetes may act as marker of metabolically unhealthy obesity within strata of BMI with the adverse effects of hypertension, dyslipidaemia, and insulin resistance added to the adverse effects of hyperglycaemia. Such findings are a reminder that BMI by itself is an imperfect measure of adiposity.
Recall of body shape is also an imperfect measure. Correlation coefficients between objective and subjective levels of adiposity varied between r=0.36 and r=0.66 in different age groups of men and women. Misclassification bias seems likely, although what effect this might have on the study’s findings is unclear. Interestingly, the authors did not identify repeated loss and regain of weight (weight cycling) as a separate trajectory. This pattern of weight change is thought to increase risk of diabetes, but limited evidence exists to support an effect on mortality.5 6
In conclusion, the study by Aune and colleagues suggests an optimal BMI for lowest mortality likely to apply to European and North American populations. Optimal BMI can be expected to vary by age, ethnicity, and the proportion of people with comorbidity in different populations, and secular declines in mortality may have been even more marked if the prevalence of obesity had not increased. The study by Song and colleagues is an important step forward in furthering our understanding of how weight gain over the life course, particularly in mid-life, is likely to influence health and mortality. Major challenges remain in finding effective ways to prevent weight gain, support weight loss, and prevent weight re-gain, in both individuals and populations.
Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents
The Global BMI Mortality Collaboration†
Published Online: 13 July 2016
Open Access
DOI:
http://dx.doi.org/10.1016/S0140-6736(16)30175-1 |
Summary
Background
Overweight and obesity are increasing worldwide. To help assess their relevance to mortality in different populations we conducted individual-participant data meta-analyses of prospective studies of body-mass index (BMI), limiting confounding and reverse causality by restricting analyses to never-smokers and excluding pre-existing disease and the first 5 years of follow-up.
Methods
Of 10 625 411 participants in Asia, Australia and New Zealand, Europe, and North America from 239 prospective studies (median follow-up 13·7 years, IQR 11·4–14·7), 3 951 455 people in 189 studies were never-smokers without chronic diseases at recruitment who survived 5 years, of whom 385 879 died. The primary analyses are of these deaths, and study, age, and sex adjusted hazard ratios (HRs), relative to BMI 22·5–<25·0 kg/m2.
Findings
All-cause mortality was minimal at 20·0–25·0 kg/m2 (HR 1·00, 95% CI 0·98–1·02 for BMI 20·0–<22·5 kg/m2; 1·00, 0·99–1·01 for BMI 22·5–<25·0 kg/m2), and increased significantly both just below this range (1·13, 1·09–1·17 for BMI 18·5–<20·0 kg/m2; 1·51, 1·43–1·59 for BMI 15·0–<18·5) and throughout the overweight range (1·07, 1·07–1·08 for BMI 25·0–<27·5 kg/m2; 1·20, 1·18–1·22 for BMI 27·5–<30·0 kg/m2). The HR for obesity grade 1 (BMI 30·0–<35·0 kg/m2) was 1·45, 95% CI 1·41–1·48; the HR for obesity grade 2 (35·0–<40·0 kg/m2) was 1·94, 1·87–2·01; and the HR for obesity grade 3 (40·0–<60·0 kg/m2) was 2·76, 2·60–2·92. For BMI over 25·0 kg/m2, mortality increased approximately log-linearly with BMI; the HR per 5 kg/m2 units higher BMI was 1·39 (1·34–1·43) in Europe, 1·29 (1·26–1·32) in North America, 1·39 (1·34–1·44) in east Asia, and 1·31 (1·27–1·35) in Australia and New Zealand. This HR per 5 kg/m2 units higher BMI (for BMI over 25 kg/m2) was greater in younger than older people (1·52, 95% CI 1·47–1·56, for BMI measured at 35–49 years vs 1·21, 1·17–1·25, for BMI measured at 70–89 years; pheterogeneity<0·0001), greater in men than women (1·51, 1·46–1·56, vs 1·30, 1·26–1·33; pheterogeneity<0·0001), but similar in studies with self-reported and measured BMI.
Interpretation
The associations of both overweight and obesity with higher all-cause mortality were broadly consistent in four continents. This finding supports strategies to combat the entire spectrum of excess adiposity in many populations.
BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants.
Aune D, Sen A, Prasad M, Norat T, Janszky I, Tonstad S, Romundstad P, Vatten LJ.
BMJ. 2016 May 4;353:i2156. doi: 10.1136/bmj.i2156.
PMID: 27146380
http://www.bmj.com/content/353/bmj.i2156http://www.bmj.com/content/bmj/353/bmj.i2156.full.pdfAbstract
OBJECTIVE:
To conduct a systematic review and meta-analysis of cohort studies of body mass index (BMI) and the risk of all cause mortality, and to clarify the shape and the nadir of the dose-response curve, and the influence on the results of confounding from smoking, weight loss associated with disease, and preclinical disease.
DATA SOURCES:
PubMed and Embase databases searched up to 23 September 2015.
STUDY SELECTION:
Cohort studies that reported adjusted risk estimates for at least three categories of BMI in relation to all cause mortality.
DATA SYNTHESIS:
Summary relative risks were calculated with random effects models. Non-linear associations were explored with fractional polynomial models.
RESULTS:
230 cohort studies (207 publications) were included. The analysis of never smokers included 53 cohort studies (44 risk estimates) with >738 144 deaths and >9 976 077 participants. The analysis of all participants included 228 cohort studies (198 risk estimates) with >3 744 722 deaths among 30 233 329participants. The summary relative risk for a 5 unit increment in BMI was 1.18 (95% confidence interval 1.15 to 1.21; I(2)=95%, n=44) among never smokers, 1.21 (1.18 to 1.25; I(2)=93%, n=25) among healthy never smokers, 1.27 (1.21 to 1.33; I(2)=89%, n=11) among healthy never smokers with exclusion of early follow-up, and 1.05 (1.04 to 1.07; I(2)=97%, n=198) among all participants. There was a J shaped dose-response relation in never smokers (Pnon-linearity <0.001), and the lowest risk was observed at BMI 23-24 in never smokers, 22-23 in healthy never smokers, and 20-22 in studies of never smokers with =/>20 years' follow-up. In contrast there was a U shaped association between BMI and mortality in analyses with a greater potential for bias including all participants, current, former, or ever smokers, and in studies with a short duration of follow-up (<5 years or <10 years), or with moderate study quality scores.
CONCLUSION:
Overweight and obesity is associated with increased risk of all cause mortality and the nadir of the curve was observed at BMI 23-24 among never smokers, 22-23 among healthy never smokers, and 20-22 with longer durations of follow-up. The increased risk of mortality observed in underweight people could at least partly be caused by residual confounding from prediagnostic disease. Lack of exclusion of ever smokers, people with prevalent and preclinical disease, and early follow-up could bias the results towards a more U shaped association.
Trajectory of body shape in early and middle life and all cause and cause specific mortality: results from two prospective US cohort studies.
Song M, Hu FB, Wu K, Must A, Chan AT, Willett WC, Giovannucci EL.
BMJ. 2016 May 4;353:i2195. doi: 10.1136/bmj.i2195.
PMID: 27146280
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4856853/http://www.bmj.com/content/bmj/353/bmj.i2195.full.pdfAbstract
OBJECTIVE:
To assess body shape trajectories in early and middle life in relation to risk of mortality.
DESIGN:
Prospective cohort study.
SETTING:
Nurses' Health Study and Health Professionals Follow-up Study.
POPULATION:
80 266 women and 36 622 men who recalled their body shape at ages 5, 10, 20, 30, and 40 years and provided body mass index at age 50, followed from age 60 over a median of 15-16 years for death.
MAIN OUTCOME MEASURES:
All cause and cause specific mortality.
RESULTS:
Using a group based modeling approach, five distinct trajectories of body shape from age 5 to 50 were identified: lean-stable, lean-moderate increase, lean-marked increase, medium-stable/increase, and heavy-stable/increase. The lean-stable group was used as the reference. Among never smokers, the multivariable adjusted hazard ratio for death from any cause was 1.08 (95% confidence interval 1.02 to 1.14) for women and 0.95 (0.88 to 1.03) for men in the lean-moderate increase group, 1.43 (1.33 to 1.54) for women and 1.11 (1.02 to 1.20) for men in the lean-marked increase group, 1.04 (0.97 to 1.12) for women and 1.01 (0.94 to 1.09) for men in the medium-stable/increase group, and 1.64 (1.49 to 1.81) for women and 1.19 (1.08 to 1.32) for men in the heavy-stable/increase group. For cause specific mortality, participants in the heavy-stable/increase group had the highest risk, with a hazard ratio among never smokers of 2.30 (1.88 to 2.81) in women and 1.45 (1.23 to 1.72) in men for cardiovascular disease, 1.37 (1.14 to 1.65) in women and 1.07 (0.89 to 1.30) in men for cancer, and 1.59 (1.38 to 1.82) in women and 1.10 (0.95 to 1.29) in men for other causes. The trajectory-mortality association was generally weaker among ever smokers than among never smokers (for all cause mortality: P for interaction <0.001 in women and 0.06 in men). When participants were classified jointly according to trajectories and history of type 2 diabetes, the increased risk of death associated with heavier body shape trajectories was more pronounced among participants with type 2 diabetes than those without diabetes, and those in the heavy-stable/increase trajectory and with a history of diabetes had the highest risk of death.
CONCLUSIONS:
Using the trajectory approach, we found that heavy body shape from age 5 up to 50, especially the increase in middle life, was associated with higher mortality. In contrast, people who maintained a stably lean body shape had the lowest mortality. These results indicate the importance of weight management across the lifespan.
Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence.
(2016) PLoS ONE 11(3): e0150735. doi:10.1371/journal.pone.0150735
Abstract
Background
State-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.
METHODS
Using non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.
• Results
Compared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps.
Conclusions
Twelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.
Full text
http://journals.plos.org/plosone/articl ... 150735.PDF"Our results suggest the burden of overweight and obesity on mortality is likely substantially larger than commonly appreciated. If correct, this may have serious implications for the future of life expectancy in the United States."
Revealing the burden of obesity using weight histories
PNAS
Proceedings of the National Academy of Sciences
Early Edition
Andrew Stokes,
doi: 10.1073/pnas.1515472113
Full Text (Attached)
http://www.pnas.org/content/early/2016/ ... 3.full.pdfSignificance
There is substantial uncertainty about the association between obesity and mortality. A major issue is the treatment of reverse causation, a phrase referring to the loss of weight among people who become ill. Weight histories are vital to addressing reverse causality, but few studies incorporate them. Here we introduce nationally representative data on lifetime maximum weight to distinguish individuals who were never obese from those who were formerly obese and lost weight. We formally investigate the performance of various models, finding that models that incorporate history perform better than the conventional approach based on a single observation of weight at the time of survey. We conclude that the burden of obesity is likely to be greater than is commonly appreciated.
Abstract
Analyses of the relation between obesity and mortality typically evaluate risk with respect to weight recorded at a single point in time. As a consequence, there is generally no distinction made between nonobese individuals who were never obese and nonobese individuals who were formerly obese and lost weight. We introduce additional data on an individual’s maximum attained weight and investigate four models that represent different combinations of weight at survey and maximum weight. We use data from the 1988–2010 National Health and Nutrition Examination Survey, linked to death records through 2011, to estimate parameters of these models. We find that the most successful models use data on maximum weight, and the worst-performing model uses only data on weight at survey. We show that the disparity in predictive power between these models is related to exceptionally high mortality among those who have lost weight, with the normal-weight category being particularly susceptible to distortions arising from weight loss. These distortions make overweight and obesity appear less harmful by obscuring the benefits of remaining never obese. Because most previous studies are based on body mass index at survey, it is likely that the effects of excess weight on US mortality have been consistently underestimated.
From The Discussion
"Our results suggest the burden of overweight and obesity on mortality is likely substantially larger than commonly appreciated. If correct, this may have serious implications for the future of life expectancy in the United States. Although the prevalence of obesity may level off or even decline, the history of rapidly rising obesity in the last 3 decades cannot be readily erased (63). Successive birth cohorts embody heavier and heavier obesity histories, regardless of current levels. Those histories are likely to exert upward pressure on US mortality levels for many years to come.”
Ann Intern Med. 2016 Mar 8. doi: 10.7326/M15-1181. [Epub ahead of print]
Relationship Among Body Fat Percentage, Body Mass Index, and All-Cause Mortality: A Cohort Study.
Padwal R, Leslie WD, Lix LM, Majumdar SR.
Abstract
Background:
Prior mortality studies have concluded that elevated body mass index (BMI) may improve survival. These studies were limited because they did not measure adiposity directly.
Objective:
To examine associations of BMI and body fat percentage (separately and together) with mortality.
Design:
Observational study.
Setting:
Manitoba, Canada.
Participants:
Adults aged 40 years or older referred for bone mineral density (BMD) testing.
Measurements:
Participants had dual-energy x-ray absorptiometry (DXA), entered a clinical BMD registry, and were followed using linked administrative databases. Adjusted, sex-stratified Cox models were constructed. Body mass index and DXA-derived body fat percentage were divided into quintiles, with quintile 1 as the lowest, quintile 5 as the highest, and quintile 3 as the reference.
Results:
The final cohort included 49 476 women (mean age, 63.5 years; mean BMI, 27.0 kg/m2; mean body fat, 32.1%) and 4944 men (mean age, 65.5 years; mean BMI, 27.4 kg/m2; mean body fat, 29.5%). Death occurred in 4965 women over a median of 6.7 years and 984 men over a median of 4.5 years. In fully adjusted mortality models containing both BMI and body fat percentage, low BMI (hazard ratio [HR], 1.44 [95% CI, 1.30 to 1.59] for quintile 1 and 1.12 [CI, 1.02 to 1.23] for quintile 2) and high body fat percentage (HR, 1.19 [CI, 1.08 to 1.32] for quintile 5) were associated with higher mortality in women. In men, low BMI (HR, 1.45 [CI, 1.17 to 1.79] for quintile 1) and high body fat percentage (HR, 1.59 [CI, 1.28 to 1.96] for quintile 5) were associated with increased mortality.
Limitations:
All participants were referred for BMD testing, which may limit generalizability. Serial measures of BMD and weight were not used. Some measures, such as physical activity and smoking, were unavailable.
Conclusion:
Low BMI and high body fat percentage are independently associated with increased mortality. These findings may help explain the counterintuitive relationship between BMI and mortality.
Obesity and Falls in a Prospective Study of Older Men: The Osteoporotic Fractures in Men Study.
Hooker ER, Shrestha S, Lee CG, Cawthon PM, Abrahamson M, Ensrud K, Stefanick ML, Dam TT, Marshall LM, Orwoll ES, Nielson CM; Osteoporotic Fractures in Men (MrOS) Study.
J Aging Health. 2016 Jul 27. pii: 0898264316660412. [Epub ahead of print]
PMID: 27469600
Abstract
OBJECTIVE:
The aim of this study is to evaluate fall rates across body mass index (BMI) categories by age group, considering physical performance and comorbidities.
METHOD:
In the Osteoporotic Fractures in Men (MrOS) study, 5,834 men aged ≥65 reported falls every 4 months over 4.8 (±0.8 ) years. Adjusted associations between BMI and an incident fall were tested using mixed-effects models.
RESULTS:
The fall rate (0.66/man-year overall, 95% confidence interval [CI] = [0.65, 0.67]) was lowest in the youngest, normal weight men (0.44/man-year, 95% CI = [0.41, 0.47]) and greatest in the oldest, highest BMI men (1.47 falls/man-year, 95% CI = [1.22, 1.76]). Obesity was associated with a 24% to 92% increased fall risk in men below 80 (ptrend ≤ .0001, p for interaction by age = .03). Only adjustment for dynamic balance test altered the BMI-falls association substantially.
DISCUSSION:
Obesity was independently associated with higher fall rates in men 65 to 80 years old. Narrow walk time, a measure of gait stability, may mediate the association.