Metabolically Healthy but Obese

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

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Metabolically Healthy but Obese

Postby JeffN » Tue Nov 05, 2019 9:05 am

Metabolically healthy but obese is a condition characterized by obesity which (supposedly) does not produce metabolic complications. There are several movements that promote this and their main claim is that while there are some people who may be obese, but they have normal biomarkers and so are not at an increased risk for lifestyle related chronic disease or premature death.

Another aspect of these movements is that those who are overweight/obese face size discrimination and they advocate against this and that people of every size, should be treated equally, of which I agree 100%. Another aspect is that since most approaches to losing weight fail we should stop trying to get this population group to lose weight.

Around 2005, I was on The Today Show to discuss this with the President of the one of these organizations and a Doctor who specializes in this area. While these shows and the discussion on them never allow for the topic to be adequately covered, it was a lively discussion.

When I first became a dietitian, this concept was being promoted. My main concern with it was that the main proof being used was current biomarkers and not longterm outcomes, which they didn't have, and that as time passed, we will see their health degenerate.

Sadly, that is what has happened.


Here is a group of studies, organized by year, that I have collected from the last few years on the topic. This point, made in the first study listed under 2015 sums it all up.

"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.”


2019

Associations of overweight and metabolic health with successful aging: 32-year follow-up of the Helsinki Businessmen Study.
Jyväkorpi SK, Urtamo A, Strandberg AY, von Bonsdorff M, Salomaa V, Kivimäki M, Luotola K, Strandberg TE.
Clin Nutr. 2019 Jun 21. pii: S0261-5614(19)30266-3. doi: 10.1016/j.clnu.2019.06.011. [Epub ahead of print]

Abstract
BACKGROUND & AIMS:
Prognostic significance of metabolically healthy overweight and obesity (MHO) is under debate. However the relationship between MHO and health-related quality of life (HRQoL) is less studied. We compared successful aging (longevity plus HRQoL) in men with MHO, metabolically healthy normal weight (MHN) and metabolically unhealthy overweight and obesity (MUO).
METHODS:
In the Helsinki Businessmen Study longitudinal cohort, consisting of men born 1919 to 1934. In 1985/86, overweight (BMI≥25 kg/m2) and metabolic health were determined in 1309 men (median age 60 years). HRQoL was assessed using RAND-36/SF-36 in 2000 and 2007, and all-cause mortality retrieved from registers up to 2018. The proportion of men reaching 90 years was also calculated.
RESULTS:
Of the men, 469 (35.8%), 538 (41.1%), 276 (21.1%), and 26 (2.0%) were MHN, MHO, MUO and MUN, respectively. During the 32-year follow-up, 72.3% men died. With MHN as reference, adjusted hazard ratio with all-cause mortality was 1.08 (95% confidence interval [CI] 0.93 to 1.27) for MHO, and 1.18 (95% CI 0.95 to 1.47) for MUO. During follow-up, 273 men reached 90 years. With MHN as reference, adjusted odds ratio for MHO was 0.82 (95% CI 0.59 to 1.14) and 0.62 (95% CI 0.41 to 0.95) for MUO. Men in MHN group scored generally highest in RAND-36 HRQoL subscales in 2000 and 2007, of those significantly better in Physical functioning, Role physical, Role emotional, Bodily Pain, and General health sub-scales compared to MHO group in 2000.
CONCLUSIONS:
As compared to MHN, MHO in late midlife does not increase mortality, but impairs odds for successful aging.


***Transition from metabolically benign to metabolically unhealthy obesity and 10-year cardiovascular disease incidence: The ATTICA cohort study. Metabolism. 2019 Apr;93:18-24. doi: 10.1016/j.metabol.2019.01.003. Epub 2019 Jan 11.

https://www.ncbi.nlm.nih.gov/pubmed/30639450

Abstract

BACKGROUND/OBJECTIVES:
Metabolically benign obesity remains a scientific field of considerable debate. The aim of the present work was to evaluate whether metabolically healthy obese (MHO) status is a transient condition which propagates 10-year cardiovascular disease (CVD) onset.

METHODS:
A prospective longitudinal study was conducted during 2001-2012, the ATTICA study studying 1514 (49.8%) men and 1528 (50.2%) women (aged >18 years old) free of CVD and residing in the greater Athens area, Greece. Follow-up assessment of first combined CVD event (2011-2012) was achieved in n = 2020 participants; of them, 317 (15.7%) incident cases were identified. Obesity was defined as body mass index ≥30 kg/m2 and healthy metabolic status as absence of all NCEP ATP III (2005) metabolic syndrome components (excluding waist circumference).

RESULTS:
The MHO prevalence was 4.8% (n = 146) with 28.2% of obese participants presenting metabolically healthy status at baseline. Within this group, 52% developed unhealthy metabolic status during the 10-year follow up. MHO vs. metabolically healthy non-obese participants had a higher likelihood of presenting with 10-year CVD events, yet only the subset of them who lost their baseline status reached the level of significance (Hazard Ratio (HR) = 1.43, 95% Confidence Interval (95% CI) 1.02, 2.01). Sensitivity analyses revealed that MHO status was independently associated with elevated CVD risk in women and participants with low adherence to the Mediterranean diet, low grade inflammation, and insulin resistance.

CONCLUSIONS:
MHO status is a transient condition where weight management is demanded to prevent the establishment of unhealthy cardiometabolic features. The existence of obese persons who remain "longitudinally" resilient to metabolic abnormalities is an emerging area of future research.


2018

***Metabolically Healthy Obesity, Transition to Metabolic Syndrome, and Cardiovascular Risk.
J Am Coll Cardiol. 2018 May 1;71(17):1857-1865. doi: 10.1016/j.jacc.2018.02.055.

https://www.ncbi.nlm.nih.gov/pubmed/29699611

Abstract

BACKGROUND:
Debate over the cardiometabolic risk associated with metabolically healthy obesity (MHO) continues. Many studies have investigated this relationship by examining MHO at baseline with longitudinal follow-up, with inconsistent results.

OBJECTIVES:
The authors hypothesized that MHO at baseline is transient and that transition to metabolic syndrome (MetS) and duration of MetS explains heterogeneity in incident cardiovascular disease (CVD) and all-cause mortality.

METHODS:
Among 6,809 participants of the MESA (Multi-Ethnic Study of Atherosclerosis) the authors used Cox proportional hazards and logistic regression models to investigate the joint association of obesity (≥30 kg/m2) and MetS (International Diabetes Federation consensus definition) with CVD and mortality across a median of 12.2 years. We tested for interaction and conducted sensitivity analyses for a number of conditions.

RESULTS:
Compared with metabolically healthy normal weight, baseline MHO was not significantly associated with incident CVD; however, almost one-half of those participants developed MetS during follow-up (unstable MHO). Those who had unstable MHO had increased odds of CVD (odds ratio [OR]: 1.60; 95% confidence interval [CI]: 1.14 to 2.25), compared with those with stable MHO or healthy normal weight. Dose response for duration of MetS was significantly and linearly associated with CVD (1 visit with MetS OR: 1.62; 95% CI: 1.27 to 2.07; 2 visits, OR: 1.92; 95% CI: 1.48 to 2.49; 3+ visits, OR: 2.33; 95% CI: 1.89 to 2.87; p value for trend <0.001) and MetS mediated approximately 62% (44% to 100%) of the relationship between obesity at any point during follow-up and CVD.

CONCLUSIONS:
Metabolically healthy obesity is not a stable or reliable indicator of future risk for CVD. Weight loss and lifestyle management for CVD risk factors should be recommended to all individuals with obesity.


***Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis.
Eur Heart J. 2018 Feb 1;39(5):397-406. doi: 10.1093/eurheartj/ehx448.

https://www.ncbi.nlm.nih.gov/pubmed/29020414

Abstract

AIMS:
The hypothesis of 'metabolically healthy obesity' implies that, in the absence of metabolic dysfunction, individuals with excess adiposity are not at greater cardiovascular risk. We tested this hypothesis in a large pan-European prospective study.

METHODS AND RESULTS:
We conducted a case-cohort analysis in the 520 000-person European Prospective Investigation into Cancer and Nutrition study ('EPIC-CVD'). During a median follow-up of 12.2 years, we recorded 7637 incident coronary heart disease (CHD) cases. Using cut-offs recommended by guidelines, we defined obesity and overweight using body mass index (BMI), and metabolic dysfunction ('unhealthy') as ≥ 3 of elevated blood pressure, hypertriglyceridaemia, low HDL-cholesterol, hyperglycaemia, and elevated waist circumference. We calculated hazard ratios (HRs) and 95% confidence intervals (95% CI) within each country using Prentice-weighted Cox proportional hazard regressions, accounting for age, sex, centre, education, smoking, diet, and physical activity. Compared with metabolically healthy normal weight people (reference), HRs were 2.15 (95% CI: 1.79; 2.57) for unhealthy normal weight, 2.33 (1.97; 2.76) for unhealthy overweight, and 2.54 (2.21; 2.92) for unhealthy obese people. Compared with the reference group, HRs were 1.26 (1.14; 1.40) and 1.28 (1.03; 1.58) for metabolically healthy overweight and obese people, respectively. These results were robust to various sensitivity analyses.

CONCLUSION:
Irrespective of BMI, metabolically unhealthy individuals had higher CHD risk than their healthy counterparts. Conversely, irrespective of metabolic health, overweight and obese people had higher CHD risk than lean people. These findings challenge the concept of 'metabolically healthy obesity', encouraging population-wide strategies to tackle obesity.


2016

***Aerobic fitness in late adolescence and the risk of early death: a prospective cohort study of 1.3 million Swedish men.
Int J Epidemiol. 2016 Aug;45(4):1159-1168. Epub 2015 Dec 20.

https://www.ncbi.nlm.nih.gov/pubmed/26686843

Abstract

BACKGROUND:
Fitness level and obesity have been associated with death in older populations. We investigated the relationship between aerobic fitness in late adolescence and early death, and whether a high fitness level can compensate the risk of being obese.

METHODS:
The cohort comprised 1 317 713 Swedish men (mean age, 18 years) that conscripted between 1969 and 1996. Aerobic fitness was assessed by an electrically braked cycle test. All-cause and specific causes of death were tracked using national registers. Multivariable adjusted associations were tested using Cox regression models.

RESULTS:
During a mean follow-up period of 29 years, 44 301 subjects died. Individuals in the highest fifth of aerobic fitness were at lower risk of death from any cause [hazard ratio (HR), 0.49; 95% confidence interval (CI), 0.47-0.51] in comparison with individuals in the lowest fifth, with the strongest association seen for death related to alcohol and narcotics abuse (HR, 0.20; 95% CI, 0.15-0.26). Similar risks were found for weight-adjusted aerobic fitness. Aerobic fitness was associated with a reduced risk of death from any cause in normal-weight and overweight individuals, whereas the benefits were reduced in obese individuals (P < 0.001 for interaction). Furthermore, unfit normal-weight individuals had 30% lower risk of death from any cause (HR, 0.70; 95% CI, 0.53-0.92) than did fit obese individuals.

CONCLUSIONS:
Low aerobic fitness in late adolescence is associated with an increased risk of early death. Furthermore, the risk of early death was higher in fit obese individuals than in unfit normal-weight individuals.



"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.


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.i2433

People 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

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.



Increased cardiometabolic risk factors and inflammation in adipose tissue in obese subjects classified as metabolically healthy
Diabetes Care. 2014 Oct;37(10):2813-21. doi: 10.2337/dc14-0937. Epub 2014 Jul 10.
PMID: 25011950 DOI: 10.2337/dc14-0937

Abstract

Objective: It has been suggested that individuals with the condition known as metabolically healthy obesity (MHO) may not have the same increased risk for the development of metabolic abnormalities as their non-metabolically healthy counterparts. However, the validity of this concept has recently been challenged, since it may not translate into lower morbidity and mortality. The aim of the current study was to compare the cardiometabolic/inflammatory profile and the prevalence of impaired glucose tolerance (IGT) and type 2 diabetes (T2D) in patients categorized as having MHO or metabolically abnormal obesity (MAO).

Research design and methods: We performed a cross-sectional analysis to compare the cardiometabolic/inflammatory profile of 222 MHO and 222 MAO patients (62% women) matched by age, including 255 lean subjects as reference (cohort 1). In a second cohort, we analyzed the adipokine profile and the expression of genes involved in inflammation and extracellular matrix remodeling in visceral adipose tissue (VAT; n = 82) and liver (n = 55).

Results: The cardiometabolic and inflammatory profiles (CRP, fibrinogen, uric acid, leukocyte count, and hepatic enzymes) were similarly increased in MHO and MAO in both cohorts. Moreover, above 30%of patients classified as MHO according to fasting plasma glucose exhibited IGT or T2D [corrected]. The profile of classic (leptin, adiponectin, resistin) as well as novel (serum amyloid A and matrix metallopeptidase 9) adipokines was almost identical in MHO and MAO groups in cohort 2. Expression of genes involved in inflammation and tissue remodeling in VAT and liver showed a similar alteration pattern in MHO and MAO individuals.

Conclusions: The current study provides evidence for the existence of a comparable adverse cardiometabolic profile in MHO and MAO patients; thus the MHO concept should be applied with caution. A better identification of the obesity phenotypes and a more precise diagnosis are needed for improving the management of obese individuals.



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.


2011

"These individuals, now known as 'metabolically healthy but obese' (MHO), despite having excessive body fatness, display a favorable metabolic profile characterized by high levels of insulin sensitivity, no hypertension as well as a favorable lipid, inflammation, hormonal, liver enzyme and immune profile. However, recent studies have indicated that this healthier metabolic profile may not translate into a lower risk for mortality. "

Review
Characterizing the profile of obese patients who are metabolically healthy.
Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Messier V, Sladek R, Rabasa-Lhoret R.
Int J Obes (Lond). 2011 Jul;35(7):971-81. doi: 10.1038/ijo.2010.216. Epub 2010 Oct 26.
PMID: 20975726
Abstract

The presence of obesity-related metabolic disturbances varies widely among obese individuals. Accordingly, a unique subset of obese individuals has been described in the medical literature, which seems to be protected or more resistant to the development of metabolic abnormalities associated with obesity. These individuals, now known as 'metabolically healthy but obese' (MHO), despite having excessive body fatness, display a favorable metabolic profile characterized by high levels of insulin sensitivity, no hypertension as well as a favorable lipid, inflammation, hormonal, liver enzyme and immune profile. However, recent studies have indicated that this healthier metabolic profile may not translate into a lower risk for mortality. Mechanisms that could explain the favorable metabolic profile of MHO individuals are poorly understood. However, preliminary evidence suggests that differences in visceral fat accumulation, birth weight, adipose cell size and gene expression-encoding markers of adipose cell differentiation may favor the development of the MHO phenotype. Despite the uncertainty regarding the exact degree of protection related to the MHO status, identification of underlying factors and mechanisms associated with this phenotype will eventually be invaluable in helping us understand factors that predispose, delay or protect obese individuals from metabolic disturbances. Collectively, a greater understanding of the MHO individual has important implications for therapeutic decision making, the characterization of subjects in research protocols and medical education.
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Re: Metabolically Healthy Obese

Postby JeffN » Tue Nov 12, 2019 7:01 am

The above post was updated with a few more newer studies, which are highlighted with ***

After adding the new studies, the post became to large for a single post and so had to split in in two.

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Jeff

A few more recent ones on the health consequence of obesity

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.i2156
http://www.bmj.com/content/bmj/353/bmj.i2156.full.pdf

Abstract

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.pdf

Abstract

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.pdf

Significance

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.

For more on the topic see the following threads

Optimum BMI
viewtopic.php?f=22&t=6916

Optimal BMI - Redux
viewtopic.php?f=22&t=52375

Difference in food choices and BMI
viewtopic.php?f=22&t=46837

Should we all try to get to 22 BMI?
viewtopic.php?f=22&t=30838

Should we all try to get to 22 BMI Pt 2
viewtopic.php?f=22&t=43218

In Health
Jeff
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Re: Metabolically Healthy Obese

Postby JeffN » Wed Sep 02, 2020 11:40 am

Metabolically healthy overweight/obesity are associated with increased risk of cardiovascular disease in adults, even in the absence of metabolic risk factors: A systematic review and meta-analysis of prospective cohort studies.
Obes Rev. 2020 Sep 1.
doi: 10.1111/obr.13127.
Online ahead of print.PMID: 32869512

Review.

Abstract
This review examined the risk of cardiovascular disease in adults with metabolically healthy overweight/obesity. A systematic review and meta-analysis using data from Medline, EMBASE, SCOPUS and Cochrane Library searched from inception up to 31st October 2019. We included prospective cohort studies of adults who are metabolically healthy or unhealthy. Outcomes were fatal and nonfatal cardiovascular events, all-cause mortality. Pooled relative risk was calculated for each outcome in populations with metabolically healthy overweight and metabolically healthy obesity using metabolically healthy normal weight as reference. A random-effects model was used for meta-analysis, and risk of bias assessment tool for nonrandomized studies assessed risk of bias within each study. Twenty-three prospective cohort studies with 4,492,723 participants were included. Cardiovascular disease risk was increased in metabolically healthy groups with overweight (RR = 1.34, CI: 1.23-1.46, n = 20, I2 = 90.3%) and obesity (RR = 1.58, CI: 1.34-1.85, n = 21, I2 = 92.2) compared with a reference group with metabolically healthy normal weight. Cardiovascular disease risk was similar irrespective of the number of risk factors used to define metabolically healthy and the risk remained in the group with no metabolic risk factors. Cardiovascular disease risk is increased in populations with overweight and obesity classified as metabolically healthy even when there were no metabolic risk factors.
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Re: Metabolically Healthy but Obese

Postby JeffN » Thu Jun 24, 2021 8:29 am

I have been in conversation with the producer of NPR's 1A since 5/5/21 about doing a show on this. The reason is, they did a show that day on the "fat acceptance" movement and had no experts on but 2 people from the movement. And when asked about health issue with obesity, they said, the issue has been resolved and there was no health issue. I told her I agree on all the discrimination/bias issues but the health issue is far from being resolved and most of them will progress to serious disease.

https://the1a.org/segments/fat-acceptance-bias/



Are people with metabolically healthy obesity really healthy? A prospective cohort study of 381,363 UK Biobank participants
Diabetologia. Received: 11 January 2021 / Accepted: 19 March 2021 # The Author(s) 2021

Abstract

Aims/hypothesis People with obesity and a normal metabolic profile are sometimes referred to as having ‘metabolically healthy obesity’ (MHO). However, whether this group of individuals are actually ‘healthy’ is uncertain. This study aims to examine the associations of MHO with a wide range of obesity-related outcomes.

Methods This is a population-based prospective cohort study of 381,363 UK Biobank participants with a median follow-up of 11.2 years. MHO was defined as having a BMI ≥ 30 kg/m2 and at least four of the six metabolically healthy criteria. Outcomes included incident diabetes and incident and fatal atherosclerotic CVD (ASCVD), heart failure (HF) and respiratory diseases. Results Compared with people who were not obese at baseline, those with MHO had higher incident HF (HR 1.60; 95% CI 1.45, 1.75) and respiratory disease (HR 1.20; 95% CI 1.16, 1.25) rates, but not higher ASCVD. The associations of MHO were generally weaker for fatal outcomes and only significant for all-cause (HR 1.12; 95% CI 1.04, 1.21) and HF mortality rates (HR 1.44; 95% CI 1.09, 1.89). However, when compared with people who were metabolically healthy without obesity, participants with MHO had higher rates of incident diabetes (HR 4.32; 95% CI 3.83, 4.89), ASCVD (HR 1.18; 95% CI 1.10, 1.27), HF (HR 1.76; 95% CI 1.61, 1.92), respiratory diseases (HR 1.28; 95% CI 1.24, 1.33) and all-cause mortality (HR 1.22; 95% CI 1.14, 1.31). The results with a 5 year landmark analysis were similar.

Conclusions/interpretation Weight management should be recommended to all people with obesity, irrespective of their meta- bolic status, to lower risk of diabetes, ASCVD, HF and respiratory diseases. The term ‘MHO’ should be avoided as it is misleading and different strategies for risk stratification should be explored.

https://link.springer.com/content/pdf/1 ... 5484-6.pdf
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Re: Metabolically Healthy but Obese

Postby JeffN » Mon Mar 14, 2022 8:29 am

Published: 15 February 2022
Obesity, metabolic risk and adherence to healthy lifestyle behaviours: prospective cohort study in the UK Biobank
BMC Medicine volume 20, Article number: 65 (2022) Cite this article

Abstract

Background
Contested evidence suggests that obesity confers no risk to health in people who have a healthy lifestyle, particularly if there are no metabolic complications of obesity. The aim was to examine the association between adherence to lifestyle recommendations and the absence of metabolic complications on the incident or fatal cardiovascular disease and all-cause mortality across different categories of body mass index (BMI).

Methods
This contemporary prospective cohort study included 339,902 adults without cardiovascular disease at baseline, recruited between 2006 and 2010 from the UK Biobank and followed until 2018–2020. The main exposures were four healthy lifestyle behaviours: never smoker, alcohol intake ≤ 112g/ week, 150 min moderate physical activity or 75 min vigorous activity/week, ≥ 5 servings of fruit or vegetables/day, and we assessed these overall and across the BMI groups. Metabolic complications of excess adiposity were hypertension, diabetes and hyperlipidaemia, and we examined whether obesity was associated with increased risk in the absence of these complications. The outcomes were all-cause mortality, death from, and incident cardiovascular disease (CVD).

Results
Individuals who met four lifestyle recommendations but had excess weight had higher all-cause mortality; for BMI 30–34.9 kg/m2, the hazard ratio (HR) was 1.42 (95% confidence interval 1.20 to 1.68), and for BMI ≥ 35 kg/m2, HR was 2.17 (95% CI 1.71 to 2.76). The risk was lower, but still increased for people with no metabolic complications; for all-cause mortality, BMI 30–34.9 kg/m2 had an HR of 1.09 (95% CI 0.99 to 1.21), and BMI ≥ 35 kg/m2 had an HR of 1.44 (95% CI 1.19 to 1.74) for all-cause mortality. Similar patterns were found for incident and fatal CVD.

(Abstract) Conclusions
Meeting healthy lifestyle recommendations, or the absence of metabolic complications of obesity offsets some, but not all, of the risk of subsequent CVD, and premature mortality in people with overweight or obesity. Offering support to achieve and maintain a healthy weight and to adopt healthy behaviours are likely to be important components in effective preventative healthcare.


(Article) Conclusions
In conclusion, this analysis shows that adopting healthy lifestyle recommendations is of significant benefit to all, including those with a healthy weight. However, it is insufficient to offset all of the cardiovascular risk and premature mortality associated with excess weight, irrespective of whether there was evidence of metabolic complications of obesity. Policies that support the whole population to achieve and maintain a healthy weight even for people with obesity who meet healthy lifestyle recommendations or those with no metabolic complications are likely to be beneficial.
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Re: Metabolically Healthy but Obese

Postby JeffN » Wed Apr 12, 2023 5:39 am

COMMENTARY
Mar 17, 2023
This Week in Cardiology Podcast
John M. Mandrola, MD

Metabolically Healthy but Obese

JAMA Network Open has published a survey of NHANES data from 1999-2018 looking at the prevalence of metabolically healthy obese patients.

This was defined as a BMI greater than 30, but no metabolic disorders, such as hypertension, blood glucose, or lipid issues.

Their cross-sectional study suggests that the age-standardized proportion of metabolically healthy obesity increased among US adults from 1999 to 2018.
Absolute numbers went from 10.6 to 15% in 2015-2018.

Here is my problem with this: I don’t think the words healthy and obese belong in the same category. The mean age of these patients were mid 40s.

What do you think will happen to these patients in their mid-50s or mid 60s.

This is what I tell patients who are overweight. You may be skirting issues now, but to preserve quality of life in the future, not just cardiac health, but bone and joint health (mobility), we should work on strategies now.

I think we do a great disservice to patients with high BMIs, by declaring them “metabolically healthy”.

I always start my discussion by saying I am not a preacher, and I worship at the altar of liberty, and I recoil against all forms of coercion, but as your health adviser, I would encourage you to consider these strategies. Because, regardless, of your labs, having a BMI of 33 puts you at risk for future problems.
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