You know my response...
1) In regard to any new study, especially the media reports and/or abstracts of it..
http://www.jeffnovick.com/RD/Newsletter ... News!.html2) Nothing from this one analysis has changed my perspective on BMI...
viewtopic.php?f=22&t=69163) Having said that, it is already being highly criticized for its flaws. We already know from numerous previous reports/studies that the "obesity paradox" or the "J-shaped BMI-mortality curve" seen in most epidemiological studies is greatly reduced or even eliminated when you eliminate ever-smokers, and isn't when you just adjust for them statistically; which the researcher didn't do.
The same researcher was criticized for their similar findings in 2005 when they including smokers and people with health problems ranging from cancer to heart disease who tend to weigh less and skew the results of those who are in the lower range of BMI
This time, they included people too thin to fit what some consider to be normal weight, which could have taken in people emaciated by cancer or other diseases, as well as smokers with elevated risks of heart disease and cancer.
Some other criticisms of the new study..
"Some portion of those thin people are actually sick, and sick people tend to die sooner," said Donald Berry, a biostatistician at the University of Texas MD Anderson Cancer Center in Houston."
The problems created by the study's inclusion of smokers and people with pre-existing illness "cannot be ignored," said Susan Gapstur, vice president of epidemiology for the American Cancer Society."
From Dr. Walter Willett of the Harvard School of Public Health, who has done research since the 2005 study that found higher death risks from being overweight or obese: "This is an even greater pile of rubbish than the 2005 study."
Willet also said that it's not helpful to look simply at how peoples BMIs, body mass index, influence their risk of death — as this paper did without knowing something about people's health or fitness. "Some people are thin because they're ill, so of course they're at higher risk of dying. The study doesn't tease this apart."
Also, on a related but not exactly the same topic.....
This study has revived the criticisms of BMI. These criticisms are mostly done by people who do not understand BMI or are using it in a manner it was not intended to be used. For anyone who really wants to understand BMI and its value, here is the WHO paper on it.
"Physical Status: The Use and Interpretation of Anthropometry".
WHO Technical Report Series 854: 9.
http://whqlibdoc.who.int/trs/WHO_TRS_854.pdfBMI is a simple measure that is used as a marker and screening tool. That is it. It is not a clinical diagnostic tool.
You can't fault a pair of binoculars for not being a microscope.
However, when used as intended, as the 3 recent studies show, BMI is quite effective.
Jeff
1) "Using BMI instead of cholesterol in CVD risk prediction models may provide more accurate estimates. "
Research Letters | Dec 10/24, 2012
Body Mass Index vs Cholesterol in Cardiovascular Disease Risk Prediction Models
David Faeh; Julia Braun; Matthias Bopp
Arch Intern Med. 2012;172(22):1766-1768.
http://archinte.jamanetwork.com/article ... ID=1391006Article
Traditional modifiable risk factors for cardiovascular disease (CVD) are smoking, high blood pressure, and unfavorable blood lipid concentrations. Models combining these factors predict CVD more accurately than models considering CVD risk factors in an isolated manner.1- 3 Combined risk prediction models include the Framingham Risk Score or, from Europe, the SCORE (Systematic Coronary Risk Evaluation).1- 2 One disadvantage of these assessments is that they require blood sampling for lipid measurements. This precludes the estimation of the 10-year risk of a CVD event, eg, from self-reports. In electronic health records, the lack of information on cholesterol was the most common reason why CVD risk could not be calculated.4 In contrast, body height and weight are available in virtually all health data sets. On the basis of the SCORE method and using a population sample from Switzerland, we aimed at comparing the traditional prediction model using total cholesterol with a version in which we replaced cholesterol with body mass index (BMI).1
http://www.escardio.org/communities/EAC ... s-2012.pdfEuropean guidelines on cardiovascular disease prevention in clinical practice (version 2012) : the fifth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts).
Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, Verschuren WM, Albus C, Benlian P, Boysen G, Cifkova R, Deaton C, Ebrahim S, Fisher M, Germano G, Hobbs R, Hoes A, Karadeniz S, Mezzani A, Prescott E, Ryden L, Scherer M, Syvänne M, Op Reimer WJ, Vrints C, Wood D, Zamorano JL, Zannad F; Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR).
Int J Behav Med. 2012 Dec;19(4):403-88. doi: 10.1007/s12529-012-9242-5. No abstract available.
PMID:23093473
http://www.escardio.org/guidelines-surv ... ention.pdfBody Mass Index vs Cholesterol in Cardiovascular Disease Risk Prediction Models
David Faeh, MD, MPH; Julia Braun, MSc; Matthias Bopp, PhD, MPH
Arch Intern Med. 2012;172(22):1766-1768.
Traditional modifiable risk factors for cardiovascular disease (CVD) are smoking, high blood pressure, and unfavorable blood lipid concentrations. Models combining these factors predict CVD more accurately than models considering CVD risk factors in an isolated manner.1- 3 Combined risk prediction models include the Framingham Risk Score or, from Europe, the SCORE (Systematic Coronary Risk Evaluation).1- 2 One disadvantage of these assessments is that they require blood sampling for lipid measurements. This precludes the estimation of the 10-year risk of a CVD event, eg, from self-reports. In electronic health records, the lack of information on cholesterol was the most common reason why CVD risk could not be calculated.4 In contrast, body height and weight are available in virtually all health data sets. On the basis of the SCORE method and using a population sample from Switzerland, we aimed at comparing the traditional prediction model using total cholesterol with a version in which we replaced cholesterol with body mass index (BMI).1
Methods
Risk factor data stem from 17 791 men and women older than 16 years who participated in either of 2 CVD studies: the National Research Program 1A (NRP1A), a community health promotion initiative focused on CVD prevention, and the Swiss MONICA (Monitoring of Trends and Determinants in Cardiovascular Disease) population survey, an international project of the World Health Organization. We obtained mortality follow-up by anonymously linking the data from the CVD studies with the Swiss National Cohort (SNC), which encompasses all residents of Switzerland enumerated in the national 1990 or 2000 censuses as well as data from death and emigration registries until the end of 2008. Linkage success was 94% (NRP1A) and 97% (MONICA). The 95th percentile of follow-up was 31.2 years, during which 2170 men and 1761 women died (749 and 630 from CVD, respectively).5- 6
Methods
Blood sampling and cholesterol measurement were described.5- 6 Body mass index was calculated from measured (without shoes) height and weight (calculated as weight in kilograms divided by height in meters squared). We defined smoking as smoking 1 cigarette or more per day. Nonsmokers include former and never smokers. Systolic blood pressure was recorded as the mean of 2 measurements. Fatal CVD events were defined according to the Eighth Revision International Classification of Diseases codes 390 to 458 (until 1994) and International Statistical Classification of Diseases, 10th Revision codes I00 to I99.
Methods
Risk models were calculated with Weibull proportional hazards regression as previously described.1 To compare the prediction abilities of the cholesterol and BMI model, we calculated the mean cross-validated (leave-one-out) Brier score,7 which measures the mean squared difference between the risk score and the actual outcome. The lower the difference, the better the respective risk prediction model. The Brier score covers both calibration and sharpness of a prediction model.7
RESULTS.
Compared with cholesterol (eFigure), the BMI model (Figure) showed higher risks at all ages and could better discriminate persons at high and low CVD risk. Moreover, the synergistic effects in combination with smoking and in particular with blood pressure were stronger than with cholesterol. Body mass index, but not cholesterol, was significantly associated with mortality. The prediction ability of BMI was better based on the lower Brier score (eTable 1). Because explanatory variables (age, sex, smoking, and blood pressure) other than BMI or cholesterol remained the same in the 2 models, the difference between the Brier scores was small. In a common model with cholesterol, BMI remained significant, while cholesterol did not (eTable 2). Thus, cholesterol did not contribute to the explanation of the association between risk factors and mortality when BMI was included in the same model.
Figure. Absolute 10-year risk of fatal cardiovascular disease (CVD) based on the model using body mass index (BMI). Each risk percentage is calculated using a combination of given risk factor values (eg, a man aged 60 years, who is a smoker and has a systolic blood pressure of 180 and a BMI of 35 [calculated as weight in kilograms divided by height in meters squared], has an absolute risk for fatal CVD of 4%). NRP1A indicates National Research Program 1A; MONICA, Monitoring of Trends and Determinants in Cardiovascular Disease.
COMMENT.
Using BMI instead of cholesterol in CVD risk prediction models may provide more accurate estimates. Traditional models such as Framingham or SCORE include cholesterol or total to high-density lipoprotein cholesterol ratio but do not consider BMI in their equation.1- 2 In line with our results, Green et al4 found that using BMI instead of cholesterol allowed at least equivalent CVD risk estimation based on electronic health records and that the use of BMI could reduce unnecessary laboratory testing. The fact that BMI renders blood sampling unnecessary leads to a substantial increase of population-based samples available for CVD risk estimation. The use of BMI may not only ease CVD risk assessment but could have further advantages. Compared with dyslipidemia screening, screening for obesity has a stronger scientific foundation and is unconditionally recommended.4 Furthermore, lifestyle changes (diet and physical activity) promoting weight loss or preventing weight gain may improve health more strongly than lipid-lowering treatment. In contrast, knowledge of cholesterol may not lead to behavioral changes, and there are also doubts concerning the effectiveness and safety of statin treatment for primary prevention of CVD.4,8
In conclusion, our results suggest that BMI may be a valuable alternative to cholesterol in CVD risk prediction models. This finding needs to be validated in other populations.
2) "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."
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.
3) Comparison of anthropometric and body composition measures as predictors of components of the metabolic syndrome in a clinical setting
Obesity Research & Clinical Practice, Available online 19 November 2012
http://dx.doi.org/10.1016/j.orcp.2012.10.004Summary
Problem
The use of body mass index (BMI) to assess obesity and health risks has been criticized in scientific and lay publications because of its failure to account for body shape and inability to distinguish fat mass from lean mass. We sought to determine whether other anthropometric measures (waist circumference (WC), waist-to-height ratio (WtH), percent body fat (%BF), fat mass index (FMI), or fat-free mass index (FFMI)) were consistently better predictors of components of the metabolic syndrome than BMI is.
Methods
Cross-sectional measurements of height, weight, waist circumference and percent body fat were obtained from 12,294 adults who took part in annual physical exams provided by EHE International, Inc. Blood pressure was measured during the exam and HDL, LDL, and fasting glucose were measured from blood samples. Pearson correlations, linear regression, and adjusted Receiver Operator Characteristic (ROC) curves were used to relate each anthropometric measure to each metabolic risk factor.
Results
None of the measures was consistently the strongest predictor. BMI was the strongest predictor of blood pressure, measures related to central adiposity (WC and WtH) performed better at predicting fasting glucose, and all measures were roughly comparable at predicting cholesterol levels. In all, differences in areas under ROC curves were 0.03 or less for all measure/outcome pairs that performed better than BMI.
Conclusion
Body mass index is an adequate measure of adiposity for clinical purposes. In the context of lay press critiques of BMI and recommendations for alternative body-size measures, these data support clinicians making recommendations to patients based on BMI measurements.
