Odds ratios (ORs) are widely used in scientific research to demonstrate associations between outcome variables and covariates (risk factors) of interest and are often described in language suitable for risks or probabilities, but odds and probabilities are related, not equivalent. In situations where the outcome is not rare (e.g., obesity), ORs no longer approximate the relative risk ratio and may be misinterpreted. Our study examines the extent of misinterpretation of ORs in Obesity and International Journal of Obesity. We reviewed all 2010 issues of these journals to identify all articles that presented ORs. Included articles were then primarily reviewed for correct presentation and interpretation of ORs; and secondarily reviewed for article characteristics that may have been associated with how ORs are presented and interpreted. Of the 855 articles examined, 62 (7.3%) presented ORs. ORs were presented incorrectly in 23.2% of these articles. Clinical articles were more likely to present ORs correctly than social science or basic science articles. Studies with outcome variables that had higher relative prevalence were less likely to present ORs correctly. Overall, almost a quarter of the studies presenting ORs in two leading journals on obesity misinterpreted them. Furthermore, even when researchers present ORs correctly, the lay media may misinterpret them as relative risk ratios. Therefore, we suggest that when the magnitude of associations is of interest, researchers should carefully and accurately present interpretable measures of association -- including risk ratios and risk differences -- to minimize confusion and misrepresentation of research results.

Objective

To estimate fruit and vegetable (FV) intake levels of US adult population and evaluate the association between FV intake and BMI status after controlling for confounding demographic, socioeconomic and lifestyle factors. We also sought to identify moderating factors.

Methods

We used 2007 Behavior Risk Factors Surveillance System (N > 400,000) data. FV intake was dichotomized as ≥5 servings (FV5+) versus <5 servings/day. BMI status was categorized as normal, overweight, and obese. Identification of moderators was performed by testing interactions between BMI status and other variables using bivariate analyses followed by multiple logistic regression analysis incorporating complex survey sampling design features.

Results

Only 24.6% of US adults consumed ≥5 servings per day and less than 4% consumed 9 or more servings. Overweight (% FV5+ = 23.9%) and obese (21.9%) groups consumed significantly less FV than the normal-weight (27.4%) group (p < 0.0001). This inverse association remained significant even after controlling for potential confounding factors. Multivariate analysis identified five significant moderators (p < 0.0001) after controlling for all evaluated variables: race, sex, smoking status, health coverage, and physical activity. Notably, physically inactive obese males tended to consume the least FV (% FV5+ = 14.7%).

Conclusion

Current US population FV intake level is below recommended levels. The inverse association between FV intake and obesity was significant and was moderated by demographic, socioeconomic status, and lifestyle factors. These factors should be considered when developing policies and interventions to increase FV intake.

Objective

Randomized controlled trials (RCTs) in obesity are plagued by missing data due to participant drop-outs. Most methodologists and regulatory bodies agree that the primary analysis of such RCTs should be based on the intent-to-treat (ITT) principle, such that all randomized subjects are included in the analysis, even those who dropped out. Unfortunately, some authors do not include an ITT analysis in their published reports. Here we show that one form of ITT analysis, baseline observation carried forward (BOCF), can be performed utilizing only information available in a published complete case (CC) analysis, permitting readers, editors, meta-analysts, and regulators to easily conduct their own ITT analyses when the original authors do not report one.

Method

We mathematically derive a simple method for estimating and testing treatment effects using the BOCF to allow a more conservative comparison of treatment effects when there are drop outs in a clinical trial. We provide two examples of this method using available CC analysis data from reported obesity trials to illustrate the application for readers who wish to determine a range of treatment effects based on published summary statistics.

Conclusion

Commonly used CC analyses may lead to inflated Type I error rates and/or treatment effect estimates. The method described herein can be useful for researchers who wish to estimate a conservative range of plausible treatment effects based on limited reported data. Limitations of this method are discussed.

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.

The role of telomere attrition in limiting the replicative capacity of cells in culture is well established. In humans, epidemiologic evidence suggests telomere length (TL) in leukocytes is highly variable at birth and inversely related to age. Although calorie restriction (CR) significantly increases life span in most rodent models, its association with TL is unknown. Using linear regression analysis, TLs (as measured by Southern blot analysis) of skeletal muscle (a postmitotic tissue that largely represents early development TL), fat, leukocytes, and skin were tested for effects of age, sex, and diet in 48 control and 23 calorie restriction rhesus monkeys. After controlling for the individual's muscle mean TL, differences between leukocytes muscle and skin muscle were significantly associated with age (p = .002; p = .002) and sex (p = .003; p = .042), but not calorie restriction (p = .884; p = .766). Despite an age-dependent shortening of TL in leukocytes and skin, calorie restriction did not significantly affect TL dynamics in these samples.

Much has been written regarding p-values below certain thresholds (most notably 0.05) denoting statistical significance and the tendency of such p-values to be more readily publishable in peer-reviewed journals. Intuition suggests that there may be a tendency to manipulate statistical analyses to push a “near significant p-value” to a level that is considered significant. This article presents a method for detecting the presence of such manipulation (herein called “fiddling”) in a distribution of p-values from independent studies. Simulations are used to illustrate the properties of the method. The results suggest that the method has low type I error and that power approaches acceptable levels as the number of p-values being studied approaches 1000.

Objective

Current clinical guidelines and public health statements generically prescribe body mass index (BMI; kgm2) categories regardless of the individual’s situation (age, risk for diseases, etc.). However, regarding BMI and mortality rate (MR), two well-established observations are (1) there is a U-shaped (i.e., concave) association - people with intermediate BMIs tend to outlive people with higher or lower BMIs; and (2) the nadirs of these curves tend to increase monotonically with age. Multiple hypotheses have been advanced to explain either of these two observations. Here we introduce a new hypothesis that may explain both phenomena, by drawing on the so-called obesity paradox: the unexpected finding that obesity is often associated with increased survival time among people who have some serious injury or illness despite being associated with reduced survival time among the general population.

Results

We establish that the obesity paradox offers one potential explanation for two curious but consistently observed phenomena in the obesity field.

Conclusion

Further research is needed to determine the extent to which the obesity paradox is actually an explanation for these phenomena, but if our hypothesis proves true the common practice of prescribing overweight patients to lower their BMI should currently be applied with caution. In addition, the statistical modeling technique employed here could be applied in such other areas involving survival analysis of disjoint subgroups, in order to explain possible interacting causal associations and to determine clinical practice.

In many circumstances, individuals do not respond identically to the same treatment. This phenomenon, which is called treatment response heterogeneity (TRH), appears to be present in treatments for many conditions, including obesity. Estimating the total amount of TRH, predicting an individual’s response, and identifying the mediators of TRH are of interest to biomedical researchers. Clinical investigators and physicians commonly postulate that some of these mediators could be genetic. Current designs can estimate TRH as a function of specific, measurable observed factors; however, they cannot estimate the total amount of TRH, nor provide reliable estimates of individual persons’ responses. We propose a new repeated randomizations design (RRD), which can be conceived as a generalization of the Balaam design, that would allow estimates of that variability and facilitate estimation of the total amount of TRH, prediction of an individual’s response, and identification of the mediators of TRH. In a pilot study, we asked 118 subjects entering a weight loss trial for their opinion of the RRD, and they stated a preference for the RRD over the conventional two-arm parallel groups design. Research is needed as to how the RRD will work in practice and its relative statistical properties, and we invite dialog about it.

Genetic factors are believed to account for 25% of the interindividual differences in Years of Life (YL) among humans. However, the genetic loci that have thus far been found to be associated with YL explain a very small proportion of the expected genetic variation in this trait, perhaps reflecting the complexity of the trait and the limitations of traditional association studies when applied to traits affected by a large number of small-effect genes. Using data from the Framingham Heart Study and statistical methods borrowed largely from the field of animal genetics (whole-genome prediction, WGP), we developed a WGP model for the study of YL and evaluated the extent to which thousands of genetic variants across the genome examined simultaneously can be used to predict interindividual differences in YL. We find that a sizable proportion of differences in YL—which were unexplained by age at entry, sex, smoking and BMI—can be accounted for and predicted using WGP methods. The contribution of genomic information to prediction accuracy was even higher than that of smoking and body mass index (BMI) combined; two predictors that are considered among the most important life-shortening factors. We evaluated the impacts of familial relationships and population structure (as described by the first two marker-derived principal components) and concluded that in our dataset population structure explained partially, but not fully the gains in prediction accuracy obtained with WGP. Further inspection of prediction accuracies by age at death indicated that most of the gains in predictive ability achieved with WGP were due to the increased accuracy of prediction of early mortality, perhaps reflecting the ability of WGP to capture differences in genetic risk to deadly diseases such as cancer, which are most often responsible for early mortality in our sample.

Background: The genetic predisposition to obesity may have contributed to the obesity epidemic through assortative mating. We investigated whether spouses were positively assorted by body mass index (BMI; = kg/m2) in late childhood, and whether changes in assorted marriage by upper BMI-percentiles occurred during the obesity epidemic. Methods: In the Copenhagen School Health Records Register (CSHRR) boys and girls with measures of BMI at age 13 years later became 37,792 spousal-pairs who married between 1945 and 2010. Trends in the spousal BMI correlations using sex-, age-, and birth cohort-specific BMI z-scores across time were investigated. Odds ratios (ORs) of marriage among spouses both with BMI z-scores >90th or >95th percentile compared with marriage among spouses ≤90th percentile were analyzed for marriages entered during the years prior to (1945–1970), and during the obesity epidemic (1971–2010). Findings: Spousal BMI correlations were around 0.05 and stayed similar across time. ORs of marriage among spouses with BMIs >90th percentile at age 13 were 1.21, 1.05–1.39, in 1945–1970, and increased to 1.63, 1.40–1.91, in 1971–2010 (p = 0.006). ORs of marriage among spouses both >95th BMI percentile were higher and increased more; from 1.39, 1.10–1.81, to 2.39, 1.85–3.09 (p = 0.004). Interpretation: Spousal correlations by pre-marital BMIs were small and stable during the past 65 years. Yet, there were assorted marriages between spouses with high BMI at age 13 years and the tendency increased alongside with the obesity epidemic which may increase the offsprings' predisposition to obesity.

Although offering many benefits for several psychiatric disorders, antipsychotic drugs (APDs) as a class have a major liability in their tendency to promote adiposity, obesity, and metabolic dysregulation in an already metabolically vulnerable population. The past decade has witnessed substantial research aimed at investigating the mechanisms of these adverse effects and mitigating them. On July 11 and 12, 2011, with support from 2 NIH institutes, leading experts convened to discuss current research findings and to consider future research strategies. Five areas where significant advances are being made emerged from the conference: (1) methodological issues in the study of APD effects; (2) unique characteristics and needs of pediatric patients; (3) genetic components underlying susceptibility to APD-induced metabolic effects; (4) APD effects on weight gain and adiposity in relation to their acute effects on glucose regulation and diabetes risk; and (5) the utility of behavioral, dietary, and pharmacological interventions in mitigating APD-induced metabolic side effects. This paper summarizes the major conclusions and important supporting data from the meeting.

Negatively skewed data arise occasionally in statistical practice; perhaps the most familiar example is the distribution of human longevity. Although other generalizations of the normal distribution exist, we demonstrate a new alternative that apparently fits human longevity data better. We propose an alternative approach of a normal distribution whose scale parameter is conditioned on attained age. This approach is consistent with previous findings that longevity conditioned on survival to the modal age behaves like a normal distribution. We derive such a distribution and demonstrate its accuracy in modeling human longevity data from life tables. The new distribution is characterized by 1. An intuitively straightforward genesis; 2. Closed forms for the pdf, cdf, mode, quantile, and hazard functions; and 3. Accessibility to non-statisticians, based on its close relationship to the normal distribution.

Nutritively sweetened beverages (NSBs) may play a role in the obesity epidemic. We abstracted data from randomized controlled trials (RCTs) and evidence-based reviews through January 2009 concerning effects of consumption of NSBs on changes in body weight and adiposity. Studies included were those 1) conducted in humans; 2) lasting at least 3 weeks; 3) incorporating random assignment of subjects to conditions that differed only in the consumption of NSBs; and 4) including an adiposity indicator as an outcome. Twelve studies met the inclusion criteria. Meta-analysis of 6 studies that added NSBs to persons’ diets showed dose-dependent increases in weight. Contrarily, meta-analysis of studies that attempted to reduce NSB consumption consistently showed no effect on BMI when all subjects were considered. Meta-analysis of studies providing access to results separately for subjects overweight at baseline showed a significant effect of a roughly 0.35 standard deviations lesser BMI change (i.e., more weight loss or less weight gain) relative to controls. The current evidence does not demonstrate conclusively that NSB consumption has uniquely contributed to obesity or that reducing NSB consumption will reduce BMI levels in general. We recommend an adequately powered RCT among overweight persons, among whom there is suggestive evidence of an effect.

Many rodent experiments have assessed effects of diets, drugs, genes, and other factors on life span. A challenge with such experiments is their long duration, typically over 3.5 years given rodent life spans, thus requiring significant time costs until answers are obtained. We collected longevity data from 15 rodent studies and artificially truncated them at 2 years to assess the extent to which one will obtain the same answer regarding mortality effects. When truncated, the point estimates were not significantly different in any study, implying that in most cases, truncated studies yield similar estimates. The median ratio of variances of coefficients for truncated to full-length studies was 3.4, implying that truncated studies with roughly 3.4 times as many rodents will often have equivalent or greater power. Cost calculations suggest that shorter studies will be more expensive but perhaps not so much to not be worth the reduced time.

Background

Obesity and motor vehicle crash (MVC) injuries are two parallel epidemics in the United States. An important unanswered question is if there are sex differences in the associations between the presence of obesity and non-fatal MVC injuries.

Objectives

To further understand the association between obesity and non-fatal motor vehicle crash injuries, particularly the sex differences in these relations.

Methods

We examined this question by analyzing data from the 2003 to 2007 National Automotive Sampling System Crashworthiness Data System (NASS CDS). A total of 10, 962 drivers who were aged 18 years or older and who survived frontal collision crashes were eligible for study.

Results

Male drivers experienced a lower rate of overall non-fatal MVC injuries than did female drivers (38.1% vs. 52.2%) but a higher rate of severe injuries (0.7% vs. 0.2%). After adjusting for change in velocity (ΔV) during the crashes, obese male drivers showed a much higher risk [logistic coefficients of BMI for moderate, serious, and severe injury are 0.0766, 0.1470, and 0.1792, respectively; all p<0.05] of non-fatal injuries than did non-obese male drivers and these risks increased with injury severity. Non-fatal injury risks were not found to be increased in obese female drivers. The association between obesity and risk of non-fatal injury was much stronger for male drivers than for female drivers.

Conclusion

The higher risk of non-fatal MVC injuries in obese male drivers might result from their different body shape and fat distribution compared with obese female drivers. Our findings should be considered for obesity reduction, traffic safety evaluation and vehicle design for obese male drivers and provide testable hypotheses for future studies.

Background

The association between waist circumference (WC) and mortality is particularly strong and direct when adjusted for body mass index (BMI). One conceivable explanation for this association is that WC adjusted for BMI is a better predictor of the presumably most harmful intra-abdominal fat mass (IAFM) than WC alone. We studied the prediction of abdominal subcutaneous fat mass (ASFM) and IAFM by WC alone and by addition of BMI as an explanatory factor.

Methodology/Principal Findings

WC, BMI and magnetic resonance imaging data from 742 men and women who participated in clinical studies in Canada and Finland were pooled. Total adjusted squared multiple correlation coefficients (R2) of ASFM and IAFM were calculated from multiple linear regression models with WC and BMI as explanatory variables. Mean BMI and WC of the participants in the pooled sample were 30 kg/m2 and 102 cm, respectively. WC explained 29% of the variance in ASFM and 51% of the variance in IAFM. Addition of BMI to WC added 28% to the variance explained in ASFM, but only 1% to the variance explained in IAFM. Results in subgroups stratified by study center, sex, age, obesity level and type 2 diabetes status were not systematically different.

Conclusion/Significance

The prediction of IAFM by WC is not improved by addition of BMI.

`White hat bias' (WHB) (bias leading to distortion of information in the service of what may be perceived to be righteous ends) is documented via quantitative data and anecdotal evidence from the research record regarding the postulated predisposing and protective effects respectively of nutritively-sweetened beverages and breastfeeding on obesity. Evidence of an apparent WHB is found in a degree sufficient to mislead readers. WHB bias may be conjectured to be fuelled by feelings of righteous zeal, indignation toward certain aspects of industry, or other factors. Readers should beware of WHB and our field should seek methods to minimize it.

Objectives

This study examined the association of physical fitness, as assessed by ability and time to complete a 400-meter walk, on changes in body composition and muscle strength over a subsequent 7-year period.

Design

Prospective observational cohort study

Setting

Memphis, Tennessee and Pittsburgh, Pennsylvania

Participants

2,949 black and white men and women aged 70-79 participating in the Health, Aging and Body Composition (Health ABC) study.

Measurements

Body composition (fat and bone-free lean mass) was assessed by dual-energy x-ray absorptiometry in years 1-6, and 8. Knee extension strength was measured with isokinetic dynamometry and grip strength with isometric dynamometry in years 1,2,4,6, and 8.

Results

Compared to very fit men and women at baseline, less fit people had a higher weight, higher total percent fat, and lower total percent lean mass (p<0.01). Additionally, the least fit lost significantly more weight, fat mass, and lean mass over time compared to the very fit (p<0.01). Very fit people had the highest grip strength and knee extensor strength at baseline and follow-up; the decline in muscle strength was similar in every fitness group.

Conclusions

Low fitness in old age was associated with greater weight loss and loss of lean mass relative to having high fitness. Despite having lower muscle strength, the rate of decline in the least fit persons was similar to the most fit. In clinical practice, a long distance walk test as a measure of fitness might be useful to identify people at risk for these adverse health outcomes.

Cardiovascular disease has a progressively earlier age of onset, and disproportionately affects African Americans in the US. It has been difficult to establish the extent to which group differences are due to physiological, genetic, social, or behavioral factors. In this study, we examined the association between blood pressure and these factors among a sample of 294 children, identified as African-, European-, or Hispanic-American. We use body composition, behavioral (diet and physical activity), and survey-based measures (socio-economic status and perceived racial discrimination), as well as genetic admixture based on 142 ancestry informative markers (AIM) to examine associations with systolic and diastolic blood pressure. We find that associations differ by ethnic/racial group. Notably, among African Americans, physical activity and perceived racial discrimination, but not African genetic admixture, are associated with blood pressure, while the association between blood pressure and body fat is nearly absent. We find an association between blood pressure and an AIM near a marker identified by a recent genome-wide association study. Our findings shed light on the differences in risk factors for elevated blood pressure among ethnic/racial groups, and the importance of including social and behavioral measures to grasp the full genetic/environmental etiology of disparities in blood pressure.

In genetic studies, associations between genotypes and phenotypes may be confounded by unrecognized population structure and/or admixture. Studies have shown that even in European populations, which are thought to be relatively homogeneous, population stratification exists and can affect the validity of association studies. A number of methods have been proposed to address this issue in recent years. Among them, the mixed-model based approach and the principal component-based approach have several advantages over other methods. However, these approaches have not been thoroughly evaluated on large human datasets. The objectives of this study are to (1) evaluate and compare the performance of the mixed-model approach and the principal component-based approach for genetic association mapping using human data consisting of unrelated individuals, and (2) understand the relationship between these two approaches. To achieve these goals, we simulate datasets based on the HapMap data under various scenarios. Our results indicate that the mixed-model approach performs well in controlling for population structure/admixture. It has similar performance as that based on principal component analysis. However, the approach combining mixed-model and principal component analysis does not perform as well as either method itself.

Insulin resistance has been associated with the accumulation of fat within skeletal muscle fibers as intramyocellular lipid (IMCL). Here, we have examined in a cross-sectional study the interrelationships among IMCL, insulin sensitivity, and adiposity in European Americans (EAs) and African Americans (AAs). In 43 EA and 43 AA subjects, we measured soleus IMCL content with proton-magnetic resonance spectroscopy, insulin sensitivity with hyperinsulinemic–euglycemic clamp, and body composition with dual-energy X-ray absorptiometry. The AA and EA subgroups had similar IMCL content, insulin sensitivity, and percent fat, but only in EA was IMCL correlated with insulin sensitivity (r = −0.47, P < 0.01), BMI (r = 0.56, P < 0.01), percent fat (r = 0.35, P < 0.05), trunk fat (r = 0.47, P < 0.01), leg fat (r = 0.40, P < 0.05), and waist and hip circumferences (r = 0.54 and 0.55, respectively, P < 0.01). In a multiple regression model including IMCL, race, and a race by IMCL interaction, the interaction was found to be a significant predictor (t = 1.69, DF = 1, P = 0.0422). IMCL is related to insulin sensitivity and adiposity in EA but not in AA, suggesting that IMCL may not function as a pathophysiological factor in individuals of African descent. These results highlight ethnic differences in the determinants of insulin sensitivity and in the pathogenesis of the metabolic syndrome trait cluster.

Background: Obesity is a serious chronic disease. Controlled-release phentermine/topiramate (PHEN/TPM CR), as an adjunct to lifestyle modification, has previously shown significant weight loss compared with placebo in a 56-wk study in overweight and obese subjects with ≥2 weight-related comorbidities.

Objective: This study evaluated the long-term efficacy and safety of PHEN/TPM CR in overweight and obese subjects with cardiometabolic disease.

Design: This was a placebo-controlled, double-blind, 52-wk extension study; volunteers at selected sites continued with original randomly assigned treatment [placebo, 7.5 mg phentermine/46 mg controlled-release topiramate (7.5/46), or 15 mg phentermine/92 mg controlled-release topiramate (15/92)] to complete a total of 108 wk. All subjects participated in a lifestyle-modification program.

Results: Of 866 eligible subjects, 676 (78%) elected to continue in the extension. Overall, 84.0% of subjects completed the study, with similar completion rates between treatment groups. At week 108, PHEN/TPM CR was associated with significant, sustained weight loss (intent-to-treat with last observation carried forward; P < 0.0001 compared with placebo); least-squares mean percentage changes from baseline in body weight were –1.8%, –9.3%, and –10.5% for placebo, 7.5/46, and 15/92, respectively. Significantly more PHEN/TPM CR–treated subjects at each dose achieved ≥5%, ≥10%, ≥15%, and ≥20% weight loss compared with placebo (P < 0.001). PHEN/TPM CR improved cardiovascular and metabolic variables and decreased rates of incident diabetes in comparison with placebo. PHEN/TPM CR was well tolerated over 108 wk, with reduced rates of adverse events occurring between weeks 56 and 108 compared with rates between weeks 0 and 56.

Conclusion: PHEN/TPM CR in conjunction with lifestyle modification may provide a well-tolerated and effective option for the sustained treatment of obesity complicated by cardiometabolic disease. This trial was registered at clinicaltrials.gov as NCT00796367.

Summary

Objective: To assesss the inappropriate use of causal language in studies on obesity and nutrition.

Methods

Titles and abstracts of 525 peer-reviewed papers in the 4 leading journals in the fields of obesity and nutrition were scrutinized for language implying causality in observational studies published in 2006.

Results

Such misleading language appeared in 161 papers (31%) independent of funding source. Remarkably 49% of studies lacking statistically significant primary outcomes used misleading language compared to 29% of those with p values ≤0.05 (chi square p < 0.001). Exculpatory language was present in the body of the text in 19%; of the 161 studies.

Conclusion

We suggest that editors and reviewers evaluate submissions for misleading reporting.