Bias in Hazard Ratios Arising From Misclassification According to Self-Reported Weight and Height in Observational Studies of Body Mass Index and Mortality

Application of a Web-based Tool for Quantitative Bias Analysis: The Example of Misclassification Due to Self-reported Body Mass Index

BACKGROUND

We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes.

METHODS

We used publicly available data from the National Health and Nutrition Examination Survey. The analysis consisted of: (1) estimating bias parameter values (sensitivity, specificity, negative predictive value, and positive predictive value) for self-reported obesity by sex, age, and race-ethnicity compared to obesity defined by measured BMI, and (2) using Apisensr to adjust for exposure misclassification.

RESULTS

The discrepancy between self-reported and measured obesity varied by demographic group (sensitivity range: 75%-89%; specificity range: 91%-99%). Using Apisensr for quantitative bias analysis, there was a clear pattern in the results: the relationship between obesity and diabetes was underestimated using self-report in all age, sex, and race-ethnicity categories compared to measured obesity. For example, in non-Hispanic White men aged 40-59 years, prevalence odds ratios for diabetes were 3.06 (95% confidence inerval = 1.78, 5.30) using self-reported BMI and 4.11 (95% confidence interval = 2.56, 6.75) after bias analysis adjusting for misclassification.

CONCLUSION

Apisensr is an easy-to-use, web-based Shiny app designed to facilitate quantitative bias analysis. Our results also provide estimates of bias parameter values that can be used by other researchers interested in examining obesity defined by self-reported BMI.

Review of codelists used to define hypertension in electronic health records and development of a codelist for research

BACKGROUND AND AIMS

Hypertension is a leading risk factor for cardiovascular disease. Electronic health records (EHRs) are routinely collected throughout a person's care, recording all aspects of health status, including current and past conditions, prescriptions and test results. EHRs can be used for epidemiological research. However, there are nuances in the way conditions are recorded using clinical coding; it is important to understand the methods which have been applied to define exposures, covariates and outcomes to enable interpretation of study findings. This study aimed to identify codelists used to define hypertension in studies that use EHRs and generate recommended codelists to support reproducibility and consistency.

ELIGIBILITY CRITERIA

Studies included populations with hypertension defined within an EHR between January 2010 and August 2023 and were systematically identified using MEDLINE and Embase. A summary of the most frequently used sources and codes is described. Due to an absence of Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codelists in the literature, a recommended SNOMED CT codelist was developed to aid consistency and standardisation of hypertension research using EHRs.

FINDINGS

375 manuscripts met the study criteria and were eligible for inclusion, and 112 (29.9%) reported codelists. The International Classification of Diseases (ICD) was the most frequently used clinical terminology, 59 manuscripts provided ICD 9 codelists (53%) and 58 included ICD 10 codelists (52%). Informed by commonly used ICD and Read codes, usage recommendations were made. We derived SNOMED CT codelists informed by National Institute for Health and Care Excellence guidelines for hypertension management. It is recommended that these codelists be used to identify hypertension in EHRs using SNOMED CT codes.

CONCLUSIONS

Less than one-third of hypertension studies using EHRs included their codelists. Transparent methodology for codelist creation is essential for replication and will aid interpretation of study findings. We created SNOMED CT codelists to support and standardise hypertension definitions in EHR studies.

Bidirectional associations between workplace bullying and sickness absence due to common mental disorders - a propensity-score matched cohort study

BACKGROUND

The link between workplace bullying and poor mental health is well-known. However, little is known about the prospective and potentially reciprocal association between workplace bullying and mental health-related sickness absence. This 2-year prospective study examined bidirectional associations between exposure to workplace bullying and sickness absence due to common mental disorders (SA-CMD) while controlling for confounding factors from both work and private life.

METHODS

The study was based on propensity score-matched samples (N = 3216 and N = 552) from the Swedish Longitudinal Occupational Survey of Health, using surveys from years 2012, 2014 and 2016. Self-reported exposure to workplace bullying was linked to registry-based information regarding medically certified SA-CMD (≥ 14 consecutive days). The associations were examined by means of Cox proportional hazards regression and via conditional logistic regression analysis. Hazard ratios and odds ratios with 95% confidence intervals were estimated.

RESULTS

Exposure to workplace bullying was associated with an increased risk of incident SA-CMD (HR: 1.3, 95% CI: 1.0-1.8), after accounting for the influence of job demands, decision authority, previous SA-CMD, as well as other sociodemographic covariates. However, we found no statistically significant association between SA-CMD and subsequent workplace bullying (OR 1.2, 95% CI 0.7-1.9).

CONCLUSIONS

The results support an association between self-reported workplace bullying and SA-CMD, independent of other sociodemographic factors and workplace stressors. Preventing workplace bullying could alleviate a share of the individual and societal burden caused by SA globally.

The psychometric upgrade psychophysiology needs

Although biological measurements are constrained by the same fundamental psychometric principles as self-report measurements, these essential principles are often neglected in most fields of neuroscience, including psychophysiology. Potential reasons for this neglect could include a lack of understanding of appropriate measurement theory or a lack of accessible software for psychometric analysis. Generalizability theory is a flexible and multifaceted measurement theory that is well suited to handling the nuances of psychophysiological data, such as the often unbalanced number of trials and intraindividual variability of scores of event-related brain potential (ERP) data. The ERP Reliability Analysis Toolbox (ERA Toolbox) was designed for psychophysiologists and is tractable software that can support the routine evaluation of psychometrics using generalizability theory. Psychometrics can guide task refinement, data-processing decisions, and selection of candidate biomarkers for clinical trials. The present review provides an extensive treatment of additional psychometric characteristics relevant to studies of psychophysiology, including validity and validation, standardization, dimensionality, and measurement invariance. Although the review focuses on ERPs, the discussion applies broadly to psychophysiological measures and beyond. The tools needed to rigorously assess psychometric reliability and validate psychophysiological measures are now readily available. With the profound implications that psychophysiological research can have on understanding brain-behavior relationships and the identification of biomarkers, there is simply too much at stake to ignore the crucial processes of evaluating psychometric reliability and validity.

Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology

Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

Pancreatic Tumor Organoid-Derived Factors from Cachectic Patients Disrupt Contractile Smooth Muscle Cells

Patients with pancreatic cancer often suffer from cachexia and experience gastrointestinal symptoms that may be related to intestinal smooth muscle cell (SMC) dysfunction. We hypothesized that pancreatic tumor organoids from cachectic patients release factors that perturb the SMC's contractile characteristics. Human visceral SMCs were exposed to conditioned medium (CM) from the pancreatic tumor organoid cultures of cachectic (n = 2) and non-cachectic (n = 2) patients. Contractile proteins and markers of inflammation, muscle atrophy, and proliferation were evaluated by qPCR and Western blot. SMC proliferation and migration were monitored by live cell imaging. The Ki-67-positive cell fraction was determined in the intestinal smooth musculature of pancreatic cancer patients. CM from the pancreatic tumor organoids of cachectic patients did not affect IL-1β, IL-6, IL-8, MCP-1, or Atrogin-1 expression. However, CM reduced the α-SMA, γ-SMA, and SM22-α levels, which was accompanied by a reduced SMC doubling time and increased expression of S100A4, a Ca2+-binding protein associated with the synthetic SMC phenotype. In line with this, Ki-67-positive nuclei were increased in the intestinal smooth musculature of patients with a low versus high L3-SMI. In conclusion, patient-derived pancreatic tumor organoids release factors that compromise the contractile SMC phenotype and increase SMC proliferation. This may contribute to the frequently observed gastrointestinal motility problems in these patients.

Self-reported and measured anthropometric variables in association with cardiometabolic markers: A Danish cohort study

General obesity is a recognized risk factor for various metabolically related diseases, including hypertension, dyslipidemia, and pre-diabetes. In epidemiological studies, anthropometric variables such as height and weight are often self-reported. However, misreporting of self-reported data may bias estimates of associations between anthropometry and health outcomes. Further, few validation studies have compared self-reported and measured waist circumference (WC). This study aimed to quantify the agreement between self-reported and measured height, weight, body mass index (BMI), WC, and waist-to-height ratio (WHtR), and to investigate associations of these anthropometric measures with cardiometabolic biomarkers. A total of 39,514 participants aged above 18 years were included into the Diet, Cancer, and Health-Next Generation Cohort in 2015-19. Self-reported and measured anthropometric variables, blood pressure, and cardiometabolic biomarkers (HbA1c, lipid profiles, C-reactive protein and creatinine) were collected by standard procedures. Pearson correlations (r) and Lin's concordance correlations were applied to evaluate misreporting. Misreporting by age, sex and smoking status was investigated in linear regression models. Multivariable regression models and Receiver Operating Characteristic analyses assessed associations of self-reported and measured anthropometry with cardiometabolic biomarkers. Self-reported height was overreported by 1.07 cm, and weight was underreported by 0.32 kg on average. Self-reported BMI and WC were 0.42 kg/m2 and 0.2 cm lower than measured, respectively. Self-reported and measured height, weight, BMI, WC and WtHR were strongly correlated (r = 0.98, 0.99, 0.98, 0.88, 0.86, respectively). Age, sex, smoking, and BMI contributed to misreporting of all anthropometric measures. Associations between self-reported or measured anthropometric measures and cardiometabolic biomarkers were similar in direction and strength. Concordance between self-reported and measured anthropometric measures, including WC, was very high. Self-reported anthropometric measures were reliable when estimating associations with cardiometabolic biomarkers.

Body mass index and all-cause mortality in a 21st century U.S. population: A National Health Interview Survey analysis

INTRODUCTION

Much of the data on BMI-mortality associations stem from 20th century U.S. cohorts. The purpose of this study was to determine the association between BMI and mortality in a contemporary, nationally representative, 21st century, U.S. adult population.

METHODS

This was a retrospective cohort study of U.S. adults from the 1999-2018 National Health Interview Study (NHIS), linked to the National Death Index (NDI) through December 31st, 2019. BMI was calculated using self-reported height & weight and categorized into 9 groups. We estimated risk of all-cause mortality using multivariable Cox proportional hazards regression, adjusting for covariates, accounting for the survey design, and performing subgroup analyses to reduce analytic bias.

RESULTS

The study sample included 554,332 adults (mean age 46 years [SD 15], 50% female, 69% non-Hispanic White). Over a median follow-up of 9 years (IQR 5-14) and maximum follow-up of 20 years, there were 75,807 deaths. The risk of all-cause mortality was similar across a wide range of BMI categories: compared to BMI of 22.5-24.9 kg/m2, the adjusted HR was 0.95 [95% CI 0.92, 0.98] for BMI of 25.0-27.4 and 0.93 [0.90, 0.96] for BMI of 27.5-29.9. These results persisted after restriction to healthy never-smokers and exclusion of subjects who died within the first two years of follow-up. A 21-108% increased mortality risk was seen for BMI ≥30. Older adults showed no significant increase in mortality between BMI of 22.5 and 34.9, while in younger adults this lack of increase was limited to the BMI range of 22.5 to 27.4.

CONCLUSION

The risk of all-cause mortality was elevated by 21-108% among participants with BMI ≥30. BMI may not necessarily increase mortality independently of other risk factors in adults, especially older adults, with overweight BMI. Further studies incorporating weight history, body composition, and morbidity outcomes are needed to fully characterize BMI-mortality associations.

Body composition and mortality from middle to old age: a prospective cohort study from the UK Biobank

BACKGROUND

How the association between adiposity and the risk of death changes with age, and which is the optimal level of adiposity to reduce mortality in older ages, is still not completely understood. We aimed to ascertain the age-specific risks of mortality associated with different measures of adiposity.

METHODS

This was a prospective UK Biobank cohort study. Participants were categorized based on five different adiposity and body composition metrics. We explored the age-varying associations between body composition indices and all-cause mortality from 45 to 85 years of age at follow-up using hazard ratios (HR) from flexible parametric survival models with multivariable adjustment and age as timescale. Participants were followed from baseline (2006-2010) through 31 March 2020.

RESULTS

We included 369,752 participants (mean baseline age = 56.3 ± 8.1 years; range 38.9-73.7 years; 54.1% women) and 10,660 deaths during a median follow-up of 11.4 years. Associations between body mass index and mortality were similar when using the fat mass index in magnitude and shape. Compared to participants with normal weight, overweight was not associated with the risk of death regardless of age and the adiposity measure used. Participants with obesity class I showed an HR of 1.20 (95% confidence interval [CI]: 1.08, 1.33) and 1.14 (95%CI: 0.98, 1.30) at ages 60 and 80, respectively, and participants with obesity class II an HR about 1.55 across all age. More attenuated associations with higher age were found in individuals with the highest obesity using the fat mass index. Very high lean mass was associated with an increased risk of mortality in those aged 55-75 years (HR about 1.20 across all ages).

CONCLUSION

Obesity should be prevented at any age. Attenuated associations with older age were observed only among the individuals with the highest obesity, but the risk remained higher compared to normal-weight participants. Lean mass did not reduce mortality risk at any age.

Reliability of Self-Reported Height and Weight in Children: A School-Based Cross-Sectional Study and a Review

Since anthropometric measurements are not always feasible in large surveys, self-reported values are an alternative. Our objective was to assess the reliability of self-reported weight and height values compared to measured values in children with (1) a cross-sectional study in Switzerland and (2) a comprehensive review with a meta-analysis. We conducted a secondary analysis of data from a school-based study in Switzerland of 2616 children and a review of 63 published studies including 122,629 children. In the cross-sectional study, self-reported and measured values were highly correlated (weight: r = 0.96; height: r = 0.92; body mass index (BMI) r = 0.88), although self-reported values tended to underestimate measured values (weight: -1.4 kg; height: -0.9 cm; BMI: -0.4 kg/m²). Prevalence of underweight was overestimated and prevalence of overweight was underestimated using self-reported values. In the meta-analysis, high correlations were found between self-reported and measured values (weight: r = 0.94; height: r = 0.87; BMI: r = 0.88). Weight (-1.4 kg) and BMI (-0.7 kg/m²) were underestimated, and height was slightly overestimated (+0.1 cm) with self-reported values. Self-reported values tended to be more reliable in children above 11 years old. Self-reported weight and height in children can be a reliable alternative to measurements, but should be used with caution to estimate over- or underweight prevalence.

Body mass index and survival after cancer diagnosis: A pan-cancer cohort study of 114 430 patients with cancer

The recommendation encouraging patients with cancer to keep a normal body mass index (BMI) is largely extrapolated from data on risk of developing cancer. We tested the prospective association between peri-diagnostic (within 1 year post-diagnosis) BMI and all-cause mortality in patients with incident cancers. During 7.2 years of follow-up, 42% (48,340) of the 114 430 patients with cancer died. Spline analysis revealed that compared with a BMI of 22.5, a BMI lower than 22.5 was associated with increased risk of all-cause mortality across 24 cancer types. A BMI higher than 22.5 was associated with reduced all-cause mortality, while a non-linear association was observed; the lowest risk was found at a BMI of 29.6-34.2, and the risk started to return to and above unity at very high BMI values. The reduced mortality risk of high BMI was observed in 23 of 24 cancer types and maintained after attempts to remove potential selection bias, confounding by smoking and comorbidities, and reserve causality. Compared with a normal BMI of 18.5-24.9, the hazard ratios were 0.85 (95% confidence interval [CI], 0.83-0.87) for an overweight BMI (25-29.9) and 0.82 (0.80-0.85) for an obese BMI (≥30), and the associations were generally consistent across cancer types and various subgroups. Obese BMI was associated with increased life expectancy, up to 6 years among men and 3 years among women. In conclusion, while overweight/obese BMI increases the risk of developing cancer in the general population, overweight/obese peri-diagnostic BMI was associated with longer survival in cancer patients.

Improving the accuracy of self-reported height and weight in surveys: an experimental study

Background

Many studies rely on self-reported height and weight. While a substantial body of literature exists on misreporting of height and weight, little exists on improving accuracy. The aim of this study was to determine, using an experimental design and a comparative approach, whether the accuracy of self-reported height and weight data can be increased by improving how these questions are asked in surveys, drawing on the relevant evidence from the psychology and survey research literatures.

Methods

Two surveys from two separate studies were used to test our hypotheses (Science Survey, n = 1,200; Eating Behaviours Survey, n = 200). Participants were randomly assigned to one of six conditions, four of which were designed to improve the accuracy of the self-reported height and weight data (“preamble”), and two of which served as the control conditions ( “no preamble”). Four hypotheses were tested: (H1) survey participants read a preamble prior to being asked their height and weight will report lower heights and higher weights than those not read a preamble; (H2) the impact of question-wording (i.e., preamble vs. no preamble) on self-reported weight will be greater for participants with higher BMIs; (H3) the impact of question-wording on height will be greater for older participants; (H4) either version of the weight question – standard or “weight-specific”—may result in participants reporting more accurate self-reported weight. One-way MANOVA was conducted to test Hypothesis 1; two-way analysis of variance were conducted to test Hypothesis 2; moderation analysis was used to test Hypothesis 3; independent samples t-test was conducted to test Hypothesis 4.

Results

None of the hypotheses was supported.

Conclusions

This paper provides an important starting point from which to inform further work exploring how question wording can improve self-reported measurement of height and weight. Future research should explore how question preambles may or may not operationalise hypothesised underlying mechanisms, the sensitivity or intrusiveness of height and weight questions, individual beliefs about one’s height and weight, and survey context.

Relationship between BMI trajectories and cardiometabolic outcomes in postmenopausal women: a growth mixture modeling approach

PURPOSE

The objective of this manuscript is to identify longitudinal trajectories of change in body mass index (BMI) after menopause and investigate the association of BMI trajectories with risk of diabetes and cardiovascular disease (CVD) among postmenopausal women.

METHODS

Using data from 54,073 participants in the Women's Health Initiative (WHI) clinical trials, we used growth mixture modeling (GMM) to develop BMI trajectories. Cox proportional hazards models were used to examine the relationship between BMI trajectories with incident diabetes and CVD. Further, we stratified by hormone therapy trial arm and time since menopause.

RESULTS

Using GMM, we identified five BMI trajectories. We did not find evidence of substantial change in BMI over time; the trajectories were stable over the study follow-up period in this sample of postmenopausal women. Risk of diabetes and CVD increased by BMI trajectory; risk was greater for women in moderate-high, high, and very high BMI trajectories compared to those in the lowest trajectory group.

CONCLUSIONS

Despite minimal change in BMI over the follow-up period, our results demonstrate a strong association of high BMI with diabetes and CVD. These results highlight the importance of further longitudinal research focused on adverse health effects of BMI in older women.

Paradoxical mortality of high estimated glomerular filtration rate reversed by 24-h urine creatinine excretion rate adjustment: sarcopenia matters

BACKGROUND

Muscle wasting may explain the paradoxical mortality of patients with high estimated glomerular filtration rates (eGFRs) derived from equation methods. However, empirical evidence and solutions remain insufficient.

METHODS

In this retrospective cohort study, we compared the performance of equation methods for predicting all-cause mortality; we used 24-h creatinine clearance (24-h CrCl), equation-based eGFRs, and a new eGFR estimating equation weighting for population 24-h urine creatinine excretion rate (U-CER). From 2003 to 2018, we identified 4986 patients whose data constituted the first 24-h CrCl measurement data in the Clinical Research Data Repository of China Medical University Hospital and were followed up for at least 5 years after careful exclusion. Three GFR estimation equations [the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Modification of Diet in Renal Disease (MDRD) Study, and Taiwanese MDRD], 24-h CrCl, and 24-h U-CER-adjusted eGFR were used.

RESULTS

A high correlation was observed among the eGFR levels derived from the equation methods (0.995-1.000); however, the correlation decreased to 0.895-0.914 when equation methods were compared with the 24-h CrCl or 24-h U-CER-adjusted equation-based eGFR. In the Bland-Altman plots, the average discrepancy between the equation methods and the 24-h CrCl method was close to zero (maximal bias range: 5.12 for the Taiwanese MDRD equation vs. 24-h CrCl), but the range in limit of agreement was wide, from ±43.7 mL/min/1.73 m² for the CKD-EPI equation to ±54.3 mL/min/1.73 m² for the Taiwanese MDRD equation. A J-shaped dose-response relationship was observed between all equation-based eGFRs and all-cause mortality. Only 24-h CrCl exhibited a non-linear negative dose-response relationship with all-cause mortality. After adjustment for 24-h U-CER in the statistical model, the paradoxical increase in mortality risk for an eGFR of >90 mL/min/1.73 m² returned to null. When 24-h U-CER was used directly to correct eGFR, the monotonic non-linear negative relationship with all-cause mortality was almost identical to that of 24-h CrCl.

CONCLUSIONS

The 24-h U-CER-adjusted eGFR and 24-h CrCl are viable options for informing mortality risk. The 24-h U-CER adjustment method can be practically implemented to eGFR-based care and effectively mitigate the inherent confounding biases from individual's muscle mass amount due to both sex and racial differences.

The association of high-sensitivity C-reactive protein with future weight gain in adults

Background

Obesity is associated with low-grade systemic inflammation, and it has been suggested that increased inflammation markers could predict future weight gain. Our aim was to investigate the associations of high-sensitivity C-reactive protein (hs-CRP) concentration with changes in weight and waist circumference in adults during 11 years of follow-up.

Methods

We used data from the Health 2000 and Health 2011 surveys consisting of a population-based sample of Finnish adults. We included those 3143 participants, aged 30–75 years at baseline, whose baseline hs-CRP was measured, and who had information on measured weight and height at both time points. Associations between baseline hs-CRP and changes in weight and waist circumference were analyzed using multinomial logistic regression, adjusted for sociodemographic factors (age, sex, marital status, and educational status), lifestyle factors (smoking, alcohol consumption, leisure-time physical activity, sitting time, sleeping time, and psychological distress), and baseline values of BMI and waist circumference.

Results

Hs-CRP was not associated with weight gain (≥5%) when adjusted for potential confounders (OR 0.99, 95% CI 0.96–1.01), compared to stable weight (change <±5%). Higher baseline hs-CRP was associated with decrease in weight (≤−5%) in the unadjusted (OR 1.03, 1.01–1.05), but not in the adjusted (OR 1.01, 0.99–1.03) model. No association was observed between hs-CRP and waist circumference.

Conclusions

Hs-CRP was not associated with future changes in weight or waist circumference in adults. These findings suggest that hs-CRP concentration does not predict future weight gain.

Higher Body Mass Index Values Do Not Impact Physical Function and Lower-Extremity Muscle Strength Performance in Active Older Individuals

This study examined the potential impact of BMI on physical function and lower-extremity muscle strength (leg extension and flexion peak torque) performance in active/trained older individuals. Sixty-four active/trained older individuals were enrolled, and later allocated to groups according to BMI categories (normal [≤ 24.9 kg/m²], overweight [25 to 29.9 kg/m²] and obese [≥ 30 kg/m²]). Sixty-four active/trained older individuals were enrolled, and later allocated to groups according to BMI categories (normal [≤ 24.9 kg/m²], overweight [25 to 29.9 kg/m²] and obese [≥ 30 kg/m²]). Assessments were conducted in two separate visits to the laboratory. In the first visit, participants underwent measures of height, body mass, and peak torque leg extension and flexion using an isokinetic dynamometer. On visit two, participants performed the 30-second Sit and Stand test (30SST), Timed Up and Go (TUG), and 6-minute Walk (6MW) tests. ANOVA one-way was used to analyze the data and significance was set at P < .05. One-way ANOVAs did not reveal significance differences among BMI categories for leg extension peak torque (F(2,61) = 1.11; P = 0.336), leg flexion peak torque (F(2,61) = 1.22; P = 0.303), 30SST (F(2,61) =1.28; P = 0.285), TUG (F(2,61) = 0.238; P = 0.789), and 6MW (F(2,61) = 2.52; P = 0.089)]. Our findings indicated that for older individuals who exercise regularly, physical function tests which mimic ordinary activities of daily living, are not impacted by BMI status. Thus, being physically active may counteract some of the negative effects of high BMI observed in the older adult population.

Mortality Beyond the First Year After Spinal Cord Injury: Does Body Mass Index Matter?

OBJECTIVE

To examine the association between body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]) and mortality after the first year post spinal cord injury (SCI) overall and across demographic and injury characteristics.

DESIGN

Cohort study.

SETTING

Sixteen Spinal Cord Injury Model Systems (SCIMS) centers.

PARTICIPANTS

SCIMS Database participants age 20 years or older and having a BMI assessment during the 2007-2011 wave of data collection.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

All-cause mortality rate. Life table method and log-rank test were used to estimate and compare mortality rates across BMI groups and other factors. Cox proportional hazard regression model was conducted to estimate hazard ratio (HR) and 95% confidence interval (CI).

RESULTS

A total of 2346 participants (N=2346) with SCI were classified into 1 of the 8 BMI groups: <18.5 (6.9%), 18.5-19.9 (7.3%), 20.0-22.49 (15.0%), 22.5-24.9 (18.8%), 25.0-27.49 (17.5%), 27.5-29.9 (13.2%), 30.0-34.9 (13.5%), and ≥35.0 (7.8%). Compared with people with BMI of 22.5-29.9, a higher mortality risk was observed among people with BMI<18.5 (HR, 1.76; 95% CI, 1.25-2.49), 18.5-19.9 (HR, 1.51; 95% CI, 1.06-2.15), and ≥35.0 (HR, 1.51; 95% CI, 1.11-2.07) after adjusting for confounding factors (sex, age at the time of BMI assessment, marital status, neurologic status). The U-shape BMI-mortality relationship varied by age, sex, neurologic status, and years since injury.

CONCLUSIONS

To improve life expectancy after SCI, health care professionals could focus on weight management among patients with relatively low and extremely high BMI, defined by demographic and injury-related characteristics. Future studies should explore factors that contribute to such a higher mortality after SCI, including pre-existing conditions, poor diet and/or nutrition, and cardiorespiratory fitness.

Mortality and Obesity Among U.S. Older Adults: The Role of Polygenic Risk

OBJECTIVES

To examine the relationship between obesity and mortality as a function of polygenic risk for obesity among older U.S. adults.

METHOD

Using data from the 1994-2014 Health and Retirement Study in conjunction with genome-wide data, we evaluated the risk of mortality as a function of obesity classification, an individual's polygenic risk score (PGS) for obesity, and their interaction, stratified by sex. We conducted our analyses using cox proportional hazard models.

RESULTS

Among those with an average PGS for obesity (8,143 [68.8%]), obese I (hazard ratio [HR] = 0.79, p = .336) adults show no difference in their risk for mortality and obese II/III (HR = 3.17, p = .000) adults present higher risk of mortality relative to non-obese adults. The interaction of obesity classification and PGS suggests that obese II/III respondents with low PGS in the total sample (HR = 2.71, p = .006) and among women (HR = 3.02, p = .023) are at significantly higher risk of death when compared to obese II/III respondents with average or high PGS.

DISCUSSION

We posit that these findings suggest that the pathway to obesity, in this case, more socio-behavioral rather than genetic, may influence subsequent risk of death in older adults. We suggest that practitioners and population researchers be mindful of these pathways as to better identify and understand mortality risk.

The impact of body mass index on one-year mortality after spinal cord injury

Objective: Evaluate the association between body mass index (BMI, kg/m²) and one-year mortality among people who survived the first 90 days after spinal cord injury (SCI).Design: Cohort study.Setting: Eighteen SCI Model Systems centers throughout the United States.Participants: 6640 participants (men, 79.6%; mean age, 42.8 ± 17.7y; Whites, 62.3%) who had an SCI between October 2006 and March 2017.Interventions: Not applicable.Outcome Measures: All-cause mortality and causes of death. Life table method was used to estimate mortality rates, while Cox proportional hazard model was conducted to assess the impact of BMI on mortality after adjusting for demographic and injury-related factors.Results: Based on BMI obtained during initial rehabilitation, participants were classified into underweight (4.2%), normal weight (41.2%), overweight (30.9%) and obese (23.8%) groups, and their corresponding one-year mortality rates were 2.6%, 1.8%, 3.1%, 3.5%, respectively (P = 0.002). After adjusting for potential confounding factors, people with obesity had a higher mortality risk than those with normal weight (hazard ratio, 1.51; 95% confidence interval, 1.00-2.28). The most frequent causes of death for people with obesity were infective and parasitic diseases and respiratory diseases, while respiratory diseases were the most frequent for people with other BMI statuses.Conclusion: People with obesity who incur an SCI need special attention to prevent early mortality. Future studies should explore factors that contribute to such a higher mortality after SCI, such as preexisting conditions and comorbidities. The effects of BMI on long-term mortality also deserve further investigation.

Utilizing genome wide data to highlight the social behavioral pathways to health: The case of obesity and cardiovascular health among older adults

We use genome-wide data from the 1992-2016 Health and Retirement Study (n = 12,090) to characterize obesity among older adults as genetically or socially oriented. To illustrate the significance of this approach for social epidemiological research, we deem those with the lowest genetic risk for obesity to be socially-behaviorally obese and obesity among those with the highest polygenic risk is characterized as genetically oriented. We then examine the association between obesity and four indicators of cardiovascular health (type-2 diabetes, hypertension, heart problems, and stroke) among those with low, average, and high genetic risk. Our results show that the association between obesity and cardiovascular health is significantly higher for those with the lowest genetic risk (e.g., social-behavioral obesity). We also demonstrate important sex differences such that this association is particularly strong for heart problems among men and hypertension and stroke among women. Our results further demonstrate the centrality of the social and behavioral determinants of health by utilizing detailed information across the human genome and add to both social and genetic epidemiology literatures.

Excess mortality during the Covid-19 pandemic: Early evidence from England and Wales

The Covid-19 pandemic has claimed many lives in the UK and globally. The objective of this paper is to study whether the number of deaths not registered as Covid-19-related has increased compared to what would have been expected in the absence of the pandemic. Reasons behind this might include Covid-19 underreporting, avoiding visits to hospitals or GPs, and the effects of the lockdown. I used weekly ONS data on the number of deaths in England and Wales that did not officially involve Covid-19 over the period 2015-2020. Simply observing trends is not sufficient as spikes in deaths may occasionally occur. I thus followed a difference-in-differences econometric approach to study whether there was a relative increase in deaths not registered as Covid-19-related during the pandemic, compared to a control. Results suggest that there were an additional 968 weekly deaths that officially did not involve Covid-19, compared to what would have otherwise been expected. It is possible that some people are dying from Covid-19 without being diagnosed, and/or that there are excess deaths due to other causes as a result of the pandemic. Analysing the cause of death for any excess non-covid-19 deaths will shed light upon the reasons for the increase in such deaths and will help design appropriate policy responses to save lives.

Adolescent Obesity and Early-Onset Type 2 Diabetes

OBJECTIVE

Type 2 diabetes (T2D) is increasingly diagnosed at younger ages. We investigated the association of adolescent obesity with incident T2D at early adulthood.

RESEARCH DESIGN AND METHODS

A nationwide, population-based study evaluated 1,462,362 adolescents (59% men, mean age 17.4 years) during 1996-2016. Data were linked to the Israeli National Diabetes Registry. Weight and height were measured at study entry. Cox proportional models were applied.

RESULTS

During 15,810,751 person-years, 2,177 people (69% men) developed T2D (mean age at diagnosis 27 years). There was an interaction among BMI, sex, and incident T2D (P _interaction = 0.023). In a model adjusted for sociodemographic variables, the hazard ratios for diabetes diagnosis were 1.7 (95% CI 1.4-2.0), 2.8 (2.3-3.5), 5.8 (4.9-6.9), 13.4 (11.5-15.7), and 25.8 (21.0-31.6) among men in the 50th-74th percentile, 75th-84th percentile, overweight, mild obesity, and severe obesity groups, respectively, and 2.2 (1.6-2.9), 3.4 (2.5-4.6), 10.6 (8.3-13.6), 21.1 (16.0-27.8), and 44.7 (32.4-61.5), respectively, in women. An inverse graded relationship was observed between baseline BMI and mean age of T2D diagnosis: 27.8 and 25.9 years among men and women with severe obesity, respectively, and 29.5 and 28.5 years among low-normal BMI (5th-49th percentile; reference), respectively. The projected fractions of adult-onset T2D that were attributed to high BMI (≥85th percentile) at adolescence were 56.9% (53.8-59.9%) and 61.1% (56.8-65.2%) in men and women, respectively.

CONCLUSIONS

Severe obesity significantly increases the risk for incidence of T2D in early adulthood in both sexes. The rise in adolescent severe obesity is likely to increase diabetes incidence in young adults in coming decades.

Validation of self-reported height and weight in a large, nationwide cohort of U.S. adults

Background

Height and weight are commonly used metrics in epidemiologic studies to calculate body mass index. Large cohort studies generally assess height and weight by self-report rather than by measurement. The aim of this study was to assess the validity of self-reported height and weight in the Cancer Prevention Study-3 (CPS-3), a large, nationwide cohort recruited by the American Cancer Society between 2006–2013.

Methods

In a subset of CPS-3 participants (n = 2,643), weight and height were assessed at the same time via self-report and in-person measurement. BMI was calculated and classified underweight (<18.5 kg/m²), normal (18.5-<25 kg/m²), overweight (25-<30 kg/m²), or obese (≥30 kg/m²). Self-reported and measured height, weight, and BMI were compared using mean differences and Bland-Altman plots and examined by sex, race/ethnicity, education, marital status, age group, and BMI category.

Results

Men and women slightly overreported height and underreported weight. BMI calculated from self-reported data was lower than for measured data for men and women. In analyses stratified by race/ethnicity, age, education, and marital status, older women and women with less than a college degree overreported height. Approximately 13% of men and 7% of women were misclassified into a lower self-reported BMI category, with misclassification of BMI being greatest in obese men and women.

Conclusions

Overall, height, weight, and BMI were well-reported, and this study further suggests that BMI computed from self-reported weight and height is a valid measure in men and women across different socio-demographic groups.

Psychosocial stress and longitudinally measured gestational weight gain throughout pregnancy: The Ulm SPATZ Health Study

Psychosocial stress is thought to influence gestational weight gain (GWG), but results are inconsistent. We investigated the relationship of questionnaire-based maternal stress and related constructs assessed at childbirth with maternal weight measured throughout pregnancy. Data were derived from the Ulm SPATZ Health Study, a birth cohort recruited from the general population (04/2012-05/2013, Ulm, Germany). Adjusted generalized estimating equations were performed. Regression coefficients (b) and 95% confidence intervals, each highest versus lowest tertile of stress or related constructs, are presented. In 748 women, we observed positive associations for maternal chronic stress (b = 4.36 kg (1.77; 6.95)), depressive symptoms (b = 2.50 kg (0.14; 4.86)), anxiety symptoms (b = 3.26 kg (0.62, 5.89)), and hair cortisol (b = 3.35 kg (0.86; 5.83)) with maternal weight at the first gestational month. GWG was considerably lower in mothers with higher chronic stress. Pregnancy-related anxiety was positively related to weight at first month (b = 4.16 kg (1.74; 6.58)) and overall GWG. In contrast, no association was observed between anxiety symptoms and GWG. Odds ratios for association with inadequate weight gain according to Institute of Medicine recommended cutoffs differed from the results presented obove. There is evidence of an association between stress and weight gain lying beyond the recommended cut-offs, which however needs further corroboration.

National Trends in American Heart Association Revised Life's Simple 7 Metrics Associated With Risk of Mortality Among US Adults

IMPORTANCE

The American Heart Association (AHA) introduced the Life's Simple 7 (LS7) metrics to assess and promote cardiovascular health. However, several shortcomings of these metrics have been identified. Therefore, a revised set of LS7 metrics was developed.

OBJECTIVES

To evaluate national trends in the metrics addressed by the revised LS7 and the individual and combined associations of the revised LS7 metrics with all-cause and cause-specific mortality and to compare these measures with the AHA recommended LS7 metrics.

DESIGN, SETTING, AND PARTICIPANTS

This national cross-sectional study used data from the National Health and Nutrition Examination Survey from 1988 to 2016. The revised LS7 metrics included a combination of the body mass index and waist to hip ratio, Healthy Eating Index-2010, and a lower blood pressure threshold of greater than or equal to 130/80 mm Hg in addition to physical activity, smoking, total cholesterol, and fasting blood glucose. Data for this study were analyzed from June 1, 2017, to December 31, 2017.

MAIN OUTCOMES AND MEASURES

The primary outcome was all-cause mortality. The secondary outcome was cancer and cardiovascular disease (CVD) mortality.

RESULTS

Data were available for 13 606 adults in 1988 to 1994 (7329 [53%] female; mean [SD] age, 47 [17.7] years), 6360 in 1999 to 2004 (3442 [54%] female; mean [SD] age, 47 [18.6] years), 10 618 in 2005 to 2010 (5428 [51%] female; mean [SD] age, 47 [17.5] years), and 10 773 in 2011 to 2016 (5474 [50%] female; mean [SD] age, 48 [17.4] years). Compared with a revised LS7 score of 0 to 1, the adjusted hazard ratios for a revised LS7 score of 5 to 7 were 0.46 (95% CI, 0.35-0.61) for all-cause mortality, 0.42 (95% CI, 0.25-0.68) for cancer mortality, and 0.37 (95% CI, 0.24-0.55) for CVD mortality, respectively. The adjusted hazard ratios for participants who met 6 or more AHA recommended ideal LS7 metrics were 0.49 (95% CI, 0.33-0.74) for all-cause mortality, 0.60 (95% CI, 0.29-1.25) for cancer mortality, and 0.24 (95% CI, 0.13-0.47) for CVD mortality. Participants with a body mass index of 29.9 or less but without central obesity were independently associated with lower risk of all-cause and CVD mortality. Blood pressure was associated with 36.7% or more of the observed population-attributable fraction of mortality.

CONCLUSIONS AND RELEVANCE

The individual revised LS7 metrics with modified criteria regarding weight, blood pressure, and diet provide more information about factors associated with cancer mortality than the original AHA LS7 metrics.

Comparisons of Self-Reported and Measured Height and Weight, BMI, and Obesity Prevalence from National Surveys: 1999-2016

OBJECTIVE

The aim of this study was to compare national estimates of self-reported and measured height and weight, BMI, and obesity prevalence among adults from US surveys.

METHODS

Self-reported height and weight data came from the National Health and Nutrition Examination Survey (NHANES), the National Health Interview Survey, and the Behavioral Risk Factor Surveillance System for the years 1999 to 2016. Measured height and weight data were available from NHANES. BMI was calculated from height and weight; obesity was defined as BMI ≥ 30.

RESULTS

In all three surveys, mean self-reported height was higher than mean measured height in NHANES for both men and women. Mean BMI from self-reported data was lower than mean BMI from measured data across all surveys. For women, mean self-reported weight, BMI, and obesity prevalence in the National Health Interview Survey and Behavioral Risk Factor Surveillance System were lower than self-report in NHANES. The distribution of BMI was narrower for self-reported than for measured data, leading to lower estimates of obesity prevalence.

CONCLUSIONS

Self-reported height, weight, BMI, and obesity prevalence were not identical across the three surveys, particularly for women. Patterns of misreporting of height and weight and their effects on BMI and obesity prevalence are complex.

Comparing Anthropometric Indicators of Visceral and General Adiposity as Determinants of Overall and Cardiovascular Mortality

BACKGROUND

It is unclear which anthropometric obesity indicator best predicts adverse health outcomes. This study aimed to investigate the association of body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), and hip-adjusted WC with all-cause and cardiovascular mortality.

METHODS

50045 people aged 40-75 (58% women, median BMI: 26.3 kg /m2 ) participated in the population-based Golestan Cohort Study. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (95% CI) for the association of obesity indicators with mortality. We also examined the association of these indicators with intermediate outcomes, including hypertension, blood glucose, dyslipidemia, carotid atherosclerosis, nonalcoholic fatty liver, and visceral abdominal fat.

RESULTS

After a median follow-up of 10.9 years (success rate: 99.1%), 6651 deaths (2778 cardiovascular) occurred. Comparing 5th to the 1st quintile, HRs (95% CIs) for all-cause and cardiovascular mortality were 1.12 (1.02-1.22) and 1.59 (1.39-1.83) for BMI, 1.16 (1.07-1.27) and 1.66 (1.44-1.90) for WC, 1.28 (1.17-1.40) and 1.88 (1.63-2.18) for WHtR, 1.44 (1.32-1.58) and 2.04 (1.76-2.36) for WHR, and 1.84 (1.62-2.09) and 2.72 (2.23-3.32) for hip-adjusted WC, respectively. Hip-adjusted WC had the strongest associations with the intermediate outcomes.

CONCLUSION

Indicators of visceral adiposity (e.g., hip-adjusted WC) were much stronger predictors of overall and cardiovascular mortality than were indicators of general adiposity (e.g., BMI). The full-strength effect of visceral adiposity becomes apparent only when both WC, as a risk factor, and hip circumference, as a protective factor, are individually and simultaneously taken into consideration.

Flawed methods and inappropriate conclusions for health policy on overweight and obesity: the Global BMI Mortality Collaboration meta-analysis

Guideline recommendations and health policy decisions rely on evidence from clinical and epidemiological studies. Adequate methodology and appropriate conclusions are essential to support healthcare and health policy decisions. An analysis of body mass index and mortality by the Global BMI Mortality Collaboration (GBMC) concluded that the association of excess body weight with higher mortality was similar worldwide and that overweight and obesity should be combated everywhere. To reach this conclusion, the GBMC used highly selected data, rather than a systematic approach. The GBMC initially chose individual participant data from 239 prospective studies with approximately 10.6 million participants. The GBMC then excluded over 60% of data and over 75% of fatal events by eliminating all cases with any reported disease at baseline or smoking history and all events within the first 5 years of follow-up. After applying these restrictions, the association of overweight with lower mortality was reversed and the association of obesity with higher mortality was increased. Given the major flaws in the selection process, in the adequacy of the data, in the data analysis, and in the interpretation, the GBMC conclusions should be viewed sceptically as a guide to action, either for clinical decisions or for public health in general. The flawed conclusion that overweight is uniformly associated with substantially increased risk of death and thus should be combated in any circumstances may lead not only to unjustified treatment efforts and potential harm in a wide range of clinical conditions but also to a tremendous waste of resources.

Examining validity of body mass index calculated using height and weight data from the US driver license

BACKGROUND

Driver license departments in many US states collect data on individuals' height and weight. These data can be useful to researchers in epidemiological and public health studies. As height and weight on driver license are self-reported, they may be prone to reporting bias. We compare height and weight obtained from driver license records and clinically measured height and weight, as well as body mass index (BMI) values calculated using the two data sources for the same individual.

METHODS

We linked individual height and weight records obtained from the Driver License Division (DLD) in the Utah Department of Public Safety to clinical records from one of the largest healthcare providers in the state of Utah. We then calculated average differences between height, weight and BMI values separately for women and men in the sample, as well as discrepancies between the two sets of measures by age and BMI category. We examined how well self-reported height and weight from the driver licenses classify individuals into specific BMI categories based on clinical measures. Finally, we used two sets of BMI values to estimate individuals' relative risk of type II diabetes.

RESULTS

Individuals, on average, tend to overestimate their height and underestimate their weight. Consequently, the value of BMI calculated using driver license records is lower than BMI calculated using clinical measurements. The discrepancy varies by age and by BMI category. Despite the discrepancy, BMI based on self-reported height and weight allows for accurate categorization of individuals at the higher end of the BMI scale, such as the obese. When used as predictors of relative risk of type II diabetes, both sets of BMI values yield similar risk estimates.

CONCLUSIONS

Data on height and weight from driver license data can be a useful asset for monitoring population health in states where such information is collected, despite the degree of misreporting associated with self-report.

Association of Obesity With Mortality Over 24 Years of Weight History: Findings From the Framingham Heart Study

IMPORTANCE

Many studies of the association between obesity and mortality rely on weight status at a single point in time, making it difficult to adequately address bias associated with reverse causality.

OBJECTIVE

To investigate the association between maximum body mass index (BMI) and all-cause mortality without the consequences of reverse causality.

DESIGN, SETTING, AND PARTICIPANTS

Prospective cohort studies for the original and offspring cohorts of the Framingham Heart Study. The follow-up period started from baseline examination 13 for the original cohort and from baseline examination 6 for the offspring cohort and ended December 31, 2014. The analyses were conducted in 2017. Participants were 6197 individuals with 3478 deaths during a mean of 17 years of follow-up.

MAIN OUTCOMES AND MEASURES

Maximum BMI over 24 years of weight history before the beginning of follow-up for all-cause mortality and cause-specific mortality. All-cause mortality and cause-specific mortality (deaths due to cardiovascular disease, cancer, or other causes).

RESULTS

Among 6197 participants (mean [SD] age at baseline, 62.79 [8.98] years; 55.5% female), 3478 (56.1%) died during the follow-up. A monotonic association was observed between maximum BMI and mortality, with increasing risks observed across obese I (BMI of 30 to <35; hazard ratio [HR], 1.27; 95% CI, 1.14-1.41) and obese II (BMI of 35 to <40; HR, 1.93; 95% CI, 1.68-2.20) categories. A significant association was not observed for the overweight category (BMI of 25 to <30; HR, 1.08; 95% CI, 0.99-1.18). Among never smokers, the risks increased, with a significant association emerging for individuals with maximum BMI in the overweight range (HR, 1.31; 95% CI, 1.13-1.51). The mortality rates of normal-weight individuals who were formerly overweight or obese were 47.48 and 66.67 per 1000 person-years, respectively, while individuals who never exceeded normal weight had a mortality rate of 27.93 per 1000 person-years.

CONCLUSIONS AND RELEVANCE

A monotonic association was found between maximum BMI over 24 years of weight history and subsequent all-cause mortality. Maximum BMI in the normal-weight range was associated with the lowest risk of mortality in this cohort, highlighting the importance of obesity prevention.

Validity of self-reported weight, height, and body mass index among African American breast cancer survivors

PURPOSE

Self-reported weight, height, and body mass index (BMI) are commonly used in cancer epidemiology studies, but information on the validity of self-reports among cancer survivors is lacking. This study aimed to evaluate the validity of these self-reported measures among African American (AA) breast cancer survivors, known to have high obesity prevalence.

METHODS

We compared the self-reported and measured values among 243 participants from the Women's Circle of Health Follow-Up Study (WCHFS), a population-based longitudinal study of AA breast cancer survivors. Multivariable-adjusted linear regressions were used to identify factors associated with reporting errors. We also examined the associations of self-reported and measured BMI with obesity-related health outcomes using multivariable logistic regressions, with hypertension as an example, to evaluate the impact of misreporting.

RESULTS

We found that self-reported and measured values were highly correlated among all and when stratified by participants' characteristics (intraclass correlation coefficients ≥ 0.99, 0.84, and 0.96 for weight, height, and BMI, respectively). The agreement between BMI categories (normal, overweight and obese) based on self-reported and measured data was excellent (kappa = 0.81). Women who were older, never smoked, had higher grade tumors, or had greater BMI tended to have overestimated BMI calculated from self-reported weight and height. The BMI-hypertension association was similar using self-reported (OR per 5 kg/m2 increase 1.63; 95% CI 1.27-2.10) and measured BMI (1.58; 95% CI 1.23-2.03).

CONCLUSIONS

Self-reported weight, height, and BMI were reasonably accurate in the WCHFS.

IMPLICATIONS FOR CANCER SURVIVORS

Our study supports the use of these self-reported values among cancer survivors when direct measurements are not possible.

How to improve health literacy to reduce short- and long-term consequences of maternal obesity?

Aim: To demonstrate the global challenge of maternal obesity and to propose models to increase awareness and health literacy. Methods: The regional perinatal data base and the international literature were reviewed to demonstrate the rising rates of maternal overweight and obesity causing major public health problems in low and high-resourced countries. A preliminary systematic review analyzing interventions in maternal obesity and a fact box based on a recent Cochrane review on dietary interventions were performed. Results: Between 2000 and 2015, the regional rates of maternal overweight and obesity have significantly increased, and the rate of morbid maternal obesity has even doubled. Pregnant women were insufficiently informed about the health risks and international recommendations for weight gain associated with pre-pregnancy body mass index. Scientific publications and guidelines of professional boards have not yet interrupted the vicious cycle of transgenerational transfer of associated health risks for the offspring. For the first time we propose a fact box to translate the results from a Cochrane review about dietary interventions into a transparent information for health care providers and patients which could help to improve awareness. Conclusions: Improving health literacy and translating clinical science into models which are understandable by policy makers, health care providers and parents is a challenge mainly if health risks are modifiable during gestation and could prevent the increasing burden of obesity for future societies.

Comparative effects of the restriction method in two large observational studies of body mass index and mortality among adults

BACKGROUND

A method applied in some large studies of weight and mortality is to begin with a well-defined analytic cohort and use successive restrictions to control for methodologic bias and arrive at final analytic results.

MATERIALS AND METHODS

Two observational studies of body mass index and mortality allow a comparative assessment of these restrictions in very large data sets. One was a meta-analysis of individual participant data with a sample size of 8 million. The second was a study of a South Korean cohort with a sample size of 12 million. Both presented results for participants without pre-existing disease before and after restricting the sample to never-smokers and deleting the first 5 years of follow-up.

RESULTS

Initial results from both studies were generally similar, with hazard ratios (HRs) below 1 for overweight and above 1 for underweight and obesity. The meta-analysis showed higher HRs for overweight and obesity after the restrictions, including a change in the direction of the HR for overweight from 0·99 (95% CI: 0·98-1·01) to 1·11 (95% CI: 1·10, 1·11). The South Korean data showed little effect of the restrictions and the HR for overweight changed from 0·85 (95% CI: 0·84-0·86) to 0·91 (95% CI: 0·90, 0·91). The summary effect size for overweight was 0·90 (95% CI: 0·89-0·91) before restrictions and 1·02 (95% CI: 1·02, 1·03) after restrictions.

CONCLUSIONS

The effect of the restrictions is not consistent across studies, weakening the argument that analyses without such restrictions lack validity.