Childhood body mass index trajectories: modeling, characterizing, pairwise correlations and socio-demographic predictors of trajectory characteristics

Background: Modeling childhood body mass index (BMI) trajectories, versus estimating change in BMI between specific ages, may improve prediction of later body-size-related outcomes. Prior studies of BMI trajectories are limited by restricted age periods and insufficient use of trajectory information. Methods: Among 3,289 children seen at 81,550 pediatric well-child visits from infancy to 18 years between 1980 and 2008, we fit individual BMI trajectories using mixed effect models with fractional polynomial functions. From each child's fitted trajectory, we estimated age and BMI at infancy peak and adiposity rebound, and velocity and area under curve between 1 week, infancy peak, adiposity rebound, and 18 years. Results: Among boys, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.2 (0.9) and 49.2 (11.9) months, respectively. Among girls, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.4 (1.1) and 46.8 (11.0) months, respectively. Ages at infancy peak and adiposity rebound were weakly inversely correlated (r = -0.09). BMI at infancy peak and adiposity rebound were positively correlated (r = 0.76). Blacks had earlier adiposity rebound and greater velocity from adiposity rebound to 18 years of age than whites. Higher birth weight z-score predicted earlier adiposity rebound and higher BMI at infancy peak and adiposity rebound. BMI trajectories did not differ by birth year or type of health insurance, after adjusting for other socio-demographics and birth weight z-score. Conclusions: Childhood BMI trajectory characteristics are informative in describing childhood body mass changes and can be estimated conveniently. Future research should evaluate associations of these novel BMI trajectory characteristics with adult outcomes

Validity of a self-reported measure of familial history of obesity

<p>Abstract</p> <p>Background</p> <p>Familial history information could be useful in clinical practice. However, little is known about the accuracy of self-reported familial history, particularly self-reported familial history of obesity (FHO).</p> <p>Methods</p> <p>Two cross-sectional studies were conducted. The aims of study 1 was to compare self-reported and objectively measured weight and height whereas the aims of study 2 were to examine the relationship between the weight and height estimations reported by the study participants and the values provided by their family members as well as the validity of a self-reported measure of FHO. Study 1 was conducted between 2004 and 2006 among 617 subjects and study 2 was conducted in 2006 among 78 participants.</p> <p>Results</p> <p>In both studies, weight and height reported by the participants were significantly correlated with their measured values (study 1: r = 0.98 and 0.98; study 2: r = 0.99 and 0.97 respectively; p < 0.0001). Estimates of weight and height for family members provided by the study participants were strongly correlated with values reported by each family member (r = 0.96 and 0.95, respectively; p < 0.0001). Substantial agreement between the FHO reported by the participants and the one obtained by calculating the BMI of each family members was observed (kappa = 0.72; p < 0.0001). Sensitivity (90.5%), specificity (82.6%), positive (82.6%) and negative (90.5%) predictive values of FHO were very good.</p> <p>Conclusion</p> <p>A self-reported measure of FHO is valid, suggesting that individuals are able to detect the presence or the absence of obesity in their first-degree family members.</p

A procedure to correct proxy-reported weight in the National Health Interview Survey, 1976–2002

<p>Abstract</p> <p>Background</p> <p>Data from the National Health Interview Survey (NHIS) show a larger-than-expected increase in mean BMI between 1996 and 1997. Proxy-reports of height and weight were discontinued as part of the 1997 NHIS redesign, suggesting that the sharp increase between 1996 and 1997 may be artifactual.</p> <p>Methods</p> <p>We merged NHIS data from 1976–2002 into a single database consisting of approximately 1.7 million adults aged 18 and over. The analysis consisted of two parts: First, we estimated the magnitude of BMI differences by reporting status (i.e., self-reported versus proxy-reported height and weight). Second, we developed a procedure to correct biases in BMI introduced by reporting status.</p> <p>Results</p> <p>Our analyses confirmed that proxy-reports of weight tended to be biased downward, with the degree of bias varying by race, sex, and other characteristics. We developed a correction procedure to minimize BMI underestimation associated with proxy-reporting, substantially reducing the larger-than-expected increase found in NHIS data between 1996 and 1997.</p> <p>Conclusion</p> <p>It is imperative that researchers who use reported estimates of height and weight think carefully about flaws in their data and how existing correction procedures might fail to account for them. The development of this particular correction procedure represents an important step toward improving the quality of BMI estimates in a widely used source of epidemiologic data.</p

Validation of self-reported anthropometrics in the Adventist Health Study 2

<p>Abstract</p> <p>Background</p> <p>Relying on self-reported anthropometric data is often the only feasible way of studying large populations. In this context, there are no studies assessing the validity of anthropometrics in a mostly vegetarian population. The objective of this study was to evaluate the validity of self-reported anthropometrics in the Adventist Health Study 2 (AHS-2).</p> <p>Methods</p> <p>We selected a representative sample of 911 participants of AHS-2, a cohort of over 96,000 adult Adventists in the USA and Canada. Then we compared their measured weight and height with those self-reported at baseline. We calculated the validity of the anthropometrics as continuous variables, and as categorical variables for the definition of obesity.</p> <p>Results</p> <p>On average, participants underestimated their weight by 0.20 kg, and overestimated their height by 1.57 cm resulting in underestimation of body mass index (BMI) by 0.61 kg/m². The agreement between self-reported and measured BMI (as a continuous variable), as estimated by intraclass correlation coefficient, was 0.97. The sensitivity of self-reported BMI to detect obesity was 0.81, the specificity 0.97, the predictive positive value 0.93, the predictive negative value 0.92, and the Kappa index 0.81. The percentage of absolute agreement for each category of BMI (normoweight, overweight, and obese) was 83.4%. After multivariate analyses, predictors of differences between self-reported and measured BMI were obesity, soy consumption and the type of dietary pattern.</p> <p>Conclusions</p> <p>Self-reported anthropometric data showed high validity in a representative subsample of the AHS-2 being valid enough to be used in epidemiological studies, although it can lead to some underestimation of obesity.</p

Validity of self-reported weight, height, and body mass index among university students in Thailand: Implications for population studies of obesity in developing countries

<p>Abstract</p> <p>Background</p> <p>Large-scale epidemiological studies commonly use self-reported weights and heights to determine weight status. Validity of such self-reported data has been assessed primarily in Western populations in developed countries, although its use is widespread in developing countries. We examine the validity of obesity based on self-reported data in an Asian developing country, and derive improved obesity prevalence estimates using the "reduced BMI threshold" method.</p> <p>Methods</p> <p>Self-reported and measured heights and weights were obtained from 741 students attending an open university in Thailand (mean age 34 years). Receiver operator characteristic techniques were applied to derive "reduced BMI thresholds."</p> <p>Results</p> <p>Height was over-reported by a mean of 1.54 cm (SD 2.23) in men and 1.33 cm (1.84) in women. Weight was under-reported by 0.93 kg (3.47) in men and 0.62 kg (2.14) in women. Sensitivity and specificity for determining obesity (Thai BMI threshold 25 kg/m²) using self-reported data were 74.2% and 97.3%, respectively, for men and 71.9% and 100% for women. For men, reducing the BMI threshold to 24.5 kg/m²increased the estimated obesity prevalence based on self-reports from 29.1% to 33.8% (true prevalence was 36.9%). For women, using a BMI threshold of 24.4 kg/m², the improvement was from 12.0% to 15.9% (true prevalence 16.7%).</p> <p>Conclusion</p> <p>Young educated Thais under-report weight and over-report height in ways similar to their counterparts in developed countries. Simple adjustments to BMI thresholds will overcome these reporting biases for estimation of obesity prevalence. Our study suggests that self-reported weights and heights can provide economical and valid measures of weight status in high school-educated populations in developing countries.</p

Accuracy and usefulness of BMI measures based on self-reported weight and height: findings from the NHANES & NHIS 2001-2006

<p>Abstract</p> <p>Background</p> <p>The Body Mass Index (BMI) based on self-reported height and weight ("self-reported BMI") in epidemiologic studies is subject to measurement error. However, because of the ease and efficiency in gathering height and weight information through interviews, it remains important to assess the extent of error present in self-reported BMI measures and to explore possible adjustment factors as well as valid uses of such self-reported measures.</p> <p>Methods</p> <p>Using the combined 2001-2006 data from the continuous National Health and Nutrition Examination Survey, discrepancies between BMI measures based on self-reported and physical height and weight measures are estimated and socio-demographic predictors of such discrepancies are identified. Employing adjustments derived from the socio-demographic predictors, the self-reported measures of height and weight in the 2001-2006 National Health Interview Survey are used for population estimates of overweight & obesity as well as the prediction of health risks associated with large BMI values. The analysis relies on two-way frequency tables as well as linear and logistic regression models. All point and variance estimates take into account the complex survey design of the studies involved.</p> <p>Results</p> <p>Self-reported BMI values tend to overestimate measured BMI values at the low end of the BMI scale (< 22) and underestimate BMI values at the high end, particularly at values > 28. The discrepancies also vary systematically with age (younger and older respondents underestimate their BMI more than respondents aged 42-55), gender and the ethnic/racial background of the respondents. BMI scores, adjusted for socio-demographic characteristics of the respondents, tend to narrow, but do not eliminate misclassification of obese people as merely overweight, but health risk estimates associated with variations in BMI values are virtually the same, whether based on self-report or measured BMI values.</p> <p>Conclusion</p> <p>BMI values based on self-reported height and weight, if corrected for biases associated with socio-demographic characteristics of the survey respondents, can be used to estimate health risks associated with variations in BMI, particularly when using parametric prediction models.</p

Recruitment into diabetes prevention programs : what is the impact of errors in self-reported measures of obesity?

BackgroundError in self-reported measures of obesity has been frequently described, but the effect of self-reported error on recruitment into diabetes prevention programs is not well established. The aim of this study was to examine the effect of using self-reported obesity data from the Finnish diabetes risk score (FINDRISC) on recruitment into the Greater Green Triangle Diabetes Prevention Project (GGT DPP).MethodsThe GGT DPP was a structured group-based lifestyle modification program delivered in primary health care settings in South-Eastern Australia. Between 2004&ndash;05, 850 FINDRISC forms were collected during recruitment for the GGT DPP. Eligible individuals, at moderate to high risk of developing diabetes, were invited to undertake baseline tests, including anthropometric measurements performed by specially trained nurses. In addition to errors in calculating total risk scores, accuracy of self-reported data (height, weight, waist circumference (WC) and Body Mass Index (BMI)) from FINDRISCs was compared with baseline data, with impact on participation eligibility presented.ResultsOverall, calculation errors impacted on eligibility in 18 cases (2.1%). Of n&thinsp;=&thinsp;279 GGT DPP participants with measured data, errors (total score calculation, BMI or WC) in self-report were found in n&thinsp;=&thinsp;90 (32.3%). These errors were equally likely to result in under- or over-reported risk. Under-reporting was more common in those reporting lower risk scores (Spearman-rho&thinsp;=&thinsp;&minus;0.226, p-value&thinsp;&lt;&thinsp;0.001). However, underestimation resulted in only 6% of individuals at high risk of diabetes being incorrectly categorised as moderate or low risk of diabetes.ConclusionsOverall FINDRISC was found to be an effective tool to screen and recruit participants at moderate to high risk of diabetes, accurately categorising levels of overweight and obesity using self-report data. The results could be generalisable to other diabetes prevention programs using screening tools which include self-reported levels of obesity.<br /

Maternal risk factors for abnormal placental growth: The national collaborative perinatal project

<p>Abstract</p> <p>Background</p> <p>Previous studies of maternal risk factors for abnormal placental growth have focused on placental weight and placental ratio as measures of placental growth. We sought to identify maternal risk factors for placental weight and two neglected dimensions of placental growth: placental thickness and chorionic plate area.</p> <p>Methods</p> <p>We conducted an analysis of 24,135 mother-placenta pairs enrolled in the National Collaborative Perinatal Project, a prospective cohort study of pregnancy and child health. We defined growth restriction as < 10^thpercentile and hypertrophy as > 90^thpercentile for three placental growth dimensions: placental weight, placental thickness and chorionic plate area. We constructed parallel multinomial logistic regression analyses to identify (a) predictors of restricted growth (vs. normal) and (b) predictors of hypertrophic growth (vs. normal).</p> <p>Results</p> <p>Black race was associated with an increased likelihood of growth restriction for placental weight, thickness and chorionic plate area, but was associated with a reduced likelihood of hypertrophy for these three placental growth dimensions. We observed an increased likelihood of growth restriction for placental weight and chorionic plate area among mothers with hypertensive disease at 24 weeks or beyond. Anemia was associated with a reduced likelihood of growth restriction for placental weight and chorionic plate area. Pre-pregnancy BMI and pregnancy weight gain were associated with a reduced likelihood of growth restriction and an increased likelihood of hypertrophy for all three dimensions of placental growth.</p> <p>Conclusion</p> <p>Maternal risk factors are either associated with placental growth restriction or placental hypertrophy not both. Our findings suggest that the placenta may have compensatory responses to certain maternal risk factors suggesting different underlying biological mechanisms.</p

Prostate Cancer Postoperative Nomogram Scores and Obesity

Nomograms are tools used in clinical practice to predict cancer outcomes and to help make decisions regarding management of disease. Since its conception, utility of the prostate cancer nomogram has more than tripled. Limited information is available on the relation between the nomograms' predicted probabilities and obesity. The purpose of this study was to examine whether the predictions from a validated postoperative prostate cancer nomogram were associated with obesity.We carried out a cross-sectional analysis of 1220 patients who underwent radical prostatectomy (RP) in southern California from 2000 to 2008. Progression-free probabilities (PFPs) were ascertained from the 10-year Kattan postoperative nomogram. Multivariable logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs).In the present study, aggressive prostate cancer (Gleason ≥7), but not advanced stage, was associated with obesity (p = 0.01). After adjusting for age, black race, family history of prostate cancer and current smoking, an inverse association was observed for 10-year progression-free predictions (OR = 0.50; 95% CI = 0.28–0.90) and positive associations were observed for preoperative PSA levels (OR = 1.23; 95% CI = 1.01–1.50) and Gleason >7 (OR = 1.45; 95% CI = 1.11–1.90).Obese RP patients were more likely to have lower PFP values than non-obese patients, suggesting a higher risk of experiencing prostate cancer progression. Identifying men with potentially higher risks due to obesity may improve disease prognosis and treatment decision-making