Moonseong Heo

Weight management using a meal replacement strategy: meta and pooling analysis from six studies

OBJECTIVE: Although used by millions of overweight and obese consumers, there has not been a systematic assessment on the safety and effectiveness of a meal replacement strategy for weight management. The aim of this study was to review, by use of a meta- and pooling analysis, the existing literature on the safety and effectiveness of a partial meal replacement (PMR) plan using one or two vitamin/mineral fortified meal replacements as well as regular foods for long-term weight management.DESIGN: A PMR plan was defined as a program that prescribes a low calorie (>800≤1600 kcal/day) diet whereby one or two meals are replaced by commercially available, energy-reduced product(s) that are vitamin and mineral fortified, and includes at least one meal of regular foods. Randomized, controlled PMR interventions of at least 3 months duration, with subjects 18 y of age or older and a BMI≥25 kg/m2, were evaluated. Studies with self-reported weight and height were excluded. Searches in Medline, Embase, and the Cochrane Clinical Trials Register from 1960 to January 2001 and from reference lists identified 30 potential studies for analysis. Of these, six met all of the inclusion criteria and used liquid meal replacement products with the associated plan. Overweight and obese subjects were randomized to the PMR plan or a conventional reduced calorie diet (RCD) plan. The prescribed calorie intake was the same for both groups. Authors of the six publications were contacted and asked to supply primary data for analysis. Primary data from the six studies were used for both meta- and pooling analyses.RESULTS: Subjects prescribed either the PMR or RCD treatment plans lost significant amounts of weight at both the 3-month and 1-year evaluation time points. All methods of analysis indicated a significantly greater weight loss in subjects receiving the PMR plan compared to the RCD group. Depending on the analysis and follow-up duration, the PMR group lost ∼7–8% body weight and the RCD group lost ∼3–7% body weight. A random effects meta-analysis estimate indicated a 2.54 kg (P<0.01) and 2.43 kg (P=0.14) greater weight loss in the PMR group for the 3-month and 1-y periods, respectively. A pooling analysis of completers showed a greater weight loss in the PMR group of 2.54 kg (P<0.01) and 2.63 kg (P<0.01) during the same time period. Risk factors of disease associated with excess weight improved with weight loss in both groups at the two time points. The degree of improvement was also dependent on baseline risk factor levels. The dropout rate for PMR and RCD groups was equivalent at 3 months and significantly less in the PMR group at 1 y. No reported adverse events were attributable to either weight loss regimen.CONCLUSION: This first systematic evaluation of randomized controlled trials utilizing PMR plans for weight management suggests that these types of interventions can safely and effectively produce significant sustainable weight loss and improve weight-related risk factors of disease.

A mixture model approach for the analysis of microarray gene expression data

Microarrays have emerged as powerful tools allowing investigators to assess the expression of thousands of genes in different tissues and organisms. Statistical treatment of the resulting data remains a substantial challenge. Investigators using microarray expression studies may wish to answer questions about the statistical significance of differences in expression of any of the genes under study, avoiding false positive and false negative results. We have developed a sequence of procedures involving finite mixture modeling and bootstrap inference to address these issues in studies involving many thousands of genes. We illustrate the use of these techniques with a dataset involving calorically restricted mice.

Weight loss increases and fat loss decreases all-cause mortality rate: Results from two independent cohort studies

OBJECTIVE: In epidemiological studies, weight loss is usually associated with increased mortality rate. Contrarily, among obese people, weight loss reduces other risk factors for disease and death. We hypothesised that this paradox could exist because weight is used as an implicit adiposity index. No study has considered the independent effects of weight loss and fat loss on mortality rate. We studied mortality rate as a function of weight loss and fat loss.DESIGN: Analysis of ‘time to death’ in two prospective population-based cohort studies, the Tecumseh Community Health Study (1890 subjects; 321 deaths within 16 y of follow-up) and the Framingham Heart Study (2731 subjects; 507 deaths within 8 y of follow-up), in which weight and fat (via skinfolds) loss were assessable.RESULTS: In both studies, regardless of the statistical approach, weight loss was associated with an increased, and fat loss with a decreased, mortality rate (P<0.05). Each standard deviation (s.d.) of weight loss (4.6 kg in Tecumseh, 6.7 kg in Framingham) was estimated to increase the hazard rate by 29% (95% confidence interval CI), (14%, 47%, respectively) and 39% (95% CI, 25%, 54% respectively), in the two samples. Contrarily, each s.d. of fat loss (10.0 mm in Tecumseh, 4.8 mm in Framingham) was estimated to reduce the hazard rate 15% (95% CI, 4%, 25%) and 17% (95% CI, 8%, 25%) in Tecumseh and Framingham, respectively. Generalisability of these results to severely (that is, body mass index BMI) ≥34) obese individuals is unclear.CONCLUSIONS: Among individuals that are not severely obese, weight loss is associated with increased mortality rate and fat loss with decreased mortality rate.

Body mass index and all-cause mortality among people age 70 and over: the Longitudinal Study of Aging

OBJECTIVES: To assess the relationship between body mass index (BMI; kg/m2) and mortality in a large nationally representative sample of US adults over age 70 years. DESIGN: Prospective longitudinal cohort study, the Longitudinal Study of Aging (LSOA). Subjects were all those 7260 black and white people (2769 men, 4491 women) initially interviewed in 1984 for whom height and weight were available. These subjects were followed through to 1990. MEASUREMENTS: Measurements included self-reported height and weight, date of death if subjects died, sex, age, race, measures of socio-economic status, number of living first degree relatives, and responses to questions asking whether the subject had retired due to poor health, had difficulty eating, worried about their health, and felt their health was worse than during the prior year. Smoking status was not assessed. RESULTS: When analyzed via Cox proportional hazard regression, the relationship between BMI and mortality, represented by means of hazard ratio, was clearly U-shaped for both men and women. The base of the curves was fairly wide suggesting that a broad range of BMIs are well tolerated by older adults. The minimum mortality (estimated from the fitted proportional hazard models) occurred at a BMI of approximately 31.7 for women and 28.8 for men. The results were essentially unchanged, if analyses were weighted, if various disease states were controlled for, and if apparently unhealthy subjects were excluded. CONCLUSIONS: The finding of the relatively high BMI (27–30 for men, 30–35 for women) associated with minimum hazard in persons older than seventy years supports some previously documented findings and opposes others and, if confirmed in future research, has implications for public health and clinical recommendations.

Meta-analysis of the association of the Trp64Arg polymorphism in the β3 adrenergic receptor with body mass index

OBJECTIVE: As a result of efforts to isolate obesity-promoting genes, the Trp64Arg polymorphism in the β3 adrenergic receptor locus, has been studied by many investigators. Results of the studies have varied in statistical significance and magnitude of the association of the polymorphism with body mass index (BMI: kg/m2). This has led to controversy about whether this polymorphism is associated with meaningful changes in BMI. To clarify the possible association, we conducted a meta-analysis.DESIGN: Meta-analytic study.MEASUREMENTS: For each genotype of the β3 adrenergic receptor (Trp/Trp; Trp/Arg; Arg/Arg), we extracted the number of subjects, mean and standard deviation of BMI from 23 studies, including 36 different subgroups with a total of 7399 subjects. Other indices and obesity-related variables were not considered.RESULTS: No significant association of the Trp64Arg polymorphism with BMI was found. The weighted mean BMI difference beween Trp/Trp homozygotes and Trp/Arg heterozygotes was 0.19 (s.e.=0.11; P=0.07). In addition, the distribution of effect sizes was not significantly heterogeneous (χ2=38.68; df 35; P=0.31) suggesting that the variation of the effect sizes across the subgroups is not significant. A further weighted regression analysis, utilizing all three genotypes and adjusting for the random subgroup effect, also showed the effect of the polymorphism on BMI is not significant (F=1.72, df=(2,54), P=0.19).CONCLUSION: Based on existing data, the Trp64Arg polymorphism does not appear to be significantly associated with BMI. Moreover, we found no evidence for effect heterogeneity, suggesting that the effect of the polymorphism is not moderated by ethnicity or diabetic status.

Sibling-Based Tests of Linkage and Association for Quantitative Traits

The transmission/disequilibrium test (TDT) developed by Spielman et al. can be a powerful family-based test of linkage and, in some cases, a test of association as well as linkage. It has recently been extended in several ways; these include allowance for implementation with quantitative traits, allowance for multiple alleles, and, in the case of dichotomous traits, allowance for testing in the absence of parental data. In this article, these three extensions are combined, and two procedures are developed that offer valid joint tests of linkage and (in the case of certain sibling configurations) association with quantitative traits, with use of data from siblings only, and that can accommodate biallelic or multiallelic loci. The first procedure uses a mixed-effects (i.e., random and fixed effects) analysis of variance in which sibship is the random factor, marker genotype is the fixed factor, and the continuous phenotype is the dependent variable. Covariates can easily be accommodated, and the procedure can be implemented in commonly available statistical software. The second procedure is a permutation-based procedure. Selected power studies are conducted to illustrate the relative power of each test under a variety of circumstances.

Evidence for Independent Genetic Influences on Fat Mass and Body Mass Index in a Pediatric Twin Sample

Objective. Insight into genetic and environmental influences on fat mass, independent of body mass index (BMI; kg/m2), is expected to enhance methods for treating pediatric obesity. However, few studies have estimated the heritability of fat mass in pediatric samples, and those conducted have relied primarily on BMI measurements. Present Study. Using bioimpedance analysis, the present study tested a series of hypotheses predicting significant genetic and environmental influences on percent body fat (PBF) above and beyond BMI. Subjects were 66 pairs of twins, including 41 monozygotic and 25 dizygotic pairs, from 3 to 17 years of age. Structural equation modeling tested hypotheses, adjusting for demographic variables. Results. Analyses indicated significant genetic influences on PBF, with genes estimated to account for 75% to 80% of the phenotypic variation. The remaining variation was attributable to nonshared environmental influences. Multivariate analyses revealed sizable genetic correlations and environmental correlations between BMI and PBF (rg = .74 andre = .67, respectively), suggesting that some genes and environmental experiences influence both phenotypes. However, analyses confirmed genetic and environmental influences on PBF above and beyond BMI. For example, 62.5% of the total genetic variation in PBF was attributable to genes that influenced PBF but not BMI. Conclusion. There seems to be a substantial genetic contribution to fat mass distinct from BMI in a sample of children and adolescents. Studies testing putative genetic or environmental determinants of pediatric obesity might be strengthened further by including research-based body composition methods. pediatric obesity, twin design, heritability, nonshared environment, bioimpedance analysis, body mass index, body composition.

Body Weight and Cancer Screening among Women

Obesity increases cancer risk, yet small-scale surveys indicate that obese women delay or avoid cancer screening even more so than do nonobese women. We sought to estimate the association between body mass index (BMI) (kg/m(2)) and delayed cancer screening among adult women in a population-based survey. Subjects were women classified by BMI as underweight (<18.5), desirable weight (18.5-24.9), overweight (25-29.9), obese class I (30-34.9), obese class II (35-39.9), and obese class III (> or =40). Outcome measures were intervals (0 for < or =2 years versus 1 for >2 years) since most recent screening for Papanicolaou (Pap) smear, mammography, and clinical breast examination (CBE). Adjusting for age, race, smoking, and health insurance, we observed J-shaped associations between BMI and screening. Compared with desirable weight women, underweight women (odds ratios [OR] = 1.21, 95% confidence interval [95% CI] 1.09-1.34), overweight women (OR = 1.13, 95% CI 1.07-1.18), and obese women (OR range 1.22-1.69) were significantly more likely to delay Pap smear testing for >2 years. Underweight (OR = 1.32, 95% CI 1.13-1.54), obesity class I (OR = 1.12, 95% CI 1.02-1.23), and obesity class III women (OR = 1.32, 95% CI 1.10-1.54) were more likely to delay mammography, and overweight (OR = 1.10, 95% CI 1.01-1.19), obesity class I (OR = 1.18, 95% CI 1.08-1.30), and obesity class III women (OR = 1.47, 95% CI 1.23-1.75) were more likely to delay CBE. White women were more likely to delay CBE as a function of BMI than were non-white women. Weight may be an important correlate of cancer screening behavior, particularly for white women.

Associations of Body Mass Index and Anthropometric Indicators of Fat Mass and Fat Free Mass with All-cause Mortality among Women in the First and Second National Health and Nutrition Examination Surveys Follow-up Studies

PURPOSE This study tests whether fat mass (FM) and fat free mass (FFM) have opposite associations with mortality in a nationally representative sample of females. METHODS Data on 13,369 female participants from National Health and Nutrition Examination Surveys (NHANES) I and II (aged 25 to 75 years) were analyzed. Mean follow-up time was 16.1 years. Ninety-seven percent of the participants with 3020 deaths were successfully followed. Subscapular and triceps skinfolds thickness were used as a FM indicator (FMI). Upper arm circumference was used as a FFM indicator (FFMI). Cox regression tested the relationships of BMI, FM and FFM with all-cause mortality adjusting for various socio-demographic variables. RESULTS BMI had a U-shaped relationship with mortality with a nadir of approximately 27 kg/m(2). When FFMI was added to the model, the relationship between BMI and mortality became more monotonic increasing. FMI showed a significant negative relationship with mortality. CONCLUSIONS Contrary to expectations, both FFMI and FMI had negative relationships with mortality. These results differ from patterns previously observed in males and may reflect sex differences in fat distribution. Research using superior measures of body fat amount and distribution may resolve these discrepancies.

Bias in Estimates of Quantitative-Trait–Locus Effect in Genome Scans: Demonstration of the Phenomenon and a Method-of-Moments Procedure for Reducing Bias

An attractive feature of variance-components methods (including the Haseman-Elston tests) for the detection of quantitative-trait loci (QTL) is that these methods provide estimates of the QTL effect. However, estimates that are obtained by commonly used methods can be biased for several reasons. Perhaps the largest source of bias is the selection process. Generally, QTL effects are reported only at locations where statistically significant results are obtained. This conditional reporting can lead to a marked upward bias. In this article, we demonstrate this bias and show that its magnitude can be large. We then present a simple method-of-moments (MOM)-based procedure to obtain more-accurate estimates, and we demonstrate its validity via Monte Carlo simulation. Finally, limitations of the MOM approach are noted, and we discuss some alternative procedures that may also reduce bias.