Shape of the BMI-mortality association by cause of death, using generalized additive models: NHIS 1986-2006

Weight Change Since Age 20 and the Risk of Cardiovascular Disease Mortality: A Prospective Cohort Study

Aim: Weight change could have many health outcomes. This study aimed to investigate the association between weight change and mortality risk due to total cardiovascular disease (CVD), ischemic heart disease (IHD), and stroke among Japanese.

Methods: We used Suita Study data from 4,746 people aged 30-79 years in this prospective cohort study. Weight change was defined as the difference between baseline weight and weight at age 20. We used Cox proportional hazards models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) of total CVD, IHD, and stroke mortality for 1) participants with a weight change (＞10, 5 to 10, -5 to -10, and ＜-10 kg) compared to those with stable weight (-4.9 to 4.9 kg) and 2) participants who moved from one body mass index category (underweight, normal weight, or overweight) to another compared to those with normal weight at age 20 and baseline.

Results: Within a median follow-up period of 19.9 years, the numbers of total CVD, IHD, and stroke mortality were 268, 132, and 79, respectively. Weight loss of ＞10 kg was associated with the increased risk of total CVD mortality 2.07 (1.29, 3.32) and stroke mortality 3.02 (1.40, 6.52). Moving from normal weight at age 20 to underweight at baseline was associated with the increased risk of total CVD, IHD, and stroke mortality: 1.76 (1.12, 2.77), 2.10 (1.13, 3.92), and 2.25 (1.05, 4.83), respectively.

Conclusion: Weight loss, especially when moving from normal to underweight, was associated with the increased risk of CVD mortality.

The Role of Underweight in Active Life Expectancy Among Older Adults in Japan

OBJECTIVES

It is underweight, rather than overweight or obesity, that has been a pressing public health concern in Japan. This study examines the impact of being underweight on the health of older Japanese men and women, measured by active life expectancy at age 65. Following the Japanese government's guideline, underweight in this study is defined using the body mass index (BMI) value of 20.

METHOD

Data came from five waves (1999-2009) of the Nihon University Japanese Longitudinal Study of Aging (NUJLSOA). We used the Interpolation of Markov Chain approach to estimate the number of years underweight (BMI < 20), normal weight (20 < BMI < 25), and overweight (25 < BMI) individuals were expected to live without difficulty in activities of daily living (ADLs) or instrumental ADLs.

RESULTS

We found differences in life and health expectancies across the three weight categories. Underweight people were expected to live the shortest lives and spend the fewest years in an active state compared with normal and overweight individuals. Results remained unchanged even when accounting for educational attainment, smoking history, and a count of existing chronic conditions.

DISCUSSION

Being underweight is associated with poor quality of life lived among Japanese older adults. This finding suggests the importance of maintaining proper weight and avoids nutritional risks at advanced ages.

Shape of BMI-Mortality Risk Associations: Reverse Causality and Heterogeneity in a Representative Cohort of US Adults

OBJECTIVE

This study examines BMI-mortality associations and evaluates strategies intended to limit reverse causality. Heterogeneity in BMI-mortality risk associations across subgroups and causes of death is explored.

METHODS

A cohort of 654,382 adults from the US National Health Interview Survey was constructed. Associations between unit BMI levels and mortality were estimated using Cox proportional hazards models, including and excluding the first 5 years of follow-up, with and without controls for smoking or preexisting conditions, and including and excluding ever-smokers and individuals with preexisting conditions. Stratified analyses by individual characteristics were performed.

RESULTS

Addressing reverse causality led to reduced risk of mortality among those with low BMI levels (<18 kg/m² ). Excluding ever-smokers and individuals with preexisting conditions further led to increased risk among those with high BMI levels (between 33 kg/m² and >40 kg/m² ) and lowered the estimated nadir risk from 27 kg/m² to 23 kg/m² . After excluding ever-smokers and individuals with preexisting conditions, limiting the analysis to >5 years of follow-up produced no substantive changes. Heterogeneous results were observed across individual characteristics, particularly age and causes of death.

CONCLUSIONS

The exclusion of smokers and individuals with preexisting conditions alters the BMI-mortality risk association and results in a somewhat lower range of BMI with minimum mortality risk.

A kernel- and optimal transport- based test of independence between covariates and right-censored lifetimes

We propose a nonparametric test of independence, termed optHSIC, between a covariate and a right-censored lifetime. Because the presence of censoring creates a challenge in applying the standard permutation-based testing approaches, we use optimal transport to transform the censored dataset into an uncensored one, while preserving the relevant dependencies. We then apply a permutation test using the kernel-based dependence measure as a statistic to the transformed dataset. The type 1 error is proven to be correct in the case where censoring is independent of the covariate. Experiments indicate that optHSIC has power against a much wider class of alternatives than Cox proportional hazards regression and that it has the correct type 1 control even in the challenging cases where censoring strongly depends on the covariate.

Effects of categorization and self-report bias on estimates of the association between obesity and mortality

PURPOSE

The health consequences of obesity are often assessed using categorical, self-reported data on body mass index (BMI). This article investigates the combined effects of categorization and self-report bias on the estimated association between obesity and mortality.

METHODS

We used the National Health and Nutrition Examination Survey (1988-2008) linked to death records through 2011. Cox models and age-standardized death rates were used to evaluate the effects of categorization and self-report bias on the mortality risks and percent of deaths attributable to obesity.

RESULTS

Despite a correlation between measured and self-reported BMI of 0.96, self-reports miscategorized 20% of adults. Hazard ratios using self-reports were overstated for the obese 1 (BMI, 30-35 kg/m(2)) and obese 2 (BMI ≥ 35 kg/m(2)) categories. The bias was much smaller using a continuous measure of BMI. In contrast, the percent of deaths attributable to excess weight was lower using self-reported versus measured data because self-reports led to systematic downward bias in the BMI distribution.

CONCLUSIONS

Categorization of BMI and self-report bias combine to produce substantial error in the estimated hazard ratios and percent of deaths attributable to obesity. Future studies should use caution when estimating the association between obesity and mortality using categorical self-reported data.

Reverse Epidemiology of Traditional Cardiovascular Risk Factors in the Geriatric Population

Traditional risk factors of cardiovascular death in the general population, including body mass index (BMI), serum cholesterol, and blood pressure (BP), are also found to relate to outcomes in the geriatric population, but in an opposite direction. Some degrees of elevated BMI, serum cholesterols, and BP are reportedly associated with lower, instead of higher, risk of death among the elderly. This phenomenon is termed "reverse epidemiology" or "risk factor paradox" (such as obesity paradox) and is also observed in a variety of chronic disease states such as end-stage renal disease requiring dialysis, chronic heart failure, rheumatoid arthritis, and AIDS. Several possible causes are hypothesized to explain this risk factor reversal: competing short-term and long-term killers, improved hemodynamic stability in the obese, adipokine protection against tumor necrosis factor-α, lipoprotein protection against endotoxins, and lipophilic toxin sequestration by the adipose tissue. It is possible that the current thresholds for intervention and goal levels for such traditional risk factors as BMI, serum cholesterol, and BP derived based on younger populations do not apply to the elderly, and that new levels for such risk factors should be developed for the elderly population. Reverse epidemiology of conventional cardiovascular risk factors may have a bearing on the management of the geriatric population, thus it deserves further attention.

Long-Term BMI Trajectories and Health in Older Adults: Hierarchical Clustering of Functional Curves

OBJECTIVE

This project contributes to the emerging research that aims to identify distinct body mass index (BMI) trajectory types in the population. We identify clusters of long-term BMI curves among older adults and determine how the clusters differ with respect to initial health.

METHOD

Health and Retirement Study cohort (N = 9,893) with BMI information collected in up to 10 waves (1992-2010) is analyzed using a powerful cutting-edge approach: hierarchical clustering of BMI functions estimated via the Principal Analysis by Conditional Expectations (PACE) algorithm.

RESULTS

Three BMI trajectory clusters emerged for each gender: stable, gaining, and losing. The initial health of the gaining and stable groups in both genders was comparable; the losing cluster experienced significantly poorer health at baseline.

DISCUSSION

BMI trajectories among older adults cluster into distinct types in both genders, and the clusters vary substantially in initial health. Weight loss but not gain is associated with poor initial health in this age group.

How Much Should We Weigh for a Long and Healthy Life Span? The Need to Reconcile Caloric Restriction versus Longevity with Body Mass Index versus Mortality Data

Total caloric restriction (CR) without malnutrition is a well-established experimental approach to extend life span in laboratory animals. Although CR in humans is capable of shifting several endocrinological parameters, it is not clear where the minimum inflection point of the U-shaped curve linking body mass index (BMI) with all-cause mortality lies. The exact trend of this curve, when used for planning preventive strategies for public health is of extreme importance. Normal BMI ranges from 18.5 to 24.9; many epidemiological studies show an inverse relationship between mortality and BMI inside the normal BMI range. Other studies show that the lowest mortality in the entire range of BMI is obtained in the overweight range (25-29.9). Reconciling the extension of life span in laboratory animals by experimental CR with the BMI-mortality curve of human epidemiology is not trivial. In fact, one interpretation is that the CR data are identifying a known: "excess fat is deleterious for health"; although a second interpretation may be that: "additional leanness from a normal body weight may add health and life span delaying the process of aging." This short review hope to start a discussion aimed at finding the widest consensus on which weight range should be considered the "healthiest" for our species, contributing in this way to the picture of what is the correct life style for a long and healthy life span.

Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.

The impact of body mass index and weight changes on disability transitions and mortality in brazilian older adults

The aim of this study was to examine the association between body mass index and weight changes on disability transitions and mortality among Brazilian older adults. Longitudinal data from the Health, Well-Being, and Aging in Latin America and the Caribbean Study conducted in São Paulo, Brazil (2000 and 2006), were used to examine the impact of obesity on disability and mortality and of weight changes on health transitions related to disability. Logistic and multinomial regression models were used in the analyses. Individuals who were obese were more likely than those of normal weight to have limitations on activities of daily living (ADL), instrumental activity of daily living (IADL), and Nagi's limitations. Obesity was associated with higher incidence of ADL and IADL limitations and with lower recovery from Nagi's limitations. Compared to those who maintained their weight, those who gained weight experienced higher incidence of ADL and Nagi's limitations, even after controlling for initial body mass index. Higher mortality among overweight individuals was only found when the reference category was "remaining free of Nagi limitations." The findings of the study underline the importance of maintaining normal weight for preventing disability at older ages.