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

Physical activity positively impacts disability outcomes during transition from midlife to early older age irrespective of body mass index

We examined the effects of physical activity (PA) and body mass index (BMI) longitudinal patterns (trajectories) on subjective measures of mobility, function, and disability in adults and assessed whether effects of PA trajectories on function varied due to BMI. Group-based trajectory analyses were used to determine patterns of change in PA and BMI using data from the Health and Retirement Study 1931-1941 birth cohort (n = 10,507). Physical function was assessed by Mobility Limitations (0-5 scale) and Large Muscle Function (0-4 scale) Indexes, as well as with score for activities of daily living (ADLs) and instrumental activities of daily living (IADLs), with higher scores being worse. Our analyses estimated four distinct PA trajectories: decreasing, (2) fluctuating, (3) stable high, and (4) emergent (previously low/sedentary with increased PA over the study period). Worse mobility limitations, large muscle function, ADLs, and IADLs were associated with Decreasing and Fluctuating PA groups. Better outcomes were associated with Emergent and Stable High PA groups. The five BMI trajectories were stable normal/overweight, modest decreasing, fluctuating, steep decreasing, and increasing. No significant interaction existed between PA and BMI trajectories for Mobility Limitations (P= 0.577), Large Muscle Function (P= 0.511), ADLs (P= 0.600), and IADLs (P= 0.152). These findings may empower clinicians to promote messages to midlifers that meaningful changes in PA can improve function in older age.

BMI trajectory in adulthood in relation to all-cause and cause-specific mortality: A retrospective cohort study in Taiwan

A dynamic change of weight over time has been known as an important factor that impacts mortality risk. The aims of this study were to identify the heterogeneity of BMI trajectory groups and to examine the association of the trajectories of BMI and all-cause and cause-specific mortality. The data for this study were obtained from a large prospective cohort study in Taiwan between 1998 and 2019 that was linked to the National Death Registry for death information. The participants were stratified into four groups by age and gender; self-reported demographics and measured BMI data were used. We used group-based trajectory analysis to identify the distinct trajectories of changes in BMI. A Cox proportional hazards model was used to assess the hazard ratio (HR) of all-cause and cause-specific mortality risk. Data were analyzed in April 2020 and included 89,886 participants. Four trajectory groups were identified by the pattern of BMI change over time. Our study shows that different trajectories were associated with mortality. Our findings suggest that the mortality risk differs in each trajectory group and in each age and gender stratification. It appears that obesity is a protective factor in cancer-related mortality in females but not in males in group of old age participants; low-normal weight is a risk factor in respiratory-related mortality in all participants. Our findings can be used to suggest the appropriate BMI in each age and gender groups and thereby earlier health interventions can be taken to avoid mortality.

Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank

BACKGROUND

An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants.

METHODS

In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation.

RESULTS

Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent-offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother-son) and 1.23 (95% CI: 1.16, 1.29; father-daughter).

CONCLUSION

Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them.

Association between physical activity and longitudinal change in body mass index in middle-aged and older adults

BACKGROUND

In middle-aged and particularly older adults, body mass index (BMI) is associated with various health outcomes. We examined associations between physical activity (PA) and longitudinal BMI change in persons aged ≥ 50 years.

METHODS

The sample included 5159 community-dwelling individuals aged ≥ 50 years (50.5% males, mean (SD) age 73.0 (10.2) years at baseline) who were enrolled in the Mayo Clinic Study of Aging (MCSA). Participants had information on PA within one year of baseline assessment, BMI at baseline, and potential follow-up assessments (mean (SD) follow-up 4.6 (3.7) years). Linear mixed-effect models were used to calculate the association between PA (moderate-vigorous physical activity, MVPA; and all PA composite score) and the longitudinal change in BMI, adjusted for baseline age, sex, education and medical comorbidities. In addition to interactions between years since baseline and PA, we also included 2- and 3-way interactions with baseline age to further assess whether age modifies the trajectory of BMI over time.

RESULTS

We observed a decrease in BMI among participants engaging at a mean amount of PA (i.e.

, MVPA

2.7; all PA: 6.8) and with a mean age (i.e., 73 years) at baseline (MVPA: estimate = -0.047, 95% CI -0.059, -0.034; all PA: estimate = -0.047, 95% CI -0.060, -0.035), and this decline is accelerated with increasing age. Participants with a mean age (i.e., 73 years) that engage at an increased amount of MVPA or all PA at baseline (i.e., one SD above the mean) do not decrease as fast with regard to BMI (MVPA: estimate = -0.006; all PA: estimate = -0.016), and higher levels of MVPA or all PA at baseline (i.e., two SD above the mean) were even associated with an increase in BMI (MVPA: estimate = 0.035; all PA: estimate = 0.015). Finally, MVPA but not all PA is beneficial at slowing BMI decline with increasing age.

CONCLUSION

PA, particularly at moderate-vigorous intensity, is associated with slower decline in longitudinal BMI trajectories. This implies that engaging in PA may be beneficial for healthy body weight regulation in middle and late adulthood.

Forecasting the Health Transition and Medical Expenditure of the Future Elderly in China: A Longitudinal Study Based on Markov Chain and Two Part Model

Set in the rapid development of population aging, this study focuses on the relationship between health and medical expenditure of the elderly population. Taking the health and medical expenditure of the elderly as the research object, this study analyzes the characteristics and the intrinsic relationship between them. Based on the future elderly model, this study calculates the transition probability of the elderly's self-assessment health state using the Health Transition Model and estimates the medical expenditure of the elderly by the Two-Part Model. Based on the above, this study predicts the trend of the population size and medical expenditure of the elderly in the next 15 years (2020–2035). Based on the results, the policy suggestions are put forward. To begin with, strengthening health management and health services for the elderly in the construction of healthy China. Next, building a comprehensive system of health care for the elderly in government, society, family, and individual. Then, establishing a long-term care service system as soon as possible. In addition, it is better to establish lifelong health consciousness and cultivate healthy accomplishment behavior. Finally, it is necessary to promote gender mainstreaming in the health field.

Predictors of change in BMI over 10 years among midlife and older adults: Associations with gender, CVD risk status, depressive symptoms, and social support

RATIONALE

Change in BMI is recognized as a key health indicator among midlife and older adults, though predictors of BMI change in this group have received little attention.

OBJECTIVE

The aim of this study was to examine relations between hypothesized predictors (i.e., gender, cardiovascular disease [CVD] risk status, depressive symptoms, social support) and BMI change over 10 years, among midlife and older adults.

METHODS

Participants were adults ages 50-74 at baseline (N = 5,688, 64% women) who completed four assessments over 10 years. Gender, CVD risk status (i.e., diagnosis of hypertension, type 2 diabetes, or both), depressive symptoms, and perceived social support were assessed at baseline, and BMI was calculated from height and weight reports at all assessments. Multilevel models tested for concurrent and prospective relations between predictors and BMI change (effect size estimates as semipartial correlation coefficients, sr), as well as whether observed relations were further moderated by baseline BMI category (underweight, healthy weight, overweight, or obese).

RESULTS

Baseline BMI was higher among those with (vs. without) CVD risk, higher (vs. lower) depressive symptoms, and lower (vs. higher) social support; all of these relations were moderated by gender (ps < 0.05, srs 0.03-0.32). Moreover, BMI showed significant change over 10 years, and BMI variability during this time was higher among women (vs. men) and those with (vs. without) CVD risk (ps < 0.0001). BMI change also differed by CVD risk status, and this relation was moderated by gender, baseline depressive symptoms, and baseline BMI category (ps < 0.05, srs 0.03-0.08).

CONCLUSIONS

Although the predictors of interest were not associated with steady BMI decreases (which are associated with long term health risks for older adults), findings reveal unique patterns of change in BMI among subgroups of midlife and older adults, and may allow for early identification of those with noteworthy BMI changes after age 50.

Body mass index trajectories among people with obesity and association with mortality: Evidence from a large Israeli database

Objective

Previous studies using longitudinal weight data to characterize obesity are based on populations of limited size and mostly include individuals of all body mass index (BMI) levels, without focusing on weight changes among people with obesity. This study aimed to identify BMI trajectories over 5 years in a large population with obesity, and to determine the trajectories' association with mortality.

Methods

For inclusion, individuals aged 30–74 years at index date (1 January 2013) with continuous membership in Clalit Health Services from 2008 to 2012 were required to have ≥1 BMI measurement per year in ≥3 calendar years during this period, of which at least one was ≥30 kg/m². Latent class analysis was used to generate BMI trajectories over 5 years (2008–2012). Cox proportional hazards models were used to assess the association between BMI trajectories and all‐cause mortality during follow‐up (2013–2017).

Results

In total, 367,141 individuals met all inclusion criteria. Mean age was 57.2 years; 41% were men. The optimal model was a quadratic model with four classes of BMI clusters. Most individuals (90.0%) had stable high BMI over time. Individuals in this cluster had significantly lower mortality than individuals in the other trajectory clusters (p < 0.01), including clusters of people with dynamic weight trajectories.

Conclusions

The results of the current study show that people with stable high weight had the lowest mortality of all four BMI trajectories identified. These findings help to expand the scientific understanding of the impact that weight trajectories have on health outcomes, while demonstrating the challenges of discerning the cumulative effects of obesity and weight change, and suggest that dynamic historical measures of BMI should be considered when assessing patients' future risk of obesity‐related morbidity and mortality, and when choosing a treatment strategy.

Trajectories of BMI Before Diagnosis of Type 2 Diabetes: The Rotterdam Study

OBJECTIVE

People with diabetes show great variability in weight gain and duration of obesity at the time of diagnosis. BMI trajectories and other cardiometabolic risk factors prior to type 2 diabetes were investigated.

METHODS

A total of 6,223 participants from the Rotterdam Study cohort were included. BMI patterns before diagnosis of diabetes were identified through latent class trajectories.

RESULTS

During a mean follow-up of 13.7 years, 565 participants developed type 2 diabetes. Three distinct trajectories of BMI were identified, including the "progressive overweight" group (n = 481, 85.1%), "progressive weight loss" group (n = 59, 10.4%), and "persistently high BMI" group (n = 25, 4.4%). The majority, the progressive overweight group, was characterized by a steady increase of BMI in the overweight range 10 years before diabetes diagnosis. The progressive weight loss group had fluctuations of glucose and marked beta cell function loss. The persistently high BMI group was characterized by a slight increase in insulin levels and sharp increase of insulin resistance accompanied by a rapid decrease of beta cell function.

CONCLUSIONS

Heterogeneity of BMI changes prior to type 2 diabetes was found in a middle-aged and elderly white population. Prevention strategies should be tailored rather than focusing only on high-risk individuals.

Weight Gain Trajectory and Pain Interference in Young Adulthood: Evidence from a Longitudinal Birth Cohort Study

OBJECTIVE

Obesity is associated with chronic pain, but the contribution of body mass index (BMI) trajectories over the life course to the onset of pain problems remains unclear. We retrospectively analyzed how BMI trajectories during the transition to adulthood were associated with a measure of pain interference obtained at age 29 in a longitudinal birth cohort study.

METHODS

Data from the National Longitudinal Survey of Youth, 1997 Cohort (follow-up from 1997 to 2015), were used to determine BMI trajectories from age 14 to 29 via group trajectory modeling. At age 29, respondents described whether pain interfered with their work inside and outside the home over the past four weeks (not at all, a little, or a lot). Multivariable ordinal logistic regression was used to evaluate pain interference according to BMI trajectory and study covariates.

RESULTS

Among 7,875 respondents, 11% reported "a little" and 4% reported "a lot" of pain interference at age 29. Four BMI trajectory groups were identified, varying in starting BMI and rate of weight gain. The "obese" group (8% of respondents) had a starting BMI of 30 kg/m2 and gained an average of 0.7 kg/m2/y. On multivariable analysis, this group was the most likely to have greater pain interference, compared with "high normal weight" (odds ratio [OR] = 1.47, 95% confidence interval [CI] = 1.14-1.88), "low normal weight" (OR = 1.45, 95% CI = 1.13-1.87), and "overweight" trajectories (OR = 1.33, 95% CI = 1.02-1.73).

CONCLUSIONS

Obesity and rapid weight gain during the transition to adulthood were associated with higher risk of pain interference among young adults.

Years of life with and without limitation in physical function and in activities of daily living by body mass index among older adults

BACKGROUND

While older adults with pre-obesity and Class I obesity have similar or lower mortality risk versus those with normal weight, a heavier body mass index (BMI) may not translate into more healthy life years. Utilizing longitudinal data on 3452 older (≥60 years) Singaporeans, we assessed the association between BMI and years of remaining life overall with and without limitation in physical function and in activities of daily living (ADLs).

METHODS

Difficulty in any of nine tasks involving upper or lower extremities was considered as limitation in physical function, and health-related difficulty in any basic ADL or instrumental ADL as limitation in ADLs. We utilized multistate life tables, including BMI as a time-varying covariate.

RESULTS

At age 60, life expectancy (LE) was similar for those with normal weight, pre-obesity and obesity. However, those with obesity, versus normal weight, had 6.3 [95% confidence interval: 3.4-9.2] more years with limitation in physical function and 4.9 [3.4-6.5] less years without limitation in physical function. Those with pre-obesity, versus normal weight, also had 3.7 [1.9-5.3] more years with limitation in physical function. The same pattern across BMI categories was observed for years of life with and without limitation in ADLs. In stratified analyses, similar associations of BMI with years of life with and without limitation in physical function and in ADLs were observed across gender, ethnicity, and educational status.

CONCLUSIONS

The increasing global prevalence of obesity may result in an increase in years of life with limitation in physical function and in ADLs at older ages. Older adults, their families and healthcare systems should be cognizant of this issue.

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand.

Body mass trajectories, diabetes mellitus, and mortality in a large cohort of Austrian adults

There are only few studies on latent trajectories of body mass index (BMI) and their association with diabetes incidence and mortality in adults.We used data of the Vorarlberg Health Monitoring & Prevention Program and included individuals (N=24,875) with BMI measurements over a 12-year period. Trajectory classes were identified using growth mixture modeling for predefined age groups (<50, 50-65, >65 years of age) and men, women separately. Poisson models were applied to estimate incidence and prevalence of diabetes for each trajectory class. Relative all-cause mortality and diabetes-related mortality was estimated using Cox proportional hazard regression.We identified 4 trajectory classes for the age groups <50 years and 50 to 65 years, and 3 for age groups >65 years. For all age groups, a stable BMI trajectory class was the largest, with about 90% of men and 70% to 80% of women. For the low stable BMI classes, the corresponding fasting glucose levels were the lowest. The highest diabetes prevalences were observed for decreasing trajectories. During subsequent follow-up of mean 8.1 (SD 2.0) years, 2741 individuals died. For men <50 years, highest mortality was observed for steady weight gainers. For all other age-sex groups, mortality was the highest for decreasing trajectories.We found considerably heterogeneity in BMI trajectories by sex and age. Stable weight, however, was the largest class over all age and sex groups, and was associated with the lowest diabetes incidence and mortality suggesting that maintaining weight at a moderate level is an important public health goal.

Validating a summary measure of weight history for modeling the health consequences of obesity

PURPOSE

Data on weight history may enhance the predictive validity of epidemiologic models of the health risks of obesity, but collecting such data is often not feasible. In this study, we investigate the validity of a summary measure of weight history.

METHODS

We evaluated the quality of reporting of maximum weight in a sample of adults aged 50-84 years using data from the Health and Retirement Study. Recalled max body mass index (BMI, measured in kilogram per square meter) based on recalled weight in 2004 was compared with calculated max BMI based on self-reported weight collected biennially between 1992 and 2004. Logistic regression was used to assess similarity between the measures in predicting prevalent conditions.

RESULTS

The correlation coefficient between recalled and calculated max weight in the overall sample was 0.95. Recalled max BMI value was within three BMI units of the calculated value 91.4% of the time. The proportions of individuals with obese I (BMI: 30.0-34.9), obese II (BMI: 35.0-39.9), and obese III (BMI: 40.0 and above) were 28.8%, 12.7%, and 6.6% using recalled values compared with 27.1%, 10.5%, and 4.9% using calculated values. In multivariate analyses, the two BMI measures similarly predicted disease prevalence across a number of chronic conditions.

CONCLUSIONS

Recalled max BMI was strongly correlated with max BMI calculated over the 12-year period before recall, suggesting that this measure can serve as a reliable summary measure of recent weight status.