Body Mass Index and Mortality in Elderly Subjects from the Moli-Sani Study: A Possible Mediation by Low-Grade Inflammation?

The Obesity Paradox and Mortality in Older Adults: A Systematic Review

“Obesity paradox” describes the counterintuitive finding that aged overweight and obese people with a particular disease may have better outcomes than their normal weight or underweight counterparts. This systematic review was performed to summarize the publications related to the obesity paradox in older adults, to gain an in-depth understanding of this phenomenon. PubMed©, Embase©, and Scopus© were used to perform literature search for all publications up to 20 March 2022. Studies were included if they reported data from older adults on the relation between BMI and mortality. The following article types were excluded from the study: reviews, editorials, correspondence, and case reports and case series. Publication year, study setting, medical condition, study design, sample size, age, and outcome(s) were extracted. This review has been registered with PROSPERO (no. CRD42021289015). Overall, 2226 studies were identified, of which 58 were included in this systematic review. In all, 20 of the 58 studies included in this review did not find any evidence of an obesity paradox. Of these 20 studies, 16 involved patients with no specific medical condition, 1 involved patients with chronic diseases, and 2 involved patients with type 2 diabetes mellitus. Seven out of the nine studies that looked at short-term mortality found evidence of the obesity paradox. Of the 28 studies that examined longer-term mortality, 15 found evidence of the obesity paradox. In the studies that were conducted in people with a particular medical condition (n = 24), the obesity paradox appeared in 18 cases. Our work supports the existence of an obesity paradox, especially when comorbidities or acute medical problems are present. These findings should help guide strategies for nutritional counselling in older populations.

Optimal body composition indices cutoff values based on all-cause mortality in the elderly

The cutoffs of body composition indices are inconclusive in older populations. This study is designed toward determining the optimal cutoffs of the body composition indices based on the association with all-cause mortality. During 2009 and 2010, a cohort population of 1200 was enrolled in central western Taiwan. Of the 1200 subjects, 428 older subjects (mean age: 72.5 ± 5.4 yrs.; 47.7 % were women) were censored in this study. The waist circumference (WC) and body mass index (BMI) were measured using standard anthropometric methods. A multi-frequency bioelectrical impedance analysis device was utilized to estimate each participant's body composition indices, including percent body fat (PBF) and skeletal muscle mass index (SMMI). All claims records of death from 2009 to 2018 in the National Health Insurance Research Databank were identified. A receiver operating characteristic curve method and the highest Youden index were used to identify the optimal cutoffs. A Cox proportional hazards regression analysis was used to model associations between each of the recommended cutoff values with all-cause mortality. The all-cause mortality rate was 20.09 % after a follow-up period of 5.86 ± 2.39 person-years. The significant indices cutoff value was identified to be WC (86.7 cm) for older women and BMI (23.8 kg/m²) and as WC (77.6 cm), and SMMI (8.7 kg/m²) for older men. The recommended optimal cutoffs of the body composition indices were gender-specific and can be utilized to predict the risk of all-cause mortality.

Understanding the risk of developing weight-related complications associated with different body mass index categories: a systematic review

BACKGROUND

Obesity and overweight are major risk factors for several chronic diseases. There is limited systematic evaluation of risk equations that predict the likelihood of developing an obesity or overweight associated complication. Predicting future risk is essential for health economic modelling. Availability of future treatments rests upon a model's ability to inform clinical and decision-making bodies.  This systematic literature review aimed to identify studies reporting (1) equations that calculate the risk for individuals with obesity, or overweight with a weight-related complication (OWRC), of developing additional complications, namely T2D, cardiovascular (CV) disease (CVD), acute coronary syndrome, stroke, musculoskeletal disorders, knee replacement/arthroplasty, or obstructive sleep apnea; (2) absolute or proportional risk for individuals with severe obesity, obesity or OWRC developing T2D, a CV event or mortality from knee surgery, stroke, or an acute CV event.

METHODS

Databases (MEDLINE and Embase) were searched for English language reports of population-based cohort analyses or large-scale studies in Australia, Canada, Europe, the UK, and the USA between January 1, 2011, and March 29, 2021. Included reports were quality assessed using an adapted version of the Newcastle Ottawa Scale.

RESULTS

Of the 60 included studies, the majority used European cohorts. Twenty-nine reported a risk prediction equation for developing an additional complication. The most common risk prediction equations were logistic regression models that did not differentiate between body mass index (BMI) groups (particularly above 40 kg/m²) and lacked external validation. The remaining included studies (31 studies) reported the absolute or proportional risk of mortality (29 studies), or the risk of developing T2D in a population with obesity and with prediabetes or normal glucose tolerance (NGT) (three studies), or a CV event in populations with severe obesity with NGT or T2D (three studies). Most reported proportional risk, predominantly a hazard ratio.

CONCLUSION

More work is needed to develop and validate these risk equations, specifically in non-European cohorts and that distinguish between BMI class II and III obesity. New data or adjustment of the current risk equations by calibration would allow for more accurate decision making at an individual and population level.

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.

Synergistic Impact of Body Mass Index and Cognitive Function on All-Cause Mortality in Older Adults: A Nationwide Longitudinal Study

BACKGROUND

Body mass index (BMI) and cognitive function are independent predictors of mortality risk. However, little is known about the combined impact of BMI and cognitive function on the risk of all-cause mortality in older adults. In this study, we aimed to examine the associations between BMI, cognitive function, and all-cause mortality, including between-factor interactions, in the general population of older adults in China.

METHODS

We used the data between 2011 and 2018 from the Chinese Longitudinal Healthy Longevity Survey that included adults aged ≥65 years residing in the 23 provinces of China. The association between BMI and cognitive function on all-cause mortality was examined with the Cox proportional hazards regression model.

RESULTS

The study included 8,293 Chinese older adults. Low BMI (underweight) and cognitive impairment were associated with the highest risk of death after adjustments [hazard ratio (HR) = 2.18; 95% confidence interval (CI), 1.96-2.41]; this combined effect was more prominent among adults aged <100 years and women. In addition, there was an interaction effect of BMI and cognitive impairment on all-cause mortality (P <0.001). Concurrently, among older adults with normal cognition, the risk of mortality related to underweight was higher than among their cognitively impaired counterparts [55% (normal cognition) vs. 38% (cognitive impairment)].

CONCLUSIONS

Low BMI (underweight) and cognitive impairment were independently and jointly associated with increased risk of all-cause mortality among Chinese older adults, and females showed a stronger effect in this association. The association between BMI and mortality was more pronounced in the participants with normal cognition than in their cognitively impaired counterparts.