INTRODUCTION
Obesity has been a major health issue in developed nations such as the United States for several decades, with a large increase now also occurring in lower- and middle-income countries.1,2) According to the National Health and Morbidity Survey 2019, 50.1% of Malaysian are now overweight or obese.3) The negative consequences of obesity on health and quality of life within the general population, particularly among those of younger age, has been apparent. While excess body fat within any population increases with age, in older adults, available evidence for excess body fat or adiposity and susceptibility to adverse outcomes such as co-morbidities, frailty, and death has been potentially conflicting.1) Conversely, with increasing age, the reduction in lean body mass is well documented and has an established relationship with adverse health outcomes.4)
Several studies have suggested that the presence of excess fat protects older adults against negative health outcomes. Within these studies individuals determined as overweight using the widely accepted body mass index (BMI) emerged as the longest survivors.5,6) This supposed paradox may have occurred due to the limitations of BMI as an indicator of adiposity, which is more apparent with older age.7,8) While increased body fat occurs alongside a reduction in lean body mass with increasing age, within the older age group, those with higher BMI usually also have higher muscle mass. The BMI does not take into consideration fat to muscle ratio or the quality of lean body mass, hence disregarding the metabolic effects of excess adiposity.7,8) Alternative measures of adiposity using the indices of waist circumference (WC), waist-to-hip ratio (WHR), and percentage body fat (%BF) have been suggested as alternatives to BMI.7)
While older age is inevitably associated with increased likelihood of death, good health maintenance remains necessary in order to enhance quality of life, reduce functional dependence, and reduce healthcare burden.9) While several studies have reported sex-specific mortality rate, there remains limited information on the consequence of sex-specific adiposity in older adults and role of muscle in the relationship. In this study, we sought to investigate the sex-specific relationship between excess body fat and mortality in community-dwelling older adults using various adiposity indices with further consideration towards the influence of muscle strength.
MATERIALS AND METHODS
Study Cohort
Baseline data were obtained from the Malaysian Elders Longitudinal Research (MELoR) study comprising 1,361 individuals aged 55 years and above residing in the Klang Valley of Malaysia, recruited by simple stratified, random electoral roll sampling from 2013 to 2015. This study received approval from the University of Malaya Medical Centre Medical Research Ethics Committee (Ref: 925.4). Participants who were terminally ill with life expectancy of 6 months or less, unable to respond to questionnaire, and unable to attend physical health checks were excluded. All participants provided written informed consent for longitudinal data collection prior to inclusion.
Data Collection
Sociodemographic data such as age, ethnicity and educational background, as well as medical history were obtained during home-based computer-aided interviews at baseline. Participant medical histories recorded consisted of self-reported non-communicable disorders. They were asked, “Has a doctor ever told you that you have any of the following conditions?” The conditions listed included heart disease, respiratory disease, cerebrovascular disease, other neurologic conditions, malignancy, and other health conditions—high blood pressure, high cholesterol, diabetes, vitamin B12 deficiency, intermittent claudication, chronic kidney disease (CKD) or failure, thyroid disease, indigestion, liver disease, arthritis, osteoporosis, gout, depression, anxiety, other psychiatric conditions, and incontinence. The medical history questionnaire is available upon request. Physical assessments were obtained during hospital-based health checks.
Anthropometric and adiposity measurements
Height and weight were first measured using a height stadiometer (SECA220; SECA GmbH, Hamburg, Germany) and a calibrated weighing scale (SECA769), respectively. The BMI was then calculated by dividing weight, measured in kilograms (kg), by the square of height in meters (m2). The study population was then categorized based on the standard BMI groups—underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30 kg/m2). Percentage body fat (%BF) was determined with bioimpedance analysis (QuadScan 4000; Bodystat, London, UK). Circumferences of the waist and hip were measured in centimeters (cm) using a measuring tape. The WHR was then calculated. WC, WHR, and %BF were categorized into quartile derived separately for men and female.
Muscle strength
Muscle strength was determined at baseline using the handgrip strength (HGS) test. Participants were instructed to sit upright with their forearms flexed at 90˚ on a chair with back and arm support. Their grip strength was measured thrice in kilograms (kg) using a handgrip dynamometer (Patterson Medical/Sammons Preston, Bolingbrook, IL, USA) by asking them to grip or squeeze with maximal strength. The mean of all three measurements from the dominant hand was recorded as the participants' HGS. The cut-off values of ≥28 kg for male and ≥18 kg female from the Asian Working Group of Sarcopenia (AWGS) guidelines were utilized to determine normal muscle strength.4)
Outcome
Vital status of included participants was determined through the National Registry Department of Malaysia (NRD). Dates of death were provided alongside the vital status. All Malaysian residents are assigned with unique identity card numbers which allowed for accurate tracking of deaths registered with the NRD. Only non-coronavirus disease 2019 (COVID-19) related deaths were included for analysis.
Statistical Analysis
Data analyses were carried out using the Statistical Package for Social Science Statistical Package version 26.0 (IBM, Armonk, NY, USA). The significance level was set at two-sided p<0.05. Descriptive statistics for male and female were presented separately as mean±standard deviation and frequency (percentage, %) as appropriate. The analysis of variance (ANOVA) test was applied for normally distributed numerical data and chi-squared test was used for categorical variables in bivariate analyses. Subsequently, hazard ratios (HR) with 95% confidence intervals (CI) were determined using Cox proportional hazards regression for mortality with normal BMI and quartile 1 (Q1) for %BF, WC and WHR categories as reference categories. Three Cox regression models were constructed with Model 1 as the crude HR adjusted for age, Model 2 adjusted for age and educational level, and Model 3 included an additional adjustment for comorbidities (heart disease, hypertension, diabetes, stroke, cancer, CKD, and liver disease). The regression analysis was further stratified into normal and poor muscle strength categories to explore the influence of muscle strength on the relationship between high adiposity and mortality risk. A Kaplan–Maier survival curve was plotted alongside the log-rank test and number at risk life table.
RESULTS
Participant Characteristics
Data were available for 1,361 individuals aged (mean) 68.4±7.2 years at recruitment. A total of 161 (11.8%) deaths had been reported to the registry department from January 2014 to June 2022, 9 (0.7%) were COVID-19 related death. Five (0.4%) participants without HGS measurements at baseline were excluded. A total of 1,347 participants (57.1% female) with 151 (11.2%) reported deaths were, therefore, included for analysis after excluding those without HGS measurements and COVID-19 related death. Male had a higher percentage mortality reported at the 7th to 9th year compared to female (14.9% vs. 8.5%, p<0.001). Prevalence of heart disease was higher among male (12.1% vs. 3.3%), while a higher percentage of male with reduced muscle strength had heart disease compared to male with normal muscle strength at baseline (15.4% vs 9.6%). The percentage of male and female with a history of cancer was higher in those with normal muscle strength compared to those with low muscle strength. Overall, both male and female with low HGS at baseline were more likely to be older at baseline, had lower educational attainment, with higher numbers of individuals with diabetes and hypertension, higher %BF, and higher numbers of reported death. The sex-specific characteristics categorized by normal or low muscle strength were reported in Table 1.
Adiposity and All-cause Mortality
Sex-specific Cox proportional hazards analysis was performed on all four adiposity measures (Table 2). No significant relationship existed between WC and mortality. Both male (HR=4.72, 95% CI 2.07–10.78) and female (HR=2.12, 95% CI 1.04–4.34) with %BF in the highest quartile (Q4; Male ≥30.3 %, Female ≥44.6%) were at increased risk of mortality compared to those %BF within Q1 (Male ≤23.7%, Female ≤36.5%). This association remained significant among male after adjusting for differences in age, education, heart disease, hypertension, diabetes, stroke, cancer, CKD, and liver disease (HR=2.73, 95% CI 1.09–6.82), while the association among female was attenuated once adjusted for age differences. Both male (HR=2.19, 95% CI 1.18–4.05) and female (HR=2.20, 95% CI 1.07–4.51) with WHR in Q4 (Male ≥0.9890, Female ≥0.9362) had higher mortality compared to male and female in Q1 (Male ≤0.9063, Female ≤0.8281), respectively in unadjusted analyses, which was then attenuated following adjustment for age differences. Male who were underweight (BMI <18.5 kg/m2) at baseline had increased mortality (HR=3.17, 95% CI 1.35–7.47) compared to male within the normal weight category (BMI 18.5–24.9 kg/m2). This association remained after adjusting for differences in age, education, heart disease, hypertension, diabetes, stroke, cancer, CKD, and liver disease (HR=2.70, 95% CI 1.11–6.59). No association existed between BMI and mortality among female.
The unadjusted Kaplan–Meier survival plot by sex within each adiposity measure is presented in Fig. 1. There was no difference in survival probability for both sexes between the WC quartiles, while no difference in survival between BMI categories existed among female. Individuals in Q4 for %BF and WHR in both sexes had the lowest survival compared to Q1. Differences in survival between the BMI categories was observed in male.
Muscle Strength, Adiposity, and Mortality
One hundred and two (18.5%) participants with poor muscle strength died after 7–9 years compared to 49 (6.2%) with normal muscle strength at baseline. Stratified Cox regression for muscle strength was, therefore, conducted (normal HGS male ≥28, female ≥18 kg).
Table 3 provides a summary of the Cox proportional hazards models for individual adiposity indices stratified by muscle strength for male. There was no difference in mortality rates in between WC and WHR quartiles for male regardless of muscle strength at baseline. Male with normal muscle strength and %BF in Q4 (%BF ≥30.4%) at baseline had an increase in mortality risk (HR=4.62, 95% CI 1.39–15.35) compared to those in Q1 (%BF ≤23.7%). The increase in mortality in male with %BF the highest quartile remained significant after adjustment for age (HR=3.82, 95% CI 1.10–13.21) but was then attenuated after additional adjustment for education, suggesting that the difference in mortality in male with %BF between Q4 and Q1 were accounted for by educational differences. Among male with low muscle strength, having a BMI within the underweight category was independently associated with increased mortality (HR=5.17, 95% CI 2.12–12.62) compared to those in the normal weight category. This remained significant after adjustment for all potential confounders (HR=7.06, 95% CI 2.66–18.73). Compared to male, differences in muscle strength at baseline was not associated with mortality after 7–9 years for all indices of adiposity among female (Table 4). The association for female with BMI in Q4 with mortality was only significantly strengthen with adjustments for age, low education, heart disease, hypertension, diabetes, stroke, cancer, CKD, and liver disease (HR=3.26, 95% CI 1.03–10.38) (Model 3). Subgroup analyses for age categories are reported in the Supplementary Table S1. The results showed similar trends across the age groups when visualized against the whole group analyses. However, as the entire cohort is now divided into six groups, three per sex category, the ability to interpret the parameter estimates is limited.
DISCUSSION
Overall, older male in the highest %BF quartile had increased mortality at 7–9-year follow-up compared to those in the lowest %BF quartile. This difference persisted in male with normal muscle strength but not those with reduced muscle strength. High %BF among female displayed age-associated increased in mortality risk but does not show an association with muscle-stratified analysis. Male who fell into the underweight BMI category were significantly more likely to have lower survival at 7–9-year follow-up compared to male within the normal BMI category for the overall male population, as well as male with reduced muscle strength. Male and female within the highest WHR quartile had increased mortality compared to the lowest WHR quartile. For both male and female, there was no differences in mortality between the WC quartiles. Our findings suggest that anthropometric assessments of adiposity in individuals aged 55 years and over have limited value in the prediction of long-term mortality, with %BF estimated through bioimpedance analysis being of value among male with normal muscle strength. For male with reduced muscle strength, however, only being underweight predicted increased mortality.
The increased mortality with increased %BF in male is attenuated after adjustment for educational attainment provided muscle strength is normal. Educational attainment was measured according to the number of years of formal education completed by individuals. This implies that the increased risk of death among male with normal muscle strength is potentially attributed to lower educational attainment. MELoR participants, who were aged 55 years and over at recruitment in 2013 to 2015, may have started their primary education during the colonial and pre-independence era in Malaya. The ability to receive formal education throughout the period may influence the attitude towards healthcare among our participants. High %BF has been found to be associated with higher mortality in previous studies.10,11) A separate study found that male with obesity and low educational attainment had a greater mortality rate, which supports our study findings.12) Differences in education may influence access to self-help resources and exposure to potential risk attributed to obesity and mortality risk.13) Normal muscle strength with obesity (%BF ≥30.4%) at baseline but limited exposure to educational resources appears to contribute to increased risk of death in male within this study population.
In female, %BF ≥44.6% was associated a two-fold increase in risk of death within the unadjusted analysis, with this association accounted for by age-related differences. Previous studies have reported high fat mass in older female to be protective against mortality.14,15) Male begin to lose muscle mass at the end of their fifth decade, whereas female have a similar drop in lean mass but gain more fat mass.16) A significant decline in lean mass due to an increment in fat percentage is likely to affect survival among female. The association between low lean body mass and increased mortality risk was consistent in both male and female (ALM/ht2 <7.26 in male and <5.45 in female) in a previous study.17) Increased %BF was, however, not significantly associated with in mortality among female.
Increased WHR in both male (≥0.9890) and female (≥0.9832) were not associated with any mortality differences in our study population. A previous study had reported WHR as a good prognostic indicator for cardiovascular diseases (CVD) and mortality in middle-aged and older adults while a different study reported an association between greater WHR with mortality in female but not in male.18,19) While numerous previous studies have shown that increased WC is linked to a high prevalence of CVD and a greater risk of mortality,20-22) this does not seem to be the case in our population, compared to other studies which had included adults of a younger age. Greater abdominal adiposity is said to be linked to insulin resistance, dyslipidemia, and systemic inflammation, all of which play important roles in the development of CVD, metabolic syndrome, and some malignancies.23) Waist circumference may also increase as a result of increased abdominal girth in older adults from underlying gastrointestinal conditions or abdominal muscle weakness, while the WHR is also dependent on the hip circumference which may actually be reduced due to muscle wasting in the gluteal region.24) Having greater hip circumference than WC was shown to lower risk of death, suggesting a protective effect of the lower-body gluteofemoral adipose tissue against mortality risk when both circumferences were considered simultaneously, which is a better predictor compared to using WC alone.25)
The obesity paradox, defined using BMI, does not appear to exist in participants of the MELoR study. The increased mortality in male who were underweight (BMI <18.5 kg/m2), appeared confined to male with poor muscle strength. Low BMI and reduced muscle strength suggest the presence of sarcopenia and frailty, which are well-established age-related conditions associated with increased adverse outcomes.4) Continuous weight loss may have exacerbated the risk of mortality in later life. Kong et al.26) suggested obesity status at baseline and weight loss during 10-year follow-up led to increased mortality risk. Risk factors for both sarcopenia and frailty include malnutrition, sedentary lifestyle, chronic diseases or poor physical activity.4,27) In those with preserved muscle strength, BMI appeared to have limited application in predicting mortality, confirming previous suspicions that the BMI may not be a useful measure for adiposity for older adults given it does not take muscle mass into account.7,8)
By stratifying our study population according to muscle strength, we were able to demonstrate that in male with low muscle strength, adiposity, regardless of whether anthropometric or bioimpedance is used, is not associated with increased mortality. In those with normal BMI, however, higher body fat does still matter. In female, muscle weakness does not seem to increase mortality risk after 7–9 years. The emphasis on adiposity, therefore, appears no longer relevant once muscle weakness sets in, and instead male within the lowest BMI category are most likely to succumb. Hence the management of adiposity of older adults should be individualized, taking into account the frailty status of the older person, a management strategy now widely advocated in the management of hypertension and diabetes in older persons.28,29)
Our study findings had included muscle strength using handgrip measurements and not muscle mass. Nevertheless, Newman et al.30) have shown that muscle strength, as a measure of muscle quality, more accurately predicts mortality risk compared to muscle quantity. Further, HGS yielded risk estimations that were equivalent to quadriceps strength.30) We only determined all-cause mortality instead of specific cause as excess adiposity has long been linked to a higher risk of all-cause death, with blood pressure, lipoprotein particles, and diabetes all playing a role in a systemic inflammation pathway.31) Apart from adiposity measures as mortality predictors, specific analyses between male and female show the significance of fat and muscle for each sex. The lower number of deaths among female and those with normal muscle strength, however, should draw caution towards the interpretation of the findings in these two groups, as the study may not be adequately powered to produce differences in these subgroups.
Conversely, despite the large number of variables included in the study, some information on some potential confounders for mortality may not have been accounted for in this study. Our adjustment models did not include socioeconomic covariates as questions such as household income was not collected at baseline. Muscle strength could affect survival by preserving major functional status and lessen the detrimental impact of falls, fractures, and sedentary lifestyles.32) The maintenance of both muscle strength and metabolic function may, therefore, play a major part in longevity and may be achieved through optimal dietary intake and physical activity.33) Larger datasets with longer follow-up periods, should be explored to help identify potential modifiable risk factors for increased mortality in male with increased %BF. Future studies should also seek to identify strategies to counter age-related increase in %BF in female.
In conclusion, sex differences exist in the relationship between adiposity with mortality with increased %BF appearing to be of value in the prediction of mortality in male. In contrast, mortality risk was greater in male with lower muscle strength who were underweight. The relationship between BMI and %BF with mortality in male, therefore, differed between those with normal and reduced muscle strength. Future studies should evaluate the value of %BF as an indicator of adiposity beyond midlife. In addition, the determination of the effects of adiposity on survival should take into account muscle strength and should be individualized.
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