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Body composition parameters and mortality

Updated: Oct 6, 2022

By Benjamin Chan and Dr Vince Vardhanabhuti


Can DEXA scans predict our mortality risk in the clinic?

Longevity – to live past the average life expectancy of your demography – has received increasing attention from medicine in recent decades. In the developed world, we strive not only to live long but also to lead a healthy life – free of chronic disease and pain – for as long as we can. Presenting the pursuit of longevity as such, it becomes immediately apparent that preventative and personalised medicine are disruptive forces that are here to stay and dominate the future of healthcare – no longer should patients only ask for one-size-fits-all pills to treat diseases that have already manifested – medical field is now moving towards informing individual patients of their risk of known diseases and ways to mitigate their risk, most commonly through unique dietary, exercise and attitude changes, in an attempt of attaining longevity. Body composition (BC) changes with age (more to follow), which can be applied to predicting disease risk and mortality, with reference to specific sample demography – therefore, there is increasing demand for accessible, affordable and reliable methods to measure BC, e.g., dual-energy X-ray absorptiometry (DEXA) scans, compared to expensive and less available magnetic resonance imaging (MRI) and computed tomography (CT) scans. This article will discuss the basics of BC parameters, and how DEXA-derived parameters may predict disease risk and mortality, informing patients of individual and unique changes in lifestyle.



Fat mass, lean mass and what DEXA tells us

In order to understand the changes in BC that occur, it is crucial to define terms related to BC – our total body weight is the sum of our fat mass and lean mass. Our fat mass is the sum of three categories of fat – essential, subcutaneous and visceral fats. Fat serves as energy storage and several physiological functions, the critical mass of which for maintaining good health is referred to as essential fats – the American Council on Exercise define the norm of essential fats as 2-5% in men and 10-13% in women. Subcutaneous fat is in addition to essential fats and is located under the skin, for functions like heat insulation and shock absorbance [1]. Visceral fat is fat under the abdomen – belly fat – and fat surrounding the organs, too much of which leads to visceral obesity. Excess fat accumulation relative to height leads to obesity, as defined by the World Health Organisation as body-mass index (BMI) > 30 kg/m2. Obesity leads to 2.8 million deaths directly from being overweight, it is an established risk factor for co-morbidity diseases, e.g. cardiovascular disease which accounted for 32% of global deaths in 2019. Nonetheless, it is debated whether obesity defined by BMI is a good predictor of obesity-related comorbidities – recent evidence suggests that DEXA-derived fat distribution presents more accurate predictions of disease than anthropometric measurements like BMI [2].


Lean mass is the sum of the weights of multiple body components (skin, bones, skeletal muscle mass, other organs, and importantly including water). Lean mass makes up the majority of body weight, accounting for between 60-90% of the subjects to relative fat mass. Research has indicated that lean mass is a reliable correlate and predictor of prevalent diseases, especially its sub-component – skeletal muscle mass.


Fat and lean masses can be obtained through DEXA scans [Check the earlier blog post for the merits and mechanism of DEXA]. In short, DEXA scans rely on relative X-ray absorbance to measure the bone mineral density (BMD), but can also be applied to measure fat and lean mass. Compared to the current gold standard CT scan for BC measurements, DEXA scan measurements agree with CT scans within 90% accuracy, and are cheaper, quicker, more accessible, and present significantly lower radiation dose – even lower than natural daily exposure. The correlates of the three parameters – BMD, fat mass and lean mass – can be applied to predicting disease risk and mortality.


How does our body change with age and lifestyle?

Our body allows us to adapt to changing environmental and internal demands through body composition changes – for example, studies have found that exposure to cool temperatures overnight for a month increased metabolism and brown fat volume in men [3]. In developed countries, the two most common effectors of body composition changes are lifestyle and ageing.


Lifestyle is an umbrella factor that encompasses our daily activities, influenced predominantly by dietary intake and exercise. We gain weight when caloric input exceeds caloric output and store some of the excess energy as fat, which raises our fat mass. Caloric input can be modified by diet by intaking different foods – calorie-dense foods include chips, and salmon, in contrast to low-calorie foods, such as vegetables and fruits. Caloric output can be modified by changing physical activity – exercise increases energy output and disposes of the body of high metabolism for breaking down foods. Besides modulating energy balance directly, diet and exercise initiate other BC changes through other pathways, e.g. hormonal changes, and emotional well-being. Notably, regular exercise increases muscle and thus lean mass. Proper weight training, especially, leads to hypertrophy of skeletal muscles.


Ageing is a natural process and often presents with drastic BC changes and a decrease in resting metabolic rate [4]. It is unknown whether BC changes are a result or cause of decreased metabolism, but while long-term tracking studies are lacking in the literature, short-term studies suggest that changes in metabolism cannot be accounted for solely by BC changes [5]. However, these studies do not consider changes in cellular quality in ageing, precluding the possibility of changes in cellular metabolic capacity. BC changes include an increase in fat mass and a decrease in muscle mass. A previous study revealed that muscle mass decrease results from increased protein oxidation in older adults [6]. The following section will discuss the correlation of these BC changes with diseases and death, providing predictive insight into patient health.


DEXA-scan-measured parameters predict disease and mortality risk

DEXA scan measures BMD, fat mass and lean mass, which can be applied to evaluating disease risk through research-backed correlation with these measurements. It is important to note that while no unifying theory is available for how these BC parameters may interact to cause disease, the presence of a strong correlation suggests that there is certainly a predictor element, if not a relationship, between BC and diseases and mortality. So while researchers work hard to uncover how BC components cross-talk, we should begin to inform ourselves about correlations that suggest inadequate aspects of our health and work to mitigate these risks.


Conventionally applied mainly to study BMD, DEXA scans inform physicians and patients about their bone health and risk of bone-related diseases, e.g., osteoporosis. It is difficult to increase BMD once it is lost in adulthood – prevention of BMD is crucial for maintaining bone health. A long-term study of more than 9000 patients was done by Berger et al with the Canadian Multicentre Osteoporosis Study Research Group (2008) who tracked the changes in BMD with age using DEXA scans [7]. With this data, we learned how BMD changes with time, alongside the risks that BMD decline presents. For instance, there is rapid bone loss in the perimenopause period in women and after 65 years old in men. Bone loss in the hips of elderly men and women predisposes them to hip fracture. Timely administration of medication that slows bone loss – antiresorptive drugs – contributes to maintaining bone strength and repair. Furthermore, recent research revealed that fat distribution alters the relationship between fat mass and BMD – more belly fat lowers BMD; this suggests the possibility of increased bone fractures and bone loss in obese individuals with much visceral fat. Integrating available data, there is much hope in producing predictive models for disease risk with measured values of BMD with DEXA – not only allowing patients to assess their risk of bone-related diseases but also helping doctors in preventing these diseases from manifesting.


In addition to BMD, measurements of fat and lean mass are invaluable to personalised health, given the numerous implications of fat and muscle mass in chronic diseases, especially cardiovascular disease. Muscle mass can be calculated from DEXA lean mass measurements with a prediction equation. Using data from the UK Biobank, fat mass and skeletal muscle mass have been found to strongly correlate with cardiovascular disease risk [8], with each being an independent predictor of mortality due to all causes [9,10]. Research in the field of BC and longevity emphasise the importance of high muscle mass and low fat mass in its possibility of reducing mortality risk in all ages, though maintaining relatively higher muscle mass in old age, and lower fat mass in young age, are especially correlative in increasing healthspan and lifespan at the respective age group [10]. However, whether this correlation exists equally in both sexes remains controversial – while previous studies suggest that both fat and muscle mass can predict mortality in both sexes at all ages, other studies such as Santana et al (2019) suggest that only muscle mass predicts for all-cause mortality in women [11]. In fact, it seems that muscle mass is a more robust predictor compared to fat mass as most studies agree that high muscle mass decreased mortality in all age groups and sexes; but some studies found a correlation between moderately high fat mass in elderly women and decreased mortality (Srikanthan, 2021; Toss, 2012) – is it possible that slightly greater fat mass is protective specifically to elderly women? [12,13] or is this association spurious? What remains certain is the key to longevity for all people – both sexes and across age groups – includes an appropriate amount of fat mass – not too high or too low, though details are to be uncovered – and maintaining high muscle mass.


What is intriguing about muscle prediction is that muscular function possesses strong predictive power for mortality risk, if not stronger than overall muscle mass. Perhaps what has been overlooked is that muscle quality may play a role in predicting mortality as well, besides just muscle quantity. Then how do we explain previous studies that found a correlation between muscle mass and mortality? This may be due to a confounding factor – as people with larger muscle quantity (i.e. higher muscle mass) tend to exercise regularly, they may also have higher muscle quality, and it is possible that their muscle quality is what contributes most to reducing mortality risk.


To illustrate this interesting “quality over quantity” observation, some studies found a correlation between muscle function and mortality, regardless of muscle mass. It has been found that walking speed is a powerful predictor of mortality, despite low calf muscle mass not necessarily being a risk factor for mortality [14]. Moreover, grip strength and quadricep strength have long been known to be strongly associated with mortality risk. These studies controlled for possible confounding factors, e.g. arm muscle area or regional lean mass and found that grip and quadricep strength remained correlative with mortality risk. This observation could be explained by the fact that a strong grip strength could prevent one from falling after a slip (e.g. being able to quickly grab and reach a nearby supporting structure). If this was not possible, a fall could predispose to fractures potentially leading to or hastening mortality. We also know that unintentional injuries are consistently the top 10 causes of mortality in people over the age of 65 [15].


Summary

Most of the relationships we know regarding BC parameters and longevity are correlative. Despite the lack of accepted unifying theory that describes how these parameters act to increase or decrease mortality risk, these correlations are very real and may provide insights for preventative medicine to prevent disease development. That is to say that if one acts sooner and change these parameters early on through individualised diet and exercise plans they could have a positive impact on their healthspan and longevity. DEXA scans provide an accessible and affordable alternative to CT and MRI scans, without compromising much quality, for patients and physicians to come up with action plans based on BC parameters – BMD, fat and lean mass are the primary outcomes in DEXA scans.


What we do know:

BMD declines with age, especially around menopause for women and after 65 years old in men, and can be delayed with appropriate intervention; higher muscle strength, and seemingly higher muscle mass, are associated with lowered mortality risk; too low or too high-fat mass is counterproductive to longevity. Nonetheless, these are only parameters that DEXA tells us; there are many other factors affecting longevity, e.g., our emotional health, sociocultural factors and environment.


Though as cliche as this is, our BC is effectively optimised for longevity through regular exercise and proper diet. In this era, how regular and intense your exercise should be and the balance of your nutrients is increasingly unique and customised – feel free to check out some contents that touch upon these topics in our website and YouTube channels. Finally, we hope that this article is useful and might inspire you to find a new gym or a diet partner to pursue your longevity pursuit.


References:

  1. American Council On Exercise. Percent Body Fat Calculator: Skinfold Method. Retrieved 4 July 2022. https://www.acefitness.org/resources/everyone/tools-calculators/percent-body-fat-calculator/

  2. Vasan, S. K., Osmond, C., Canoy, D., Christodoulides, C., Neville, M. J., Di Gravio, C., Fall, C., & Karpe, F. (2018). Comparison of regional fat measurements by dual-energy X-ray absorptiometry and conventional anthropometry and their association with markers of diabetes and cardiovascular disease risk. International journal of obesity (2005), 42(4), 850–857. https://doi.org/10.1038/ijo.2017.289

  3. Lee, P., Smith, S., Linderman, J., Courville, A. B., Brychta, R. J., Dieckmann, W., Werner, C. D., Chen, K. Y., & Celi, F. S. (2014). Temperature-acclimated brown adipose tissue modulates insulin sensitivity in humans. Diabetes, 63(11), 3686–3698. https://doi.org/10.2337/db14-0513

  4. St-Onge, M. P., & Gallagher, D. (2010). Body composition changes with aging: the cause or the result of alterations in metabolic rate and macronutrient oxidation?. Nutrition (Burbank, Los Angeles County, Calif.), 26(2), 152–155. https://doi.org/10.1016/j.nut.2009.07.004

  5. St-Onge M. P. (2005). Relationship between body composition changes and changes in physical function and metabolic risk factors in aging. Current opinion in clinical nutrition and metabolic care, 8(5), 523–528.

  6. Davy, K. P., Horton, T., Davy, B. M., Bessessen, D., & Hill, J. O. (2001). Regulation of macronutrient balance in healthy young and older men. International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity, 25(10), 1497–1502. https://doi.org/10.1038/sj.ijo.0801718

  7. Berger, C., Langsetmo, L., Joseph, L., Hanley, D. A., Davison, K. S., Josse, R., Kreiger, N., Tenenhouse, A., Goltzman, D., & Canadian Multicentre Osteoporosis Study Research Group (2008). Change in bone mineral density as a function of age in women and men and association with the use of antiresorptive agents. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 178(13), 1660–1668. https://doi.org/10.1503/cmaj.071416

  8. Knowles, R., Carter, J., Jebb, S. A., Bennett, D., Lewington, S., & Piernas, C. (2021). Associations of Skeletal Muscle Mass and Fat Mass With Incident Cardiovascular Disease and All-Cause Mortality: A Prospective Cohort Study of UK Biobank Participants. Journal of the American Heart Association, 10(9), e019337. https://doi.org/10.1161/JAHA.120.019337

  9. Farsijani, S., Xue, L., Boudreau, R. M., Santanasto, A. J., Kritchevsky, S. B., & Newman, A. B. (2021). Body Composition by Computed Tomography vs Dual-Energy X-ray Absorptiometry: Long-Term Prediction of All-Cause Mortality in the Health ABC Cohort. The journals of gerontology. Series A, Biological sciences and medical sciences, 76(12), 2256–2264. https://doi.org/10.1093/gerona/glab105

  10. Liu, M., Zhang, Z., Zhou, C., Ye, Z., He, P., Zhang, Y., Li, H., Liu, C., & Qin, X. (2022). Predicted fat mass and lean mass in relation to all-cause and cause-specific mortality. Journal of cachexia, sarcopenia and muscle, 13(2), 1064–1075. https://doi.org/10.1002/jcsm.12921

  11. de Santana, F. M., Domiciano, D. S., Gonçalves, M. A., Machado, L. G., Figueiredo, C. P., Lopes, J. B., Caparbo, V. F., Takayama, L., Menezes, P. R., & Pereira, R. M. (2019). Association of Appendicular Lean Mass, and Subcutaneous and Visceral Adipose Tissue With Mortality in Older Brazilians: The São Paulo Ageing & Health Study. Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, 34(7), 1264–1274. https://doi.org/10.1002/jbmr.3710

  12. Srikanthan, P., Horwich, T. B., Calfon Press, M., Gornbein, J., & Watson, K. E. (2021). Sex Differences in the Association of Body Composition and Cardiovascular Mortality. Journal of the American Heart Association, 10(5), e017511. https://doi.org/10.1161/JAHA.120.017511

  13. Toss, F., Wiklund, P., Nordström, P., & Nordström, A. (2012). Body composition and mortality risk in later life. Age and ageing, 41(5), 677–681. https://doi.org/10.1093/ageing/afs087

  14. Cesari, M., Pahor, M., Lauretani, F., Zamboni, V., Bandinelli, S., Bernabei, R., Guralnik, J. M., & Ferrucci, L. (2009). Skeletal muscle and mortality results from the InCHIANTI Study. The journals of gerontology. Series A, Biological sciences and medical sciences, 64(3), 377–384. https://doi.org/10.1093/gerona/gln031

  15. Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Data are from the Multiple Cause of Death Files, 1999-2020, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Jul 10, 2022 10:20:00 AM

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