Body composition assessment methods

The identification of SO hinges on diagnosing sarcopenia and obesity, typically necessitating a quantitative assessment of body composition. Various methods have been employed for quantitative body composition assessment, including non-anthropometric techniques (e.g. DXA, BIA, CT and MRI) and anthropometric indices (e.g. BMI, WC, mid-arm muscle circumference (MAMC) and calf circumference)^{(Reference Batsis and Villareal14,Reference Donini, Busetto and Bauer19,Reference Kawakami, Murakami and Sanada34)} . Among the non-anthropometric techniques, DXA is considered a reliable option for SO identification in both research and clinical practice due to its availability, sensitivity, repeatability and safety^{(Reference Donini, Busetto and Bischoff20)}. However, it is imperative to acknowledge its limitations such as the inability to measure body composition directly, potential interference from changes in tissue hydration status and challenges encountered when scanning individuals with large body sizes that may exceed the scanner’s specifications^{(Reference Donini, Busetto and Bischoff20,Reference Toomey, McCormack and Jakeman35,Reference Shepherd, Ng and Sommer36)} . Another non-anthropometric method, BIA, is valued for its quickness and portability^{(Reference Bosy-Westphal, Jensen and Braun37)}. Nonetheless, caution is warranted in its use, as hydration status may also affect its diagnostic accuracy^{(Reference Bosy-Westphal, Jensen and Braun37)}. For accurate BIA measurements, it is assumed that tissue hydration remains constant and body shape is cylindrical; however, these assumptions are challenged in individuals with sarcopenia and obesity^{(Reference Batsis and Villareal14,Reference Houtkooper, Lohman and Going38)} . Furthermore, despite CT and MRI being deemed gold standard methods for precise body composition analysis, the high cost, limited availability and X-ray exposure associated with CT preclude their routine use in SO diagnosis^{(Reference Woodrow39)}, while MRI is limited to research settings due to similar constraints regarding cost and availability. Regarding anthropometric approaches used in SO diagnosis, they are generally less sensitive than precise analytical techniques^{(Reference Donini, Busetto and Bischoff20)}. In a study utilising DXA-assessed percentage fat mass (%FM) as the gold standard for identifying obesity, BMI incorrectly classified 19.2% of males and 21.5% of females as having obesity, while WC yielded percentages of 35.8% and 19%, respectively^{(Reference Batsis, Mackenzie and Bartels40)}. For further details on body composition assessment, we direct readers to additional literature on the subject^{(Reference Prado and Heymsfield41,Reference Prado, Landi and Chew42)} .

Parameters and cut-point values

Mainstream definitions and diagnostic criteria for SO, as discussed previously, involve identifying obesity and the loss of SM and function (e.g. skeletal muscle strength). According to a systematic review, the assessment of SM commonly relies on parameters measured through DXA or BIA; these parameters include ALM adjusted by weight, ASM divided by weight (ASM/W), ASM adjusted by height in meters squared (ASM/h²) and ASM adjusted by BMI (ASM/BMI)^{(Reference Donini, Busetto and Bauer19)}. Regarding the evaluation of muscle function, hand grip strength (HGS), gait speed and chair-stand time have been recommended^{(Reference Cruz-Jentoft, Bahat and Bauer4,Reference Studenski, Peters and Alley5,Reference Fielding, Vellas and Evans8,Reference Donini, Busetto and Bischoff20)} . Nevertheless, the existing body of evidence does not conclusively indicate the superiority of any specific muscle function parameter^{(Reference Donini, Busetto and Bischoff20)}. On the other hand, as mentioned previously, adiposity can be identified using anthropometric parameters such as BMI and WC, as well as non-anthropometric body composition parameters such as %FM^{(Reference Ji, Li and Ma24)}. Although anthropometric parameters have relatively modest sensitivity, they are frequently employed in adiposity diagnosis due to their simplicity and widespread availability^{(Reference Donini, Busetto and Bauer19)}. Notably, the cut-offs for the same parameters used in SO diagnosis may vary across studies as few universally accepted cut-off values for most of these parameters exist. Previous studies predominantly adhered to established guidelines, such as a BMI of ≥30 kg/m² denoting obesity, or adopted population-specific cut-offs derived from statistical measures such as n-tiles, SD or z scores based on individual parameter values^{(Reference Donini, Busetto and Bauer19)}. For further information regarding the various cut-point values for these parameters, we refer readers to the ESPEN-EASO consensus statement, which provides a detailed summary of these cut-offs^{(Reference Donini, Busetto and Bischoff20,Reference Donini, Busetto and Bischoff21)} .

So and risk of CVD

In recent years, the roles of sarcopenia and its concurrence with obesity—a well-established risk factor for CVD—have received increasing attention in the development of CVD^{(Reference Koliaki, Liatis and Kokkinos43)}. Analysis of data from a nationally representative sample of middle-aged and older adults (≥45 years) in China revealed that sarcopenia was associated with an increased risk of CVD, as demonstrated in both cross-sectional and longitudinal analyses (with a follow-up period of 3.6 years)^{(Reference Gao, Cao and Ma44)}. Furthermore, a cross-sectional study of Korean older adults (≥65 years) found a positive association between sarcopenia and CVD risk^{(Reference Chin, Rhee and Chon45)}. To date, a small number of observational studies have investigated the association between SO and the risk of CVD; however, the current body of evidence remains inconclusive^{(Reference Atkins and Wannamathee11,Reference Atkins and Wannamethee46)} . In addition to the SO diagnostic criteria and definition discrepancies between studies, different sample sizes, populations, study designs and statistical approaches used to assess CVD risk could further contribute to contradictory findings. Furthermore, most investigations are cross-sectional, thereby limiting the ability to discern long-term associations or causality. Therefore, to reconcile previously inconsistent findings, we synthesised evidence from previously published longitudinal studies that quantitatively assessed the association between SO and CVD risk.

Findings from these longitudinal studies are summarised in Table 1^{(Reference Stephen and Janssen47–Reference Jiang, Ren and Han52)}, four of which revealed a significant association between SO and an elevated risk of overall CVD^{(Reference Fukuda, Bouchi and Takeuchi49–Reference Jiang, Ren and Han52)}. Atkins et al., leveraging data from a British prospective cohort over an average follow-up duration of 11.3 years, reported no significant association between SO and CVD risk^{(Reference Atkins, Whincup and Morris48)}. In their study, sarcopenia was defined solely by anthropometrics (MAMC and WC) or BIA-estimated muscle mass (fat mass index (FMI) and fat free mass index (FFMI)), without consideration of muscle function. On the contrary, using data from an American prospective cohort with an 8-year follow-up, Stephen and Janssen suggested that SO, when sarcopenia was assessed based on HGS, was modestly associated with an elevated risk of overall CVD (HR = 1.23; 95% CI = 0.99–1.54) and significantly associated with the a higher coronary heart disease (CHD) risk (HR = 1.42; 95% CI = 1.05–1.91)^{(Reference Stephen and Janssen47)}. Notably, this association was not observed when sarcopenia was defined by SM, highlighting the importance of incorporating muscle function assessments in SO diagnosis^{(Reference Stephen and Janssen47)}. Using data from a Japanese retrospective cohort with a median follow-up of 2.6 years, Fukada et al. reported that SO (obesity was identified based on the android to gynoid ratio (A/G ratio) and android fat mass (AF) but not BMI and %FM) was significantly associated with elevated risk of incident CVD, whereas neither sarcopenia nor obesity alone was linked to a significant increase in risk^{(Reference Fukuda, Bouchi and Takeuchi49)}. Furthermore, two studies suggested that SO had a stronger association with CVD risk than either sarcopenia or obesity alone^{(Reference Farmer, Mathur and Schmidt50,Reference Chuan, Chen and Ye51)} .

Table 1. Summary of longitudinal studies assessing the association between SO and CVD risk

SO, sarcopenic obesity; WC, waist circumference; SM, skeletal muscle mass; BIA, bioelectrical impedance analysis; HR, hazard ratio; MAMC, mid-arm muscle circumference; FMI, fat mass index; FFMI, fat free mass index; DXA, dual-energy X-ray absorptiometry; SMI, skeletal muscle index; A/G ratio, android to gynoid ratio; AF, android fat mass; FM, fat mass; WHR, waist:hip ration; HGS, hand grip strength; ASM, appendicular skeletal muscle mass; VFA, visceral fat area.

*In all studies, the group of “normal, i.e. without obesity and without sarcopenia” was regarded as the reference group.

^†The specific cut-off values were not mentioned.

Insulin resistance, inflammation and oxidative stress

Insulin resistance, chronic inflammation and oxidative stress are associated with vascular endothelial dysfunction, potentially precipitating atherosclerosis—a dominant contributor to various CVD such as myocardial infarction, heart failure and stroke^{(Reference Frostegård54)}. Skeletal muscle serves as a primary site for glucose uptake, storage and myokine secretion. In the context of SO, both obesity and decline of skeletal muscle mass may decrease insulin sensitivity, leading to insulin resistance^{(Reference Srikanthan, Hevener and Karlamangla55)}. This condition can cause hyperinsulinemia, which in turn diminishes the release of nitric oxide (NO), a critical regulator of vascular homeostasis^{(Reference Zeng, Nystrom and Ravichandran56)}. NO plays an essential role in regulating vascular tone and local blood flow, platelet aggregation and adhesion and leukocyte-endothelial cell interactions^{(Reference Huang57)}. A reduction in NO availability can impair vasodilation and endothelial function, thereby accelerating atherosclerosis^{(Reference Hong and Choi58)}. Furthermore, as SO progresses, fat accumulation can lead to the dysregulated production of adipokines and the infiltration of macrophages and other immune cells into adipose tissue^{(Reference Di Pino and DeFronzo59)}. This results in the production of a variety of pro-inflammatory cytokines such as IL-6 and TNF-α, exacerbating systemic, chronic low-grade inflammation in the absence of infection^{(Reference Esser, Legrand-Poels and Piette60)}. Concurrently, the decline in muscle mass may reduce myokine secretion, further deteriorating inflammation and insulin resistance^{(Reference Pedersen and Febbraio61)}. These alterations in humoral factors could induce or amplify oxidative stress^{(Reference Kim and Choi62)}, a phenomenon characterised by an imbalance between production and accumulation of oxygen reactive species (ROS) in cells and tissues and the biological system’s ability to detoxify these reactive products^{(Reference Pizzino, Irrera and Cucinotta63)}. Oxidative stress can lead to the oxidation of low-density lipoprotein, obstruction of cholesterol efflux and the aggregation of collagen fibres in fibroatheroma plaques. Collectively, these processes exacerbate endothelial dysfunction and accelerate atherogenesis^{(Reference Korytowski, Wawak and Pabisz64)}.

On the other hand, myocardial fibrosis, another well-recognized cardiac condition, and significant risk factor for CVD, is likewise linked to SO. The chronic low-grade inflammatory activity may be involved in myocardial fibrosis, with evidence indicating that inflammatory cells may secrete profibrogenic cytokines such as transforming growth factor-β (TGF-β)^{(Reference Kalogeropoulos, Georgiopoulou and Butler65)}. During the pathophysiological development of SO, hyperinsulinemia, induced by insulin resistance, may trigger the renin-angiotensin-aldosterone system^{(Reference Hong and Choi58)}. This activation leads to elevated levels of angiotensin II and aldosterone, which in turn activate the angiotensin II type 1 and mineralocorticoid receptors. The engagement of these receptors initiates the TGF-β1-SMAD signaling pathway, ultimately leading to the development of myocardial fibrosis^{(Reference Hong and Choi58)}. Moreover, research has highlighted that the augmentation of myocardial oxidative stress, induced by angiotensin II, is a pivotal factor in the onset and progression of myocardial fibrosis^{(Reference Lijnen, Van Pelt and Fagard66)}.

Hormonal shifts and mitochondrial dysfunction

Hormonal changes associated with ageing play a crucial role in the onset and progression of SO. An ageing-related decline in growth hormone (GH) levels leading to numerous adverse consequences for skeletal muscle structure and strength; such decline also reduces liver-derived insulin-like growth factor-I (IGF-I), a principal regulator of muscle mass^{(Reference Kob, Bollheimer and Bertsch67,Reference Cappola, Bandeen-Roche and Wand68)} . Both GH and IGF-I are considered atheroprotective, with evidence suggesting IGF-1 promotes a more stable status of atherosclerotic plaques and GH improves endothelial dysfunction^{(Reference Caicedo, Díaz and Devesa69,Reference Higashi, Gautam and Delafontaine70)} . Concurrently, the reduction in sex hormone levels (testosterone and oestrogen) associated with ageing leads to diminished muscle mass and strength^{(Reference Gupta and Kumar71)}. These sex hormones also modulate CVD risk factors and vascular biology in a gender-specific manner. For instance, oestrogen is known to lower systemic vascular resistance and enhance endothelial function in coronary vessels in postmenopausal women^{(Reference Volterrani, Rosano and Coats72,Reference Collins, Rosano and Sarrel73)} . On the other hand, mutations that leads to impaired oestrogen synthesis or dysfunctional oestrogen receptors are associated with impaired endothelial function and the premature development of atherosclerosis in males^{(Reference Smith, Boyd and Frank74)}. Additionally, testosterone can improve vascular functions and risk factors in men; however, in women, the effects of testosterone are contingent upon estrogen levels^{(Reference Vitale, Mendelsohn and Rosano75)}.

Mitochondrial dysfunction represents a common risk factor among SO and CVD. As the primary sites of aerobic respiration within cells, mitochondria are crucial for generating the energy required through the oxidative phosphorylation system and for regulating cellular metabolism^{(Reference Liu, Huang and Xu76)}. Mitochondrial dysfunction, arising from mutations in either mitochondrial DNA or nuclear DNA, as well as from ageing, various diseases and environmental stressors, can induce significant cellular disturbances^{(Reference Manolis, Manolis and Manolis77)}. These include excessive production of ROS, impaired energy production, dysregulated autophagy and activated apoptosis, all of which may contribute to the pathogenesis of CVD and SO^{(Reference Chistiakov, Shkurat and Melnichenko78–Reference Marzetti, Hwang and Lees81)}.

Role of dietary intake

Numerous research has reported that adherence to certain dietary patterns, such as the Mediterranean diet and the Dietary Approach to Stop Hypertension diet, is associated with a lower risk of CVD^{(Reference Siervo, Lara and Chowdhury82,Reference Grosso, Marventano and Yang83)} . On the contrary, unbalanced dietary patterns such as low protein consumption and excessive high-calorie food intake, may increase the risk of CVD^{(Reference Bowen, Sullivan and Kris-Etherton84)}. These dietary patterns may also play a crucial role in the development and progression of SO. The underlying pathophysiologic mechanisms are twofold: First, older adults are susceptible to low protein consumption (both quantity and quality) and/or metabolism, potentially leading to inadequate levels of amino acids essential for muscle protein synthesis and, consequently, the onset of sarcopenia^{(Reference Deutz, Bauer and Barazzoni85)}. Second, excessive consumption of high-calorie foods, which leads to obesity, may induce abnormal surges in serum free fatty acids and glucose levels. These changes are associated with an increased production of ROS, resulting in elevated levels of oxidative stress—a key factor in the development and progression of SO^{(Reference O’Keefe and Bell86,Reference Gonzalez, Simon and Achiardi87)} .

Alteration of energy balance

The link between SO and an increased CVD risk could also be partially attributed to disruptions in energy expenditure observed in both conditions. In SO, decreased muscle mass results in a lower basal metabolic rate and consequently, leading to decreased energy expenditure; this reduction creates an energy surplus that favours adipose tissue accumulation^{(Reference Prado, Batsis and Donini53)}. Sarcopenia-related muscle loss and dysfunction make physical activity challenging, while insulin resistance, induced by physical inactivity, further intensifies obesity-related muscle loss^{(Reference Yaribeygi, Maleki and Sathyapalan88)}. Consequently, the cycle of reduced physical activity, muscle loss and increased fat accumulation may perpetuate a sedentary lifestyle. This lifestyle is linked to complications such as diabetes, hypertension, and dyslipidaemia, all of which are well-recognised risk factors for CVD^{(Reference Stamatakis, Hamer and Dunstan89)}. Additionally, individuals with CVD may also experience difficulties in maintaining physical activity due to symptoms such as shortness of breath and fatigue. This can lead to obesity-related muscle loss^{(Reference Yaribeygi, Maleki and Sathyapalan88)}, thereby contributing to the development of SO.