Skip to main content
Download PDF
Download ePub
  • Original investigation
  • Open access
  • Published:

Sex-specific associations of fat mass and muscle mass with cardiovascular disease risk factors in adults with type 2 diabetes living with overweight and obesity: secondary analysis of the Look AHEAD trial

Cardiovascular Diabetology volume 21, Article number: 40 (2022) Cite this article

Abstract

Background

Distinguishable sex differences exist in fat mass and muscle mass. High fat mass and low muscle mass are independently associated with cardiovascular disease (CVD) risk factors in people living with type 2 diabetes; however, it is unknown if the association between fat mass and CVD risk is modified by muscle mass, or vice versa. This study examined the sex-specific interplay between fat mass and muscle mass on CVD risk factors in adults with type 2 diabetes living with overweight and obesity.

Methods

Dual-energy X-ray absorptiometry (DXA) measures were used to compute fat mass index (FMI) and appendicular muscle mass index (ASMI), and participants were separated into high-fat mass vs. low-fat mass and high-muscle mass vs. low-muscle mass. A two-way analysis of covariance (ANCOVA: high-FMI vs. low-FMI by high-ASMI vs. low-ASMI) was performed on CVD risk factors (i.e., hemoglobin A1C [A1C]; high-density lipoprotein cholesterol; low-density lipoprotein cholesterol; triglycerides; systolic and diastolic blood pressure; cardiorespiratory fitness, depression and health related-quality of life [HR-QoL]) at baseline and following a 1-year intensive lifestyle intervention (ILI) for females and males separately, with a primary focus on the fat mass by muscle mass interaction effects.

Results

Data from 1,369 participants (62.7% females) who completed baseline DXA were analyzed. In females, there was a fat mass by muscle mass interaction effect on A1C (p = 0.016) at baseline. Post-hoc analysis showed that, in the low-FMI group, A1C was significantly higher in low-ASMI when compared to high-ASMI (60.3 ± 14.1 vs. 55.5 ± 13.5 mmol/mol, p = 0.023). In the high-FMI group, there was no difference between high-ASMI and low-ASMI (56.4 ± 12.5 vs. 56.5 ± 12.8 mmol/mol, p = 0.610). In males, only high-FMI was associated with higher A1C when compared to low-FMI (57.1 ± 14.4 vs. 54.2 ± 12.0 mmol/mol, p = 0.008) at baseline. Following ILI, there were significant fat mass by muscle mass interaction effects on changes in the mental component of HR-QoL in males.

Conclusion

Considering that A1C predicts future CVD, strategies to lower A1C may be especially important in females with low fat and low muscle mass living with type 2 diabetes. Our results highlight the complicated and sex-specific contribution of fat mass and muscle mass to CVD risk factors.

Introduction

In the United States, 43.3% of middle-aged (40–59 years) females and 46.4% of middle-aged males were living with obesity between 2017 and 2018 [1], and up to 30% of community-dwelling females and males > 50 years of age were living with sarcopenia, an age-related decline in muscle mass [2]. The coexistence of obesity and sarcopenia is often termed sarcopenic obesity. Data from the National Health and Nutrition Examination Survey (NHANES) showed that 33.5% of females and 12.6% of males over 60 years of age are characterized as sarcopenic obese [3].

Type 2 diabetes is also prevalent among US adults, affecting 12.0% of females and 14.0% of males [4]. Excess body fat in type 2 diabetes, especially when distributed in the abdominal region, contributes to the clustering of cardiovascular disease (CVD) risk factors, including insulin resistance [5], glucose intolerance [6, 7], dyslipidemia [6], hypertension [7], low cardiorespiratory fitness (CRF) [8], depression and poor health-related quality of life (HR-QoL) [9]. While maintaining high muscle mass is critical to offset the negative effects of high fat mass on CVD risk factors [10], adults with type 2 diabetes show accelerated muscle mass loss [11]. Low muscle mass exacerbates insulin resistance [12], glycated hemoglobin A1C (A1C) [13], dyslipidemia [14], hypertension [14], CRF [15], depression [16] and HR-QoL [17], and predicts greater 10-year CVD incidences [18]. In type 2 diabetes, CVD accounts for ~ 65% of deaths [19]; the impact of diabetes on the risk of CVD mortality is greater for females than males [20]. While high fat mass and low muscle mass synergistically increase the risk of cumulative cardiovascular events in type 2 diabetes (e.g., stroke, myocardial infarction and death) [21], it remains unclear how fat mass and muscle mass simultaneously affect CVD risk factors.

Fat mass, fat distribution and muscle mass differ between sexes. Males generally have lower fat mass than females but have higher insulin resistance, blood glucose and lipid concentrations partly due to a greater amount of fat distributed in the abdominal region [10]. Females generally have lower absolute and body mass-adjusted muscle mass when compared to males [22], and females with type 2 diabetes are at heightened risk for muscle mass loss [11]. Lifestyle interventions including physical activity reduce fat mass while preserving muscle mass [23]. However, previous work has suggested that concurrent presentation of high fat mass and low muscle mass attenuates improvements in some CVD risk factors following exercise training, such as insulin resistance, fasting blood glucose and triglycerides in adults with type 2 diabetes [24]. It is unknown if the influence of fat mass and muscle mass on lifestyle-induced CVD risk improvements in type 2 diabetes is sex-specific.

Several studies have demonstrated that high fat mass and low muscle mass independently exacerbate CVD risks. However, the interplay between high fat mass and low muscle mass on CVD risk factors in adults with type 2 diabetes is less known. Given the sex-differences in body composition and risk of CVD mortality in type 2 diabetes, a sex-specific investigation of associations between body composition and CVD risk factors is warranted to understand how the unique body compositions of females and males contribute to CVD. The primary purpose of this study was to assess how fat mass and muscle mass would simultaneously predict CVD risk factors in females and males with type 2 diabetes. The secondary purpose was to examine how fat mass and muscle mass would simultaneously predict changes in CVD risk factors following a lifestyle intervention in females and males with type 2 diabetes. We hypothesized that, regardless of sex, high fat mass and low muscle mass would synergistically be associated with greater CVD risk factors and smaller improvements in CVD risk factors.

Methods

Study design

A secondary analysis was conducted on the longitudinal data collected in the Look AHEAD trial (ClinicalTrial.gov, number NCT00017953), the largest multicenter clinical trial designed to examine the effects of an intensive lifestyle intervention (ILI) on the prevention of CVD in individuals with type 2 diabetes [25]. The study protocol was approved by the Ottawa Health Science Network Research Ethics Board (Protocol #: 20200690-01H). The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement [26].

From August 2001 to April 2004, adults with type 2 diabetes with a body mass index (BMI) ≥ 25 kg/m2 and between the ages of 45–76 years were recruited from 16 clinical sites across the United States. These participants were randomized (1:1) to either ILI or diabetes support and education (DSE). Details of ILI have been described elsewhere [25]. Briefly, ILI aimed at achieving and maintaining body mass loss of ≥ 7% by reducing caloric intake to 1200–1800 kcal/day and increasing caloric expenditure by ≥ 175 min/week of moderate intensity physical activity. ILI participants met with registered dieticians, behavioural psychologists, and exercise specialists weekly for the first 6 months (three group sessions and one individual session each month) and thrice monthly for the following 6 months (two group sessions and one individual session per month). At each session, the experts certified by Look AHEAD weighed participants, reviewed self-monitoring records completed by participants, and discussed behavioural strategies for weight loss, such as limiting times and places of eating; methods for exercising safely; and, reducing barriers to exercise [27]. To help participants achieve their dietary goals, meal replacement products, including Slim-Fast (Slim-Fast Foods Company, Englewood, NJ), Glucerna (Ross Laboratories, Columbus, OH), OPTIFAST (Novartis Nutrition, Fremont, MI), and HMR (Health Management Resources, Boston, MA) were provided at no cost. Participants were encouraged to increase caloric expenditure by modifying lifestyle behaviours, such as using stairs rather than elevators and walking rather than riding [28], and by accumulating bouts of physical activity at least 10 min in duration (e.g., brisk walking or similar aerobic activity). Participants were also provided with pedometers and encouraged to walk 10,000 steps each day.

Participants randomized to the DSE group received general information related to healthy eating and physical activity but did not receive the comprehensive components of the intervention nor specific strategies for weight loss. For this secondary analysis, data from phase I of the original trial [25] (baseline measures and measures taken one year following the intervention) were accessed because the lifestyle interventions were most strictly implemented and improvements in CVD risk factors were most prominent during this period.

Participants

Of a total of 5,145 females and males with type 2 diabetes enrolled in the Look AHEAD trial [25], the subset of participants who completed dual-energy X-ray absorptiometry (DXA) at baseline were included in the analysis. All participants provided written informed consent, using a form approved by their local institutional board. This secondary data analysis was conducted on participants who consented to data sharing.

Body composition phenotypes

Dual-energy X-ray absorptiometry was conducted at four sites using QDR-4500A fan beam densitometers (Hologic Inc., Bedford, MA, USA) at baseline and one-year following the interventions. DXA uses two-compartment models to distinguish fat mass and fat-free mass, and the fat-free mass can be subdivided into bone mineral and soft tissue. Skeletal muscle mass was calculated as the difference between fat-free mass and bone mineral content. Within-subject coefficient of variations for fat mass and muscle mass are 1.5% and 0.80%, respectively [29].

To quantify fat mass, fat mass index (FMI) was calculated by dividing whole body fat mass by height squared (m2) to account for different body sizes [30]. For muscle mass, appendicular skeletal muscle mass (ASM) was determined as the sum of muscle masses of both right and left extremities. To account for different body sizes, ASM index (ASMI) was calculated by adjusting the ASM for BMI as recommended by the Foundation for the National Institutes of Health (FNIH) [31]. Based on baseline FMI and ASMI, female and male participants were divided separately into high-fat mass (i.e. 50–100 FMI deciles) and low-fat mass (i.e. 0–49.99 FMI deciles) and into high-muscle mass (50–100 ASMI deciles) and low-muscle mass (0–49.99 ASMI deciles) [30] using sex-specific cutoffs.

CVD risk factors

The outcome measures were CVD risk factors at baseline and following ILI, including A1C; fasting blood glucose; HDL-C; LDL-C; triglycerides; systolic and diastolic blood pressure (BP); CRF, depression and HR-QoL. Details of the blood glucose and lipid measures, the graded exercise test used for CRF assessment [32], the Beck Depression Inventory (BDI) used to assess depression severity (higher scores denote greater severity) [33], and the Medical Outcome Study Short Form-36 (SF-36) for physical component summary (PCS) and mental component summary (MCS) scores of HR-QoL (higher scores denote better QoL) [33] are described elsewhere.

Statistical analysis

Data analyses were performed using IBM SPSS Statistics 27 for Windows (IBM Corp., Armonk, NY, USA). Data normality was tested with Kolmogorov–Smirnov test. BMI, A1C, fasting blood glucose, HDL-C, LDL-C, triglycerides, systolic BP, depression, PCS, MCS and CRF violated normality assumption. These data were normalized using a two-step approach [34]. Because the transformed data showed consistent results with non-transformed data, outputs using the non-transformed data are reported. Missing data were excluded from the analyses. Categorical variables are presented as frequencies and percentages, and continuous variables as mean ± standard deviation. Statistical significance was set at p < 0.05.

To assess the associations of fat mass and muscle mass with CVD risk factors at baseline, a two-way analysis of covariance (ANCOVA: high-FMI vs. low-FMI by high-ASMI vs. low-ASMI) was performed on baseline measures for females and males separately, with a primary focus on the fat mass by muscle mass interaction effects. The analyses were adjusted for age; race/ethnicity; income; duration of diabetes; and, prescribed medications to manage hyperglycemia, dyslipidemia, hypertension and depression. Details of medications are provided in Additional file 1: Tables S1 and S2. For females only, all analyses were additionally adjusted for the menopausal status. When significant interaction effects were found, we performed post-hoc analyses comparing high-ASMI and low-ASMI separately within high-FMI and low-FMI deciles. For sensitivity analysis, the truncal fat mass (calculated as the difference between total fat mass and appendicular fat mass) was used in place of FMI.

To examine if ILI was associated with improvements in CVD risk factors in the subset of participants included in the analysis, dependent t-tests were conducted for females and males separately to compare baseline and 1-year CVD risk factors. To assess the interaction between fat mass and muscle mass on changes in CVD risk factors, a two-way ANCOVA (high-FMI vs. low-FMI by high-ASMI vs. low-ASMI) on changes in blood glucose and lipids, BP, CRF, depression and HR-QoL was performed in females and males randomized to ILI. The analyses were adjusted for the same covariates as described above.

Results

Participants

Of 5,145 participants enrolled in the Look AHEAD trial, 1,369 (females, n = 858, 62.7%) completed DXA measures at baseline. A larger proportion of participants who completed DXA (i.e., those included in the analyses) were females when compared to those who did not undergo DXA assessment (62.5 vs. 57.1%, p < 0.001). Participants who completed DXA were younger (58 ± 6 vs. 59 ± 6 years old, p = 0.001); had significantly lower body mass (96.8 ± 16.7 vs. 102.7 ± 19.8 kg, p < 0.001), BMI (35.2 ± 5.3 vs. 36.2 ± 6.1 kg/m2, p < 0.001), waist circumference (111.0 ± 12.3 vs. 115.1 ± 14.4 cm, p < 0.001), triglyceride concentrations (175.2 ± 107.0 vs. 195.3 ± 133.9 mg/dL, p < 0.001) and MCS scores (53.1 ± 8.5 vs. 54.6 ± 7.8 points, p < 0.001); and, had higher depression scores (5.6 ± 5.1 vs. 5.3±4.8 point, p = 0.030) and CRF (7.5 ± 1.9 vs. 7.1 ± 2.0 metabolic equivalents [METs], p < 0.001) when compared to those who did not undergo DXA assessment.

Baseline characteristics of females

Baseline characteristics of females (n = 858) are summarized in Table 1. When compared to females with low-FMI, those with high-FMI were significantly younger (56.8 ± 6.4 vs. 58.0 ± 6.0 years, p = 0.001) and had a shorter history of type 2 diabetes (5.9 ± 5.8 vs. 6.7 ± 6.1 years, p = 0.004). Females with low-ASMI were significantly older (57.7 ± 6.2 vs. 57.1 ± 6.6 years, p = 0.029) and had longer history of type 2 diabetes (6.8 ± 6.5 vs. 5.8 ± 5.4 years, p = 0.001) when compared to those with high-ASMI.

Table 1 Baseline characteristics of female participants
Full size table

The CVD risk factors at baseline are also summarized in Table 1. After adjusting for the covariates (i.e., age, duration of type 2 diabetes, race, income, prescribed medication and menopausal status), there was a significant FMI-by-ASMI interaction effect on A1C (interaction effect, p = 0.016, Fig. 1). Post-hoc analysis showed that, in the low-FMI group, A1C was significantly higher in low-ASMI when compared to high-ASMI (60.3 ± 14.1 vs. 55.5 ± 13.5 mmol/mol [7.7 ± 1.3 vs. 7.2 ± 1.2%], p = 0.023). In the high-FMI group, there was no difference between high-ASMI and low-ASMI (56.4 ± 12.5 vs. 56.5 ± 12.8 mmol/mol [7.3 ± 1.1 vs. 7.3 ± 1.2%] p = 0.610). Our sensitivity analysis using truncal fat mass instead of FMI showed consistent findings (interaction effect, p = 0.049). Females with high-FMI had higher systolic BP (132 ± 17 vs. 128 ± 16 mmHg, p = 0.006), lower CRF (6.4 ± 1.4 vs. 7.5 ± 1.7 METs, p < 0.001), higher depression (6.9 ± 5.5 vs. 5.7 ± 4.8 points, p = 0.004) and lower physical HR-QoL (i.e., PCS score: 46.0 ± 8.2 vs. 49.5 ± 7.4 points, p < 0.001) scores when compared to low-FMI. Females with low-ASMI had lower CRF when compared to high-AMSI (6.7 ± 1.5 vs. 7.3 ± 1.7 METs, p < 0.001).

Fig. 1
figure 1

Glycated hemoglobin A1C concentration of females (left) and males (right) at baseline. In females, there was a significant FMI-by-ASMI interaction effect (p = 0.016). Post hoc analyses showed no significant difference between high-ASMI and low-ASMI in the high-FMI group (p = 0.610), whereas significantly higher A1C in low-ASMI when compared to high-ASMI in the low-FMI group (p = 0.023). In males, the high-FMI group had higher A1C when compared to low-FMI (p = 0.008)

Full size image

Baseline characteristics of males

Baseline characteristics and CVD risk factors of males (n = 511) are summarized in Table 2. There were no FMI-by-ASMI interaction effects on CVD risk factors at baseline. Males with high-FMI had higher A1C (57.1 ± 14.4 vs. 54.2 ± 12.0 mmol/mol [7.4 ± 1.3 vs. 7.1 ± 1.1%], p = 0.008, Fig. 1) and systolic BP (131 ± 16 vs. 127 ± 16 mmHg, p = 0.015), and lower CRF (7.5 ± 1.7 vs. 9.1 ± 2.2 METs, p < 0.001) and physical HR-QoL scores (PCS score: 47.4 ± 8.4 vs. 50.9 ± 6.6 points, p < 0.001) when compared to low-FMI. When comparing high- and low-ASMI, males with low-ASMI were significantly older (60.5 ± 6.4 vs. 59.7 ± 6.6 years old, p = 0.041) and had lower CRF (7.7 ± 1.8 vs. 9.0 ± 2.2 METs, p = 0.006).

Table 2 Baseline characteristics of male participants
Full size table

Changes in CVD risk factors in females

Of 858 females, 410 were randomized to ILI. Dependent t-tests showed a significant reduction in FMI, increase in ASMI, and improvements in all CVD risk factors following ILI (all p < 0.05). Changes in CVD risk factors according to FMI and ASMI are summarized in Table 3. No FMI-by-ASMI interaction effect was observed on changes in CVD risk factors. Females with high-FMI at baseline showed greater reductions in body mass, BMI, and depression score (all p < 0.05). Between low- and high-ASMI, females with low-ASMI at baseline showed a greater decrease in systolic BP (-9 ± 17 vs. -7 ± 17 mmHg, p = 0.045) when compared to high-ASMI.

Table 3 Changes in cardiovascular disease risk factors of females following intensive lifestyle intervention
Full size table

Changes in CVD risk factors in males

Of 511 males, 235 were randomized to ILI. Dependent t-tests showed a significant decrease in FMI and improvements in all CVD risk factors (all p < 0.05). However, ASMI decreased significantly over time (− 0.13 ± 0.22, p < 0.001). Changes in CVD risk factors according to FMI and ASMI are summarized in Table 4. Males with high-FMI at baseline experienced a significantly greater decrease in A1C (− 31.8 ± 13.8 vs. − 30.2 ± 9.6 mmol/mol [− 0.8 ± 1.3 vs. − 0.6 ± 0.9%], p = 0.038) and fasting blood glucose concentration (− 30.2 ± 55.6 vs. − 16.7 ± 41.0 mg/dL, p = 0.008) when compared to those with low-FMI at baseline. Between low- and high-ASMI, reductions in body mass and BMI were greater in males with low-ASMI than high-ASMI (both p < 0.05).

Table 4 Changes in cardiovascular disease risk factors of males following intensive lifestyle intervention
Full size table

There was a significant FMI-by-ASMI interaction effect on the changes in mental HR-QoL (i.e., MCS scores: interaction effect, p = 0.003, Fig. 2). Post hoc analyses showed a significantly greater increase in MCS scores in males with high-ASMI when compared to low-ASMI in high-FMI at baseline (5.2 ± 9.4 vs. 1.2 ± 9.5 points, p = 0.016). In contrast, the MCS score increased more in males with low-ASMI when compared to high-ASMI in low-FMI at baseline (3.5 ± 6.5 vs. 0.1 ± 6.6 points, p = 0.042). Our sensitivity analysis using the truncal fat mass did not confirm the FMI-by-ASMI interaction effect (p = 0.197).

Fig. 2
figure 2

Changes in mental component summary (MCS) scores following intensive lifestyle intervention in females (left) and males (right). In females, there were no differences in changes in MCS scores associated with different body composition phenotypes. In males, there was an FMI-by-ASMI interaction effect on changes in MCS scores (p = 0.003). Post hoc analyses showed a greater increase in MCS scores in high-ASMI when compared to low-ASMI in the high-FMI group. In the low-FMI group, MCS scores increased more in low-ASMI when compared to high-ASMI

Full size image

Discussion

The combination of high fat mass and low muscle mass increase the risk of CVD incidences [21]. This study assessed the sex-specific interplay between fat mass and muscle mass on CVD risk factors in adults with type 2 diabetes living with overweight or obesity. Contrary to our hypothesis that high fat mass and low muscle mass would synergistically worsen CVD risks, we found that, in females, low fat mass and low muscle mass were synergistically associated with higher A1C. This result suggests that low muscle mass has deleterious effects on A1C only when combined with low fat mass in females. In males, no significant influence of muscle mass on A1C was found and high-fat mass was significantly associated with higher A1C. Regarding the changes in CVD risk factors following ILI, we found a significant fat mass by muscle mass interaction effect on ILI-induced changes in MCS scores in males.

No previous study has examined the sex-specific interplay between fat mass and muscle on A1C. This is the first study to report synergistic exacerbation of A1C by low fat mass and low muscle mass among females with type 2 diabetes living with overweight or obesity. This finding, although unexpected, was similar to previous large scale retrospective studies demonstrating significantly higher A1C in underweight (BMI < 18.5 kg/m2, 9.6 ± 2.7%) when compared to higher BMI (≥ 27.5 kg/m2, 8.2 ± 1.8%, p < 0.001) [35], and non significantly higher A1C in females with lower BMI (20 to 25 kg/m2, 8.1 ± 2.2%) when compared to higher BMI categories (e.g., ≥ 30 kg/m2, ≤ 7.9%) [36]. Because BMI reflects the sum of both fat mass and muscle mass, lower BMI in these studies likely reflects low fat mass and low muscle mass. Every 0.5% increment in A1C is positively associated with increased risk of CVD (RR:1.29, 95% CI 1.11 to 1.50) [37], and those with A1C ≥ 7.5% face elevated future risks of total CVD (HR: 1.82, 95% CI: 1.01–3.26) and all cause mortality (HR: 2.45, 95% CI: 1.45–4.14) when compared to A1C < 5.5% [38]. Consequently, the mean A1C of 7.7% observed in females with low fat mass and low muscle compared to A1C of ≤ 7.3% in the other body composition phenotypes may be clinically meaningful. Interventions that target A1C and low muscle mass may be necessary for females with low fat mass and low muscle mass to avoid future complications.

The mechanism behind synergistic exacerbation of A1C by low fat mass and low muscle mass in females is unclear. It is possible that higher systemic inflammation contributed to muscle mass loss and higher A1C [39]. Insulin resistance may have also increased A1C and decreased stimulation of protein synthesis while promoting activation of protein degradation [40]. Insulin resistance is generally higher in those with more fat distributed in the android region and visceral adipose tissue is more strongly linked to insulin resistance in females [41]. We conducted sensitivity analysis using truncal fat mass and confirmed that lower truncal fat mass and lower muscle mass are synergistically associated with exacerbated A1C in females. However, our data did not allow us to distinguish visceral and subcutaneous fat amounts. Interestingly, while fat mass was positively associated with A1C in males, in females there were no significant associations between baseline A1C and FMI, body mass or BMI (all p > 0.05, results not shown). Further studies simultaneously examining sex-specific muscle mass, fat mass and fat distribution are warranted to elucidate the intricate interplay between fat mass and muscle mass and to develop more tailored programs to manage unique body compositions of both sexes.

With regards to the other CVD risk factors at baseline, we found that high fat mass was associated with worse systolic BP, CRF, depression and PCS scores regardless of sex. We also found that low muscle mass was associated with lower CRF in both sexes. These findings are in line with a previous study [15] and highlight the negative effects of high fat mass on CVD risks factors. Because lower CRF is associated with the development of CVD [42] and predicts mortality in type 2 diabetes [43], our results suggest that high fat mass and low muscle mass in adults with type 2 diabetes are independently associated with higher risk of future CVD.

Following ILI, consistent to the previous report including all participants (N = 5,145) [25], we found significant improvement in body compositions in females (i.e., decreased fat mass and increased muscle mass). Such improvements were accompanied by overall improvements in CVD risk factors. Between high-FMI and low-FMI at baseline, depression improved significantly more in females with high-FMI at baseline when compared to females with low-FMI. A greater decrease in fat mass in females with high-FMI at baseline when compared to low-FMI (− 5.0 ± 5.1 vs. 1.1 ± 4.3 kg/m2, p < 0.001) may have positively affected societal stigmatization and self-esteem [44] and resulted in enhanced reduction in depression scores [45]. The systolic BP decreased significantly more in females with low-ASMI when compared to high-ASMI (− 10 ± 17 vs. − 7 ± 18 mmHg, p = 0.045). However, this difference may be of little clinical importance. Overall, our results highlight positive effects of ILI on CVD risk factors in females regardless of fat mass or muscle mass before participating in ILI.

In males, weight loss was accompanied by significant decreases in FMI and ASMI. The reduction in FMI was significantly greater in males with high-FMI at baseline when compared to males with low-FMI, which may explain greater decreases in A1C and fasting blood glucose in high-FMI compared to low-FMI. The loss of muscle mass at 1 year corresponds to previous findings [46]. Loss of muscle mass only in males is in line with a previous study reporting that males lose greater muscle mass than females when total body mass is reduced [47]. ILI represented by exercise and hypocaloric diet is a more effective intervention for weight loss than either of the interventions alone; however, the skeletal muscle mass is not completely preserved [23]. Our results suggest that males require strategies to maintain muscle mass when ILI targeting weight loss is implemented. Considering blunted protein synthetic response to the anabolic stimuli in individuals with obesity, they may have higher protein needs compared to leaner individuals [23]. Addition of resistance exercise and increased protein intake to counteract the loss of muscle mass may be more important in males aiming to lose weight through lifestyle interventions.

In males, there was a significant fat mass by muscle mass interaction effect on the mental component of HR-QoL (i.e., MCS scores). There is no clear explanation for such different degrees of improvements. However, when we examined subscales of HR-QoL, mental health showed the same interaction effect (p = 0.001), and social function also showed the same patterns of improvements as the MCS while the interaction effects did not reach statistical significance (p = 0.087), highlighting that the changes in mental component of HR-QoL are simultaneously influenced by fat mass and muscle mass. Nonetheless, this interaction effect needs to be interpreted with caution as it was not confirmed by our sensitivity analysis.

There are limitations to the current study. First, while the European Working Group for the Study of Sarcopenia proposed the presence of both low muscle mass and low muscle function (strength or performance) for the diagnosis of sarcopenia [48], our data did not include muscle functions. Because some studies have shown that dynapenia (i.e., loss of muscle function) but not low muscle mass is associated with functional limitations, disability, institutionalization and mortality [49], inclusion of muscular function is warranted in future studies. Additionally, there is a large heterogeneity in methods for determining fat mass and muscle mass, which is compounded by the heterogeneous body composition measures (e.g., bioelectrical impedance analysis, computed tomography, and DXA). We calculated the ASMI by adjusting muscle mass for BMI instead of height as the latter strongly correlates with BMI and identifies few individuals with low-muscle mass when BMI is high [50]. Indeed, in our cohort, the correlation between the muscle mass adjusted for height and BMI was significant (r = 0.503, p < 0.001). Our approach to determine ASMI has been indicated to detect sarcopenia in patients with higher BMI [51] and predict cardiometabolic risk factors in low muscle mass [52]. The cut-offs we used for the ASMI to separate high- and low- muscle mass were 0.580 and 0.877 for females and males, respectively. Our cut-offs were slightly higher than those previously reported by the FNIH: 0.512 for females and 0.789 for males [31]. The differences are probably due to younger age of our study participants. Second, although we divided participants into high fat and low fat based on FMI deciles, the mean BMI of females with low fat mass was 31.7 ± 2.6 kg/m2. For males, it was 31.0 ± 2.5 kg/m2 for low fat mass. It is important to note that the mean BMI of both sexes with low fat mass fell in the obesity category. Third, there were significant differences in baseline characteristic between participants who underwent DXA and those who did not and, thus, were excluded from our study. Participants who completed DXA were younger and metabolically healthier than those who did not undergo DXA. This limits the generalizability of our results to those who have more favorable cardiometabolic conditions. Lastly, because this is a retrospective analysis of existing data, it can only provide associative evidence, not causal.

Conclusion

In conclusion, our sex-specific assessment of fat mass and muscle mass showed that low fat mass and low muscle mass are synergistically associated with higher A1C in females with type 2 diabetes, whereas high fat mass and low muscle mass independently contribute to other cardiovascular health risk factors regardless of sex, such as worse systolic BP, CRF, depression and HR-QoL scores. These findings suggest that strategies to increase muscle mass are necessary to reduce CVD risks in people living with type 2 diabetes, especially in females with low fat mass to control A1C. Our results highlight the complicated and sex-specific contribution of fat mass and muscle mass on CVD risk factors.

Availability of data and materials

The data from Look AHEAD are available in the NIDDK Central Repository.

Abbreviations

ANCOVA:

Analysis of covariance

ASMI:

Appendicular muscle mass index

A1C:

Glycated hemoglobin A1C

BDI:

Beck Depression Inventory

BMI:

Body mass index

BP:

Blood pressure

CRF:

Cardiorespiratory fitness

CVD:

Cardiovascular disease

DSE:

Diabetes support and education

DXA:

Dual-energy X-ray absorptiometry

FMI:

Fat mass index

FNIH:

Foundation for the National Institutes of Health

HDL-C:

High-density lipoprotein cholesterol

HR-QoL:

Health-related quality of life

ILI:

Intensive lifestyle intervention

LDL-C:

Low-density lipoprotein cholesterol

MCS:

Mental component summary

NHANES:

National Health and Nutrition Examination Survey

PCS:

Physical component summary

SF-36:

Medical Outcomes Study Short Form-36

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

References

  1. Hales CM CM, Cheryl D, Fryar CD, Ogden CL. Revalence of obesity and severe obesity among adults: United States, 2017–2018. NCHS Data Brief, No 360. Hyattsville, MD: National Center for Health Statistics. 2020.

  2. Cruz-Jentoft AJ, Landi F, Schneider SM, et al. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing 2014; 43(6):748–759.

  3. Batsis JA, Mackenzie TA, Emeny RT, Lopez-Jimenez F, Bartels SJ. Low lean mass with and without obesity, and mortality: results from the 1999–2004 National Health and Nutrition Examination Survey. J Gerontol A Biol Sci Med Sci. 2017;72(10):1445–51.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Center for Disease Control and Prevention. National Diabetes Statistics Report 2020 (2020) Atlanta, GA, U.S. Dept of Health and Human Services; 2020. https://www.cdc.gov/diabetes/library/features/diabetes-stat-report.html. Accessed 20 May 2021

  5. Despres JP, Lamarche B. Effects of diet and physical activity on adiposity and body fat distribution: implications for the prevention of cardiovascular disease. Nutr Res Rev. 1993;6(1):137–59.

    Article  CAS  PubMed  Google Scholar 

  6. Fujioka S, Matsuzawa Y, Tokunaga K, Tarui S. Contribution of intra-abdominal fat accumulation to the impairment of glucose and lipid metabolism in human obesity. Metabolism. 1987;36(1):54–9.

    Article  CAS  PubMed  Google Scholar 

  7. Marin P, Andersson B, Ottosson M, et al. The morphology and metabolism of intraabdominal adipose tissue in men. Metabolism. 1992;41(11):1242–8.

    Article  CAS  PubMed  Google Scholar 

  8. Arsenault BJ, Lachance D, Lemieux I, et al. Visceral adipose tissue accumulation, cardiorespiratory fitness, and features of the metabolic syndrome. Arch Intern Med. 2007;167(14):1518–25.

    Article  CAS  PubMed  Google Scholar 

  9. van Hout GC, van Oudheusden I, van Heck GL. Psychological profile of the morbidly obese. Obes Surg. 2004;14(5):579–88.

    Article  PubMed  Google Scholar 

  10. Schorr M, Dichtel LE, Gerweck AV, et al. Sex differences in body composition and association with cardiometabolic risk. Biol Sex Differ. 2018;9(1):28.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Park SW, Goodpaster BH, Lee JS, et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care. 2009;32(11):1993–7.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Srikanthan P, Hevener AL, Karlamangla AS. Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: findings from the National Health and Nutrition Examination Survey III. PLoS ONE. 2010;5(5):e10805.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tanaka K, Kanazawa I, Sugimoto T. Reduction in endogenous insulin secretion is a risk factor of sarcopenia in men with type 2 diabetes mellitus. Calcif Tissue Int. 2015;97(4):385–90.

    Article  CAS  PubMed  Google Scholar 

  14. Karakelides H, Nair KS. Sarcopenia of aging and its metabolic impact. Curr Top Dev Biol. 2005;68:123–48.

    Article  CAS  PubMed  Google Scholar 

  15. Kim TN, Park MS, Kim YJ, et al. Association of low muscle mass and combined low muscle mass and visceral obesity with low cardiorespiratory fitness. PLoS ONE. 2014;9(6):e100118.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Olgun Yazar H, Yazar T. Prevalence of sarcopenia in patients with geriatric depression diagnosis. Ir J Med Sci. 2019;188(3):931–8.

    Article  PubMed  Google Scholar 

  17. Beaudart C, Reginster JY, Petermans J, et al. Quality of life and physical components linked to sarcopenia: the SarcoPhAge study. Exp Gerontol. 2015;69:103–10.

    Article  CAS  PubMed  Google Scholar 

  18. Tyrovolas S, Panagiotakos D, Georgousopoulou E, et al. Skeletal muscle mass in relation to 10 year cardiovascular disease incidence among middle aged and older adults: the ATTICA study. J Epidemiol Community Health. 2020;74(1):26–31.

    Article  PubMed  Google Scholar 

  19. Grundy SM, Benjamin IJ, Burke GL, et al. Diabetes and cardiovascular disease: a statement for healthcare professionals from the American Heart Association. Circulation. 1999;100(10):1134–46.

    Article  CAS  PubMed  Google Scholar 

  20. Lee WL, Cheung AM, Cape D, Zinman B. Impact of diabetes on coronary artery disease in women and men: a meta-analysis of prospective studies. Diabetes Care. 2000;23(7):962–8.

    Article  CAS  PubMed  Google Scholar 

  21. Fukuda T, Bouchi R, Takeuchi T, et al. Sarcopenic obesity assessed using dual energy X-ray absorptiometry (DXA) can predict cardiovascular disease in patients with type 2 diabetes: a retrospective observational study. Cardiovasc Diabetol. 2018;17(1):55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Janssen I, Heymsfield SB, Wang ZM, Ross R. Skeletal muscle mass and distribution in 468 men and women aged 18–88 yr. J Appl Physiol. 2000;89(1):81–8.

    Article  CAS  PubMed  Google Scholar 

  23. Trouwborst I, Verreijen A, Memelink R, et al. Exercise and nutrition strategies to counteract sarcopenic cbesity. Nutrients. 2018;10(5):605.

    Article  PubMed Central  Google Scholar 

  24. Terada T, Boulé NG, Forhan M, et al. Cardiometabolic risk factors in type 2 diabetes with high fat and low muscle mass: at baseline and in response to exercise. Obesity. 2017;25(5):881–91.

    Article  CAS  PubMed  Google Scholar 

  25. Wing RR, Bolin P, Brancati FL, et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013;369(2):145–54.

    Article  CAS  PubMed  Google Scholar 

  26. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The strengthening the reporting of observational studies in epidemiology (STROBE) Statement: guidelines for reporting observational studies. Int J Surg. 2014;12(12):1495–9.

    Article  Google Scholar 

  27. Look AHEAD Research Group. Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial. Arch Intern Med. 2010;170(17):1566–75.

    Google Scholar 

  28. Wadden TA, West DS, Delahanty L, et al. The Look AHEAD study: a description of the lifestyle intervention and the evidence supporting it. Obesity. 2006;14(5):737–52.

    Article  PubMed  Google Scholar 

  29. Galgani JE, Smith SR, Ravussin E. Assessment of EchoMRI-AH versus dual-energy X-ray absorptiometry to measure human body composition. Int J Obes. 2011;35(9):1241–6.

    Article  CAS  Google Scholar 

  30. Van Aller C, Lara J, Stephan BCM, et al. Sarcopenic obesity and overall mortality: Results from the application of novel models of body composition phenotypes to the National Health and Nutrition Examination Survey 1999–2004. Clin Nutr. 2019;38(1):264–70.

    Article  PubMed  Google Scholar 

  31. Studenski SA, Peters KW, Alley DE, et al. The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol A Biol Sci Med Sci. 2014;69(5):547–58.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Terada T, Boulé NG. Does metformin therapy influence the effects of intensive lifestyle intervention? Exploring the interaction between first line therapies in the Look AHEAD trial. Metabolism. 2019;94:39–46.

    Article  CAS  PubMed  Google Scholar 

  33. Rubin RR, Wadden TA, Bahnson JL, et al. Impact of intensive lifestyle intervention on depression and health-related quality of life in type 2 diabetes: the Look AHEAD Trial. Diabetes Care. 2014;37(6):1544–53.

    Article  PubMed  Google Scholar 

  34. Templeton GF. A two-step approach for transforming continuous variables to normal: implications and recommendations for IS research. Commun Assoc Inf Systems. 2011;28:4.

    Google Scholar 

  35. Lin CC, Li CI, Liu CS, et al. Obesity paradox in associations between body mass index and diabetes-related hospitalization and mortality in patients with type 2 diabetes: retrospective cohort studies. Diabetes Metab. 2019;45(6):564–72.

    Article  PubMed  Google Scholar 

  36. Logue J, Walker JJ, Leese G, et al. Association between BMI measured within a year after diagnosis of type 2 diabetes and mortality. Diabetes Care. 2013;36(4):887–93.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Pai JK, Cahill LE, Hu FB, Rexrode KM, Manson JE, Rimm EB. Hemoglobin a1c is associated with increased risk of incident coronary heart disease among apparently healthy, nondiabetic men and women. J Am Heart Assoc. 2013;2(2):e000077.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chen YY, Lin YJ, Chong E, et al. The impact of diabetes mellitus and corresponding HbA1c levels on the future risks of cardiovascular disease and mortality: a representative cohort study in Taiwan. PLoS ONE. 2015;10(4):e0123116.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Roubenoff R. Physical activity, inflammation, and muscle loss. Nut Rev. 2007;65(12 Pt 2):S208-212.

    Article  Google Scholar 

  40. Kalyani RR, Corriere M, Ferrucci L. Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol. 2014;2(10):819–29.

    Article  PubMed  PubMed Central  Google Scholar 

  41. de Mutsert R, Gast K, Widya R, et al. Associations of abdominal subcutaneous and visceral fat with insulin resistance and secretion differ between men and women: The Netherlands epidemiology of obesity study. Metab Syndr Relat Disord. 2018;16(1):54–63.

    Article  PubMed  Google Scholar 

  42. Chu DJ, Al Rifai M, Virani SS, Brawner CA, Nasir K, Al-Mallah MH. The relationship between cardiorespiratory fitness, cardiovascular risk factors and atherosclerosis. Atherosclerosis. 2020;304:44–52.

    Article  CAS  PubMed  Google Scholar 

  43. Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med. 2002;346(11):793–801.

    Article  PubMed  Google Scholar 

  44. Patsalos O, Keeler J, Schmidt U, Penninx B, Young AH, Himmerich H. Diet, obesity, and depression: a systematic review. J Pers Med. 2021;11(3):176.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Fuller NR, Burns J, Sainsbury A, et al. Examining the association between depression and obesity during a weight management programme. Clin Obes. 2017;7(6):354–9.

    Article  CAS  PubMed  Google Scholar 

  46. Pownall HJ, Bray GA, Wagenknecht LE, et al. Changes in body composition over 8 years in a randomized trial of a lifestyle intervention: the look AHEAD study. Obesity. 2015;23(3):565–72.

    Article  PubMed  Google Scholar 

  47. Hughes VA, Frontera WR, Roubenoff R, Evans WJ, Singh MA. Longitudinal changes in body composition in older men and women: role of body weight change and physical activity. Am J Clin Nutr. 2002;76(2):473–81.

    Article  CAS  PubMed  Google Scholar 

  48. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, et al. Sarcopenia: European consensus on definition and diagnosis: report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39(4):412–23.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Roh E, Choi KM. Health consequences of sarcopenic obesity: a narrative review. Front Endocrinol. 2020;11:332.

    Article  Google Scholar 

  50. Newman AB, Kupelian V, Visser M, et al. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriat Soc. 2003;51(11):1602–9.

    Article  PubMed  Google Scholar 

  51. Fonseca G, Dos Santos MR, de Souza FR, et al. Discriminating sarcopenia in overweight/obese male patients with heart failure: the influence of body mass index. ESC Heart Failure. 2020;7(1):84–91.

    Article  CAS  PubMed  Google Scholar 

  52. Kim TN, Park MS, Lee EJ, Chung HS, Yoo HJ, Kang HJ, Song W, Baik SH, Choi KM. Comparisons of three different methods for defining sarcopenia: an aspect of cardiometabolic risk. Sci Rep. 2017;7(1):6491.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Look AHEAD was conducted by the Look AHEAD Research Group and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); the National Heart, Lung, and Blood Institute (NHLBI); the National Institute of Nursing Research (NINR); the National Institute on Minority Health and Health Disparities (NIMHD); the Office of Research on Women's Health (ORWH); and the Centers for Disease Control and Prevention (CDC). The data from Look AHEAD were supplied by the NIDDK Central Repository. This manuscript was not prepared under the auspices of the Look AHEAD and does not represent analyses or conclusions of the Look AHEAD Research Group, the NIDDK Central Repository, or the NIH.

Funding

TT was supported by a Canadian Institutes of Health Research Postdoctoral Research Fellowship and a Jan & Ian Craig Cardiac Prevention and Rehabilitation Endowed Fellowship from the University of Ottawa Heart Institute.

Author information

Authors and Affiliations

  1. Exercise Physiology and Cardiovascular Health Lab, Division of Cardiac Prevention and Rehabilitation, University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, ON, K1Y 4W7, Canada

    Tasuku Terada, Jennifer L. Reed, Sol Vidal-Almela, Matheus Mistura & Kimberley L. Way

  2. School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

    Jennifer L. Reed

  3. School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada

    Jennifer L. Reed & Sol Vidal-Almela

  4. School of Allied Health Sciences, Kitasato University, Sagamihara, Japan

    Kentaro Kamiya

  5. Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia

    Kimberley L. Way

Authors
  1. Tasuku Terada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennifer L. Reed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sol Vidal-Almela
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matheus Mistura
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kentaro Kamiya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kimberley L. Way
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Tasuku Terada: Conceptualization, methodology, data curation, analysis, writing—original draft, visualization; Jennifer L. Reed: writing—review and editing, supervision; Sol Vidal-Almela: writing—review and editing; Matheus Mistura: data curation, writing—review and editing; Kentaro Kamiya: writing—review and editing; Kimberley L. Way: writing—review and editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Tasuku Terada.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Ottawa Health Science Network Research Ethics Board (Protocol #: 20200690-01H).

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1

. Female, medication details. Table S2. Male, medication details.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Terada, T., Reed, J.L., Vidal-Almela, S. et al. Sex-specific associations of fat mass and muscle mass with cardiovascular disease risk factors in adults with type 2 diabetes living with overweight and obesity: secondary analysis of the Look AHEAD trial. Cardiovasc Diabetol 21, 40 (2022). https://doi.org/10.1186/s12933-022-01468-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12933-022-01468-x

Keywords

Cardiovascular Diabetology

ISSN: 1475-2840