Association between body composition and subsequent cardiovascular diseases among 5-year breast cancer survivors

doi:10.21203/rs.3.rs-1984230/v1

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Association between body composition and subsequent cardiovascular diseases among 5-year breast cancer survivors

https://doi.org/10.21203/rs.3.rs-1984230/v1

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published 01 Mar, 2024

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Purpose: Cardiovascular diseases (CVDs) remain one of the leading causes of mortality in breast cancer survivors. This study aimed to investigate the association between body composition and subsequent CVDs in breast cancer survivors.

Methods: A retrospective cohort study of more than 70 thousand 5-year breast cancer survivors aged 40 years or older was conducted using data from the National Health Insurance Service of South Korea. Based on the percentage of predicted lean body mass (pLBMP), appendicular skeletal muscle mass (pASMP), and body fat mass (pBFMP), which were calculated using prediction equations with anthropometric data and health habits, groups were equally divided into quartiles. The risk of CVD was evaluated using multivariate Cox proportional hazards regression.

Results: Compared to those with the lowest pLBMP and pASMP, those with the highest pLBMP and pASMP had a 37% and 42% lower risk of CVDs, respectively. In contrast, those with the highest pBFMP had a 57% higher risk of CVDs compared to those with the lowest pBFMP. Each 1 % increase in pLBMP and pASMP was associated with a decreased risk of CVDs [pLBMP, adjusted hazard ratio (aHR): 0.96, 95% CI 0.94–0.98, p<0.05; pASMP, aHR: 0.91, 95% CI 0.87–0.95, p<0.05] while each 1 % increase in pBFMP was associated with the increased risk of CVDs (aHR: 1.05, 95% CI 1.03–1.07, p<0.01).

Conclusions: In this cohort study, high pLBMP, a high pASMP, and a low pBFMP were associated with reduced risk of CVDs.

Breast cancer survivor

body composition

cardiovascular diseases

Survivors of most site-specific cancers have an increased mid-to long-term risk for one or more cardiovascular diseases (CVDs) compared to that of the general population [1]. The risk of atherosclerosis and cardiovascular diseases (ASCVDs) varies by cancer type, and those with bladder/kidney, prostate, colorectal, lung, melanoma, or testicular cancer have a 2.72–10.47 higher 10-year risk of ASCVDs [2]. The risk of CVDs in breast cancer survivors has also been investigated in previous studies [3]. Results of a previous studies showed CVDs to be the primary cause of death in older females diagnosed with early-stage breast cancer and the second leading cause of death in breast cancer survivors.

The majority of breast cancer survivors experience weight gain and body fat increase after treatment [4]. Excessive body fat in cancer survivors has been shown to affect the quality of life and disease-free survival [5]. As with the obesity paradox, survival rates were improved in overweight and early obese cancer patients, however, there were also observational studies showing that intentional body fat reduction and maintenance of skeletal muscle in overweight and obese cancer survivors have health benefits. Studies conducted on breast cancer patients have also shown varying results. In a study conducted by Shang et al. on breast cancer patients in their 50s, body mass index loss was found to be a strong predictor of poor prognosis [6]. On the contrary, according to the results of a meta-analysis, weight gain after diagnosis of breast cancer was associated with a high mortality rate, and the risk was higher when weight gain was 10% or more compared to that of weight gain of less than 10% [7]. Besides one study [8], which examined for association between adipose distribution and CVD, prior research on breast cancer survivors, let alone breast cancer patients, were limited to investigating association between BMI and mortality, without measuring body composition or CVD.

Body composition has been associated with risk of metabolic syndrome and CVD in the general population [9, 10]. Body fat percentage has been advocated to be a more accurate measure of obesity than BMI, but imaging modalities such as computerized tomography [4], dual-energy X-ray absorptiometry (DXA), or bioelectric impendence analysis (BIA) are indispensable for measurement. Therefore, prediction equations using anthropometric measures were validated and used to estimate body composition, including body fat mass (BFM), lean body mass (LBM), and appendicular skeletal muscle mass (ASM).

In this study, the relationship between body composition and newly developed CVDs was studied among 5-year breast cancer survivors, who were screened by the National Health Insurance Service during 2011–2019. Accordingly, we aimed to provide evidence for the holistic health management, in terms of body composition, of breast cancer survivors.

Study Population

The study population was based on the National Health Insurance Service (NHIS) database. NHIS provides nearly all kinds of health services and insurance claims in Korea. With this health insurance claims information, researchers could analyze the NHIS database for specific research purposes [11, 12]. In the NHIS database, we used information about sociodemographic factors (income level), all forms of hospital use (history of radiotherapy), pharmaceutical drug prescriptions (for chemotherapy and hormonal therapy), and results from health screening examinations (health habits, anthropometric results, and results of laboratory test).

Among 142,899 5-year breast cancer survivors in females aged 40 years or older during 2011 ~ 2019, we excluded 67,093 participants who did not take the health screening examination during the previous 3-year before the index date (after 5 years from initial breast cancer diagnosis). A total of 2,535 participants with previous CVDs before the index date were excluded. An additional 1,102 participants who had missing necessary variables to calculate predictive body composition (age, sex, height, weight, waist circumference, etc.) and covariates (income level, blood pressure, total cholesterol, etc.) were excluded. As a result, the final study population consisted of 72,169 5-year breast cancer survivors (Fig. 1). All participants were followed up from the index date to date of newly diagnosed CVD, date of death, or 31 December 2020, whichever came first.

Breast cancer survival and cardiovascular diseases

We recruited all breast cancer patients based on disease information recorded on the NHIS database. Breast cancer subjects were collected from 1 January 2006 to 31 December 2014 based on the International Classification of Diseases, Tenth Revision (ICD-10; C50) and the special assessment code (V-code that clearly distinguishes cancer patients in Korea; V193 and V194) [13]. Among 5-years breast cancer survivors, we used ICD-10 codes to identify CVDs (I20–I25, I60–I69), coronary heart disease (CHD; I20–I25), and stroke (including ischemic stroke and hemorrhage; I60–I69) [14, 15]. To exclude events that are not actual CVDs, we defined CVD events as 2- or more days of hospitalization with ICD-10 codes for CVDs.

Key variables

Age, sex, weight, height, waist circumference, serum creatine level, smoking status, alcohol consumption, and physical activity were used to evaluate for predicted body composition among a total of 72,169 participants who underwent the health check-up. Predicted mass of body composition (kg) including predicted lean body mass (pLBM), predicted appendicular skeletal muscle mass (pASM), and predicted body fat mass (pBFM) was assessed and calculated using a proven equation [16]. These prediction equations have been previously used in studies related to muscle and fat mass [9, 17]. The prediction equations were developed using multiple linear regressions to predict pASM, pLBM, and pBFM. The correlation coefficient for the variables which were consisted of the equation was then derived. Also, a test of predicted body composition calculated from the equations with actual measurement value showed high predictive power, low bias, and moderate agreement. In this study, we used age, height, weight, waist circumference, serum creatine level, physical activity, alcohol consumption, smoking status, and their correlation coefficients to calculate the pLBM, pASM, and pBFM. As there is a difference in pLBM, pASM, and pBFM according to the individual height, predicted lean body mass index (pLBMi), predicted appendicular skeletal muscle mass (pASMi), and predicted body fat mass index (pBFMi), which is divided by the square of height (kg/m²), were used to compensate for the individual height. In addition, the percentage of predicted lean body mass (pLBMP), percentage of predicted appendicular skeletal muscle mass (pASMP), and percentage of predicted body fat mass (pBFMP), percentage of the index to body mass index (BMI), were mainly used since the pLBMi, pASMi, and pBFMi is correlated with BMI [18, 19]. The 1st, 2nd, 3rd, and 4th quartile groups were equally divided according to percentage of body composition (pLBMP, pASMP, and pBFMP). The 1st quartile group has the lowest percentage of body composition. On the other hand, the 4th quartile group has the highest percentage of body composition. In addition, participants were also divided into four groups (Low body fat mass (BFM)-Low lean body mass (LBM), Low BFM-High LBM, High BFM-Low LBM, and High BFM-High LBM) according to whether pLBMi and pBFMi were lower or higher than each median.

Statistical Analysis

Participants were evaluated for adjusted hazard ratios (aHR) and 95% confidence intervals (CI) of CVD risk according to the percentage of body composition using multivariate Cox proportional hazards regression after adjustments for the covariates. The considered covariates included age (continuous, years), income level (categorical, first, second, third, and fourth quartiles), smoking status (categorical, never-, past, and current smokers), alcohol consumption (categorical, 0, 1–2, 3–4, and 5 or more times per week), exercise (categorical, 0, 1–2, 3–4, and 5 or more times per week), BMI (continuous, kg/m²), Charlson comorbidity index (continuous), history of chemotherapy (categorical; cyclophosphamide, trastuzumab, doxorubicin, epirubicin, docetaxel, paclitaxel, and cisplatin), history of radiation therapy, history of hormone therapy (categorical; tamoxifen, anastrozole, and letrozole), diastolic blood pressure (continuous, mmHg), systolic blood pressure (continuous, mmHg), total cholesterol (continuous, mg/dL), and fasting serum glucose (continuous, mg/dL). Income level was derived from the insurance premium. BMI was calculated by dividing body weight by the square of height (kg/m²). Smoking status, alcohol consumption, and exercise were assessed by a self-reported questionnaire at the health check-up. The algorithm for calculating Charlson comorbidity index (CCI) was adapted from a previous study [20]. Prescription of anti-cancer drugs known to cause heart disease was collected on the NHIS database [21]. Through the insurance claims, the history of radiation treatment was also collected [22]. Chi-squared test for categorical variables and analysis of variance for continuous variables were used to compare the differences in the distribution of covariates.

Compared to the 1st quartile group, CVDs risk and aHR in the other quartile groups were assessed. Kaplan–Meier curves for risk of CVDs according to pLBMP, pASMP, and pBFMP were constructed. Blood pressure, total cholesterol, and fasting glucose serum, known to cause CVDs, tend to increase as fat mass increases [23]. The percentage of excess risk mediated (PERM) was used as an indicator to evaluate the impact of these mediators. PERM was calculated by the corresponding equation with aHR of the confounder (blood pressure, total cholesterol, and fasting glucose serum was not adjusted at all) and aHR of the mediator (blood pressure, total cholesterol, and fasting glucose serum was additionally adjusted) [24]. Statistical significance was defined as p-value < 0.05. p-value by chi-square test for categorical variables and analysis of variance (ANOVA) for continuous variables were used to determine the risk of CVDs. All data collection and statistical analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

Table 1 depicts the descriptive characteristics of the study population according to pLBMP. The mean percentage of predicted lean body mass in the 1st, 2nd, 3rd, and 4th quartile groups were 61.67%, 64.83%, 67.03%, and 70.35%, respectively. Mean ± standard deviation of age in the 1st, 2nd, 3rd, and 4th quartile groups were 59.21 ± 9.54 years, 57.67 ± 9.00 years, 55.87 ± 8.59 years, 53.92 ± 8.35 years, respectively. Most individuals in all quartiles were never-smokers, did not drink alcohol, and underwent chemotherapy and radiotherapy.

Table 1

Descriptive characteristics of the study population.
	Percentage of predicted lean body mass, quartiles				p-value
	1st (lowest)	2nd	3rd	4th (highest)	p-value
Study population, N	18,042	18,042	18,043	18,042
Percentage [%], mean (SD)	61.67 (1.60)	64.83 (0.67)	67.03 (0.65)	70.35 (1.88)	< 0.001
Percentage [%], range	51.37, 63.63	63.63, 65.94	65.94, 68.21	68.21, 86.17
Age [years], mean (SD)	59.21 (9.54)	57.67 (9.00)	55.87 (8.59)	53.92 (8.35)	< 0.001
Age [years], N (%)					< 0.001
40 ~ 49	2,917 (16.17)	3,354 (18.59)	4,308 (23.88)	5,919 (32.81)
50 ~ 59	6,879 (38.13)	7,788 (43.17)	8,462 (46.90)	8,220 (45.56)
≥ 60	8,246 (45.70)	6,900 (38.24)	5,273 (29.22)	3,903 (21.63)
Income, quartiles, N (%)					< 0.001
1st (highest)	4,004 (22.19)	4,499 (24.94)	4,789 (26.54)	5,271 (29.22)
2nd	3,577 (19.83)	3,520 (19.51)	3,406 (18.88)	3,438 (19.06)
3rd	3,587 (19.88)	3,423 (18.97)	3,372 (18.69)	3,240 (17.96)
4th (lowest)	6,874 (38.10)	6,600 (36.58)	6,476 (35.89)	6,093 (35.77)
Smoking, N (%)					< 0.001
Never-smoker	17,329 (96.05)	17,440 (96.66)	17,446 (96.69)	17,336 (96.09)
Past smoker	462 (2.56)	400 (2.22)	373 (2.07)	413 (2.29)
Current smoker	251 (1.39)	202 (1.12)	224 (1.24)	293 (1.62)
Alcohol consumption [times per week], N (%)					< 0.001
0	16,455 (91.20)	16,220 (89.90)	16,007 (88.72)	15,832 (87.75)
1 ~ 2	1,370 (7.59)	1,560 (8.65)	1,810 (10.03)	1,958 (10.85)
3 ~ 4	158 (0.88)	184 (1.02)	165 (0.91)	181 (1.00)
≥ 5	59 (0.33)	78 (0.43)	61 (0.34)	71 (0.39)
Exercise [times per week], N (%)					< 0.001
0	9,401 (52.11)	7,856 (43.54)	6,926 (38.39)	6,221 (34.48)
1 ~ 2	2,484 (13.77)	2,636 (14.61)	2,709 (15.01)	2,994 (16.59)
3 ~ 4	2,381 (13.20)	2,684 (14.88)	3,084 (17.09)	3,291 (18.24)
≥ 5	3,776 (20.93)	4,866 (26.97)	5,324 (29.51)	5,536 (30.68)
Appendicular skeletal muscle mass [kg], mean (SD)	16.61 (1.84)	15.39 (1.43)	14.81 (1.31)	14.08 (1.34)	< 0.001
Fat mass [kg], mean (SD)	25.02 (3.73)	20.18 (1.75)	17.58 (1.45)	14.35 (1.89)	< 0.001
BMI [kg/m²], mean (SD)	27.76 (2.41)	24.14 (0.86)	22.20 (0.73)	19.89 (1.22)	< 0.001
BMI [kg/m²], N (%)					< 0.001
< 23.0	1 (0.01)	1,386 (7.68)	15,273 (84.65)	17,958 (99.53)
≥ 23.0	18,041 (99.99)	16,656 (92.32)	2,770 (15.35)	84 (0.47)
Charlson comorbidity index, N (%)					< 0.001
≤ 2	6,833 (37.87)	8,225 (45.59)	9,096 (50.41)	9,837 (54.52)
3 ~ 4	7,251 (40.19)	6,936 (38.44)	6,609 (36.63)	6,241 (34.59)
≥ 5	3,958 (21.94)	2,881 (15.97)	2,338 (12.96)	1,964 (10.89)
Chemotherapy^a, N (%)	10,133 (56.16)	9,945 (55.12)	9,682 (53.66)	9,736 (53.96)	< 0.001
Doxorubicin	4,905 (27.19)	4,604 (25.52)	4,734 (26.24)	5,538 (30.70)
Cyclophosphamide	9,015 (49.97)	8,986 (49.81)	8,767 (48.59)	8,820 (48.89)
Paclitaxel	1,954 (10.83)	1,809 (10.31)	1,752 (9.71)	1,627 (9.02)
Radiation therapy, N (%)	10,622 (58.87)	10,856 (60.17)	10,930 (60.58)	10,880 (60.30)	0.005
Hormone therapy^b, N (%)	2,022 (11.21)	1,802 (9.99)	1,611 (8.93)	1,589 (8.81)	< 0.001
Tamoxifen	1,175 (6.51)	1,092 (6.05)	1,089 (6.04)	1,153 (6.39)	0.148
Diastolic blood pressure [mmHg], mean (SD)	77.86 (9.64)	75.22 (9.41)	73.32 (9.43)	71.34 (9.35)	< 0.001
Systolic blood pressure [mmHg], mean (SD)	126.33 (14.87)	121.59 (14.50)	117.94 (14.32)	113.99 (13.95)	< 0.001
Total cholesterol [mg/dL], mean (SD)	196.36 (39.00)	195.87 (37.48)	193.71 (38.46)	189.24 (36.37)	< 0.001
Fasting serum glucose [mg/dL], mean (SD)	103.63 (24.86)	98.38 (20.07)	95.23 (17.12)	92.77 (16.23)	< 0.001
p-values calculated via Chi-squared test for categorical variables and analysis of variance (ANOVA) for continuous variables.
^aTreatment patterns including cyclophosphamide, trastuzumab, doxorubicin, epirubicin, docetaxel, paclitaxel, and cisplatin
^bTreatment patterns including tamoxifen, anastrozole, and letrozole
Acronyms: standard deviation (SD); body mass index (BMI).

The risk for CVDs according to quartiles of pLBMP, pASMP, and pBFMP is shown in Table 2. Compared to those with the lowest pLBMP, those with the highest pLBMP had a 37% lower risk of CVDs. Similarly, compared to those with the lowest pASMP, those with the highest pASMP had a 42% lower risk of CVDs. In contrast, those with the highest pBFMP had a 57% higher risk of CVDs compared to those with the lowest BFMP. The risk reduction of CVDs tended to be higher according to the higher quartiles of pLBMP and pASMP (both p for trend < 0.001). As pBFMP increased, statistically significant increased risk of developing CVDs was observed (p for trend < 0.001). The results after adjusting for additional covariates (Model 2 and 3) and BMI (Model B) were also consistent with the main findings presented in Model 1. Kaplan-Meier curves demonstrated significantly shorter CVDs survival for those with the highest quartile of pBFMP and lowest quartile of pLBMP and pASMP (Fig. 2). Interestingly, the risk of all-cause mortality tended to be higher according to higher quartiles of pLBMP and pASMP and lower according to higher quartiles of pBFMP (Online Resource 1).

Table 2

Hazard ratios for cardiovascular diseases according to quartiles of a percentage of predicted body composition.
	Percentage of predicted body composition, quartiles				p_trend
	1st (lowest)	2nd	3rd	4th (highest)	p_trend
pLBMP
Study population, N	18,042	18,042	18,043	18,042
Percentage [%], range	51.37, 63.63	63.63, 65.94	65.94, 68.21	68.21, 86.17
CVD; events, N (%)	383 (2.12)	331 (1.83)	220 (1.22)	161 (0.89)
aHR (95% CI) of Model A^a
Model 1	1.00 (reference)	0.98 (0.85, 1.14)	0.74 (0.62, 0.87)	0.63 (0.52, 0.76)	< 0.001
Model 2	1.00 (reference)	1.00 (0.86, 1.16)	0.76 (0.64, 0.90)	0.65 (0.54, 0.78)	< 0.001
Model 3	1.00 (reference)	1.06 (0.91, 1.23)	0.84 (0.70, 0.99)	0.74 (0.61, 0.90)	< 0.001
aHR (95% CI) of Model B^b
Model 1	1.00 (reference)	1.01 (0.83, 1.23)	0.78 (0.60, 1.01)	0.67 (0.48, 0.94)	0.003
Model 2	1.00 (reference)	1.04 (0.86, 1.27)	0.81 (0.62, 1.06)	0.71 (0.50, 1.00)	0.009
Model 3	1.00 (reference)	1.05 (0.86, 1.28)	0.83 (0.64, 1.08)	0.73 (0.52, 1.03)	0.016
pASMP
Study population, N	18,042	18,042	18,043	18,042
Percentage [%], range	17.04, 25.55	25.55, 26.52	26.52, 27.49	27.49, 46.07
CVD; events, N (%)	436 (2.42)	312 (1.73)	210 (1.16)	137 (0.76)
aHR (95% CI) of Model A^a
Model 1	1.00 (reference)	0.90 (0.78, 1.05)	0.73 (0.62, 0.87)	0.58 (0.48, 0.71)	< 0.001
Model 2	1.00 (reference)	0.93 (0.80, 1.08)	0.76 (0.64, 0.91)	0.61 (0.50, 0.75)	< 0.001
Model 3	1.00 (reference)	0.98 (0.84, 1.14)	0.84 (0.70, 1.00)	0.70 (0.57, 0.87)	< 0.001
aHR (95% CI) of Model B^b
Model 1	1.00 (reference)	0.94 (0.77, 1.12)	0.78 (0.62, 0.98)	0.64 (0.47, 0.86)	0.002
Model 2	1.00 (reference)	0.98 (0.82, 1.17)	0.83 (0.66, 1.05)	0.69 (0.51, 0.93)	0.010
Model 3	1.00 (reference)	0.98 (0.82, 1.18)	0.85 (0.67, 1.07)	0.72 (0.53, 0.97)	0.020
pBFMP
Study population, N	18,042	18,042	18,043	18,042
Percentage [%], range	13.07, 30.85	30.85, 33.13	33.13, 35.36	35.36, 47.60
CVD; events, N (%)	163 (0.90)	221 (1.22)	326 (1.81)	385 (2.13)
aHR (95% CI) of Model A^a
Model 1	1.00 (reference)	1.17 (0.96, 1.44)	1.51 (1.25, 1.83)	1.57 (1.30, 1.89)	< 0.001
Model 2	1.00 (reference)	1.17 (0.96, 1.43)	1.50 (1.24, 1.81)	1.52 (1.26, 1.84)	< 0.001
Model 3	1.00 (reference)	1.12 (0.91, 1.37)	1.38 (1.14, 1.67)	1.32 (1.09, 1.61)	0.001
aHR (95% CI) of Model B^b
Model 1	1.00 (reference)	1.13 (0.91, 1.41)	1.42 (1.11, 1.81)	1.39 (0.99, 1.96)	0.014
Model 2	1.00 (reference)	1.13 (0.91, 1.41)	1.40 (1.10, 1.79)	1.35 (0.96, 1.90)	0.024
Model 3	1.00 (reference)	1.11 (0.89, 1.38)	1.36 (1.07, 1.74)	1.30 (0.92, 1.83)	0.041
Adjusted hazard ratios were calculated by multivariable Cox proportional hazards regression analysis after adjustments for the following covariates.
^aModel A: not including BMI.
^bModel B: including BMI.
Model 1: age, income, chemotherapy, radiation therapy, hormone therapy, and Charlson comorbidity index.
Model 2: Model 1 + smoking, alcohol, and exercise.
Model 3: Model 2 + blood pressures, total cholesterol, and fasting glucose serum.
Acronyms: cardiovascular disease (CVD); hazard ratio (HR); confidence interval (CI); percentage of predicted lean body mass (pLBMP); percentage of predicted appendicular skeletal mass (pASMP); percentage of predicted body fat mass (pBFMP).

The associations in pLBMP, pASMP, and pBFMP with the risk of CVD stratified by BMI are presented in Table 3. Overall, in Model A, each 1% increase in pLBMP and pASMP was associated with a decreased risk of CVDs [pLBMP, aHR: 0.96, 95% CI 0.94–0.98, p < 0.05; pASMP, aHR: 0.91, 95% CI 0.87–0.95, p < 0.05]. In contrast, each 1% increase in pBFMP was associated with an increased risk of CVDs (aHR: 1.05, 95% CI 1.03–1.07, p < 0.01). Adjustments for combinations of three confounders showed blood pressure to be the most important mediator for the association of pLBMP, pASMP, and pBFMP with CVDs (Fig. 3).

Table 3

Hazard ratios for cardiovascular diseases per 1% increase in the percentage of predicted body composition.
	per 1% increase
	pLBMP	pASMP	pBFMP
Overall
Study population, N	72,169	72,169	72,169
CVDs, N	1,095	1,095	1,095
aHR (95% CI)
Model A^a	0.96 (0.94, 0.98)*	0.91 (0.87, 0.95)*	1.05 (1.03, 1.07)**
Model B^b	0.95 (0.90, 1.02)	0.98 (0.90, 1.05)	1.01 (0.90, 1.13)
BMI (< 23.0 kg/m²)
Study population, N	34,618	34,618	34,618
CVDs, N	375	375	375
aHR (95% CI)	0.97 (0.93, 1.02)	0.97 (0.88, 1.07)	1.04 (0.99, 1.09)
BMI (≥ 23.0 kg/m²)
Study population, N	37,551	37,551	37,551
CVDs, N	720	720	720
aHR (95% CI)	0.97 (0.94, 1.01)	0.96 (0.89, 1.03)	1.03 (1.00, 1.07)
Adjusted hazard ratios were calculated by multivariable Cox proportional hazards regression analysis after adjustments for the following covariates: age, income, chemotherapy, radiation therapy, hormone therapy, Charlson comorbidity index, smoking, alcohol, and exercise.
^aModel A: above covariates and not including BMI.
^bModel B: above covariates and BMI.
Acronyms: cardiovascular disease (CVD); hazard ratio (HR); confidence interval (CI); percentage of predicted lean body mass (pLBMP); percentage of predicted appendicular skeletal mass (pASMP); percentage of predicted body fat mass (pBFMP); body mass index (BMI).
p-value < 0.05, p-value < 0.01, and **p-value < 0.001.

In Table 4, the risk of CVDs was observed according to groups classified by pLBMi and pBFMi. Compared to those with Low BFM-Low LBM, those with High BFM-Low LBM and High BFM-High LBM had 59% and 35% higher risk of CVDs, respectively. Though statistically insignificant, those with High BFM-High LBM had a lower risk of CVDs compared to those with High BFM-Low LBM. Results from the stratified analysis on the association of pLBMP, pASMP, and pBFMP with CVDs according to subgroups of age, CCI, and treatment pattern are shown in Table 5. Compared to those with the lowest pLBMP and pASMP, those with the highest pLBMP and pASMP, respectively, had the significantly lower risk of CVDs in all age groups above 40, those with lower CCI, and those who underwent radiotherapy. Highest quartile pBFMP patients in all age groups above 40, those with lower CCI, and those who underwent radiotherapy had the significantly higher risk of CVDs compared to that of lowest quartile pBFMP survivors.

Table 4

Hazard ratios for cardiovascular diseases according to the groups classified by predicted lean body mass and fat mass index.
		pBFMi [kg/m²]
		Low (Range: 1.71, 7.65)		High (Range: 7.65, 26.99)
pLBMi [kg/m²]	Low (Range:11.06, 15.24)	Low BFM-Low LBM		High BFM-Low LBM
pLBMi [kg/m²]	High (Range:15.24, 29.13)	Low BFM-High LBM		High BFM-High LBM
		Low BFM-Low LBM	Low BFM-High LBM	High BFM-Low LBM	High BFM-High LBM
Study population, N		34,006	2,078	2,078	34,007
pLBMi [kg/m²], mean (Range)		14.34 (11.06, 15.24)	15.43 (15.24, 17.84)	15.09 (14.33, 15.24)	16.47 (15.24, 29.13)
pBFMi [kg/m²], mean (Range)		6.36 (1.71, 7,65)	7.41 (3.48, 7.65)	7.87 (7.65, 10.32)	9.42 (7.66, 26.99)
BMI [kg/m²], mean (SD)		20.89 (1.44)	23.02 (0.40)	23.19 (0.26)	26.15 (2.50)
CVDs; events, N (%)		363 (1.07)	28 (1.35)	46 (2.21)	658 (1.93)
aHR (95% CI)		1.00 (reference)	1.15 (0.78, 1.69)	1.59 (1.17, 2.16)*	1.35 (1.18, 1.54)***
aHR (95% CI)		0.87 (0.59, 1.28)	1.00 (reference)	1.38 (0.86, 2.22)	1.18 (0.80, 1.72)
aHR (95% CI)		0.63 (0.46, 0.86)*	0.72 (0.45, 1.16)	1.00 (reference)	0.85 (0.63, 1.15)
5-year cancer survivors divided into 4 groups based on pLBMi and pBFMi: the Low BFM-Low LBM group (those who have relatively low fat mass and low lean body mass), the Low BFM-High LBM group (those who have relatively low fat mass and high lean body mass), the High BFM-Low LBM group (those who have relatively high fat mass and low lean body mass), and the High BFM-High LBM group (those who have relatively high fat mass and high lean body mass).
Adjusted hazard ratios were calculated by multivariable Cox proportional hazards regression analysis after adjustments for the following covariates: age, income, chemotherapy, radiation therapy, hormone therapy, Charlson comorbidity index, smoking, alcohol, and exercise.
Acronyms: cardiovascular disease (CVD); hazard ratio (HR); confidence interval (CI); lean body mass (LBM); body fat mass (BFM); predicted lean body mass index (pLBMi); predicted body fat mass index (pBFMi).
p-value < 0.05, p-value < 0.01, and **p-value < 0.001.

Table 5

Stratified analysis of hazard ratios for cardiovascular diseases according to the subgroups divided by other covariates.
	Predicted body composition, quartiles				p_trend	p_interaction
	1st (lowest)	2nd	3rd	4th	p_trend	p_interaction
pLBMP	aHR (95% CI)
Age [years]						0.095
40 ~ 49	1.00 (reference)	0.79 (0.43, 1.48)	0.53 (0.27, 1.02)	0.40 (0.21, 0.77)	0.003
50 ~ 59	1.00 (reference)	1.06 (0.80, 1.40)	0.84 (0.63, 1.13)	0.58 (0.41, 0.81)	< 0.001
≥ 60	1.00 (reference)	0.97 (0.81, 1.16)	0.73 (0.58, 0.90)	0.77 (0.50, 0.98)	0.003
Charlson comorbidity index						0.272
1 ~ 3	1.00 (reference)	1.03 (0.85, 1.25)	0.75 (0.60, 0.93)	0.60 (0.47, 0.76)	< 0.001
4 or more	1.00 (reference)	0.96 (0.76, 1.21)	0.81 (0.62, 1.06)	0.79 (0.58, 1.08)	0.062
Treatment pattern, yes
Radiation therapy	1.00 (reference)	0.95 (0.75, 1.19)	0.77 (0.60, 0.99)	0.62 (0.46, 0.83)	< 0.001	0.475
Chemotherapy	1.00 (reference)	1.23 (0.97, 1.55)	0.94 (0.72, 1.22)	0.79 (0.59, 1.07)	0.079	0.671
Hormone therapy	1.00 (reference)	1.18 (0.75, 1.85)	0.98 (0.58, 1.63)	0.77 (0.42, 1.39)	0.400	0.473
pASMP	aHR (95% CI)
Age [years]						0.054
40 ~ 49	1.00 (reference)	0.41 (0.19, 0.86)	0.61 (0.33, 1.11)	0.31 (0.16, 0.61)	0.004
50 ~ 59	1.00 (reference)	0.99 (0.74, 1.31)	0.74 (0.54, 1.00)	0.60 (0.43, 0.84)	< 0.001
≥ 60	1.00 (reference)	0.88 (0.74, 1.06)	0.72 (0.58, 0.90)	0.69 (0.52, 0.92)	< 0.001
Charlson comorbidity index						0.794
1 ~ 3	1.00 (reference)	0.91 (0.75, 1.10)	0.75 (0.60, 0.93)	0.54 (0.42, 0.70)	< 0.001
4 or more	1.00 (reference)	0.95 (0.75, 1.20)	0.80 (0.60, 1.07)	0.81 (0.58, 1.13)	0.101
Treatment pattern, yes
Radiation therapy	1.00 (reference)	0.79 (0.63, 1.00)	0.70 (0.54, 0.90)	0.59 (0.44, 0.80)	< 0.001	0.098
Chemotherapy	1.00 (reference)	1.10 (0.88, 1.39)	0.87 (0.66, 1.14)	0.74 (0.54, 1.01)	0.038	0.914
Hormone therapy	1.00 (reference)	0.92 (0.58, 1.46)	0.93 (0.55, 1.57)	0.87 (0.48, 1.58)	0.652	0.848
pBFMP	aHR (95% CI)
Age [years]						0.087
40 ~ 49	1.00 (reference)	1.84 (0.97, 3.52)	1.39 (0.67, 2.90)	2.57 (1.33, 4.96)	0.014
50 ~ 59	1.00 (reference)	1.43 (1.03, 1.99)	1.79 (1.30, 2.47)	1.70 (1.22, 2.37)	< 0.001
≥ 60	1.00 (reference)	0.90 (0.68, 1.19)	1.24 (0.97, 1.59)	1.28 (1.01, 1.63)	0.003
Charlson comorbidity index						0.777
1 ~ 3	1.00 (reference)	1.24 (0.97, 1.59)	1.63 (1.29, 2.06)	1.62 (1.28, 2.05)	< 0.001
4 or more	1.00 (reference)	1.02 (0.72, 1.45)	1.22 (0.88, 1.69)	1.28 (0.94, 1.75)	0.046
Treatment pattern, yes
Radiation therapy	1.00 (reference)	1.13 (0.83, 1.53)	1.43 (1.07, 1.91)	1.52 (1.14, 2.02)	0.001	0.472
Chemotherapy	1.00 (reference)	1.19 (0.87, 1.61)	1.46 (1.10, 1.95)	1.26 (0.94, 1.69)	0.088	0.675
Hormone therapy	1.00 (reference)	1.15 (0.61, 2.16)	1.36 (0.75, 2.45)	1.29 (0.72, 2.30)	0.356	0.525
Adjusted hazard ratios were calculated by multivariable Cox proportional hazards regression analysis after adjustments for the following covariates: age, income, chemotherapy, radiation therapy, hormone therapy, Charlson comorbidity index, smoking, alcohol, and exercise.
Acronyms: cardiovascular disease (CVD); hazard ratio (HR); confidence interval (CI); percentage of predicted lean body mass (pLBMP); percentage of predicted appendicular skeletal mass (pASMP); percentage of predicted body fat mass (pBFMP).

In this study, we showed that a high percentage of predicted body fat mass was associated with increased risk of CVDs among 5-year breast cancer survivors. Furthermore, a high percentage of predicted lean body mass and predicted appendicular skeletal mass was associated with reduced risk of CVDs. In all three components of body composition, blood pressure showed to be the most important mediator for the association. To our knowledge, this is the first study, in this scale, to show comprehensively that low percentage of body fat mass and high percentage of lean body mass and appendicular skeletal mass are associated with decreased risk of CVDs among breast cancer survivors.

The results from previous studies are in accordance with increased risk of CVDs associated with a high percentage of predicted body fat mass observed in our study. For example, in a study investigating the association between adipose tissue distribution with CVDs risk, each standard deviation increase in visceral or intramuscular adiposity was associated with an increase in CVDs risk (aHR, 1.15 [95% CI, 1.03 to 1.29] and aHR, 1.21 [95% CI, 1.06 to 1.37]), respectively) [8]. Similarly, in our study, each 1% increase in pBFMP was associated with an increased risk of CVDs (aHR: 1.05, 95% CI 1.03–1.07, p < 0.01). Additionally, a relatively recent report observed the association of body mass index, central obesity, and body composition with mortality in black breast cancer survivors [25]. High body mass index, central obesity (waist-to-hip ratio and waist circumference), and body composition (percent body fat and fat mass index) were associated with higher overall mortality (1.57 [95% CI, 1.11–2.22], 1.74 [95% CI, 1.26–2.41], and 1.53 [95% CI, 1.09–2.15], respectively) and breast cancer-related mortality. However, in this study, association with CVDs was not investigated and there were potential confounding variables, because the number of subjects was limited to 1,891, and data on underlying heart disease were not collected.

Breast cancer patients experience cardiotoxic agents, and it has been suggested that CVD risk may also be related to anti-estrogen therapy such as aromatase inhibitors [26]. Breast cancer survivors tend to also have other risk factors for CVDs. Increased body fat mass may lead to metabolic disturbances related to increased adiposity including insulin resistance, dyslipidemia, and chronic inflammation [27]. Also, visceral fat has been suggested to alter cardiac autonomic activity in breast cancer survivors [28]. Specifically, diminished parasympathetic activity and heart rate variability have been associated with loss of cholinergic anti-inflammatory pathway, enabling enhanced cytokine responses to the otherwise normal stimuli [29]. Finally, increased intramuscular fat was associated with reduced peak exercise capacity in cancer survivors [30] [31]. This, in addition to breast cancer-related skeletal muscle damage, may have reduced exercise-based opportunities for CVD risk reduction.

Reduced muscle mass, which is prevalent in breast cancer survivors, is a risk factor for mortality in early breast cancer patients [32, 33]. A UK study examined appendicular skeletal muscle mass to have a curvilinear association with CVD events in women [34]. In our study of breast cancer survivors, both lean body mass and appendicular skeletal mass were associated with reduced risk of CVDs. Though not significant, this was also evident in the lower risk of CVDs observed in those with higher lean body mass (High BFM-High LBM) compared to those with lower lean body mass (High BFM-Low LBM) among those with high body fat mass. In breast cancer survivors who had received radiation therapy, there was a significantly higher CVD risk was associated with higher pBFMP and lower risk in higher pLBMP and pASMP. The risk of CVD may have been more prominent in this subgroup due to the significant changes seen in body composition post-radiation [35].

Blood pressure, among the three confounders that were adjusted for in combinations, was observed to be the most important mediator for the association of pLBMP, pASMP, and pBFMP with CVD risk. Though further research is warranted to elucidate the mechanism in which body composition and metabolic changes lead to CVD, studies have already examined a close link between body composition and blood pressure. Both lean body mass, the majority constituted by muscle mass, and fat mass, at a lesser degree, were significant determinants of blood pressure level; relatively high muscle mass was associated with high blood pressure levels in this report [36]. In a study on middle-aged adults, higher visceral adiposity was associated with higher blood pressure level with lower variability, independent of BMI, and this persistently elevated blood pressure may impose cardiac burden [37].

Our study is not without limitations. First, body composition based on the prediction equation is an imperfect measurement. However, a previous large validation study based on Korean National Health and Nutrition Examination Survey showed high predictive values, including low bias, high intraclass correlation coefficient, high adjusted R², and low SEE, allowing it to apply to large-scale research and epidemiological studies. Second, body composition was observed at one time point. Third, the results of our studies were limited to Asians and may not represent other ethnicities. Body composition, metabolic syndrome, and the nature of cancer varies among ethnicities. A major strength of our study was the large sample size and inclusion of other indices (lean body mass and appendicular skeletal mass) in addition to body fat mass. Body composition was investigated without the use of imaging modalities and presented in terms of percentile. This allows for a cost-effective means, without radiation exposure, to help identify and modify body composition in breast cancer survivors, including those who are normal weight or have few comorbidities.

In conclusion, in this cohort study, a high percentage of predicted lean body mass and predicted appendicular skeletal mass and a low percentage of predicted body fat mass were associated with reduced risk of CVDs. Blood pressure showed to be the most important mediator for the association.

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: Ji Soo Kim, Jihun Song, Seulggie Choi, and Sang Min Park contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jihun Song. The first draft of the manuscript was written by Ji Soo Kim and Jihun Song, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data Availability: The datasets generated and/or analyzed during the current study are not publicly available due to institutional policy but are available from the corresponding author upon reasonable request.

Ethics approval: The Institutional Review Board (IRB) of Seoul National University Hospital, which complies with the principles of the Declaration of Helsinki, approved this study (IRB no: 2206-162-1335). The NHIS database was provided through legal procedures after deliberation only for research purposes.

Informed consent: The requirement for informed consent was waived as the NHIS database is anonymized according to strict confidentiality guidelines prior to distribution.

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Association between body composition and subsequent cardiovascular diseases among 5-year breast cancer survivors

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