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Predicting cardiovascular disease with body mass index

Predicting cardiovascular disease with Body Mass Index

BMI and CVD: An Introduction

Obesity, as indexed by the high body mass index (BMI), is a major risk factor for cardiovascular disease (CVD). About 60 – 80% of patients with CVD are overweight. However, there are mixed findings on the effect of decreasing BMI or weight loss on CVD. Some studies found no effect of weight loss on CVD although a recent meta-analysis of trials reported a moderate lower risk of CVD following weight loss. Observational studies of weight loss or a change in BMI conflicting, with studies reporting variously increased risk of CVD or no association .

One possible explanation for the moderate risk of CVD following weight loss is that it could be related to the obesity assessment tools. Large-scale studies use anthropometric measures for assessing adiposity-related risk. These include BMI, waist circumference (WC), Hip Circumference (HP), Waist-to-Hip Ratio (WHR), and Waist-to-Stature Ratio (WSR). The limitation of anthropometric measurements is that they cannot identify small changes in proportions of body fat to lean body mass nor detect changes in nutritional status. To accurately measure the association of changes in BMI with CVD, fat, and muscle must be distinguished with regard to body composition and identify their physiological effects of them on CVD.

Dual-energy X-ray absorptiometry (DXA), computed tomography (CT) scan, and magnetic resonance imaging (MRI) can accurately distinguish fat mass, lean muscle mass, and body fat percentage. However, from a large-scale epidemiologic study’s perspective, their use is limited due to the high cost and time required.

Therefore, to estimate fat and muscle mass and determine their independent effects on CVD in young adults aged 20 – 39 years, the authors developed robust prediction equations for BFM, LBM, and appendicular skeletal muscle mass (ASM).

METHODS

Study population

A total of 3,742,621 young adults (age 20 to 39 years) without a previous history of CVD and underwent two health screening examinations during 2009–2010 and 2011–2012 were enrolled. The sample was primarily male (n=2,406,046; 64.5%). The study excluded data of the i) deceased (n=1176) and ii) the ones lacking data on covariates before the index date of 1 January 2013. A total of 2,727,738 individual [2,406, 046 (64.5%) men and 1,321,692 (35.5%) women] were included and followed up from 1 January 2013 to 31 December 2018.

Assessment of lean body mass, appendicular skeletal muscle mass, and fat mass

To perform multivariable linear regression, a representative sample of 17608 individuals (male=7599; women=10009) was extracted from the Korean National Health and Nutrition Examination Survey. Using validated and robust prediction equations, the changes in predicted LBMI, ASMI, and BFMI were calculated. Bland–Altman plot and intraclass correlation coefficient (ICC) in the independent validation group were used to validate the prediction equations. Based on the Bland–Altman plot by Lee et al, the 95% limits of agreement between each actual mass index and predicted mass index was approximately estimated to be ±1–1.5 kg/m2.

Follow up for CVD

Hospital admission records from the NHIS along with codes from the International Classification of Diseases, Tenth Revision (ICD-10) were collected to determine CVD events, including coronary heart disease (CHD) and stroke, which occurred between 1 January 2012 and 31 December 2018.

Statistical analysis

Cox proportional hazard analysis was done to assess the hazard ratios and 95% confidence intervals. The authors also presented restricted cubic splines of changes in predicted LBMI, ASMI, and BFMI to visually assess the association of the changes in predicted LBMI, ASMI, and BFMI with CVD events. Subgroup analyses were also done stratifying by age, physical activity, alcohol intake, smoking status, Charlson comorbidity index (CCI), systolic blood pressure, fasting serum glucose, and total cholesterol. Physical activity, smoking status, and alcohol intake were assessed by a self-reported questionnaire. All analyses, data collection, and data mining were performed using SAS Version 9.4 and R programming version 3.3.3. Statistical significance was defined as P < 0.05.

RESULTS

  • The mean age of the study population was 32.2 (4.9) years, and 64.5% (n=2,406,046) of the participants were men.
  • A total of 23,344 CVD events were identified during 22,257,632 person-years of follow-up.
  • Each 1 kg/m2 increase in predicted LBMI and ASMI change was shown to have a reduced risk of CVD among men [LBMI, adjusted hazard ratio (aHR): 0.86, 95% CI 0.82–0.91, P < 0.001; ASMI, aHR: 0.76, 95% CI 0.69–0.82, P< 0.001] and women (LBMI, aHR: 0.77, 95%CI 0.63–0.95, P <0.01; ASMI, aHR: 0.75, 95% CI 0.59–0.96, P < 0.01).
  • In contrast, each 1 kg/m2 increase in predicted BFMI change was shown to increase the risk of CVD among men (aHR: 1.16, 95% CI 1.10–1.22, P < 0.001) and women (aHR: 1.32, 95% CI 1.06–1.65, P < 0.01).
  • In both men and women, a decrease in predicted LBMI and ASMI was associated with increased CVD risk and decreased predicted BFMI was associated with a reduced CVD risk.
  • Those who maintained their BMI between -1 and +1 kg/m2 also had a decreased risk of CVD per 1 kg/m2 increase in predicted LBMI and ASMI change among men (LBMI, aHR: 0.86, 95%CI 0.80–0.92, P <0.001; ASMI, aHR: 0.85, 95% CI 0.76–0.95, P <0.001) and women (LBMI, aHR: 0.62, 95% CI 0.47–0.83, P < 0.001; ASMI, aHR: 0.59, 95% CI0.44–0.80, P < 0.001). These group also had a greater risk of CVD per 1 kg/m2 increase in predicted BFMI change among men (aHR: 1.17,95% CI 1.10–1.25) and women (aHR: 1.64, 95% CI 1.20–2.23, P < 0.001).

DISCUSSION

This population‐based historical cohort study demonstrated that patients with increased predicted LBMI and ASMI or decreased BFMI had a lower risk of CVD. Conversely, patients with decreased predicted LBMI and ASMI or increased BFMI had a greater risk of CVD. Surprisingly, these results were consistent irrespective of changes in weight, such as from normal to obese or vice versa.

Multiple studies have shown an association between changes in BMI and the onset of CVD in the past. However, the current study is the very first one to investigate the association of changes in muscle or fat mass with CVD. Galanis et al showed that each 5kg/m2 increase in BMI increases the risk of CVD by 30% in adults aged 30-59 years. The results from the Nurses’ Health Study and US men in the Health Professionals Follow-Up Study cohorts showed that weight gain or obesity significantly increased the risk of incident CVD. Furthermore, it was reported that weight gain in early adulthood increases the risk of CVD in midlife. In this study, the authors found that an increase in fat mass can elevate the risk of CVD among young adults.

The physiological consequences of being overweight or obese have been well documented in the literature. It is estimated that obesity accounts for about 65–78% of cases of primary hypertension, a risk factor for CVD. The mechanisms through which excess adiposity causes hypertension are complex and include sympathetic nervous system overactivation and stimulation of the renin-angiotensin-aldosterone system (RAAS), changes in adipose-derived cytokines, insulin resistance, and structural and functional renal changes. Furthermore, observational studies have reported that various conditions, including type 2 diabetes (T2D), sleep apnoea, and osteoarthritis can raise blood pressure. To sum it up, obesity may be a risk factor for CVD by causing neurohormonal activation and cardiovascular dysregulation, which partly supports the findings of this study.

The effects of decreasing BMI or weight loss on CVD risk are still debatable. Nurses Health Study on middle-aged US women found no relationship between weight loss and CHD. Some previous studies reported that weight loss can be associated with an increased risk of CVD or mortality, especially in older adults and patients suffering from chronic illness. One possible explanation why weight loss failed to prevent the onset of CVD may be explained based on the results of this study. The authors reported that decreasing fat mass and decreasing muscle mass have different effects on the development of CVD. Unlike weight gain, which is due to an increase in fat mass, weight loss may be purposeful (mainly due to a decrease in fat mass) or unintended (mainly due to decreased muscle mass). This study found that the decreased fat mass significantly reduced the risk of CVD development, and decreased muscle mass elevated the risk of CVD development. These results imply that one of the major reasons for the unexpectedly negative effect of weight loss on CVD might be the loss of muscle mass more than the loss of fat mass.

For men, the results of the association of changes in predicted LBMI, ASMI, and BFMI with CVD risk in most subgroups were consistent with the overall results. Whereas in women, the results were statistically significant, especially in the ones who are physically active, non-drinker, never smoker, with CCI 0, has systolic pressure <130 mmHg, with fasting serum glucose < 126 mg/dL, or with total cholesterol < 200 mg/dL.

This study has several limitations. First, the predicted LBMI, ASMI, and BFMI were not precise values of actual LBMI, ASMI, and BFMI. Nevertheless, the prediction equations showed a high predictive power and validation. Second, confounding factors were not fully considered due to the retrospective study design. Finally, the predicted equations were not validated for the changes in body components.

CONCLUSION

Among young adults, increased predicted muscle mass or decreased predicted fat mass was associated with a reduction in the risk of developing cardiovascular disease. Conversely, decreased predicted muscle mass or increased predicted fat mass were associated with a significant increase in the risk of developing CVD.

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