Summary
Objective
The objective of this study is to determine associations between anthropometry and echocardiographic measures of cardiac structure and function in Hispanic/Latinos.
Methods
A total of 1,824 participants from ECHO‐SOL were included. We evaluated associations between echocardiographic measures of left ventricular structure and function and anthropometric measures using multivariable‐adjusted linear and logistic regression models adjusting for traditional cardiovascular risk factors.
Results
The mean age was 56 ± 0.17 years, 57% were women. The mean body mass index (BMI) was 30 ± 9.4 kg m−2, waist circumference (WC) was 100 ± 18 cm, and waist‐to‐hip ratio (WHR) was 0.93 ± 0.15. Adjusted analysis showed that 5‐unit increment in BMI and 5‐cm increase in WC was associated with 3.4 ± 0.6 and 1.05 ± 0.05 g m−2.7 (p < 0.05 for both) higher left ventricular (LV) mass index, respectively. Similarly, 0.1‐unit increment in WHR was associated with 2.0 ± 0.16 g m−2.7 higher LV mass index (p < 0.01). WHR was associated with 0.22 ± 0.08% decrease in ejection fraction (p < 0.05). Concomitantly, 5‐unit increment in BMI and WC was associated with increased odds of abnormal LV geometry (odds ratio 1.40 and 1.16, p = 0.03 and <0.01, respectively); 0.1‐unit increment in WHR was associated with increased odds of abnormal LV geometry (odds ratio 1.51, p < 0.01).
Conclusions
Among Hispanic/Latinos, higher anthropometric measures were associated with adverse cardiac structure and function.
Keywords: anthropometrics, cardiac structure and function, Hispanics
1. Introduction
Obesity is an independent risk factor for the development of heart failure (HF) 1, 2, 3, 4. The increasing prevalence of HF, in particular, HF with preserved ejection fraction in the setting of the obesity pandemic 5, 6, is a significant and burgeoning public health problem 7, 8. Hispanic/Latinos are a fast‐growing population with a high prevalence of obesity 9. The overall unadjusted prevalence of obesity among Hispanic adults from National Health and Nutrition Examination Survey data between 2014 and 2015 was 42.7% 10. This estimate makes Hispanic/Latinos the second leading ethnic group with obesity in the United States.
Concomitantly, there is accumulating evidence to support a predisposition for left ventricular (LV) dysfunction and HF among Hispanic/Latinos 11. In this regard, Mehta et al. showed that 50.3% of individuals from various Hispanic/Latino backgrounds have echocardiographic evidence of LV diastolic dysfunction 12. Similarly, higher prevalence of LV hypertrophy and abnormal LV remodelling has been observed in different Hispanic/Latino groups compared with non‐Hispanic Whites, even after adjusting for hypertension 13.
Obesity increases the risk of developing diabetes 14, hypertension 15 and dyslipidaemia, all major risk factors for the development of coronary heart disease 16, 17. Coronary heart disease is an important cause of HF 18. In addition, both hypertension and diabetes are independent risk factors for the development of HF. Furthermore, obesity may lead to undesirable left ventricular remodelling 19. Left ventricular chamber dilatation is observed due to the chronic elevation of filling pressures and volume overload in obese individuals 20. Left ventricular hypertrophy (LVH), concentric remodelling and concentric LVH are associated with obesity independent of age and arterial pressure 20. In addition to structural abnormalities, obesity has adverse effects on systolic and diastolic functions 21, 22.
Preclinical echocardiographic findings of LV dysfunction in populations affected by overweight and obesity can serve as a fundamental step to develop effective HF preventive interventions. Hispanic/Latinos have been identified as a group at risk for HF 11 and have high rates of obesity 23. However, there is a lack of data evaluating the associations between anthropometric measures and left ventricular structure and function in US Hispanic/Latinos. Therefore, we aimed to determine the associations between different anthropometric and body composition variables with echocardiographic measures of LV structure and function in a diverse cohort of US Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The hypothesis for this analysis was that higher levels of adiposity are significantly associated with larger LV mass and volumes but smaller ejection fraction and cardiac output. Also, that fat fee mass will be significantly associated with larger LV mass and volumes, as well as ejection fraction and cardiac output.
2. Methods
2.1. Population
The HCHS/SOL is a multicentre community‐based, prospective cohort study of 16,415 self‐identified Hispanics ages 18–74 recruited from four field centres in Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA. The HCHS/SOL was designed to examine the risk and protective factors for chronic diseases and associated morbidity and mortality among Hispanic/Latinos in the United States. The HCHS/SOL is composed of individuals from the following heritage: Central American (n = 1,732), Cuban (n = 2,348), Dominican (n = 1,473), Mexican (n = 6,472), Puerto‐Rican (n = 2,728) and South American (n = 1,072). Details on the sampling and designs have been published separately 24.
The echocardiographic study of Latinos (ECHO‐SOL) is an ancillary study to the HCHS/SOL designed to characterize cardiac structure and function and their determinants. The study population consists of 1,824 participants, approximately 450 individuals from each field centre. Individuals older than 45 years of age who met eligibility criterial for the HCHS/SOL and who had completed all baseline examinations were eligible participants for ECHO‐SOL. All echocardiograms for ECHO‐SOL were completed after baseline examination but within 36 months of initial collection of clinical and laboratory data. Stratified random sampling was used, drawing participants from the communities representing the four field centres to provide a population sample representative of the HCHS/SOL population and the Hispanic subgroups characteristic of the field centres. Details on the methodology for the ECHO‐SOL study have been published 25.
2.2. Echocardiographic measurements
ECHO‐SOL field imaging centres were established using existing echocardiographic equipment from the HCHS/SOL at each of the four field centres. Phillips Ultrasounds IE‐33 (Philips Health Care, Andover, MA, USA) or Sonos 5500/7500 ultrasound imaging platform were used in all field imaging centres. Under a standardized protocol and after completion of in‐person training, experienced sonographers in each field centre completed standard echocardiographic examinations including two‐dimensional imaging with harmonics spectral Doppler and tissue Doppler. Digital echocardiographic images were electronically transferred from the field centres to the Core Lab at Wake Forest School of Medicine. All ECHO‐SOL echocardiograms were read by a cardiologist with experience in echocardiography (Dr Rodriguez). In order to confirm accuracy of key quantitative measures, echocardiogram over‐reads were performed. Inter and intra‐reader reproducibility was assessed and previously published 25. Intra‐class correlations for inter‐reader reproducibility ranged from 0.80 to 0.99 with left atrial volume and LV end‐diastolic volumes having the highest intra‐class correlation values (0.97–0.99).
LV chamber size and wall thickness linear dimensions were used to calculate LV mass (LVM). LVM was indexed (LVMI) to height2.7. Relative wall thickness (RWT) was defined as 2 * (LV Posterior Wall Thickness ∕ LV internal diameter at diastole). The bi‐plane method of disc (Simpson's method) was used to obtain LV volumes, and ejection fraction (EF) was calculated from the formula EF = EDV – ESV ∕ EDV; where EDV and ESV are the volumetric measurements of LV end‐diastolic and end‐systolic volumes, respectively. LV stroke volume (SV), reported in mL, was assessed utilizing LV outflow tract (LVOT) dimensions, measured in cm2 and pulse wave spectral Doppler for LV outflow tract Velocity Time Integral. Cardiac output (CO) is the SV multiplied by heart rate. LV diastolic function was assessed with pulse wave mitral inflow velocities (E and A), tissue Doppler mitral annular velocities (e' septal and e' lateral) and left atrial volume (LAV) measured in biplane views index to body surface area. LV diastolic function was categorized as Grade I (mild) to Grade III (severe) according to published ASE (American Society of Echocardiography) and Redfield definitions 26. Left ventricular geometry was categorized as normal, concentric remodelling, concentric hypertrophy and eccentric hypertrophy using LVMI2.7 and RWT. The cuff off value for normal LVMI2.7 was 45 and for RWT was 0.42 27.
2.3. Anthropometric measurements
The participants wore light clothing and slippers for all measurements and were barefoot when measuring weight and body composition. Standing height was measured with a fixed stadiometer with a vertical backboard and moveable headboard. Weight and body composition was measured with the Tanita Body Composition Analyser, TBF‐300A (Tanita Corporation of America, Inc., Arlington Heights, IL, USA). Body mass index (BMI) was calculated averaging two body weight measurements and two standing height measures. The World Health Organization BMI classification was used: normal = 18.5–25 kg m−2, overweight = 25–29.99 kg m−2 and obese ≥ 30 kg m−2. Waist circumference (WC) was measured at the level of the upper border of the iliac crest. WC measurement was done using Gulick II 150 and 250 anthropometric tapes (Sammons Preston, Chicago, IL, USA). Hip circumference (HC) was measured at the level of maximum protrusion of the buttocks. Waist‐to‐hip ratio (WHR) was defined as the WC divided by the HC. The Tanita scale used bioelectrical impedance to measure the participant's total body fat percent, fat mass (FM) and lean body mass to calculate the fat‐free mass (FFM).
2.4. Clinical covariates
All variables were obtained using methods that have been previously described 24. Briefly, an interviewer‐administered questionnaire was used to assess participants' medical history. Participants were fasting 12 h before providing venous blood samples. Laboratory measurements from blood included total cholesterol, high‐density lipoprotein (HDL) cholesterol, triglycerides, glucose and creatinine. Dyslipidaemia was defined as total cholesterol of 240 mg dL−1 or greater, low‐density lipoprotein cholesterol 160 mg dL−1 or greater or HDL cholesterol less than 40 mg dL−1, or receiving cholesterol‐lowering medication. Diabetes was defined as a fasting plasma glucose level of 126 mg dL−1 or greater, 2 h post‐load glucose 200 mg dL−1 or greater, or A1c of 6.5 or greater or taking antihyperglycaemic medication. Normal range plasma creatinine was 0.5–1.2 mg dL−1 for men and 0.4–1.0 mg dL−1 for women 28. Three‐seated blood pressure measurements were obtained with an automatic sphygmomanometer; the second and third reading were averaged. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg, or diastolic blood pressure ≥ 90 mmHg or taking blood pressure medication.
2.5. Data analysis
Continuous variables were presented as means (SD) and categorical variables as counts (percentage). Continuous variables were compared using simple regression analysis across categories of BMI. Categorical variables were compared using Rao–Scott Chi‐square test across categories of BMI. We tested associations between various anthropometric parameter (BMI, WC, HC, WHR, FM and FFM) and the echocardiographic parameter of cardiac structure and function using multivariable linear regression models. Using multivariable logistic regression, we also tested the association between anthropometric measures and diastolic dysfunction and left ventricular geometry. Diastolic dysfunction was defined as normal or abnormal (class I‐III), and left ventricular geometry was normal or abnormal (if concentric remodelling, eccentric remodelling or concentric hypertrophy was identified).
The covariates, for both multivariable linear and logistic regression, included age, gender, height (except when BMI was the predictor variable), FFM (except when FFM was the predictor variable), hypertension and taking antihypertensive medications, diabetes, dyslipidaemia, smoking and family history of cardiac disease. Statistical significance was defined as a p value < 0.05. Survey methods were utilized, and all reported values were weighted to adjust for sampling probability and nonresponse bias to make the inference applicable to the ECHO‐SOL target population. All analyses were performed using PROC SURVEY with SAS version 9.3 (SAS Institute Inc, Cary, NC).
3. Results
The average age was 56 ± 0.17 years, and 57% of participants were women. Mean values for blood pressure (systolic blood pressure 136 ± 38 mmHg and diastolic blood pressure 78 ± 34 mmHg), HDL cholesterol (50 ± 17 mg dL−1) and serum creatinine (0.8 ± 0.13 mg dL−1) were within the normal range. The mean BMI was 30 ± 9.4 kg m−2, WC = 100 ± 18 cm, fat mass = 29 ± 16 kg, and fat‐free mass = 50 ± 13 kg. The mean LVEF was 60 ± 8.5%, LVMI = 42 ± 15 g m−2.7, ESV = 34 ± 17 mL, EDV = 84 ± 37 mL and CO = 5 ± 2.1 L min−1. Please refer to Table 1.
Table 1.
Baseline characteristics
| Mean (SE) | |
|---|---|
| Age (years) | 56 ± 0.17 |
| SBP (mmHg) | 136 ± 38 |
| DBP (mmHg) | 78 ± 34 |
| Heart rate (bpm) | 66 ± 24 |
| HDL cholesterol (mg dL−1) | 50 ± 17 |
| Serum Cr (mg dL−1) | 0.8 ± 0.13 |
| BMI (kg m−2) | 30 ± 9.4 |
| Waist circumference (cm) | 100 ± 18 |
| Hip circumference (cm) | 107 ± 21 |
| Waist‐to‐hip ratio | 0.93 ± 0.15 |
| Fat mass (kg) | 29 ± 16 |
| Fat free mass (kg) | 50 ± 13 |
| Height (cm) | 163 ± 17 |
| N (%) | |
| Women (%) | 882 (57) |
| Diabetes (%) | 523 (28) |
| Current smokers (%) | 210 (18) |
| Echocardiographic parameters | Mean (SE) |
| Left ventricular mass index (g m−2.7) | 42 ± 15 |
| End systolic volume (mL) | 34 ± 17 |
| End diastolic volume (mL) | 84 ± 37 |
| Ejection fraction (%) | 60 ± 8.5 |
| Fractional shortening (%) | 30 ± 12 |
| Cardiac output (L min−1) | 5 ± 2.1 |
| Lateral peak systolic velocity (cm s−1) | 8 ± 4.3 |
Data are presented as mean ± SD or N (%) using weighted row percentages.
BMI, body mass index; DBP, diastolic blood pressure; HDL, high‐density lipoprotein; SBP, systolic blood pressure; SE, standard error.
As shown in Table 2, among those with obesity, 67% were women, those with overweight included 51% women compared with 48% women in those with normal BMI (p value < 0.0001). People with obesity had lower rates of smoking compared with individuals with normal BMI (13% vs. 29%, p < 0.0001). The proportion of participants with diabetes, hypertension and dyslipidaemia was higher in individuals with obesity (37%, 56.2% and 44.3%) and overweight (23.1%, 46.3% and 40.9%) compared with those with a normal BMI (17.5%, 41.6% and 31.4%).
Table 2.
Unadjusted associations between baseline characteristics and BMI
| Clinical and echo characteristic | BMI | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| <25 (N = 225) | 25–29 (N = 529) | >30 (N = 598) | Overall p value | ||||||
| Mean | SE | Mean | SE | p value | Mean | SE | p value | ||
| Age (years) | 56 | 0.73 | 57 | 0.46 | 0.68 | 56 | 0.63 | 0.82 | <0.0001 |
| Lean body mass (kg) | 46 | 0.6 | 49 | 0.48 | 0.0007 | 52 | 0.53 | <0.0001 | <0.0001 |
| Fat mass (kg) | 16 | 0.29 | 24 | 0.19 | <0.0001 | 38 | 0.5 | <0.0001 | <0.0001 |
| WC (cm) | 85.5 | 0.61 | 95 | 0.33 | <0.0001 | 109.1 | 0.69 | <0.0001 | <0.0001 |
| SBP (mmHg) | 133.3 | 0.43 | 135.4 | 0.99 | 0.24 | 137.9 | 1.07 | 0.01 | <0.0001 |
| DBP (mmHg) | 74.8 | 0.76 | 77.7 | 0.68 | <0.01 | 79.2 | 0.49 | <0.0001 | <0.0001 |
| HDL (mg dL−1) | 55.5 | 1.27 | 54.8 | 0.59 | <0.01 | 48.3 | 0.53 | <0.0001 | <0.0001 |
| Creatinine (mg dL−1) | 0.94 | 0.06 | 0.86 | 0.015 | 0.21 | 0.8 | 0.01 | 0.03 | <0.0001 |
| LVMI (g m−2.7) | 34.7 | 0.57 | 40.1 | 0.53 | <0.0001 | 45.2 | 0.55 | <0.0001 | <0.0001 |
| LVPWd (cm) | 0.81 | 0.01 | 0.89 | 0.01 | <0.0001 | 0.91 | 0.01 | <0.0001 | <0.0001 |
| IVSd (cm) | 1 | 0.01 | 1 | 0.01 | <0.0001 | 1.11 | 0.01 | <0.0001 | <0.0001 |
| RWT | 0.37 | 0.01 | 0.37 | 0.01 | <0.0001 | 0.4 | 0.01 | <0.0001 | <0.0001 |
| ESV, mean | 32.4 | 0.88 | 32.9 | 0.59 | 0.59 | 35 | 0.48 | 0.01 | <0.0001 |
| EDV, mean | 79.4 | 1.89 | 81.5 | 1.07 | 0.31 | 86.8 | 1.02 | <0.01 | <0.001 |
| EF, mean | 59.4 | 0.4 | 59.9 | 0.29 | 0.2 | 59.8 | 0.25 | 0.32 | <0.0001 |
| SV (LVOT) (mL) | 65.7 | 1.1 | 69.1 | 0.97 | 0.02 | 73 | 0.95 | <0.0001 | <0.0001 |
| CO (L) | 4.2 | 0.079 | 4.5 | 0.071 | <0.01 | 4.8 | 0.06 | <0.0001 | <0.0001 |
| E/E' lateral ratio (mean) | 8.2 | 0.18 | 8.7 | 0.27 | 0.04 | 9.3 | 0.29 | <0.0001 | <0.0001 |
| E/E' septal ratio (mean) | 10.7 | 0.25 | 11.1 | 0.19 | 0.29 | 12.5 | 0.2 | <0.0001 | <0.0001 |
| LA volume | 39.6 | 1.15 | 42.4 | 0.73 | 0.04 | 43.5 | 0.6 | 0.001 | <0.001 |
| Percent | Percent | Percent | p value | ||||||
| Women | 48 | 51 | 67 | <0.0001 | |||||
| Current smoker | 29 | 18 | 13 | <0.0001 | |||||
| Current alcohol use | 47 | 49 | 38 | 0.014 | |||||
| Diabetes | 17.6 | 23.1 | 37 | <0.0001 | |||||
| Hypertension | 41.5 | 46.3 | 56.2 | 0.0024 | |||||
| Dyslipidaemia | 32.2 | 40.9 | 44.3 | 0.05 | |||||
| Left ventricular geometry | <0.0001 | ||||||||
| Normal (%) | 76.9 | 56.8 | 46.4 | ||||||
| CR (%) | 16.6 | 23.3 | 15.6 | ||||||
| ER (%) | 2.7 | 7.7 | 16.8 | ||||||
| CH (%) | 3.8 | 12.2 | 21.2 | ||||||
| LV diastolic function | <0.0001 | ||||||||
| Normal | 55.4 | 41.1 | 31.8 | ||||||
| Grade 1 (%) | 12.2 | 19.7 | 17.4 | ||||||
| Grade 2 (%) | 30.3 | 37.4 | 48.6 | ||||||
| Grade 3 (%) | 2.1 | 1.8 | 2.2 | ||||||
Data are presented as mean or N (%) using weighted row percentages; N's presented are unweighted counts of total participants in the HCHS‐SOL with respective characteristic.
BMI, body mass index; CO, cardiac output; CR, concentric hypertrophy; DBP, diastolic blood pressure; EDV, end‐diastolic volume; EF, ejection fraction; ER, eccentric hypertrophy; ESV, end‐systolic volume; HDL, high‐density lipoprotein; IVSd, interventricular septum thickness; LA, left atrial; LV, left ventricular; LVMI, left ventricular mass index; LVOT, left ventricular outflow tract; LVPWd, left ventricular posterior wall thickness; RWT, relative wall thickness; SBP, systolic blood pressure; SE, standard error; SV, stroke volume; WC, waist circumference.
Mean echocardiographic measures of LVMI, ESV, EDV, SV and CO were higher among those with obesity (45.2 g m−2.7, 35 mL, 86.8 mL, 73 mL and 4.8 L) compared with those with normal BMI (34.7 g m−2.7, 32.4 mL, 79.4 mL, 65.7 mL and 4.2 L, and 79.4 mL, p value < 0.05 for all comparisons) The lateral and septal E/E' was higher for those with obesity (9.3 and 12.5 m s−1) compared with people with normal BMI (8.2 and 10.7 m s−1; p value < 0.0001 for both comparisons). The rate of eccentric remodelling and concentric hypertrophy was higher in people with obesity (16.8% and 21.2%) than those with normal BMI (2.7% and 3.8%; p value < 0.0001). Furthermore, the rate of normal LV geometry was higher in those with normal BMI compared with those with obesity (76.9% and 46.4%; p value < 0.0001). Diastolic dysfunction grade 1 and grade 2 were higher among people with obesity (17.4% and 48.6%) and overweight (19.7% and 37.4%) compared with the normal BMI group (12.2% and 30.3%; p value = 0.0001). Similar diastolic dysfunction, the proportion of individuals with normal LV geometry was lower in people with obesity compared with those with normal BMI (46.4% and 76.9%; p value < 0.0001).
In linear regression models adjusted for age, gender, FFM and cardiovascular risk factors, every 5‐unit increment in BMI was associated with a 3.4 g m−2.7 increase in LVMI (p < 0.05), a 101.5‐mL increase in CO (p < 0.05) and a 0.45 higher E/E' ratio (p < 0.05). Similarly, for every 5‐cm increase in WC was associated with a 1.05 g m−2.7 increase in LVMI (p < 0.01), a 0.15‐mL increase in ESV (p < 0.02), a 0.40‐mL increase in EDV (p < 0.05), a 43.5 mL min−1 increase in CO (p < 0.01), a 0.35‐ml increase LAV (p < 0.01) and a 0.1 higher E/E' ratio (p < 0.01). Similar findings were noted for HC, FM, and FFM, please refer to Table 3. However, each 0.1‐unit increment in WHR was associated with a 2.0 g m−2.7 increase in LVMI (p < 0.01), and a 0.22 decrease in EF (p < 0.01), as well as a 0.66‐mL decrease in ESV (p < 0.01) and 1.71‐mL decrease in EDV (p < 0.01).
Table 3.
Multivariable linear regression models of the associations between anthropometric measures and echocardiographic parameters
| LVMI | ESV | EDV | EF | CO | LAV | E/E' | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictor | B (SE) | R 2 | B (SE) | R 2 | B (SE) | R 2 | B (SE) | R 2 | B (SE) | R 2 | B (SE) | R 2 | B (SE) | R 2 |
| BMI | 3.4 (0.60)* | 0.21 | 0.5 (0.45) | 0.3 | 1.75 (1) | 0.34 | 0.1 (0.20) | 0.06 | 101.5 (49.5)* | 0.19 | 0.3 (0.6) | 0.12 | 0.45 (0.12)* | 0.21 |
| WC | 1.05 (0.05)* | 0.22 | 0.15 (0.05)* | 0.3 | 0.40 (0.1)* | 0.34 | −0.05 (0.04) | 0.07 | 43.5 (12.5)* | 0.19 | 0.35 (0.1)* | 0.12 | 0.10 (0.01)* | 0.21 |
| HC | 0.55 (0.04)* | 0.2 | 0.30 (0.05)* | 0.3 | 0.85 (0.1)* | 0.35 | −0.01 (0.03) | 0.07 | 38 (11.5)* | 0.19 | 0.25 (0.05)* | 0.12 | 0.10 (0.01)* | 0.21 |
| WHR | 2.00 (0.16)* | 0.2 | −0.66 (0.13)* | 0.3 | −1.71 (0.29)* | 0.35 | −0.22 (0.08)* | 0.07 | 29.3 (18.5)* | 0.19 | 0.32 (0.23) | 0.12 | 0.05 (0.05) | 0.20 |
| FM | 0.19 (0.01)* | 0.21 | 0.08 (0.01)* | 0.3 | 0.26 (0.03)* | 0.35 | −0.001 (0.01) | 0.07 | 12.5 (3.7)* | 0.19 | 0.1 (0.02)* | 0.12 | 0.02 (0.00)* | 0.21 |
| FFM | 0.41 (0.06)* | 0.15 | 0.52 (0.07)* | 0.3 | 1.2 (0.16)* | 0.34 | −0.01 (0.03) | 0.06 | 68.0 (8.3)* | 0.19 | 0.57 (0.08)* | 0.12 | 0.04 (0.01)* | 0.19 |
Model adjusted for age, gender, height (except for BMI models where height has been omitted), FFM (except for FFM models), HTN, DM, dyslipidaemia, smoking and family history of coronary heart disease.
p value <0.05.
BMI, body mass index; CO, cardiac output; DM, diabetes mellitus; EDV, end‐diastolic volume; EF, ejection fraction; ESV, end‐systolic volume; FFM, fat‐free mass; FM, fat mass; HC, hip circumference; HTN, hypertension; LAV, left atrial volume; LVMI, left ventricular mass index; SE, standard error; WC, waist circumference; WHR, waist‐to‐hip ratio.
To summarize, BMI, WC, HC, FM and FFR were all associated with higher LVMI, CO, LAV and E/E' independent of age, sex, hypertension and antihypertensive medication use, diabetes, dyslipidaemia, smoking and family history of coronary heart disease. In the other hand, WHR was associated with higher LVMI but lower ESV, EDV and EF.
In multivariable logistic regression models (Table 4), every 5 kg m‑2 increase in BMI was associated with higher odds of abnormal left ventricular geometry (odds ratio, OR 1.40, 95% confidence interval, CI [1.3, 1.9]) and diastolic dysfunction (OR 1.48, 95% CI [1.2, 1.8], p < 0.01). Similarly, each 5‐cm increase in WC was associated with increased odds of abnormal left ventricular geometry (OR 1.17, 95% CI [1.09, 1.26]) and increased odds of diastolic dysfunction (OR 1.1, 95% CI [1.01, 1.2]). Each 0.1‐unit increase in WHR was associated with higher odds of abnormal left ventricular geometry (OR 1.51, 95% CI [1.12, 2.03]), but WHR was not significantly associated with diastolic dysfunction (OR 1.18, 95% CI [0.93, 1.48]). Every 1‐kg increase in FM and FFM was associated higher odds of abnormal left ventricular geometry (OR 1.03, 95% [CI 1.0, 1.05] and OR 1.02, 95% CI [0.98, 1.05], respectively) and higher odds of diastolic dysfunction (OR 1.02, 95% CI [1.0, 1.04] and OR 1.06, 95% CI [1.02, 1.10]).
Table 4.
Multivariable logistic regression models of the associations between anthropometric measures and echocardiographic parameters
| LV geometry | Diastolic dysfunction | |||||
|---|---|---|---|---|---|---|
| Odds ratio | 95% CI | p value | Odds ratio | 95% CI | p value | |
| BMI | 1.40 | [1.30, 1.90] | 0.03 | 1.48 | [1.20, 1.80] | <0.01 |
| WC | 1.17 | [1.09, 1.26] | <0.01 | 1.10 | [1.01, 1.20] | 0.04 |
| HC | 1.05 | [0.94, 1.17] | 0.41 | 1.07 | [0.98, 1.17] | 0.13 |
| WHR | 1.51 | [1.12, 2.03] | <0.01 | 1.18 | [0.93, 1.48] | 0.22 |
| FM | 1.03 | [1.00, 1.05] | 0.02 | 1.02 | [1.0, 1.04] | 0.05 |
| FFM | 1.02 | [0.98, 1.05] | 0.42 | 1.06 | [1.02, 1.10] | 0.03 |
Model 1: Age, gender, height (except for BMI models where height has been omitted), FFM (except FFM which is adjusted for FM), HTN, DM, dyslipidaemia, smoking and family history of coronary heart disease.
Left ventricular geometry is categorized as normal versus abnormal. The abnormal category includes left ventricular eccentric remodelling, eccentric hypertrophy and concentric hypertrophy.
BMI, body mass index; CI, confidence interval; FFM, fat‐free mass; FM, fat mass; HC, hip circumference; WC, waist circumference; WHR, waist‐to‐hip ratio.
In summary, BMI, WC, WHR and FM were associated with statistically significant higher odds of abnormal left ventricular geometry. Similarly, BMI, WC, FM and FFM were associated with higher odds of diastolic dysfunction.
4. Discussion
In this cross‐sectional analysis of the association between anthropometrics, body composition measures and echocardiographic measures of left ventricular structure and function in Hispanic/Latinos, we found that higher anthropometrics and body composition measures were associated with undesirable cardiac structure and function. Specifically, BMI, WC, HC, FM and FFM were associated with higher LVMI, CO and E/E', while WHR was significantly associated with higher LVMI and lower ESV, EDV and lower EF. Similarly, BMI, WC and FM were associated with abnormal left ventricular geometry (eccentric remodelling, eccentric hypertrophy and concentric hypertrophy) and diastolic dysfunction (grades I–III). These associations were independent of age, sex, diabetes, hypertension, dyslipidaemia, smoking and personal and family history of cardiovascular disease.
Hispanic/Latinos are a fast‐growing minority in the USA with high prevalence of obesity and other cardiovascular risk factors 9 that place them at increased risk for the development of HF 11. Our study shows that higher anthropometric measures are associated with higher LVMI and CO but lower EF except for BMI for which EF is higher. Also, higher anthropometrics are associated with abnormal left ventricular geometry (concentric remodelling, concentric hypertrophy and eccentric hypertrophy) after adjusting for traditional cardiovascular risk factors including hypertension, dyslipidaemia, smoking and personal and family history of cardiovascular disease. Abnormal LV geometry is commonly observed in patients with normal LV systolic function. Unfortunately, abnormal LV geometry with or without LVH is associated with adverse outcomes. Older and obese patients are more commonly found to have abnormal LV geometry patterns 29.
Obesity can influence the development of left ventricular hypertrophy, and therefore, abnormalities in left ventricular geometry through increase haemodynamic load 3 and inflammation THAT promote adverse cardiac remodeling 30. Hypertension is strongly correlated with obesity 15, and hypertension is known to be associated with eccentric left ventricular hypertrophy. Although it is difficult to separate the effect of hypertension from that of obesity, we included hypertension in our linear and logistic regression models to account for this known association. Similarly, obesity and diabetes are strongly associated 31, and both are pro‐inflammatory conditions that could lead to adverse cardiac remodelling (high LVMI) 30. Diabetes was therefore included in our models as a confounder for the association between obesity and cardiac structure. Our final results demonstrate an independent association between obesity and higher LVMI and abnormal left ventricular geometry after adjusting for major confounders such as hypertension and diabetes.
Higher BMI, WC, HC, FM and FFM were associated with higher E/E' and left ventricular diastolic dysfunction independent of age, sex and common cardiovascular risk factors. Left ventricular diastolic dysfunction is a finding related to abnormal cardiac filling pressure during the relaxation phase of the cardiac cycle and is associated with an increased risk of future HF risk 32. Similarly, E/E' is a widely used indicator of left ventricular filling pressures and predictor of HF risk 33, 34. While previous studies have documented the association between obesity and abnormal cardiac structure and function 21, 35, 36, 37, 38, there is lack of data describing these associations in US Hispanic/Latinos. Among the existing studies, the sample might not be representative or the US Hispanic/Latino population 39. Our study provides data to fill in this gap and serves as the groundwork to inform prospective and intervention studies aimed to determine the use of echocardiography as a risk stratification tool for HF in US Hispanic/Latinos affected by overweight and obesity.
Our study limitations include recruitment of specific Hispanic/Latino background groups by study site because of differential geographic distribution among Hispanic/Latino groups residing in the United States. The ECHO‐SOL echocardiograms were not contemporaneous with data from the HCHS/SOL. However, the time intervals between ECHO‐SOL assessment and HCHS/SOL measurements were reduced to the best possible, and the most updated HCHS/SOL data on key covariates (hypertension, diabetes mellitus and self‐reported HF) were used. The cross‐sectional nature of the analysis limits causal inference and is prone to potential reverse causality. Furthermore, our study lacks clinical outcomes.
The strengths of this study include the use of a large and diverse US Hispanic/Latino population sample with standardized comprehensive anthropometric and echocardiographic measures of cardiac structure and function. We provide information on the association between various measures of adiposity rather than limiting to commonly used BMI and WHR. The inclusion of WC, HC, FM and FFM allows for assessment of different body fat distribution patterns important to the population studied.
5. Conclusion
Understanding anthropometrics and body composition measures and their relationship to echocardiographic markers of left ventricular dysfunction aid in identifying populations at risk for HF. The findings from this cross‐sectional analysis contribute the first comprehensive anthropometric and body composition analysis reporting the associations with echocardiographic measures of LV structure and function in a large cohort of US Hispanic/Latinos.
Funding
The Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina (N01‐HC65233), University of Miami (N01‐HC65234), Albert Einstein College of Medicine (N01‐HC65235), Northwestern University (N01‐HC65236) and San Diego State University (N01‐HC65237). The following Institutes/Centres/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke and NIH Institution‐Office of Dietary Supplements.
ECHO‐SOL was supported by a grant from the National Heart, Lung, and Blood Institute (R01 HL104199, Epidemiologic Determinants of Cardiac Structure and Function among Hispanics: Carlos J. Rodriguez, MD, MPH Principal Investigator).
Sonia G. Ponce was supported by the UCSD Cardiovascular Epidemiology and Prevention T32 postdoctoral fellowship (NHLBI 5T32HL079891).
Conflict of Interest Statement
The authors declare no conflict of interest.
Author Contributions
Dr Ponce interpreted the data and drafted the manuscript. Katrina Swett completed the analyses for this study. Dr Allison conceived the study, develop the manuscript proposal and interpreted the data. Dr Cai, Desai, Hurwitz, Ni, Schneiderman, Shah, Spevack, Talavera and Rodriguez contributed to the methodological aspects of this study. All authors were involved in the manuscript writing and approved the final version submitted for publication.
Acknowledgements
The authors acknowledge the investigators, the staff and the participants of HCHS‐SOL and ECHO‐SOL for their dedication and commitment to the success of this study. Investigators website ‐ http://www.cscc.unc.edu/hchs/
Ponce, S. , Allison, M. A. , Swett, K. , Cai, J. , Desai, A. A. , Hurwitz, B. E. , Ni, A. , Schneiderman, N. , Shah, S. J. , Spevack, D. M. , Talavera, G. A. , and Rodriguez, C. J. (2018) The associations between anthropometric measurements and left ventricular structure and function: the Echo‐SOL Study. Obesity Science & Practice, 4: 387–395. 10.1002/osp4.279.
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