Influence of Obesity on the Relationship of Cardiometabolic Risks Factors With Cardiovascular Disease in Older African and European Americans

Abstract

Purpose

This study evaluated the impact of obesity on cardiometabolic risk factors (CRF) interrelationships and predictive efficiency of CVD development in older African (AA) and European Americans (EA).

Design

A comparative research design evaluated CRF risk profile differences between participant groups.

Setting

Seven neighborhoods in a southern US city.

Subjects

A sample of 179 older AA (n = 128) and EA (n = 51) adults.

Measures

Non-fasting blood samples were evaluated for lipids and lipoproteins, glycosylated hemoglobin, systolic –(SBP) and diastolic blood pressure (DBP), body mass index (BMI), body fat percentage (BF%) and physical function.

Analysis

Data were analysis with descriptive statistics, t-tests, and correlations.

Results

AA were heavier than EA although all had above average age-appropriate fitness. Means and relationships between CRF and other variables were different (P < .05) based on race. Both AA (41.3 + 5.8) and EA (38.6 + 6.4) BF% were CRF risks. Holding BMI constant, CRF were generally not related, and the relationships were different for AA and EA. AA had a range of 13.0 to 27.2% more favorable values for cholesterol, HDL-C, and triglyceride. EA had favorable A1c (EA 5.8 vs AA 6.2%) values.

Conclusions

A limitation of this report is the small sample size. Although further research is warranted, these findings suggest population specific CRF selections would improve CVD prediction in AA.

Keywords

cardiometabolic risk factors African Americans European Americans cardiovascular disease obesity non-fasting blood samples

Purpose

African Americans (AA) experience an earlier onset of cardiovascular disease (CVD) with greater severity and mortality than European Americans (EA).¹ In young and middle age adults, lipids and lipoproteins are better predictors of CVD development in EA, while blood pressure (BP) and obesity are better determinants of CVD development in AA.^2,3 Race differences in CRF clustering are reported to continue into older age (65+).⁴

Obesity has a differential effect on the pathogenic mechanisms underlying glucose homeostasis and atherogenesis in middle age obese AA and EA.² Being overweight or obese increases CRF, but the number and nature of CRF increases vary by age, race, and level of obesity.⁵

Fasting samples are typically used in research, however; no sound scientific evidence supports why fasting is considered superior to non-fasting when evaluating a lipid profile for CVD predictions.³ This study purpose was to evaluate the influence of body adiposity/obesity on interrelationships among CRF, measured with non-fasting samples, to predict the development of CVD in older AA and EA. The hypothesis evaluated was that body adiposity would not influence relationships among CRF in older AA and EA.

Participants and Methods

A comparative research design was employed to determine the CRF risk profiles of 179 older age adults age (AA 74.1 ± 8.9: EA 77.8 ± 6.9 yrs.), race (AA n = 128 and EA n = 51; power ≤.80),) from seven neighborhoods in a southern U.S. city. Participants displaying no indications of exercise contraindications were selected based on their participation in age-appropriate senior exercise classes for a minimum of 6 months.⁵ They signed an approved informed consent form and completed a health questionnaire regarding overall health, age, and socioeconomic status. CRF blood profiles were based on non-fasting samples per participants’ preference. Testing required 2 hours to complete and was conducted an average of 2 hours after the participants’ stated time of breakfast between 9:00 am and 12:00 noon.

Blood profiles were completed via a calibrated PTS Diagnostics Analyzer (total cholesterol, HDL-C, LDL-C, triglyceride, and glycosylated hemoglobin (A1c), and glucose). Systolic blood pressure (SBP) and diastolic blood pressure (DBP), pulmonary functions and physical functions (timed up and go- TUG) were measured following standard procedures. A Bioelectric Impedance Analyzer (BIA) system assessed height, weight, body mass index (BMI) and body fat percentage (BF%).

The CRF data were converted to CRF indexes by dividing measured CRF value by the CRF risk value. A CRF risk index ≥1 indicated an increased likelihood of developing CVD. However, a CRF risk index <1 for HDL-C indicated an increased risk of developing CVD. Descriptive statistics calculated means and standard deviations of the AA and EA. Independent t-tests evaluated AA and EA for CRF differences. Correlations evaluated relationships among the different variables for AA and EA, respectively. To further evaluate the relationships among other CRF within AA and EA samples, correlations were assessed with BF% and BMI held constant.

Results

The AA were younger (All 75.2 ± 8.5: AA 74.1 ± 8.9: EA 77.8 ± 6.9 yrs.), and heavier than the EA (AA 82.0 ± 19.4; and EA 71.8 ± 15.7 kg – Table 1) and all had above average age-appropriate fitness.⁵ The participants’ incomes ranged from $33,912 to $103,416 with EA having a higher average income.

Table 1.

Lipids, Lipoproteins, Circulatory and Morphological Responses in Older.

Variable	African Americans N = 128	European Americans N = 51
AgeYrs	74.1 ± 8.9	77.8 ± 6.9^*
Weight (kg)	82.0 ± 19.1	71.8 ± 15.7^*
Cholesterol (mg/dL)	162.8 ± 42.2	184.6 ± 39.4^*
HDL cholesterol (mg/dL)	58.3 ± 16.9	51.6 ± 16.3^*
LDL cholesterol (mg/dL)	84.5 ± 35.0	100.7 ± 32.2^*
Triglycerides (mg/dL)	123.1 ± 63.7	156.6 ± 83.8^*
Glucose (mg/dL)	123.2 ± 50.7	123.2 ± 48.8
A1c (mmol/mol)	6.2 ± 1.2	5.8 ± .84^*
Systolic BP (mmHg)	141.9 ± 21.8	130.4 ± 16.5^*
Diastolic BP (mmHg)	75.5 ± 11.6	69.7 ± 11.2^*
Body Mass index (kg/m²)	30.7 ± 6.8	27.6 ± 4.2^*
Body fat%	41.3 ± 5.8	38.6 ± 6.4

Mg/dL = milligram per deciliter; mmol/mol = millimole per mol. Kg = kilogram. Yrs. = yearsA1c is glycosylated Hemoglobin; mmHg = millimeters of mercury; kg/m² = kilograms per/meters of height squared; ^*P < .05.

African Americans SBP and DBP values were higher and were CRF risks. AA had higher BMI and BF% than EA, BMI was a CRF risk for AA and both the AA and EA were morbidly obese (AA body fat% 41.3 and EA 38.6 - Table 1). Different mean values were observed for blood lipids and lipoproteins as older AA had a range of 13.0 to 27.2% more favorable values for cholesterol, HDL-C, and triglyceride. Triglyceride and LDL-C for EA were the only lipid or lipoprotein variables that were CRF risk in either group. EA had favorable A1c (EA 5.8 vs AA 6.2%) values and blood glucose was at prediabetes levels but not different between the 2 races.

Glucose was a CRF index risk for AA while glucose and triglyceride indexes were CRF index risks for EA. SBP for AA was the only circulatory variable that was a CRF index risk. BF% index was a CRF index risk for both AA and EA while BMI index was a risk factor for AA. Total cholesterol, LDL-C and HDL-C were the CRF indexes significantly different (P < .05) between AA and EA.

As displayed in Table 1 AA were more obese than EA but had favorable lipid and lipoprotein profiles. European Americans had a greater risk profile for triglyceride and HDL-C while AA had greater risk profiles for obesity and BP.

Glycosylated hemoglobin shared a relationship with BMI and BF% in AA. In EA DBP was related to LDL-C while glucose, and SBP was related to A1c. Holding BMI constant glucose was related to TUG and BF% in AA. In EA TUG increased while GLU and all lipids except HDL-C (r values ranged from −.38 to −.43 – P < .05) decreased. Holding BF% constant TUG shared a negative relationship with glucose, and all lipids except HDL-C in EA, while DBP shared relationships with TC, LDL-C, glucose, and TUG. In AA TC, LDL-C and glucose increased as DBP (ranged from .34 to .66) increased.

Discussion

The hypothesis evaluated in this study was that body adiposity contributes to different relationships among CRF when predicting the development of CVD in older AA compared with older EA. The CRF, blood glucose and pressure, A1c and anthropometric variables of the older adults were measured in the morning using non-fasting blood samples as participants did not wish to skip breakfast. Non-fasting-blood samples provided results reasonably similar to fasting values observed in older AA and EA adults in the literature.⁶ Our data are not supportive of the hypothesis when BF% was held constant as different CRF variables relationships and patterns were observed for older AA compared with older EA. This finding is consistent with findings by Liu et al.⁷ who found that obesity impacted the relationships of other CRF variables in younger AA and EA. Based on BF% (>35%) both AA and EA were morbidly obese. One study showed that 51% of overweight adults and 32% of obese adults had no more than one CRF.⁷ Differences in body morphology suggests that obesity may contribute to difference relationships observed among CRF, CRF indexes, and the development of CVD in AA and EA.^4,7

The issue of obesity, when comparing AA and EA, is complicated by the relationship of obesity with other CRF.^6,8 AA have a greater prevalence of obesity which is related more to CVD morbidity and mortality than in EA.¹ SBP was a CRF risk in AA, but not in EA.¹ These findings may partially explain why BP and obesity are better at predicting CVD in AA than lipids and lipoproteins.^1,6,8 Glucose values (123 mg/dL each) suggest both groups were pre-diabetic, nearing diabetes levels. This finding is consistent with some and different from other results that reported a tendency for AA to have higher A1c at similar glucose levels compared with EA.^4,8,9

Lipids, lipoproteins, and glucose values increased as DBP value decreased and a similar pattern was observed when physical ability was correlated with lipids, lipoproteins, and glucose in EA with BMI held constant. Glucose increased as body fat increased in AA. When body fat was held constant, DBP increased as TG, LDL-C and glucose increased in EA. For AA HDL-C, TG, and glucose increased as physical ability increased. The finding in this study that adiposity/obesity influences the relationships among CRF differently for older AA compared with EA is consistent with findings by Enyeji et al.¹⁰ that AA consistently have lower odds than EA of having good cardiovascular health. Their recommendation was for governmental intervention to decrease the gap between EA and AA cardiovascular health.^7,10

Conclusions

A weakness of the present study is the small unequal samples (n < 284 participants; power ≤.80) sensitivity would not be large enough to accurately evaluate the hypothesis. Thus, further investigations are needed to validate these findings. Based on our findings, BF% or BMI and hypertension are overpowering CRF factors that make older AA more prone to developing CVD. Therefore, population specific clustering and CRF selections appear to be the most effective at predicting CVD in older AA and EA and reducing the CVD racial disparity.

SO WHAT?

What is Known on This Topic?

A single CRF protocol and cut points are used to predict the likelihood of developing CVD in AA and EA even though older AA have a greater prevalence of CVD.

What Does This Article Add?

Obesity and evaluated BP appears to be CRF that contribute disproportionately to the greater prevalence of CVD in older AA and older AA have disproportionate obesity and hypertension levels. Lipids and lipoproteins contribute most to the development of CVD in EA.

What are the Implications for Health Promotion Practice or Research?

This study suggests greater emphasis should be placed on controlling BP and obesity in older AA. A1c provides different information about glucose levels and CVD development in older AA and EA. Further, this study suggests that non-fasting blood samples can effectively assess blood profiles for CVD in older adults.

Footnotes

Author Contributions

All authors of this article brief contributed the concept or design (TMH, KK, LJB), analysis (TMH, GS, LJB) and interpretation of the data (TMH, LJB, CSB); also, all authors significantly contributed to the drafting of the work, final approval, and agree to the accuracy of the investigation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors wish to thank the following for their support of this research through funding: Mecklenburg County Government IPF 2023-1070 and The Sharon at Southpark IPF2023-1040.

Ethical Statement

ORCID iDs

Trudy Moore-Harrison

Candace S. Brown

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