The American Heart Association Ideal Cardiovascular Health and Incident Type 2 Diabetes Mellitus Among Blacks: The Jackson Heart Study
The concept of ideal cardiovascular health (CVH), defined by the American Heart Association primarily for coronary heart disease and stroke prevention, may apply to diabetes mellitus prevention among blacks.
Methods and Results
Our sample included 2668 adults in the Jackson Heart Study with complete baseline data on 6 of 7 American Heart Association CVH metrics (body mass index, healthy diet, smoking, total cholesterol, blood pressure, and physical activity). Incident diabetes mellitus was defined as fasting glucose ≥126 mg/dL, physician diagnosis, use of diabetes mellitus drugs, or glycosylated hemoglobin ≥6.5%. A summary CVH score from 0 to 6, based on presence/absence of ideal CVH metrics, was derived for each participant. Cox regression was used to estimate adjusted hazard ratios. Mean age was 55 years (65% women) with 492 incident diabetes mellitus events over 7.6 years (24.6 cases/1000 person‐years). Three quarters of participants had only 1 or 2 ideal CVH metrics; no participant had all 6. After adjustment for demographic factors (age, sex, education, and income) and high‐sensitivity C‐reactive protein, each additional ideal CVH metric was associated with a 17% diabetes mellitus risk reduction (hazard ratio, 0.83; 95% CI, 0.74–0.93). The association was attenuated with further adjustment for homeostasis model assessment for insulin resistance (hazard ratio, 0.89; 95% CI, 0.79–1.00). Compared with participants with 1 or no ideal CVH metric, diabetes mellitus risk was 15% and 37% lower in those with 2 and ≥3 ideal CVH metrics, respectively.
The AHA concept of ideal CVH is applicable to diabetes mellitus prevention among blacks. These associations were largely explained by insulin resistance.
What is New?
Our findings show a robust association between the American Heart Association–defined ideal cardiovascular health (CVH; healthy body mass index, healthy diet, nonsmoking status, normal total cholesterol, normal blood pressure, and physical activity) and diabetes mellitus risk reduction among middle‐aged blacks.
This association is driven, in large part, by insulin resistance.
The effects of the ideal CVH metrics on reducing diabetes mellitus risk may be cumulative and synergistic.
What are the Clinical Implications?
To prevent 1 case of diabetes mellitus in the population, it would require 55 people to meet 3 American Heart Association ideal CVH targets and 49 people to meet 4 or more targets.
Primary care interventions with a focus on improving health behaviors so that more blacks achieve the American Heart Association targets for ideal CVH may have a significant impact in reducing diabetes mellitus risk.
For clinicians, encouraging their patients to meet these American Heart Association ideal CVH targets could represent a great step forward in preventing diabetes mellitus.
Blacks have a disproportionately higher risk of type 2 diabetes mellitus and higher rates of diabetes mellitus–related complications and death than non‐Hispanic whites.1 Overweight and obesity, fasting hyperglycemia, physical inactivity, and poor dietary habits are important modifiable risk factors for the development of diabetes mellitus, and interventions targeting these factors have been shown to delay or prevent the development of diabetes mellitus.2, 3, 4, 5 Based on evidence on the role of 7 modifiable cardiovascular health (CVH) factors and behaviors (smoking, total cholesterol, fasting glucose, body mass index [BMI], healthy diet, blood pressure, and physical activity), the American Heart Association (AHA) defined the concept of ideal CVH as part of efforts to decrease cardiovascular disease (CVD) and stroke incidence and mortality.6 Given the commonality in risk factors between CVD and diabetes mellitus, the concept of ideal CVH may be applicable to the primordial and primary prevention of diabetes mellitus.
Fewer Americans, particularly blacks, achieve ideal CVH, especially for BMI, physical activity, and healthy diet, which have been strongly associated with incident diabetes mellitus.7, 8, 9 The association between AHA‐defined ideal CVH and the development of diabetes mellitus is less clear among blacks than among other race‐ethnic groups. Fretts et al, using data on American Indians from the SHFS (Strong Heart Family Study), showed that having at least 2 AHA‐defined ideal CVH metrics, compared with 1 or none, significantly reduced the odds of developing diabetes mellitus.10 In MESA (Multi‐Ethnic Study of Atherosclerosis),11 participants with 2 to 3 and 4 or more ideal CVH metrics had a 34% and 75% lower risk of incident diabetes mellitus, respectively. In MESA, the association between ideal CVH and diabetes mellitus was less robust among blacks and Hispanics compared with whites and Chinese.11 Among blacks, though there was a significant trend in diabetes mellitus risk reduction with increasing number of ideal CVH metrics, there was no apparent association between each level of ideal CVH metric and the referent group (no ideal CVH metric). This lack of association could be attributed, in large part, to insufficient power in the stratified analysis. The MESA study and JHS (Jackson Heart Study) recruited blacks from different geographical regions in the United States. The JHS is a single‐site study and recruited all blacks from Jackson, Mississippi, a state with the highest rates of diabetes mellitus in the nation.12 In contrast, blacks in the MESA study were recruited primarily from Maryland and North Carolina, and to a lesser extent New York, California, and Illinois. The JHS enrolled more blacks, and there are more incident cases of diabetes mellitus among blacks in the JHS than in MESA, and, as such, the JHS is well powered to address this question.
If ideal CVH, as defined by the AHA, is applicable to diabetes mellitus prevention among blacks, future joint efforts for CVD and diabetes mellitus prevention in this population may provide 1 effective and cost‐efficient strategy for public health experts and policy makers to tackle the growing challenge of diabetes mellitus and CVD. To investigate this further, we examined the association between AHA‐defined ideal CVH and incident diabetes mellitus in the JHS, a large cohort of blacks. Specifically, we sought to determine whether there was a graded relationship between increasing number of ideal CVH metrics and diabetes mellitus risk reduction, and the role insulin resistance plays in this association. We hypothesized that increasing number of ideal CVH metrics will be significantly associated with a lower incidence of diabetes mellitus.
The JHS enrolled 5301 participants aged 21 to 94 years at the time of the baseline assessment (2000–2004) from the Jackson, Mississippi, metropolitan area. The goal of the study was to examine factors that influence the development of CVD in black men and women to learn how to prevent this group of diseases in this population. Two subsequent in‐person follow‐up visits have been completed since baseline (2005–2008 and 2009–2013). Details about the study design and recruitment process have been published.13, 14
Baseline information, including demographics, socioeconomic status, lifestyle data, and medication use, was obtained by study personnel through interviews during clinic visits or at home. Medication use in the 2 weeks preceding each clinic visit was assessed at the time of the clinic visit. Blood samples were collected according to standard procedures and metabolic variables (glucose, insulin, and lipids) were analyzed at a central laboratory (University of Minnesota).15
Ascertainment of Incident Type 2 Diabetes Mellitus
At baseline, fasting plasma glucose was measured by the glucose oxidase colorimetric method using a Vitros 950 or 250 (Ortho Clinical Diagnostics analyzer; Ortho Clinical Diagnostics, Raritan, NJ), and at visits 2 and 3, it was measured using a Roche Modular P Chemistry analyzer (Roche Diagnostics, Indianapolis, IN). Glycated hemoglobin was measured using a Tosoh high‐performance liquid chromatography system (Tosoh Corporation, Tokyo, Japan). We defined type 2 diabetes mellitus as having either: (1) fasting glucose ≥126 mg/dL; (2) glycated hemoglobin ≥6.5%; (3) use of diabetes mellitus medication; or (4) a physician diagnosis of the condition. We assessed incident type 2 diabetes mellitus at follow‐up visits 2 and 3. For each incident case of type 2 diabetes mellitus, time to event was considered as midpoint between last exam without diabetes mellitus and the exam at which diabetes mellitus developed. For participants who remained event free, follow‐up time was censored at their last available visit.
AHA's CVH Metrics
Resting blood pressure (BP) was measured twice at 5‐minute intervals, the average of which was used in our analysis. Ideal BP was defined as a systolic BP of <120 mm Hg and a diastolic BP of <80 mm Hg if untreated; intermediate BP was defined as a systolic BP of 120 to 139 mm Hg or a diastolic BP of 80 to 89 mm Hg, of if treated to goal; and poor BP was defined as a systolic BP of ≥140 mm Hg or a diastolic BP ≥90 mm Hg.6
Participants completed an interviewer‐administered physical activity (PA) questionnaire at baseline, modified from the Baeke PA survey,16 which was also the parent document for the ARIC (Atherosclerosis Risk in Communities Study) study's survey.17, 18 This instrument was identical to the one used during the Kaiser PA survey, which showed good validity and reliability in a multiethnic sample.19 Exercise was reported by the average amount of time per week spent, and metabolic equivalent levels were defined for each activity. Moderate activity was defined as 3 to 6 metabolic equivalents and vigorous activity as >6 metabolic equivalents. Ideal PA was defined as ≥150 minutes of moderate‐intensity activity or ≥75 minutes of vigorous activity or ≥150 minutes of combined moderate‐intensity and vigorous activity per week; intermediate PA was defined as 1 to 149 minutes of moderate‐intensity activity or 1 to 74 minutes of vigorous activity or 1 to 149 minutes of combined moderate‐intensity and vigorous activity per week; poor PA was defined as no amount of activity (0 minutes per week).6
Body Mass Index
BMI was determined as weight (in kilograms) divided by the square of height (in meters). BMI was defined as ideal, <25 kg/m2; intermediate, 25 to 29.9 kg/m2; and poor, ≥30 kg/m2.6
Cigarette smoking was as self‐reported and participants were asked about the quantity and duration of smoking. Ideal smoking was defined as participants who had never smoked or who quit smoking more than 12 months preceding the clinic visit; intermediate was defined as former smokers who quit smoking within 12 months of the clinic visit; and poor was defined as a current smoker.6
Total cholesterol was measured by the cholesterol oxidase method (Roche COBAS Fara analyzer; Roche Diagnostics), as previously described.15 Ideal total cholesterol was defined as a level of <200 mg/dL if untreated; intermediate total cholesterol defined by a level of 200 to 239 mg/dL or if treated to goal; and poor was defined by a level of ≥240 mg/dL.6
In the JHS, dietary intake was assessed using the Delta NIRI food frequency questionnaire with 158 items.20, 21 The 5 components used to compute the AHA score, based on a 2000‐kcal diet, were: (1) ≥4.5 cups daily of fruits and vegetables; (2) >3.5 ounces, twice per week, of fish; (3) <450 kcal per week of sugary beverages; (4) ≥3 daily servings of whole grains; and (5) <1500 mg daily of sodium. Ideal diet was defined by a diet including 4 to 5 components; intermediate diet, 2 to 3 components; and poor diet, 0 to 1 component.6
Measurement of Covariates
Baseline waist circumference was the average of 2 measurements about the umbilicus and in the upright position. Current alcohol drinking was defined as alcohol use in the past 12 months. Education level was dichotomized as having at least a college education or higher versus no college education. Income status was divided into 3 categories based on family size and income: low income, middle income, and affluent income groups. Fasting insulin concentration was measured on a Vitros 950 or 250, Ortho Clinical Diagnostics analyzer (Ortho Clinical Diagnostics) using standard procedures that met the College of American Pathologists accreditation requirement.15 Insulin resistance was estimated using the homeostasis model assessment for insulin resistance (HOMA‐IR)=(fasting plasma insulin [μU/mL])×(fasting plasma glucose [mmol/L])÷22.5.22 hs‐CRP was measured by the immunoturbidimetric CRP‐Latex assay (Kamiya Biomedical Company, Seattle, WA) using a Hitachi 911 analyzer (Roche Diagnostics).23
Our analyses were performed using SAS software (version 9.3; SAS Institute Inc, Cary, NC). Using 6 of the 7 AHA health metrics (BMI, diet, smoking status, BP, total cholesterol, and PA), for each participant, and for each health metric, presence of ideal health was scored as 1 and absence as 0. A summary AHA CVH score was computed ranging from 0 (least ideal CVH) to 6 (most ideal CVH).
We compared differences in baseline characteristics by categories of the CVH score using the analysis of variance test or appropriate nonparametric tests for continuous variables, and the chi‐square test for categorical variables. Categories of the CVH score were defined based on the distribution of participants in each score group. Variables with non‐normal distributions were log‐transformed as required.
Using Cox regression analysis, we assessed the prospective graded association between the CVH score (modeled as a continuous variable) and the risk of incident diabetes mellitus. Hazard ratios (HRs) for each additional ideal health metric were estimated. We also examined the association of categories of the CVH score (≤1, 2, and ≥3) and incident diabetes mellitus. A sequential modeling approach was used as follows: model 1 (base model) was unadjusted; model 2 was adjusted for age, sex, education, and income; model 3 was further adjusted for hs‐CRP; and model 4 was additionally adjusted for HOMA‐IR.
Because of a previous report of effect modification of the association between low‐grade systemic inflammation and incident diabetes mellitus by BMI,24 we also assessed for interaction by waist circumference in our analysis. Interactions were assessed by including an interaction term in the regression model. Finally, based on a 5‐year risk, we estimated the number of participants who need to have increments of the ideal CVH score in order to prevent 1 case of incident diabetes mellitus. For all analyses, a P value of ≤0.05 was considered statistically significant.
After exclusions, our final sample size was 2668 adults. The proportions of participants with an ideal health score of 0, 1, 2, 3, 4, 5, and 6 were 3%, 36.1%, 39.9%, 16.1%, 4.5%, 0.4%, and 0%, respectively. Table 1 shows baseline characteristics of participants across categories of the ideal health summary score; score ≤1 (least ideal), score 2 to 3, and score ≥4 (most ideal). Compared with participants in the most ideal category, those in the least ideal category were older, less likely to have completed college with higher waist circumference, glycated hemoglobin, markers of systemic inflammation (hs‐CRP), and insulin resistance (as measured by HOMA‐IR). There were no differences in family history of diabetes mellitus or sex between categories.
|Characteristic||Categories of AHA CVH Score||P Value|
|Scores ≤1 (n=1042)||Score=2 (n=1065)||Score ≥3 (n=561)|
|Men, n, %||359 (34.5)||366 (34.4)||198 (35.3)||0.9|
|Waist circumference, cm||103±15||99±15||92±14||<0.0001|
|Current alcohol drinker, n, %||467 (45.0)||482 (45.3)||307 (55.1)||<0.001|
|At least college education, n, %||561 (53.8)||660 (62.0)||409 (73.2)||<0.0001|
|Income status, n, %|
|Low||105 (11.9)||99 (10.9)||50 (10.3)||<0.001|
|Middle||501 (56.6)||477 (52.7)||220 (45.2)|
|Affluent||279 (31.5)||329 (36.4)||217 (44.6)|
|Incident diabetes mellitus, n, %||235 (22.6)||191 (17.9)||66 (11.8)||<0.0001|
|Family history of diabetes mellitus, n, %||482 (46.4)||502 (47.3)||232 (41.4)||0.07|
|Lipid‐lowering medication, n, %||157 (17.4)||69 (6.8)||15 (2.7)||<0.0001|
|HOMA‐IRa||3.4 (2.3)||3.1 (2.2)||2.5 (1.6)||<0.0001|
|hs‐CRPa, mg/L||3.1 (4.5)||2.6 (4.7)||1.7 (3.6)||<0.0001|
Incident type 2 diabetes mellitus occurred in 492 persons (rate, 24.6 per 1000 person‐years) during a median follow‐up of 7.6 years (range, 3.5–12.2). Rates of diabetes mellitus decreased across increasing categories of ideal CVH score (Figure). Rates were highest among those with a score of 0 (30.8 events per 1000 person‐years; 95% CI, 19.4–48.9) and those with a score of 1 (30.2; 95% CI, 26.4–34.5). Rates among those with scores of 2 and 3 were (24.0; 95% CI, 20.9–27.7) and (17.1; 95% CI, 13.2–22.3), respectively. The lowest rate was observed among those with an ideal CVH score of 4 or more (10.2; 95% CI, 5.5–18.9).
The HR of incident diabetes mellitus per each additional ideal CVH metric in the unadjusted model was 0.77 (95% CI, 0.69–0.85; Table 2). The HR for incident diabetes mellitus was mildly attenuated, but remained significant after adjustment for age, sex, education, and income (HR, 0.80; 95% CI, 0.71–0.90) and for hs‐CRP (HR, 0.83; 95% CI, 0.74–0.93). With additional adjustment for HOMA‐IR (HR, 0.89; 95% CI, 0.79–1.00), the association was no longer significant.
|Model||HR||95% CI||P Value|
|CVH score||0.77||0.69 to 0.85||<0.0001|
|CVH score+demographic factorsa||0.80||0.71 to 0.90||<0.001|
|CVH score+demographic factors+hs‐CRP||0.83||0.74 to 0.93||0.002|
|CVH score+demographic factors+Hs‐CRP+HOMA‐IR||0.89||0.79 to 1.00||0.06|
|CVH Score+HOMA‐IR||0.85||0.76 to 0.94||0.002|
We examined the association between ideal CVH summary score and incident diabetes mellitus across categories of the score (Table 3): score ≤1, score=2, and score ≥3. In the unadjusted model, compared with participants with 1 or no CVH metric (reference category, score ≤1), the HR for incident diabetes mellitus was 0.82 (95% CI, 0.67–0.99) for those with a score of 2, and 0.51 (95% CI, 0.39–0.68) for those with a score ≥3 (P for trend, <0.0001). The HR for incident diabetes mellitus for those with a score ≥3 was 0.58 (95% CI, 0.43–0.77; P for trend, <0.001) when adjusted for age, sex, education, and income and 0.63 (95% CI, 0.47–0.85; P for trend=0.002) when further adjusted for hs‐CRP. The association was no longer significant when adjusted for HOMA‐IR (HR, 0.74; 95% CI, 0.55–0.99; P for trend=0.06).
|Models||Categories of AHA CVH Score||P Trend|
|Score ≤1 HR (95% CI)||Score=2 HR (95% CI)||Score ≥3 HR (95% CI)|
|CVH score||1.0||0.82 (0.67–0.99)||0.51 (0.39–0.68)||<0.0001|
|CVH score+demographic factorsa||1.0||0.83 (0.68–1.02)||0.58 (0.43–0.77)||<0.001|
|CVH score+demographic factors+hs‐CRP||1.0||0.85 (0.69–1.04)||0.63 (0.47–0.85)||0.002|
|CVH score+demographic factors+hs‐CRP+HOMA‐IR||1.0||0.95 (0.77–1.17)||0.74 (0.55–0.99)||0.06|
|CVH score+HOMA‐IR||1.0||0.92 (0.76–1.11)||0.64 (0.49–0.84)||0.003|
We did not formally test for mediation effects of HOMA‐IR. However, when only HOMA‐IR was added to the unadjusted model with ideal CVH score modeled as a continuous variable (Table 2), there was a substantial decrease in the HR for incident diabetes mellitus, from HR 0.77 (CI, 0.69–0.85) to HR 0.85 (CI, 0.76–0.94). Similarly, when only HOMA‐IR was added to the model containing categorical ideal CVH score (Table 3), the HR decreased from 0.51 (CI, 0.39–0.68) to 0.64 (CI, 0.49–0.84) for those with a score ≥3. These represented similar magnitudes in attenuation of effects compared to when both demographic factors and hs‐CRP were adjusted for.
The association between ideal CVH and incident diabetes mellitus was modified by central obesity (measured by waist circumference; Pinteraction<0.001 in the demographic model). We stratified our analysis by the median waist circumference (98.0 cm). Compared with the upper median of waist circumference, the lower median had significantly higher proportions of participants with ideal BMI (28.6% versus 0.7%), ideal BP (21.4% versus 10.5%), and ideal PA level (24.1% versus 18.6%; Table 4). There were no differences in ideal diet and ideal total cholesterol between these 2 groups. In analysis adjusted for age, sex, education, income, and hs‐CRP, among participants in the lower median of waist circumference, increasing ideal CVH score was significantly associated with a 21% decrease in risk of incident diabetes mellitus (HR, 0.79; 95% CI, 0.66–0.97; Table 5). Accounting for the effects of HOMA‐IR attenuated this relationship (HR, 0.83; 95% CI, 0.68–1.02). Among participants in the upper median, there was no association between ideal CVH score and incident diabetes mellitus even in the unadjusted model (HR, 0.96; 95% CI, 0.84–1.10).
|CVH Metric||Median WC <98.0 cm||Median WC ≥98.0 cm||P Value|
|Ideal CVH score||2.1±1.0||1.6±0.8||<0.0001|
|Categories ideal CVH score, n (%)||<0.0001|
|≤1||406 (30.7)||636 (47.2)|
|2||523 (39.6)||542 (40.2)|
|≥3||392 (29.7)||169 (12.6)|
|AHA body mass index category, n (%)||<0.0001|
|Not ideal, ≥25 kg/m2||943 (71.4)||1338 (99.3)|
|Ideal, <25 kg/m2||378 (28.6)||9 (0.7)|
|AHA blood pressure category, n (%)||<0.0001|
|Not ideal, ≥120/ or ≥80 mm Hg||1038 (78.6)||1206 (89.5)|
|Ideal, <120/<80 mm Hg without treatment||283 (21.4)||141 (10.5)|
|AHA total cholesterol category, n (%)||0.45|
|Not ideal, ≥200 mg/dL||742 (56.2)||776 (57.6)|
|Ideal, <200 mg/dL without treatment||579 (43.8)||571 (42.4)|
|AHA diet category, n (%)||0.079|
|Not ideal, <4 components||1306 (98.9)||1340 (99.5)|
|Ideal, 4 to 5 components||15 (1.1)||7 (0.5)|
|AHA smoking category, n (%)||0.024|
|Not ideal, current or former smoker or quit less ≤12 months ago||169 (12.8)||135 (10.0)|
|Ideal, never smoker or quit >12 months ago||1152 (87.2)||1212 (90.0)|
|AHA physical activity category, n (%)||<0.001|
|Not ideal, none or <150 mod. or <75 vig. or <150 mod. +vig.||1002 (75.9)||1096 (81.4)|
|Ideal, ≥150 mod. or ≥75 vig. or ≥150 mod. +vig.||319 (24.1)||251 (18.6)|
|Model||Hazard Ratio||95% CI||P Value|
|Waist circumference <98.0 cm||0.67||0.57 to 0.82||<0.0001|
|CVH score||0.75||0.62 to 0.92||0.006|
|CVH score+demographic factorsa||0.79||0.66 to 0.97||0.022|
|CVH score+demographic factors+hs‐CRP||0.83||0.68 to 1.02||0.081|
|CVH score+demographic factors+hs‐CRP+HOMA‐IR||0.67||0.57 to 0.82||<0.0001|
|Waist circumference ≥98.0 cm||0.96||0.84 to 1.10||0.56|
|CVH score||0.97||0.84 to 1.13||0.71|
|CVH score+demographic factors||0.97||0.84 to 1.13||0.70|
|CVH score+demographic factors+hs‐CRP||1.01||0.87 to 1.17||0.92|
|CVH score+demographic factors+hs‐CRP+HOMA‐IR||0.96||0.84 to 1.10||0.56|
Association Between Individual Ideal CVH Metrics and Diabetes Mellitus
Table S1 displays the results of multivariable analysis for each individual CVH metric with incident diabetes mellitus. Only BMI and BP were associated with diabetes mellitus after adjustment for demographic factors and hs‐CRP. The association between ideal PA and diabetes mellitus was significant in the unadjusted model, but this association was totally explained by age, sex, education, and income. Smoking status, total cholesterol, and diet were not associated with diabetes mellitus.
Number Needed to Prevent
Based on a 5‐year risk, we estimated the number of participants who need to have increments of the ideal CVH score in order to prevent 1 case of incident diabetes mellitus. In the unadjusted model, compared to participants with no ideal CVH metric, the number of participants needed to have 1, 2, 3, and ≥4 ideal CVH metrics to prevent 1 case of incident diabetes mellitus were 167, 56, 29, and 20, respectively. After accounting for age, sex, education, income, hs‐CRP, and HOMA‐IR, the number of participants needed to have 1, 2, 3, and ≥4 ideal CVH metrics to prevent 1 case of incident diabetes mellitus were 279, 156, 55, and 49, respectively.
In this contemporary middle‐aged black cohort, we found that the AHA‐defined ideal CVH metrics for CVD and stroke prevention are associated with a decreased risk of incident type 2 diabetes mellitus. Specifically, the risk of developing diabetes mellitus decreased by 17% for each additional baseline ideal CVH metric that a participant had, and participants with at least 3 CVH metrics had a 37% risk reduction, after accounting for demographic risk factors and systemic inflammation. Our findings also suggest that the association between the ideal CVH metrics and incident diabetes mellitus is, in large part, explained by insulin resistance (measured by HOMA‐IR) and may differ according to a participant's waist circumference. Our findings differ from those in the MESA study, in which the association was less robust among blacks. The MESA study did not take into account the effects of systemic inflammation and insulin resistance, which we found to play a significant role in the association between ideal CVH and incident diabetes mellitus.
The prevalence of ideal CVH in our sample was very low. No participant met all 6 ideal CVH goals and less than 1% of them met 5 of the goals used in our analyses. Three quarters of participants met only 1 or 2 ideal CVH metrics goals. Previous studies have reported lower prevalence values especially for persons with 1 or 2 ideal CVH metrics.7, 25, 26, 27 Folsom et al, using data from the ARIC study,9 reported that around 54% of black men and 55% of black women had between 1 and 2 ideal CVH metrics. Prevalence values in our study may be higher because of the fact that we used 6 of the 7 CVH metrics in defining ideal health. A previous report from the JHS cohort, using all 7 CVH metrics, showed a prevalence of around 56% among those with 1 or 2 ideal CVH metrics, similar to previous studies.8
Our findings support a protective role of ideal CVH, as defined by the AHA, on diabetes mellitus risk among blacks and are consistent with previous reports on this association in other populations using data from the SHFS10 and MESA.11 In the SHFS, a lower odds of developing diabetes mellitus was reported among American Indians who met at least 2 of the ideal CVH goals, compared with those who met only 1 ideal CVH goal or none. In MESA, an interaction by race/ethnicity was found. Ideal CVH was associated with incident diabetes mellitus in the general MESA study population, but when stratified by race/ethnicity, the association was less robust among blacks and Hispanics compared with whites and Chinese. Findings from our study showed that the association between ideal CVH and incident diabetes mellitus is, in fact, robust and consistent among blacks and independent of age, sex, socioeconomic status, and the presence of systemic inflammation. The magnitude of risk reduction differs across studies; compared with our study where participants with 3 or more ideal CVH metrics had a 37% reduction in risk of diabetes mellitus, in the SHFS and in MESA, American Indians, non‐Hispanic whites, and Chinese Americans with 4 or more ideal CVH metrics had an 89%, 87%, and 88% reduction in risk, respectively. These differences may be explained, in part, by differences in methodology across studies, but may also reflect potential race/ethnic differences in the association between ideal CVH and incident diabetes mellitus. In recent genetic studies, single‐nucleotide polymorphism variants in the APOE locus, which has been associated with clinical ideal CVH,28 were found to be associated with multiple components of metabolic syndrome in blacks.29
When the CVH metrics were considered individually, the association between ideal CVH and incident diabetes mellitus appeared to be driven primarily by BMI, which could explain why insulin resistance plays a significant role in the association. There were no apparent PA/diabetes mellitus, diet/diabetes mellitus, smoking/diabetes mellitus, and total cholesterol/diabetes mellitus associations. Similar findings were reported in the SHFS and MESA.10, 11 Given that less than 1% of our participants met the dietary targets for ideal CVH, we had limited power to adequately examine the association between diet and diabetes mellitus. As such, our results for healthy diet need to be interpreted with caution. The effects of diet and PA may not have been adequately captured in our sample by questionnaires, and their true effects may not be independent of BMI. There is evidence that adherence to the Dietary Approach to Stop Hypertension diet reduces insulin resistance30 and the risk of incident diabetes mellitus.31 Although only 2 (BMI and BP) of 6 CVH metrics were individually associated with diabetes mellitus risk, the finding of a significant trend in the association between categories of the CVH score (0–1, 2, and 3+) and incident diabetes mellitus suggests that the beneficial effects of the CVH metrics on diabetes mellitus risk is synergistic and cumulative. In fact, when compared with those with 0 to 1 ideal CVH metric, having 3 or more ideal CVH metrics was associated with a 37% reduction in diabetes mellitus risk. Other risk factors taken into account, 55 people in the population would need to meet 3 AHA ideal CVH targets to prevent 1 case of diabetes mellitus, and 49 people would need to meet 4 or more AHA ideal CVH targets to prevent 1 case of diabetes mellitus.
Our results suggest that less‐than‐ideal levels of AHA CVH may reflect a state of insulin resistance, which would promote the occurrence of not only diabetes mellitus, but also probably of CVDs. Although there was no apparent association between ideal PA and ideal diet with incident diabetes mellitus in our analysis, both have been associated with a reduction in diabetes mellitus risk in previous studies.32, 33, 34, 35 Data from the Diabetes Prevention Program showed that weight loss achieved through PA and a healthy low‐fat, low‐calorie diet reduced the incidence of diabetes mellitus by 61% in blacks, when compared with placebo.5 Diabetes mellitus risk reduction was greatest in the lifestyle group, compared with the other groups. The reduction in diabetes mellitus risk is thought to be mediated by improvements in insulin sensitivity, as a result of weight loss and PA. Although there were no differences in CVD events (myocardial infarction, stroke, and coronary revascularization) after 3 years between treatment arms in the Diabetes Prevention Program, there were significant reductions in cardiometabolic risk factors in the lifestyle arm, including high triglyceride levels, hypertension, low high‐density lipoprotein, and small dense low‐density lipoprotein.36 In other clinical trials with longer durations of follow‐up, such cardiometabolic risk factor reductions have resulted in reductions in both fatal and nonfatal CVD events.37, 38
The association between CVH metrics and diabetes mellitus differed according to waist circumference levels. The finding of a significant association among participants in the lower median of waist circumference, but not among those in the upper median, may simply reflect a greater heterogeneity in the distribution of ideal CVH metrics as indicated by the differences observed in the proportions of ideal CVH between participants in the lower and upper median of waist circumference. This pattern warrants further investigation and replication in other cohorts.
The strengths of our analysis include the use of a sizable cohort of blacks with more incident diabetes mellitus events compared to previous reports in the same population.11 Not only were we able to more robustly assess the extent of the associations between ideal CVH and diabetes mellitus, we also conducted a more comprehensive ascertainment of diabetes mellitus (using the American Diabetes Association 2010 criteria, including HbA1c level), thus reducing the potential for misclassifications. Furthermore, in contrast to previous studies, we explored the potential role of insulin resistance, measured by HOMA‐IR, on the observed association between CVH metrics and diabetes mellitus. Some limitations of the present study should be addressed. Because of the small number of participants and events for some CVH metrics (diet and smoking), we were unable to fully explore the individual effects of these health behaviors on incident diabetes mellitus. Also, it is difficult to assess the true independent effects of inter‐related parameters such as BMI, PA, diet, BP, and HOMA‐IR. In addition, although validated instruments were used in the JHS to measure PA39 and diet,40 an association between PA and diet with diabetes mellitus may not have been apparent owing to the self‐reported nature of these measures. Because some of the CVH metrics were self‐reported (PA, diet, and smoking), residual confounding could be present. Time to incident diabetes mellitus was interval‐censored and the midpoint between 2 study visits was imputed as the time to incident diabetes mellitus. This approach of imputing time to event has been validated in previous studies.41, 42
Our study showed that each additional ideal AHA CVH metric was associated with a 17% reduction in the risk of incident diabetes mellitus in blacks, after accounting for demographic factors and systemic inflammation. The association between the ideal CVH metrics and incident diabetes mellitus may be synergistic and cumulative and was largely explained by insulin resistance. These data support the application the concept of ideal CVH as defined by the AHA in diabetes mellitus prevention in this population.
Our findings, which support a protective role of ideal CVH metrics (healthy BMI, healthy diet, nonsmoking status, normal total cholesterol, normal BP, and PA) have implications from a primary prevention standpoint. The overall low prevalence of higher levels of ideal CVH, as defined by the AHA, among blacks is alarming and calls for targeted public health interventions to improve on disparities for CVD and diabetes mellitus among blacks, a population that is disproportionately affected by these 2 conditions.
Our study has shown that if people meet increasing targets of AHA ideal CVH metrics, diabetes mellitus can be prevented. It would need 55 people in the population to meet 3 AHA ideal CVH targets to prevent 1 case of diabetes mellitus and 49 people to meet 4 or more AHA ideal CVH targets to prevent 1 case of diabetes mellitus. Interventions with a primary focus of improving health behaviors so that more blacks achieve the AHA targets for ideal CVH are important and may have a significant impact in reducing diabetes mellitus risk. Our findings suggest that the effects of these ideal CVH metrics may be cumulative and synergistic. In this light, future lifestyle or behavioral interventions should take into account as many AHA ideal CVH metrics as possible, and not just 1 particular metric, to potentially increase the impact of the interventions on reducing diabetes mellitus risk. Also, future joint efforts for CVD and diabetes mellitus prevention among blacks may provide 1 effective and cost‐efficient strategy for public health experts and policy makers to tackle the growing challenge of diabetes mellitus and CVD.
The authors thank all study participants and staff of the Jackson Heart Study for their contributions to the development and completion of this research project.
Sources of Funding
The JHS Diabetes and Obesity Working Group is supported by 1 R01 HL117285‐01 from the National Heart, Lung, and Blood Institute. The Jackson Heart Study is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, and HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. The views expressed in this article are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the US Department of Health and Human Services.
Table S1. Unadjusted and Adjusted Hazard Ratios and 95% CIs for Incident Diabetes Mellitus for Each Individual American Heart Association Cardiovascular Health Metric
Parts of this study were presented as an oral presentation at the American Diabetes Association 76th Scientific Sessions, June 10–14, 2016, in New Orleans, LA.
- 1 Centers for Disease Control and Prevention . National Diabetes Statistics Report: Estimates of Diabetes and its Burden in the United States. Atlanta, GA: US Department of Health and Human Services; 2014.Google Scholar
- 2 Association AD. Nutrition recommendations and interventions for diabetes: a position statement of the American diabetes association. Diabetes Care. 2008; 31:S61–S78.Google Scholar
- 3 Nguyen N, Nguyen X‐M, Lane J, Wang P. Relationship between obesity and diabetes in a US adult population: findings from the National Health and Nutrition Examination Survey, 1999–2006. Obes Surg. 2011; 21:351–355.Google Scholar
- 4 The Diabetes Prevention Program Research G . The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002; 25:2165–2171.Google Scholar
- 5 Diabetes Prevention Program Research Group . Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346:393–403.Google Scholar
- 6 Lloyd‐Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, Greenlund K, Daniels S, Nichol G, Tomaselli GF, Arnett DK, Fonarow GC, Ho PM, Lauer MS, Masoudi FA, Robertson RM, Roger V, Schwamm LH, Sorlie P, Yancy CW, Rosamond WD; Force obotAHASPT, Committee S . Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation. 2010; 121:586–613.Google Scholar
- 7 Bambs C, Kip KE, Dinga A, Mulukutla SR, Aiyer AN, Reis SE. Low prevalence of “ideal cardiovascular health” in a community‐based population: the heart strategies concentrating on risk evaluation (Heart SCORE) study. Circulation. 2011; 123:850–857.Google Scholar
- 8 Djousse L, Petrone AB, Blackshear C, Griswold M, Harman JL, Clark CR, Talegawkar S, Hickson DA, Gaziano JM, Dubbert PM, Correa A, Tucker KL, Taylor HA. Prevalence and changes over time of ideal cardiovascular health metrics among African‐Americans: the Jackson Heart Study. Prev Med. 2015; 74:111–116.Google Scholar
- 9 Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol. 2011; 57:1690–1696.Google Scholar
- 10 Fretts AM, Howard BV, McKnight B, Duncan GE, Beresford SAA, Mete M, Zhang Y, Siscovick DS. Life's simple 7 and incidence of diabetes among American Indians: the Strong Heart Family Study. Diabetes Care. 2014; 37:2240–2245.Google Scholar
- 11 Joseph JJ, Echouffo‐Tcheugui JB, Carnethon MR, Bertoni AG, Shay CM, Ahmed HM, Blumenthal RS, Cushman M, Golden SH. The association of ideal cardiovascular health with incident type 2 diabetes mellitus: the Multi‐Ethnic Study of Atherosclerosis. Diabetologia. 2016; 59:1893–1903.Google Scholar
- 12 Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the US: a diabetes belt. Am J Prev Med. 2011; 40:434–439.Google Scholar
- 13 Fuqua SR, Wyatt SB, Andrew ME, Sarpong DF, Henderson FR, Cunningham MF, Taylor HA. Recruiting African‐American research participation in the Jackson Heart Study: methods, response rates, and sample description. Ethn Dis2005; 15(S6):18–29.Google Scholar
- 14 Taylor HA, Wilson JG, Jones DW, Sarpong DF, Srinivasan A, Garrison RJ, Nelson C, Wyatt SB. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn Dis. 2005; 15:S6‐4–S6‐17Google Scholar
- 15 Carpenter MA, Crow R, Steffes M, Rock W, Heilbraun J, Evans G, Skelton T, Jensen R, Sarpong D. Laboratory, reading center, and coordinating center data management methods in the Jackson Heart Study. Am J Med Sci. 2004; 328:131–144.Google Scholar
- 16 Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982; 36:936–942.Google Scholar
- 17 Richardson MT, Ainsworth BE, Wu HC, Jacobs DR, Leon AS. Ability of the Atherosclerosis Risk in Communities (ARIC)/Baecke Questionnaire to assess leisure‐time physical activity. Int J Epidemiol. 1995; 24:685–693.Google Scholar
- 18 Dubbert PM, Carithers T, Ainsworth BE, Taylor HA, Wilson G, Wyatt SB. Physical activity assessment methods in the Jackson Heart Study. Ethn Dis. 2005; 15:S6‐56–S6‐61.Google Scholar
- 19 Ainsworth BE, Sternfeld B, Richardson MT, Jackson K. Evaluation of the kaiser physical activity survey in women. Med Sci Sports Exerc. 2000; 32:1327–1338.Google Scholar
- 20 Tucker KL, Maras J, Champagne C, Connell C, Goolsby S, Weber J, Zaghloul S, Carithers T, Bogle ML. A regional food‐frequency questionnaire for the US Mississippi Delta. Public Health Nutrition. 2005; 8:87–96.Google Scholar
- 21 Carithers T, Dubbert PM, Crook E, Davy B, Wyatt SB, Bogle ML, Taylor HA, Tucker KL. Dietary assessment in African Americans: methods used in the Jackson Heart Study. Ethn Dis. 2005; 15:S6‐49–S6‐55.Google Scholar
- 22 Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta‐cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28:412–419.Google Scholar
- 23 Fox ER, Benjamin EJ, Sarpong DF, Rotimi CN, Wilson JG, Steffes MW, Chen G, Adeyemo A, Taylor JK, Samdarshi TE, Taylor HA. Epidemiology, heritability, and genetic linkage of C‐reactive protein in African Americans (from the Jackson Heart Study). Am J Cardiol. 2008; 102:835–841.Google Scholar
- 24 Effoe VS, Correa A, Chen H, Lacy ME, Bertoni AG. High‐sensitivity C‐reactive protein is associated with incident type 2 diabetes among African Americans: the Jackson Heart Study. Diabetes Care. 2015; 38:1694–1700.Google Scholar
- 25 Shay CM, Ning H, Allen NB, Carnethon MR, Chiuve SE, Greenlund KJ, Daviglus ML, Lloyd‐Jones DM. Status of cardiovascular health in US adults: prevalence estimates from the National Health and Nutrition Examination Surveys (NHANES) 2003–2008. Circulation. 2012; 125:45–56.Google Scholar
- 26 Dong C, Rundek T, Wright CB, Anwar Z, Elkind MS, Sacco RL. Ideal cardiovascular health predicts lower risks of myocardial infarction, stroke, and vascular death across whites, blacks, and hispanics: the northern Manhattan Study. Circulation. 2012; 125:2975–2984.Google Scholar
- 27 Yang Q, Cogswell ME, Flanders WD, Hong Y, Zhang Z, Loustalot F, Gillespie C, Merritt R, Hu FB. Trends in cardiovascular health metrics and associations with all‐cause and CVD mortality among US adults. JAMA. 2012; 307:1273–1283.Google Scholar
- 28 Allen NB, Lloyd‐Jones D, Hwang SJ, Rasmussen‐Torvik L, Fornage M, Morrison AC, Baldridge AS, Boerwinkle E, Levy D, Cupples LA, Fox CS, Thanassoulis G, Dufresne L, Daviglus M, Johnson AD, Reis J, Rotter J, Palmas W, Allison M, Pankow JS, O'Donnell CJ. Genetic loci associated with ideal cardiovascular health: a meta‐analysis of genome‐wide association studies. Am Heart J. 2016; 175:112–120.Google Scholar
- 29 Carty CL, Bhattacharjee S, Haessler J, Cheng I, Hindorff LA, Aroda V, Carlson CS, Hsu C‐N, Wilkens L, Liu S, Selvin E, Jackson R, North KE, Peters U, Pankow JS, Chatterjee N, Kooperberg C. Analysis of metabolic syndrome components in >15 000 African Americans identifies pleiotropic variants: results from the population architecture using genomics and epidemiology study. Circ Cardiovasc Genet. 2014; 7:505–513.Google Scholar
- 30 Shirani F, Salehi‐Abargouei A, Azadbakht L. Effects of dietary approaches to stop hypertension (dash) diet on some risk for developing type 2 diabetes: a systematic review and meta‐analysis on controlled clinical trials. Nutrition2013; 29:939–947.Google Scholar
- 31 Liese AD, Nichols M, Sun X, D'Agostino RB, Haffner SM. Adherence to the DASH Diet is inversely associated with incidence of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes Care. 2009; 32:1434–1436.Google Scholar
- 32 Balkau B, Mhamdi L, Oppert JM, Nolan J, Golay A, Porcellati F, Laakso M, Ferrannini E; Group E‐RS . Physical activity and insulin sensitivity: the RISC study. Diabetes. 2008; 57:2613–2618.Google Scholar
- 33 Cespedes EM, Hu FB, Tinker L, Rosner B, Redline S, Garcia L, Hingle M, Van Horn L, Howard BV, Levitan EB, Li W, Manson JE, Phillips LS, Rhee JJ, Waring ME, Neuhouser ML. Multiple healthful dietary patterns and type 2 diabetes in the women's health initiative. Am J Epidemiol. 2016; 183:622–633.Google Scholar
- 34 Jacobs S, Harmon BE, Boushey CJ, Morimoto Y, Wilkens LR, Le Marchand L, Kroger J, Schulze MB, Kolonel LN, Maskarinec G. A priori‐defined diet quality indexes and risk of type 2 diabetes: the multiethnic cohort. Diabetologia. 2015; 58:98–112.Google Scholar
- 35 Mayer‐Davis EJ, D'Agostino R, Karter AJ, Haffner SM, Rewers MJ, Saad M, Bergman RN. Intensity and amount of physical activity in relation to insulin sensitivity: the insulin resistance atherosclerosis study. JAMA : the journal of the American Medical Association. 1998; 279:669–674.Google Scholar
- 36 Ratner R, Goldberg R, Haffner S, Marcovina S, Orchard T, Fowler S, Temprosa M. Impact of intensive lifestyle and metformin therapy on cardiovascular disease risk factors in the diabetes prevention program. Diabetes Care. 2005; 28:888–894.Google Scholar
- 37 Haffner SM, Alexander CM, Cook TJ, Boccuzzi SJ, Musliner TA, Pedersen TR, Kjekshus J, Pyorala K. Reduced coronary events in simvastatin‐treated patients with coronary heart disease and diabetes or impaired fasting glucose levels: subgroup analyses in the Scandinavian Simvastatin Survival Study. Arch Intern Med. 1999; 159:2661–2667.Google Scholar
- 38 Goldberg RB, Mellies MJ, Sacks FM, Moye LA, Howard BV, Howard WJ, Davis BR, Cole TG, Pfeffer MA, Braunwald E. Cardiovascular events and their reduction with pravastatin in diabetic and glucose‐intolerant myocardial infarction survivors with average cholesterol levels: subgroup analyses in the cholesterol and recurrent events (care) trial. The Care Investigators. Circulation. 1998; 98:2513–2519.Google Scholar
- 39 Smitherman TA, Dubbert PM, Grothe KB, Sung JH, Kendzor DE, Reis JP, Ainsworth BE, Newton RL, Lesniak KT, Taylor HA. Validation of the Jackson Heart study physical activity survey in African Americans. J Phys Act Health. 2009; 6(Suppl 1):S124–S132.Google Scholar
- 40 Carithers TC, Talegawkar SA, Rowser ML, Henry OR, Dubbert PM, Bogle ML, Taylor HA, Tucker KL. Validity and calibration of food frequency questionnaires used with African‐American adults in the Jackson Heart Study. J Am Diet Assoc. 2009; 109:1184–1193.Google Scholar
- 41 Lindsey JC, Ryan LM. Tutorial in biostatistics methods for interval‐censored data. Stat Med. 1998; 17:219–238.Google Scholar
- 42 Leffondre K, Touraine C, Helmer C, Joly P. Interval‐censored time‐to‐event and competing risk with death: is the illness‐death model more accurate than the Cox model?Int J Epidemiol. 2013; 42:1177–1186.Google Scholar