We examined the association of both aldosterone and renin, with insulin resistance, β-cell function, and incident diabetes in a large African American cohort. Renin-angiotensin-aldosterone system with higher levels of aldosterone and renin is associated with insulin resistance, compensatory increased β-cell function and incident diabetes in African Americans.
Abstract
Context:
Previous research has suggested that activation of the renin-angiotensin-aldosterone system may promote insulin resistance and β-cell dysfunction, but the association with incident diabetes in African Americans is unknown.
Objective:
We examined the association of aldosterone and renin with insulin resistance, β-cell function, and incident diabetes in a large African American cohort.
Design:
The Jackson Heart Study is a prospective study of the development and progression of cardiovascular disease in African Americans.
Setting:
Participants were recruited from the tricounty area of metropolitan Jackson, Mississippi.
Participants:
A total of 5301 African American adults, aged 21–94 years, were assessed at baseline and through 12 years of follow-up. Data on aldosterone, renin, and risk factors were collected at baseline (2000–2004). Diabetes (fasting glucose ≥ 126 mg/dL, physician diagnosis, use of diabetes drugs, or glycated hemoglobin ≥ 6.5%) was assessed at baseline and through 12 years of follow-up. Participants were excluded for missing data on baseline covariates or diabetes follow-up. Cox regression was used to estimate hazard ratios (HR) for incident diabetes using sequential modeling adjusting for age, sex, education, occupation, systolic blood pressure, current smoking, physical activity, dietary intake, and body mass index.
Exposures:
Aldosterone, renin, and diabetes risk factors were measured.
Outcomes:
Outcomes included the homeostatic model assessment of insulin resistance (HOMA-IR) and incident diabetes.
Results:
Among 3234 participants over a median of 8.0 years of follow-up, there were 554 cases of incident diabetes. Every 1% increase in log-transformed aldosterone was associated with a 0.18% higher log-transformed HOMA-IR in cross-sectional analyses of nondiabetic participants (P < .001). Log-transformed aldosterone and renin levels in the fifth vs first quintile were associated with a 78% (HR 1.78, 95% confidence interval 1.35–2.34) and 35% (HR 1.35, 95% confidence interval 1.06–1.72) increase in diabetes risk, respectively, in fully adjusted models.
Conclusions:
Activation of the renin-angiotensin-aldosterone system may play a significant role in the development of insulin resistance and diabetes in African Americans.
Type 2 diabetes mellitus (diabetes) is more prevalent among African Americans (AAs) than among non-Hispanic whites (NHWs) (1, 2) and is associated with a 50%–100% higher morbidity and mortality (2, 3). Although diabetes incidence has plateaued in NHWs during the period of 1980–2012, it continues to rise among AAs (1), indicating the need to identify novel preventive intervention targets. AAs also have a higher prevalence of hypertension and its complications compared with NHWs (4–6). High hypertension prevalence in AAs may be a byproduct of biological adaptations of the renin-angiotensin-aldosterone system (RAAS) that conferred a survival advantage against dehydration and death during the trans-Atlantic slave trade (7). Although the role of RAAS activation in hypertension is established (6), its role in the pathogenesis of diabetes in AAs has not been evaluated. Animal data suggest a role for RAAS in the development of both hypertension and diabetes, providing a common mechanism contributing to both conditions in AAs (8).
In the Atherosclerosis Risk in Communities study, lower potassium levels, a potential marker of elevated aldosterone levels, were associated with higher diabetes risk; lower serum potassium in AAs appeared to explain 18% of the excess risk of diabetes in AAs (9). The relationship between serum potassium and incident diabetes was thought to be mediated through low dietary potassium intake, but an association between serum aldosterone and incident diabetes would provide a physiological mechanism for this observation (9). Cell- and rodent-based studies suggest that aldosterone excess may impair insulin secretion, insulin action, or both (10). Whether and to what extent the RAAS relates to the risk of developing insulin resistance and diabetes in AAs is unknown. Therefore, we examined the relationship of both aldosterone and renin, with insulin resistance, β-cell function, and prevalent and incident diabetes in community-dwelling AAs.
Materials and Methods
Study participants
The Jackson Heart Study (JHS) is a prospective study of the development and progression of cardiovascular disease in a cohort of 5301 AA adults, aged 21–94 years from the tri-county area of metropolitan Jackson, Mississippi. Enrollment and baseline examinations were performed between 2000 and 2004. There were two subsequent in-person follow-up examinations in 2005–2008 and 2009–2013. Details about the study design, recruitment, and methods used have been described elsewhere (11). For the cross-sectional analysis, participants were excluded for missing the following data at baseline: diabetes status (n = 61), aldosterone (n = 52), and covariates (n = 193). Participants with nonphysiological hormonal variable values were excluded: aldosterone greater than 100 ng/dL (n = 2) and homeostasis model of β-cell function (HOMA-β) less than 0 (n = 2). For the longitudinal analysis, after the cross-sectional exclusions, participants were excluded if they had diabetes at baseline (n = 1081) or were missing diabetes data on follow-up (n = 676). After these exclusions, 4991 participants were included in cross-sectional analyses and 3234 in longitudinal analyses. The JHS was approved by the institutional review boards of the participating institutions and written informed consent was obtained from all subjects.
Exposure: aldosterone and renin
Fasting blood samples were drawn in the supine position and processed using a standardized protocol. Plasma and serum were prepared from samples by sedimentation in a refrigerated centrifuge within 2 hours of blood collection, stored at −70°C, and sent to central laboratories (University of Minnesota) (11, 12). Serum aldosterone was measured by a RIA (Siemens), and the intraassay coefficients of variation are reported elsewhere (13). Renin was measured at baseline using immunoradiometric assays of either plasma renin activity (PRA) in nanograms per milliliter per hour (n = 2252) or active renin mass concentration (ARMC) in picograms per milliliter (n = 2739) with intraassay coefficients of variation of 8.0% and 5.4%, respectively. PRA and ARMC were highly correlated in a subset of 99 participants (Spearman rank correlation, ρ = 0.85;P < .0001); thus, PRA and ARMC were divided into quintiles, which were combined for analysis.
Outcome: diabetes status, estimated β-cell function and insulin resistance
Fasting glucose and insulin concentrations were measured on a Vitros 950 or 250 Ortho-Clinical Diagnostics analyzer using standard procedures that met the College of American Pathologists accreditation requirement (12). A HPLC system (Tosoh Corp) was used to measure glycosylated hemoglobin A1c (HbA1c) concentrations. Diabetes was defined as having a HbA1c level of 6.5% or greater (48 mmol/mol), having a fasting blood glucose of 126 mg/dL or greater, taking diabetes medications, or having a self-reported physician diagnosis (14). Persons without diabetes at baseline, meeting criteria for diabetes at one of the two subsequent examinations were considered to have incident diabetes. Insulin resistance and β-cell function were estimated using the following formula: homeostatic model assessment of insulin resistance (HOMA-IR) = (fasting plasma glucose [millimoles per liter] × fasting plasma insulin [milliunits per milliliter])/22.5 and HOMA-β = (20 × fasting plasma insulin)/(fasting plasma glucose − 3.5)% (15).
Baseline assessments
Baseline information was obtained during clinic visits or at home using standardized questionnaires including the following: demographics, occupation (management/professional vs not), level of education (bachelor's degree or higher vs less than a bachelor's degree), tobacco use (current smoking vs not), alcohol use (in the past 12 mo vs not), medical conditions, and current prescription medication usage. Calibrated devices were used by certified technicians and nurses to measure participants' weight, waist circumference (average of two measurements around the umbilicus), and height. Body mass index (BMI) was calculated as weight (kilograms)/height 2 (meters). Resting seated blood pressure (BP) was measured twice at 5-minute intervals using an appropriately sized cuff with standard Hawksley random-zero instruments, and measurements were averaged for analysis. Hypertension was defined as systolic BP of 140 mm Hg or greater, diastolic BP of 90 mm Hg or greater, or use of antihypertensive therapy. Serum concentration of adiponectin was measured as total adiponectin with interassay coefficient reported elsewhere (16).
Physical activity was defined according to the American Heart Association (AHA) categorization (17) as poor health (0 min of moderate and vigorous activity), intermediate health (> 0 min but < 150 min of moderate activity), > 0 min but < 75 min of vigorous activity, or > 0 min but < 150 min of combined moderate and vigorous activity), and ideal health (>150 min of moderate activity, > 75 min of vigorous activity, or > 150 min of combined moderate and vigorous activity). Dietary intake was defined according to the AHA categorization, (17) using a validated 158-item food frequency questionnaire administered face to face by trained AA interviewers (18). The questionnaire had some slight differences compared with the AHA categorization regarding units of servings, requiring modification of the metrics. Components of the ideal diet were as follows: fruits and vegetables of 4.5 cups/d or more, fish of two 3.5-oz servings or more per week (nonfried), fiber-rich whole grains of three 1-oz-equivalent servings/d or more, sodium less than 1500 mg/d, and sugar-sweetened beverages of 450 kcal/wk or less (36 oz). Participants were given one point per dietary component at goal for a total score ranging from 0 to 5. Participants were classified as ideal (four or five of the five metrics), intermediate (two or three of the five metrics), or poor (none or one of the five metrics). Daily sodium intake in milligrams per day was also assessed using the food frequency questionnaire.
Statistical analysis
Due to the nonnormal distribution of aldosterone, PRA, ARMC, HOMA-IR, and HOMA-β, these variables were log transformed before analyses were performed. To explore the potential nonlinear relationships and evaluate for dose-response relationships, aldosterone and renin were divided into quintiles. Descriptive statistics were used to compare the baseline characteristics of participants by quintiles of log-transformed aldosterone using a one-way ANOVA for normally distributed continuous variables, Mann-Whitney and Kruskal-Wallis test for nonnormally distributed continuous variables (aldosterone and renin), and the χ2 test for categorical variables.
Cross-sectional analyses
We used a multivariable linear regression to examine the cross-sectional association between log aldosterone and the dependent variables log HOMA-IR and log HOMA-β in participants without prevalent diabetes (n = 3757). We used a logistic regression to examine the association between log aldosterone and prevalent diabetes at baseline. Sequential multivariable adjustment modeling was performed: model 1, age, sex, education, and current occupation status; model 2, model 1 + systolic BP (millimeters of mercury); model 3, model 2 + smoking, AHA physical activity, AHA dietary intake, and alcohol use; model 4, model 3 + BMI (kilograms per square meter); and model 5, model 3 + adiponectin. The adjustments for BMI and adiponectin were performed due to previous studies showing an association between aldosterone and visceral adiposity (19), which is independently associated with insulin resistance and diabetes (20).
Longitudinal analyses
We defined the time of incident diabetes as the midpoint between the last examination without diabetes and the examination at which diabetes developed. For participants who remained free of diabetes, the follow-up time was censored at their last available visit. Imputing the time to the onset of diabetes as the midpoint between the two study visits has been used in prior studies (21). Cox proportional hazard modeling was used to estimate hazard ratios (HR) for incident diabetes by quintiles of log aldosterone and log renin. Identical sequential modeling was performed as above for cross-sectional analyses.
We performed a series of sensitivity analyses to confirm the robustness of our results including limiting the analytic sample to the following participants: 1) those not taking medications known to affect the RAAS including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and mineralocorticoid receptor blockers; 2) those with hypertension not on medications known to effect RAAS; and 3) those without hypertension. We performed renin analyses on both of the assays separately. We tested for age and sex interactions of the association between log aldosterone with incident diabetes by inserting multiplicative interaction term models and using the likelihood ratio test. Statistical significance was defined as a two-sided alpha < .05. Analyses were performed using Stata version 13.1 (Statacorp).
Results
Baseline characteristics
The baseline characteristics of the 4991 participants (cross-sectional) analyses and 3234 participants (longitudinal) across quintiles of log aldosterone are presented in Table 1 and Supplemental Table 1. Compared with those in the lowest quintiles, participants in higher quintiles of log aldostersone at baseline were more likely to be women and had a higher BMI, waist circumference, systolic and diastolic BPs, fasting glucose, HOMA-IR, HOMA-β, and renin and lower levels of adiponectin (all comparisons, P < .05).
Table 1.
Characteristics of Participants in the Jackson Heart Study by Log Aldosterone in Quintiles at Baseline
| Baseline Characteristicsa | All (n = 4991) | 1 (n = 1044) | 2 (n = 974) | 3 (n = 1016) | 4 (n = 963) | 5 (n = 994) | P Valuea |
|---|---|---|---|---|---|---|---|
| Age | 55.1 (12.8) | 55.8 (12.9) | 54.8 (13.0) | 54.4 (12.7) | 54.9 (12.7) | 55.8 (12.7) | .06 |
| Female, sex | 63.2% | 72.1% | 64.7% | 60.3% | 57.5% | 60.9% | <.001 |
| Education > bachelor's degree | 32.6% | 29.6% | 34.3% | 32.5% | 34.8% | 32.0% | .10 |
| Occupation, management/professional | 35.7% | 34.6% | 37.2% | 35.5% | 35.9% | 35.4% | .82 |
| Current smoking | 13.1% | 13.6% | 12.3% | 13.4% | 13.6% | 12.8 | .89 |
| Current alcohol use | 46.2% | 41.3% | 47.8% | 49.4% | 46.9% | 45.9% | .003 |
| Ideal AHA physical activityb | 19.5% | 16.5% | 20.3% | 21.1% | 19.3% | 20.4% | .17 |
| Ideal AHA dietary intakeb | 0.9% | 0.5% | 1.3% | 1.0% | 1.3% | 0.7% | .01 |
| Dietary sodium intake total, mg/dc | 3447 (1523) | 3411 (1501) | 3406 (1479) | 3495 (1543) | 3520 (1525) | 3404 (1561) | .31 |
| BMI, kg/m2 | 31.8 (7.3) | 31.6 (7.2) | 31.2 (7.4) | 31.6 (7.3) | 31.7 (7.2) | 32.7 (7.0) | <.001 |
| Waist circumference, cm | 100.8 (16.2) | 99.3 (16.6) | 98.8 (15.6) | 100.6 (16.2) | 101.2 (16.3) | 103.9 (15.7) | <.001 |
| Systolic BP, mm Hg | 127.0 (18.3) | 126.5 (18.2) | 125.1 (18.0) | 126.4 (17.6) | 128.1 (18.4) | 129.0 (19.3) | <.001 |
| Diastolic BP, mm Hg | 78.9 (10.6) | 77.4 (10.2) | 77.7 (10.3) | 78.7 (10.3) | 80.2 (10.7) | 80.7 (11.0) | <.001 |
| Glucose, mg/dL | 101.9 (38.0) | 99.4 (36.8) | 98.6 (31.2) | 100.2 (34.9) | 101.8 (34.3) | 109.4 (49.0) | <.001 |
| Adiponectin, ng/mL | 5393.4 (4275.6) | 6238.7 (4543.5) | 5800.0 (4838.2) | 5029.2 (3833.4) | 5138.2 (4262.4) | 4726.9 (3615.0) | <.001 |
| HOMA-IRd | 3.5 (2.3) | 2.9 (1.6) | 3.3 (1.8) | 3.5 (1.9) | 3.8 (2.3) | 4.4 (3.2) | <.001 |
| HOMA-βd | 222.5 (128.8) | 206.0 (120.9) | 212.7 (107.3) | 219.3 (118.7) | 226.4 (127.5) | 252.1 (162.1) | <.001 |
| Aldosterone, ng/dL | 5.73 (4.80) | 1.94 (0.08) | 2.97 (0.39) | 4.44 (0.48) | 6.59 (0.80) | 12.92 (6.14) | <.001 |
| PRA, ng/mL·he | 1.59 (5.69) | 0.79 (2.23) | 1.37 (4.40) | 1.16 (3.55) | 1.84 (8.31) | 2.74 (7.41) | <.001 |
| ARMC, pg/mLf | 14.18 (51.27) | 12.13 (49.65) | 13.22 (50.40) | 11.21 (24.03) | 12.73 (41.57) | 22.19 (77.16) | .003 |
| Prevalence of diabetes | 22% | 17.6% | 18.9% | 19.8% | 18.0% | 25.7% | <.001 |
Mean (SD) or percentages are listed, P values were calculated using χ2 (categorical variables), ANOVA (parametric continuous variables), and Kruskal-Wallis test (nonparametric continuous variables).
Ideal physical activity and dietary intake recommendations were defined by AHA 2020 guidelines. Physical activity was considered ideal if participant achieved 150 min/wk or greater of moderate-intensity or 75 min/wk or greater of vigorous-intensity physical activity. Dietary Intake was considered ideal if participant met four to five of five of the following recommendations: fruits and vegetables of 4.5 cups/d or more; fish of two 3.5-oz servings per week or more (preferably oily fish); fiber-rich whole grains of three 1-oz-equivalent servings per day or more; sodium less than 1500 mg/d or more; and sugar-sweetened beverages of 450 kcal (36 oz)/wk or less.17
n = 4510 participant with dietary sodium intake calculated from Jackson Heart Study food frequency questionnaire (quintiles 1–5: n = 939, n = 878, n = 923, n = 866, n = 904).
n = 3757 participants with HOMA-IR and HOMA-β without diabetes at baseline (quintile 1–5: n = 824, n = 743, n = 770, n = 730, n = 690).
n = 2252 participants with PRA at baseline (quintile 1–5: n = 452, n = 433, n = 465, n = 427, n = 475).
n = 2739 participants with ARMC at baseline (quintile 1–5: n = 592, n = 541, n = 551, n = 536, n = 519).
Cross-sectional assessments
The cross-sectional associations between RAAS and log HOMA-IR, log HOMA-β, and prevalent diabetes are summarized in Tables 2 and 3. Among individuals without prevalent diabetes, every 1-U increase in log aldosterone was associated with a statistically significant 0.18% higher log HOMA-IR and 0.10% higher log HOMA-β, respectively, with full multivariable adjustment including BMI (P < .001) (Table 2). Participants in the fifth vs first quintile of log aldosterone had a 34% and 19% higher HOMA-IR and HOMA-β, respectively (Table 2; P < .001). Similarly, participants in the fifth vs first quintile of log renin had a 37% and 19% higher HOMA-IR and HOMA-β, respectively (Table 3; P < .001). In logistic regression models after full adjustment including BMI, those in the fifth vs first quintile of log aldosterone had significantly greater odds of prevalent diabetes (P < .001) (Table 2). A similar finding was observed for log renin (Table 3). Results were similar when substituting adiponectin for BMI (Tables 2 and 3) and in sensitivity analyses of individuals with and without hypertension, after excluding participants using RAAS antagonists and among subcategories of renin (Supplemental Tables 2–7).
Table 2.
The Association of Log Aldosterone With Prevalent Diabetes, Insulin Resistance, and β-Cell Function
| Unadjusted | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|---|
| Logistic regression: odds ratio (95% CI) of prevalent diabetes among all participants (n = 4991)a | ||||||
| Log aldosterone | ||||||
| Continuous | 1.35 (1.22, 1.50) | 1.37 (1.24, 1.52) | 1.36 (1.23, 1.51) | 1.36 (1.22, 1.50) | 1.30 (1.17, 1.45) | 1.32 (1.19, 1.47) |
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 1.19 (0.96, 1.48) | 1.27 (1.02, 1.60) | 1.28 (1.02, 1.61) | 1.26 (1.00, 1.58) | 1.30 (1.03, 1.64) | 1.24 (0.99, 1.57) |
| Quintile 3 | 1.20 (0.96, 1.49) | 1.31 (1.05, 1.64) | 1.31 (1.05, 1.64) | 1.29 (1.03, 1.62) | 1.29 (1.03, 1.63) | 1.25 (0.99, 1.57) |
| Quintile 4 | 1.14 (0.91, 1.43) | 1.23 (0.98, 1.55) | 1.22 (0.97, 1.53) | 1.20 (0.95, 1.51) | 1.19 (0.94, 1.50) | 1.16 (0.92, 1.46) |
| Quintile 5 | 1.75 (1.42, 2.15) | 1.84 (1.48, 2.28) | 1.82 (1.46, 2.26) | 1.79 (1.44, 2.23) | 1.69 (1.35, 2.11) | 1.70 (1.36, 2.12) |
| Multivariable linear regression: coefficients (nondiabetic participants, n = 3757)b | ||||||
| Log HOMA-IR | ||||||
| Continuous | 0.20 (0.17, 0.23) | 0.21 (0.18, 0.24) | 0.21 (0.18, 0.23) | 0.21 (0.18, 0.23) | 0.18 (0.16, 0.21) | 0.18 (0.15, 0.20) |
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 0.09 (0.03, 0.14) | 0.10 (0.05, 0.15) | 0.10 (0.05, 0.15) | 0.10 (0.05, 0.16) | 0.11 (0.06, 0.16) | 0.09 (0.04, 0.14) |
| Quintile 3 | 0.15 (0.10, 0.20) | 0.17 (0.12, 0.22) | 0.17 (0.11, 0.22) | 0.17 (0.12, 0.22) | 0.16 (0.11, 0.21) | 0.13 (0.08, 0.17) |
| Quintile 4 | 0.23 (0.18, 0.29) | 0.26 (0.20, 0.31) | 0.25 (0.20, 0.31) | 0.25 (0.20, 0.31) | 0.23 (0.18, 0.28) | 0.22 (0.16, 0.27) |
| Quintile 5 | 0.37 (0.31, 0.42) | 0.39 (0.33, 0.44) | 0.38 (0.33, 0.44) | 0.38 (0.33, 0.44) | 0.34 (0.29, 0.39) | 0.32 (0.27, 0.37) |
| Multivariable linear regression: coefficients (nondiabetic participants, n = 3757)b | ||||||
| Log HOMA-β | ||||||
| Continuous | 0.10 (0.07, 0.12) | 0.11 (0.09, 0.14) | 0.11 (0.09, 0.14) | 0.11 (0.09, 0.14) | 0.10 (0.07, 0.12) | 0.09 (0.07, 0.12) |
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 0.05 (−0.00, 0.10) | 0.05 (0.00, 0.10) | 0.05 (0.00, 0.10) | 0.05 (0.00, 0.10) | 0.06 (0.01, 0.10) | 0.04 (−0.00, 0.09) |
| Quintile 3 | 0.08 (0.03, 0.13) | 0.09 (0.04, 0.14) | 0.09 (0.04, 0.14) | 0.09 (0.04, 0.14) | 0.09 (0.04, 0.13) | 0.06 (0.02, 0.11) |
| Quintile 4 | 0.10 (0.05, 0.15) | 0.12 (0.07, 0.17) | 0.12 (0.07, 0.17) | 0.12 (0.07, 0.17) | 0.11 (0.06, 0.15) | 0.10 (0.05, 0.14) |
| Quintile 5 | 0.19 (0.13, 0.24) | 0.21 (0.16, 0.26) | 0.21 (0.16, 0.26) | 0.21 (0.16, 0.26) | 0.19 (0.14, 0.24) | 0.17 (0.12, 0.22) |
Models included the following: model 1, age, sex, education, and current occupation status; model 2, model 1 + systolic BP (millimeters of mercury); model 3, model 2 + smoking, physical activity (American Heart Association Life's Simple 7), dietary intake (American Heart Association Life's Simple 7), and alcohol use; model 4, model 3 + BMI (kilograms per square meter)c; and model 5, model 3 + adiponectin.
For the logistic regression, the odds ratios are expressed as a percentage of higher prevalence per log unit increase (continuous) and a percentage of higher prevalence of diabetes per quintile.
Multivariable linear regression using sequential models 1–5 expressed as a 1% increase in log aldosterone results in an X% increase in log HOMA-IR or log HOMA-β.
The BMI was calculated as the weight in kilograms divided by the square of the height in meters.
Table 3.
The Association of Renin With Prevalent Diabetes, Insulin Resistance, and β-Cell Function
| Unadjusted | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|---|
| Logistic regression: odds ratio (95% CI) of prevalent diabetes among all participants (n = 4991)a | ||||||
| Log reninb | ||||||
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 1.12 (0.88, 1.43) | 1.13 (0.88, 1.44) | 1.19 (0.93, 1.53) | 1.17 (0.91, 1.50) | 1.21 (0.94, 1.56) | 1.17 (0.91, 1.50) |
| Quintile 3 | 1.18 (0.93, 1.49) | 1.25 (0.98, 1.59) | 1.36 (1.07, 1.74) | 1.33 (1.04, 1.70) | 1.34 (1.05, 1.72) | 1.32 (1.03, 1.69) |
| Quintile 4 | 1.74 (1.40, 2.18) | 1.88 (1.49, 2.36) | 2.10 (1.66, 2.65) | 2.03 (1.61, 2.56) | 2.00 (1.58, 2.54) | 2.00 (1.58, 2.53) |
| Quintile 5 | 3.67 (2.98, 4.52) | 3.71 (2.99, 4.60) | 4.31 (3.45, 5.38) | 4.08 (3.26, 5.10) | 3.79 (3.02, 4.76) | 4.00 (3.20, 5.01) |
| Multivariable linear regression: coefficients (non-DM participants, n = 3757)c | ||||||
| Log HOMA-IR | ||||||
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 0.04 (−0.01, 0.09) | 0.04 (−0.01, 0.10) | 0.06 (0.00, 0.11) | 0.06 (0.00, 0.11) | 0.07 (0.02, 0.12) | 0.05 (0.00, 0.10) |
| Quintile 3 | 0.09 (0.04, 0.14) | 0.11 (0.05, 0.16) | 0.13 (0.08, 0.18) | 0.13 (0.08, 0.18) | 0.13 (0.08, 0.17) | 0.11 (0.06, 0.16) |
| Quintile 4 | 0.14 (0.09, 0.19) | 0.15 (0.10, 0.21) | 0.18 (0.13, 0.23) | 0.18 (0.13, 0.23) | 0.18 (0.13, 0.22) | 0.16 (0.11, 0.21) |
| Quintile 5 | 0.30 (0.24, 0.35) | 0.31 (0.26, 0.37) | 0.34 (0.29, 0.40) | 0.34 (0.29, 0.40) | 0.31 (0.26, 0.37) | 0.29 (0.24, 0.35) |
| Multivariable linear regression: coefficients (non-DM participants, n = 3757)c | ||||||
| Log HOMA-β | ||||||
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 0.06 (0.01, 0.11) | 0.06 (0.01, 0.10) | 0.06 (0.01, 0.11) | 0.06 (0.01, 0.11) | 0.07 (0.02, 0.11) | 0.06 (0.01, 0.10) |
| Quintile 3 | 0.07 (0.03, 0.12) | 0.08 (0.03, 0.13) | 0.09 (0.04, 0.13) | 0.09 (0.04, 0.13) | 0.08 (0.04, 0.13) | 0.08 (0.03, 0.12) |
| Quintile 4 | 0.13 (0.08, 0.18) | 0.13 (0.08, 0.17) | 0.13 (0.08, 0.18) | 0.13 (0.08, 0.18) | 0.13 (0.08, 0.18) | 0.12 (0.07, 0.16) |
| Quintile 5 | 0.17 (0.12, 0.22) | 0.20 (0.15, 0.25) | 0.21 (0.16, 0.26) | 0.21 (0.15, 0.26) | 0.19 (0.14, 0.24) | 0.18 (0.13, 0.23) |
Abbreviation: DM, diabetes mellitus. Models included the following: model 1, age, sex, education, and current occupation status; model 2, model 1 + systolic BP (millimeters of mercury); model 3, model 2 + smoking, physical activity (American Heart Association Life's Simple 7), dietary intake (American Heart Association Life's Simple 7), and alcohol use; model 4, model 3 + BMI (kilograms per square meter)d; and model 5, model 3 + adiponectin.
For logistic regression, the odds ratios were expressed as a percentage of higher prevalence of diabetes per quintile.
Log renin quintiles were created by combining log-transformed quintiles of PRA and ARMC.
Multivariable linear regression using sequential models 1–5 expressed as a 1% increase in log aldosterone results in an X% increase in log HOMA-IR or log HOMA-β.
The BMI was calculated as the weight in kilograms divided by the square of the height in meters.
Longitudinal assessments
During a median follow-up of 8.0 years, there were 554 participants who developed diabetes (incidence rate 21.5 per 1000 person-years). There was a graded increase in incidence rates across log aldosterone quintiles: 16.5, 19.1, 20.7, 28.4, and 29.4 per 1000 person-years from lowest to highest quintile (P < .0001) (Supplemental Tables 1–8).
The unadjusted and adjusted HRs for incident diabetes associated with log aldosterone and log renin are presented in Table 4. After full adjustment including BMI, higher quintiles of log aldosterone were associated with a significantly higher risk of incident diabetes (HR 1.78, fifth vs first quintile [95% confidence interval CI 1.35–2.34]). The fifth vs first quintile of log renin was associated with a 35% higher risk of incident diabetes (HR 1.35, 95% CI 1.06–1.72). The results were similar in sensitivity analyses of those with and without hypertension, after excluding participants taking RAAS inhibitors and when waist circumference was substituted for BMI (Supplemental Table 9). We found no evidence for interactions between the association of log aldosterone with incident diabetes by age or sex.
Table 4.
The Association of Log Aldosterone and Log Renin With Incident Diabetes
| Unadjusted | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|---|
| Cox proportional hazards model: HR (95% CI) for incident diabetes (n = 3234)a | ||||||
| Log aldosterone | ||||||
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 1.15 (0.85, 1.54) | 1.19 (0.89, 1.60) | 1.20 (0.89, 1.62) | 1.21 (0.90, 1.63) | 1.20 (0.89, 1.61) | 1.17 (0.87, 1.58) |
| Quintile 3 | 1.21 (0.91, 1.62) | 1.24 (0.93, 1.67) | 1.25 (0.94, 1.68) | 1.25 (0.93, 1.68) | 1.26 (0.94, 1.69) | 1.14 (0.85, 1.53) |
| Quintile 4 | 1.74 (1.33, 2.29) | 1.80 (1.37, 2.37) | 1.79 (1.36, 2.36) | 1.79 (1.36, 2.36) | 1.79 (1.36, 2.36) | 1.62 (1.23, 2.13) |
| Quintile 5 | 1.85 (1.41, 2.43) | 1.86 (1.42, 2.44) | 1.83 (1.40, 2.41) | 1.83 (1.39, 2.40) | 1.78 (1.35, 2.34) | 1.61 (1.22, 2.12) |
| P for trendc | <.0001 | |||||
| Cox proportional hazards model: HR (95% CI) for incident diabetes (n = 3234)a | ||||||
| Log reninb | ||||||
| Quintile 1 | Referent | Referent | Referent | Referent | Referent | Referent |
| Quintile 2 | 0.89 (0.68 1.16) | 0.88 (0.67, 1.15) | 0.90 (0.69, 1.19) | 0.90 (0.69, 1.19) | 0.91 (0.69, 1.19) | 0.88 (0.67, 1.15) |
| Quintile 3 | 1.08 (0.84, 1.40) | 1.11 (0.86, 1.44) | 1.15 (0.88, 1.49) | 1.13 (0.87, 1.47) | 1.13 (0.87, 1.46) | 1.07 (0.82, 1.39) |
| Quintile 4 | 0.88 (0.68, 1.14) | 0.88 (0.68, 1.15) | 0.95 (0.73, 1.24) | 0.94 (0.72, 1.22) | 0.92 (0.70, 1.19) | 0.87 (0.67, 1.14) |
| Quintile 5 | 1.37 (1.08, 1.73) | 1.35 (1.07, 1.72) | 1.44 (1.14, 1.84) | 1.42 (1.12, 1.81) | 1.35 (1.06, 1.72) | 1.21 (0.95, 1.55) |
| P for trendc | .036 | |||||
Models included the following: model 1, age, sex, education, and current occupation status; model 2, model 1 + systolic BP (millimeters of mercury); model 3, model 2 + smoking, physical activity (American Heart Association Life's Simple 7), dietary intake (American Heart Association Life's Simple 7), and alcohol use; model 4, model 3 + BMI (kilograms per square meter)d; and model 5, model 3 + adiponectin.
Log renin quintiles were created by combining log-transformed quintiles of PRA and ARMC.
P for trend was calculated using a log-rank test.
The BMI was calculated as the weight in kilograms divided by the square of the height in meters.
Discussion
In this prospective study of AAs, we found that among individuals without diabetes at baseline, higher aldosterone and renin were associated with greater estimates of insulin resistance at baseline. The greater insulin resistance was accompanied by a smaller compensatory increase in β-cell function, representing a risk for β-cell failure over time. Furthermore, higher aldosterone and renin were associated with an increased risk of prevalent diabetes at baseline and incident diabetes over 8 years of follow-up. These associations were independent of known diabetes risk factors, including BMI, waist circumference, and adiponectin. These results suggest that activation of the RAAS is a risk factor for the development of diabetes in AAs. In addition, these findings suggest a plausible explanation for the high degree of coexistent hypertension and diabetes in AAs (22).
Mechanisms
Cell and rodent studies
In vitro and in vivo studies characterize a reciprocal interaction between aldosterone and adiposity with mineralocorticoid receptor activation promoting inflammation, adipocyte differentiation, and the production of less metabolically favorable adipokines, which is reversed by mineralocorticoid receptor blockade (19, 23). The aforementioned interaction between aldosterone and adipocyte differentiation is important because adipocytes possess aldosterone synthase and produce aldosterone independent of the adrenal gland (24). Additionally, adipocytes stimulate production of an adrenal aldosterone secretagogue by the liver, potentially creating a cycle of increasing aldosterone (24). These adiposity-dependent actions may have a major impact on glucose homeostasis, including insulin resistance. In this analysis we found that the associations of the RAAS with glucose homeostasis and diabetes were predominantly independent of measures of generalized and visceral adiposity including BMI, adiponectin, and waist circumference, suggesting that adiposity independent aldosterone actions may be of greater importance in the development of diabetes. Cellular and animal studies suggest that aldosterone induced mineralocorticoid activation increases insulin resistance and impairs insulin secretion (10). The mechanism linking aldosterone and insulin resistance is related to the inhibitory effects of aldosterone on insulin signaling and insulin-stimulated glucose uptake via glucose transporter-4 translocation in adipocytes, skeletal muscle, and vascular smooth muscle cells as well as a reduction in adiponectin and peroxisome proliferator-activated receptor-γ (23, 25). Regarding impaired insulin secretion and β-cell dysfunction with aldosterone, in cell- and rodent-based models, aldosterone decreases glucose-stimulated insulin secretion, and in aldosterone synthase-deficient mice, glucose-stimulated insulin secretion is markedly increased (26). Consistent with these findings, we found increasing insulin resistance and decreased compensatory β-cell function with higher levels of aldosterone, suggesting the lack of ability of the β-cell to increase compensatory insulin secretion with increasing insulin resistance may represent a driving force in the development of diabetes.
Human studies
Human studies reveal greater insulin resistance with higher levels of aldosterone in those with and without primary hyperaldosteronism. Previous analyses have shown higher levels of insulin resistance in individuals with primary hyperaldosteronism, a condition in which angiotensin II and renin levels are low, suggesting an independent effect of aldosterone on insulin resistance (25). These findings were bolstered by a recent human study showing a significant improvement of the impaired first-phase insulin secretion (β-cell function) in patients with primary hyperaldosteronism after adrenalectomy (27). At the population-level, a large prospective Japanese cohort study demonstrated that plasma aldosterone levels predict the development of insulin resistance over 10 years (28). Furthermore, in AAs, serum aldosterone has been associated with incident metabolic syndrome, elevated blood glucose in a large cohort of AA (13), and insulin resistance independent of obesity in a small cohort of young AAs (29). In a multiethnic study of 84 healthy subjects on a high-salt diet, angiotensin II-stimulated aldosterone was an independent predictor of insulin sensitivity, assessed by an oral glucose tolerance test, after accounting for age, BMI, and diastolic BP (30). The results of our study confirm the relationship of aldosterone with insulin resistance and reveal the novel relationship between increasing insulin resistance and blunting of the compensatory increase in β-cell function with increasing aldosterone levels.
Our results are supported by and provide a mechanistic link to previous studies of RAAS inhibition and improved glucometabolism. Mineralocorticoid receptor blockade with spironolactone, but not angiotensin type 1 receptor blockade with irbesartan, improved chlorthalidone-induced insulin resistance in hypertensive individuals, suggesting a causative relationship between the activated mineralocorticoid receptor, increased aldosterone, and greater levels of insulin resistance (31). In a meta-analysis of randomized clinical trials, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers were associated with a 20% reduction in the cumulative risk of incident diabetes (32). The two major trials of RAAS inhibition and incident diabetes, The Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication (DREAM) study (32) and the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcome Research (NAVIGATOR) study (33) revealed conflicting findings in majority NHW populations. The DREAM study included 5269 participants, with half randomized to ramipril with or without rosiglitazone for a median of 3 years. Ramipril lowered postload plasma glucose levels compared with placebo control (mean difference 5.4 mg/dL, P < .01), although the rates of incident diabetes were not significantly different between groups (HR 0.91, 95% CI 0.81–1.03; P = .15). The NAVIGATOR study compared the effect of valsartan vs placebo on the development of diabetes among 9306 participants with impaired glucose tolerance. The valsartan-treated group had a lower incidence of diabetes (HR 0.86, 95% CI: 0.80–0.92; P < .0001) and lower fasting glucose (mean difference 0.03 mM/L, P < .01) and postload glucose (mean difference 0.17 mM/L, P < .0001) as compared with the placebo group. Population differences may explain the modestly positive result in NAVIGATOR as opposed to in DREAM, in which the participants in the NAVIGATOR study were at greater diabetes risk with more metabolic syndrome, higher fasting and 2-hour postload glucose, and hypertension. In the one trial that examined the association of RAAS inhibition and incident diabetes as a secondary end point in AAs, the AA Study of Kidney Disease and Hypertension Trial (34), ramipril compared with metoprolol or amlodipine was associated with a 36% reduction (95% CI 0.45–0.90) in incident diabetes (32). The studies of RAAS antagonism corroborate our finding of a novel graded association of aldosterone with incident diabetes over 8 years. The current understanding based on cell, animal, and human data support a relationship between aldosterone and the direct effects on insulin secretion, insulin resistance, and the development of diabetes.
The role of sodium intake in RAAS activation and glucometabolism
Salt sensitivity and dietary salt intake play a role in the RAAS. In our study 95% of participants consumed more than the recommended 1500 mg/d of sodium at baseline, with a mean intake of 3447 mg/d, consistent with prior analyses (35). Sodium intake did not differ by quintiles of log aldosterone (P = .31). In the rodent model of salt sensitivity (Dahl salt sensitive rat), aldosterone is suppressed acutely in response to salt loads, but this effect wanes and aldosterone becomes paradoxically elevated over time (36). Inappropriate RAAS activation, combined with the maladaptive inability to lower aldosterone levels in response to high salt intake, constitute a major issue in all ethnicities but may be especially relevant in AAs, who have a high prevalence and heritability of salt sensitivity (73% of hypertensive vs 36% of normotensive AAs) (37). In salt-sensitive animal models, as well as recent human trials, the deleterious end-organ effects of aldosterone are dependent on coexistent high dietary salt intake (38). The interaction of aldosterone and elevated sodium intake may be a key contributor to the effects of aldosterone in hypertension as well as aldosterone-mediated effects via the mineralocorticoid receptors on end organs including the brain, kidney, and heart (39). In the Dahl salt-sensitive rat, high sodium intake has been also associated with insulin resistance (8), and in a NHW European study of high-salt intake, salt intake predicted the risk of diabetes, independently of physical inactivity, obesity, and hypertension (40).
Strengths and limitations
Strengths of our study include a large, socioeconomically diverse, AA cohort with more than a decade of follow-up. We used validated questionnaires and a comprehensive documentation of diabetes over time including fasting glucose, HbA1c, medication use, and self-reported physician diabetes diagnosis as well as examined various aspect of glucose metabolism including estimates of insulin resistance and β-cell function. Despite these and other strengths, there are some potential limitations. First, the participants in the JHS are from one geographic area in the southeastern United States and may not be representative of all AAs. Second, renin was measured by two different assays, not allowing for continuous analysis of renin and insulin resistance in the full cohort. Third, we did not have a measure of 24-hour urinary sodium in the full cohort and thus were unable to assess the impact of dietary salt intake on aldosterone. Lastly, we may have underestimated the relation of RAAS elements with incident diabetes because individuals with 2-hour postload glucose impairment only may have remained undetected.
In conclusion, our study suggests that activation of the RAAS with higher levels of aldosterone and renin are associated with insulin resistance, compensatory increased β-cell function and incident diabetes in AAs, implicating this hormonal pathway not only in the pathophysiology of hypertension but also in diabetes. Further research exploring the impact of the RAAS and RAAS blockade on diabetes prevention in AAs, a population with a high prevalence of hypertension, is paramount, given the potential to reduce the disparities in diabetes prevalence and cardiometabolic health in AAs.
Acknowledgments
We thank the other investigators, the staff, and the participants of the Jackson Heart Study for their valuable contributions.
The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
J.J. was supported by Institutional Training Grant T32 DK062707 from the National Institute of Diabetes and Digestive and Kidney Diseases. 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.
Disclosure Summary: The authors have nothing to disclose.
Footnotes
- AA
- African American
- AHA
- American Heart Association
- ARMC
- active renin mass concentration
- BMI
- body mass index
- BP
- blood pressure
- CI
- confidence interval
- DREAM
- Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication
- HbA1c
- glycosylated hemoglobin A1c
- HOMA-β
- homeostasis model assessment of β-cell function
- HOMA-IR
- homeostatic model assessment of insulin resistance
- HR
- hazard ratio
- JHS
- Jackson Heart Study
- NAVIGATOR
- Nateglinide and Valsartan in Impaired Glucose Tolerance Outcome Research
- NHW
- non-Hispanic white
- PRA
- plasma renin activity
- RAAS
- renin-angiotensin-aldosterone system.
References
- 1. Geiss LS, Wang J, Cheng YJ, et al. Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980–2012. JAMA. 2014;312(12):1218. [DOI] [PubMed] [Google Scholar]
- 2. Golden SH, Brown A, Cauley JA, et al. Health disparities in endocrine disorders: biological, clinical, and nonclinical factors—an Endocrine Society scientific statement. J Clin Endocrinol Metab. 2012;97(9):E1579–E1639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Spanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diab Rep. 2013;13(6):814–823. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Murtaugh MA. Correlates of urinary albumin excretion in young adult blacks and whites: the Coronary Artery Risk Development in Young Adults Study. Am J Epidemiol. 2003;158(7):676–686. [DOI] [PubMed] [Google Scholar]
- 5. Drazner MH, Dries DL, Peshock RM, et al. Left ventricular hypertrophy is more prevalent in blacks than whites in the general population: the Dallas Heart Study. Hypertension. 2005;46(1):124–129. [DOI] [PubMed] [Google Scholar]
- 6. Williams SF. African Americans, hypertension and the renin angiotensin system. World J Cardiol. 2014;6(9):878. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Wilson TW, Grim CE. Biohistory of slavery and blood pressure differences in blacks today. A hypothesis. Hypertension. 1991;17(suppl 1):I122–I128. [DOI] [PubMed] [Google Scholar]
- 8. Ogihara T, Asano T, Ando K, et al. High-salt diet enhances insulin signaling and induces insulin resistance in Dahl salt-sensitive rats. Hypertension. 2002;40(1):83–89. [DOI] [PubMed] [Google Scholar]
- 9. Chatterjee R, Yeh H-C, Shafi T, et al. Serum potassium and the racial disparity in diabetes risk: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Clin Nutr. 2011;93(5):1087–1091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Corry DB, Tuck ML. The effect of aldosterone on glucose metabolism. Curr Hypertens Rep. 2003;5(2):106–109. [DOI] [PubMed] [Google Scholar]
- 11. Taylor HA, Jr, Wilson JG, Jones DW, et al. Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study. Ethn Dis. 2005;15(4 suppl 6):S6–S4–S17. [PubMed] [Google Scholar]
- 12. Carpenter MA, Crow R, Steffes M, et al. Laboratory, reading center, and coordinating center data management methods in the Jackson Heart Study. Am J Med Sci. 2004;328(3):131–144. [DOI] [PubMed] [Google Scholar]
- 13. Musani SK, Vasan RS, Bidulescu A, et al. Aldosterone, C-reactive protein, and plasma B-type natriuretic peptide are associated with the development of metabolic syndrome and longitudinal changes in metabolic syndrome components: findings from the Jackson Heart Study. Diabetes Care. 2013;36(10):3084–3092. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010;33(suppl 1):S62–S69. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–419. [DOI] [PubMed] [Google Scholar]
- 16. Bidulescu A, Liu J, Musani SK, et al. Association of adiponectin with left ventricular mass in blacks: the Jackson Heart Study. Circ Heart Fail. 2011;4(6):747–753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lloyd-Jones DM, Hong Y, Labarthe D, et al. 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(4):586–613. [DOI] [PubMed] [Google Scholar]
- 18. Carithers T, Dubbert PM, Crook E, et al. Dietary assessment in African Americans: methods used in the Jackson Heart Study. Ethn Dis. 2005;15(4 suppl 6):S6–S49–S55. [PubMed] [Google Scholar]
- 19. Goodfriend TL, Egan BM, Kelley DE. Plasma aldosterone, plasma lipoproteins, obesity and insulin resistance in humans. Prostaglandins Leukot Essent Fatty Acids. 1999;60(5–6):401–405. [DOI] [PubMed] [Google Scholar]
- 20. Joseph JJ, Golden SH. Type 2 diabetes and cardiovascular disease: what next? Curr Opin Endocrinol Diabetes Obes. 2014;21(2):109–120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. 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(9):1694–1700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Lastra G, Syed S, Kurukulasuriya LR, Manrique C, Sowers JR. Type 2 diabetes mellitus and hypertension. Endocrinol Metab Clin North Am. 2014;43(1):103–122. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Guo C, Ricchiuti V, Lian BQ, et al. Mineralocorticoid receptor blockade reverses obesity-related changes in expression of adiponectin, peroxisome proliferator-activated receptor-γ, and proinflammatory adipokines. Circulation. 2008;117(17):2253–2261. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Briones AM, Cat AND, Callera GE, et al. Adipocytes produce aldosterone through calcineurin-dependent signaling pathways implications in diabetes mellitus-associated obesity and vascular dysfunction. Hypertension. 2012;59(5):1069–1078. [DOI] [PubMed] [Google Scholar]
- 25. Underwood PC, Adler GK. The renin angiotensin aldosterone system and insulin resistance in humans. Curr Hypertens Rep. 2013;15(1):59–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Luther JM. Effects of aldosterone on insulin sensitivity and secretion. Steroids. 2014;91:54–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Fischer E, Adolf C, Pallauf A, et al. Aldosterone excess impairs first phase insulin secretion in primary aldosteronism. J Clin Endocrinol Metab. 2013;98(6):2513–2520. [DOI] [PubMed] [Google Scholar]
- 28. Kumagai E, Adachi H, Jacobs DR, et al. Plasma aldosterone levels and development of insulin resistance: prospective study in a general population. Hypertension. 2011;58(6):1043–1048. [DOI] [PubMed] [Google Scholar]
- 29. Huan Y, DeLoach S, Keith SW, Goodfriend TL, Falkner B. Aldosterone and aldosterone: renin ratio associations with insulin resistance and blood pressure in African Americans. J Am Soc Hypertens. 2012;6(1):56–65. [DOI] [PubMed] [Google Scholar]
- 30. Garg R, Hurwitz S, Williams GH, Hopkins PN, Adler GK. Aldosterone production and insulin resistance in healthy adults. J Clin Endocrinol Metab. 2010;95(4):1986–1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Raheja P, Price A, Wang Z, et al. Spironolactone prevents chlorthalidone-induced sympathetic activation and insulin resistance in hypertensive patients. Hypertension. 2012;60(2):319–325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Al-Mallah M, Khawaja O, Sinno M, Alzohaili O, Samra ABA. Do angiotensin converting enzyme inhibitors or angiotensin receptor blockers prevent diabetes mellitus? A meta-analysis. Cardiol J. 2010;17(5):448–456. [PubMed] [Google Scholar]
- 33. McMurray JJ, Holman RR, Rutten G, et al. Effect of valsartan on the incidence of diabetes and cardiovascular events. N Engl J Med. 2010;362(16):1477–1490. [DOI] [PubMed] [Google Scholar]
- 34. Norris K, Bourgoigne J, Gassman J, et al. Cardiovascular outcomes in the African American Study of Kidney Disease and Hypertension (AASK) trial. Am J Kidney Dis. 2006;48(5):739–751. [DOI] [PubMed] [Google Scholar]
- 35. Cogswell ME, Zhang Z, Carriquiry AL, et al. Sodium and potassium intakes among US adults: NHANES 2003–2008. Am J Clin Nutr. 2012;96(3):647–657. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Morizane S, Mitani F, Ozawa K, et al. Biphasic time course of the changes in aldosterone biosynthesis under high-salt conditions in Dahl salt-sensitive rats. Arterioscler Thromb Vasc Biol. 2012;32(5):1194–1203. [DOI] [PubMed] [Google Scholar]
- 37. Svetkey LP, McKeown SP, Wilson AF. Heritability of salt sensitivity in black Americans. Hypertension. 1996;28(5):854–858. [DOI] [PubMed] [Google Scholar]
- 38. du Cailar G, Fesler P, Ribstein J, Mimran A. Dietary sodium, aldosterone, and left ventricular mass changes during long-term inhibition of the renin-angiotensin system. Hypertension. 2010;56(5):865–870. [DOI] [PubMed] [Google Scholar]
- 39. Catena C, Colussi G, Nait F, Martinis F, Pezzutto F, Sechi LA. Aldosterone and the heart: still an unresolved issue? Front Endocrinol. 2014;5:168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Hu G, Jousilahti P, Peltonen M, Lindström J, Tuomilehto J. Urinary sodium and potassium excretion and the risk of type 2 diabetes: a prospective study in Finland. Diabetologia. 2005;48(8):1477–1483. [DOI] [PubMed] [Google Scholar]