Abstract
We examined the association of plasma lactate at rest, a marker of oxidative capacity, with incident cardiovascular outcomes in 10,006 participants in the Atherosclerosis Risk in Communities (ARIC) Study visit 4 (1996–1998). We used Cox proportional-hazards models to estimate hazard ratios of incident coronary heart disease, stroke, heart failure, and all-cause mortality by quartiles of plasma lactate (Q1, ≤5.3 mg/dL; Q2, 5.4–6.6; Q3, 6.7–8.6; and Q4 ≥8.7). During a median follow-up time of 10.7 years, there were 1,105 coronary heart disease cases, 379 stroke cases, 820 heart failure cases, and 1,408 deaths. A significant graded relation between lactate level and cardiovascular events was observed in the demographically adjusted model (all P for trend < 0.001). After further adjustment for traditional and other potential confounders, the association remained significant for heart failure (Q4 vs. Q1: hazard ratio (HR) = 1.35, 95% confidence interval (CI): 1.07, 1.71) and all-cause mortality (HR = 1.27, 95% CI: 1.07, 1.51) (P for trend < 0.02 for these outcomes) but not for coronary heart disease (HR = 1.02, 95% CI: 0.84, 1.24) and stroke (HR = 1.26, 95% CI: 0.91, 1.75). The results for heart failure were robust across multiple subgroups, after further adjustment for N-terminal pro–B-type natriuretic peptide and after exclusion of participants with incident heart failure within 3 years. The independent associations of plasma lactate with heart failure and all-cause mortality suggest an important role for low resting oxidative capacity.
Keywords: cardiovascular disease, epidemiology, heart failure, oxidative capacity, plasma lactate
Growing evidence indicates that low oxidative capacity plays an important role in the development of metabolic illnesses and their complications, such as insulin resistance, hypertension, and atherosclerosis (1, 2). Insulin resistance and type 2 diabetes are associated with decreased systemic aerobic capacity (3, 4), probably because of decreased oxidative phosphorylation, gene expression related to that process (3, 5–8), and mitochondrial size and density (7, 9). However, clinical or epidemiologic research on oxidative capacity as a predictor of age-related degenerative diseases has been limited by the absence of a simple, noninvasive technique to measure oxidative capacity.
Blood lactate at rest is an indicator of low oxidative capacity: When oxidative capacity decreases, blood lactate rises as a consequence of increased flux through glycolytic pathways (10, 11). Previous studies suggested that lactate level is elevated among individuals with insulin resistance and obesity (12, 13). Furthermore, a few studies have shown that blood lactate level is correlated positively with blood pressure (10, 14, 15). However, these studies were mainly cross sectional (14, 15) or limited to obese subjects (10), leaving uncertainty as to whether elevated lactate level predates the development of cardiovascular disease in the general population. The objective of the present study was to investigate a possible relation between blood lactate and the incidence of cardiovascular disease in a middle-aged, biracial population.
MATERIALS AND METHODS
Study participants
The Atherosclerosis Risk in Communities (ARIC) Study is a community-based cohort study of 15,792 people aged 45–64 years at baseline sampled from 4 communities in the United States: Forsyth County, North Carolina; suburban Minneapolis, Minnesota; Washington County, Maryland; and Jackson, Mississippi (16). The first examination was conducted during 1987–1989, with 3 triennial follow-up visits. Visit 4 (1996–1998) was the only visit at which blood lactate was measured and was the baseline for the present study. A total of 11,656 participants attended visit 4. Of these, we excluded participants who reported race other than white or black (n = 31), who were missing values of lactate (n = 170), or who had prevalent cardiovascular disease, including coronary heart disease (CHD) (n = 973), stroke (n = 213), and heart failure (n = 263), leaving a final study population of 10,006 participants. Prevalent heart failure was defined as self-reported treatment for heart failure or the Gothenburg stage 3, a status with dyspnea due to cardiac causes and under treatment with digitalis or loop diuretics (17, 18), at visit 1 or hospitalization for heart failure between visits 1 and 4. The study was approved by the institutional review boards of all participating institutions, and all participants gave informed consent.
Data collection at baseline
ARIC study participants provided information on demographic and behavioral variables and medical history to a trained interviewer at each visit. In the present study, we used information obtained at visit 4, unless otherwise noted. Smoking status, alcohol intake, and history of chronic lung disease were determined by self-report. Participants were asked to bring all medications, which were coded by trained personnel. Information about completed years of education was obtained at visit 1. As a measure of physical activity, a score of physical activity during leisure time was derived from questions on intensity and frequency of exercise, frequency of sweating, and a subjective comparison of physical activity to that of others one's own age at visit 3 (19). Certified technicians used a random-zero sphygmomanometer to measure 2 systolic and 2 diastolic blood pressures with participants in the sitting position after 5 minutes of rest. The average of the 2 readings was recorded. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or antihypertensive medication use. Heart rate was recorded before blood pressure measurement. Height and weight were measured with the participant in light clothing without shoes. Body mass index was calculated as weight in kilograms divided by the square of height in meters. Waist circumference was measured at the level of the umbilicus (20).
Blood samples were obtained at rest from all participants according to standardized procedures (21). To measure plasma lactate, an enzymatic reaction to convert lactate to pyruvate was conducted with a Roche Hitachi 911 Auto-Analyzer (Roche Diagnostics, Indianapolis, Indiana). The reliability coefficient for blinded replicates was 0.93 in 435 pairs, and the coefficient of variation was 10.2%. Diabetes mellitus was defined as a fasting glucose level ≥7.0 mmol/L, a nonfasting glucose level ≥11.1 mmol/L, self-reported physician diagnosis of diabetes, or use of glucose-lowering medications. Total cholesterol and high-density lipoprotein cholesterol were determined by enzymatic methods. Insulin was measured by radioimmunoassay (125Insulin kit; Cambridge Medical Diagnosis, Billerica, Massachusetts). As an index of insulin resistance, we calculated the homeostasis model assessment of insulin resistance (22). Alanine aminotransferase and aspartate aminotransferase were measured with an Olympus AU400e automated chemistry analyzer (Olympus Life Science Research Europa GmbH, Munich, Germany). High-sensitivity C-reactive protein was measured by the immunoturbidimetric assay on the BNII analyzer (Siemens Healthcare Diagnostics, Deerfield, Illinois). Serum creatinine concentration was measured according to a modified kinetic Jaffe method. Estimated glomerular filtration rate was computed by the CKD-EPI equation (23). N-terminal pro–B-type natriuretic peptide was measured with an electrochemiluminescent immunoassay on an automated Cobas e411 analyzer (Roche Diagnostics, Indianapolis, Indiana) (24).
Outcomes
The outcomes of interest were incident CHD, stroke, heart failure, and all-cause mortality. All-cause mortality was included because cardiovascular disease is the leading cause of death in the United States (25). ARIC investigators conduct continuous, comprehensive surveillance for all cardiovascular disease–related hospitalizations and deaths in the 4 communities. All potential cardiovascular events are reviewed, and CHD and stroke are adjudicated on the basis of published criteria (26–28). We defined incident CHD as a definite or probable myocardial infarction, definite coronary death, or coronary revascularization procedure. Stroke included definite or probable cases, defined as sudden or rapid onset of neurological symptoms that lasted for 24 hours or led to death in the absence of another cause (27, 28). Incident heart failure was defined as death from heart failure in any position on the death certificate or as the first heart failure hospitalization with International Classification of Diseases, Ninth Revision (ICD-9) code 428 or International Classification of Diseases, Tenth Revision (ICD-10) code I50 in any position of the hospital discharge list (29). Validation of hospitalizations for heart failure indicated that the positive predictive value was 93% for acute decompensated heart failure and 97% for chronic heart failure (30, 31). These outcomes, from visit 4 to January 1, 2009, were analyzed in the present study.
Statistical analyses
We divided the study participants into quartiles of lactate level (Q1–Q4) and used analysis of variance and χ2 tests, as appropriate, to compare continuous and categorical variables across the quartiles. Continuous associations between lactate level and incidence rates of cardiovascular outcomes were evaluated through the use of a Poisson regression model incorporating linear spline terms for lactate (with 3 knots at the cutoff points of quartiles) with adjustment for age, sex, and race. Cox proportional-hazards models were used to quantify the association between the quartiles of lactate level and incident cardiovascular disease. We constructed 3 models for the adjustment for covariates. Model 1 included demographic variables: age, sex, race, and level of education (less than high school or a minimum of high school). Model 2 further adjusted for traditional cardiovascular risk factors: blood pressure, antihypertensive medication, diabetes, smoking (current vs. never/former), total cholesterol, and high-density lipoprotein cholesterol (32). Model 3 incorporated all variables in Model 2 plus other factors potentially associated with lactate metabolism, including measures of adiposity (body mass index and waist circumference), insulin resistance index (the homeostasis model assessment of insulin resistance), physical activity, history of chronic lung disease, heart rate, high-sensitivity C-reactive protein, liver enzymes, alcohol intake, and estimated glomerular filtration rate. P for trend was evaluated from the models, with the median value of lactate in each quartile assigned to individuals in that quartile.
We conducted several sensitivity analyses to assess the robustness of our results. First, we excluded individuals who were taking biguanides, drugs well known to affect lactate levels (33). Second, we excluded participants with high lactate levels (≥45 mg/dL) that reportedly indicate systemic hypoperfusion (34). Third, we further adjusted for N-terminal pro–B-type natriuretic peptide in the analysis for heart failure. Fourth, we excluded cases of heart failure in the first 3 years of follow-up to avoid the possibility of reverse causation, postulating that latent cardiac dysfunction might contribute to high levels of lactate through hypoperfusion. Finally, to evaluate whether the association was consistent across the full range of underlying cardiovascular risk, we also examined the association of lactate level with cardiovascular risk in subgroups defined by age, sex, race, smoking, and presence/absence of diabetes, hypertension, obesity, and kidney dysfunction. The likelihood ratio test was used to test interactions. Stata version 11.2 (StataCorp LP, College Station, Texas) was used to conduct all analyses, and a 2-tailed P value less than 0.05 was considered statistically significant.
RESULTS
The mean plasma lactate level in our study population was 7.4 (standard deviation, 3.3) mg/dL. Demographic characteristics of participants by quartiles of lactate are shown in Table 1. Participants with high lactate levels were more likely to be black, obese, and diabetic and to have higher blood pressure, abnormal lipid profile, and higher liver enzymes than those in the bottom quartile (lactate level 2.2–5.3 mg/dL). In contrast, participants with high lactate levels were less likely to be current smokers and drinkers.
Table 1.
Baseline Characteristics According to Lactate Quartiles, Atherosclerosis Risk in Communities Study, 1996–2008
| Quartile of Lactatea |
P Valueb | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 (n = 2,660) |
2 (n = 2,429) |
3 (n = 2,478) |
4 (n = 2,439) |
||||||||||
| Mean (SD) | Median (IQR) | % | Mean (SD) | Median (IQR) | % | Mean (SD) | Median (IQR) | % | Mean (SD) | Median (IQR) | % | ||
| Age, years | 62.2 (5.6) | 62.9 (5.6) | 62.6 (5.6) | 62.5 (5.7) | <0.001 | ||||||||
| Female sex | 65.7 | 53.3 | 53.2 | 61.5 | <0.001 | ||||||||
| Black | 15.3 | 18.2 | 22.9 | 33.6 | <0.001 | ||||||||
| Less than high school education | 14.4 | 16.7 | 18.8 | 22.6 | <0.001 | ||||||||
| Current smoker | 15.7 | 14.6 | 15.1 | 13.5 | 0.156 | ||||||||
| Current drinker | 56.6 | 51.6 | 49.7 | 43.1 | <0.001 | ||||||||
| Body mass indexc | 27.2 (5.2) | 28.1 (5.2) | 29.2 (5.5) | 30.5 (5.9) | <0.001 | ||||||||
| Waist circumference, cm | 97.4 (13.8) | 100.1 (13.7) | 103.1 (14.0) | 106.1 (14.8) | <0.001 | ||||||||
| Heart rate, beats per 30 seconds | 32.2 (4.4) | 32.7 (4.7) | 33.0 (4.8) | 34.1 (4.9) | <0.001 | ||||||||
| Sports score | 2.6 (0.8) | 2.6 (0.8) | 2.5 (0.8) | 2.5 (0.8) | <0.001 | ||||||||
| Antihypertensive medications | 24.7 | 28.0 | 36.5 | 44.9 | <0.001 | ||||||||
| Systolic blood pressure, mm Hg | 124.3 (18.6) | 126.0 (18.1) | 128.0 (18.7) | 130.9 (19.3) | <0.001 | ||||||||
| Diastolic blood pressure, mm Hg | 70.1 (10.1) | 70.9 (9.9) | 71.8 (10.2) | 72.2 (10.5) | <0.001 | ||||||||
| Total cholesterol, mmol/L | 5.17 (0.89) | 5.18 (0.94) | 5.23 (0.95) | 5.30 (1.00) | <0.001 | ||||||||
| HDL cholesterol, mmol/L | 1.42 (0.44) | 1.32 (0.44) | 1.27 (0.40) | 1.25 (0.42) | <0.001 | ||||||||
| Diabetes | 5.3 | 8.8 | 14.9 | 32.3 | <0.001 | ||||||||
| Fasting glucose, mmol/L | 5.45 (0.82) | 5.74 (1.32) | 6.05 (1.68) | 7.13 (3.14) | <0.001 | ||||||||
| Insulin, mU/L | 8.5 (6.0–11.7) | 9.7 (6.8–13.6) | 11.2 (7.8–16.2) | 13.9 (9.4–21.3) | <0.001 | ||||||||
| HOMA-IR | 2.47 (2.50) | 3.00 (2.65) | 3.85 (4.07) | 6.61 (9.91) | <0.001 | ||||||||
| History of chronic lung disease | 7.7 | 7.7 | 7.5 | 8.1 | 0.852 | ||||||||
| hsCRP, mg/L | 1.8 (0.9–4.3) | 2.2 (1.0–4.8) | 2.4 (1.1–5.4) | 3.4 (1.5–6.9) | <0.001 | ||||||||
| Alanine aminotransferase | 13.5 (9.7) | 14.7 (11.8) | 15.5 (10.9) | 16.7 (12.5) | <0.001 | ||||||||
| Aspartate aminotransferase | 19.0 (9.3) | 19.6 (10.5) | 20.2 (13.0) | 21.0 (15.5) | <0.001 | ||||||||
| eGFR, mL/min/1.73 m2 | 79.9 (14.8) | 79.5 (14.6) | 80.3 (15.0) | 82.9 (16.5) | <0.001 | ||||||||
| NT-proBNP, pg/mL | 74.4 (38.2–134.8) | 65.3 (32.7–122.0) | 59.4 (29.5–111.8) | 53.3 (25.2–105.6) | 0.039 | ||||||||
Abbreviations: eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; HOMA-IR, homeostasis model assessment of insulin resistance; hsCRP, high-sensitivity C-reactive protein; IQR, interquartile range; NT-proBNP, N-terminal pro–B-type natriuretic peptide; SD, standard error.
a Quartile 1, 2.2–5.3 mg/dL; quartile 2, 5.4–6.6 mg/dL; quartile 3, 6.7–8.6 mg/dL; and quartile 4, 8.7–55.5 mg/dL.
b P values for comparisons across quartiles, with analysis of variance used for continuous variables and χ2 test for categorical variables.
c Weight (kg)/height (m)2.
During a median follow-up period of 10.7 years, there were 1,105 cases of CHD (136 fatal cases), 379 cases of stroke, 820 cases of incident heart failure, and 1,408 deaths. The incidence rate per 1,000 person-years was 10.8 for CHD, 3.6 for stroke, and 7.8 for heart failure. The continuous associations of lactate levels with incidence rate of cardiovascular disease with adjustment for age, sex, and race are shown in Web Figure 1, available at http://aje.oxfordjournals.org/. Overall, there was dose-response relation between lactate and the outcomes tested, with a risk gradient of 2- to 3-fold from the range of the lowest quartile (Q1, ≤5.3 mg/dL) to the range of the highest quartile (Q4, ≥8.6 mg/dL). The risk increment for CHD and stroke was flat in quartile 2, and the risk increment for heart failure, all-cause mortality, and fatal CHD was flat in quartile 1 (the lowest quartile).
We used Cox proportional-hazards models with adjustment for multiple covariates to estimate the hazard ratios and corresponding 95% confidence intervals for clinical outcomes by quartiles of lactate (Table 2). Compared with participants in the lowest quartile, the hazard ratios of cardiovascular outcomes rose progressively across lactate categories in the model adjusted for age, sex, race, and educational level (Model 1) (P for trend < 0.001 for all outcomes). These associations remained significant even after adjustment for all traditional cardiovascular risk factors except CHD (Q4 vs. Q1: for fatal CHD, HR = 1.62, 95% CI: 0.96, 2.74; for stroke, HR = 1.31, 95% CI: 0.97, 1.77; for heart failure, HR = 1.58, 95% CI: 1.27, 1.97; and for all-cause mortality, HR = 1.40, 95% CI: 1.20, 1.64; P for trend < 0.05 in Model 2 for all outcomes except CHD). Further adjustment for other potential confounders substantially attenuated the association for stroke (P for trend = 0.124 in Model 3) but not for heart failure and mortality outcomes (Q4 vs. Q1: for fatal CHD, HR = 1.72, 95% CI: 0.98, 3.02; for heart failure, HR = 1.35, 95% CI: 1.07, 1.71; and for all-cause mortality, HR = 1.27, 95% CI: 1.07, 1.51; P for trend < 0.05 in Model 3). These associations did not change appreciably after further adjustment for antidiabetic drugs including biguanides, specific types of antihypertensive drugs (diuretics, β-blockers, calcium channel blockers, and renin-angiotensin system inhibitors), or statins; they also did not change appreciably after exclusion of participants who were taking biguanides (n = 153) or had an extremely high lactate level of 45 mg/dL, indicating systemic hypoperfusion (34) (n = 2), or half that level (>22.5 mg/dL) (n = 46) (data not shown). Similar associations were observed when we repeated the analysis with quintiles of lactate (data not shown).
Table 2.
Adjusted Hazard Ratios (95% Confidence Intervals) for Cardiovascular Outcomes According to Lactate Quartiles, Atherosclerosis Risk in Communities Study, 1996–2008
| Model | Quartile of Lactatea |
P for Trend | ||||||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 |
3 |
4 |
|||||
| HR | 95% CI | HR | 95% CI | HR | 95% CI | |||
| CHD | ||||||||
| Model 1b | Referent | 1.12 | 0.94, 1.33 | 1.19 | 1.00, 1.42 | 1.50 | 1.26, 1.78 | <0.001 |
| Model 2c | Referent | 1.04 | 0.87, 1.24 | 0.98 | 0.82, 1.16 | 1.00 | 0.83, 1.20 | 0.827 |
| Model 3d | Referent | 1.04 | 0.87, 1.26 | 0.96 | 0.80, 1.16 | 1.02 | 0.84, 1.24 | 0.989 |
| Fatal CHD | ||||||||
| Model 1 | Referent | 0.77 | 0.42, 1.41 | 1.44 | 0.86, 2.42 | 1.97 | 1.20, 3.23 | <0.001 |
| Model 2 | Referent | 0.81 | 0.44, 1.50 | 1.31 | 0.77, 2.24 | 1.62 | 0.96, 2.74 | 0.014 |
| Model 3 | Referent | 0.85 | 0.45, 1.62 | 1.25 | 0.71, 2.22 | 1.72 | 0.98, 3.02 | 0.013 |
| Stroke | ||||||||
| Model 1 | Referent | 1.04 | 0.76, 1.43 | 1.13 | 0.83, 1.53 | 1.67 | 1.26, 2.23 | <0.001 |
| Model 2 | Referent | 1.03 | 0.75, 1.41 | 1.00 | 0.73, 1.36 | 1.31 | 0.97, 1.77 | 0.047 |
| Model 3 | Referent | 1.03 | 0.74, 1.43 | 1.01 | 0.73, 1.39 | 1.26 | 0.91, 1.75 | 0.124 |
| Heart failure | ||||||||
| Model 1 | Referent | 1.24 | 0.99, 1.56 | 1.69 | 1.36, 2.09 | 2.26 | 1.84, 2.78 | <0.001 |
| Model 2 | Referent | 1.21 | 0.96, 1.52 | 1.45 | 1.16, 1.80 | 1.58 | 1.27, 1.97 | <0.001 |
| Model 3 | Referent | 1.14 | 0.90, 1.45 | 1.30 | 1.03, 1.63 | 1.35 | 1.07, 1.71 | 0.013 |
| All-cause mortality | ||||||||
| Model 1 | Referent | 1.05 | 0.90, 1.23 | 1.20 | 1.03, 1.41 | 1.51 | 1.30, 1.76 | <0.001 |
| Model 2 | Referent | 1.08 | 0.92, 1.27 | 1.19 | 1.01, 1.39 | 1.40 | 1.20, 1.64 | <0.001 |
| Model 3 | Referent | 1.02 | 0.86, 1.20 | 1.15 | 0.97, 1.35 | 1.27 | 1.07, 1.51 | 0.002 |
Abbreviations: CHD, coronary heart disease; CI, confidence interval; HR, hazard ratio.
a Quartile 1, 2.2–5.3 mg/dL; quartile 2, 5.4–6.6 mg/dL; quartile 3, 6.7–8.6 mg/dL; and quartile 4, 8.7–55.5 mg/dL.
b Model 1: Adjusted for age, sex, race, and level of education.
c Model 2: Adjusted for variables in Model 1, plus systolic and diastolic blood pressure, antihypertensive medication, smoking, total cholesterol, high-density lipoprotein cholesterol, and diabetes.
d Model 3: Adjusted for variables in Model 2, plus body mass index, waist circumference, homeostasis model assessment of insulin resistance, physical activity, history of lung disease, heart rate, high-sensitivity C-reactive protein, alanine aminotransferase, aspartate aminotransferase, alcohol intake, and estimated glomerular filtration rate.
As shown by the significantly higher hazard ratios in quartiles 3 and 4, lactate level was most strongly and consistently associated with heart failure among cardiovascular outcomes, and thus we further explored this association. Neither the adjustment for N-terminal pro–B-type natriuretic peptide nor the exclusion of heart failure cases in the first 3 years of follow-up altered the results (Q4 vs. Q1: HR = 1.41, 95% CI: 1.09, 1.81; and HR = 1.39, 95% CI: 1.06, 1.81, respectively). Finally, we examined this association in various subgroups according to risk profile for heart failure (Figure 1). The results were largely consistent across the different subpopulations (all P values for interaction > 0.05). The associations in whites and blacks were similar when race-specific quartiles of lactate were used (white-specific Q1, Q2, Q3, and Q4 were ≤5.2, 5.3–6.4, 6.5–8.1, and ≥8.2 mg/dL, respectively; black-specific Q1, Q2, Q3, and Q4 were ≤5.8, 5.9–7.4, 7.5–10.0, and ≥10.1 mg/dL, respectively) (Q4 vs. Q1: HR = 1.32, 95% CI: 1.00, 1.73; and HR = 1.31, 95% CI: 0.83, 2.08, respectively).
Figure 1.
Adjusted hazard ratios of incident heart failure for top quartile of lactate (quartile 4, ≥8.7 mg/dL) compared with bottom quartile (quartile 1, ≤5.3 mg/dL) in subgroups, Atherosclerosis Risk in Communities Study, 1996–2008. The hazard ratios were adjusted for the same covariates as Model 3 in Table 2 (i.e., age, sex, race, level of education, systolic and diastolic blood pressure, antihypertensive medication, smoking, total cholesterol, high-density lipoprotein cholesterol, diabetes, body mass index, waist circumference, homeostasis model assessment of insulin resistance, physical activity, history of lung disease, heart rate, high-sensitivity C-reactive protein, alanine aminotransferase, aspartate aminotransferase, alcohol intake, and estimated glomerular filtration rate). Body mass index was defined as weight in kilograms divided by the square of height in meters. CI, confidence interval; eGFR, estimated glomerular filtration rate.
DISCUSSION
It is well accepted that very high values of lactate in resting individuals are an indicator of a severe deficit in oxygen delivery, as occurs with hypoperfusion (35). It is also well known that the extent and timing of an increase in lactate level with exercise is a robust measure of aerobic fitness (36). To our knowledge, our present work is the first to demonstrate the prospective association of a resting lactate level within normal range with cardiovascular disease and all-cause mortality. Plasma lactate was associated with all cardiovascular outcomes tested in the demographically adjusted model. Although the associations with CHD and stroke were no longer significant after further adjustment for traditional cardiovascular risk factors and other potential confounders, the association with heart failure remained significant, independent, and robust, even in various sensitivity analyses, such as subgroup analysis, further adjustment for N-terminal pro–B-type natriuretic peptide, and exclusion of individuals taking biguanides.
Given that a level of lactate was correlated with prevalence of diabetes and measures of adiposity (Table 1), it was possible that the significant association between lactate level and heart failure was confounded by these metabolic conditions, which are well-known risk factors for heart failure (29, 37). Indeed, the association was attenuated after accounting for these factors, but it should be noted that it remained statistically significant. Moreover, the association was largely consistent, regardless of the presence or absence of diabetes and obesity.
Independent association of high lactate level with heart failure suggests that low oxidative capacity plays an important role in the deterioration of cardiac function. The heart is an aerobic organ and relies almost exclusively on the oxidation of substrates for energy generation (38). In contrast to most other vascular beds, myocardial oxygen extraction is nearly maximal at rest (39). Thus, the heart could be particularly susceptible to reduced oxidative capacity. Indeed, mitochondrial disorders are known to have cardiac manifestations (40), and a recent basic study suggested that reduced energy delivery causally contributes to the contractile dysfunction of the heart (41).
The associations of plasma lactate level with CHD and stroke were no longer significant when we adjusted for traditional confounders and some other potential confounders. This might suggest that low oxidative capacity could play a less important role in the development of atherosclerosis than in the deterioration of cardiac function. A slightly stronger association with stroke (statistically significant trend in Model 2) than with CHD in the present study might be in line with this concept because stroke has etiologies other than atherosclerosis, such as cardiac embolism (42), whereas CHD is due predominantly to atherosclerosis. Another possibility is that the contribution of low oxidative capacity to atherosclerotic cardiovascular disease could be mediated largely by its contribution to other atherosclerotic risk factors, like hypertension and insulin resistance (1–10).
We observed a significant association of high lactate level with all-cause and coronary mortality. Marginally significant association with fatal CHD might be to some extent in line with the findings for heart failure. Persons with reduced oxidative capacity could have lower cardiac reserve when they develop CHD. Given that low oxidative capacity might be involved in a broad range of age-related degenerative diseases (1, 2), it is not surprising that elevated lactate level would be associated with all-cause mortality. Further evaluation of the association between lactate level and cause-specific deaths is warranted, although this is beyond the scope of the present study.
Various organs, such as skeletal muscle and adipose tissue, are involved in lactate production and release (43). We are not sure whether elevated levels of lactate in our study reflect low oxidative capacity of the whole body or of specific organs. Because we were interested in global oxidative capacity, we rigorously adjusted for physical activity and measures of obesity. Nevertheless, further investigations will be required to elucidate organ-specific involvement in the link between low oxidative capacity and poor outcome.
It is possible that elevated lactate level linked to increased risk in heart failure and mortality in the present study was due to pathophysiological conditions other than low oxidative capacity. Lactate production increases in hypoxia and hypoperfusion and with the use of some medications. It is known that lactate is a predictor of death in critically ill patients with systemic hypoperfusion, such as cardiac arrest, sepsis, and trauma (44, 45). However, it is highly unlikely that these conditions would influence risk estimates in a community-based cohort. Also, we obtained similar results after adjustment for smoking, history of pulmonary disease, an inflammatory marker, and heart rate and after exclusion of participants taking biguanides and participants with an extremely high lactate level that indicated hypoperfusion. Persons with subclinical cardiac dysfunction might have had elevated lactate levels because of low cardiac output and thus were at high risk of future heart failure. However, the association of plasma lactate with heart failure remained significant even after the adjustment for N-terminal pro–B-type natriuretic peptide, a clinical marker of cardiac overload, and after exclusion of heart failure cases within 3 years of follow-up, which suggests that reverse causation is unlikely. Lactate is metabolized in the liver and kidney, and abnormality of these organs might also contribute to increased lactate level (43). However, the association with heart failure and mortality in our study was independent of kidney function and liver enzymes. Nevertheless, further investigations with more specific markers of oxidative capacity, such as metabolomics (46), need to be performed.
Several limitations of the present study should be mentioned. First, we had only a single measurement of plasma lactate, and thus short-term variability could have resulted in a degree of misclassification (47). This type of misclassification usually biases the results toward null, however. Second, as with any observational study, we cannot rule out the possibility of residual confounding despite rigorous adjustment for all major cardiovascular risk factors. Third, identification of heart failure cases relied entirely on International Classification of Diseases codes abstracted from hospital records and death certificates (48, 49). Reliance on hospital discharge codes could result in underestimation of heart failure incidence (50). Additionally, data on ejection fraction were not available in most of the heart failure cases in this study, and thus whether lactate has a similar association with heart failure with preserved systolic function versus heart failure with reduced systolic function requires further investigation.
In conclusion, elevated lactate, a marker of low oxidative capacity, was significantly associated with increased risk of heart failure and all-cause mortality in a middle-aged, biethnic general population. Our findings suggest that low resting oxidative capacity could play an independent role in the development of cardiac dysfunction and poor prognosis in the general population.
Supplementary Material
ACKNOWLEDGMENTS
Author affiliations: Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Kunihiro Matsushita; Emma K. Williams, Morgana L. Mongraw-Chaffin, Josef Coresh); Postgraduate Program in Epidemiology, School of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul Brazil (Maria Ines Schmidt); Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland (Frederick L. Brancati, J. Hunter Young); and the Department of Medicine, Section of Atherosclerosis and Vascular Medicine, Baylor College of Medicine and Methdodist DeBakey Heart and Vascular Center, Houston, Texas (Ron C. Hoogeveen, Christie M. Ballantyne).
The authors thank the staff and participants of the Atherosclerosis Risk in Communities (ARIC) Study for their important contributions.
The ARIC Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Kunihiro Matsushita and J. Hunter Young are supported in part by an NIH/NHLBI RO1DK085458 grant. Roche Diagnostics provided reagents and loan of an instrument to conduct the N-terminal pro–B-type natriuretic peptide assay.
Siemens Healthcare Diagnostics provided reagents and loan of an instrument to conduct the high-sensitivity C-reactive protein assay. Roche and Siemens had no role in design, analysis, or manuscript preparation.
Conflict of interest: none declared.
REFERENCES
- 1.Linnane A, Ozawa T, Marzuki S, et al. Mitochondrial DNA mutations as an important contributor to ageing and degenerative diseases. Lancet. 1989;333(8639):642–645. doi: 10.1016/s0140-6736(89)92145-4. [DOI] [PubMed] [Google Scholar]
- 2.Bernal-Mizrachi C, Semenkovich CF. Fast predators or fast food, the fit still survive. Nat Med. 2006;12(1):46–47. doi: 10.1038/nm0106-46. [DOI] [PubMed] [Google Scholar]
- 3.Mootha VK, Lindgren CM, Eriksson KF, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–273. doi: 10.1038/ng1180. [DOI] [PubMed] [Google Scholar]
- 4.Wisloff U, Najjar SM, Ellingsen O, et al. Cardiovascular risk factors emerge after artificial selection for low aerobic capacity. Science. 2005;307(5708):418–420. doi: 10.1126/science.1108177. [DOI] [PubMed] [Google Scholar]
- 5.Bruce CR, Anderson MJ, Carey AL, et al. Muscle oxidative capacity is a better predictor of insulin sensitivity than lipid status. J Clin Endocrinol Metab. 2003;88(11):5444–5451. doi: 10.1210/jc.2003-030791. [DOI] [PubMed] [Google Scholar]
- 6.Petersen KF, Befroy D, Dufour S, et al. Mitochondrial dysfunction in the elderly: possible role in insulin resistance. Science. 2003;300(5622):1140–1142. doi: 10.1126/science.1082889. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Morino K, Petersen KF, Dufour S, et al. Reduced mitochondrial density and increased IRS-1 serine phosphorylation in muscle of insulin-resistant offspring of type 2 diabetic parents. J Clin Invest. 2005;115(12):3587–3593. doi: 10.1172/JCI25151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sparks LM, Xie H, Koza RA, et al. A high-fat diet coordinately downregulates genes required for mitochondrial oxidative phosphorylation in skeletal muscle. Diabetes. 2005;54(7):1926–1933. doi: 10.2337/diabetes.54.7.1926. [DOI] [PubMed] [Google Scholar]
- 9.Kelley DE, He J, Menshikova EV, et al. Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes. Diabetes. 2002;51(10):2944–2950. doi: 10.2337/diabetes.51.10.2944. [DOI] [PubMed] [Google Scholar]
- 10.Crawford SO, Ambrose MS, Hoogeveen RC, et al. Association of lactate with blood pressure before and after rapid weight loss. Am J Hypertens. 2008;21(12):1337–1342. doi: 10.1038/ajh.2008.282. [DOI] [PubMed] [Google Scholar]
- 11.Zagari F, Jordan M, Stettler M, et al. Lactate metabolism shift in CHO cell culture: the role of mitochondrial oxidative activity. N Biotechnol. 2013;30(2):238–245. doi: 10.1016/j.nbt.2012.05.021. [DOI] [PubMed] [Google Scholar]
- 12.DiGirolamo M, Newby FD, Lovejoy J. Lactate production in adipose tissue: a regulated function with extra-adipose implications. FASEB J. 1992;6(7):2405–2412. doi: 10.1096/fasebj.6.7.1563593. [DOI] [PubMed] [Google Scholar]
- 13.Doar JW, Wynn V, Cramp DG. Blood pyruvate and plasma glucose levels during oral and intravenous glucose tolerance tests in obese and non-obese women. Metabolism. 1968;17(8):690–701. doi: 10.1016/0026-0495(68)90053-x. [DOI] [PubMed] [Google Scholar]
- 14.Jansson PA, Larsson A, Lönnroth PN. Relationship between blood pressure, metabolic variables and blood flow in obese subjects with or without non-insulin-dependent diabetes mellitus. Eur J Clin Invest. 1998;28(10):813–818. doi: 10.1046/j.1365-2362.1998.00360.x. [DOI] [PubMed] [Google Scholar]
- 15.Baron AD, Brechtel-Hook G, Johnson A, et al. Skeletal muscle blood flow. A possible link between insulin resistance and blood pressure. Hypertension. 1993;21(2):129–135. doi: 10.1161/01.hyp.21.2.129. [DOI] [PubMed] [Google Scholar]
- 16.The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epidemiol. 1989;129(4):687–702. [PubMed] [Google Scholar]
- 17.Eriksson H, Caidaul K, Larsson B, et al. Cardiac and pulmonary causes of dyspnoea − validation of a scoring test for clinical-epidemiological use: The Study of Men Born in 1913. Eur Heart J. 1987;8(9):1007–1014. doi: 10.1093/oxfordjournals.eurheartj.a062365. [DOI] [PubMed] [Google Scholar]
- 18.Wilhelmsen L, Eriksson H, Svardsudd K, et al. Improving the detection and diagnosis of congestive heart failure. Eur Heart J. 1989;10(suppl C):13–18. doi: 10.1093/eurheartj/10.suppl_c.13. [DOI] [PubMed] [Google Scholar]
- 19.Evenson KR, Rosamond WD, Cai J, et al. Physical activity and ischemic stroke risk. The Atherosclerosis Risk in Communities Study. Stroke. 1999;30(7):1333–1339. doi: 10.1161/01.str.30.7.1333. [DOI] [PubMed] [Google Scholar]
- 20.Houston DK, Stevens J, Cai J. Abdominal fat distribution and functional limitations and disability in a biracial cohort: the Atherosclerosis Risk in Communities Study. Int J Obes (Lond) 2005;29(12):1457–1463. doi: 10.1038/sj.ijo.0803043. [DOI] [PubMed] [Google Scholar]
- 21.Papp AC, Hatzakis H, Bracey A, et al. ARIC hemostasis study—I. Development of a blood collection and processing system suitable for multicenter hemostatic studies. Thromb Haemost. 1989;61(1):15–19. [PubMed] [Google Scholar]
- 22.Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–419. doi: 10.1007/BF00280883. [DOI] [PubMed] [Google Scholar]
- 23.Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi: 10.7326/0003-4819-150-9-200905050-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Olsen MH, Hansen TW, Christensen MK, et al. N-terminal pro-brain natriuretic peptide, but not high sensitivity C-reactive protein, improves cardiovascular risk prediction in the general population. Eur Heart J. 2007;28(11):1374–1381. doi: 10.1093/eurheartj/ehl448. [DOI] [PubMed] [Google Scholar]
- 25.Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012;125(1):e2–e220. doi: 10.1161/CIR.0b013e31823ac046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.White AD, Folsom AR, Chambless LE, et al. Community surveillance of coronary heart disease in the Atherosclerosis Risk in Communities (ARIC) Study: methods and initial two years’ experience. J Clin Epidemiol. 1996;49(2):223–233. doi: 10.1016/0895-4356(95)00041-0. [DOI] [PubMed] [Google Scholar]
- 27.Rathore SS, Hinn AR, Cooper LS, et al. Characterization of incident stroke signs and symptoms: findings from the atherosclerosis risk in communities study. Stroke. 2002;33(11):2718–2721. doi: 10.1161/01.str.0000035286.87503.31. [DOI] [PubMed] [Google Scholar]
- 28.Rosamond WD, Folsom AR, Chambless LE, et al. Stroke incidence and survival among middle-aged adults: 9-year follow-up of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke. 1999;30(4):736–743. doi: 10.1161/01.str.30.4.736. [DOI] [PubMed] [Google Scholar]
- 29.Matsushita K, Blecker S, Pazin-Filho A, et al. The association of hemoglobin A1c with incident heart failure among people without diabetes: the Atherosclerosis Risk in Communities Study. Diabetes. 2010;59(8):2020–2026. doi: 10.2337/db10-0165. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Folsom AR, Yatsuya H, Nettleton JA, et al. Community prevalence of ideal cardiovascular health, by the American Heart Association definition, and relationship with cardiovascular disease incidence. J Am Coll Cardiol. 2011;57(16):1690–1696. doi: 10.1016/j.jacc.2010.11.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Avery CL, Loehr LR, Baggett C, et al. The population burden of heart failure attributable to modifiable risk factors: the ARIC (Atherosclerosis Risk in Communities) Study. J Am Coll Cardiol. 2012;60(17):1640–1646. doi: 10.1016/j.jacc.2012.07.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.D'Agostino RB, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–753. doi: 10.1161/CIRCULATIONAHA.107.699579. [DOI] [PubMed] [Google Scholar]
- 33.Salpeter SR, Greyber E, Pasternak GA, et al. Risk of fatal and nonfatal lactic acidosis with metformin use in type 2 diabetes mellitus: systematic review and meta-analysis. Arch Intern Med. 2003;163(21):2594–2602. doi: 10.1001/archinte.163.21.2594. [DOI] [PubMed] [Google Scholar]
- 34.Mizushima Y, Ueno M, Watanabe H, et al. Discrepancy between heart rate and makers of hypoperfusion is a predictor of mortality in trauma patients. J Trauma. 2011;71(4):789–792. doi: 10.1097/TA.0b013e31822f7bbd. [DOI] [PubMed] [Google Scholar]
- 35.Jansen TC, van Bommel J, Bakker J. Blood lactate monitoring in critically ill patients: a systematic health technology assessment. Crit Care Med. 2009;37(10):2827–2839. doi: 10.1097/CCM.0b013e3181a98899. [DOI] [PubMed] [Google Scholar]
- 36.Tanaka H, Seals DR. Endurance exercise performance in Masters athletes: age-associated changes and underlying physiological mechanisms. J Physiol. 2008;586(1):55–63. doi: 10.1113/jphysiol.2007.141879. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Loehr LR, Rosamond WD, Poole C, et al. The potentially modifiable burden of incident heart failure due to obesity: the atherosclerosis risk in communities study. Am J Epidemiol. 2010;172(7):781–789. doi: 10.1093/aje/kwq213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Kern M. Coronary blood flow and myocardial ischemia. In: Zipes D, Libby P, Bonow R, Braunwald E, editors. Braunwald's Heart Disease: A Text Book of Cardiovascular Medicine. 7th ed. Philadelphia, PA: Elsevier Saunders; 2005. pp. 1103–1127. [Google Scholar]
- 39.Canty J. Coronary blood flow and myocardial ischemia. In: Bonow R, Mann D, Zipes D, Libby P, editors. Braunwald's Heart Disease: A Text Book of Cardiovascular Medicine. 9th ed. Philadelphia, PA: Elsevier Saunders; 2012. pp. 1049–1075. [Google Scholar]
- 40.Berardo A, Musumeci O, Toscano A. Cardiological manifestations of mitochondrial respiratory chain disorders. Acta Myol. 2011;30(1):9–15. [PMC free article] [PubMed] [Google Scholar]
- 41.Gupta A, Akki A, Yibin W, et al. Creatine kinase—mediated improvement of function in failing mouse hearts provides causal evidence the failing heart is energy starved. J Clin Invest. 2012;122(1):291–302. doi: 10.1172/JCI57426. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Arboix A, Alio J. Acute cardioembolic stroke: an update. Expert Rev Cardiovasc Ther. 2011;9(3):367–379. doi: 10.1586/erc.10.192. [DOI] [PubMed] [Google Scholar]
- 43.van Hall G. Lactate kinetics in human tissues at rest and during exercise. Acta Physiol (Oxf) 2010;199(4):499–508. doi: 10.1111/j.1748-1716.2010.02122.x. [DOI] [PubMed] [Google Scholar]
- 44.Guyette F, Suffoletto B, Castillo J-L, et al. Prehospital serum lactate as a predictor of outcomes in trauma patients: a retrospective observational study. J Trauma. 2011;70(4):782–786. doi: 10.1097/TA.0b013e318210f5c9. [DOI] [PubMed] [Google Scholar]
- 45.Kessler KM, Kozlovskis P, Trohman RG, et al. Serum lactate: prognostic marker for recurrent cardiac arrest? Am Heart J. 1987;113(6):1540–1544. doi: 10.1016/0002-8703(87)90696-x. [DOI] [PubMed] [Google Scholar]
- 46.Holmes E, Loo RL, Stamler J, et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature. 2008;453(7193):396–400. doi: 10.1038/nature06882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Panteghini M, Pagani F. Biological variation of lactate and pyruvate in blood [letter] Clin Chem. 1993;39(5):908. [PubMed] [Google Scholar]
- 48.Temme EH, Zhang J, Schouten EG, et al. Serum bilirubin and 10-year mortality risk in a Belgian population. Cancer Causes Control. 2001;12(10):887–894. doi: 10.1023/a:1013794407325. [DOI] [PubMed] [Google Scholar]
- 49.Pazin-Filho A, Kottgen A, Bertoni AG, et al. HbA(1c) as a risk factor for heart failure in persons with diabetes: the Atherosclerosis Risk in Communities (ARIC) study. Diabetologia. 2008;51(12):2197–2204. doi: 10.1007/s00125-008-1164-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Ohnaka K, Kono S, Inoguchi T, et al. Inverse associations of serum bilirubin with high sensitivity C-reactive protein, glycated hemoglobin, and prevalence of type 2 diabetes in middle-aged and elderly Japanese men and women. Diabetes Res Clin Pract. 2010;88(1):103–110. doi: 10.1016/j.diabres.2009.12.022. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.