Martin G. Larson

Metabolite profiles and the risk of developing diabetes

Emerging technologies allow the high-throughput profiling of metabolic status from a blood specimen (metabolomics). We investigated whether metabolite profiles could predict the development of diabetes. Among 2,422 normoglycemic individuals followed for 12 years, 201 developed diabetes. Amino acids, amines and other polar metabolites were profiled in baseline specimens by liquid chromatography–tandem mass spectrometry (LC-MS). Cases and controls were matched for age, body mass index and fasting glucose. Five branched-chain and aromatic amino acids had highly significant associations with future diabetes: isoleucine, leucine, valine, tyrosine and phenylalanine. A combination of three amino acids predicted future diabetes (with a more than fivefold higher risk for individuals in top quartile). The results were replicated in an independent, prospective cohort. These findings underscore the potential key role of amino acid metabolism early in the pathogenesis of diabetes and suggest that amino acid profiles could aid in diabetes risk assessment.

Multiple biomarkers for the prediction of first major cardiovascular events and death

BACKGROUNDnFew investigations have evaluated the incremental usefulness of multiple biomarkers from distinct biologic pathways for predicting the risk of cardiovascular events.nnnMETHODSnWe measured 10 biomarkers in 3209 participants attending a routine examination cycle of the Framingham Heart Study: the levels of C-reactive protein, B-type natriuretic peptide, N-terminal pro-atrial natriuretic peptide, aldosterone, renin, fibrinogen, D-dimer, plasminogen-activator inhibitor type 1, and homocysteine; and the urinary albumin-to-creatinine ratio.nnnRESULTSnDuring follow-up (median, 7.4 years), 207 participants died and 169 had a first major cardiovascular event. In Cox proportional-hazards models adjusting for conventional risk factors, the following biomarkers most strongly predicted the risk of death (each biomarker is followed by the adjusted hazard ratio per 1 SD increment in the log values): B-type natriuretic peptide level (1.40), C-reactive protein level (1.39), the urinary albumin-to-creatinine ratio (1.22), homocysteine level (1.20), and renin level (1.17). The biomarkers that most strongly predicted major cardiovascular events were B-type natriuretic peptide level (adjusted hazard ratio, 1.25 per 1 SD increment in the log values) and the urinary albumin-to-creatinine ratio (1.20). Persons with multimarker scores (based on regression coefficients of significant biomarkers) in the highest quintile as compared with those with scores in the lowest two quintiles had elevated risks of death (adjusted hazard ratio, 4.08; P<0.001) and major cardiovascular events (adjusted hazard ratio, 1.84; P=0.02). However, the addition of multimarker scores to conventional risk factors resulted in only small increases in the ability to classify risk, as measured by the C statistic.nnnCONCLUSIONSnFor assessing risk in individual persons, the use of the 10 contemporary biomarkers that we studied adds only moderately to standard risk factors.

Prediction of Lifetime Risk for Cardiovascular Disease by Risk Factor Burden at 50 Years of Age

Background— Lifetime risk for atherosclerotic cardiovascular disease (CVD) has not previously been estimated, and the effect of risk factor burden on lifetime risk is unknown. Methods and Results— We included all Framingham Heart Study participants who were free of CVD (myocardial infarction, coronary insufficiency, angina, stroke, claudication) at 50 years of age. Lifetime risks to 95 years of age were estimated for men and women, with death free of CVD as a competing event. We followed up 3564 men and 4362 women for 111 777 person-years; 1757 had CVD events and 1641 died free of CVD. At 50 years of age, lifetime risks were 51.7% (95% CI, 49.3 to 54.2) for men and 39.2% (95% CI, 37.0 to 41.4) for women, with median survivals of 30 and 36 years, respectively. With more adverse levels of single risk factors, lifetime risks increased and median survivals decreased. Compared with participants with ≥2 major risk factors, those with optimal levels had substantially lower lifetime risks (5.2% versus 68.9% in men, 8.2% versus 50.2% in women) and markedly longer median survivals (>39 versus 28 years in men, >39 versus 31 years in women). Conclusions— The absence of established risk factors at 50 years of age is associated with very low lifetime risk for CVD and markedly longer survival. These results should promote efforts aimed at preventing development of risk factors in young individuals. Given the high lifetime risks and lower survival in those with intermediate or high risk factor burden at 50 years of age, these data may be useful in communicating risks and supporting intensive preventive therapy.

Impact of Obesity on Plasma Natriuretic Peptide Levels

Background—The mechanisms linking obesity to hypertension have not been established, but sodium retention and excessive sympathetic tone are key contributors. The natriuretic peptides are important regulators of sodium homeostasis and neurohormonal activation, raising the possibility that obese individuals have an impaired natriuretic peptide response. Methods and Results—We examined the relations of plasma B-type natriuretic peptide (BNP) and N-terminal proatrial natriuretic peptide (N-ANP) to body mass index in 3389 Framingham Study participants (1803 women) without heart failure. Multivariable regression analyses were performed, adjusting for clinical and echocardiographic covariates. BNP levels below the assay detection limit and N-ANP levels in the lowest sex-specific quartile were categorized as low. Multivariable-adjusted mean plasma BNP levels in lean (<25 kg/m2), overweight (25 to 29.9 kg/m2), and obese (≥30 kg/m2) men were 21.4, 15.5, and 12.7 pg/mL, respectively (trend P <0.0001). Corresponding values in women were 21.1, 16.3, and 13.1 pg/mL (trend P <0.001). A similar pattern was noted for plasma N-ANP. Obese individuals had higher odds of having low plasma BNP (multivariable-adjusted odds ratios: men, 2.51; 95% CI, 1.71 to 3.68; women, 1.84; 95% CI, 1.32 to 2.58) and low plasma N-ANP (odds ratios: men, 4.81; 95% CI, 2.98 to 7.76; women, 2.85; 95% CI, 2.01 to 4.04) compared with lean individuals. Diabetes also was associated with low plasma natriuretic peptide levels, and the negative effects of obesity and diabetes on natriuretic peptide levels were additive. Conclusions—Obese individuals have low circulating natriuretic peptide levels, which may contribute to their susceptibility to hypertension and hypertension-related disorders.

Impact of high-normal blood pressure on the risk of cardiovascular disease ☆

BACKGROUNDnInformation is limited regarding the absolute and relative risk of cardiovascular disease in persons with high-normal blood pressure (systolic pressure of 130 to 139 mm Hg, diastolic pressure of 85 to 89 mm Hg, or both).nnnMETHODSnWe investigated the association between blood-pressure category at base line and the incidence of cardiovascular disease on follow-up among 6859 participants in the Framingham Heart Study who were initially free of hypertension and cardiovascular disease.nnnRESULTSnA stepwise increase in cardiovascular event rates was noted in persons with higher baseline blood-pressure categories. The 10-year cumulative incidence of cardiovascular disease in subjects 35 to 64 years of age who had high-normal blood pressure was 4 percent (95 percent confidence interval, 2 to 5 percent) for women and 8 percent (95 percent confidence interval, 6 to 10 percent) for men; in older subjects (those 65 to 90 years old), the incidence was 18 percent (95 percent confidence interval, 12 to 23 percent) for women and 25 percent (95 percent confidence interval, 17 to 34 percent) for men. As compared with optimal blood pressure, high-normal blood pressure was associated with a risk-factor-adjusted hazard ratio for cardiovascular disease of 2.5 (95 percent confidence interval, 1.6 to 4.1) in women and 1.6 (95 percent confidence interval, 1.1 to 2.2) in men.nnnCONCLUSIONSnHigh-normal blood pressure is associated with an increased risk of cardiovascular disease. Our findings emphasize the need to determine whether lowering high-normal blood pressure can reduce the risk of cardiovascular disease.

Prognosis of left ventricular geometric patterns in the Framingham heart study

OBJECTIVESnThe goal of this study was to determine the incremental prognostic value of left ventricular geometric patterns beyond that provided by cardiovascular disease risk factors, including left ventricular mass.nnnBACKGROUNDnLeft ventricular geometry may be classified into the following four mutually exclusive groups on the basis of left ventricular mass and relative wall thickness: concentric hypertrophy (increased mass and increased relative wall thickness), eccentric hypertrophy (increased mass and normal relative wall thickness), concentric remodeling (normal mass and increased relative wall thickness) and normal geometry (normal mass and normal relative wall thickness). The prognosis associated with these patterns in a population-based sample is not known.nnnMETHODSnProportional hazards regression models were used to evaluate the prognostic importance of left ventricular geometry in 3,216 subjects in the Framingham Heart Study who were > or = 40 years old and free of clinically apparent cardiovascular disease, after adjustment for traditional cardiovascular risk factors and left ventricular mass. The follow-up period was 8 years.nnnRESULTSnSubjects with concentric hypertrophy had the worst prognosis, followed by those with eccentric hypertrophy, concentric remodeling and normal geometry. Subjects with concentric hypertrophy also had the highest left ventricular mass. The association between type of geometry and prognosis was largely attenuated by adjustment for baseline differences in left ventricular mass. The odds ratio for incident cardiovascular disease in subjects with concentric hypertrophy compared with those who had normal geometry was 1.3 (95% confidence interval [CI] 0.8 to 2.1) in men and 1.2 (95% CI 0.6 to 2.3) in women after adjustment for other cardiovascular risk factors, including left ventricular mass.nnnCONCLUSIONSnIn a population-based sample of subjects without cardiovascular disease, knowledge of left ventricular geometry provided little prognostic information beyond that available from left ventricular mass and traditional cardiovascular risk factors.

Accuracy of Death Certificates for Coding Coronary Heart Disease as the Cause of Death

The death certificate is widely used to establish cause of death in epidemiologic and clinical investigations and for national statistics. Although mortality statistics are of interest to policymakers and researchers, the certification of the underlying and contributing causes of death is the responsibility of decedents physicians, who often determine causes subjectively at the time of death. Coronary heart disease is the most common cause of death in the United States and many developed nations [1, 2]. Mortality due to coronary heart disease has been ascertained from death certificates in numerous ecological studies, in studies of secular trends in cause of death, and in therapy evaluation [3-8]. Because data on coronary heart disease mortality is used for various purposes, it is important that these data be both accurate and reliable. However, the amount of confidence that should be placed in these data depends heavily on the accuracy of the death certificate. For example, in the United States, death certificate data indicate that the rates of death from coronary heart disease increased until the mid-1960s to late 1960s and then steadily declined [8]. This trend may be the result of changes in disease rates, changes in diagnostic methods, changes in recording procedures, or a combination of these three factors. Therefore, the accuracy of death certificates must be examined, particularly with respect to chronic diseases, such as coronary heart disease, that increase in prevalence with advancing age. Data from the Framingham Heart Study allowed us to evaluate the accuracy of the death certificate diagnosis of coronary heart disease as the underlying cause of death. In this investigation, we compared cause of death obtained from the death certificate with cause of death assigned independently by a panel of trained physician-adjudicators. We also evaluated the influence of age, sex, calendar year in which death occurred, and prevalence of coronary heart disease on the accuracy of the death certificate. Methods Study Sample The Framingham Heart Study began in 1948 when 5209 residents of Framingham, Massachusetts, who were 28 to 62 years of age enrolled in a prospective epidemiologic study. The selection criteria and study design have been detailed elsewhere [9-11]. Every 2 years, members of this cohort receive follow-up evaluations that include medical histories, physical examinations, and selected laboratory tests. Ascertainment of the vital status of study participants has been essentially complete. Determination of Cause of Death Since the beginning of the study, each death has been reviewed and assigned an underlying cause by a panel of three physicians. As part of the review process, all available medical information about each death is collected. This information typically includes Framingham Heart Study records, hospitalization records, and, when available, autopsy results. In the case of an out-of-hospital, witnessed death, family members are interviewed by telephone to better ascertain the circumstances surrounding death. The death certificate is usually available to the panel, but it is not used to determine the underlying cause of death. After discussing the case, the panel jointly assigns an underlying cause of death, which is then coded into one of the following six mutually exclusive categories: 1) coronary heart disease, 2) stroke, 3) other cardiovascular disease, 4) cancer, 5) other, or 6) unknown. The panel makes a conscious effort to determine the true underlying cause of death; however, when cause cannot be reliably determined from all available data (for example, in the case of a nursing home resident with progressive inanition), the panel assigns the death to the category of unknown cause. Sudden death, defined as death occurring within 1 hour of symptom onset, is attributed to coronary heart disease unless another cause is apparent. These criteria for assigning cause of death have not changed since the beginning of the study. Diagnostic tests have improved over time, but the panel has access to the same results that are available to the physician coding the death certificate. We compared the underlying cause of death listed by each patients physician on the death certificate with the cause assigned by the Framingham Heart Study physician panel. In 1988, a trained nosologist, blinded to the findings of the physician panel, coded each death certificate according to the International Classification of Diseases, Ninth Revision (ICD-9) [12]. For our study, we used the ICD-9 code assigned by the nosologist to determine the underlying cause of death on the death certificate. Coronary heart disease was considered the underlying cause of death if the cause of death was assigned an ICD-9 code of 410 to 414 (ischemic heart disease) by the nosologist. Although ICD-9 code 427 (cardiac dysrhythmia) has also been used to identify deaths from coronary heart disease [13], none of the deaths in our sample was assigned this code by the nosologist. Other cardiovascular disease was considered to be the underlying cause of death for ICD-9 codes of 390 to 404, 415 to 425, 428, 429, and 440 to 459. Death certificates of persons who died after 1988 have not been nosologically coded. Study Design By the end of 1988, 2888 participants from the original cohort had died. Death certificates were available and nosologically coded for 2719 of these participants (94.1%). Because few deaths occurred at young ages, we excluded participants whose age at death was younger than 45 years (n = 36). We examined the utility of the death certificate for coding coronary heart disease (as opposed to any other cause) as the underlying cause of death. With the cause of death determined by the physician panel serving as the reference standard, the sensitivity, positive predictive value, specificity, and negative predictive value of the death certificate for coding coronary heart disease as the underlying cause of death were calculated according to their usual definitions [14]. In the context of vital statistics, sensitivity has also been called the detection rate and positive predictive value has been called the confirmation rate [15]. To assess the effect of such cases on the overall utility of the death certificate, these variables were calculated with inclusion and exclusion of cases with panel-assigned unknown cause of death to assess the effect of such cases on the overall utility of the death certificate. For comparative purposes, the sensitivity, positive predictive value, specificity, and negative predictive value of the death certificate for cancer and for stroke as the underlying cause of death were also calculated. Cancer was considered the underlying cause of death if the ICD-9 code was 140 to 239; stroke was considered the underlying cause of death if the ICD-9 code was 430 to 438. To determine whether there was a time trend in the accuracy of the death certificate during the study, we compared the sensitivity, positive predictive value, specificity, and negative predictive value of the death certificate for coronary heart disease during three consecutive decades from 1955 through 1984. These decades were chosen because too few deaths occurred before 1955 to allow meaningful comparisons with later periods. To account for aging of the cohort, we further restricted this time-trend analysis to decedents whose age at death was 50 to 84 years (n = 2033). Statistical Analysis All analyses were performed by using SAS software [16]. Ninety-five percent CIs for the estimates of sensitivity, specificity, and positive and negative predictive values were calculated by using asymptotic calculations of the normal distribution. The chi-square test statistic with five degrees of freedom was used to assess equality of the marginal rates of diagnosis of underlying cause of death between the death certificate and the physician panel [17, 18]. Statistics were adjusted for age, sex, calendar year in which death occurred, or prevalence of coronary heart disease by using logistic regression [19] where such adjustment was indicated. For analysis of time trends in these statistics, we did not use the aggregate sample method described by Coughlin and colleagues [20]. Instead, logistic models were fitted to various subsets of participants. We let X = 1 if the panel assigned coronary heart disease as the cause of death and let X = 0 otherwise; similarly, we let Y = 1 if the death certificate recorded coronary heart disease as the cause of death and let Y = 0 otherwise. For sensitivity, we modeled Pr(Y = 1 X = 1); for specificity, Pr(Y = 0 X = 0); for positive predictive value, Pr(X = 1 Y = 1); and for negative predictive value, Pr(X = 0 Y = 0). For example, only patients with panel-assigned coronary heart disease death were used for analysis of sensitivity. Co-variates included in the models to test for time trend in the diagnosis of death from coronary heart disease were sex of the decedent, indicators for age group at time of death (50 to 64 years of age, 65 to 74 years of age, and 75 to 84 years of age), and decade of death (0 = 1955 to 1964, 1 = 1965 to 1974, and 2 = 1975 to 1984). Hosmer-Lemeshow statistics were computed to assess goodness of fit for the trend model. Further checks were made by using models with unique coefficients for each period and by using models without any time-period variable. A P value less than 0.05 was considered statistically significant. Results We analyzed a total of 2683 decedents whose underlying causes of death were coded by the death certificate and determined by the physician panel. The distribution of this sample by age and sex is given in Table 1. Overall, 52.6% of decedents were male and 41.9% were at least 75 years of age. Table 1. Decedents by Age at Death and Sex Table 2 presents a cross-classification of the 2683 deaths by underlying cause as assigned by the physician panel and as taken from the death ce

Insulin resistance, oxidative stress, hypertension, and leukocyte telomere length in men from the Framingham Heart Study.

Insulin resistance and oxidative stress are associated with accelerated telomere attrition in leukocytes. Both are also implicated in the biology of aging and in aging‐related disorders, including hypertension. We explored the relations of leukocyte telomere length, expressed by terminal restriction fragment (TRF) length, with insulin resistance, oxidative stress and hypertension. We measured leukocyte TRF length in 327 Caucasian men with a mean age of 62.2 years (range 40–89 years) from the Offspring cohort of the Framingham Heart Study. TRF length was inversely correlated with age (r = –0.41, P < 0.0001) and age‐adjusted TRF length was inversely correlated with the Homeostatic Model Assessment of Insulin Resistance (HOMA‐IR) (r =–0.16, P = 0.007) and urinary 8‐epi‐PGF2α (r = –0.16, P = 0.005) – an index of systemic oxidative stress. Compared with their normotensive peers, hypertensive subjects exhibited shorter age‐adjusted TRF length (hypertensives = 5.93 ± 0.042 kb, normotensives = 6.07 ± 0.040 kb, P = 0.025). Collectively, these observations suggest that hypertension, increased insulin resistance and oxidative stress are associated with shorter leukocyte telomere length and that shorter leukocyte telomere length in hypertensives is largely due to insulin resistance.

Blood Pressure Response During Treadmill Testing as a Risk Factor for New-Onset Hypertension The Framingham Heart Study

BACKGROUNDnAlthough systolic blood pressure (SBP) response to exercise has been shown to predict subsequent hypertension in small samples of men, this association has not been studied in a large population-based sample of middle-aged men and women. The purpose of this study was to examine, in normotensive subjects, the relations of SBP and diastolic blood pressure (DBP) during the exercise and recovery periods of a graded treadmill test to the risk of developing new-onset hypertension.nnnMETHODS AND RESULTSnBP data from exercise testing in 1026 men and 1284 women (mean age, 42+/-10 years; range, 20 to 69 years) from the Framingham Offspring Study who were normotensive at baseline were related to the incidence of hypertension 8 years later. New-onset hypertension, defined as an SBP >/=140 mm Hg or DBP >/=90 mm Hg or the initiation of antihypertensive drug treatment, occurred in 228 men (22%) and 207 women (16%). Exaggerated SBP (Ex-SBP 2) and DBP (Ex-DBP 2) response and delayed recovery of SBP (R-SBP 3) and DBP (R-DBP 3) were defined as an age-adjusted BP greater than the 95th percentile during the second stage of exercise and third minute of recovery, respectively. After multivariable adjustment, Ex-DBP 2 was highly predictive of incident hypertension in both men (OR, 4.16; 95% CI, 2.15, 8.05) and women (OR, 2.17; CI, 1.19, 3.96). R-SBP 3 was predictive of hypertension in men in a multivariable model that included exercise duration and peak exercise BP (OR, 1.92; CI, 1.00, 3.69). Baseline resting SBP (chi2, 23.4 in men and 34.7 in women) and DBP (chi2, 11.3 in men and 13.1 in women) had stronger associations with new-onset hypertension than exercise DBP (chi2, 16.4 in men and 6.1 in women) and recovery SBP (chi2, 6.5 in men and 2.1 in women) responses.nnnCONCLUSIONSnAn exaggerated DBP response to exercise was predictive of risk for new-onset hypertension in normotensive men and women. An elevated recovery SBP was predictive of hypertension in men. These findings may reflect subtle pathophysiological features in the preclinical stage of hypertension.

Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans

Dyslipidemia is an independent risk factor for type 2 diabetes, although exactly which of the many plasma lipids contribute to this remains unclear. We therefore investigated whether lipid profiling can inform diabetes prediction by performing liquid chromatography/mass spectrometry-based lipid profiling in 189 individuals who developed type 2 diabetes and 189 matched disease-free individuals, with over 12 years of follow up in the Framingham Heart Study. We found that lipids of lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk. This pattern was strongest for triacylglycerols (TAGs) and persisted after multivariable adjustment for age, sex, BMI, fasting glucose, fasting insulin, total triglycerides, and HDL cholesterol. A combination of 2 TAGs further improved diabetes prediction. To explore potential mechanisms that modulate the distribution of plasma lipids, we performed lipid profiling during oral glucose tolerance testing, pharmacologic interventions, and acute exercise testing. Levels of TAGs associated with increased risk for diabetes decreased in response to insulin action and were elevated in the setting of insulin resistance. Conversely, levels of TAGs associated with decreased diabetes risk rose in response to insulin and were poorly correlated with insulin resistance. These studies identify a relationship between lipid acyl chain content and diabetes risk and demonstrate how lipid profiling could aid in clinical risk assessment.