Long-term C-Reactive Protein Variability and Prediction of Metabolic Risk

Article Outline

• Abstract
• Materials and Methods
  • Study Sample
  • Clinical Measurements
  • Laboratory Measurements
  • Statistical Analysis
• Results
  • Longitudinal, Intraindividual C-Reactive Protein Variability
  • Variables Predicting Long-term C-Reactive Protein Changes
  • Association of C-Reactive Protein and Related Variables with New-onset Metabolic Syndrome or Development of Diabetes
• Discussion
• Strengths and Limitations
• Conclusions
• References
• Copyright

Abstract 

Objective

This analysis was undertaken to determine the long-term intraindividual variability, determinants of change, and capacity of the inflammatory marker C-reactive protein (CRP) to predict metabolic traits and diabetes in a large community-based population.

Methods

Intraindividual CRP variability, predictors of CRP change, and metabolic events were evaluated in the Framingham Heart Study Offspring cohort using data from the same 2409 participants with CRP measured by the same methodology at each of 3 examination cycles, spanning 20 years.

Results

Between the first and second examinations (averaging 16 years apart), 23% to 47% of men and 27% to 49% of women remained within the same quintile of CRP values. An additional 24% to 51% of men and 24% to 50% of women occupied an adjacent quintile. Intermediate-term CRP variability (over 4 years) was similar to long-term variability. Both long- and intermediate-term variability of CRP were significantly less than that of plasma cholesterol measured in these same groups. Linear regression models for CRP at the intermediate examination demonstrated that CRP at the initial examination contributed the largest proportion of the variability (partial R-square = 0.27) seen in the overall model after adjustment for other covariates known to affect CRP concentrations. Although logistic regression models demonstrated that CRP over the intermediate term did not predict new-onset metabolic syndrome at the final examination, CRP did predict an increase in glucose and new-onset diabetes.

Conclusion

The results of this longitudinal analysis suggest the intraindividual, long-term variability of CRP concentrations is relatively small and predictive of new diabetes over an intermediate-term of 4 years.

Keywords: C-reactive protein, Diabetes, Epidemiology, Metabolic syndrome

C-reactive protein (CRP) is a nonspecific marker of inflammation that is notably increased in subjects with proinflammatory states of the metabolic syndrome (MetS) and diabetes,¹, ², ³ diagnoses that predispose individuals to develop cardiovascular disease.⁴ Although the extent to which high blood concentrations of CRP may be an independent predictor of cardiovascular disease is still unclear, it has been demonstrated that higher CRP identifies individuals who may have vascular or metabolic abnormalities, which are often present at early stages of cardiovascular disease.⁵

Unlike the more traditional risk factors for cardiovascular disease, such as high cholesterol, high glucose, and high blood pressure (BP), there seems to be little information related to the long-term biological (or intraindividual) variability of CRP measurements. Furthermore, no sizable population studies have assessed the extent to which the long or intermediate-term variability of CRP may be related to other major risk factors for cardiovascular disease, or the extent to which CRP measured at an earlier time point might be used to predict the development of MetS or diabetes later in life. Several analyses of CRP variability, using current sensitive CRP analytic methods, have been undertaken in studies of relatively small, ostensibly healthy groups for several months up to 1 year⁶, ⁷, ⁸ and in one case⁹ for 5 years. These studies have shown that CRP concentrations are relatively stable, and between-examination variability has ranged from 40% to 60%, which was comparable to the variability of cholesterol⁷, ⁸ or other major cardiovascular risk factors.⁷

The present analysis was undertaken to examine the intraindividual variability of CRP in the large group of men and women who constitute the Framingham Offspring cohort over intervals more prolonged than previously reported; to assess the strength of a number of CRP-related measures to predict future CRP concentrations; and in multivariable analysis, to assess the association of CRP with the development of MetS or diabetes.

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Materials and Methods 

Study Sample 

Study of the Framingham Offspring cohort was begun in 1971.¹⁰ During the period from 1979 to 2001, 7 examinations of this group were completed, usually at 4-year intervals. Serum CRP concentrations were measured using the same procedure at 3 examination periods: from 1979 to 1983 (examination 2), from 1995 to 1998 (examination 6), and from 1998 to 2001 (examination 7). The present analysis was limited to participants with CRP measured at each of these 3 examination cycles and consisted of 1123 men and 1286 women (representing 72.5% of the men and 72.1% of the women who attended the cycle 7 examination). The study was approved by the Institutional Review Board at Boston University Medicine Center, and all participants gave written informed consent.

Clinical Measurements 

Participants provided a medical history, underwent a physical examination, and had a series of laboratory tests. Subjects were considered to have cardiovascular disease if they had a history of coronary heart disease, death due to coronary heart disease, stroke, transient ischemic attack, intermittent claudication, or heart failure as determined by a panel of 3 physicians who reviewed medical and hospital records.¹¹ Fasting glucose concentrations were categorized by 2003 American Diabetes Association criteria. MetS was defined by the National Cholesterol Education Program Adult Treatment Panel III criteria, as refined in 2004.¹² For this analysis, the diagnosis of MetS did not include subjects with known diabetes.

Laboratory Measurements 

Blood was obtained from study participants who had fasted for at least 10 hours. At each of the 3 examinations, total cholesterol, triglycerides, and high-density lipoprotein-cholesterol (HDL-C) were measured by procedures previously described and modified after examination 2 only by the substitution of the apolipoprotein B-precipitant dextran-Mg⁺² for heparin-Mn⁺² for the measurement of HDL-C.¹³ Low-density lipoprotein-cholesterol values were obtained using the Friedewald formula. The intra-assay coefficient of variation was less than 5% for all lipid measurements. Glucose was measured using a hexokinase reagent with intra-assay variability less than 3%.

High-sensitivity CRP was measured at all cycles using the same nephelometric method, analytic instrument (Dade-Behring BN 100 nephelometer), and protocol for maintaining quality control over a 2-year period. Analyses were performed to routinely include blinded replicate specimens every 25th sample. Interassay and intra-assay coefficients of variation averaged 5.0% and 3.8%, respectively, for examination 2; 3.4% and 3.8%, respectively, for examination 6; and 5.1% and 3.2%, respectively, for examination 7. Although measurements were made over a 2-year period (from May 2002 to June 2004), values of CRP remeasured at the beginning and end of this 2-year period were similar, with mean ± standard deviation of 2.79 ± 2.56 mg/L in 2002 and 2.76 ± 2.34 mg/L in 2004 for 35 randomly chosen samples.

To provide a referent structure for evaluating CRP changes, the intraindividual variability of plasma cholesterol also was compared, long and intermediate-term, in the same Framingham groups used for CRP assessment. Values for cholesterol were those measured at the times that subjects were examined, using the Abell Kendall method¹⁴ at examination 2 and an automated cholesterol oxidase-based procedure at examinations 6 and 7.¹⁵ A cross-comparison of methods was conducted to ensure comparable cholesterol results between earlier and later cycles of examinations.

Statistical Analysis 

All analyses were performed with SAS 8 (SAS Institute Inc, Cary, NC). Intermediate- and long-term intra-individual CRP variability, during average follow-up times of 4 and 16 years, were compared. Shift-tables of sex-specific quintiles of CRP were compared for consistency between examinations 2 and 6 and between examinations 6 and 7 using weighted Kappa test for symmetry. CRP variability between examinations was examined using sex-specific Spearman correlation coefficients. A comparison of the intermediate and long-term variability of CRP with total cholesterol was undertaken using the McNemar's test, comparing the shift-tables of participants with more than a 1 quintile change in CRP distribution to a 1 quartile change in cholesterol distribution between the same pairs of examinations.

A variety of metabolic variables and cardiovascular risk factors have been associated with CRP concentrations, as previously reviewed.¹⁶ Two multiple linear regression models were constructed to assess which of these covariates, including initial CRP values (CRP at examination 2), best predicted future CRP concentrations (CRP at examination 6). The first model included the covariates at examination 2 of age, sex, body mass index (BMI), systolic BP, diastolic BP, smoking status, prevalent cardiovascular disease, fasting glucose, triglycerides, and the total cholesterol/HDL-C ratio, as well as aspirin use, BP therapy, lipid therapy, and hormone replacement therapy. The second multiple linear regression model used those examination 2 covariates and their examination 6 equivalents. A stepwise backward selection procedure using all examination 2 and 6 covariates was used to select the most parsimonious model.

The association of long-term change in CRP, defined as CRP at examination 6 minus CRP at examination 2, and CRP at examination 6 with incident MetS and diabetes at examination 7 were evaluated using multivariable logistic regression. Models were adjusted for the metabolic parameters and cardiovascular risk factors present at examination 6. Participants with established diagnoses of MetS or diabetes at examination 6 were excluded from analyses with either of these diagnoses as an end point. The discriminative ability of the models was assessed using a c-statistic.

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Results 

The characteristics of the study sample are shown in Table 1 at each of 3 examination cycles for men and women separately. Data are shown as mean values (standard deviation) or percentages for the same individuals at each of the 3 examination periods.

Table 1. Major Characteristics of the Framingham Offspring by Sex and Examination Cycle

Characteristics	Men (n=1123)			Women (n=1286)
	Examination			Examination
	2	6	7	2	6	7
Age, y	43(10)	59(10)	62(10)	43(10)	59(10)	62(10)
BMI, kg/m2	26.6(3.5)	28.6(4.3)	28.8(4.5)	24.5(4.7)	27.3(5.5)	27.6(5.7)
Systolic BP, mm Hg	125(14)	129(17)	128(18)	118(16)	127(20)	126(20)
Diastolic BP, mm Hg	80(9)	77(10)	75(10)	75(9)	74(9)	72(10)
Cholesterol/HDL-C	5.0(2)	4.9(2)	4.5(1)	3.9(1)	3.9(1)	3.7(1)
HDL-C, mg/dL	43(11)	43(12)	45(13)	54(13)	58(16)	60(16)
Triglycerides, mg/dL	142(79)	143(93)	142(98)	103(54)	132(80)	132(78)
Glucose, mg/dL	101(18)	107(27)	109(31)	94(12)	100(25)	100(22)
Current smoker, %	40	55	13	32	47	12
Prevalent disease
Hypertension, %	44	61	61	26	51	53
Diabetes, %	2	11	14	1	7	9
Obesity, %	15	31	33	11	25	26
MetS, %	13	44	53	4	34	43
Cardiovascular disease, %	3	15	19	2	7	9
Therapy
BP, %	9	31	38	8	25	32
Diabetes, %	1	6	8	<1	3	5
Lipid, %	2	16	25	1	10	18
Aspirin, %	25	36	40	28	23	26
Hormone, %	—	—	—	3	27	30
CRP, mg/L
Group mean	2.3	3.6	4.3	2.1	4.9	4.6
Quintile 1 mean	0.3(0.1)	0.5(0.2)	0.6(0.2)	0.2(0.1)	0.5(0.2)	0.6(0.2)
Quintile 2 mean	0.6(0.1)	1.0(0.2)	1.2(0.2)	0.4(0.1)	1.2(0.2)	1.3(0.3)
Quintile 3 mean	1.1(0.2)	1.8(0.3)	2.0(0.4)	0.8(0.2)	2.4(0.5)	2.7(0.5)
Quintile 4 mean	2.1(0.5)	3.3(0.6)	3.9(0.8)	1.9(0.4)	4.8(1.1)	5.1(1.0)
Quintile 5 mean	7.7(8.5)	11.2(12.7)	13.9(19.9)	7.1(7.6)	15.4(18.4)	13.4(7.9)

BP = blood pressure; HDL-C = high-density lipoprotein-cholesterol; MetS = metabolic syndrome; CRP = C-reactive protein.

Data shown are given as mean values (standard deviation) or percentages for the same individuals at each of 3 examination periods: examination 2, 1979 to 1983; examination 6, 1995 to 1998; examination 7, 1998 to 2001. Prevalent disease criteria: hypertension, by systolic BP >140 mm Hg, diastolic BP > 90 mm Hg, or history; diabetes by fasting glucose >125 mg/dL or history; obesity by BMI ≥ 30 kg/m²; MetS by presence of ≥ 3 National Cholesterol Educations Program III criteria (“Materials and Methods”).

Longitudinal, Intraindividual C-Reactive Protein Variability 

CRP values at each examination were divided into sex-specific quintiles. The percentages of men and women with the same quintile ranking are shown in Table 2, first comparing examination 2 with examination 6 (for long-term variability) and then comparing examination 6 with examination 7 (for intermediate-term variability). Both long- and intermediate-term CRP variability showed similar patterns in men and women. The largest percentage of subjects remaining in the same quintile between examinations occurred at the extremes of the quintile distribution (ie, at the lowest and highest quintiles) comparing examination 2 with 6 and examination 6 with 7. A comparison of CRP quintiles at examination 2 with examination 6 showed that a high of 45% to 47% of men and 49% of women stayed in the same quintile of CRP at both examinations. Qualitatively similar results for CRP variability were found in the intermediate follow-up time period between examinations 6 and 7. In the intermediate case, at the highest and lowest quintile, 52% to 56% of men and 62% to 65% of women remained within their same quintile at each examination.

Table 2. Sex-specific Cross Comparisons of the C-Reactive Protein Distribution at Different Framingham Examination Cycles

*Values shown in individual cells are percentages of men and women with the same quintile ranking for CRP at examinations 2 (1979-1983) and 6 (1995-1998).

†For men, a test of symmetry (S) = 4.53 (P = .92), weighted kappa = 0.37.

‡For women, a test of symmetry (S) = 6.77 (P = 0.75), weighted kappa = 0.40.

§Values shown in individual cells are percentages of men with the same quintile ranking for CRP at examinations 6(1995-1998) and 7(1998-2001).

‖For men, a test of symmetry(S) = 11.06(P = .35), weighted kappa = 0.46.

¶For women, a test of symmetry(S) = 7.76(P = .65), weighted kappa = 0.57.

CRP measurements at earlier and later cycles of examinations were highly correlated. For men, the Spearman correlation coefficient between CRP quintiles was 0.51 at examinations 2 and 6, 0.48 between examinations 2 and 7, and 0.60 between examinations 6 and 7. For women, similar values comparing CRP quintiles were obtained between examinations 2 and 6 (r = 0.54), examinations 2 and 7 (r = 0.51), and examinations 6 and 7 (r = 0.71). All correlation coefficients were significant (P <.0001).

Tests of symmetry for each of the sex-specific analyses were not significant (P >.10), indicating that movement of participants into adjacent quintiles between examination cycles was equally likely in both directions. Tests for intraindividual agreement using a weighted Kappa test for symmetry was highly significant at both pairs of examinations compared (P <.0001), indicating that individuals were properly assigned to their respective quintiles.

Cholesterol measurements at earlier and later cycles of examinations were highly correlated and all were significant (P <.0001), comparing examination 2 with examination 6 (r = 0.44 for men and r = 0.52 for women) and comparing examination 6 with examination 7 (r = 0.61 for men and r = 0.57 for women). As shown in Table 3, results were similar for both sexes. Over the longer interval from examination 2 to 6, 42% to 48% of the population remained within the highest quintile designations and 44% to 46% remained within the lowest quintile designations. Over the intermediate interval between examinations (examination 6 to 7), 51% of men and women remained at the highest level and 54% remained at the lowest level of a cholesterol quintile distribution. However, a sex-pooled comparison of the change in quintile distributions between cholesterol and CRP showed that a change of more than 1 quintile of CRP was significantly less frequent than a change in 1 quintile of cholesterol (P = .0001) over both long- and short-term periods of examinations (P = .0004).

Table 3. Sex-specific Cross Comparisons of the Quintile Distribution of Cholesterol at Different Framingham Examination Cycles

*Values shown within individual cells are percentages of men and women with the same quintile ranking for CRP at examinations 6(1995-1998) and 7(1998-2001).

†For men, test of symmetry(S) = 15.91(P = .10), weighted kappa = 0.29.

‡For women, test of symmetry(S) = 12.97(P = .23), weighted kappa = 0.34.

§Values shown within individual cells are percentages of men and women with the same quintile ranking for CRP at examinations 6(1995-1998) and 7(1998-2001).

‖For men, test of symmetry(S) = 30.3(P = .0008), weighted kappa = 0.43.

¶For women, test of symmetry(S) = 38.23(P <.0001), weighted kappa = 0.41.

Variables Predicting Long-term C-Reactive Protein Changes 

Two multiple linear regression models were used to explore the correlates of future CRP concentrations. The first model, predicting concentrations of CRP at examination 6 using examination 2 variables, was significant with an overall model R² of 0.32 (P <.0001). Parameter estimates for significant covariates at examination 2 are shown in the first part of Table 4 and included CRP at examination 2, sex, age, BMI, systolic and diastolic BP, and smoking status. Baseline (examination 2) factors not selected in the stepwise backward selection procedure and therefore excluded from the final model were prevalent cardiovascular disease, aspirin use, BP therapy, lipid or hormone therapy, glucose, triglycerides, and cholesterol/HDL-C ratio.

Table 4. C-Reactive Protein-Related Variables Predicting Future C-Reactive Protein

Variables at Examination 2	Parameter Estimate(SE)	P Value
CRP	0.44(0.02)	<.0001
Sex	0.43(0.04)	<.0001
Age	0.01(0.002)	<.0001
BMI	0.03(0.006)	<.0001
Systolic BP	−0.005(0.002)	.01
Diastolic BP	0.008(0.003)	.02
Smoking status	0.12(0.04)	.006
Variables at Examination 2 or 6
CRP at examination 2	0.43(0.02)	<.0001
Sex	0.22(0.04)	<.0001
Age at examination 2	0.02(0.002)	<.0001
BMI at examination 2	−0.06(0.008)	<.0001
BMI at examination 6	0.08(0.006)	<.0001
Cholesterol/HDL-C at examination 6	0.11(0.01)	<.0001
Smoking status at examination 2	0.08(0.04)	.03
BP therapy at examination 6	0.10(0.04)	.02
Hormone therapy at examination 6	0.67(0.06)	<.0001
Lipid therapy at examination 6	−0.21(0.06)	.0002

CRP = C-reactive protein; BMI = body mass index; HDL-C = high-density lipoprotein-cholesterol; BP = blood pressure; SE = standard error.

Demographic, clinical, and laboratory variables are shown, which by linear regression analysis predicted CRP change over an average of 16 years(from examination 2 [1979-1983] to examination 6 [1995-1998]).

In the second model (second part of Table 4), adding examination 6 to examination 2 covariates increased the overall model R² to 0.43 (P <.0001). CRP at examination 2 was significant (P <.05), as were the following examination 2 covariates (age, BMI, systolic BP, diastolic BP, and smoking status) and examination 6 covariates (BMI, glucose, cholesterol/HDL-C ratio, BP therapy, hormone therapy, and lipid therapy). CRP accounted for 29% of overall variability in the second model with a large extent of variability also related to hormone replacement therapy and sex.

Association of C-Reactive Protein and Related Variables with New-onset Metabolic Syndrome or Development of Diabetes 

The extent to which CRP was associated with new-onset MetS or the development of diabetes was assessed by multiple logistic regression in conjunction with a number of other variables related to CRP or the development of MetS or diabetes. More than 90% of the participants (2177/2409) had criteria available at examinations 6 and 7 to diagnose MetS. Of these, 38% (831/2177) were excluded from this analysis with a diagnosis of prevalent MetS at examination 6, leaving 1346 participants eligible for an analysis of incidence. Of these 1346 participants, 21% (276/1346) developed MetS by examination 7. As shown in Table 5, neither the change in CRP from examination 2 to 6 nor the CRP value at examination 6 predicted MetS at examination 7. In contrast with the absence of a significant association of CRP, MetS incidence was associated positively with female sex, age, BMI, diastolic BP, fasting blood glucose, and the ratio of total cholesterol to HDL-C. As also shown, the presence of MetS at examination 7 was strongly associated with the use of BP or lipid therapy at examination 6. This model was significant (P <.0001) with high discriminatory power (c-statistic = 0.808).

Table 5. Probability of C-Reactive Protein and Related Variables in the Prediction of a New Diagnosis of Metabolic Syndrome or Diabetes

	MetS		Diabetes
Covariate	Odds Ratio(95% CI)	P Value	Odds Ratio(95% CI)	P Value
CRP change(examinations 2-6)	1.02(0.88-1.19)	.78	0.81(0.60-1.09)	.16
CRP at examination 6	1.06(0.89-1.26)	.53	1.78(1.29-2.46)	.0005
Sex	1.67(1.15-2.43)	.0073	0.62(0.31-1.23)	.17
Age	1.03(1.01-1.05)	.0061	0.97(0.94-1.01)	.14
BMI	1.17(1.12-1.22)	<.0001	1.06(1.01-1.12)	.023
Systolic BP	1.001(0.99-1.01)	.86	1.01(0.99-1.03)	.60
Diastolic BP	1.04(1.01-1.06)	.0022	0.98(0.95-1.02)	.31
Glucose	1.05(1.03-1.07)	<.0001	1.19(1.15-1.23)	<.0001
Cholesterol/HDL-C	1.74(1.47-2.05)	<.0001	1.02(0.81-1.29)	.85
Triglycerides	1.49(0.96-2.30)	.074	1.73(0.88-3.38)	.11
Smoking	0.82(0.60-1.12)	.20	0.95(0.54-1.68)	.87
Cardiovascular disease	1.30(0.73-2.32)	.37	2.16(1.00-4.68)	.05
Aspirin therapy	0.86(0.59-1.25)	.44	0.82(0.43-1.55)	.54
BP therapy	2.07(1.39-3.10)	.0004	0.78(0.42-1.46)	.44
Hormone therapy	1.34(0.82-2.19)	.25	0.84(0.28-2.50)	.76
Lipid therapy	5.09(2.65-9.77)	<.0001	1.85(0.84-4.05)	.12

CRP = C-reactive protein; MetS = metabolic syndrome; BMI = body mass index; BP = blood pressure; HDL-C = high-density lipoprotein-cholesterol; CI = confidence interval.

All covariates, except for change in CRP from examination 2 (1979-1983) to examination 6 (1995-1998), were measured at examination 6 and used in logistic regression models to predict a diagnosis of new MetS or diabetes at examination 7 (1998-2001).

All 2409 participants had information available at examinations 6 and 7 to confirm or exclude the diagnosis of diabetes. From these 2409, 8% (202/2409) had diabetes at examination 6 and were excluded from the analysis of new diabetes at examination 7, leaving 2207 participants. Sixteen percent (352/2207) with glucose less than 110 mg/dL at examination 6 progressed to either impaired fasting glucose (n = 280) or diabetes with glucose greater than 125 mg/dL (n = 72) by examination 7. Sex, BMI, glucose, and prevalent cardiovascular disease at examination 6 were significant predictors of impaired glucose or diabetes at examination 7 (odds ratio and 95% confidence intervals are shown in Table 5). This model was significant (P <.0001) with high discriminatory power (c-statistic = 0.934).

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Discussion 

In an assessment of the variability and associations of the inflammatory marker CRP in the Framingham Offspring cohort, we observed that intraindividual variability of CRP was similar when CRP was remeasured over relatively long- and intermediate-term periods, averaging 16 and 4 years, respectively. After adjustment for major variables known to be related to CRP, the strongest predictor of CRP concentrations in this longitudinal analysis was CRP values measured an average of 16 years earlier, accounting for 27% of the overall variability of future CRP concentrations. Neither long-term CRP change nor CRP concentrations 4 years earlier were associated with new-onset MetS but were associated with a significant increase in fasting blood glucose and a new diagnosis of diabetes.

The variability of CRP measurements has been assessed in smaller samples and for shorter time intervals than in our present analysis.⁷, ⁸, ⁹ In the longest of these, CRP variability was evaluated in a Japanese study of 388 individuals during a 5-year period.⁹ Those investigators reported a 43% correlation between the initial and subsequent mean CRP concentrations, which is similar to the correlations between CRP measurements that we have observed in the Framingham cohort over a longer interval.

As reported by others in smaller comparative CRP studies,⁷, ⁸ we also have compared the intraindividual variability of CRP with the variability of total plasma cholesterol over the same time periods. In this analysis, we have made the assumption that although there have been large changes in certain variables that are known to influence concentrations of both CRP and cholesterol (most notably, the frequency of lipid therapy), these changes would be distributed in a similar pattern throughout the population so as not to distort a comparison (of percentages) within an ordered distribution of a population between examinations. We have found, contrary to findings in the smaller, shorter-term studies, that the extent of variability of CRP measurements in Framingham was significantly less over both relatively long and intermediate intervals than the variability of cholesterol measurements.

We tested a number of clinical and laboratory variables that have been found to influence CRP concentrations and evaluated their relation to future CRP concentrations. Although a relatively large number of measurements in Framingham were significantly related to future CRP values, the strongest single predictor of CRP values seemed to be prior CRP itself. There are well-described sex differences in CRP that seem to be more highly correlated with the use of estrogen therapy than with female sex per se or a predisposition to inflammation.¹⁷, ¹⁸ In any case, hormone replacement therapy, which is known to increase concentrations of CRP, was found to be strongly related to future CRP measurements, as was lipid therapy (predominantly with statins), which has been shown to reduce CRP concentrations¹⁹ and other markers of inflammation.²⁰

In the present analysis, we did not find that long-term change or the previous concentration of CRP predicted the development of MetS. However, we did find that MetS was associated with other variables in our multivariable model that are key components defining MetS (BMI, glucose, BP, and HDL-C relative to cholesterol) or are therapies used in the treatment of these MetS components (specifically, BP or lipid therapy). In cross-sectional analysis, CRP concentrations have been shown to correlate with a number of features of MetS¹ or to add to the prediction of vascular events in individuals with known MetS.¹, ²¹ However, we know of no studies, including one previously from Framingham,²² in which CRP, when evaluated in conjunction with other proinflammatory biomarkers, has been shown to independently predict MetS.

In contrast with MetS, CRP was associated with an increase in fasting blood glucose and the development of diabetes. Although we have no certain explanation for this association, it is notable that an increase in CRP concentrations is strongly associated with the presence of insulin resistance,²³, ²⁴, ²⁵ and higher CRP concentrations have been found to be predictive of adult-onset diabetes in at least 2 other large population studies.³, ¹⁷ In contrast with these results, an earlier study from Framingham has reported that CRP was only of borderline significance in the prediction of new diabetes.²⁶ In that analysis, however, CRP was measured by an older, less-sensitive immunologic method than the nephelometric procedure that we have used for this present analysis of trends.

It has been proposed that CRP be measured in clinical practice in addition to more classic risk factors to estimate cardiovascular risk or to better define an inflammatory component of risk that is associated with MetS or diabetes.²⁷ Our findings suggest that single measurements of CRP spaced several years apart, which may be a good reflection of usual clinical practice, can provide a highly reliable measure of this biomarker in the individual patient. The apparent stability of CRP over relatively long periods irrespective of the potential for CRP change with, for example, seasonal variation and infectious diseases, provides a strong measure of confidence that a single measurement of CRP in the individual patient is a representative CRP value.

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Strengths and Limitations 

We believe our observations have broad applicability to the general population. The Framingham Heart Study is a large longitudinal study with a well-characterized cohort and standardized methods of analysis. Although the Framingham cohort is largely a white population, National Health and Nutrition Examination Survey III data have shown that there is little difference in CRP concentrations between a variety of ethnic groups living in the United States.²⁸ Furthermore, although there has been a suggestion that CRP concentrations might be higher in Indian Asians than in European whites, this difference can be largely explained by differences in central obesity.²⁹ We believe that the scope of this analysis far exceeds any previous comparative CRP measurements and that analytic variability has been minimized by the use of the same high-sensitivity CRP method and the same laboratory throughout this analysis.

We recognize that our comparison of CRP with cholesterol variability may be faulted for a number of reasons. Most prominently this might include a variable and increasing use of cholesterol-lowering statin therapy in the Framingham cohort (as shown in Table 1). However, statin therapy also lowers CRP, often by percentages that are comparable to those attained for cholesterol reduction.¹⁹, ²⁰ We further recognize that there are measurement issues that might limit a comparison of CRP and cholesterol. Whereas CRP represents a single molecular form, cholesterol is present in the blood as unesterified and esterified molecules that are components in variable proportions of different lipoproteins that are metabolized at different rates. Finally, although our cholesterol measurements were well standardized by cross-comparisons, cholesterol was measured over longer intervals than was CRP, and this might be expected to result in inherently greater analytic variability for cholesterol than for CRP measurements.

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Conclusions 

Single measurements of CRP from specimens obtained as much as 20 years earlier seem to be more highly predictive of future concentrations of CRP than a variety of other commonly measured metabolic or cardiovascular risk factors. Although CRP is associated with many features of MetS, CRP in this sample was not associated with new-onset MetS but was positively related to the development of higher concentrations of blood glucose and the incidence of diabetes.

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