A growing body of evidence suggests that as compared with individuals without HIV, those with HIV are at greater risk of metabolic complications, including diabetes.1 Reasons for this increased risk are unclear but may include some antiretroviral therapies (ART),2–4 hepatitis coinfection,5,6 and chronic inflammation.7,8 Untreated HIV is associated with higher levels of inflammatory markers, and initiation of ART results in a decline but, on average, does not normalize inflammatory markers.9,10 Insulin resistance, which is associated with the development of diabetes, was one of the first metabolic complications associated with HIV and HIV treatments.11
It has been hypothesized that diabetes is a manifestation of an ongoing acute phase inflammatory response, and in the general population, several studies indicate that higher levels of interleukin-6 (IL-6) and high-sensitivity C-reactive protein (hsCRP) are associated with the development of diabetes.8,12–17 However, a recent meta-analysis and an analysis of 12 inflammatory biomarkers in the Framingham Heart Study indicate that associations are diminished after adjustment for other type 2 diabetes risk factors.18,19 Cross-sectional studies have also reported associations between CRP and insulin resistance in individuals without diabetes.20 Moreover, it has been shown that in healthy volunteers, administration of subcutaneous recombinant human IL-6 induces dose-dependent increases in fasting glucose.21 It has been postulated that type 2 diabetes may represent a disease of the innate immune system.22
With the exception of a small case–control study, the relationship of inflammatory markers with risk of diabetes among HIV-positive patients taking ART has not been assessed.23 Further research on this was motivated by the observation that levels of inflammatory markers are considerably higher for HIV-positive individuals, even those on suppressive ART, compared with HIV-negative age-matched men and women.9 If chronic inflammation is an important predictor of diabetes for HIV patients, this could explain, at least in part, their greater risk of diabetes compared with those without HIV. Data from the control arms of 2 large, international HIV trials are used to estimate the incidence of diabetes and study the association of IL-6 and hsCRP with risk of diabetes. Our hypothesis was that among patients prescribed combination ART aimed at virologic suppression, higher levels of each inflammatory biomarker would be associated with an increased risk of diabetes.
Data from the Strategies for Management of Anti Retroviral Therapy (SMART) and the Evaluation of Subcutaneous Proleukin in a Randomized Trial (ESPRIT) were used.24,25 Briefly, in SMART, 5472 HIV-positive adults with CD4+ cell count greater than 350 cells/mm3 were randomized by sites in 33 countries to receive uninterrupted ART with the goal of viral suppression (control group) or episodic ART guided by the CD4+ count. The SMART study was stopped early because of a safety risk in the episodic therapy arm.25 In ESPRIT, 4111 HIV-positive adults with CD4+ cell counts greater than or equal to 300 cells/mm3 were randomized by sites in 25 countries to receive continuous ART alone (control group) or continuous ART in combination with subcutaneous IL-2. ESPRIT failed to demonstrate a clinical benefit of IL-2.24
Because continuous ART represents the current standard of care for people with HIV, this investigation is restricted to patients in the control groups of these 2 studies who did not have a diagnosis of diabetes at study entry and who consented to storing plasma for the biomarker measurements.
In SMART, patients could be enrolled while on or off ART. Those not on ART were to receive combination ART aimed at virologic suppression if they were assigned to the control group.
The biomarkers, hsCRP and IL-6, were centrally measured on stored plasma collected at baseline (before randomization) for all patients who provided written consent. These 2 biomarkers were prospectively chosen, initially for studying their association for all-cause mortality and cardiovascular disease (CVD).26,27 In those investigations, both markers were associated with an increased risk of mortality and CVD. They were chosen for those investigations because they have high laboratory and biological reproducibility and have been associated with all-cause mortality and CVD in the general population.28–30
The institutional review board at the University of Minnesota approved plans for the analysis of stored specimens. For SMART participants, biomarkers were measured at the Laboratory for Clinical Biochemistry Research at the University of Vermont (Burlington, VT). In the ESPRIT trial, laboratory measurements were performed by SAIC-Frederick (Frederick, MD). IL-6 was measured by the same method at each laboratory (Chemiluminescent Sandwich ELISA; R&D Systems; Minneapolis, MN). hsCRP was measured by ELISA by both laboratories. For SMART participants, an NBTMII nephelometer, N Antiserum to Human CRP (Siemens Diagnostics; Deerfield, IL) was used. For ESPRIT participants, an R&D Systems ELISA assay was used. The hsCRP assays, although different, compared very well on duplicate samples. The lower limit of detection for IL-6 was 0.16 pg/mL, and for CRP, the lower limit was 0.16 μg/mL for SMART and 0.078 μg/mL for ESPRIT participants. All samples were analyzed masked to clinical information about the patients.
The year-to-year consistency of IL-6, but not hsCRP, was assessed in a random sample of 235 patients in the control arm of ESPRIT. IL-6 was measured at baseline, year 1, and year 3. The reliability coefficient (ratio of between-subject variability to total variability) of log IL-6 was 0.39. This is similar to that reported in a systematic overview of IL-6 and risk of coronary heart disease (0.41).31 The short-term reliability of hsCRP was estimated using 324 patients in the control arm of SMART who were seen at baseline and 6 months. It was 0.78. Based on a systematic review of CRP and coronary heart disease, the reliability coefficient for log CRP considering year-to-year variability was 0.58.32
Follow-up and Diabetes Determination
In SMART, follow-up visits were scheduled at 1 month and 2 months, every 2 months thereafter in the first year, and every 4 months in the second and subsequent years. In ESPRIT, follow-up visits were scheduled every 4 months following randomization.
In both SMART and ESPRIT, dates of diabetes diagnosis and use of drugs for diabetes were recorded. A patient was considered to have developed diabetes if he or she received a diagnosis of diabetes requiring drug treatment during follow-up. Diabetes was ascertained in the SMART study at baseline and reported during follow-up as soon as the diagnosis was made. The date of diagnosis, the drug prescribed, and plasma glucose laboratory value were collected. In the ESPRIT study, treatment for diabetes, including date of treatment initiation, was reported annually.
Fasting glucose measurements were not obtained in ESPRIT. In SMART, fasting glucose measurements were only obtained for 111 participants who also enrolled in a substudy on body composition.
Crude incidence rates of diabetes, defined as a diagnosis of diabetes requiring drug treatment, are reported as rates per 1000 person-years. Kaplan–Meier estimates of the cumulative probability of developing diabetes are also cited, both overall and by levels of the biomarkers. For patients with biomarker levels below the level of detection (4 patients for IL-6 and 33 patients for hsCRP), the value of the biomarker was imputed as the lower level of detection. Baseline measurements of hsCRP and IL-6 were used to predict the development of type 2 diabetes after adjusting for other risk factors for type 2 diabetes that were measured in the 2 studies. In both the studies, age, race, body mass index (BMI), blood pressure–lowering drugs, lipid-lowering drugs, coinfection with hepatitis, previous history of CVD, type of ART, CD4+ count, and HIV RNA level were collected at baseline. Patients in SMART also had information available on cigarette smoking, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides at baseline.
Cox regression models, with stratification by study (SMART and ESPRIT), were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) associated with higher biomarker levels. In addition to models that categorized the biomarkers into quartiles with lowest quartile as reference, models with log2 transformed biomarker levels were considered. A log transformation was used because the markers were skewed to the right. With a log2 transformation, exponentiation of the parameter estimate from the Cox regression gives the increased risk of diabetes associated with a doubling of the biomarker level. Patients were censored by death and last annual visit attended. We checked the proportional hazards assumption for each of the biomarkers.
The association of incident diabetes with each biomarker was first studied in a model with no covariates and then in 3 regression models with the following baseline covariates: (1) age, race, sex, coinfection with hepatitis C or B, CD4+ count, and HIV RNA level; (2) age, race, sex, coinfection with hepatitis C or B, CD4+ count, HIV RNA level, BMI, use of lipid-lowering drugs, use of blood pressure–lowering drugs, use of ART, and if taking ART, use of a stavudine (d4T)-containing ART regimen, use of a zidovudine (ZDV), but not d4T-containing ART regimen, and use of an ART regimen that did not contain d4T or ZDV; and (3) all the aforementioned covariates plus cigarette smoking status, total cholesterol, low-density lipoprotein, high-density lipoprotein, and triglycerides (SMART only). Model 1 considers demographic and HIV factors; model 2 adds variables significant in univariate analyses and that have been associated with risk of diabetes in other studies; and model 3 considers other potential confounding factors that were only measured in SMART.
Subgroup analyses for baseline variables that were significant predictors of diabetes were carried out to assess whether the association of each biomarker with diabetes was consistent across the subgroups. For these analyses, P-values corresponding to the interaction between each biomarker and the subgrouping variable are cited.
Statistical analyses were performed using SAS software (version 9.2; SAS Institute; Cary, NC). P values are 2-sided and 95% CIs are cited.
Characteristics of Study Cohort
Among the 4792 patients in the control arms of SMART and ESPRIT, 240 (5%) reported taking drugs for diabetes at entry. IL-6 and hsCRP were available for 214 of those taking drugs for diabetes at entry and median (interquartile range) levels were 2.44 (1.62–4.10) and 2.63 (1.14–5.78), respectively.
Among those not taking drug treatment for diabetes at entry (n = 4552), 3965 (87%) had IL-6 and hsCRP measurements and attended at least 1 annual follow-up visit (1719 in ESPRIT and 2246 in SMART). These 3965 patients, who had an average CD4+ count of 523 cells/mm3, were considered to be at risk for developing diabetes during follow-up and are the subject of this report. Using fasting glucose measurements made on a sample of SMART patients, we were able to assess the extent to which the cohort was free of diabetes. Among the 2246 patients in SMART, 111 had fasting glucose measurements at entry. None of these 111 patients had levels of 126 mg/dL or higher, 22 (19.8%) had levels between 100 and 125 mg/dL, and 89 (80.1%) had fasting glucose levels <100 mg/dL.
Attendance at annual visits ranged from 89.5% to 97.7% for these 3965 patients. The numbers of patients who missed 2, 3, and 4 or more annual visits were 72, 29, and 26, respectively.
Over an average follow-up of 4.6 years (2.9 years in SMART and 6.8 years in ESPRIT), 137 [68 (3.0%) in SMART and 69 (4.0%) in ESPRIT] of the 3965 patients in the cohort developed diabetes (3.5%, 8.18 per 1000 person-years). Cumulative percents developing diabetes after 2, 4, and 6 years were 1.4%, 3.5%, and 4.8%, respectively.
For this cohort on continuous ART, the percent with an HIV RNA level of 500 copies/mL or lower and average CD4+ cell count did not change appreciably during follow-up. At 12, 24, 36, and 48 months, percents with HIV RNA of 500 copies/mL or lower were 81.9%, 81.2%, 80.8%, and 82.6%, respectively. Average levels of CD4+ cell count at these visits were 610, 604, 592, and 593 cells/mm3, respectively.
Table 1 summarizes baseline characteristics for patients who developed diabetes (ie, initiated drug treatment for diabetes) with those who did not. In contrast to those who did not develop diabetes, incident cases were more likely to be non-white, be older in age, be nonsmokers, have higher CD4+ cell count, BMI, and triglycerides, and were more likely to be taking lipid-lowering and blood pressure–lowering drugs at baseline. Those who developed diabetes were less likely to have a viral load of 500 copies/mL or less (Table 1).
Median baseline levels of both IL-6 and hsCRP were significantly higher among those who developed diabetes: 3.45 versus 2.50 pg/mL for IL-6 and 4.91 versus 3.29 µg/mL for hsCRP (P < 0.001 for each marker) (Fig. 1). In the sample of 111 patients from SMART with fasting glucose measurements, IL-6 and hsCRP were positively correlated with fasting glucose: rank correlation 0.24 for IL-6 (P = 0.01) and 0.17 for hsCRP (P = 0.07). Median levels of the 2 biomarkers for those with fasting glucose 100–125 mg/dL (n = 22) versus <100 mg/dL (n = 89) were 2.35 versus 1.74 pg/mL for IL-6 (P = 0.01) and 2.12 versus 2.55 µg/mL for hsCRP (P = 0.40).
Baseline Levels of IL-6 and hsCRP and the Development of Diabetes Requiring Drug Treatment
Table 2 and Figures 2 and 3 summarize the association of IL-6 and hsCRP with the development of diabetes. In crude (unadjusted) analyses, both biomarkers were associated with an increased risk of developing diabetes [HR associated with a doubling of IL-6 and hsCRP were 1.47 (95% CI: 1.26 to 1.70) and 1.32 (95% CI: 1.20 to 1.45), respectively]. Those in the highest quartile of baseline plasma hsCRP had rates more than 5 times greater than those in the lowest quartile (HR = 5.13; 95% CI: 2.60 to 10.1); for those in the highest quartile of IL-6, rates were more than 3 times greater than those in the lowest quartile (HR = 3.45; 95% CI: 1.91 to 6.23). Figures 2 and 3 give the cumulative percent developing diabetes by quartile of each biomarker. There is a clear separation in risk of diabetes for the upper 2 versus lower 2 quartiles.
Covariate adjustment diminished the associations but they remained significant. For the 3 models considered, HRs associated with a doubling of biomarkers for IL-6 ranged from 1.29 to 1.36; for hsCRP, the HRs ranged from 1.22 to 1.28. The lower HRs in models 2 and 3 were due primarily to the adjustment for BMI. Table S1 (see Supplemental Digital Content,http://links.lww.com/QAI/A570) gives unadjusted and adjusted HRs for each of the covariates considered in model 3. A 1 kg/m2 higher BMI was associated with a 10% increased risk of diabetes (<0.001). Other significant predictors of diabetes in the multivariable model were older age (P = 0.013), hepatitis B or C infection (P = 0.032), use of lipid-lowering therapy (P = 0.008), and nonsmoking status (P = 0.03). Consistent with other studies,33 d4T and ZDV were associated with an increased risk of diabetes, but these associations did not achieve statistical significance.
We investigated the higher risk of diabetes with nonsmoking status further by dividing the nonsmokers into 2 groups: former smokers (n = 561) and never smokers (n = 762). The unadjusted HRs for diabetes for smokers versus former smokers and for smokers versus never smokers were 0.51 (95% CI: 0.28 to 0.93) and 0.56 (95% CI: 0.31 to 1.01), respectively. With adjustment for the covariates in Table S1 (see Supplemental Digital Content, http://links.lww.com/QAI/A570) plus hsCRP and IL-6, HRs for former versus current smokers and for never versus current smokers were reduced to 0.63 (95% CI: 0.33 to 1.19) and 0.53 (95% CI: 0.27 to 1.00), respectively.
For comparison with a recent meta-analysis,18 we also considered the risk of diabetes associated with 1 natural log higher levels of IL-6 and hsCRP. With the model 2 covariates, these HRs for IL-6 and hsCRP were 1.45 (95% CI: 1.12 to 1.88) and 1.34 (95% CI: 1.15 to 1.56), respectively.
To assess whether the risk of diabetes associated with elevated IL-6 and hsCRP was similar in baseline-defined subgroups, we carried out additional analyses. These subgroup analyses showed no interaction between the diabetes risk factors considered and the biomarkers except for race and hsCRP (see Tables S2 and S3, Supplemental Digital Content, http://links.lww.com/QAI/A570). A higher level of hsCRP was associated with significantly higher risk of developing diabetes for whites and other races, but there was no association in blacks (P = 0.009 for interaction).
The test for proportional hazard ratio assumption showed no evidence that the assumption was violated for hsCRP (P = 0.34). For IL-6, there was evidence that the proportional hazards assumption may not hold (P = 0.03). The association between IL-6 and incident diabetes was stronger early in the follow-up period compared with later (see Tables S4 and S5, Supplemental Digital Content, http://links.lww.com/QAI/A570). In the multivariable analysis (model 2), a doubling of IL-6 was associated with a HR of 1.45 (95% CI: 1.18 to 1.79; P < 0.001) in the first 3 years of follow-up when 87 of the 137 diabetes events occurred, and a HR of 1.01 (95% CI: 0.72 to 1.42; P = 0.94) after 3 years of follow-up.
In this cohort study of HIV-positive patients, higher baseline levels of IL-6 and hsCRP were significantly associated with the risk of developing diabetes over an average of 4.6 years of follow-up. These associations were independent of BMI, age, and other established diabetes risk factors.
Our findings are similar to a recent systematic review of studies in the general population.19 That review included 10 cohort studies with a total of 19,709 participants and 4480 cases of diabetes for IL-6 and 22 cohort studies with a total of 40,735 participants and 5753 cases of diabetes for hsCRP. In that review, relative risks associated with a 1 natural log higher IL-6 and hsCRP level were 1.31 (95% CI: 1.17 to 1.46) and 1.26 (95% CI: 1.16 to 1.37), respectively. Several of the studies in this overview adjusted for measures of glycemia. With adjustment for measures of glycemia, the associations of IL-6 and hsCRP with diabetes were attenuated but remained significant. For the subgroup of studies in this systematic review that did not adjust for measures of glycemia (as in this investigation), the relative risk estimates associated with a 1 natural log higher IL-6 and hsCRP were 1.46 (95% CI: 1.25 to 1.71) and 1.33 (95% CI: 1.10 to 1.62), respectively. These latter estimates are very similar to HRs for IL-6 and hsCRP in this HIV cohort: 1.45 and 1.34 for IL-6 and hsCRP, respectively.
We cannot establish in our study whether inflammation increased levels of glycemia or vice versa. The baseline cross-sectional correlations we observed between IL-6 and hsCRP with fasting glucose in the small sample of SMART patients with these biomarkers (0.24 and 0.17) are similar to those reported in other studies.20,34,35 The findings from the aforementioned systematic review19 suggest that these inflammatory markers provide independent information for predicting the development of diabetes. Experimental studies aimed at reducing inflammation are needed to establish a causal relationship.
The study of the systemic inflammation and diabetes among HIV-positive patients is limited to a single case–control study.23 Brown et al23 examined the association of hsCRP, IL-6, and soluble receptors of tumor necrosis factor-α (sTNFR1 and sTNFR2) measured 48 weeks after ART initiation with subsequent development of diabetes among HIV-positive patients. They carried out a nested case–control study that included 55 patients who developed diabetes at a median of 1.9 years after the 48-week blood measurements and 55 control patients matched on BMI at ART initiation, age, and race. Higher levels of each of the inflammatory markers were associated with an increased risk of diabetes and associations were significant for hsCRP, sTNFR1, and sTNFR2.
In the Atherosclerosis Risk in Communities Study, a significant association between a score based on several inflammatory markers and diabetes was found for nonsmokers but not smokers.36 Therefore, we considered the possibility of interaction with smoking in our investigation. We did not observe an interaction between smoking and hsCRP (P = 0.91) or IL-6 (P = 0.48). We did find a significant interaction between race and hsCRP levels. hsCRP was significantly associated with diabetes in whites and other race groups but not in blacks. An interaction with race was not found in the Multi-Ethnic Study of Atherosclerosis (MESA) study.37 There was no evidence that the association of IL-6 and hsCRP with diabetes varied by age, gender, BMI, use of lipid-lowering drug, or use of blood pressure–lowering drug. Associations were also similar in the SMART and ESPRIT studies.
The association between higher levels of IL-6, but not hsCRP, was reduced with longer follow-up. Weaker associations of diabetes with IL-6 compared with hsCRP were also observed in the Women's Health and Nurses' Health Study.10,13 This finding could be a chance finding or could be because of some patients at study entry having levels of blood glucose indicative of diabetes or pre-diabetes that led to raised levels of IL-6. Although inclusion of a large percent of patients with baseline fasting glucose levels of 126 mg/dL or higher in this cohort seems unlikely given the results from the sample of patients from SMART who had fasting glucose measurements, approximately 20% did have pre-diabetes levels (100–125 mg/dL). Similarly, in the study by Brown et al,23 fasting glucose levels at study entry were significantly higher for patients who later developed diabetes compared with controls. As in the previously mentioned overview,19 if it had been possible in our investigation to adjust for measures of glycemia at entry in our analyses, the strength of the associations of IL-6 and hsCRP with diabetes incidence would likely have been attenuated.
Consistent with a recent review of risk prediction models for type 2 diabetes that found age, BMI, and race to be among the most common predictors reported for type 2 diabetes,38 these were also important predictors in our analyses. In the HIV population, these are also important predictors.3 Inconsistent with a recent overview of studies in the general population which found that smoking was associated with a 44% increased risk of diabetes,39 we found that smoking was associated with a reduced risk of diabetes. This was also observed in the D:A:D and Swiss Cohort studies (not significant in the latter).3,38 Smoking cessation has been associated with an increased risk of diabetes,40 and this may explain this finding, in part, although HRs for former and never smokers versus smokers in our investigation were similar.
The strengths of our study include the large sample size, the standardized biomarker measurements, and the long follow-up. Patients in these trials had frequent contact with medical providers, thereby reducing the likelihood that diabetes would not be diagnosed if it developed during the follow-up period. Several limitations should be considered, however. First of all, measures of glycemia used to define diabetes were not available at baseline and during follow-up, except for a small sample of participants. Thus, we have likely underestimated the incidence of diabetes and also the strength of the association between the inflammatory markers and diabetes because those with diabetes defined by glucose measurements only are included in the no-diabetes group. Also, we could not consider covariate adjustment for baseline fasting glucose. Second, the inflammatory biomarkers were only measured once. This also results in an underestimation of risk because of measurement error and the variability in these markers over time.41 Third, some important baseline covariates were not available for ESPRIT study, therefore limiting our ability to adjust for smoking and lipoprotein levels in all participants. Nevertheless, when analyses were restricted to patients in SMART and these factors were included in the multivariate analysis (model 3), HRs were not strongly affected. Fourth, although BMI was available, information was not available for measures of adiposity such as waist circumference in either SMART or ESPRIT. Information on family history of diabetes was also not available.
As for individuals in the general population,42 diabetes is a strong risk factor for CVD among HIV-positive patients.33 Thus, our findings have important clinical and public health implications. First, elevated hsCRP and IL-6 levels in HIV patients even on suppressive ART are likely to lead to increased numbers with diabetes as the HIV population ages unless preventive measures are increased. Second, these data may offer some clues as to why HIV-positive individuals seem to have an increased risk of CVD and other serious non-AIDS diseases. Inflammation may be a common factor leading to increased risk of diabetes and serious non-AIDS conditions.
In conclusion, we found that elevated plasma levels of hsCRP and IL-6 were associated with diabetes among HIV-positive patients taking ART. Our findings support the hypothesis that low-grade systemic inflammation is an underlying factor in the pathogenesis of type 2 diabetes.
The authors acknowledge the SMART and ESPRIT participants and the SMART and ESPRIT study teams (see references 24 and 25).
1. Samaras K. The burden of diabetes and hyperlipidemia in treated HIV infection and approaches for cardiometabolic care. Curr HIV/AIDS Rep. 2012;9:206–217.
2. Brown TT, Cole SR, Li X, et al.. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med. 2005;165:1179–1184.
3. De Wit S, Sabin CA, Weber R, et al.; Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. Incidence and risk factors for new-onset diabetes in HIV-infected patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. Diabetes Care. 2008;31:1224–1229.
4. Tien PC, Schneider MF, Cole SR, et al.. Antiretroviral therapy exposure and incidence of diabetes mellitus in the women's interagency HIV study. AIDS. 2007;21:1739–1745.
5. Mocroft A, Neuhaus J, Peters L, et al.; INSIGHT SMART Study Group; ESPRIT Study Group. Hepatitis B and C co-infection are independent predictors of progressive kidney disease in HIV-positive, antiretroviral-treated adults. PLoS One. 2012;7:e40245.
6. Reingold J, Wanke C, Kotler D, et al.. Association of HIV infection and HIV/HCV coinfection with C-reactive protein levels: the fat redistribution and metabolic change in HIV infection (FRAM) study. J Acquir Immune Defic Syndr. 2008;48:142–148.
7. Stankov M, Behrens GMN. Contribution of inflammation to fat redistribution and metabolic disturbances in HIV-1 infected patients. Curr Pharm Des. 2010;16:3361–3371.
8. Donath M. Type 2 diabetes as an inflammatory disease. Nat Rev Immunol. 2011;11:98–107.
9. Neuhaus J, Jacobs D, Baker J, et al.. Markers of inflammation, coagulation, and renal function are elevated in adults with HIV infection. J Infect Dis. 2010;201:1788–1795.
10. Baker JV, Neuhaus J, Duprez D, et al.; INSIGHT SMART Study Group. Changes in inflammatory and coagulation biomarkers: a randomized comparison of immediate versus deferred antiretroviral therapy in patients with HIV infection. J Acquir Immune Defic Syndr. 2011;56:36–43.
11. Feeney ER, Mallon PW. Insulin resistance in treated HIV infection. Best Pract Res Clin Endocrinol Metab. 2011;25:443–458.
12. Pradhan AD, Manson JE, Rifai N, et al.. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA. 2001;286:327–334.
13. Hu F, Meigs J, Li T, et al.. Inflammatory markers
and risk of developing type 2 diabetes in women. Diabetes. 2004;53:693–700.
14. Spranger J, Kroke A, Mohlig M, et al.. Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer And Nutrition (EPIC)-Potsdam Study. Diabetes. 2003;52:812–817.
15. Sjoholm A, Nystrom T. Inflammation and the etiology of type 2 diabetes. Diabetes Metab Res Rev. 2006;22:4–10.
16. Akash MS, Rehman K, Chen S. Role of inflammatory mechanisms in pathogenesis of type 2 diabetes mellitus. J Cell Biochem. 2013;114:525–531.
17. Herder C, Brunner EJ, Rathmann W, et al.. Elevated levels of the anti-inflammatory interleukin-1 receptor antagonist precede the onset of type 2 diabetes: the Whitehall II study. Diabetes Care. 2009;32:421–423.
18. Dallmeier D, Larson MG, Wang N, et al.. Addition of inflammatory biomarkers did not improve diabetes prediction in the community: the Framingham Heart Study. J Am Heart Assoc. 2012;1:e000869.
19. Wang X, Bao W, Liu J, et al.. Inflammatory markers
and risk of type 2 diabetes: a systematic review and meta-analysis. Diabetes Care. 2013;36:166–175.
20. Festa A, D'Agostino R Jr, Howard G, et al.. Chronic subclinical inflammation as part of the insulin resistance syndrome: the insulin resistance atherosclerosis study (IRAS). Circulation. 2000;102:42–47.
21. Tsigos C, Papanicolaou DA, Kyrou I, et al.. Dose-dependent effects of recombinant human interleukin-6 on glucose regulation. J Clin Endocrinol Metab. 1997;82:4167–4170.
22. Pickup JC, Crook MA. Is type II diabetes mellitus a disease of the innate immune system? Diabetologia. 1998;41:1241–1248.
23. Brown TT, Tassiopoulos K, Bosch RJ, et al.. Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy. Diabetes Care. 2010;33:2244–2249.
24. INSIGHT-ESPRIT Study Group; SILCAAT Scientific Committee; Abrams D, Levy Y, Losso MH, et al.. Interleukin-2 therapy in patients with HIV infection. N Engl J Med. 2009;361:1548–1559.
25. Strategies for Management of Antiretroviral Therapy (SMART) Study Group; El-Sadr WM, Lundgren J, Neaton JD, et al.. CD4+ count-guided interruption of antiretroviral treatment. N Engl J Med. 2006;355:2283–2296.
26. Kuller LH, Tracy R, Belloso W, et al.; INSIGHT SMART Study Group. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med. 2008;5:e203.
27. Duprez D, Neuhaus J, Kuller L, et al.. Inflammation, coagulation and cardiovascular disease in HIV-infected individuals. PLoS One. 2012;7:e44454.
28. Kuller LH, Tracy RP, Shaten J, et al.. Relation of C-reactive protein and coronary heart disease in the MRFIT nested case-control study. Multiple risk factor intervention trial. Am J Epidemiol. 1996;144:537–547.
29. Harris TB, Ferrucci L, Tracy RP, et al.. Associations of elevated interleukin-6 and C-reactive protein levels with mortality in the elderly. Am J Med. 1999;106:506–512.
30. Ridker PM, Rifai N, Stampfer MJ, et al.. Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation. 2000;101:1767–1772.
31. Danesh J, Kaptoge S, Mann AG, et al.. Long-term interleukin-6 levels and subsequent risk of coronary heart disease: two new prospective studies and a systematic review. PLoS Med. 2008;5:e78.
32. Emerging Risk Factors Collaboration; Kaptoge S, Di Angelantonio E, Lowe G, et al.. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis. Lancet. 2010;375:132–140.
33. Worm SW, De Wit S, Weber R, et al.. Diabetes mellitus, preexisting coronary heart disease, and the risk of subsequent coronary heart disease events in patients infected with human immunodeficiency virus: the data collection on adverse events of anti-HIV drugs (D:A:D study). Circulation. 2009;119:805–811.
34. Aronson D, Bartha P, Zinder O, et al.. Association between fasting glucose and C-reactive protein in middle-aged subjects. Diabet Med. 2004;21:39–44.
35. Cardellini M, Andreozzi F, Laratta E, et al.. Plasma interleukin-6 levels are increased in subjects with impaired glucose tolerance but not in those with impaired fasting glucose in a cohort of Italian Caucasians. Diabetes Metab Res Rev. 2007;23:141–145.
36. Duncan B, Schmidt M, Pankow J, et al.. Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes. 2003;52:1799–1805.
37. Bertoni AG, Burke GL, Owusu JA, et al.. Inflammation and the incidence of type 2 diabetes: the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care. 2010;33:804–810.
38. Ledergerber B, Furrer H, Rickenbach M, et al.; Swiss HIV Cohort Study. Factors associated with the incidence of type 2 diabetes mellitus in HIV-infected participants in the Swiss HIV Cohort Study. Clin Infect Dis. 2007;45:111–119.
39. Willi C, Bodenmann P, Ghali WA, et al.. Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA. 2007;298:2654–2664.
40. Davey Smith G, Bracha Y, Svendsen KH, et al.; Multiple Risk Factor Intervention Trial Research Group. Incidence of type 2 diabetes in the randomized multiple risk factor intervention trial. Ann Intern Med. 2005;142:313–322.
41. Clarke R, Shipley M, Lewington S, et al.. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol. 1999;150:341–353.
42. Stamler J, Vaccaro O, Neaton JD, et al.. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the multiple risk factor intervention trial. Diabetes Care. 1993;16:434–444.