Literature DB >> 20664016

Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy.

Todd T Brown1, Katherine Tassiopoulos, Ronald J Bosch, Cecilia Shikuma, Grace A McComsey.   

Abstract

OBJECTIVE: To determine whether systemic inflammation after initiation of HIV-antiretroviral therapy (ART) is associated with the development of diabetes. RESEARCH DESIGN AND METHODS: We conducted a nested case-control study, comparing 55 previously ART-naive individuals who developed diabetes 48 weeks after ART initiation (case subjects) with 55 individuals who did not develop diabetes during a comparable follow-up (control subjects), matched on baseline BMI and race/ethnicity. Stored plasma samples at treatment initiation (week 0) and 1 year later (week 48) were assayed for levels of high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and the soluble receptors of tumor necrosis factor-α (sTNFR1 and sTNFR2).
RESULTS: Case subjects were older than control subjects (median age 41 vs. 37 years, P = 0.001), but the groups were otherwise comparable. Median levels for all markers, except hs-CRP, decreased from week 0 to week 48. Subjects with higher levels of hs-CRP, sTNFR1, and sTNFR2 at 48 weeks had an increased odds of subsequent diabetes, after adjustment for baseline marker level, age, BMI at week 48, CD4 count at week 48 (< vs. >200 cells/mm(3)), and indinavir use (all P(trend) ≤ 0.05). After further adjustment for week 48 glucose, effects were attenuated and only sTNFR1 remained significant (odds ratio, highest quartile vs. lowest 23.2 [95% CI 1.28-423], P = 0.03).
CONCLUSIONS: Inflammatory markers 48 weeks after ART initiation were associated with increased risk of diabetes. These findings suggest that systemic inflammation may contribute to diabetes pathogenesis among HIV-infected patients.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20664016      PMCID: PMC2945167          DOI: 10.2337/dc10-0633

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


With the advent of effective antiretroviral therapy (ART), morbidity and mortality have decreased dramatically among HIV-infected patients and the proportion of older HIV-infected patients is rising. As a result, aging-related comorbidities, such as diabetes, have become increasingly important in the management of HIV-infected patients. Abnormalities in glucose metabolism occur commonly in HIV-infected patients, and some cohorts have shown a higher than expected risk of insulin resistance and diabetes compared with that for HIV-negative control populations (1,2). The etiology is multifactorial. Certain protease inhibitors, such as indinavir (IDV), lopinavir, and ritonavir, have been shown to reversibly induce insulin resistance, probably by inhibition of glucose translocation through GLUT4 (3). The nucleoside reverse transcriptase inhibitors, zidovudine and stavudine, also have direct and indirect effects on glucose metabolism (4,5). Chronic infection with HIV may also contribute to glucose abnormalities among HIV-infected patients. In the Multicenter AIDS Cohort Study, insulin resistance markers were higher in all groups of HIV-infected men compared with HIV-uninfected control subjects, even among those who were not receiving ART (6), suggesting an effect of HIV infection itself. Systemic inflammation has been associated with incident diabetes in multiple cohorts in the general population (7–9). Proinflammatory cytokines, such as tumor necrosis factor (TNF)-α, may induce insulin resistance by binding to insulin-responsive elements in skeletal muscle (10). Among HIV-infected patients, markers of systemic inflammation decrease quickly with ART initiation (11) but do not normalize (12). It is speculated that this residual inflammation with effective ART may contribute to the pathogenesis of comorbidities in HIV-infected patients, including diabetes (13). We undertook a case-control study, nested within an observational study of the AIDS Clinical Trial Group (ACTG) to determine whether markers of systemic inflammation measured 48 weeks after ART initiation were associated with the development of diabetes.

RESEARCH DESIGN AND METHODS

The ACTG Longitudinal Linked Randomized Trials (ALLRT) cohort was designed to enroll HIV-infected individuals previously randomized into approved parent ACTG clinical trials (parent studies) for the purpose of evaluating clinical, virological, immunological, metabolic, and pharmacological outcomes associated with long-term treatment with potent ARTs. Individuals could be treatment-naive or treatment-experienced at entry into their parent study. Enrollment into ALLRT began in January 2000. As of October 2008, 3,405 previously treatment-naive individuals and 1,225 treatment-experienced individuals were enrolled into ALLRT. All subjects provided written informed consent, and each ACTG study site received approval from the designated institutional review board before protocol initiation. Details of the ALLRT protocol have been described previously (14). The eligible population for this prospective, nested case-control study included all HIV-infected individuals from treatment-naive parent studies co-enrolled in ALLRT, with the exception of parent studies for which ART data were still blinded. Eligible individuals were required to have had fasting or nonfasting blood glucose measurements before and after ART initiation and during the follow-up period, as well as plasma samples taken at the time of treatment initiation and at week 48, for measurement of inflammatory markers. We used data submitted to our data management center as of October 2008. All potential case or control subjects were required to have fasting or nonfasting blood glucose levels <100 mg/dl at the time of treatment initiation (baseline) or within 16 weeks after initiation and could not have a history of hypoglycemic medication use or diabetes. All individuals who developed diabetes ≥1 years after ART initiation were eligible to be included as case subjects. Control subjects (non–case subjects) were randomly selected from those individuals who did not develop diabetes and who maintained fasting blood glucose levels <100 mg/dl through the study period. Control subjects were matched to case subjects 1:1 by BMI at treatment initiation and by race/ethnicity. Control subjects were followed for at least as long as their matched case subject. The glucose and medication histories of each of these case subjects and control subjects were reviewed by hand by an endocrinologist (T.T.B.) to confirm their case and control status.

Laboratory methods

After identification of case subjects and control subjects, frozen plasma samples at weeks 0 and 48 were pulled from the repository at the same time and forwarded to the Johns Hopkins Bayview Advanced Chemistry Laboratory (Baltimore, MD). Markers were measured in duplicate using commercially available enzyme-labeled immunosorbent sandwich assays, and values were averaged for analysis. The intra-assay precision of these assays range from 4.4 to 7.6% coefficient of variation (average 5.6% coefficient of variation). The sensitivity of this assay system for these cytokines ranged from 0.5 to 16 pg/ml. C-reactive protein was measured using a highly sensitive ELISA (ALPCO Diagnostics, Windham, NH). The linear range of the assay standards is 0.0019–0.15 mg/l, and samples with values greater than the highest standard were diluted and reanalyzed.

Measures

Outcome.

An incident case of diabetes was defined as having either two fasting blood glucose levels ≥126 mg/dl (or two consecutive nonfasting blood glucose levels ≥200 mg/dl) or a diagnosis of diabetes recorded in the medical record. The first high glucose level and the diagnosis date had to occur at least 1 year after ART initiation.

Inflammatory markers.

Week 48 levels of high-sensitivity (hs-CRP), interleukin (IL)-6, and the soluble receptors of tumor necrosis factor-α (sTNFR1 and sTNFR2) were each grouped into quartiles. Baseline levels of each marker were natural log-transformed. In addition to the matching factors BMI at baseline and race/ethnicity (white non-Hispanic, black non-Hispanic, and Hispanic), we considered several other potential confounding variables: glucose levels at week 48 (continuous), age (continuous), sex, calendar year of treatment initiation, smoking status (ever yes or no), personal and family history of any cardiovascular disease, specific antiretroviral regimens taken through week 48 (in particular use of protease inhibitors or thymidine-analog nucleoside reverse transcriptase inhibitors), CD4+ cell count <200 cells/mm3 at weeks 0 and 48, HIV-1 RNA ≥100,000 copies/ml at baseline, HIV-1 RNA <400 copies/ml at week 48, BMI at week 48 (continuous), and history of hepatitis C infection.

Statistical analysis

Demographic, health, and treatment characteristics were summarized and compared between case subjects and control subjects. P values from univariate conditional logistic regression models were used for statistical comparisons of all covariates by case status with the exception of race/ethnicity, which was a matching factor. Differences in marker levels at baseline and at week 48 were also summarized and compared using the Wilcoxon signed-rank test. Conditional logistic regression was used to model the association between week 48 levels of each inflammatory marker and diabetes incidence, taking into account matching and adjusting for the other potential confounders. The lowest marker quartile was the reference category in each model. Each model included categories of week 48 marker quartile, natural log-transformed baseline marker level and age. Model building proceeded by adding, one at a time, univariate predictors of diabetes (P ≤ 0.10). P ≤ 0.05 was considered statistically significant. Variables that changed odds ratios by ≥15% were kept in the model as potential confounders. To assess trend in diabetes incidence over marker quartiles, quartiles for each marker were included in the model as ordinal variables. Because of concern that hyperglycemia in the nondiabetic range may lead to inflammation, we reevaluated each model after adjustment for glucose values at 48 weeks. In these models, a variable indicating whether the glucose determination was made in the fasting state (yes or no) was also included.

RESULTS

Of the 3,405 individuals from the treatment-naive studies enrolled as of October 2008, 1,217 were from parent studies with ART data still blinded and therefore were not eligible for this analysis. Of the 2,188 remaining individuals, 2,161 (99%) had fasting or nonfasting glucose values within 16 weeks of ART initiation, and 231 (10.7%) had glucose values between 100 and 125 mg/dl pretreatment and were excluded. Another 63 individuals had either a prevalent diagnosis of diabetes, hypoglycemic medication use, fasting blood glucose ≥126 mg/dl, or nonfasting blood glucose ≥200 mg/dl at baseline and were also excluded. From this at-risk group of 1,867, 55 cases of incident diabetes (defined as occurring at least 1 year after ART initiation) were identified. An additional 22 individuals developed diabetes within the 1st year of ART initiation and were not included in analyses. Twenty-seven case subjects (49%) were identified through ≥2 fasting glucose values ≥126 mg/dl, with no clinical diagnosis or medication use recorded. Twenty-four (44%) had elevated fasting glucose and either a clinical diagnosis or medication use, and the remainder (7%) had a clinical diagnosis with or without medication use reported, but no elevated glucose. All case subjects with high glucose had high fasting glucose levels. Fifty-five control subjects matched on race/ethnicity and BMI were identified from those individuals who did not develop diabetes and who maintained blood glucose levels <100 mg/dl throughout follow-up. For 90% of case-control pairs the difference in BMI was <1 kg/m2; five pairs differed by a greater amount (range 1.7–6.5 kg/m2). The median time to diabetes incidence after week 48 was 1.9 years (interquartile range 1.3–3.8 years). In Table 1, we summarize demographic, treatment, and health characteristics for case and control subjects. Case subjects were significantly older than control subjects (median 41 vs. 37 years, P < 0.01). There was no difference by case status in calendar year of treatment initiation. Approximately 50% of each group had HIV RNA levels ≥100,000 copies/ml at baseline, and a greater proportion of case subjects than control subjects had CD4 counts <200 cells/mm3 at baseline. A higher proportion of case subjects versus control subjects had some use of IDV through week 48 (16 vs. 6%, P = 0.08).
Table 1

Baseline and on-treatment characteristics of case and control subjects

CovariateCase subjectsControl subjectsP value*
n5555
Sex
    Male42 (76)42 (76)1.00
    Female13 (24)13 (24)
Age in years at week 041 (37–46)37 (31–42)0.001
Race/ethnicity
    White26 (47)26 (47)
    African American13 (24)13 (24)
    Hispanic16 (29)16 (29)
BMI at week 0 (kg/m2)25.1 (22.7–30.0)25.1 (22.7–29.8)0.14
BMI at week 48 (kg/m2)28.1 (24.7–33.1)27.6 (24.1–30.4)0.01
hs-CRP at week 0 (mg/l)2.41 (1.36–4.37)1.57 (0.85–4.33)0.07
IL-6 at week 0 (pg/ml)1.74 (1.16–2.95)1.32 (0.80–2.52)0.08
TNFR1 at week 0 (pg/ml)1,386 (1,132–1,725)1,213 (1,081–1,470)0.03
TNFR2 at week 0 (pg/ml)4,741 (3,292–6,950)4,298 (3,646–5,185)0.14
Glucose at/before week 48 (mg/dl)
    Fasting (n = 40 case subjects; n = 35 control subjects)102 (91–111)83 (80–89)<0.001
    Nonfasting (n = 15 case subjects; n = 20 control subjects)89 (81–97)81 (76–87)0.08 <0.001
Family history of CVD
    Yes11 (20)6 (11)0.18
Smoking status
    Ever28 (51)29 (53)0.84
Calendar year of treatment initiation
    1998–199920 (36)24 (44)
    2001–2002 (none in 2000)22 (40)19 (34)
    2003–200413 (24)12 (22)0.69
Hepatitis C
    Ever5 (9)4 (8)0.74
HIV RNA >100,000 copies/ml at week 0
    Yes32 (58)30 (55)0.67
HIV RNA <400 copies/ml at week 48
    Yes5 (9)1 (2)0.14
CD4 <200 cells/mm3 at week 0
    Yes36 (66)23 (42)0.02
CD4 <200 cells/mm3 at week 48
    Yes8 (15)4 (7)0.14
ART in first 48 weeks
    Any stavudine19 (35)14 (26)0.32
    Any zidovudine43 (78)37 (67)0.19
    Any nelfinavir12 (22)17 (31)0.26
    Any IDV9 (16)3 (6)0.08
    Any lopinavir/ritonavir7 (13)8 (15)0.76

Data are median (interquartile range) or n (%). N = 110.

*From univariate conditional logistic model, unless otherwise indicated.

†Matching factor.

‡From a univariate conditional logistic model (continuous glucose term in model along with fasting status indicator).

Baseline and on-treatment characteristics of case and control subjects Data are median (interquartile range) or n (%). N = 110. *From univariate conditional logistic model, unless otherwise indicated. †Matching factor. ‡From a univariate conditional logistic model (continuous glucose term in model along with fasting status indicator). Case subjects had significantly higher glucose levels at week 48 than did control subjects. Seventy-five individuals (40 case subjects and 35 control subjects) had fasting glucose levels at or before week 48, and 35 individuals (15 case subjects and 20 control subjects) had only nonfasting glucose levels. Median fasting levels for case subjects and control subjects were 102 and 83 mg/dl, respectively (P value <0.001); median nonfasting values were 89 and 81 mg/dl, respectively (P value = 0.08). Case subjects tended to have higher baseline levels of inflammatory markers than control subjects (Table 1), adding further justification for including baseline marker levels in each multivariate model. These baseline levels were not significantly associated with diabetes after adjustment for age (data not shown). In Table 2, we present the median values of each marker at baseline and week 48. hs-CRP significantly increased from week 0 to 48; all other markers decreased significantly from week 0 to 48.
Table 2

Marker levels at week 0 and week 48 after initiation of antiretroviral therapy

Week 0Week 48Change (week 48–week 0)P value (H0: Δ = 0)
hs-CRP (mg/l)2.0 (1.0–4.3)3.0 (1.5–6.1)0.6 (−0.7 to 3.6)0.01
IL-6 (pg/ml)1.6 (0.9–2.7)1.3 (0.7–2.1)−0.2 (−0.9 to 0.4)0.01
TNFR1 (pg/ml)1,279 (1,109–1,573)1,173 (992–1,388)−109 (−387 to 40)<0.0001
TNFR2 (pg/ml)4,414 (3,550–6,300)2,983 (2,310–3,795)−1,390 (−2,932 to −479)<0.0001

Data are median (interquartile range).

Marker levels at week 0 and week 48 after initiation of antiretroviral therapy Data are median (interquartile range). Figure 1 summarizes the odds ratios for the association of each inflammatory marker at week 48 with subsequent diabetes incidence, adjusted for age, log of baseline marker value, CD4 at week 48, BMI at week 48, and IDV use through week 48. Higher hs-CRP, sTNFR1, and sTNFR2 levels, but not IL-6 levels, 1 year after ART initiation were all significantly associated with diabetes incidence before adjustment for glucose level at week 48. After additional adjustment for week 48 glucose, only sTNFR1 remained significantly associated with diabetes incidence (Table 3).
Figure 1

Odds ratios (95% CI) for incident diabetes in HIV-infected individuals by quartile (Q) of hs-CRP (A), IL-6 (B), sTNFR1 (C), and sTNFR2 (D) measured 48 weeks after initiation of antiretroviral therapy, adjusted for baseline marker level, age, CD4 <200 cells/mm3 at week 48, 48-week BMI, and use of IDV.

Table 3

Relationship between inflammatory marker level 48 weeks after initiation of ART and incident diabetes in HIV-infected persons, with and without additional adjustment for week 48 glucose levels

MarkerModel excluding week 48 glucoseModel including week 48 glucose
hs-CRP
    Quartile 1ReferentReferent
    Quartile 21.09 (0.19–6.36)0.65 (0.09–4.69)
    Quartile 32.62 (0.51–13.5)1.88 (0.28–12.8)
    Quartile 44.87 (0.87–27.2)2.52 (0.36–17.6)
    P value, test for trend0.050.18
IL-6
    Quartile 1ReferentReferent
    Quartile 21.38 (0.29–6.54)1.52 (0.21–10.9)
    Quartile 33.94 (0.83–18.6)3.34 (0.53–21.1)
    Quartile 41.62 (0.31–8.47)1.39 (0.23–8.28)
    P value, test for trend0.240.94
TNFR1
    Quartile 1ReferentReferent
    Quartile 25.36 (0.77–37.5)2.88 (0.34–24.5)
    Quartile 314.0 (1.37–143)6.09 (0.61–60.8)
    Quartile 439.4 (2.17–716)23.2 (1.28–423)
    P value, test for trend0.010.03
TNFR2
    Quartile 1ReferentReferent
    Quartile 22.64 (0.56–12.5)1.49 (0.25–8.95)
    Quartile 32.38 (0.43–13.1)1.01 (0.12–8.31)
    Quartile 47.39 (1.18–46.4)4.63 (0.60–35.8)
    P value, test for trend0.040.27

Data are odds ratio (95% CI) vs. quartile 1. All models also include age, natural log of baseline marker level, BMI at week 48, CD4 <200 at week 48, and IDV use through week 48.

Odds ratios (95% CI) for incident diabetes in HIV-infected individuals by quartile (Q) of hs-CRP (A), IL-6 (B), sTNFR1 (C), and sTNFR2 (D) measured 48 weeks after initiation of antiretroviral therapy, adjusted for baseline marker level, age, CD4 <200 cells/mm3 at week 48, 48-week BMI, and use of IDV. Relationship between inflammatory marker level 48 weeks after initiation of ART and incident diabetes in HIV-infected persons, with and without additional adjustment for week 48 glucose levels Data are odds ratio (95% CI) vs. quartile 1. All models also include age, natural log of baseline marker level, BMI at week 48, CD4 <200 at week 48, and IDV use through week 48. Six individuals (five case subjects and one control subject) had not reached viral suppression by week 48. In a sensitivity analysis that excluded these six individuals and their matched case/control subject, the results were relatively unchanged. For sTNFR1 the association between the highest quartile and the lowest) was somewhat attenuated and for sTNFR2 the association between the highest quartile and the lowest) was stronger (data not shown).

CONCLUSIONS

In this case-control study nested within a multicenter, observational cohort of HIV-infected individuals receiving ART, we found that, despite a decrease in most inflammatory markers with ART initiation, markers of TNF-α activation 48 weeks after treatment were associated with incident diabetes. After further adjustment for glucose levels at 48 weeks, the associations were attenuated, but higher levels of sTNFR1 remained a significant predictor of incident diabetes in adjusted analyses. TNF-α is an proinflammatory cytokine produced by multiple cell types including macrophages, lymphocytes, and endothelial cells that contributes to the inflammatory cascade (15) and is highly expressed in both treated and untreated HIV-infected patients (12,13). TNF-α is also produced by adipose tissue and has been shown to interact with the insulin receptor substrate to attenuate the effect of insulin binding to its receptor (10). As a result, TNF-α has been proposed as a mediating factor between adiposity and insulin resistance. Blockers of TNF-α, however, such as infliximab and etanercept, have shown conflicting effects on insulin resistance in human populations (16,17). In epidemiological studies, the association between incident diabetes and TNF activity, either measured by concentrations of the soluble receptors to TNF-α or by TNF-α itself, has also been inconsistent. In several studies, sTNFR2 was associated with incident diabetes, but this association was lost after mutual adjustment for other inflammatory cytokines or measures of adiposity (8,9,18). We found that higher concentrations of sTNFR1 and sTNFR2 measured 48 weeks after ART initiation were associated with incident diabetes, independent of BMI, suggesting that TNF-α activity in HIV-infected individuals may contribute to the pathogenesis of diabetes. It is not known whether the origin of TNF-α is the adipose tissue, infected lymphocytes, or other cell types, but this is an important area for further inquiry. Although all subjects were normoglycemic before ART initiation, subjects who were to develop diabetes already had significantly higher glucose concentrations at 48 weeks after ART initiation, although within the nondiabetic range. After we adjusted for glucose levels at 48 weeks, the associations between markers of TNF-α activity were attenuated, but higher levels of sTNFR1 remained associated with incident diabetes. These findings may suggest that the effect of TNF-α activity on diabetes risk is present early on in the pathogenesis of diabetes among HIV-infected individuals treated with ART and adjustment for glucose concentrations at 48 weeks obscured the early effect. Whether this effect is mediated by increased insulin resistance or a decline in β-cell function is unclear. A prospective study examining inflammatory and insulin resistance markers at earlier time points after ART initiation would be necessary to further investigate the mechanisms. Without adjustment for week 48 glucose levels, we found that higher concentrations of hs-CRP at 48 weeks after ART initiation were associated with incident diabetes. hs-CRP has been associated with incident diabetes in the general population (7), but this association is partially explained by excess adiposity (19). Evidence against a causal effect of hs-CRP on diabetes risk is derived from a Mendelian randomization study, which found no association between haplotypes associated with higher hs-CRP concentrations and diabetes risk (20). In our study, after adjustment for 48-week glucose values, the association between 48-week hs-CRP and incident diabetes was no longer present, suggesting that hs-CRP may be an early marker of diabetes risk in HIV-infected patients starting ART. Importantly, hs-CRP is associated with other adverse outcomes in HIV-infected patients, including myocardial infarction (21) and all-cause mortality (22). Whether therapies aimed to decrease hs-CRP will improve outcomes in HIV-infected individuals is unknown. Modern ART is very effective in suppressing HIV replication. However, in all studies of patients initiating ART, there is a subset in whom HIV is not suppressed to undetectable levels. Six subjects in the study had viral RNA >400 copies/ml at 48 weeks. In a sensitivity analysis, we excluded those who had uncontrolled HIV infection and found similar association between inflammatory markers and incident diabetes. Although not nominally statistically significant, this analysis suggests that our findings were not influenced by a small number of subjects with uncontrolled HIV. Our study had several limitations that should be noted. Our sample size was small, and, as a result, the effect estimates were imprecise. Larger, confirmatory studies should be performed to better estimate the association between inflammation and diabetes in HIV-infected individuals initiating ART. A larger study would also have sufficient power to determine whether the association between inflammation and diabetes in HIV-infected individuals differs by sex. Second, we examined only four different inflammatory markers. Studies in the general population have shown that other markers may be associated with diabetes, such as IL-18 (23). Third, without an otherwise similar HIV-uninfected control group, it is difficult to interpret the absolute levels of inflammatory markers in our study. Finally, family history of diabetes was not available in our dataset, and therefore we were unable to adjust for this factor. However, in the Multi-Ethnic Study of Atherosclerosis (MESA) study, the inclusion of family history did not alter the relationship between inflammation and diabetes (19). In summary, we found that inflammatory markers, particularly those associated with TNF-α activity, 48 weeks after ART initiation were associated incident diabetes. Chronic inflammation among HIV-infected individuals, even those receiving effective antiretroviral therapy has been hypothesized to be an important contributor to the development of comorbid conditions. Our findings suggest that inflammation contributes to the pathogenesis of diabetes among treated HIV-infected individuals. The discovery of antiretroviral medication that suppresses inflammation in addition to HIV replication or adjunctive therapy targeting uncontrolled inflammation may be important in the prevention of comorbid conditions associated with chronic HIV infection, such as diabetes.
  21 in total

1.  Effects of etanercept in patients with the metabolic syndrome.

Authors:  L Elizabeth Bernstein; Jacqueline Berry; Sunnie Kim; Bridget Canavan; Steven K Grinspoon
Journal:  Arch Intern Med       Date:  2006-04-24

2.  Effects of a nucleoside reverse transcriptase inhibitor, stavudine, on glucose disposal and mitochondrial function in muscle of healthy adults.

Authors:  Amy Fleischman; Stine Johnsen; David M Systrom; Mirko Hrovat; Christian T Farrar; Walter Frontera; Kathleen Fitch; Bijoy J Thomas; Martin Torriani; Hélène C F Côté; Steven K Grinspoon
Journal:  Am J Physiol Endocrinol Metab       Date:  2007-02-06       Impact factor: 4.310

Review 3.  The potential biological and clinical significance of the soluble tumor necrosis factor receptors.

Authors:  D Aderka
Journal:  Cytokine Growth Factor Rev       Date:  1996-10       Impact factor: 7.638

4.  C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus.

Authors:  A D Pradhan; J E Manson; N Rifai; J E Buring; P M Ridker
Journal:  JAMA       Date:  2001-07-18       Impact factor: 56.272

5.  Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study.

Authors:  Todd T Brown; Stephen R Cole; Xiuhong Li; Lawrence A Kingsley; Frank J Palella; Sharon A Riddler; Barbara R Visscher; Joseph B Margolick; Adrian S Dobs
Journal:  Arch Intern Med       Date:  2005-05-23

6.  Cumulative exposure to nucleoside analogue reverse transcriptase inhibitors is associated with insulin resistance markers in the Multicenter AIDS Cohort Study.

Authors:  Todd T Brown; Xiuhong Li; Stephen R Cole; Lawrence A Kingsley; Frank J Palella; Sharon A Riddler; Joan S Chmiel; Barbara R Visscher; Joseph B Margolick; Adrian S Dobs
Journal:  AIDS       Date:  2005-09-02       Impact factor: 4.177

7.  IRS-1-mediated inhibition of insulin receptor tyrosine kinase activity in TNF-alpha- and obesity-induced insulin resistance.

Authors:  G S Hotamisligil; P Peraldi; A Budavari; R Ellis; M F White; B M Spiegelman
Journal:  Science       Date:  1996-02-02       Impact factor: 47.728

8.  A prospective study of inflammatory cytokines and diabetes mellitus in a multiethnic cohort of postmenopausal women.

Authors:  Simin Liu; Lesley Tinker; Yiqing Song; Nader Rifai; Denise E Bonds; Nancy R Cook; Gerardo Heiss; Barbara V Howard; Gokhan S Hotamisligil; Frank B Hu; Lewis H Kuller; JoAnn E Manson
Journal:  Arch Intern Med       Date:  2007 Aug 13-27

9.  Inflammatory markers and risk of developing type 2 diabetes in women.

Authors:  Frank B Hu; James B Meigs; Tricia Y Li; Nader Rifai; JoAnn E Manson
Journal:  Diabetes       Date:  2004-03       Impact factor: 9.461

10.  Loss of bone mineral density after antiretroviral therapy initiation, independent of antiretroviral regimen.

Authors:  Todd T Brown; Grace A McComsey; Martin S King; Roula B Qaqish; Barry M Bernstein; Barbara A da Silva
Journal:  J Acquir Immune Defic Syndr       Date:  2009-08-15       Impact factor: 3.771

View more
  111 in total

1.  Interleukin-6, high sensitivity C-reactive protein, and the development of type 2 diabetes among HIV-positive patients taking antiretroviral therapy.

Authors:  Claude Béténé A Dooko; Stephane De Wit; Jacqueline Neuhaus; Adrian Palfreeman; Rosalie Pepe; James S Pankow; James D Neaton
Journal:  J Acquir Immune Defic Syndr       Date:  2014-12-15       Impact factor: 3.731

2.  Serum leptin level mediates the association of body composition and serum C-reactive protein in HIV-infected persons on antiretroviral therapy.

Authors:  John R Koethe; Aihua Bian; Ayumi K Shintani; M Sean Boger; Valerie J Mitchell; Husamettin Erdem; Todd Hulgan
Journal:  AIDS Res Hum Retroviruses       Date:  2012-02-02       Impact factor: 2.205

Review 3.  HIV and inflammation: mechanisms and consequences.

Authors:  Peter W Hunt
Journal:  Curr HIV/AIDS Rep       Date:  2012-06       Impact factor: 5.071

Review 4.  Management of the metabolic effects of HIV and HIV drugs.

Authors:  Todd T Brown; Marshall J Glesby
Journal:  Nat Rev Endocrinol       Date:  2011-09-20       Impact factor: 43.330

5.  Risk of type 2 diabetes among HIV-infected and healthy subjects in Italy.

Authors:  Laura Galli; Stefania Salpietro; Gabriele Pellicciotta; Alberto Galliani; Piermarco Piatti; Hamid Hasson; Monica Guffanti; Nicola Gianotti; Alba Bigoloni; Adriano Lazzarin; Antonella Castagna
Journal:  Eur J Epidemiol       Date:  2012-06-22       Impact factor: 8.082

6.  Higher CD163 levels are associated with insulin resistance in hepatitis C virus-infected and HIV-infected adults.

Authors:  Michael Reid; Yifei Ma; Rebecca Scherzer; Jennifer C Price; Audrey L French; Michael W Plankey; Carl Grunfeld; Phyllis C Tien
Journal:  AIDS       Date:  2017-01-28       Impact factor: 4.177

7.  New-onset type 2 diabetes mellitus among patients receiving HIV care at Newlands Clinic, Harare, Zimbabwe: retrospective cohort analysis.

Authors:  Cleophas Chimbetete; Catrina Mugglin; Tinei Shamu; Bindu Kalesan; Barbara Bertisch; Matthias Egger; Olivia Keiser
Journal:  Trop Med Int Health       Date:  2017-06-08       Impact factor: 2.622

8.  Effect of statin therapy in reducing the risk of serious non-AIDS-defining events and nonaccidental death.

Authors:  E T Overton; D Kitch; C A Benson; P W Hunt; J H Stein; M Smurzynski; H J Ribaudo; P Tebas
Journal:  Clin Infect Dis       Date:  2013-02-05       Impact factor: 9.079

Review 9.  Study design issues in evaluating immune biomarkers.

Authors:  Ronald J Bosch; Xinyan Zhang; Netanya G Sandler
Journal:  Curr Opin HIV AIDS       Date:  2013-03       Impact factor: 4.283

10.  HIV-exposed-uninfected infants have increased inflammation and monocyte activation.

Authors:  Sahera Dirajlal-Fargo; Marisa M Mussi-Pinhata; Adriana Weinberg; Qilu Yu; Rachel Cohen; D Robert Harris; Emily Bowman; Janelle Gabriel; Manjusha Kulkarni; Nicholas Funderburg; Nahida Chakhtoura; Grace A McComsey
Journal:  AIDS       Date:  2019-04-01       Impact factor: 4.177

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.