Clinical Role of Inflammation in Metabolic Diseases Won-Young Lee Department of Endocrinology and Metabolism Kangbuk Samsung Hospital Sungkyunkwan University Medical School, Seoul, Korea (2009-Nov-19)
Contents 1. Inflammation & insulin resistance : FFA, TNFα, JNK, IKKβ, SOCS, ASK1 2. Inflammation & islet dyfunction 3. Clinical data on inflammation & metabolic diseases
Adipokine expession in VAT according to high-fat h vs standard d diet in obesity-prone C57BL/6J mice. Metformin & 30% (2nd) Metformin & 30% (2nd) Metformin & 30% (2nd) 60 10 35 tin mrna Ad diponectin/β-act 50 40 30 20 10 TNF-α/β-actin mrna 8 6 4 2 hemerin/β-actin n mrna C 30 25 20 15 10 5 0 C Low fat C Metf 30% High fat 0 C Low fat C Metf 30% High fat 0 C Low fat C Metf 30% High fat Low C: High C (P value 0.0467797957) High C: High + 30% (P value 0.0007487272) Low C: High + 30% (P value 0.0176246810) Low C: High C (P value 0.0005133969) High C: High + Metf (P value 0.0138635845) High C: High + 30% (P value 0.0047228495) Low C: High + 30% (P value 0.0097507644) Low C: High C (P value 0.0005930242) High C: High + 30% (P value 0.0186465573) Low C: High + 30% (P value 0.0019570097) Adiponectin TNFα Chemerin (cf) Chemerin is a novel adipocyte-derived factor in inducing IR in primary human skeletal M. cells (Diabetes 2009)
Mechanism involved in inflammation-induced IR. 1. IKKbeta-NFkB activation is a mediator of TNF-induced IR. - Overexpression of IKKbeta atteniuated insulin signaling,. - ob/ob mice with IKKbeta attenuation were protected against IR. (Yuan M. Science 293: 1673-1677, 2001) 2. JNK activation (family of serine/threonine protein kinase) - JNK activity is increased in liver, muscle and AT in genetic, DIO. - JNK loss prevents IR. (Hirosumi J. Nature 420: 333-336, 2002) 3. ER stress promotes IR. - ER stress -> JNK activation -> IRS-1 serine phosphorylation. - XBP1 attenuation develop IR. 4. IL-6-> SOCS protein expression : IRS-1,2 degradation --> IR. SREBP-1 expression fatty liver 5. TLR signaling -> ASK1 JNK activation.
Potential cellular mechanisms for activating inflammatory signaling. (JCI 116:1793, 2006)
NF-κB is a proinflammatory master switch that controls the production of inflammatory markers & mediators (JCI 116:1793, 2006)
Model of overlapping metabolic and inflammatory signal pathways in adipocytes or macrophages : The absence of FABPs is antiinflammatory. -FABP4 : = induced by Ox-LDL, TLR agnoists = reduced by statin. (J Clin Invest. 115:1111, 2005)
Contents 1. Inflammation & insulin resistance : Macrophage polarity, JNK, IKKβ, SOCS, ASK1 2. Inflammation & islet dyfunction 3. Clinical data on inflammation & metabolic diseases
Diabetes 56: 2356-2370, 2007
Islet inflammation in type 2 DM Inflammatory process is important in the pathogenesis of glucotoxicity in type 2 DM. High glucose -> IL-1β/NF-κB, JNK -> β cell apoptosis.
NEJM 256:15, 2007
Inflammation and β Cell loss Obesity, IR
Contents 1. Inflammation & insulin resistance : Macrophage polarity, JNK, IKKβ, SOCS, ASK1 2. Inflammation & islet dyfunction 3. Clinical data on inflammation & metabolic diseases
The Relative Contribution of Adipokines vs Inflammatory Markers on the Future Development of New DM : The Rancho Bernardo Study Won-Young Lee 1, Elizabeth Barrett-Connor 2 Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine 1 Department of Family and Preventive Medicine, UCSD 2
Methods - Subjects : = 1920 adults (57.5% women, mean age 69.1) = Among 2765 subjects in 1984-1987 1987 visit, 1920 subjects did not have DM. =!984-1987 visit 4: DM verification by 75g OGTT, DM Dx Hx, DM drug Hx 8 yrs F/U Visit 1 Visit 4 Visit 7 (1972-1974) (1984-1987) (1992-1995) - Hx, 75g OGTT - Leptin, Adiponectin - IL-6/ CRP - Hx, 75g OGTT
Table 1. Age and age-adjusted adjusted characteristics : RBS 1984-1987 1987 No DM (n=1846) New DM (n=74: 3.9%) Age (yrs) 69.2±0.3 65.8±1.3 Women (%) 57.5% 56.3% BMI (kg/m2) 24.7±0.1 26.0±0.4 Waist Circum (cm) 85.7 ±0.9 86.6±4.4 WHR 0.84 ±0.01 0.85±0.01 SBP (mmhg) 136.7±0.4 140.4±2.2 DBP (mmhg) 75.9±0.2 77.0±1.1 TC (mg/dl) 220.0±0.9 216.6±4.6 TG (mg/dl) 113.1±1.7 130.7±8.5 LDL-C (mg/dl) 135.0±0.8 129.8±4.2 HDL-C (mg/dl) 63.0±0.4 61.2±2.2 FPG (mg/dl) 95.9±0.2 102.0±1.2 Leptin (ng/ml) 10.63±0.25 12.89±1.29 1289±1 29 Adiponectin (mg/ml) 13.99±0.20 12.38±1.04 Leptin/Adpn ratio 1.028±0.037 1.823±0.190 IL-6 6(pg/mL) 2.96±0.07 296±0 07 3.11±0.31 311±0 31 CRP (mg/l) 3.21±0.15 4.95±0.74 Data are means±se or percent. P<0.05, 05 P<0.01, 01 P<0.001001
Table 4. Odd ratio and 95% confidence intervals for biomarkers and New DM from multiple logistic regression analyses : the Rancho Bernardo Study, 1984-1987 Independent Variables Odd ratio 95% Confidence Interval P-value Leptin/Adpn ratio 1.67 1.00-2.78 0.049 IL-6 0.81 0.42-1.55 NS CRP 0.84 0.56-1.27 NS - Covariates were age, sex, BMI, TC, TG, FPG. -TG, FPG, Leptin/Adpn, IL-6 and CRP were log-transformed for analyses.
Figure 1. Age and sex-adjusted incidence of new DM by the tertiles of L/A ratio : the Rancho-Bernardo Study. P<0.01 70 60 P=0.01 (P for trend <0.01) DM (% Incidence e of new 50 40 30 20 10 NS 2.3% 2.7% 6.1% 0 1st tertile 2nd tertile 3rd tertile Leptin/adiponectin ratio
Metabolism 57:268-273, 2008-139 type2 DM patients. - In clamp study, L/A correlated with GIR (r 2 =0.26), stronger correlation with either leptin (r 2 =0.144 P=0.03), or adiponectin alone or HOMA-IR (r 2 =0.103, P=0.08)
Baseline hscrp could impact later development of MS : Longitudinal observation study CH Jung, EJ Rhee, WY Lee et al. Endocrinology and Metabolism Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine (Int J Cardiol 129: 266-71, 2008)
Mthd Methods Retrospective study : A total of 1132 subjects who underwent health examination in both 2002 & 2005 were enrolled (767 men, 365 women, mean age of 49 years). 3 yrs F/U 1132 subjects Baseline (2002) 3 yrs after (2005) -Hx, FBG -Hx, FBG - hscrp - hscrp
Multivariate relative risks for the highest vs lowest quartiles were 2.3 (95% CI, 1.3-4.1; P for trend =0.005). This longitudinal study shows that elevated levels of hscrp could be associated with incident MS.
Insulin resistance indices were higher in the C allele carriers compared with the non-carriers, raising possibility of candidate gene in IR among Koreans.
Longitudinal observation study : KBSMC-Adipokine Study (KangBuk Samsung Medical Center- Adipokine Study) - Among 1500 subjects examined at 2003, July, 500 subjects who underwent health examination in both 2003 & 2007 will be enrolled. - It will be examined if basline adipokines could impact progression of glucose status. 4yrsF/U Baseline (2003) 4 yrs after (2007) - Biomarkers : =afabp, RBP-4, visfatin =IL-6, TNFa, MCP-1 - Progression to IFG/DM from NFG/IFG - Progression of NAFLD - Progression of MS
Baseline characteristics of the participants Mean±SM Age (yrs) 40.81±6.3 BMI (kg/m 2 ) 23.63±3.0 Fasting glucose (mg/dl) 94.4±12.1 GOT 26.87±10.4 GPT 28.77±21.3 Uric acid 5.57±1.4 Total Cholesterol 200.96±35.7 Triglycerides 132.37±111.3 LDL cholesterol 114.53±28.2 HDL-C 54.34±11.3 γ-gtp 27.16±30.1 SBP (mmhg) 112.95±12.2 DBP (mmhg) 72.65±9.5 Fasting insulin (μiu/ml) 6.71±3.4
The comparisons among the baseline cardiovascular risk factors according to MS progression status analyzed in 409 subjects who didn t have MS in 2003 No MS (n=344) MS developed (n=65) P values Age (yrs) 40.45±6.0 41.34±7.8 0.302 Log(RBP4) 3.40±0.9 3.50±0.9 0.374 Log(IL-6) 1.01±1.0 1.13±1.0 0.414 Log(visfatin) 2.12±1.4 2.55±1.4 0.044 Log(TNFα) 0.96±0.8 1.20±0.5 0.021 Log(FABP4)* 2.13±0.4 2.47±0.4 <0.01 Log(MCP-1) 5.61±0.6 5.72±0.7 0.080 *These significance is consistent even after adjustment for age and BMI.
Comparisons of the mean values of adipokine levels according to the glucose tolerance progression status Maintained Progressor Regressor (n=28) P value (n=381) (n=70) RBP4 (ug/ml) 49.22±56.5 54.5 ±53.6 50.33 ±61.9 0.779 IL-6 (pg/ml) 4.75 ±6.4 6.24 ±8.6 3.47 ±4.1 0.121 MCP-1 (pg/ml) 344.11 ±218.22 368.8181 ±314.11 375.75 ±493.33 0.450 TNF-α (pg/ml) 3.35 ±1.7 4.07 ±1.8 3.00 ±2.3 0.004 Visfatin (ng/ml) 37.5 ±272.5 36.61 ±85.3 58.64 ±223.5 0.917 A-FABP (ng/ml) 9.78 ±5.5 a 11.97 ±5.2 a,b 8.37 ±3.8 b 0.002 Same footnotes denote significant differences in multiple comparison
Summary of KBSMC-adipokine study FABP4 and TNF-alpha might be associated with future development of metabolic syndrome and glycemic progression.
Serum MCP-1 levels and Metabolic Syndrome : KBSMC-Adipokine Study 496 participants. cross-sectional anlaysis. Table 2. Bivariate correlation between serum MCP-1 levels and metabolic parameters Correlation coefficient P value Age -0.033033 0.465 p=0.025 0025 Weight 0.241 <0.01 BMI 0.156 <0.01 SBP 0.176 <0.01 001 DBP 0.151 <0.01 FBS 0.074 0.101 Insulin -0.104 0.024 AST 0.138 0.002 ALT 0.198 <0.01 TC 0.065 0.146 TG 0.149 0.001 HDL-C -0.137 0.002 LDL-C 0.083 0.074 Correlation analyses were performed with Spearman s correlation analyses. p=0.015 p=0.017 p=0.010 (Kim JH, Lee WY et al. Kor Diab J. 2008)
(Diabetes Care 31: 562-568, 2008)
New development of NAFLD according to baseline adipokines : KBSMC-Adipokine study 07 US Non-NAFLD NAFLD P-value 03 (n=257) (n=106) IL-6 2.50 (1.47-4.71) 2.53 (1.40-5.30) 0.928 MCP-1 257 (196-370) 365 (204-444) 0.122 TNF-a 3.15 ± 1.79 3.65 ± 1.78 0.016 Visfatin i 764(403 7.64 (4.03 16.31) 31) 868(44019 8.68 (4.40-19.27) 0.429 A-FABP 867(687-11 8.67 (6.87 11.17) 17) 857(667-11 8.57 (6.67 11.56) 0.797 RBP-4 25.55 (15.1-58.1) 29.42 (15.3-8.9) 0.505 Odds ration of becoming NAFLD in the highest tertile group of baseline TNFα was 2.0 compared to the lowest tertile group when adjusting age, BMI, smoking.
This study explored whether elevated levels of inflammation-sensitive plasma proteins (ISPs) (fibrinogen, orosomucoid, 1-antitrypsin, haptoglobin, and ceruloplasmin) are associated with future weight gain. Five ISPs were measured in 2,821 non-diabetic healthy men who were reexamined after a mean follow-up of 6.1 years. (Engström G. Diabetes 52: 2097 2101, 2003)
Relation of baseline IL-6 and future weight gain : KBSMC-adipokine study Comparisons of mean weight changes according to the quartiles of baseline serum IL-6 levels l in 4 years of ffollow-up Quartiles of IL-6 levels mean±sd I( 1.47 pg/ml) -0.14±3.16 a,b II (1.47~2.59 pg/ml) -0.06±3.32 c,d III (2.60~5.14 pg/ml) 0.82±3.17 a,c IV ( 5.15 pg/ml) 0.76±3.03 b,d P value in one-way ANOVA 0.023 Same letters denote significant differences in post-hoc analyses. These significant differences were consistent after adjustment for age and baseline weight (p=0.049). 049)
Relation of baseline IL-6 and future weight gain : KBSMC-adipokine study Bivariate correlations between baseline serum IL-6 levels and weight change in 4 yr F/U r=0.112 p=0.014 This significant difference was consistent after adjustment for age and baseline weight.
Importance of weight gain and physical activity in metabolic diseases among Koreans : KBSMC data
Association of glycemic & BP progression with weight change in subjects without history of diabetes: A 4-year follow-up study Eun-Jung Rhee, Won-Young Lee, Seung-Hyun Yoo, Ji-Cheol Bae, Won-Jun Kim, Eun-Suk Choi, Se-Eun Park, Cheol-Young Park, Ki-Won Oh, Sung-Woo Park, Sun-Woo Kim Endocrinology and metabolism, Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine, Seoul, Korea (KDA, 2009 Autumn Congress)
Selection of study subjects in this study 68,051 participants who performed medical check-up in 2004 92,473 participants who performed medical check-up in 2008 31,900 subjects who performed medical checkup in both 2004 and 2008 (after exclusion criteria in 2004) 29,339 subjects selected for analyses 3850 (13.1%) out of 29,339 subjects progressed to more serious glucose status in 4 years (NFG-> IFG or IFG-> DM).
Glycemic progression according to weight change in 29,339 apparently healthy Koreans : 4 year follow-up study (KBSMC) Quartiles of Incident case OR 95% CI percent weight change (%) <-5.0 50 2793 100( 1.00 (reference) - -5.0 ~ -1.0 6864 1.215 1.050~1.406-1.0 10~ 10 1.0 5438 1.381 1.189~1.604189 1 1.0 ~ 5.0 9128 1.587 1.378~1.826 >5.0 5070 2.354 2.025~2.737 2 P for trend Total 29293 <0.01 Analyzed with logistic regression model with adjustments for confounding variables (Age,,g gender, BMI, HOMA-IR 2004, FBS, TG, TC, SBP in 2004) (Rhee EJ, Lee WY et al. in submisison)
BP progression according to weight change in 19,903 apparently healthy Koreans : 4 year follow-up study (KBSMC) Changes in BP across the quartiles of weight change (Park SE, Lee WY et al. in submission)
Both physical activity and obesity could impact IR in 40779 Koreans (KBSMC study) (Bae JC, Lee WY et al. in submission) (n=5,694) (n=22,965) (n=2,677) (n=9,623)
Odds ratios for being IR (HOMA-IR >2) categorized by obesity and physical activity Odd ti f b i b h i ll i ti Odds ratio for being obese-physically inactive: 1.63 and 1,59 compared to being obese-physically active.
Summary Atherosclerosis Inflammation Adipose Tissue Adipokines (wt gain) DM (type 2)
Acknowledgements Kangbuk Samsung Hospital Diabetes Center Kim, Sun-Woo Park, Sung-Woo Oh, Ki-Won Park, Cheol-Young Rhee, Eun-Jung Park, Se-Eun Choi, Eun-Sook Kim, Won-Jun Bae, Ji-Chul Yoo, Seung-Hyun Laboratory members Kim, Se-Yeon Lee, Jin-Mee Park, Hee-Soon Choi, Jung-Mook