Title: Receptor Activator of Nuclear Factor-κB (RANKL) and Risk of Type 2 Diabetes Mellitus

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1 1 Title: Receptor Activator of Nuclear Factor-κB (RANKL) and Risk of Type 2 Diabetes Mellitus Authors: Stefan Kiechl, Jürgen Wittmann, Andrea Giaccari, Michael Knoflach, Peter Willeit, Aline Bozec, Alexander Moschen, Giovanna Muscogiuri, Gian Pio Sorice, Trayana Kireva, Monika Summerer, Stefan Wirtz, Julia Luther, Dirk Mielenz, Ulrike Billmeier, Georg Egger, Agnes Mayr, Friedrich Oberhollenzer, Florian Kronenberg, Michael Orthofer, Josef Penninger, James B. Meigs, Enzo Bonora, Herbert Tilg, Johann Willeit, Georg Schett

2 2 Supplementary Text Association between soluble RANKL concentration and insulin resistance In subjects free of diabetes, 1990 soluble RANKL concentrations (n = 844) showed weak associations with insulin resistance as estimated by HOMA-IR or the inverse of Gutt s ISI 0,120 : age- and sex-adjusted partial correlation coefficients r = (P = 0.016) and r = (P = 0.011), respectively. Concentration of soluble RANKL was higher in individuals with four or more components of the metabolic syndrome. In particular, mean concentration of soluble RANKL was 1.15, 1.10, 1.61 and 1.64 pmol L 1 in subjects with zero to two, three, four and five components (1990; P = 0.008) and 1.15, 1.18, 1.63 and 2.13 pmol L 1 (1995; P < 0.001), respectively. There was a tendency to an inverse correlation between RANKL and ß-cell secretory capacity as estimated by Sluiter s index, but this correlation fell short of significance. Risk prediction In order to assess the potential usefulness of soluble RANKL as a risk predictor for T2DM, we estimated its effect on model discrimination and calibration and on risk classification. Discriminative accuracy of the prediction models, i.e. the ability to correctly classify subjects into one of two categories, was calculated with the C statistic, which is analogous to the area under the receiver-operating-characteristic curve (AUC) (larger values indicate better discrimination). Comparison of AUCs based on models including and not including RANKL was performed according to the method of DeLong. 1 Findings remained robust when applying the algorithms proposed by Hanley. 2 To assess model calibration or how closely the predicted probabilities reflect actual risk, we computed the Hosmer-Lemeshow calibration statistics comparing observed and predicted risk in decile categories of predicted risk (higher P values indicate better calibration). 3 Finally, we calculated the net reclassification improvement. 4 Results are as follows. Addition of RANKL to the model already including age, gender, period of follow-up, body mass index and fasting glucose concentration resulted in a significantly higher AUC

3 3 (0.858 [ ] to [ ], ΔAUC [ ], P = 0.011), indicating a significant gain in model discrimination. There was a highly significant improvement in the likelihood function ( 2 Log-likelihood, 26.3, P < 0.001), but only a marginal gain in model calibration (P value of the Hosmer-Lemeshow calibration statistics comparing observed and predicted risk in decile categories of predicted risk: and 0.616). Finally, consideration of soluble RANKL resulted in a more accurate classification of subjects. A total of nine of the 78 subjects with incident T2DM were correctly reclassified from <2.50, and % categories to the 10.0% category, whereas only three were erroneously down-graded from the high to low-to-medium risk groups. The net reclassification improvement for subjects who developed T2DM or remained free of T2DM was 6.4% and 2.5%, respectively, yielding a global NRI of 9.0% (P = 0.083). ROCs for an analysis restricted to individuals with fasting glucose concentrations equal to or greater than 100 mg dl 1 again indicated that the model including RANKL provides a better fit (AUC [ ] versus [ ], ΔAUC [ ] P = ). The global NRI in this group was 20.2% (P = ). A total of 71 of the 78 incident cases of diabetes occurred in this subgroup. Measurement of soluble RANKL in previous studies While OPG measurement in numerous studies yielded largely comparable serum concentrations and consistent association with risk factors, diabetes and cardiovascular disease, the same was not true for soluble RANKL. Median concentrations of free soluble RANKL in samples of the general community varied from around 0.05 pmol L 1 (Framingham or Tromso) to more than 8 pmol L 1 (EPIC controls, 8.43) 5 9 [for transformation of pg to pmol a molecular weight of 35 kda for soluble RANKL was assumed]. The proportions of individuals with no RANKL detectable ranged from 0% to more than 60%. Reasons for these discrepant findings and distinct association patterns are not entirely clear, but several issues may be of relevance. (a) Concentrations of free soluble RANKL were shown to be unaffected by sample storage of up to ten years when blood specimens were immediately frozen, stored at 80 ºC

4 4 and assayed shortly after thawing (no thawing and re-freezing cycle). However, less stringent storage conditions have been proposed to substantially affect RANKL concentrations. 10 Quality of blood specimens may account in part for the discrepancy observed. (b) Two groups of assays measure either free (biologically active) soluble RANKL or total soluble RANKL, i.e. also the larger amount of RANKL complexed to OPG. There is no doubt that these parameters have a distinct meaning. Not all manufacturers provide detailed information in this regard. Moreover, not all assays were licensed for human application (mouse assay). (c) The various assays used in previous studies differed substantially in their lower detection limit. Problems with the detection limit have recently been resolved by the development of new generation assays like the one applied in the current evaluation (Biomedica; lower detection limit 0.08 pmol L 1 ). 10 (d) Sources and clearance of soluble RANKL may be subject to change. Owing to a lack of associations between soluble RANKL concentration and bone mineral density as well as genetic polymorphisms linked to bone mineral density, the skeletal system is unlikely to be a major source.

5 5 Supplementary Table 1: Baseline characteristics in the Bruneck study population according to tertile groups of soluble RANKL (n = 844). Characteristic a Tertile group for RANKL (pmol L 1 ) Low Medium High [ ] [ ] [ ] P value b Age, years 58.9± ± ± Male sex, n (%) 147 (52.1) 141 (51.6) 137 (47.4) Social status, n (%) Low 189 (67.0) 163 (59.7) 165 (57.1) Middle 51 (18.1) 62 (22.7) 69 (23.9) High 42 (14.9) 48 (17.6) 55 (19.0) Alcohol consumption, n (%) g d (75.2) 206 (75.5) 240 (83.0) > 50 g d 1 70 (24.8) 67 (24.5) 49 (17.0) Cigarette smoking, n (%) 88 (31.2) 59 (21.6) 63 (21.8) Physical activity, score 4.22± ± ± T2DM family history, n (%) 82 (29.1) 78 (28.6) 88 (30.4) Body mass index, kg m ± ± ± Waist-to-hip ratio, cm cm ± ± ± HbA1c, % 5.45± ± ± Fasting glucose, mg dl ± ± ± h glucose, mg dl 1 c 93 [75 118] 93 [76 114] 91 [75 112] High-sensitivity CRP, mg L 1 c 1.2 [ ] 1.3 [ ] 1.1 [ ] Osteoprotegerin, pmol L ± ± ± T2DM, type two diabetes; CRP, C-reactive protein; RANKL, receptor activator of NF-κB. a Values presented are means ± standard deviation or numbers (percentages). b P values for difference in variable levels between individuals with and without incident T2DM were calculated with logistic regression analysis or general linear models with adjustment for age and sex. c Variables with a skewed distribution were log e -transformed for statistical analysis.

6 6 Supplementary Table 2: Baseline characteristics in the total study population (left column) and in participants with and without incident T2DM ( ). Characteristic a Total (n = 844) Incident T2DM No (n = 766) Yes (n = 78) P Value b Age, years 58.1± ± ± Male sex, n (%) 425 (50.4) 386 (50.4) 39 (50.0) Social status, n (%) Low 517 (61.3) 459 (59.9) 58 (74.4) Middle 182 (21.6) 172 (22.5) 10 (12.8) High 145 (17.2) 135 (17.6) 10 (12.8) Alcohol consumption, n (%) g d (78.0) 602 (78.6) 56 (71.8) > 50 g d (22.0) 164 (21.4) 22 (28.2) Cigarette smoking, n (%) 210 (24.9) 192 (25.1) 18 (23.1) Physical activity, score 4.36± ± ± T2DM family history, n (%) 248 (29.4) 224 (29.2) 24 (30.8) Body mass index, kg m ± ± ±4.5 <0.001 Waist-to-hip ratio, cm cm ± ± ± HbA1c, % 5.41± ± ±0.44 <0.001 Fasting glucose, mg dl ± ± ±10.1 < h glucose, mg dl 1 c 92 [75 114] 90 [73 110] 127 [ ] < ± ± ±52.5 High-sensitivity CRP, mg L 1 c 1.2 [ ] 1.2 [ ] 2.0 [ ] < ± ± ±8.95 Soluble RANKL, pmol L 1 c 1.1 [ ] 1.0 [ ] 1.1 [ ] < ± ± ±2.25 Osteoprotegerin, pmol L ± ± ± T2DM, type two diabetes; CRP, C-reactive protein; RANKL, receptor activator of NF-κB. a Values presented are means ± standard deviation or numbers (percentages). For variables with a skewed distribution both medians [inter-quartile range] and means ± standard deviation are shown to enable comparisons with previous studies. b P values for difference in variable levels between individuals with and without incident T2DM were calculated with Chi- Square or Fisher s exact test or with independent-samples T test. c Variables with a skewed distribution were log e -transformed for statistical analysis.

7 7 Supplementary Table 3: Association of soluble RANKL concentration with T2DM in various subgroups (Bruneck Study, ). OR (95%CI) for a one Subgroup n of cases Person -years Incidence rate (95%CI) s.d. unit higher RANKL concentration P value for interaction Sex Male ( ) 1.95 ( ) Female ( ) 1.87 ( ) Age <60 years ( ) 1.69 ( ) years ( ) 2.00 ( ) Social class Low ( ) 1.79 ( ) High ( ) 2.14 ( ) Body mass index <25 kg m ( ) 1.50 ( ) kg m ( ) 2.08 ( ) Smoking status Current smoker ( ) 1.82 ( ) Never/ex-smoker ( ) 1.85 ( ) Waist hip ratio < ( ) 1.72 ( ) ( ) 1.94 ( ) Physical activity Low (score 4) ( ) 1.74 ( ) High (score >4) ( ) 2.26 ( ) Alcohol consumption No/low alcohol ( ) 1.77 ( ) Modest/high alcohol ( ) 2.25 ( ) Fasting glucose <100 mg dl ( ) 1.56 ( ) mg dl ( ) 1.91 ( ) C-reactive protein <3 mg L ( ) 1.87 ( ) mg L ( ) 2.06 ( ) Time period ( ) 1.85 ( ) ( ) 1.63 ( ) ( ) 2.30 ( ) Odds ratios and 95% confidence intervals (95%CI) derived from pooled logistic regression analysis and calculated for a one s.d. unit higher concentration of loge-transformed soluble RANKL. Analyses were adjusted for age, sex, period of follow-up, social status, cigarette smoking, alcohol consumption, physical activity, family history for diabetes, body mass index and waist-to-hip ratio.

8 8 Supplementary Table 4: Association between baseline concentrations of markers of inflammation, endothelial activation, bone metabolism and T2DM risk in the Bruneck Study (n = 844, ). Characteristic a No (n = 766) Incident T2DM Yes (n = 78) Logistic regression Models b Odds ratio (95%CI) P Value c C-reactive protein, mg L [ ] 2.0 [ ] 1.36 ( ) Fibrinogen, mg dl [ ] 265 [ ] 1.05 ( ) IL-6, pg ml [ ] 4.0 [ ] 1.17 ( ) sicam-1, ng ml [ ] 350 [ ] 1.52 ( ) c svcam-1, ng ml [ ] 678 [ ] 1.28 ( ) E-selectin, ng ml [ ] 56.2 [ ] 1.25 ( ) P-selectin, ng ml [ ] 204 [ ] 1.33 ( ) MMP-9, ng ml [ ] 305 [ ] 1.32 ( ) TIMP-1, ng ml [ ] 189 [ ] 0.99 ( ) MCP-1, pg ml [ ] 219 [ ] 1.00 ( ) Adiponectin, μg ml [ ] 9.9 [ ] 0.71 ( ) Hydroxyvitamin D, ng ml [ ] 29.3 [ ] 1.03 ( ) Osteocalcin, ng ml [ ] 23.1 [ ] 0.80 ( ) Soluble RANKL, pmol L [ ] 1.1 [ ] 1.58 ( ) c sicam-1 and svcam-1, soluble intracellular and vascular adhesion molecule-1, MMP-9, metalloproteinase-9; TIMP-1, tissue inhibitor of metalloproteinase-1; MCP-1, monocyte chemoattractant protein-1; RANKL, receptor activator of nuclear factor κb ligand. a Values presented are medians [inter-quartile range]. b Odds ratios (95%CI) and P values were derived from logistic regression analysis adjusted for age, sex, social status, cigarette smoking, alcohol consumption, physical activity, family history of diabetes, body mass index and waist-to-hip ratio. To facilitate a comparison between the various risk predictors all odds ratios were calculated for a one s.d. unit increase in log e - transformed variable levels. A one s.d. unit decrease in adiponectin concentration resulted in an odds ratio [95%CI] of 1.41 [ ]. All analyses consider baseline variable levels (instead of updated variable levels as in Table 2) because most of these variables were determined only once in c These variables are significant when controlling for the multiple comparisons performed (Bonferroni-corrected threshold for significance < ).

9 9 Supplementary Table 5: Risk for T2DM according to concentration of osteoprotegerin in the Bruneck Study. Tertile group for osteoprotegerin a Low Medium High Osteoprotegerin (pmol L 1 ) Median Range Incident T2DM No. of events Person-years of follow-up 4, , ,964.3 Incidence rate per 1,000 person 5.4 [ ] 7.1 [ ] 9.8 [ ] years [95%CI]) Pooled logistic regression (n = 2,278) b Odds ratio (95%CI) P for trend Odds ratio (95%CI) for a one s.d. unit higher OPG concentration P value Adjusted for age, sex and period ( ) 1.26( ) ( ) Multivariate adjustment (model 1) c ( ) 1.31( ) ( ) Multivariate adjustment (model 2) c ( ) 1.31( ) ( ) Multivariate adjustment (model 3) c ( ) 1.32( ) ( ) Extended Follow-up (n = 2,852) d Adjusted for age, sex and period ( ) 1.06( ) ( ) Multivariate adjustment (model 1) c ( ) 1.08( ) ( ) Multivariate adjustment (model 2) c ( ) 1.09( ) ( ) Multivariate adjustment (model 3) c ( ) 1.08( ) ( ) T2DM, type II diabetes ascertained according to American Diabetes Association criteria; CI, confidence interval, OPG, osteoprotegerin. a Categorization of OPG tertile group was based on the entire study population (n = 909). Seeming differences in the incidence rates across tertile groups for OPG are due to an uneven age distribution in these groups and disappear after adjustment for age.

10 10 b Odds ratios (95%CI) were derived from pooled logistic regression analysis and calculated for a one s.d. unit higher OPG concentration (right-hand columns) and in separate models for OPG tertile groups (left-hand columns). The bottom tertile group served as the reference category. Base models were adjusted for age, sex and period of follow-up ( , , ). Odds ratios reflect the risk for new-onset T2DM in a 5-year period. All covariates were up-dated every five years. c Analysis was further adjusted for social status, cigarette smoking, alcohol consumption, physical activity, and family history of diabetes (model 1), plus body mass index and waist-to-hip ratio (model 2), plus fasting glucose and log e -transformed concentration of high-sensitivity C-reactive protein (model 3), plus common types of medication (statins, ß-blockers, diuretics, calcium channel blockers, angiotensin-converting enzyme and angiotensin receptor inhibitors, corticosteroids, digitalis drugs, platelet inhibitors, oral anticoagulation and hormone replacement therapy, each considered a separate variable) (model 4). d These analyses focused on the extended follow-up between 1990 and 2010 (n = 2,852 observation periods) and considered 100 cases of incident T2DM. Because OPG concentration was not measured in 2005 blood samples, OPG concentration assessed in 2000 samples was used to predict T2DM risk in both the and the follow-up period.

11 11 Reference List 1. DeLong,E.R., DeLong,D.M., & Clarke-Pearson,D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, (1988). 2. Hanley,J.A. & McNeil,B.J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, (1983). 3. Hosmer,D.W. & Lemeshow,S. Applied logistic regression(john Wiley, New York, 1989). 4. Pencina,M.J., D'Agostino,R.B., Sr., D'Agostino,R.B., Jr., & Vasan,R.S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat. Med. 27, (2008). 5. Kiechl,S. et al. Soluble receptor activator of nuclear factor-kappa B ligand and risk for cardiovascular disease. Circulation 116, (2007). 6. Schett,G. et al. Soluble RANKL and risk of nontraumatic fracture. JAMA 291, (2004). 7. Lieb,W. et al. Biomarkers of the Osteoprotegerin Pathway. Clinical Correlates, Subclinical Disease, Incident Cardiovascular Disease, and Mortality. Arterioscler. Thromb. Vasc. Biol.(2010). 8. Semb,A.G. et al. Osteoprotegerin and soluble receptor activator of nuclear factor-kappab ligand and risk for coronary events: a nested case-control approach in the prospective EPIC- Norfolk population study Arterioscler. Thromb. Vasc. Biol. 29, (2009). 9. Jorgensen,L. et al. Bone loss in relation to serum levels of osteoprotegerin and nuclear factorkappab ligand: the Tromso Study. Osteoporos. Int. 21, (2010). 10. Rogers,A. & Eastell,R. Circulating osteoprotegerin and receptor activator for nuclear factor kappab ligand: clinical utility in metabolic bone disease assessment. J. Clin. Endocrinol. Metab 90, (2005).

12 a Osteoprotegerin [pmol L 1 ] > < Number of participants Males Females RANKL [pmol L 1 ] < Number of participants b Osteoprotegerin level [pmol L 1 ] prior to T2D manifestation ** ** Osteoprotegerin level [pmol L 1 ] after T2D manifestation Soluble RANKL level [pmol L 1 ] prior to T2D manifestation * * Soluble RANKL level [pmol L 1 ] after T2D manifestation Supplementary Figure 1 (a) Distribution of serum concentration of soluble RANKL and osteoprotegerin in Bruneck Study 1990 (n = 844). (b) Serum concentrations of OPG and soluble RANKL before and after T2DM manifestation. The dotted grey lines show individual changes and the solid red lines show mean changes of OPG and soluble RANKL in 61 patients with incident T2DM and two or more available measurements of OPG and RANKL ( ). The solid green lines represent mean changes in subjects who remained free of T2DM. * P < 0.05, ** P < 0.01 compared to baseline levels (change with manifestation of T2DM) calculated using the paired T test. * P < 0.05, ** P < 0.01 for a comparison of changes in OPG and RANKL concentration between the two groups of subjects with and without incident T2DM (general linear models adjusted for age and sex and allowing for repeated measurements).

13 a d g b c e f Supplementary Figure 2 Generation of Rank shrna (RANKi) vectors and bockade of Rank expression by RANKi. (a) Plasmid maps of the plko.1 control vector (CTRLi) (non-target shrna) and the plko.1 RANKi 1 vector. (b) Tested shrna binding sites in mouse Rank [TNFRSF11A] (NM009399). (c) List of the target site and target sequence of the seven RANKi 1 vectors as well as CTRLi vectors. Vectors in red were used in the in vivo experiments. (d) Ponceau S staining of nitrocellulose membranes after Western transfer showing total protein content of HEK293 cells transfected with the pcdna3 mrank 3xFLAG expression vector, an EGFP fusion protein expression vector for normalization of transfection efficiency as well as seven different RANKi or CTRLi vectors. (e) Western blot showing FLAG staining of HEK293 cells transfected with pcdna3 mrank 3xFLAG expression vector, an EGFP fusion protein expression vector for normalization of transfection efficiency as well as seven different RANKi versus CTRLi vectors. Red arrows mark RANKi vectors with prominent downregulation of RANK expression selected for the in vivo experiments. (f) Western blot showing Enhanced Green Fluorescent Protein (EGFP) staining of HEK293 cells transfected with pcdna3 mrank 3xFLAG expression vector, an EGFP fusion protein expression vector for normalization of transfection efficiency and seven different RANKi and CTRLi vectors. (g Plasmid map of the Rank expression vector used for transfection of HEK293 cells.

14 Supplementary Figure 3 Specific blockade of hepatic Rank expression. (a) Real-time PCR analysis for Rank, RANKL, and Opg mrna expression in the liver of mice fed with high-fat diet (HFD) and treated with CTRLi versus RANKi by hydrodynamic injection. Data presented are means + s.e.m. (n = 5). *** P < compared to control calculated using the Mann Whitney U test. (b) Western blot of liver, adipose and muscle tissue for expression of Rank, RANKL and Opg in mice fed a HFD and treated with CTRLi versus RANKi by hydrodynamic injection. Data from three representative mice per group are shown. (c) Western blot of liver, adipose and muscle tissue for expression of Rank in control rank WT and rank LKO mice. Data from three representative mice per group are shown.

15 Supplementary Figure 4 Effects of Rank inhibition on glucose tolerance in obese (ob/ob) mice. (a) Fasting glucose concentration, insulin concentration, and insulin resistance as estimated by HOMA-IR in 10- and 14-week-old control rank WT ob/ob (bars in white) versus littermate rank LKO ob/ob mice (bars in black) (n = 5 per group). (b) Fasting glucose concentration, insulin concentration, and insulin resistance as estimated by HOMA-IR in 8-week-old ob/ob mice (week 0) and after 4 weeks of treatment with CTRLi vector (bars in white) versus RANKi vector (bars in black) (n = 6 per group). (c) Fasting glucose concentration, insulin concentration, and insulin resistance as estimated by HOMA-IR in 8-week-old ob/ob mice (week 0) and after 6 weeks of treatment with PBS (bars in white) versus osteoprotegerin (OPG) (bars in black) (n = 8 per group). Data presented are means + s.e.m. * P < 0.05, ** P < 0.01 compared to control calculated using the Mann Whitney U test.

16 a b c Supplementary Figure 5 Hepatic triglyceride and cholesterol content and effect of RANKL stimulation on the expression of NF- B-inducible cytokines in hepatocytes. (a) Liver triglyceride (upper graph) and cholesterol content (lower graph) of 8- week-old control rank WT (bars in white) versus littermate rank LKO mice (bars in black) at baseline and after 4 weeks of normal-fat diet (NFD) or HFD (n = 5 per group). Data presented are means. Error bars indicate s.e.m. * P < 0.05 compared to control calculated using the Mann Whitney U test. (b) Real time PCR analysis of tumor necrosis factor alpha (TNF-α), interleukin(il)-1, and chemokine (C-X-C motif) ligand 1 (CXCL1) mrna expression in cultured mouse hepatocytes exposed to PBS versus 50 ng ml 1 mouse RANKL (n = 3 for all groups). Data presented are fold increases to baseline (means + s.e.m.) elicited by RANKL stimulation. Measurements were performed 1, 3 and 12 h after stimulation. (c) Real time PCR analysis of TNF-α, IL-1, and CXCL1 mrna expression in cultured mouse hepatocytes exposed to PBS versus 50 ng ml 1 mouse RANKL with or without IκB-kinase(IKK)ß inhibitor AS (10 μg ml 1 ) (n = 3 for all groups). Data presented are fold increases to baseline (means + s.e.m.) elicited by RANKL stimulation. Measurements were performed 1 and 3 h after stimulation. * P < 0.05, ** P < 0.01 compared to control calculated using the Mann Whitney U test.

Supplementary Figure 1. DNA methylation of the adiponectin promoter R1, Pparg2, and Tnfa promoter in adipocytes is not affected by obesity.

Supplementary Figure 1. DNA methylation of the adiponectin promoter R1, Pparg2, and Tnfa promoter in adipocytes is not affected by obesity. (a) Relative amounts of adiponectin, Ppar 2, C/ebp, and Tnf mrna