Path Analysis Relationship of HOMA-IR, IGF-1, and

60 In structural phase model we obtained direct effect to BPHLUTS: HOMA-IR toward BPHLUTS was 0,31 31, IGF- 1 toward BPHLUTS was 0,03 3, IL-6 toward BPHLUTS was 0,10 10 and hsCRP toward BPHLUTS was 0,05 5. On the other hand direct effect of HOMA-IR toward other variable: effect HOMA-IR toward IGF-1, IL-6, hsCRP consecutively was 3,79 379, 0,005 0,5 and 0,015 1,5. From this structural model it appears that HOMA-IR has the most powerfull effect toward IGF-1, while the most powerful variable that influence BPHLUTS was HOMA-IR. Relationships between constructs was presented in Table 5.4 once analyzed with AMOS. The magnitude of each independent variable toward dependent variable represented by critical ratio CR. CR Value is obtained from estimate valued divided by standard error SE. The higher the value of CR hence the effect is more significant. 61 Table 5.4 Correlation between 2 constructive variable Variable Weight Regression Standardiz e weight regression Estimate CR p HOMA-IR--IGF-1 3,79 4,61 0,46 HOMA-IR--IL-6 0,00 0,30 0,76 0,03 HOMA-IR--hsCRP -0,015 -0,19 0,85 -0,02 HOMA-IR--Prostate HyperplasiaLUTS 0,31 3,64 0,37 IGF-1--Prostate Hyperplasia LUTS 0,03 3,19 0,001 0,32 IL-6-- Prostate Hyperplasia LUTS -0,10 -0,18 0,85 -0,02 hsCRP-- Prostate Hyperplasia LUTS -0,05 -0,46 0,64 -0,04 From the analysis of relationship between two variables constructs in Table 5.4, we could interpreted: there is a significant relationship between HOMA-IR with IGF-1 CR=4,61; p0,001, HOMA-IR with prostate hyperplasia CR=3,64; p0,001, IGF-1 with hyperplasia prostate CR=3,19; p=0,001. On the other hand, we found no significant relationship between IL-6 and hsCRP toward prostate hyperplasia. Based on the value of Standardize weight regression we can see how strong the relationship between variables constructs. Loading factor HOMA-IR and IGF-1 toward prostate hyperplasia consecutively 0,37 and 0,32. This means HOMA-IR and IGF-1 can 62 explain the incident of BPH, there is a close relationship between HOMA-IR and IGF-1 toward prostate hyperplasia. On the other hand loading factor HOMA-IR toward IGF-1 was 0,46. This suggests the close relationship between HOMA-IR and IGF-1. Patterns of relationship between constructs variables, direct or indirect relationship presented in Table 5.5 and 5.6. Table 5.5 Correlation pattern between constructive variable with Prostate Hyperplasia as dependent variable Outcome HOMA-IR -- Prostate Hyperplasia LUTS IGF-1 --Prostate Hyperplasia LUTS IL-6-- Prostate Hyperplasia LUTS hsCRP-- Prostate Hyperplasia LUTS Total outcome 0,44 0,03 -0,10 -0,05 Direct outcome 0,31 0,03 -0,1 -0,05 Indirect outcome 0,13 0,00 0,00 0,00 63 Table 5.6 Correlation pattern between constructive variable with HOMA-IR as dependent variable Outcome HOMA- IR --IGF-1 HOMA- IR --hsCRP HOMA- IR --IL-6 HOMA-IR -- Hiperplasia ProstatLUTS Total outcome 3,79 -0,01 0,00 0,44 Direct outcome 3,79 -0,01 0,00 0,31 Indirect outcome 0,00 0,00 0,00 0,13 From table 5.5 we can see HOMA-IR have the most powerful total and direct effect against prostate hyperplasia. On the other hand in Table 5.6 clearly show the most powerful direct and total effect HOMA-IR to IGF-1, followed by HOMA-IR toward prostate hyperplasia. In Table 5.5 we can also see HOMA-IR still contributes an indirect effect on prostate hyperplasia, while IGF-1 only have direct effect toward hyperplasia prostate. Figure 5.1 From structural model analysis and Table 5.6 shows that there is a close and significant relationship between HOMA-IR with prostate hyperplasia CR=3,64; p0,001, total effect 44, direct effect 31 and indirect 13. From this analysis we also found a significant relationship between HOMA-IR with IGF-1 CR=4,61; p0,001, immediate effect and the indirect effect is the same magnitude 379. There is also a very clear direct relationship between IGF-1 against prostate hyperplasia CR=3,19; p=0,001, direct effect 3. 64 Thus it can be said that HOMA-IR and IGF-1 has significant effects on prostate hyperplasia. From the structural analysis model is presented in Figure 5.1 and a path analysis above, we can see a direct relationship between HOMA-IR toward hyperplasia prostate through increased of IGF-1. 6. Discussion 6.1 Insulin Resistance and Prostate Hyperplasia on Ab-Ob This Research get result if HOMA-IR more higher and significant on Ab-Ob group with prostate hyperplasia compared with group without prostate hyperplasia 1,88 vs 1,19, p=0,002. High HOMA IR 2,7 or in resistance insulin condition increase prostate hyperplasia risk with OR=1,94 IK 1,30-2,89, and significant p=0,005. This research is consistent with what Zhang et al. achieved, through this epidemiology study which involve 401 geriatric male sample also achieved if male with MS has prostate volume bigger and significant p=0,000 if compared with without MS Zhang,et al.,2014. Further sub analysis in this research get significant positive correlation between prostate volume and BMI P=0,000, HOMA-IR P=0,003 and negative correlation with HDL level p=0,000. On linear regression multivariate analysis prostate volume significant correlated with HOMA-IR p=0,015. Kim et al., which examine 212 BPH patient without DM get significant positive correlation between fasting glucose level with prostate size r=0,186; p=0,007 Kim et al., 2011. This Study also examine BMI, fasting glucose level and insulin resistance towards prostate size which assessed form transrectal USG. But on this research didn’t get significant correlation between insulin resistance HOMA-IR and fasting glucose level and prostate size. MS prevalence on community with prostate hyperplasia also higher if compared with without MS 60,3 vs 46,1, p=0,018 Ryl et al., 2015. This research uses case control design with healthy people control. Further in this research also proved significant correlation between BPH with several MS component like glucose level, total cholesterol, LDL, HDL and systolic and diastolic blood pressure. But on this research not obtained 65 significant correlation between prostate hyperplasia with waist circumference, weight, and TG concentration. Ozden et al., in a more detail examined correlation between MS with increase of prostate size every year, which involve 78 patient with prostate hyperplasia symptom divided into group with MS and without MS. Ozden et al., 2007. On MS group with prostate hyperplasia median value weight, BMI, TG and blood glucose which higher if compared with BPH patient without MS. On MS group with prostate hyperplasia in the end of research found increase of total prostate volume 1 ml every year while BPH group without MS only 0,93 ml, with significant difference in statistic p0,05. From this study pictured insulin resistance central role on MS in its correlation with prostate hyperplasia. Obesity consistency as prostate hyperplasia risk factor also proved on research by Lee et al., which examined Obesity effect which measured by BMI parameter to prostate hyperplasia Lee et al., 2006. In this research included 146 male patient age 40 years old which not experience DM. Prostate size examination with USG transrectal. Research subject divided into 3 group consist of normal BMI 22,9 kgm 2 , overweight 23-24,9 kgm 2 and obesity 25 kgm 2 and two group based on waist circumference 90cm and 90 cm Prostate volume obtained the higher and significant on obesity group p=0,03 and on abdominal obesity p=0,002. After doing adjusted to some confounding variable, abdominal obesity become primary risk factor incidence of prostate hyperplasia prostate volume 20 mL OR=3,7,p=0,037. Hyperinsulinemia which inducted by high carbohydrate and fat diet, can caused increased of cell activation for excessive proliferation. This research show hyperinsulinemia correlated with increase of prostate size about 45 from rat experiment. This result can give picture that obesity with hyperinsulinemia have strong relation with prostate hyperplasia incident Renehan et al,. 2008, Allotet al., 2013 Hyperinsulinemia and insulin resistance in other way can caused increased sympathetic neuron system activity, by increasing cytosolic-free calcium on smooth muscle cell and neural network