Circulating IL-17A Levels in Postmenopausal Women with Primary Hyperparathyroidism

Abstract

Background. Primary hyperparathyroidism (PHPT) is a common cause of secondary osteoporosis in postmenopausal women. Th17 lymphocytes and the released cytokine IL-17A play an important role in bone metabolism. Th17 cells have been shown to be activated by PTH, and peripheral blood T cells from patients affected with PHPT express higher levels of IL-17A mRNA than controls. Aim. To investigate circulating levels of IL-17A and the ratio RANKL/OPG, as markers of osteoclastogenesis, in 50 postmenopausal PHPT women compared with postmenopausal osteoporotic non-PHPT women (n=20). Results. Circulating levels of IL-17A were similarly detectable in most PHPT and non-PHPT osteoporotic women (12.9 (8.4-23.1) vs. 11.3 (8.3-14.3) pg/ml, median (range interquartile), P=0.759), at variance with premenopausal women where IL-17A was undetectable. In PHPT women, any significant correlations could be detected between circulating IL-17A levels and PTH levels. Nonetheless, significant negative correlations between circulating IL-17A and ionized calcium levels (r=‐0.294, P=0.047) and urine calcium excretions (r=‐0.300, P=0.045) were found. Moreover, PHPT women were characterized by positive correlations between IL-17A levels and femur neck (r=0.364, P=0.021) and total hip (r=0.353, P=0.015) T-scores. Circulating IL-17A levels did not show any significant correlation with sRANKL, OPG, and sRANKL/OPG ratio in PHPT women. Conclusions. In postmenopausal PHPT women, circulating IL-17A levels were similar to those detected in postmenopausal non-PHPT women, showing a disruption of the relationship observed in postmenopausal osteoporosis among circulating PTH, sRANKL, OPG, IL-17A, and bone demineralization in postmenopausal PHPT women. The data support an osteogenic effect of IL-17A in postmenopausal PHPT women.

Clinical Data for Parametrization of In Silico Bone Models Incorporating Cell-Cytokine Dynamics: A Systematic Review of Literature

“…(1,900 non−OP 2,300 OP ) (Zhao et al, 2016;Coulson et al, 2017); (2,490 non−OP 2,880 OP ) (Molnár et al, 2014); 14,600 (D'Amelio et al, 2010a) pM 0.04 (Jørgensen et al, 2009); 0.07 (Gossiel et al, 2016);0.10 (Jørgensen et al, 2011);0.1 (LaCroix et al, 2013); (0.14 OP 0.27 non−OP ) (Pino et al, 2010) 0.31 (Rahnama et al, 2013) 0.505 (Anastasilakis et al, 2008b); (0.37 non−OP 0.66 OP ) (Jabbar et al, 2011); 1.23 (Li et al, 2011);155.4 (Dozio et al, 2020); 4,203 (Mödder et al, 2011); 8,600 (Poornima et al, 2014) C OPG pg/ml 68.4 (Chiba et al, 2009); 83.7 (Dobnig et al, 2006) (Anastasilakis et al, 2008a); 1,359 (Oh et al, 2004); (1,300 non−OP 1,700 OP ) (Zhao et al, 2016;Coulson et al, 2017); 3,521 (Jørgensen et al, 2009); 9,300 (Kim et al, 2007) pM 1.8 (Abrahamsen et al, 2005); 3.32 (Messalli et al, 2007); 3.7 (Poornima et al, 2014); 4.35 (Wu et al, 2010); 5.0 (LaCroix et al, 2013); 5.3 (Indridason et al, 2005); 5.8 (Samelson et al, 2008); 6.27 (Rahnama et al, 2013); 6.94 (Li et al, 2011); 6.96 (Jiang et al, 2008); 7.5 (Dozio et al, 2020);11.6 (Karadag-Saygi et al, 2011);12.3 (Rogers et al, 2002) Ahlborg et al, 2003); 55 (Orwoll et al, 1989); 60.7 (Slemenda et al, 1987); 71.7 (Ouyang et al, 1984…”