ObjectivesThis study examines the relationship between synovial hypoxia and cellular bioenergetics with synovial inflammation.MethodsPrimary rheumatoid arthritis synovial fibroblasts (RASF) were cultured with hypoxia, dimethyloxalylglycine (DMOG) or metabolic intermediates. Mitochondrial respiration, mitochondrial DNA mutations, cell invasion, cytokines, glucose and lactate were quantified using specific functional assays. RASF metabolism was assessed by the XF24-Flux Analyzer. Mitochondrial structural morphology was assessed by transmission electron microscopy (TEM). In vivo synovial tissue oxygen (tpO2 mmHg) was measured in patients with inflammatory arthritis (n=42) at arthroscopy, and markers of glycolysis/oxidative phosphorylation (glyceraldehyde 3-phosphate dehydrogenase (GAPDH), PKM2, GLUT1, ATP) were quantified by immunohistology. A subgroup of patients underwent contiguous MRI and positron emission tomography (PET)/CT imaging. RASF and human dermal microvascular endothelial cells (HMVEC) migration/angiogenesis, transcriptional activation (HIF1α, pSTAT3, Notch1-IC) and cytokines were examined in the presence of glycolytic inhibitor 3-(3-Pyridinyl)-1-(4-pyridinyl)-2-propen-1-one (3PO).ResultsDMOG significantly increased mtDNA mutations, mitochondrial membrane potential, mitochondrial mass, reactive oxygen species and glycolytic RASF activity with concomitant attenuation of mitochondrial respiration and ATP activity (all p<0.01). This was coupled with altered mitochondrial morphology. Hypoxia-induced lactate levels (p<0.01), which in turn induced basic fibroblast growth factor (bFGF) secretion and RASF invasiveness (all p<0.05). In vivo glycolytic markers were inversely associated with synovial tpO2 levels <20 mm Hg, in contrast ATP was significantly reduced (all p<0.05). Decrease in GAPDH and GLUT1 was paralleled by an increase in in vivo tpO2 in tumour necrosis factor alpha inhibitor (TNFi) responders. Novel PET/MRI hybrid imaging demonstrated close association between metabolic activity and inflammation. 3PO significantly inhibited RASF invasion/migration, angiogenic tube formation, secretion of proinflammatory mediators (all p<0.05), and activation of HIF1α, pSTAT3 and Notch-1IC under normoxic and hypoxic conditions.ConclusionsHypoxia alters cellular bioenergetics by inducing mitochondrial dysfunction and promoting a switch to glycolysis, supporting abnormal angiogenesis, cellular invasion and pannus formation.

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Background: Macroprolactin (big big prolactin) has reduced bioactivity and is measured by immunoassays for prolactin when it accumulates in the plasma of some individuals. We applied normative data for serum prolactin after treatment of sera to remove macroprolactin to elucidate the contribution of macroprolactin to misleading diagnoses, inappropriate investigations, and unnecessary treatment. Methods: We reviewed records of women attending a tertiary referral center who had prolactin >1000 mIU/L. Application of a reference interval to polyethylene glycol (PEG)-treated hyperprolactinemic sera identified 21 patients in whom hyperprolactinemia was accounted for entirely by the presence of macroprolactin. Presenting clinical features, diagnoses, and treatment were compared in these patients and 42 age-matched true hyperprolactinemic patients.

ISRCTN registry, https://www.isrctn.com/, ISRCTN29316280.

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Probiotic treatment of 4 wk during pregnancy did not influence maternal fasting glucose, the metabolic profile, or pregnancy outcomes in obese women.

A high molecular mass form of prolactin (PRL), macroprolactin, accumulates in the sera of some subjects. Although macroprolactin exhibits limited bioactivity in vivo, it retains immunoreactivity. We examined the frequency of macroprolactinemia in clinical practice and the ability of immunoassay systems to distinguish between macroprolactin and monomeric PRL. Of 300 hyperprolactinemic sera identified, 71 normalized following treatment of sera with polyethylene glycol, indicating that 24% of hyperprolactinemia could be accounted for by macroprolactin. Ten of these macroprolactinemic sera were circulated to 18 clinical laboratories. Two sets of PRL measurements of the 10 untreated sera were obtained from each of the nine most commonly used immunoassay systems. Across the nine assay systems, differences in the PRL estimates ranged from 2.3- to 7.8-fold. Elecsys users reported the highest PRL levels. Somewhat lower values were reported for DELFIA systems followed by Immuno-1, AxSYM, and Architect assays. The Immulite 2000 assay generated PRL levels equivalent to approximately 50% of those reported by the high-reading methods. The lowest PRL levels were reported by Access, ACS:180, and Centaur systems. To avoid confusion caused by the frequent presence of macroprolactin accounting for hyperprolactinemia, secondary screening for the presence of macroprolactin is recommended.

Background: Macroprolactin is an important source of immunoassay interference that commonly leads to misdiagnosis and mismanagement of hyperprolactinemic patients. We used the predominant immunoassay platforms for prolactin to assay serum samples treated with polyethylene glycol (PEG) and establish and validate reference intervals for total and monomeric prolactin. Methods: We used the Architect (Abbott), ADVIA Centaur and Immulite (Siemens Diagnostics), Access (Beckman Coulter), Elecsys (Roche Diagnostics), and AIA (Tosoh) analyzers with samples from healthy males (n = 53) and females (n = 93) to derive parametric reference intervals for total and post-PEG monomeric prolactin. Concentrations of immunoreactive prolactin isoforms in serum samples from healthy individuals were established by gel filtration chromatography (GFC). We then used samples from 22 individuals whose hyperprolactinemia was entirely attributable to macroprolactin and 32 patients with true hyperprolactinemia to compare patient classifications and prolactin concentrations measured by GFC with the newly derived post-PEG reference intervals. Results: Parametric reference intervals for post-PEG prolactin in male and female serum samples, respectively, were (in mIU/L): 61–196, 66–278 (Centaur); 63–245, 75–381 (Elecsys); 70–301, 92–469 (Access); 72–229, 79–347 (Architect); 73–247, 83–383 (AIA); and 78–263, 85–394 (Immulite). Concordance between GFC and immunoassay-specific post-PEG reference intervals was observed in 311 of 324 cases and for 31 of 32 patients with true hyperprolactinemia and 17 of 22 patients with macroprolactinemia. Results leading to misclassification occurred in a few analyzers for 5 macroprolactinemia patient samples with relatively minor increases in post-PEG prolactin (mean 61 mIU/L). Conclusions: Our validated normative reference data for sera pretreated with PEG and analyzed on the most commonly used immunoassay platforms should facilitate the more widespread introduction of macroprolactin screening by clinical laboratories.