The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year old Danes

Background: Research into the clinical importance of spinal MRI findings in patients with low back pain (LBP) has primarily focused on single imaging findings, such as Modic changes or disc degeneration, and found only weak associations with the presence of pain. However, numerous MRI findings almost always co-exist in the lumbar spine and are often present at more than one lumbar level. It is possible that multiple MRI findings are more strongly associated with LBP than single MRI findings. Latent Class Analysis is a statistical method that has recently been tested and found useful for identifying latent classes (subgroups) of MRI findings within multivariable datasets. The purpose of this study was to investigate the association between subgroups of MRI findings and the presence of LBP in people from the general population. Methods: To identify subgroups of lumbar MRI findings with potential clinical relevance, Latent Class Analysis was initially performed on a clinical dataset of 631 patients seeking care for LBP. Subsequently, 412 participants in a general population cohort (the ‘Backs on Funen’ project) were statistically allocated to those existing subgroups by Latent Class Analysis, matching their MRI findings at a segmental level. The subgroups containing MRI findings from the general population were then organised into hypothetical pathways of degeneration and the association between subgroups in the pathways and the presence of LBP was tested using exact logistic regression. Results: Six subgroups were identified in the clinical dataset and the data from the general population cohort fitted the subgroups well, with a median posterior probability of 93%–100%. These six subgroups described two pathways of increasing degeneration on upper (L1-L3) and lower (L4-L5) lumbar levels. An association with LBP was found for the subgroups describing severe and multiple degenerative MRI findings at the lower lumbar levels but none of the other subgroups were associated with LBP

A review of the value of MRI signs in low back pain

AbstractLow back pain is a common symptom that can lead to disability and major socio-professional repercussions. Despite advances in imaging, the etiology of the pain often remains unknown. Morphological changes related to normal ageing of the disc appear on MR imaging without any symptoms. The potential impact of changes seen on imaging, especially MRI, also warrants discussion. The purpose of this work is to review the state-of-the-art of this subject, underlining relevant key features for routine radiological practice. We will first discuss anterior and posterior segments of the spine with a focus on anatomical, physiopathological and semiological findings. Secondly we will discuss the diagnostic value of each sign

La maladie des exostoses multiples

National audienceHereditary multiple exostosis (HEM) is one of the most common hereditary diseases. It is characterized by the proliferation of bone protuberances, especially located in the metaphysis of long bones. The disease phenotype may also associate abnormalities in the shape and length of long bones, such as the typical “Bessel Hagen” deformity. Clinically, the main complaint of patients remains pain, but the psychological and social consequences should not be minimized. The rare complication (2 to 5% of cases) but the most feared is the transformation into chondrosarcoma, which motivates regular clinical and radiological monitoring of these patients. This follow-up, although essential, remains poorly defined, including at a minimum, an annual clinical examination and radiological follow-up based on the symptoms described by the patients. The treatment remains mainly surgical, with patients being most often multi-operated during the course of the disease. Medical treatment remains secondary. In recent years, progress has been made in understanding the pathophysiology of this disease, particularly with the discovery of the mutation of EXT genes, found in 80% of HME cases. These tumor suppressor genes encode proteins that act in the synthesis of heparan sulfates (HS). The decrease in the amount of HS leads to changes in certain metabolic pathways, which explains the development of ectopic growth plaques, which are the cause of bone exostosis, but also the poor longitudinal growth of long bones. The greater knowledge of the mechanisms underlying this disease makes it possible to consider potential therapeutic targets. © 2019 Société Française de RhumatologieLa maladie des exostoses multiples (MEM) est l’une des maladies héréditaires les plus fréquentes. Elle est caractérisée par la prolifération de protubérances osseuses, surtout localisées à proximité de la métaphyse des os longs. Le phénotype de la maladie peut aussi associer des anomalies de formes et de longueurs des os longs, comme la déformation typique dite de « Bessel Hagen ». Cliniquement, la plainte principale des patients reste la douleur, mais les conséquences psychologiques et sociales ne doivent pas être minimisées. La complication rare (2 à 5 % des cas), mais la plus redoutée, reste la transformation en chondrosarcome, ce qui motive une surveillance régulière clinique et radiologique de ces patients. Ce suivi, bien qu’indispensable, reste mal défini et comprend au minimum un examen clinique annuel et un suivi radiologique orienté en fonction des symptômes décrits par les patients. Le traitement reste principalement chirurgical, les patients étant le plus souvent multi-opérés au cours de la maladie. Les traitements médicaux restent secondaires. Les dernières années ont permis d’avancer sur la connaissance de la physiopathologie de cette maladie, notamment avec la découverte de la mutation des gènes EXT, retrouvée dans 80 % des cas des MEM. Ces gènes, suppresseurs de tumeurs, codent pour des protéines agissant dans la synthèse des héparanes sulfates (HS). La diminution de la quantité d’HS entraîne des modifications de certaines voies métaboliques expliquant le développement de plaques de croissance ectopiques, à l’origine des exostoses osseuses, mais aussi la faible croissance longitudinale des os longs. La plus grande connaissance des mécanismes sous-tendant cette maladie permet d’envisager des cibles thérapeutiques potentielles

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Rationale The urinary proteome reflects molecular drivers of disease. Objectives To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and >= 1.19 (P = + 0.50), NRI (>= + 53.7), and AUC (>= + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. Conclusions The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Rationale The urinary proteome reflects molecular drivers of disease. Objectives To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. Methods In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Measurements and main results In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and >= 1.19 (P = + 0.50), NRI (>= + 53.7), and AUC (>= + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. Conclusions The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome

Year in review in Intensive Care Medicine, 2007. I. Experimental studies. Clinical studies: Brain injury and neurology, renal failure and endocrinology

SCOPUS: re.jinfo:eu-repo/semantics/publishe

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

RATIONALE: The urinary proteome reflects molecular drivers of disease. OBJECTIVES: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. METHODS: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. MEASUREMENTS AND MAIN RESULTS: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. CONCLUSIONS: The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.status: publishe