Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.

Meta-analysis of prevalence

. Based on the publication in this issue of AI&R of a metanalytic assessment of the degree and the quality of the available evidences on the clinical efficacy of an otherwise diffuse utilization of nutritional integration on a spectrum of fragile patients, the methodological comment is focused on the limitations, biases, misleading implications which are today threatening the role of metanalytical analyses of existing literature, to produce useful-reliable evidences in areas where the representativeness of the source of data is substantially scarce. The exposure to such risk appears to be specifically worrying in the field of nursing care, due to the very limited availability of randomized and other "controlled" prospective original studies with hard end-points.

[About meta-analyses].

Background— In view of the increase in plasma concentrations of proinflammatory mediators tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP) in obesity, we investigated whether peripheral blood mononuclear cells (MNC) from obese subjects are in a proinflammatory state. Methods and Results— MNC were prepared from fasting blood samples of obese (n=16; body mass index [BMI]=37.7±5.0 kg/m2) and normal-weight control (n=16; BMI=23.8±1.9 kg/m2) subjects. Nuclear factor κB (NF-κB) binding to DNA in nuclear extracts was elevated (P<0.05) and the inhibitor of NFκB-β (IκB-β) was significantly lower (P<0.001) in the obese group. Reverse transcription–polymerase chain reaction revealed elevated levels of migration inhibitor factor (MIF), IL-6, TNF-α, and matrix metalloproteinase-9 (MMP-9) mRNA expression in the obese subjects (P<0.05). Plasma concentrations of MIF, IL-6, TNF-α, MMP-9, and CRP were also significantly higher. Plasma glucose, insulin, and free fatty acids (FFAs) were mea...

Circulating Mononuclear Cells in the Obese Are in a Proinflammatory State

Osteoarthritis (OA) is a disease of the whole joint organ, characterized by the loss of cartilage, and structural changes in bone including the formation of osteophytes, causing disability and loss of function. It is also associated with systemic mediators and low-grade inflammation. Currently, there is negligible/no availability of specific biomarkers that can be used to facilitate the diagnosis and treatment of OA. The most unmet clinical need is, however, related to the monitoring of disease progression over a short period that can be used in clinical trials. In this review, the value of biomarkers identified over the past decade has been highlighted. These biomarkers are associated with the synthesis and breakdown of cartilage, including collagenous and noncollagenous biomarkers, inflammatory and anti-inflammatory biomarkers, expressed in the biological fluid such as serum, synovial fluid, and urine. Broad validation of novel and clinically applicable biomarkers and their involvement in the pathways are particularly needed for early-stage diagnosis, monitoring disease progression, and severity and examining new drugs to mitigate the effects of this highly prevalent and debilitating condition.

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Biomarkers of Joint Damage in Osteoarthritis: Current Status and Future Directions.

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren–Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.

https://downloads.hindawi.com/journals/jhe/2022/4138666.pdf

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies

Background Periprosthetic joint infection (PJI) is a disastrous complication after total hip arthroplasty (THA) and total knee arthroplasty (TKA). The relationship between body mass index (BMI) and the incidence of PJI remains controversial. To better understand the impact of increasing BMI on PJI, we conducted this study to investigate the dose-response relationship between BMI and the risk of PJI after primary THA or TKA. Methods A systematic search was conducted in PubMed, Embase, and Cochrane Library databases from inception to August 17, 2019. After study selection and data extraction, a dose-response meta-analysis was performed to investigate the relationship between BMI and PJI. Adjusted relative risks (RRs) with 95% confidence intervals (CIs) were pooled using fixed-effects or random-effects models. Results Eleven studies comprising 505,303 arthroplasties were included. The dose-response analysis showed a significant non-linear relationship between BMI and the risk of PJI (Pnon-linearity <0.001). Patients following THA (RR, 1.489; 95% CI, 1.343-1.651; P<0.001) were more likely to suffer from PJI than patients following TKA. Furthermore, American Society of Anesthesiologists (ASA) score ≥3 (RR, 2.287; 95% CI, 1.650-3.170; P<0.001), lung disease (RR, 1.484; 95% CI, 1.208-1.823; P<0.001) and diabetes (RR, 1.695; 95% CI, 1.071-2.685; P=0.024) were identified as risk factors for PJI, but male (RR, 1.649; 95% CI, 0.987-2.755; P=0.056) and hypertension (RR, 0.980; 95% CI, 0.502-1.916; P=0.954) were not recognized as risk factors for PJI. Conclusions The J-shaped non-linear relationship demonstrated that increased BMI was associated with an increased risk for PJI after primary THA or TKA. Patients following THA were more likely to suffer from PJI than patients following TKA. Also, patients with ASA score ≥3, lung disease and diabetes have a higher risk of PJI. Gender and hypertension did not influence the incidence of PJI.

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Relationship between body mass index and the risk of periprosthetic joint infection after primary total hip arthroplasty and total knee arthroplasty

Diagnostic and therapeutic values of PMEPA1 and its correlation with tumor immunity in pan-cancer.

Osteoarthritis (OA) is a common joint disease featured by the deterioration of articular cartilage and chondrocyte death. Emerging evidence has indicated that circular RNAs (circRNAs) play an essential role in OA progress. Here, we found that the expression of circHIPK3 was significantly decreased in human and mouse OA cartilage. Knocking down circHIPK3 increased apoptosis and intracellular ROS level in HC‐a chondrocytes. We performed proteomic studies and identified that circHIPK3 regulated chondrocyte apoptosis through the mitochondrial pathway. Results of JC‐1 staining and western blot further confirmed that mitochondrial outer membrane permeabilization was promoted in HC‐a chondrocytes transfected by circHIPK3 siRNA. In terms of mechanism, we showed that PON2 functioned as a potential target of circHIPK3 to regulate chondrocyte apoptosis. Moreover, we revealed that circHIPK3 interacted with miR‐30a‐3p to regulate PON2 expression in chondrocytes. Taken together, our findings suggested that circHIPK3 regulated chondrocyte apoptosis by mitochondrial pathway, and targeting the circHIPK3/miR‐30a‐3p/PON2 axis might be a potential strategy for OA treatment.

Yufeng Chen

Papers

Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies

Relationship between body mass index and the risk of periprosthetic joint infection after primary total hip arthroplasty and total knee arthroplasty

Diagnostic and therapeutic values of PMEPA1 and its correlation with tumor immunity in pan-cancer.

CircHIPK3 prevents chondrocyte apoptosis and cartilage degradation by sponging miR‐30a‐3p and promoting PON2

Clock knockdown attenuated reactive oxygen species-mediated senescence of chondrocytes through restoring autophagic flux.