DESIGN: MEDLINE, EMBASE, CINAHL were systematically searched (1990-April 2020) for studies describing the prevalence of NP and PS in knee and hip osteoarthritis using self-report questionnaires. Random-effects meta-analysis was performed. Statistical heterogeneity between studies and sub-groups (affected joint and population source as a proxy for disease severity) was assessed (I2 statistic and the Chi-squared test).
RESULTS: From 2,706 non-duplicated references, 39 studies were included (2011-2020). Thirty-six studies reported on knee pain and six on hip pain. For knee osteoarthritis, the pooled prevalence of NP was: using PainDETECT, possible NP(score ≥13) 40% (95%CI 32-48%); probable NP(score >18) 20% (95%CI 15-24%); using Self-Report Leeds Assessment of Neuropathic Symptoms and Signs, 32% (95%CI 26-38%); using Douleur Neuropathique (DN4) 41% (95% CI 24-59%). The prevalence of PS using Central Sensitization Inventory (CSI) was 36% (95% CI 12-59%). For hip osteoarthritis, the pooled prevalence of NP was: using PainDETECT, possible NP 29% (95%CI 22-37%%); probable NP 9% (95%CI 6-13%); using DN4 22% (95%CI 12-31%) in one study. The prevalence of possible NP pain was higher at the knee (40%) than the hip (29%) (difference 11% (95% CI 0-22%), P = 0.05).
CONCLUSIONS: Using self-report questionnaire tools, NP was more prevalent in knee than hip osteoarthritis. The prevalence of NP in knee and hip osteoarthritis were similar for each joint regardless of study population source or tool used. Whether defining NP using self-report questionnaires enables more effective targeted therapy in osteoarthritis requires investigation.
METHODOLOGY: We recruited 175 subjects, aged 7 to 18 years old, referred for obesity. We studied their demography (age, gender, ethnicity, family background), performed clinical/auxological examinations [weight, height, body mass index (BMI), waist circumference (WC), blood pressure (BP)], and analyzed their biochemical risks associated with metabolic syndrome [fasting plasma glucose (FPG), fasting lipid profile (FLP), fasting insulin, liver function tests (LFT)]. MetS was identified according to the criteria proposed by the International Diabetes Federation (IDF) for pediatric obesity. Multiple logistic regression models were used to examine the associations between risk variables and MetS.
RESULTS: The prevalence of metabolic syndrome among children with obesity was 56% (95% CI: 48.6 to 63.4%), with a mean age of 11.3 ± 2.73 years. Multiple logistic regression analysis showed age [adjusted odds ratio (OR) 1.27, 95% CI: 1.15 to 1.45] and sedentary lifestyle (adjusted OR 3.57, 95% CI: 1.48 to 8.59) were the significant factors associated with metabolic syndrome among obese children.
CONCLUSION: The prevalence of metabolic syndrome among obese children referred to our centers was 56%. Older age group, male gender, birth weight, sedentary lifestyle, puberty and maternal history of gestational diabetes mellitus (GDM) were found to be associated with MetS. However, older age group and sedentary lifestyle were the only significant predictors for metabolic syndrome.