Browsing by Author "Zhang, Y."

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Can pain flares in knee osteoarthritis be predicted?
(:Taylor & Francis-Informa Healthcare, 2021) Atukorala, I.; Pathmeswaran, A.; Makovey, J.; Metcalf, B.; Bennell, K.L.; March, L.; Chang, T.; Zhang, Y.; Hunter, D.J.
OBJECTIVES: This study examined whether risk factors for knee osteoarthritis (KOA) pain such as age, gender, body mass index (BMI), baseline pain, and other putative risk factors for knee osteoarthritis pain flares (KOAF) (e.g. knee buckling, injury, mood/stress/social support scores, and footwear) could predict KOAF. METHOD: People with KOA and previous history of KOAF were selected from a 3-month web-based longitudinal study. KOAF was defined as an increase of ≥ 2 points on a numeric rating scale (compared with background pain) which resolved within 20 days. Predictors assessed at baseline were gender, age, duration of KOA, BMI, pain, knee injury (7 days before), knee buckling (2 days before), Lubben Social Support, Knee Injury and Osteoarthritis Outcome Score, Intermittent and Constant Osteoarthritis Pain score (ICOAP), Positive/Negative Affect Score, and footwear stability/heel height. Outcome was occurrence of any KOAF during the ensuing 30 days. The combined ability of the above variables to predict occurrence of any KOAF was evaluated by multiple logistic regression with a 10-fold cross-validation method to build and internally validate the model. Variables that assessed similar domains were eliminated using receiver operating characteristics curve assessment for best fit. RESULTS: Complete data were available for 313 people (66.6% female, mean ± sd age 62.3 ± 8.2 years, BMI 29.7 ± 6.5 kg/m2). Increasing age, years of osteoarthritis, BMI, background/worst levels of pain, knee injury, knee buckling, ICOAP, and footwear category/heel height significantly predicted the occurrence of KOAF during the following 30 days, with an area under the curve of 0.73 (95% confidence interval 0.67-0.80). Conclusion: A combination of risk factors assessed at baseline, including exposures with potential to vary, successfully predicts the KOAF in the ensuing 30 days.
Do traditional risk factors for knee osteoarthritis predict pain flares in knee osteoarthritis?.
(BMJ Publishing, 2016) Atukorala, I.; Pathmeswaran, A.; Chang, T.; Zhang, Y.; Hunter, D.J.
BACKGROUND: Knee pain is the main cause of disability and reduced function in knee osteoarthritis (KOA). Though knee pain in osteoarthritis was previously perceived as a chronic condition it is now established that KOA pain fluctuates. There is emerging evidence that time variant risk factors-such as knee injury, buckling and mood- are associated with knee pain flares. But, it is not known whether conventional risk factors associated with KOA - age, gender, body mass index-are associated with pain flares in KOA. OBJECTIVES: This study examines whether conventional time invariant risk factors for KOA and baseline pain felt by the patient are associated with KOA pain flares. METHODS: Study participants were selected from a 3-month web-based longitudinal follow up study developed to identify risk factors for KOA pain flares. Participants were requested to complete online questionnaire at days 0, 30, 60 and 90 (control period assessment points) and at time points whenever they experienced knee pain flare (case period assessment points) during the follow up period. A KOA pain flare was defined as current pain with a greater than 2 point increase (on a 0-10 point numeric rating scale) from the mildest KOA pain intensity reported at day 0. The association of pain flares with traditional risk factors for knee osteoarthritis -gender, weight, height, body mass index- was assessed by negative binomial regression. The duration of knee osteoarthritis, baseline pain intensity (lowest pain and highest pain scores at baseline) were similarly evaluated. The best explanatory variable was decided by forward selection. RESULTS: 345 persons (61.2% females) with multiple KOA pain flares were selected. Their mean age was 62.1years (SD +/-8.2). The mean body mass index was 29.8kg/m2 (SD +/-6.5). The participants rated their baseline pain (on a numeric rating scale) as being 4.41 (SD+/- 2.02) and their worst pain as being 7.91 (SD +/-1.74). An average of 1.92 (SD 2.59) flares were documented during the 3-month period. The levels of baseline pain - usual and worst pain felt at baseline- were the only parameters significantly associated with KOA pain flares (Table 1). CONCLUSIONS: The baseline pain scores were the strongest predictors of pain flares of knee osteoarthritis. The traditional risk factors associated with knee osteoarthritis did not usefully predict pain flares. The traditional time invariant risk factors may not be associated with short term variability in pain though they are associated with long term outcomes of knee osteoarthritis. It is postulated that as knee pain is already present, time invariant risk factors that contributed to the original symptom causation are not associated with pain flare. (Table Presented).
Is being barefoot, wearing shoes and physical activity associated with knee osteoarthritis pain flares? Data from a usually barefoot Sri Lankan cohort
(Oxford, 2021) Atukorala, I.; Pathmeswaran, A.; Batuwita, N.; Rajapaksha, N.; Ratnasiri, V.; Wijayaratne, L.; de Silva, M.; Chang, T.; Zhang, Y.; Hunter, D.J.
AIM: To identify the association between hours of being barefoot/wearing footwear, physical activity (PA) and knee osteoarthritis pain flares (KOAF). METHODS: Persons with a diagnosis of knee osteoarthritis, who reported previous KOAF, were followed up in a 3 months long telephone-based case-crossover study. Exposures to risk factors were assessed every 10 days and whenever the participants experienced a KOAF. Conditional logistic regression examined associations of KOAF with following: hours of being barefoot/using footwear and PA performed (P < .05). RESULTS: There were 260 persons recruited, of whom 183 continued longitudinal follow up. Of them, 120 persons had at least one valid KOAF and control period. Participants were female (90%) with mean (SD) age and body mass index of 59.9 (7.0) years, 28.0 (5.0) kg/m2 respectively. Participants were barefoot for a mean duration of 12.7 hours (SD 4.6) and used footwear for 5.1 (SD 4.7) hours daily; 99% wore heel heights <2.5 cm. Duration of being barefoot, 1 and 2 days before, demonstrated reduced multivariate odds of KOAF (odds ratio [OR] = 0.85; 95% CI 0.80-0.90). Moderate PA performed 1, 2 days prior was associated with a significantly increased risk of KOAF (multivariate OR 4.29; 2.52-7.30 and OR 3.36; 2.01-5.61). Similarly, hours of using footwear 1 and 2 days before flare demonstrated increased odds of KOAF (OR 1.15; 1.07-1.23 and 1.10; 1.03-1.18). CONCLUSIONS: Increased duration of being barefoot 1 to 2 days before is associated with reduced risk of KOAF. Performing moderate PA 1 to 2 days before was associated with an increased risk of KOAF. KEYWORDS: knee osteoarthritis pain.
Is there a relationship between the intermittent and constant osteoarthritis pain score (ICOAP) and pain flares in knee osteoarthritis?.
(W.B. Saunders-Elsevier, 2016) Atukorala, I.; Pathmeswaran, A.; Makovey, J.; Metcalf, B.; March, L.; Bennell, K.L.; Chang, T.; Zhang, Y.; Hunter, D.J.
PURPOSE: The Intermittent and Constant Osteoarthritis Pain Score (ICOAP) is a recently validated multidimensional osteoarthritis pain measure. This 11-item tool takes into account both the constant (6 items) and intermittent (5 items) pain of knee osteoarthritis (KOA) within 7 days summated to a single score. These items are scored from 0 (no pain) to 4 (extremely severe pain). The intent of this project was to assess the association and utility of ICOAP and its subscales in predicting pain flares in KOA identified by a 0-10 point numerical rating scale (NRS). METHODS: Study participants were selected from a 3-month web-based longitudinal follow up study developed to identify risk factors for KOA pain flares. Participants were requested to complete the ICOAP questionnaire at days 0, 30, 60 and 90 (control period assessment points) and at time points whenever they experienced knee pain flare (case period assessment points) during the follow up period. A KOA pain flare was defined as current pain with a greater than 2 point increase (on a 0-10point NRS) from the mildest KOA pain intensity reported at day 0. The ICOAP score at point of a KOA pain flare was used to identify whether ICOAP was associated with occurrence of a pain flare. Conditional logistic regression was used to identify the odds of association with pain flare by the individual subscales and total ICOAP. Receiver Operating Characteristic Curves (ROC curves) were used to assess the utility of the ICOAP and its subscales (immediately preceding the flare) in predicting pain flares using the pain flares identified by the numeric rating scale as the gold standard. The ICOAP value for the first flare during the follow up period was used to predict pain. RESULTS: 213 persons (61%females) with multiple KOA pain flares were selected. Their mean age was 62.1 years (SD 8.5). The mean body mass index was 29.8 kg/m2 (SD 6.5). There were 652 flares documented with 1232 control periods over a 3- month period. 325 flares had a documented ICOAP within the preceding 30 days. The time gap between control period and flare period assessment points differed between subjects with the mean time gap being 18.5 days (SD 9.3). The mean number of flares per person per month was 1.97 (SD 2.65). None of the patients had a pain flare at baseline ICOAP total, constant and intermittent subscales had a significant association with pain flare (Table 1). However, the ICOAP scores (total, constant and intermittent) did not usefully predict pain flares and demonstrated an area under the ROC curves of 0.69 (95% confidence interval (CI)0.67-0.72), 0.69 (95% CI 0.67-0.72), 0.67 (95% CI 0.64-0.69) for total ICOAP score, constant pain and intermittent pain subscales respectively. CONCLUSIONS: The total ICOAP score (as well as the Constant and Intermittent subscales) recorded at point of flare was associated with KOA pain flares identified by the NRS. However, the ICOAP and its subscales did not usefully predict a pain flare. The lack of difference between the constant and intermittent ICOAP score can be attributed to correlation of items in the two subscales. The lack of complete correlation between the ICOAP values and pain flare assessed by the NRS is possibly due to the multidimensional nature of the ICOAP in contrast to the uni-dimensional nature of NRS. (Table Presented).
Polarimetry and 13C n.m.r. show that the hydrolyses of B -D-glucopyranosyl fluoride by B-(1-3)- glucanase from Phanerochaete chrysosporium and Sporotrichum dimorphosporum have opposite stereochemistries
(Biochem Journal, 1993) Patino, J.C.; Zhang, Y.; Jayawardena, B.M.; Broda, P.; Sinnott, M.L.
Short-term pain trajectories in patients with knee osteoarthritis
(Wiley on behalf of the Asia Pacific League of Associations for Rheumatology, 2022) Atukorala, I.; Downie, A.; Pathmeswaran, A.; Deveza, L.M.A.; Chang, T.; Zhang, Y.; Hunter, D.J.
Aim: It is unknown if pain in knee osteoarthritis (KOA) follows distinct patterns over the short term. Therefore, the aim of this study was to identify whether persons with a previous history of KOA pain fluctuations have distinct trajectories of pain over 90 days and to examine associations between baseline characteristics and pain trajectories. Method: People with a previous history of KOA were selected from a web-based longitudinal study. Baseline variables were sex, age, being obese/overweight, years of KOA, knee injury, knee buckling, satisfactory Lubben Social Support Score, pain and stress scales, Intermittent Constant Osteoarthritis Pain Score (ICOAP), medication use, and physical activity. Participants completed a Knee Injury and Osteoarthritis Outcomes Score (KOOS) pain subscale (KOOS-p, rated 0 = extreme to 100 = no knee problems) at 10-day intervals for 90 days. Short-term KOOS-p trajectories were identified using latent growth mixture modeling and the baseline risk factors for these pain trajectories were examined. Results: Participants (n = 313) had a mean age of 62.2 (SD ± 8.1) years and and a body mass index of 29.8 (SD ± 6.6) kg/m2 . The three-class latent growth mixture modeling quadratic model with best fit indices was chosen (based on lowest sample-size-adjusted Bayesian Information Criterion, high probability of belonging, interpretability). Three distinct pain trajectory clusters (over 90 days) were identified: low-moderate pain at baseline with large improvement (n = 11), minimal change in pain over 90 days (n = 248), and moderate-high pain with worsening (n = 46). Higher ICOAP (intermittent scale), perceived stress, negative affect score, and knee buckling at baseline were associated with a worse knee pain trajectory (P < 0.05). Conclusions: Persons with KOA showed unique short-term pain trajectories over 90 days, with distinct characteristics at baseline associated with each trajectory.
Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation
(Nature Publishing Company, 2015) Kato, N.; Loh, M.; Takeuchi, F.; Verweij, N.; Wang, X.; Zhang, W.; Kelly, T.N.; Saleheen, D.; Lehne, B.; Leach, I.M.; Drong, A.W.; Abbott, J.; Wahl, S.; Tan, S.T.; Scott, W.R.; Campanella, G.; Chadeau-Hyam, M.; Afzal, U.; Ahluwalia, T.S.; Bonder, M.J.; Chen, P.; Dehghan, A.; Edwards, T.L.; Esko, T.; Go, M.J.; Harris, S.E.; Hartiala, J.; Kasela, S.; Kasturiratne, A.; Khor, C.C.; Kleber, M.E.; Li, H.; Mok, Z.Y.; Nakatochi, M.; Sapari, N.S.; Saxena, R.; Stewart, A.F.; Stolk, L.; Tabara, Y.; Teh, A.L.; Wu, Y.; Wu, J.Y.; Zhang, Y.; Aits, I.; Da Silva Couto Alves, A.; Das, S.; Dorajoo, R.; Hopewell, J.C.; Kim, Y.K.; Koivula, R.W.; Luan, J.; Lyytikäinen, L.P.; Nguyen, Q.N.; Pereira, M.A.; Postmus, I.; Raitakari, O.T.; Bryan, M.S.; Scott, R.A.; Sorice, R.; Tragante, V.; Traglia, M.; White, J.; Yamamoto, K.; Zhang, Y.; Adair, L.S.; Ahmed, A.; Akiyama, K.; Asif, R.; Aung, T.; Barroso, I.; Bjonnes, A.; Braun, T.R.; Cai, H.; Chang, L.C.; Chen, C.H.; Cheng, C.Y.; Chong, Y.S.; Collins, R.; Courtney, R.; Davies, G.; Delgado, G.; Do, L.D.; Doevendans, P.A.; Gansevoort, R.T.; Gao, Y.T.; Grammer, T.B.; Grarup, N.; Grewal, J.; Gu, D.; Wander, G.S.; Hartikainen, A.L.; Hazen, S.L.; He, J.; Heng, C.K.; Hixson, J.E.; Hofman, A.; Hsu, C.; Huang, W.; Husemoen, L.L.; Hwang, J.Y.; Ichihara, S.; Igase, M.; Isono, M.; Justesen, J.M.; Katsuya, T.; Kibriya, M.G.; Kim, Y.J.; Kishimoto, M.; Koh, W.P.; Kohara, K.; Kumari, M.; Kwek, K.; Lee, N.R.; Lee, J.; Liao, J.; Lieb, W.; Liewald, D.C.; Matsubara, T.; Matsushita, Y.; Meitinger, T.; Mihailov, E.; Milani, L.; Mills, R.; Mononen, N.; Müller-Nurasyid, M.; Nabika, T.; Nakashima, E.; Ng, H.K.; Nikus, K.; Nutile, T.; Ohkubo, T.; Ohnaka, K.; Parish, S.; Paternoster, L.; Peng, H.; Peters, A.; Pham, S.T.; Pinidiyapathirage, M.J.; Rahman, M.; Rakugi, H.; Rolandsson, O.; Rozario, M.A.; Ruggiero, D.; Sala, C.F.; Sarju, R.; Shimokawa, K.; Snieder, H.; Sparso, T.; Spiering, W.; Starr, J.M.; Stott, D.J.; Stram, D.O.; Sugiyama, T.; Szymczak, S.; Tang, W.H.; Tong, L.; Trompet, S.; Turjanmaa, V.; Ueshima, H.; Uitterlinden, A.G.; Umemura, S.; Vaarasmaki, M.; van Dam, R.M.; van Gilst, W.H.; van Veldhuisen, D.J.; Viikari, J.S.; Waldenberger, M.; Wang, Y.; Wang, A.; Wilson, R.; Wong, T.Y.; Xiang, Y.B.; Yamaguchi, S.; Ye, X.; Young, R.D.; Young, T.L.; Yuan, J.M.; Zhou, X.; Asselbergs, F.W.; Ciullo, M.; Clarke, R.; Deloukas, P.; Franke, A.; Franks, P.W.; Franks, S.; Friedlander, Y.; Gross, M.D.; Guo, Z.; Hansen, T.; Jarvelin, M.R.; Jorgensen, T.; Jukema, J.W.; Kähönen, M.; Kajio, H.; Kivimaki, M.; Lee, J.Y.; Lehtimäki, T.; Linneberg, A.; Miki, T.; Pedersen, O.; Samani, N.J.; Sorensen, T.I.; Takayanagi, R.; Toniolo, D.; BIOS-consortium; CARDIo GRAMplusCD; LifeLines Cohort Study; InterAct Consortium; Ahsan, H.; Allayee, H.; Chen, Y.T.; Danesh, J.; Deary, I.J.; Franco, O.H.; Franke, L.; Heijman, B.T.; Holbrook, J.D.; Isaacs, A.; Kim, B.J.; Lin, X.; Liu, J.; März, W.; Metspalu, A.; Mohlke, K.L.; Sanghera, D.K.; Shu, X.O.; van Meurs, J.B.; Vithana, E.; Wickremasinghe, A.R.; Wijmenga, C.; Wolffenbuttel, B.H.; Yokota, M.; Zheng, W.; Zhu, D.; Vineis, P.; Kyrtopoulos, S.A.; Kleinjans, J.C.; McCarthy, M.I.; Soong, R.; Gieger, C.; Scott, J.; Teo, Y.Y.; He, J.; Elliott, P.; Tai, E.S.; van der Harst, P.; Kooner, J.S.; Chambers, J.C.
We carried out a trans-ancestry genome-wide association and replication study of blood pressurephenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNAmethylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNAmethylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.