Last week saw three papers published by Doctor Ruth, one was a review. Some of the Ram-Magic is clearly rubbing off.
Dobson R, Leddy SG, Gangadharan S, Giovannoni G. Assessing fracture risk in people with MS: a service development study comparing three fracture risk scoring systems. BMJ Open. 2013 Mar 11;3(3). doi:pii: e002508. 10.1136/bmjopen-2012-002508. Print 2013.
BACKGROUND: Suboptimal bone health is increasingly recognised as an important cause of morbidity. Multiple sclerosis (MS) has been consistently associated with an increased risk of osteoporosis and fracture. Various fracture risk screening tools have been developed, two of which are in routine use and a further one is MS-specific. We set out to compare the results obtained by these in the MS clinic population.
DESIGN: This was a service development study. The 10-year risk estimates of any fracture and hip fracture generated by each of the algorithms were compared.
PARTICIPANTS: 88 patients with a confirmed diagnosis of MS.
OUTCOME MEASURES: Mean 10-year overall fracture risk and hip fracture risk were calculated using each of the three fracture risk calculators. The number of interventions that would be required as a result of using each of these tools was also compared.
RESULTS: Mean 10-year fracture risk was 4.7%, 2.3% and 7.6% using FRAX, QFracture and the MS-specific calculator, respectively (p<0.0001 for difference). The agreement between risk scoring tools was poor at all levels of fracture risk.
CONCLUSIONS: The agreement between these three fracture risk scoring tools is poor in the MS population. Further work is required to develop and validate an accurate fracture risk scoring system for use in MS.
Dr Ruth has been investigating bone health and this study looks at tools to predict risks of fracture. As you can see they are not very similar and not very accurate, so something is wrong and time for a rethink.
Dobson R, Topping J, Davis A, Thompson E, Giovannoni G. Cerebrospinal fluid and urinary biomarkers in multiple sclerosis. Acta Neurol Scand. 2013 Mar 7. doi: 10.1111/ane.12119. [Epub ahead of print]
OBJECTIVES: Biomarkers with the potential for longitudinal measurements are needed in multiple sclerosis (MS). Urine is easy to collect, and repeated sampling is possible.
METHODS: 39 paired CSF and urine samples were taken. Oligoclonal bands (OCBs) were measured in CSF. Kappa and lambda free light chain (FLC), neopterin and ubiquitin C-terminal hydrolase-L1 (UCHL1) were measured in CSF and urine.
RESULTS: 16/39 samples had OCBs unique to the CSF. CSF FLC levels (P < 0.0001) were higher in OCB-positive subjects, with no difference in urinary FLC. CSF and urinary FLC did not correlate. There were a significant correlation between total CSF FLC and CSF neopterin in MS samples (correlation coefficient = 0.588, P = 0.016) and a strong correlation between CSF lambda FLC and CSF neopterin in MS samples (correlation coefficient = 0.875, P < 0.001). There was a correlation between urinary neopterin/creatinine levels and urinary total FLC/protein levels (correlation coefficient = 0.452, P = 0.004). Only three CSF samples (8%) had detectable levels of UCHL1. 18/38 (48%) (8/15 MS and 10/23 control) urine samples had detectable levels of UCLH1.
CONCLUSIONS: This study confirms the relationship between CSF OCBs and CSF FLCs, highlighting the importance of intrathecal B- and plasma-cell activation in MS. There is a relationship between CSF FLC and CSF neopterin in MS, highlighting the multifaceted immune activation seen in MS. Correlations in the OCB-positive group highlight the multifaceted immune activation seen in MS.
BACKGROUND: Suboptimal bone health is increasingly recognised as an important cause of morbidity. Multiple sclerosis (MS) has been consistently associated with an increased risk of osteoporosis and fracture. Various fracture risk screening tools have been developed, two of which are in routine use and a further one is MS-specific. We set out to compare the results obtained by these in the MS clinic population.
DESIGN: This was a service development study. The 10-year risk estimates of any fracture and hip fracture generated by each of the algorithms were compared.
PARTICIPANTS: 88 patients with a confirmed diagnosis of MS.
OUTCOME MEASURES: Mean 10-year overall fracture risk and hip fracture risk were calculated using each of the three fracture risk calculators. The number of interventions that would be required as a result of using each of these tools was also compared.
RESULTS: Mean 10-year fracture risk was 4.7%, 2.3% and 7.6% using FRAX, QFracture and the MS-specific calculator, respectively (p<0.0001 for difference). The agreement between risk scoring tools was poor at all levels of fracture risk.
CONCLUSIONS: The agreement between these three fracture risk scoring tools is poor in the MS population. Further work is required to develop and validate an accurate fracture risk scoring system for use in MS.
Dr Ruth has been investigating bone health and this study looks at tools to predict risks of fracture. As you can see they are not very similar and not very accurate, so something is wrong and time for a rethink.
Dobson R, Topping J, Davis A, Thompson E, Giovannoni G. Cerebrospinal fluid and urinary biomarkers in multiple sclerosis. Acta Neurol Scand. 2013 Mar 7. doi: 10.1111/ane.12119. [Epub ahead of print]
OBJECTIVES: Biomarkers with the potential for longitudinal measurements are needed in multiple sclerosis (MS). Urine is easy to collect, and repeated sampling is possible.
METHODS: 39 paired CSF and urine samples were taken. Oligoclonal bands (OCBs) were measured in CSF. Kappa and lambda free light chain (FLC), neopterin and ubiquitin C-terminal hydrolase-L1 (UCHL1) were measured in CSF and urine.
RESULTS: 16/39 samples had OCBs unique to the CSF. CSF FLC levels (P < 0.0001) were higher in OCB-positive subjects, with no difference in urinary FLC. CSF and urinary FLC did not correlate. There were a significant correlation between total CSF FLC and CSF neopterin in MS samples (correlation coefficient = 0.588, P = 0.016) and a strong correlation between CSF lambda FLC and CSF neopterin in MS samples (correlation coefficient = 0.875, P < 0.001). There was a correlation between urinary neopterin/creatinine levels and urinary total FLC/protein levels (correlation coefficient = 0.452, P = 0.004). Only three CSF samples (8%) had detectable levels of UCHL1. 18/38 (48%) (8/15 MS and 10/23 control) urine samples had detectable levels of UCLH1.
CONCLUSIONS: This study confirms the relationship between CSF OCBs and CSF FLCs, highlighting the importance of intrathecal B- and plasma-cell activation in MS. There is a relationship between CSF FLC and CSF neopterin in MS, highlighting the multifaceted immune activation seen in MS. Correlations in the OCB-positive group highlight the multifaceted immune activation seen in MS.
"Sire you look like the p**s boy"......and you look like a bucket of s**t
(History of the World part 1)
CoI: Work done by members of Team G