Doctor Ruth Week

D
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.



PARTICIPANTS88 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.



CONCLUSIONSThe
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)

When I first met Prof G doing research, he was knee deep in p**s, and so he has enthused others to follow in this urinary habit and is still chasing the elusive biomarkers in urine. Wouldn’t it be great if you could have a quick test of your pee to know how your MS was doing. Needs to keep searching!

CoI: Work done by members of Team G

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