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BMJ Diabetes Day

27 Jun, 13 | by Dr Dean Jenkins

Yesterday at BMA House, the team from BMJ Informatica hosted a day dedicated to diabetes. It asked the question “How can we prevent diabetes from bankrupting the NHS?“.

The audience consisted of doctors and pharmacists  involved with the commissioning of diabetes care. There was a lively discussion on the challenges that diabetes, and obesity, present to the health service. The debate was informed by the a research update on screening and risk scores from Dr David Webb at the University of Leicester, the use of routine clinical data and risk scores from Dr Pete Green from Medway CCG, and an international perspective from Professor Henk Bilo of the Netherlands. There was also a presentation from Professor Kilm McPherson looking at updated models of future obesity rates, John Stewart describing the NHS Outcomes Frameworks, and Professor Stephen Bloom looking at the future therapeutic trends in the management of obesity.

The meeting was chaired by Professor Sir Charles George and myself. Although we didn’t come to any simple answer, plenty of interesting ideas shared. Prioritising patient reviews, using data to focus resources, virtual clinics, the efficient division of labour between primary and secondary care, strategies for local negotiation and methods for overcoming various forms of stakeholder resistance were all explored.

What I found the most fascinating was the data analysis that Paul Barbour from BMJ Informatica had prepared. Each delegate had an estimated prevalence and cost summary for their CCG derived from various data sources. There was also a screen with a Google Map of the UK overlaid with every GP practice so that the number of QOF-registered people with Type 2 Diabetes could be compared with the risk-score estimate of prevalence. The data made for a great focus of discussion when displayed on a large screen.

Map of GP practice data on people with diagnosed and undiagnosed Type 2 Diabetes.

BMJ Informatica map of GP practice data on people with diagnosed and undiagnosed Type 2 Diabetes.


The real world versus lifestyle change trials in diabetes

23 Apr, 13 | by Dr Dean Jenkins

Unfortunately it seems that in the real world primary care can only stabilise weight and HbA1c in people with Type 2 Diabetes. A study of electronic records in primary care in the Netherlands has shown that the effect of lifestyle interventions was not as great as that seen in published research.

“Despite effective lifestyle interventions in controlled trial settings, we found that real-world primary care is only able to stabilize weight and HbA1c in patients with T2DM over time. Medical registration can be used to monitor the actual effectiveness of interventions in primary care.” [1]

CAPHRI logoThe authors felt that more future research (of effectiveness) should take place in real-world primary care settings and especially those that have electronic records. This would better reflect the feasibility of translating this research into practice.

Another finding from the study was that the authors felt most of the variability of outcomes was explained by differences between patients rather than healthcare staff. This has implications for correct interpretation of variability in apparent performance.

1. Linmans JJ, Viechtbauer W, Koppenaal T, Spigt M, Knottnerus JA. Using electronic medical records analysis to investigate the effectiveness of lifestyle programs in real-world primary care is challenging: a case study in diabetes mellitus. J Clin Epidemiol 2012 Jul;65(7):785–792. Available from:

Filtering the diabetes noise

20 Mar, 13 | by Dr Dean Jenkins

I’ve always been interested in how to keep up to date. Staying abreast of developments in a specialty is an important aspect of the role of a physician. You can share this knowledge with others. I came across these three papers today and I’ll explain how in a moment.

“Soft drink consumption is significantly linked to overweight, obesity, and diabetes worldwide, including in low- and middle-income countries.”

Basu S, McKee M, Galea G, Stuckler D. Relationship of Soft Drink Consumption to Global Overweight, Obesity, and Diabetes: A Cross-National Analysis of 75 Countries. Am J Public Health 2013 Mar;

“Low self-rated health was associated with a higher risk of type 2 diabetes. The association could be only partly explained by other health-related variables, of which obesity was the strongest.”

Wennberg P, Rolandsson O, Van der A DL, Spijkerman AMW, Kaaks R, Boeing H, Feller S, Bergmann MM, Langenberg C, Sharp SJ, Forouhi N, Riboli E, Wareham N. Self-rated health and type 2 diabetes risk in the European Prospective Investigation into Cancer and Nutrition-InterAct study: a case-cohort study. BMJ Open 2013;3(3)

“long-term BPA exposure [a compound in plastic bottles] at a dose three times higher than the tolerable daily intake of 50 µg/kg, appeared to accelerate spontaneous insulitis and diabetes development in NOD mice.”

Bodin J, Bølling AK, Samuelsen M, Becher R, Løvik M, Nygaard UC. Long-term bisphenol A exposure accelerates insulitis development in diabetes-prone NOD mice. Immunopharmacol Immunotoxicol 2013 Mar;

If I were presented with these papers as someone with a keen interest in the clinical care of diabetes then I think I’d find them rather interesting. Apart from the plastic bottles and NOD mice – which I know less about – the papers would seem to shape my understanding of the causes of diabetes. The first paper would raise my awareness of the importance of soft drinks (which has been in the news recently as well so patients might visit with questions). The second would highlight the psychological factors behind the risks for Type 2 Diabetes and obesity.

So, where did they come from?

From the library? No.

From a news agency on a website or email spam? No.

A PubMed search and crawl? No.

From my mates on Twitter? Not exactly.

From collecting all 40,000 tweets in the past 48 hours mentioning ‘diabetes’ and analysing them using various algorithms. Well yes.

Is this another way of filtering the diabetes noise? By tapping into the collaborative work of others. I think we’ll see more of it … perhaps, in the 21st Century, we already do.

New formula suggested for BMI

24 Jan, 13 | by Dr Dean Jenkins

In 1832, whilst trying to define ‘normal’, Adolphe Quetelet, Belgian polymath, defined an index – the Quetelet Index – which later became known as the Body Mass Index (BMI) and is used as an indicator of obesity which has become recognised as an important marker of early mortality. [1] Typical BMI chart

The formula is well known to us though rather cumbersome to calculate. It is weight in kilograms divided by the square of height in metres. We are all familiar with the swathes of colour that map out underweight, normal, overweight and obese. We may very well say to people in our clinics that ‘according to your BMI you seem to be obese!’.

However, there is a problem with the index in that it doesn’t quite catch the nature of obesity and human growth. Those who tend to be below average height score slightly lower on this index than perhaps they should. The opposite is true of people who are taller than average. Clearly we can’t change our height but when certain recommendations [2] have explicit levels of BMI we need to be sure it is representing what we feel it should. The problem is mainly to do with the power that the height is raised to and Quetelet seems to have recognised that too (though he wasn’t so concerned with obesity). Babies seem to require a power of 3 to correctly recognise their growth in all dimensions and children would probably benefit from a figure of 2 since they grow more vertically. Adults fall in between and 2.5 seems to fit better.

So, Professor Nick Trefethen of Oxford University, made all these points and has suggested a refined formula following a letter to The Economist where he said:

“millions of short people think they are thinner than they are, and millions of tall people think they are fatter”

His formula is:

BMI = 1.3*weight(kg)/height(m)2.5

In the diabetes literature there is concern over the suitability of BMI in those with short-stature [3] and its usefulness when compared with other measures [4].

So, will you be using the new (slightly more cumbersome) formula in your clinic?


1. Garabed Eknoyan. Adolphe Quetelet (1796–1874)—the average man and indices of obesity.  Nephrol. Dial. Transplant. (2008) 23 (1): 47-51. doi: 10.1093/ndt/gfm517

2. NICE Clinical Guideline 43. Obesity.

3. Lara-Esqueda A, Aguilar-Salinas CA, Velazquez-Monroy O, Gómez-Pérez FJ, Rosas-Peralta M, Mehta R, Tapia-Conyer R. The body mass index is a less-sensitive tool for detecting cases with obesity-associated co-morbidities in short stature subjects. Int J Obes Relat Metab Disord. 2004 Nov;28(11):1443-50.

4. Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008 Jul;61(7):646-53. doi: 10.1016/j.jclinepi.2007.08.012.

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