Diabetes HealthRisk Assessment

Discover your Diabetes risk and take a step in the right direction. 

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Medically Reviewed By: Expert-24 Medical Review Board on March 27, 2014 | References | Terms of Use & Privacy

HEALTHTOOLS™ (HEALTHRISK™ AND HEALTHAGE™) DOES NOT PROVIDE MEDICAL ADVICE. It is intended for informational purposes only. It is not a substitute for professional medical advice, diagnosis or treatment. Never ignore professional medical advice in seeking treatment because of something you have read on the site. If you think you may have a medical emergency, immediately call your doctor or dial 911.

Expert Review Panel – Expert-24 Ltd

Terms of reference

The aim of the Expert Review Panel is to ensure that all Expert-24 clinical and epidemiological content is robust, independent and up to date.

Qualifications

Medical Director and Editor

Dr. Timothy Dudley

Chairman of the Expert Review Panel

Dr. Robin Christie

Current authors and reviewers for the Health Risk Assessment

Dr. Martin Dawes

Dr. Jonathan Mant

Emeritus authors and reviewers for the Health Risk Assessment

The following individuals were deeply involved in the creation of the health risk assessment at its inception, but are no longer active reviewers on the panel:

Dr. John Fletcher

Dr. Emma Boulton

Professor Larry Ramsay

Professor Klim McPherson



Patient-centered health risk using an Evidence Based Medicine approach

Who created it and how often is it reviewed and updated?

This health risk assessment is brought to you by Expert-24 Limited. Expert-24 Ltd has full editorial control over content and strives to ensure that the content is: 

  • Robust - All information used is derived from reputable, referenced sources and subject to rigorous expert review. The content is written by the medical staff of Expert-24 and reviewed by an independent Expert Review Panel. All content is subject to regular review and updated to incorporate the latest evidence. Oxford Health Consulting was commissioned to conduct independent research to determine the model for disease and mortality-specific risks, the contents and its assumptions. The research and statistical modeling behind the risk assessment has been led by Dr. John Fletcher. Dr. Fletcher is deputy editor of the Canadian Medical Association Journal. He holds a Masters degree in Public Health Quantitative Methods and is a member of the Royal College of General Practitioners. 
  • Independent - The content on the site is provided by Expert-24 Limited, an independent UK company providing knowledge automation and decision support tools to improve health and wellbeing. No member of the Expert Review Panel has any financial stake in Expert-24 Ltd. Content creation and ongoing Quality Assurance is provided by Expert-24 Ltd and its Expert Review Panel. 
  • Up to date - All clinical material is subject to review by Expert-24 and its Expert Review Panel at least annually.

Why is this health risk assessment different than others?

Most health risk assessments say if a person is at high, medium or low risk of either dying from or developing a given medical condition. Most also indicate what lifestyle factors contribute to this risk. What they do not say is the magnitude of each risk for an individual and how much that person’s risk will decrease if they change their lifestyle. For example, if one is at moderate risk of two diseases, say bowel cancer and heart disease, most people would be unaware that their risk of heart disease is still five times higher than their risk of bowel cancer. 

In order to construct an electronic risk assessment tool for health and disease states, it is necessary to provide supporting research evidence and a method of encapsulating the best estimate of relative risk. For each medical condition, it is necessary to present credible estimates of risk, based on evidence from relevant, peer reviewed medical research. Important features of the risk assessment tool are: 
  • The tool gives numerical estimates of risk, rather than an imprecise statement such as "increased risk" or "reduced risk". 
  • The tool has the capability for interaction, allowing users to explore the impact on their personal risk of changing individual risk factors. 
  • The tool utilizes best available medical evidence 

The aim of this project is to provide healthy people with a quantitative assessment of their personal risk of developing some important diseases and some of the factors that influence their risk. This is an ambitious task and we would not claim to have produced the definitive approach. Although we believe this is the most informative collection of disease prediction equations available at the present time they do have limitations. The ones we are aware of are outlined below.

What exactly does a given percentage risk mean?

Someone looking at their risk of lung cancer until the age of 50 should read this model as saying, "Assuming survival to age 50 the chance of developing lung cancer during that time would be (some predicted value)". This approach has the appeal that changing risk factors will have the expected impact on cumulative risk and the mathematics remains transparent. We chose the risk of developing a certain condition rather than the risk of dying from it because for many people the fear of living and dealing with a disabling disease is as frightening as dying from it. 

This is different than lifetime risk calculations, which generally calculate the risk of dying from a given condition. Lifetime risk must take account of the fact that we all die of something in the end and calculating the relative contribution of common competing causes of death at various ages is difficult. Not only that, but the interpretation by users is complex. For example, a user of an interactive model predicting lifetime risk of lung cancer would see their individual risk of lung cancer fall with increasing cigarette consumption, because they would be dying of heart disease and chronic lung disease before they could get lung cancer.


How accurate are these percentages?

These models are good for illustrating the change in risk due to the presence or absence of single risk factors for prediction times of up to 5 years. They are likely to be reasonably good for 15 or 20 years and for combinations of several risk factors. For longer prediction times and varying more than, say, four risk factors the results should be regarded as illustrative rather than precise. The absolute level of risk for an individual may also be wide of the mark because the majority of overall risk remains unexplained in most research studies. This is why "confidence intervals" have not been included. That said these prediction equations do calculate the best estimate of risk that can be provided on the data given. 

Is this useful in the end? We believe it is. We believe that putting some quantification on risk allows users to explore the possible impact on their health of altering what they do. We find this approach more informative than a bland statement of "high risk" that is often value laden or that a certain action will "cut down" a risk without any indication of by how much.

Is risk really reversible?

This is a difficult question to answer, but in many cases the answer seems to be, "yes". This is good news for people with high risks who are older. Intuition might tell you that you are constantly doing damage to your body that accumulates over time, and in many cases that may be true. An example of this is in skin cancer, where the earlier and more often you are badly burned in life, the higher your risk of skin cancer. Staying out of the sun when you are old cannot reverse this risk. 
However, there is good evidence that for heart disease, for example, your risks can be significantly reduced no matter what your age. Cholesterol reduction by medications called "statins" reduces the risk of heart attack, angina or sudden death from heart problems by up to 30%, and this is entirely independent of age. Similarly, blood pressure reduction by drugs reduces the risk of stroke and heart disease by 25% - again entirely independent of age. Because in general it is older people who have the highest risks, they actually stand to benefit the most from treatment. 

The risk for developing heart disease in tobacco users has been shown to decline to a level comparable with a person who has never smoked within 2-3 years of giving up. Furthermore, the risk of having a stroke is reversed after 5-10 years of stopping. Studies have also shown that life expectancy improves even in people who stop smoking later in life (i.e. at 65 years or older). 

The reduction of risk that can be obtained from changing lifestyle habits such as diet, alcohol consumption and exercise is largely unknown. Therefore, the amount of risk reduction that can be expected from optimizing these habits needs to be viewed with caution. Certainly they should not take the place of blood pressure control, cholesterol control, and smoking cessation as goals.


How good is the evidence?

Our aim in searching for evidence was to identify up to ten high quality, relevant research studies for each topic. We used Medline to search using free text, MeSH terms and thesaurus search terms specific to each medical condition. To narrow the documents we used filters using "risk" and study design type; cohorts, case control, longitudinal, follow up. Searches were limited to studies published in English language and human studies. Although a comprehensive systematic review of the literature on each disease was not possible due to the scope of this project, we feel that the evidence used represents a reasonable cross-section of high-quality literature on the subjects in question. 
What we have done is to seek out plausible values of relative risk to use in the prediction equations. We have used an approach that searches for high quality research studies and have then applied our judgment tempered by Austin Bradford Hill's criteria for causation when selecting which risks to use. Hill's criteria are: strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experimental evidence and analogy. 

If this sometimes appears somewhat subjective then that is because at times it is a matter of judgment. The judgments have seldom altered the relative risk by more than a small amount. For each risk factor we had to choose a value to use in the model and have been faced at times with a range from which to choose. While a meta-analysis may provide the best point estimate, one is not always available and would be spurious to conduct on the sample of studies we have used for each condition. Given the level of uncertainty surrounding an individual's absolute personal risk we are comfortable with a comparatively lesser degree of uncertainty regarding a risk factor's relative risk.

What is the mathematical model that is used?

The actual mathematical and statistical models and risk coefficients that are used to determine risk are proprietary at this time, but have been validated by the authors and reviewers to be appropriate for use in this setting. 


References: Diabetes

Most recently reviewed:

  1. Salas-Salvado J, et al. Reduction in the incidence of type 2 diabetes with the Mediterranean diet. Results of the PREDIMED-Reus nutrition intervention randomized trial. Diabetes Care 2011; 34(1): 14-19
  2. Hu FB et al. Diet, Lifestyle and the risk of type 2 diabetes in women. NEJM September 13, 2001; 345(11): 790-7.

Guidelines reviewed annually:

  1. National Collaborating Centre for Chronic Conditions. Type 2 diabetes: national clinical guideline for management in primary and secondary care (update). London: Royal College of Physicians, 2008. http://www.nice.org.uk/nicemedia/pdf/CG66diabetesfullguideline.pdf
  2. http://www.diabetes.org.uk, accessed November 21, 2010

Selected articles from previous reviews:

  1. Weycker D et al. Excess risk of diabetes in persons with hypertension. J Diabetes Complications 1 Sep 2009;23(5): 330-6
  2. Carter et al. Fruit & vegetable intake and incidence of type 2 diabetes: systematic review and meta analysis. BMJ 2010; 341 c4229
  3. Solomon, D et al. Risk of diabetes among patients with rheumatoid arthritis, psoriatic arthritis and psoriasis. Ann Rheum Dis 2010;69:2114-2117
  4. Valdez, R et al. "Family history and prevalence of diabetes in the US population. The 6 year results from the NHANES 1999-2004." Diabetes Care, Oct. 2007; Vol 30 (10):2517-2522
  5. Wilson et al. "Prediction of incident diabetes mellitus in Middle-aged adults: the Framingham Offspring Study. Arch. Int. Med. 2007
  6. Siegel, LC et al. "Physical Activity, BMI, and diabetes risk in men: a prospective study. Am. J. Med. 2009 Dec;122:1115-21
  7. Narayan, KMV et al. "Effect of BMI on Lifetime Risk for Diabetes in the US. Diabetes Care June 2007 30(6):1562-1566
  8. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore, BMJ 2009;338:b880
  9. Elvira Luján Massó-González et al. Trends in the Prevalence and Incidence of Diabetes in the UK - 1996 to 2005 J Epidemiol Community Health doi:10.1136/jech.2008.080382
  10. Guangwei Li et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet 2008; 371:1783-89
  11. Nield L, Summerbell CD, Hooper L, Whittaker V, Moore H. Dietary advice for the prevention of type 2 diabetes mellitus in adults. Cochrane Database of Systematic Reviews 2008, Issue 3. Art. No.: CD005102. DOI: 10.1002/14651858.CD005102.pub2.
  12. Choi HK, et al. "Dairy consumption and risk of type 2 diabetes mellitus in men: a prospective study", Arch Int Med May 9, 2005, 165(9): 997-1000
  13. Weinstein AR, et al. "Relationship of physical activity vs body mass index with type 2 diabetes in women", JAMA Sep 8, 2004, 292(10): 1188-94
  14. Whincip PH, et al. "British South Asians aged 13-16 years have higher fasting glucose and insulin levels than Europeans", Diab Med Sep 2005 22(9):1275-7
  15. Lauenborg J "The prevalence of Metabolic Syndrome in a Danish population of women with previous gestational diabetes is three fold higher than in the general population" J Clin Endocrin Metab July 2005 90(7): 4004-10
  16. Damm P "Predictive factors for the development of diabetes in women with previous gestational diabetes mellitus", Am J Obst Gyn Sep 1992 167(3): 607-16
  17. Bhargava, A., "A longitudinal analysis of the risk factors for diabetes and coronary heart disease in the Framingham Offspring Study", Popul Health Metr. 2003; 1 (1): 3
  18. Schaefer-Graf, U.M., "Clinical predictors for a high risk for the development of diabetes mellitus in the early puerperium in women with recent gestational diabetes mellitus." Am J Obstet Gynecol. 01 Apr 002; 186(4): 751-6.
  19. Verma, A., "Insulin resistance syndrome in women with prior history of gestational diabetes mellitus." J Clin Endocrinol Metab 01 Jul 2002; 87(7): 3227-35.
  20. Albareda, M., "Diabetes and abnormal glucose tolerance in women with previous gestational diabetes." Diabetes Care 01 Apr 2003; 26(4): 1199-205.
  21. Legro, R.S., "Prevalence and predictors of risk for type 2 diabetes mellitus and impaired glucose tolerance in polycystic ovary syndrome: a prospective, controlled study in 254 affected women." J Clin Endocrinol Metab 01 Jan 1999; 84(1): 165-9.
  22. Yildiz, B.O., "Glucose intolerance, insulin resistance, and hyperandrogenemia in first degree relatives of women with polycystic ovary syndrome." J Clin Endocrinol Metab 01 May 2003; 88(5): 2031-6.
  23. Ehrmann, D.A., "Prevalence of impaired glucose tolerance and diabetes in women with polycystic ovary syndrome." Diabetes Care 01 Jan 1999; 22(1): 141-6.
  24. Legro, R.S., "Diabetes prevalence and risk factors in polycystic ovary syndrome." Obstet Gynecol Clin North Am 01 Mar 2001; 28(1): 99-109.

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The HealthTools™ assessment is an educational tool made available to you at no charge.  It is designed for adults 18 years and older living in the United States.  It was developed using current national standard guidelines.

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