Type 2 Diabetes: Prediction and Prevention (Wiley Practical Diabetes Series)

Type 2 Diabetes: Prediction and Prevention: Medicine & Health Science Books @ leondumoulin.nl Series: Wiley Practical Diabetes Series.
Table of contents

In such cases, subjects could only be classified as having diabetes, based on high fasting value. The year incidence of drug-treated diabetes during the follow-up was 4. The baseline survey questionnaire included several questions about blood pressure. Overall, high blood pressure was associated with higher incidence of drug-treated diabetes. The question about history of blood pressure medication was selected into the Diabetes Risk Score because it is an unequivocal marker of clinically evident hypertension and can be determined without blood pressure measurement.

The question about history of latent diabetes or diabetes covered transient or borderline elevated blood glucose and gestational diabetes, as well as diabetes treated with diet alone at baseline. A total of 35 subjects reported at baseline that they had been told they had diabetes but never had any antidiabetic drug treatment. Of these individuals, 32 had at least fasting glucose levels measured at baseline; 16 had glucose values considered diabetic. During follow-up, 21 of these individuals started using antidiabetic drugs according to the drug register data 15 of these subjects had glucose levels considered diabetic at baseline.

The multivariable logistic regression models based on the follow-up of the survey are shown in Table 1. Statistically significant independent predictors of future drug-treated diabetes were age, BMI, waist circumference, antihypertensive drug therapy, and history of high blood glucose levels. The concise model includes only these statistically significant variables. The full model includes also physical activity and fruit and vegetable consumption. Even though these two variables did not add much to the predictive power of the statistical model, they were included in the Diabetes Risk Score to emphasize the importance of physical activity and diet in the prevention of diabetes.

Nevertheless, it was included in the final Diabetes Risk Score because it is obviously the intermediate stage between normal weight and obesity, with a reasonably high impact on diabetes risk odds ratio 2. In the multivariate model, male sex was a statistically significant predictor of drug-treated diabetes risk; the odds ratio was 1.

On the other hand, inclusion or exclusion of sex into the models changed the coefficients of the other independent variables only slightly. Therefore, we did not include sex in the final multivariate model and the final Diabetes Risk Score.

A practical tool to predict type 2 diabetes risk

A total of 4, subjects had complete baseline data for the concise model, and of these individuals, drug-treated diabetes developed in during follow-up. For the full model, 4, subjects had complete baseline data and drug-treated diabetes developed in subjects. The total Diabetes Risk Score was calculated as the sum of the individual scores and varied from 0 to The and surveys had similar data, except for the intake of vegetables, fruits, or berries: The ROC curves Fig.

The Diabetes Risk Score value 9 was selected as the cut point for increased risk of drug-treated diabetes, along with sensitivity of 0. The positive predictive value PPV , the probability of drug-treated diabetes developing during follow-up if the Diabetes Risk Score was 9 or higher, was 0. The overall incidence was lower in the cohort due to the shorter follow-up period.

In Table 2 , the men and women of both cohorts are classified into four Diabetes Risk Score categories. The incidence of drug-treated diabetes was markedly elevated in the two highest categories. We also analyzed the performance of the Diabetes Risk Score cross-sectionally in identifying subjects who had either fasting or 2-h glucose levels exceeding the threshold of diabetes. A total of 2, subjects in the cohort and 1, subjects in the cohort could be classified according to results of oral glucose tolerance test and had complete Diabetes Risk Score data. The crude prevalence of undiagnosed diabetes was 3.

Recent studies have shown that type 2 diabetes can be prevented in high-risk subjects with impaired glucose tolerance by lifestyle intervention 1 — 3. Therefore, a strong argument exists in favor of screening for subjects who are at increased risk for diabetes Our study is unique in that it focuses on predicting future drug-treated diabetes with several factors that are easy to measure with noninvasive methods, are known to be associated with risk of type 2 diabetes, are easily comprehensible, and direct attention to modifiable risk factors of diabetes.

Drug-treated diabetes is very unlikely to develop in individuals with a low Diabetes Risk Score. Therefore, these individuals can be excluded from further procedures such as glucose testing without causing a problem of false-negative results.

Defining a suitable cut point is a trade-off between sensitivity and specificity. We included in the analyses all subjects who were not on antidiabetic drug therapy at baseline. Therefore, patients with diabetes who were treated with diet alone were included in the prospective follow-up, where the outcome was initiation of antidiabetic drug treatment.

Initiation of drug therapy indicates a deterioration of glucose homeostasis also in patients who, at baseline, may have been treated with diet alone. This approach decreased the possibility of bias because, during follow-up, it would not have been possible to ascertain diet-treated cases. It is obvious that the recent incident cases, typically treated with diet, were missed in follow-up.

Therefore, incidence of diabetes is an underestimate of the true value. We are also aware of the possibility of circular argument of identifying subjects based on the same risk factors that would evoke their physician to prescribe blood glucose testing, missing the diagnosis of less typical cases. However, the finding that the Diabetes Risk Score performed equally well in the cross-sectional analysis attenuated this concern.

Diabetes Type 1 and Type 2, Animation.

We did not exclude people with high glucose levels at baseline because we tested the Diabetes Risk Score under the assumption that no biochemical tests are performed at that stage. As shown by our analyses in the subset in which glucose values were available at baseline, use of a high Diabetes Risk Score value as a primary screening tool would efficiently identify unrecognized diabetes. Most cases of diabetes would then be diagnosed at the subsequent oral glucose tolerance test in individuals with a high Diabetes Risk Score value.

The Diabetes Risk Score values were derived from the coefficients of the logistic model by classifying them into five categories.

The Diabetes Risk Score

A more precise method would be to sum the original coefficients or their expansions. The sum of the coefficients would have a wide distribution and would therefore be impractical in clinical use. In Table 1 , we have shown the coefficients for the full model that was used to formulate the Diabetes Risk Score as well as the concise model with fewer variables.

We also calculated the model excluding those subjects who, at baseline, reported that they had diet-treated diabetes. The coefficient for history of high blood glucose was reduced from 2. A few reports 15 — 20 have suggested methods of screening for undiagnosed diabetes. In these assessments, the outcome was prevalent diabetes in a cross-sectional setting.


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In a follow-up study 21 with a median follow-up of 8 years, BMI at baseline predicted diabetes as well as fasting or 2-h plasma glucose; in that study, no other risk factors for diabetes were analyzed. In a recent follow-up study, Stern et al. Therefore, they included in their models most of the parameters of the metabolic syndrome as defined by the WHO Consultation Their finding is not surprising, because it is well known that people with signs of the metabolic syndrome have increased risk of type 2 diabetes.

The PPVs of the reported predictive models in identifying prevalent, undiagnosed diabetes have ranged from 5. Therefore, our method, even though it was developed using incident drug-treated diabetes as the outcome, might also be accurate in predicting earlier stages of type 2 diabetes.

This will be seen when our Diabetes Risk Score is applied in such situations in the future.

Definition of prediabetes

A 4-Week Introductory Guid This book has helped thousands of guys build their best bodies ever. Will you be next? Unleash Your Inner Diabetes Dominator: Never Binge Again tm: Wiley Practical Diabetes Series Hardcover: Wiley; 1 edition May 7, Language: Be the first to review this item Amazon Best Sellers Rank: Related Video Shorts 0 Upload your video. Try the Kindle edition and experience these great reading features: Customer reviews There are no customer reviews yet.

Share your thoughts with other customers. Write a customer review. Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Learn more about Amazon Giveaway. Set up a giveaway. There's a problem loading this menu right now. Get fast, free shipping with Amazon Prime. Your recently viewed items and featured recommendations. View or edit your browsing history. Novel approaches to training are required to meet the global demands of caring for patients with chronic conditions [ 82 ].

The healthcare system needs to transition from a reactive to a proactive perspective with regard to prevention and approach this issue on a population basis beyond caring for the individual patient [ 82 ]. Healthcare workers will, therefore, need to develop a broad approach to patient care considering the entire continuum from community prevention to palliative care [ 82 ]. Establishing new core competencies will require restructuring of training to include knowledge, skills and abilities designed to prepare 21st century health workers to address current challenges [ 82 ].

Ground level proposals, for primary prevention, listed in Box 1 , complement community programs such as the National DPP. A limiting factor in prevention is the inability to recognize metabolic disorders early in their course, because these tend to be subtle and often are not considered given the significant challenges of primary care practitioners in managing ever-increasing patient volumes in the context of decreased reimbursement. The latter appears to be increasingly undertaken by third-party insurers.

The development of accredited, cost-effective hospital-based initiatives should be encouraged to supplement prevention programs in the community. All healthcare professionals, regardless of specialty, should be instructed to refer individuals at risk to their physician for further evaluation and referral. Continuing medical education courses, utilizing web-based and other formats, as well as satellite symposia held at scientific congresses, should be focused particularly on pediatricians, primary care and family physicians.

Clinical case presentations should be utilized to illustrate screening, diagnosis and treatment principles. Given the enormous population at risk, community-based strategies involving local health departments should also be considered. The overwhelming burden of screening and treatment may, therefore, be mitigated and achieved by integrating diabetes prevention programs within extant hospitals or community clinics garnered with necessary training and duly recognized.


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As current medical school curricula offer basic public health principles, perhaps because it tends not to adequately focus on chronic disease management [ 83 ], consideration should be given to the development of more extensive prevention modules in collaboration with public health faculty.

This should enhance life-long awareness of the importance of prevention and may also provide motivation to pursue a career in public health practice and research. It is the responsibility of the medical community to reach out to the broader public raising awareness of the current epidemic and offering basic instruction in the importance of lifestyle modification while encouraging referral for those at risk. Governments, in concert with the private sector, need to set policies that promote healthy nutritional and agricultural policies, favor modifications in the environment that encourage greater physical activity and make prevention affordable for all citizens at high risk.

The clinical sector has the formidable challenge of screening and identifying those at high risk and referring to accredited intervention programs. Thus, all three sectors, government, public health and clinical, each have a critical role in this process and by working as a partnership, ought to create the necessary synergies essential for making substantial forays in the prevention of diabetes.

The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the CDC. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.

This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript. National Center for Biotechnology Information , U. Author manuscript; available in PMC Sep 1. See other articles in PMC that cite the published article.

Type 2 Diabetes Prediction and Prevention (Wiley Practical Diabetes Series), requested file xbd

Caveats of current definitions The early identification of prediabetes permits intensive management to delay the progression to diabetes and to potentially prevent the development of chronic complications [ 19 ]. Open in a separate window. Prevention of Type 2 diabetes: Program recognition The CDC Diabetes Prevention Recognition Program DPRP [ ] assures program quality, consistency, provides a registry of recognized programs and implements standardized reporting on performance of recognized programs. Health marketing Participant engagement and healthcare provider referrals are important for program success.

Identification of individuals at increased risk The preponderance of evidence for diabetes prevention is derived from initiatives focusing on those at increased risk i. Standardization of lifestyle intervention Policy development requires utilization of evidence-based, standardized lifestyle intervention recommendations that are customized to reflect cultural and individual circumstances.

Monitoring The public health sector can play an important role in continuous evaluation and monitoring to ensure successful implementation of diabetes prevention programs. Ground level proposals for the primary prevention of diabetes While confronting NCDs in general and diabetes in particular at the highest levels of national and international governmental agencies is absolutely necessary to promote shifts in healthcare delivery, this process will clearly take time.

Box 1 Ground level proposals for the primary prevention of diabetes. Contradictory data with regard to sensitivity may indicate that the ability to accurately diagnose prediabetes is limited. Footnotes Disclaimer The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the CDC.

References Papers of special note have been highlighted as: Diabetes Atlas Fourth Edition. International Diabetes Federation; Brussels, Belgium: Impaired fasting glucose and impaired glucose tolerance. Diabetes risk reduction behaviors among U. Glycated hemoglobin, diabetes and cardiovascular risk in nondiabetic adults. Concise and makes the point that economic and health consequences of diabetes will not be reduced until individuals with prediabetes are identified and offered access to lifestyle intervention at reasonable cost. Diabetes prevention program research group: Global estimates of the prevalence of diabetes for and Prevention of Type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.

Ramachandran A, Snehalatha C. The Expert Committee on the diagnosis and classification of diabetes mellitus: The International Expert Committee: Glycemic thresholds for diabetes-specific retinopathy. Lowering the criterion for impaired fasting glucose will not provide clinical benefit. Buysschaert M, Bergman M. Diabetes Prevention — from Science to Practice.


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Diagnosis of prediabetes and diabetes prevention. A1c versus glucose testing: Screening for Type 2 diabetes and dysglycemia. Position statement executive summary: Diagnostic testing for diabetes using HbA 1c in the Abu Dhabi population. Hemoglobin A 1c as a screen for previously undiagnosed prediabetes and diabetes in an acute-care setting. Glycated haemoglobin HbA 1c for the diagnosis of diabetes mellitus — practical implications.

Ageing and associations of fasting plasma glucose and 2h plasma glucose with HbA 1c in apparently healthy population. High-normal HbA 1c is a strong predictor of Type 2 diabetes in the general population. Hemoglobin A 1c predicts diabetes but not cardiovascular disease in non-diabetic women.

Impact of A1c screening criterion on the diagnosis of pre-diabetes among U. Clinicians and health systems need to understand the issues in using A1c or IFG in diagnosing prediabetes. Prevalence of diabetes and high risk for diabetes using A1C criteria in the U. Screening for diabetes and pre-diabetes with proposed A1c-based diagnostic criteria.

A1c cut points to define various glucose intolerance groups in Asian Indians. Inadequacies of absolute threshold levels for diagnosing prediabetes. Bonora E, Tuomilehto J. The pros and cons of diagnosing diabetes with A1c. Global and societal implications of the diabetes epidemic. Albright A, Williamson DF. Community approaches to diabetes prevention. Prevention of Type 2 Diabetes: Long-term benefits from lifestyle interventions for Type 2 diabetes prevention: Are we really active in the prevention of obesity and Type 2 diabetes at the community level? Lifestyle intervention for prevention of Type 2 diabetes in primary healthcare.

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The Finnish diabetes risk score is associated with insulin resistance and progression towards Type 2 diabetes. A European evidence-based guideline for the prevention of Type 2 diabetes. Preventing Type 2 Diabetes: Systematic review of intervention components associated with increased effectiveness in dietary and physical activity interventions. For the image study group.