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Type 2 diabetes (T2D) is a common disease caused by a complex interplay between many genetic and environmental factors. Both a population-based approach and a targeted high-risk approach are recommended as strategies for prevention of T2D. Genetic susceptibility testing for T2D is currently offered by several commercial companies that use genome-wide scans to deliver information about risk for many common complex diseases (see Table 1 ). Direct-to-consumer risk companies sell risk profiles that differ in the number of genetic markers included and in the exact SNPs used. T2D is a metabolic disorder characterized by hyperglycemia, insulin resistance and relative insulin deficiency. Preventive interventions for T2D, including medication, weight loss and increased physical activity, can slow or even reverse the disease process.
A European multidisciplinary consortium developed an evidence-based guideline for the prevention of T2D. Analytic Validity : Test accuracy and reliability in identifying multiple SNPs (analytic sensitivity and specificity).
Navigenics reports an analytic accuracy of 99%, [15] deCODEme does not provide a measure of accuracy but describes the methods used to ensure good analytic validity, [16] 23andMe does not disclose the methods used, [17] and the same applies to Pathway Genomics. Direct-to-consumer genetic testing services are not clearly regulated by governmental agencies. Clinical Validity : Clinical validity refers to test accuracy and reliability in predicting risk of T2D (discrimination and calibration). Discrimination shows how well the model can distinguish between individuals with and without disease.
In most empirical studies, the genetic risk scores had lower discriminative accuracy than the clinical risk factors. Search strategy: We performed a search in PubMed and HuGE Navigator to identify relevant studies, scanned the reference lists from the retrieved articles to identify additional studies, and further used Web of Science to identify studies that cited the selected articles.
Another important aspect when testing the performance of a prediction model is the model calibration. We assessed clinical utility as the added benefit of the test beyond traditional clinical predictors in improving health outcomes, and as the impact of genetic testing on attitudes, beliefs and health related behavior in individuals who receive genetic risk information.
First, clinical utility is reflected in the impact of a risk prediction model on the classification of individuals in risk groups for which the preventive interventions differ. Second, when the impact on outcome prediction is not available, clinical utility is reflected in the public interest and health care provider interest in genetic testing, the uptake of the tests and the effect of testing on outcomes such as adherence to lifestyle changes or to medication for prevention and treatment of disease.
A survey conducted among primary care physicians and endocrinologists (n = 304) and patients (152 non-diabetic and 89 with T2D) assessed beliefs regarding the clinical use of genetic testing for T2D. This study was supported by the Centre for Medical Systems Biology (CMSB) in the framework of the Netherlands Genomics Initiative (NGI). Gene Mutations parallel Biological Alterations: The New War against Five Stages of type 2 Diabetes Mellitus. You will receive an email whenever this article is corrected, updated, or cited in the literature. Abstract Progressive hyperglycemia is a characteristic of type 2 diabetes mellitus (T2DM) that poses a challenge to maintaining optimal glycemic control.
Approximately 26 million Americans were living with diabetes in 2010.1 Data from a 2012 report2 indicated a substantial increase in the prevalence of diagnosed diabetes mellitus throughout the 50 states, Washington, DC, and Puerto Rico during a 16-year period (1995-2010), with the age-adjusted prevalence increasing by more than 50% in most states and by 100% or greater in 18 states. Figure 2.Proportion of 18-year-olds in the United States who will develop diabetes, by sex, body mass index (BMI), and period, as determined by the American Diabetes Association.
Like many chronic conditions, type 2 diabetes mellitus (T2DM) has a prolonged asymptomatic phase. Type 2 diabetes mellitus is a disease of dysfunctional glucose metabolism that is characterized by worsening hyperglycemia and a loss of response to therapy over time.
In many patients, the metabolic abnormalities associated with persistent hyperglycemia lead to complications such as vision loss, renal failure, and neuropathy.
The goal of T2DM therapy is to reestablish normoglycemia and avoid both the excesses of hyperglycemia and the dangers associated with hypoglycemia.
The Irish Health Services Executive (HSE) pharmacy claims database was used to identify new users of metformin or sulphonylureas, aged ?25 years, initiating therapy between June 2009 and December 2010. Interventions to optimise medication-taking in patients with T2DM should be implemented nationally to improve the overall level of adherence and persistence, especially in patients with no comorbidity.
References1.American Diabetes Association (2005) Diagnosis and classification of diabetes mellitus. 32.Zhang Y, Wu SH, Fendrick AM et al (2013) Variation in medication adherence in heart failure. 33.Curkendall SM, Thomas N, Bell KF et al (2013) Predictors of medication adherence in patients with type 2 diabetes Mellitus. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser. Find out more about how you can improve the care in your institution, for your child or yourself! Psychiatric disorders, particularly major depressive disorder (MDD), generalized anxiety disorder and eating disorders, are more prevalent in people with diabetes compared to the general population.
People diagnosed with serious mental illnesses, such as MDD, bipolar disorder and schizophrenia, have a higher risk of developing diabetes than the general population.
All individuals with diabetes should be regularly screened for the presence of depressive and anxious symptoms. Compared to those with diabetes only, individuals with diabetes and mental health disorders have decreased medication adherence, decreased compliance with diabetes self-care, increased functional impairment, increased risk of complications associated with diabetes, increased healthcare costs and an increased risk of early mortality.
The following treatment modalities should be incorporated into primary care and self-management education interventions to facilitate adaptation to diabetes, reduce diabetes-related distress and improve outcomes: motivational interventions, stress management strategies, coping skills training, family therapy and collaborative case management. Individuals taking psychiatric medications, particularly atypical antipsychotics, benefit from regular screening of metabolic parameters. Research is increasingly demonstrating a relationship between mental health disorders and diabetes.
The prevalence of clinically relevant depressive symptoms among patients with diabetes is in the range of 30% (4–6).
Psychological stress, leading to chronic hypothalamic-pituitary-adrenal activation with cortisol release (20–25). Comorbid depression worsens clinical outcomes in diabetes, possibly because the accompanying lethargy lowers motivation for self-care, resulting in lowered physical and psychological fitness, higher use of healthcare services and reduced adherence to medication regimens (26,27). Patients with bipolar disorder have been found to have prevalence rates estimated to be double that of the general population for metabolic syndrome and triple for diabetes (34–36). The interplay between diabetes, major depressive disorder, and other psychiatric conditions. Eating disorders, such as anorexia nervosa, bulimia nervosa and binge eating, have been found to be more common in individuals with diabetes (both type 1 and type 2) than in the general population (39,40). Schizophrenia (SZ) and other psychotic disorders may contribute an independent risk factor for diabetes.
Regular and comprehensive monitoring of metabolic parameters is recommended for all persons who receive antipsychotic medications, whether or not they have diabetes.
Diabetes, both type 1 and 2, is a psychologically challenging disease for patients and their family members (57). Diabetes distress describes the despondency and emotional turmoil related specifically to having the condition, the need for continual monitoring and treatment, persistent concerns about complications and the potential erosion of personal and professional relationships.
Individuals with diabetes should be regularly screened for psychological distress and psychiatric disorders via directed interviews.
Table 2 illustrates the differences between the principal features and assessment methods of diabetes distress and MDD. Given the burden associated with the demands of diabetes self-management, efforts to promote well-being and moderate distress should be incorporated into diabetes management for all individuals (86). Gains from treatment with psychotherapy are more likely to benefit psychological symptoms and glycemic control in adults than will psychiatric medications (which usually only reduce psychological symptoms) (98).
Psychiatric medications have the capacity to affect metabolic parameters and cause changes in weight, glycemic control and lipid profile and, in some cases, can have immunomodulating effects (22,100–103). The CATIE study investigated 4 aspects of the effectiveness of antipsychotic medications: efficacy, tolerability, emergence of medical problems and patient choice (1,106). Individuals with diabetes should be regularly screened for subclinical psychological distress and psychiatric disorders (e.g. Candidate gene studies and recent collaborative genome-wide association efforts revealed at least 38 common single nucleotide polymorphisms (SNPs) associated with increased risk of T2D. Several recent guidelines advocate screening for individuals at risk to develop T2D followed by blood glucose measurements to detect individuals with impaired fasting glucose (IFG) or impaired glucose tolerance (IGT). For example, deCODEme offers predictions for 50 complex diseases and non-disease phenotypes that vary from breast cancer, atrial fibrillation, T2D or psoriasis, to eye color and bitter taste perception. For example, deCODEme uses 21 SNPs from the genome-wide scan to calculate the risk of T2D for individuals with European descent, 9 SNPs for East Asians and 2 SNPsfor African Americans. The companies take an average risk from some epidemiological study and multiply this with the odds ratios from published meta-analyses or large scale genome-wide association studies. Diabetes is a leading cause of blindness, renal failure and limb amputation, and a major risk factor for cardiovascular morbidity and mortality.

The consortium advocates the use of clinical risk scores as primary screening tools to identify high-risk groups in whom T2D screening may be targeted more efficiently. Their services may bypass healthcare providers who are typically responsible for appropriate ordering of lab tests and for discussing with patients the implications of test results. A commonly used measure of discrimination is the area under the receiver operating characteristic curve (AUC). The first column lists all SNPs included in genetic tests for T2D, either used by DTC companies or available from published studies.
Measures of calibration were presented in some of the T2D risk prediction studies and generally showed sufficient model fit. Percentage of reclassification and the net reclassification improvement (NRI) are recently developed measures that assess this aspect of clinical utility. Subjects answered questions related to three domains: testing for risk prediction, testing to motivate behavior change and testing to guide medication prescription. To retrieve information about companies that offer DTC genetic testing for T2D risk prediction we performed a search in Google, followed the list of companies from a published review on DTC genomic companies 27 and collected additional information from discussions with other researchers. Furthermore, this project was sponsored by the VIDI grant of the Netherlands Organization for Scientific Research (NWO). Identifying and Addressing Barriers to Insulin Acceptance and Adherence in Patients With Type 2 Diabetes Mellitus. Achieving glycemic control early in the course of disease can minimize or prevent serious complications. Reprinted with permission from the American Diabetes Association, from Cunningham SA, et al.
Even after T2DM has been diagnosed, symptoms (eg, fatigue, weight loss, increased thirst, frequent urination, blurred vision) are nonspecific rather than acute. Insulin resistance is an early factor in the pathophysiologic profile of T2DM, which may be associated with unhealthy lifestyle choices and weight gain. Moreover, T2DM is the leading cause of kidney failure, non-traumatic lower-limb amputations, and new cases of blindness among adults in the United States, and it is a major cause of heart disease and stroke.1 It is imperative that increased measures are taken to improve rates of glycemic control in patients with T2DM. The purpose of this study was to investigate whether there are regional differences in non-persistence and non-adherence to oral anti-hyperglycaemic agents in patients initiating therapy and examine if any association exists between different types of comorbidity in terms of medication-taking behaviour. Adjusted hazard ratios (HRs) and 95 % confidence intervals (95 % CIs) for non-persistence were determined in relation to both HSE region and comorbidity type using Cox proportional hazards model, adjusting for age, sex and initial OAH prescribed.
The product line was developed in Sweden ("medos") and is sold in North America under the MeDose name.
Patients with serious mental illnesses, particularly those with depressive symptoms or syndromes, and patients with diabetes share reciprocal susceptibility and a high degree of comorbidity ( Figure 1).
Some evidence shows that treatment for mental health disorders may actually increase the risk of diabetes, particularly when second-generation (atypical) antipsychotic agents are prescribed (1). The prevalence of major depressive disorder (MDD) is approximately 10% (7,8) , which is double the overall prevalence in people without a chronic medical illness.
One study estimated that 14% of individuals with diabetes suffered from generalized anxiety disorder, with double this figure experiencing a subclinical anxiety disorder and triple this figure having at least some anxiety symptoms (38).
Depressive symptoms are highly comorbid with eating disorders, affecting up to 50% of patients (41).
A weight gain of between 2 to 3 kg was found within a 1-year time frame with amitriptyline, mirtazapine and paroxetine (51).
It interferes with quality of life and is a risk factor for diabetes-related distress as well as the psychiatric disorders listed above. Distinguishing between diabetes distress, MDD and the presence of depressive symptoms is important (72,73) because these psychological experiences are different, and, of the three, diabetes distress may be most strongly related to adverse diabetes outcomes (72,74,75). No data presently demonstrate the superiority of one particular depression screening tool over another (76).
Motivational interventions (68,87,88) , coping skills, self-efficacy enhancement, stress management (89,90) and family interventions (91–93) all have been shown to be helpful. A meta-analysis of psychological interventions found that glycemic control (A1C) is improved in children and adolescents with type 1 diabetes (99).
The results did indicate that some antipsychotic medications were more likely to cause weight gain, worsen glycemic control and induce unfavourable changes in lipid profile. Cognitive behaviour therapy (CBT) alone [Grade B, Level 2 (33)] or in combination with antidepressant medication [Grade A, Level 1 (95)] may be used to treat depression in individuals with diabetes. Regular metabolic monitoring is recommended for patients with and without diabetes who are treated with such medications [Grade D, Consensus]. Genetic testing of multiple SNPs is considered a potentially useful tool for early detection of individuals at high diabetes risk leading to improved targeting of preventive interventions. One such example is the Finnish risk test (FINDRISC) that provides ten-year risks to develop T2D. Not all companies explicitly report the analytic and clinical performance characteristics of their test systems. Like companies, all studies used multiplicative models or additive genetic effects, [24][25][26] but whether this is correct has not been demonstrated.
Reclassification is the percentage of individuals that change from one risk category based on the original prediction model to a different risk category based on the updated model. Most patients with T2DM eventually require insulin replacement therapy to attain and preserve satisfactory glucose control.
These characteristics of T2DM contribute to the challenges of achieving early diagnosis, intervention, and active follow-up. Logistic regression analysis, adjusting for these covariates, was used to determine the adjusted odds ratios (ORs) and 95 % CIs for non-adherence for both HSE region and comorbidity type. University College Cork15.O’Sullivan T (2006) National survey of diabetes care in general practice. Biochemical changes due to the mental health disorders themselves also may play a role (2). Individuals with depression have an approximately 60% increased risk of developing type 2 diabetes (9).
One study found that the requirement for insulin was the factor associated with the highest rate of depression, regardless of the type of diabetes involved (14). Treating depressive symptoms more reliably improves mood than it does glycemic control (30–33). Type 1 diabetes in young adolescent women appears to be a risk factor for development of an eating disorder, both in terms of an increased prevalence of established eating disorder features (42,43) as well as through deliberate insulin omission or underdosing (called diabulimia).
The Clinical Antipsychotic Trials for Intervention Effectiveness (CATIE) study found, at baseline, that of the individuals with SZ who participated in the study, 11% had diabetes (type 1 and 2 combined) (1). A study of patients with type 2 diabetes and SZ who were treated with antipsychotic medications also showed worsening glycemic control requiring the addition of insulin therapy over a 2-year period with a hazard ratio of 2.0 (52). Challenges accompanying the diagnosis of diabetes include adjustment to the disease, adherence to the treatment regimen and psychosocial difficulties at both a personal and an interpersonal level (58,59). Currently available screening instruments have a sensitivity of between 80% and 90% and a specificity of 70% to 85% (76). Case management by a nurse working with the patient’s primary care physician and providing guideline-based, patient-centred care resulted in improved glycated hemoglobin (A1C), lipids, blood pressure and depression scores (94).
Furthermore, evidence suggests interventions are best implemented in a collaborative fashion and when combined with self-management interventions (95).
The consensus statement issued by the American Diabetes Association in 2004 contains recommendations regarding almost all of the atypical agents currently available in Canada (55) , as does the Canadian Diabetes Association position paper from 2005 (105).
However, when these effects were considered in the context of efficacy, tolerability and patient choice, no conclusive statements could be made about which medications to clearly use or which to clearly avoid.
When available, some companies use sex, ethnicity and age matched population risks to depart from. This number is expected to increase by more than 50% in the next 20 years if no preventive strategies are implemented. The FINDRISC score contains eight items: age, BMI, waist circumference, antihypertensive medication, history of elevated blood glucose, daily physical activity and daily intake of fruits or vegetables. Following a recent Government Accountability Office investigation of companies providing direct-to-consumer genetic tests, the US Food and Drug Administration is considering premarket review of some laboratory-derived tests that pose higher clinical risks, assuring that the tests are evaluatedfor analytical and clinical validity. AUC indicates the probability that, on average, an individual with the disease will be assigned a higher predicted risk than an individual without the disease. However, patients were more likely than physicians to request genetic testing for risk prediction and treatment guidance. For decades, the use of insulin to address the primary defect of T2DM has been a cornerstone of diabetes therapy.
Unlike patients with acute diseases, patients with diabetes mellitus who have few or no symptoms may not visit a physician.
Insulin release declines progressively in patients with T2DM and begins well before diagnosis. Lifestyle changes and symptoms of mental health disorders are also likely to contribute (3). The prognosis for comorbid depression and diabetes is worse than when each illness occurs separately (10). Night eating syndrome (NES) has been noted to occur in individuals with type 2 diabetes who have depressive symptoms.

The prevalence of metabolic syndrome was approximately twice that of the general population (47). Stress, deficient social supports and negative attitudes toward diabetes can impact on self-care and glycemic control (60–64). A website that contains a wide variety of downloadable scales that are in the public domain is available here. A comprehensive review and meta-analysis looked at the effect of antidepressants on body weight (51). Consequently, all 4 aspects are important and reinforce the need for regular and comprehensive metabolic monitoring. In the context of targeted screening, the guideline includes the followingrecommendation about genetic testing : “despite the encouraging progress in our understanding of the genetic basis of T2DM, it is too early to use genetic information as a tool for targeting preventive efforts”. Calibration indicates how close the risks predicted by the model are to the actual observed risks.
Disagreement between results for identical DNA samples sent to 4 different companies reflects the use of different sets of markers to predict risk of disease and the use of different average risks to depart from. Cases are correctly classified when they move to a higher risk category and wrongly classified when they move to a lower category.
Patients, and to a lesser extent physicians, expressed expectations that knowledge of genetic risk would motivate adoption of preventive lifestyle recommendations and increase adherence to treatment. Secondly, according to WHO competent Authorities, there were in 2010 250 milion of diabetics, and they will be 366 milion in 2030, indicating that type 2 DM is today’s growing epidemics (1-15). Insulin is indicated for patients with T2DM presenting with clinically significant hyperglycemia, and it is mandatory for patients exhibiting signs of catabolism. In fact, studies suggest that 50% to 80% of ?-cell function is lost by the time of diagnosis.5-7 The decline continues as the disease progresses, from impaired fasting glucose levels and impaired glucose tolerance to full-blown T2DM, and it continues to progress until the patient becomes increasingly insulin deficient. This is characterized by the consumption of >25% of daily caloric intake after the evening meal and waking at night to eat, on average, at least 3 times per week. Whether the increased prevalence of diabetes is due to the effect of the illness, antipsychotic medications or other factors, individuals with psychotic disorders represent a particularly vulnerable population. Olanzapine and clozapine have been shown to have the greatest weight gain, with a mean increase of >6 kg over a 1-year span compared with 2 to 3 kg for quetiapine and risperidone, and 1 kg for aripiprazole and ziprasidone, also over a 1-year time frame.
Diabetes management strategies ideally incorporate a means of addressing the psychosocial factors that impact on individuals and their families. Evidence from systematic reviews of randomized controlled trials supports cognitive behaviour therapies (CBT) and antidepressant medication, both solely or in combination (33,95). Should medical problems arise while a patient is taking psychiatric medications, clinical judgement will dictate, on a case-by-case basis, as to whether modifications such as diet or exercise, adding a medication to address the emergent issue (e.g. The Hosmer-Lemeshow (H-L) chi-square test is a commonly used summary measure of calibration.
Insulin should be considered for patients in whom hyperglycemia persists despite attempts to control the condition through diet and exercise modifications and the use of noninsulin therapies. Episodes of MDD in individuals with diabetes are likely to last longer and have a higher chance of recurrence compared to those without diabetes (12).
NES can result in weight gain, poor glycemic control and an increased number of diabetic complications (44). The main impact on lipid profile is an increase in triglyceride and total cholesterol levels, especially with clozapine, olanzapine and quetiapine (1,53).
No evidence presently shows that the combination of CBT and medication is superior to these treatments given individually.
The H-L test compares the observed and predicted number of patients within specified risk groups, usually deciles of risk.
Table 4 shows the SNPs included in genetic risk scores in the studies summarized in Table 1 and the SNPs used by three commercial companies to predict T2D risk.
NRI is the sum of the net correct moves: the proportion of cases moving up minus the proportion of cases moving down, plus the proportion of non-cases moving down minus the proportion of non-cases moving up.
Many physicians delay initiation of insulin until absolutely necessary, sometimes overestimating patient concerns about its use. A pilot study of 50 patients with type 2 diabetes who initially had a moderate level of depression at baseline showed an improvement in the severity of their depression (moving to the mild range) with a 12-week intervention of 10 CBT sessions combined with exercise in the form of 150 minutes of aerobic activity weekly.
Handbooks are available that allow clinicians to quickly review the major side effect profiles of psychiatric medications (108,109). The other companies do not specify on their websites which SNPs they use for T2D risk prediction.
Modern insulin analogs, treatment regimens, and delivery devices make insulin more user friendly, and physicians can promote patient acceptance of insulin by reviewing the benefits of controlled glycated hemoglobin levels and addressing patient concerns.
For most companies, algorithms or criteria for interpreting SNP results are not made clear to the consumer.
Obviously that happens in individuals with defined Biophysical Semeiotics Constitutions, in our case, Diabetic “and” Dislipidaemic, according to Joslin(1-6, 12-15). Given that the person with diabetes carries out 95% of diabetes management (65) , identifying depressive syndromes in diabetes is important since depression is a risk factor for poor diabetes self-management (66–68) and outcomes, including early mortality (69,70). Even when this information is made available, [24][25][26] it is sometimes difficult to know which effect sizes and genotype frequencies are used to calculate a composite risk. To realize on vast scale Diabetes both Pre-Primary, and Primary Prevention (PP),enrolling exclusively individuals at type 2 DM Inherited Real Risk, we need new clinical tools, aiming to lower the increasing number of patients, because the present, expensive screening has failed (14).
As a result, no clinically defined risk categories exist that can be applied across different populations where the underlying risk of T2D varies and, therefore, the cut-off values chosen to define the risk groups differ among studies. For instance, in the normal Langheran’s islets microcirculatory bed, there are exclusively “normal” type II (= in arterioles, according to Hammersen), but not type I (= in small arterioles) endoarteriolar blocking devices, i.e. This is an important aspect in the interpretation of reclassification measures, as the choice of cut-off has a high impact on the percentage of reclassification observed. In addition, the evaluation of Insulin Secretion Acute Pick Renal Test is significantly impaired, corroborating the clinical diagnosis (1-3) (See above cited- website, Practical Applications, and Glossary). Considering the frequent association between hypertension and diabetes, more important, in my opinion based on 53-year-long clinical experience, is bedside recognizing diabetic predisposition, now-a-days possible since birth, utilising a lot of methods, different in difficulty, but all reliable.
For the first time, from the clinical view-point, I have recently illustrated an original manoeuvre, based on a singular activity of osteocalcin, and reliable in bedside detecting diabetes in one minute, with the aid of a stethoscope (10). In fact, osteocalcin, a product of osteoblasts, among other action mechanisms, stimulates both insulin secretion and insulin receptor sensitivity. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism. Improvement of risk prediction by genomic profiling: reclassification measures versus the area under the receiver operating characteristic curve. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction.
Post genome-wide association studies of novel genes associated with type 2 diabetes show gene-gene interaction and high predictive value. Predicting type 2 diabetes based on polymorphisms from genome-wide association studies: a population-based study. Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study. Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of gene-gene interaction.
Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study.
Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Identification of undiagnosed type 2 diabetic individuals by the finnish diabetes risk score and biochemical and genetic markers: a population-based study of 7232 Finnish men. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association.
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey.

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