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Use of an expanded genetic score in conjunction with the Framingham risk score improved risk prediction for type 2 diabetes, according to findings recently published in the journal Diabetes.
In an analysis of seven cohorts of European patients, a 65-SNP (single nucleotide polymorphism) genetic score alone detected 19.9% of incident cases with a 10% false-positive rate, compared with the Framingham risk model, which detected 30.7% (95% confidence interval, 27.5-33.8) of cases.
When combined, the two measures detected 37.3% (95% CI, 33.9-40.6) of cases, reported Dr. Philippa J. Talmud of the Centre for Cardiovascular Genetics at the Institute of Cardiovascular Science, University College London, and her associates.
The addition of a 65-SNP gene score improved correct classification of patients into higher risk disease categories by 6.2%.
Of the 13,294 patients included in the study, 804 developed type 2 diabetes during the study period. Patients were classified as having type 2 diabetes according to self-report, medical record review, prescription of glucose-lowering drugs, or a recorded fasting glucose of 7 mmol/L or greater, the authors said.
Genotyping was performed using MetaboChip array, which includes nearly 200,000 SNP loci for cardiometabolic disease and their genetic variants.
Area under the curve values for genetic score, Framingham risk model, and combined score were 0.60 (95% CI, 0.58-0.62), 0.75 (95% CI, 0.73-0.77), and 0.76 (95% CI, 0.75-0.78), respectively. The combined risk score net reclassification improvement (NRI) was 8.1% (P = 3.31 × 10–7), the investigators reported (Diabetes 2015;64:1830-40).
The findings suggest that common variants of small effects in combination may help improve risk prediction for type 2 diabetes, Dr. Talmud and her coauthors said.
“For a gene score to be effective, it should improve the reclassification of individuals with [type 2 diabetes] into a more accurate risk category over and above the phenotypic risk score. The 65 SNP–weighted gene score did this,” they said.
“In actual terms, at a 10% [false-positive rate], the combined phenotypic and genetic risk score led to the correct identification of an additional 53 (6.6%) of the 804 cases,” they added.
This slight improvement demonstrates that the addition of a gene score to a phenotyping risk model could be a “potentially clinically important improvement” in disease discrimination and classification, Dr. Talmud and her associates concluded.
The study authors reported several sources of funding, including the British Heart Foundation, the Stroke Association, the National Heart, Lung, and Blood Institute, and others.
Though some at-risk populations may benefit from genetic risk scores in type 2 diabetes, “there are major implementation challenges ahead,” wrote Brendan J. Keating, Ph.D., in an editorial published with the study.
Potential challenges to the integration of genetic risk scores include implementation of clinical genotyping and sequencing; consent between patients, physicians, and the health care system; and the use of clinical decision support models, Dr. Keating noted.
Although type 2 diabetes genetic risk scores “will likely improve incrementally, the clinical utility remains to be determined at national scales, although it is likely that benefits will be reaped by at-risk populations such as lean individuals with [type 2 diabetes] who may not present to primary care with later stages of disease manifestation,” he wrote (Diabetes 2015;64:1495-7).
Dr. Keating is with the division of genetics at the Children’s Hospital of Philadelphia. He did not report any financial disclosures.
Though some at-risk populations may benefit from genetic risk scores in type 2 diabetes, “there are major implementation challenges ahead,” wrote Brendan J. Keating, Ph.D., in an editorial published with the study.
Potential challenges to the integration of genetic risk scores include implementation of clinical genotyping and sequencing; consent between patients, physicians, and the health care system; and the use of clinical decision support models, Dr. Keating noted.
Although type 2 diabetes genetic risk scores “will likely improve incrementally, the clinical utility remains to be determined at national scales, although it is likely that benefits will be reaped by at-risk populations such as lean individuals with [type 2 diabetes] who may not present to primary care with later stages of disease manifestation,” he wrote (Diabetes 2015;64:1495-7).
Dr. Keating is with the division of genetics at the Children’s Hospital of Philadelphia. He did not report any financial disclosures.
Though some at-risk populations may benefit from genetic risk scores in type 2 diabetes, “there are major implementation challenges ahead,” wrote Brendan J. Keating, Ph.D., in an editorial published with the study.
Potential challenges to the integration of genetic risk scores include implementation of clinical genotyping and sequencing; consent between patients, physicians, and the health care system; and the use of clinical decision support models, Dr. Keating noted.
Although type 2 diabetes genetic risk scores “will likely improve incrementally, the clinical utility remains to be determined at national scales, although it is likely that benefits will be reaped by at-risk populations such as lean individuals with [type 2 diabetes] who may not present to primary care with later stages of disease manifestation,” he wrote (Diabetes 2015;64:1495-7).
Dr. Keating is with the division of genetics at the Children’s Hospital of Philadelphia. He did not report any financial disclosures.
Use of an expanded genetic score in conjunction with the Framingham risk score improved risk prediction for type 2 diabetes, according to findings recently published in the journal Diabetes.
In an analysis of seven cohorts of European patients, a 65-SNP (single nucleotide polymorphism) genetic score alone detected 19.9% of incident cases with a 10% false-positive rate, compared with the Framingham risk model, which detected 30.7% (95% confidence interval, 27.5-33.8) of cases.
When combined, the two measures detected 37.3% (95% CI, 33.9-40.6) of cases, reported Dr. Philippa J. Talmud of the Centre for Cardiovascular Genetics at the Institute of Cardiovascular Science, University College London, and her associates.
The addition of a 65-SNP gene score improved correct classification of patients into higher risk disease categories by 6.2%.
Of the 13,294 patients included in the study, 804 developed type 2 diabetes during the study period. Patients were classified as having type 2 diabetes according to self-report, medical record review, prescription of glucose-lowering drugs, or a recorded fasting glucose of 7 mmol/L or greater, the authors said.
Genotyping was performed using MetaboChip array, which includes nearly 200,000 SNP loci for cardiometabolic disease and their genetic variants.
Area under the curve values for genetic score, Framingham risk model, and combined score were 0.60 (95% CI, 0.58-0.62), 0.75 (95% CI, 0.73-0.77), and 0.76 (95% CI, 0.75-0.78), respectively. The combined risk score net reclassification improvement (NRI) was 8.1% (P = 3.31 × 10–7), the investigators reported (Diabetes 2015;64:1830-40).
The findings suggest that common variants of small effects in combination may help improve risk prediction for type 2 diabetes, Dr. Talmud and her coauthors said.
“For a gene score to be effective, it should improve the reclassification of individuals with [type 2 diabetes] into a more accurate risk category over and above the phenotypic risk score. The 65 SNP–weighted gene score did this,” they said.
“In actual terms, at a 10% [false-positive rate], the combined phenotypic and genetic risk score led to the correct identification of an additional 53 (6.6%) of the 804 cases,” they added.
This slight improvement demonstrates that the addition of a gene score to a phenotyping risk model could be a “potentially clinically important improvement” in disease discrimination and classification, Dr. Talmud and her associates concluded.
The study authors reported several sources of funding, including the British Heart Foundation, the Stroke Association, the National Heart, Lung, and Blood Institute, and others.
Use of an expanded genetic score in conjunction with the Framingham risk score improved risk prediction for type 2 diabetes, according to findings recently published in the journal Diabetes.
In an analysis of seven cohorts of European patients, a 65-SNP (single nucleotide polymorphism) genetic score alone detected 19.9% of incident cases with a 10% false-positive rate, compared with the Framingham risk model, which detected 30.7% (95% confidence interval, 27.5-33.8) of cases.
When combined, the two measures detected 37.3% (95% CI, 33.9-40.6) of cases, reported Dr. Philippa J. Talmud of the Centre for Cardiovascular Genetics at the Institute of Cardiovascular Science, University College London, and her associates.
The addition of a 65-SNP gene score improved correct classification of patients into higher risk disease categories by 6.2%.
Of the 13,294 patients included in the study, 804 developed type 2 diabetes during the study period. Patients were classified as having type 2 diabetes according to self-report, medical record review, prescription of glucose-lowering drugs, or a recorded fasting glucose of 7 mmol/L or greater, the authors said.
Genotyping was performed using MetaboChip array, which includes nearly 200,000 SNP loci for cardiometabolic disease and their genetic variants.
Area under the curve values for genetic score, Framingham risk model, and combined score were 0.60 (95% CI, 0.58-0.62), 0.75 (95% CI, 0.73-0.77), and 0.76 (95% CI, 0.75-0.78), respectively. The combined risk score net reclassification improvement (NRI) was 8.1% (P = 3.31 × 10–7), the investigators reported (Diabetes 2015;64:1830-40).
The findings suggest that common variants of small effects in combination may help improve risk prediction for type 2 diabetes, Dr. Talmud and her coauthors said.
“For a gene score to be effective, it should improve the reclassification of individuals with [type 2 diabetes] into a more accurate risk category over and above the phenotypic risk score. The 65 SNP–weighted gene score did this,” they said.
“In actual terms, at a 10% [false-positive rate], the combined phenotypic and genetic risk score led to the correct identification of an additional 53 (6.6%) of the 804 cases,” they added.
This slight improvement demonstrates that the addition of a gene score to a phenotyping risk model could be a “potentially clinically important improvement” in disease discrimination and classification, Dr. Talmud and her associates concluded.
The study authors reported several sources of funding, including the British Heart Foundation, the Stroke Association, the National Heart, Lung, and Blood Institute, and others.
FROM DIABETES
Key clinical point: When combined with the Framingham risk score, use of an expanded gene score improved risk prediction for type 2 diabetes.
Major finding: With a 10% false-positive rate, a 65-SNP genetic score alone detected 19.9% of incident cases, compared with the Framingham risk model, which detected 30.7% (95% CI; 27.5-33.8) of cases; when combined, the two measures detected 37.3% (95% CI; 33.9-40.6) of cases.
Data source: An analysis of 13,294 European patients, 804 of whom developed type 2 diabetes over the course of study.
Disclosures: The study authors reported several sources of funding, including the British Heart Foundation, the Stroke Association, the National Heart, Lung, and Blood Institute, and others.