User login
High Breast Cancer Risk With Menopausal Hormone Therapy & Strong Family History
TOPLINE:
These women have a striking cumulative risk of developing breast cancer (age, 50-80 years) of 22.4%, according to a new modelling study of UK women.
METHODOLOGY:
This was a modeling study integrating two data-sets of UK women: the BOADICEA dataset of age-specific breast cancer risk with family history and the Collaborative Group on Hormonal Factors in Breast Cancer, which covers relative risk for breast cancer with different types and durations of MHT.
Four different breast cancer family history profiles were:
- “Average” family history of breast cancer has unknown affected family members;
- “Modest” family history comprises a single first-degree relative with breast cancer at the age of 60 years.
- “Intermediate” family history comprises a single first-degree relative who developed breast cancer at the age of 40 years.
- “Strong” family history comprises two first-degree relatives who developed breast cancer at the age of 50 years.
TAKEAWAY:
- The lowest risk category: “Average” family history with no MHT use has a cumulative breast cancer risk (age, 50-80 years) of 9.8% and a risk of dying from breast cancer of 1.7%. These risks rise with 5 years’ exposure to MHT (age, 50-55 years) to 11.0% and 1.8%, respectively.
- The highest risk category: “Strong” family history with no MHT use has a cumulative breast cancer risk (age, 50-80 years) of 19.6% and a risk of dying from breast cancer of 3.2%. These risks rise with 5 years’ exposure to MHT (age, 50-55 years) to 22.4% and 3.5%, respectively.
IN PRACTICE:
The authors concluded that, “These integrated data will enable more accurate estimates of absolute and attributable risk associated with MHT exposure for women with a family history of breast cancer, informing shared decision-making.”
SOURCE:
The lead author is Catherine Huntley of the Institute of Cancer Research, London, England. The study appeared in the British Journal of General Practice.
LIMITATIONS:
Limitations included modeling study that did not directly measure individuals with combined risks.
DISCLOSURES:
The study was funded by several sources including Cancer Research UK. The authors reported no conflicts of interest.
A version of this article first appeared on Medscape.com.
TOPLINE:
These women have a striking cumulative risk of developing breast cancer (age, 50-80 years) of 22.4%, according to a new modelling study of UK women.
METHODOLOGY:
This was a modeling study integrating two data-sets of UK women: the BOADICEA dataset of age-specific breast cancer risk with family history and the Collaborative Group on Hormonal Factors in Breast Cancer, which covers relative risk for breast cancer with different types and durations of MHT.
Four different breast cancer family history profiles were:
- “Average” family history of breast cancer has unknown affected family members;
- “Modest” family history comprises a single first-degree relative with breast cancer at the age of 60 years.
- “Intermediate” family history comprises a single first-degree relative who developed breast cancer at the age of 40 years.
- “Strong” family history comprises two first-degree relatives who developed breast cancer at the age of 50 years.
TAKEAWAY:
- The lowest risk category: “Average” family history with no MHT use has a cumulative breast cancer risk (age, 50-80 years) of 9.8% and a risk of dying from breast cancer of 1.7%. These risks rise with 5 years’ exposure to MHT (age, 50-55 years) to 11.0% and 1.8%, respectively.
- The highest risk category: “Strong” family history with no MHT use has a cumulative breast cancer risk (age, 50-80 years) of 19.6% and a risk of dying from breast cancer of 3.2%. These risks rise with 5 years’ exposure to MHT (age, 50-55 years) to 22.4% and 3.5%, respectively.
IN PRACTICE:
The authors concluded that, “These integrated data will enable more accurate estimates of absolute and attributable risk associated with MHT exposure for women with a family history of breast cancer, informing shared decision-making.”
SOURCE:
The lead author is Catherine Huntley of the Institute of Cancer Research, London, England. The study appeared in the British Journal of General Practice.
LIMITATIONS:
Limitations included modeling study that did not directly measure individuals with combined risks.
DISCLOSURES:
The study was funded by several sources including Cancer Research UK. The authors reported no conflicts of interest.
A version of this article first appeared on Medscape.com.
TOPLINE:
These women have a striking cumulative risk of developing breast cancer (age, 50-80 years) of 22.4%, according to a new modelling study of UK women.
METHODOLOGY:
This was a modeling study integrating two data-sets of UK women: the BOADICEA dataset of age-specific breast cancer risk with family history and the Collaborative Group on Hormonal Factors in Breast Cancer, which covers relative risk for breast cancer with different types and durations of MHT.
Four different breast cancer family history profiles were:
- “Average” family history of breast cancer has unknown affected family members;
- “Modest” family history comprises a single first-degree relative with breast cancer at the age of 60 years.
- “Intermediate” family history comprises a single first-degree relative who developed breast cancer at the age of 40 years.
- “Strong” family history comprises two first-degree relatives who developed breast cancer at the age of 50 years.
TAKEAWAY:
- The lowest risk category: “Average” family history with no MHT use has a cumulative breast cancer risk (age, 50-80 years) of 9.8% and a risk of dying from breast cancer of 1.7%. These risks rise with 5 years’ exposure to MHT (age, 50-55 years) to 11.0% and 1.8%, respectively.
- The highest risk category: “Strong” family history with no MHT use has a cumulative breast cancer risk (age, 50-80 years) of 19.6% and a risk of dying from breast cancer of 3.2%. These risks rise with 5 years’ exposure to MHT (age, 50-55 years) to 22.4% and 3.5%, respectively.
IN PRACTICE:
The authors concluded that, “These integrated data will enable more accurate estimates of absolute and attributable risk associated with MHT exposure for women with a family history of breast cancer, informing shared decision-making.”
SOURCE:
The lead author is Catherine Huntley of the Institute of Cancer Research, London, England. The study appeared in the British Journal of General Practice.
LIMITATIONS:
Limitations included modeling study that did not directly measure individuals with combined risks.
DISCLOSURES:
The study was funded by several sources including Cancer Research UK. The authors reported no conflicts of interest.
A version of this article first appeared on Medscape.com.
Colorectal Cancer: New Primary Care Method Predicts Onset Within Next 2 Years
TOPLINE:
Up to 16% of primary care patients are non-compliant with FIT, which is the gold standard for predicting CRC.
METHODOLOGY:
- This study was retrospective cohort of 50,387 UK Biobank participants reporting a CRC symptom in primary care at age ≥ 40 years.
- The novel method, called an integrated risk model, used a combination of a polygenic risk score from genetic testing, symptoms, and patient characteristics to identify patients likely to develop CRC in the next 2 years.
- The primary outcome was the risk model’s performance in classifying a CRC case according to a statistical metric, the receiver operating characteristic area under the curve. Values range from 0 to 1, where 1 indicates perfect discriminative power and 0.5 indicates no discriminative power.
TAKEAWAY:
- The cohort of 50,387 participants was found to have 438 cases of CRC and 49,949 controls without CRC within 2 years of symptom reporting. CRC cases were diagnosed by hospital records, cancer registries, or death records.
- Increased risk of a CRC diagnosis was identified by a combination of six variables: older age at index date of symptom, higher polygenic risk score, which included 201 variants, male sex, previous smoking, rectal bleeding, and change in bowel habit.
- The polygenic risk score alone had good ability to distinguish cases from controls because 1.45% of participants in the highest quintile and 0.42% in the lowest quintile were later diagnosed with CRC.
- The variables were used to calculate an integrated risk model, which estimated the cross-sectional risk (in 80% of the final cohort) of a subsequent CRC diagnosis within 2 years. The highest scoring integrated risk model in this study was found to have a receiver operating characteristic area under the curve value of 0.76 with a 95% CI of 0.71-0.81. (A value of this magnitude indicates moderate discriminative ability to distinguish cases from controls because it falls between 0.7 and 0.8. A higher value [above 0.8] is considered strong and a lower value [< 0.7] is considered weak.)
IN PRACTICE:
The authors concluded, “The [integrated risk model] developed in this study predicts, with good accuracy, which patients presenting with CRC symptoms in a primary care setting are likely to be diagnosed with CRC within the next 2 years.”
The integrated risk model has “potential to be immediately actionable in the clinical setting … [by] inform[ing] patient triage, improving early diagnostic rates and health outcomes and reducing pressure on diagnostic secondary care services.”
SOURCE:
The corresponding author is Harry D. Green of the University of Exeter, England. The study (2024 Aug 1. doi: 10.1038/s41431-024-01654-3) appeared in the European Journal of Human Genetics.
LIMITATIONS:
Limitations included an observational design and the inability of the integrated risk model to outperform FIT, which has a receiver operating characteristic area under the curve of 0.95.
DISCLOSURES:
None of the authors reported competing interests. The funding sources included the National Institute for Health and Care Research and others.
A version of this article first appeared on Medscape.com.
TOPLINE:
Up to 16% of primary care patients are non-compliant with FIT, which is the gold standard for predicting CRC.
METHODOLOGY:
- This study was retrospective cohort of 50,387 UK Biobank participants reporting a CRC symptom in primary care at age ≥ 40 years.
- The novel method, called an integrated risk model, used a combination of a polygenic risk score from genetic testing, symptoms, and patient characteristics to identify patients likely to develop CRC in the next 2 years.
- The primary outcome was the risk model’s performance in classifying a CRC case according to a statistical metric, the receiver operating characteristic area under the curve. Values range from 0 to 1, where 1 indicates perfect discriminative power and 0.5 indicates no discriminative power.
TAKEAWAY:
- The cohort of 50,387 participants was found to have 438 cases of CRC and 49,949 controls without CRC within 2 years of symptom reporting. CRC cases were diagnosed by hospital records, cancer registries, or death records.
- Increased risk of a CRC diagnosis was identified by a combination of six variables: older age at index date of symptom, higher polygenic risk score, which included 201 variants, male sex, previous smoking, rectal bleeding, and change in bowel habit.
- The polygenic risk score alone had good ability to distinguish cases from controls because 1.45% of participants in the highest quintile and 0.42% in the lowest quintile were later diagnosed with CRC.
- The variables were used to calculate an integrated risk model, which estimated the cross-sectional risk (in 80% of the final cohort) of a subsequent CRC diagnosis within 2 years. The highest scoring integrated risk model in this study was found to have a receiver operating characteristic area under the curve value of 0.76 with a 95% CI of 0.71-0.81. (A value of this magnitude indicates moderate discriminative ability to distinguish cases from controls because it falls between 0.7 and 0.8. A higher value [above 0.8] is considered strong and a lower value [< 0.7] is considered weak.)
IN PRACTICE:
The authors concluded, “The [integrated risk model] developed in this study predicts, with good accuracy, which patients presenting with CRC symptoms in a primary care setting are likely to be diagnosed with CRC within the next 2 years.”
The integrated risk model has “potential to be immediately actionable in the clinical setting … [by] inform[ing] patient triage, improving early diagnostic rates and health outcomes and reducing pressure on diagnostic secondary care services.”
SOURCE:
The corresponding author is Harry D. Green of the University of Exeter, England. The study (2024 Aug 1. doi: 10.1038/s41431-024-01654-3) appeared in the European Journal of Human Genetics.
LIMITATIONS:
Limitations included an observational design and the inability of the integrated risk model to outperform FIT, which has a receiver operating characteristic area under the curve of 0.95.
DISCLOSURES:
None of the authors reported competing interests. The funding sources included the National Institute for Health and Care Research and others.
A version of this article first appeared on Medscape.com.
TOPLINE:
Up to 16% of primary care patients are non-compliant with FIT, which is the gold standard for predicting CRC.
METHODOLOGY:
- This study was retrospective cohort of 50,387 UK Biobank participants reporting a CRC symptom in primary care at age ≥ 40 years.
- The novel method, called an integrated risk model, used a combination of a polygenic risk score from genetic testing, symptoms, and patient characteristics to identify patients likely to develop CRC in the next 2 years.
- The primary outcome was the risk model’s performance in classifying a CRC case according to a statistical metric, the receiver operating characteristic area under the curve. Values range from 0 to 1, where 1 indicates perfect discriminative power and 0.5 indicates no discriminative power.
TAKEAWAY:
- The cohort of 50,387 participants was found to have 438 cases of CRC and 49,949 controls without CRC within 2 years of symptom reporting. CRC cases were diagnosed by hospital records, cancer registries, or death records.
- Increased risk of a CRC diagnosis was identified by a combination of six variables: older age at index date of symptom, higher polygenic risk score, which included 201 variants, male sex, previous smoking, rectal bleeding, and change in bowel habit.
- The polygenic risk score alone had good ability to distinguish cases from controls because 1.45% of participants in the highest quintile and 0.42% in the lowest quintile were later diagnosed with CRC.
- The variables were used to calculate an integrated risk model, which estimated the cross-sectional risk (in 80% of the final cohort) of a subsequent CRC diagnosis within 2 years. The highest scoring integrated risk model in this study was found to have a receiver operating characteristic area under the curve value of 0.76 with a 95% CI of 0.71-0.81. (A value of this magnitude indicates moderate discriminative ability to distinguish cases from controls because it falls between 0.7 and 0.8. A higher value [above 0.8] is considered strong and a lower value [< 0.7] is considered weak.)
IN PRACTICE:
The authors concluded, “The [integrated risk model] developed in this study predicts, with good accuracy, which patients presenting with CRC symptoms in a primary care setting are likely to be diagnosed with CRC within the next 2 years.”
The integrated risk model has “potential to be immediately actionable in the clinical setting … [by] inform[ing] patient triage, improving early diagnostic rates and health outcomes and reducing pressure on diagnostic secondary care services.”
SOURCE:
The corresponding author is Harry D. Green of the University of Exeter, England. The study (2024 Aug 1. doi: 10.1038/s41431-024-01654-3) appeared in the European Journal of Human Genetics.
LIMITATIONS:
Limitations included an observational design and the inability of the integrated risk model to outperform FIT, which has a receiver operating characteristic area under the curve of 0.95.
DISCLOSURES:
None of the authors reported competing interests. The funding sources included the National Institute for Health and Care Research and others.
A version of this article first appeared on Medscape.com.