Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes
Background: Peripheral arterial disease (PAD) is known to be strongly linked to major adverse limb events, ultimately leading to an increased risk of limb-threatening conditions. We developed a predictive model using five identified biomarkers to predict major adverse limb events, limb loss, diabeti...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Diabetology |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4540/6/1/2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588691444334592 |
|---|---|
| author | Kian Draper Ben Li Muzammil Syed Farah Shaikh Abdelrahman Zamzam Batool Jamal Abuhalimeh Kharram Rasheed Houssam K. Younes Rawand Abdin Mohammad Qadura |
| author_facet | Kian Draper Ben Li Muzammil Syed Farah Shaikh Abdelrahman Zamzam Batool Jamal Abuhalimeh Kharram Rasheed Houssam K. Younes Rawand Abdin Mohammad Qadura |
| author_sort | Kian Draper |
| collection | DOAJ |
| description | Background: Peripheral arterial disease (PAD) is known to be strongly linked to major adverse limb events, ultimately leading to an increased risk of limb-threatening conditions. We developed a predictive model using five identified biomarkers to predict major adverse limb events, limb loss, diabetic (DM) foot ulcers, and vascular intervention in patients with underlying PAD and DM over 2 years. Methods: A single-center prospective case control study with was conducted with 2 years’ follow up. In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. Demographic data were analyzed by independent <i>t</i>-test and chi-square test. The importance of predictive features was calculated using the variable importance (gain) score. The model was used and assessed for its ability to diagnose PAD, predict limb loss, predict major adverse limb events (MALEs), predict diabetic foot ulcers, and predict the need for vascular surgery. The model was evaluated using area under the receiver operating characteristic curve and net reclassification index. Results: The cohort of 392 patients was matched for age, sex, and comorbidities. Five proteins were identified (TNFa: tumor necrosis factor alpha, BMP-10: bone morphogenic protein 10, CCL15/MIP1 delta: chemokine (c-c motif) ligand 15/macrophage inflammatory protein 1 delta, MMP-10: matrix metalloprotease 10, and HTRA2/Omi: HTRA2, also known as Omi) as having a significantly higher level of expression in the DM PAD group. HTRA/Omi had the highest contribution to the model’s ability to diagnose PAD in diabetic patients. Model performance was best when combined with clinical characteristics to predict limb loss (AUROC 0.86, 0.76, 0.80), foot ulcer (AUROC 0.87, 0.82, 0.67), MALE (AUROC 0.81, 0.78, 0.67), and the need for vascular surgery (AUROC 0.82, 0.81, 0.61). Conclusions: In this study, we describe a biomarker panel that can be used in combination with clinical characteristics to create an accurate prediction model for diagnosis and prognostication of PAD in the setting of DM. |
| format | Article |
| id | doaj-art-3f26605d6b494c1ea43bc94633dc9d09 |
| institution | Kabale University |
| issn | 2673-4540 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diabetology |
| spelling | doaj-art-3f26605d6b494c1ea43bc94633dc9d092025-01-24T13:28:45ZengMDPI AGDiabetology2673-45402024-12-0161210.3390/diabetology6010002Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with DiabetesKian Draper0Ben Li1Muzammil Syed2Farah Shaikh3Abdelrahman Zamzam4Batool Jamal Abuhalimeh5Kharram Rasheed6Houssam K. Younes7Rawand Abdin8Mohammad Qadura9Department of Surgery, University of Toronto, Toronto, ON M5T 1P5, CanadaDepartment of Surgery, University of Toronto, Toronto, ON M5T 1P5, CanadaDepartment of Surgery, University of Toronto, Toronto, ON M5T 1P5, CanadaCleveland Clinic Abu Dhabi, Department of Vascular Surgery Heart and Vascular Institute, Abu Dhabi P.O. Box 112412, United Arab EmiratesDivision of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, 30 Bond Street, Suite 7-076, Toronto, ON M5B 1W8, CanadaCleveland Clinic Abu Dhabi, Department of Vascular Surgery Heart and Vascular Institute, Abu Dhabi P.O. Box 112412, United Arab EmiratesCleveland Clinic Abu Dhabi, Department of Vascular Surgery Heart and Vascular Institute, Abu Dhabi P.O. Box 112412, United Arab EmiratesCleveland Clinic Abu Dhabi, Department of Vascular Surgery Heart and Vascular Institute, Abu Dhabi P.O. Box 112412, United Arab EmiratesDepartment of Medicine, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Surgery, University of Toronto, Toronto, ON M5T 1P5, CanadaBackground: Peripheral arterial disease (PAD) is known to be strongly linked to major adverse limb events, ultimately leading to an increased risk of limb-threatening conditions. We developed a predictive model using five identified biomarkers to predict major adverse limb events, limb loss, diabetic (DM) foot ulcers, and vascular intervention in patients with underlying PAD and DM over 2 years. Methods: A single-center prospective case control study with was conducted with 2 years’ follow up. In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. Demographic data were analyzed by independent <i>t</i>-test and chi-square test. The importance of predictive features was calculated using the variable importance (gain) score. The model was used and assessed for its ability to diagnose PAD, predict limb loss, predict major adverse limb events (MALEs), predict diabetic foot ulcers, and predict the need for vascular surgery. The model was evaluated using area under the receiver operating characteristic curve and net reclassification index. Results: The cohort of 392 patients was matched for age, sex, and comorbidities. Five proteins were identified (TNFa: tumor necrosis factor alpha, BMP-10: bone morphogenic protein 10, CCL15/MIP1 delta: chemokine (c-c motif) ligand 15/macrophage inflammatory protein 1 delta, MMP-10: matrix metalloprotease 10, and HTRA2/Omi: HTRA2, also known as Omi) as having a significantly higher level of expression in the DM PAD group. HTRA/Omi had the highest contribution to the model’s ability to diagnose PAD in diabetic patients. Model performance was best when combined with clinical characteristics to predict limb loss (AUROC 0.86, 0.76, 0.80), foot ulcer (AUROC 0.87, 0.82, 0.67), MALE (AUROC 0.81, 0.78, 0.67), and the need for vascular surgery (AUROC 0.82, 0.81, 0.61). Conclusions: In this study, we describe a biomarker panel that can be used in combination with clinical characteristics to create an accurate prediction model for diagnosis and prognostication of PAD in the setting of DM.https://www.mdpi.com/2673-4540/6/1/2peripheral arterial diseasediabetesbiomarkers |
| spellingShingle | Kian Draper Ben Li Muzammil Syed Farah Shaikh Abdelrahman Zamzam Batool Jamal Abuhalimeh Kharram Rasheed Houssam K. Younes Rawand Abdin Mohammad Qadura Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes Diabetology peripheral arterial disease diabetes biomarkers |
| title | Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes |
| title_full | Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes |
| title_fullStr | Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes |
| title_full_unstemmed | Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes |
| title_short | Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes |
| title_sort | identification of inflammatory biomarkers for predicting peripheral arterial disease prognosis in patients with diabetes |
| topic | peripheral arterial disease diabetes biomarkers |
| url | https://www.mdpi.com/2673-4540/6/1/2 |
| work_keys_str_mv | AT kiandraper identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT benli identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT muzammilsyed identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT farahshaikh identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT abdelrahmanzamzam identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT batooljamalabuhalimeh identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT kharramrasheed identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT houssamkyounes identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT rawandabdin identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes AT mohammadqadura identificationofinflammatorybiomarkersforpredictingperipheralarterialdiseaseprognosisinpatientswithdiabetes |