BACKGROUND: Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality. METHODS: We completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population. RESULTS: Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals. CONCLUSIONS: Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.
BACKGROUND: Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality. METHODS: We completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population. RESULTS: Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals. CONCLUSIONS: Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.
Authors: A J Moss; R E Goldstein; V J Marder; C E Sparks; D Oakes; H Greenberg; H J Weiss; W Zareba; M W Brown; C S Liang; E Lichstein; W C Little; J A Gillespie; L Van Voorhees; R J Krone; M M Bodenheimer; J Hochman; E M Dwyer; R Arora; F I Marcus; L F Watelet; R B Case Journal: Circulation Date: 1999-05-18 Impact factor: 29.690
Authors: Michael E Burleigh; Vladimir R Babaev; John A Oates; Raymond C Harris; Shiva Gautam; Denis Riendeau; Lawrence J Marnett; Jason D Morrow; Sergio Fazio; MacRae F Linton Journal: Circulation Date: 2002-04-16 Impact factor: 29.690
Authors: M M McDermott; P Greenland; K Liu; J M Guralnik; M H Criqui; N C Dolan; C Chan; L Celic; W H Pearce; J R Schneider; L Sharma; E Clark; D Gibson; G J Martin Journal: JAMA Date: 2001-10-03 Impact factor: 56.272
Authors: G Rioufol; G Finet; I Ginon; X André-Fouët; R Rossi; E Vialle; E Desjoyaux; G Convert; J F Huret; A Tabib Journal: Circulation Date: 2002-08-13 Impact factor: 29.690
Authors: Philip Greenland; Maria Deloria Knoll; Jeremiah Stamler; James D Neaton; Alan R Dyer; Daniel B Garside; Peter W Wilson Journal: JAMA Date: 2003-08-20 Impact factor: 56.272
Authors: Mary McGrae McDermott; Philip Greenland; Kiang Liu; Jack M Guralnik; Lillian Celic; Michael H Criqui; Cheeling Chan; Gary J Martin; Joseph Schneider; William H Pearce; Lloyd M Taylor; Elizabeth Clark Journal: Ann Intern Med Date: 2002-06-18 Impact factor: 25.391
Authors: Maksim Zagura; Jaak Kals; Kalle Kilk; Martin Serg; Priit Kampus; Jaan Eha; Ursel Soomets; Mihkel Zilmer Journal: Hypertens Res Date: 2015-07-02 Impact factor: 3.872
Authors: Margaux M Luck; Laurence Le Moyec; Eric Barrey; Mohamed N Triba; Nadia Bouchemal; Philippe Savarin; Céline Robert Journal: Front Physiol Date: 2015-07-15 Impact factor: 4.566
Authors: Ahmed Ismaeel; Marco E Franco; Ramon Lavado; Evlampia Papoutsi; George P Casale; Matthew Fuglestad; Constance J Mietus; Gleb R Haynatzki; Robert S Smith; William T Bohannon; Ian Sawicki; Iraklis I Pipinos; Panagiotis Koutakis Journal: J Clin Med Date: 2019-09-14 Impact factor: 4.241