ABSTRACT
Objective:To investigate the metabolic profile of fatty acids in elderly frail patients, and its value as a biomarker of frailty.Methods:Forty-nine older adults were recruited, of whom 19 were non-frail while 15 were in the pre-frail or frail phase.Targeted metabolomics was used to detect the serum levels of fatty acids, concerning 38 short-, medium-and long-chain fatty acids.Results:Metabolomics indicated elevated levels of 9 long-chain fatty acids in the serum of the elderly frail patients, with a 1.056-fold increase in the risk of fatigue for every 1 unit increase in the level of HOMO-γ-linolenic acid( OR=2.056, P=0.016). No metabolic differences were found between the pre-frail and non-frail groups.Three and seven long-chain fatty acids were negatively correlated with the grip strength and gait speed, respectively.The γ-linolenic acid was positively correlated with body mass index(BMI), percent body fat, visceral fat area and other indicators reflecting adipose tissue.However, no correlation was found between skeletal muscle, laboratory indicators or fatty acids.Five metabolic pathways were correlated with frailty, namely fatty acid biosynthesis, fatty acid metabolism, fatty acid elongation in mitochondria, linoleic acid metabolism and α-linolenic acid metabolism. Conclusions:Nine unsaturated fatty acids, including HOMO-γ-linolenic acid and γ-linolenic acid, may be potential biomarkers of frailty in the elderly.However, the value of fatty acid metabolomics for identifying pre-frail elderly people needs to be further investigated.
ABSTRACT
Objective:To construct a risk prediction score for the needs of coronary care unit (CCU) care in stable condition acute ST-segment elevation myocardial infarction (STEMI) patients who receive percutaneous coronary intervention (PCI) treatment.Methods:The clinical data of 805 STEMI patients who accepted PCI in the First Hospital of Jilin University from November 2017 to October 2018 were retrospectively analyzed. Among the patients, 654 patients from November 2017 to July 2018 were served as the modeling group, the patients with needs of CCU had 125 cases, and the patients without needs of CCU had 529 cases; 151 patients from August 2018 to October 2018 were served as the validation group, the patients with needs of CCU had 28 cases, and the patients without needs of CCU had 123 cases. Binary Logistic regression analysis was used to establish the risk prediction model and determine the score standards. The critical value was determined according to the best Youden index of receiver operating characteristic (ROC) curve.Results:Among 805 patients with STEMI, 153 cases (19.01%) had the needs of CCU, and the most common reason was pump failure (heart failure and cardiogenic shock, 113 cases). In the modeling group, age (60 to 74 years old, OR = 1.513, 95% CI 0.945 to 2.424, P = 0.085; ≥75 years old, OR = 2.740, 95% CI 1.371 to 5.478, P = 0.004), total ischemic time>4 h ( OR = 1.701, 95% CI 1.022 to 2.831, P = 0.041), admission shock index ≥0.8 ( OR = 1.910, 95% CI 1.178 to 3.099, P = 0.009), multi-vessel disease ( OR = 2.090, 95% CI 1.272 to 3.432, P = 0.004), preoperative diseased vessels thrombolysis in myocardial ischemia (TIMI) blood flow grade 0 ( OR = 2.099, 95% CI 1.313 to 3.353, P = 0.002), acute anterior myocardial infarction ( OR = 3.696, 95% CI 2.347 to 5.819, P<0.001) and previous history of stroke ( OR = 3.927, 95% CI 2.057 to 7.500, P<0.001) were independent risk factors for CCU needs in STEMI patients undergoing PCI. The scoring criteria were as followings: age<60 years old was given 0 score, 60 to 74 years old 1 score, ≥75 years old 2 score; total ischemic time>4 h in 1 score, admission shock index ≥0.8 2 scores, multi-vessel disease 2 scores, preoperative diseased vessels TIMI blood flow grade 0 2 scores, acute anterior myocardial infarction 3 scores, previous history of stroke 3 scores, and the total score was 15 scores. The patients with 0 to 6 scores were low-risk, and the patients with 7 to 15 scores were high-risk. ROC curve analysis result showed that, in modeling group, the area under curve (AUC) of risk prediction score for predicting the needs of CCU in STEMI patients was 0.740 (95% CI 0.692 to 0.788, P = 0.580); in validation group, the AUC of risk prediction score for predicting the needs of CCU in STEMI patients was 0.755 (95% CI 0.658 to 0.853, P = 0.755). Conclusions:A predictive risk score based on seven risk factors such as age, total ischemic time, admission shock index, multi-vessel disease, preoperative diseased vessels TIMI blood flow grade, acute anterior myocardial infarction and previous history of stroke is constructed in order to predict the needs of CCU in STEMI patients with stable condition who receive PCI treatment. It can be used to help doctors to identify high-risk patients before the admission to CCU, thus providing simple and practical clinical tool for rational allocation of limited CCU resources.