Biomarker-based predictive model for calcific aortic valve disease

Abstract

Abstract:

ObjectiveTo establish a new biomarker predictive model for calcific aortic valve disease (CAVD) by analyzing the relationship between new serum markers and CAVD.MethodsThe peripheral blood and clinical data of 446 in-patients in the department of cardiology were collected. The patients were randomly divided into a screening cohort (n=202) and a verification cohort(n=244) by SPSS20.0. The enzyme-linked immunosorbent assay was used to detect the serum concentrations of glucagon-like peptide 1(GLP-1) and osteopontin (OPN). By analyzing the relationship between multiple factors and clinical variables in the screening cohort and CAVD, and verifying it in the verification cohort, the CAVD predictive model was constructed.ResultsThe binary regression analysis showed that multiple factors in the screening cohort were significantly related to CAVD. Based on it, it was determined that high density lipoprotein cholesterol (HDL-C), GLP-1 and OPN could be included in the predictive model, and a nomogram was drawn. Through Pass C statistics [screening cohort: 0.73(95% CI: 0.66-0.80), verification cohort 0.70 (95% CI: 0.64-0.77)] and Hosmer-Lemeshow test (screening cohort:P=0.14, verification cohort:P=0.23), it was found that the discrimination and consistency of the model in the screening and validation cohorts performed well. Decision curve analysis indicated that the biomarker model had a higher net benefit than that in the clinical factor model. In addition, the model showed nice discriminating ability in different gender and age groups.ConclusionsGLP-1, OPN and HDL-C levels are significantly correlated with CAVD. Based on it, a CAVD predictive model which performed well in discriminating ability and accuracy was successfully constructed and verified, and provided the possibility for CAVD prediction.

Key words:Calcific aortic valve disease,Biomarker,Predictive model,Glucagon-like peptide-1,Osteopontin

CLC Number:

R542.5

YANG Ling, ZHA Qing, ZHANG Qianru, YE Jiawen, YANG Ke, LIU Yan. Biomarker-based predictive model for calcific aortic valve disease[J]. Journal of Internal Medicine Concepts & Practice, 2022, 17(04): 324-329.

Figures/Tables7

变量	β	SE	Wald χ²	OR（95%CI）	P
年龄	0.228	0.032	50.054	1.26（1.18~1.34）	0.000
吸烟	1.099	0.490	5.038	3.00（1.15~7.84）	0.025
HDL-C	-1.551	0.674	5.290	0.21（0.06~0.80）	0.021
GLP-1	-0.062	0.027	5.390	0.94（0.89~0.99）	0.020
OPN	0.000 12	0.000 048	5.780	0.999 8（0.999 7~0.999 9）	0.016

指标	非CAVD（n=99）	CAVD（n=103）	t/U/χ²	P
男性[n（%）]	54（54.5）	64（62.1）	1.197	0.274
年龄（岁）	60.19±8.24	73.44±7.65	-11.845	0.000
BMI（kg/m²）	24（22,26）	25（23,27）	-1.366	0.172
饮酒[n（%）]	14（14.1）	11（10.7）	0.558	0.455
吸烟[n（%）]	24（24.2）	26（25.2）	0.027	0.869
GGT（U/L）	19（13,35）	21（16,28）	-0.819	0.413
TG（mmol/L）	1.48（1.15,2.10）	1.43（1.02,2.14）	-0.612	0.541
TC（mmol/L）	4.04±1.13	4.02±1.22	-0.119	0.906
HDL-C（mmol/L）	1.14（0.94,1.33）	1.08（0.90,1.24）	-1.670	0.191
LDL-C（mmol/L）	2.34（1.72,2.77）	2.42（1.76,2.92）	-0.479	0.584
空腹血糖（mmol/L）	5.1（4.57,5.84）	5.1（4.70,5.90）	-0.053	0.958
BUN（mmol/L）	5.0（4.2,5.7）	5.6（4.5,6.7）	-2.647	0.008
Cr（umol/L）	73（63,84）	82（71,97）	-4.162	0.000
eGFR[mL/（min·1.73m²）]	85.69±21.22	65.50±23.69	6.370	0.000
糖尿病[n（%）]	35（35.4）	33（32.0）	0.248	0.618
高血压[n（%）]	66（66.7）	79（76.7）	2.508	0.113
CAD[n（%）]	65（65.7）	88（85.4）	10.750	0.001
降糖药物治疗[n（%）]	16（16.2）	13（12.6）	0.515	0.473
服用他汀类药物[n（%）]	75（75.8）	88（85.4）	3.036	0.081
GLP-1（ng/L）	14.6（9.43,20.50）	11.14（7.68,13.84）	-3.543	0.000
OPN（ng/L）	5 374.07（4 248.37,7 030.09）	4 391.5（1 496.13,7 436.65）	-2.650	0.008

指标	非CAVD（n=125）	CAVD（n=119）	t/U/χ²	P
男性[n（%）]	69（55.2）	71（59.7）	0.497	0.481
年龄(岁)	59.26±9.32	74.08±7.92	-13.357	0.000
BMI（kg/m²)	24（23,27）	25（23,28）	-1.090	0.276
饮酒[n（%）]	15（12.0）	7（5.9）	2.781	0.095
吸烟[n（%）]	42（33.6）	27（22.7）	3.578	0.059
GGT（U/L）	20（14,33）	21（14,30）	-0.147	0.883
TG（mmol/L）	1.44（1.12,2.08）	1.21（0.94,1.77）	-3.213	0.001
TC（mmol/L）	4.20±1.02	3.81±1.10	2.885	0.004
HDL-C（mmol/L）	1.09（0.92,1.29）	1.04（0.91,1.19）	-1.992	0.046
LDL-C（mmol/L）	2.35（1.82,3.08）	2.07（1.65,2.76）	-2.390	0.017
空腹血糖（mmol/L）	4.82（4.49,5.59）	4.94（4.55,5.86）	-0.682	0.495
BUN（mmol/L）	5.1（3.9,6.1）	5.9（4.7,7.0）	-3.451	0.000
Cr（μmol/L）	72（62,85）	82（69,96）	-3.967	0.000
eGFR[mL/（min·1.73m²）]	87.71±28.09	62.62±18.19	8.237	0.000
糖尿病[n（%）]	40（32.0）	33（27.7）	0.530	0.467
高血压[n（%）]	78（62.9）	91（76.5）	5.278	0.022
CAD[n（%）]	93（74.4）	104（87.4）	6.620	0.010
降糖药物治疗[n（%）]	24（19.2）	15（12.6）	1.974	0.160
他汀类药物[n（%）]	109（87.2）	94（79.0）	2.938	0.087
GLP-1（ng/L）	14.58（9.82,18.74）	11.82（7.62,15.96）	-3.108	0.003
OPN（ng/L）	5 242.36（3 732.77,6799.75）	3 941.64（1 469.97,7067.90）	-2.937	0.003

分组	C统计量	95%CI
男性组	0.79	0.70~0.88
女性组	0.64	0.52~0.76
青中年组（年龄<65岁）	0.80	0.67~0.94
老年组（年龄≥65岁）	0.73	0.65~0.81

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