Patients population

Among 380 consecutive non-diabetic patients with AF who underwent radiofrequency ablation, 65 patients who met the major exclusion criteria and 40 patients lost to follow-up were excluded (Fig. 1). Consequently, a total of 275 AF patients (29.1% persistent AF and 70.9% paroxysmal AF) were entered into the analysis. The baseline demographic characteristics, demographical laboratory data, and procedural details of both cohorts are summarized in Table 1. The mean patient age was 57.32 ± 9.57 years, and 69.4% were male. After a median follow-up time of 26.1 months, late recurrence of AF was observed in 70 patients (25.5%). We observed late AF recurrence in 24 patients with persistent AF and 46 patients with paroxysmal AF. As was shown in Table 1, patients with late AF recurrence exhibited larger LA diameter (LAD) (42.4 ± 4.6 versus 37.7 ± 4.1 mm; p = 0.001), longer AF duration (79.43 ± 65.7 versus 60.20 ± 58.27 months; p = 0.04), older age( 64.38 ± 8.04 versus 55.07 ± 8.93 years; p < 0.001), higher number of patients with long-standing persistent AF ( 21.4% versus 8.3%, p = 0.010), more patients with early AF recurrence ( 70% versus 12.7%; p = 0.001), more patients with hypertension (62.8% versus 46.3%; p = 0.02),a higher prevalence of amiodarone treatment (54.3% versus 17.6%; p = 0.01), higher CHA2DS2-VASc score (p = 0.02), as well as higher APPLE (p = 0.01) and DR-FLASH (p = 0.01) scores, compared to patients without late AF recurrence (Table 1). Moreover, the mean value of the TyG index (9.42 ± 0.6 versus 8.68 ± 0.70; p < 0.001), high sensitivity C-reactive protein (hs-CRP) (p < 0.001), and N-terminal pro B-type natriuretic peptide (NT-proBNP) (483 ± 411 versus 237 ± 205 pg/mL; p = 0.005) were significantly greater in patients with late AF recurrence compared to those without late AF recurrence (Table 1). Furthermore, the patients were stratified into three groups according to the value of pre-ablation TyG, as described above, and subgroup analysis showed that patients in T3 group ( tertile 3) had a higher rate of late AF recurrence than those in T1 group ( tertile 1) (54% versus 12%, p < 0.001) (Fig. 2). Likewise, patients with a higher TyG index (tertile 3) tended to be older (60.6 ± 10.4 versus 55.1 ± 7.5 years; p = 0.001), had elevated BMI (26.3 ± 3.1versus 25.2 ± 2.0 kg/m2, p = 0.027), enlarged LAD (41.9 ± 4.8 versus 38.1 ± 4.7 mm; p = 0.035), higher hs-CRP level ( 4.8 ± 4.9 versus 2.4 ± 1.6 mg/L; p = 0.001), higher NT-proBNP level ( 368.2 ± 379.0 versus 245.7 ± 282.7 pg/mL; p = 0.001), higher APPLE score (p = 0.01), as well as higher DR-FLASH (p = 0.01) and CHA2DS2-VASc (p = 0.025) scores, compared to those in the first tertile (tertile 1) (Table 2).

Fig. 1
figure 1

The flow chart of the present study

Table 1 Clinical characteristics of study population
Fig. 2
figure 2

Percentage of the patients developing late AF recurrence post-ablation stratified by tertiles of pre-ablation triglyceride-glucose (TyG) index

Table 2 Clinical characteristics of AF patients according to tertiles of TyG index

Comparison of TyG index

As was shown in Fig. 3, the TyG index was significantly higher in patients with late AF recurrence compared to patients without late AF recurrence (medians: 9.55 vs 8.75; p < 0.001). Moreover, the TyG index was elevated in patients with LAD > 40 mm, compared to patients with LAD < 40 mm (medians: 9.26 vs 8.95; p = 0.010). Similarly, the TyG index was elevated in the obese patients compared to non-obese patients (medians: 9.25 vs 8.97; p = 0.011).

Fig. 3
figure 3

Comparison of triglyceride-glucose index. Box plots represent median levels with 25th and 75th percentiles of the value of TyG index

Prediction of late AF recurrence using clinical variables

Univariate Cox proportional hazards regression analysis showed that older age (> 65 years), LA diameter, AF type (persistent AF), AF history > 5 years, early AF recurrence, TyG index, LDL-C level, hs-CRP level, NT-proBNP level, CHA2DS2-VASc score, APPLE score and DR-FLASH score, were significantly associated with late AF recurrence ( all of the variables, p < 0.05) (Table 3). Multivariate Cox regression analysis confirmed that TyG index (HR:2.015, 95% CI: 1.408–4.117, p = 0.009), LA diameter (HR:3.514, 95% CI: 2.083–5.929, p = 0.001), older age(> 65 years) (HR: 1.165,95% CI: 1.013–1.340, p = 0.032), early AF recurrence (HR: 1.093, 95% CI: 1.001–1.193, p = 0.042), APPLE score (HR: 1.697, 95% CI: 1.116–2.581, p = 0.010) and DR-FLASH score (HR: 1.387, 95% CI: 1.052–1.830, p = 0.021), were significantly associated with late AF recurrence (Table 3). According to the ROC curve analysis, the TyG index was a significant predictor of late AF recurrence (AUC = 0.737, 95% CI: 0.657–0.816; p < 0.001). Additionally, LAD, CHA2DS2-VASc score, APPLE score, and DR-FLASH score were also significant predictors of late AF recurrence after RFCA (LAD: AUC = 0.780, 95%CI:0.703–0.857, p < 0.001;APPLE score: AUC = 0.752, 95% CI:0.675–0.830, p < 0.001; DR-FLASH score: AUC = 0.797, 95% CI:0.723–0.871, p < 0.001; CHA2DS2-VASc score: AUC = 0.624, 95% CI:0.533–0.715, p = 0.006, respectively) (Fig. 4). The cutoff value for the TyG index was 9.24 based on the ROC analysis, and the corresponding sensitivity and specificity were 89.1% and 57.3%, respectively. Kaplan–Meier analyses revealed that patients in highest tertile of TyG index (T3) presented lower event-free survival, compared to those in the first tertile (p < 0.001 by log-rank test, Fig. 4). In this case, the TyG index may be a reliable predictor for late AF recurrence after RFCA, similar to traditional risk factors, such as old age, LAD, APPLE, and DR-FLASH scores.

Table 3 Univariate and multivariate Cox proportional hazards regression analysis of late AF recurrence
Fig. 4
figure 4

Receiver operating characteristic curve (ROC) of triglyceride-glucose index for predictor of late recurrence of atrial fibrillation after RFCA; Event-free survival analyses according to three tertiles of pre-ablation TyG index

Correlations between TyG index and key cardiac variables and blood biomarkers

In Spearman correlation analyses, an elevated TyG index was positively correlated with LAD (r = 0.133, p = 0.027), hs-CRP (r = 0.132, p = 0.028) and NT-proBNP (r = 0.291, p < 0.001) (Fig. 5) in non-diabetic patients. However, the correlation coefficients were low, and studies with larger sample sizes are required to validate our results.

Fig. 5
figure 5

Correlation between LAD, hs-CRP, NT-proBNP and pre-ablation TyG index. a TyG index is positively correlated with LAD (r = 0.133, p = 0.027); b TyG index is positively correlated with hs-CRP level (r = 0.132, p = 0.028); c TyG index is positively correlated with NT-proBNP level (r = 0.291, p < 0.001)

Value of the TyG index according to prognostic risk scoring system

In this study, we found that various prognostic risk scoring systems (APPLE, DR-FLASH and CHA2DS2-VASc scores) were closely associated with late AF recurrence after RFCA. Furthermore, we investigated the TyG index distribution according to those risk scoring systems. We found that the value of TyG index significantly increased as the mentioned scores elevated: (i) APPLE score (median TyG index 9.65 in APPLE score of 4 points versus 8.80 in APPLE score of 0 points; p < 0.001); (ii) DR-FLASH score: median TyG index 9.52 in DR-FLASH score of 5 points versus 8.70 in DR-FLASH score of 0 points; p = 0.001); iii) CHA2DS2-VASc score: median TyG index 9.36 in CHA2DS2-VASc score > 3 points versus median 8.89 in CHA2DS2-VASc score of 0 points; p = 0.015) (Fig. 6).

Fig. 6
figure 6

Box plot representing the median value of pre-ablation TyG index at increasing APPLE, DR-FLASH and CHA2DS2-VASc scores. To compare the value of TyG index according to each of the APPLE, DR-FLASH and CHA2DS2-VASc scores, ANOVA test was used. ***p < 0.001: the median value of TyG index in the APPLE score of 4 points versus APPLE score of 0 points; ***p = 0.001: the median value of TyG index in the DR-FLASH score of 5 points versus DR-FLASH score of 0 points; the median value of TyG index in the CHA2DS2-VASc score of > 3 points versus CHA2DS2-VASc score of 0 points indicated p = 0.015

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Disclaimer:

This article is autogenerated using RSS feeds and has not been created or edited by OA JF.

Click here for Source link (https://www.biomedcentral.com/)