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10.1371/journal.pone.0309758
PONE-D-24-19204
Research Article
Medicine and Health Sciences
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Hemorrhage
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Hemorrhage
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Physiological Parameters
Body Weight
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Arrhythmia
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Cardiovascular Diseases
Coronary Heart Disease
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Vascular Medicine
Coronary Heart Disease
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Pharmaceutics
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Anticoagulant Therapy
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Antiplatelet Therapy
Medicine and Health Sciences
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Physiological Parameters
Body Weight
Relationship between body mass index and clinical events in patients with atrial fibrillation undergoing percutaneous coronary intervention
Body mass index and clinical events
Yamazaki Tatsuro Conceptualization Data curation Formal analysis Investigation Visualization Writing – original draft 1
https://orcid.org/0000-0002-2888-6160
Kitahara Hideki Conceptualization Data curation Formal analysis Investigation Supervision Writing – review & editing 1 *
Yamashita Daichi Data curation Investigation 1
Sato Takanori Data curation Investigation 1
Suzuki Sakuramaru Data curation Investigation 2
Hiraga Takashi Data curation Investigation 1
Matsumoto Tadahiro Data curation Investigation 1
Kobayashi Takahiro Data curation Investigation 1
Ohno Yuji Data curation Investigation 3
Harada Junya Data curation Investigation 4
Fukushima Kenichi Data curation Investigation 5
Asano Tatsuhiko Data curation Investigation 6
Ishio Naoki Data curation Investigation 7
Uchiyama Raita Data curation Investigation 8
Miyahara Hirofumi Data curation Investigation 9
Okino Shinichi Data curation Investigation 10
Sano Masanori Data curation Investigation 11
Kuriyama Nehiro Data curation Investigation 12
Yamamoto Masashi Data curation Investigation 13
Sakamoto Naoya Data curation Investigation 14
Kanda Junji Data curation Investigation 15
Kobayashi Yoshio Conceptualization Data curation Investigation Project administration Supervision Writing – review & editing 1
1 Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
2 Department of Cardiovascular Medicine, Eastern Chiba Medical Center, Togane, Japan
3 Department of Cardiovascular Medicine, Narita Red Cross Hospital, Narita, Japan
4 Division of Cardiology, Chiba Cerebral and Cardiovascular Center, Ichihara, Japan
5 Department of Cardiology, Matsudo City General Hospital, Matsudo, Japan
6 Department of Cardiology, Chiba Rosai Hospital, Ichihara, Japan
7 Department of Cardiology, Chiba Aoba Municipal Hospital, Chiba, Japan
8 Department of Cardiovascular Medicine, Japan Community Healthcare Organization Chiba Hospital, Chiba, Japan
9 Department of Cardiology, Chiba Kaihin Municipal Hospital, Chiba, Japan
10 Department of Cardiology, Funabashi Municipal Medical Center, Funabashi, Japan
11 Department of Cardiology, Chiba Emergency Medical Center, Chiba, Japan
12 Cardiovascular Center, Miyazaki Medical Association Hospital, Miyazaki, Japan
13 Department of Cardiology, Kimitsu Central Hospital, Kisarazu, Japan
14 Division of Cardiology, Chibaken Saiseikai Narashino Hospital, Narashino, Japan
15 Department of Cardiovascular Medicine, Asahi General Hospital, Asahi, Japan
Fukumoto Yoshihiro Editor
Kurume University School of Medicine, JAPAN
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: hidekitahara0306@gmail.com
19 9 2024
2024
19 9 e03097581 6 2024
18 8 2024
© 2024 Yamazaki et al
2024
Yamazaki et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Background

It is still unclear whether body mass index (BMI) affects bleeding and cardiovascular events in patients requiring oral anticoagulants (OAC) for atrial fibrillation (AF) and antiplatelet agents after percutaneous coronary intervention (PCI) for coronary artery disease (CAD). The aim of this study was to evaluate the relationship between BMI and clinical events in patients who underwent PCI under OAC therapy for AF.

Method

This was a multicenter, observational cohort study conducted at 15 institutions in Japan. AF patients who underwent PCI with drug-eluting stents for CAD were retrospectively and prospectively included. Patients were divided into the Group 1 (BMI <21.3 kg/m2) and the Group 2 (BMI ≥21.3 kg/m2) according to the first-quartile value of BMI. The primary endpoint was net adverse clinical events (NACE), a composite of major adverse cardiovascular events (MACE) and major bleeding events within one year after index PCI procedure.

Results

In the 720 patients, 180 patients (25.0%) had BMI value <21.3 kg/m2. While the rates of NACE and MACE were significantly higher in the Group 1 than the counterpart (21.1% vs. 11.9%, p = 0.003 and 17.2% vs. 8.9%, p = 0.004), that of major bleeding did not differ significantly between the 2 groups (5.6% vs. 4.3%, p = 0.54). The cumulative rate of NACE and MACE was significantly higher in the Group 1 than the Group 2 (both log-rank p = 0.002), although that of major bleeding events was equivalent between the 2 groups (log-rank p = 0.41). In multivariable Cox regression analyses, while BMI value <21.3 kg/m2 was not associated with major bleeding events, that cut-off value was an independent predictor for increased NACE and MACE.

Conclusions

Among the patients undergoing PCI for CAD and requiring OAC for AF, BMI value was a useful indicator to predict major adverse clinical events.

The author(s) received no specific funding for this work. Data AvailabilityAll relevant data are within the manuscript and its Supporting Information files.
Data Availability

All relevant data are within the manuscript and its Supporting Information files.
==== Body
pmcIntroduction

Body mass index (BMI), calculated as body weight in kilograms divided by height in meters squared, is a common and simple indicator, which can evaluate physique easily in daily clinical practice. In general, patients with a high value of BMI are likely to have chronic diseases associated with progression of arteriosclerosis, such as diabetes, hypertension, and dyslipidemia [1, 2], resulting in increased cardiovascular events and mortality [1, 3]. On the other hand, several reports have suggested that patients with low BMI values have a higher incidence of mortality and cardiovascular events than those with normal and high range of BMI in populations with cardiovascular disease, including coronary artery disease, heart failure, and atrial fibrillation (AF) [4–7]. These phenomena have been proposed as “obesity paradox” and still remain controversial. Given that BMI value is associated with the prognosis of patients, it is clinically important to include optimal BMI and body weight for preventing cardiovascular events.

AF is a common cardiac arrhythmia worldwide, and its prevalence is reportedly associated with BMI value [8]. Due to increasing average life expectancy, improving survival rate of various diseases, and facilitation of rhythm monitoring devices [8, 9], the number of patients with AF is increasing, accompanied by the increasing number of patients with AF undergoing percutaneous coronary intervention (PCI) for concomitant coronary artery disease [10]. Previous studies have reported that approximately 10% of patients have a history of AF when they undergo PCI [11, 12]. While using oral anticoagulant (OAC), including vitamin K antagonist (VKA) or direct oral anticoagulant (DOAC), it is recommended as standard therapy for AF patients to prevent systemic embolism [13–15]. Moreover, OAC is recognized as an important risk factor for bleeding complications in patients who need antiplatelet drugs after PCI [16, 17]. The recent Japanese guideline pertaining to antithrombotic therapy for patients with coronary artery disease (CAD) demonstrated that both low body weight and OAC are important risk factors for major bleeding complications [16]. Given that Asians, especially East Asians, are likely to have lower body weight compared with non-Asians [18, 19], it is clinically relevant to evaluate the risk of bleeding events as well as adverse cardiovascular events in patients with low BMI requiring OAC for AF and antiplatelet drugs after PCI. Thus, the aim of this study was to evaluate the relationship between BMI and clinical events in patients who underwent PCI under OAC therapy for AF.

Materials and methods

Study population

This was a multicenter and observational cohort study performed in 15 hospitals in Japan (CHIBA AF-PCI registry) [20]. The study flow is summarized in Fig 1. We enrolled the AF patients who underwent PCI with drug-eluting stents (DES) for coronary artery disease prospectively from 9th July 2019 to 26th March 2021 and included them retrospectively from 1st June 2015 to 31st March 2021 (n = 949). The main exclusion criteria were as follows: PCI with two stent technique for bifurcation lesion, cardiogenic shock, history of stent thrombosis, history of aortic or mitral valve replacement, and severe liver dysfunction. Next, patients with OAC used for reasons other than AF (n = 11), no DES implantation (n = 25), no PCI information (n = 3), no OAC used (n = 134), and no detailed follow-up information (n = 30) were excluded. Last, patients who discontinued OAC without any clinical events (n = 17) and those without BMI data (n = 9) were also excluded. Thus, 720 patients were included in the present analysis, and were divided into 2 groups according to their BMI value. Patients who had less than the first quartile value of BMI were classified as “Group 1” and remaining patients were classified as “Group 2” (Fig 1). This study was performed in accordance with the principles of the Declaration of Helsinki and registered in the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (ID: UMIN000047503). The study protocol was approved by the ethics committee of Chiba University Graduate School of Medicine (unique identifier: 3443), and also approved by the institutional review board or ethics committee at each participating institution. Prospectively enrolled patients provided written informed consent for research before study entry. In retrospectively included patients, informed consent was ascertained in the form of opt-out. An anonymization process was performed, removing all identifiable information. Authors did not access to information that could identify individual participants during or after data collection. The extracted data were stored into a password-protected electronic database.

10.1371/journal.pone.0309758.g001 Fig 1 Study flow.

AF, atrial fibrillation; BMI, body mass index; DES, drug-eluting stents; OAC, oral anticoagulant; PCI, percutaneous coronary intervention.

Medical treatment

In this study, PCI was performed per local protocol using DES in each institution [21, 22]. Antithrombotic therapy for patients requiring OAC after performing PCI was left to the operator’s discretion based on recent recommendations and guidelines [16, 17, 23]. In Japan, prasugrel has been used with OAC due to the approval of low-dose prasugrel (loading/maintenance dose: 20/3.75 mg/day), which is about one-third of that in other countries, as an antithrombotic therapy after performing PCI [24, 25]. In addition, optimal medical therapy and life guidance to prevent recurrence of cardiovascular disease were left to operators in each institution.

Clinical events

In this study, the primary endpoint was the cumulative incidence of net adverse clinical events (NACE), a composite of major adverse cardiovascular events (MACE) and major bleeding events within 1 year after index PCI procedure [26, 27]. MACE was defined as a composite of all-cause death, non-fatal myocardial infarction, stent thrombosis, and stroke. Major bleeding events were defined as Bleeding Academic Research Consortium (BARC) types 3 or 5. The secondary endpoints were the cumulative incidence MACE and major bleeding events, respectively.

Statistical analysis

The main purpose of this study was to compare clinical outcomes between the Group 1 and 2. The categorical variables were shown as n (%) and analyzed with Fisher’s exact test or Pearson’s chi-square test. The continuous variables were presented as mean ± standard deviation and analyzed with one-way analysis of variance (ANOVA). To evaluate the cumulative events of NACE, MACE and major bleeding, Kaplan-Meier curve analysis and log-lank test were performed. Multivariable Cox regression analysis was performed to calculate the hazard ratio (HR) to NACE, MACE, and major bleeding events. Variables included in the multivariable analysis were selected referencing the recent Japan circulation society guideline and previous reports [16, 28], in which the factors associated with thrombotic events were included in the analysis of MACE and those proposed as predictors for bleeding events in the Japanese high bleeding risk were done in analysis of major bleeding. In addition, the multivariable analysis of NACE included the variables associated with thrombotic and major bleeding events. As a sub-analysis, propensity score matching was performed with propensity score calculated using logistic regression analysis to adjust between-group differences in age. In this study, the patients with eGFR <30ml/min/1.73 m2, platelets level <10×104/μL, or hemoglobin level <11g/dl were defined as having severe chronic kidney dysfunction (CKD), thrombocytopenia, or moderate to severe anemia, respectively [16]. A value of p <0.05 was considered statistically significant. All statistical analyses were performed with JMP pro version 16.0 (SAS Institute Inc., Cary, USA) and EZR version 1.55 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R version 2.7–1 (The R Foundation for Statistical Computing, Vienna, Austria).

Results

In a total of 720 patients enrolled, the first quartile value of BMI was 21.3 kg/m2, and based on this value, patients were divided into the Group 1 (n = 180) and Group 2 (n = 540) groups. Baseline characteristics are shown in Table 1.

10.1371/journal.pone.0309758.t001 Table 1 Baseline characteristics.

Variables	All
(n = 720)	Group 1
(n = 180)	Group 2
(n = 540)	p value	
Age (years)	74.0±9.0	77.3±8.4	72.8±9.0	<0.001	
Male	580 (80.6%)	132 (73.3%)	448 (83.0%)	0.006	
Body weight (kg)	64.8±14.1	50.5±7.4	69.6±12.6	<0.001	
BMI (kg/m2)	24.4±4.3	19.5±1.5	26.0±3.6	<0.001	
Hypertension	610 (84.7%)	145 (80.6%)	465 (86.1%)	0.09	
Diabetes	352 (48.9%)	75 (41.7%)	277 (51.3%)	0.03	
Dyslipidemia	535 (74.3%)	119 (66.1%)	416 (77.0%)	0.004	
Current smoking	131 (18.3%)	22 (12.2%)	109 (20.4%)	0.01	
Family history of CAD	95 (14.5%)	17 (10.2%)	78 (16.0%)	0.07	
Prior angina pectoris	203 (28.2%)	45 (25.0%)	158 (29.3%)	0.29	
Previous MI	186 (25.8%)	45 (25.0%)	141 (26.1%)	0.84	
Prior PCI	238 (33.1%)	51 (28.3%)	187 (34.6%)	0.14	
Prior CABG	37 (5.1%)	14 (7.8%)	23 (4.3%)	0.08	
Hemodialysis	20 (2.8%)	7 (3.9%)	13 (2.4%)	0.30	
Paroxysmal AF	375 (52.2%)	93 (51.7%)	282 (52.3%)	0.93	
Peripheral artery disease	55 (7.6%)	13 (7.2%)	42 (7.8%)	0.87	
Prior heart failure	185 (25.7%)	59 (32.8%)	126 (23.4%)	0.01	
Prior major bleeding events	45 (6.3%)	15 (8.3%)	30 (5.6%)	0.21	
Severe CKD	71 (9.9%)	17 (9.4%)	54 (10.0%)	0.89	
Thrombocytopenia	12 (1.7%)	5 (2.8%)	7 (1.3%)	0.19	
Moderate to severe anemia	114 (15.8%)	45 (25%)	69 (12.8%)	<0.001	
Liver cirrhosis	3 (0.4%)	3 (1.7%)	0 (0.0%)	0.02	
Active cancer	40 (5.6%)	15 (8.3%)	25 (4.6%)	0.09	
Prior bleeding stroke	22 (3.1%)	5 (2.8%)	17 (3.2%)	1.00	
Prior ischemic stroke	141 (19.9%)	36 (20.0%)	105 (19.4%)	0.91	
LVEF (%)	51.3±14.3	49.5±15.2	51.9±14.0	0.06	
CHADS2 score	2.6±1.3	2.8±1.3	2.6±1.3	0.10	
CHA2DS2-VASc score	3.8±1.6	4.0±1.5	3.7±1.6	0.01	
HAS-BLED score	3.2±0.9	3.3±1.0	3.1±0.9	0.06	
Laboratory data					
Hemoglobin (g/dl)	13.1±2.2	12.3±2.2	13.4±2.1	<0.001	
Platelet (×104/μL)	20.4±6.4	21.7±10.6	20.2±6.1	0.03	
eGFR (ml/min/1.73 m2)	53.9±18.9	52.2±20.3	54.5±18.4	0.21	
Total cholesterol (mg/dL)	167.6±38.7	167.9±39.7	167.5±38.4	0.89	
HDL cholesterol (mg/dL)	48.8±14.1	52.5±15.4	47.5±13.5	<0.001	
LDL cholesterol (mg/dl)	96.1±31.9	94.7±33.0	96.6±31.6	0.50	
Triglycerides (mg/dL)	129.6±92.7	95.7±51.4	141.1±100.4	<0.001	
Glycated hemoglobin (%)	6.4±1.1	6.3±1.2	6.4±1.0	0.43	
Lesion and procedure					
Acute coronary syndrome	288 (40.0%)	84 (46.7%)	204 (37.8%)	0.04	
Multivessel disease	329 (45.8%)	74 (41.3%)	255 (47.2%)	0.19	
Bifurcation lesion	133 (18.5%)	37 (20.7%)	96 (17.8%)	0.44	
Femoral approach	112 (15.8%)	26 (14.6%)	86 (16.1%)	0.72	
Mechanical support	23 (3.2%)	12 (6.7%)	11 (2.0%)	0.005	
DES type					
Everolimus	444 (61.7%)	111 (61.7%)	333 (61.7%)	0.20	
Zotarolimus	100 (13.9%)	28 (15.6%)	72 (13.3%)		
Sirolimus	121 (16.8%)	30 (16.7%)	91 (16.9%)		
Biolimus	11 (1.5%)	5 (2.8%)	6 (1.1%)		
Multiple	44 (6.1%)	6 (3.3%)	38 (7.0%)		
Number of stents	1.5±0.8	1.5±0.8	1.5±0.8	0.35	
Mean stent diameter (mm)	3.0±0.5	2.9±0.5	3.0±0.5	0.20	
Total stent length (mm)	37.3±22.9	36.3±22.2	37.6±23.1	0.49	
AF, atrial fibrillation; BMI, Body mass index; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CKD, chronic kidney disease; DES, drug-eluting stent; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; MI, myocardial infarction; PCI, percutaneous coronary intervention.

The mean BMI values were 19.5±1.5 and 26.0±3.6 kg/m2, for the Group 1 and 2, respectively. Age was significantly higher, and the proportion of men was lower in the Group 1. The prevalence of conventional risk factors for cardiovascular disease, such as diabetes, dyslipidemia, and current smoking, were significantly higher in the Group 2. CHA2DS2-VASc and HAS-BLED scores were higher in the Group 1 than the counterpart. Patients in the Group 1 had a lower value of hemoglobin, platelet, and triglycerides, and a higher value of high-density lipoprotein cholesterol. Lesion and procedure information did not differ significantly between the 2 groups except for the rate of using mechanical support device. The comparison of medications in the present study is summarized in Table 2.

10.1371/journal.pone.0309758.t002 Table 2 Medications.

Variables	All
(n = 720)	Group 1
(n = 180)	Group 2
(n = 540)	p value	
Medication at discharge					
Aspirin	575 (79.9%)	139 (77.2%)	436 (80.7%)	0.33	
P2Y12 inhibitor	679 (94.3%)	171 (95.0%)	508 (94.1%)	0.71	
VKA	105 (14.6%)	35 (19.4%)	70 (13.0%)	0.04	
DOAC	610 (84.7%)	143 (79.4%)	467 (86.5%)	0.03	
ACE-i/ARB	461 (64.0%)	104 (57.8%)	357 (66.1%)	0.049	
β-blocker	536 (74.6%)	133 (73.9%)	403 (74.8%)	0.84	
Statins	607 (84.3%)	145 (80.6%)	462 (85.6%)	0.12	
Oral antidiabetic agents	200 (27.8%)	43 (23.9%)	157 (29.1%)	0.21	
Insulin	62 (8.6%)	16 (8.9%)	46 (8.5%)	0.88	
PPI	629 (87.4%)	158 (87.8%)	471 (87.2%)	0.90	
Steroid	18 (2.5%)	8 (4.4%)	10 (1.9%)	0.09	
Medication at 6 months					
Aspirin	318 (47.7%)	72 (45.6%)	246 (48.3%)	0.58	
P2Y12 inhibitor	438 (65.7%)	93 (58.9%)	345 (67.8%)	0.04	
OAC	649 (97.3%)	152 (96.2%)	497 (97.6%)	0.40	
Duration of triple therapy	78.9±101.1	69.3±98.6	82.1±101.8	0.14	
ACE-i, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; DOAC, direct oral anticoagulant; OAC, oral anticoagulant; PPI, proton pump inhibitor; VKA, vitamin K antagonist.

The use of antihypertensive agents was more frequent along with the frequency of hypertension in the Group 2. The rate of DOAC rather than VKA was significantly lower in the Group 1 than in the Group 2. At 6 months follow-up, the percentage of patients taking P2Y12 inhibitors was significantly lower in the Group 1.

The crude incidence of clinical events is shown in Table 3.

10.1371/journal.pone.0309758.t003 Table 3 Adverse clinical events at 1 year.

Variables	All
(n = 720)	Group 1
(n = 180)	Group 2
(n = 540)	OR*	95% CI	p value†	
NACE	102 (14.2%)	38 (21.1%)	64 (11.9%)	1.99	1.28–3.10	0.003	
MACE	79 (11.0%)	31 (17.2%)	48 (8.9%)	2.13	1.31–3.41	0.004	
All-cause death	52 (7.2%)	24 (13.3%)	28 (5.2%)	2.81	1.58–5.00	<0.001	
Cardiovascular death	27 (3.8%)	12 (6.7%)	15 (2.8%)	2.50	1.15–5.45	0.02	
Myocardial infarction	8 (1.1%)	3 (1.7%)	5 (0.9%)	1.81	0.43–7.65	0.42	
Stent thrombosis	5 (0.7%)	1 (0.6%)	4 (0.7%)	0.75	0.08–6.74	1.00	
Ischemic stroke	22 (3.1%)	4 (2.2%)	18 (3.3%)	0.66	0.22–1.97	0.62	
Major bleeding (BARC 3 or 5)	33 (4.6%)	10 (5.6%)	23 (4.3%)	1.32	0.62–2.83	0.54	
All bleeding	71 (9.9%)	26 (14.4%)	45 (8.3%)	1.86	1.11–3.11	0.02	
Values are expressed as n (%). BARC, Bleeding Academic Research Consortium; CI, confidence interval; MACE, major adverse cardiovascular events; NACE, net adverse clinical events; OR, odds ratio.

* The ratio of the event risk in the Group 1 to that in the Group 2

† Comparing the rate of each event between the Group 1and 2 with Fisher’s exact test

The rates of NACE and MACE were significantly higher in the Group 1 (21.1% vs. 11.9%, p = 0.003, and 17.2% vs. 8.9%, p = 0.004). In the analysis of each component of MACE, all-cause death and the cardiovascular death more frequently occurred in the Group 1 than the counterpart (13.3% vs. 5.2%, p<0.001, and 6.7% vs. 2.8%, p = 0.002). While the incidence of all bleeding events was significantly higher in the Group 1 (14.4% vs. 8.3%, p = 0.02), that of major bleeding did not differ significantly between the 2 groups (5.6% vs. 4.3%, p = 0.54). Kaplan-Meier curve showed significantly higher cumulative rate of NACE in the Group 1 at one year after performing PCI (log-rank p = 0.002) (Fig 2). Whereas the cumulative rate of major bleeding was not significantly different between the 2 groups (log-rank p = 0.41), that of MACE was significantly higher in the Group 1 (log-rank p = 0.002) (S1 Fig). The multivariable Cox regression analysis revealed that male sex, low BMI value (<21.3 kg/m2), current smoker, prior coronary artery bypass grafting, peripheral artery disease, severe CKD, thrombocytopenia, and moderate to severe anemia were independent predictors for NACE within 1 year after performing PCI (Table 4).

10.1371/journal.pone.0309758.g002 Fig 2 Comparison of the cumulative incidence of the NACE between the Group 1 and 2.

BMI, body mass index; NACE, net adverse clinical events.

10.1371/journal.pone.0309758.t004 Table 4 Multivariable Cox regression model of predictors for NACE.

Variables	Hazard ratio	95% CI	p value	
Age (per year)	1.02	0.99–1.05	0.08	
Male sex	1.34	0.79–2.30	0.29	
Low BMI (< 21.3 kg/m2)	1.66	1.08–2.57	0.02	
Diabetes	1.20	0.79–1.83	0.39	
Current smoking	1.77	1.05–2.98	0.03	
Prior CABG	2.23	1.15–4.33	0.02	
Peripheral artery disease	1.92	1.07–3.45	0.03	
Prior heart failure	0.84	0.52–1.37	0.49	
Severe CKD	1.97	1.10–3.52	0.02	
Thrombocytopenia	6.72	3.04–14.9	<0.001	
Moderate to severe Anemia	2.02	1.24–3.30	0.005	
Acute coronary syndrome	1.16	0.77–1.77	0.48	
VKA at discharge	0.97	0.55–1.68	0.90	
BMI, body mass index; CABG, coronary artery bypass grafting; CKD, chronic kidney disease; CI, confidence interval; NACE, net adverse clinical events; VKA vitamin K antagonist.

Although low BMI value independently promoted MACE (S1 Table), there was no significant association between low BMI value and major bleeding events (S2 Table). In sub-analyses which adjust several confounding variables, the rate of NACE was significantly higher in the patients with BMI value <21.3 kg/m2 than those without in only men (22.0% vs. 11.8%, p = 0.006) (S3 Table). On the other hand, that was not significantly different in only women (18.8% vs. 12.0%, p = 0.31) (S3 Table). In the older adults (i.e. age ≥65 years), the patients with BMI value <21.3 kg/m2 had significantly higher rate of NACE than their counterpart (21.0% vs. 12.1%, p = 0.007) (S4 Table). Baseline characteristics and information on medications after adjusting for age were summarized in S5 and S6 Tables. The rate of NACE was significantly higher after adjusting for age or excluding the patients with acute coronary syndrome or active cancer (S7 and S8 Tables). While the incidence of NACE did not differ significantly between the two groups of patients with low-dose prasugrel (14.8% vs. 13.7%, p = 0.85), there was a significant difference in the incidence of NACE between the two groups of the patients without low-dose prasugrel (26.3% vs. 10.0%, p<0.001) (S9 Table). In additional analyses which use other cut-off value of BMI, the rate of NACE was not significantly but numerically higher in the patients with BMI value <18.5 kg/m2 than their counterpart (20.5% vs. 13.8%, p = 0.26) (S10 Table). When dividing the patients using median value of BMI in this study (i.e. 24.0 kg/m2), NACE more frequently occurred in the patients with BMI value <24.0 kg/m2 than those with BMI ≥24.0 kg/m2 (16.9% vs. 11.4%, p = 0.04) (S10 Table).

Discussion

In the present study, patients in the Group 1 had a higher risk of NACE and MACE, and BMI value <21.3 kg/m2 was independent predictor for NACE and MACE within 1 year after performing PCI in patients under OAC therapy for AF. On the other hand, BMI value <21.3 kg/m2 was not significantly associated with major bleeding events.

The relationship between BMI and clinical events has been validated in previous studies and a matter of debates. In traditional knowledge, patients with high BMI value, meaning obese patients, are likely to have an increased mortality and risk of cardiovascular events [1, 3]. However, several studies reported that obese patients had a favorable prognosis compared with patients with low or normal body weight in subjects with cardiovascular disease [4–7]. The Korean retrospective observational nationwide study, investigating the relationship between BMI value and clinical outcomes in Asian patients with AF receiving OAC, previously suggested that higher BMI value, per 5 kg/m2 increase, was associated with lower risk of the composite clinical outcomes (hazard ratio, 0.751 [95% confidence interval, 0.706–0.799]) [4]. In contrast, patients having low BMI value, less than 18.5 kg/m2, had a 1.4-fold risk of composite clinical outcomes compared with normal weight patients [4]. Another previous dose-response meta-analysis with almost 140,000 participants from 15 studies reported that, in cases undergoing PCI for coronary artery disease, obese patients had a lower risk of all-cause mortality than underweight patients [6]. These phenomena were observed in several studies and proposed as “obesity paradox”. Although it has been considered that high usage rates of medications for comorbidities in obese patients, such as beta-blockers, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, and statins, might influence the paradox [26, 29, 30], details were still unclear. In the present study, the usage rates of such medications were almost similar between the Group 1 and 2. On the other hand, patients in the Group 1 were older and had a higher rate of acute coronary syndrome, mechanical support, and higher CHA2DS2-VASc score than the counterpart, potentially resulting in the increased risk of NACE and MACE as similarly shown in previous reports [4–7]. In multivariable Cox regression analysis, low BMI value (i.e. <21.3 kg/m2) was an independent predictor for NACE and MACE. Similar results were observed in a recent sub-analysis of a Korean prospective and randomized trial which compared the efficacy of antiplatelet drugs in the patients who underwent PCI for CAD, in which a combination risk of major cardiovascular and bleeding events was higher in the underweight patients and lower in obese patients compared with normal weight patients [26]. Considering these results, we believe that it is clinically relevant to recognize patients with low BMI value who undergo PCI with OAC therapy for AF as potentially high-risk subjects for adverse clinical events.

In many previous studies, low body weight and low BMI have been reported as independent risk factors for bleeding events in post-PCI or AF patients [4, 26, 31–33], and low body weight is proposed as one of the major criteria of high bleeding risk in recent Japanese guidelines on antithrombotic therapy in patients with CAD [16]. In the present study, however, the incidence of major bleeding events was not significantly higher in patients with BMI value <21.3 kg/m2. The duration of triple therapy, using an oral anticoagulant and dual antiplatelet therapy simultaneously, was not significantly but numerically shorter in the Group 1 than the counterpart. In addition, the appropriately reduced dose of DOAC should have been used in patients with old age, low body weight, or kidney dysfunction, which were frequently observed in the Group 1, according to the guidelines [16]. These backgrounds might prevent bleeding events in the Group 1, resulting in the equivalent rate of major bleeding events between the 2 groups in the present study. Practically, a variety of associations between BMI and bleeding events have been previously reported. There are several reports that high BMI is associated with an increased risk of bleeding events [5, 34], that both low and high BMI can accelerate the bleeding risk (i.e. U-shaped association) [35, 36], and that there is no relationship between BMI and bleeding events [37]. Therefore, no definite conclusion has yet been reached regarding the relationship between BMI and bleeding events. Further studies are warranted to clarify this issue in AF patients after PCI.

Limitations

The present study has several limitations. First, since this was an observational study based on the registry data, confounding factors might influence on the results. Second, this study did not use the classification of BMI recommended by World Health Organization (WHO) because of the limited number of underweight (BMI <18.5 kg/m2) and obese (BMI ≥30.0 kg/m2) patients: 44 patients (6.1%) had BMI <18.5 kg/m2 and 69 (9.6%) had BMI ≥30.0 kg/m2 in the present study [38]. When using the WHO standard cut-off, the rate of NACE was not significantly but numerically higher in the underweight patients than their counterpart (S10 Table). However, a previous individual-participant data meta-analysis which evaluated association of all-cause mortality with BMI in populations without chronic diseases demonstrated that the BMI value with the lowest risk of mortality gradually increased as age increased [39]. Similar results were shown in another study with Japanese population [40]. On this basis, the target BMI value for elderly people was higher than younger people as per the recommendation from the Health, Labor and Welfare Ministry in Japan [39, 40]. In that recommendation, 21.5 kg/m2 is the proposed value for patients more than 65 years old, which applies to most of the patients in the present study, whose mean age was 74.0±9.0 years old. Given that the cut-off value defining the Group 1 in the present study (i.e. 21.3 kg/m2) was in line with the target value for Japanese elderly people (i.e. 21.5 kg/m2), it was thought that the classification of BMI in the present study was acceptable. Third, the small number of bleeding events did not allow inclusion of a sufficient number of variables in multivariable analysis which evaluated the factors associated with major bleeding events. Finally, because this study was a registry study, medical treatment was left to each physician. It was difficult to unify the characteristics of included patients, such as baseline characteristics, strategy of PCI, and medications. Although the choice and duration of antithrombotic therapy were decided based on thrombotic and bleeding events risk of each patient, the difference of antithrombotic therapy might be associated with the event rates seen in the present study.

Conclusions

Among the patients undergoing PCI for CAD and requiring OAC for AF, low BMI value was independently associated with the incidence of NACE and MACE, although not with the incidence of major bleeding events. BMI value may be a useful indicator to predict major adverse clinical events.

Supporting information

S1 Table Multivariable Cox regression model of predictors for MACE.

(DOCX)

S2 Table Multivariable Cox regression model of predictors for major bleeding events.

(DOCX)

S3 Table Adverse clinical events at 1 year in only men or women.

(DOCX)

S4 Table Adverse clinical events at 1 year in the patients with <65 years or ≥65 years.

(DOCX)

S5 Table Baseline characteristics after adjusted by age.

(DOCX)

S6 Table Medications after adjusted by age.

(DOCX)

S7 Table Adverse clinical events at 1 year after adjusted by age.

(DOCX)

S8 Table Adverse clinical events at 1 year after excluding the patients with ACS or active cancer.

(DOCX)

S9 Table Adverse clinical events at 1 year in the patients with and without low-dose prasugrel.

(DOCX)

S10 Table Adverse clinical events at 1 year in the patients classified by WHO criteria or median value.

(DOCX)

S1 Fig Comparison of the cumulative incidence of the MACE and major bleeding events between the Group 1 and 2.

(TIF)

S1 Dataset All data used for analyses in this study.

(XLSX)

We thank Heidi N. Bonneau, RN, MS, CCA for her editorial review of the manuscript.

10.1371/journal.pone.0309758.r001
Decision Letter 0
Fukumoto Yoshihiro Academic Editor
© 2024 Yoshihiro Fukumoto
2024
Yoshihiro Fukumoto
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
21 Jun 2024

PONE-D-24-19204Relationship Between Body Mass Index and Clinical Events in Patients With Atrial Fibrillation Undergoing Percutaneous Coronary InterventionPLOS ONE

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Reviewer #1: In the present study, the authors evaluated the relationship between BMI and clinical events in patients who underwent PCI under OAC therapy for AF. Low BMI value was independently associated with the incidence of NACE and MACE, although not with the incidence of major bleeding events. The authors concluded that BMI value may be a useful indicator to predict major adverse clinical events in this specific patient population. It is certainly informative that low BMI may be relevant to cardiovascular risk, however, the novelty of this study is rather low and significant population biases may exist. The reviewer has comments as below.

# It is obvious that the Low BMI group has more severe clinical background. It includes higher age, more cancer patients, more anemic patients, more ACS, and more mechanical supports during PCI. Thus, the main result of the study that the Low BMI group had more clinical events, seems a matter of course. Do the results unchanged if they are age-matched or those with ACS and with cancer are excluded?

# Relevance of low BMI for NACE was significant in the multivariable Cox regression model in this study. What about for MACE?

# In table 3, sum of the number of MACE and major bleeding is not equal to NACE. Why?

# If the authors has a K-M analysis of MACE, it may be provided as a supplemental figure.

# The studied patients contains a special cohort with dual anti-thrombotic therapy with prasugrel. What is the number of them? No sub-analysis for them is provided. Why?

Reviewer #2: Review Comments

1.　Selection of BMI Cut-off Values

I understand from the authors' description in the Limitations section that the cutoff value of 21.3 for BMI is based on the first quartile of the study population. However, it is unclear to what extent this value can be generalized to other studies or clinical trials. Additionally, as it differs from the WHO standards, it makes international comparisons difficult. Therefore, to further validate the appropriateness of this cutoff value, it is recommended to conduct supplementary analyses using other cutoff values (e.g., 18.5 or 25) or analyses based on the second quartile.

2.　The need for further adjustment of confounding variables

In this study, multivariable Cox regression analysis was used to adjust for confounding factors, but further subgroup analyses are needed to more precisely evaluate the impact of sex and age. Since sex and age are likely to independently affect cardiovascular events and bleeding risks apart from BMI, additional analyses considering these variables are recommended. Specifically, please conduct subgroup analyses by sex to evaluate the relationship between BMI and clinical outcomes separately for men and women. Furthermore, instead of including age only as a continuous variable, it is advisable to perform age-stratified subgroup analyses to assess the relationship between BMI and clinical outcomes within each age group. This will provide a more accurate assessment of the relevance of BMI.

3.　Table3：Adverse clinical events at 1 year

In Table 3, the incidence rates of NACE and MACE are shown, but the confidence intervals (CI) for these rates are not provided. Adding 95% confidence intervals for the incidence rates of each event would make it easier to evaluate the precision of the results. Therefore, it is desirable to include these intervals.

4.　Regarding Group Classification and Naming

Generally, a BMI of 18.5 to 24.9 is defined as normal weight. From the context of the paper, it is understood that BMI 21.3 is used as the cutoff for grouping in this study. However, classifying the groups as Low BMI group and Normal-High BMI group feels somewhat inappropriate. It would be less confusing to categorize them as Group 1 and Group 2 or Group A and Group B, for example.

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Reviewer #2: No

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10.1371/journal.pone.0309758.r002
Author response to Decision Letter 0
Submission Version1
4 Aug 2024

Responses to the comments of Reviewer #1

We thank the Reviewer for his/her time and input. We have modified the manuscript according to the suggestions.

Reviewer#1 comment (1)

It is obvious that the Low BMI group has more severe clinical background. It includes higher age, more cancer patients, more anemic patients, more ACS, and more mechanical supports during PCI. Thus, the main result of the study that the Low BMI group had more clinical events, seems a matter of course. Do the results unchanged if they are age-matched or those with ACS and with cancer are excluded?

Author comment (1)

First and foremost, thank you so much for your time and commitment to improving the quality of our manuscript. As you pointed out, several clinical backgrounds in patients with low BMI value might be associated with poor clinical outcomes in the Group 1 (Low BMI group). According to the reviewer’s comments, we would like to provide the results of sub-analyses. First, propensity score matching was performed with propensity score calculated using logistic regression analysis to adjust between-group differences in age. Between the Group 1 (Low BMI group) and Group 2 (Normal-high BMI group), 177 patients were included in each group in the age-adjusted model. Baseline characteristics and information on medications were summarized in S5 and S6 Table. After age-matched, the rate of NACE and MACE was significantly higher in the Group 1 than their counterpart (S7 Table). Second, we provide the comparison of adverse events between 2 groups after excluding the patients with ACS and active cancer (S8 Table). While the rate of MACE did not differ significantly (12.8% vs. 6.9%, p=0.12), that of NACE was significantly higher in the Group 1 than the Group 2 (18.6% vs. 9.5%, p=0.04) (S8 Table). Considering these results, clinical outcomes did not change significantly after adjusting for age or excluding ACS and active cancer.

In the revised manuscript, we have added S5-S8 Table and the sentence “As a sub-analysis, propensity score matching was performed with propensity score calculated using logistic regression analysis to adjust between-group differences in age.” (page 10, paragraph 1, line 9-10) and modified the sentence “All statistical analyses were performed with JMP pro version 16.0 (SAS Institute Inc., Cary, USA) and EZR version 1.55 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R version 2.7-1 (The R Foundation for Statistical Computing, Vienna, Austria)” in the Methods (page 10, paragraph 1, line 14-17). In addition, we have added the sentence “Baseline characteristics and information on medications after adjusting for age were summarized in S5 and S6 Table. The rate of NACE was significantly higher after adjusting for age or excluding the patients with acute coronary syndrome or active cancer (S7 and S8 Table)” in the Results (page 16, paragraph 1, line 8-11).

Reviewer#1 comment (2)

Relevance of low BMI for NACE was significant in the multivariable Cox regression model in this study. What about for MACE?

Author comment (2)

In multivariable Cox regression analysis, low BMI value (<21.3kg/m2) was an independent predictor for MACE (HR 1.89, 95%CI [1.16-3.07], p=0.01). The results of the multivariable analysis for MACE and major bleeding are provided as S1 and S2 Table in the manuscript (page 16, paragraph 1, line 1-2).

Reviewer#1 comment (3)

In table 3, sum of the number of MACE and major bleeding is not equal to NACE. Why?

Author comment (3)

I appreciate your meaningful comment. Since Table 3 describes the crude incidence of each event, it is possible that multiple events have occurred in the same case, which would result in the total number of MACE and major bleeding not being equal to NACE.

Reviewer#1 comment (4)

If the authors has a K-M analysis of MACE, it may be provided as a supplemental figure.

Author comment (4)

Thank you for your valuable comments. We have added the Kaplan-Meier curve for MACE and major bleeding as S1 Fig in the Results (page 15, paragraph 1, line 1-3), as you have indicated.

Reviewer#1 comment (5)

The studied patients contains a special cohort with dual anti-thrombotic therapy with prasugrel. What is the number of them? No sub-analysis for them is provided. Why?

Author comment (5)

In this study, treatment strategies, including details of medication, were left to each operator. In Japan, prasugrel has been used with OAC due to the approval of low-dose prasugrel (loading/maintenance dose: 20/3.75 mg/day), which is about one-third of that in other countries, as an antithrombotic therapy after performing PCI [Cardiovasc Interv Ther 2022; 37: 269-278. J Atheroscler Thromb 2015; 22: 557-569.]. Low-dose prasugrel was used in 352 (48.9%) patients at performing PCI. A comparison of the clinical outcomes between the Group 1 and Group 2 was conducted for each patient, with and without low-dose prasugrel and results were summarized in Table S9. While the incidence of NACE did not differ significantly between the two groups of patients with low-dose prasugrel (14.8% vs. 13.7%, p=0.85), there was a significant difference in the incidence of NACE between the two groups of patients without low-dose prasugrel (26.3% vs. 10.0%, p<0.001) (S9 Table).

We added new references as reference No. 24 and 25. In addition, we have added S9 Table and the sentence “In Japan, prasugrel has been used with OAC due to the approval of low-dose prasugrel (loading/maintenance dose: 20/3.75 mg/day), which is about one-third of that in other countries, as an antithrombotic therapy after performing PCI [24,25].” in the Methods (page 8, paragraph 2, line 3 – page 9, paragraph 1, line 2) and “While the incidence of NACE did not differ significantly between the two groups of patients with low-dose prasugrel (14.8% vs. 13.7%, p=0.85), there was a significant difference in the incidence of NACE between the two groups of patients without low-dose prasugrel (26.3% vs. 10.0%, p<0.001) (S9 Table).”in the Results (page 16, paragraph 1, line 11 – page 17, paragraph 1, line 2).

Thank you again for your valuable contribution to elevating the quality of our manuscript.

Responses to the comments of Reviewer #2

We thank the Reviewer for his/her time and input. We have modified the manuscript according to the suggestions.

Reviewer#2 comment (1)

Selection of BMI Cut-off Values

I understand from the authors' description in the Limitations section that the cutoff value of 21.3 for BMI is based on the first quartile of the study population. However, it is unclear to what extent this value can be generalized to other studies or clinical trials. Additionally, as it differs from the WHO standards, it makes international comparisons difficult. Therefore, to further validate the appropriateness of this cutoff value, it is recommended to conduct supplementary analyses using other cutoff values (e.g., 18.5 or 25) or analyses based on the second quartile.

Author comment (1)

First and foremost, thank you so much for your time and commitment to improving the quality of our manuscript. Following the reviewer's comments, we would like to provide the results of the sub-analyses. First, we compared clinical outcomes using the WHO standard cut-off. In this study population, 44 patients (6.1%) were defined as underweight patients (i.e. BMI <18.5 kg/m2) according to WHO criteria. The rate of NACE was not significantly but numerically higher in the patients with BMI value <18.5 kg/m2 than their counterpart (20.5% vs. 13.8%, p=0.26) (S10 Table). Given the common use of WHO criteria in the assessment of BMI, we agree with the reviewer that the results of the assessment using WHO criteria should be presented. Second, we used the value of second quartile to divide the patients into two groups. The second quartile value of BMI in this study population was 24.0 kg/m2 and the comparison of clinical outcomes between the patients with BMI value <24.0 kg/m2 and ≥24.0 kg/m2 is summarized in S10 Table. The rate of NACE was significantly higher in the patients with BMI value <24.0 kg/m2 than those with BMI ≥24.0 kg/m2 (S10 Table).

We have newly added S10 Table and added the sentence “In additional analyses which use other cut-off value of BMI, the rate of NACE was not significantly but numerically higher in the patients with BMI value <18.5 kg/m2 than their counterpart (20.5% vs. 13.8%, p=0.26) (S10 Table). When dividing the patients using median value of BMI in this study (i.e. 24.0 kg/m2), NACE more frequently occurred in the patients with BMI value <24.0 kg/m2 than those with BMI ≥24.0 kg/m2 (16.9% vs. 11.4%, p=0.04) (S10 Table).” in the Results (page 17, paragraph 1, line 2-7). In addition, the authors modified sentence “Second, this study did not use the classification of BMI recommended by World Health Organization (WHO) because of the limited number of underweight (BMI <18.5 kg/m2) and obese (BMI ≥30.0 kg/m2) patients: 44 patients (6.1%) had BMI <18.5 kg/m2 and 69 (9.6%) had BMI ≥30.0 kg/m2 in the present study [38]. When using the WHO standard cut-off, the rate of NACE was not significantly but numerically higher in the underweight patients than their counterpart (S10 Table).” in the Discussion (page 20, paragraph 2, line 2-8).

Reviewer#2 comment (2)

The need for further adjustment of confounding variables

In this study, multivariable Cox regression analysis was used to adjust for confounding factors, but further subgroup analyses are needed to more precisely evaluate the impact of sex and age. Since sex and age are likely to independently affect cardiovascular events and bleeding risks apart from BMI, additional analyses considering these variables are recommended. Specifically, please conduct subgroup analyses by sex to evaluate the relationship between BMI and clinical outcomes separately for men and women. Furthermore, instead of including age only as a continuous variable, it is advisable to perform age-stratified subgroup analyses to assess the relationship between BMI and clinical outcomes within each age group. This will provide a more accurate assessment of the relevance of BMI.

Author comment (2)

Thank you for your comment. The author agrees with reviewer’s comment that additional analyses should be conducted, correcting for the effects of sex and age. First, the author conducted subgroup analysis to investigate the relationship between BMI value and clinical outcomes separately for men and women. In only men (n=580), 132 (22.8%) patients had low BMI value (i.e. <21.3 kg/m2) and the rate of NACE was significantly higher in the patients with BMI value <21.3 kg/m2 (22.0% vs. 11.8%, p=0.006) (S3 Table). On the other hand, in only women (n=140), low BMI value was observed in 48 (34.3%) patients and the rate of NACE was not significantly different between the patients with and without BMI value <21.3 kg/m2 (18.8% vs. 12.0%, p=0.31) (S3 Table). Although the multivariable analysis in the present study did not show sex as an independent predictor for NACE (Table 4), the results of sub-analyses might suggest that the impact of BMI on clinical outcomes differs between men and women. Second, according to reviewer’s comment, we performed age-stratified subgroup analyses to assess the relationship between BMI value and clinical outcomes. We divided the patients into 2 groups as follows: age <65 years group (n=90) and age ≥65 years group (n=630) because the cases over 65 years were defined as older adults in WHO criteria. In the patients with age <65 years, the rate of NACE did not differ significantly between the patients with and without BMI value <21.3 kg/m2 (23.1% vs. 10.4%, p=0.19) (Table S4). On the other hand, in the older adults (i.e. age ≥65 years), the rate of NACE was significantly higher in the Low BMI group than their counterpart (21.0% vs. 12.1%, p=0.007) (S4 Table), suggesting that BMI value may be associated with clinical outcome in the population limited to older people. These results provide new insights into the relationship between BMI value and clinical outcomes.

We have newly added S3 and S4 Table as supplemental materials. Additionally, we have added the sentence “In sub-analyses which adjust several confounding variables, the rate of NACE was significantly higher in the patients with BMI value <21.3 kg/m2 than those without in only men (22.0% vs. 11.8%, p=0.006) (Table S3). On the other hand, that was not significantly different in only women (18.8% vs. 12.0%, p=0.31) (S3 Table). In the older adults (i.e. age ≥65 years), the patients with BMI value <21.3 kg/m2 had significantly higher rate of NACE than their counterpart (21.0% vs. 12.1%, p=0.007) (S4 Table).” in the Results (page 16, paragraph 1, line 2-8)

Reviewer#2 comment (3)

Table3：Adverse clinical events at 1 year

In Table 3, the incidence rates of NACE and MACE are shown, but the confidence intervals (CI) for these rates are not provided. Adding 95% confidence intervals for the incidence rates of each event would make it easier to evaluate the precision of the results. Therefore, it is desirable to include these intervals.

Author comment (3)

I appreciate your meaningful comment. The authors agree with the reviewer’s suggestion that addition of 95% confidence intervals for the incidence rates of each event would be a better way of assessing the precision of the results. We conducted additional analyses to provide odds ratio and 95% confidence intervals in the comparison of the incidence rate of clinical events between the Group 1 (Low BMI group) and Group 2 (Normal-high BMI group) (Table 3_revised).

Reviewer#2 comment (4)

Regarding Group Classification and Naming

Generally, a BMI of 18.5 to 24.9 is defined as normal weight. From the context of the paper, it is understood that BMI 21.3 is used as the cutoff for grouping in this study. However, classifying the groups as Low BMI group and Normal-High BMI group feels somewhat inappropriate. It would be less confusing to categorize them as Group 1 and Group 2 or Group A and Group B, for example.

Author comment (4)

As you pointed out, the name of each group, “Low BMI group” and “Normal-High BMI”, may be inappropriate and cause confusing. We renamed “Low BMI group” to “Group 1” and “Normal-high BMI group” to “Group 2”.

We have modified the sentence “Patients who had less than the first quartile value of BMI were classified as “Group 1” and remaining patients were classified as “Group 2” (Fig 1).” In the Methods (page 8, paragraph 1, line 1-3).

Thank you again for your valuable contribution to elevating the quality of our manuscript.

Attachment Submitted filename: Response to reviewers.docx

10.1371/journal.pone.0309758.r003
Decision Letter 1
Fukumoto Yoshihiro Academic Editor
© 2024 Yoshihiro Fukumoto
2024
Yoshihiro Fukumoto
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
19 Aug 2024

Relationship Between Body Mass Index and Clinical Events in Patients With Atrial Fibrillation Undergoing Percutaneous Coronary Intervention

PONE-D-24-19204R1

Dear Dr. Kitahara,

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10.1371/journal.pone.0309758.r004
Acceptance letter
Fukumoto Yoshihiro Academic Editor
© 2024 Yoshihiro Fukumoto
2024
Yoshihiro Fukumoto
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
11 Sep 2024

PONE-D-24-19204R1

PLOS ONE

Dear Dr. Kitahara,

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