Metabolically healthy obesity and the risk for subclinical atherosclerosis

Accepted Manuscript Metabolically healthy obesity and the risk for subclinical atherosclerosis Tae Jun Kim, Hee-Young Shin, Yoosoo Chang, Mira Kang, Jaehwan Jee, Yoon-Ho Choi, Hyeon Seon Ahn, Soo Hyun Ahn, Hee Jung Son, Seungho Ryu PII:

S0021-9150(17)30138-7

DOI:

10.1016/j.atherosclerosis.2017.03.035

Reference:

ATH 15010

To appear in:

Atherosclerosis

Received Date: 30 October 2016 Revised Date:

27 February 2017

Accepted Date: 22 March 2017

Please cite this article as: Kim TJ, Shin H-Y, Chang Y, Kang M, Jee J, Choi Y-H, Ahn HS, Ahn SH, Son HJ, Ryu S, Metabolically healthy obesity and the risk for subclinical atherosclerosis, Atherosclerosis (2017), doi: 10.1016/j.atherosclerosis.2017.03.035. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Metabolically healthy obesity and the risk for subclinical atherosclerosis

Tae Jun Kim a,*, Hee-Young Shin a,*, Yoosoo Chang b,c,d, Mira Kang a, Jaehwan Jee, a, Yoon-

a

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Ho Choi b,e, Hyeon Seon Ahn f, Soo Hyun Ahn f, Hee Jung Son b,e, Seungho Ryu b,c,d

Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School

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of Medicine, Seoul, Republic of Korea

Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital,

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Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital,

Sungkyunkwan University School of Medicine, Seoul, Republic of Korea d

Department of Health Sciences and Technology, Samsung Advanced Institute for Health

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Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of

Medicine, Seoul, Republic of Korea

Biostatistics and Clinical Epidemiology Center, Samsung Medical Center, Sungkyunkwan

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University School of Medicine, Seoul, Republic of Korea

*These authors contributed equally to this work as co-first authors of this paper.

Corresponding authors: Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea (H. J. Son). Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga , Jung-gu , Seoul 100-742, Republic of Korea (S. Ryu).

ACCEPTED MANUSCRIPT E-mail addresses: [email protected] (H. J. Son); [email protected] (S. Ryu)

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Keywords: obesity, metabolic syndrome, subclinical atherosclerosis, cohort study.

Abbreviations: MHO, metabolically healthy obese; HbA1c, glycated hemoglobin; CIMT,

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carotid intima-media thickness; HOMA-IR, homeostatic model assessment-insulin resistance.

ACCEPTED MANUSCRIPT ABSTRACT Background and aims: Although obesity and metabolic abnormalities are known risk factors for cardiovascular disease, the risk of cardiovascular disease among obese individuals

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without obesity-related metabolic abnormalities, referred to as metabolically healthy obese (MHO), remains unclear. We examined the association between body mass index categories and the development of subclinical carotid atherosclerosis in a cohort of metabolically

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healthy individuals.

Methods: We conducted a cohort study of 6,453 men without subclinical carotid

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atherosclerosis or metabolic abnormalities at baseline, who underwent repeated health checkup examinations that included carotid ultrasound. A metabolically healthy state was defined as having no metabolic syndrome components and a homeostasis model assessment of insulin resistance < 2.5. Subclinical carotid atherosclerosis was assessed using ultrasound.

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Results: During the follow-up period of 34,797.9 person-years, subclinical carotid atherosclerosis developed in 1,916 participants. Comparing overweight and obese with normal weight participants, the multivariable adjusted hazard ratios (95% confidence

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intervals) for incident subclinical carotid atherosclerosis were 1.24 (1.12-1.38) and 1.54 (1.38-1.72), respectively. The association persisted after further adjustment for metabolic

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variables. This association was also evident in MHO men without abdominal obesity (waist circumference > 90 cm) and it did not differ across any clinically relevant subgroups evaluated.

Conclusions: In a large cohort study of strictly defined metabolically healthy participants, the MHO phenotype was associated with an increased risk of incident subclinical carotid atherosclerosis, providing evidence that the MHO phenotype is not protective from cardiovascular risk.

ACCEPTED MANUSCRIPT Introduction The global obesity epidemic continues to worsen, imposing a considerable burden on global health due to chronic diseases, such as cardiovascular disease, diabetes mellitus, and several

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cancers [1-4]. The major impact of obesity on the development of cardiovascular disease is often accompanied by a number of metabolic abnormalities, such as hypertension,

hyperglycemia, and dyslipidemia [5]. Most obese individuals have one or more metabolic

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abnormalities. However, some obese individuals are metabolically healthy and have

preserved insulin sensitivity. Recent interest has focused on a unique subgroup of obese

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individuals who do not have metabolic abnormalities, referred to as metabolically healthy obese (MHO), despite their increased adiposity [6,7].

There are numerous adverse effects of obesity on health, especially cardiovascular health. As assessed via body mass index (BMI) and several other measures of adiposity, obesity has

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shown consistent associations with cardiovascular disease [8,9]. However, the association of the MHO phenotype with cardiovascular disease is controversial [10,11]. The development of clinical cardiovascular disease events from the associated risk factors usually requires a long

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period of time [5]. However, compared to the estimation of overt cardiovascular events, the confirmation of subclinical atherosclerosis, including coronary artery calcification [12] and

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abnormal carotid intima-media thickness (CIMT) [13], might better evaluate the impact of obesity related factors on the development of cardiovascular disease within a shorter period [5,10,14]. Furthermore, abnormal CIMT and carotid plaque were stronger predictors of cardiovascular disease than other measurements such as the coronary calcium score, Creactive protein level, and left ventricular structure and function [15-22]. Previous studies evaluated the cross-sectional associations between the MHO phenotype and subclinical carotid atherosclerosis, as measured by CIMT and carotid plaque, but showed inconsistent results [13,23,24]. One cohort study evaluated the 3 year incidence of CIMT

ACCEPTED MANUSCRIPT progression, but, that study included a limited number of MHO participants and did not include insulin sensitivity as a criterion of metabolic abnormality [25]. Therefore, we examined the longitudinal association between BMI categories and the development of

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subclinical carotid atherosclerosis in a large cohort of metabolically healthy individuals who had preserved insulin sensitivity and did not have any metabolic syndrome components.

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Materials and methods Study population

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We conducted a cohort study of healthy participants who underwent a routine health check-up examination at the Center for Health Promotion of Samsung Medical Center, South Korea, between January 2005 and December 2013. The study population consisted of men who underwent at least two health check-ups that included carotid ultrasonography, performed at

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least 1 year apart (n = 21,198). We excluded 11,084 participants for the following reasons: abnormal CIMT or carotid plaque at baseline (n = 10,038); self-reported history of malignancy (n = 221); self-reported history of cardiovascular disease (n = 937); taking aspirin

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or other antiplatelet drugs (n = 2,463); and missing data describing important covariates such as anthropometric data, metabolic parameters, or carotid ultrasonography (n = 658).

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We then selected metabolically healthy participants by excluding participants who had any of the following metabolic abnormalities at baseline [6]: (i) high serum triglycerides, defined as triglycerides ≥ 150 mg/dL (1.7 mmol/L) or drug treatment for this lipid abnormality (n = 1,936); (ii) low high-density lipoprotein-cholesterol (HDL-C) level, defined as HDL-C ≤ 40 mg/dL (1.0 mmol/L) for men or drug treatment for this lipid abnormality (n = 638); (iii) high blood pressure, defined as blood pressure ≥ 130/85 mmHg or drug treatment for previously diagnosed hypertension ( n = 1,091); (iv) high fasting blood glucose level, defined as glucose >100 mg/dL (5.6 mmol/L) or drug treatment for previously diagnosed

ACCEPTED MANUSCRIPT diabetes (n = 1,273) [26]; or (v) homeostasis model assessment of insulin resistance (HOMAIR) value ≥ 2.5 (n = 1,175) [6]. Finally, 6,453 metabolically healthy men without abnormal CIMT or carotid plaque were included in the cohort study population (Fig. 1). This study was

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approved by the Institutional Review Board of the Samsung Medical Center.

Data collection

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The comprehensive health-screening program assessed demographic characteristics,

anthropometric measurements, and serum biochemical measurements, and it included a self-

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administered health questionnaire on smoking habits, alcohol consumption, physical activity, medication history (including current use), and personal medical history. Personal medical history included hypertension, diabetes mellitus, dyslipidemia, malignancy, stroke and cardiovascular disease. Medication history included current use of antihypertensive drugs,

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hypoglycemic agents, lipid-lowering drugs, and antiplatelet agents, including aspirin. Smoking status was categorized into three groups: never a smoker, former smoker, and current smoker. Alcohol consumption status was divided into non-heavy (≤ 20 g/day) and

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heavy (> 20 g/day). Regular exercise was defined as exercising three or more times per week with moderate intensity physical activity.

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Height and weight were calculated to the nearest 0.1 cm and 0.1 kg, respectively, using an Inbody 720 machine (Biospace, Seoul, Korea), with the participants wearing light clothing and bare feet. BMI was calculated as the weight in kilograms divided by height in square meters (kg/m2). Waist circumference was measured in a horizontal plane at the midpoint between the inferior margin of the last rib and the superior iliac crest. Blood pressure was measured in a seated position after > 5 minutes of quiet rest, using an automated blood pressure monitor (Dinamap PRO 100; GE Healthcare, Milwaukee, Wisconsin). After a ≥ 12 hour fast, blood samples were collected in the morning and analyzed at the

ACCEPTED MANUSCRIPT hospital clinical laboratory. Serum levels of glucose, insulin, glycated hemoglobin (HbA1c), total cholesterol, triglycerides, low-density lipoprotein-cholesterol (LDL-C), and HDL-C were measured using enzymatic colorimetric and liquid-selective detergent methods with a

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Hitachi 7600 (Hitachi, Tokyo, Japan). Plasma insulin levels were measured using a radioimmunoassay method with the Packard Cobra II 5010 (Packard Instrument, Baltimore, MD). HbA1c levels were measured using a high-performance liquid chromatography method

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with a Tosoh Glycohemoglobin Analyzer (Tosoh Bioscience Inc, Tokyo, Japan). Serum

glucose levels were measured using the hexokinase/glucose-6-phosphate dehydrogenase

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method with a Hitachi 7600 Modular Dp-110 autoanalyzer (Hitachi, Tokyo, Japan). The interand intra-assay coefficients of variation for quality control specimens were < 5% for the blood variables. HOMA-IR was used to evaluate insulin resistance, which was calculated as

Carotid ultrasonography

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follows: (fasting insulin [µU/mL] × fasting glucose [mg/dL])/405.

Subclinical carotid atherosclerosis was assessed using carotid ultrasonography and was

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defined as the presence of an abnormally increased CIMT or carotid plaque. These are wellestablished markers of subclinical atherosclerosis, which indicate an increased risk of

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cardiovascular disease [14,16,22,27]. Experienced sonographers performed the carotid artery sonography using a B-mode ultrasound system (Logiq 7, GE Medical System, Milwaukee, WI, USA) with a 9-MHz linear array transducer [28]. Carotid examination included bilateral visualization of the common, internal, and external carotid arteries. Abnormal CIMT was defined as a CIMT more than 0.9 mm [27]. Carotid plaque was defined as a focal wall thickening of at least 0.5 mm or 50% of the surrounding CIMT that encroaches into the arterial lumen, or a focal region with CIMT greater than 1.5 mm that protrudes into any carotid segment [14,27].

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Statistical analysis Continuous variables are reported as means ± standard deviations, and categorical variables

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are presented as percentages. To test for linear trends, we considered the BMI categorical variable as a continuous variable by imputing the median value of each BMI category in the linear regression for continuous variables or Chi-squared linear trend test for categorical

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variables. Descriptive statistics were used to summarize the baseline characteristics of the participants by BMI category. BMI was categorized on the basis of Asian-specific criteria

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[29]: underweight, BMI < 18.5 kg/m2; normal weight, BMI of 18.5 to 22.9 kg/m2; overweight, BMI of 23.0 to 24.9 kg/m2; and obese, BMI ≥ 25.0 kg/m2. The primary endpoint was the development of incident subclinical carotid atherosclerosis. Participants were followed from the baseline exam until the development of subclinical carotid atherosclerosis or the last

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health exam among those who did not develop subclinical carotid atherosclerosis. We used Cox regression models to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) for incident subclinical atherosclerosis comparing BMI categories

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at baseline with the normal weight BMI category. We fit three multivariable models with progressive adjustment for potential confounding factors. Model 1 was adjusted for age, year

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of screening exam, smoking status, alcohol intake, regular exercise, and glomerular filtration rate (GFR). Model 2 was further adjusted for metabolic factors that could be potential mediators of the association between obesity and subclinical carotid atherosclerosis, including systolic blood pressure, blood glucose, triglycerides, and HDL-C. Model 3 was further adjusted for central obesity represented by waist circumference, which is a primary component of metabolic syndrome. In addition, we conducted subgroup analyses to identify interactions between BMI categories and clinically relevant groups, defined by age (< 50 vs. ≥ 50 years), smoking status (non-current vs. current smokers), alcohol intake (non-heavy vs.

ACCEPTED MANUSCRIPT heavy drinkers), regular exercise (< 3 vs. ≥ 3 times per week), high-sensitivity C-reactive protein (hsCRP; < 0.3 mg/dL vs. ≥ 0.3 mg/dL), and metabolic syndrome development prior to incident subclinical atherosclerosis (no vs. yes). A p-value < 0.05 was considered statistically

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significant. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).

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Results

The mean (SD) age of the 4,506 metabolically healthy participants was 48.0 (7.6) years. The

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median follow-up time for the study participants was 4.2 years (interquartile range, 2.2-6.9). The average number of ultrasound exams per person was 4.4. The median follow-up period was not significantly different among the BMI categories, but the frequency of ultrasound increased across increasing BMI categories. Participants in higher BMI categories were more

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likely to exercise regularly. Waist circumference, body fat percentage, systolic and diastolic blood pressure, fasting blood glucose, HbA1c, uric acid, total cholesterol, LDL-C, triglycerides, alanine aminotransferase, gamma-glutamyltransferase, insulin, and HOMA-IR

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increased gradually across BMI categories, whereas levels of HDL-C and estimated GFR decreased (Table 1). Regarding the incidence of metabolic abnormalities such as prediabetes

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or diabetes, prehypertension or hypertension, high triglycerides, and low HDL-C, participants in higher BMI categories developed metabolic abnormalities more frequently during the follow-up period.

During the 34,797.9 person-years of follow-up, subclinical carotid atherosclerosis developed in 1,916 participants. In our cohort of metabolically healthy men, increasing baseline BMI categories showed a positive association with the incidence of subclinical carotid atherosclerosis. The incidence rates (per 1,000 person-years) of subclinical carotid atherosclerosis for each of the BMI categories were 22.4 for underweight, 44.9 for normal

ACCEPTED MANUSCRIPT weight, 56.1 for overweight, and 70.6 for obese participants (Table 2). In multivariable model 1, adjusted for age, year of screening exam, smoking status, alcohol intake, regular exercise, and GFR, HRs (95% CI) for incident subclinical carotid atherosclerosis comparing

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underweight, overweight, and obese participants with normal weight participants were 0.46 (0.26-0.81), 1.24 (1.12-1.38), and 1.54 (1.38-1.72), respectively (p for trend, < 0.001). To evaluate whether the increased risk of subclinical carotid atherosclerosis with increased BMI

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was mediated by metabolic risk factors in MHO participants, we conducted additional

analyses adjusting for fasting blood glucose, systolic blood pressure, triglycerides, and HDL-

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C (Model 2) or the metabolic variables plus waist circumference (Model 3). After adjusting for the metabolic risk factors, the association was attenuated slightly but it remained significant (Model 2 and Model 3). Additionally, the association was evident even among MHO men without central obesity (waist circumference > 90 cm) (Table 3). We performed

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additional analyses to explore whether the association remained when only one outcome variable, either abnormal CIMT or carotid plaque, was used. MHO was still associated with an increased risk of either abnormal CIMT or carotid plaque, but the association was stronger

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with incident abnormal CIMT (Table 4).

The relationship between BMI categories and incident subclinical carotid atherosclerosis

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was similar across participant subgroups (Table 5), with no significant interactions by age (< 50 vs. ≥ 50 years), smoking status (non-current vs. current smokers), alcohol intake (nonheavy vs. heavy drinkers), regular exercise (< 3 vs. ≥ 3 times per week), hsCRP level (< 0.3 mg/dL vs. ≥ 0.3 mg/dL), or metabolic syndrome development prior to incident subclinical atherosclerosis (no vs. yes). The association between BMI and the risk of subclinical atherosclerosis was observed even among the participants without preceding development of metabolic syndrome. In this subgroup, the aHRs (95% CI) for incident subclinical atherosclerosis comparing overweight and obese with normal-weight participants were 1.45

ACCEPTED MANUSCRIPT (1.18-1.77) and 2.02 (1.64-2.48), respectively.

Discussion

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In this cohort study of metabolically healthy men without subclinical carotid atherosclerosis at baseline, we found that overweight and obese participants were at a higher risk for

developing subclinical carotid atherosclerosis than metabolically healthy normal weight

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participants. This association was attenuated but present after adjustment for metabolic risk factors. In addition, the association between MHO and incident subclinical carotid

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atherosclerosis was evident even among MHO men without central obesity, and it was observed similarly in all subgroups evaluated. Our findings indicate that MHO is not a harmless condition and it can induce the development of carotid atherosclerosis. Previous reports describing the effects of MHO on cardiovascular disease have yielded

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contradictory results [5,10,11,30,31]. These previous studies have several limitations. First, the manner in which MHO is defined may be critically important. Although there is no consensus regarding the definition of the MHO phenotype, previous studies have defined

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MHO as having two or fewer metabolic syndrome components [6]. However, the risk of cardiovascular disease increases progressively with the number of metabolic components,

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beginning with one component. Each metabolic parameter is an established risk factor of cardiovascular disease [32-34]. Second, it is important to consider the potential effect of the MHO phenotype on the development of cardiovascular disease using a longitudinal study with adequate follow-up. The cross-sectional design of other studies limits the assessment of causality. In our study, even though we used a very strict definition of the metabolically healthy state as having no metabolic abnormalities and no insulin resistance, the MHO phenotype was not a benign condition for the development of atherosclerosis. Several previous studies have reported an association between the MHO phenotype and

ACCEPTED MANUSCRIPT subclinical atherosclerosis [23,30]. A recent Korean cross-sectional analysis reported that MHO participants had a higher prevalence of subclinical coronary atherosclerosis as measured by coronary artery calcium scores [30]. Increased CIMT and carotid plaque as early

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signs of cardiovascular disease are associated with an increased risk of stroke and coronary heart disease [35]. Two cross-sectional studies also revealed that MHO was associated with a higher prevalence of subclinical carotid atherosclerosis, as measured by carotid ultrasound

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[13,23]. One cohort study demonstrated that metabolically healthy overweight and obese individuals have a greater risk of CIMT progression than metabolically healthy normal-

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weight individuals [25]. However, that study used a small sample size of 152 metabolically healthy overweight and obese participants and measured only CIMT, and did not measure carotid plaque. Increased CIMT is widely accepted as an indicator of atherosclerosis. However, carotid plaque appears to be a more powerful predictor of cardiovascular disease

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than CIMT alone [18,36,37]. Therefore, measuring CIMT along with carotid plaque is an important method in the prediction of cardiovascular disease. In our study, the association between MHO and the development of carotid

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atherosclerosis was attenuated after adjusting for metabolic risk factors, which suggests that the effects of MHO may be mediated by metabolic factors even though their metabolic

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parameters were below the levels considered abnormal. Adiposity increases blood pressure through renal salt retention, leptin concentrations, hyperinsulinemia, and subsequently increased peripheral vascular resistance [38]. Recent longitudinal studies with long follow-up periods have suggested that MHO individuals have a higher risk of cardiovascular events than normal weight individuals [5,10]. These studies only assessed metabolic status at baseline, and did not evaluate metabolic changes over long-term follow-up periods. The transition from an MHO status to a metabolically unhealthy status may affect disease risk. Indeed, our findings revealed that metabolically healthy participants with a higher BMI

ACCEPTED MANUSCRIPT developed metabolic abnormalities more frequently during their follow-up period. MHO status is unstable and can progress toward overt metabolic abnormalities. Adiposity is also associated with dyslipidemia, insulin resistance, and subsequently glucose intolerance [39].

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The unexplained risk associated with these metabolic mediators might be caused by other mechanisms such as endothelial dysfunction, systemic inflammation, or increased

thrombogenic factors [39,40]. Indeed, adipose tissue is now recognized as an important

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endocrine organ that secretes numerous cytokines. Among the adipocytokines, tumor necrosis factor, interleukin-6, and plasminogen activator inhibitor may also be involved in pro-

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thrombotic effects and atherogenesis [39,41-43].

Several limitations need to be a considered when interpreting the results of our study. First, although carotid ultrasound is an easily available and safe method for assessing CIMT or plaque, it is associated with measurement error. In addition, due to the long study period,

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different sonographers were involved in performing carotid ultrasound, though all of the procedures followed a standardized protocol. Furthermore, the study participants were consecutively enrolled and the personnel who collected the data were unaware of the study

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aims; thus, measurement error in carotid ultrasound is likely non-differential, possibly resulting in an underestimation of the association between the MHO phenotype and the risk

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of carotid atherosclerosis. Second, although we measured several important confounders in the multivariable analysis, we cannot exclude the possibility of residual confounding factors due to unmeasured parameters such as dietary variables and socioeconomic status. Third, we used BMI as a measure of obesity; however, BMI does not contain information on the composition and distribution of fat and muscle mass. If the MHO participants had a higher proportion of lean mass, then the association between the MHO phenotype and carotid atherosclerosis risk may have been attenuated. Finally, our study population included healthy men who underwent a routine health check-up; thus, it may be difficult to generalize our

ACCEPTED MANUSCRIPT findings to other populations. This study also has several strengths. First, its cohort study design, relatively large sample size, and exclusion of baseline abnormal CIMT or carotid plaque allowed us to identify a

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temporal relationship, which is not usually possible in cross-sectional studies. Additional strengths include the use of high-quality, standardized clinical and laboratory methods and the use of a strict definition of metabolically healthy status (no metabolic abnormalities and

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preserved insulin sensitivity).

In conclusion, this study showed that the MHO phenotype was associated with an

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increased risk of developing subclinical carotid atherosclerosis in healthy men without baseline carotid atherosclerosis. These findings provide strong support for the hypothesis that the effects of the MHO phenotype are sufficient to increase the risk of disease development. The association seems to be mediated by metabolic risk factors. The risk of subclinical

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carotid atherosclerosis development was also higher among MHO participants without central obesity. Therefore, because of the increased risk of cardiovascular disease, weight reduction

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should be recommended to MHO individuals.

Conflict of interest

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The authors declared they do not have anything to disclose regarding conflict of interest with respect to this manuscript.

Author contributions Study concept and design: Seungho Ryu. Acquisition, analysis, or interpretation of data: Tae Jun Kim, Hee-Young Shin, Mira Kang, and Jaehwan Jee.

ACCEPTED MANUSCRIPT Writing and drafting of the manuscript: Tae Jun Kim. Critical revision of the manuscript for important intellectual content: Yoosoo Chang, YoonHo Choi, Hee Jung Son and Seungho Ryu.

Study supervision: Hee Jung Son and Seungho Ryu.

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All authors read and approved the final manuscript.

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Statistical analysis: Hyeon Seon Ahn and Soo Hyun Ahn.

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Table 1 Characteristics of metabolically healthy participants by BMI category. Underweight (<18.5)

Normal weight (18.5-22.9)

Overweight (23.0-24.9)

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Number of participants 6,453 104 2,215 2,165 Age (years) 49.5 ± 8.4 48.0 ± 10.7 49.5 ± 8.6 49.9 ± 8.2 Waist circumference (cm) 84.5 ± 6.7 70.0 ± 4.1 79.8 ± 4.6 85.6 ± 4.0 Body fat percentage (%) 19.5 ± 4.4 12.3 ± 3.3 17.0 ± 3.6 20.0 ± 3.4 Current smoker (%) 39.5 43.3 40.9 37.3 Heavy alcohol intake (%) 6.5 3.9 6.4 6.4 Regular exercise (%) 46.9 36.5 44.4 48.9 Systolic BP (mmHg) 112.6 ± 12.1 107.6 ± 11.0 110.9 ± 12.1 113.1 ± 12.0 Diastolic BP (mmHg) 71.2 ± 8.7 69.6 ± 8.7 70.3 ± 8.9 71.3 ± 8.5 FBG (mg/dl) 87.6 ± 6.8 84.2 ± 7.4 86.7 ± 7.0 88.1 ± 6.7 HbA1c (%) 5.3 ± 0.3 5.3 ± 0.4 5.3 ± 0.3 5.3 ± 0.3 Uric acid (mg/dl) 5.8 ± 1.2 5.4 ± 1.2 5.6 ± 1.1 5.9 ± 1.2 Total cholesterol (mg/dl) 191.5 ± 30.0 177.4 ± 29.1 188.6 ± 29.7 193.5 ± 29.8 LDL-C (mg/dl) 125.9 ± 28.2 106.7 ± 26.3 121.5 ± 27.7 128.7 ± 28.0 HDL-C (mg/dl) 56.9 ± 11.6 64.3 ± 12.5 59.0 ± 12.2 56.1 ± 11.0 Triglycerides (mg/dl) 93 (73-117) 77 (59-100) 87 (68-110) 96 (75-119) ALT (U/L) 20 (16-27) 17 (13-24) 18 (15-24) 21 (16-27) GGT (U/L) 25 (18-37) 18 (15-24) 22 (16-31) 26 (19-38) Estimated GFR (mL/min) 88.8 ± 12.1 93.3 ± 12.7 90.0 ± 12.0 88.0 ± 12.0 hsCRP (mg/dl) 0.06 (0.03-0.11) 0.04 (0.03-0.10) 0.05 (0.03-0.10) 0.06 (0.04-0.11) Insulin (uIU/ml) 6.4 (4.7-8.1) 4.6 (2.5-6.5) 5.8 (4.2-7.5) 6.6 (5.0-8.2) HOMA-IR 1.4 (1.0-1.8) 1.0 (0.5-1.4) 1.2 (0.9-1.6) 1.4 (1.1-1.8) Follow-up period (years) 4.2 (2.2-6.9) 4.2 (2.2-6.8) 4.1 (2.1-6.9) 4.3 (2.2-7.0) Frequency of ultrasound 4.4 3.8 4.2 4.4 Incidence of metabolic abnormalities (%) 1 component 24.2 15.8 21.4 24.9 2 component 10.3 3.9 8.1 11.1 ≥ 3 component 16.6 3.9 12.2 17.7 Values are expressed as means ± standard deviation, medians (interquartile range), or percentages.

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1,669 49.2 ± 8.2 91.2 ± 5.2 23.2 ± 3.8 40 7 51.5 115.0 ± 11.8 72.5 ± 8.4 88.6 ± 6.4 5.3 ± 0.3 6.0 ± 1.2 194.2 ± 30.3 130.2 ± 27.8 54.4 ± 10.6 100 (80-122) 23 (18-31) 29 (21-44) 88.2 ± 12.1 0.07 (0.04-0.14) 7.3 (5.6-8.8) 1.6 (1.2-2.0) 4.2 (2.1-6.9) 4.6

0.005 <0.001 <0.001 0.065 0.058 0.001 <0.001 <0.001 <0.001 0.007 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.066 <0.001 <0.001 0.014 0.039

27.8 12.7 22.4

<0.001 <0.001 <0.001

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BMI, body mass index; BP, blood pressure; FBG, fasting blood glucose; HbA1c, glycated hemoglobin; LDL-C, low-density lipoprotein cholesterol; HDLC, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; GGT, gamma-glutamyltransferase; GFR, glomerular filtration rate; hsCRP, highsensitivity C-reactive protein; HOMA-IR, homeostasis model assessment of insulin resistance.

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Table 2 Development of subclinical carotid atherosclerosis by body mass index categories in metabolically healthy participants.

Person-years

Incident cases

Incidence density (per 1,000 person-years)

Multivariable-adjusted HRa (95% CI) Model 2c 0.51 (0.29-0.90) 1.00 (reference) 1.17 (1.05-1.30) 1.40 (1.25-1.57) <0.001

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Model 1b 0.46 (0.26-0.81) 1.00 (reference) 1.24 (1.12-1.38) 1.54 (1.38-1.72) <0.001

Model 3d 0.36 (0.18-0.72) 1.00 (reference) 1.13 (1.00-1.28) 1.28 (1.09-1.51) <0.001

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<18.5 580.1 13 22.4 18.5-22.9 13115 589 44.9 23.0-24.9 12124.2 680 56.1 ≥25 8978.6 634 70.6 p for trend BMI, body mass index; HR, hazards ratio; CI, confidence intervals. a Estimated from Cox proportional hazard models. b Multivariable model 1 was adjusted for age, year of screening exam, smoking status, alcohol intake, regular exercise, and glomerular filtration rate. c Model 2: model 1 plus adjustment for fasting blood glucose, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol. d Model 3: model 2 plus adjustment for waist circumference.

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BMI categories (kg/m2) Underweight (<18.5)

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Overweight (23.0-24.9)

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Table 3 Risk of subclinical carotid atherosclerosis by body mass index categories in metabolically healthy participants with waist circumference ≤ 90 cm.

Obese (≥25)

p for trend

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Model 1a 0.46 (0.26-0.81) reference 1.23 (1.11-1.38) 1.53 (1.33-1.75) <0.001 Model 2b 0.51 (0.29-0.90) reference 1.16 (1.04-1.30) 1.42 (1.24-1.63) <0.001 Model 3c 0.37 (0.18-0.76) reference 1.10 (0.96-1.26) 1.31 (1.09-1.56) <0.001 BMI, body mass index; HR, hazards ratio; CI, confidence intervals. Estimated from Cox proportional hazard models. a Multivariable model 1 was adjusted for age, year of screening exam, smoking status, alcohol intake, regular exercise, and glomerular filtration rate. b Model 2: model 1 plus adjustment for fasting blood glucose, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol. c Model 3: model 2 plus adjustment for waist circumference. BMI, body mass index; HR, hazards ratio; CI, confidence intervals.

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BMI categories (kg/m2) Underweight (<18.5)

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Table 4 Risk of abnormal IMT and carotid plaque by body mass index categories in metabolically healthy participants.

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Abnormal IMT Model 1a 0.46 (0.22-0.97) reference 1.36 (1.19-1.56) 1.88 (1.64-2.15) <0.001 b Model 2 0.53 (0.25-1.13) reference 1.26 (1.10-1.44) 1.65 (1.44-1.90) <0.001 Model 3c 0.38 (0.14-1.02) reference 1.12 (0.95-1.31) 1.30 (1.05-1.60) <0.001 Carotid plaque Model 1a 0.51 (0.26-0.99) reference 1.08 (0.94-1.25) 1.24 (1.07-1.45) <0.001 Model 2b 0.55 (0.28-1.07) reference 1.04 (0.90-1.21) 1.18 (1.01-1.39) <0.001 0.55 (0.27-1.14) reference 1.03 (0.87-1.21) 1.08 (0.86-1.35) 0.012 Model 3c BMI, body mass index; HR, hazards ratio; CI, confidence intervals. Estimated from Cox proportional hazard models. a Multivariable model 1 was adjusted for age, year of screening exam, smoking status, alcohol intake, regular exercise, and glomerular filtration rate. b Model 2: model 1 plus adjustment for fasting blood glucose, systolic blood pressure, triglycerides, and high-density lipoprotein cholesterol. c Model 3: model 2 plus adjustment for waist circumference.

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Table 5 Risk of subclinical carotid atherosclerosis by body mass index category in clinically-relevant subgroups of metabolically healthy participants. BMI categories (kg/m2) Underweight (<18.5)

Normal weight (18.5-22.9)

Overweight (23.0-24.9)

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Obese (≥25)

p for interaction

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Age 0.794 < 50 years 0.49 (0.12-1.99) reference 1.47 (1.15-1.89) 1.91 (1.48-2.45) ≥ 50 years 0.42 (0.17-1.02) reference 1.27 (1.08-1.49) 1.72 (1.46-2.01) Current smoking 0.061 No 0.31(0.10-0.97) reference 1.52 (1.29-1.78) 2.08 (1.76-2.47) Yes 0.76 (0.28-2.05) reference 1.15(0.91-1.45) 1.59 (1.27-2.01) Alcohol intake 0.246 Non-heavy 0.48 (0.23-1.01) reference 1.40 (1.22-1.61) 1.86 (1.62-2.14) Heavy 0.89 (0.35-2.07) reference 1.07 (0.67-1.71) 2.23 (1.41-3.54) Regular exercise 0.538 No 0.71(0.29-1.71) reference 1.38 (1.13-1.67) 1.96 (1.61-2.39) Yes 0.24 (0.06-0.96) reference 1.37 (1.14-1.64) 1.82 (1.51-2.19) hsCRP 0.771 < 0.3mg/dl 0.48 (0.23-1.02) reference 1.39 (1.20-1.60) 1.88 (1.63-2.16) ≥ 0.3mg/dl 0.64 (0.18-2.41) reference 1.19 (0.74-1.90) 1.90 (1.21-3.00) Preceding development of MS 0.216 No 0.21 (0.05-0.85) reference 1.45 (1.18-1.77) 2.02 (1.64-2.48) Yes 0.87 (0.36-2.12) reference 1.30 (1.09-1.55) 1.77 (1.48-2.12) BMI, body mass index; hsCRP, high-sensitivity C-reactive protein; MS, metabolic syndrome. Estimated from Cox proportional hazard models. Multivariable model was adjusted for age, sex, BMI, year of screening exam, smoking status, alcohol intake, and regular exercise.

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FIGURE LEGEND

Flow diagram of study participants.

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Fig. 1.

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ACCEPTED MANUSCRIPT Highlights

Obesity and metabolic abnormalities are known risk factors for cardiovascular disease.

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Risk of cardiovascular disease among obese people without metabolic abnormalities remains unclear.

Metabolically healthy obese phenotype was associated with an increased risk of incident

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subclinical carotid atherosclerosis,