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Imaging-based body fat depots and new-onset atrial fibrillation typically population: a prospective cohort study | BMC Medicine

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Study population

The current study was embedded within the Rotterdam Study (RS) [21]. The Rotterdam Study is a prospective population-based cohort study that goals to evaluate the occurrence and progression of risk aspects for chronic diseases in middle-age and elderly individuals. During 1990–1993, all inhabitants of Ommoord, a district in town of Rotterdam, the Netherlands, aged ≥ 55 years were invited for the study. A complete of 7983 (78% of all invitees) agreed to participate (RS-I). In 2000, the cohort was prolonged with 3011 latest participants who had turn into ≥ 55 years or had migrated into the research area (RS-II). Participants attend followed-up examinations every 3–4 years. Final result data on morbidity and mortality were constantly collected through linkage with digital files from general practitioners within the study area [21].

The Rotterdam Study has been approved by the Medical Ethics Committee of Erasmus MC (registration number MEC 02.1015) and by the Dutch Ministry of Health, Welfare, and Sport (Population Screening Act WBO, license number 1071272-159521-PG). The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and into the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalog number NTR6831. All participants provided written informed consent to take part in the study and to have their information obtained information from their treating physicians.

Body composition was assessed through DXA throughout the fourth visit of RS-I (RS-I-4) and the second visit of RS-II (RS-II-2), between 2002 and 2006 [22]. In total, 3724 participants were scanned by DXA. We excluded participants with prevalent AF at baseline (N = 256). Thus, 3468 individuals were included within the analyses for DXA-assessed fat depots and incident AF. From 2003 to 2006, all participants who visited the research center were asked to take part in a computed tomography (CT) study geared toward visualizing vascular calcification in multiple vessel beds. In total, 2524 participants were scanned by CT [23]. Amongst 2230 participants with sufficient quality CT scan (N-excluded = 294), 91 participants with prevalent AF at baseline were excluded. In total, 2139 participants were included within the CT-assessed fat depot analyses. Further analyses of body composition rating and fat distribution in association with AF risk included participants who had each DXA and CT measurements available (n = 1297). (Fig. 1)

Fig. 1figure 1

Flowchart of the study population

Assessment of body composition

Body composition was assessed by DXA using a ProdigyTM total body-fan beam densitometer (GE Lunar Corp, Madison, WI, USA) following the manufacturer protocols, with scans analyzed with enCORE software V13.6 (from GE Lunar) using pre-determined regions of interest. Total lean mass is the sum of trunk lean mass and appendicular lean mass (the sum of lean tissue from the legs and arms), and total fat mass is the sum of android fat mass (localized across the waist), gynoid fat mass (localized across the breasts, hips, and thighs), and fat mass not otherwise specified [22, 24]. As reported, repeatability of the DXA-based measurements was excellent with a Pearson correlation coefficient of 0.991 for total fat mass and 0.994 for total lean mass [25]. The android-to-gynoid fat mass ratio was also calculated. Along with the online mass of every fat depot, we calculated the share of fat mass, android fat mass, and gynoid fat mass as well by the use of dividing each of them by total body mass.

Assessment of liver fat and epicardial fat

A 16-slice (n = 785) or 64-slice (n = 1739) multidetector CT scanner (Somatom Sensation 16 or 64, Siemens, Forchheim, Germany) was used to perform non-enhanced cardiac, ECG-gated CT scanning. Detailed information regarding imaging parameters of the scans is provided elsewhere [23].

Liver fat was assessed by utilizing a standardized procedure which was described previously [23]. In short, we placed three circular regions of interest within the liver using Philips iSite Enterprise software (Royal Philips Electronics N.V. 2006) [26]. These regions were drawn throughout the imaged liver tissue (including each the left and right liver lobes) and were fastidiously chosen to incorporate only liver tissue, and no disruptive tissue reminiscent of large blood vessels, cysts, or focal lesions. Then, we calculated the mean Hounsfield unit (HU) value from these three measurements as a marker of the full amount of liver fat, which is a reliable proxy for the mean HU value of the entire liver [26]. We used a totally automatic method [27] to quantify the quantity of epicardial fat in milliliters. Briefly, this method consisted of whole heart segmentation and epicardial fat volume calculation. For the segmentation, we used a multi-atlas-based approach, through which eight manually segmented contrast-enhanced cardiac scans (atlases) were registered (spatially aligned) with every participant’s CT scan. Next, we used this segmentation to find out the quantity of epicardial fat.

Assessment of atrial fibrillation

Methods on event adjudication for prevalent and incident AF have been described previously [13, 22, 28, 29].. Ascertainment of AF at baseline and follow-up examinations in our study has been based on clinical information from the medical records for all participants of the Rotterdam Study. Inside the Rotterdam Study, data on medical history and drugs use are constantly being collected through multiple sources including a baseline home interview, a physical examination at our research center, the pharmacy prescription records, the Nationwide Medical Registry of all primary and secondary hospital discharge diagnosis, and screening of general practitioner’s records. As well as, a resting 10-s 12-lead electrocardiogram (ECG) used with an ACTA Gnosis IV ECG recorder (Esaote; Biomedical, Florence Italy) is obtained from all participants at every visit of the Rotterdam Study to confirm AF. The ECG records were stored digitally and analyzed with the Modular ECG Evaluation system (MEANS). The ECG records were stored digitally and analyzed with the Modular ECG Evaluation system (MEANS). Subsequently, the AF outcomes are adjudicated independently by two research physicians. In case of disagreement, a senior cardiologist is consulted. Participants were followed from the date of enrolment within the RS until the date of onset of AF, date of death, loss to follow-up, or to January 1, 2014, whichever occurred first.

Assessment of cardiovascular risk aspects

Methods for assessment of cardiovascular risk aspects are detailed in Additional file 1: Methods [22, 28, 30].

Statistical evaluation

Descriptives were presented as mean ± standard deviation (SD) or median (interquartile range—IQR) for continuous variables and numbers (percentages) for categorical variables. Because the HU values (A) had a left-skewed, non-normal distribution, we used exponential transformed values (B) [B = A3.5/10,000] [23]. Also, since a lower value of HU represents a bigger amount of liver fat, we inversed the HU values (-HU) to represent levels of liver fat.

Values of every fat depot were standardized to permit for direct comparisons. Cox proportional hazards regression evaluation was used to look at whether various measures of fat depot including fat mass, android fat mass, gynoid fat mass, android to gynoid fat ratio, liver fat, and epicardial fat were related to the new-onset AF. Besides, within the evaluation with DXA measures, we also assessed each percentage of fat mass, android fat mass, and gynoid fat mass of total body mass in association with incident AF. In the primary model, we calculated the age- and sex-adjusted hazard ratio (HR) and their 95% confidence intervals (CIs). Model 2 was moreover adjusted for cardiovascular risk aspects including total and HDL cholesterol, history of hypertension, history of diabetes mellitus (DM), history of coronary heart disease (CHD), history of heart failure (HF), history of left ventricular hypertrophy, smoking status, total alcohol intake, use of lipid-lowering medication, and use of cardiac medication. Furthermore, we moreover adjusted for total lean mass in Model 3 and for total fat mass in Model 4, respectively. Finally, potential nonlinear associations were examined between various fat depots and incident AF using splines analyses within the Cox models. Nevertheless, there was no indication for significant nonlinearity (all P for nonlinearity >0.05, result not shown). In sensitivity analyses, effect modification by sex was tested. Besides, the analyses were repeated after stratifying participants by BMI; categories of BMI<25 and BMI≥25 kg/m2. We also preformed all analyses amongst participants without prevalent cardiovascular diseases (CHD, HF, and stroke) at baseline.

To analyze the potential cumulative effect of fat depots, we further included the five fat depots (fat mass, android fat mass, gynoid fat mass, liver fat, and epicardial fat) to generate a body fat rating. Each fat depot was scored from 0 to 2 in keeping with the respective tertile. All component scores were summed to acquire a complete rating starting from 0 to 10 in our population, with higher scores indicating higher total body fat. Consequently, Cox proportional hazards regression analyses, with the identical multivariate-adjusted models as aforementioned, were conducted to research the impact of body fat rating (as tertiles: rating 0–2, rating 3–6, and rating 7–10 with the primary tertile because the reference group) on incident AF.

We then developed fat distribution patterns. The distribution patterns were derived using principal component evaluation (PCA) on values of the fat depots (including fat mass, android fat mass, gynoid fat mass, liver fat, and epicardial fat) [31]. We used varimax rotation to acquire potential principal components. Factor loadings, which reflect the standardized correlation between each fat depot and a body fat distribution pattern, were used to characterize potential patterns using a cut-off of 0.5. For every participant, pattern-adherence scores were constructed by summing up observed values of the pattern’s fat depots weighted by the corresponding factor loading for every of the 2 patterns individually. Further, Cox proportional hazards regression evaluation was used to evaluate how the identified fat distribution patterns (as quartiles with the primary quartile as reference) were related to incident AF.

All missing values in covariates were imputed under the idea of missing at random using multiple imputation [32]. For multiple imputation, all available data was used to generate 5 imputed data sets. Statistical significance was considered at two-tailed P-value <0.05. The analyses were done using R software (R 4.0.3; R Foundation for Statistical Computing, Vienna, Austria).

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