Adipose tissue biomarkers and type 2 diabetes incidence in normoglycemic participants in the MESArthritis Ancillary Study: A cohort study.

BACKGROUND: Given the central role of skeletal muscles in glucose homeostasis, deposition of adipose depots beneath the fascia of muscles (versus subcutaneous adipose tissue [SAT]) may precede insulin resistance and type 2 diabetes (T2D) incidence. This study was aimed to investigate the associations between computed tomography (CT)-derived biomarkers for adipose tissue and T2D incidence in normoglycemic adults. METHODS AND
FINDINGS: This study was a population-based multiethnic retrospective cohort of 1,744 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) with normoglycemia (baseline fasting plasma glucose [FPG] less than 100 mg/dL) from 6 United States of America communities. Participants were followed from April 2010 and January 2012 to December 2017, for a median of 7 years. The intermuscular adipose tissue (IMAT) and SAT areas were measured in baseline chest CT exams and were corrected by height squared (SAT and IMAT indices) using a predefined measurement protocol. T2D incidence, as the main outcome, was based on follow-up FPG, review of hospital records, or self-reported physician diagnoses. Participants' mean age was 69 ± 9 years at baseline, and 977 (56.0%) were women. Over a median of 7 years, 103 (5.9%) participants were diagnosed with T2D, and 147 (8.4%) participants died. The IMAT index (hazard ratio [HR]: 1.27 [95% confidence interval [CI]: 1.15-1.41] per 1-standard deviation [SD] increment) and the SAT index (HR: 1.43 [95% CI: 1.16-1.77] per 1-SD increment) at baseline were associated with T2D incidence over the follow-up. The associations of the IMAT and SAT indices with T2D incidence were attenuated after adjustment for body mass index (BMI) and waist circumference, with HRs of 1.23 (95% CI: 1.09-1.38) and 1.29 (95% CI: 0.96-1.74) per 1-SD increment, respectively. The limitations of this study include unmeasured residual confounders and one-time measurement of adipose tissue biomarkers.
CONCLUSIONS: In this study, we observed an association between IMAT at baseline and T2D incidence over the follow-up. This study suggests the potential role of intermuscular adipose depots in the pathophysiology of T2D. TRIAL REGISTRATION: ClinicalTrials.gov NCT00005487.

Introduction

Type 2 diabetes (T2D) affects more than 20 million new cases every year, and, in addition to the attributable high morbidity and mortality, imposes an increased financial burden on healthcare systems worldwide [1]. However, most of this burden can be avoided by early identification of at-risk individuals and the rapid implementation of primary and secondary preventive measures [2]. Therefore, identifying the biomarkers associated with T2D incidence is crucial to minimize the morbidity, mortality, and healthcare financial burden attributable to this prevalent and chronic disease.

There are known associations between obesity or excessive overall adipose depots and the T2D incidence [3-5]. However, recent data from genome-wide association studies [6,7] and imaging assessments [7] suggest a stronger association between ectopic adipose depots (in liver, visceral organs, and muscles) and T2D incidence (compared to excessive overall adipose depots) [8-10]. Specifically, adipose depots deposition beneath the fascia of skeletal muscles (extramyocellular and intramyocellular lipid content) may contribute to T2D incidence, owing to the skeletal muscles cardinal role in glucose homeostasis [11]. The body mass index (BMI) and other clinical anthropometric indices, despite quantifying the excessive overall adipose depots, do not provide data on ectopic adipose depots distribution.

Imaging-derived adipose tissue biomarkers have the potential to provide a better characterization of ectopic adipose depots distribution [12]. In addition to dedicated imaging techniques for evaluating body composition and adipose depots distribution (e.g., dual-energy X-ray absorptiometry or bioelectrical impedance), chest computed tomography (CT) exams that are commonly performed in the routine clinical practice for cardiopulmonary indications (e.g., coronary calcium scoring [13] or lung cancer screening [14]) retain data on adipose depots distribution, and there is an opportunity to extract biomarkers on the distribution of ectopic adipose depots from these CT exams, at zero additional cost or radiation exposure [15-17].

To our knowledge, no prior studies assessed the associations between CT-derived adipose depots biomarkers and T2D incidence using longitudinal analysis [3,8-10] after adjusting for the effects of traditional risk factors of the disease and clinical anthropometric indices (e.g., BMI and waist circumference) [8,9] or stratified for baseline glycemic status (normoglycemia versus prediabetes) [9].

In the present study, we used the population-based multiethnic cohort of participants in the Multi-Ethnic Study of Atherosclerosis (MESA) to assess the associations between CT-derived adipose depots biomarkers and T2D incidence. We characterized intermuscular adipose tissue (IMAT), subcutaneous adipose tissue (SAT), and intramyocellular lipid contents in the baseline chest CT exams and studied the potential associations between these biomarkers and T2D incidence.

Methods

The MESA is a population-based multiethnic cohort of 6,814 participants from 6 communities across the USA to investigate the features of subclinical and clinical cardiovascular diseases and to determine their relevant risk factors (see www.MESA-NHLBI.org) [18]. The MESA was approved by the institutional review boards of the 6 participating field centers (Columbia University, Johns Hopkins University, Northwestern University, University of California, University of Minnesota, and Wake Forest University) and the coordinating center (University of Washington). All participants in the MESA provided written informed consent (registered at ClinicalTrials.gov as NCT00005487).

MESArthritis Ancillary Study

Between April 2010 and January 2012 (fifth MESA exam), 3,137 participants in the MESA consented to participate in the MESA Lung Ancillary Study and underwent chest CT exams [19]. The MESArthritis Ancillary Study is an analysis of the available CT exams of 3,083 of these participants [20]. It is aimed to investigate the roles of CT-derived soft tissue and bone biomarkers associated with incidence and clinical outcomes of several cardiometabolic and cardiopulmonary diseases. Before this study, 86 participants with low-quality chest CT exams (n = 52), unknown baseline glycemic status (n = 21), or missing follow-up information (n = 13) were excluded ().

In the baseline exam, information on various demographic and clinical characteristics was collected. Participants were visited to obtain plasma samples after 12 hours of fasting to measure plasma glucose, insulin, HbA1c, triglyceride, and high-density lipoprotein (HDL) cholesterol [21,22]. In this study, normoglycemic participants (baseline fasting plasma glucose [FPG] less than 100 mg/dL) were included (; participants with prediabetes, FPG of 100 to 125 mg/dL, were included in a supplementary analysis).

T2D diagnosis

Between baseline (April 2010 to January 2012) to December 2017, participants were contacted by interviewers at intervals of 9 to 12 months to inquire about new disease diagnoses and interim hospital admissions. Moreover, between September 2016 and June 2018, participants completed a follow-up exam to recollect information on clinical characteristics and obtain fasting plasma samples (sixth MESA exam). This information was supplemented by the data collected through reviews of the hospital records ().

T2D diagnosis over the follow-up was based on at least 1 of the following criteria: (1) physician-diagnosed T2D based on the review of the hospital records (based on the relevant codes of the ninth and 10th editions of the International Classification of Diseases, , time of incidence was the time of hospital admission); (2) self-reported physician-diagnosed T2D (time of incidence was the midpoint between the last interview without and the interview with the self-reported physician-diagnosed disease); or (3) use of insulin or oral hypoglycemic agents or FPG ≥126 mg/dL in the follow-up exam (time of incidence was the time of the follow-up exam).

In a sensitivity analysis, self-reported physician-diagnosed T2D (the second criterion) was confirmed with the use of insulin or oral hypoglycemic agents or FPG ≥126 mg/dL in the follow-up exam, and participants with self-reported T2D but missing information on the use of insulin or oral hypoglycemic agents or FPG in the follow-up exam (n = 17) were excluded.

Adipose tissue biomarkers

The non-contrast–enhanced chest CT exams (acquired at suspended full inspiration using 64-slice multidetector row CT scanners, Siemens Medical Solutions, Erlangen, Germany or GE Healthcare, Waukesha, Wisconsin, USA) were used to measure the IMAT and SAT areas as well as the pectoralis muscles (PMs) density [19,23]. The cross-sectional SAT area was measured as the area between the PM and skin surface in the slice just above the superior margin of the aortic arch [20,24]. Density of the SAT area was analyzed to estimate an individualized attenuation threshold for the IMAT (i.e., the extramyocellular lipid content) [20,25]. The cross-sectional areas within the PM with attenuation below the estimated threshold were measured as the IMAT (). The IMAT and SAT areas were corrected as IMAT and SAT indices (area by height squared, cm2/m2) to account for the anthropometric variations. Moreover, the mean density of the PM area (after excluding the IMAT area) was also measured as a surrogate measure of the intramyocellular lipid content, Hounsfield unit by area, HU/cm2, ).

Statistical analysis

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (). The statistical analyses of this study were planned a priori at the time of preparing the research proposal and was approved by the MESA publication and steering committees. Few additions (e.g., assessing the linearity of the associations between adipose tissue biomarkers and T2D incidence and multiply imputing the missing data points) or changes (e.g., reporting the associations per 1-standard deviation [SD] increment in the biomarkers) were made to the planned statistical analyses at the time of data analysis or per peer reviewers comments ().

Descriptive statistics were used to compare baseline demographic and clinical characteristics between normoglycemic participants with and without T2D incidence over the follow-up.

The correlations between adipose tissue biomarkers with baseline BMI, waist circumference, triglyceride, HDL cholesterol, FPG, insulin, HbA1c, and homeostatic model assessment–insulin resistance (HOMA-IR, FPG × insulin/405) were analyzed using the Pearson correlation analysis and illustrated using a symmetric correlation matrix.

The generalized additive Cox proportional hazard models with integrated smoothness estimation were used to assess and illustrate the linearity of the associations between adipose tissue biomarkers and T2D incidence. Moreover, several adjusted Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence interval (CI) for the T2D incidence according to adipose tissue biomarkers (per 1-SD increment). Models were tested for the proportional hazard assumption by regressing the Schoenfeld residuals over time. Models were adjusted for the traditional risk factors of T2D, HOMA-IR, and clinical anthropometric indices (i.e., BMI and waist circumference) at baseline. The traditional risk factors of T2D included age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity (vigorous and moderate metabolic equivalents [METs]), HDL cholesterol, triglyceride, and hypertension [3]. Decedents without T2D incidence were right-censored at the time of death.

Stratified analyses for the traditional risk factors of T2D, HOMA-IR, BMI, and waist circumference were conducted. In the HOMA-IR stratified analyses, the mean HOMA-IR in normoglycemic participants without T2D incidence over the follow-up was used as the cut point. High HDL cholesterol, high triglyceride, hypertension, and central obesity were defined according to the updated metabolic syndrome guideline of the National Cholesterol Education Program Adult Treatment Panel III [26]. Heterogeneity of the association between adipose tissue biomarkers and T2D incidence in the levels of the stratification variable was tested using the significance of a multiplicative interaction term between adipose tissue biomarkers and the stratification variable.

In a sensitivity analysis, self-reported physician-diagnosed T2D (the second criterion) was confirmed with the use of insulin or oral hypoglycemic agents or FPG ≥126 mg/dL in the follow-up exam, and similar Cox proportional hazard models were used to study the associations between adipose tissue biomarkers and the T2D incidence.

In a supplementary analysis, similar Cox proportional hazard models were used to study the associations between adipose tissue biomarkers and the T2D incidence in participants with prediabetes at baseline.

The missing data points were multiply-imputed with chained equations and predictive mean matching method before Cox proportional hazard modeling and the stratified analyses to produce 5 datasets [27] Each dataset was analyzed separately, and the results were pooled across the datasets using Rubin’s rule (missing values were infrequent among the traditional risk factors of T2D, ).

We applied the Benjamini–Hochberg procedure to correct the p-values for multiple comparisons. The p-values from main, sensitivity, and supplementary analyses were batched and corrected separately using this procedure [28]. The associations with p-values <0.05 were considered statistically significant. All analyses were performed in the R platform, version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Baseline characteristics

Out of the 3,083 participants in the MESArthritis Ancillary Study, 1,744 normoglycemic participants at baseline were included in this study (). Participants’ mean age was 68.7 ± 9.3 years at baseline, and 977 (56.0%) participants were female (). Participants were followed for a median of 6.8 [6.2 to 7.2] years, and during this period, 103 (5.9%) participants were diagnosed with T2D, and 147 (8.4%) participants died (137 [7.9%] without T2D, ).

Flow diagram of participants and the timing of T2D diagnoses.

*Self-reported physician-diagnosed T2D. †Decedents without T2D at the time of death. FPG, fasting plasma glucose; T2D, type 2 diabetes.

Intermuscular adiposity and T2D incidence

IMAT index correlated with BMI (r: 0.41, p-value: <0.001), waist circumference (r: 0.35, p-value: <0.001), HOMA-IR (r: 0.21, p-value: <0.001), and the HDL cholesterol (r: −0.15, p-value: <0.001) at baseline ().

The generalized additive Cox proportional hazard models showed little evidence for a nonlinear association between the IMAT index and T2D incidence (). In the models adjusted for traditional risk factors of T2D, higher IMAT index quartiles were associated with T2D incidence, and 1-SD increment in the IMAT index was associated with the disease incidence (HR: 1.27 [95% CI: 1.15 to 1.41], p-value <0.001, per 1-SD increment). This association was attenuated but remained statistically significant after adjusting for the effects of the HOMA-IR (HR: 1.26 [95% CI: 1.13 to 1.40], p-value: <0.001, per 1-SD increment) or BMI and waist circumference (HR: 1.23 [95% CI: 1.09 to 1.38], p-value: 0.010, per 1-SD increment) ().

In the stratified analyses, our models showed similar associations between IMAT index and T2D incidence in the strata of the traditional risk factors of T2D, HOMA-IR, BMI, and waist circumference (, ).

Forest plot of the associations of adipose tissue biomarkers and T2D incidence by stratification variables.

*p-value for interaction. Models were adjusted for covariates in Model 1 (categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension), except for the stratification variable. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; IMAT, intermuscular adipose tissue; SAT, subcutaneous adipose tissue; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

Subcutaneous adiposity and T2D incidence

The SAT index correlated with BMI (r: 0.65, p-value: <0.001), waist circumference (r: 0.46, p-value: <0.001), and HOMA-IR (r: 0.34, p-value: <0.001) at baseline ().

The generalized additive Cox proportional hazard models showed little evidence for a nonlinear association between the SAT index and T2D incidence (). Higher SAT index quartiles were associated with T2D incidence, and 1-SD increment in the SAT index was associated with T2D incidence (HR: 1.43 [95% CI: 1.16 to 1.77], p-value: 0.010, per 1-SD increment) after adjusting for traditional risk factors of T2D. After including the HOMA-IR (HR: 1.34 [95% CI: 1.07 to 1.68], p-value: 0.078, per 1-SD increment) or BMI and waist circumference (HR: 1.29 [95% CI: 0.96 to 1.74], p-value: 0.325, per 1-SD increment), the associations between the SAT index and T2D incidence were attenuated toward null hypothesis and no longer were statistically significant ().

In the stratified analyses, our models showed similar associations between SAT index and T2D incidence in the strata of the traditional risk factors of T2D, HOMA-IR, BMI, and waist circumference (, ).

Intramuscular adiposity and T2D incidence

The PM density correlated with BMI (r: −0.33, p-value: <0.001), waist circumference (r: −0.28, p-value: <0.001), and HOMA-IR (r: −0.16, p-value: <0.001) at baseline ().

The generalized additive Cox proportional hazard models showed little evidence for a nonlinear association between PM density and T2D incidence (). The models adjusted for traditional risk factors of T2D, HOMA-IR, or BMI and waist circumference failed to reject the null hypothesis for lack of any associations between PM density quartiles or 1-SD increment in the PM density and T2D incidence ().

In the stratified analyses, our models showed similar associations between PM density and T2D incidence in the strata of the traditional risk factors of T2D, HOMA-IR, BMI, and waist circumference ().

Sensitivity and supplementary analyses

In the sensitivity analysis, self-reported physician-diagnosed T2D (the second criterion) was confirmed with the use of insulin or oral hypoglycemic agents or FPG ≥126 mg/dL in the follow-up exam. Similar to the results in the main analysis, the IMAT index was associated with T2D incidence, after adjusting for the effects of traditional T2D risk factors, HOMA-IR, and clinical anthropometric indices (i.e., BMI and waist circumference, ).

In a supplementary analysis in participants with prediabetes at baseline, the models failed to reject the null hypothesis for lack of associations between adipose tissue biomarkers and T2D incidence ().

Discussion

In this study, we investigated the associations between CT-derived adipose tissue biomarkers and T2D incidence over a median of 6.8 years in a population-based multiethnic cohort of normoglycemic participants. We showed that high IMAT indices were associated with T2D incidence. The association was attenuated after accounting for effects of traditional T2D risk factors, HOMA-IR, and clinical anthropometric indices (i.e., BMI and waist circumference) but remained statistically significant.

The burden attributable to T2D can be potentially avoidable through primary and secondary preventive measures in the early stages of the disease [29]. Despite the robust literature on the predictive roles of genetic, cultural, behavioral, and environmental risk factors in T2D incidence [30,31], there is a paucity of research on the association between imaging biomarkers and T2D incidence. In routine clinical practice, chest CT exams are commonly used for various cardiopulmonary indications [32,33]. Specifically, chest CT exams are well-established tools for screening for coronary artery plaques and coronary calcium scoring in participants with intermediate 10-year atherosclerotic cardiovascular disease (ASCVD) risk [13] as well as for annual screening for lung cancer in high-risk adults [14]. All these CT exams retain data on adipose depots distribution, and there is the opportunity to extract CT-derived adipose depots biomarkers from these exams.

We showed that IMAT in chest CT exams (i.e., high IMAT index) is associated with T2D incidence. The IMAT index in chest CT exams may reflect the overall (upper and lower bodies) deposition of extramyocellular adipose depots, which is associated with insulin resistance [17,34,35], and, possibly, T2D incidence. The IMAT is located close to the muscle fibers and may play an intermediary role in insulin resistance through secreting pro-inflammatory cytokines, extracellular matrix proteins, and increasing local free fatty acids, or collectively, through altering the skeletal muscle microenvironment [34,35]. High BMI (or other clinical anthropometric indices) may be cited as a confounding factor in the association between this biomarker and the T2D incidence [36,37]. However, the findings of this study showed that when the effects of these clinical anthropometric indices and the IMAT are both accounted for in the models, the IMAT index remained associated with T2D incidence.

In this study, we have demonstrated that subcutaneous adipose depots in chest CT exams (i.e., high SAT index) were associated with T2D incidence. This finding was in line with prior studies on this topic, which suggested that (upper body) subcutaneous adipose depots on the chest CT exams may be associated with adverse cardiometabolic risk factors [38]. However, SAT index correlated with BMI and obesity, and the association between this CT-derived adipose tissue biomarker and T2D incidence was (at least partly) due to the role of high BMI (the association between SAT index and T2D incidence was attenuated toward the null after adjusting for effects of BMI and waist circumference). We also did not show any associations between intramyocellular lipid content (i.e., PM density) and T2D incidence.

Compared to preceding works [8-10], in this study, the adipose tissue biomarkers were obtained from the chest CT exams that were primarily performed to assess the lung parenchymal structure. The use of these exams shows the fact that the CT-derived adipose tissue biomarkers can be extracted opportunistically from the commonly performed chest CT exams for routine cardiopulmonary clinical indications, i.e., coronary calcium scoring and lung cancer screening. Although future studies are required to confirm the findings of this study, our study builds on the recommendation of clinical guidelines on the use of chest CT exams for coronary calcium scoring and lung cancer screening and can extend the value of these CT exams. The 2018 ACC/AHA Cholesterol Guideline recommends using chest CT exams for coronary artery calcium scoring in adults without T2D for making decisions about statin therapy [39]. The CT-derived adipose tissue biomarkers can be extracted from these CT exams and may be used in recommending T2D-related preventive measures in addition to potentially making decisions about statin therapy.

This study has a few but important limitations. This population-based study was observational, and, therefore, our findings are limited by the lack of interventions to control for potential effects of residual confounders. Moreover, this study was nested within the MESA, which is primarily designed to study cardiovascular diseases and outcomes. Contrary to the majority of cardiovascular diseases, T2D diagnosis is usually made in outpatient settings, and the review of the hospital records may not be able to capture all T2D diagnoses. To address this limitation, we supplemented the review of the hospital records with self-reported physician-diagnosed T2D. Although the potential inconsistencies in the timing of the self-reported physician-diagnosed T2D may have confounded our findings, we tested the possible effects of outliers on the observed results using a sensitivity analysis. Finally, the change of CT-derived adipose tissue biomarkers over the follow-up may be a potential source of unmeasured confounding effects in this study. The trajectory of these biomarkers may provide a better understanding of their association with T2D incidence.

In conclusion, this study showed an association between IMAT at baseline and T2D incidence over the follow-up in normoglycemic participants and suggested the potential role of intermuscular adipose depots in the pathophysiology of T2D.

STROBE Statement.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

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Relevant Codes of ICD-9 and ICD-10.

ICD, International Classification of Diseases.

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Statistical analyses.

SD, standard deviation.

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Associations of PM density and T2D incidence.

Model 0: unadjusted. Model 1: adjusted for categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; HU, Hounsfield unit; PM, pectoralis muscle; PY, person-year; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

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Associations of adipose tissue biomarkers and T2D incidence (sensitivity analysis).

Model 0: unadjusted. Model 1: adjusted for categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension. In this sensitivity analysis, self-reported physician-diagnosed T2D (the second criterion) was confirmed with the use of insulin or oral hypoglycemic agents or FPG ≥126 mg/dL in the follow-up exam. Participants with self-reported T2D but missing information on the use of insulin or oral hypoglycemic agents or FPG in the follow-up exam (n = 17) were excluded. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; FPG, fasting plasma glucose; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; HU, Hounsfield unit; IMAT, intermuscular adipose tissue; PM, pectoralis muscle; PY, person-year; SAT, subcutaneous adipose tissue; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

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Associations of adipose tissue biomarkers and T2D incidence in participants with prediabetes (supplementary analysis).

*Models did not meet the proportional hazard assumption. Model 0: unadjusted. Model 1: adjusted for categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension. In this supplementary analysis, Cox proportional hazard models were used to study the associations between adipose tissue biomarkers and T2D incidence in participants with prediabetes at baseline. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; HU, Hounsfield unit; IMAT, intermuscular adipose tissue; PM, pectoralis muscle; PY, person-year; SAT, subcutaneous adipose tissue; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

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Flow diagram of the MESArthritis Ancillary Study.

CT, computed tomography.

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Timeline of the MESArthritis Ancillary Study.

MESA, Multi-Ethnic Study of Atherosclerosis.

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IMAT, SAT, and PM in the chest CT exam of a participant.

The chest CT exam of a 69-year-old normoglycemic male participant is shown here. (A) The coronal reconstruction of the CT exam is shown. The blue dashed line indicates the slice above the superior aspect of the aortic arch. (B) The area within the PM with attenuation below an individualized threshold (−89 HU in this participant) was measured as the IMAT (red). (C) The area between the PM and skin surface was measured as the SAT (red). (D) The density of PM (red) was measured as the mean HU of the pixels in the PM (after removing the pixels in the IMAT). CT, computed tomography; HU, Hounsfield unit; IMAT, intermuscular adipose tissue; PM, pectoralis muscle; SAT, subcutaneous adipose tissue.

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Pattern of missing values.

In the dataset, there were missing data points in the HOMA-IR (161 [9.2%] data points), physical activity (13 [0.7%] data points), smoking status (10 [0.6%] data points), alcohol drinking status (8 [0.5%] data points), TG, HDL cholesterol, systolic and diastolic blood pressures, BMI, and waist circumference (2 [0.1%] data points in each). In this figure, the blue bars show number of participants with missing data points in the covariates marked with red circles below each bar. BMI, body mass index; HDL, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment–insulin resistance; TG, triglyceride; Waist C., waist circumference.

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Symmetric correlation matrix for adipose tissue biomarkers and traditional T2D risk factors.

The blue color is used to display the positive correlations, and the red color is used for negative correlations. The attenuation of the color is proportional to the estimated Pearson correlation coefficients (numbers in the boxes). BMI, body mass index; FPG, fasting plasma glucose; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; IMAT, intermuscular adipose tissue; PM, pectoralis muscle; SAT, subcutaneous adipose tissue; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

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Smooth function estimates (red lines) obtained from fitting a generalized additive Cox proportional hazard models with integrated smoothness estimation on the dataset, with estimated 95% CI (blue lines) is shown. The results are reported on the scale of the adipose tissue biomarkers (per 1-SD increment), and the models were adjusted for covariates in Model 1 (categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension). The numbers in brackets in the captions are the estimated degrees of freedom of the smooth curves. The rug marks along the x-axis indicate the adipose tissue biomarkers values. CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; IMAT, intermuscular adipose tissue; PM, pectoralis muscle; SAT, subcutaneous adipose tissue; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride.

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Forest plot of the association of IMAT index and T2D incidence by stratification variables.

*p-value for interaction. Models were adjusted for covariates in Model 1 (categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension), except for the stratification variable. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; IMAT, intermuscular adipose tissue; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

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Forest plot of the association of SAT index and T2D incidence by stratification variables.

*p-value for interaction. Models were adjusted for covariates in Model 1 (categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension), except for the stratification variable. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; SAT, subcutaneous adipose tissue; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

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Forest plot of the association of PM density and T2D incidence by stratification variables.

*p-value for interaction. Models were adjusted for covariates in Model 1 (categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension), except for the stratification variable. Reported p-values were corrected for multiple comparisons. BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; PM, pectoralis muscle; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride, Waist C., waist circumference.

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3 Feb 2021

Dear Dr Demehri,

Thank you for submitting your manuscript entitled "Adipose tissue biomarkers and risk of type 2 diabetes incidence in normoglycemic participants:  The MESArthritis Ancillary Study" for consideration by PLOS Medicine.

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18 Mar 2021

Dear Dr. Demehri,

Thank you very much for submitting your manuscript "Adipose tissue biomarkers and risk of type 2 diabetes incidence in normoglycemic participants:  The MESArthritis Ancillary Study" (PMEDICINE-D-21-00345R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

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In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Requests from the editors:

- Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" or “risk of” should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A retrospective study,") in the subtitle (i.e., after a colon). For example, “Adipose tissue biomarkers and type 2 diabetes incidence in normoglycemic participants in The MESArthritis Ancillary Study: A retrospective cohort study”

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- Abstract:

1. Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.

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3. Please include the actual amounts and/or absolute risk(s) of relevant outcomes, not just relative risks or correlation coefficients

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- Please conclude the Introduction with a clear description of the study question or hypothesis.

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- Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

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- Your study is observational and therefore causality cannot be inferred. Please remove language that implies causality, such as - predict, risk, effect, etc. Instead, refer to associations consistently throughout the text.

- Please define the length of follow up (eg, in mean, SD, and range).

- How was race/ethnicity defined and by whom? Why was race/ethnicity considered important in this study and what it is believed to represent e.g., are SES or genetic differences being attributed to race/ethnicity?

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- Please replace “nondiabetics” with “persons without diabetes.”

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Comments from the reviewers:

Reviewer #1: The authors examined the associations of CT-measured indices with incident type 2 diabetes (T2D). This study has a novel component of the use of CT-derived variables. Its contributions to science and clinical practice may be meaningful, while the reviewer is not an expert on it. While positive remarks are possible, some concerns are present as described in the following paragraphs: Major Comments and Minor Comments.

Major comments:

0.

The authors identified a significant interaction by BMI for the primary finding. Thus, the average association presented in the abstract and the main results is not meaningful. The abstract and "Author Summary" should clarify that the positive association was present among those with overweight or obesity (BMI>24.9).

The authors need to assess the association stratified by BMI with statistical adjustment for BMI (within stratum) and waist circumference. It is not clear if the authors did it.

1. The authors should revise the description of the results throughout the manuscript. The authors dichotomized their inference into two mainly: significant or not. That is malpractice in recent medical science. The authors should avoid it throughout the manuscript.

Accounting for no perfect measure of central or whole-body adiposity, the authors need to keep the following interpretations:

i) there were associations of the SAT and IMAT indices with T2D among the participants with overweight or obesity.

ii) the association was partly related to insulin resistance and anthropometric measures.

iii) The intermuscular fat related to the development of T2D. Thus, this study demonstrated that the possible failure of storing fat intracellularly in adults with overweight or obesity could be the consequence, pathological process, or both of the development of T2D.

The authors should avoid describing that one type of association was independent of the adiposity or that the IMAT index predicted T2D. This kind of interpretation would be too strong given the observational analyses with measurement errors. Of note, even if the association became null after adjustment for BMI and waist circumference, this study seems to be pathophysiologically important. However, for the particular argument, interaction by overweight or obesity status is essential to highlight.

2. The introduction and discussion are insufficient.

The authors should cite several genetic, Mendelian randomization studies relating anthropometric measures to T2D, such as the one in JAMA (LA Lotta et al., JAMA, 2018). Genetic evidence is already available to indicate that fat storage and its location matter in the development of T2D. The authors should present it as background information for this work and discuss the pathophysiology of T2D.

3. The reviewer has considered the pathophysiological importance of this work. The authors emphasized it in the introduction and because the reviewer has recognized the sparsity of the relevant evidence in large-scale epidemiological research. However, the authors may eagerly want to establish the importance of the CT-derived measures to predict T2D risk regardless of the etiological pathway. If so, the authors should revise the introduction to explain why the prediction modelling for T2D should be improved, citing previous studies on T2D prediction (e.g. ones from Framingham Offspring Study, ARIC, MESA). The authors have not done it, leaving the argument about prediction unconvincing or not structured well.

Moreover, if the authors are interested in prediction, its evaluation requires refinement. The authors mainly interpreted whether the measures of associations were significant or not. This interpretation was substandard for its utility for the prediction. A further quantitative evaluation scoping possible clinical actions will be essential (see Cook et al., Annals Intern Med, 2009;150(11):795-802).

Finally, if the authors want to argue the utility of a CT exam for the prediction of T2D, the evaluation should include other CT results in addition to the chest ones. The emphasis for prediction would not make sense in this manuscript.

4.

S7 Fig is incorrect. Hazard ratios cannot be negative. If an author estimates incident rate ratio or hazard ratio, one reference point without any standard errors should be available. The current figure does not have it. A valid explanation is necessary if the figure is valid.

Minor comments:

The primary results are owing to an assessment of statistical significance applied to p-value=0.036. Given the number of tests operated in this study (for the two CT measures, for instance), the inference based on the p-value is inappropriate. As generally recommended against the dichotomic use of p-value, the authors should revise the manuscript to judge the results based on statistical significance.

Abstract:

Background: "and contribute to" should be deleted. After the deletion, the sentence still works, and it will turn out to be sharp and straightforward.

Calendar years of the follow-up start and finish should be available in the abstract. The MESA started around 2000, but this particular study's baseline started around 2010 from the CT measurements. Thus, for some readers, years should be documented.

Use "T2D" after spell out once the authors explain the abbreviation throughout the abstract.

The authors should not provide a positive or negative connotation based on statistical significance. The authors have stated, "The association of the IMAT index remained statistically significant after adjusting for body mass index and waist circumference (HR:1.30 [95%CI: 1.02-1.65]), however, the SAT index was no longer associated with type 2 diabetes incidence." This sentence should be revised as follows: "The associations of the SAT and IMAT indices with T2D incidence were attenuated toward the null after adjustment for body mass index and waist circumference, with HR (95% CI) of X.XX (X.XX-X.XX) and 1.30 (1.02-1.65), respectively".

Given the possible measurement errors of body mass and waist circumference, the authors should consider a more substantial attenuation after the adjustment and describe the results and interpretation, accounting for the consideration.

The conclusion is too strong and odd. The authors must not be interested in the improvement of prediction via the index they used. Instead, the authors must be interested in how important the intermuscular fat may be in the pathophysiology of T2D. In a sense, the authors may not want to digress the logical flow to a prediction story. The authors may want to state the association partly related to adiposity and highlight the etiological importance of the intermuscular fat. The conclusion requires such a revision.

Author Summary:

The first text read as if the participants had used time-to-event data. The authors can delete "using longitudinal time-to-event analysis". By saying "baseline" and "incidence", the longitudinal aspect is readable.

Delete "in this study" and start the bullet point with "We".

Line 75: The authors should avoid the dichotomic expression of the results.

Methods

Line 186-187: For additive modelling and smoothness estimation, the statistical function should be clear enough for readers to replicate the analysis. There can be different ways.

Line 188: "consecutive nested" is not necessary unless this phrase means something extraordinary.

BMI and alcohol can have non-linear associations with T2D incidence. The current approach would be suboptimal. The authors are, in particular, interested in the role of BMI in the evaluation of the association. Therefore, additive modelling for BMI is reasonable in this study.

Results:

The authors should document the increase of standard errors or confidence intervals after adjusting BMI and waist circumference. Table 2 clarifies the collinearity of the SAT with BMI and waist circumference.

Delete S6 Fig or delete S2 Table and include the numeric info into S6 Fig.

Line 228-229. There was no apparent cubic association. Also, the authors did not formally test whether or not there was a cubic or non-linear association. The inflexion point the authors described did not reflect an objective assessment. The authors should delete the sentence without any statistical approach to identify such a value. Also, log-2 values are hard to interpret. The authors should convert it to a raw value to facilitate an interpretation when needed.

The authors should describe the results accounting for the interaction by BMI. It is critically important to state the results after the stratification and after adjusting BMI and waist circumference.

Discussion.

The first paragraph is misleading. The association was present among those with overweight and obesity. This should be the basis to characterize the association.

The authors' discussion is appropriate for the potential use of CT scanning for the prediction of T2D and other diseases. However, this study is too far from the argument because the authors did not interpret the results quantitatively for the clinical application. Also, this study identified the interaction by BMI and therefore has not granted the wide application of the CT scanning to a general population.

The reviewer is reserving other minor comments.

Reviewer #2: Statistical review

This paper reports an observational study that assessed association between biomarkers and risk of developing diabetes.

I had some comments on the statistical methods and reporting, which are provided below.

1. Abstract: I'd recommend providing the estimated HR and 95%CI for adjusted SAT association.

2. Abstract line 62 "can predict a" - I recommend slightly less causal language such as 'is associated with'.

3. Abstract/Methods/Results: Were IMAT and SAT included in the same adjusted model? It would be useful to note whether IMAT was an independent risk variable after adjustment for SAT.

4. Line 197 - I'd recommend adding how many patients were excluded from the complete cases analysis. I think Figure S5 requires more information in the caption as it wasn't clear to me how to interpret it currently.

5. Statistical analysis: it would be useful to provide a pre-specified analysis plan or otherwise note which analyses described here were specified prior to the data being available.

6. Line 188-190: I did not follow what the authors meant by 'consecutive nested … were used to estimate' - is this some type of model building procedure? More details on this would be useful.

7. Line 199 - did the stratified analyses also adjust for the variables in the previous paragraph?

8. Line 222 - I'd recommend clarifying the 74 does not include the 17 self-identified participants mentioned on line 160?

9. Line 266 - I would add results from the Intramuscular adiposity section to the abstract as currently it looks a bit selective to report only two of the three main sets of results. I'd also add the results on this to table 2 from the supplementary table.

James Wason

Reviewer #3: See file

Any attachments provided with reviews can be seen via the following link:

[LINK]

Submitted filename: Plos_Medicine-D-00345-1.docx

Click here for additional data file.

11 May 2021

Submitted filename: renamed_bf73f.docx

Click here for additional data file.

10 Jun 2021

Dear Dr. Demehri,

Thank you very much for re-submitting your manuscript "Adipose tissue biomarkers and type 2 diabetes incidence in normoglycemic participants in The MESArthritis Ancillary Study: A retrospective cohort study" (PMEDICINE-D-21-00345R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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We look forward to receiving the revised manuscript by Jun 17 2021 11:59PM.

Sincerely,

Beryne Odeny,

Associate Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

1. Please remove the word “retrospective” from the title

2. Please use the "Vancouver" style for reference formatting and see our website for other reference guidelines. For example:

a. Please ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases, and are appropriately formatted and capitalized. https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references. For example, in references # 37, 38 etc., the journal titles need to be abbreviated in line with the Vancouver style.

b. Please remove excess text from Reference # 6, “AstraZeneca, MedImmune, Novo Nordisk, and ERX Pharmaceuticals and grants from Aegerion outside the submitted work. Dr Scott is an employee and shareholder in GlaxoSmithKline. Dr Burgess reported grants from Wellcome Trust/Royal Society and the UK Medical Research Council during the conduct of the study. No other disclosures were reported”

Comments from Reviewers:

Reviewer #1: The authors have revised the manuscript well. Some minor comments are come up with as the followings.

In the imputation, the authors stated the use of "Multivariate Imputation by Chained Equations (MICE)". The name is correct, according to the publication referred to by the authors. However, the approach has now been well-known as "multiple imputation by chained equations (MICE)". Technically, both are correct, but the latter is standard as far as the Reviewer reviewed some publications. To avoid a potentially negative impression readers may come up with, the authors may want to avoid stating the procedure's name: for example, to state, "imputing missing values with chained equations and predictive mean matching method".

In a couple of tables, p-values presented appeared to be after adjustment for false discovery rate. Some readers would be confused, and therefore the application of the FDR should be noted in the footnote wherever appropriate.

S4 Table was not clear what was different from the main table. The authors should confirm that each table and figure stands alone so that readers can understand the material. Also, person-time values across categories should be available. Otherwise, future reviewers cannot use the statistics for meta-analysis readily.

In the discussion section, the authors should discuss the possible mechanism of the authors' findings. The current statement is insufficient and very naive (Page 18). The authors should discuss why the IMAT of the chest showed an association with T2D incidence. For example, mechanisms of T2D development include skeletal muscle insulin resistance, hepatic insulin resistance, and beta-cell dysfunction. Then, it is not explicitly clear why the chest IMAT relates to the incidence of T2D. General correlates with IMAT around any tissues are unhelpful.

Reviewer #2: Thank you to the authors for addressing my previous comments well. I have no further issues to raise.

Reviewer #3: I Think the authors have addressed all questions adequately and I have no further comments.

Any attachments provided with reviews can be seen via the following link:

[LINK]

13 Jun 2021

Submitted filename: Response to reviewers.docx

Click here for additional data file.

16 Jun 2021

Dear Dr Demehri,

On behalf of my colleagues and the Academic Editor, Dr. Weiping Jia, I am pleased to inform you that we have agreed to publish your manuscript "Adipose tissue biomarkers and type 2 diabetes incidence in normoglycemic participants in The MESArthritis Ancillary Study: A cohort study" (PMEDICINE-D-21-00345R3) in PLOS Medicine.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Beryne Odeny

Associate Editor

PLOS Medicine

Table 1

Baseline demographic and clinical characteristics of normoglycemic participants.

Characteristics	Without T2D incidence over the follow-up (n = 1,641)	With T2D incidence over the follow-up (n = 103)
Traditional risk factors of T2D
Age (years)	68.7 ± 9.3	68.7 ± 9.5
Sex (female)	924 (56.3%)	53 (51.5%)
Race/ethnicity
White	750 (45.7%)	35 (34.0%)
Black	418 (25.5%)	30 (29.1%)
Hispanic	279 (17.0%)	28 (27.2%)
Chinese-American	194 (11.8%)	10 (9.7%)
Physical activity (METs hours/week)	88.6 ± 96.5	85.1 ± 92.1
Alcohol drinking status (current)	768 (46.8%)	32 (31.1%)
Smoking status
Never	757 (46.1%)	42 (40.8%)
Former	747 (45.5%)	51 (49.5%)
Current	127 (7.7%)	10 (9.7%)
Systolic blood pressure (mm Hg)	122.2 ± 19.8	125.6 ± 21.3
Diastolic blood pressure (mm Hg)	68.4 ± 9.9	70.0 ± 9.2
TG (mg/dL)	100.8 ± 52.5	118.4 ± 64.7
HDL cholesterol (mg/dL)	58.5 ± 17.2	52.4 ± 15.2
HOMA-IR
log2 (HOMA-IR)	3.2 ± 0.9	3.6 ± 1.0
Clinical anthropometric indices
BMI (kg/m2)	27.4 ± 5.1	29.7 ± 5.6
Waist C. (cm)	96.1 ± 13.4	100.7 ± 13.0
Adipose tissue biomarkers
IMAT index (cm2/m2)	0.3 ± 0.3	0.5 ± 0.7
SAT index (cm2/m2)	18.8 ± 11.2	22.5 ± 14.6
PM density (HU/cm2)	24.0 ± 10.3	23.4 ± 11.1

Quantitative variables are shown in mean ± SD, and qualitative variables are shown in number (%).

BMI, body mass index; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HU, Hounsfield unit; IMAT, intermuscular adipose tissue; IQR, interquartile range; MET, metabolic equivalent; PM, pectoralis muscle; SAT, subcutaneous adipose tissue; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.

Table 2

Associations of adipose tissue biomarkers and T2D incidence.

	Index				p-value for trend	HR (95% CI), p-value per 1-SD increment
	Quartile 1	Quartile 2	Quartile 3	Quartile 4
IMAT index
Mean (cm2/m2)	0.1	0.2	0.3	0.7	-	-
Incident cases	16	19	26	42	-	-
Incidence rate (per 1,000 PYs)	5.9	6.9	9.6	15.6	-	-
HR (95% CI)
Model 0	1 (reference)	1.18 (0.60–2.31)	1.62 (0.86–3.04)	2.66 (1.49–4.77)	<0.001	1.27 (1.17–1.38), <0.001
Model 1	1 (reference)	1.11 (0.55–2.25)	1.37 (0.70–2.69)	1.93 (1.03–3.64)	0.100	1.27 (1.15–1.41), <0.001
Model 1 + HOMA-IR	1 (reference)	1.08 (0.54–2.18)	1.24 (0.63–2.45)	1.72 (0.91–3.26)	0.216	1.26 (1.13–1.40), <0.001
Model 1 + BMI and Waist C.	1 (reference)	0.98 (0.48–1.99)	1.13 (0.56–2.27)	1.42 (0.71–2.86)	0.485	1.23 (1.09–1.38), 0.010
SAT index
Mean (cm2/m2)	7.6	13.1	20.4	35.1	-	-
Incident cases	19	22	26	36	-	-
Incidence rate (per 1,000 PYs)	7.3	8.0	9.3	13.2	-	-
HR (95% CI)
Model 0	1 (reference)	1.09 (0.58–2.02)	1.25 (0.69–2.28)	1.76 (1.00–3.09)	0.150	1.27 (1.08–1.50), 0.043
Model 1	1 (reference)	1.09 (0.57–2.07)	1.53 (0.78–3.00)	2.65 (1.22–5.77)	0.078	1.43 (1.16–1.77), 0.010
Model 1 + HOMA-IR	1 (reference)	1.00 (0.53–1.91)	1.26 (0.62–2.52)	2.02 (0.90–4.54)	0.300	1.34 (1.07–1.68), 0.078
Model 1 + BMI and Waist C.	1 (reference)	0.98 (0.50–1.90)	1.26 (0.59–2.69)	1.79 (0.68–4.71)	0.495	1.29 (0.96–1.74), 0.325

Model 0: unadjusted.

Model 1: adjusted for categorical age, sex, race/ethnicity, smoking status, alcohol drinking status, physical activity, TG, HDL cholesterol, and hypertension.

Reported p-values were corrected for multiple comparisons.

BMI, body mass index; CI, confidence interval; HDL, high-density lipoprotein; HOMA-IR, homeostatic model assessment–insulin resistance; HR, hazard ratio; HU, Hounsfield unit; IMAT, intermuscular adipose tissue; PY, person-year; SAT, subcutaneous adipose tissue; SD, standard deviation; T2D, type 2 diabetes; TG, triglyceride; Waist C., waist circumference.