Association of Visceral Adiposity Surrogates with Impaired Fasting Glucose in Nonobese Individuals

Abstract

Background:

Nonobese individuals with disproportionate body fat distribution are also vulnerable to dysglycemia. This study aimed to evaluate the association between three visceral adiposity surrogates and impaired fasting glucose (IFG) in nonobese Chinese individuals.

Methods:

A total of 70,200 nonobese adults without diabetes were included in this analysis. Two diagnostic criteria (IFG-ADA and IFG-WHO) were used to define IFG. The values of the visceral adiposity index, lipid accumulation product index (LAP), and cardiometabolic index (CMI) were calculated. Multivariable logistic analysis was used to evaluate the association between these surrogates and IFG.

Results:

Among the three indicators, only LAP and CMI were positively correlated with fasting plasma glucose (all P < 0.001). After fully adjusting for confounders, only LAP and CMI exhibited significant associations with IFG. For women, the odds ratios (ORs) for IFG-ADA in the highest quartile of the LAP and CMI were 1.967 (95% confidence interval [CI]: 1.645–2.353) and 1.594 (95% CI: 1.383–1.836), respectively; and were 2.025 (95% CI: 1.597–2.567) and 2.017 (95% CI: 1.647–2.470), respectively, for IFG-WHO (all P < 0.001). For men, the ORs for IFG-ADA of the LAP and CMI were 1.503 (95% CI: 1.233–1.833) and 2.045 (95% CI: 1.752–2.388), respectively; and were 1.534 (95% CI: 1.174–2.005) and 2.541 (95% CI: 2.025–3.188), respectively, for IFG-WHO (all P < 0.001).

Conclusions:

The LAP and CMI, cost-effective and simple visceral adiposity surrogates, are strongly associated with IFG in nonobese Chinese individuals. These surrogates might be potential targets to monitor for the recognition and management of excess visceral adiposity in nonobese individuals with prediabetes.

Introduction

Prediabetes, which includes impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT), is associated with increased risks of diabetes and cardiovascular disease (CVD).¹ Worryingly, the prevalence of prediabetes is increasing worldwide. According to a 2013 survey, the prevalence of prediabetes was 35.7% among the Chinese adult population.² Compared with IGT, the diagnosis of IFG is relatively simple and only needs fasting plasma glucose (FPG) level. But the diagnostic cutoff point for IFG is still controversial. The World Health Organization (WHO)/International Expert Committee (IEC) defines IFG as an FPG of 6.1–6.9 mM,³ while the criteria of the ADA is an FPG of 5.6–6.9 mM.⁴ A recent study has shown that an increased risk of CVD and all cause mortality have been observed when FPG was as low as 5.6 mM according to the current ADA. Therefore, active and effective prevention and intervention of IFG is essential for the prevention of CVD.⁵

Disproportionate body fat distribution is a well-established risk factor for prediabetes.^6,7 Compared with Caucasian and African individuals, East Asian individuals are more likely to store excess fat in the liver and visceral compartment than in the subcutaneous compartment.⁸ Traditional obesity indicators, such as body mass index (BMI) and waist circumference (WC), have a weaker ability to reflect visceral fat accumulation, especially for East Asian populations. Therefore, dysglycemia is also common in nonobese individuals (with normal BMI and WC) who may have excessive visceral fat accumulation in China. Therefore, looking for some indicators that could reflect visceral obesity in nonobese individuals may be conducive to the prevention and management of IFG.

In recent years, several visceral adiposity surrogates (visceral adiposity index [VAI],⁹ lipid accumulation product index [LAP],¹⁰ and cardiometabolic index [CMI]¹¹) have been developed. However, these adiposity surrogates were developed for Caucasians and we do not know whether they are applicable for Asians. In addition, a large sample size study that assessed the relationship between these adiposity surrogates and IFG in nonobese individuals is rare. With this in mind, we therefore undertook the present study to evaluate the associations between IFG and the above three visceral adiposity surrogates simultaneously and to determine which indicators may be potential anthropometric measurements to monitor for the recognition and management of excess visceral adiposity in nonobese individuals with prediabetes.

Materials and Methods

Subjects

This cross-sectional study was based on the data of subjects who received a routine physical examination (including blood pressure measurement, the collection of anthropometric information, blood biochemistry analyses, and abdominal B-ultrasound analyses) between November 2015 and September 2018 in China's Yangtze River Delta region (Shanghai Municipality, Zhejiang Province, and Jiangsu Province). There were a total of 174,698 adults who at least underwent routine anthropometric examination and biochemical tests. We first excluded 30,361 individuals who were diagnosed with diabetes or with a FPG >7.0 mM. According to the cutoff points for general and central obesity for Chinese,^12,13 71,419 individuals with a BMI ≥24 kg/m² and (or) a WC ≥85 cm in men and ≥80 cm in women were excluded. We also excluded 2718 individuals who were pregnant or with a self-reported use of statins. Finally, a total of 70,200 adults with normal BMI and WC were available for this analysis. The present study was approved by the Ethics Committee of Hangzhou Aeronautical Sanatorium of the Chinese Air Force.

Anthropometric and biochemical measurements

Height and weight were measured with participants barefoot and in light clothing on an electronic scale. WC and hip circumference (HC) were measured by well-trained nurses. Serum levels of FPG, plasma uric acid (UA), total cholesterol, triglyceride (TG), low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol (HDLc) were measured by an automatic blood biochemistry analyzer. These measurements are supervised by the government's unified quality control system.

Definitions

In this study, we used both diagnostic criteria (IFG-ADA and IFG-WHO) to define IFG. BMI was calculated as weight divided by the height squared, WC divided by the HC was the waist to hip ratio (WHR), and WC divided by the height was the waist height ratio (WHtR). Nonobesity was defined as BMI <24 kg/m² and WC <85 cm in men and <80 cm in women.^12,13

The visceral adiposity surrogates were calculated with the following formulas: $V A I (m e n) = (\frac{W C}{39.68 + (1.88 \times B M I)}) \times (\frac{T G}{1.03}) \times (\frac{1.31}{H D L c})$ ⁹; $V A I (w o m e n) = (\frac{W C}{36.58 + (1.89 \times B M I)}) \times (\frac{T G}{0.81}) \times (\frac{1.52}{H D L c})$ ; $L A P (m e n) = (W C - 65) \times T G$ ; and $L A P (w o m e n) = (W C - 58) \times T G$ ¹⁰; $C M I = \frac{T G}{H D L c} \times W H t R$ .¹¹

Statistical analysis

Data are expressed as numbers (percentage) or mean ± standard deviation. Statistical analysis was performed using SPSS 18.0 (SPSS, Inc.). The t-test was used to test differences for continuous variables. Partial correlation was used to check the correlation between the visceral adiposity surrogates and the level of FPG. After adjustment for age, smoking status, and antihypertensive agents, multivariable logistic regression analyses were applied to examine the association between the three visceral adiposity surrogates and IFG. The visceral adiposity surrogates were divided into four quartiles, and the first quartile was used as a reference. Receiver operating characteristic (ROC) analyses and the area under ROC curves (AUC) were also used to evaluate the distinguishing ability of the three visceral adiposity surrogates for identifying IFG. All probability values were two-tailed and a P-value <0.05 was considered to indicate statistical significance.

Results

The clinical characteristics of nonobese individuals are summarized in Table 1. Of the 70,200 nonobese individuals with an FPG <7.0 mM, 46.5% were male and 53.5% were female, and the mean age was 42.2 ± 12.3 years. Because the scope of the ADA classification criteria of IFG is larger, the frequency of IFG-ADA was significantly higher than that of IFG-WHO (35.7% vs. 13%, P < 0.001). Compared to adults with normal glycemia, adults with IFG did not have higher average values of traditional anthropometric measurements (BMI, WC, WHR, and WHtR) and blood pressure levels but had higher average values of metabolic indicators (FPG, lipid parameters, and UA). Among the three visceral adiposity surrogates, the average values of LAP (P < 0.001) and CMI (P < 0.001) but not VAI (P = 0.794) were higher in individuals with IFG than in those with normal glycemia.

Table 1.

Clinical Characteristics of Nonobese Individuals With and Without Impaired Fasting Glucose

Variable	FBG <5.6 mM	IFG-ADA	P	FBG <6.1 mM	IFG-WHO	P
No., n (%)	42,358 (60.3)	27,842 (35.7)	<0.001	61,083 (87)	9117 (13)	<0.001
Age (years)	42.2 ± 12.3	42.1 ± 12.2	0.188	42.2 ± 12.3	42.1 ± 12.3	0.402
Female (%)	54.2	52.4	0.006	53.7	51.9	0.005
BMI (kg/m²)	21.2 ± 1.7	21.1 ± 1.7	0.007	21.2 ± 1.7	21.1 ± 1.7	0.054
WC (cm)	72.2 ± 6.1	72.2 ± 6.2	0.524	72.2 ± 6.2	72.2 ± 6.2	0.825
WHR	0.800 ± 0.056	0.802 ± 0.056	0.030	0.801 ± 0.056	0.801 ± 0.056	0.453
WHtR	0.439 ± 0.033	0.438 ± 0.033	0.001	0.439 ± 0.033	0.438 ± 0.033	0.005
SBP (mmHg)	118.0 ± 15.6	117.9 ± 15.5	0.212	117.9 ± 15.6	118.0 ± 15.5	0.587
DBP (mm Hg)	72.1 ± 10.4	72.0 ± 10.3	0.269	72.1 ± 10.3	72.2 ± 10.4	0.379
FPG (mM)	5.17 ± 0.29	6.00 ± 0.33	<0.001	5.37 ± 0.38	6.41 ± 0.24	<0.001
TC (mM)	4.68 ± 0.86	4.88 ± 0.90	<0.001	4.73 ± 0.87	4.98 ± 0.93	<0.001
TG (mM)	1.38 ± 1.06	1.73 ± 1.36	<0.001	1.46 ± 1.12	1.93 ± 1.57	<0.001
HDLc (mM)	1.51 ± 0.33	1.43 ± 0.32	<0.001	1.49 ± 0.33	1.41 ± 0.31	<0.001
LDLc (mM)	2.63 ± 0.73	2.80 ± 0.77	<0.001	2.68 ± 0.75	2.83 ± 0.79	<0.001
Non-HDLc (mM)	3.75 ± 1.12	3.94 ± 1.11	<0.001	3.80 ± 1.11	4.02 ± 1.12	<0.001
UA (μM)	336.4 ± 87.9	360.2 ± 88.2	<0.001	342.0 ± 88.1	371.2 ± 88.7	<0.001
VAI	1.46 ± 1.57	1.46 ± 1.46	0.794	1.46 ± 1.53	1.46 ± 1.47	0.821
LAP	15.50 ± 14.66	19.18 ± 17.97	<0.001	16.30 ± 15.28	21.40 ± 20.55	<0.001
CMI	0.483 ± 0.499	0.619 ± 0.636	<0.001	0.513 ± 0.521	0.703 ± 0.758	<0.001

ADA, fasting plasma glucose of 5.6–6.9 mM; BMI, body mass index; CMI, cardiometabolic index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HDLc, high-density lipoprotein cholesterol; IFG, impaired fasting glucose; LAP, lipid accumulation product index; LDLc, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; UA, plasma uric acid; VAI, visceral adiposity index; WC, waist circumference; WHO, fasting plasma glucose of 6.1–6.9 mM; WHR, waist to hip ratio; WHtR, waist height ratio.

After controlling for age, only LAP and CMI but not VAI were positively correlated with FPG detected by partial correlation (Table 2). For men, the correlation coefficients between LAP and CMI and FPG were 0.135 and 0.142, respectively (all P < 0.001); for women, the correlation coefficients between LAP and CMI and FPG were 0.134 and 0.144, respectively (all P < 0.001).

Table 2.

Partial Correlations Coefficients Between Visceral Adiposity Surrogates and Fasting Plasma Glucose in Nonobese Individuals

Variables	Men		Women
Variables	r	P	r	P
VAI	0.020	0.021	−0.006	0.445
LAP	0.135	<0.001	0.134	<0.001
CMI	0.142	<0.001	0.144	<0.001

All adjusted for age.

The proportions of IFG-ADA and IFG-WHO showed an obvious trend with a stepwise increase across the ascending quartiles of LAP and CMI but not that of VAI (Fig. 1A, B). The odds ratio (OR) for IFG-ADA and IFG-WHO in the highest quartiles compared to the first quartile of sex-specific six visceral adiposity surrogates are shown in (Fig. 1C, D). Among the three visceral adiposity surrogates, only the ORs for IFG of LAP and CMI were statistically significant after adjusting for confounding variables. For women, the ORs for IFG-ADA in the highest quartile of the LAP and CMI were 1.967 (95% confidence interval [CI]: 1.645–2.353) and 1.594 (95% CI: 1.383–1.836), respectively; and the ORs for IFG-ADA of LAP and CMI were 1.503 (95% CI: 1.233–1.833) and 2.045 (95% CI: 1.752–2.388), respectively, in men (all P < 0.001) (Fig. 1C). For women, the ORs for IFG-WHO in the highest quartile of the LAP and CMI were 2.025 (95% CI: 1.597–2.567) and 2.017 (95% CI: 1.647–2.470), respectively; and the ORs for IFG-WHO of the LAP and CMI were 1.534 (95% CI: 1.174–2.005) and 2.541 (95% CI: 2.025–3.188), respectively, in men (all P < 0.001) (Fig. 1D).

FIG. 1.

The proportions and ORs of IFG-ADA and IFG-WHO by quartiles of visceral adiposity surrogates. When performing multivariable logistic regression analyses, the potential confounding variables such as age, smoking status, and antihypertensive agents were adjusted. ADA, fasting plasma glucose of 5.6–6.9 mM; CMI, cardiometabolic index; IFG, impaired fasting glucose; LAP, lipid accumulation product index; OR, odds ratio; Q, quartile; VAI, visceral adiposity index; WHO, fasting plasma glucose of 6.1–6.9 mM.

The ROC curves for the three visceral adiposity surrogates for IFG are shown in Supplementary Fig. S1. Among them, LAP and CMI but not VAI were significant distinguishers for IFG. For IFG-ADA, the AUC value of VAI, LAP, and CMI were 0.506 (95% CI: 0.499–0.513), 0.572 (95% CI: 0.565–0.579), and 0.596 (95% CI: 0.589–0.603); respectively; For IFG-WHO, the AUC value of VAI, LAP, and CMI were 0.505 (95% CI: 0.495–0.515), 0.589 (95% CI: 0.579–0.599), and 0.618 (95% CI: 0.608–0.627); respectively.

Discussion

In this large-scale cross-sectional study, we explored the associations between IFG and three visceral adiposity surrogates in nonobese Chinese individuals simultaneously. The result showed that LAP and CMI, which incorporate WC or WHtR and lipid parameters, were significantly associated with IFG (according to both ADA and WHO criteria) in nonobese Chinese individuals. This finding supports the close relationship between excess visceral fat and prediabetes and provides some potential target anthropometric measurements to monitor for the recognition and management of excess visceral adiposity in nonobese individuals with prediabetes.

Although there is still some controversy about the ADA category of IFG,¹⁴ several studies have demonstrated that the risk of CVD and all-cause mortality increased in people with an FPG as low as 5.55 mM.^5,15 In China, the two most recent nationwide surveys of diabetes used the ADA classification.² For a more comprehensive view of the association of the three visceral adiposity surrogates with IFG, we used both ADA and WHO classification criteria in this study. Among adults in China, the estimated overall prevalence of prediabetes was 35.7% in 2013 according to the ADA criteria.² In the present study, the percentage of IFG-ADA was also 35.7% in nonobese individuals with an FPG <7.0 mM. If those with IGT and high hemoglobin A_1c (HbA_1c) were included, the prevalence of prediabetes in the nonobese individuals in this study would be higher than that on the national level.

If untreated, 37% of prediabetes may progress to diabetes in 4 years, and 17.1% of subjects with IFG may develop diabetes by a 3-year follow-up.¹⁶ In contrast, lifestyle modifications, such as weight loss and moderate physical activity, can reduce the risk of diabetes by as much as 58%.¹⁷ Therefore, a proactive approach and targeted interventions for prediabetes can make health care more affordable and save more lives. However, the current recommendations of diabetes for weight management are mostly based on BMI or WC and are less concerned about excess visceral adiposity in normal weight groups.¹⁸ In the present study, we explored the relationship between visceral adiposity surrogates and IFG in nonobese people and provided new anthropometric options for the management of excess visceral adiposity in nonobese individuals with prediabetes.

Compared with subcutaneous adipose tissue, visceral adipose tissue (VAT) has a greater impact on dysglycemia.¹⁹ Therefore, assessing the amount of VAT is more efficient than simply using BMI in the management of prediabetes. The most accurate method for detecting the distribution and amount of VAT is the imaging approach, such as dual-energy X-ray absorptiometry and magnetic resonance imaging. However, these methods are often expensive and impractical in clinical practice. Therefore, finding simple and efficient anthropometric tools for VAT assessments is necessary and has special importance in the nonobese population. The most important purpose of this study is to find some of these indicators.

VAI is a gender-specific mathematical model that was extrapolated from the linear relationship between BMI and WC⁹ and was associated with poor metabolic outcomes in obese individuals.²⁰ However, our recent study also showed that VAI was not significantly associated with hyperuricemia.²¹ In this study, the included population was nonobese, which may limit the discriminative function of VAI. Therefore, the current study showed a negative association between VAI and IFG. The relationship between VAI and IFG requires further study in different BMI categorizations.

LAP is also a gender-specific index based on a combination of WC and TG and has been reported to be better than other variables (BMI and WC) at predicting metabolic abnormalities in both obese and normal weight subjects.^22,23 In the present study, LAP was significantly associated with IFG. This finding is consistent with the results of two other studies on the relationship between prediabetes and LAP.^24,25 Because of the addition of lipid parameters to the formula, LAP was better at distinguishing IFG than VAI. Because LAP comprises only two easily available variables of WC and TG and is simple to calculate, it has a large clinical application potential in the weight management of patients with prediabetes.

Apart from LAP, CMI also showed a significant correlation with IFG in this study. To date, only one study has explored the association between CMI and prediabetes.²⁶ However, IFG was not listed separately from IGT and elevated HbA_1c in the studies. Therefore, future studies are needed to confirm the relationship of IFG with CMI. Although TG and HDLc are included in the formula of VAI and CMI, the CMI directly uses two well-established indicators: WHtR and TG/HDLc. Compared with BMI, WC, and WHR, the WHtR demonstrated stronger associations with CVD risk in both sexes and in children.^27
–29 Similarly, TG/HDLc is also a well-documented indicator of insulin resistance.^30,31 Therefore, CMI, the combination of WHtR and TG/HDLc, is theoretically closely related to IFG.

This study has several strengths. First, the sample size of this study was relatively large. Second, to the best of our knowledge, this is the first study focusing on the association between visceral adiposity surrogates and IFG in nonobese individuals. We must also acknowledge limitations to this study. First, this cross-sectional study design cannot show a causal association between these adiposity surrogates and IFG. Second, this study was based on the data of subjects who received a routine physical examination, and not everyone underwent a standard oral glucose tolerance test or HbA_1c test, therefore this finding cannot be translatable to all individuals with prediabetes.

In conclusion, we demonstrated that LAP and CMI were strongly associated with IFG in nonobese Chinese individuals. The availability of the two visceral adiposity surrogates without costly and complex examination might be more practical in the comprehensive management of patients with prediabetes. However, a further large prospective epidemiological study is needed to confirm this finding.

Footnotes

Acknowledgments

In the preparation and implementation of this study, we received a lot of selfless help. All of our authors thank all those who have helped us.

Author Disclosure Statement

No conflicting financial interests exist.

Funding Information

This study was funded by Shanghai Jiaotong University Curriculum Construction Fund Project (TRJX201907) and Key Project of Shanghai Changning District Natural Science Foundation (CNKW2016Z03).

Supplementary Material

Supplementary Figure S1

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