Risk Factors Correlated With Risk of Insulin Resistance Using Homeostasis Model Assessment in Adolescents in Taiwan

Abstract

The study aims to discover risk factors significantly correlated with insulin resistance among adolescents in Taiwan. A total of 339 study subjects were recruited in this cross-sectional study. A self-administered questionnaire and physical examinations including anthropometrics and biochemistry profiles were collected. Insulin resistance was assessed using homeostasis model assessment for insulin resistance (HOMA-IR). Study subjects had a significantly increased risk of IR for those with abnormal level of body mass index (odds ratio [OR] = 3.54; 95% confidence interval [CI] = 1.81-6.91), body fat (OR = 2.71; 95% CI = 1.25-5.88), and waist circumference (OR = 25.04; 95% CI = 2.93-214.14) when compared with those who have normal values. Furthermore, a significantly joint effect of 10.86-fold risk for HOMA-IR abnormality among body fat, body mass index, and systolic blood pressure was observed. The identification of risk factors significantly correlated with IR will be important to prevent metabolic syndrome–related diseases and complications for adolescents in their future life.

Keywords

metabolic syndrome risk factors insulin resistance HOMA-IR adolescents

Introduction

The concept of metabolic syndrome (MetS) has been receiving worldwide attention for decades since the first published article on MetS in the mid 20th century.¹ The major features of the syndrome include central obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hyperglycemia, and hypertension. However, the prevalence for each component can be different depending on the underlying genetic and environmental influences; therefore, their corresponding criteria vary across the globe. Organizations such as Adult Treatment Panel III (ATP III), International Diabetes Federation, World Health Organization (WHO), American Diabetes Association, and American Heart Association (AHA) all have their own slightly modified versions of the definition for MetS.^2-4

Metabolic syndrome is a major risk factor for developing cardiovascular disease, type 2 diabetes, nonalcoholic fatty liver disease, and polycystic ovary syndrome.^5,6 Despite being infamous for its associated diseases, MetS still has high prevalence in both developed and developing countries. It affects 25% of the people in the United States,⁷ 31.2% of the population in Venezuala,⁸ and 16.5% of the people in China.⁸ In Taiwan, the age-standardized prevalence of MetS was 15.7% by the modified ATP III criteria.⁹ Coronary heart disease (CHD), cardiovascular disease (CVD), and total mortality are significantly higher in US adults with MetS than in those without MetS. The adjusted hazard ratios (HRs) for CHD, CVD, and total mortality were 2.02, 1.82, and 1.42, respectively.⁶

Prevalence rate of complications and diseases that are induced by MetS has persistently risen over the past decades for adolescents worldwide. Obesity has a direct impact on hypertension incidence during childhood with relatively higher body mass index (BMI) and waist circumference (WC) compared with non–overweight adolescents.⁹ Additionally, abdominal adiposity has also been linked to a higher risk of insulin resistance and its associated morbidities.¹⁰ Risk factors for MetS and cardiovascular diseases share a lot in similarities that accelerates the process of atherosclerosis. Obesity-related hypertension during adolescence can be the key factor leading to cardiovascular diseases by abnormal sodium retention, increased sympathetic nervous system activity, activation of rennin–angiotensin–aldosterone system, and altered vascular function.¹¹ According to the International Diabetes Federation, insulin resistance was identified in all patients with MetS.³ Univariate analysis of adolescents revealed that MetS had a statistically significant association with insulin over 25 µIU/mL, homeostasis model for assessment of insulin resistance (HOMA-IR) equal to 3.16.¹² The aim of the study is to discover the risk factors correlated with insulin resistance among adolescents in Taiwan. We can provide evidence of risk factors significantly related with insulin resistance to develop some preventive strategies for decreasing incidence of insulin resistance and then prevent MetS-related diseases and complications in adolescents in Taiwan.

Methods

Study Population

Two cooperation junior high schools were purposively selected from Taipei City, Taiwan. Among the 2 schools, all first-year students carried a research packet home for the parent or legal custodian to read and provide informed consent before study activities begin. After returning the informed consent form, student volunteers and their parents, students’ parents, or legal custodian were asked to complete the self-administered questionnaires for data collection. During September 2010 to November 2011, we recruited 340 junior high school first-grade students (13.4 ± 0.63 years, 182 boys and 158 girls) in this study. However, a total of 339 subjects were eligible for analysis since we excluded 1 subject whose blood sample could not be obtained. This study was approved by the Research Ethics Committee of Taipei Medical University Hospital (TMUH-02-08-10) and was consistent with the World Medical Association Declaration of Helsinki. Parents provided informed consent and adolescents provided informed assent before testing commenced.

Anthropometrics Measurement and Blood Pressure

All participants were invited to accept anthropometrics measurement and questionnaire interview in the health center in the cooperative school. Height and weight were obtained with cloth and shoes off using the SuperView HW686. BMI of study subjects were calculated using weight (kg) divided by the square of height (m²). We used national guideline for age-sex specific cut point of BMI of overweight and obese for children and adolescents published by Taiwan Food and Drug Administration, Department of Health, Executive Yuan, to define study subjects. Those whose BMI were more than 85th percentile of age-sex-specific value were grouped as overweight and those whose BMI were greater than 95th were obese.¹³ WC was measured using the horizontal plane at a midway between the lowest rib and the superior border of the iliac crest at the end of normal expiration respiration. WC ≥ 90th were classified to abnormal group based on criteria in adolescents from the AHA.² Percentage of body fat was estimated with bioelectrical impedance (Omron Body Fat Analyzer HBF-306) by trained research assistants. All participants were asked to stand with feet, shoulder-width apart, and grip the handles. Wrap the palms around the electrode pads (metallic pads) with thumbs pointing vertically. Hold the body fat analyzer straight out in front, with arms parallel to the ground. According to the statistics from the 1999-2004 National Health and Nutrition Examination Survey, percentage body fat defined as abnormal were 50th smoothed percentile among adolescents aged 13.0 to 13.49 (24.6% for boys and 31.8% for girls, respectively).¹⁴ Systolic blood pressure (SBP), diastolic blood pressure (DBP), arterial stiffness index (ASI), and ankle brachial index (ABI) were measured using a noninvasive computerized oscillometric device (CardioVision, Ver 3.10) that was calibrated before use. The subjects sat and relaxed for 5 minutes before we measured the blood pressure on their upper arms. Each subject was measured following the same protocol, and the average of 2 blood pressure measurements for each subject was used in the analysis. SBP ≥ 90th were classified into abnormal group in accordance with the definition criteria in adolescent from the AHA.²

Questionnaire Interview

Study subjects’ demographics characteristics and lifestyle behaviors were obtained through self-administered questionnaire. The structured questionnaire was distributed to be filled out by their parents or legal custodian at home. The questionnaire takes about 10 to 15 minutes on average to complete. Information included parents’ socioeconomic characteristics, lifestyle behaviors such as cigarette smoking and alcohol consumption, and personal and family disease histories were collected.

Serum Biochemical Examination

All study subjects provided fasting blood samples. A total of 10 mL blood for each study subject was collected by a disposable vacuum blood collector. All blood samples were separated into red blood cells and serum within 4 hours and then be frozen at −80°C for further examination. Low-density lipoprotein cholesterol (LDL-C), HDL-C, total cholesterol, and triglyceride were determined by an autoanalyzer (Hitachi 737, USA) and kit. The detection technology was obtained from Boehringer Mannheim Diagnostics (Indianapolis, IN). An additional 2 mL blood was collected using a vacuum blood collector with EDTA to examine blood glucose level. Fasting plasma glucose concentration was detected using a glucose oxidase method (YSI 203 Glucose Analyzer; Yellow Springs Instruments, Yellow Springs, OH). Serum homocysteine was described by enzymatic assay.¹⁵ HOMA-IR was used as an index of insulin resistance (IR) in this study. HOMA-IR was calculated using fasting blood glucose (mmol/L) × insulin (µIU/mL)/22.5. Those whose HOMA-IR > 3.16 were diagnosed as HOMA-IR abnormal group, otherwise they were in the HOMA-IR normal group in this study.¹²

Statistical Analysis

All statistical analyses were conducted with SAS 9.1 (SAS Institute, Cary, NC). Student’s t test was used to compare the means of continual variables and χ² was used to compare the categorical variables between HOMA-IR abnormal and normal groups. The effect of BMI, body fat, WC, and SBP were assessed by calculating odds ratio (OR) and 95% confidence intervals (CIs) for the HOMA-IR abnormal group versus the HOMA-IR normal group with the multivariate logistic regression model since these factors are significant in the univariate model. After adjustment for risk factors, we combined effects of BMI, WC, and SBP to examine the joint effect of risk for HOMA-IR abnormality. Two-sided P values <.05 were considered as statistically significant.

Results

Table 1 shows the physical characteristics of the study subjects in the HOMA-IR abnormal and normal groups. BMI, body fat, WC, and SBP were significantly lower in the HOMA-IR (HOMA-IR ≤ 3.16) normal group than in the abnormal group (P < .001). However, gender and DBP were similar between HOMA-IR abnormal and normal groups.

Table 1.

Physical Characteristics of the Study Subjects by Various HOMA-IR Groups.^a

	HOMA-IR Abnormal			HOMA-IR Normal			P Value
	N	Mean	SD	N	Mean	SD
Total number	147	43.3%		192	56.6%
Age	147	13.4	0.6	192	13.3	0.6	0.450
Gender (male)	77	52.4%		105	54.7%		.673
BMI (kg/m2)	147	22.6	4.5	192	18.9	2.7	<.001
Body fat (%)	147	25.4	8.4	191	19.2	6.2	<.001
WC (cm)	144	75.9	11.9	191	65.7	7.4	<.001
SBP (mm Hg)	147	114.2	8.6	192	109.3	8.9	<.001
DBP (mm Hg)	147	64.3	7.9	192	62.8	8.6	.090

Abbreviations: HOMA-RI, homeostasis model assessment for insulin resistance; SD, standard deviation; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Data are expressed as % or mean (SD).

The clinical and laboratory data of all the 339 subjects distributed according to their HOMA-IR level are shown in Table 2. Fasting glucose level, insulin level, triglyceride, HDL-C, LDL-C, and glutamic pyruvic transaminase (GPT) significantly differed between HOMA-IR abnormal and HOMA-IR normal groups. However, total cholesterol, glutamic oxaloacetic transaminase (GOT), and homocysteine did not show significantly differences between the 2 HOMA-IR groups.

Table 2.

Serum Markers of the Study Subjects by Various HOMA-IR Groups.

	HOMA-IR Abnormal			HOMA-IR Normal
	N	Mean	SD	N	Mean	SD	P Value
Fasting glucose level (mmol/L)	147	5.3	0.5	192	4.9	0.3	<.001
Insulin (µIU/mL)	147	25.3	16.5	192	10.3	2.5	<.001
Total cholesterol (mmol/L)	147	4.1	0.7	192	4.1	0.7	.808
Triglyceride (mmol/L)	147	1.0	0.5	192	0.7	0.3	<.001
HDL-C (mmol/L)	147	1.3	0.3	192	1.5	0.3	<.001
LDL-C (mmol/L)	147	2.4	0.6	192	2.3	0.6	<.001
GOT (IU/L)	147	21.5	9.8	192	20.5	6.1	.252
GPT (IU/L)	147	20.7	23.8	192	12.2	6.6	<.001
Homocysteine (µmol/L)	147	9.8	3.0	192	9.5	2.6	.326

Abbreviations: HOMA-RI, homeostasis model assessment for insulin resistance; SD, standard deviation; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase.

As shown in Table 3, the crude ORs of HOMA-IR abnormality were 7.7, 6.9, 57.1, and 2.7, respectively, for abnormality of BMI, body fat, WC, and SBP, showing statistically significance. After the adjustment of gender, cholesterol, triglyceride, HDL-C, LDL-C, GOT, and GPT, the multivariate adjusted ORs of HOMA-IR abnormality were 3.54 (95% CI = 1.81-6.91) for BMI, 2.71 (95% CI = 1.25-5.88) for body fat, and 25.04 (95% CI = 2.93-214.14) for WC.

Table 3.

Multivariate Adjusted Odds Ratios of HOMA-IR Abnormality by Body Mass Index, Body Fat, Waist Circumference, and Systolic Blood Pressure.

		HOMA
		Abnormal	Normal	Crude OR	OR^a (95% CI)	OR^b (95% CI)
BMI	Normal	72	169	1.00	1.00	1.00
	Abnormal	74	23	7.55 (4.39-13.00)***	7.66 (4.44-13.22)***	3.54 (1.81-6.91)**
Body fat	Normal	95	175	1.00	1.00	1.00
	Abnormal	51	16	5.87 (3.17-10.86)***	6.85 (3.61-13.00)***	2.71 (1.25-5.88)*
WC	Normal	110	190	1.00	1.00	1.00
	Abnormal	33	1	56.99 (7.69-422.47)***	57.09 (7.70-423.28)***	25.04 (2.93-214.14)**
SBP	Normal	122	179	1.00	1.00	1.00
	Abnormal	24	13	2.71 (1.33-5.53)**	2.71 (1.33-5.54)**	1.62 (0.70-3.76)

Abbreviations: HOMA-RI, homeostasis model assessment for insulin resistance; OR, odds ratio; CI, confidence interval; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase.

OR adjusted for gender.

OR adjusted for gender, cholesterol, triglyceride, HDL-C, LDL-C, GOT, and GPT.

P < .05. **P < .01. ***P < .001.

A joint effect on the risk of abnormal HOMA between 3 risk factors—body fat, BMI, and SBP—is listed in Table 4. Compared with a reference group that included subjects without all 3 risk factors mentioned above, those with all risk factors would have a significantly higher risk of abnormal HOMA (OR = 10.86; 95% CI = 1.19-99.35). Furthermore, study subjects with any combination of 1 or 2 abnormalities of body fat, BMI, and SBP will also have 2.51- or 3.75-fold risk for HOMA-IR abnormality, respectively. Our finding shows a significant joint effect on risk of HOMA-IR abnormality among WC, BMI, and SBP among adolescents in Taiwan.

Table 4.

Joint Effects of Body Fat, Body Mass Index, and Systolic Blood Pressure on Risk of HOMA Abnormality.

				HOMA
Group	Body Fat	BMI	SBP	Abnormal	Normal	OR (95% CI)	OR^a (95% CI)	OR (95% CI)	OR^a (95% CI)
I	−	−	−	58	149	1.00	1.00	1.00^†	1.00^†
II	+	−	−	9	7	3.30 (1.18-9.28)*	3.74 (1.21-11.52)*
II	−	+	−	26	14	4.77 (2.33-9.77)***	3.56 (1.58-8.02)**
II	−	−	+	5	12	1.07 (0.36-3.17)	0.84 (0.26-2.69)	3.11 (1.79-5.41)***	2.51 (1.37-4.60)**
III	+	+	−	29	8	9.31 (4.02-21.56)***	2.87 (1.00-8.21)*
III	+	−	+	0	0	—	—
III	−	+	+	6	0	—	—	11.24 (4.92-25.67)***	3.75 (1.39-10.12)**
IV	+	+	+	13	1	33.39 (4.27-261.10)**	11.86 (1.27-110.96)*	33.39 (4.27-261.038)**	10.86 (1.19-99.35)*

Abbreviations: HOMA-RI, homeostasis model assessment for insulin resistance; OR, odds ratio; CI, confidence interval; BMI, body mass index; SBP, systolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase.

OR adjusted for gender, cholesterol, triglyceride, HDL-C, LDL-C, GOT, and GPT.

P < .05. **P < .01. ***P < .001. ^†P for trend < .001.

Discussion

The study result shows positive finding in HOMA-IR association with BMI, body fat, WC, and SBP with significance. While few studies working on the theme among school adolescents, however, this adolescent study has findings similar to many earlier adult studies.¹⁶ The present investigation presents the joint effect profile among WC, BMI, and SBP toward HOMA values to be traced as a proxy for insulin resistance. It shows the joint effects of WC and BMI as well as WC, BMI, and SBP all together in significance independence of gender, blood lipids, and liver enzymes. This study is a pioneering one in the current literature among adolescent studies on insulin resistance since it evaluates the joint association of multiple biological abnormalities in light of future metabolic disorders.

Our study found a significant joint effect of risk for HOMA-IR abnormality among WC, BMI, and SBP. Elevated blood pressure can be the outcome of insulin resistance. Clinical evidence show a link between insulin resistance and essential hypertension.¹⁷ Obese patients with hypertension are at an increased risk of more insulin resistance than normotensive obese patients.¹⁷ This shows the possible causative role of hyperinsulinemia (a marker of insulin resistance) in the development of hypertension. However, some of the hypertension patients do not show evidence of insulin resistance.¹⁷ The possible explanation could be due to sympathetic reaction to the kidney leading to blood pressure elevation activated by leptin, angiotensinogen, and insulin itself.¹⁸ Similarly, reabsorption of sodium in the kidneys is noted activated by high insulin resistance.¹⁹ Furthermore, endothelial dysfunction may also lead to the development of elevated blood pressure secondary to the decreased release of nitric oxide and increased expression of adhesion molecules, platelets, and monocytes.¹⁹

The progression of insulin resistance to CVD or type II diabetes can be classified into 4 stages.^20-22 Stage I is with carbohydrates craving, mild insulin resistance, and easy weight gain as increased quantity of food energy (transformed into blood sugar) is channeled through the liver, turned into blood fat, and then stored in fat cells. Stage II is with normal or elevated fasting insulin levels, normal blood glucose, mild-to-moderate central obesity, elevated blood pressure, early atherogenic dyslipidemia, vascular inflammation with high circulating levels of inflammatory markers, and endothelial dysfunction. Stage III is with elevated fasting insulin levels, low blood sugar swings with impaired glucose intolerance (prediabetes), advanced atherogenic dyslipidemia comprising elevated lipoproteins containing apolipoprotein B, triglycerides, increased small dense LDL particles, and low levels of HDLs. Stage IV is with the total body cell resistance to insulin and the stage is marked by elevated levels of fasting insulin and blood glucose levels.

Weight reduction, increased physical activity, diet, and tobacco cessation are primary lifestyle intervention strategies to ameliorate the adverse effects of insulin resistance, thus reducing the risk of development of CVD or type II diabetes.²³ Obesity, regional fat distribution, central adiposity, and lean body mass that mediate insulin resistance may be at least partly relieved by lifestyle modification.²⁴ Therefore, feasible lifestyle modifications approaches, as mentioned above, should be encouraged and promoted.

Pharmaceuticals can also ameliorate the adverse effect of insulin resistance. New drugs, sibutramine and orlisat, approved by the Food and Drug Administration, are prescribed for weight reduction.²⁵ Rimonabant, a selective blocker of the cannabinoid receptor type I(CB-1), is another promising weight-loss drug in development.²⁶ Other than their primary goal of weight loss, these drugs have been shown to improve insulin resistance and associated with favorable changes in serum lipid levels, metabolic risk factors, and glucose levels.^26,27 Ruboxistaruin, a PKC-β isoform selective inhibitor, has been shown to normalize endothelial dysfunction, diabetic nephropathy, and CVD risk factors.²⁸ Dipeptidyl peptidase 4 (DDP-4) inhibitors are commonly used in clinical practice for the treatment of type II diabetes to control blood sugar through increased insulin secretion and insulin sensitivity.²⁹ DDP-4 inhibitors block the inactivation of incretin hormone. The 2 most important incretin-producing hormones are glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide (GLP-1). Sitagliptin and vildagliptin are 2 DDP-4 inhibitors with a long duration effect that inhibit inactivation of GLP-1 and thus improve insulin sensitivity and prevent the vascular and metabolic adverse effects in type II diabetes.³⁰

On account of the study design, this study is cross-sectional and a case–control study, which is a limitation for the detection of a causal relationship. In addition, the health of adolescents is not only on their current health status but also their future health and lifestyle. Future cohort data collection for a longitudinal study is warranted.

Conclusion

Our results indicate that some physical markers, including WC, BMI, and SBP, and some serological markers, including blood sugar, blood lipids, and the liver enzyme, may represent an important risk factor to the genesis of IR in an Asia-Pacific adolescent population at schools in Taiwan; furthermore, these markers may have a major role in MetS, type 2 diabetes, and cardiovascular diseases in adult life. The identification of adolescents at risk of IR by the convenient measurements of some biological factors such as physical and serological markers may be useful for the design and implementation of early intervention in the adolescent stage to prevent MetS-related diseases in the future life stage.

Footnotes

Authors’ Note

Authors Shiyng-Yu Lin and Chien-Tien Su contributed equally to this work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the Taipei Medical University Hospital (98TMU-TMUH-01-4, 99TMU-TMUH-02-1). Additional support was received from a Ministry of Education Topnotch Stroke Research Center grant and the Department of Health Center of Excellence for Clinical Trial and Research in Neuroscience (DOH99-TD-B-111-003, DOH100-TD-B-111-003, DOH101-TD-B-111-003, DOH102-TD-B-111-003) and Dr Chi-Chin Huang Stroke Research Center.

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