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Abstract

Aim:

To investigate the association between visceral adipose tissue loss and insulin resistance and hyperleptinemia in adolescents with obesity submitted to interdisciplinary weight-loss therapy.

Methods:

A total of 172 post-pubertal adolescents (body mass index greater than the 95th percentile of the Centers for Disease Control and Prevention reference growth charts) were recruited for the study. The adolescents were assigned to long-term weight-loss therapy. Body composition, visceral and subcutaneous fat, glucose metabolism, lipid profile, hepatic enzymes and leptin concentration were measured. After the therapy, the adolescents were allocated to three different groups according to the tertile of visceral fat reduction.

Results:

Positive effects on body composition were observed in all analysed groups independent of visceral fat reduction. It was found that visceral fat was an independent predictor of insulin resistance in the investigated population. Obese adolescents who lost a higher proportion of visceral adipose tissue (>1.8 cm) demonstrated improved metabolic and inflammatory parameters twice as much than those who presented smaller losses. Positive correlations between visceral fat reduction and glucose metabolism, lipid profile, hepatic enzymes and homeostasis model assessment of insulin resistance index were demonstrated.

Conclusion:

The magnitude of the reduction in visceral fat was an independent predictor of insulin resistance, hyperleptinemia and metabolic disorders related to obese adolescents.

Keywords

Visceral fat insulin resistance obesity cardiometabolic risk hyperleptinemia state

Introduction

In his 1988 Banting Lecture, Dr Gerald Reaven^1,2 presented the theory that insulin resistance played a key role in the aetiology and prognosis of a group of linked metabolic diseases, including, diabetes, hypertension and cardiovascular diseases. He named this group of symptoms ‘Syndrome X’, which later became known as metabolic syndrome (MetS). This has since been extensively studied in different conditions in the elderly, adults, adolescents and children.^3–6

In addition, Dr Reaven² suggested that insulin resistance presenting compensatory hyperinsulinemia could lead not only to pathogenesis of noninsulin-dependent diabetes mellitus (NIDDM) but also to an increase in the risk of coronary heart disease (CHD).

To corroborate this, he presented an in-depth discussion of the mechanisms involved in Syndrome X, which result in an increased risk of cardiovascular disease (insulin resistance, compensatory hyperinsulinemia, glucose intolerance, increased triglycerides (TGs) and reduced high-density lipoprotein (HDL) cholesterol concentration). In addition, he extensively investigated insulin resistance syndrome and showed that a large cohort of metabolic abnormalities associated with insulin resistance/hyperinsulinemia can lead to numerous clinical manifestations, including type 2 diabetes, hypertension, cardiovascular disease, polycystic ovary syndrome, non-alcoholic fatty liver disease (NAFLD), certain types of cancer and sleep apnea.^7–10 MetS involves a variety of altered metabolic and inflammatory processes, including increases in visceral adiposity. The identification of individuals with this condition is important so that interventions can target lifestyle changes to decrease not only the incidence of diabetes but also the risk of developing cardiovascular disease.¹¹ Our research team has already started to develop a multicomponent therapy trying to help adolescents with obesity recover their health, taking into account the multifactorial components involved in the aetiology of obesity and its comorbidities.¹² In this respect, we found that they presented high prevalences of insulin resistance (about 70%), dyslipidemias, carotid intima media thickness (cIMT) alterations, hyperleptinemia and hypoadiponectinemia, resulting in an increased prevalence of MetS^13,14 with almost 30% of the adolescents having a diagnosis of MetS.^14,15 Interestingly, there was a higher prevalence of NAFLD in adolescents with obesity, reaching 40% and 60% in girls and boys, respectively, aged between 14 and 19 years.¹⁶

In addition, we were able to show that the most important factors in the aetiology of MetS and NAFLD development in these adolescents with obesity were insulin resistance and visceral adiposity.^14,17 Moreover, de Lima Sanches et al.,¹³ showed that an improved insulin resistance index was an independent predictor of cIMT alterations in adolescents with obesity. In addition, Masquio et al.¹⁴ demonstrated that the presence of MetS impaired the reduction in cIMT in adolescents with obesity and that hyperleptinemia correlated with the increased prevalence of NAFLD and the development of atherosclerosis.¹⁸

However, as far as we are aware, the association between visceral adipose tissue reduction and the impact on insulin resistance and hyperleptinemia, according to the level of reduction, has not been explored in adolescents with obesity analysed after long-term interdisciplinary weight-loss therapy.

Methods

Population

This study uses data collected between 2004 and 2012 by the obesity study group. A total of 172 post-pubertal obese adolescents aged 15–19 years of both genders (102 girls and 70 boys) were recruited. The inclusion criteria were as follows: adolescents at Tanner stage five¹⁹, the presence of primary obesity and a body mass index (BMI) >95th percentile of the Centers for Disease Control and Prevention (CDC) reference growth charts.²⁰ Non-inclusion criteria were the use of birth control pills, cortisone, anti-epileptic drugs, a history of renal disease, alcohol intake, smoking and secondary obesity due to endocrine disorders. The study was conducted following the principles of the Declaration of Helsinki and was approved by the Ethics Committee on Research at the Universidade Federal de São Paulo, UNIFESP (152.281), clinical trial Id: NCT01358773. All procedures were explained to those responsible for the volunteers, and a free and informed consent form was signed.

Anthropometric measurements

The adolescents were weighed on a scale and after the plethysmograph was used for body composition. (BOD POD equipment - Cosmed, Life Measurement Instruments, Concord, CA, USA), with patients wearing the minimum clothing possible, and height was measured using a stadiometer (Sanny-model ES 2030). BMI was calculated by dividing the weight by height squared (kg/m²). Body composition, including fat mass (percentage and kilogrammes) and lean mass (percentage and kilogrammes) was obtained through air displacement plethysmography (BOD POD equipment). Waist circumference was obtained at the midpoint between the last rib and iliac crest.²¹

Serum analysis

Blood samples were collected after an overnight fast in the outpatient clinic at approximately 08:00 h. After collection, the blood was centrifuged for 10 min at 5000 r/min and stored at −70°C for future analyses. The materials used for collection were disposable and adequately labelled. Blood was collected by a skilled and qualified technician. Insulin resistance was assessed according to the homeostasis model assessment of insulin resistance (HOMA-IR) index. The HOMA-IR was calculated as the product of the fasting blood glucose and the immunoreactive insulin (I) levels: [fasting blood glucose (mg/dL) × fasting insulin (mU/L)]/405. The quantitative insulin sensitivity check index (QUICKI) was calculated as [1/(log(fasting insulin) + log(fasting glucose)]. Total cholesterol (TC), TG, HDL, low-density lipoprotein (LDL), very low-density lipoprotein (VLDL) and hepatic enzymes were analysed using a commercial kit (CELM, Barueri, Brazil). Reference values adopted were as follows: glucose (60–110 mg/dL), insulin (<20 lU/mL), aspartate aminotransferase (AST, 10–40 U/L), alanine aminotransferase (ALT, 10–35 U/L), and c-glutamyl transferase (GGT, 17–30 U/L) as previously described by Sartorio et al.;²² HOMA-IR (<3.16) according to Keskin et al.;²³ QUICKI (>0.339) according to Schwimmer et al.;²⁴ TC (<170 mg/dL), TG (33–129 mg/dL), HDL cholesterol (>38 mg/dL), LDL cholesterol (<130 mg/dL), VLDL cholesterol (10–50 mg/dL) according to Giuliano et al.²⁵

The leptin concentration was measured using a commercially available enzyme-linked immunosorbent assay kit from R&D Systems (Minneapolis, MN), according to the manufacturer’s instructions. Leptin values between 1 and 20 ng/mL for males and between 4.9 and 24 ng/mL for females, as previously described by Gutin et al.,²⁶ were adopted.

The ratios of the lipoprotein levels (TC/HDL and TG/HDL) were calculated because these ratios have been described in the literature as predictors of cardiovascular disease and MetS in adults and in children.^27–29

Visceral and subcutaneous adiposity measurements

The abdominal ultrasonography procedures and the measurements of visceral and subcutaneous fat tissue and fatty liver were performed by the same physician, who was blinded to the subject assignment groups at baseline and at follow-up 1 year after the start of the therapy. This physician was a specialist in imaging diagnostics. A 3.5 MHz multifrequency transducer (broadband) was used to reduce the risk of misclassification. The intra-examination coefficient of the variation for ultrasound (US) was 0.8%. US-determined subcutaneous fat was defined as the distance between the skin and the external face of the rectus abdominal muscle, and visceral fat was defined as the distance between the internal face of the same muscle and the anterior wall of the aorta. The cut-off points for the definition of visceral obesity by ultrasonography were based on the methodology previously described by Ribeiro-Filho et al.³⁰

Descriptive methodology of interdisciplinary intervention

The interventions were conducted by an interdisciplinary group of health professionals. All adolescents performed the same tests both at the beginning and at the end of the study (Figure 1). Once each month, the adolescents visited the endocrinologist to evaluate the treatment. The adolescents participated in three, 2-h supervised therapy sessions per week (on non-consecutive days) combining physical exercise, physiotherapy, nutritional advice sessions and psychological therapy for 1 year (Figure 1).

Figure 1.

Methodological design.

Clinical approach

All obese adolescents visited the endocrinologist with their parents once every month. The medical follow-up was based on the initial medical history and comprised a physical examination, and the measurement of blood pressure and body mass. The adherence of the adolescents to the interdisciplinary therapies was also assessed. The team discussed some possible changes in lifestyle to promote their health with the adolescents and their parents (Figure 1).

Physical exercise practice

Aerobic plus resistance training intervention

During the 1-year therapy period, the adolescents followed a combined physical exercise training therapy. The protocol was performed three times per week for 1 year and included 30 min of aerobic training plus 30 min of resistance training per session. The subjects were instructed to reverse the order of the physical exercises (aerobic and resistance) at each training session. The aerobic training consisted of running on a motor-driven treadmill (Life Fitness – model TR 9700HR) or riding an exercise bicycle at a cardiac frequency intensity representing ventilatory threshold 1 (VT1) (±4 bpm), which was determined by the results of an initial oxygen uptake test for aerobic exercises (ergoespirometry). The physical exercise therapy was based on the guidelines from the American College of Sports Medicine (ACSM).³¹ Resistance training was also designed based on ACSM recommendations (Figure 1).³²

Nutrition counselling

Energy intake was set at the levels recommended by the Institute of Medicine’s dietary reference intake (DRI) for subjects with low levels of physical activity of the same age and gender following a balanced diet.³³ No pharmacotherapies or antioxidants were recommended. Once a week, adolescents had dietetics lessons educating participants on the food pyramid, were taught how to keep diet records and were given information on weight loss diets, fad diets, food labels, dietetics, fat-free foods, low-calorie foods and other related topics. They also had monthly individual consultations (Figure 1).

Physiotherapy intervention

The adolescents were monitored by a physiotherapist during the therapy in order to prevent musculoskeletal injuries. Once a week, the volunteers had lessons regarding such topics as posture, the prevention of musculoskeletal injuries, diaphragmatic breathing, hydrotherapy, isostretching, and balance.

Psychological counselling

Psychological therapy treatment plans were established based on validated questionnaires that considered some of the psychological problems caused by obesity, as described in the literature. These include depression, eating disorders, anxiety, decreased self-esteem and body image disorders. Interdisciplinary therapy consisted of a weekly 1-h group session. Individualized psychological therapy was recommended when it was necessary according to the psychological assessment (Figure 1).

Statistical analysis

Statistical analysis was performed using the Statistica 12 (StatSoft Inc, Tulsa, USA) software package. The adopted significant value was α ⩽ 5%. Data normality was verified with the Kolmogorov–Smirnov test. Data were expressed as mean ± standard deviation (SD). The effects of 1 year of interdisciplinary therapy were assessed using repeated measures analysis of variance (ANOVA) and Tukey post hoc test. Correlations were established through the Pearson’s test. Multivariate regression analysis was performed to look for independent predictors for insulin resistance.

After long-term weight-loss therapy, they were analysed according to the tertile of visceral fat reduction (delta values) – 1st tertile (high): more than 1.8 cm (n = 58); 2nd tertile (moderate): between 1.8 cm and 0.79 cm (n = 55); and 3rd tertile (low): less than or equal to 0.79 cm (n = 59).

Results

Baseline condition

Analysing the baseline condition, the adolescents were paired according to BMI and body fat mass as shown in Table 1. This is relevant information that highlights the homogeneity of the sample studied at the initial moment of the interventions. It justifies that the differences that were found between the groups at the final moment are dependent on the effect of the therapy and the group evaluated, according the magnitude of reduction in visceral fat depot.

Table 1.

Effects of interdisciplinary Intervention according to visceral fat group distribution.

	All		High group		Moderate group		Low group
	(n = 172)		(n = 58)		(n = 55)		(n = 59)
	Baseline after intervention		Baseline after intervention		Baseline after intervention		Baseline after intervention
Body mass (kg)	100.56 ± 15.01	92.36 ± 15.03^d	101.88 ± 14.11	88.22 ± 13.07^d	103.34 ± 17.52	95.43 ± 18.03^d	96.73 ± 12.80	93.28 ± 12.86^d
BMI (kg/m²)	35.98 ± 4.59	32.91 ± 4.84^d	36.06 ± 4.17	30.95 ± 3.82^d	36.42 ± 5.10	33.57 ± 5.34^d	35.61 ± 4.51	34.29 ± 4.69^d
Body fat mass (%)	44.09 ± 6.97	38.02 ± 8.26^d	44.82 ± 5.90	35.74 ± 7.35^d	43.16 ± 8.01	37.20 ± 9.20^d	44.41 ± 6.85	41.19 ± 7.29^d
Lean body mass (%)	56.00 ± 6.91	61.99 ± 8.25^d	55.18 ± 5.90	64.26 ± 7.35^d	57.13 ± 7.80	62.80 ± 9.19^d	55.60 ± 6.89	58.81 ± 7.29^d
Body fat mass (kg)	44.50 ± 10.97	35.60 ± 11.17^d	45.88 ± 10.05	31.87 ± 9.18^d	44.51 ± 12.87	36.01 ± 12.86^d	43.29 ± 9.99	38.95 ± 10.28^d
Body lean mass (kg)	56.02 ± 9.27	56.93 ± 9.47	56.01 ± 8.41	56.79 ± 9.15	58.48 ± 10.77	59.21 ± 10.83	53.60 ± 7.99	54.60 ± 7.80
Waist circumference (cm)	101.55 ± 10.44	94.66 ± 11.27^d	103.23 ± 9.35	91.61 ± 9.81^d	102.21 ± 11.14	95.20 ± 11.47^d	98.87 ± 10.61	97.36 ± 11.96
Visceral fat (cm)	4.41 ± 1.52	3.21 ± 1.33^d	5.27 ± 1.27	2.65 ± 1.06^d	4.48 ± 1.54^a	3.22 ± 1.50^d	3.52 ± 1.21^a,b	3.72 ± 1.22^c
Subcutaneous fat (cm)	3.78 ± 0.90	3.20 ± 0.91^d	3.75 ± 0.84	3.09 ± 0.91^d	3.85 ± 1.02	3.20 ± 0.93^d	3.79 ± 0.82	3.33 ± 0.85^d
Glucose (mg/dL)	90.54 ± 6.54	90.34 ± 7.40	90.61 ± 6.51	87.91 ± 6.64	89.33 ± 6.83	91.44 ± 8.06	91.54 ± 6.24	91.67 ± 7.01
Insulin (uU/mL)	16.64 ± 9.22	13.12 ± 8.95^d	17.05 ± 9.23	11.76 ± 7.24^d	17.07 ± 11.02	14.58 ± 12.09	16.00 ± 7.29	13.21 ± 6.49
HOMA-IR	3.74 ± 2.21	3.00 ± 2.37^d	3.80 ± 1.97	2.58 ± 1.60^d	3.86 ± 2.87	3.44 ± 3.38	3.62 ± 1.70	3.03 ± 1.69
QUICKI	0.32 ± 0.02	0.34 ± 0.03^d	0.32 ± 0.02	0.34 ± 0.03^d	0.32 ± 0.02	0.33 ± 0.03^d	0.32 ± 0.02	0.33 ± 0.02
Total cholesterol (mg/dL)	164.89 ± 33.04	155.51 ± 29.64^d	170.25 ± 36.72	156.63 ± 31.14^d	165.53 ± 29.77	158.65 ± 31.21^d	160.00 ± 31.51	151.87 ± 26.41
HDL cholesterol (mg/dL)	45.51 ± 8.91	47.31 ± 10.02^d	44.07 ± 9.49	45.21 ± 9.37	44.11 ± 8.55	46.72 ± 9.90^d	48.24 ± 8.22	50.07 ± 10.42
LDL cholesterol (mg/dL)	98.78 ± 28.68	90.55 ± 25.29^d	105.26 ± 33.87	94.18 ± 27.96^d	98.98 ± 22.82	94.06 ± 25.23	92.98 ± 26.95	83.75 ± 21.31^d
VLDL cholesterol (mg/dL)	20.51 ± 10.28	17.65 ± 9.27^d	20.84 ± 9.58	17.23 ± 8.69^d	21.39 ± 10.36	17.87 ± 9.28^d	19.55 ± 10.92	18.05 ± 9.92
Triglycerides (mg/dL)	104.54 ± 60.59	87.97 ± 46.01^d	104.19 ± 48.00	86.14 ± 42.86^d	114.40 ± 76.86	89.04 ± 46.45^d	96.69 ± 53.70	89.71 ± 49.24
TC/HDL	3.74 ± 1.01	3.41 ± 0.90^d	4 ± 1.12	3.57 ± 0.91^d	3.85 ± 0.89	3.52 ± 0.99^d	3.41 ± 0.90	3.13 ± 0.76^d
TG/HDL	2.46 ± 1.64	0.55 ± 0.23^d	2.59 ± 1.55	0.55 ± 0.26^d	2.72 ± 1.94	0.54 ± 0.19^d	2.31 ± 1.38	0.57 ± 0.24^d
AST enzyme (U/L)	24.74 ± 8.17	23.42 ± 14.27	25.07 ± 8.10	21.73 ± 6.28^d	24.56 ± 7.46	22.22 ± 6.17	24.26 ± 8.71	26.36 ± 23.06
ALT enzyme (U/L)	31.49 ± 18.46	26.73 ± 18.37^d	32.77 ± 23.66	24.59 ± 14.93	30.18 ± 13.14	25.96 ± 12.85	31.67 ± 17.24	29.91 ± 24.97
GGT enzyme (U/L)	25.52 ± 15.11	20.22 ± 9.77^d	27.93 ± 22.11	20.93 ± 11.01^d	25.89 ± 11.61	20.54 ± 9.55^d	23.07 ± 7.55	19.36 ± 8.68
Leptin (ng/mL)	37.74 ± 24.30	24.86 ± 19.83^d	37.58 ± 27.10	19.03 ± 17.24^d	35.70 ± 24.25	24.93 ± 21.75^d	40.34 ± 21.51	32.24 ± 18.80

BMI: body mass index; HOMA-IR: homeostasis model assessment insulin resistance; QUICKI: quantitative insulin sensitivity check index; HLD: high-density lipoprotein; LDL: low-density lipoprotein; VLDL: very low-density lipoprotein; TC/HDL: total cholesterol/high-density lipoprotein ratio; TG/HDL: triglyceride/high-density lipoprotein ratio; AST: aspartate aminotransferase; ALT: alanine aminotransferase; GGT: c-glutamyl transferase.

Reference values: glucose (60–110 mg/dL), insulin (<20 lU/mL), AST (10–40 U/L), ALT (10–35 U/L) and GGT (17–30 U/L) as previously described by Sartorio et al.;²² HOMA-IR (<3.16) according Keskin et al.;²³ QUICKI (>0.339) according to Schwimmer et al.;²⁴ total cholesterol (<170 mg/dL), TG (33–129 mg/dL), HDL cholesterol (>38 mg/dL), LDL cholesterol (<130 mg/dL), VLDL cholesterol (10–50 mg/dL) according to Giuliano et al.²⁵ Leptin values between 1 and 20 ng/mL for males and between 4.9 and 24 ng/mL for females were adopted as previously described by Gutin et al.²⁶ Statistical test applied ANOVA-two way post hoc Tukey.

Statistical difference of high group in baseline.

Statistical difference of moderate group in baseline condition.

Statistical difference of high group in after intervention.

Statistical difference between values at baseline and after intervention.

Effects of interdisciplinary intervention

All sample

Considering the total sample (n = 172), there were a statistically significant reduction in body mass (kg), BMI (kg/m²), body fat mass (% and kg), waist circumference (cm), visceral fat (cm), subcutaneous fat (cm), insulin (µU/mL), HOMA-IR, TC (mg/dL), LDL cholesterol (mg/dL), VLDL cholesterol (mg/dL), TGs (mg/dL), TC/HDL ratio, TG/HDL ratio, ALT enzyme (U/L), GGT enzyme (U/L) and leptin concentration (ng/mL). On the other hand, there were statistically significant increases in lean body mass (%), QUICKI and HDL cholesterol (mg/dL). For lean body mass (kg), glucose (mg/dL) and AST enzyme (U/L) no statistically significant changes were observed (Table 1).

High group (1st tertile: >1.8 cm)

Similarly to the all sample analysis, in the high group, there was a statistically significant reduction in body mass (kg), BMI (kg/m²), body fat mass (% and kg), waist circumference (cm), visceral fat (cm), subcutaneous fat (cm), insulin (µU/mL), HOMA-IR, TC (mg/dL), LDL cholesterol (mg/dL), VLDL cholesterol (mg/dL), TGs (mg/dL), TC/HDL ratio, TG/HDL ratio, AST enzyme (U/L), GGT enzyme (U/L) and leptin concentration (ng/mL). In addition, it is important to note that only in this group was the hyperleptinemia state normalized. On the other hand, there was a statistically significant increase in lean body mass (%) and QUICKI. For lean body mass (kg), glucose (mg/dL), HDL cholesterol (mg/dL) and ALT enzyme (U/L) no statistically significant changes were observed (Table 1).

Moderate group (2nd tertile: between 1.8 and 0.79 cm)

In the moderate group, there was a statistically significant reduction in body mass (kg), BMI (kg/m²), body fat mass (% and kg), waist circumference (cm), visceral fat (cm), subcutaneous fat (cm), TC (mg/dL), VLDL cholesterol (mg/dL), TGs (mg/dL), TC/HDL ratio, TG/HDL ratio, GGT enzyme (U/L) and leptin concentration (ng/mL). Although, this hormone was not normalized. On the other hand, there was a statistically significant increase in lean body mass (%), QUICKI and HDL cholesterol (mg/dL). For lean body mass (kg), glucose (mg/dL), insulin (µU/mL), HOMA-IR, LDL cholesterol (mg/dL), AST enzyme (U/L) and ALT enzyme (U/L) no statistically significant changes were observed (Table 1).

Low group (3rd tertile: less than or equal 0.79 cm)

In the low group, there was a statistically significant reduction in body mass (kg), BMI (kg/m²), body fat mass (% and kg), subcutaneous fat (cm), LDL cholesterol (mg/dL), TC/HDL ratio and TG/HDL ratio. On the other hand, there was a statistically significant increase in lean body mass (%) and visceral fat (cm). For lean body mass (kg), waist circumference (cm), glucose (mg/dL), insulin (µU/mL), HOMA-IR, QUICKI, TC (mg/dL), HDL cholesterol, VLDL cholesterol (mg/dL), TGs (mg/dL), and AST enzyme (U/L), ALT enzyme (U/L), GGT enzyme (U/L) and leptin concentration (ng/mL) no statistically significant changes were observed (Table 1).

Analysing the delta values

According to the delta values analysed, it is possible to note a greater effectiveness of high group than the moderate and low groups in promoting a reduction in body mass (kg), body fat mass (% and kg), visceral fat (cm) and leptin concentration (ng/mL). Interestingly, the programme was more effective in the high group in respect of improved BMI (kg/m²) and QUICKI than in the moderate group. For the variables lean body mass (kg), waist circumference (cm), subcutaneous fat (cm), glucose (mg/dL), insulin (µU/mL), HOMA-IR, TC (mg/dL), HDL cholesterol (mg/dL), LDL cholesterol (mg/dL), VLDL cholesterol (mg/dL), TGs (mg/dL), TC/HDL ratio, TG/HDL ratio, AST, ALT and GGT enzymes no significant differences were observed (Table 2).

Table 2.

Comparison of delta values among visceral fat groups.

	All	high group	moderate group	low group
	(n = 172)	(n = 58)	(n = 55)	(n = 59)
Body mass (kg)	−11.87 ± 21.88	−18.30 ± 22.04	−7.76 ± 20.73^a	−9.64 ± 21.89^a
BMI (kg/m²)	−4.41 ± 7.96	−6.74 ± 7.56	−2.79 ± 7.75^a	−3.72 ± 8.18
Body fat mass (%)	−7.34 ± 9.73	−10.96 ± 8.56	−5.85 ± 9.78^a	−5.32 ± 9.96^a
Lean body mass (%)	4.08 ± 14.91	5.70 ± 15.93	6.61 ± 14.18	0.13 ± 14.08^a
Body fat mass (kg)	−9.76 ± 11.54	−15.69 ± 9.62	−7.54 ± 12.87^a	−6.20 ± 9.77^a
Body lean mass (kg)	−0.74 ± 14.60	−2.22 ± 17.67	1.78 ± 14.03	−1.68 ± 11.52
Waist circumference (cm)	−12.81 ± 58.92	−21.24 ± 57.50	−12.91 ± 50.58	−4.42 ± 67.22
Visceral fat (cm)	−1.21 ± 1.36	−2.63 ± 0.82	−1.26 ± 0.28^a	0.20 ± 0.83^a,b
Subcutaneous fat (cm)	−0.62 ± 0.81	−0.66 ± 0.78	−0.73 ± 0.89	−0.47 ± 0.74
Glucose (mg/dL)	−2.84 ± 17.21	−4.25 ± 13.89	0.45 ± 14.78	−4.62 ± 21.71
Insulin (uU/mL)	−4.01 ± 9.71	−5.50 ± 9.15	−2.75 ± 12.60	−3.76 ± 6.70
HOMA-IR	−0.86 ± 2.47	−1.27 ± 2.03	−0.48 ± 3.45	−0.82 ± 1.60
QUICKI	0.00 ± 0.07	0.02 ± 0.05	0.01 ± 0.06^a	−0.01 ± 0.09
Total cholesterol (mg/dL)	−13.91 ± 33.53	−16.37 ± 31.45	−9.76 ± 24.78	−15.86 ± 41.98
HDL cholesterol (mg/dL)	0.45 ± 10.04	0.35 ± 9.30	1.76 ± 7.93	−0.71 ± 12.38
LDL cholesterol (mg/dL)	−10.12 ± 24.95	−12.74 ± 23.16	−4.84 ± 24.67	−12.95 ± 26.50
VLDL cholesterol (mg/dL)	−3.08 ± 9.81	−3.91 ± 7.58	−3.45 ± 11.18	−1.91 ± 10.47
Triglycerides (mg/dL)	−19.07 ± 50.40	−19.56 ± 37.87	−26.98 ± 60.26	−11.27 ± 50.99
TC/HDL	−0.33 ± 0.53	−0.43 ± 0.52	−0.33 ± 0.58	−0.24 ± 0.46
TG/HDL	−1.90 ± 1.53	−2.03 ± 1.39	−2.1 ± 1.84	−150, ± 1.26
AST enzyme	−1.86 ± 15.07	−3.72 ± 7.24	−2.75 ± 7.32	−8.61 ± 23.53
ALT enzyme	−5.4 ± 18.95	−8.61 ± 17.56	−4.69 ± 10.18	−3.00 ± 25.57
GGT enzyme	−5.82 ± 10.89	−7.37 ± 15.17	−5.73 ± 8.04	−4.46 ± 7.76
Leptin (ng/mL)	−8.62 ± 22.80	−15.62 ± 25.56	−9.28 ± 19.47^a	0.83 ± 19.65^a

BMI: body mass index; HOMA-IR: homeostasis model assessment insulin resistance; QUICKI: quantitative insulin sensitivity check index; HLD: high-density lipoprotein; LDL: low-density lipoprotein; VLDL: very low-density lipoprotein; TC/HDL: total cholesterol/high-density lipoprotein ratio; TG/HDL: triglycerides/high-density lipoprotein ratio; AST: aspartate aminotransferase; ALT: alanine aminotransferase; GGT: c-glutamyl transferase.

Statistical test applied ANOVA-one way post hoc Tukey.

Statistical difference compared to high group.

Statistical difference compared to moderate group.

Multivariate logistic regression analysis

In the regression analysis of the total sample, both visceral fat and BMI were predictors of increased insulin resistance. Moreover, for the high and moderate groups, the regression model demonstrated that only visceral fat was an independent predictor of an increase in the insulin resistance index (HOMA-IR). Considering the low group, only BMI (kg/m²) was an independent predictor of an increase in the insulin resistance index (HOMA-IR) (Table 3).

Table 3.

Multivariate logistic regression analysis of association between insulin resistance and body composition parameters.

Variables	95% CI			p value
Variables	OR	Lower	Upper	p value
Total sample
Visceral fat (cm)	1.604	1.303	1.975	0.0001
Lean body mass (%)	0.698	0.003	146.86	0.895
Body fat mass (%)	0.692	0.003	147.28	0.893
BMI (kg/m²)	1.149	1.056	1.249	0.001
High group
Visceral fat (cm)	1.876	1.246	2.798	0.002
Lean body mass (%)	0.001	10⁻¹¹	104.243	0.423
Body fat mass (%)	0.001	10⁻¹¹	147.86	0.422
BMI (kg/m²)	1.07	0.922	1.241	0.375
Moderate group
Visceral fat (cm)	2.567	1.676	3.931	0.0001
Lean body mass (%)	1.03	0.934	1.135	0.55
Body fat mass (%)	1	0.989	1.354	_
BMI (kg/m²)	1.157	0.922	_	0.069
Low group
Visceral fat (cm)	1.068	0.745	1.53	0.722
Lean body mass (%)	1.324	0.014	126.81	0.904
Body fat mass (%)	1.321	0.013	130.27	0.905
BMI (kg/m²)	1.207	1.072	1.36	0.002

Statistical significance is bold in the Table. BMI: body mass index; CI: confidence interval; OR: odds ratio; HOMA-IR: homeostasis model assessment insulin resistance. Multivariate logistic regression analysis was realized considering HOMA-IR as a dependent variable. The analysis was performed in different conditions: all sample; high group, moderate group and low group.

BMI: body mass index; CI: confidence interval; OR: odds ratio; HOMA-IR: homeostasis model assessment insulin resistance.

Model considering HOMA-IR as a dependent variable. All sample (n = 172). BMI: body mass index (kg/m²).

Model considering HOMA-IR as a dependent variable. Only high group considered in the analysis. BMI: body mass index (kg/m²).

Model considering HOMA-IR as a dependent variable. Only moderate group considered in the analysis. BMI: body mass index (kg/m²).

Model considering HOMA-IR as a dependent variable. Only low group considered in the analysis. BMI: body mass index (kg/m²).

Correlations analysis

Performing the correlation analysis, as demonstrated in Figure 2(a) to (d), visceral fat (cm) was positively correlated with glucose (mg/dL): r = 0.22, p = 0.001; insulin (µU/L): r = 0.38, p = 0.0001 and HOMA-IR: r = 0.40, p = 0.001. Moreover, negative correlations were found between visceral fat and QUICKI: r = –0.38, p = 0.0001. Considering the lipid profile, as shown in Figure 3(a) to (d), there were positive correlations between visceral fat (cm) and TC (mg/dL): r = 0.23, p = 0.006; LDL cholesterol (mg/dL): r = 0.22, p = 0.01; VLDL cholesterol (mg/dL): r = 0.26, p = 0.002 and TGs (mg/dL): r = 0.26, p = 0.002. Regarding the hepatics enzymes, as shown in Figure 4(a) to (d), there were positive correlations between visceral fat (cm) and AST enzyme (U/L): r = 0.35, p = 0.0001; ALT enzyme (U/L): r = 0.37, p = 0.0001 and GGT enzyme (U/L): r = 0.46, p = 0.0001.

Figure 2.

Correlations of visceral fat with: (a) glucose (mg/dL): r = 0.22, p = 0.001; (b) insulin (μU/L): r = 0.38, p = 0.0001; (c) HOMA-IR: r = 0.40, p = 0.001 and (d) QUICKI: r = −0.38, p = 0.0001.

Figure 3.

Correlations of visceral fat with: (a) total cholesterol (mg/dL): r = 0.23, p = 0.006; (b) LDL cholesterol (mg/dL): r = 0.22, p = 0.01; (c) VLDL cholesterol (mg/dL): r = 0.26, p = 0.002 and (d) triglycerides (mg/dL): r = 0.26, p = 0.002.

Figure 4.

Correlations of visceral fat with: (a) AST enzyme (U/L): r = 0.35, p = 0.0001; (b) ALT enzyme (U/L): r = 0.37, p = 0.0001 and (c) GGT enzyme (U/L): r = 0.46, p = 0.0001.

Discussion

This study aimed to investigate the impact of visceral adipose tissue reductions, analysed in terms of their magnitude, on insulin resistance and hyperleptinemia in a sample of adolescents with obesity. Interestingly, the most important finding was that a greater reduction in visceral adipose tissue (>1.8 cm; according to the delta value analysis) was a cut-off point to obtain a significant decrease in insulin resistance and normalization of leptin concentration (Table 1).

Corroborating this, the multivariate logistic analysis showed that increased visceral fat is an independent predictor of increased insulin resistance in the groups with a high and moderate reduction of adipose deposits (Table 3). In a previous investigation, it was shown that improved HOMA-IR was an independent predictor of cIMT alterations in a sample of adolescents with obesity.¹³ In fact, in this investigation, there was only a significant decrease in insulin resistance and insulin concentration in the group which presented a higher reduction in visceral fat after long-term interdisciplinary weight-loss therapy occurs.

Remarkably, our data indicate that when adolescents with obesity have a high or moderate decrease in the visceral adipose tissue, there are improvements in 20 and 16 analysed variables, respectively, including body composition and glucose and lipid metabolism. However, when there was only a small reduction in visceral adipose tissue, there were only improvements in nine parameters (Table 1).

These results show that reduced visceral adipose tissue after long-term weight-loss therapy is a key factor in the control of insulin resistance and a range of metabolic conditions, looking at a sample of obese adolescents with similar body mass, BMI, body fat and subcutaneous fat at baseline, as shown in Table 1. This strongly corroborates the thesis of Dr Reaven^34–36 regarding insulin resistance as a key aspect in the development of many chronic diseases. Our research suggests that if these conditions are not treated early on in obese adolescents, they may develop not only insulin resistance but also MetS,¹⁵ NAFLD,³⁷ sleep apnea,³⁸ bone metabolism³⁹ and cardiovascular disease.¹³

The present study showed the key role of visceral fat in the modulation of metabolic and hormonal conditions in a sample of adolescents with obesity, including positive correlations with TC, LDL cholesterol, VLDL cholesterol, TGs, glucose, insulin, HOMA-IR and hepatic enzymes. Moreover, negative correlations between visceral fat were observed with the insulin sensitivity index (QUICKI) (Figures 2 to 4). The data demonstrate the intrinsic link between high visceral fat and insulin resistance in adolescents with obesity, which may lead to the development of metabolic alterations and a consequent increase in cardiovascular risk in this population.^13,17,40

In addition, it is important to note that leptin levels, a potent biomarker of energy balance and inflammatory processes related to obesity, were significantly lower after both high and moderate reductions in visceral adipose tissue. In the group presenting a high reduction of visceral fat, the leptin values reached normal concentrations, while the adolescents with a moderate reduction continued to present hyperleptinemia. In agreement with our hypothesis, the group presenting a low delta reduction in deposits showed no significant change in leptin concentration (Table 1).

Importantly, hyperleptinemia can impair energy balance, specifically in its anorexigenic effects upregulating α-melanocyte-stimulating hormone (α-MSH).⁴¹ Indeed, hyperleptinemia can weaken the anti-inflammatory effects of adiponectin, the key hormone involved in the modulation of glucose and lipid metabolism in insulin-sensitive tissues.⁴²

In summary, the metabolic and inflammatory parameters of the adolescents who lost a high proportion of visceral adipose tissue (>1.8 cm) improved, while they did not in those who had a small reduction (<0.79 cm). Therefore, long-term interdisciplinary therapy was shown to contribute to the control of related comorbidities.

In conclusion, the present study showed that the magnitude of reduction in visceral fat was an independent predictor for insulin resistance control, hyperleptinemia and a range of altered metabolic conditions observed in obese adolescents.

Footnotes

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 study was supported by the Foundation of São Paulo Research–FAPESP (grant nos.: 2017∕07372-1; 2015/14309-9; 2013/08522-6; 2011/50414-0; 2011/50356-0; 2008/53069-0, Fundação de Amparo à Pesquisa do Estado de São Paulo–FAPESP), the National Council for Scientific and Technological Development – CNPq (grant nos.: 409943/2016-9 and 301322/2017-1) and the Coordination of Higher Education Personnel Training – CAPES.

ORCID iD

Raquel Munhoz da Silveira Campos

References

Reaven

GM.

Banting lecture 1988. Role of insulin resistance in human disease.1988. Nutrition 1997; 13: 65; discussion 64, 66.

Reaven

GM.

Syndrome X: is one enough. Am Heart J 1994; 127: 1439–1442.

Pérez-Tasigchana

León-Muñoz

Lopez-Garcia

et al . Metabolic syndrome and insulin resistance are associated with frailty in older adults: a prospective cohort study. Age Ageing 2017; 46: 807–812.

Baek

Kim

et al . Insulin resistance and the risk of diabetes and dysglycemia in Korean general adult population. Diabetes Metab J 2018; 42: 296–307.

Caprio

Pierpont

Kursawe

The ‘adipose tissue expandability’ hypothesis: a potential mechanism for insulin resistance in obese youth. Horm Mol Biol Clin Investig. Epub ahead of print 29 March 2018. DOI: 10.1515/hmbci-2018-0005.

Barbosa

dos Santos

Barbosa

et al . Metabolic syndrome, insulin resistance and other cardiovascular risk factors in university students. Cien Saude Colet 2016; 21: 1123–1136.

Reaven

GM.

Why syndrome X? From Harold Himsworth to the insulin resistance syndrome. Cell Metab 2005; 1: 9–14.

Zavaroni

Bonini

Fantuzzi

et al . Hyperinsulinaemia, obesity, and syndrome X. J Intern Med 1994; 235: 51–56.

Reaven

GM.

Syndrome X: 10 years after. Drugs 1999; 58: 19–20; discussion 75–82.

10.

Reaven

GM.

Syndrome X. Curr Treat Options Cardiovasc Med 2001; 3: 323–332.

11.

Reaven

GM.

Is diagnosing metabolic syndrome a uniquely simple way to predict incident type 2 diabetes mellitus?

CMAJ 2009; 180: 601–602.

12.

Dâmaso

de Piano Ganen

Campos

et al . Multidisciplinary approach to the treatment of obese adolescents: effects on cardiovascular risk factors, inflammatory profile, and neuroendocrine regulation of energy balance. Int J Endocrinol 2013; 2013: 541032.

13.

De Lima Sanches

de Mello

Elias

et al . Improvement in HOMA-IR is an independent predictor of reduced carotid intima-media thickness in obese adolescents participating in an interdisciplinary weight-loss program. Hypertens Res 2011; 34: 232–238.

14.

Masquio

de Piano Ganen

Campos

et al . The role of multicomponent therapy in the metabolic syndrome, inflammation and cardiovascular risk in obese adolescents. Br J Nutr 2015; 113: 1920–1930.

15.

Caranti

Tock

do Prado

et al . Long-term multidisciplinary therapy decreases predictors and prevalence of metabolic syndrome in obese adolescents. Nutr Metab Cardiovasc Dis 2007; 17: e11–e13.

16.

Tock

do Prado

Caranti

et al . Nonalcoholic fatty liver disease decrease in obese adolescents after multidisciplinary therapy. Eur J Gastroenterol Hepatol 2006; 18: 1241–1245.

17.

Dâmaso

do Prado

de Piano Ganen

et al . Relationship between nonalcoholic fatty liver disease prevalence and visceral fat in obese adolescents. Dig Liver Dis 2008; 40: 132–139.

18.

De Lima Sanches

de Piano Ganen

Campos

et al . Association of nonalcoholic fatty liver disease with cardiovascular risk factors in obese adolescents: the role of interdisciplinary therapy. J Clin Lipidol 2014; 8: 265–272.

19.

Tanner

Whitehouse

RH.

Clinical longitudinal standards for height, weight, height velocity, weight velocity, and stages of puberty. Arch Dis Child 1976; 51: 170–179.

20.

Prevalence of overweight among children and adolescents: United States 1999–2002. Hyattsville, MD: National Center for Health Statistics, Centers for Disease Control and Prevention, http://www.cdc.gov/nchs/products/pubs/pubd/hestats/overwght99.htm (2007, accessed 11 August 2013).

21.

Lohman

Roche

Martorrel

Anthropometric standardization reference manual. Champaign, IL: Human Kinetic Books, 1991.

22.

Sartorio

Del Col

Agosti

et al . Predictors of non-alcoholic fatty liver disease in obese children. Eur J Clin Nutr 2007; 61: 877–883.

23.

Keskin

Kurtoglu

Kendirci

et al . Homeostasis model assessment is more reliable than the fasting glucose/insulin ratio and quantitative insulin sensitivity check index for assessing insulin resistance among obese children and adolescents. Pediatrics 2005; 115: e500–e503.

24.

Schwimmer

Deutsch

Rauch

et al . Obesity, insulin resistance, and other clinicopathological correlates of pediatric nonalcoholic fatty liver disease. J Pediatr 2003; 143: 500–505.

25.

Giuliano

ICB

Caramelli

Pellanda

et al . Guideline for preventing atherosclerosis in childhood and adolescence. Int J Atheroscler 2006; 1: 1–30.

26.

Gutin

Ramsey

Barbeau

et al . Plasma leptin concentrations in obese children: changes during 4-mo periods with and without physical training. Am J Clin Nutr 1999; 69: 388–394.

27.

Quijada

Paoli

Zerpa

et al . The triglyceride/HDL-cholesterol ratio as a marker of cardiovascular risk in obese children: association with traditional and emergent risk factors. Pediatr Diabetes 2008; 9: 464–471.

28.

Stampfer

Sacks

Salvini

et al . A prospective study of cholesterol, apolipoproteins, and the risk of myocardial infarction. N Engl J Med 1991; 325: 373–381.

29.

McLaughlin

Reaven

Abbasi

et al . Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease. Am J Cardiol 2005; 96: 399–404.

30.

Ribeiro-Filho

Faria

Azjen

et al . Methods of estimation of visceral fat: advantages of ultrasonography. Obes Res 2003; 11: 1488–1494.

31.

Donnelly

Blair

Jakicic

et al . American College of Sports Medicine Position Stand. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc 2009; 41: 459–471.

32.

Kraemer

Ratamess

French

DN.

Resistance training for health and performance. Curr Sports Med Rep 2002; 1: 165–171.

33.

Dietary reference intakes: applications in dietary assessment. Washington, DC: National Academies Press, 2001.

34.

Reaven

GM.

Insulin resistance: the link between obesity and cardiovascular disease. Med Clin North Am 2011; 95: 875–892.

35.

Reaven

GM.

The metabolic syndrome or the insulin resistance syndrome? Different names, different concepts, and different goals. Endocrinol Metab Clin North Am 2004; 33: 283–303.

36.

Reaven

GM.

Role of insulin resistance in human disease (syndrome X): an expanded definition. Annu Rev Med 1993; 44: 121–131.

37.

De Piano Ganen

do Prado

Caranti

et al . Metabolic and nutritional profile of obese adolescents with nonalcoholic fatty liver disease. J Pediatr Gastroenterol Nutr 2007; 44: 446–452.

38.

Corgosinho

Ackel-D’Elia

Tufik

et al . Beneficial effects of a multifaceted 1-year lifestyle intervention on metabolic abnormalities in obese adolescents with and without sleep-disordered breathing. Metab Syndr Relat Disord 2015; 13: 110–118.

39.

Campos

Lazaretti-Castro

Mello

et al . Influence of visceral and subcutaneous fat in bone mineral density of obese adolescents. Arq Bras Endocrinol Metabol 2012; 56: 12–18.

40.

Hedley

Ogden

Johnson

et al . Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 2004; 291: 2847–2850.

41.

Dâmaso

de Piano Ganen

de Lima Sanches

et al . Hyperleptinemia in obese adolescents deregulates neuropeptides during weight loss. Peptides 2011; 32: 1384–1391.

42.

De Lima Sanches

de Mello

Elias

et al . Hyperleptinemia: implications on the inflammatory state and vascular protection in obese adolescents submitted to an interdisciplinary therapy. Inflammation 2014; 37: 35–43.

Effects of magnitude of visceral adipose tissue reduction: Impact on insulin resistance,hyperleptinemia and cardiometabolic risk in adolescents with obesity after long-term weight-loss therapy

Abstract

Aim:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Population

Anthropometric measurements

Serum analysis

Visceral and subcutaneous adiposity measurements

Descriptive methodology of interdisciplinary intervention

Clinical approach

Physical exercise practice

Aerobic plus resistance training intervention

Nutrition counselling

Physiotherapy intervention

Psychological counselling

Statistical analysis

Results

Baseline condition

Effects of interdisciplinary intervention

All sample

High group (1st tertile: >1.8 cm)

Moderate group (2nd tertile: between 1.8 and 0.79 cm)

Low group (3rd tertile: less than or equal 0.79 cm)

Analysing the delta values

Multivariate logistic regression analysis

Correlations analysis

Discussion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References