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Relationship between Sex Hormone-Binding Globulin (SHBG) and Insulin-Like Growth Factor-I (IGF-I) with Metabolic Syndrome

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GLUCOSE intolerance, insulin resistance, hypertension, visceral obesity, and dyslipidemia are the major components of metabolic syndrome (MS). To evaluate the association between serum SHBG and IGF-1 levels and the risk of MS, Furthermore, to determine the correlations between SHBG and IGF-1 and the main components of MS. A total of 402 subjects with and without MS were enrolled in this study (MS=156, Non-MS=246) aged > 18 years. The age, height, weight, BMI, HC, WC, and incidence of diabetes, hypertension and dyslipidemia of all cases were recorded. The collected serum samples were used to assess lipid profile, glucose and insulin levels. The levels of LDL-cholesterol were calculated using Friedewald's formula. Insulin resistance was measured (as HOMA score). The levels of serum SHBG and IGF-1 were measured using Elisa technique. A positive relationship between SHBG and MS was detected, however no such correlation was observed concerning IGF-1. There were positive correlations between SHBG and main components of MS; with insulin, HOMA-index, TC, TG and HDL. Conversely, IGF-1 showed negative correlations. Finally, SHGB was more sensitive (63.5%), accurate (61.9%) than IGF-1 (51.9%), accuracy (59%). Our study reveals that lower SHBG is more strongly associated with metabolic syndrome and its main components than lower IGF-1. SHBG could be the essential driver of these relations, conceivably reflecting its association with insulin sensitivity; however more studies are required to confirm this relationship.
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Egypt. J. Chem. Vol. 60, No.5, pp. 957 - 964 (2017)
*Corresponding author e-mail: ymshaker@gmail. com
DOI : 10.21608/ejchem.2017.1510.1111
©2017 National Information and Documentation Center (NIDOC)
GLUCOSE intolerance, insulin resistance, hypertension, visceral obesity, and dyslipidemia
are the major components of metabolic syndrome (MS). To evaluate the association
between serum SHBG and IGF-1 levels and the risk of MS, Furthermore, to determine the
correlations between SHBG and IGF-1 and the main components of MS. A total of 402 subjects
with and without MS were enrolled in this study (MS=156, Non-MS=246) aged > 18 years. The
age, height, weight, BMI, HC, WC, and incidence of diabetes, hypertension and dyslipidemia of

and insulin levels. The levels of LDL-cholesterol were calculated using Friedewald’s formula.
Insulin resistance was measured (as HOMA score). The levels of serum SHBG and IGF-1 were
measured using Elisa technique. A positive relationship between SHBG and MS was detected,
however no such correlation was observed concerning IGF-1. There were positive correlations
between SHBG and main components of MS; with insulin, HOMA-index, TC, TG and HDL.
Conversely, IGF-1 showed negative correlations. Finally, SHGB was more sensitive (63.5%),
accurate (61.9%) than IGF-1 (51.9%), accuracy (59%). Our study reveals that lower SHBG is
more strongly associated with metabolic syndrome and its main components that lower IGF-


Keywords: Metabolic syndrome (MS); Sex hormone-binding globulin (SHBG); Insulin like
growth factor-1 (IGF-1); Insulin resistance; Visceral obesity.
70
Relationship between Sex Hormone-Binding Globulin (SHBG) and
Insulin-Like Growth Factor-I (IGF-I) with Metabolic Syndrome
Weaam Gouda1, Lamiaa Mageed1, Esmat Ashour11, Mona Awad2, Said
Shalby34,Yehia Shaker1*
1Biochemistry Department, 2Department of Clinical Pathology ,3Department of
Complementary Medicine, 4Department of Internal Medicine, National Research
Center, Giza, Egypt.
Introduction
Metabolic syndrome (MS) is an important reason
for mortality and morbidity in industrial nations
[1]. It is described by the mixture of several
disorders including insulin resistance, high
blood pressure, obesity, dyslipidemia, and a pro-
 
is intensely related to a lifestyle characterized by
an easy access to unlimited supply of high caloric,
little nutrient-dense, foods and physical inactivity
[3]. Psychosocial stress has also been proposed to
contribute, with most metabolic constituents are
more prevalent in socioeconomically deprived
populations [4]. The incidence of MS associates
with the worldwide prevalence of obesity and is
   
20% of the global grown-up population [5].
Recently, in the pathogenesis of MS, non-
alcoholic fatty liver disease (NAFLD) and type
2 diabetes mellitus (T2DM), further consideration
has been paid to the supposed organosilanes,
proteins with both endocrine or/and paracrine
     
adipokines (mostly created by adipose tissue),
myokines (principally formed by skeletal muscles)
and hepatokines (mainly made by the liver) [7].
 
glucose and lipid metabolism by hepatokines
discharged into the blood and MS appears to be
accompanying with altered hepatokines creation.
Insulin like growth factor-1 (IGF-1) and sex
hormone-binding globulin (SHBG) are considered
as the most important hepatokines.
Sex hormone-binding globulin is a serum
steroid-transporting protein that is made in the
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Egypt. J. Chem. 60, No.5 (2017)
WEAAM GOUDA et al.
liver. Many reports have demonstrated that
decreased serum SHBG levels are associated
with MS components (insulin resistance and
obesity), T2DM and NAFLD [8; 9]. Insulin-
like growth factor-I is a polypeptide hormone
formed mostly by the liver in response to the
endocrine growth hormone stimulus and controls
both body composition and metabolism. There is
mounting evidence suggesting that IGF-I, besides
its mitogenic action, plays an active role in the
regulation of protein, carbohydrate and lipid
metabolism [10]. Insulin like growth factor-1
has been reported to predict the occurrence of
liver steatosis in obese patients [11]. It codes
for a membrane glycoprotein involved in insulin
sensitivity [12]. Our study was to explore the

as well as with its major components.
Subjects and Methods
Subjects
The study was performed on consecutive
adults (of both sexes) who were recruited from
the Medical Center of Excellence - National
Research Center. This study was conducted from
May 2015 to June 2016. All subjects were of the
age more than 18 years old and were asked about
their family history, individual health history
and current medications (anti-hypertensive,
oral hypoglycemic agents and lipid-lowering
medicine). Subjects with any malignancy, liver
cirrhosis, taking hormones, or antifungal agents,
were excluded from the study. Written informed
consent was obtained from each individual, and
the study protocol was reviewed and approved
by the Medical ethics Committee of National
Research Center.
All subjects (n= 402) in the study were divided
into MS (n=156) and Non-MS (n=246) groups;
metabolic syndrome was diagnosed according
to guidelines from the National Heart, Lung, and
Blood Institute (NHLBI) and the American Heart
Association (AHA) [13], metabolic syndrome
was diagnosed when a patient has at least 3 of the
following 5 conditions: 1) Waist circumference
 

for hypertension; 3) High-density lipoprotein
(HDL) < 40 mg/dL in men or Cholesterol < 50 mg/
dL in women or lipid medication use; 4) Fasting

     
receiving drug therapy for hypertriglyceridemia.
Measurements
All subjects underwent the physical
examination and fasting blood samples (3 ml)
were withdrawn by venipuncture for laboratory
evaluation after 14 h of overnight fasting. The
body mass index (BMI) was derived from body
weight (in kilograms) divided by the square of
body height in meters. Waist circumferences
(WC) were measured by standard form to the
nearest 0.1 cm. Hip circumference (HC) was
measured at the maximum 2 protruding part of
buttocks at the level of the greater trochanter with
the patient wearing minimal clothing and feet
together. Subjects were seated with legs uncrossed
and were asked to refrain from talking for 10
min. Blood pressure and heart rate measurement
were taken three times, with at least a 1-min
interval between two consecutive readings using
an automatic blood pressure monitor (using a
mercury sphygmomanometer).
Biochemical analyses
Fasting plasma glucose levels and serum levels
of total cholesterol, triglyceride, and HDL were
measured with an enzymatic colorimetric method
(Stanbio Laboratory, USA). LDL was calculated
using Friedewald’s formula [14]. Serum sex-
hormone-binding globulin (SHBG) and serum
human insulin-like growth factors 1(IGF-1) were
assayed by an enzyme-linked immunosorbent
assay (SHBG, IBL International GmbH, Germany
and EIAab system, respectively).
Statistical analysis
Sample size calculation was done using
Stats Direct statistical software version 2.8 for
MS Windows, Stats Direct Ltd., Cheshire, UK.
Analysis of data was done by IBM computer using
SPSS (statistical program for social science version
20) (SPSS Inc., Chicago, IL, USA). Independent
sample -t- test was used for comparison between
the two groups. Correlations between different
variables and metabolic syndrome were analyzed
using Spearman correlation test.
Results
Characteristics of subjects with and without
metabolic syndrome
The general characteristics of patients with and

differences were found according to age, BMI,
WC, HC, waist/hip ratio, obesity (extreme obesity
group only), DBP, SBP, insulin, HOMA-index,

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Egypt. J. Chem. 60, No.5 (2017)
RELATIONSHIP BETWEEN SEX HORMONE-BINDING GLOBULIN ...
were found in obesity (for overweight and obese
groups).
Frequencies of SHBG and IGF-1 in subjects with
and without metabolic syndrome
The frequencies of SHBG and IGF-1 in
subjects with and without MS are presented
      
difference occurred with the p value <0.05 but
     
the p value >0.05.
Correlation between ILGF-1 and metabolic
indices and lipid prole
Table 3 shows the spearman’s correlation

        
negatively correlated with insulin, HOMA-index,
TC and LDL and positively correlated with FBG,
TG and HDL.
TABLE 
Characteristics Total
(n = 402)
None Metabolic
Syndrome
(n = 246)
Metabolic
Syndrome
(n = 156)
P value P* value
Male / Female 171 / 231 129 / 117 42 / 114 - -
Age (Years) 39.53 ± 10.69 37.61 ± 10.66 42.56 ± 10.11 0.009 <0.05
BMI 32.89 ± 9.55 28.26 ± 8.44 40.18 ± 5.99 0.000 <0.05
Waist Circumference (WC) 98.57 ± 16.02 91.24 ± 14.88 110.12 ± 9.78 0.000 <0.05
Hip Circumference (HC) 113.66 ± 13.2 107.83 ± 11.78 122.85 ± 9.65 0.000 <0.05
Waist/Hip Ratio 0.86 ± 0.07 0.84 ± 0.07 0.9 ± 0.07 0.000 <0.05
Obesity
Overweight 27 (6.7%) 21 (77.8%) 6 (22.2%) 0.096 <0.05
Obese 135 (33.6%) 66 (48.9%) 69 (51.1%) 0.881 >0.05
Extreme Obesity 102 (25.4%) 21 (20.6%) 71 (79.4%) 0.001 <0.05
Diastolic BP (mmHg) 121.17 ± 15.17 114.27 ± 11.76 132.06 ± 13.53 0.000 <0.05
Systolic BP(mmHg) 79.76 ± 12.05 75.3 ± 9.73 86.79 ± 12.08 0.000 <0.05
FBG (mmol/l) 27.19 ± 43.71 24.6 ± 36.58 31.26 ± 53.2 0.003 <0.05
Insulin (mIU/ml) 8.87 ± 4.35 7.92 ± 3.48 10.37 ± 5.14 0.002 <0.05
HOMA-index 2.05 ± 1.18 1.68 ± 0.77 2.63 ± 1.45 0.000 <0.05
TC (mg/dl) 236.32 ± 64.05 212.91 ± 58.98 273.23 ± 53.88 0.000 <0.05
TG (mg/dl) 179.27 ± 78.31 144.18 ± 69.93 234.61 ± 55.88 0.000 <0.05
HDL (mg/dl) 56.58 ± 21.9 65.24 ± 22.61 42.91 ± 11.29 0.000 <0.05
LDL (mg/dl) 143.89 ± 66.01 118.83 ± 60.42 183.41 ± 54.43 0.000 <0.05
BMI: Body mass index; TC: Total cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein;
FPG: Fasting plasma glucose.
Numeric variables are described by mean ± SD, and categorical data are expressed as number (%).
P value for comparison between total subjects, MS and Non-MS groups.
P* value for comparison between the MS and Non-MS groups.

Correlation between SHGB and metabolic indices
and lipid prole
Table 4 shows the spearman’s correlation

        
negatively correlated with FBG and LDL and
positively correlated with insulin, HOMA-index,
TC, TG and HDL.
Percent sensitivity, specicity, positive and
negative predictive values (PPV, NPV) and SHGB
and IGF-1 accuracy in MS
Table 5 indicates that SHGB has more
sensitivity (sn=63.5%), accuracy (61.9%) with
     
sensitivity (sn=51.9%), accuracy (59%) with
P-value= 0.089 (Fig.1&2).
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WEAAM GOUDA et al.
TABLE  metabolic syndrome.
Characteristics
Total
(n = 402)
None Metabolic Syndrome
(n = 246)
Metabolic Syndrome
(n = 156)
P value
P* value
ILGF1 (Pg/ml) 6.75 ± 1.59 6.92 ± 1.63 6.49 ± 1.49 0.130 >0.05
SHGB (nmol/L) 3.37 ± 1.35 3.52 ± 1.28 3.13 ± 1.43 0.020 <0.05
SHBG: Sex-hormone-binding globulin; IGF-1: Human insulin-like growth factors 1.
Data are presented as mean ± SD.
P value for comparison between total subjects, MS and Non-MS groups.
P* value for comparison between the MS and Non-MS groups.

Discussion
Metabolic syndrome is considered as one
of the main public health problems of the
21th century. In the current study, we found
significant differences between metabolic and
non-metabolic syndrome groups according
to BMI; WC; HC; SBP; DBP; FBG; HOMA-
index; and lipids; which are the main
components of the metabolic syndrome. Our
finding could be explained as MS is a group
of risk factors; containing increased TG levels,
decreased HDL, raised central abdominal
obesity, increased FBS, hyperinsulinemia, and/
or high BP [15].
Growth hormone (GH) is the main regulator
of postnatal growth and also controls both
body composition and metabolism. The growth
promoting the action of GH is mainly mediated
by IGF-I, a component of the insulin-like growth
factor system [16].
TABLE
Variables  P value
FBG 0.411 0.002
Insulin -0.018 0.920
HOMA-index -0.008 0.965
TC -0.189 0.286
TG 0.008 0.966
HDL 0.083 0. 64
LDL -0.228 0.194
The mechanisms underlying the association
between IGF-I levels and MS are still largely
unknown. The insulin-like activity of IGF-I may
account for a positive effect on insulin resistance
which is closely associated with metabolic
syndrome [17]. This may be due to resemblances
between insulin and IGF-I indicate the probability
of IGF-I involvement in the phenotypic
expression of this disorder [18]. The increased
insulin levels can induce a down-regulation of
IGF-I secretion by the liver and other tissues, as
a compensatory homeostatic mechanism, caused
most likely through a differential variation of
IGF-I production. This could be responsible
for the increment levels of IGF-I indicated in
accordance with states of IR, as the MS [19].
On the contrary, the present study suggested
      
associated with MS. This could be explained
by the greater incidence of IR and MS in adult
population compared with younger individuals
TABLE 
Variables  P value
FBG -0.116 0.513
Insulin 0.220 0.21
HOMA-index 0.277 0.045
TC 0.084 0.636
TG 0.255 0.146
HDL 0.068 0.704
LDL -0.009 0.961
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Egypt. J. Chem. 60, No.5 (2017)
RELATIONSHIP BETWEEN SEX HORMONE-BINDING GLOBULIN ...
TABLE 
Parameter
Area
under the
curve

value Sensitivity %  PPV NPV Test
Accuracy 95% CI P value
SHGB 0.620 2.936 63.5 % 61.0 % 50.8 % 72.5 % 61.9 % 0.518 to 0.721 0.020
ILGF1 0.587 6.150 51.9 % 63.4 % 47.4 % 67.5 % 59.0 % 0.488 to 0.687 0.089
might also be attributable, nevertheless partially,
to the decay concentrations of serum and tissue
IGF-I with progressing age [20]. Reduced IGF-I
levels are independently associated with glucose
intolerance, diabetes, abdominal obesity [21;
22] and atherogenic dyslipidemia [23]. There
are interesting discrepancies for understanding
the physiological relevance of the reduced IGF-I
axis in aging. Several studies have suggested
that reduced IGF-I activity promotes longevity
       
been accumulated indicating that IGF-I might
play a role in several pathological conditions
commonly seen during aging. These pathologies
are associated with oxidative tissular damage.
This effect can be an additional mechanism to
explain the antioxidant activity displayed by this

The mechanisms responsible for the effects of
IGF-I are not fully understood that require further
investigation [26].
Our data reported that serum levels of
SHBG were decreased in MS group as compared

et al. [19] and Liao et al. [20]; who found that the
serum concentration of SHBG was associated with
MS. Our data could be explained on the basis that
the crucial abnormality detected in MS seems to be
IR in peripheral tissues. Since insulin is a powerful
suppressor of SHBG generation in the liver, it is
conceivable that reduced levels of SHBG might
be an initial indicator for MS. Likewise, Heald
et al. [27] in an investigation examining Afro-
Caribbean, European and Pakistani populaces
and Chubb et al. [28] in a population-based study,
recommended that SHBG is a potential marker for
MS. Recently, Caldas et al., [29] described that a
rise of one unit in insulin concentrations lead to
a drop of 0.25 units in SHBG concentrations, in
a non-interventional study examining 80 subjects
with MS.
A powerful relationship was observed
between lipids and SHBG, making SHBG to be
expected as a valuable predictor for the metabolic
syndrome distinct by the National Cholesterol
Education Program Adult Treatment Panel [30].
This description excludes insulin resistance as a
risk factor for this disorder, and along these lines,
it has a tendency to be more weighted toward lipid
constituents and abdominal obesity as compared to
the WHO explanation of the metabolic syndrome.
Since IGF-1 does not have a relationship with
either insulin or insulin resistance, and its relation
with SHBG is stronger than for lipids, IGF-1
       
      
 
962
Egypt. J. Chem. 60, No.5 (2017)
WEAAM GOUDA et al.
Cholesterol Education.
Conclusion
In conclusion, serum SHBG level inversely
correlates with the prevalence of metabolic
syndrome, but not serum level of IGF-1.
Metabolic Syndrome is increasing in emerging
countries, making this disease a public health
problem. Although, the exact mechanisms is
linking MS disease remain only partly known.
We recommended that further research is
warranted for the better understanding of the
pathophysiology of MS and for better identifying
potential therapeutic targets in this ever growing
disease.


interest that could be perceived as prejudicing the
impartiality of the research reported.
Funding
This study was supported by project grants
from the National Research Center (Project no.
10010205).

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WEAAM GOUDA et al.

SHBG    
-IGF
-IGFSHBG

     

-IGFSHBG 
 SHBG 
SHBG-IGF.
HOMA
-IGFSHBG-IGF
SHBG
SHBG-IGF

-IGF) SHBG


 
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Background Obesity is heritable and is known to predispose to many diseases. Obesity is considered as a complex or multifactorial condition that is associated with complicated interplay between genetic and non-genetic factors. Genetic factors play an indispensable role in the individual's predisposition to obesity. Objective To conduct a genome-wide association study, to determine the relation between obesity and adiposity and different diabetes GWAS loci, by analysing the effect of six different types namely, rs3923113 (GRB14), rs16861329 (ST6GAL1), rs1802295 (VPS26A), rs7178572 (HMG20A), rs2028299 (AP3S2) and rs4812829 (HNF4A) on obesity. Subjects and methods This observational study included 456 subjects (obese = 300, controls = 156), who were genotyped by TaqMan assay and their biochemical parameters were determined. The frequencies of their genotypes were calculated and their association with obesity and lipid profile were analyzed. Results A significant association was found between the dominant model of rs 3,923,113 (GRB14; P = .05), dominant model of rs 1,802,295 (VPS26A; P = .03), co-dominant and recessive models of rs 2,028,299 (APsS2; P = .001 & P = .000), respectively; and co-dominant, dominant and recessive models of rs 4,812,829 (HNf4A; P = .000) polymorphisms and the risk of obesity, whereas there was no association for both rs 16,861,329 (ST6GAL1) and rs 7,178,572 (HMG20A) of six type II diabetes GWAS. Conclusion SNPsrs2028299 (AP3S2) and rs4812829 (HNF4A) are significantly associated with obesity.
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The metabolic syndrome is a condition characterized by a special constellation of reversible major risk factors for cardiovascular disease and type 2 diabetes. The main, diagnostic, components are reduced HDL-cholesterol, raised triglycerides, blood pressure and fasting plasma glucose, all of which are related to weight gain, specifically intra-abdominal/ectopic fat accumulation and a large waist circumference. Using internationally adopted arbitrary cut-off values for waist circumference, having metabolic syndrome doubles the risk of cardiovascular disease, but offers an effective treatment approach through weight management. Metabolic syndrome now affects 30–40% of people by age 65, driven mainly by adult weight gain, and by a genetic or epigenetic predisposition to intra-abdominal/ectopic fat accumulation related to poor intra-uterine growth. Metabolic syndrome is also promoted by a lack of subcutaneous adipose tissue, low skeletal muscle mass and anti-retroviral drugs. Reducing weight by 5–10%, by diet and exercise, with or without, anti-obesity drugs, substantially lowers all metabolic syndrome components, and risk of type 2 diabetes and cardiovascular disease. Other cardiovascular disease risk factors such as smoking should be corrected as a priority. Anti-diabetic agents which improve insulin resistance and reduce blood pressure, lipids and weight should be preferred for diabetic patients with metabolic syndrome. Bariatric surgery offers an alternative treatment for those with BMI ≥ 40 or 35–40 kg/m2 with other significant co-morbidity. The prevalence of the metabolic syndrome and cardiovascular disease is expected to rise along with the global obesity epidemic: greater emphasis should be given to effective early weight-management to reduce risk in pre-symptomatic individuals with large waists.
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Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease worldwide, affecting 20–30% of adults and 3–10% of children in Western countries. The pathogenesis of NAFLD is considered to be multifactorial and factors such as insulin resistance, intrahepatic fat accumulation, oxidative stress, mitochondrial alterations, and stellate cell activation appear to substantially contribute to the development and progression of the disease. In this Editorial, we highlight some evidence suggesting a close link between NAFLD and growth hormone (GH)–IGF (insulin-like growth factor) axis.
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Objective: High as well as low levels of IGF1 have been associated with cardiovascular diseases (CVD). The relationship of IGF1 with (components of) the metabolic syndrome could help to clarify this controversy. The aims of this study were: i) to investigate the association of IGF1 concentration with prevalent (components of) the metabolic syndrome; and ii) to examine the role of (components of) the metabolic syndrome in the relationship between IGF1 and incident CVD during 11 years of follow-up. Methods: Data were used from the Longitudinal Aging Study Amsterdam, a cohort study in a representative sample of the Dutch older population (≥65 years). Data were available in 1258 subjects. Metabolic syndrome was determined using the definition of the US National Cholesterol Education Program Adult Treatment Panel III. CVD were ascertained by self-reports and mortality data. Results: Levels of IGF1 in the fourth quintile were associated with prevalent metabolic syndrome compared with the lowest quintile (odds ratio: 1.59, 95% confidence interval (CI) 1.09-2.33). The middle up to the highest quintile of IGF1 was positively associated with high triglycerides in women. Metabolic syndrome was not a mediator in the U-shaped relationship of IGF1 with CVD. Both subjects without the metabolic syndrome and low IGF1 levels (hazard ratio (HR) 1.75, 95% CI 1.12-2.71) and subjects with the metabolic syndrome and high IGF1 levels (HR 2.28, 95% CI 1.21-4.28) demonstrated increased risks of CVD. Conclusions: In older people, high-normal IGF1 levels are associated with prevalent metabolic syndrome and high triglycerides. Furthermore, this study suggests the presence of different pathomechanisms for both low and high IGF1 levels and incident CVD.
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Nonalcoholic fatty liver disease (NAFLD) is a multifactorial condition, ranging from simple steatosis to nonalcoholic steatohepatitis (NASH) with or without fibrosis. NAFLD affects both adults and children who present with particular risk factors, including obesity, sedentary lifestyle and/or a predisposing genetic background. The escalation of the prevalence of NAFLD in children worldwide is a worrying phenomenon because this disease is closely associated with the development of both cirrhosis and cardiometabolic syndrome in adulthood. The etiopathogenesis of primary NAFLD in children is unknown; however, considerable knowledge about the mechanisms of liver damage that occur during disease progression has been gathered over the past 30 years. Understanding the pathogenetic mechanisms, together with the histological pattern, provide the basis to characterize potential early predictors of the disease, suitable noninvasive diagnostic tools and design novel specific treatments and possible management strategies. Despite a few clinical trials on the use of antioxidants combined with lifestyle intervention for NAFLD that showed encouraging results, to date, no treatment guidelines exist for children with NAFLD. In this Review, we provide an overview of current concepts in epidemiology, histological features, etiopathogenesis, diagnosis and treatment of NAFLD in children and adolescents.
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In this study, we aimed to investigate the relationship between the histological features of nonalcoholic fatty liver disease (NAFLD) and serum insulin-like growth factor-1 (IGF-1) and insulin-like growth factor-binding protein-5 (IGFBP-5) to determine the usefulness of this relationship in clinical practice. Serum samples were collected from 92 patients with biopsy-proven NAFLD and 51 healthy controls and serum levels of IGF-1 and IGFBP-5 were assayed by enzyme-linked immunosorbent assay. Serum IGFBP-5 levels were correlated with liver steatosis, fibrosis, and nonalcoholic steatohepatitis scores. IGF-1 levels were significantly decreased in patients with moderate-to-severe fibrosis compared with patients with no or mild fibrosis. Serum IGFBP-5 levels may be useful to differentiate both advanced fibrosis and definite nonalcoholic steatohepatitis from other NAFLD groups. Also, serum IGF-1 levels may be useful to differentiate advanced fibrosis in patients with NAFLD.
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The metabolic syndrome is a combination of metabolic and clinical features that aggregate in individuals and increase cardiovascular disease (CVD) risk considerably. It is believed, although sometimes controversially, that the underlying basis for this syndrome is insulin resistance (IR) and accompanying compensatory hyperinsulinemia. Insulin and insulin-like growth factors (IGFs) have significant homology and interact with differing affinity with the same receptors. Therefore, their actions can be complementary, and this becomes particularly significant clinico-pathologically when their circulating levels are altered. This review of currently available information attempts to answer the following questions: (1) Is there any evidence for changes in the components of the IGF system in individuals with established CVD or with increased CVD risk as with the metabolic syndrome? (2) What are the underlying mechanisms for interactions, if any, between insulin and the IGF system, in the genesis of CVD? (3) Can knowledge of the pathophysiological changes in the IGF system observed in macrosomic newborn infants and growth hormone (GH)-treated children and adults explain some of the observations in relation to the IGF system and the metabolic syndrome? (4) Can the experimental and clinical evidence adduced from the foregoing be useful in designing novel therapies for the prevention, treatment, and assignment of prognosis in metabolic syndrome-associated disease, particularly ischemic heart disease? To answer these questions, we have performed a literature review using bibliographies from PubMed, Medline, and Google Scholar published within the last 10 years. We suggest that IGF-1 levels are reduced consistently in individuals with the metabolic syndrome and its components and in those with ischemic CVD. Such changes are also seen with GH deficiency in which these changes are partially reversible with GH treatment. Furthermore, changes are seen in levels and interactions of IGF-binding proteins in these disorders, and some of these changes appear to be independent of IGF-binding capability and could potentially impact on risk for the metabolic syndrome and CVD. The promising therapeutic implications of these observations are also discussed.