Magnesium-Rich Indonesian Brown Rice ‘Sintanur’ Improves Insulin
Sensitivity in High Fat High Fructose Diet-Induced Obesity Sprague
Dawley Rats
SRI ANDARINI1, GATI LINGGA KIWARI2, DIAN HANDAYANI3
1Department of Public Health, Faculty of Medicine, Universitas Brawijaya, Jl. Veteran, Malang,
INDONESIA
2Master Program in Biomedical Sciences, Faculty of Medicine, Universitas Brawijaya, Jl. Veteran,
Malang, INDONESIA
3Department of Nutrition, Faculty of Medicine, Universitas Brawijaya, Jl. Veteran, Malang,
INDONESIA
Abstract: This study aimed to analyze the effect of 'Sintanur' brown rice on Lee's index, fasting blood glucose
levels, and HOMA-IR administered to male Sprague Dawley rats. This research was an experimental laboratory
study with a post-test-only control group design. The subjects were thirty-five male Sprague Dawley rats
divided into five groups. Group 1 consisted of the negative control with a standard diet. Group 2 consisted of
the positive control with HFFD-induced obesity for 20 weeks, while groups 3, 4, and 5 were the treatment
groups with HFFD-induced obesity for 12 weeks, which were intervened with different dosages of brown rice
diet from week 13 to week 20. At week 21, the rats were sacrificed. Fasting blood glucose levels were tested
using a glucometer. Fasting serum insulin levels were tested using ELISA. HOMA-IR was calculated using
fasting glucose and insulin levels. Serum magnesium levels were tested using Atomic Absorption
Spectrophotometry. A non-parametric test of Kruskal-Wallis was used to analyze differences in mean dietary
intake, Lee index, fasting blood glucose, and HOMA-IR. As a result, there were significant differences between
groups (p<0.05). Spearman correlation test was used to analyze the relationship between the Lee index, fasting
blood glucose levels, and HOMA-IR with serum magnesium levels. As a result, there were negative
correlations between parameters (r=-0.299; r=-0.393; r=-0.257). Group 5 had the best results in lowering
insulin resistance. In conclusion, consuming local 'Sintanur' brown rice decreased the Lee index, fasting blood
glucose levels, and HOMA-IR by increasing serum magnesium levels in obese rats. High magnesium intake
reduces insulin resistance by correcting the disruption of glucose metabolism and insulin signaling pathways.
Key-Words: Brown Rice; Insulin Resistance; HOMA-IR; Magnesium; Obesity
Received: August 29, 2021. Revised: October 22, 2022. Accepted: November 19, 2022. Published: December 20, 2022.
1 Introduction
Obesity is an accumulation of excessive fat in the
body that can impair health, [1]. Body mass index
(BMI) is used for clinical obesity screening, [2]. A
BMI same as or more than 30 kg/m2 is considered
obese, [1]. In Indonesia, the prevalence of obesity in
the adult population reaches 23.1% , [3]. The risk
factors of obesity include an energy imbalance,
genetic, and socioeconomic determinants, [4], [5],
[6]. Some of the comorbidities associated with
obesity are type 2 diabetes, osteoarthritis,
hypertension, congestive heart failure, coronary
artery disease, pulmonary embolism, and
cerebrovascular accident, [7]. Type II diabetes
mellitus is the most common type of diabetes today
and is characterized by a series of malfunctions such
as insulin resistance, the inadequate secretion of
insulin and excessive or inappropriate glucagon
secretion. This type of diabetes affects mainly
adults, [8], [9].
Obesity is related to type 2 diabetes based on its
ability to induce insulin resistance. Insulin is a
hormone secreted by pancreatic beta cells. It is the
most important hormone in the regulation of blood
glucose levels, [10].
Insulin resistance is a decrease in the ability of
tissues to respond to insulin action, [11]. In obese
individuals, adipocytes lose their effectiveness in
responding to insulin’s antilipolytic action, [12].
Homeostasis Model Assessment for Insulin
Resistance (HOMA-IR) is a method to predict the
occurrence of insulin resistance by using the fasting
blood glucose and fasting serum insulin level in the
calculation. The cut-off values differ depending on
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race, age, gender, and disease, [13]. Obese
individuals have higher levels of HOMA-IR than
those with average body weight, indicating higher
insulin resistance, [14].
Maintaining diet, physical activity, adherence to
treatment, and education may control type 2
diabetes, [15]. Dietary regulation can stabilize blood
glucose and lipid levels within the normal range,
[16]. Brown rice is a highly nutritious food obtained
through a milling process by removing the husk
from the paddy to obtain rice grain that contains a
brown outer layer of bran, [17]. 'Sintanur' is one of
the brown rice variants in Indonesia. A 100 grams of
local ‘Sintanur’ brown rice contains 386.67 calories
of energy, 8.39 grams of protein, 86.19 grams of
carbohydrates, 0.91 grams of fat, 22.04 grams of
fiber, 230 mg of magnesium, 4.41 mg of
manganese, and 340 mg of potassium. The
micronutrients in local Sintanur’ brown rice are
higher than those in white rice of the same variety,
i.e., magnesium content is seven times higher, [18].
Local brown rice also has a higher fiber content than
local white rice. Therefore, brown rice has a longer
digestion time and lower glycemic index, [19], [20].
Indonesian local brown rice ‘Sintanur’ is a good
magnesium source, [18]. Magnesium contributes to
the insulin-mediated regulation of glucose
absorption and increases insulin sensitivity, [20].
Numerous enzymes in the metabolic cycles need
Mg2+ or MgATP as a cofactor throughout the
processes. Mg2+ may immediately impact the
glucokinase activity in pancreatic β-cells since the
glucokinase action relies upon MgATP.
Glucokinase is needed for converting glucose to
glucose-6-phosphatase (G6P), [21]. Serum
magnesium homeostasis is regulated by the
reabsorption of magnesium in the kidneys. Serum
magnesium levels were found to be inversely
correlated with blood glucose levels. Renal tubular
reabsorption of magnesium decreases in the
presence of severe hyperglycemia, [22].
A trial on overweight, non-diabetic subjects
shows significant evidence that oral magnesium
supplementation (365 mg/day) for six months
improves insulin sensitivity, [23]. A Japanese study
reports that the consumption of brown rice may be
advantageous because of its effect on lowering
glycemic response and protecting postprandial
endothelial function in individuals with metabolic
syndrome. It also helps lower body weight and
insulin resistance risk, [24]. A study in India on
overweight subjects shows that consumption of
brown rice can reduce blood glucose and fasting
insulin responses, [25]. Research in Malaysia
suggests that consumption of brown rice on female
Sprague Dawley rats shows better oral glucose
tolerance test, lower weights, and HOMA-IR values
compared to the white rice group, [26].
2 Materials and Methods
2.1 Research Design
This study used an experimental laboratory method
and a post-test-only control group design. This study
was part of extensive research exploring the overall
benefits of Indonesian local brown rice ‘Sintanur’
and received a certificate of ethical eligibility from
the Health Research Ethics Commission, Faculty of
Medicine, Universitas Brawijaya.
2.2 Subjects
The subjects of this study were 35 Sprague Dawley
male rats, white fur, ages 70-90 days post-natal, and
weights 200-250 grams. The sample quantity was
calculated by using Federer’s formula. The rats were
obtained from the Animal Laboratory of Institut
Pertanian Bogor, Bogor, Indonesia. The rats were
treated at the Biosciences Institute, Universitas
Brawijaya, Malang, Indonesia. The brown rice used
in the study was Indonesia's local brown rice,
‘Sintanur’. The rice was purchased from a local
market. The subjects were randomly divided into
five groups. Group 1 was the negative control with a
standard diet AIN-93M. Group 2 was the positive
control with HFFD (High Fat, High Fructose Diet)-
induced obesity for 20 weeks. Groups 3, 4, and 5
were the treatment groups with HFFD-induced
obesity for 12 weeks, which were intervened with
different dosages of brown rice diet from week 13 to
week 20. At week 21, the rats were sacrificed by
ketamine injection, and the parameters were tested.
During the research period, three experimental
animals died (dropped out). Therefore, there were
32 rats at the end of the study, namely seven in
group 1, six in group 2, five in group 3, seven in
group 4, and seven in group 5.
2.3 Dietary Intake
The standard diet consisted of cornstarch,
dextrinized cornstarch, sucrose, soybean, gelatin,
fish flour, casein, egg white, fiber, minerals,
vitamins, L-cystine, and choline bitartrate. HFFD
was a modification that substituted carbohydrate and
protein sources in the standard diet with animal fat,
lard, and 30% fructose. Rice consumption per capita
in March 2015 was 98 kilograms/year or 370-380
grams/day, [27]. The carbohydrate content of brown
rice added replaced the carbohydrate source from
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HFFD. Rat feed consumption was 15-30 grams/day,
and an average of 20 grams/day was used. The
addition of brown rice to rat feed per 20 grams was
converted to 0.018 as a human-to-mouse dose
conversion factor, [28]. In humans, brown rice
needed per day was 125 grams for one meal, 250
grams for two meals, and 375 grams for three meals.
Conversion to rat feed per day was 2.25 grams, 4.5
grams, and 6.75 grams. In making 1000 grams of
feed, 112.5 grams of brown rice were added for
dose I (group 3), 225 grams of brown rice was
added for dose II (group 4), and 337.5 grams of
brown rice were added for dose III (group 5) to
substitute cornstarch, dextrinized cornstarch,
sucrose, and fructose (carbohydrate source in rat
feed).
2.4 Parameters Test
The blood glucose levels were examined using
Nesco’s glucometer. The fasting serum insulin
levels were examined using Bioassay Technology
Laboratory’s ELISA kit, [29]. The HOMA-IR
values were calculated by multiplying fasting blood
glucose (mg/dL) and fasting serum insulin (μU/mL)
levels, divided by 405. The Lee index was obtained
by calculating the cube root of body weight (grams),
divided by the length of the nasal-anal (cm), and
multiplied by 1000. This study was a part of
extensive research; the serum magnesium levels
were already examined using Atomic Absorption
Spectrophotometry, [28].
2.5 Data Analysis
Data were analyzed using IBM SPSS 26.0 for
windows, with a significance level of 0.05 and a
confidence level of 95% (p<0.05 indicates a
significant difference/relationship). ANOVA
(normal distribution and homogeneous data) or
Kruskal-Wallis (abnormal distribution and
heterogeneous data) test was used to analyze the
difference in mean data of dietary intake, Lee index,
fasting blood glucose levels, and HOMA-IR values
between treatment groups. The Post Hoc Tukey
HSD (normal distribution and homogeneous data) or
Mann-Whitney (abnormal distribution and
heterogeneous data) test was used to analyze further
where the difference lay. The Pearson (normal
distribution and homogeneous data) or Spearman
correlation test (normally distributed data), or
Spearman (abnormal distribution and heterogeneous
data) test was used to analyze the correlation
between the Lee index, fasting blood glucose levels,
and HOMA-IR values with serum magnesium
levels.
3 Results and Discussion
3.1 Dietary Intake
Based on the normality and homogeneity tests, the
only normally distributed and homogeneous data
was the fructose intake data (p>0.05). Therefore, the
ANOVA test was used on fructose intake data. The
Kruskal Wallis tests were used on average daily
feed, brown rice, fiber, and total energy intake data.
As a result, significant differences were found in the
data on average daily feed intake (p=0.000), brown
rice (p=0.000), fructose intake (p=0.000), fiber
(p=0.001), and total energy (p=0.002). Group 2 had
the highest total energy, and group 1 had the lowest
total energy. Group 5 had the lowest total energy
among the brown rice intervention groups. Group 1
had the highest average daily feed intake
(13.60±2.78 g), while group 5 had the lowest
(7.94±0.71 g).
Table 1. The Average Daily Intake during the
Intervention
Parameter
Group
1
3
4
5
Average
daily feed
intake (g)#
13.60
±
2.78a
10.09
±
1.29b
8.33
±
0.91c
7.94
±
0.71c
Brown rice
(g)#
0.00
±
0.00
1.25
±
0.16a
2.07
±
0.23b
2.96
±
0.26c
Fructose
intake (ml)*
0.00
±
0.00
33.94
±
3.59b
33.70
±
4.13b
33.72
±
3.31b
Fiber (g)#
3.86
±
0.79a
2.86
±
0.28b,c
2.89
±
0.32b,c
3.17
±
0.28c
Total energy
(kcal)#
55.62
±
11.38a
86.62
±
7.01b
78.07
±
5.25b,c
76.21
±
5.03c
Note:
The data are mean ± standard deviation
*One Way Anova test p<0.05; #Kruskal Wallis test
p<0.05
Data accompanied by different notations show significant
differences
Group 1: Negative control
Group 2: Positive control
Group 3: HFFD + brown rice dose I (11.25%)
Group 4: HFFD + brown rice dose II (22.5%)
Group 5: HFFD + brown rice dose III (33.75%)
3.2 Lee Index
Based on the normality test, Lee index data was not
normally distributed (p<0.05). Therefore, the
Kruskal-Wallis and Post Hoc Mann-Whitney tests
were used to see the relationship between variables.
The average Lee index at the end of the intervention
significantly differed between groups (p=0.000)
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(Figure 1). Group 2 (positive control) had the
highest average Lee index, while group 1 (negative
control) had the lowest average Lee index. The Lee
index values in all rats administered with Sintanur
rice (groups 3, 4, and 5) were lower than in group 2.
Lee index was observed throughout the intervention;
the changes were 7.43±12.03 g/cm3 in group 1,
4.50±9.57 g/cm3 in group 2, -10.77±7.77 g/cm3 in
group 3, -16.61±7.77 g/cm3 in group 4, and -
17.29±12.39 g/cm3 in group 5. Group 5 had the
most significant decrease in the Lee index.
Fig. 1: Average Lee Index at the End of Intervention
Note:
The data are mean ± standard deviation
#Kruskal Wallis test p<0.05
Data accompanied by different notations show significant
differences
Group 1: Negative control
Group 2: Positive control
Group 3: HFFD + brown rice dose I (11.25%)
Group 4: HFFD + brown rice dose II (22.5%)
Group 5: HFFD + brown rice dose III (33.75%)
Correlation analysis showed that the Lee index
and serum magnesium levels had a fair negative
correlation, but it was insignificant (r=-0.299;
p=0.060) (Figure 2).
Fig. 2: The Spearman Correlation Test between the
Lee Index and Serum Magnesium Levels
The Lee index negatively correlated with serum
magnesium levels but was insignificant (r=-0.299;
p=0.060).
3.3 Fasting Blood Glucose
Based on the normality and homogeneity tests,
fasting blood glucose data were normally distributed
(p>0.05) but heterogeneous (p<0.05). Therefore, the
Kruskal-Wallis and Post Hoc Mann-Whitney tests
were used to see the relationship between variables.
The average fasting blood glucose levels
significantly differed between groups (p=0.000)
(Figure 3), but group 5 had no significant difference
from group 1 (negative control). Group 2 had the
highest average fasting blood glucose levels, while
group 1 had the lowest average fasting blood
glucose levels. Group 5 had the lowest average
fasting blood glucose levels among the brown rice
intervention groups.
Fig. 3: Average Fasting Blood Glucose Levels at the
End of Intervention
Note:
The data are mean ± standard deviation
#Kruskal Wallis test p<0.05
Data accompanied by different notations show significant
differences
Group 1: Negative control
Group 2: Positive control
Group 3: HFFD + brown rice dose I (11.25%)
Group 4: HFFD + brown rice dose II (22.5%)
Group 5: HFFD + brown rice dose III (33.75%)
Correlation analysis showed that the fasting
blood glucose levels and serum magnesium levels
had a significantly fair negative correlation (r=-
0.393; p=0.019) (Figure 4). The lower the serum
magnesium levels, the higher the fasting blood
glucose levels.
287.42
319.93
303.91 299.41 302.47
260
270
280
290
300
310
320
330
Group 1 Group 2 Group 3 Group 4 Group 5
Lee Index (gr/cm)
Group of controls and treatments
132.14
331.83
230.4
183.29 156
0
50
100
150
200
250
300
350
400
Group 1 Group 2 Group 3 Group 4 Group 5
Fasting blood glucose (mg/dL)
Group of controls and treatments
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Fig. 4: The Spearman Correlation Test between
Fasting Blood Glucose Levels and Serum
Magnesium Levels
The fasting blood glucose levels negatively
correlated with serum magnesium levels, significant
(r=-0.393; p=0.019).
3.4 HOMA-IR
Based on the normality test, HOMA-IR data was not
normally distributed (p<0.05). Therefore, the
Kruskal-Wallis and Post Hoc Mann-Whitney tests
were used to see the relationship between variables.
The average HOMA-IR values significantly differed
between groups (p=0.003) (Figure 5), but group 5
had no significant difference from group 1 (negative
control). Group 2 had the highest average HOMA-
IR, and group 1 had the lowest average HOMA-IR.
Group 5 had the lowest average HOMA-IR among
the brown rice intervention groups.
Fig. 5: Average HOMA-IR values at the End of
Intervention
Note:
The data are mean ± standard deviation
#Kruskal Wallis test p<0.05
Data accompanied by different notations show significant
differences
Group 1: Negative control
Group 2: Positive control
Group 3: HFFD + brown rice dose I (11.25%)
Group 4: HFFD + brown rice dose II (22.5%)
Group 5: HFFD + brown rice dose III (33.75%)
Correlation analysis showed that the
HOMA-IR values and serum magnesium levels
had a fair negative correlation but were
insignificant (r=-0.257; p=0.093) (Figure 6).
Fig. 6: The Spearman Correlation Test between
HOMA-IR Values and Serum Magnesium Levels
The HOMA-IR values negatively correlated with
serum magnesium levels but were insignificant (r=-
0.257; p=0.093).
3.5 Discussion
Body mass index is a typical formula to describe
levels of obesity in humans, the same as the Lee
index in an animal experiment, [30]. Lee index is
calculated by dividing the cube root of body weight
(g) by the length of the nasal-anal (cm), then
multiplied by 1000. Obesity is Lee index above 300,
[31]. Obesity occurs due to an imbalance between
energy needs and intake. A high-fat and high-
fructose diet can lead to a positive energy balance
and obesity, [32]. Obesity can increase the risk of
type 2 diabetes, [33]. Meanwhile, a healthy diet can
reduce the risk of type 2 diabetes, [34].
Rice meets the energy needs of billions of people
worldwide. Rice quality is currently focused more
on excellent taste and less on nutrition, mainly due
to the milling process that removes the nutritious
bran (white rice), [35]. Meanwhile, Indonesian local
brown rice ‘Sintanur’ still has nutritious bran. It
contains relatively higher amounts of protein, fiber,
unsaturated lipids, and micronutrients (Magnesium,
Manganese, and Potassium) than the same variety of
local white rice, [18]. This study examines the
potential of 'Sintanur' local brown rice as a diet
therapy for obesity.
In this study, group 1 was given a standard diet
for twenty weeks. Group 2 was given an HFFD for
twelve weeks until the Lee index>300 and then an
HFFD again for another eight weeks. Groups 3, 4,
2.97
6.97 6.13
4.44
3.06
0
2
4
6
8
10
12
Group 1 Group 2 Group 3 Group 4 Group 5
HOMA-IR
Group of controls and treatments
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and 5 were given an HFFD for twelve weeks until
the Lee index>300, then an HFFD modified with
different dosages of brown rice diet for another
eight weeks. During the intervention, significant
differences were found in dietary intake data
(average daily feed intake, brown rice, fructose
intake, fiber, and total energy).
Among the three treatment groups, group 5 had
the highest average intake of brown rice and fiber
but had the lowest average daily feed intake and
total energy. Local brown rice 'Sintanur' contains
more fiber and energy than local white rice of the
same variety. One hundred grams of local brown
rice, 'Sintanur', contains 386.67 calories of energy
and 22.04 grams of fiber, whereas, in the same
variety, local white rice only contains 376.43
calories of energy and 20.58 grams of fiber, [18].
Soluble fiber can form a thick liquid in the digestive
tract, so it takes longer to be digested in the
stomach. Fiber will also attract water and give a
more prolonged feeling of fullness, thus preventing
from consuming more food, [19].
Based on the study results, there was a
significant difference in Lee index values at the end
of the intervention, especially between the controls
and the intervention groups. The results showed that
all rats administered with Sintanur rice (groups 3, 4,
and 5) had a lower Lee index than the positive
control (group 2). Group 5 had the most significant
change in the Lee index among the treatment
groups.
Brown rice intake is associated with weight loss
and reduced adipocytes. Brown rice contains high
dietary fiber. High dietary fiber is correlated with a
low glycemic index because glucose digestion and
absorption into the circulation occurs more slowly,
[36]. Consumption of brown rice has been reported
to reduce hunger and increase satiety, leading to
lower energy intake, [24]. This study’s result is
consistent with a previous study in Malaysia, which
showed that consumption of brown rice (30
kcal/100 grams body weight/day) in female Sprague
Dawley rats for eight weeks reduced body weight
and Lee index compared to white rice, [26].
Obesity is related to type 2 diabetes based on its
ability to induce insulin resistance. Insulin
resistance is a decrease in the ability of tissues to
respond to insulin action, [11]. In obese individuals,
adipocytes are ineffective in responding to the
antilipolytic action of insulin, [12]. HOMA-IR is a
method for predicting the occurrence of insulin
resistance, by using fasting glucose levels and
fasting insulin levels in the calculations, [13].
Individuals with obesity have higher HOMA-IR
levels than those with average weight, indicating a
higher risk of developing insulin resistance, [14].
This study showed significant differences in
fasting blood glucose levels and HOMA-IR values
at the end of the intervention. Among the three
treatment groups, group 5 had the most significant
difference from group 2 (positive control). This
study’s result is consistent with a previous study that
found that after ten weeks of intervention, fasting
blood glucose levels in the Sprague Dawley rat
group fed a high-fat diet modified with brown rice
(20%) were significantly lower (p<0.05) than
positive controls, [37]. In addition, research stated
that by consuming brown rice (30 kcal/100 grams
body weight/day) for eight weeks in Sprague
Dawley rats, fasting insulin levels and HOMA-IR
are lower than white rice, [26].
Brown rice has high fiber content, [25]. Dietary
fiber has consistently been associated with increased
insulin sensitivity and reduced risk of type 2
diabetes, [38]. The fiber in food is negatively
correlated with the glycemic index. High fiber can
cause glucose absorption into circulation to occur
more slowly, [19], [39].
The fiber in food can absorb water and bind
glucose, thereby reducing glucose availability. A
high-fiber diet can also cause the formation of
complex carbohydrates and fiber, which reduces the
digestibility of carbohydrates. This situation can
reduce the increase in blood glucose levels and
insulin demand, [39]. Foods with high fiber content
and low glycemic index can act on digestion and
absorption of nutrients by reducing glucose/insulin
levels, chylomicron production and secretion, and
de novo lipogenesis, [40]. Fiber can act through the
fermentation of indigestible carbohydrates in the
large intestine by improving hepatic glucose
regulation. Fiber may also increase satiety signals in
the hypothalamus, [41].
One hundred grams of Indonesian local brown
rice, 'Sintanur', contains 230 mg of magnesium,
whereas, in the same variety, local white rice only
contains 30 mg of magnesium. The intake of local
brown rice strongly correlates with serum
magnesium levels, [18]. Based on the results of the
Spearman correlation test, showed that the Lee
index (insignificant)/ fasting blood glucose levels
(significant)/ HOMA-IR values (insignificant) had
negative correlations with magnesium levels.
Local brown rice, 'Sintanur', is a good
magnesium source, [18]. Magnesium is a cofactor
required in many enzymatic reactions and is
involved in the metabolism of glucose and insulin
homeostasis, [42]. Glucose enters the pancreatic β-
cells by passing through glucose transporter type 2
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(GLUT2). After that, glucokinase transforms
glucose into glucose-6-phosphate (G6P), [21].
GLUT2 and glucokinase activity are glucose
sensors that control blood glucose levels, [43]. Mg2+
can directly affect glucokinase by acting as a
cofactor for adenine nucleotides. G6P, the enzyme
reaction's product, is processed to form ATP. The
KATP channel's opening depends on the binding of
MgATP and SUR1 subunits. Meanwhile, the KATP
channel’s closure depends on the binding of ATP
and Kir6.2 subunits, which causes depolarization of
the membrane. It then triggers the Ca2+ influx
through the L-type Ca2+ channels to initiate the
release of insulin vesicles, [21].
Homeostasis of magnesium regulated by
magnesium reabsorption in the kidneys is found to
be inversely correlated with blood glucose levels.
Hyperglycemia condition can decrease renal
reabsorption of magnesium, [22]. Magnesium
deficiency can disrupt glucokinase function, G6P
formation, and ATP accumulation in the pancreatic
β-cells. It can interfere with the closure of KATP
channels and delay the initial and late phases of
insulin responses to glucose. In magnesium
deficiency, MgATP intracellular levels decrease. It
can interfere with the opening of KATP channels
and prolong the depolarization of the β-cells plasma
membrane, which causes more insulin release. Thus,
magnesium deficiency can lead to β-cells
dysfunction in type 2 diabetes, [44].
Two major signaling pathways activate most
insulin actions. The first signaling pathway is the
Ras/mitogen-activated protein kinases (Ras/MAPK).
It modulates the expression of genes and insulin-
associated mitogenic reactions. The second
signaling pathway is the phosphatidylinositol-3-
kinase/protein kinase B (PI3K/Akt). It manages
most insulin metabolic activities and significant
functions in insulin signaling. Its activation leads to
the phosphorylation of many substrates that play
essential roles in biological processes, such as
stimulation of glucose transport, synthesis of
glycogen and protein, and lipogenesis. Akt has a
crucial role in insulin metabolic actions, including
glucose uptake in muscle and adipose tissue through
glucose transporter type 4 (GLUT4) translocation
from the intracellular compartments to the cell
membrane, [45]. Magnesium deficiency can lead to
insulin resistance associated with decreased
PI3K/Akt pathway activity and impaired expression
and function of GLUT4. These can reduce glucose
uptake in muscle and adipose tissue and trigger
changes in the metabolic level, [44].
Several international studies have provided
relevant associations between magnesium and
insulin resistance. Magnesium supplementation (365
mg/day) can reduce fasting blood glucose levels and
insulin resistance in obese subjects, [23]. In one
study, serum magnesium levels in diabetic subjects
were significantly lower than in healthy controls
(p<0.001), [46]. Animal studies have shown that
giving magnesium supplements (50 mg/mL in
drinking water) for six weeks lowers blood glucose
levels, improves mitochondrial function and reduces
oxidative stress in diabetic rats, [47]. In one study, it
was found that there were significant negative
correlations between serum magnesium levels with
fasting insulin levels (r=-0.396, p<0.01) and the
HOMA-IR (r=-0.518, p<0.001), [48]. Hence,
correcting hypomagnesemia is expected to deliver
better management of type 2 diabetes.
A magnesium deficiency can trigger beta-cell
dysfunction in conditions of hyperglycemia and
cause disruption of the main insulin signaling
pathway and glucose uptake in muscle and adipose
tissue, which triggers insulin resistance. Group 5
had the best results in reducing insulin resistance
because it significantly reduced the Lee index,
fasting blood glucose levels, and HOMA-IR values.
The results of research using magnesium-rich
Indonesian brown rice 'Sintanur' in experimental
animals showed excellent benefits in reducing the
risk of insulin resistance. The brown rice
intervention in this study took eight weeks, the same
as the brown rice intervention time in humans in
studies in Japan, [24].
In other clinical medicine examples, a study
involving 417 people with prediabetes or type 2
diabetes shows that a brown rice diet can lower
body weight. It is discovered that brown rice may be
used as a substitute for white rice in such patients,
[49]. Research involving 60 people with impaired
glucose tolerance demonstrates that replacing white
rice with brown rice as a staple food may help
control body weight and blood glucose, [50]. A
study involving 58 Chinese Americans with
prediabetes also verifies decreased HOMA in the
brown rice group compared to the white rice group,
[51]. Whole-grain intake has a protective
association with type 2 diabetes risk by decreasing
energy intake, preventing weight gain and
increasing insulin sensitivity, [52].
4 Conclusion
Magnesium deficiency that happens in
hyperglycemia conditions can lead to β-cells
dysfunction. It also induces insulin resistance by
disrupting the main insulin signaling pathways and
impairing GLUT4 expression and function. The
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intervention of Indonesian local brown rice
'Sintanur', which contains high magnesium content,
can improve serum magnesium levels. The serum
magnesium levels correlate negatively with the Lee
index, fasting blood glucose levels, and HOMA-IR
values in HFFD-induced obesity Sprague Dawley
rats. The study’s results using Indonesian local
brown rice, 'Sintanur', shows a tremendous
advantage in reducing the risk of insulin resistance,
especially in group 5. Furthermore, it is necessary to
carry out further research and its implementation in
humans using local brown rice 'Sintanur' to test the
benefits of local rice against obesity and insulin
resistance it causes in humans. It is also necessary to
research other local brown rice varieties.
Acknowledgments:
We thank the Faculty of Medicine Universitas
Brawijaya for supporting the facilities and aids for
this study.
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