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The Association Between Body Composition, Blood Pressure, Fasting Blood Glucose, Lipid Profile and Iron Profile Among Overweight/ Obese University Students

Fiona Ann Christianus^*, Ramlah George^**, Yasmin Beng Houi Ooi
Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia

Received Date: September 14, 2022; Accepted Date: September 26, 2022; Published Date: October 01, 2022; 

^* Corresponding author:  Fiona Ann Christianus, Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia. Email: mn1911018t@student.ums.edu.my
^**Corresponding author: Ramlah George, Faculty of Food Science and Nutrition, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia. Email: ramlah@ums.edu.my

Citation: Christianus F A, George R, Ooi Y B H (2022) The Association Between Body Composition, Blood Pressure, Fasting Blood Glucose, Lipid Profile and Iron Profile Among Overweight/ Obese University Students. Adv in Nutri and Food Sci: ANAFS-240.

DOI: 10.37722/ANAFS.2022403

Abstract
      Evidence from epidemiological studies of association between body composition, blood pressure and metabolic parameters in overweight/obese individuals is of public health interest. This study aimed to determine the association between body composition, blood pressure, fasting blood glucose, lipid profile and iron profile among overweight/obese university students. A total of 28-university students consented to participate in the study. Participants were measured for body composition (body fat percentage and visceral fat percentage) height, weight, waist circumference and blood pressure. Participants’ venous blood samples were analysed for fasting blood glucose level, lipid profile and iron profile. Statistical analysis of data used IBM SPSS statistics. Results were presented as mean and standard deviation. Independent sample t-tests or Mann-Whitney tests were used to determine the P-value of variables. Pearson correlation or Spearman rho were used in determining the association between variables. Study findings showed significant difference in height (p=0.000), body fat percentage (p=0.000), systolic blood pressure (p=0.000), HDL-cholesterol (p=0.029), iron (p=0.000), UIBC (p=0.001), saturation (p=0.000), ferritin (p=0.000) between males and females. Body mass index positively correlated with diastolic blood pressure (p=0.001). Body fat percentage negatively correlated with HDL-cholesterol (p=0.002). Visceral fat percentage positively correlated with diastolic blood pressure (p=0.002). Systolic blood pressure positively correlated with ferritin (p=0.001). Diastolic blood pressure positively correlated with triglycerides (p=0.001), iron (p=0.020), saturation (p=0.023), and ferritin (p=0.015), but negatively correlated with HDL-cholesterol (p=0.002), UIBC (p=0.027) and transferrin (p=0.015). In conclusion, this study found an association between body composition, blood pressure, fasting blood glucose, lipid profile and iron profile among university students.

Keywords: Body Mass Index; Blood Pressure; Fasting Blood Glucose; Iron Profile; Lipid Profile

Introduction
      Malaysian healthcare is challenged by the increasing trend of overweightness/obesity, abdominal obesity, diabetes, hypertension, heart disease, raised cholesterol levels and anaemia among Malaysians (NIH, 2020). An estimated 1 in 2 adults is overweight or obese (NIH, 2020). According to the National Health and Morbidity Survey (NHMS) 2019, the prevalence risk for abdominal obesity, hypertension, diabetes, high cholesterol, and anaemia were 50%, 30%, 18.3%, 38% and 21.3% (NIH, 2020). Among the 18.3 % of diabetes prevalence, 9.4% have been diagnosed, and 8.9% did not know they had diabetes (NIH, 2020). In the National Diabetes Registry 2020, the registrations were 57% women and 42% men (MOH, 2021). The ethnicity consists of Malay (59%), Chinese (19%), Indian (12%), and other (8%) (MOH, 2021).

      The body mass index cut-off in Malaysia public health care uses the Asian cut-off where overweight is ≥ 23 kg/ m² and obese is ≥ 27.5 kg/ m² (CPG, 2003). Being overweight or obese increase the risk of non-communicable diseases such as Type 2 diabetes mellitus, coronary heart disease and hypertension (CPG, 2003). In the study by Mohd-Sidik et al. (2021), 18.6% obesity prevalence among adults in Selangor, 14.2% of men and 21.1% of women. Tan et al. (2019) reported the prevalence of overweight and obesity in undergraduate males is 11.0% to 18.9% and 3.0% to 4.9%. There is a 6.11 % to 8.5 % overweight prevalence and a 0.56% to 3.8% prevalence of obesity among undergraduate females (Tan et al., 2019).

      Abdominal obesity can be defined as having a waist circumference of more than 90 cm for males and 80 cm for females (NIH, 2020). Tan et al. (2019) reported abdominal obesity prevalence in Malaysia ranges from 11.4% to 18.1%. According to MOH (2015), there was a 2% increase in abdominal obesity prevalence. Lukács et al. (2019) reported that abdominal obesity is a strong indicator of metabolic risk factors for metabolic syndrome and cardiovascular disease, particularly among non-obese individuals.

      Hypertension is a condition with a high blood pressure value  (WHO, 2021). The value to diagnose hypertension is when the systolic blood pressure, the pressure in the blood vessel when the heart contract or beats, is ≥ 140 mmHg, and when diastolic pressure, the pressure in the blood vessel when the heart rest between beats, is ≥ 90 mmHg  (WHO, 2021). Khoo et al. (2022) observed that from 2006 to 2015, there was a 17% -18.4% prevalence of young-onset hypertension. Obese individuals and diabetic individuals were at a higher risk of developing hypertension (Azahadi Omar et al., 2016).

      Diabetes mellitus type two is diagnosed by fasting plasma glucose and random plasma glucose ≥ 7.0 mmol/L (MOH, 2020). The normal HbA1C value is below 5.7% (<39 mmol/mol), the pre-diabetes and diabetes mellitus type two diagnostic HbA1C values 5.7 to 6.3 % (39 to 44 mmol/mol) for pre-diabetes mellitus and 6.3 % (≥ 45 mmol/mol) for diabetes mellitus (MOH, 2020). The gradual fluctuation of fasting glucose correlated with the risk of myocardial infarction, stroke, and all-cause mortality (G. Lee et al., 2018).

      Dyslipidaemia can be categorized by the value of total cholesterol > 5.2 mmol/L, low-density lipoprotein cholesterol > 3.4 mmol/L, high-density lipoprotein cholesterol of < 1.0mmol/L (males) and <1.2 mmol/L (females), triglycerides of > 1.7 mmol/L and high-density lipoprotein cholesterol level will depend on the cardiovascular risk (MOH, 2017). Hyperlipidaemia is a type of dyslipidaemia categorized in abnormally high lipid levels in the blood (Wang et al., 2022). High levels of triglycerides, total cholesterol, LDL- cholesterol, or low levels of HDL-cholesterol are all predictors of hyperlipidaemia (Wang et al., 2022).

      The condition in which there are not enough healthy red blood cells to carry oxygen in the body is called anaemia (NIH, 2020). A prolonged anaemic condition without treatment can lead to a heart problem (NIH, 2020). There is a 12.6 % anaemia prevalence among younger men and increases in older men and diabetics (Awaluddin et al., 2021). The anaemic prevalence in men is less than in females (Abdullah et al., 2020). Abdullah et al. (2020) reported 13.8% anaemia prevalence among Malaysians, Indians are more susceptible, followed by Malays and Chinese (Awaluddin et al., 2021).

      Early screening using indicators such as body mass index and measurement of body composition and metabolic profiles is crucial to detect early signs of non-communicable disease among young adults, particularly the overweight and obese who are more susceptible. Appropriate intervention and treatment need to be applied in order to treat and prevent any progression of non-communicable disease. This study aimed to determine body composition, blood pressure, fasting blood glucose, lipid profile and iron profile among 28 overweight/obese university students. The secondary objective of this study is to determine the relationship between body composition, blood pressure, fasting blood glucose, lipid profile and iron profile.

Method
Samples

      A total of 28 University students participated in this study. The recruitment process used purposive sampling. Participants were provided with information and details on this research. Participants were asked to provide their informed consent to participate voluntarily in the study. Participants were university students, within the age of 18-25 years, and body mass index of more than 23.0 kgm^-2. Body mass index cut-off value of 23.0 kgm^-2 to 27.4 kgm^-2 was categorised as overweight, 27.5 kgm^-2 to 32.4 kgm^-2 was categorised as pre-obese, 32.5 kgm^-2 to 37.4 kgm^-2 was categorised as obese class I, and more than 37.5 kgm^-2 was categorised as obese class II (NCCN, 2010).

Location

      This research was conducted at University Malaysia Sabah Kota Kinabalu campus.

Procedure

      Study participants were briefed about the research. All participants provided their sociodemographic information (gender, ethnicity, and year of study). Study participants were measured for body composition (body fat percentage and visceral fat percentage) height, weight, waist circumference and blood pressure. Participants’ venous blood samples were drawn for analysis of fasting blood glucose level, lipid profile and iron profile.

Measurement

Height, Weight, Body Mass Index and Waist Circumference

      Participants were informed on the proper attire for height, weight and waist circumference measurement sessions. Height was measured using a stadiometer, Seca 213 (Seca, Germany). Study participants were barefoot for height measurement. The participant was required to stand straight, looking forward, heels together, arms at the side, and shoulder relax. The participant's head was perpendicular to the horizontal Frankfort bar. The participant's heels, back, shoulder, and head were parallel to the vertical bar of the stadiometer. The participants had to take a deep breath during the height measurement (Lee & Nieman, 2016).

      The participant's weight was measured using a digital weighing scale Omron HN-286 (Omron, China). The participant stood straight at the centre of the digital weighing scale without holding anything. The participant was informed to put equal pressure on both standing legs during weight measurement (Lee & Nieman, 2016). Body mass index was calculated using the following formula: BMI= (weight in kg)/(height in meter)² (Lee & Nieman, 2016).

      Waist circumference was determined using a measuring tape, Seca 201 (Seca, Germany). Before the measurement, the participant had removed any outer clothing that restricted the access of the measuring tape to the abdomen, waist, and bare skin. The participant was required to stand upward and relax their abdomen during the measurement. The participant's top right iliac crest, the highest point of the hip bone on the right side, was located. Then the measuring tape was placed at horizontal plane around the abdomen at the level of the iliac crest, not too tight or too loose. The reading of the measurement was recorded at the end of the normal expiration (Lee & Nieman, 2016).

Body Fat Percentage and Visceral Fat Percentage

      The participant's body fat percentage was determined by bioelectrical impedance method using the Omron Body Fat Monitor HBF-302 (Omron Marsusaka Co. Ltd, Japan). Before the measurement, the participant's information (age, gender, and height) was keyed into the Omron Body Fat Monitor HBF-302. The participant had to stand at the centre of the device barefooted. The participant had to put both palms facing downward while grabbing the electrode rod. Then, the participant placed their arms at a 90⁰ angle perpendicular to the floor (Lee & Nieman, 2016).

Systolic Blood Pressure and Diastolic Blood Pressure

      The participant's blood pressure was measured using an Automatic Blood Pressure Monitor Omron SEM-1 (Omron Marsusaka Co. Ltd, Japan). Before the blood pressure measurement, the participant was informed not to smoke, eat, take caffeine or exercise for at least 30 minutes (MSH, 2011). The participant was asked to stay rested for 10 minutes (Tice et al., 2019). During the blood pressure measurement, subjects were seated with their arm rested at the level of their heart and their feet flat on the ground with legs uncrossed (MSH, 2011). The participant was relaxed during blood pressure measurement (MSH, 2011). The correct bladder cuff size was used and placed at heart level (MSH, 2011). The blood pressure measurement was repeated two times with a gap of 1 to 2 minutes apart at the same position and using the same device (MSH, 2011).

Venous Blood Sampling for Fasting Blood Glucose, Lipid Profile, and Iron Profile

      The participant was instructed to fast 8 - 12 hours before the blood sampling (Tomkins-Lane et al., 2015; Batch et al., 2014; Hageman et al., 2014). Venous blood collection was conducted by qualified medical personnel at Pusat Rawatan Warga, UMS. 10ml of venous blood was drawn. Blood samples were sent to university-accredited hospital lab for analysis (Fasting blood sample, lipid profile, and iron profile). After receiving the blood sampling result, the participants were consulted by qualified medical personnel.

Ethical Consideration

      Ethics approval to conduct the research was granted from UMS Human Ethics Committee (Approval Code: JKEtika 2/21 (15)). All study participants were briefed on and provided with subject information sheet regarding the study. All study participants volunteered to take part in the study and provided their written informed consent before participating in this study.

Data Analysis

      Statistical analysis of data used IBM SPSS statistics. The normality test and the descriptive test were performed. Results were reported in mean and standard deviation. Pearson correlation test or Spearman correlation test was performed to determine the association between variables. Variables found to correlate were further analyzed by comparing means between two groups (low and high values) using an independent t-test test or the Man-Whitney U test depending on normality of the data distribution.

Results
Socio-demographic information

      Table 1 shows sociodemographic information of the study participants. In this study, 18 females and 10 males consented to partake in the study. The study participants were Bumiputera Sabah (20), Malay (4), Chinese (3) and Bumiputera Sarawak (1). The participants range from a different year of study. Most participants were overweight (19), pre-obese (7) and obese (2) following the body mass index cut-off point for Malaysia's public health action (NCCN, 2010).

      There was no significant difference in age (p= 0.077), waist circumference (p=0.090), and body mass index (p=0.737) between males and females (Table 2). There was a significant difference in height (p=0.000) between females (1.56 ± 0.06) and males (1.67 ± 0.04). The body fat percentage (p=0.000) of females (33.72 ± 4.05%) was significantly higher than that of males (21.59 ± 3.04 %). There were no significant differences in visceral fat percentage between females (7.05 ± 3.40%) and males (9.03 ± 1.71%). Males (123.2 ± 9.39 mmHg) had significantly higher systolic blood pressure (p=0.000) than females (111 ±6.74 mmHg). However, there was no significant difference in diastolic blood pressure (p=0.457) between females (71.33 ± 9.66 mmHg) and males (75.5 ± 12.43 mmHg). There was no significant difference in fasting blood glucose (p=0.592) between females (4.71± 0.35 mmol/L) and males (4.64 ± 0.23 mmol/L). For lipid profiles, total cholesterol (p=0.215), triglycerides (p=0.429) and LDL-cholesterol (p=0.631) did not differ between males and females. However, HDL-cholesterol (p=0.029) of females (1.60 ± 0.35 mmol/L) was significantly higher than that of males (1.31 ± 0.21 mmol/L). There was significant difference in iron (p=0.000), UIBC (p=0.001), saturation (p=0.000) and ferritin (p=0.000) between males and females. Males had higher iron levels (22.64 ± 7.27 umol/L), saturation (49.50 ± 13.00%), and ferritin (143.72 ± 61.45 ug/L) than females. Females (38.13 ± 11.98 umol/L) had higher UIBC mean than males (23.01 ± 6.33 umol/L). The mean TIBC and transferrin value between females and males showed no statistically significant difference.

Correlation between body composition, fasting blood glucose, lipid profile and iron profile

      Weight positively correlated with systolic blood pressure (r=.381, p=0.045), diastolic blood pressure (r=.522, p=0.004), iron (r=.476, p=0.010), saturation (r=.409, p=0.031) and ferritin (r=.398, p=0.036), but negatively correlated with HDL-cholesterol (r=-.492, p=0.008). Height positively correlated with systolic blood pressure (r=.567, p=0.002), iron (r=.635, p=0.000), saturation (r=.585, p=0.001), and ferritin (r=.477, p=0.010), but negatively correlated with HDL-cholesterol (r=-.381, p=0.046) and UIBC (r=-.445, p=0.018). Waist circumference positively correlated with systolic blood pressure (r=.567, p=0.002).

      Body mass index positively correlated with diastolic blood pressure (r=.574, p=0.001), total cholesterol (r=.381, p=0.046), triglycerides (r=.483, p=0.009), and UIBC (r=.460, p=0.014), but negatively correlated with systolic blood pressure (r=-.412, p=0.029), iron (r=-.440, p=0.019), and saturation (r=-.461, p=0.014). There was a significant difference in diastolic blood pressure (p=0.015) between females with low (18.5 - 27.45 kgm^-2) and high (27.45 - 34.9 kgm^-2) body mass index.

      Body fat percentage positively correlated with iron (r=.396, p=0.037), but negatively correlated with HDL-cholesterol (r=-.562, p=0.002) and ferritin (r=-.549, p=0.002). HDL-cholesterol (p=0.031) was significantly different between males with low (17.50 - 22.5%) and high (22.6 - 27.6 %) body fat percentage.

      Visceral fat percentage positively correlated with diastolic blood pressure (r=.555, p=0.002) and ferritin (r=.426, p=0.024). Diastolic blood pressure (p=0.016) was significantly different between females with low (4 – 8%) and high (9 -14%) visceral fat percentages.

      Systolic blood pressure positively correlated with iron (r=.581, p=0.001), saturation (r=.603, p=0.001), and ferritin (r=.606, p=0.001), but negatively correlated with HDL-cholesterol (r=-.419, p=0.027) and UIBC (r=-.515, p=0.005). Ferritin (p=0.016) is significantly different in males with low (108 - 123 mmHg) and high (124 - 139 mmHg) systolic blood pressure.

      Diastolic blood pressure positively correlated with triglycerides (r=.579, p=0.001), iron (r=.438, p=0.020), saturation (r=.427, p=0.023), ferritin (r=.453, p=0.0215), but negatively correlated with HDL-cholesterol (r=-.568, p=0.028), UIBC (r=-.419, p=0.027), and transferrin (r=-.456, p=0.015). Triglycerides (p=0.015), HDL-cholesterol (p=0.028), iron (p=0.018), UIBC (p=0.001), saturation (p=0.001), ferritin (p=0.034), and transferrin (p=0.003) were significantly different in females with low (59 - 76 mmHg) and high (77 - 95 mmHg) diastolic blood pressure.

      Triglycerides positively correlated with iron (r=.509, p=0.006) and saturation (r=.452, p=0.016), but negatively correlated with UIBC (r=-.429, p=0.023). HDL-cholesterol positively correlated with UIBC (r=.430, p=0.023) and transferrin (r=.377, p=0.048), but negatively correlated with iron (r=-.463, p=0.013), saturation (r=-.508, p=0.006) and ferritin (r=-.505, p=0.006).

Discussion
      This study aimed to determine the association between body composition, fasting blood glucose, lipid profile and iron profile among 28 overweight/obese university students. Findings of this study showed no significant differences in age, weight, waist circumference, body mass index, and visceral fat percentages between genders. This study found that females had significantly higher body fat percentage than males. Sukkriang et al. (2021) reported that females had larger waist circumference, higher body fat percentage, and lower visceral fat than males. According to Mohd-Sidik et al. (2021), women with higher or obese body mass index tend to have low physical activity, albeit having a healthier dietary intake.

      This study found that waist circumference positively correlated with systolic blood pressure. This is consistent with findings by Song (2014) in which waist circumference positively correlated with systolic blood pressure among normal-weight and overweight individuals. Lukács et al. (2019) reported that a larger waist circumference is associated with the risk of high systolic blood pressure, increased fasting blood glucose, high cholesterol level and high triglycerides. A larger waist circumference is linked to a higher body mass index (Lukács et al., 2019).

      This study also found that body mass index is positively associated with diastolic blood pressure, total cholesterol, triglycerides and UIBC, but negatively associated with systolic blood pressure, iron, and saturation. Moreover, there was a significant difference in the mean diastolic blood pressure among females with low and high body mass index. These results are consistent with the suggestion by Sukkriang et al. (2021) that obese individuals with higher body mass indexes have high serum triglycerides. Past studies showed that higher body mass index correlated with higher diastolic blood pressure (Landi et al., 2018 ; Chen et al., 2018; Dua et al., 2014). Landi et al. (2018) explained that the positive correlation between body mass index and blood pressure causes hypertension. However, Khalid et al. (2020) found negative association between body mass index and male’s diastolic blood pressure. Choma et al. (2015) discovered body mass index links inversely with serum iron, haemoglobin, ferritin, and transferrin saturation but positively correlated with TIBC. Aderibigbe et al. (2011) addressed that obese individuals had lower serum iron than normal-weight and pre-obese women.

      This study showed that body fat percentage was only positively associated with iron and negatively correlated with HDL-cholesterol and ferritin. Sukkriang et al. (2021) reported body fat percentage positively correlated with total cholesterol. Aderibigbe et al. (2011) found that high body fat percentages were associated with high ferritin levels. Body mass index and fat percentage may be associated with the induction of non-alcoholic fatty liver disease (NAFLD) (Martins, 2016) that determine diastolic blood pressure, total cholesterol, triglycerides, UIBC, blood pressure, iron, and saturation. The organ for lipid and iron haemostasis is the liver (Martins, 2017), and any changes in liver homeostasis may affect diastolic blood pressure, total cholesterol, triglycerides, UIBC, blood pressure, iron, and saturation.

      Visceral fat percentage positively correlated with diastolic blood pressure and ferritin. There is significant difference in diastolic blood pressure between females with low and high visceral fat percentages. Guo et al. (2019) observed that men's visceral fat reduction was associated with systolic and diastolic blood pressure improvement. Goswami et al. (2020) reported a positive association between visceral fat and hypertension among the indigenous tribal population of Tripura. Sukkriang et al. (2021) reported a negative association between visceral fat rating and HDL-cholesterol.

      In this current study, males had significantly higher systolic blood pressure than females, but there was no difference in diastolic blood pressure between gender. According to Dua et al. (2014), the difference in blood pressure between gender begins during adolescence. Azahadi Omar et al. (2016) reported blood pressure differences in gender were more significant during young adulthood. Males were more susceptible to higher systolic blood pressure and diastolic blood pressure due to smoking and drinking habits (Dua et al., 2014). Females who benefit from the protective effect of oestrogen tend to have lower blood pressure (Dua et al., 2014). The prevalence of hypertension is also lower in females (Azahadi Omar et al., 2016). The risk of hypertension in Bumiputera was also higher than in another ethnicity in Malaysia (Azahadi Omar et al., 2016).

      The findings in this study showed that systolic blood pressure positively correlated with iron, saturation, and ferritin but negatively correlated with HDL-cholesterol and UIBC. Ferritin level in males was significant when compared to low and high systolic blood pressure. Diastolic blood pressure positively correlated with triglycerides, iron, saturation, and ferritin but negatively correlated with HDL-cholesterol, UIBC and transferrin. This study showed significant difference in triglycerides, HDL-cholesterol, iron, UIBC, saturation, ferritin, and transferrin between females with low and high diastolic blood pressure. Results of this study are consistent with work by Zhu et al. (2019) that reported diastolic blood pressure positively correlated with serum ferritin. Zhu et al. (2019) reported serum transferrin is positively associated with systolic and diastolic blood pressure, which contradicts this study. The increase in transferrin and haemoglobin concentration increases the risk of hypertension (Zhu et al., 2019). Vento et al. (2022) reported that an increase in systolic pressure was linked to higher total HDL-cholesterol. Chen et al. (2018) also discovered systolic blood pressure positively correlated with total cholesterol. Chen et al. (2018) suggested systolic blood pressure positively correlated with body mass index, age, heart rate, total cholesterol, being male, having diabetes, having hypertension, and taking antihypertensive drugs.

      Dua et al. (2014) suggested systolic and diastolic blood pressure positively correlated with age, anthropometric measures, and fat percentage. Azahadi Omar et al. (2016) noted that the development of hypertension progress with age. Past literature showed that serum ferritin was a predictor of hypertension, particularly in middle age Korean men (Kim et al., 2012) and men who are shift workers (D. H. Lee et al., 2018). D. H. Lee et al. (2018) noticed that serum ferritin was positively associated with hypertension, while Kim et al. (2012) discovered that increased ferritin and TIBC were predictors of hypertension. Wang et al. (2019)  observed increased ferritin levels and abnormal systolic and diastolic blood pressure in first-time male blood donors, albeit normal ferritin levels for regular blood donors. He et al. (2021) noticed that higher triglycerides and lower HDL-cholesterol increased the risk of new-onset hypertension in the Chinese community. Mohamed-Yassin et al. (2021) found elevated triglyceride among participants with hypertension.

      Study participants’ fasting blood glucose between gender was in the normal range and showed no difference. In previous studies, either moderate or high physical activity (Teh et al., 2015) or frequent physical activity for four or more days (Sedodo et al., 2020) lowered fasting blood glucose. According to Sukkriang et al. (2021), females have a lower fasting blood sample and fasting blood glucose is positively correlated with visceral fat rating. This study shows that fasting blood sugar has no association with other variables, albeit visceral fat in this study was lower. Mehdad et al. (2012) found that among overweight/ obese adolescent females, elevated fasting blood glucose correlated to higher fat mass, higher body fat percentage, greater body mass index, and larger waist circumference. There was no correlation between fasting blood glucose and fat mass, body fat percentage, body mass index, or larger waist circumference found in adolescent males (Mehdad et al., 2012). Research findings by Lee et al. (2011) showed that hypertensive people with high fasting glucose levels and high body mass index had a 40-fold increased risk of developing diabetes mellitus.

      This current study reported that total cholesterol, triglycerides, and LDL-cholesterol did not differ between gender. Females showed significantly higher blood HDL-cholesterol levels than males. Total cholesterol, triglycerides, and LDL-cholesterol were also in normal ranges. However, both genders have HDL-cholesterol levels higher than the normal range. Previous studies show mixed results in HDL-cholesterol between gender. Mohamed-Yassin et al. (2021) found that females tended to have lower levels of HDL-cholesterol, lower triglycerides, and lower non-HDL-cholesterol than males. Kim et al. (2011) noticed gender differences in HDL-cholesterol among participants below 60 years, with females having a higher HDL-cholesterol. Kim et al. (2011) explained that the gender difference was due to oestrogens hormone, which functions in reducing the metabolic activity of macrophage by lipid accumulation, while testrogen promotes it. The high values of HDL-cholesterol may be due to higher dietary fat intake (Kim et al., 2011).

      Previous literature reported that males tend to have higher HDL-cholesterol and LDL-cholesterol levels than females (Ariaratnam et al., 2017 ; Sukkriang et al., 2021). Female athletes have lower total cholesterol (Vento et al., 2022), and regular take-out habits tend to influence the higher level of LDL-cholesterol and total cholesterol (Jafri, 2015). Ariaratnam et al. (2017) reported that in older age, males and non-Malays have higher triglycerides values.

      Triglycerides were positively correlated with iron and saturation but negatively correlated with UIBC. HDL-cholesterol was positively correlated with UIBC but negatively correlated with iron, saturation, and ferritin. This study contradicts He et al. (2020) which suggested that triglyceride is negatively associated with serum iron, and high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and total cholesterol are positively correlated with serum iron. Triglycerides over 150mg/dL were significantly associated with lower HDL-cholesterol (Kim et al. (2011). He et al. (2020) also observed that total cholesterol positively correlated with serum iron. Sawada et al. (2020) noted that groups with Iron deficiency without anaemia (IDNA) have higher serum HDL-cholesterol. No association was found between total cholesterol, triglycerides, and LDL-cholesterol in the iron deficiency without anaemia group (Sawada et al., 2020). The increased concentration of serum LDL-cholesterol is linked to a higher serum ferritin level (Sawada et al., 2020). There was no correlation between HDL-cholesterol with serum ferritin levels (Sawada et al., 2020).

      In this study, males had significantly higher iron, saturation, and ferritin but lower UIBC than females. TIBC and transferrin showed no difference between gender. According to Ajugwo et al. (2014), male participants had higher iron and a lower UIBC than female participants but Ajugwo et al. (2014) reported significant difference between the TIBC mean value of males and females. For iron status, serum ferritin level was a sign of depleted iron stores or severe risk of iron overload. The risk of iron overload is suspected when serum ferritin exceeds 200 ug/L for males and 150 ug/L for females  (WHO, 2011). In this study, the participants have a healthy range of serum ferritin and no sign of iron store depletion (<15 ug/L).

Conclusion
      In conclusion, height, body fat percentage, systolic blood pressure, HDL-cholesterol, iron, UIBC, saturation and ferritin between gender were different. Body mass index was associated with diastolic blood pressure, total cholesterol, triglycerides, UIBC, blood pressure, iron, and saturation. Body fat percentage was associated with iron, HDL-cholesterol, and ferritin. Visceral fat percentages were associated with diastolic blood pressure and ferritin. Systolic blood pressure was associated with iron, saturation, ferritin, HDL-cholesterol, and UIBC. Diastolic blood pressure was associated with triglycerides, iron, saturation, ferritin, HDL-cholesterol, UIBC, and transferrin. Triglycerides was associated with iron and saturation, and UIBC. HDL-cholesterol was associated with UIBC, transferrin, iron, saturation, and ferritin. The study sample size was small and only among university students. Therefore, these study findings cannot be generalized to the whole population. The work described in this article did not include the dietary intake data of the study participants. This is recommended in future publication.

Acknowledgments: The authors would like to express their appreciation to all study participants and Universiti Malaysia Sabah for the research grant with project code SDN0079-2019.

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