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Esmaeili Shahmirzadi F, Ghavamzadeh S, Rashidi A. The Effect of Conjugated Linoleic Acid Supplementation on Lipid-Related Cardiovascular Biomarkers in Obese Adults. JNFS 2024; 9 (3) :385-395
URL: http://jnfs.ssu.ac.ir/article-1-742-en.html
URL: http://jnfs.ssu.ac.ir/article-1-742-en.html
Food and Beverage Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran
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1 Food and Beverage Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran; 2 Department of Nutrition, Medicine Faculty, Urmia University of Medical Sciences, Urmia, Iran; 3 Maternal and Child Obesity Research Center, Urmia University of Medical Sciences, Urmia, Iran; 4 Nutrition Research Department, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Introduction
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The Effect of Conjugated Linoleic Acid Supplementation on Lipid-Related Cardiovascular Biomarkers in Obese Adults
Fatemeh Esmaeili Shahmirzadi; MSc1, 2, Saeid Ghavamzadeh; PhD*1- 3 & Arash Rashidi; PhD4
Fatemeh Esmaeili Shahmirzadi; MSc1, 2, Saeid Ghavamzadeh; PhD*1- 3 & Arash Rashidi; PhD4
1 Food and Beverage Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran; 2 Department of Nutrition, Medicine Faculty, Urmia University of Medical Sciences, Urmia, Iran; 3 Maternal and Child Obesity Research Center, Urmia University of Medical Sciences, Urmia, Iran; 4 Nutrition Research Department, National Nutrition and Food Technology Research Institute, Faculty of Nutrition Sciences and Food Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| ARTICLE INFO | ABSTRACT | |
| ORIGINAL ARTICLE | Background: Studies have shown incompatible findings regarding the effects of conjugated linoleic acid (CLA) supplementation on cardiovascular diseases (CVDs) risk factors. The aim of this study was to evaluate the effect of daily CLA supplementation on serum insulin and lipid- related CV biomarkers in obese adults. Methods: This randomized double-blind clinical trial was conducted on 54 adults categorized as class I obesity. The participants were randomly assigned into two groups (n=27) receiving a total of 3,000 mg/d of a 50:50 mixture of CLA isomers for three months in intervention group (IG) and 500 mg/d paraffin in placebo group (PG). Moreover, fasting serum levels of insulin, lipid profile, non-HDL-Cholesterol (non-HDL-C), atherogenic index of plasma (AIP), total triglyceride (TG)/HDL-C, and cholesterol/HDL-C ratio were measured. The main statistical analysis method was independent t-test for changes. Results: Changes between the groups showed a significant decrease in total cholesterol (P=0.03), LDL-C (P=0.04), and non-HDL-C (P=0.03), and also a significant increase in AIP (P=0.04) in IG compared to the PG. A remarkable decrease was found in HDL-C and cholesterol/HDL-C ratio. In addition, a remarkable increase was observed in TG in this context. Serum insulin, VLDL-C, and LDL-C/HDL-C ratio showed no significant changes during the intervention period. The use of CLA supplementation could help reduce some adverse fractions of serum lipid profile, particularly TC, non-HDL-C and LDL-C. Conclusions: Regarding the augmenting effects of CLA intake on AIP as a strong predictive marker for CVDs, it is difficult to confirm the beneficial effects of CLA supplementation in preventing CVDs. Keywords: Linoleic acid; Conjugated linoleic acid: Cardiovascular risk; Obesity. |
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| Article history: Received: 18 Oct 2022 Revised: 30 Jul 2023 Accepted: 23 Sep 2023 |
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| *Corresponding author: ghavamzadeh@hotmail.com Medicine Faculty, Urmia University of Medical Sciences, Nazloo Highway, Pardis-e-Daneshgah, Urmia, Iran. Postal code: 5714783734 Tel: +98 914 341 3616 |
Introduction
Obesity refers to excessive accumulation of fat in body, which is known not only as a fitness problem, but also a multi-causative disease (Finucane et al., 2011). The danger of growing prevalence of obesity as an epidemic has long been highlighted in Iran, and its prevalence has been rising in recent decades in this country (Bakhshi et al., 2016, Musaiger, 2011).
Evidences has suggested that obesity and excess mortality are linked to each other through increased risk of essential hypertension, type 2 diabetes mellitus (T2DM), and cardiovascular diseases (CVDs) among obese individuals (Chaudhary et al., 2019, Dal Canto et al., 2020, Wang et al., 2021). Insulin could stimulate glucose disposal in non-diabetic healthy population by various degrees. As a result, a large number of these individuals may be known as insulin resistant subjects (Park et al., 2021, Reaven et al., 1993).
Insulin resistance and compensatory hyperinsulinemia were strongly correlated with CVDs, independent from the other risk factors (Ormazabal et al., 2018).
The disturbance of the lipolysis in adipose tissue can lead to increased plasma free fatty acid levels. It seems that this mechanism is responsible for insulin resistance in obesity and T2DM. Hormone sensitive lipase in adipose probably results in the production of most of the free fatty acids. However, there is a separate process, known as spillover, in which endothelial lipase, especially lipoprotein lipase, acts on triglyceride-rich lipoproteins (Eckel et al., 2005, Miles and Nelson, 2007). Both lipolysis inhibitory effect and the stimulation of lipoprotein lipase depend on insulin. It seems that the substantial substrate for spillover would be chylomicrons derived from dietary fat.
In insulin resistance status, the adipose tissue produces more fatty acids due to increased amount of lipolysis of stored triacylglycerol molecules. These fatty acids play an inhibitory role in antilipolytic effect of insulin, developing additional lipolysis (Eckel et al., 2005, Ormazabal et al., 2018). It seems that chronic hyperinsulinemia could stimulate very low-density lipoprotein (VLDL) production, too (Julius, 2003).
Evidences has suggested that obesity and excess mortality are linked to each other through increased risk of essential hypertension, type 2 diabetes mellitus (T2DM), and cardiovascular diseases (CVDs) among obese individuals (Chaudhary et al., 2019, Dal Canto et al., 2020, Wang et al., 2021). Insulin could stimulate glucose disposal in non-diabetic healthy population by various degrees. As a result, a large number of these individuals may be known as insulin resistant subjects (Park et al., 2021, Reaven et al., 1993).
Insulin resistance and compensatory hyperinsulinemia were strongly correlated with CVDs, independent from the other risk factors (Ormazabal et al., 2018).
The disturbance of the lipolysis in adipose tissue can lead to increased plasma free fatty acid levels. It seems that this mechanism is responsible for insulin resistance in obesity and T2DM. Hormone sensitive lipase in adipose probably results in the production of most of the free fatty acids. However, there is a separate process, known as spillover, in which endothelial lipase, especially lipoprotein lipase, acts on triglyceride-rich lipoproteins (Eckel et al., 2005, Miles and Nelson, 2007). Both lipolysis inhibitory effect and the stimulation of lipoprotein lipase depend on insulin. It seems that the substantial substrate for spillover would be chylomicrons derived from dietary fat.
In insulin resistance status, the adipose tissue produces more fatty acids due to increased amount of lipolysis of stored triacylglycerol molecules. These fatty acids play an inhibitory role in antilipolytic effect of insulin, developing additional lipolysis (Eckel et al., 2005, Ormazabal et al., 2018). It seems that chronic hyperinsulinemia could stimulate very low-density lipoprotein (VLDL) production, too (Julius, 2003).
Biological active isomers of conjugated linoleic acid (CLAs) are one of the supplements used in weight loss. Since the discovery of CLA, many studies focused on the traits of CLA in animal models and cell culture environments (humans and animals); therefore, in one study, it was concluded that CLA supplementation could reduce body fat and serum leptin (Esmaeili Shahmirzadi et al., 2019). It has also been shown CLA could have beneficial effects on health including antiadipogenic (den Hartigh, 2019, Pariza et al., 2001) anti-carcinogenic (den Hartigh, 2019, McGowan et al., 2013), anti-inflammatory properties (Bassaganya-Riera et al., 2003, Fleck et al., 2021, Lordan et al., 2018), anti-diabetic (Moloney et al., 2007), and also antiatherogenic properties (Koba et al., 2002, Kritchevsky et al., 2004). According to the current documents, CLA’s effect on CVDs biomarkers remains indecisive. It has been found that CLA could result in hypocholesterolemic, hypotriglyceridemic, and anti-atherosclerotic properties in animals (McLeod et al., 2004). CLA supplementation for 8 weeks reduced plasma total triglycerides (TG) and very low-density lipoprotein cholesterol (VLDL-C) among normolipidemic men and women (Noone et al., 2002). However, other studies reported no effects on different doses of CLA supplements regarding blood lipids (Eftekhari et al., 2014, Joseph et al., 2011). Because of the importance of CVDs, as one of the most common problems in Iranian society, and due to the above-mentioned contradictory findings regarding the effect of CLA on CVDs risk indexes; the authors decided to design this study to evaluate the effect of CLA supplementation on serum lipid-related CVDs biomarkers in obese adults. The secondary objective was to study the effect of CLA supplementation on insulin sensitivity as a link between obesity and CVDs in the studied population.
Materials and Methods
Subjects and study design
In line with Nikan Hospital in Tehran, 120 obese people who went to Weight Loss Clinic for weight reduction were considered as candidates for this study, and afterwards, 64 people were assigned to this study in terms of the inclusion criteria. All the eligible participants were allocated into two groups using random allocation rule method as follows: the intervention group (IG, n=32) and the placebo group (PG, n=32).
According to the inclusion criteria, these individuals were included in the study if they were between 18 and 65, and were at grade 1 obesity (BMI of 30-34.9 kg/m2). The exclusion criteria were having any illnesses e.g., diabetes and CVDs, a history of cigarette smoking, taking any medications, taking CLA supplement, and having a weight loss diet in the past 3 months. As a result, none of the participants were addicted to cigarettes, alcohol, and narcotics. They suffered from no other diseases such as endocrine, liver, diabetes, renal, cardiac or respiratory illnesses. The studied subjects have also followed no therapeutic diet since the last three months. All these mentioned characteristics were considered as exclusion criteria in this study. In total, 120 participants were included with grade 1 obesity; however, 56 subjects were excluded in the eligibility phase due to not meeting inclusion criteria. 5subjects in each group were dropped out due to some reasons reflected in Figure 1. Therefore, the rate of compliance was 85% for both groups.
Random allocation was performed by selection of a total study size of 64. Afterwards, two groups of cards, named A and B, were placed in a hat and were randomly drawn without replacement. The sequence generation would randomly order 32 subjects in IG and 32 participants in the PG.
From a statistical viewpoint, using the formula suggested for clinical trials, having 15 participants in each group was adequate; however, Considering a type 1 error (α) of 0.05 and type 2 error (β) of 0.05 (power = 95%) according to Blankson et al.’s study (Blankson et al., 2000), the mean±SD of changes in body fat mass (BFM) in IG (3.4 g) and PG were 1.73±1.9 and 1.47±2.43 during 12 weeks of intervention, respectively. However, with regard to the participants who dropped out of the study and enhanced its validity, the authors decided to increase the sample size as far as possible (n=32). Finally, there were 5 dropouts for each group (Figure 1) and we completed the study with 27 people in each of the studied groups.
Data collection
In this randomized double-blind clinical trial, the participants were divided into two groups as follows: IG received a daily dose of 3000 mg of CLA supplements (1000 mg t.d.s containing 50:50 mixture of cis-9, trans-11 and trans-10, and cis-12 CLA isomers), and the PG received the same number of placebos daily (500 mg t.d.s paraffin oil) for 12 weeks. CLA capsules used in this study were prepared by Nutricentury®/Canada, and placebo capsules were from Zahravi Co. Pharmaceutical Company in Iran. Blinding was applied at two levels of the participants and data gatherer co-worker. Both CLA and placebo capsules were completely similar in size and color. The encoded boxes of both capsules were presented to the participants by an assistant who was aware of the boxes’ contents. They were instructed to take 3 capsules before eating each meal. Individuals who reported that they had taken less than 80% of the supplements based on self-reports or counting the number of capsules in the package delivered at the end of the study, were excluded from the final analysis of the data. Participants were urged not to change their physical activity, diet, or lifestyle during the study and quickly report any abnormal feelings. Furthermore, at the beginning and at the end of the study, general information questionnaires, dietary intake, and international physical activity questionnaire (IPAQ) were completed and collected. In order to measure physical activity, a Persian translated IPAQ was used. The validation and reliability of the questionnaire had been confirmed in a published document (Vasheghani-Farahani et al., 2011).
The questionnaire was completed by self-administering the samples themselves at the beginning and end of the study. They were asked to not change their activities until the end of the study.
.JPG)
Subjects and study design
In line with Nikan Hospital in Tehran, 120 obese people who went to Weight Loss Clinic for weight reduction were considered as candidates for this study, and afterwards, 64 people were assigned to this study in terms of the inclusion criteria. All the eligible participants were allocated into two groups using random allocation rule method as follows: the intervention group (IG, n=32) and the placebo group (PG, n=32).
According to the inclusion criteria, these individuals were included in the study if they were between 18 and 65, and were at grade 1 obesity (BMI of 30-34.9 kg/m2). The exclusion criteria were having any illnesses e.g., diabetes and CVDs, a history of cigarette smoking, taking any medications, taking CLA supplement, and having a weight loss diet in the past 3 months. As a result, none of the participants were addicted to cigarettes, alcohol, and narcotics. They suffered from no other diseases such as endocrine, liver, diabetes, renal, cardiac or respiratory illnesses. The studied subjects have also followed no therapeutic diet since the last three months. All these mentioned characteristics were considered as exclusion criteria in this study. In total, 120 participants were included with grade 1 obesity; however, 56 subjects were excluded in the eligibility phase due to not meeting inclusion criteria. 5subjects in each group were dropped out due to some reasons reflected in Figure 1. Therefore, the rate of compliance was 85% for both groups.
Random allocation was performed by selection of a total study size of 64. Afterwards, two groups of cards, named A and B, were placed in a hat and were randomly drawn without replacement. The sequence generation would randomly order 32 subjects in IG and 32 participants in the PG.
From a statistical viewpoint, using the formula suggested for clinical trials, having 15 participants in each group was adequate; however, Considering a type 1 error (α) of 0.05 and type 2 error (β) of 0.05 (power = 95%) according to Blankson et al.’s study (Blankson et al., 2000), the mean±SD of changes in body fat mass (BFM) in IG (3.4 g) and PG were 1.73±1.9 and 1.47±2.43 during 12 weeks of intervention, respectively. However, with regard to the participants who dropped out of the study and enhanced its validity, the authors decided to increase the sample size as far as possible (n=32). Finally, there were 5 dropouts for each group (Figure 1) and we completed the study with 27 people in each of the studied groups.
Data collection
In this randomized double-blind clinical trial, the participants were divided into two groups as follows: IG received a daily dose of 3000 mg of CLA supplements (1000 mg t.d.s containing 50:50 mixture of cis-9, trans-11 and trans-10, and cis-12 CLA isomers), and the PG received the same number of placebos daily (500 mg t.d.s paraffin oil) for 12 weeks. CLA capsules used in this study were prepared by Nutricentury®/Canada, and placebo capsules were from Zahravi Co. Pharmaceutical Company in Iran. Blinding was applied at two levels of the participants and data gatherer co-worker. Both CLA and placebo capsules were completely similar in size and color. The encoded boxes of both capsules were presented to the participants by an assistant who was aware of the boxes’ contents. They were instructed to take 3 capsules before eating each meal. Individuals who reported that they had taken less than 80% of the supplements based on self-reports or counting the number of capsules in the package delivered at the end of the study, were excluded from the final analysis of the data. Participants were urged not to change their physical activity, diet, or lifestyle during the study and quickly report any abnormal feelings. Furthermore, at the beginning and at the end of the study, general information questionnaires, dietary intake, and international physical activity questionnaire (IPAQ) were completed and collected. In order to measure physical activity, a Persian translated IPAQ was used. The validation and reliability of the questionnaire had been confirmed in a published document (Vasheghani-Farahani et al., 2011).
The questionnaire was completed by self-administering the samples themselves at the beginning and end of the study. They were asked to not change their activities until the end of the study.
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Dietary assessment: Dietary assessment was fulfilled by the Three-Day Written Diet Record form. For this purpose, the participants were trained in two public sessions by an experienced fieldworker. Accordingly, energy and macronutrients intakes were estimated in 3 consecutive days (one day off and two working days). A modified version of Nutritionist IV software for Iranian community was used to analyze nutritional data. The energy and macronutrients intake as well as vitamin D and calcium intake were reported based on kilocalories and grams per day, respectively.
Laboratory tests
Fasting venous blood samples (5 ml) were collected from the participants by a trained laboratory technician. Blood samples were moved to anticoagulant tubes of EDTA and non-anticoagulant tubes, and were then transferred to Nikan hospital’s clinical laboratory. EDTA-containing tubes were closed by parafilm and slowly mixed with anticoagulants to prevent clotting. To prepare serum, the tubes were centrifuged for 10 minutes at 1500 rpm. Serum samples were then stored in encoded microtubes for each patient and stored at -80° C to retain, if needed. Biochemical measures included the measurement of serum insulin, fasting plasma glucose, total cholesterol (TC), TG, LDL-C, and High-density lipoprotein cholesterol (HDL-C). In order to obtain greater reliability, biochemical tests, food intake, and anthropometric measurements were performed by a certain person and/or device.
The concentrations of TC, TG, HDL-C, and LDL-C were measured using an automatic biochemistry analyzer (BT 1500, Italy). Also, TC, TG, HDL-C, and LDL-C levels were measured using an enzymatic assay (Biosystem, Spain). VLDL-C was calculated based on Friedewald estimation: VLDL-C = TG/5 (Friedewald et al., 1972) .
AIP was derived as the logarithmically transformed ratio of molar concentrations of TG to HDL-C (Dobiásová and Frohlich, 2001, Wu et al., 2018). Non-HDL Cholesterol was obtained by subtracting HDL-C level from TC (Jiang et al., 2004). LDL-C/HDL-C ratio was calculated to estimate CVDs risk (Khazaál, 2014, Wu et al., 2018), and cholesterol/HDL-C ratio was calculated via dividing TC by HDL-C number (Lemieux et al., 2001). Seated blood pressure (BP) was measured by mercury sphygmomanometer (Riester Co., Germany) after a 10-min rest in waiting room.
Ethical considerations
All the participants were requested to complete the informed consent form approved by the Ethics Committee of Urmia University of Medical Sciences. All the information collected from the subjects remained confidential at all the stages of the investigation. In addition, as the research protocol states, the participant was urged to quit the research in case of any problem. All the stages of study were conducted by a trained expert. This research was approved by the Ethics Committee of Urmia University of Medical Sciences under the number umsu.rec.202. It was also registered at Iran Clinical Trials Center (www.irct.ir) under the code IRCT2014052413678N2.
Data analysis
Data were reported as mean±SD. The statistical significance level in all the analyses was considered less than or equal to 0.05. Chi-square test and/or Fisher’s exact test were used to examine statistical relationships between qualitative variables. Normality of data was examined by Kolmogorov-Smirnov test, and if data distribution was normal, paired t-test was used to compare the values ​​before and after the intervention in each of the IG and PG. If not, Wilcoxon Signed Rank test was used. Mann-Whitney U test was also used to compare the mean of data in IG, and for PG, given they were normal, independent t-test was used. Analysis between groups regarding differences in changes was also done using independent t-test. Data were analyzed using SPSS software version 20.0.
Results
General characteristics are shown in Table 1. The age of all the participants was between 29 and 64 with the mean±SD of 36.72±5.78 year for PG and 38.22±7.74 year for IG (P=0.23). No significant difference in literacy levels, occupational, or marital status was found among the groups at the beginning of the study (P>0.05).
Laboratory tests
Fasting venous blood samples (5 ml) were collected from the participants by a trained laboratory technician. Blood samples were moved to anticoagulant tubes of EDTA and non-anticoagulant tubes, and were then transferred to Nikan hospital’s clinical laboratory. EDTA-containing tubes were closed by parafilm and slowly mixed with anticoagulants to prevent clotting. To prepare serum, the tubes were centrifuged for 10 minutes at 1500 rpm. Serum samples were then stored in encoded microtubes for each patient and stored at -80° C to retain, if needed. Biochemical measures included the measurement of serum insulin, fasting plasma glucose, total cholesterol (TC), TG, LDL-C, and High-density lipoprotein cholesterol (HDL-C). In order to obtain greater reliability, biochemical tests, food intake, and anthropometric measurements were performed by a certain person and/or device.
The concentrations of TC, TG, HDL-C, and LDL-C were measured using an automatic biochemistry analyzer (BT 1500, Italy). Also, TC, TG, HDL-C, and LDL-C levels were measured using an enzymatic assay (Biosystem, Spain). VLDL-C was calculated based on Friedewald estimation: VLDL-C = TG/5 (Friedewald et al., 1972) .
AIP was derived as the logarithmically transformed ratio of molar concentrations of TG to HDL-C (Dobiásová and Frohlich, 2001, Wu et al., 2018). Non-HDL Cholesterol was obtained by subtracting HDL-C level from TC (Jiang et al., 2004). LDL-C/HDL-C ratio was calculated to estimate CVDs risk (Khazaál, 2014, Wu et al., 2018), and cholesterol/HDL-C ratio was calculated via dividing TC by HDL-C number (Lemieux et al., 2001). Seated blood pressure (BP) was measured by mercury sphygmomanometer (Riester Co., Germany) after a 10-min rest in waiting room.
Ethical considerations
All the participants were requested to complete the informed consent form approved by the Ethics Committee of Urmia University of Medical Sciences. All the information collected from the subjects remained confidential at all the stages of the investigation. In addition, as the research protocol states, the participant was urged to quit the research in case of any problem. All the stages of study were conducted by a trained expert. This research was approved by the Ethics Committee of Urmia University of Medical Sciences under the number umsu.rec.202. It was also registered at Iran Clinical Trials Center (www.irct.ir) under the code IRCT2014052413678N2.
Data analysis
Data were reported as mean±SD. The statistical significance level in all the analyses was considered less than or equal to 0.05. Chi-square test and/or Fisher’s exact test were used to examine statistical relationships between qualitative variables. Normality of data was examined by Kolmogorov-Smirnov test, and if data distribution was normal, paired t-test was used to compare the values ​​before and after the intervention in each of the IG and PG. If not, Wilcoxon Signed Rank test was used. Mann-Whitney U test was also used to compare the mean of data in IG, and for PG, given they were normal, independent t-test was used. Analysis between groups regarding differences in changes was also done using independent t-test. Data were analyzed using SPSS software version 20.0.
Results
General characteristics are shown in Table 1. The age of all the participants was between 29 and 64 with the mean±SD of 36.72±5.78 year for PG and 38.22±7.74 year for IG (P=0.23). No significant difference in literacy levels, occupational, or marital status was found among the groups at the beginning of the study (P>0.05).
| Table 1. General characteristics of the participants at baseline (n=54). | |||
| P-valueb | IG (n=27) | PG (n=27) | Variables |
| 0.78 | 12 (44.4) 15 (55.6) |
13 (48.2)a 14 (51.8) |
Gender Male Female |
| 0.23 |
1 (3.7) 10 (37.0) 7 (26.0) 9 (33.3) |
0 (0.0) 8 (29.7) 9 (33.3) 10 (37.0) |
Literacy levels Illiterate Below high school diploma High school diploma Academic literacy |
| 0.46 |
10 (37.0) 9 (33.3) 8 (29.7) |
9 (33.3) 6 (22.3) 12 (44.4) |
Occupational status Business Governmental employee Housekeeper |
| 0.88 | 23 (85.2) 0 (0.0) 4 (14.8) |
21 (77.7) 1 (3.7) 5 (18.6) |
Marital status Married Divorced Single |
| a: n (%); b: Chi square test. PG: Placebo group ; IG: Intervention group | |||
Table 2 shows the findings of the study on laboratory indicators of the participants. None of the laboratory indices pointed out any significant differences among the two studied groups, neither at beforee nor after intervention stage. However, group changes showed a significant decrease in TC, LDL-C, and Non-HDL-C and a significant increase in AIP in IG in comparison with the PG. Furthermore, a remarkable decrease was found (but not significant) in HDL-C and cholesterol//HDL-C ratio and a remarkable increase was also observed in TG in this regard. The researchers also compared the data of systolic BP and diastolic BP between the groups. According to Table 2, of systolic BP and diastolic BP were not altered during the intervention.
Table 2 illustrates dietary intake of the subjects. As shown in the tables, among the variables studied, the intake of no studied nutrients significantly changed during the study period. Analysis of physical activity data revealed that most of the subjects were at the minimally inactive category in both groups and in all of the studied phases within a range of 38% to 48% (P<0.05).
Table 2 illustrates dietary intake of the subjects. As shown in the tables, among the variables studied, the intake of no studied nutrients significantly changed during the study period. Analysis of physical activity data revealed that most of the subjects were at the minimally inactive category in both groups and in all of the studied phases within a range of 38% to 48% (P<0.05).
| Table 2. Comparison of the laboratory indicators, blood pressure and dietary intake at the two studied stages in studied groups. | |||
| P-valuea | Intervention group (n=27) | Placebo group (n=27) | Variables |
| Total cholesterol (mg/dl) | |||
| 0.11 | 193.11±35.76 | 179.63±46.3c | Before |
| 0.02 | 177.07±35.77 | 173.13±36.57 | After |
| 0.08 | 0.22 | P-valueb | |
| 0.03 | -16.04±10.24 | -6.50±9.32 | Changes |
| Triglyceride (mg/dl) | |||
| 0.16 | 148.51±92.44 | 145.81±93.27 | Before |
| 0.09 | 151.59±84.58 | 147.40±85.30 | After |
| 0.24 | 0.54 | P-value | |
| 0.08 | 3.08±31.34 | 1.59±22.63 | Changes |
| Low density lipoprotein (mg/dl) | |||
| 0.11 | 113.37±22.69 | 105.36±25.21 | Before |
| 0.16 | 108.14±21.97 | 104.13±22.75 | After |
| 0.26 | 0.31 | P-value | |
| 0.04 | -5.23±10.12 | -1.23±6.26 | Changes |
| High density lipoprotein (mg/dl) | |||
| 0.14 | 43.69±9.61 | 42.22±35.76 | Before |
| 0.09 | 40.36±10.30 | 42.63±9.96 | After |
| 0.12 | 0.61 | P-value | |
| 0.07 | -3.33±4.62 | 0.41±3.69 | Changes |
| Very low density lipoprotein (mg/dl) | |||
| 0.44 | 28.67±6.93 | 28.89±8.72 | Before |
| 0.14 | 30.49±4.46 | 29.48±7.72 | After |
| 0.29 | 0.35 | P-value | |
| 0.19 | 1.82±4.96 | 0.59±3.64 | Changes |
| Atherogenic index of plasma | |||
| 0.44 | 0.18±0.10 | 0.17±0.06 | Before |
| 0.09 | 0.21±0.08 | 0.18±0.03 | After |
| 0.29 | 0.35 | P-value | |
| 0.04 | 0.04±0.87 | 0.01±0.60 | Changes |
| Non-High density lipoprotein (mg/dl) | |||
| 0.44 | 123.89±25.67 | 116.25±23.45 | Before |
| 0.30 | 115.43±26.17 | 113.56±29.21 | After |
| 0.29 | 0.35 | P-value | |
| 0.03 | -8.46±26.24 | -2.69±14.57 | Changes |
| Low density lipoprotein /High density lipoprotein | |||
| 0.19 | 2.34±1.03 | 2.78±1.12 | Before |
| 0.09 | 2.86±1.42 | 2.56±1.11 | After |
| 0.29 | 0.35 | P-value | |
| 0.10 | 0.52±0.96 | -0.22±1.25 | Changes |
| Cholesterol//HDL-C ratio | |||
| 0.33 | 4.84±2.32 | 4.56±2.12 | Before |
| 0.64 | 4.22±2.01 | 4.33±1.96 | After |
| 0.43 | 0.77 | P-value | |
| 0.07 | -0.62±1.95 | -0.23±1.22 | Changes |
| Insulin (mIU/l) | |||
| 0.98 | 14.82±8.04 | 14.81±7.27 | Before |
| 0.22 | 14.22±7.64 | 15.07±8.61 | After |
| 0.18 | 0.32 | P-value | |
| 0.27 | -0.60±5.24 | 0.26±4.26 | Changes |
| Diastolic blood pressure (mmHg) | |||
| 0.32 | 7.19±1.62 | 7.45±1.67 | Before |
| 0.43 | 7.42±1.32 | 7.34±1.21 | After |
| 0.21 | 0.29 | P-value | |
| 0.15 | 0.23±1.75 | -0.11±2.07 | Changes |
| Systolic blood pressure (mmHg) | |||
| 0.64 | 12.89±1.12 | 12.35±2.12 | Before |
| 0.28 | 12.23±1.96 | 11.86±2.32 | After |
| 0.31 | 0.32 | P-value | |
| 0.35 | -0.66±1.69 | -0.49±1.94 | Changes |
| Energy (kcal/d) | |||
| 0.39 | 1890±350 | 1853±279 | Before |
| 0.34 | 1820±379 | 1813±318 | After |
| 0.16 | 0.25 | P-value | |
| 0.23 | -70±316 | -40±259 | Changes |
| Protein (g/d) | |||
| 0.17 | 66±22 | 64±17 | Before |
| 0.17 | 64±18 | 62±13 | After |
| 0.29 | 0.34 | P-value | |
| 0.33 | -2±18 | -2±14 | Changes |
| Carbohydrate (g/d) | |||
| 0.49 | 241±58 | 234±59 | Before |
| 0.45 | 236±53 | 228±57 | After |
| 0.44 | 0.39 | P-value | |
| 0.50 | -5±46 | -6±49 | Changes |
| Fat (g/d) | |||
| 0.68 | 67±24 | 66±19 | Before |
| 0.81 | 65±20 | 66±17 | After |
| 0.43 | 0.79 | P-value | |
| 0.12 | -2±17 | 0±14 | Changes |
| Calcium (mg/d) | |||
| 0.37 | 636±78 | 689±62 | Before |
| 0.50 | 654±59 | 629±73 | After |
| 0.29 | 0.10 | P-value | |
| 0.49 | 18±52 | -60±64 | Changes |
| Vitamin D (μg/d) | |||
| 0.22 | 2.96±0.68 | 3.32±0.52 | Before |
| 0.23 | 3.12±0.69 | 3.46±0.79 | After |
| 0.65 | 0.54 | P-value | |
| 0.16 | 0.16±032 | 0.14±0.24 | Changes |
| Soluble fiber (g/d) | |||
| 0.25 | 1.77±0.28 | 1.73±0.27 | Before |
| 0.33 | 1.71±0.29 | 1.59±0.32 | After |
| 0.41 | 0.24 | P-value | |
| 0.20 | -0.06±0.08 | -0.14±0.28 | Changes |
| Insoluble fiber (g/d) | |||
| 0.27 | 12.64±1.48 | 12.24±1.18 | Before |
| 0.22 | 12.12±1.63 | 11.63±1.22 | After |
| 0.49 | 0.43 | P-value | |
| 0.44 | -0.52±1.09 | -0.61±0.99 | Changes |
| Sodium (mg/d) | |||
| 0.09 | 3281±489 | 3234±451 | Before |
| 0.12 | 3517±422 | 3356±522 | After |
| 0.17 | 0.29 | P-value | |
| 0.08 | 236±462 | 122±375 | Changes |
| a: Independent sample t-test; b: Paired t-test; c: Mean±SD. | |||
Discussion
While studies are still at their preliminary phase, the desirable potential effects of dairy fats including CLA on CV biomarkers have attracted remarkable research interests (Lordan et al., 2018). Despite the fact that several investigations have revealed no significant effect on serum lipid profile, some trials reported both beneficial and adverse effects on TC, LDL-c, HDL-c, and TGs (Agueda et al., 2009, Eftekhari et al., 2014, Joseph et al., 2011, Steck et al., 2007).
The study by Steck et al. was similar to this study in terms of dose and complementary CLA and duration of the intervention. This clinical trial was performed on obese people with a BMI of 30-35 kg/m2 for 12 weeks with CLA supplementation (50:50 ratios of cis-9, trans-11 and trans-10, cis-12 isomers). Also, the supplements were given in two doses of 3.2 and 6.4 g/d CLA. The results showed that CLA supplementation may lead to a significant decrease (P<0.05) in serum HDL-C among the participants receiving 6.4 g/d CLA. Furthermore, a significant fall could be observed in HDL-C in placebo group. Also, no changes were observed in serum TG, TC, and LDL-C in studied groups (Steck et al., 2007). Based on the findings, a remarkable decrease could be detected in HDL-C levels; however, it was not a statistically significant decrease. These effects were shown at a dose of 3 g/d while in Steck et al’s study, it was achieved only at a dose of 6.4 g/d. The results of the changes regarding differences between the groups were not seen in Steck et al’s study, whereas a significant decrease was detected between the groups regarding HDL-C in this study. The study population of the survey, unlike the sample population of this study, belonged to different racial groups. Moreover, there were differences in sex ratio between the two studies. Besides, dietary intake of sodium and soluble and insoluble fibers were not reported. It was possible that the participants could not achieve a stable dietary intake of effective nutrients in serum lipid profile during the study period. Accordingly, the findings of this study showed that the studied groups achieved no significant difference in terms of the above-mentioned nutrients (P>0.05). Based on the findings reported by Josef et al., CLA’s 8-week supplementation could not achieve a significant decline in serum lipid profile including TC, TG, VLDL-C, LDL-C, and HDL-C. The participants this study were overweight hyperlipidemic men, and the dietary intake had not been controlled in that study (Joseph et al., 2011).
In their report, Mougios et al. showed that serum HDL-C significantly decreased (P <0.001) and TG as well as TC tended to be reduced in CLA group receiving 0.7 g of CLA for four weeks. These data indicated that daily supplementation of 0.7–1.4 g CLA daily for 4–8 weeks may modulate serum lipids and increase CLA content of serum lipids in humans (Mougios et al., 2001). Participants were not obese (BMI<30) and dietary control had been fulfilled. Although such changes in serum lipids have not been documented by this low dose of CLA in other sources, the results were approximately similar to the findings except for TG figures. Also, dietary intakes of three nutritional factors affecting serum lipid profile including fibers, calcium, and vitamin D were not analyzed in this paper.
According to our results, serum insulin was not altered during the intervention period. Although the associations between serum insulin and some components of serum lipids were reported by some studies conducted on patients with diabetes (Moussa et al., 1998, Yan et al., 2016); Based on some recent studies, no clear correlations were found between them in participants such as non-diabetic people (Bedogni et al., 2019). Accordingly, that may explain the significant changes in serum lipid components in spite of no statistical changes in serum insulin in this study.
Regarding serum lipid-related CVDs predicting indexes, the findings of this study were more confusing. In IG, while Non-HDL-C were significantly decreased (P=0.03) and cholesterol//HDL-C ratio tended to decline (P=0.07), AIP was significantly risen (P=0.04). The mentioned variables were rarely reflected in findings of the available studies. Eftekhari et al. reported no changes in LDL-C/HDL-C among CLA supplemented group in comparison with placebo group. Serum LDL-C was not directly measured, and also it was obtained using Friedewald equation (Eftekhari et al., 2014). It seems that AIP as a novel predictive indicator for CVDs is a stronger predictor for the disease compared to others (Cai et al., 2017). Nevertheless, the researchers could reveal no changes in systolic BP, Diastolic BP, dietary intakes of macronutrients, calcium, sodium, soluble and insoluble dietary fibers, and physical activity among the two studied groups during the intervention. Additionally, no changes were observed in serum insulin as a major hormone affecting the lipid metabolism. In a meta-analysis, Yang et al. included 8 studies with 9 trials, which involved 638 participants with CLA supplementation ranging from 2.0 g/day to 6.8 g/day (Yang et al., 2015). The findings of this meta-analysis did not support the overall favorable effect of CLA supplementation on BP regulation.
It should be noted that in this research, the authors measured no inflammation markers affecting the lipid levels such as hs-CRP and TNF-α. They also could not measure CLA dietary intake. To the best of the author’s knowledge, the effect of CLA on some lipid-related biomarkers including AIP, Non-HDL-C, LDL-C/HDL-C, and Cholesterol//HDL-C ratio in obese adults was studied for the first time.
Conclusion
The authors concluded that the use of CLA supplementation could help reduce some adverse fractions of serum lipid profile, particularly TC, Non-HDL-C, and LDL-C. Regarding the augmenting effects of CLA intake on AIP as a strong predictive marker for CVDs, it is difficult to confirm the beneficial effects of CLA supplementation on preventing CVDs. The authors suggest that epidemiological and animal studies be continued in this field.
Acknowledgments
of the authors would like to thank Urmia University of Medical Sciences for financial support. They also thank Mr. Naser Sheikhi for his valuable help in statistical analysis of the data.
Authors’ contributions
All the authors were involved in designing, data gathering, statistical analysis and writing the original draft of the manuscript, and they approved of the final manuscript.
Conflicting interests
The authors declared no conflict of interests.
Funding
All the costs of this study have been done with the full financial support of Urmia University of Medical Sciences
References
Agueda M, Zulet MA & Martínez JA 2009. Effect of conjugated linoleic acid (CLA) on human lipid profile. Archivos latinoamericanos de nutricion. 59 (3): 245-252.
Bakhshi E, et al. 2016. Changes in Obesity Odds Ratio among Iranian Adults, since 2000: Quadratic Inference Functions Method. Computational and mathematical methods in medicine. 2016 (1): 7101343.
Bassaganya-Riera J, et al. 2003. Conjugated linoleic acid ameliorates viral infectivity in a pig model of virally induced immunosuppression. Journal of nutrition. 133 (10): 3204-3214.
Bedogni G, et al. 2019. Association of serum lipids with β-cell function in obese children and adolescents. Endocrine Connections. 8 (10): 1318-1323.
Blankson H, et al. 2000. Conjugated linoleic acid reduces body fat mass in overweight and obese humans. Journal of nutrition. 130 (12): 2943-2948.
Cai G, Shi G, Xue S & Lu W 2017. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population. Medicine. 96 (37): e8058.
Chaudhary GMD, et al. 2019. Association of Obesity Indicators with Hypertension in Type 2 Diabetes Mellitus Patients. Cureus. 11 (7): e5050.
Dal Canto E, et al. 2020. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. European journal of preventive cardiology. 26 (2_suppl): 25-32.
den Hartigh LJ 2019. Conjugated Linoleic Acid Effects on Cancer, Obesity, and Atherosclerosis: A Review of Pre-Clinical and Human Trials with Current Perspectives. Nutrients. 11 (2): 573.
Dobiásová M & Frohlich J 2001. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clinical biochemistry. 34 (7): 583-588.
Eckel RH, Grundy SM & Zimmet PZ 2005. The metabolic syndrome. Lancet (London, England). 365 (9468): 1415-1428.
Eftekhari MH, Aliasghari F, Beigi MA & Hasanzadeh J 2014. The effect of conjugated linoleic acids and omega-3 fatty acids supplementation on lipid profile in atherosclerosis. Advanced biomedical research. 3 (1): 15.
Esmaeili Shahmirzadi F, Ghavamzadeh S & Zamani T 2019. The Effect of Conjugated Linoleic Acid Supplementation on Body Composition, Serum Insulin and Leptin in Obese Adults. Archives of Iranian medicine. 22 (5): 255-261.
Finucane MM, et al. 2011. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet (London, England). 377 (9765): 557-567.
Fleck AK, et al. 2021. Dietary conjugated linoleic acid links reduced intestinal inflammation to amelioration of CNS autoimmunity. Brain : a journal of neurology. 144 (4): 1152-1166.
Friedewald WT, Levy RI & Fredrickson DS 1972. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical chemistry. 18 (6): 499-502.
Jiang R, et al. 2004. Non-HDL cholesterol and apolipoprotein B predict cardiovascular disease events among men with type 2 diabetes. Diabetes care. 27 (8): 1991-1997.
Joseph SV, et al. 2011. Conjugated linoleic acid supplementation for 8 weeks does not affect body composition, lipid profile, or safety biomarkers in overweight, hyperlipidemic men. Journal of nutrition. 141 (7): 1286-1291.
Julius U 2003. Influence of plasma free fatty acids on lipoprotein synthesis and diabetic dyslipidemia. Experimental and clinical endocrinology & diabetes. 111 (5): 246-250.
Khazaál MS 2014. Atherogenic Index of Plasma ( AIP ) As a Parameter in Predicting Cardiovascular Risk in Males Compared To the Conventional Dyslipidemic Indices ( Cholesterol Ratios ).
Koba K, et al. 2002. Dietary conjugated linolenic acid in relation to CLA differently modifies body fat mass and serum and liver lipid levels in rats. Lipids. 37 (4): 343-350.
Kritchevsky D, et al. 2004. Conjugated linoleic acid isomer effects in atherosclerosis: growth and regression of lesions. Lipids. 39 (7): 611-616.
Lemieux I, et al. 2001. Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: the Quebec Cardiovascular Study. Archives of internal medicine. 161 (22): 2685-2692.
Lordan R, Tsoupras A, Mitra B & Zabetakis I 2018. Dairy Fats and Cardiovascular Disease: Do We Really Need to be Concerned? Foods (Basel, Switzerland). 7 (3): 29.
McGowan MM, et al. 2013. A proof of principle clinical trial to determine whether conjugated linoleic acid modulates the lipogenic pathway in human breast cancer tissue. Breast cancer research and treatment. 138: 175-183.
McLeod RS, LeBlanc AM, Langille MA, Mitchell PL & Currie DL 2004. Conjugated linoleic acids, atherosclerosis, and hepatic very-low-density lipoprotein metabolism. American journal of clinical nutrition. 79 (6 Suppl): 1169s-1174s.
Miles JM & Nelson RH 2007. Contribution of triglyceride-rich lipoproteins to plasma free fatty acids. Hormone and metabolic research. 39 (10): 726-729.
Moloney F, et al. 2007. Antidiabetic effects of cis-9, trans-11-conjugated linoleic acid may be mediated via anti-inflammatory effects in white adipose tissue. Diabetes. 56 (3): 574-582.
Mougios V, et al. 2001. Effect of supplementation with conjugated linoleic acid on human serum lipids and body fat. Journal of nutritional biochemistry. 12 (10): 585-594.
Moussa MA, et al. 1998. Association of fasting insulin with serum lipids and blood pressure in Kuwaiti children. Metabolism: clinical and experimental. 47 (4): 420-424.
Musaiger AO 2011. Overweight and obesity in eastern mediterranean region: prevalence and possible causes. Journal of obesity. 2011 (1): 407237.
Noone EJ, Roche HM, Nugent AP & Gibney MJ 2002. The effect of dietary supplementation using isomeric blends of conjugated linoleic acid on lipid metabolism in healthy human subjects. British journal of nutrition. 88 (3): 243-251.
Ormazabal V, et al. 2018. Association between insulin resistance and the development of cardiovascular disease. Cardiovascular diabetology. 17 (1): 122.
Pariza MW, Park Y & Cook ME 2001. The biologically active isomers of conjugated linoleic acid. Progress in lipid research. 40 (4): 283-298.
Park SY, Gautier JF & Chon S 2021. Assessment of Insulin Secretion and Insulin Resistance in Human. Diabetes & metabolism journal. 45 (5): 641-654.
Reaven GM, Brand RJ, Chen YD, Mathur AK & Goldfine I 1993. Insulin resistance and insulin secretion are determinants of oral glucose tolerance in normal individuals. Diabetes. 42 (9): 1324-1332.
Steck SE, et al. 2007. Conjugated linoleic acid supplementation for twelve weeks increases lean body mass in obese humans. Journal of nutrition. 137 (5): 1188-1193.
Vasheghani-Farahani A, et al. 2011. The Persian, last 7-day, long form of the International Physical Activity Questionnaire: translation and validation study. Asian journal of sports medicine. 2 (2): 106-116.
Wang C, et al. 2021. Assessment of Cardiovascular Risk Factors and Their Interactions in the Risk of Coronary Heart Disease in Patients with Type 2 Diabetes with Different Weight Levels, 2013-2018. Metabolic syndrome and obesity. 14: 4253-4262.
Wu TT, Gao Y, Zheng YY, Ma YT & Xie X 2018. Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women. Lipids in health and disease. 17 (1): 197.
Yan Y, et al. 2016. Association of Insulin Resistance with Glucose and Lipid Metabolism: Ethnic Heterogeneity in Far Western China. Mediators of inflammation. 2016 (1): 3825037.
Yang J, et al. 2015. Effect of conjugated linoleic acid on blood pressure: a meta-analysis of randomized, double-blind placebo-controlled trials. Lipids in health and disease. 14: 1-6.
While studies are still at their preliminary phase, the desirable potential effects of dairy fats including CLA on CV biomarkers have attracted remarkable research interests (Lordan et al., 2018). Despite the fact that several investigations have revealed no significant effect on serum lipid profile, some trials reported both beneficial and adverse effects on TC, LDL-c, HDL-c, and TGs (Agueda et al., 2009, Eftekhari et al., 2014, Joseph et al., 2011, Steck et al., 2007).
The study by Steck et al. was similar to this study in terms of dose and complementary CLA and duration of the intervention. This clinical trial was performed on obese people with a BMI of 30-35 kg/m2 for 12 weeks with CLA supplementation (50:50 ratios of cis-9, trans-11 and trans-10, cis-12 isomers). Also, the supplements were given in two doses of 3.2 and 6.4 g/d CLA. The results showed that CLA supplementation may lead to a significant decrease (P<0.05) in serum HDL-C among the participants receiving 6.4 g/d CLA. Furthermore, a significant fall could be observed in HDL-C in placebo group. Also, no changes were observed in serum TG, TC, and LDL-C in studied groups (Steck et al., 2007). Based on the findings, a remarkable decrease could be detected in HDL-C levels; however, it was not a statistically significant decrease. These effects were shown at a dose of 3 g/d while in Steck et al’s study, it was achieved only at a dose of 6.4 g/d. The results of the changes regarding differences between the groups were not seen in Steck et al’s study, whereas a significant decrease was detected between the groups regarding HDL-C in this study. The study population of the survey, unlike the sample population of this study, belonged to different racial groups. Moreover, there were differences in sex ratio between the two studies. Besides, dietary intake of sodium and soluble and insoluble fibers were not reported. It was possible that the participants could not achieve a stable dietary intake of effective nutrients in serum lipid profile during the study period. Accordingly, the findings of this study showed that the studied groups achieved no significant difference in terms of the above-mentioned nutrients (P>0.05). Based on the findings reported by Josef et al., CLA’s 8-week supplementation could not achieve a significant decline in serum lipid profile including TC, TG, VLDL-C, LDL-C, and HDL-C. The participants this study were overweight hyperlipidemic men, and the dietary intake had not been controlled in that study (Joseph et al., 2011).
In their report, Mougios et al. showed that serum HDL-C significantly decreased (P <0.001) and TG as well as TC tended to be reduced in CLA group receiving 0.7 g of CLA for four weeks. These data indicated that daily supplementation of 0.7–1.4 g CLA daily for 4–8 weeks may modulate serum lipids and increase CLA content of serum lipids in humans (Mougios et al., 2001). Participants were not obese (BMI<30) and dietary control had been fulfilled. Although such changes in serum lipids have not been documented by this low dose of CLA in other sources, the results were approximately similar to the findings except for TG figures. Also, dietary intakes of three nutritional factors affecting serum lipid profile including fibers, calcium, and vitamin D were not analyzed in this paper.
According to our results, serum insulin was not altered during the intervention period. Although the associations between serum insulin and some components of serum lipids were reported by some studies conducted on patients with diabetes (Moussa et al., 1998, Yan et al., 2016); Based on some recent studies, no clear correlations were found between them in participants such as non-diabetic people (Bedogni et al., 2019). Accordingly, that may explain the significant changes in serum lipid components in spite of no statistical changes in serum insulin in this study.
Regarding serum lipid-related CVDs predicting indexes, the findings of this study were more confusing. In IG, while Non-HDL-C were significantly decreased (P=0.03) and cholesterol//HDL-C ratio tended to decline (P=0.07), AIP was significantly risen (P=0.04). The mentioned variables were rarely reflected in findings of the available studies. Eftekhari et al. reported no changes in LDL-C/HDL-C among CLA supplemented group in comparison with placebo group. Serum LDL-C was not directly measured, and also it was obtained using Friedewald equation (Eftekhari et al., 2014). It seems that AIP as a novel predictive indicator for CVDs is a stronger predictor for the disease compared to others (Cai et al., 2017). Nevertheless, the researchers could reveal no changes in systolic BP, Diastolic BP, dietary intakes of macronutrients, calcium, sodium, soluble and insoluble dietary fibers, and physical activity among the two studied groups during the intervention. Additionally, no changes were observed in serum insulin as a major hormone affecting the lipid metabolism. In a meta-analysis, Yang et al. included 8 studies with 9 trials, which involved 638 participants with CLA supplementation ranging from 2.0 g/day to 6.8 g/day (Yang et al., 2015). The findings of this meta-analysis did not support the overall favorable effect of CLA supplementation on BP regulation.
It should be noted that in this research, the authors measured no inflammation markers affecting the lipid levels such as hs-CRP and TNF-α. They also could not measure CLA dietary intake. To the best of the author’s knowledge, the effect of CLA on some lipid-related biomarkers including AIP, Non-HDL-C, LDL-C/HDL-C, and Cholesterol//HDL-C ratio in obese adults was studied for the first time.
Conclusion
The authors concluded that the use of CLA supplementation could help reduce some adverse fractions of serum lipid profile, particularly TC, Non-HDL-C, and LDL-C. Regarding the augmenting effects of CLA intake on AIP as a strong predictive marker for CVDs, it is difficult to confirm the beneficial effects of CLA supplementation on preventing CVDs. The authors suggest that epidemiological and animal studies be continued in this field.
Acknowledgments
of the authors would like to thank Urmia University of Medical Sciences for financial support. They also thank Mr. Naser Sheikhi for his valuable help in statistical analysis of the data.
Authors’ contributions
All the authors were involved in designing, data gathering, statistical analysis and writing the original draft of the manuscript, and they approved of the final manuscript.
Conflicting interests
The authors declared no conflict of interests.
Funding
All the costs of this study have been done with the full financial support of Urmia University of Medical Sciences
References
Agueda M, Zulet MA & Martínez JA 2009. Effect of conjugated linoleic acid (CLA) on human lipid profile. Archivos latinoamericanos de nutricion. 59 (3): 245-252.
Bakhshi E, et al. 2016. Changes in Obesity Odds Ratio among Iranian Adults, since 2000: Quadratic Inference Functions Method. Computational and mathematical methods in medicine. 2016 (1): 7101343.
Bassaganya-Riera J, et al. 2003. Conjugated linoleic acid ameliorates viral infectivity in a pig model of virally induced immunosuppression. Journal of nutrition. 133 (10): 3204-3214.
Bedogni G, et al. 2019. Association of serum lipids with β-cell function in obese children and adolescents. Endocrine Connections. 8 (10): 1318-1323.
Blankson H, et al. 2000. Conjugated linoleic acid reduces body fat mass in overweight and obese humans. Journal of nutrition. 130 (12): 2943-2948.
Cai G, Shi G, Xue S & Lu W 2017. The atherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han population. Medicine. 96 (37): e8058.
Chaudhary GMD, et al. 2019. Association of Obesity Indicators with Hypertension in Type 2 Diabetes Mellitus Patients. Cureus. 11 (7): e5050.
Dal Canto E, et al. 2020. Diabetes as a cardiovascular risk factor: An overview of global trends of macro and micro vascular complications. European journal of preventive cardiology. 26 (2_suppl): 25-32.
den Hartigh LJ 2019. Conjugated Linoleic Acid Effects on Cancer, Obesity, and Atherosclerosis: A Review of Pre-Clinical and Human Trials with Current Perspectives. Nutrients. 11 (2): 573.
Dobiásová M & Frohlich J 2001. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clinical biochemistry. 34 (7): 583-588.
Eckel RH, Grundy SM & Zimmet PZ 2005. The metabolic syndrome. Lancet (London, England). 365 (9468): 1415-1428.
Eftekhari MH, Aliasghari F, Beigi MA & Hasanzadeh J 2014. The effect of conjugated linoleic acids and omega-3 fatty acids supplementation on lipid profile in atherosclerosis. Advanced biomedical research. 3 (1): 15.
Esmaeili Shahmirzadi F, Ghavamzadeh S & Zamani T 2019. The Effect of Conjugated Linoleic Acid Supplementation on Body Composition, Serum Insulin and Leptin in Obese Adults. Archives of Iranian medicine. 22 (5): 255-261.
Finucane MM, et al. 2011. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet (London, England). 377 (9765): 557-567.
Fleck AK, et al. 2021. Dietary conjugated linoleic acid links reduced intestinal inflammation to amelioration of CNS autoimmunity. Brain : a journal of neurology. 144 (4): 1152-1166.
Friedewald WT, Levy RI & Fredrickson DS 1972. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical chemistry. 18 (6): 499-502.
Jiang R, et al. 2004. Non-HDL cholesterol and apolipoprotein B predict cardiovascular disease events among men with type 2 diabetes. Diabetes care. 27 (8): 1991-1997.
Joseph SV, et al. 2011. Conjugated linoleic acid supplementation for 8 weeks does not affect body composition, lipid profile, or safety biomarkers in overweight, hyperlipidemic men. Journal of nutrition. 141 (7): 1286-1291.
Julius U 2003. Influence of plasma free fatty acids on lipoprotein synthesis and diabetic dyslipidemia. Experimental and clinical endocrinology & diabetes. 111 (5): 246-250.
Khazaál MS 2014. Atherogenic Index of Plasma ( AIP ) As a Parameter in Predicting Cardiovascular Risk in Males Compared To the Conventional Dyslipidemic Indices ( Cholesterol Ratios ).
Koba K, et al. 2002. Dietary conjugated linolenic acid in relation to CLA differently modifies body fat mass and serum and liver lipid levels in rats. Lipids. 37 (4): 343-350.
Kritchevsky D, et al. 2004. Conjugated linoleic acid isomer effects in atherosclerosis: growth and regression of lesions. Lipids. 39 (7): 611-616.
Lemieux I, et al. 2001. Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: the Quebec Cardiovascular Study. Archives of internal medicine. 161 (22): 2685-2692.
Lordan R, Tsoupras A, Mitra B & Zabetakis I 2018. Dairy Fats and Cardiovascular Disease: Do We Really Need to be Concerned? Foods (Basel, Switzerland). 7 (3): 29.
McGowan MM, et al. 2013. A proof of principle clinical trial to determine whether conjugated linoleic acid modulates the lipogenic pathway in human breast cancer tissue. Breast cancer research and treatment. 138: 175-183.
McLeod RS, LeBlanc AM, Langille MA, Mitchell PL & Currie DL 2004. Conjugated linoleic acids, atherosclerosis, and hepatic very-low-density lipoprotein metabolism. American journal of clinical nutrition. 79 (6 Suppl): 1169s-1174s.
Miles JM & Nelson RH 2007. Contribution of triglyceride-rich lipoproteins to plasma free fatty acids. Hormone and metabolic research. 39 (10): 726-729.
Moloney F, et al. 2007. Antidiabetic effects of cis-9, trans-11-conjugated linoleic acid may be mediated via anti-inflammatory effects in white adipose tissue. Diabetes. 56 (3): 574-582.
Mougios V, et al. 2001. Effect of supplementation with conjugated linoleic acid on human serum lipids and body fat. Journal of nutritional biochemistry. 12 (10): 585-594.
Moussa MA, et al. 1998. Association of fasting insulin with serum lipids and blood pressure in Kuwaiti children. Metabolism: clinical and experimental. 47 (4): 420-424.
Musaiger AO 2011. Overweight and obesity in eastern mediterranean region: prevalence and possible causes. Journal of obesity. 2011 (1): 407237.
Noone EJ, Roche HM, Nugent AP & Gibney MJ 2002. The effect of dietary supplementation using isomeric blends of conjugated linoleic acid on lipid metabolism in healthy human subjects. British journal of nutrition. 88 (3): 243-251.
Ormazabal V, et al. 2018. Association between insulin resistance and the development of cardiovascular disease. Cardiovascular diabetology. 17 (1): 122.
Pariza MW, Park Y & Cook ME 2001. The biologically active isomers of conjugated linoleic acid. Progress in lipid research. 40 (4): 283-298.
Park SY, Gautier JF & Chon S 2021. Assessment of Insulin Secretion and Insulin Resistance in Human. Diabetes & metabolism journal. 45 (5): 641-654.
Reaven GM, Brand RJ, Chen YD, Mathur AK & Goldfine I 1993. Insulin resistance and insulin secretion are determinants of oral glucose tolerance in normal individuals. Diabetes. 42 (9): 1324-1332.
Steck SE, et al. 2007. Conjugated linoleic acid supplementation for twelve weeks increases lean body mass in obese humans. Journal of nutrition. 137 (5): 1188-1193.
Vasheghani-Farahani A, et al. 2011. The Persian, last 7-day, long form of the International Physical Activity Questionnaire: translation and validation study. Asian journal of sports medicine. 2 (2): 106-116.
Wang C, et al. 2021. Assessment of Cardiovascular Risk Factors and Their Interactions in the Risk of Coronary Heart Disease in Patients with Type 2 Diabetes with Different Weight Levels, 2013-2018. Metabolic syndrome and obesity. 14: 4253-4262.
Wu TT, Gao Y, Zheng YY, Ma YT & Xie X 2018. Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women. Lipids in health and disease. 17 (1): 197.
Yan Y, et al. 2016. Association of Insulin Resistance with Glucose and Lipid Metabolism: Ethnic Heterogeneity in Far Western China. Mediators of inflammation. 2016 (1): 3825037.
Yang J, et al. 2015. Effect of conjugated linoleic acid on blood pressure: a meta-analysis of randomized, double-blind placebo-controlled trials. Lipids in health and disease. 14: 1-6.
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