Association of Circulating Omega 3, 6 and 9 Fatty Acids with Gestational Diabetes Mellitus: A Systematic Review

doi:10.21203/rs.3.rs-150357/v1

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Association of Circulating Omega 3, 6 and 9 Fatty Acids with Gestational Diabetes Mellitus: A Systematic Review

https://doi.org/10.21203/rs.3.rs-150357/v1

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Background: Gestational diabetes mellitus (GDM) is associated with increased risks of disease for mother and child during pregnancy and after that. Early diagnosis of GDM would promote both maternal and fetal health. Metabolomics can simplify and develop our understanding of the etiology, manifestation, or pathophysiology of the disease. This systematic review investigates the association of circulating omega 3, 6, and 9 fatty acids with GDM.

Methods: We conducted a systematic search of PubMed, Scopus, Web of Science and EMBASE databases up to May 8, 2020, using the key term combinations of all types of omega fatty acids with gestational diabetes mellitus. Additional articles were identified through searching the reference lists of included studies.

Results: This systematic review included 14 articles. four were cohort studies, four included case-control studies and four were case-control studies. Omega fatty acids, as potential biomarkers, are considered to be associated with GDM risk and thus provide useful information regarding the prevention and early diagnosis of GDM. Moreover, existing metabolomic studies on GDM are shown to provide conflicting results about metabolite profile characteristics.

Conclusions: The results of this study demonstrate an increasing trend in the amount of oleic acid and palmitoleic acid in the second trimester and an increase in decosahexanoic acid in the third trimester of GDM mothers. The changes in other fatty acids of interest are either not significant or if significant, their results are inconsistent with the other exisiting articles.

This systematic review was registered at PROSPERO (www.crd.york.ac.uk/PROSPERO) as CRD42020196122.

Endocrinology & Metabolism

Omega fatty acids

Gestational diabetes mellitus

GDM

Maternal lipids

Systematic review

Gestational diabetes mellitus (GDM) is a hyperglycemia state that develops or is ﬁrst recognized during pregnancy. Development of GDM increases the risk for adverse outcomes such as hypoglycemia, respiratory distress syndrome and macrosomia in the newborns, as well as increased risk of cesarean section and development postpartum type 2 diabetes (T2D) in mothers (1). Disparities in race-ethnicity and country of birth are considered to be risk factors for GDM, however, maternal obesity is the strongest risk factor ever known (2) It is also cruicial to note that, due to an increasing prevalence of obesity and diabetes melitus worldwide, the number of pregnant women with GDM is rising.

Altered maternal lipid metabolism in pregnancy with a moderate increase of lipids early in pregnancy and a signiﬁcant increase of lipids later in pregnancy is also prevalent (2). Studies support primary, narrow, and potentially transitory effects of prenatal long-chain omega 3 polyunsaturated fatty acid (LC-n3 PUFAs) amounts on child metabolic status (3). Moreover, the fetal PUFA concentrations and composition essentially depend on the maternal metabolic status because fetal PUFA synthesis is limited and they are transferred to the fetus through the placenta (4). Maternal fatty acid and lipid metabolism change during pregnancy to facilitate fetal growth and development. Adequate amounts of fat is required during pregnancy as both deficiency and excess consumption can have negative effects on fetal as well as maternal health (5). Excessive consumption of fatty foods or imbalance in the consumption of different types of fats, especially omega 3 and omega 6 fatty acids as essential fatty acids and saturated fatty acids, plus changes in fatty acid metabolism during pregnancy can potentially lead to an increased risk for GDM by developing insulin resistance and increasing inflammatory factors. Maternal fatty acid metabolism changes during pregnancy, especially in regards with PUFAs which are reflected in the blood. This matter is considered to be due to insulin resistance in adipose tissues and the release of excess fat into the bloodstream (6). we can get a good interpretation of fatty acid metabolism changes during pregnancy and its relevant complications such as GDM by assessing the concentrations of omega fatty acids during pregnancy is capable of providing us with an excellent interpretation of changes in fatty acid metabolism and other relevant complications, and also gives us the chance to use them as predictors or early diagnostic biomarkers (7).

The metabolomics as a downstream product of gene expression are able to facilitate our understanding of the etiology, manifestation, or pathophysiology of this disease. Compared to biomarkers that are clinically common and diagnostic approaches that are used, Metabolomics also provide proper sensitivity and accuracy. Metabolomics have a promising vision for identifying and measuring considerable metabolic characteristics which lead to pathological conditions like GDM. Studies have employed nuclear magnetic resonance (NMR), targeted or complementary non-targeted gas/liquid chromatography-mass spectrometry (GC/ LC-MS) based assays, etc. to evaluate lipid profiles in GDM (8). Lipidomics studies have led to advances in the structural recognition, classification, and naming of lipids. However, due to the structural and functional complexities of lipids as well as the diversity of their abundance in biological samples, more comprehensive studies with strong methodologies are needed to better understand these compounds (9).

There is a major debate in the literature as to whether lipid patterns differ in women with GDM early in pregnancy and if these early patterns are potential markers of preexisting insulin resistance (2). So, in this study, we intend to systematically investigate the literature to evaluate the omega-3,6 and 9 fatty acid levels in women with GDM, compared to healthy pregnant women based on metabolomic analytical techniques.

This systematic review is in accordance with the preferred reporting items for the systematic reviews and meta-analysis (PRISMA) statement.

1) Protocol and registration

The systematic review was registered at PROSPERO (www.crd.york.ac.uk/PROSPERO) as CRD42020196122.

2) Eligibility criteria

For the eligible studies, inclusion criteria included: (1) case-control or cohort studies design, (2) comparing the concentration levels of omega 3, 6 and 9 fatty acids in maternal blood, serum, or plasma, (3) participants included pregnant women classified as having GDM and controls (healthy pregnant women with normal glucose tolerance), (4) metabolomic techniques such as GC-MS or NMR spectroscopy were used to measure metabolite profile; and (5) published in English with the full-text articles available.

Exclusion criteria for the articles included: (1) duplicated publications, (2) animal studies, (3) pre-pregnancy diabetes, (4) letters, meta-analyses, reviews, expert opinions, case series, case reports, also articles that did not meet the inclusion criteria were excluded.

3) Information sources & Search

A comprehensive literature search was carried out in the PubMed, Scopus, Web of Science and EMBASE databases to obtain relevant studies and the cut-oﬀ date was up to May 8, 2020. In addition, the reference list of all included articles were analyzed carefully to recognize additional articles.

We used the following MeSH terms: "hypogeic acid" OR "Oleic Acid" OR "elaidic acid" OR gondoic acid OR "mead acid" OR "erucic acid" OR "nervonic acid" OR "ximenic acid" OR "Hexadecatrienoic acid OR "alpha-linolenic acid" OR "stearidonic acid" OR “Eicosatetraenoic acid” OR "Eicosapentaenoic Acid" OR "heneicosapentaenoic acid" "Clupanodonic acid" OR "Docosahexaenoic Acids" OR "Tetracosahexaenoic acid " OR "Tetracosapentaenoic acid" OR "Linoleic Acid" OR "gamma linolenic acid" OR "Calendic acid" OR "Eicosadienoic acid" OR "Eicosatrienoic Acid" OR "Arachidonic Acid" OR "Docosadienoic acid" OR "Adrenic acid OR "Tetracosatetraenoic acid OR "tetracosapentaenoic acid" OR AND ("Diabetes, Gestational"[Mesh] OR "Pregnancy-Induced Diabetes" OR "Pregnancy Induced Diabetes" OR "gestational diabetes")

4) Study selection & Data collection process & Data items

Study selection and data collection from all eligible studies were independently evaluated by two investigators. A third person resolved the diﬀerences until all reached a set of similar statements. Data on population characteristics and metabolomic techniques and study results were extracted from all eligible studies. Due to the limited number of GDM and metabolic studies, methodological heterogeneity, and considerable differences in the characteristics of the study populations, a quantitative meta-analysis could not be done.

1) Study selection

We identiﬁed 703 records from Scopus (n=228), Web of Science (n=117), PubMed (n=90) and EMBASE (n=268) and 1 article identified through manually searching bibliographies; after elimination of duplicates (n=542), 162 records remained. The selection algorithm for the 14 studies that met the inclusion/exclusion criteria for our systematic review is detailed in Fig. 1.

2) Study characteristic

Among the included articles, 10 were original articles and we had access to their full text (10-19) and the other 5 were conference abstracts (20-24).

Five were cohort studies (10, 11, 20, 22, 24), four were nested case-control studies (12, 13, 17, 19) and four were case-control studies (14-16, 18).

All included studies compared metabolites in women with GDM with those in women with normal glucose tolerance (NGT).

The studies used various GDM diagnostic criteria as follows: 5 studies used Carpenter–Coustan criteria (11, 13, 15, 17, 19), 3 studies referenced the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria (10, 12, 14), 1 study referenced the National Diabetes Data Group criteria (18), 1 study specified using O’Sullivan and Mahan criteria (16), and 5 studies did not specify the GDM diagnostic criteria used (20-24).

The sample size of the GDM study population varied among the studies, ranging from 7 to 305 participants.

The mean age of participants was 32.19 for GDM and 27.02 years for the control group and the mean pre-pregnancy BMI was 27.03 for GDM and 24.04 kg/m² for control group (mean of ages and BMI all studies are reported, except that two studies were conducted on obese women (10, 24)). Accordingly, women who developed GDM had higher BMI and were older than those who didn’t.

In 4 articles sampling had taken place in both second and third trimesters (10, 17, 19, 20). 3 articles took samples in the second trimester (11-13) and 4 articles took samples in the third trimester (14-16, 21). There were also 3 articles with sampling in time of delivery (18, 22, 24) and one of the conference abstracts didn’t report the time at which sampling had been done(23).

Results have been reported in 2 categories: plasma free omega fatty acids (10 articles) (10-14, 19-21, 23, 24) and plasma phospholipid omega fatty acids (5 articles) (15-18, 22).

9 studies applied GC-MS as an analytical technique (11-13, 15-17, 19-21) and others used NMR (10), LC-MS (23), FIA-MS/MS (14), or liquid-gas chromatography (18).

4 articles had estimated the intake of macronutrients and individual fatty acids during the pregnancy and evaluated the effect of dietary intake on plasma omega fatty acids (11, 15, 16, 19).

Data on population characteristics and metabolomic techniques in each study are summarized in Table 1 and the results of each study are summarized in Table 2.

Among the evaluated fatty acids, DHA did not change significantly in the second trimester, but in most studies, the third trimester was associated with an increasing trend in this regard. The amount of eicosapentaenoic acid (EPA) measured during pregnancy was generally either increased or did not change significantly. Among the studies conducted in the second trimester, α-Linolenic acid (ALA) did not change significantly and its changes in the third trimester were very contradictory. Changes in omega-6 fatty acids during pregnancy were not significant, if significant, conflicting results were reported. Oleic acid and palmitoleic acid concentrations increased in the second trimester of pregnancy, but their changes in the third trimester were shown to be contradictory.

Summary of evidence

This systematic review summarizes the available data on the association of circulating omega 3, 6, and 9 fatty acids with GDM. Most included studies have revealed correlations between metabolites and GDM risk factors, especially age, pre-pregnancy BMI, family history of T2D, smoking, ethnicity, systolic blood pressure and parity. Just one of the included studies evaluated the frequency of physical activity during pregnancy. Studies mostly used Carpenter–Coustan criteria and then IADPSG criteria as diagnostic criteria. GC-MS was the most applied analytical technique.

Mammals can make saturated and omega-9 monounsaturated fatty acids but are unable to synthesize precursors required to produce omega-3 and omega-6 fatty acids which are needed for the body cells to function properly, so these fatty acids, called essential fatty acids, must be supplied through food. However, mammals can produce some longer-chain unsaturated fatty acids through unsaturation and elongation processes. linoleic acid as an essential omega 6 fatty acid, is a precursor of dihomogamma linoleic acid, arachidonic acid (20:4 n-6, AA) and α-linoleic acid as an essentioal omega 3 fatty acid, is a precursor of EPA and docosaexahenoic acid (DHA) which are their long-chain metabolites (long-chain PUFAs, LC-PUFAs) (27). AA and DHA are important components of the cell membranes. For instance, AA is used in the phospholipid part of the membrane of all cells and DHA is an important part of the phospholipids that make up the membrane of the central nervous system (28). Also, prostaglandins and prostacyclins are among the eicosanoid metabolites of LC-PUFAs that play a key role in placental growth and development, gestational length, and initiation of labor (30).

Pregnancy is associated with changes in the mother’s metabolism to provide the fetus' needs for proper growth. This period is associated with increased accumulation of lipids in maternal tissue and the later development of maternal hyperlipidemia (29). During the first months of pregnancy, LC-PUFAs stored in the mother's adipose tissue acts as the only source of LC-PUFAs for fetal growth (30).

Dietary intake

studies conducted on the connection between fatty acids received via dietary and their amounts (that is reflected in blood) revealed that n-3 and n-6 PUFA, in comparison with saturated and monounsaturated fatty acids, have reliable diagnostic worthiness (9). Omega-6 / omega-3 ratio is an important criteria for determining health. A balance in the consumption of omega 3 and 6 fatty acids is necessary to maintain health throughout the lifecycle as well as a successful pregnancy. An imbalance in this ratio leads to auto-immune and mental disorders, chronic inﬂammation, and diabetes. Over the past few decades, consumption of Western diets which consist of large amounts of omega-6 fatty acids has increased, leading to a loss of fatty acid balance (28). A summary of national nutrition studies shows a reduction in the consumption of PUFA, especially omega-3 PUFA, derived from seafood on a global scale (9). This issue is important and noteworthy in maintaining the health of people in the community, especially pregnant women who are carriers of the next generation.

Insulin resistance

On the other hand, in pregnancy, to ensure the supply of nutrients needed for fetal growth, insulin resistance and hyperinsulinemia can develop to some extent, which in some women leads to gestational diabetes (31). The main pathophysiological causes of GDM are considered to be decreased insulin secretion and abnormal insulin resistance, which are associated with impaired fatty acid metabolism (28).

The amount of fat received through the diet and type of fatty acids that make up the structure of these fats are among the factors that are effective in moderating insulin resistance.

Chen XH et al. reported when HOMA-IR and C-peptide increased, GDM risk has increased by twofold to fourfold. there was a negative relationship between palmitoleic, oleic, linolenic and HOMA-IR and C-peptide levels. In contrast, arachidonic, dihomo-γ-linolenic (DGLA) and DHAs were positively associated with HOMA-IR and C-peptide (11). As reported by Muñoz-Nava MA et al., HOMA-IR positively correlated with dihomo-gamma linolenic acids, and negatively with oleic and linoleic acids. Beta-cell function in the GDM group positively correlated with linolenic acid and negatively with oleic acid (23). In Zhu YY et al. study, among plasma phospholipid n-3 PUFAs at gestational weeks 10–14, EPA inversely correlated with insulin and HOMA-IR (17).

Inflammatory markers

Diabetes is a metabolic disorder caused by increased Inflammation. Fatty acids in the diet are among the factors that can affect the inflammatory pathways (32). Adipose tissue has a considerable role in GDM developement through synthesis and secretion of a great number of adipocytokines and biologically active materials such as proinflammatory cytokines, acute phase reactants, leptin, resistin, adiponectin and others. These compounds can lead to insulin resistance when synthesized at high levels. There is a basic connection between fatty acids and inflammation, especially as precursors to eicosanoids as modifiers of inflammation (29). plasma total antioxidant capacity could be adjusted by the administration of omega-3 fatty acids and vitamin E in women with GDM (34).

In a study by Chen XH et al., cytokine or adipokine levels could be predicted by the individual FFAs. For instance, women with increased DGLA levels were twice as likely to have higher interleukin (IL)-8. Conversely, women who had higher oleic, palmitoleic, and linolenic acid, had decreased odds for having higher Interleukin-6 (IL-6), Interleukin-8 (IL-8), or tumor necrosis factor-alpha (TNF-α) (11). Results of a study by Burlina S et al. show that all the inﬂammatory parameters considered (TNFa, IL6, IL-10 and C-reactive protein) were signiﬁcantly higher in GDM women than in the NGT group, both during the pregnancy and after delivery (15). In another study by Zhu YY et al., DHA correlated positively with adiponectin, among plasma phospholipid n-3 PUFAs at gestational weeks 10–14 (17).

BMI

Pre-pregnancy BMI is a considerable risk factor of GDM by which the risks for developing GDM are 2-6 folds greater in women with either pre-pregnancy obesity or extensive gestational weight gain (GWG) (36). In most obese individuals, elevated Plasma FFA concentrations are observed because extended and stressed adipose cells release more FFA and FFA elimination may be diminished. Moreover, elevated FFA can prevent insulin’s antilipolytic action which leads to releasing more FFA into the blood circulation (35). By characterizing the relationship between maternal BMI and GDM with maternal metabolites and neonatal or cord blood metabolites, the theory of the transgenerational cycle of obesity and diabetes can be verified (37).

In a study by White SL et al., it was indicated that obese GDM women exhibited overstated dyslipidaemic profiles compared with obese non-GDM women, that integrated the effects of insulin resistance inn the lipid metabolism pathways and subsequently reduced insulin sensitivity from an earlier gestation in pregnant women (10). De La Garza Puentes A et al. reported a BMI ≥ 25 in GDM women with a higher rate of LCn6 to LCn3 fatty acids (22). Wijendran V et al. showed that in women with GDM, Maternal plasma phospholipid DHA and n-3 long-chain PUFAs were significantly lower in overweight (BMI: >25.5 to <30) than in normal-weight (BMI >19.8 to ≤25.5) subjects (16).

In this study, a part of the included studies were conference abstracts which just reported the significance of results without measuring the levels of omega fatty acids. Among original articles, some of them did not report the concentration levels, and some were reported as fold changes or adjusted results or expressed as a percent of total fatty acids whereas the measures of association were not consistently reported across studies. On the other hand, the amounts of omega fatty acids were measured in various structures such as free in plasma, plasma phospholipids and erythrocyte phospholipids which didn’t have enough articles in each subtype, hence impeding meta-analysis.

Although statistically, encouraging progress in the AUC and relation of recognized metabolites in GDM diagnosis and pathogenesis have been observed, there is a need for further investigation in regards of clinical consequences followed by employing these metabolites as predictors of GDM. Advanced findings regarding metabolite disturbances in GDM can be gained by similar investigations that evaluate the abundance of metabolites in more details, incorporate metabolites with clinical characteristics, use multiple platforms, and analyses of various specimen kinds.

Although the amount of fats consumed through food was shown to be in correlation with the amount of free fatty acids circulating in the bloodstream, the impact of dietary intake in most of the studies was not assessed or results presented conflicting relationships. Also, in studies that have measured the effects of diet on blood fatty acids, 24 hour recalls has been used to assess the dietary behaviors. However, it is clear that a 24-hour assessment is not capable of showing a strong correlation between lipid intake and the amount of fatty acid measured in the blood.

It can be one of the strengths of future studies, to design a large characterized study including the assessment of FFAs indices concurrent with insulin resistance or secretion, inflammatory biomarkers which have been accredited and accepted in experimental studies on a large scale. it would be possible to evaluate the distinction potential of the biomarkers by performing the analyses at an early stage of gestation which results in a reliable early diagnosis of GDM. Moreover, evaluation of the the necessity of fasting before tests in pregnant women is yet another subject that can be investigated.

This study is the ﬁrst review to comprehensively and rigorously summarize the available data on the association between circulating omega 3, 6 and 9 fatty acids with GDM and by increasing the number of articles in this field, we will repeat this review by performing a meta-analysis.

Gestational diabetes mellitus (GDM), type 2 diabetes (T2D), normal glucose tolerance (NGT), International Association of Diabetes and Pregnancy Study Groups (IADPSG), α-Linolenic acid (ALA), Eicosapentaenoic acid (EPA), Docosapentaenoic acid (DPA), Docosahexaenoic acid (DHA), Linoleic acid (LA), Gamma-linolenic acid (GLA), Eicosadienoic acid (EDA), Dihomo-gamma-linolenic acid (DGLA), Arachidonic acid (AA), body mass index (BMI), free fatty acid (FFA), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), flow injection analysis-mass spectrometry (FIA-MS), monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), long-chain polyunsaturated fatty acid (LC PUFAs), Interleukin-6 (IL-6), Interleukin-8 (IL-8), tumor necrosis factor-alpha (TNF-α).

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials: All data generated or analysed during this study are included in this published article.

Competing interests: The authors declare that they have no financial and non-financial competing interests.

Funding: There is no funding for this study.

Authors’ contributions: PP and MA constructed the design of study. HA led survey implementation. SH and HD led data screening, extraction and analysis. SH prepared the manuscript. FR supervised the study and resolved the diﬀerences until all reach a set of similar statements. All authors read and approved the paper prior to submission.

Acknowledgements: Not applicable.

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Table 1. Characteristics of the Included Studies

	First auther/ year	Type of study	country	GDM/ control	age(year) GDM/ control	BMI(kg/m²) GDM/ control	Diagnostic criteria	sample type
1	White SL 2017	cohort	UK	198/ 448	31.5 ± 4.6 30.5 ± 5.6	36.1 (33.0–39.4) 34.7 (32.6–38.5)	IADPSG	plasma, serum or whole blood FFAs
2	Chen XH 2019	cohort	USA	81/ 1287	26.01±6.05 21.90±5.03	30.08±6.42 25.37±6.21	Carpenter/Coustan	plasma FFAs
3	Chen XH 2010	nested case-control	USA	49/ 98	25.63 ± 0.81 21.29 ± 0.54	30.76 ± 0.93 25.40 ± 0.56	Carpenter/Coustan	serum FFAs
4	Li XP 2020	nested case-control	China	305/ 305	30.6 ± 4.4 30.1 ± 4.1	22.4 ± 3.2 20.5 ± 2.6	IADPSG	plasma FFAs
5	Enquobahrie DA 2015	nested case-control	USA	178/ 180	34.0 ± 4.8 33.1 ± 4.3	26.7 ± 7.2 23.4 ± 5.3	Carpenter/Coustan	Serum FFAs
6	Lehmann R 2015	case-control	Germany	9/ 15	32 ± 1 30 ± 1	29.8 ± 2.5 25.6 ± 0.8	IADPSG	plasma FFAs
7	Burlina S 2016	case-control	Italy	21/ 21	33.9 ± 3.9 33.2 ± 4.6	24.6 ± 6.2 22.8 ± 4.0	Carpenter/Coustan	Plasma phospholipid fatty acids
8	Wijendran V 1999	case-control	Hartford, CT	15/ 15	32.13± 3.253 29.33 ± 3.99	25.95 ± 5.89 23.30 ± 2.98	O’Sullivan and Mahan	plasma phospholipid fatty acids
9	Zhu YY 2019	nested case-control	California	107/ 214	30.5± 5.7 30.4± 5.4	-	Carpenter/Coustan	plasma phospholipid fatty acids
10	Min YJ 2006	case-control	Korea	12/ 12	31.5 ±4.4 28.3 ±2.8	21.4 ±2.6 21.0±1.7	National Diabetes Data Group Criteria	plasma phospholipid fatty acids
11	Kumatani S 2019	cohort	Japan	17/ 188	-	-	-	plasma FFAs
12	Johansen NJ 2018	-	Denmark	9/ 6	31 ± 6 28 ± 3	31.6 ± 6.4 29.7 ± 5.4	-	plasma FFAs
13	De La Garza Puentes A 2016	Population based cohort	Spain	44/ 135	-	-	-	plasma phospholipid fatty acids
14	Muñoz-Nava, M. A. 2018	-	Mexico	50/ 50	-	-	-	serum FFAs
15	Poole A 2015	cohort	Galvestone,TX	7/ 6	33.6 ±5.9 28.3 ±5.2	41 ±11.4 38 ±6.2	-	serum FFAs

Table 2. Results of the included studies.

First author

sampling time (weeks)

inc

w3 FAs

dec

Not.sig

inc

w6 FAs

dec

Not.sig

inc

w9 FAs

dec

Not.sig

analytical technique

White SL

15 - 18

MUFA

NMR

23 - 30

DHA

Chen XH

ALA, EPA, DHA

LA, AA, DGLA

palmitoleic, oleic

GC-MS

Chen XH

EPA

palmitoleic

GC-MS

20-28

ALA, EPA, DHA

LA, AA

palmitoleic, oleic, Total MUFAs

Li XP

EPA, n-3/n-6 ratio

ALA, DPA, DHA

GLA, DGLA

LA, EDA, AA/EPA ratio

hypogeic acid, oleic acid, Total MUFAs

GC-MS

Enquobahrie DA

Oleic acid

GC-MS

Lehmann R

24–27

FIA-MS/MS

Burlina S

third trimester

ALA, EPA, DPA, DHA

LA, DGLA, AA

GC-MS

Wijendran V

third trimester

DHA

ALA, DPA, EPA

ratio of AA to DGLA

EDA, DGLA

LA, AA

oleic acid

GC-MS

Zhu YY

10–14

DHA, Total n-3 PUFAs

ALA, EPA, DPA

GLA, DGLA,

LA, EDA, AA, DTA, n6-DPA

GC-MS

15–26

EPA, DPA, Total n-3 PUFAs

ALA, DHA

DGLA

EDA, DTA

LA, GLA, AA, n6-DPA

Min YJ

delivery

DHA

ALA

LA, AA

Oleic, total MUFA

LC- GC

Kumatani S

12-23

palmitoleic acid

GC-MS

24-34

palmitoleic acid

Johansen NJ

third trimester

oleic acid

GC-MS

De La Garza Puentes A

at delivery

EPA, DHA, n3 PUFAs, LC-n3 PUFAs

AA, LC-n6 PUFAs

Muñoz-Nava, M. A.

not reported

Oleic acid

LC-MS

Poole A

not reported

ALA

DHA

Abbreviations: increase (inc), decrease (dec), not significant (Not.sig), α-Linolenic acid (ALA), Eicosapentaenoic acid (EPA), Docosapentaenoic acid (DPA), Docosahexaenoic acid (DHA), Linoleic acid (LA), Gamma-linolenic acid (GLA), Eicosadienoic acid (EDA), Dihomo-gamma-linolenic acid (DGLA), Arachidonic acid (AA), body mass index (BMI), free fatty acid (FFA), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS), flow injection analysis-mass spectrometry (FIA-MS), monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), long-chain omega 3 polyunsaturated fatty acid (LC-n3 PUFAs), long-chain omega 6 polyunsaturated fatty acid (LC-n6 PUFAs).

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Editorial decision: Major revision
21 Mar, 2021
Review #3 received at journal
15 Mar, 2021
Reviewer #3 agreed at journal
28 Feb, 2021
Review #1 received at journal
27 Feb, 2021
Review #2 received at journal
27 Feb, 2021
Reviewer #2 agreed at journal
20 Feb, 2021
Reviewer #1 agreed at journal
20 Feb, 2021
Reviewers invited by journal
16 Feb, 2021
Editor invited by journal
10 Jan, 2021
First submitted to journal
26 Dec, 2020
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Association of Circulating Omega 3, 6 and 9 Fatty Acids with Gestational Diabetes Mellitus: A Systematic Review

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