Next Article in Journal
Artificial Intelligence in the Management of Women with Endometriosis and Adenomyosis: Can Machines Ever Be Worse Than Humans?
Previous Article in Journal
Performance of Risk Scores in Predicting Infective Endocarditis in Patients with Staphylococcus aureus Bacteraemia in a Prospective Asian Cohort
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 13
Issue 10
10.3390/jcm13102948
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dietary Intake and Oral Glucose Tolerance Test Results in Women with Gestational Diabetes

by
Lucas Almeida das Chagas
1,
Maria Regina Torloni
1,2,
Luiz Gonzaga Ribeiro Silva-Neto
3,
Patricia Medici Dualib
4,
Rosângela Maria Lopes de Sousa
5,
Jalila Andréa Sampaio Bittencourt
6,
Edward Araujo Júnior
1,
Roberta Granese
7,* and
Rosiane Mattar
1
1
Department of Obstetrics, Paulista School of Medicine, Federal University of São Paulo (EPM-UNIFESP), São Paulo 04023-062, SP, Brazil
2
Evidence Based Health Care, Department of Medicine, Paulista School of Medicine, Federal University of São Paulo (EPM-UNIFESP), São Paulo 04023-900, SP, Brazil
3
Department of Nutrition, Paulista School of Medicine, Federal University of São Paulo (EPM-UNIFESP), São Paulo 04023-062, SP, Brazil
4
Discipline of Endocrinology, Department of Medicine, Paulista School of Medicine, Federal University of São Paulo (EPM-UNIFESP), São Paulo 04038-001, SP, Brazil
5
Department of Nutrition, CEUMA University, São Luiz 65075-120, MA, Brazil
6
Laboratory of Biological Information Processing, Department of Electrical Engineering, Federal University of Maranhão (UFMA), São Luiz 65080-805, MA, Brazil
7
Department of Biomedical and Dental Sciences and Morphofunctional Imaging, “G. Martino” University Hospital, 98100 Messina, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2024, 13(10), 2948; https://doi.org/10.3390/jcm13102948
Submission received: 2 April 2024 / Revised: 2 May 2024 / Accepted: 15 May 2024 / Published: 16 May 2024
(This article belongs to the Section Obstetrics & Gynecology)
Versions Notes

Abstract

:
Background/Objective: Diet is a risk factor for gestational diabetes mellitus (GDM). There are few studies on women’s diet and glucose tolerance test (GTT) results during pregnancy. The objective of this study was to evaluate the relationship between one’s previous diet and the number of abnormal values on the diagnostic GTT in women with GDM. We hypothesized that there would be an inverse relation between antioxidant micronutrient consumption and the number of abnormal GTT values. Methods: This cross-sectional study included 60 women diagnosed with GDM (2-h, 75 g-GTT), divided in two groups as follows: 1 abnormal glucose value and 2–3 abnormal values. Shortly after the diagnosis, participants answered a validated food frequency questionnaire to assess their food consumption in the last 6 months. The Mann–Whitney test was used to compare the dietary intake of the participants in the two groups. Results: The participant characteristics were similar. The median intake of total calories, carbohydrates, lipids, and proteins did not differ significantly between groups. Participants with 1 abnormal GTT value had significantly higher intakes of fiber (11.9 vs. 11.0 g/day p = 0.049), vitamin D (40.6 vs. 40.4 mcg/day p = 0.049), and vitamin C (180.0 vs. 151.0 mg/day p = 0.008) than those with 2–3 abnormal values. Conclusions: Our results suggest a possible association between the consumption of fiber and antioxidant micronutrients and the number of abnormal GTT values.

1. Introduction

Gestational diabetes mellitus (GDM) is defined as glucose intolerance of variable severity, with the onset or first recognition occurring during pregnancy, which does not meet the diagnostic criteria for pre-existing diabetes, and may or may not persist after delivery [1]. Over the course of normal pregnancy, maternal and placental hormones induce a physiologic insulin resistance to ensure that the fetus receives a constant and adequate amount of glucose. Normal women respond to this by increasing the pancreatic production of insulin to maintain glucose homeostasis. Women whose pancreatic response is insufficient to overcome this pregnancy-induced insulin resistance develop GDM [2]. GDM is one of the most common complications of pregnancy, with a worldwide prevalence between 8.4% and 30.9% [3,4]. This variation is due to differences in population characteristics (such as age and obesity) and diagnostic criteria [3,4]. In Brazil, the prevalence of GDM is 18% according to the criteria proposed by the International Association of Diabetes and Pregnancy Study Group [5]. Rates of GDM are increasing, in parallel with the increasing rates of obesity, advanced maternal age, and unhealthy lifestyle habits across most populations [6,7,8].
GDM increases the risks of obstetric and perinatal complications (e.g., gestational hypertension, neonatal hypoglycemia, macrosomia) [9], the later development of type 2 diabetes in the mother, and abnormal glucose metabolism in exposed offspring [10]. However, timely and appropriate GDM treatment can decrease the risks of these complications [11,12]. Due to its high prevalence and negative impact on maternal and child health, GDM is an important public health issue.
According to recommendations from the World Health Organization (WHO), the diagnosis of GDM should be made in pregnant women who have ≥1 abnormal glucose values on a 75 g oral glucose tolerance test (GTT), which consists of measuring the serum glucose levels at three time points (fasting, 1 h, and 2 h after the glucose load) [1]. A larger number of abnormal glucose values on the GTT has been associated with higher risks of adverse maternal and perinatal outcomes and the need of antenatal insulin treatment for glycemic control [13,14,15,16]. Investigators suggest that the glucose metabolism and insulin sensitivity of women with >1 abnormal glucose value on the GTT are more compromised than those of women with only 1 abnormal value, characterizing a more severe subtype of GDM [13,14,15,16].
There is ample evidence suggesting that changing eating habits before and at the beginning of pregnancy can reduce the risk of developing GDM [17,18,19,20,21,22]. However, there are fewer studies on the effects of diet on the severity of GDM, and we could not identify any Brazilian study concerning this topic. A large trial conducted in Spain showed that a nutritional intervention in early pregnancy, consisting of a Mediterranean diet with added extra virgin olive oil and nuts, reduced the incidence of GDM in 30%, and significantly fewer women who developed GDM required insulin therapy, suggesting that women in the intervention group developed a milder form of the disease [23,24,25]. Adopting a diet which is rich in dietary fiber and antioxidants, with a greater variety of fruits and vegetables, could minimize the effect of oxidative stress, thus helping to prevent and control GDM and its adverse effects [26,27]. Oxidative stress is the result of an imbalance between the production and detoxication of free radicals, which are highly reactive molecules that include reactive oxygen species (ROS). The excessive production of ROS can induce inflammatory responses and cause direct tissular damage and cell death, especially in mitochondria-rich tissues, such as pancreatic Beta cells, thus reducing insulin production [28]. Therefore, since oxidative stress is involved in the pathogenesis of GDM, diets rich in antioxidants could modulate this effect.
The objective of this study was to evaluate the relationship between the type of diet consumed before the diagnosis of GDM and the number of abnormal glucose values on the GTT, an indicator of disease severity. Our hypothesis was that women with a larger number of abnormal glucose values on the GTT would exhibit worse diet quality (poorer in antioxidants and fibers) than those with fewer abnormal values.

2. Methods

2.1. Study Design and Period

This is a secondary analysis of a case–control study carried out at the Federal University of São Paulo (UNIFESP), a public institution located in São Paulo, Brazil, in 2018–2019, that evaluated the food consumption of women with GDM with and without hepatic steatosis [29]. The study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the institutional review board of Federal University of São Paulo (protocol code CAEE 86210318.0.0000.5505, 16 May 2018). All participants signed an informed consent form at the time of recruitment.

2.2. Participants

All pregnant women managed at UNIFESP´s antenatal care clinics routinely perform a fasting blood glucose test in the first trimester of pregnancy. All those with normal fasting blood glucose (<92 mg/dL) undergo a 75 g 2 h GTT at 24–32 weeks of gestation. Women with any of the following receive a diagnosis of GDM: fasting 92–125 mg/dL, 1 h ≥ 180 mg/dL, or 2 h 153–199 mg/dL [1]. Women who are diagnosed with GDM are referred to UNIFESP´s Diabetes Center, a free public outpatient clinic where they continue prenatal care with a specialized multiprofessional team which includes obstetrician–gynecologists, nurses, endocrinologists, and nutritionists. At their first visit to the Diabetes Center, before they have had any contact with other healthcare professionals, all women with a recent diagnosis of GDM were approached by the first author, who invited them to participate in the study. Women who were 18–45 years of age, with a live singleton pregnancy between 26–34 weeks, having received a diagnosis of GDM in the last 2 weeks, and not yet undergoing any GDM treatment (including nutritional counselling), were eligible to participate. The exclusion criteria included the following: illiteracy, having a mental, hearing, or visual disability that made data collection impossible, foreigners who were not fluent in Portuguese, women with bariatric surgery, infectious diseases that can affect eating habits (such as tuberculosis), women who were on any type of diet, with a history or current alcohol consumption > 20 g/day, type 1 or 2 diabetes, ascites, a history or current diagnosis of liver diseases (including hepatitis, cirrhosis, alcoholic fatty liver disease, or malignant tumors), use of medications potentially associated with hepatic steatosis (including tamoxifen, estrogen, or diltiazem) in the last six months, and women who could not remember their weight before pregnancy. We also excluded from the study women who did not complete the food frequency questionnaire for any reason, including the inability to recall their food consumption, portions, or frequency in the last six months. For this study, we categorized participants into two groups, based on the results of their GTT, as follows: those with only 1 abnormal value and those with 2–3 abnormal values.

2.3. Data Collection

Immediately after recruitment, all eligible women underwent an abdominal ultrasound (US) scan at the Diabetes Center. The same physician used portable equipment (Sanoace Pico, Medison, Seoul, Republic of Korea) to perform all exams. He classified each woman as having a normal liver, or as having mild, moderate, or severe diffuse fatty infiltration [29,30]. The principal investigator then weighed and measured each participant using an electronic digital scale (MEA 07700, Plenna, Sao Paulo, Brazil) and a stadiometer. The investigator then interviewed women to collect sociodemographic data, family and obstetric history, and information regarding pre-pregnancy weight. Women were also asked about the duration and frequency of regular physical activity in the six months before getting pregnant; those who reported at least 30 min of regular physical activity/week were considered physically active [31]. Participants then completed a written quantitative food frequency questionnaire (FFQ) which was validated for pregnant women in Brazil [32].
Based on women´s pre-pregnancy weight and measured height, we classified them into one of four pre-pregnancy body mass index (BMI) categories (<18.5, 18.5–24.9, 25.0–29.9, or ≥30.0 kg/m2) [33]. According to weight gain at the time of recruitment (weight measured by the researcher minus the self-reported pre-pregnancy weight), we also classified participants into one of three gestational weight gain categories (insufficient, adequate, or excessive) according to the recommendations of the Institute of Medicine [33].
The FFQ assessed food consumption in the last six months. To facilitate the quantification of portions and to reduce recall bias, we showed participants photos of standard serving tools (cups, plates, spoons), as well as photos of different food portion sizes. Using the dietary guidelines proposed for the Brazilian population, we converted portion sizes to grams or milliliters [34]. Portion sizes were categorized as small, medium, large, and extra large using the 25th, 50th, 75th, and 100th percentiles, respectively. We then calculated the distribution of the reported food consumption of the past six months via assessing the daily, weekly, and monthly food frequency [32]. Participant´s macro and micronutrient intake were calculated using the Dietpro Clinic nutrition software (AS Systems, 2019, version 6.1, Viçosa, Brazil). To assess the adequacy of participants´ food consumption, we followed the dietary reference intake (DRI 2002) recommendations for pregnant women [35]. If the frequency was between the estimated average need (average reference value) and the upper tolerable limit of intake, food intake was categorized as adequate.

2.4. Sample Size

We used the first 60 eligible pregnant women with GDM (convenience sample). The 60 participants (30 with hepatic steatosis and 30 with a normal liver US) were matched for self-reported skin color (brown versus other), pre-pregnancy BMI category (<18.5 versus 18.5–24.9 versus ≥25 Kg/m2), and for age (± 2 years) [29].

2.5. Statistical Analysis

We used the Student’s t test or Fisher’s exact test to compare participant characteristics in the two groups. We present the median (1st–3rd quartile) macronutrient and micronutrient intake of participants in each group. Differences in dietary intakes between the two groups were assessed using the Mann–Whitney test. p values < 0.05 were considered statistically significant.
Multivariate logistic regression was performed to examine the relationship between energy, macro, and micronutrient intake and the number of abnormal values on the GTT. For this analysis, consumption for each of the dietary items was categorized in tertiles (T1: low, T2: average, T3: high). Each of the dietary items was analyzed separately to estimate the crude odds ratios (ORs) and the 95% confidence intervals (CIs) for the likelihood of having 2–3 abnormal glucose values on the GTT. An adjusted model was created to adjust for the following covariates: age, race, income, and education.
We used the software SPSS version 19.0 (IBM Corp., Armonk, NY, USA) for all analyses.

3. Results

Most (51.7%, n = 31) of the participants had only 1 abnormal GTT value. The participant characteristics did not differ significantly between the two groups (Table 1).
Table 2 presents the participants’ dietary pattern over the last six months. Participants in both groups had an average dietary intake of fiber, vitamin A, and vitamin E below the recommended level, and a higher than recommended protein intake. There were no significant differences between groups in caloric, macronutrient, and vitamin A, E, selenium, and zinc intake. The average daily consumption of fiber (11.9 vs. 11.0 g, p = 0.049), vitamin D (40.6 vs. 40.4 mcg, p = 0.049), and vitamin C (180.0 vs. 151.0 mg, p = 0.008) was significantly higher in the group with 1 abnormal GTT value than in the group with 2–3 abnormal GTT values.
Table 3 presents the risk of having 2–3 abnormal GTT values according to tertiles of dietary items consumed. The likelihood of having 2–3 abnormal values decreased significantly as the consumption of fiber increased in tertiles. When compared with the lowest tertile, higher fiber consumption was associated with an 80% lower likelihood of having 2–3 abnormal GTT values (highest versus lowest tertile: aOR 0.20, 95% CI 0.04 to 0.95, p = 0.043). Likewise, higher vitamin D consumption was associated with an 81% lower likelihood of having 2–3 abnormal GTT values (highest versus lowest tertile: aOR 0.19, 95% CI 0.04 to 0.90, p = 0.036). Similarly, when compared to women with the lowest ingestion, those showing an average ingestion of vitamin C had an 83% lower likelihood of having 2–3 abnormal GTT values (second versus first tertile: aOR 0.17, 95% CI 0.03 to 0.82, p = 0.028), while women showing the highest ingestion of vitamin C had a 92% lower likelihood of having 2–3 abnormal GTT values (third versus first tertile: aOR 0.08, 95% CI 0.01 to 0.51, p = 0.007).

4. Discussion

We found significant differences between the food intake of women with 1 abnormal GTT value and those with 2–3 abnormal values. There were no significant differences in the consumption of macronutrients between the two groups. However, the consumption of fibers, as well as vitamins C and D were significantly higher in women with 1 abnormal value than in those with 2–3 abnormal values. The likelihood of having 2–3 abnormal GTT values decreased significantly as the consumption of fibers, vitamin C, and vitamin D increased.
GDM is a disease that involves genetic factors, lifestyle, and eating habits [36,37,38]. Several studies confirm the role of diet in the development of GDM. Women with diets which are rich in ultra-processed foods, low in fibers, and rich in simple carbohydrates associated with the consumption of sugary drinks have a higher risk of developing GDM [39,40,41,42,43]. Our study advances the knowledge in this field by showing an inverse relationship between the consumption of fibers, and of vitamins C and D, and the number of abnormal glucose values on the GTT.
Our finding of a lower fiber intake in the group with 2–3 abnormal GTT values, a possibly more severe subtype of GDM [13,14,15,16], is consistent with the literature. Xu et al. showed that the intake of total fiber and fruit fiber in the first and second trimester of pregnancy was significantly correlated with a decreased risk of GDM [44]. A meta-analysis of 8 trials involving 752 patients with GDM concluded that additional fortification with dietary fiber supplements significantly reduced fasting glucose, 2 h postprandial glucose, and the glycated hemoglobin levels in the study participants [45]. Dietary fiber plays an important role in controlling postprandial blood glucose because it delays gastric emptying, blood glucose levels, insulin resistance, and the metabolic profile of healthy and diabetic individuals [46,47].
There is evidence that a diet which is rich in antioxidants could reduce the incidence of GDM [48]. Normal pregnancy is characterized by increased levels of free radicals and lipid peroxides [49]. However, hyperglycemia during pregnancy is associated with increased oxidative stress, which impairs the insulin-dependent glucose uptake and increases apoptosis and placental dysfunction. These factors can contribute to a greater pro-inflammatory state which, in turn, may worsen hyperglycemia and the severity of GDM [50,51].
Our findings of lower vitamin C and D values in women with 2–3 abnormal GTT values align with those reported in the literature, showing that adequate vitamin D and C intake can have a protective role in the development GDM and may improve glycemic control [52,53,54,55,56]. In a meta-analysis involving 15 observational studies with over 8000 participants, Zhou et al. showed that, among women with low vitamin C ingestion, the risk of developing GDM was 2.72-fold higher than in those with higher vitamin C ingestion [55]. The beneficial effect of vitamin C on glucose metabolism is probably due to its anti-inflammatory and antioxidant activities, which could help to compensate for the increased oxidative stress and insulin resistance which are typical of pregnancy. On the other hand, vitamin D affects glucose metabolism by increasing insulin sensitivity, and women with low levels of vitamin D have a higher risk of developing GDM [53].
This is the first study that investigated the association between usual food consumption and the number of abnormal glucose values on the GTT. This approach is innovative and aligns with the recent concept that GDM is a disease with different phenotypes [57]. Several investigators suggest the existence of different metabolic subtypes of GDM, possibly associated with better or worse maternal/perinatal outcomes, which could be identified based on GTT results [14,15,16,58,59,60,61,62]. The study has several limitations. Due to its small sample size, the study may have been underpowered to detect other associations between dietary patterns and the number of abnormal GTT values. Moreover, the study design and the absence of a control group (pregnant women with a normal GTT) prevent inferring a causal relation between dietary intake and GTT results. Another study limitation was that, since we used only one FFQ for the six months that preceded the diagnosis of GDM, we cannot exclude recall bias.
Our study does not conclusively demonstrate an association between the intake of the various nutrients studied and the number of abnormal values on the GTT. More studies are needed to confirm these findings. Future studies should involve a larger number of participants, a more diverse group of women with GDM, and a control group of pregnant women with normal GTT results.

5. Conclusions

Our results suggest a possible association between the consumption of dietary fiber and some antioxidant nutrients (vitamin D and C) and the number of abnormal glucose values on the GTT at the time of GDM diagnosis. As hypothesized, women with a larger number of abnormal glucose values on the GTT had diets that were poorer in antioxidants and fibers. These findings contribute to the evolving field of research concerning the role of diet in the development of different subtypes of GDM.

Author Contributions

Conceptualization M.R.T. and R.M.; methodology P.M.D.; validation L.G.R.S.-N. and R.M.L.d.S.; formal analysis J.A.S.B.; investigation L.A.d.C.; resources R.G.; data curation L.A.d.C.; writing—original draft preparation E.A.J.; writing—review and editing M.R.T.; visualization L.A.d.C., M.R.T., L.G.R.S.-N., R.M.L.d.S., P.M.D., J.A.S.B., R.G., E.A.J. and R.M.; supervision M.R.T.; project administration R.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Our investigations were carried out following the rules of the Declaration of Helsinki of 1975, revised in 2013. The study was approved by the Ethics Committee of the Federal University of São Paulo (CAEE 86210318.0.0000.5505) on 16 May 2018.

Informed Consent Statement

Informed consent was obtained from all participants.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. World Health Organization. Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy: A World Health Organization Guideline. Diabetes Res. Clin. Pract. 2014, 103, 341–363. [Google Scholar] [CrossRef] [PubMed]
  2. Sweeting, A.; Wong, J.; Murphy, H.R.; Ross, G.P. A Clinical Update on Gestational Diabetes Mellitus. Endocr. Rev. 2022, 43, 763–793. [Google Scholar] [CrossRef] [PubMed]
  3. Magliano, D.J.; Boyko, E.J.; IDF Diabetes Atlas 10th Edition Scientific Committee. IDF Diabetes Atlas, 10th ed.; International Diabetes Federation: Brussels, Belgium, 2021; ISBN 978-2-930229-98-0. [Google Scholar]
  4. Guariguata, L.; Linnenkamp, U.; Beagley, J.; Whiting, D.R.; Cho, N.H. Global Estimates of the Prevalence of Hyperglycaemia in Pregnancy. Diabetes Res. Clin. Pract. 2014, 103, 176–185. [Google Scholar] [CrossRef] [PubMed]
  5. Trujillo, J.; Vigo, A.; Duncan, B.B.; Falavigna, M.; Wendland, E.M.; Campos, M.A.; Schmidt, M.I. Impact of the International Association of Diabetes and Pregnancy Study Groups Criteria for Gestational Diabetes. Diabetes Res. Clin. Pract. 2015, 108, 288–295. [Google Scholar] [CrossRef] [PubMed]
  6. Shah, N.S.; Wang, M.C.; Freaney, P.M.; Perak, A.M.; Carnethon, M.R.; Kandula, N.R.; Gunderson, E.P.; Bullard, K.M.; Grobman, W.A.; O’Brien, M.J.; et al. Trends in Gestational Diabetes at First Live Birth by Race and Ethnicity in the US. 2011–2019. JAMA 2021, 326, 660–669. [Google Scholar] [CrossRef] [PubMed]
  7. Anna, V.; van der Ploeg, H.P.; Cheung, N.W.; Huxley, R.R.; Bauman, A.E. Sociodemographic Correlates of the Increasing Trend in Prevalence of Gestational Diabetes Mellitus in a Large Population of Women between 1995 and 2005. Diabetes Care 2008, 31, 2288–2293. [Google Scholar] [CrossRef] [PubMed]
  8. Linder, T.; Dressler-Steinbach, I.; Tura, A.; Göbl, C. New Developments. Challenges and Open Questions in Diagnosis and Treatment of Gestational Diabetes Mellitus. J. Clin. Med. 2022, 11, 7197. [Google Scholar] [CrossRef] [PubMed]
  9. HAPO Study Cooperative Research Group; Metzger, B.E.; Lowe, L.P.; Dyer, A.R.; Trimble, E.R.; Chaovarindr, U.; Coustan, D.R.; Hadden, D.R.; McCance, D.R.; Hod, M.; et al. Hyperglycemia and Adverse Pregnancy Outcomes. N. Engl. J. Med. 2008, 358, 1991–2002. [Google Scholar] [CrossRef] [PubMed]
  10. Lowe, W.L.; Scholtens, D.M.; Kuang, A.; Linder, B.; Lawrence, J.M.; Lebenthal, Y.; McCance, D.; Hamilton, J.; Nodzenski, M.; Talbot, O.; et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): Maternal Gestational Diabetes Mellitus and Childhood Glucose Metabolism. Diabetes Care 2019, 42, 372–380. [Google Scholar] [CrossRef]
  11. Hartling, L.; Dryden, D.M.; Guthrie, A.; Muise, M.; Vandermeer, B.; Donovan, L. Benefits and Harms of Treating Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis for the U.S. Preventive Services Task Force and the National Institutes of Health Office of Medical Applications of Research. Ann. Intern. Med. 2013, 159, 123–129. [Google Scholar] [CrossRef]
  12. ElSayed, N.A.; Aleppo, G.; Aroda, V.R.; Bannuru, R.R.; Brown, F.M.; Bruemmer, D.; Collins, B.S.; Cusi, K.; Das, S.R.; Gibbons, C.H.; et al. Summary of Revisions: Standards of Care in Diabetes—2023. Diabetes Care 2022, 46, S5–S9. [Google Scholar] [CrossRef]
  13. Ding, T.-T.; Xiang, J.; Luo, B.-R.; Hu, J. Relationship between the IADPSG-Criteria-Defined Abnormal Glucose Values and Adverse Pregnancy Outcomes among Women Having Gestational Diabetes Mellitus: A Retrospective Cohort Study. Medicine 2018, 97, e12920. [Google Scholar] [CrossRef]
  14. Ikenoue, S.; Miyakoshi, K.; Saisho, Y.; Sakai, K.; Kasuga, Y.; Fukutake, M.; Izumi, Y.; Matsumoto, T.; Minegishi, K.; Yoshimura, Y. Clinical Impact of Women with Gestational Diabetes Mellitus by the New Consensus Criteria: Two Year Experience in a Single Institution in Japan. Endocr. J. 2014, 61, 353–358. [Google Scholar] [CrossRef] [PubMed]
  15. Feng, H.; Zhu, W.-W.; Yang, H.-X.; Wei, Y.-M.; Wang, C.; Su, R.-N.; Hod, M.; Hadar, E. Relationship between Oral Glucose Tolerance Test Characteristics and Adverse Pregnancy Outcomes among Women with Gestational Diabetes Mellitus. Chin. Med. J. 2017, 130, 1012–1018. [Google Scholar] [CrossRef] [PubMed]
  16. Bhavadharini, B.; Anjana, R.M.; Deepa, M.; Pradeepa, R.; Uma, R.; Saravanan, P.; Mohan, V. Association between Number of Abnormal Glucose Values and Severity of Fasting Plasma Glucose in IADPSG Criteria and Maternal Outcomes in Women with Gestational Diabetes Mellitus. Acta Diabetol. 2022, 59, 349–357. [Google Scholar] [CrossRef] [PubMed]
  17. Tobias, D.K.; Zhang, C.; Chavarro, J.; Bowers, K.; Rich-Edwards, J.; Rosner, B.; Mozaffarian, D.; Hu, F.B. Prepregnancy Adherence to Dietary Patterns and Lower Risk of Gestational Diabetes Mellitus. Am. J. Clin. Nutr. 2012, 96, 289–295. [Google Scholar] [CrossRef]
  18. Donazar-Ezcurra, M.; López-del Burgo, C.; Bes-Rastrollo, M. Primary Prevention of Gestational Diabetes Mellitus through Nutritional Factors: A Systematic Review. BMC Pregnancy Childbirth 2017, 17, 30. [Google Scholar] [CrossRef] [PubMed]
  19. Koivusalo, S.B.; Rönö, K.; Klemetti, M.M.; Roine, R.P.; Lindström, J.; Erkkola, M.; Kaaja, R.J.; Pöyhönen-Alho, M.; Tiitinen, A.; Huvinen, E.; et al. Gestational Diabetes Mellitus Can Be Prevented by Lifestyle Intervention: The Finnish Gestational Diabetes Prevention Study (RADIEL): A Randomized Controlled Trial. Diabetes Care 2016, 39, 24–30. [Google Scholar] [CrossRef]
  20. Griffith, R.J.; Alsweiler, J.; Moore, A.E.; Brown, S.; Middleton, P.; Shepherd, E.; Crowther, C.A. Interventions to Prevent Women from Developing Gestational Diabetes Mellitus: An Overview of Cochrane Reviews. Cochrane Database Syst. Rev. 2020, 6, CD012394. [Google Scholar] [CrossRef]
  21. Karamanos, B.; Thanopoulou, A.; Anastasiou, E.; Assaad-Khalil, S.; Albache, N.; Bachaoui, M.; Slama, C.B.; El Ghomari, H.; Jotic, A.; Lalic, N.; et al. Relation of the Mediterranean Diet with the Incidence of Gestational Diabetes. Eur. J. Clin. Nutr. 2014, 68, 8–13. [Google Scholar] [CrossRef]
  22. Schoenaker, D.A.J.M.; Mishra, G.D.; Callaway, L.K.; Soedamah-Muthu, S.S. The Role of Energy. Nutrients. Foods. and Dietary Patterns in the Development of Gestational Diabetes Mellitus: A Systematic Review of Observational Studies. Diabetes Care 2016, 39, 16–23. [Google Scholar] [CrossRef]
  23. De la Torre, N.G.; Assaf-Balut, C.; Jiménez Varas, I.; Del Valle, L.; Durán, A.; Fuentes, M.; Del Prado, N.; Bordiú, E.; Valerio, J.J.; Herraiz, M.A.; et al. Effectiveness of Following Mediterranean Diet Recommendations in the Real World in the Incidence of Gestational Diabetes Mellitus (GDM) and Adverse Maternal-Foetal Outcomes: A Prospective. Universal. Interventional Study with a Single Group. The St Carlos Study. Nutrients 2019, 11, 1210. [Google Scholar] [CrossRef] [PubMed]
  24. Assaf-Balut, C.; García de la Torre, N.; Durán, A.; Fuentes, M.; Bordiú, E.; Del Valle, L.; Familiar, C.; Ortolá, A.; Jiménez, I.; Herraiz, M.A.; et al. A Mediterranean Diet with Additional Extra Virgin Olive Oil and Pistachios Reduces the Incidence of Gestational Diabetes Mellitus (GDM): A Randomized Controlled Trial: The St. Carlos GDM Prevention Study. PLoS ONE 2017, 12, e0185873. [Google Scholar] [CrossRef]
  25. Assaf-Balut, C.; Garcia de la Torre, N.; Durán, A.; Fuentes, M.; Bordiú, E.; Del Valle, L.; Valerio, J.; Familiar, C.; Jiménez, I.; Herraiz, M.A.; et al. Medical Nutrition Therapy for Gestational Diabetes Mellitus Based on Mediterranean Diet Principles: A Subanalysis of the St Carlos GDM Prevention Study. BMJ Open Diabetes Res. Care 2018, 6, e000550. [Google Scholar] [CrossRef] [PubMed]
  26. Markovic, T.P.; Muirhead, R.; Overs, S.; Ross, G.P.; Louie, J.C.Y.; Kizirian, N.; Denyer, G.; Petocz, P.; Hyett, J.; Brand-Miller, J.C. Randomized Controlled Trial Investigating the Effects of a Low-Glycemic Index Diet on Pregnancy Outcomes in Women at High Risk of Gestational Diabetes Mellitus: The GI Baby 3 Study. Diabetes Care 2016, 39, 31–38. [Google Scholar] [CrossRef]
  27. Wan, C.S.; Nankervis, A.; Teede, H.; Aroni, R. Dietary Intervention Strategies for Ethnic Chinese Women with Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Nutr. Diet. J. Dietit. Assoc. Aust. 2019, 76, 211–232. [Google Scholar] [CrossRef] [PubMed]
  28. Joo, E.H.; Kim, Y.R.; Kim, N.; Jung, J.E.; Han, S.H.; Cho, H.Y. Effect of Endogenic and Exogenic Oxidative Stress Triggers on Adverse Pregnancy Outcomes: Preeclampsia, Fetal Growth Restriction, Gestational Diabetes Mellitus and Preterm Birth. Int. J. Mol. Sci. 2021, 19, 10122. [Google Scholar] [CrossRef]
  29. Chagas, L.; Torloni, M.R.; Sanchez, V.H.S.; Pititto, B.A.; Dualib, P.M.; Mattar, R. Dietary Intake of Pregnant Women with Non-Alcoholic Fatty Liver Disease: A Case-Control Study. Clin. Nutr. ESPEN 2023, 57, 630–636. [Google Scholar] [CrossRef]
  30. Lee, S.S.; Park, S.H. Radiologic Evaluation of Nonalcoholic Fatty Liver Disease. World J. Gastroenterol. WJG 2014, 20, 7392–7402. [Google Scholar] [CrossRef]
  31. Chasan-Taber, L.; Schmidt, M.D.; Roberts, D.E.; Hosmer, D.; Markenson, G.; Freedson, P.S. Development and Validation of a Pregnancy Physical Activity Questionnaire. Med. Sci. Sports Exerc. 2004, 36, 1750–1760. [Google Scholar] [CrossRef]
  32. Barbieri, P.; Nishimura, R.Y.; Crivellenti, L.C.; Sartorelli, D.S. Relative Validation of a Quantitative FFQ for Use in Brazilian Pregnant Women. Public Health Nutr. 2013, 16, 1419–1426. [Google Scholar] [CrossRef] [PubMed]
  33. Institute of Medicine (US) and National Research Council (US) Committee to Reexamine IOM Pregnancy Weight Guidelines. Weight Gain During Pregnancy: Reexamining the Guidelines; Rasmussen, K.M., Yaktine, A.L., Eds.; The National Academies Collection: Reports Funded by National Institutes of Health; National Academies Press (US): Washington, DC, USA, 2009; ISBN 978-0-309-13113-1. [Google Scholar]
  34. Brazilian Ministry of Health. Dietary Guidelines for the Brazilian Population; Brazilian Ministry of Health: Brasilia, Brazil, 2015; ISBN 978-85-334-2242-1. [Google Scholar]
  35. Institute of Medicine (US). Panel on Micronutrients. In Dietary Reference Intakes for Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium, and Zinc; National Academies Press (US): Washington, DC, USA, 2001; ISBN 978-0-309-07279-3. [Google Scholar]
  36. Zhang, C.; Ning, Y. Effect of Dietary and Lifestyle Factors on the Risk of Gestational Diabetes: Review of Epidemiologic Evidence. Am. J. Clin. Nutr. 2011, 94, 1975S–1979S. [Google Scholar] [CrossRef] [PubMed]
  37. Bao, W.; Michels, K.B.; Tobias, D.K.; Li, S.; Chavarro, J.E.; Gaskins, A.J.; Vaag, A.A.; Hu, F.B.; Zhang, C. Parental Smoking during Pregnancy and the Risk of Gestational Diabetes in the Daughter. Int. J. Epidemiol. 2016, 45, 160–169. [Google Scholar] [CrossRef] [PubMed]
  38. Zhang, C.; Bao, W.; Rong, Y.; Yang, H.; Bowers, K.; Yeung, E.; Kiely, M. Genetic Variants and the Risk of Gestational Diabetes Mellitus: A Systematic Review. Hum. Reprod. Update 2013, 19, 376–390. [Google Scholar] [CrossRef] [PubMed]
  39. Bao, W.; Tobias, D.K.; Olsen, S.F.; Zhang, C. Pre-Pregnancy Fried Food Consumption and the Risk of Gestational Diabetes Mellitus: A Prospective Cohort Study. Diabetologia 2014, 57, 2485–2491. [Google Scholar] [CrossRef] [PubMed]
  40. Bao, W.; Tobias, D.K.; Hu, F.B.; Chavarro, J.E.; Zhang, C. Pre-Pregnancy Potato Consumption and Risk of Gestational Diabetes Mellitus: Prospective Cohort Study. BMJ 2016, 352, h6898. [Google Scholar] [CrossRef] [PubMed]
  41. Chen, L.; Hu, F.B.; Yeung, E.; Willett, W.; Zhang, C. Prospective Study of Pre-Gravid Sugar-Sweetened Beverage Consumption and the Risk of Gestational Diabetes Mellitus. Diabetes Care 2009, 32, 2236–2241. [Google Scholar] [CrossRef]
  42. Zhang, C.; Liu, S.; Solomon, C.G.; Hu, F.B. Dietary Fiber Intake. Dietary Glycemic Load. and the Risk for Gestational Diabetes Mellitus. Diabetes Care 2006, 29, 2223–2230. [Google Scholar] [CrossRef] [PubMed]
  43. Leone, A.; Martínez-González, M.Á.; Craig, W.; Fresán, U.; Gómez-Donoso, C.; Bes-Rastrollo, M. Pre-Gestational Consumption of Ultra-Processed Foods and Risk of Gestational Diabetes in a Mediterranean Cohort. The SUN Project. Nutrients 2021, 13, 2202. [Google Scholar] [CrossRef]
  44. Xu, Q.; Tao, Y.; Zhang, Y.; Zhang, X.; Xue, C.; Liu, Y. Dietary Fiber Intake. Dietary Glycemic Load. and the Risk of Gestational Diabetes Mellitus during the Second Trimester: A Nested Case-Control Study. Asia Pac. J. Clin. Nutr. 2021, 30, 477–486. [Google Scholar] [CrossRef]
  45. Sun, J.; Wang, J.; Ma, W.; Miao, M.; Sun, G. Effects of Additional Dietary Fiber Supplements on Pregnant Women with Gestational Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. Nutrients 2022, 14, 4626. [Google Scholar] [CrossRef] [PubMed]
  46. Yu, K.; Ke, M.-Y.; Li, W.-H.; Zhang, S.-Q.; Fang, X.-C. The Impact of Soluble Dietary Fibre on Gastric Emptying. Postprandial Blood Glucose and Insulin in Patients with Type 2 Diabetes. Asia Pac. J. Clin. Nutr. 2014, 23, 210–218. [Google Scholar] [CrossRef] [PubMed]
  47. Chen, C.; Zeng, Y.; Xu, J.; Zheng, H.; Liu, J.; Fan, R.; Zhu, W.; Yuan, L.; Qin, Y.; Chen, S.; et al. Therapeutic Effects of Soluble Dietary Fiber Consumption on Type 2 Diabetes Mellitus. Exp. Ther. Med. 2016, 12, 1232–1242. [Google Scholar] [CrossRef] [PubMed]
  48. Daneshzad, E.; Tehrani, H.; Bellissimo, N.; Azadbakht, L. Dietary Total Antioxidant Capacity and Gestational Diabetes Mellitus: A Case-Control Study. Oxid. Med. Cell Longev. 2020, 2020, 5471316. [Google Scholar] [CrossRef] [PubMed]
  49. Chen, X.; Scholl, T.O. Oxidative Stress: Changes in Pregnancy and with Gestational Diabetes Mellitus. Curr. Diab. Rep. 2005, 5, 282–288. [Google Scholar] [CrossRef] [PubMed]
  50. Chatzakis, C.; Sotiriadis, A.; Tsakmaki, E.; Papagianni, M.; Paltoglou, G.; Dinas, K.; Mastorakos, G. The Effect of Dietary Supplements on Oxidative Stress in Pregnant Women with Gestational Diabetes Mellitus: A Network Meta-Analysis. Nutrients 2021, 13, 2284. [Google Scholar] [CrossRef] [PubMed]
  51. Li, H.; Yin, Q.; Li, N.; Ouyang, Z.; Zhong, M. Plasma Markers of Oxidative Stress in Patients with Gestational Diabetes Mellitus in the Second and Third Trimester. Obstet. Gynecol. Int. 2016, 2016, 3865454. [Google Scholar] [CrossRef]
  52. Chen, Q.; Feng, Y.; Yang, H.; Wu, W.; Zhang, P.; Wang, K.; Wang, Y.; Ko, J.; Shen, J.; Guo, L.; et al. A Vitamin Pattern Diet Is Associated with Decreased Risk of Gestational Diabetes Mellitus in Chinese Women: Results from a Case Control Study in Taiyuan. China J. Diabetes Res. 2019, 2019, 5232308. [Google Scholar] [CrossRef]
  53. Aljanahi, A.; Hadhiah, H.; Al-Nasr, W.; Abuzaid, O.; Al Qahtani, N.; Sebastian, T.; Metwally, R. The Effect of Dietary Intake of Vitamin D on Gestational Diabetes Mellitus. Nutr. Metab. Insights 2020, 13, 2164. [Google Scholar] [CrossRef]
  54. Mahjoub, F.; Ben Jemaa, H.; Ben Sabeh, F.; Ben Amor, N.; Gamoudi, A.; Jamoussi, H. Impact of Nutrients and Mediterranean Diet on the Occurrence of Gestational Diabetes. Libyan J. Med. 2021, 16, 1930346. [Google Scholar] [CrossRef]
  55. Zhou, L.; Liu, J.; Zhou, M. A Comprehensive Meta-Analysis on the Association between Vitamin C Intake and Gestational Diabetes Mellitus: Insights and Novel Perspectives. Medicine 2023, 102, e34740. [Google Scholar] [CrossRef] [PubMed]
  56. Zhang, Q.; Cheng, Y.; He, M.; Li, T.; Ma, Z.; Cheng, H. Effect of Various Doses of Vitamin D Supplementation on Pregnant Women with Gestational Diabetes Mellitus: A Randomized Controlled Trial. Exp. Ther. Med. 2016, 12, 1889–1895. [Google Scholar] [CrossRef] [PubMed]
  57. Yeung, R.O.; Retnakaran, R.; Savu, A.; Butalia, S.; Kaul, P. Gestational Diabetes: One Size Does Not Fit All-an Observational Study of Maternal and Neonatal Outcomes by Maternal Glucose Profile. Diabet. Med. J. Br. Diabet. Assoc. 2024, 41, e15205. [Google Scholar] [CrossRef]
  58. Rostin, P.; Balke, S.; Sroka, D.; Fangmann, L.; Weid, P.; Henrich, W.; Königbauer, J.T. The CHANGED Score-A New Tool for the Prediction of Insulin Dependency in Gestational Diabetes. J. Clin. Med. 2023, 12, 7169. [Google Scholar] [CrossRef]
  59. Pertot, T.; Molyneaux, L.; Tan, K.; Ross, G.P.; Yue, D.K.; Wong, J. Can Common Clinical Parameters Be Used to Identify Patients Who Will Need Insulin Treatment in Gestational Diabetes Mellitus? Diabetes Care 2011, 34, 2214–2216. [Google Scholar] [CrossRef]
  60. Balke, S.; Weid, P.; Fangmann, L.; Rostin, P.; Henrich, W.; Koenigbauer, J.T. Glucose Levels of the Oral Glucose Tolerance Test (oGTT) Can Predict Adverse Pregnancy Outcomes in Women with Gestational Diabetes (GDM). J. Clin. Med. 2023, 12, 3709. [Google Scholar] [CrossRef] [PubMed]
  61. Chatzakis, C.; Eleftheriades, A.; Demertzidou, E.; Dinas, K.; Vlahos, N.; Sotiriadis, A.; Eleftheriades, M. Pregnancy Outcomes in the Different Phenotypes of Gestational Diabetes Mellitus Based on the Oral Glucose Tolerance Test. A Systematic Review and Meta-Analysis. Diabetes Res. Clin. Pract. 2023, 204, 110913. [Google Scholar] [CrossRef]
  62. Bai, W.; Wang, H.; Fang, R.; Lin, M.; Qin, Y.; Han, H.; Cui, J.; Zhang, R.; Ma, Y.; Chen, D.; et al. Evaluating the Effect of Gestational Diabetes Mellitus on Macrosomia Based on the Characteristics of Oral Glucose Tolerance Test. Clin. Chim. Acta Int. J. Clin. Chem. 2023, 544, 117362. [Google Scholar] [CrossRef]
Table 1. Characteristics of 60 pregnant women with gestational diabetes mellitus according to the glucose tolerance test results.
Table 1. Characteristics of 60 pregnant women with gestational diabetes mellitus according to the glucose tolerance test results.
VariablesNumber of Abnormal Glucose Values on GTT *p Value #
1
(n = 31)		2–3
(n = 29)
Age, years, mean (SD)33.0 (5.7)34.5 (6.1)0.337
Gestational age, weeks, mean (SD)28.0 (2.9)27.2 (2.6)0.268
Skin color 0.146
  Brown11 (35.5)10 (34.5)
  White2 (6.5)7 (24.1)
  Black18 (58.1)12 (41.4)
Marital status 0.782
  Married10 (32.3)8 (27.6)
  Single21 (67.7)21 (72.4)
Education, years 0.140
  0–72 (6.5)6 (20.7)
  ≥829 (93.5)23 (79.3)
Monthly family income 0.208
  <256 USD17 (54.8)11 (37.9)
  ≥256 USD14 (45.2)18 (62.1)
Religion 0.177
  Catholic18 (58.1)22 (75.9)
  Other13 (41.9)7 (24.1)
Family history of diabetes 0.800
  Yes17 (54.8)17 (58.6)
  No14 (45.2)12 (41.4)
Chronic hypertension 0.229
  No31 (100.0)27 (93.1)
  Yes0 (0.0)2 (6.9)
Physically active 0.783
  No22 (71.0)19 (65.5)
  Yes9 (29.0)10 (34.5)
Pre-pregnancy BMI, kg/m2 0.398
  <18.50 (0.0)1 (3.4)
  18.5–24.93 (9.7)2 (6.9)
  25.0–29.910 (32.3)14 (48.3)
  ≥30.018 (58.1)12 (41.4)
Gestational weight gain 1.000
  Insufficient14 (45.2)13 (44.8)
  Adequate5 (16.1)5 (17.2)
  Excessive12 (38.7)11 (37.9)
Hepatic steatosis 0.930
  Absent16 (51.6)14 (48.3)
  Mild12 (38.7)13 (44.8)
  Moderate3 (9.7)2 (6.9)
Abnormal glucose value in GTT * EUR 0.945
  Fasting19 (61.3)21 (72.4)
  1 h5 (16.1)19 (65.5)
  2 h7 (22.6)27 (93.1)
BMI: Body mass index, GTT: Glucose tolerance test, SD: Standard deviation, USD: US dollars. All values represent numbers (%) unless otherwise noted. * 75 g GTT cutoffs for GDM diagnosis: fasting 92–125, 1 h ≥ 180, 2 h 153–199 mg/dL. # Student’s t test or Fisher’s exact test. EUR The total number in the group with 2–3 abnormal glucose values is > total group number (n = 29) because each participant had 2 or more abnormal glucose values.
Table 2. Food consumption of 60 pregnant women with gestational diabetes according to the number of abnormal values on the glucose tolerance test.
Table 2. Food consumption of 60 pregnant women with gestational diabetes according to the number of abnormal values on the glucose tolerance test.
VariableNutritional
Recommendation *		Number of Abnormal Glucose Values on GTT **p Value #
1
(n = 31)		2–3
(n = 29)
Calories1800–25002000.5 (1779.0–2230.0)1905.0 (1696.0–2111.0)0.105
Carbohydrate45–65%57.9 (50.0–63.5)56.4 (53.5–63.9)0.676
Protein10–15%15.8 (14.3–18.5)16.1 (13.5–16.9)0.245
Lipid25–30%27.5 (22.4–32.1)27.1 (23.1–30.6)0.471
Dietary fiber28 g11.9 (10.8–16.2)11.0 (9.1–14.2)0.049
Vitamin A550–3000 μg144.0 (68.7–298.0)146.0 (77.1–199.0)0.270
Vitamin D50 µg40.6 (40.3–80.2)40.4 (20.8–40.7)0.049
Vitamin E12–1000 mg6.0 (4.5–8.3)6.0 (4.6–8.1)0.427
Vitamin C70–2000 mg180.0 (157.0–275.0)151.0 (117.0–204.0)0.008
Selenium49–400 μg43.5 (32.0–47.1)39.0 (32.2–49.2)0.350
Zinc9.5–40 mg5.6 (4.2–6.9)5.2 (4.1–6.0)0.185
GTT: Glucose tolerance test. All values represent median (1st–3rd quartile). * Estimated average needs to assess the adequacy of intake of pregnant women based on the recommended dietary intake and maximum tolerable intake. ** 75 g GTT cutoffs for GDM diagnosis: fasting 92–125, 1 h ≥ 180, 2 h 153–199 mg/dL. # Mann–Whitney test.
Table 3. Likelihood of having 2–3 abnormal glucose values on the GTT according to tertiles of nutrient intake.
Table 3. Likelihood of having 2–3 abnormal glucose values on the GTT according to tertiles of nutrient intake.
Nutrients2–3 Abnormal Values
(n = 29)		1 Abnormal Value
(n = 31)		OR95% CIp ValueaOR95%CIp Value
Calories
T1: <1809.411 (37.9%)9 (29.0%)1.00 1.00
T2: 1809.4–2128.911 (37.9%)9 (29.0%)1.000.29; 3.481.0001.010.25; 4.020.996
T3: ≥2129.07 (24.1%)13 (41.9)0.440.12; 1.570.2070.540.13; 2.280.400
Carbohydrate
T1: <255.112 (41.4%)8 (25.8)1.00 1.00
T2: 255.1–297.19 (31.0%)11 (35.5%)0.540.15; 1.910.3440.270.05; 1.250.095
T3: ≥297.28 (27.6%)12 (38.7%)0.440.12; 1.570.2090.400.09; 1.740.225
Protein
T1: <68.413 (44.8%)7 (22.6%)1.00 1.00
T2: 68.4–87.69 (31.0%)11 (35.5%)0.440.12; 1.570.2070.500.10; 2.280.371
T3 ≥87.77 (24.1%)13 (49.1%)0.290.07; 1.060.0620.340.08; 1.430.143
Lipid
T1: <52.112 (41.4%)8 (25.8%)1.00 1.00
T2: 52.1–64.610 (34.5%)10 (32.3%)0.660.19; 2.330.5260.840.20; 3.390.807
T3: ≥64.77 (24.1%)13 (41.9%)0.350.09; 1.290.1170.670.14; 3.110.616
Dietary fiber
T1: <10.613 (44.8%)7 (22.6%)1.00 1.00
T2: 10.6–13.98 (27.6%)12 (38.7%)0.360.10; 1.290.1170.330.07; 1.480.150
T3: ≥14.08 (27.6%)12 (38.7%)0.350.09; 1.290.1170.200.04; 0.950.043
Vitamin A
T1: <79.610 (34.5%)10 (32.3%)1.00 1.00
T2: 79.6–199.012 (41.4%)8 (25.8%)1.500.42; 5.250.5261.030.23; 4.600.960
T3: ≥199.17 (24.1%)13 (41.9%)0.530.15; 1.920.3390.540.13; 2.130.384
Vitamin D
T1: <40.313 (44.8%)7 (22.6%)1.00 1.00
T2: 40.3–40.99 (31.0%)11 (35.5%)0.440.12; 1.570.2070.380.10; 1.760.219
T3: ≥41.07 (24.1%)13 (41.9%)0.290.79; 1.060.0620.190.04; 0.900.036
Vitamin C
T1: <145.114 (41.3%)6 (19.4%)1.00 1.00
T2: 145.1–204.08 (27.6%)12 (38.7%)0.280.07; 1.050.0610.170.03; 0.820.028
T3: ≥204.17 (24.1%)13 (41.9%)0.230.06; 0.860.0300.080.01; 0.510.007
Vitamin E
T1: <4.910 (34.5%)10 (32.3%)1.00 1.00
T2: 4.9–7.810 (34.5%)11 (35.5%)0.910.26; 3.100.8790.970.23; 4.030.975
T3: ≥7.99 (31.0%)10 (32.3%)0.900.25; 3.160.8690.860.20; 3.630.843
Selenium
T1: <40.011 (37.9%)9 (29.9%)1.00 1.00
T2: 40.0–45.68 (27.6%)12 (38.7%)0.540.15; 1.910.3440.780.18; 3.250.735
T3: ≥45.710 (34.5%)10 (32.3%)0.810.23; 2.840.7520.840.21; 3.340.810
Zinc
T1: <4.79 (31.0%)11 (35.5%)1.00 1.00
T2: 4.7–5.911 (37.9%)9 (29.0%)1.490.43; 5.190.5282.090.44; 9.790.349
T3: ≥6.09 (31.0%)11 (35.5%)1.000.28; 3.481.0000.920.22; 3.850.912
CI: Confidence interval. GTT: Glucose tolerance test. OR: Odds ratio. aOR: adjusted OR (age, race, income, and education). T: tertile.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

das Chagas, L.A.; Torloni, M.R.; Silva-Neto, L.G.R.; Dualib, P.M.; de Sousa, R.M.L.; Bittencourt, J.A.S.; Araujo Júnior, E.; Granese, R.; Mattar, R. Dietary Intake and Oral Glucose Tolerance Test Results in Women with Gestational Diabetes. J. Clin. Med. 2024, 13, 2948. https://doi.org/10.3390/jcm13102948

AMA Style

das Chagas LA, Torloni MR, Silva-Neto LGR, Dualib PM, de Sousa RML, Bittencourt JAS, Araujo Júnior E, Granese R, Mattar R. Dietary Intake and Oral Glucose Tolerance Test Results in Women with Gestational Diabetes. Journal of Clinical Medicine. 2024; 13(10):2948. https://doi.org/10.3390/jcm13102948

Chicago/Turabian Style

das Chagas, Lucas Almeida, Maria Regina Torloni, Luiz Gonzaga Ribeiro Silva-Neto, Patricia Medici Dualib, Rosângela Maria Lopes de Sousa, Jalila Andréa Sampaio Bittencourt, Edward Araujo Júnior, Roberta Granese, and Rosiane Mattar. 2024. "Dietary Intake and Oral Glucose Tolerance Test Results in Women with Gestational Diabetes" Journal of Clinical Medicine 13, no. 10: 2948. https://doi.org/10.3390/jcm13102948

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop