Women post-gestational diabetes mellitus (GDM) have more than 7-fold increased risk of having future type 2 diabetes mellitus (T2DM). While a healthful dietary pattern reduces the risk of diabetes in post-GDM, no data support a dietary pattern tailored to the Malaysian diet. To address this issue, the investigators propose to determine the effects of dietary patterns and plasma metabolites in predicting the risk of T2DM known as the Nutritype model. The aim of this study is to identify Nutritype signatures of T2DM risk in women post-GDM using metabolomics approach.
Women with a history of gestational diabetes mellitus (GDM) or post-GDM are at high risk of developing type 2 diabetes (T2DM). This is important in the present context because T2DM has reached epidemic proportions. In Malaysia, the prevalence of T2DM has increased by almost 80% in just over a 10 year period. Current recommendation supports early screening at 6 weeks postpartum via oral glucose tolerance testing (OGTT) after GDM. However, the screening of women after GDM remains suboptimal, with a very low compliance rate up to almost 20%. Also, none of the recommendations highlights the need of having nutrition screening assessments despite the fact that nutritional stimuli are highly relevant to expedite disease progression in women post-GDM. As such, the metabolomics technique can be used as a tool to measure the full profile of small-molecule metabolites in bio-fluids. This technique has been expanded beyond biological disciplines towards nutrition research leading to the emerging concept of Nutritype. Nutritype refers to the expression of overall dietary intake in metabolites; work that capable to classify individuals into a certain dietary pattern based on the metabolomics profiles. While the role of metabolomics is significance, no exploration of the Nutritype signatures has been established. Potential significant determinants for the progression from GDM to T2DM include genetics, factors during the index pregnancy, exogenous modifiable risk factors and factors specific to intermediate biological mechanisms with no data on metabolites profile. Although the metabolomic signatures predicting GDM transition to T2DM in women post-GDM have been identified, its metabolites related to a protective dietary pattern is unknown. This concept is timely needed as the objective assessment of dietary intake is a huge challenge that lacks biological validation. Although several biomarkers of foods exist, identification of metabolites signature that reflects overall dietary patterns is scarce. While a healthful dietary pattern such as the alternate Healthy Eating Index (aHEI) reduces the risk of T2DM among women post-GDM, none of the patterns tailored to Malaysian diet. Direct extrapolation of these findings to the overall Malaysian diet is unknown. Therefore, the study aims to discover and identify the Nutritype signatures which combine information on dietary pattern biomarkers and metabolites profiles of T2DM risk in women post-GDM using metabolomics approach. The data will then be used to identify a predictive model of Nutritype signatures to develop protective dietary pattern works according to individuals' metabolite in preventing T2DM among women post-GDM. The findings aid in establishing an early measure of T2DM prevention in women post-GDM based on the metabolite profile that reflects the overall diet. This new exciting work leads to the goal of achieving precision diabetes-nutrition prevention using a multi-pronged strategy. This is a cross-sectional comparative study involving women post-GDM. Women with a history of GDM will have their nutritional status, metabolite profile, dietary pattern and lifestyle practices assessed. They will undergo Oral Glucose Tolerance Test (OGTT) to determine T2DM diagnosis, based on Clinical Practice Guidelines Malaysia. Based on their OGTT results, they will be divided into 3 groups: T2DM, prediabetes (impaired fasting glucose \[IFG\] or impaired glucose tolerance \[IGT\]), or non-T2DM.
Study Type
OBSERVATIONAL
Enrollment
270
Cross-sectional only
Universiti Putra Malaysia
Serdang, Selangor, Malaysia
Nutritype signature of T2DM risks in women post-GDM
To identify the nutritype signatures of T2DM risks in women post-GDM using proton nuclear magnetic resonance (1H NMR) based metabolomics approach.
Time frame: Through study completion, an average of 1 year
Prevalence of glucose intolerance
To determine prevalence of glucose intolerance (T2DM and pre-diabetes) among women post-GDM
Time frame: Through study completion, an average of 1 year
Socio-demographic background
Difference in socio-demographic background between T2DM, pre-diabetes and non-T2DM groups.
Time frame: Through study completion, an average of 1 year
Obstetric history
Difference in obstetric history between T2DM, pre-diabetes and non-T2DM groups.
Time frame: Through study completion, an average of 1 year
Nutritional status
Difference in nutritional status between T2DM, pre-diabetes and non-T2DM groups.
Time frame: Through study completion, an average of 1 year
Metabolite profile
Difference in metabolite profile between T2DM, pre-diabetes and non-T2DM groups. Metabolite profile will be analyzed based plasma blood and urine samples, using 1H NMR metabolomics approach.
Time frame: Through study completion, an average of 1 year
Dietary pattern
Difference in dietary pattern between T2DM, pre-diabetes and non-T2DM groups. Dietary pattern will be assessed using Food Frequency Questionnaire.
Time frame: Through study completion, an average of 1 year
Sleeping pattern
Difference in sleeping pattern between T2DM, pre-diabetes and non-T2DM groups. Sleeping pattern will be assessed using a questionnaire.
Time frame: Through study completion, an average of 1 year
Perceived Stress Scale score
Difference in Perceived Stress Scale score between T2DM, pre-diabetes and non-T2DM groups. PSS scores are obtained by reversing responses (e.g., 0 = 4, 1 = 3, 2 = 2, 3 = 1 \& 4 = 0) to the four positively stated items (items 4, 5, 7, \& 8) and then summing across all scale items. Minimum score is 10, whereas maximum score is 40.
Time frame: Through study completion, an average of 1 year
Physical activity level
Difference in physical activity level between T2DM, pre-diabetes and non-T2DM groups. Physical activity level will be assessed by International Physical Activity Questionnaire (IPAQ).
Time frame: Through study completion, an average of 1 year
Smoking habit and exposure
Difference in smoking habit and exposure between T2DM, pre-diabetes and non-T2DM groups. Questions on smoking habit and exposure are based on the Global Adult Tobacco Survey.
Time frame: Through study completion, an average of 1 year
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