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| Name | Class |
|---|---|
| Ministry of Health, Malaysia | OTHER_GOV |
| Universiti Teknologi Mara | OTHER |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Type 2 diabetes mellitus (T2DM) | Subjects in this group are diagnosed with type 2 diabetes mellitus. They receive no intervention. Their nutritional status, metabolite profile, dietary pattern, and lifestyle practices will be assessed. |
| |
| Pre-diabetes (IFG or IGT) | Subjects in this group are diagnosed with pre-diabetes (impaired fasting glucose [IFG] or impaired glucose tolerance [IGT]). They receive no intervention. Their nutritional status, metabolite profile, dietary pattern, and lifestyle practices will be assessed. |
| |
| Non-type 2 diabetes mellitus (healthy) | Subjects in this group are not diagnosed with type 2 diabetes mellitus or pre-diabetes (healthy). They receive no intervention. Their nutritional status, metabolite profile, dietary pattern, and lifestyle practices will be assessed. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Observational | Other | Cross-sectional only |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Through study completion, an average of 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Prevalence of glucose intolerance | To determine prevalence of glucose intolerance (T2DM and pre-diabetes) among women post-GDM | Through study completion, an average of 1 year |
| Socio-demographic background |
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Inclusion Criteria:
Exclusion Criteria
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Women with previous GDM will be screened for inclusion and exclusion criteria. Those eligible will be asked to come to the clinic for the further ascertainment of the current status of T2DM by undergoing oral glucose tolerance test (OGTT) based on the criteria set by CPG DM (2015). Based on this diagnostic criteria, they will be divided into 3 groups: either T2DM, pre-diabetes (IFG/IGT), or non-T2DM.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Barakatun Nisak Mohd. Yusof, PhD | Contact | +603-97692606 | bnisak@upm.edu.my | |
| Farah Yasmin Hasbullah, MSc | Contact | +60123235874 | farahyasmin90@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Barakatun Nisak Mohd. Yusof, PhD | Universiti Putra Malaysia | Principal Investigator |
| Farah Yasmin Hasbullah, MSc | Universiti Putra Malaysia | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Universiti Putra Malaysia | Serdang | Selangor | 43400 | Malaysia |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 27338739 | Background | Allalou A, Nalla A, Prentice KJ, Liu Y, Zhang M, Dai FF, Ning X, Osborne LR, Cox BJ, Gunderson EP, Wheeler MB. A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes. Diabetes. 2016 Sep;65(9):2529-39. doi: 10.2337/db15-1720. Epub 2016 Jun 23. | |
| 28089710 | Background |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D016640 | Diabetes, Gestational |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D057832 | Watchful Waiting |
| ID | Term |
|---|---|
| D017063 | Outcome Assessment, Health Care |
| D010043 | Outcome and Process Assessment, Health Care |
| D011787 | Quality of Health Care |
| D006298 | Health Services Administration |
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Plasma blood and urine
Difference in socio-demographic background between T2DM, pre-diabetes and non-T2DM groups.
| Through study completion, an average of 1 year |
| Obstetric history | Difference in obstetric history between T2DM, pre-diabetes and non-T2DM groups. | Through study completion, an average of 1 year |
| Nutritional status | Difference in nutritional status between T2DM, pre-diabetes and non-T2DM groups. | 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. | 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. | 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. | 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. | 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). | 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. | Through study completion, an average of 1 year |
| Geeta Appannah, PhD |
| Universiti Putra Malaysia |
| Principal Investigator |
| Rohana Abdul Ghani, PhD | Universiti Teknologi Mara (UiTM) | Principal Investigator |
| Zulfitri 'Azuan Mat Daud, PhD | Universiti Putra Malaysia | Principal Investigator |
| Winnie Chee, PhD | International Medical University (IMU) | Principal Investigator |
| Bhupathiraju SN, Hu FB. One (small) step towards precision nutrition by use of metabolomics. Lancet Diabetes Endocrinol. 2017 Mar;5(3):154-155. doi: 10.1016/S2213-8587(17)30007-4. Epub 2017 Jan 13. No abstract available. |
| Background | Institute for Public Health [IPH]. 2015. Vol. II: Non-communicable diseases, risk factors and other health problems. National Health and Morbidity Survey (NHMS 2015). Kuala Lumpur, Malaysia: IPH, Ministry of Health. |
| Background | Malaysian Endocrine & Metabolic Society [MEMS] and Ministry of Health [MOH] Malaysia. 2015. Management of type 2 diabetes mellitus (5th Edition). Kuala Lumpur, Malaysia: MEMS & MOH. |
| 24450389 | Background | Nielsen KK, Kapur A, Damm P, de Courten M, Bygbjerg IC. From screening to postpartum follow-up - the determinants and barriers for gestational diabetes mellitus (GDM) services, a systematic review. BMC Pregnancy Childbirth. 2014 Jan 22;14:41. doi: 10.1186/1471-2393-14-41. |
| 28091346 | Background | O'Gorman A, Brennan L. The role of metabolomics in determination of new dietary biomarkers. Proc Nutr Soc. 2017 Aug;76(3):295-302. doi: 10.1017/S0029665116002974. Epub 2017 Jan 16. |
| 28513624 | Background | Tee ES, Yap RWK. Type 2 diabetes mellitus in Malaysia: current trends and risk factors. Eur J Clin Nutr. 2017 Jul;71(7):844-849. doi: 10.1038/ejcn.2017.44. Epub 2017 May 17. |
| 22987062 | Background | Tobias DK, Hu FB, Chavarro J, Rosner B, Mozaffarian D, Zhang C. Healthful dietary patterns and type 2 diabetes mellitus risk among women with a history of gestational diabetes mellitus. Arch Intern Med. 2012 Nov 12;172(20):1566-72. doi: 10.1001/archinternmed.2012.3747. |
| 24828694 | Background | Zhang C, Hu FB, Olsen SF, Vaag A, Gore-Langton R, Chavarro JE, Bao W, Yeung E, Bowers K, Grunnet LG, Sherman S, Kiely M, Strom M, Hansen S, Liu A, Mills J, Fan R; DWH study team. Rationale, design, and method of the Diabetes & Women's Health study--a study of long-term health implications of glucose intolerance in pregnancy and their determinants. Acta Obstet Gynecol Scand. 2014 Nov;93(11):1123-30. doi: 10.1111/aogs.12425. Epub 2014 Jun 9. |
| 12351492 | Background | Kim C, Newton KM, Knopp RH. Gestational diabetes and the incidence of type 2 diabetes: a systematic review. Diabetes Care. 2002 Oct;25(10):1862-8. doi: 10.2337/diacare.25.10.1862. |
| D004700 | Endocrine System Diseases |
| D011248 | Pregnancy Complications |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |