METHODS: A total of 174 subjects were divided into NGT (n=58), pre-DM (n=54), and T2DM (n=62). Plasma total GLP-1 concentrations were measured at 0, 30, and 120 min during a 75-g OGTT. Homeostasis model assessment of insulin resistance (HOMA-IR), HOMA of insulin sensitivity (HOMA-IS), and triglyceride-glucose index (TyG) were calculated.
RESULTS: Total GLP-1 levels at fasting and 30 min were significantly higher in T2DM compared with pre-DM and NGT (27.18 ± 11.56 pmol/L vs. 21.99 ± 10.16 pmol/L vs. 16.24 ± 7.79 pmol/L, p=0.001; and 50.22 ± 18.03 pmol/L vs. 41.05 ± 17.68 pmol/L vs. 31.44 ± 22.59 pmol/L, p<0.001; respectively). Ethnicity was a significant determinant of AUCGLP-1, with the Indians exhibiting higher GLP-1 responses than Chinese and Malays. Indians were the most insulin resistant, whereas Chinese were the most insulin sensitive. The GLP-1 levels were positively correlated with HOMA-IR and TyG but negatively correlated with HOMA-IS. This relationship was evident among Indians who exhibited augmented GLP-1 responses proportionately to their high insulin-resistant states.
CONCLUSION: This is the first study that showed GLP-1 responses are augmented as IR states increase. Fasting and post-OGTT GLP-1 levels are raised in T2DM and pre-DM compared to that in NGT. This raises a possibility of an adaptive compensatory response that has not been reported before. Among the three ethnic groups, the Indians has the highest IR and GLP-1 levels supporting the notion of an adaptive compensatory secretion of GLP-1.
METHODS: This was a cross sectional study carried out in two primary care clinics in a semi-urban locality of Ampangan, Negeri Sembilan, Malaysia. Data was collected through self-administered questionnaires assessing the demographic characteristics, medical history, lifestyle and physical activity. The Short Form 36-items health survey was used to measure HRQOL among the pre-diabetics. Data entry and analysis were performed using the SPSS version 19.
RESULTS: A total of 268 eligible pre-diabetics participated in this study. The prevalence of normal weight, overweight and obesity were 7.1%, 21.6% and 71.3% respectively. Their mean (SD) age was 52.5 (8.3) years and 64.2% were females. Among the obese pre-diabetics, 42.2% had both IFG and IGT, 47.0% had isolated IFG and 10.8% had isolated IGT, 36.2% had combination of hypertension, dyslipidemia and musculoskeletal diseases. More than 53.4% of the obese pre-diabetics had family history of diabetes, 15.7% were smokers and 60.8% were physically inactive with mean PA of <600 MET-minutes/week. After adjusted for co-variants, Physical Component Summary (PCS) was significantly associated with BMI categories [F (2,262)=11.73, p<0.001] where pre-diabetics with normal weight and overweight had significantly higher PCS than those obese; normal vs obese [Mdiff=9.84, p=0.006, 95% CIdiff=2.28, 17.40] and between overweight vs obese [Mdiff=8.14, p<0.001, 95% CIdiff=3.46, 12.80].
CONCLUSION: Pre-diabetics who were of normal weight reported higher HRQOL compared to those overweight and obese. These results suggest a potentially greater risk of poor HRQOL among pre-diabetics who were overweight and obese especially with regard to the physical health component. Promoting recommended amount of physical activity and weight control are particularly important interventions for pre-diabetics at the primary care level.
METHODS: Studies were identified using electronic search and manual search techniques by choosing keywords for prediabetes, physical activity and inflammatory marker. Randomized controlled trials on individuals diagnosed with prediabetes and provided intervention in the form of physical activity were included in this review. Adiponectin, leptin, C-reactive protein, interleukin-6 and tumour necrosis factor-α were the considered outcome measures.
RESULTS: Our search retrieved 1,688 citations, 31 full-text articles assessed for eligibility of inclusion. Nine studies satisfied the pre-specified criteria for inclusion. Meta-analysis found that physical activity with or without dietary or lifestyle modification reduces level of leptin (MD-2.11 ng/mL, 95% CI -3.81 - -0.42) and interleukin-6 (MD -0.15 pg/mL, 95% CI -0.25--0.04). It has no effect on level of adiponectin (MD 0.26 µg/mL, 95% CI -0.42- 0.93), C-reactive protein (MD -0.05 mg/L, 95% CI -0.33-0.23) and tumour necrosis factor-α (MD 0.67 pg/mL, 95% CI -2.56-3.89).
CONCLUSIONS: This review suggests that physical activity promotion with dietary and lifestyle modification may reduce the level of leptin and interleukin-6 but are uncertain if there is any effect on levels of adiponectin, C-reactive protein and tumour necrosis factor-α in the individuals with prediabetes.
METHOD: This systematic review was conducted to identify and describe FFQs that measure dietary intake of pre-diabetic patients and to examine their relative validity and reliability. The systematic search was done through electronic databases such as PubMed, CINAHL, PsycINFO, ProQuest and Scopus. Methodological quality of included studies and results of study outcome was also summarized in this review.
RESULT: The search identified 445 papers, of which 18 studies reported 15 FFQs, met inclusion criteria. Most of the FFQs (n = 12) were semi-quantitative while three were frequency measures with portion size estimation of selected food items. Test-retest reliability of FFQ was reported in 7 (38.3%) studies with the correlation coefficient of 0.33-0.92. Relative validity of FFQ was reported in 16 (88.8%) studies with the range of correlation coefficient of 0.08-0.83. Dietary patterns rich in carbohydrate, fat, animal protein and n-3 fatty acids were associated with increased risk of pre-diabetes.
CONCLUSION: No well-established disease-specific FFQ identified in the literature. Development of a valid, practical and reliable tool is needed for better understanding of the impact of diet in pre-diabetic population.
METHODS: We surveyed 16 512 adults from July 2020 to August 2021 in 30 territories. Participants self-reported their medical histories and the perceived impact of COVID-19 on 18 lifestyle factors and 13 health outcomes. For each disease subgroup, we generated lifestyle, health outcome, and bridge networks. Variables with the highest centrality indices in each were identified central or bridge. We validated these networks using nonparametric and case-dropping subset bootstrapping and confirmed central and bridge variables' significantly higher indices through a centrality difference test.
FINDINGS: Among the 48 networks, 44 were validated (all correlation-stability coefficients >0.25). Six central lifestyle factors were identified: less consumption of snacks (for the chronic disease: anxiety), less sugary drinks (cancer, gastric ulcer, hypertension, insomnia, and pre-diabetes), less smoking tobacco (chronic obstructive pulmonary disease), frequency of exercise (depression and fatty liver disease), duration of exercise (irritable bowel syndrome), and overall amount of exercise (autoimmune disease, diabetes, eczema, heart attack, and high cholesterol). Two central health outcomes emerged: less emotional distress (chronic obstructive pulmonary disease, eczema, fatty liver disease, gastric ulcer, heart attack, high cholesterol, hypertension, insomnia, and pre-diabetes) and quality of life (anxiety, autoimmune disease, cancer, depression, diabetes, and irritable bowel syndrome). Four bridge lifestyles were identified: consumption of fruits and vegetables (diabetes, high cholesterol, hypertension, and insomnia), less duration of sitting (eczema, fatty liver disease, and heart attack), frequency of exercise (autoimmune disease, depression, and heart attack), and overall amount of exercise (anxiety, gastric ulcer, and insomnia). The centrality difference test showed the central and bridge variables had significantly higher centrality indices than others in their networks (P