METHODOLOGY: A cross-sectional study was conducted for young-onset T2DM individuals aged 18-35 years with a disease duration of not more than 5 years. Plasma C-peptide was measured before and after intravenous glucagon injection. Demographic data, medical history and complications were obtained from medical records and clinical assessment. Continuous data were expressed as median and interquartile range (IQR). Categorical variables were described as frequency or percentage. Multivariable linear regression analysis was used to determine factors associated with C-peptide levels.
RESULTS: 113 participants with young-onset T2DM with a median (IQR) age of 29.0 (9.5) years and 24 (36) months were included in this study. The median (IQR) basal and stimulated C-peptide was 619 (655) pmol/L and 1231 (1024) pmol/L. Adequate beta-cell function was present in 78-86% of the participants based on the basal and stimulated C-peptide levels. We found hypertension, obesity and diabetic kidney disease (DKD) to be independently associated with higher C-peptide levels. In contrast, females, smokers, those on insulin therapy and with longer duration of disease had lower C-peptide levels.
CONCLUSION: Most recently diagnosed young-onset T2DM have adequate beta-cell function. Elevated C-peptide levels associated with obesity, hypertension and diabetic kidney disease suggest insulin resistance as the key driving factor for complications.
OBJECTIVE: To assess the chronic effects of the substitution of refined carbohydrate or MUFA for SAFA on insulin secretion and insulin sensitivity in centrally obese subjects.
METHODS: Using a crossover design, randomized controlled trial in abdominally overweight men and women, we compared the effects of substitution of 7% energy as carbohydrate or MUFA for SAFA for a period of 6 weeks each. Fasting and postprandial blood samples in response to corresponding SAFA, carbohydrate, or MUFA-enriched meal-challenges were collected after 6 weeks on each diet treatment for the assessment of outcomes.
RESULTS: As expected, postprandial nonesterified fatty acid suppression and elevation of C-peptide, insulin and glucose secretion were the greatest with high-carbohydrate (CARB) meal. Interestingly, CARB meal attenuated postprandial insulin secretion corrected for glucose response; however, the insulin sensitivity and disposition index were not affected. SAFA and MUFA had similar effects on all markers except for fasting glucose-dependent insulinotropic peptide concentrations, which increased after MUFA but not SAFA when compared with CARB.
CONCLUSION: In conclusion, a 6-week lower-fat/higher-carbohydrate (increased by 7% refined carbohydrate) diet may have greater adverse effect on insulin secretion corrected for glucose compared with isocaloric higher-fat diets. In contrast, exchanging MUFA for SAFA at 7% energy had no appreciable adverse impact on insulin secretion.
METHODS: Twenty healthy subjects were enrolled in a randomized, 3-way, blinded cross-over trial. The study was registered under ClinicalTrials.gov Identifier no. NCT00123456. At each test day, the subjects received one of three meals comprising 30 g of starch with 5 g of LD or UP or an energy-adjusted control meal containing pea protein. Fasting and postprandial blood glucose, insulin, C-peptide and glucagon-like peptide-1 (GLP-1) concentrations were measured. Subjective appetite sensations were scored using visual analogue scales (VAS).
RESULTS: Linear mixed model (LMM) analysis showed a lower blood glucose, insulin and C-peptide response following the intake of LD and UP, after correction for body weight. Participants weighing ≤ 63 kg had a reduced glucose response compared to control meal between 40 and 90 min both following LD and UP meals. Furthermore, LMM analysis for C-peptide showed a significantly lower response after intake of LD. Compared to the control meal, GLP-1 response was higher after the LD meal, both before and after the body weight adjustment. The VAS scores showed a decreased appetite sensation after intake of the seaweeds. Ad-libitum food intake was not different three hours after the seaweed meals compared to control.
CONCLUSIONS: Concomitant ingestion of brown seaweeds may help improving postprandial glycaemic and appetite control in healthy and normal weight adults, depending on the dose per body weight.
CLINICAL TRIAL REGISTRY NUMBER: Clinicaltrials.gov (ID# NCT02608372).
METHODS: Genetic analysis was performed in 42 patients with MODY aged 1 month to 18 years among a cohort of 759 patients with diabetes, identified with the following four clinical criteria: age of diagnosis ≤18 years; negative pancreatic autoantibodies; family history of diabetes; or persistently detectable C-peptide; or diabetes associated with extrapancreatic features. GCK gene mutations were first screened by Sanger sequencing. GCK mutation-negative patients were further analyzed by WES.
RESULTS: Mutations were identified in 24 patients: 20 mutations in GCK, 1 in HNF4A, 1 in INS, 1 in ABCC8, and a 17q12 microdeletion. Four previously unpublished novel GCK mutations: c.1108G>C in exon 9, and c.1339C>T, c.1288_1290delCTG, and c.1340_1343delGGGGinsCTGGTCT in exon 10 were detected. WES identified a novel missense mutation c.311A>G in exon 3 in the INS gene, and copy number variation analysis detected a 1.4 Mb microdeletion in the long arm of the chromosome 17q12 region. Compared with mutation-negative subjects, the mutation-positive subjects had lower hemoglobin A1c and initial blood glucose levels.
CONCLUSIONS: Most MODY cases in this study were due to GCK mutations, which is in contrast to previous reports in Chinese patients. Diabetes associated with extrapancreatic features should be a clinical criterion for MODY genetic analysis. Mutational analysis by WES provided a precise diagnosis of MODY subtypes. Moreover, WES can be useful for detecting large deletions in coding regions in addition to point mutations.
METHODS: Post hoc analysis of patient-level data (Asian n = 235; non-Asian n = 3351) from 16 trials.
RESULTS: At baseline, Asian patients were younger with lower body mass index (BMI), fasting C-peptide, and fasting plasma glucose (FPG) than non-Asian patients (all P
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.
METHODS: Using a randomized, crossover and double-blinded design, 15 men and 15 women with metabolic syndrome consumed high-fat meals enriched with SFA, MUFA or n-6 PUFA, or a low-fat/high-sucrose (SUCR) meal. C-peptide, insulin, glucose, gastrointestinal peptides and satiety were measured up to 6 h.
RESULTS: As expected, SUCR meal induced higher C-peptide (45 %), insulin (45 %) and glucose (49 %) responses compared with high-fat meals regardless of types of fatty acids (P < 0.001). Interestingly, incremental area under the curve (AUC0-120min) for glucagon-like peptide-1 was higher after SUCR meal compared with MUFA (27 %) and n-6 PUFA meals (23 %) (P = 0.01). AUC0-120min for glucose-dependent insulinotropic polypeptide was higher after SFA meal compared with MUFA (23 %) and n-6 PUFA meals (20 %) (P = 0.004). Significant meal x time interaction (P = 0.007) was observed for ghrelin, but not cholecystokinin and satiety.
CONCLUSIONS: The amount of fat regardless of the types of fatty acids affects insulin and glycemic responses. Both the amount and types of fatty acids acutely affect the gastrointestinal peptide release in metabolic syndrome subjects, but not satiety.