The Type 2 Diabetes Susceptibility PROX1 Gene Variants Are Associated with Postprandial Plasma Metabolites Profile in Non-Diabetic Men

The prospero homeobox 1 (PROX1) gene may show pleiotropic effects on metabolism. We evaluated postprandial metabolic alterations dependently on the rs340874 genotypes, and 28 non-diabetic men were divided into two groups: high-risk (HR)-genotype (CC-genotype carriers, n = 12, 35.3 ± 9.5 years old) and low-risk (LR)-genotype (allele T carriers, n = 16, 36.3 ± 7.0 years old). Subjects participated in two meal-challenge-tests with high-carbohydrate (HC, carbohydrates 89%) and normo-carbohydrate (NC, carbohydrates 45%) meal intake. Fasting and 30, 60, 120, and 180 min after meal intake plasma samples were fingerprinted by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). In HR-genotype men, the area under the curve (AUC) of acetylcarnitine levels was higher after the HC-meal [+92%, variable importance in the projection (VIP) = 2.88] and the NC-meal (+55%, VIP = 2.00) intake. After the NC-meal, the HR-risk genotype carriers presented lower AUCs of oxidized fatty acids (−81–66%, VIP = 1.43–3.16) and higher linoleic acid (+80%, VIP = 2.29), while after the HC-meal, they presented lower AUCs of ornithine (−45%, VIP = 1.83), sphingosine (−48%, VIP = 2.78), linoleamide (−45%, VIP = 1.51), and several lysophospholipids (−40–56%, VIP = 1.72–2.16). Moreover, lower AUC (−59%, VIP = 2.43) of taurocholate after the HC-meal and higher (+70%, VIP = 1.42) glycodeoxycholate levels after the NC-meal were observed. Our results revealed differences in postprandial metabolites from inflammatory and oxidative stress pathways, bile acids signaling, and lipid metabolism in PROX1 HR-genotype men. Further investigations of diet–genes interactions by which PROX1 may promote T2DM development are needed.

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Body Weight Control by a High-Carbohydrate/Low-Fat Diet Slows the Progression of Diabetic Kidney Damage in an Obese, Hypertensive, Type 2 Diabetic Rat Model

Journal of Obesity ◽

10.1155/2010/136502 ◽

2010 ◽

Vol 2010 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Shuichi Ohtomo ◽

Yuko Izuhara ◽

Masaomi Nangaku ◽

Takashi Dan ◽

Sadayoshi Ito ◽

...

Keyword(s):

Body Weight ◽

Weight Control ◽

Advanced Glycation ◽

Low Fat ◽

High Carbohydrate ◽

Low Fat Diet ◽

Body Weight Control ◽

And Oxidative Stress ◽

Type 2 Diabetic

Obesity is one of several factors implicated in the genesis of diabetic nephropathy (DN). Obese, hypertensive, type 2 diabetic rats SHR/NDmcr-cp were given, for 12 weeks, either a normal, middle-carbohydrate/middle-fat diet (MC/MF group) or a high-carbohydrate/low-fat diet (HC/LF group). Daily caloric intake was the same in both groups. Nevertheless, the HC/LF group gained less weight. Despite equivalent degrees of hypertension, hyperglycemia, hyperlipidemia, hyperinsulinemia, and even a poorer glycemic control, the HC/LF group had less severe renal histological abnormalities and a reduced intrarenal advanced glycation and oxidative stress. Mediators of the renoprotection, specifically linked to obesity and body weight control, include a reduced renal inflammation and TGF-beta expression, together with an enhanced level of adiponectin. Altogether, these data identify a specific role of body weight control by a high-carbohydrate/low-fat diet in the progression of DN. Body weight control thus impacts on local intrarenal advanced glycation and oxidative stress through inflammation and adiponectin levels.

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Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study

American Journal of Clinical Nutrition ◽

10.1093/ajcn/nqy262 ◽

2019 ◽

Vol 109 (3) ◽

pp. 626-634 ◽

Cited By ~ 12

Author(s):

Christopher Papandreou ◽

Mònica Bulló ◽

Miguel Ruiz-Canela ◽

Courtney Dennis ◽

Amy Deik ◽

...

Keyword(s):

Risk Factors ◽

Insulin Resistance ◽

Type 2 Diabetes ◽

Area Under The Curve ◽

Predictive Ability ◽

Plasma Metabolites ◽

Incident Type ◽

Dieta Mediterranea ◽

Dieta Mediterránea

ABSTRACT Background Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D). Objectives The aim of this study was to test whether baseline metabolites can additionally improve the prediction of insulin resistance beyond classical risk factors. Furthermore, we examined whether a multimetabolite model predicting insulin resistance in nondiabetics can also predict incident T2D. Methods We used a case-cohort study nested within the Prevención con Dieta Mediterránea (PREDIMED) trial in subsets of 700, 500, and 256 participants without T2D at baseline and 1 and 3 y. Fasting plasma metabolites were semiquantitatively profiled with liquid chromatography–tandem mass spectrometry. We assessed associations between metabolite concentrations and the homeostasis model of insulin resistance (HOMA-IR) through the use of elastic net regression analysis. We subsequently examined associations between the baseline HOMA-IR–related multimetabolite model and T2D incidence through the use of weighted Cox proportional hazard models. Results We identified a set of baseline metabolites associated with HOMA-IR. One-year changes in metabolites were also significantly associated with HOMA-IR. The area under the curve was significantly greater for the model containing the classical risk factors and metabolites together compared with classical risk factors alone at baseline [0.81 (95% CI: 0.79, 0.84) compared with 0.69 (95% CI: 0.66, 0.73)] and during a 1-y period [0.69 (95% CI: 0.66, 0.72) compared with 0.57 (95% CI: 0.53, 0.62)]. The variance in HOMA-IR explained by the combination of metabolites and classical risk factors was also higher in all time periods. The estimated HRs for incident T2D in the multimetabolite score (model 3) predicting high HOMA-IR (median value or higher) or HOMA-IR (continuous) at baseline were 2.00 (95% CI: 1.58, 2.55) and 2.24 (95% CI: 1.72, 2.90), respectively, after adjustment for T2D risk factors. Conclusions The multimetabolite model identified in our study notably improved the predictive ability for HOMA-IR beyond classical risk factors and significantly predicted the risk of T2D.

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The Differences in Postprandial Serum Concentrations of Peptides That Regulate Satiety/Hunger and Metabolism after Various Meal Intake, in Men with Normal vs. Excessive BMI

Nutrients ◽

10.3390/nu11030493 ◽

2019 ◽

Vol 11 (3) ◽

pp. 493 ◽

Cited By ~ 5

Author(s):

Edyta Adamska-Patruno ◽

Lucyna Ostrowska ◽

Joanna Goscik ◽

Joanna Fiedorczuk ◽

Monika Moroz ◽

...

Keyword(s):

Body Weight ◽

Normal Weight ◽

Serum Concentrations ◽

High Fat ◽

Obese People ◽

High Carbohydrate ◽

Meal Intake ◽

Total Ghrelin ◽

Normal Body Weight ◽

Challenge Tests

The energy balance regulation may differ in lean and obese people. The purposes of our study were to evaluate the hormonal response to meals with varying macronutrient content, and the differences depending on body weight. Methods. The crossover study included 46 men, 21–58 years old, normal-weight and overweight/obese. Every subject participated in two meal-challenge-tests with high-carbohydrate (HC), and normo-carbohydrate (NC) or high-fat (HF) meals. Fasting and postprandial blood was collected for a further 240 min, to determine adiponectin, leptin and total ghrelin concentrations. Results. In normal-weight individuals after HC-meal we observed at 60min higher adiponectin concentrations (12,554 ± 1531 vs. 8691 ± 1070 ng/mL, p = 0.01) and significantly (p < 0.05) lower total ghrelin concentrations during the first 120 min, than after HF-meal intake. Fasting and postprandial leptin levels were significantly (p < 0.05) higher in overweigh/obese men. Leptin concentrations in normal-weight men were higher (2.72 ± 0.8 vs. 1.56 ± 0.4 ng/mL, p = 0.01) 180 min after HC-meal than after NC-meal intake. Conclusions. Our results suggest that in normal-body weight men we can expect more beneficial leptin, adiponectin, and total ghrelin response after HC-meal intake, whereas, in overweight/obese men, the HC-meal intake may exacerbate the feeling of hunger, and satiety may be induced more by meals with lower carbohydrate content.

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A High-Protein Breakfast Induces Greater Insulin and Glucose-Dependent Insulinotropic Peptide Responses to a Subsequent Lunch Meal in Individuals with Type 2 Diabetes

Journal of Nutrition ◽

10.3945/jn.114.202549 ◽

2014 ◽

Vol 145 (3) ◽

pp. 452-458 ◽

Cited By ~ 17

Author(s):

Young-Min Park ◽

Timothy D Heden ◽

Ying Liu ◽

Lauryn M Nyhoff ◽

John P Thyfault ◽

...

Keyword(s):

Area Under The Curve ◽

Postprandial Glucose ◽

High Protein ◽

Glucose Response ◽

C Peptide ◽

High Carbohydrate ◽

Glucagon Like Peptide 1 ◽

Insulinotropic Peptide ◽

Type 2 Diabetic

Abstract Background: The previous meal modulates the postprandial glycemic responses to a subsequent meal; this is termed the second-meal phenomenon. Objective: This study examined the effects of high-protein vs. high-carbohydrate breakfast meals on the metabolic and incretin responses after the breakfast and lunch meals. Methods: Twelve type 2 diabetic men and women [age: 21–55 y; body mass index (BMI): 30–40 kg/m2] completed two 7-d breakfast conditions consisting of 500-kcal breakfast meals as protein (35% protein/45% carbohydrate) or carbohydrate (15% protein/65% carbohydrate). On day 7, subjects completed an 8-h testing day. After an overnight fast, the subjects consumed their respective breakfast followed by a standard 500-kcal high-carbohydrate lunch meal 4 h later. Blood samples were taken throughout the day for assessment of 4-h postbreakfast and 4-h postlunch total area under the curve (AUC) for glucose, insulin, C-peptide, glucagon, glucose-dependent insulinotropic peptide (GIP), and glucagon-like peptide 1 (GLP-1). Results: Postbreakfast glucose and GIP AUCs were lower after the protein (17%) vs. after the carbohydrate (23%) condition (P < 0.05), whereas postbreakfast insulin, C-peptide, glucagon, and GLP-1 AUCs were not different between conditions. A protein-rich breakfast may reduce the consequences of hyperglycemia in this population. Postlunch insulin, C-peptide, and GIP AUCs were greater after the protein condition vs. after the carbohydrate condition (second-meal phenomenon; all, P < 0.05), but postlunch AUCs were not different between conditions. The overall glucose, glucagon, and GLP-1 responses (e.g., 8 h) were greater after the protein condition vs. after the carbohydrate condition (all, P < 0.05). Conclusions: In type 2 diabetic individuals, compared with a high-carbohydrate breakfast, the consumption of a high-protein breakfast meal attenuates the postprandial glucose response and does not magnify the response to the second meal. Insulin, C-peptide, and GIP concentrations demonstrate the second-meal phenomenon and most likely aid in keeping the glucose concentrations controlled in response to the subsequent meal. The trial was registered at www.clinicaltrials.gov/ct2/show/NCT02180646 as NCT02180646.

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