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Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics.
Valsesia, A, Chakrabarti, A, Hager, J, Langin, D, Saris, WHM, Astrup, A, Blaak, EE, Viguerie, N, Masoodi, M
Scientific reports. 2020;(1):9236
Abstract
Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.
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A carbohydrate-reduced high-protein diet improves HbA1c and liver fat content in weight stable participants with type 2 diabetes: a randomised controlled trial.
Skytte, MJ, Samkani, A, Petersen, AD, Thomsen, MN, Astrup, A, Chabanova, E, Frystyk, J, Holst, JJ, Thomsen, HS, Madsbad, S, et al
Diabetologia. 2019;(11):2066-2078
Abstract
AIMS/HYPOTHESIS Dietary recommendations for treating type 2 diabetes are unclear but a trend towards recommending a diet reduced in carbohydrate content is acknowledged. We compared a carbohydrate-reduced high-protein (CRHP) diet with an iso-energetic conventional diabetes (CD) diet to elucidate the effects on glycaemic control and selected cardiovascular risk markers during 6 weeks of full food provision of each diet. METHODS The primary outcome of the study was change in HbA1c. Secondary outcomes reported in the present paper include glycaemic variables, ectopic fat content and 24 h blood pressure. Eligibility criteria were: men and women with type 2 diabetes, HbA1c 48-97 mmol/mol (6.5-11%), age >18 years, haemoglobin >6/>7 mmol/l (women/men) and eGFR >30 ml min-1 (1.73 m)-2. Participants were randomised by drawing blinded ballots to 6 + 6 weeks of an iso-energetic CRHP vs CD diet in an open label, crossover design aiming at body weight stability. The CRHP/CD diets contained carbohydrate 30/50 energy per cent (E%), protein 30/17E% and fat 40/33E%, respectively. Participants underwent a meal test at the end of each diet period and glycaemic variables, lipid profiles, 24 h blood pressure and ectopic fat including liver and pancreatic fat content were assessed at baseline and at the end of each diet period. Data were collected at Copenhagen University Hospital, Bispebjerg and Copenhagen University Hospital, Herlev. RESULTS Twenty-eight participants completed the study. Fourteen participants carried out 6 weeks of the CRHP intervention followed by 6 weeks of the CD intervention, and 14 participants received the dietary interventions in the reverse order. Compared with a CD diet, a CRHP diet reduced the primary outcome of HbA1c (mean ± SEM: -6.2 ± 0.8 mmol/mol (-0.6 ± 0.1%) vs -0.75 ± 1.0 mmol/mol (-0.1 ± 0.1%); p < 0.001). Nine (out of 37) pre-specified secondary outcomes are reported in the present paper, of which five were significantly different between the diets, (p < 0.05); compared with a CD diet, a CRHP diet reduced the secondary outcomes (mean ± SEM or medians [interquartile range]) of fasting plasma glucose (-0.71 ± 0.20 mmol/l vs 0.03 ± 0.23 mmol/l; p < 0.05), postprandial plasma glucose AUC (9.58 ± 0.29 mmol/l × 240 min vs 11.89 ± 0.43 mmol/l × 240 min; p < 0.001) and net AUC (1.25 ± 0.20 mmol/l × 240 min vs 3.10 ± 0.25 mmol/l × 240 min; p < 0.001), hepatic fat content (-2.4% [-7.8% to -1.0%] vs 0.2% [-2.3% to 0.9%]; p < 0.01) and pancreatic fat content (-1.7% [-3.5% to 0.6%] vs 0.5% [-1.0% to 2.0%]; p < 0.05). Changes in other secondary outcomes, i.e. 24 h blood pressure and muscle-, visceral- or subcutaneous adipose tissue, did not differ between diets. CONCLUSIONS/INTERPRETATION A moderate macronutrient shift by substituting carbohydrates with protein and fat for 6 weeks reduced HbA1c and hepatic fat content in weight stable individuals with type 2 diabetes. TRIAL REGISTRATION ClinicalTrials.gov NCT02764021. FUNDING The study was funded by grants from Arla Food for Health; the Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen; the Department of Clinical Medicine, Aarhus University; the Department of Nutrition, Exercise and Sports, University of Copenhagen; and Copenhagen University Hospital, Bispebjerg.
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Transcriptome profiling from adipose tissue during a low-calorie diet reveals predictors of weight and glycemic outcomes in obese, nondiabetic subjects.
Armenise, C, Lefebvre, G, Carayol, J, Bonnel, S, Bolton, J, Di Cara, A, Gheldof, N, Descombes, P, Langin, D, Saris, WH, et al
The American journal of clinical nutrition. 2017;(3):736-746
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Abstract
Background: A low-calorie diet (LCD) reduces fat mass excess, improves insulin sensitivity, and alters adipose tissue (AT) gene expression, yet the relation with clinical outcomes remains unclear.Objective: We evaluated AT transcriptome alterations during an LCD and the association with weight and glycemic outcomes both at LCD termination and 6 mo after the LCD.Design: Using RNA sequencing (RNAseq), we analyzed transcriptome changes in AT from 191 obese, nondiabetic patients within a multicenter, controlled dietary intervention. Expression changes were associated with outcomes after an 8-wk LCD (800-1000 kcal/d) and 6 mo after the LCD. Results were validated by using quantitative reverse transcriptase-polymerase chain reaction in 350 subjects from the same cohort. Statistical models were constructed to classify weight maintainers or glycemic improvers.Results: With RNAseq analyses, we identified 1173 genes that were differentially expressed after the LCD, of which 350 and 33 were associated with changes in body mass index (BMI; in kg/m2) and Matsuda index values, respectively, whereas 29 genes were associated with both endpoints. Pathway analyses highlighted enrichment in lipid and glucose metabolism. Classification models were constructed to identify weight maintainers. A model based on clinical baseline variables could not achieve any classification (validation AUC: 0.50; 95% CI: 0.36, 0.64). However, clinical changes during the LCD yielded better performance of the model (AUC: 0.73; 95% CI: 0.60, 0.87]). Adding baseline expression to this model improved the performance significantly (AUC: 0.87; 95% CI: 0.77, 0.96; Delong's P = 0.012). Similar analyses were performed to classify subjects with good glycemic improvements. Baseline- and LCD-based clinical models yielded similar performance (best AUC: 0.73; 95% CI: 0.60, 0.86). The addition of expression changes during the LCD improved the performance substantially (AUC: 0.80; 95% CI: 0.69, 0.92; P = 0.058).Conclusions: This study investigated AT transcriptome alterations after an LCD in a large cohort of obese, nondiabetic patients. Gene expression combined with clinical variables enabled us to distinguish weight and glycemic responders from nonresponders. These potential biomarkers may help clinicians understand intersubject variability and better predict the success of dietary interventions. This trial was registered at clinicaltrials.gov as NCT00390637.
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Dietary protein and urinary nitrogen in relation to 6-year changes in fat mass and fat-free mass.
Ankarfeldt, MZ, Gottliebsen, K, Ängquist, L, Astrup, A, Heitmann, BL, Sørensen, TI
International journal of obesity (2005). 2015;(1):162-8
Abstract
BACKGROUND In contrast to the physiological expectation, observational studies show that greater protein intake is associated with subsequent body weight (BW) gain. An increase in fat-free mass (FFM) due to the anabolic effects of protein could explain this. OBJECTIVE To examine associations between protein intake and subsequent changes in fat mass (FM) and FFM in longitudinal, observational data. DESIGN A health examination, including measures of FM and FFM by bioelectrical impedance at baseline and follow-up 6 years later, was conducted. Diet history interviews (DHI) were performed, and 24-h urinary nitrogen collection at baseline was done. In total, 330 participants with DHI, of whom 227 had validated and complete 24-h urine collection data, were analyzed. Macronutrient energy substitution models were used. RESULTS Mean estimated protein intake was 14.6 E% from DHI and 11.3 E% from urinary nitrogen. Estimated from DHI, FM increased 46 g per year, with every 1 E% protein substituted for fat (95% confidence interval (CI) = 13, 79; P = 0.006), and FFM increased 15 g per year (1, 30; P = 0.046). Results were similar in other substitution models. Estimated from urinary nitrogen, FM increased 53 g per year, with 1 E% protein substituted for other macronutrients (24, 81; P < 0.0005), and FFM increased 18 g per year (6, 31; P = 0.004). CONCLUSION Within a habitual range, a greater protein intake was associated with BW gain, mostly in FM. This is in contrast to the expectations based on physiological and clinical trials, and calls for a better understanding of how habitual dietary protein influences long-term energy balance, versus how greater changes in dietary proteins may influence short-term energy balance.