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Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study.

Molnos, Sophie; Wahl, Simone; Haid, Mark; Eekhoff, E Marelise W; Pool, René; Floegel, Anna; Deelen, Joris; Much, Daniela; Prehn, Cornelia; Breier, Michaela; Draisma, Harmen H; van Leeuwen, Nienke; Simonis-Bik, Annemarie M C; Jonsson, Anna; Willemsen, Gonneke; Bernigau, Wolfgang; Wang-Sattler, Rui; Suhre, Karsten; Peters, Annette; Thorand, Barbara; Herder, Christian; Rathmann, Wolfgang; Roden, Michael; Gieger, Christian; Kramer, Mark H H; van Heemst, Diana; Pedersen, Helle K; Gudmundsdottir, Valborg; Schulze, Matthias B; Pischon, Tobias; de Geus, Eco J C; Boeing, Heiner; Boomsma, Dorret I; Ziegler, Anette G; Slagboom, P Eline; Hummel, Sandra; Beekman, Marian; Grallert, Harald; Brunak, Søren; McCarthy, Mark I; Gupta, Ramneek; Pearson, Ewan R; Adamski, Jerzy; 't Hart, Leen M.
Diabetologia; 61(1): 117-129, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-28936587

AIMS/HYPOTHESIS:

Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes.

METHODS:

We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders.

RESULTS:

There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10-7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10-3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [ß 0.97 ± 0.09], p = 1.0 × 10-27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [ß 0.45 ± 0.06]; p = 1.3 × 10-15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose).

CONCLUSIONS/INTERPRETATION:

In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.
Selo DaSilva