Bariatric surgery enhances glucose metabolism, yet the detailed postprandial joint glucose and insulin responses, variability in individual outcomes and differences in surgical approaches remain poorly understood. To address this, we used hierarchical multi-output Gaussian process (HMOGP) regression to model the individual postprandial glucose and insulin responses and to estimate the average response curves from individual data. Our study included 44 patients with obesity who underwent either Roux-en-Y gastric bypass (RYGB) (n=24) or One-Anastomosis gastric bypass (OAGB) (n=20) surgery. The patients were followed up at 6th and 12th months after the operation, during which they underwent an oral glucose tolerance test (OGTT) and a mixed meal test (MMT). A marked reduction in glycaemia, an earlier glucose peaking time and increase and sharpening in the postprandial glucose and insulin responses were evident in both metabolic tests after the operations. MMT resulted in higher postprandial glucose and insulin peaks compared with OGTT and higher glucose and insulin responses were observed after RYGB compared with OAGB. Women and persons without T2DM had a healthier postprandial response before and after surgery. Computational modeling with HMOGP regression can be used to, in detail, predict the combined responses of patient cohorts to ingested glucose or a mixed meal, and help in assessing individual metabolic improvement after weight loss. This can lead to new knowledge in personalized metabolic interventions.

The authors have declared no competing interest.

NCT02882685

The study was supported by the Research Council of Finland (272376, 266286, 314383, and 335443 to KHP, 314457 to AJ, 338417 to SH), the Finnish Medical Foundation (KHP, SH and AJ), the Finnish Diabetes Research Foundation (SH and KHP), the Orion Foundation (SH), the Novo Nordisk Foundation (NNF10OC1013354, NNF17OC0027232, and NNF20OC0060547 to KHP. NNF23SA0083953 for SH), the Paulo Foundation (SH), the Gyllenberg Foundation (KHP), Paavo Nurmi Foundation (SH), the Sigrid Juselius Foundation (KHP), Helsinki University Hospital Research Funds (SH, KHP, AJ), Government Research Funds (KHP, SH), the Research Council of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI, and grants 352986, 358246 to PM) and EU (H2020 grant 101016775 and NextGenerationEU to PM).

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