Associations between dietary intake and glucose tolerance in clinical and metabolomics-based metabotypes

**Background:** Metabotyping groups metabolically similar individuals and may become a tool in precision nutrition, but it is unclear whether metabotypes based on comprehensive omics data are more useful than those based on a few clinical variables. This study investigated whether associations between habitual dietary intake and glucose tolerance depend on metabotypes identified from standard clinical variables or comprehensive NMR metabolomics.

**Methods:** Cross-sectional data were collected from 203 participants recruited through advertisements aimed at people at risk of type 2 diabetes mellitus in Norway (August 2018–September 2019). Glucose tolerance was assessed with a 2-hour OGTT. Habitual dietary intake was recorded using a 270-item food frequency questionnaire. Lipoprotein subclasses and metabolites were quantified by NMR spectroscopy (Nightingale Health platform, 168 metabolites used for clustering), and plasma carotenoids by HPLC-UV. Participants were divided into favorable (n=99) and unfavorable (n=104) clinical metabotypes based on established cutoffs: fasting glucose >5.5 mmol/L, 2-h glucose >6.4 mmol/L, or HbA1c ≥5.8%. Favorable (n=127) and unfavorable (n=76) NMR metabotypes were created using k-means clustering of NMR metabolites. Linear models with food group–metabotype interaction terms were used, adjusted for sex, age, BMI, smoking, statin use, and energy intake.

**Key Results:** Clinical metabotypes were separated by glycemic variables, while NMR metabotypes were mainly separated by lipoprotein-related variables. The unfavorable clinical metabotype had higher mean fasting glucose (5.8 vs. 4.9 mmol/L), 2-h glucose (6.6 vs. 4.7 mmol/L), HbA1c (33 vs. 27 mmol/mol), BMI (33.3 vs. 30.9 kg/m²), and age (55 vs. 44 years) compared to the favorable clinical metabotype. The unfavorable NMR metabotype had higher total cholesterol (5.7 vs. 4.6 mmol/L), LDL-C (4.1 vs. 3.0 mmol/L), triglycerides (1.9 vs. 1.06 mmol/L), and lower HDL-C (1.1 vs. 1.4 mmol/L) compared to the favorable NMR metabotype. A high intake of vegetables was associated with better glucose tolerance in the unfavorable but not the favorable clinical metabotype (interaction p=0.01). This interaction was confirmed using plasma lutein (interaction p=0.04) and zeaxanthin (interaction p=0.04). The association between glucose tolerance and fiber intake depended non-significantly on clinical metabotypes (interaction p=0.14). The association between glucose tolerance and intake of saturated fatty acids and dietary fat sources depended non-significantly on NMR metabotypes (e.g., oil and oil products interaction p=0.08; fish and shellfish interaction p=0.17). Higher intake of mono- and disaccharides (E%) was associated with lower 2-h glucose in the whole sample (p=0.02, FDR <10%).

**Clinical Implications:** This study suggests that individuals with unfavorable metabolic profiles may derive greater glucose tolerance benefits from dietary improvements, particularly increased vegetable and fiber intake, compared to those with favorable profiles. The findings indicate that the choice of variables used to define metabotypes (clinical vs. omics-based) affects which diet–disease associations are detected, with clinical metabotypes better capturing vegetable–glucose interactions and NMR metabotypes better capturing fat quality–glucose interactions. Metabotyping could guide the design of targeted dietary interventions in precision nutrition, though the added value of expensive omics-based metabotyping over simple clinical metabotyping remains uncertain. The study is limited by its small sample size (n=203), cross-sectional design (no causality), and potential residual confounding by age and sex differences between metabotypes.