Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population

Zaghlool, Shaza B.; Halama, Anna; Stephan, Nisha; Guðmundsdóttir, Valborg; Gudnason, Vilmundur; Jennings, Lori L.; Thangam, Manonanthini; Ahlqvist, Emma; Malik, Rayaz A.; Abou‐Samra, Abdul‐Badi; Suhre, Karsten

doi:10.1038/s41467-022-34754-z

Cited by 36 publications

(26 citation statements)

References 106 publications

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“…Hydroxyasparagine, the metabolite with the highest positive weight in our plasma and multi-uid signatures for BMI, is found in the prenatal metabolomic pro les of mothers of obese children 28 . In another study among Arabs, high levels of hydroxyasparagine were found in individuals with mild-obesity-related diabetes 29 . Kynurenine, a metabolite identi ed in our plasma pro le for BMI, is converted from tryptophan in the brain and the periphery and affects various biological actions, including modulating immune response and in ammation 30 .…”

Section: Discussionmentioning

confidence: 90%

Saliva, plasma, and multi-fluid metabolomic profiles of excess adiposity and their associations with diabetes progression among Puerto Ricans

Bhupathiraju,

Wang,

Haslam

et al. 2024

Preprint

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Introduction: Plasma metabolomic profiles of BMI are associated with type 2 diabetes (T2D) risk, but none have examined saliva or combined information from multiple biofluids. Our aim was to characterize saliva, plasma, and plasma-saliva multi-fluid metabolomic profiles of BMI and waist circumference (WC) and examine their associations with diabetes progression. Methods We included 911 participants from the San Juan Overweight Adult Longitudinal Study, a 3-year prospective cohort of overweight Puerto Ricans. At baseline, using LC-MS, we quantified metabolites from saliva (n = 635) and plasma (n = 1,051). We used elastic net regression with 10-fold cross-validation to identify features from saliva, plasma, and saliva and plasma (multi-fluid) that were predictive of BMI and WC. We used Cox-proportional hazard models to evaluate associations between metabolomic profiles and diabetes progression, adjusting for age, sex, socioeconomic factors, lifestyle, and medication use. Results For BMI metabolomic profiles, we identified 207 metabolites in plasma, 118 metabolites in saliva, and 225 in the multi-fluid profile. For WC, we identified 157, 89, and 210 metabolites for saliva, plasma, and multi-fluid profiles, respectively. Highly positively weighted metabolites across all BMI and WC metabolomic profiles included those in pathways of alanine and aspartate metabolism, purine metabolism, and sphingomyelins. Each SD increase in saliva, but not plasma or multi-fluid, metabolic profile of BMI was significantly associated with all stages of diabetes progression. Saliva, but not plasma or multi-fluid, metabolomic profile of WC was significantly associated with progression from pre-diabetes to T2D. All associations became stronger after further adjustment for anthropometric measures of BMI and WC. Conclusion Saliva is an underexplored and easily accessible biofluid to measure metabolites that are reflective of adiposity measures. Validation in future studies will confirm our findings that saliva, plasma, and multi-fluid metabolites can supplement measures of BMI and WC to better predict diabetes progression among overweight and obese Puerto Rican adults.

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Section: Discussionmentioning

confidence: 90%

Saliva, plasma, and multi-fluid metabolomic profiles of excess adiposity and their associations with diabetes progression among Puerto Ricans

Bhupathiraju,

Wang,

Haslam

et al. 2024

Preprint

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“…Of note, N-acetylglucosamine is a recognized contributor to insulin resistance 38 and has been linked with insulin resistance and weight gain in mouse models 38,39 High levels of hydroxyasparagine were reported to play a role in obesity. 25 In addition, both Lachnoclostridium and Acidaminococcus were inversely associated with serum levels of 3 beneficial metabolites: indolepropionate, 3-phenylpropionate, and cinnamoylglycine. The associations between these 2 potentially pathogenic bacteria and T2D could be partially explained by their related microbial metabolites.…”

Section: Discussionmentioning

confidence: 98%

“…In contrast, Lachnoclostridium, which was associated with lower fiber intake and higher odds of T2D, showed negative correlations with indolepropionate and 3-phenylpropionate, and was positively linked with several unfavorable metabolites, including N-acetylglucosamine, a metabolite associated with insulin resistance and weight gain, 24 and hydroxyasparagine, a marker of the mild obesity-related diabetes. 25 Because fecal samples were collected after a blood draw, with a median of 10 (interquartile range, 6-14) days, we conducted stratified analyses on the correlations between gut bacteria and serum metabolites by the sample collection time difference (≤10 versus 10-30 days), and correlations between genera and metabolites were highly consistent (Figure S13A and S13B).…”

Section: Integrated Analyses Of Gut Microbiota and Circulating Metabo...mentioning

confidence: 99%

Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes

Wang,

Peters,

et al. 2024

Circulation Research

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BACKGROUND: Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly the role of gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate gut microbiota and circulating metabolites associated with dietary fiber intake and their relationships with type 2 diabetes (T2D). METHODS: This study included up to 11 394 participants from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Diet was assessed with two 24-hour dietary recalls at baseline. We examined associations of dietary fiber intake with gut microbiome measured by shotgun metagenomics (350 species/85 genera and 1958 enzymes; n=2992 at visit 2), serum metabolome measured by untargeted metabolomics (624 metabolites; n=6198 at baseline), and associations between fiber-related gut bacteria and metabolites (n=804 at visit 2). We examined prospective associations of serum microbial-associated metabolites (n=3579 at baseline) with incident T2D over 6 years. RESULTS: We identified multiple bacterial genera, species, and related enzymes associated with fiber intake. Several bacteria (eg, Butyrivibrio , Faecalibacterium ) and enzymes involved in fiber degradation (eg, xylanase EC3.2.1.156) were positively associated with fiber intake, inversely associated with prevalent T2D, and favorably associated with T2D-related metabolic traits. We identified 159 metabolites associated with fiber intake, 47 of which were associated with incident T2D. We identified 18 of these 47 metabolites associated with the identified fiber-related bacteria, including several microbial metabolites (eg, indolepropionate and 3-phenylpropionate) inversely associated with the risk of T2D. Both Butyrivibrio and Faecalibacterium were associated with these favorable metabolites. The associations of fiber-related bacteria, especially Faecalibacterium and Butyrivibrio , with T2D were attenuated after further adjustment for these microbial metabolites. CONCLUSIONS: Among United States Hispanics/Latinos, dietary fiber intake was associated with favorable profiles of gut microbiota and circulating metabolites for T2D. These findings advance our understanding of the role of gut microbiota and microbial metabolites in the relationship between diet and T2D.

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“…More recent evidence suggested that phenylalanine impairs insulin signaling and inhibits glucose uptake through modification of insulin receptor (IR) β [45]. Phenylalanine and 1-carboxyethylphenylalanine were found significantly higher in severe insulin resistant (SIRD) [46].…”

Section: Discussionmentioning

confidence: 99%

The Metabolic Switch of Physical Activity in Non-Obese Insulin Resistant Individuals

Almuraikhy

Anwardeen

Doudin

et al. 2023

IJMS

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Healthy non-obese insulin resistant (IR) individuals are at higher risk of metabolic syndrome. The metabolic signature of the increased risk was previously determined. Physical activity can lower the risk of insulin resistance, but the underlying metabolic pathways remain to be determined. In this study, the common and unique metabolic signatures of insulin sensitive (IS) and IR individuals in active and sedentary individuals were determined. Data from 305 young, aged 20–30, non-obese participants from Qatar biobank, were analyzed. The homeostatic model assessment of insulin resistance (HOMA-IR) and physical activity questionnaires were utilized to classify participants into four groups: Active Insulin Sensitive (ISA, n = 30), Active Insulin Resistant (IRA, n = 20), Sedentary Insulin Sensitive (ISS, n = 21) and Sedentary Insulin Resistant (SIR, n = 23). Differences in the levels of 1000 metabolites between insulin sensitive and insulin resistant individuals in both active and sedentary groups were compared using orthogonal partial least square discriminate analysis (OPLS-DA) and linear models. The study indicated significant differences in fatty acids between individuals with insulin sensitivity and insulin resistance who engaged in physical activity, including monohydroxy, dicarboxylate, medium and long chain, mono and polyunsaturated fatty acids. On the other hand, the sedentary group showed changes in carbohydrates, specifically glucose and pyruvate. Both groups exhibited alterations in 1-carboxyethylphenylalanine. The study revealed different metabolic signature in insulin resistant individuals depending on their physical activity status. Specifically, the active group showed changes in lipid metabolism, while the sedentary group showed alterations in glucose metabolism. These metabolic discrepancies demonstrate the beneficial impact of moderate physical activity on high risk insulin resistant healthy non-obese individuals by flipping their metabolic pathways from glucose based to fat based, ultimately leading to improved health outcomes. The results of this study carry significant implications for the prevention and treatment of metabolic syndrome in non-obese individuals.

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Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population

Cited by 36 publications

References 106 publications

Saliva, plasma, and multi-fluid metabolomic profiles of excess adiposity and their associations with diabetes progression among Puerto Ricans

Saliva, plasma, and multi-fluid metabolomic profiles of excess adiposity and their associations with diabetes progression among Puerto Ricans

Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes

The Metabolic Switch of Physical Activity in Non-Obese Insulin Resistant Individuals

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