Coffee Types and Type 2 Diabetes Mellitus: Large-Scale Cross-Phenotype Association Study and Mendelian Randomization Analysis

Wang, Xinpei; Jia, Jinzhu; Huang, Tao

doi:10.3389/fendo.2022.818831

Cited by 10 publications

(11 citation statements)

References 43 publications

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“…However, it is important to note that decaffeinated and instant coffee have shown detrimental effects [ 26 ], and further research is needed to unravel the underlying pathophysiological mechanisms. Additionally, our finding on the preventive effect of alcohol and wine consumption aligns with a previous longitudinal study [ 27 ]. The presence of natural phytochemical compounds, such as polyphenols with antioxidant properties, has been demonstrated to improve insulin sensitivity [ 27 ].…”

Section: Discussionsupporting

confidence: 92%

Diet-Wide Association Study for the Incidence of Type 2 Diabetes Mellitus in Community-Dwelling Adults Using the UK Biobank Data

Liu,

Shang,

Chen

et al. 2023

Nutrients

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This longitudinal study used diet-wide association studies (DWAS) to investigate the association between diverse dietary food and nutrient intakes and the onset of type 2 diabetes mellitus (T2DM). Out of 502,505 participants from the UK Biobank, 119,040 with dietary data free of T2DM at the baseline were included, and 3241 developed T2DM during a median follow-up of 11.7 years. The DWAS analysis, which is based on Cox regression models, was used to analyse the associations between dietary food or nutrient intake factors and T2DM risk. The study found that 10 out of 225 dietary factors were significantly associated with the T2DM risk. Total alcohol (HR = 0.86, 0.85–0.92, p = 1.26 × 10−32), red wine (HR = 0.89, 0.88–0.94, p = 7.95 × 10−19), and fresh tomatoes (HR = 0.92, 0.89–0.94, p = 2.3 × 10−11) showed a negative association with T2DM risk, whereas sliced buttered bread exhibited a positive association. Additionally, 5 out of 21 nutrient intake variables revealed significant associations with the T2DM risk, with iron having the highest protective effect and starch as a risk factor. In conclusion, DWAS is an effective method for discovering novel associations when exploring numerous dietary variables simultaneously and could provide valuable insight into future dietary guidance for T2DM.

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Section: Discussionsupporting

confidence: 92%

Diet-Wide Association Study for the Incidence of Type 2 Diabetes Mellitus in Community-Dwelling Adults Using the UK Biobank Data

Liu,

Shang,

Chen

et al. 2023

Nutrients

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“…Whether it corresponds to causality requires further confirmation, as discordant MR results were reported previously (43,44). Further research may pay more attention to different coffee and tea types, as the composition of bioactive substances may differ across types, thus leading to inconsistent effects (44). In regard to alcohol consumption, there is emerging evidence (including our results) sustaining its risk-increasing effect on hypertension (45,46).…”

Section: Applications To Inferring Causality On Cardiometabolic Diseasessupporting

confidence: 64%

“…In contrast, our MR results suggested a hazardous effect of coffee consumption on T2D (Table S1). Whether it corresponds to causality requires further confirmation, as discordant MR results were reported previously (43,44). Further research may pay more attention to different coffee and tea types, as the composition of bioactive substances may differ across types, thus leading to inconsistent effects (44).…”

Section: Applications To Inferring Causality On Cardiometabolic Diseasesmentioning

confidence: 80%

Mendelian randomization analysis with pleiotropy-robust log-linear model for binary outcomes

Huang

Jia

2023

Preprint

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Mendelian randomization (MR) is a statistical technique that uses genetic variants as instrumental variables to infer causality between traits. In dealing with a binary outcome, there are two challenging barriers on the way toward a valid MR analysis, that is, the inconsistency of the traditional ratio estimator and the existence of horizontal pleiotropy. Recent MR methods mainly focus on handling pleiotropy with summary statistics. Many of them cannot be easily applied to one-sample MR. We propose two novel individual data-based methods, respectively named random-effects and fixed-effects MR-PROLLIM, to surmount both barriers. These two methods adopt risk ratio (RR) to define the causal effect for a continuous or binary exposure. The random-effects MR-PROLLIM models correlated pleiotropy, accounts for variant selection, and allows weaker instruments. The fixed-effects MR-PROLLIM can function with only a few selected variants. We demonstrate in this study that the random-effects MR-PROLLIM exhibits high statistical power while yielding fewer false-positive detections than its competitors. The fixed-effects MR-PROLLIM generally performs at an intermediate level between the classical median and mode estimators. In our UK Biobank data analyses, we also found (i) the MR ratio method tended to underestimate binary exposure effects to a large extent; (ii) about 26.5% of the trait pairs were detected to have significant correlated pleiotropy; (iii) the pleiotropy-sensitive method showed estimated relative biases ranging from -103.7% to 178.0% for inferred non-zero effects. MR-PROLLIM exhibits the potential to facilitate a more rigorous and robust MR analysis for binary outcomes.

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“…A robust inverse association between coffee consumption and type 2 diabetes risk has been reported in many observational studies [34]. However, genetically predicted higher coffee consumption was not associated with a decreased risk of type 2 diabetes in MR studies [13,35]. Pleiotropic effects of the SNPs used may cause this lack of association (e.g.…”

Section: Weak Instrument Biasmentioning

confidence: 96%

Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges

2023

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Diabetes and its complications cause a heavy disease burden globally. Identifying exposures, risk factors and molecular processes causally associated with the development of diabetes can provide important evidence bases for disease prevention and spur novel therapeutic strategies. Mendelian randomisation (MR), an epidemiological approach that uses genetic instruments to infer causal associations between an exposure and an outcome, can be leveraged to complement evidence from observational and clinical studies. This narrative review aims to summarise the evidence on potential causal risk factors for diabetes by integrating published MR studies on type 1 and 2 diabetes, and to reflect on future perspectives of MR studies on diabetes. Despite the genetic influence on type 1 diabetes, few MR studies have been conducted to identify causal exposures or molecular processes leading to increased disease risk. In type 2 diabetes, MR analyses support causal associations of somatic, mental and lifestyle factors with development of the disease. These studies have also identified biomarkers, some of them derived from the gut microbiota, and molecular processes leading to increased disease risk. These studies provide valuable data to better understand disease pathophysiology and explore potential therapeutic targets. Because genetic association studies have mostly been restricted to participants of European descent, multi-ancestry cohorts are needed to examine the role of different types of physical activity, dietary components, metabolites, protein biomarkers and gut microbiome in diabetes development. Graphical abstract

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Coffee Types and Type 2 Diabetes Mellitus: Large-Scale Cross-Phenotype Association Study and Mendelian Randomization Analysis

Cited by 10 publications

References 43 publications

Diet-Wide Association Study for the Incidence of Type 2 Diabetes Mellitus in Community-Dwelling Adults Using the UK Biobank Data

Diet-Wide Association Study for the Incidence of Type 2 Diabetes Mellitus in Community-Dwelling Adults Using the UK Biobank Data

Mendelian randomization analysis with pleiotropy-robust log-linear model for binary outcomes

Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges

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