Associations between smoking, glucose metabolism and lipid levels: A cross-sectional study

High levels of blood glycemic indicators are associated with chronic kidney disease prevalence in non-diabetic adults: Cross-sectional data from the national health and nutrition examination survey 2005-2016

Objective

Hyperglycemia in individuals with diabetes is associated with chronic kidney disease (CKD); however, little is known about its association with those without diabetes. Our goal was to investigate the association between glycemic indicators and CKD in individuals without diabetes.

Methods

This cross-sectional study included 9610 participants without diabetes who participated in the Health and Nutrition Examination Survey between 2005 and 2016. Exposures included postprandial glucose dip (PGD), fasting blood glucose (FBG), oral glucose tolerance test two-hour blood glucose (OGTT-2HBG), and glycated hemoglobin (HbA1C) levels. Moreover, CKD was defined as an estimated glomerular filtration rate below 60 mL/min per 1.73 m2 or a urinary albumin-creatinine ratio of ≥ 30 mg/g. Two multivariate models were constructed. Interaction effects were also explored.

Results

The mean age of the participants was 46.0 years, with 50.3 % being females. The prevalence of CKD was 12.6 %. In the final multivariable models, the odds ratios (ORs) for CKD were 1.51 (95 % confidence interval [CI]: 1.22,1.88, p < 0.001) for participants in the highest quartile of PGD,1.46 (95 %CI: 1.13,1.87, p = 0.004) for OGTT-2HBG, and 1.33 (95 %CI: 1.04,1.70, p = 0.020) for HbA1C, when compared with the quartile 1. No significant association was observed between FBG levels and CKD in the final model. Additionally, interactions were observed between PGD and body mass index, as well as between PGD and alcohol consumption in relation to CKD.

Conclusion

The study identified that high levels of PGD, OGTT-2HBG, and HBA1C were significantly associated with a high prevalence of CKD in individuals without diabetes.

Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting

BACKGROUND AND OBJECTIVES

Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting.

METHODS

Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min.

RESULTS

A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two.

CONCLUSIONS

The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.

Genetic associations between circulating metabolic biomarkers and lung cancer in East Asians and Europeans

BACKGROUND

Metabolic biomarkers are reported to be associated with the risk of lung cancer (LC). However, the observed associations from epidemiological studies are either inconsistent or inconclusive.

METHODS

The genetic summary data of high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TC), triglyceride (TG), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) and those of the LC and its histological subtypes were retrieved from previous GWASs. We performed two-sample Mendelian randomization (MR) and multivariable MR analyses to examine the associations between genetically predicted metabolic biomarkers and LC in East Asians and Europeans.

RESULTS

In East Asians, the inverse-variance weighted (IVW) method suggests that LDL (odds ratio [OR] = 0.799, 95% CI 0.712-0.897), TC (OR = 0.713, 95% CI 0.638-0.797), and TG (OR = 0.702, 95% CI 0.613-0.804) were significantly associated with LC after correction for multiple testing. For the remaining three biomarkers, we did not detect significant association with LC by any MR method. Multivariable MR (MVMR) analysis yielded an OR of 0.958 (95% CI 0.748-1.172) for HDL, 0.839 (95% CI 0.738-0.931) for LDL, 0.942 (95% CI 0.742-1.133) for TC, 1.161 (95% CI 1.070-1.252) for TG, 1.079 (95% CI 0.851-1.219) for FPG, and 1.101 (95% CI 0.922-1.191) for HbA1c. In Europeans, the univariate MR analyses did not detect significant association between exposures and outcomes. However, in MVMR analysis integrating circulating lipids and lifestyle risk factors (smoking, alcohol drinking, and body mass index), we found that TG was positively associated with LC in Europeans (OR = 1.660, 95% CI 1.060-2.260). Subgroup and sensitivity analysis yielded similar results to the main analyses.

CONCLUSIONS

Our study provides genetic evidence that circulating levels of LDL was negatively associated with LC in East Asians, whereas TG was positively associated with LC in both populations.

Commonalities in Metabolic Reprogramming between Tobacco Use and Oral Cancer

Tobacco use is a major public health concern and is linked to myriad diseases, including cancer. The link between tobacco use and oral cancer, specifically, is very strong, making tobacco use one of the primary risk factors for oral cancer. While this association is well known, the underlying biochemical changes that result from tobacco use, and how this links to metabolic phenotypes of oral cancer, is not well understood. To address this knowledge gap, a combination of literature reviews and metabolomics studies were performed to identify commonalities in metabolic perturbations between tobacco use and oral cancers. Metabolomics analysis was performed on pooled reference urine from smokers and non-smokers, healthy and malignant oral tissues, and cultured oral cells with or without treatment of the well-known tobacco carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Alterations in amino acid metabolism, carbohydrates/oxidative phosphorylation, fatty acid oxidation, nucleotide metabolism, steroid metabolism, and vitamin metabolism were found to be shared between tobacco use and oral cancer. These results support the conclusion that tobacco use metabolically reprograms oral cells to support malignant transformation through these pathways. These metabolic reprogramming events may be potential targets to prevent or treat oral cancers that arise from tobacco use.

Penalty weighted glucose prediction models could lead to better clinically usage

BACKGROUND AND OBJECTIVE

Numerous attempts to predict glucose value from continuous glucose monitors (CGM) have been published. However, there is a lack of proper analysis and modeling of penalty for errors in different glycemic ranges. The aim of this study was to investigate the potential for developing glucose prediction models with focus on the clinical aspects.

METHODS

We developed and compared six different models to test which approach were best suited for predicting glucose levels at different lead times between 10 and 60 min. The models were: last observation carried forward, linear extrapolation, ensemble methods using LSBoost and bagging, neural networks, one without error-weights and one with error-weights. The modeling and test were based on 225 type 1 diabetes patients with 315,000 h of CGM data.

RESULTS

Results show that it is possible to predict glucose levels based on CGM with a reasonable accuracy and precision with a 30-min prediction lead time. A comparison of different methods shows that there are improvements on performance gained from using more advanced machine learning algorithms (MARD 10.26-10.79 @ 30-min lead time) compared to a simple modeling (MARD 10.75-12.97 @ 30-min lead time). Moreover, the proposed use of error weights could lead to better clinical performance of these models, which is an important factor for real usage. E.g., the percentages in the C-zone of the consensus error grid without error-weights (0.57-0.68%) vs including error-weights (0.28%).

CONCLUSIONS

The results point toward that using error weighting in the training of the models could lead to better clinical performance.