New onset of type 2 diabetes after colorectal cancer diagnosis: Results from three prospective US cohort studies, systematic review, and meta-analysis

Casual effects of type 1 diabetes mellitus on site-specific digestive cancers: a Mendelian randomisation analysis

Objective

Despite several observational studies attempting to investigate the potential association between type 1 diabetes mellitus (T1DM) and the risk of digestive cancers, the results remain controversial. The purpose of this study is to examine whether there is a causal relationship between T1DM and the risk of digestive cancers.

Methods

We conducted a Mendelian randomisation (MR) study to systematically investigate the effect of T1DM on six most prevalent types of digestive cancers (oesophageal cancer, stomach cancer, hepatocellular carcinoma, biliary tract cancer, pancreatic cancer, and colorectal cancer). A total of 1,588,872 individuals were enrolled in this analysis, with 372,756 being the highest number for oesophageal cancer and 3,835 being the lowest for pancreatic cancer. Multiple MR methods were performed to evaluate the causal association of T1DM with the risk of six site-specific cancers using genome-wide association study summary data. Sensitivity analyses were also conducted to assess the robustness of the observed associations.

Results

We selected 35 single nucleotide polymorphisms associated with T1DM as instrumental variables. Our findings indicate no significant effect of T1DM on the overall risk of oesophageal cancer (OR= 0.99992, 95% CI: 0.99979-1.00006, P= 0.2866), stomach cancer (OR=0.9298,95% CI: 0.92065-1.09466, P= 0.9298), hepatocellular carcinoma (OR= 0.99994,95% CI: 0.99987-1.00001, P= 0.1125), biliary tract cancer (OR=0.97348,95% CI: 0.8079-1.1729, P= 0.7775)), or pancreatic cancer (OR =1.01258, 95% CI: 0.96243-1.06533, P= 0.6294). However, we observed a causal association between T1DM and colorectal cancer (OR=1.000, 95% CI: 1.00045-1.0012, P<0.001), indicating that T1DM increases the risk of colorectal cancer. We also performed sensitivity analyses, which showed no heterogeneity or horizontal pleiotropy. For the reverse MR from T1DM to six digestive cancers, no significant causal relationships were identified.

Conclusions

In this MR study with a large number of digestive cancer cases, we found no evidence to support the causal role of T1DM in the risk of oesophageal cancer, stomach cancer, hepatocellular carcinoma, biliary tract cancer, or pancreatic cancer. However, we found a causal positive association between T1DM and colorectal cancer. Further large-scale prospective studies are necessary to replicate our findings.

Exploring the Molecular Mechanism of Comorbidity of Type 2 Diabetes Mellitus and Colorectal Cancer: Insights from Bulk Omics and Single-Cell Sequencing Validation

The relationship between type 2 diabetes mellitus (T2DM) and colorectal cancer (CRC) has long been extensively recognized, but their crosstalk mechanisms based on gene regulation remain elusive. In our study, for the first time, bulk RNA-seq and single-cell RNA-seq data were used to explore the shared molecular mechanisms between T2DM and CRC. Moreover, Connectivity Map and molecular docking were employed to determine potential drugs targeting the candidate targets. Eight genes (EVPL, TACSTD2, SOX4, ETV4, LY6E, MLXIPL, ENTPD3, UGP2) were identified as characteristic comorbidity genes for T2DM and CRC, with EVPL and ENTPD3 further identified as core comorbidity genes. Our results demonstrated that upregulation of EVPL and downregulation of ENTPD3 were intrinsic molecular features throughout T2DM and CRC and were significantly associated with immune responses, immune processes, and abnormal immune landscapes in both diseases. Single-cell analysis highlighted a cancer-associated fibroblast (CAF) subset that specifically expressed ENTPD3 in CRC, which exhibited high heterogeneity and unique tumor-suppressive features that were completely different from classical cancer-promoting CAFs. Furthermore, ENTPD3+ CAFs could notably predict immunotherapy response in CRC, holding promise to be an immunotherapy biomarker at the single-cell level. Finally, we identified that droperidol may be a novel drug simultaneously targeting EVPL and ENTPD3. In conclusion, previous studies have often focused solely on metabolic alterations common to T2DM and CRC. Our study establishes EVPL and ENTPD3 as characteristic molecules and immune biomarkers of comorbidity in T2DM and CRC patients, and emphasizes the importance of considering immunological mechanisms in the co-development of T2DM and CRC.

Diabetes Risk Reduction Diet and Colorectal Cancer Risk

BACKGROUND

Diabetes has been associated with colorectal cancer. We evaluated whether adherence to a diabetes risk reduction diet (DRRD) can favorably influence the risk of colorectal cancer.

METHODS

Data came from a multicentric Italian case-control study including 1,953 histologically confirmed colorectal cancer cases and 4,154 hospital controls admitted for acute nonneoplastic diseases. Diet was assessed through a validated and reproducible food frequency questionnaire. The DRRD score was computed assigning higher values for higher consumption of cereal fiber, fruit, coffee, nuts and a higher polyunsaturated/saturated fats ratio and for lower glycemic index and lower consumption of red/processed meat and sweetened beverages and fruit juices. The ORs and the corresponding 95% confidence intervals (CI) of colorectal cancer according to the DRRD score were obtained using logistic regression models adjusting for total energy intake and other major confounders.

RESULTS

The DRRD was inversely related to colorectal cancer risk. The ORs of colorectal cancer were 0.77 (95% CI, 0.67-0.89) for the third versus first score tertile (Ptrend < 0.001) and 0.92 (95% CI, 0.87-0.96) for a 3-point increment in the score. Inverse associations were observed for colon and rectal cancers and were consistent in strata of sex, age, and other major covariates.

CONCLUSIONS

A higher adherence to a DRRD was inversely associated with colorectal cancer risk.

IMPACT

Given the high incidence and mortality rates of colorectal cancer, adherence to a DRRD can have relevant prevention and public health implications.

Jinkui Shenqi pills ameliorate diabetes by regulating hypothalamic insulin resistance and POMC/AgRP expression and activity

BACKGROUND

Research on the imbalance of proopiomelanocortin (POMC)/agouti-related protein (AgRP) neurons in the hypothalamus holds potential insights into the pathophysiology of diabetes. Jinkui Shenqi pills (JSP), a prevalent traditional Chinese medicine, regulate hypothalamic function and treat diabetes.

PURPOSE

To investigate the hypoglycemic effect of JSP and explore the probable mechanism in treating diabetes.

METHODS

A type 2 diabetes mouse model was used to investigate the pharmacodynamics of JSP. The glucose-lowering efficacy of JSP was assessed through various metrics including body weight, food consumption, fasting blood glucose (FBG), serum insulin levels, and an oral glucose tolerance test (OGTT). To elucidate the modulatory effects of JSP on hypothalamic mechanisms, we quantified the expression and activity of POMC and AgRP and assessed the insulin-mediated phosphoinositide 3-kinase (PI3K)/protein kinase A (AKT)/forkhead box O1 (FOXO1) pathway in diabetic mice via western blotting and immunohistochemistry. Additionally, primary hypothalamic neurons were exposed to high glucose and palmitic acid levels to induce insulin resistance, and the influence of JSP on POMC/AgRP protein expression and activation was evaluated by PI3K protein inhibition using western blotting and immunofluorescence.

RESULTS

Medium- and high-dose JSP treatment effectively inhibited appetite, resulting in a steady declining trend in body weight, FBG, and OGTT results in diabetic mice (p < 0.05). These JSP groups also had significantly increased insulin levels (p < 0.05). Importantly, the medium-dose group exhibited notable protection of hypothalamic neuronal and synaptic structures, leading to augmentation of dendritic length and branching (p < 0.05). Furthermore, low-, medium-, and high-dose JSP groups exhibited increased phosphorylated (p) INSR, PI3K, pPI3K, AKT, and pAKT expression, as well as decreased FOXO1 and increased pFOXO1 expression, indicating improved hypothalamic insulin resistance in diabetic mice (p < 0.05). Treatment with 10% JSP-enriched serum produced a marked elevation of both expression and activation of POMC (p < 0.05), with a concurrent reduction in AgRP expression and activation within primary hypothalamic neurons (p < 0.05). Intriguingly, these effects could be attributed to the regulatory dynamics of PI3K activity.

CONCLUSION

Our findings suggest that JSP can ameliorate diabetes by regulating POMC/AgRP expression and activity. The insulin-mediated PI3K/AKT/FOXO1 pathway plays an important regulatory role in this intricate process.

Diabetes mellitus in long-term survivors with colorectal, breast, or prostate cancer: Prevalence and prognosis. A population-based study

BACKGROUND

Patients with cancer are at increased risk of diabetes mellitus (DM). Previous studies on the prevalence and prognostic impact of DM in cancer survivors were limited by small sample sizes or short follow-up times. We aimed to compare the patient-reported prevalence of DM in long-term cancer survivors (LTCS), who survived 5 years or more after cancer diagnosis, with that in cancer-free controls, and to estimate the mortality risk among LTCS according to DM status.

METHODS

Our population-based cohort comprised 6952 LTCS diagnosed with breast, colorectal, or prostate cancer between 1994 and 2004, recruited in 2008-2011 (baseline), and followed until 2019. A total of 1828 cancer-free individuals served as controls. Multivariable logistic regression was used to compare the prevalence of DM in LTCS and controls, and according to covariates at baseline. Mortality among LTCS according to DM was assessed by Cox proportional hazards regression.

RESULTS

A total of 962 (13.8%) LTCS at baseline reported DM. Prevalence of DM in LTCS was not higher than in cancer-free controls, both at baseline (odds ratio, 0.80; 95% CI, 0.66-0.97) and at follow-up (odds ratio, 0.83; 95% CI, 0.67-1.04). Prevalence of DM in LTCS was associated with cancer site, older age, lower education, higher socioeconomic deprivation, higher body mass index, physical inactivity, other comorbidities, and poorer prognosis (adjusted hazard ratio [all-cause mortality] = 1.29; 95% CI, 1.15-1.44).

CONCLUSION

DM in LTCS is prevalent, but not higher than in cancer-free population controls. Cancer survivors with concurrent DM are at a potentially higher risk of death.

PLAIN LANGUAGE SUMMARY

Cancer and diabetes mellitus (DM) are two serious threats to global health. In our study, prevalence of DM in long-term cancer survivors who survived 5 years or more after cancer diagnosis was not higher than in cancer-free controls. This should not be interpreted as an indication of a lower risk of DM in cancer survivors. Rather, it highlights the potentially poor prognosis in diabetic cancer survivors. Therefore, keeping a continuous satisfactory DM and hyperglycemia management is essential during long-term cancer survivorship.

Predicting coronary heart disease in Chinese diabetics using machine learning

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.