Lifestyle modifies the associations of early-life smoking behaviors and genetic susceptibility with type 2 diabetes: A prospective cohort study involving 433,872 individuals from UK Biobank.

BACKGROUND: To investigate whether and what lifestyle factors in later life modify the associations of early-life smoking behaviors and genetic susceptibility with type 2 diabetes (T2D). METHODS: In the UK Biobank, in utero tobacco exposure (n = 354,493) and age of smoking initiation (n = 353,557) were self-reported. A composite lifestyle score was calculated based on diet, physical activity, nicotine exposure, sleep duration, and BMI. Hazard ratio (HR) and absolute risk difference (ARD) were used to estimate the associations of early-life smoking behaviors and genetic risk with incident T2D, as well as the effect modification of the lifestyle score. RESULTS: During a median follow-up of 14.6 years, the HRs (95 % CIs) of T2D for in utero tobacco exposure, and smoking initiation in adulthood, adolescence, and childhood, compared with no smoking behavior, were 1.19 (1.16-1.23), 1.34 (1.29-1.39), 1.58 (1.53-1.64), 2.22 (2.11-2.32), respectively (P for trend<0.001). Early-life smoking behaviors and high genetic risk (vs no smoking behavior and low genetic risk) were associated with a 302%-593 % higher T2D risk (P for additive interaction<0.05). Compared to participants with early-life smoking behaviors, high genetic risk, and an unfavorable lifestyle, those who adhered to a favorable lifestyle had a lower T2D risk in all subgroups (HRs from 0.05 to 0.36 and ARD from -14.97 % to -9.51 %), with the highest ARD attributable to lifestyle in participants with early-life smoking behaviors and high genetic risk. CONCLUSIONS: The T2D risk associated with early-life smoking behaviors and genetic risk was modified by a favorable lifestyle.

Unveiling the Roles of Immune Function and Inflammation in the Associations Between Dietary Patterns and Incident Type 2 Diabetes.

OBJECTIVE: To investigate the associations between data-driven dietary patterns, immune function, and incident type 2 diabetes (T2D) and the mediating effects of immune function. METHODS: This study included 375,665 participants without diabetes at baseline in the UK Biobank study. Dietary patterns were derived through principal component analysis of food frequency questionnaire data. Immune function was assessed using 14 individual inflammatory markers and an integrated low-grade inflammation score (INFLA-score). Cox proportional hazard models were used to estimate the associations of dietary patterns or immune function with incident T2D. Linear regressions were used to estimate the associations of dietary patterns with immune function. Mediating effects of immune function were quantified. RESULTS: During a median 14.6-year follow-up, 13,932 participants developed T2D. Four dietary patterns were identified: prudent diet (high in whole grains, vegetables, fruits, fish), wheat/dairy/eggs restrictive diet (limiting these foods), meat-based diet (high in red/processed meat, salt), and full-cream dairy diet (preference for full cream milk or dairy products). The prudent diet was negatively (HRQ4 vs Q1, 0.69 [95% CI, 0.65-0.72]), while the wheat/dairy/eggs restrictive diet (HRQ4 vs Q1, 1.08 [95% CI, 1.03-1.13]), meat-based diet (HRQ4 vs Q1, 1.12 [95% CI, 1.06-1.17]), and full-cream dairy diet (HRQ4 vs Q1, 1.08 [95% CI, 1.03-1.12]) were positively associated with incident T2D (all p for trend ≤0.04). The prudent diet was negatively and the full-cream dairy diet was positively associated with most inflammatory markers. Most inflammatory markers, especially INFLA-score (HR, 1.18 [95% CI, 1.16-1.20]), were positively associated with incident T2D. INFLA-score mediated 13% of the association with incident T2D for the prudent diet and 34% for the full-cream dairy diet. CONCLUSIONS: This study identified four distinct dietary patterns and a range of inflammatory markers associated with incident T2D. A notable proportion of the associations between dietary patterns and T2D was mediated by immune function.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population.

BACKGROUND: Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. METHODS: The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n = 3,770), along with the NHANES follow-up cohort (n = 25,317) and the UK Biobank follow-up cohort (n = 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10 kPa were defined as compensated advanced chronic liver disease (cACLD). FINDINGS: Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. FUNDING: The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

OPA1 supports mitochondrial dynamics and immune evasion to CD8+ T cell in lung adenocarcinoma.

Background: Mitochondrial fusion and fission were identified to play key roles during multiple biology process. Thus, we aim to investigate the roles of OPA1 in mitochondria fusion and immune evasion of non-small cell lung cancer cells. Methods: The transcriptional activation of genes related to mitochondrial dynamics was determined by using multi-omics data in lung adenocarcinoma (LUAD). We elucidated the molecular mechanism and roles of OPA1 promoting lung cancer through single-cell sequencing and molecular biological experiments. Results: Here, we found that copy number amplification of OPA1 and MFN1 were co-occurring and synergistically activated in tumor epithelial cells in lung cancer tissues. Both of OPA1 and MFN1 were highly expressed in LUAD tumor tissues and OPA1 high expression was associated with poor prognosis. In terms of mechanism, the damaged mitochondria activated the apoptotic signaling pathways, inducing cell cycle arrest and cell apoptosis. More interestingly, OPA1 deficiency damaged mitochondrial dynamics and further blocked the respiratory function to increase the sensitivity of tumor epithelial to CD8+ T cells in non-small cell lung cancer. Conclusions: Our study demonstrated the high co-occurrence of copy number amplification and co-expression of OPA1 and MFN1 in LUAD tissue, and further revealed the contribution of OPA1 in maintaining the mitochondria respiratory function and the ability of immune evasion to CD8+ T cells of LUAD.