Early-life tobacco smoke exposure, genetic susceptibility and the risk of type 2 diabetes in adulthood: A large prospective cohort study.

OBJECTIVES: We aimed to assess the relationships between early-life tobacco smoke exposures and the incident risk of type 2 diabetes (T2D) in later life as well as the joint effects and interactions between genetic susceptibility and early-life tobacco exposures. METHODS: We used data on in utero tobacco exposure and the age of smoking initiation to estimate the status of early-life tobacco exposure in the UK Biobank. Cox proportion hazard models were applied to estimate the associations between early-life tobacco exposure and T2D risk and investigate joint effects and interactions of early-life tobacco smoke exposure with genetic susceptibility. RESULTS: Among 407,943 subjects from the UK Biobank, 17,115 incident cases were documented during a median follow-up of 12.80 years. Compared with subjects without prenatal tobacco exposure, those with in utero tobacco exposure had a higher risk of T2D with a hazard ratio (HR) (95 % confidence interval [CI]) of 1.11 (1.08, 1.15). Besides, the HRs (95 % CIs) of incident T2D for smoking initiation in adulthood, adolescence, and childhood (vs. never smokers) were 1.36 (1.31, 1.42), 1.44 (1.38, 1.50), and 1.78 (1.69, 1.88), respectively (P trend <0.001). No interaction between early-life tobacco exposure and genetic susceptibility was observed. In addition, participants with prenatal (HR 4.67 [95 % CI 4.31, 5.06]) or childhood (6.91 [6.18, 7.72]) tobacco exposure combined with high genetic risk showed the highest risk of T2D, compared to low genetic risk subjects without early-life smoke exposure. CONCLUSION: Early-life tobacco exposure was associated with an increased risk of T2D later in life regardless of genetic background. This highlights the significance of education campaigns aimed at reducing smoking among children, adolescents, and pregnant women as an effective measure to combat the T2D epidemic.

Rational N,P-Codoped pH-Activatable Red Carbon Dot for In Vitro and In Vivo Tumor Imaging.

Tumor detection and imaging via tumor microenvironmental indicators can have practical value. Here, a low-pH-responsive red carbon dot (CD) was prepared via a hydrothermal reaction for specific tumor imaging in vitro and in vivo. The probe responded to the acidic tumor microenvironment. The CDs are codoped by nitrogen and phosphorene and contain anilines on the surface. These anilines are efficient electron donors and modulate the pH response: Fluorescence is undetectable at common physical pH (>7.0), but red fluorescence (600-720 nm) increases with decreasing pH. The inactivation of fluorescence is due to three aspects: photoinduced electron transfer from anilines, deprotonation-induced energy states changing, and particle aggregation-induced quenching. It is believed that this pH-responsive character of CD is better than other reported CDs. Thus, in vitro images of HeLa cells show strong fluorescence that is 4-fold higher than normal cells. Subsequently, the CDs are used for in vivo imaging of tumors in mice. Tumors can be clearly observed within 1 h, and clearance of CDs will be finished within 24 h due to the small size of the CDs. The CDs offer excellent tumor-to-normal tissue (T/N) ratios and have great potential for biomedical research and disease diagnosis.

Immune response and barrier dysfunction-related proteomic signatures in preclinical phase of Crohn's disease highlight earliest events of pathogenesis.

OBJECTIVE: The measure of serum proteome in the preclinical state of Crohn's disease (CD) may provide insight into biological pathways involved in CD pathogenesis. We aimed to assess associations of serum proteins with future CD onset and with other biomarkers predicting CD risk in a healthy at-risk cohort. DESIGN: In a nested case-control study within the Crohn's and Colitis Canada Genetics Environment Microbial Project (CCC-GEM) cohort, which prospectively follows healthy first-degree relatives (FDRs), subjects who developed CD (n=71) were matched with four FDRs remaining healthy (n=284). Using samples at recruitment, serum protein profiles using the Olink Proximity Extension Assay platform was assessed for association with future development of CD and with other baseline biomarkers as follows: serum antimicrobial antibodies (AS: positive antibody sum) (Prometheus); faecal calprotectin (FCP); gut barrier function using the fractional excretion of lactulose-to-mannitol ratio (LMR) assay. RESULTS: We identified 25 of 446 serum proteins significantly associated with future development of CD. C-X-C motif chemokine 9 (CXCL9) had the highest OR with future risk of CD (OR=2.07 per SD, 95% CI 1.58 to 2.73, q=7.9e-5), whereas matrix extracellular phosphoglycoprotein had the lowest OR (OR 0.44, 95% CI 0.29 to 0.66, q=0.02). Notably, CXCL9 was the only analyte significantly associated with all other CD-risk biomarkers with consistent direction of effect (FCP: OR=2.21; LMR: OR=1.67; AS: OR=1.59) (q<0.05 for all). CONCLUSION: We identified serum proteomic signatures associated with future CD development, reflecting potential early biological processes of immune and barrier dysfunction.

An observational study of the effects of smoking cessation earlier on the clinical characteristics and course of acute exacerbations of chronic obstructive pulmonary disease.

BACKGROUND: Previous studies have focused on the negative effects of continued smoking on chronic obstructive pulmonary disease (COPD). However, few studies have investigated the positive effects of long-term smoking cessation on patients suffering from acute exacerbations of COPD. METHODS: The study recruited and followed current or former smokers who had been hospitalized and diagnosed with AECOPD. An in-depth analysis of clinical and laboratory indicators was conducted. RESULTS: 125 patients were covered, including 72 short-term quitters and 53 long-term quitters. The results showed that long-term smoking cessation may result in milder dyspnea and cough, a higher oxygenation index, a lower arterial partial pressure of carbon dioxide, and milder pulmonary hypertension, airflow restriction, and gas retention in patients with AECOPD. However, despite the lower treatment intensity for long-term quitters, improvement in dyspnea and an increase in oxygenation index were comparable to those achieved by short-term quitters. Furthermore, patients with mild phlegm, which accounted for 84% of all subjects, showed greater improvement in phlegm in AECOPD patients with long-term cessation of smoking. CONCLUSION: It was found that in patients with AECOPD who quit smoking for a long period of time, there was a reduction in symptoms, improvement in lung function, reduction in treatment intensity, and better improvement in phlegm symptoms after therapy. It is beneficial and necessary to quit smoking early, even if you smoke a small amount of cigarettes.

Altered Gut Microbiome Composition and Function Are Associated With Gut Barrier Dysfunction in Healthy Relatives of Patients With Crohn's Disease.

BACKGROUND & AIMS: The gut microbiome has been suggested to play a role in gut barrier hemostasis, but data are scarce and limited to animal studies. We therefore aimed to assess whether alterations in gut microbial composition and functional pathways are associated with gut barrier function in a cohort of healthy first-degree relatives of patients with Crohn's disease. METHODS: We used the Crohn's and Colitis Canada Genetic Environmental Microbial (CCC-GEM) cohort of healthy first-degree relatives of patients with Crohn's disease. Gut barrier function was assessed using the urinary fractional excretion of lactulose-to-mannitol ratio (LMR). Microbiome composition was assessed by sequencing fecal 16S ribosomal RNA. The cohort was divided into a discovery cohort (n = 2472) and a validation cohort (n = 655). A regression model was used to assess microbial associations with the LMR. A random forest classifier algorithm was performed to assess microbial community contribution to barrier function. RESULTS: Individuals with impaired barrier function (LMR >0.025) had reduced alpha-diversity (Chao1 index, P = 4.0e-4) and altered beta-diversity (Bray-Curtis dissimilarity index, R2 = 0.001, P = 1.0e-3) compared with individuals with an LMR ≤0.025. When taxa were assessed individually, we identified 8 genera and 52 microbial pathways associated with an LMR >0.025 (q < 0.05). Four genera (decreased prevalence of Adlercreutzia, Clostridia UCG 014, and Clostridium sensu stricto 1 and increased abundance of Colidextribacter) and 8 pathways (including decreased biosynthesis of glutamate, tryptophan, and threonine) were replicated in the validation cohort. The random forest approach revealed that the bacterial community is associated with gut barrier function (area under the curve, 0.63; P = 1.4e-6). CONCLUSIONS: The gut microbiome community and pathways are associated with changes in gut barrier function. These findings may identify potential microbial targets to modulate gut barrier.

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population.

Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.

A Non-Invasive Prediction Model for Non-Alcoholic Fatty Liver Disease in Adults with Type 2 Diabetes Based on the Population of Northern Urumqi, China.

BACKGROUND: High prevalence of non-alcoholic fatty liver disease (NAFLD) occurs in type 2 diabetes mellitus (T2DM), and about 13% of diabetic patients eventually die of liver cirrhosis or liver cancer. The purpose of our research was to develop a non-invasive predictive model of NAFLD in adults with T2DM. PATIENTS AND METHODS: Adult patients diagnosed with T2DM during physical examination in 2018 in Urumqi were recruited, in total 40,921 cases. We chose questionnaire and physical measurement variables to build a simple, low-cost model. Variables were selected by the least absolute shrinkage and selection operator regression (LASSO). The features chosen by LASSO were used to build the nomogram prediction model of NAFLD. The receiver operating curve (ROC) and calibration were used for model validation. RESULTS: Determinants in the nomogram included age, ethnicity, sex, exercise, smoking, dietary ratio, heart rate, systolic blood pressure (SBP), BMI, waist circumference, and atherosclerotic vascular disease (ASCVD). The area under ROC of developing group and validation group was 0.756 (95% confidence interval 0.750-0.761) and 0.755 (95% confidence interval 0.746-0.763), respectively, and the P values of the two calibration curves were 0.694 and 0.950, suggesting that the nomogram had good disease recognition ability and calibration. CONCLUSION: A nomogram constructed with accuracy can calculate the possibility of NAFLD in adults with T2DM. If validated externally, this tool could be utilized as a non-invasive method to diagnose non-alcoholic fatty liver in adults with T2DM.

Prevalence and Correlation of Metabolic Syndrome: A Cross-Sectional Study of Nearly 10 Million Multi-Ethnic Chinese Adults.

PURPOSE: This study aimed to examine the prevalence and correlates of metabolic syndrome (MetS) in multi-ethnic populations of Northwest China based on Large-scale provincial health checking data. PATIENTS AND METHODS: A total of 9,745,640 Chinese aged ≥18 years in Xinjiang, the largest autonomous region of multi-ethnic in China, were enrolled from Feb. to Sep. 2019. MetS was defined by modified Adult Treatment Panel (ATP III) criteria. RESULTS: The overall prevalence of MetS was 20.85% [Sex: 20.06% female, 21.56% male; Age: 39.22% 60-years and above, 26.32% 40- to 59-years and 9.56% 18- to 39-years; Ethnicity: 28.19% Hui followed by Han (26.39%), Uyghur (18.56%), Other (18.61%), Kazak (17.98%), Mongolian (17.87%), Kyrgyz (14.44%)]. People living in cities and towns (23.03%) or North (24.78%) had higher prevalence of MetS than rural (19.94%) and South (17.66%). Although the prevalence of hypertension, smoking, drinking and lack of physical activity were higher in Kyrgyz, Mongolian and Kazak than those of other ethnic groups, the risk of MetS was lower. CONCLUSION: The results indicated that a high prevalence of MetS in Hui and Han groups and people living in cities and towns. Living in North Xinjiang and elder population tend to have higher prevalence of MetS. The prevention and management of MetS in these populations should be prioritized.

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.

BACKGROUND: An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. METHODS: A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables' importance scores of T2DM. RESULTS: The results indicated that XGBoost had the best performance (accuracy = 0.906, precision = 0.910, recall = 0.902, F-1 = 0.906, and AUC = 0.968). The degree of variables' importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). CONCLUSIONS: We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables' importance scores gives a clue to prevent diabetes occurrence.

A simple nomogram score for screening patients with type 2 diabetes to detect those with hypertension: A cross-sectional study based on a large community survey in China.

OBJECTIVES: Compared with unaffected individuals, patients with type 2 diabetes (T2DM) have higher risk of hypertension, and diabetes combined with hypertension can lead to server cardiovascular disease. Therefore, the purpose of this study was to establish a simple nomogram model to identify the determinants of hypertension in patients with T2DM and to quickly calculate the probability of hypertension in individuals with T2DM. MATERIALS AND METHODS: A total of 643,439 subjects participating in the national physical examination has been recruited in this cross-sectional study. After excluding unqualified subjects, 30,507 adults with T2DM were included in the final analysis. 21,355 and 9,152 subjects were randomly assigned to the model developing group and validation group, respectively, with a ratio of 7:3. The potential risk factors used in this study to assess hypertension in patients with T2DM included questionnaire investigation and physical measurement variables. We used the least absolute shrinkage and selection operator models to optimize feature selection, and the multivariable logistic regression analysis was for predicting model. Discrimination and calibration were assessed using the receiver operating curve (ROC) and calibration curve. RESULTS: The results showed that the major determinants of hypertension in patients with T2DM were age, gender, drinking, exercise, smoking, obesity and atherosclerotic vascular disease. The area under ROC curve of developing group and validation group are both 0.814, indicating that the prediction model owns high disease recognition ability. The p values of the two calibration curves are 0.625 and 0.445, suggesting that the nomogram gives good calibration. CONCLUSION: The individualized nomogram model can facilitate improved screening and early identification of patients with hypertension in T2DM. This procedure will be useful in developing regions with high epidemiological risk and poor socioeconomic status just like Urumqi, in Northern China.

A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants.

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

Smoking status affects clinical characteristics and disease course of acute exacerbation of chronic obstructive pulmonary disease: A prospectively observational study.

Existing studies primarily explored chronic obstructive pulmonary disease (COPD) in smokers, whereas the clinical characteristics and the disease course of passive or nonsmokers have been rarely described. In the present study, patients hospitalized and diagnosed as acute exacerbation of COPD (AECOPD) were recruited and followed up until being discharged. Clinical and laboratory indicators were ascertained and delved into. A total of 100 patients were covered, namely, 52 active smokers, 34 passive smokers, and 14 nonsmokers. As revealed from the results here, passive or nonsmokers developed less severe dyspnea (patients with modified Medical Research Council scale (mMRC) <2, 0.0% vs. 8.8% vs. 14.3%, p < 0.05, active, passive, and nonsmokers, respectively), higher oxygenation index (206.4 ± 45.5 vs. 241.2 ± 51.1 vs. 242.4 ± 41.8 mmHg, p < 0.01), as well as lower arterial partial pressure of carbon dioxide (70.8 ± 12.7 vs. 58.85 ± 9.9 vs. 56.6 ± 6.5 mmHg, p < 0.001). Despite lower treatment intensity over these patients, amelioration of dyspnea, mitigation of cough, and elevation of oxygenation index were comparable to those of active smokers. However, in terms of patients exhibiting mMRC ≥2 and type 2 respiratory failure, amelioration of dyspnea was more common in nonsmokers as compared with passive smokers (46.4% vs. 83.3%, p < 0.05, passive and nonsmokers, respectively). In terms of patients exhibiting Global Initiative for COPD severity <3, mMRC ≥2, and type 2 respiratory failure, active smokers achieved the least mitigation of cough symptom (8.7% vs. 35.0% vs. 44.4%, p < 0.05). Similar results could be achieved after the effects of confounders were excluded, with the most prominent amelioration of dyspnea (odds ratio (OR) 3.8, 95% confidence interval (CI) 1.1-13.6, p < 0.05, as compared with active smokers) and cough (OR 3.3, 95% CI 1.0-10.7, p < 0.05) in nonsmokers, and relatively better amelioration of hypoxemia in passive smokers (oxygenation index change, 39.0 ± 34.6 vs. 51.5 ± 32.4 vs. 45.3 ± 25.4 mmHg, p < 0.05). In brief, passive or nonsmokers with AECOPD were subjected to less severe disease, and nonsmokers, especially patients with more severe disease, might achieve the optimal enhancement of clinical presentation after treatment.

Heavy metals and associated health risk of wheat grain in a traditional cultivation area of Baoji, Shaanxi, China.

As a staple food for people worldwide, wheat is one of the major exposure pathways for heavy metals (HMs). Therefore, the safety of the wheat grain directly affects food security and human health. Long-term agricultural activities are sources of heavy metal pollution in farmland ecosystems. This study assessed the pollution situation of HMs in wheat grain from the major wheat-cultivation areas of Baoji, a typical agricultural area in Shaanxi, to assess the dietary health risks caused by consuming wheat grains and to prevent food pollution. The results showed that the mean grain concentration of Cr, Ni, Cu, Zn, Cd and Pb were 0.11, 0.09, 4.41, 26.79, 0.01 and 0.03 mg/kg, respectively. These values were all remarkably lower than the tolerance limits of the Chinese food hygiene standard (GB2762-2017). According to the metal pollution index (MPI) analysis, wheat grain consumption poses no direct threat to human health. The health risk assessment showed that there was a noncarcinogenic risk to adults and children for wheat consumed in the study area. In the study area, no carcinogenic risk was manifested. Principal component analysis (PCA) indicated that the source of Ni was different from that of the other tested HMs and was mainly from industry, where as the others were mainly derived from agricultural activities. Therefore, more attention should be paid to Cu and Zn input through agricultural activities in fields to further prevent the accumulation of these HMs in wheat grains and their related human health risks.

Dual functionalized natural biomass carbon dots from lychee exocarp for cancer cell targetable near-infrared fluorescence imaging and photodynamic therapy.

Photodynamic therapy (PDT) is a non-invasive phototherapy that has gained significant attention for cancer therapy. However, image-guided PDT still remains a considerable challenge. Herein, we developed a targeted, near-infrared (NIR) fluorescence imaging nanoprobe for cancer cells by preparing natural biomass carbon dots (NBCDs) from lychee exocarp, and loading transferrin and a photosensitizer on the NBCD surfaces for image-guided PDT of cancer cells and mouse tumors. Because the surfaces of cancer cells exhibit more transferrin receptors, the proposed NIR fluorescent nanoprobe can better penetrate cancer cells for cancer cell targetable fluorescence imaging. Thus, the dual-function nanoprobe made from natural biomass can be used as a specific agent for NIR fluorescence imaging and PDT. More importantly, the functional nanoprobe prepared from natural biomass emits NIR fluorescence, shows very low biological toxicity, and can minimize side effects on normal cells. After directly injecting the nanoprobes into tumor tissues, the photosensitizers on the surface of the NBCDs can produce singlet oxygen (1O2) through photodynamic reactions when irradiated with 650 nm light to kill cancer cells, thus inhibiting tumor growth in PDT-treated mice. Therefore, the functional fluorescent nanoprobe made from natural biomass has been employed as a PDT agent, and holds great promise in image-guided tumor PDT.

Sulfur and nitrogen binary doped carbon dots derived from ammonium thiocyanate for selective probing doxycycline in living cells and multicolor cell imaging.

A novel sulfur and nitrogen binary doped carbon dots (S,N-CDs) was synthesized by one-step manner through the hydrothermal treatment of citric acid (CA) and ammonium thiocyanate, and the procedures for biomedical applications, including probing doxycycline in living cells and multicolor cell imaging were developed. The obtained S,N-CDs are stable in aqueous solution, possess a very high quantum yield (QY, 74.15%) and good photostability. The fluorescence of S,N-CDs can be specifically quenched by doxycycline, providing a convenient turn-off assay of doxycycline. This assay shows a wide linear detection range from 0.08 to 60 µM with a low detection limit of 20 nM. The present method also displays a good selectivity. More importantly, the S,N-CDs have an excellent biocompatibility and low cytotoxicity, allowing the multicolor cell imaging and doxycycline detection in living cells. Consequently, the developed doxycycline methods is facile, low-cost, biocompatible, sensitive and selective, which may hold the potential applications in the fields of food safety and environmental monitoring, as well as cancer therapy and related mechanism research.