Anti-SARS-CoV-2 IgG responses are powerful predicting signatures for the outcome of COVID-19 patients.

Introduction: The COVID-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for early predicting the outcome of COVID-19. Growing evidences have revealed that SARS-CoV-2 specific antibodies evolved with disease progression and severity in COIVD-19 patients. Objectives: We assumed that antibodies may serve as biomarkers for predicting the clinical outcome of hospitalized COVID-19 patients on admission. Methods: By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgG responses against 20 proteins of SARS-CoV-2 in 1034 hospitalized COVID-19 patients on admission and followed till 66 days. The microarray results were further correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 mortality. Results: Nonsurvivors (n = 955) induced higher levels of IgG responses against most of non-structural proteins than survivors (n = 79) on admission. In particular, the magnitude of IgG antibodies against 8 non-structural proteins (NSP1, NSP4, NSP7, NSP8, NSP9, NSP10, RdRp, and NSP14) and 2 accessory proteins (ORF3b and ORF9b) possessed significant predictive power for patient death, even after further adjustments for demographics, comorbidities, and common laboratory biomarkers for disease severity (all with p trend < 0.05). Additionally, IgG responses to all of these 10 non-structural/accessory proteins were also associated with the severity of disease, and differential kinetics and serum positive rate of these IgG responses were confirmed in COVID-19 patients of varying severities within 20 days after symptoms onset. The area under curves (AUCs) for these IgG responses, determined by computational cross-validations, were between 0.62 and 0.71. Conclusions: Our findings might have important implications for improving clinical management of COVID-19 patients.

COVID-ONE-hi: The One-stop Database for COVID-19-specific Humoral Immunity and Clinical Parameters.

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, varies with regard to symptoms and mortality rates among populations. Humoral immunity plays critical roles in SARS-CoV-2 infection and recovery from COVID-19. However, differences in immune responses and clinical features among COVID-19 patients remain largely unknown. Here, we report a database for COVID-19-specific IgG/IgM immune responses and clinical parameters (named COVID-ONE-hi). COVID-ONE-hi is based on the data that contain the IgG/IgM responses to 24 full-length/truncated proteins corresponding to 20 of 28 known SARS-CoV-2 proteins and 199 spike protein peptides against 2360 serum samples collected from 783 COVID-19 patients. In addition, 96 clinical parameters for the 2360 serum samples and basic information for the 783 patients are integrated into the database. Furthermore, COVID-ONE-hi provides a dashboard for defining samples and a one-click analysis pipeline for a single group or paired groups. A set of samples of interest is easily defined by adjusting the scale bars of a variety of parameters. After the "START" button is clicked, one can readily obtain a comprehensive analysis report for further interpretation. COVID-ONE-hi is freely available at www.COVID-ONE.cn.

Kinetics of Neutralizing Antibody Response Underscores Clinical COVID-19 Progression.

BACKGROUND: Neutralizing antibody (nAb) response is generated following infection or immunization and plays an important role in the protection against a broad of viral infections. The role of nAb during clinical progression of coronavirus disease 2019 (COVID-19) remains little known. METHODS: 123 COVID-19 patients during hospitalization in Tongji Hospital were involved in this retrospective study. The patients were grouped based on the severity and outcome. The nAb responses of 194 serum samples were collected from these patients within an investigation period of 60 days after the onset of symptoms and detected by a pseudotyped virus neutralization assay. The detail data about onset time, disease severity and laboratory biomarkers, treatment, and clinical outcome of these participants were obtained from electronic medical records. The relationship of longitudinal nAb changes with each clinical data was further assessed. RESULTS: The nAb response in COVID-19 patients evidently experienced three consecutive stages, namely, rising, stationary, and declining periods. Patients with different severity and outcome showed differential dynamics of the nAb response over the course of disease. During the stationary phase (from 20 to 40 days after symptoms onset), all patients evolved nAb responses. In particular, high levels of nAb were elicited in severe and critical patients and older patients (≥60 years old). More importantly, critical but deceased COVID-19 patients showed high levels of several proinflammation cytokines, such as IL-2R, IL-8, and IL-6, and anti-inflammatory cytokine IL-10 in vivo, which resulted in lymphopenia, multiple organ failure, and the rapidly decreased nAb response. CONCLUSION: Our results indicate that nAb plays a crucial role in preventing the progression and deterioration of COVID-19, which has important implications for improving clinical management and developing effective interventions.

Antibody landscape against SARS-CoV-2 reveals significant differences between non-structural/accessory and structural proteins.

The immunogenicity of the SARS-CoV-2 proteome is largely unknown, especially for non-structural proteins and accessory proteins. In this study, we collect 2,360 COVID-19 sera and 601 control sera. We analyze these sera on a protein microarray with 20 proteins of SARS-CoV-2, building an antibody response landscape for immunoglobulin (Ig)G and IgM. Non-structural proteins and accessory proteins NSP1, NSP7, NSP8, RdRp, ORF3b, and ORF9b elicit prevalent IgG responses. The IgG patterns and dynamics of non-structural/accessory proteins are different from those of the S and N proteins. The IgG responses against these six proteins are associated with disease severity and clinical outcome, and they decline sharply about 20 days after symptom onset. In non-survivors, a sharp decrease of IgG antibodies against S1 and N proteins before death is observed. The global antibody responses to non-structural/accessory proteins revealed here may facilitate a deeper understanding of SARS-CoV-2 immunology.

Linear epitope landscape of the SARS-CoV-2 Spike protein constructed from 1,051 COVID-19 patients.

To fully decipher the immunogenicity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike protein, it is essential to assess which part is highly immunogenic in a systematic way. We generate a linear epitope landscape of the Spike protein by analyzing the serum immunoglobulin G (IgG) response of 1,051 coronavirus disease 2019 (COVID-19) patients with a peptide microarray. We reveal two regions rich in linear epitopes, i.e., C-terminal domain (CTD) and a region close to the S2' cleavage site and fusion peptide. Unexpectedly, we find that the receptor binding domain (RBD) lacks linear epitope. We reveal that the number of responsive peptides is highly variable among patients and correlates with disease severity. Some peptides are moderately associated with severity and clinical outcome. By immunizing mice, we obtain linear-epitope-specific antibodies; however, no significant neutralizing activity against the authentic virus is observed for these antibodies. This landscape will facilitate our understanding of SARS-CoV-2-specific humoral responses and might be useful for vaccine refinement.

Systematic evaluation of IgG responses to SARS-CoV-2 spike protein-derived peptides for monitoring COVID-19 patients.

Serological tests play an essential role in monitoring and combating the COVID-19 pandemic. Recombinant spike protein (S protein), especially the S1 protein, is one of the major reagents used for serological tests. However, the high cost of S protein production and possible cross-reactivity with other human coronaviruses pose unavoidable challenges. By taking advantage of a peptide microarray with full spike protein coverage, we analyzed 2,434 sera from 858 COVID-19 patients, 63 asymptomatic patients and 610 controls collected from multiple clinical centers. Based on the results, we identified several S protein-derived 12-mer peptides that have high diagnostic performance. In particular, for monitoring the IgG response, one peptide (aa 1148-1159 or S2-78) exhibited a sensitivity (95.5%, 95% CI 93.7-96.9%) and specificity (96.7%, 95% CI 94.8-98.0%) comparable to those of the S1 protein for the detection of both symptomatic and asymptomatic COVID-19 cases. Furthermore, the diagnostic performance of the S2-78 (aa 1148-1159) IgG was successfully validated by ELISA in an independent sample cohort. A panel of four peptides, S1-93 (aa 553-564), S1-97 (aa 577-588), S1-101 (aa 601-612) and S1-105 (aa 625-636), that likely will avoid potential cross-reactivity with sera from patients infected by other coronaviruses was constructed. The peptides identified in this study may be applied independently or in combination with the S1 protein for accurate, affordable, and accessible COVID-19 diagnosis.

Antibody dynamics to SARS-CoV-2 in asymptomatic COVID-19 infections.

BACKGROUND: The missing asymptomatic COVID-19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic. MEASURE: Utilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty-three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS-CoV-2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset. RESULTS: A combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS-CoV-2. Different from strong and persistent N-specific antibodies, S1-specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months. CONCLUSION: Our findings might have important implications for the definition of asymptomatic COVID-19 infections, diagnosis, serological survey, public health, and immunization strategies.

Association between urinary metals levels and metabolic phenotypes in overweight and obese individuals.

Epidemiologic studies suggest that circulating metals from the natural environment are linked with cardiometabolic health. However, few studies examined the relationship between multiple metals exposure and metabolic phenotypes, especially in obese individuals. We conducted a cross-sectional study to explore the association between 23 urinary metals and metabolic phenotypes in 1392 overweight and obese individuals (592 males, 800 females, mean age 43.1 ± 9.8 years). Participants were classified as metabolically unhealthy if they had ≥2 of the following metabolic abnormalities: elevated blood pressure, elevated fasting blood glucose, elevated triglycerides, and reduced high-density lipoprotein cholesterol. Odds ratios (ORs) of unhealthy metabolic phenotypes for metal levels categorized into tertiles were assessed using logistic regression models. Five metals (barium, copper, iron, uranium, and zinc) were associated with unhealthy metabolic phenotypes in single-metal models, while in the multiple-metal model, only zinc and zinc-copper ratio remained significant. The ORs (95% CIs) comparing extreme tertiles were 2.57 (1.69, 3.89) for zinc and 1.68 (1.24, 2.27) for zinc-copper ratio after adjustment for confounders (both p-trends were <0.001). The numbers of metabolic abnormalities significantly increased with the levels of zinc and the zinc-copper ratio increased. Similar associations were observed with metabolic syndrome risk. High levels of urinary zinc were positively associated with elevated fasting blood glucose (p-trend < 0.001) and elevated triglycerides (p-trend = 0.003). The results suggest that urinary zinc and zinc-copper ratio are positively associated with increased risk of unhealthy metabolic phenotype. Further prospective studies with a larger sample size are required to verify these findings.

Epidemiological and clinical characteristics of 1663 hospitalized patients infected with COVID-19 in Wuhan, China: a single-center experience.

BACKGROUND: The COVID-19 outbreak in late December 2019 has quickly emerged into pandemic in 2020. We aimed to describe the epidemiology and clinical characteristics of hospitalized COVID-19 patients, and to investigate the potential risk factors for COVID-19 severity. METHOD: 1663 hospitalized patients with laboratory-confirmed diagnosed COVID-19 from Tongji Hospital between January 14, 2020, and February 28, 2020 were included in the present study. Demographic information, exposure history, medical history, comorbidities, signs and symptoms, chest computed tomography (CT) scanning, severity of COVID-19 and laboratory findings on admission were collected from electronic medical records. Multivariable logistic regression was used to explore the association between potential risk factors with COVID-19 severity. RESULTS: In the present study, the majority (79%) of 1663 COVID-19 patients were aged over 50 years old. A total of 2.8% were medical staff, and an exposure history of Huanan seafood market was document in 0.7%, and 7.4% were family infection. Fever (85.8%), cough (36.0%), fatigue (23.6%) and chest tightness (11.9%) were the most common symptoms in COVID-19 patients. As of February 28, 2020, of the 1663 patients included in this study, 26.0% were discharged, 10.2% were died, and 63.8% remained hospitalized. More than 1/3 of the patients had at least one comorbidity. Most (99.8%) patients had abnormal results Chest CT, and the most common manifestations of chest CT were local patchy shadowing (70.7%) and ground-glass opacity (44.8%). On admission, lymphocytopenia was present in 51.1% of the patients, mononucleosis in 26.6%, and erythrocytopenia in 61.3%. Most of the patients had increased levels of C-reactive protein (80.4%) and D-dimer (64.4%). Compared with non-severe patients, severe patients had more obvious abnormal laboratory results related to inflammation, coagulation disorders, liver and kidney damage (all P < 0.05). Older age (OR = 2.37, 95% CI: 1.47-3.83), leukocytosis (OR = 2.37, 95% CI: 1.47-3.83), and increased creatine kinase (OR = 2.37, 95% CI: 1.47-3.83) on admission were significantly associated with COVID-19 severity. CONCLUSION: Timely medical treatment and clear diagnosis after the onset might be beneficial to control the condition of COVID-19. Severe patients were more likely to be to be elder, and tended to have higher proportion of comorbidities and more prominent laboratory abnormalities. Older age, leukocytosis, and increased creatine kinase might help clinicians to identify severe patients with COVID-19.

Clinical Characteristics, Associated Factors, and Predicting COVID-19 Mortality Risk: A Retrospective Study in Wuhan, China.

INTRODUCTION: COVID-19 has become a serious global pandemic. This study investigates the clinical characteristics and the risk factors for COVID-19 mortality and establishes a novel scoring system to predict mortality risk in patients with COVID-19. METHODS: A cohort of 1,663 hospitalized patients with COVID-19 in Wuhan, China, of whom 212 died and 1,252 recovered, were included in this study. Demographic, clinical, and laboratory data on admission were collected from electronic medical records between January 14, 2020 and February 28, 2020. Clinical outcomes were collected until March 26, 2020. Multivariable logistic regression was used to explore the association between potential risk factors and COVID-19 mortality. The receiver operating characteristic curve was used to predict COVID-19 mortality risk. All analyses were conducted in April 2020. RESULTS: Multivariable regression showed that increased odds of COVID-19 mortality was associated with older age (OR=2.15, 95% CI=1.35, 3.43), male sex (OR=1.97, 95% CI=1.29, 2.99), history of diabetes (OR=2.34, 95% CI=1.45, 3.76), lymphopenia (OR=1.59, 95% CI=1.03, 2.46), and increased procalcitonin (OR=3.91, 95% CI=2.22, 6.91, per SD increase) on admission. Spline regression analysis indicated that the correlation between procalcitonin levels and COVID-19 mortality was nonlinear (p=0.0004 for nonlinearity). The area under the receiver operating curve of the COVID-19 mortality risk was 0.765 (95% CI=0.725, 0.805). CONCLUSIONS: The independent risk factors for COVID-19 mortality included older age, male sex, history of diabetes, lymphopenia, and increased procalcitonin, which could help clinicians to identify patients with poor prognosis at an earlier stage. The COVID-19 mortality risk score model may assist clinicians in reducing COVID-19-related mortality by implementing better strategies for more effective use of limited medical resources.

Clinical characteristics of 80 hospitalized frontline medical workers infected with COVID-19 in Wuhan, China.

More than 1,000 medical workers have been infected with COVID-19 in China. The 80 hospitalized patients included 57 SARS-CoV-2 confirmed and 23 clinically diagnosed. The median age was 39 years, 49 (61.25%) were women, and one patient died. The most common symptoms at onset were fever (65, 81.25%), cough (47, 58.75%), fatigue (28, 35%), myalgia (19, 23.75%), expectoration (19, 23.75%), and diarrhea (15, 18.75%). Patients of frontline medical workers at a single-center hospital showed some unique clinical and laboratory findings compared with other patients in Wuhan and outside of Wuhan. This study provides our experience for other frontline medical workers.

Metabolically healthy obesity increased diabetes incidence in a middle-aged and elderly Chinese population.

BACKGROUND: We examined the association between metabolically healthy obese (MHO) and diabetes incidence in a middle-aged and elderly population and whether the association differed by the presence of nonalcoholic fatty liver disease (NAFLD). METHODS: We examined 17 801 participants without diabetes at study entry (7980 males and 9821 females with a mean age of 63.2 years) derived from the Dongfeng-Tongji cohort study (median follow-up: 4.6 years). Participants were divided into six groups based on BMI (normal weight, overweight, or obese) and metabolic health (healthy/unhealthy) defined by the Adult Treatment Panel III criteria. The MHO was defined as BMI greater than 28.0 kg/m2 with 0 or 1 of four metabolic abnormalities (elevated blood pressure, triglyceridaemia, hyperglycaemia, low HDL cholesterol). The hazard ratio (HR) and 95% confidence interval (CI) for incident diabetes were derived from the Cox proportional hazard regression model. RESULTS: During 79 843 person-years of follow-up, 1453 individuals developed diabetes. Compared with metabolically healthy normal weight (MH-NW) individuals, the multivariable-adjusted HRs (95% CI) were 1.74 (1.16-2.59) for MHO and 2.15 (1.65-2.81) for metabolically unhealthy obese subjects after adjusting for age, sex, smoking, alcohol drinking, physical activity, fruit and vegetable consumption, family history of diabetes, fasting glucose, waist circumference, and NAFLD. Among those without NAFLD, MHO individuals showed higher incidence of diabetes (multivariate-adjusted HR = 2.71, 95% CI: 1.47-5.00) than MH-NW individuals. CONCLUSIONS: The MHO phenotype was associated with increased incidence of diabetes in a middle-aged and elderly population, and the association did not differ by the presence or absence of NAFLD.

Serum alanine transaminase levels predict type 2 diabetes risk among a middle-aged and elderly Chinese population.

INTRODUCTION AND AIM: It is indicated that high levels of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are associated with increased incident type 2 diabetes risk. However, whether serum ALT levels could improve the discrimination of type 2 diabetes remains unclear. METHODS: The data was derived from the Dongfeng-Tongji cohort study, which was established in 2008 and followed until October 2013. A total of 17,173 participants free of type 2 diabetes at baseline were included and 1159 participants developed diabetes after 4.51 (0.61) years of follow-up. Cox proportional hazard regression model was used to calculate the hazard ratios (HRs) for the association between ALT and AST levels with incident diabetes risk. Receiver-operating characteristic (ROC) curves analysis was used to evaluate the predictive accuracy of models incorporating traditional risk factors with and without ALT. RESULTS: Compared with the lowest quartile of ALT and AST levels, the highest quartile had a significantly higher risk of developing type 2 diabetes (HR: 2.17 [95% CI: 1.78-2.65] and 1.29 [1.08-1.54], respectively) after adjustment for potential confounders. The addition of ALT levels into the traditional risk factors did not improve the predictive ability of type 2 diabetes, with AUC increase from 0.772 to 0.774; P=0.86. CONCLUSIONS: Although elevated ALT or AST levels increased incident type 2diabetes risk, addition of ALT levels into the prediction model did not improve the discrimination of type 2 diabetes.

Association between resting heart rate and incident diabetes risk: a Mendelian randomization study.

AIMS: Observational studies indicated that resting heart rate (RHR) was associated with diabetes mellitus (DM) risk; however, it remains unclear whether the association between RHR and DM is causal. We aimed to examine whether there was causal association of RHR with DM risk. METHODS: A prospective study including 16,201 middle-aged and older Chinese (7031 males and 9170 females) derived from the Dongfeng-Tongji cohort was performed. Cox proportional hazard regression models were conducted to estimate the associations between RHR and incident DM risk. In 7481 participants, 65 single nucleotide polymorphisms related to RHR were genotyped. A genetic risk score (GRS) of RHR was calculated based on the RHR-associated variants. The causal associations of RHR with DM risk were investigated by Mendelian randomization analysis. RESULTS: During a mean (SD) follow-up of 4.5 (0.5) years, 1110 diabetes were identified. Compared with the referential RHR group (≤ 60 beats per minute [bpm]), individuals with RHR > 80 bpm have a higher incident diabetes risk, with a hazard ratio of 1.40 (95% confidence interval [CI], 1.05-1.88). With per SD increase in the weighted genetic risk score, the resting heart rate increased by 0.71 bpm (95% CI 0.49-0.93). By using the GRS to estimate the unconfounded effect, we found that higher resting heart rate did not have a causal effect on diabetes risk (OR 1.00 [95% CI 0.95-1.05]). CONCLUSIONS: The present study supported a positive but not a causal association of RHR with incident diabetes risk. More studies are needed to verify our findings.

Serum carbohydrate antigen 125 levels and incident risk of type 2 diabetes mellitus in middle-aged and elderly Chinese population: The Dongfeng-Tongji cohort study.

OBJECTIVES: The aim of this study was to investigate the association between serum carbohydrate antigen 125 concentrations and incident type 2 diabetes mellitus risk in a prospective cohort. METHODS: We included 18,983 eligible participants aged 63.1 years derived from the Dongfeng-Tongji cohort at baseline from September 2008 to June 2010, and they were followed until October 2013. Cox proportional-hazards models were used to estimate the hazard ratios and 95% confidence interval of type 2 diabetes mellitus incidence in relation to carbohydrate antigen 125 concentrations. RESULTS: In all, 1594 incident cases of type 2 diabetes mellitus were observed after a median follow-up of 4.6 years. Carbohydrate antigen 125 concentrations were categorized into four groups according to the quartiles of distribution: <1.1, 1.1-5.6, 5.6-10.0 and ⩾10 U/mL. Compared with participants in the lowest quartile, the hazard ratio (95% confidence interval) of type 2 diabetes mellitus was 0.97 (0.81-1.15), 1.23 (1.05-1.45) and 1.48 (1.27-1.74) for quartile 2-quartile 4 of carbohydrate antigen 125 concentrations after adjustment for potential confounders (p for trend < 0.001). With per-standard deviation increase in carbohydrate antigen 125 levels, the hazard ratio of type 2 diabetes mellitus increased 12% (95% confidence interval, 8-16). CONCLUSION: Findings from this study indicated that serum carbohydrate antigen 125 concentrations were positively correlated with incident type 2 diabetes mellitus risk among a middle-aged and elderly Chinese population.

Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study.

BACKGROUND: Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese. METHODS: Nine thousand nine hundred sixty-two elderly individuals (age ≥ 60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices (body mass index [BMI], waist circumference [WC], waist-to-height ratio [WHtR]), novel anthropometric indices (visceral adiposity index [VAI], a body shape index [ABSI], body roundness index [BRI]) and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction. RESULTS: During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC = 0.655) comparable with the other anthropometric indices (P < 0.05). Similar as men, BMI was the strongest predictor (AUC = 0.635) in women. Except for WC, the AUC of BMI was larger than WHtR, VAI, and BRI. In contrast, ABSI was not a good predictor in either men (AUC = 0.507) or women (AUC = 0.503). CONCLUSIONS: In elderly Chinese, BMI, WC, WHtR, VAI and BRI were positively associated with incident T2D risk. Among them, BMI was the strongest predictor in both men and women.

Victims of Chinese famine in early life have increased risk of metabolic syndrome in adulthood.

OBJECTIVES: To investigate the association of exposure to the Chinese famine during early life with metabolic syndrome risk in adults. METHODS: There were 7,915 participants from Dongfeng-Tongji cohort were included in the present study. Participants were classified as non-exposed group, fetal exposed group, early childhood-, mid childhood-, and late childhood-exposed groups, respectively. Metabolic syndrome was defined according to International Diabetes Foundation criteria (2005). Logistic regression model was used to explore the association between famine exposure in early life and metabolic syndrome risk in adults. RESULTS: The metabolic syndrome prevalence in non-, fetal-, early childhood-, mid childhood-, and late childhood- exposed groups were 25.2%, 26.9%, 30.3%, 32.7%, and 32.7%, respectively. Compared with non-exposed group, participants exposed to famine in the fetal (0.96, 95% CI: 0.77-1.20), early childhood (1.24, 95% CI: 1.01-1.52), mid childhood (1.39, 95% CI: 1.13-1.72), and late childhood (1.33, 95% CI: 1.08-1.63) had higher metabolic syndrome prevalence risk in adults after adjustment for potential confounders (P for trend < 0.0001). In gender-specific analyses, women exposed to famine in early childhood (1.26, 95% CI: 1.02-1.56), mid childhood (1.43, 95% CI: 1.14-1.78), and late childhood (1.47, 95% CI: 1.18-1.84) had higher metabolic syndrome prevalence risk than non-exposed women (P for trend < 0.0001). There was a famine-gender interaction on metabolic syndrome prevalence risk (P for interaction = 0.0001). CONCLUSION: Results in the present study indicated that exposure to famine in early life increases the risk of metabolic syndrome in adulthood, particularly in women.

Serum bilirubin concentrations and incident coronary heart disease risk among patients with type 2 diabetes: the Dongfeng-Tongji cohort.

AIMS: Elevated serum bilirubin levels are associated with decreased coronary heart disease (CHD) risk in cross-sectional studies among diabetic patients, but prospective evidence is limited. We investigated the relationship of serum bilirubin levels with incident CHD risk among type 2 diabetes patients. METHODS: In a prospective study of 2918 type 2 diabetes embedded in the Dongfeng-Tongji cohort, serum total bilirubin (TBil), direct bilirubin (DBil), and indirect bilirubin (IBil) were measured at baseline. Cox proportional hazards models were used to examine the association between serum bilirubin levels and CHD risk. RESULTS: A total of 440 CHD cases were identified during 12,017 person-years of follow-up. Compared with extreme quartiles, the adjusted hazard ratio and 95% confidence interval of incident CHD were 0.74 (0.56-0.99) with P trend = 0.08 in IBil, while in TBil and DBil, the bilirubin-CHD associations were not significant. Moreover, serum TBil and IBil levels were interacted with drinking status on the risk of incident CHD (P interaction = 0.021 and 0.037, respectively), and the associations were evident in ever drinkers. In drinkers, when serum TBil or IBil concentrations increased 1 µmol/L, the CHD risk both decreased 6% (95% CIs 0.89-0.99 and 0.87-1.00, respectively). CONCLUSIONS: Serum IBil levels were marginally related to decreased incident CHD risk among type 2 diabetes. Drinking could potentially enhance the associations of serum TBil and DBil levels with incident CHD risk.