Image_1_Long-term trajectories of BMI and cumulative incident metabolic syndrome: A cohort study.png

BackgroundBody mass index (BMI) has been widely recognized as a risk factor for metabolic syndrome (MetS). However, the relationship between the trajectory of BMI and cumulative incident MetS is still unclear. We investigate the associations of long-term measurements of BMI with MetS among young adults in the China Health and Nutrition Survey.MethodsWe enrolled individuals aged 10 to 20 at baseline with recorded BMI at each follow-up interview, and 554 participants were finally included in our study. The assessment and incidence of MetS were evaluated by blood tests and physical examinations in their adulthood. A latent class growth mixed model was used to identify three BMI trajectory patterns: a low baseline BMI with slow development (low-slow, n=438), a low baseline BMI with fast development (low-fast, n=66), and a high baseline BMI with fast development (high-fast, n=50). Logistic regression was used to explore the relationship between different BMI trajectories and the incidence of MetS.ResultDuring a follow-up of 16 years, 61 (11.01%) participants developed MetS. The combination of elevated triglycerides and reduced high-density lipoprotein cholesterol was most frequent in diagnosed MetS. In multivariate adjusted models, the low-fast and high-fast BMI trajectories showed a significantly higher risk of MetS than those with the low-slow BMI trajectory (low-high: OR = 3.40, 95% CI: 1.14-10.13, P ConclusionOur study identified three BMI trajectories in young adults and found that long-term measurements of BMI were also associated with cumulative incident MetS.</p

Image_2_Long-term trajectories of BMI and cumulative incident metabolic syndrome: A cohort study.png

BackgroundBody mass index (BMI) has been widely recognized as a risk factor for metabolic syndrome (MetS). However, the relationship between the trajectory of BMI and cumulative incident MetS is still unclear. We investigate the associations of long-term measurements of BMI with MetS among young adults in the China Health and Nutrition Survey.MethodsWe enrolled individuals aged 10 to 20 at baseline with recorded BMI at each follow-up interview, and 554 participants were finally included in our study. The assessment and incidence of MetS were evaluated by blood tests and physical examinations in their adulthood. A latent class growth mixed model was used to identify three BMI trajectory patterns: a low baseline BMI with slow development (low-slow, n=438), a low baseline BMI with fast development (low-fast, n=66), and a high baseline BMI with fast development (high-fast, n=50). Logistic regression was used to explore the relationship between different BMI trajectories and the incidence of MetS.ResultDuring a follow-up of 16 years, 61 (11.01%) participants developed MetS. The combination of elevated triglycerides and reduced high-density lipoprotein cholesterol was most frequent in diagnosed MetS. In multivariate adjusted models, the low-fast and high-fast BMI trajectories showed a significantly higher risk of MetS than those with the low-slow BMI trajectory (low-high: OR = 3.40, 95% CI: 1.14-10.13, P ConclusionOur study identified three BMI trajectories in young adults and found that long-term measurements of BMI were also associated with cumulative incident MetS.</p

Table_1_Long-term trajectories of BMI and cumulative incident metabolic syndrome: A cohort study.docx

BackgroundBody mass index (BMI) has been widely recognized as a risk factor for metabolic syndrome (MetS). However, the relationship between the trajectory of BMI and cumulative incident MetS is still unclear. We investigate the associations of long-term measurements of BMI with MetS among young adults in the China Health and Nutrition Survey.MethodsWe enrolled individuals aged 10 to 20 at baseline with recorded BMI at each follow-up interview, and 554 participants were finally included in our study. The assessment and incidence of MetS were evaluated by blood tests and physical examinations in their adulthood. A latent class growth mixed model was used to identify three BMI trajectory patterns: a low baseline BMI with slow development (low-slow, n=438), a low baseline BMI with fast development (low-fast, n=66), and a high baseline BMI with fast development (high-fast, n=50). Logistic regression was used to explore the relationship between different BMI trajectories and the incidence of MetS.ResultDuring a follow-up of 16 years, 61 (11.01%) participants developed MetS. The combination of elevated triglycerides and reduced high-density lipoprotein cholesterol was most frequent in diagnosed MetS. In multivariate adjusted models, the low-fast and high-fast BMI trajectories showed a significantly higher risk of MetS than those with the low-slow BMI trajectory (low-high: OR = 3.40, 95% CI: 1.14-10.13, P ConclusionOur study identified three BMI trajectories in young adults and found that long-term measurements of BMI were also associated with cumulative incident MetS.</p

Additional file 1 of The role of lifestyle in the association between long-term ambient air pollution exposure and cardiovascular disease: a national cohort study in China

Additional file 1: Method S1. Ambient air pollution exposure acquisition. Figure S1. Sampling procedure. Figure S2. Study flowchart. Figure S3. The association of different lifestyle factors. Figure S4. (a) The proportion of single ideal factor in different lifestyle groups. (b) The proportion of ideal factors in different lifestyle groups. Figure S5. Directed acyclic graph. Figure S6. The marginal effect of lifestyle on CVD and in the relationship between ambient air pollutant exposure and CVD. Table S1. The score criteria of different lifestyle factors. Table S2. The exposure level of different air pollutants among the study population. Table S3. The exposure level by quintile of air pollutant. Table S4. The HRs (95% CIs) of the associations between lifestyle and CVD with and without adjustment for ambient air pollutant exposure. Table S5. Joint effects of lifestyle and air pollutant exposure on the incidence of CVD. Table S6. The HRs (95% CIs) of incident CVD associated with each lifestyle factor at different levels of air pollutant exposure. Table S7. Subgroup analysis of the additive interactions analysis of the effect of dichotomized lifestyle on the association between ambient air pollutant exposure and CVD in high air pollutant exposure levels (Q2–Q5). Table S8. The HRs (95% CIs) of associations between air pollutant exposure (per 10 μg/m3 increase) and incident CVD, and the mediation effect of lifestyle categories on air pollution and CVD in different sensitivity analysis models. Table S9. The HRs (95% CIs) of the association between ambient air pollutant exposure (per 10 μg/m3 increase) and CVD in different lifestyle categories in different sensitivity analysis models. Table S10. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between time-varying ambient air pollutant exposure and CVD. Table S11. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle on the association between 3 years of ambient air pollutant exposure and CVD. Table S12. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle considering new categories and nighttime sleep duration on the association between ambient air pollutant exposure and CVD. Table S13. Multiplicative and additive interaction analysis of the effect of dichotomized lifestyle considering new assignment of lifestyle categories on the association between ambient air pollutant exposure and CVD. Table S14. The subdistribution HRs (sHRs, 95% CI) of the associations between ambient air pollutant exposure (per 10 μg/m3) and CVD in different lifestyle categories. Table S15. Baseline characteristics of included and excluded participants. Table S16. Baseline characteristics of included participants and those without lifestyle scores

Additional file 3: Table S2. of A simple prediction model to estimate obstructive coronary artery disease

Multivariate logistic regression for the final modified Framingham model of complete and imputation data. (DOCX 19 kb

Additional file 2: Table S1. of A simple prediction model to estimate obstructive coronary artery disease

Baseline Characteristics and incidence of obstructive coronary artery disease for patients with and without miss data of variables included in the final model. (DOCX 32 kb

Additional file 1: Figure S1. of A simple prediction model to estimate obstructive coronary artery disease

Study flow. (PDF 54 kb

Table_1_Coronary artery disease as an independent predictor of short-term and long-term outcomes in patients with type-B aortic dissection undergoing thoracic endovascular repair.docx

Background and aimsPrevious studies reported a high prevalence of concomitant coronary artery disease (CAD) in patients with Type B aortic dissection (TBAD). However, there is too limited data on the impact of CAD on prognosis in patients with TBAD. The present study aimed to assess the short-term and long-term impact of CAD on patients with acute or subacute TBAD undergoing thoracic endovascular aortic repair (TEVAR).MethodsWe retrospectively evaluated 463 patients with acute or subacute TBAD undergoing TEVAR from a prospectively maintained database from 2010 to 2017. CAD was defined before TEVAR by coronary angiography. Multivariable logistic and cox regression analyses were performed to evaluate the relationship between CAD and the short-term as well as long-term outcomes.ResultsAccording to the results of coronary angiography, the 463 patients were divided into the following two groups: CAD group (N = 148), non-CAD group (N = 315). In total, 12 (2.6%) in-hospital deaths and 54 (12%) all-cause deaths following a median follow-up of 48.1 months were recorded. Multivariable analysis revealed that CAD was an independent predictor of in-hospital major adverse clinical events (MACE) (odd ratio [OR], 2.33; 95% confidence interval [CI], 1.07–5.08; p = 0.033), long-term mortality [hazard ratio (HR), 2.11, 95% CI, 1.19–3.74, P = 0.011] and long-term MACE (HR, 1.95, 95% CI, 1.26–3.02, P = 0.003). To further clarify the relationship between the severity of CAD and long-term outcomes, we categorized patients into three groups: zero-vessel disease, single-vessel disease and multi-vessel disease. The long-term mortality (9.7 vs. 14.4 vs. 21.2%, P = 0.045), and long-term MACE (16.8 vs. 22.2 vs. 40.4%, P = 0.001) increased with the number of identified stenosed coronary vessels. Multivariable analysis indicated that, multi-vessel disease was independently associated with long-term mortality (HR, 2.38, 95% CI, 1.16–4.89, P = 0.018) and long-term MACE (HR, 2.79, 95% CI, 1.65–4.73, P = 0.001), compared with zero-vessel disease.ConclusionsCAD was associated with short-term and long-term worse outcomes in patients with acute or subacute TBAD undergoing TEVAR. Furthermore, the severity of CAD was also associated with worse long-term prognosis. Therefore, CAD could be considered as a useful independent predictor for pre-TEVAR risk stratification in patients with TBAD.</p

Study schematic and immuno-phenotype of ASCs.

<p>Panel (a) Schematic of the <i>in vivo</i> and <i>in vitro</i> experiment. Panel (b) CD29, CD31, CD44 and CD45 were detected by flow cytometry. Results showed that the fourth passage ASCs were largely positive for CD29 (99.80±0.10%) and CD44 (99.60±0.20%), and only minority of ASCs were positive for CD31 (0.30±0.10%) and CD45 (0.45±0.10%). The expression of CXCR4 on ASCs was 9.96±0.07%.</p

Comparisons of eNOS, p-eNOS, NO and SDF-1α levels in each group.

<p>Panel (a) Western-blot analysis showed that a significant increment of eNOS expression in group Ator, A+L-NAME and A+AMD3100 for <i>in vivo</i> study when compared with blank control. (sham operated: 0.28±0.07, blank control: 0.41±0.10*, Ator: 0.88±0.16*#, A+L-NAME: 0.93±0.15*#, A+AMD3100: 0.92±0.13*#). Similar changes were found in p-eNOS expression among each group (sham operated: 0.33±0.06, blank control: 0.46±0.11*, Ator: 0.93±0.14*#, A+L-NAME: 1.01±0.18*#, A+AMD3100: 0.95±0.16*#). Panel (b) Analyses of eNOS expression by Western-blot for <i>in vitro</i> study showed that as compared with group blank control, eNOS was significantly increased in group Ator, A+L-NAME and A+AMD3100. (sham operated: 0.42±0.08, blank control: 0.61±0.10*, Ator: 0.86±0.13*#, A+L-NAME: 0.89±0.11*#, A+AMD3100: 0.92±0.14*#). Similar changes were found in p-eNOS expression among each group (sham operated: 0.33±0.06, blank control: 0.50±0.09*, Ator: 0.76±0.11*#, A+L-NAME: 0.81±0.13*#, A+AMD3100: 0.78±0.10*#). Panel(c) When compared with group blank control, the NO productions <i>in vitro</i> and <i>in vivo</i> were identically and significantly increased in group Ator and A+AMD3100, whereas was abolished in group A+L-NAME. (<i>In vivo</i> study, sham operated: 6.03±1.10, blank control: 6.22±0.98, Ator: 10.31±0.86*#, A+L-NAME: 8.86±1.03*#, A+AMD3100: 11.10±0.92*#) and (in vitro study, sham operated: 3.62±0.42, blank control: 4.11±0.66, Ator: 7.03±0.71*#, A+L-NAME: 5.02±0.69*#, A+AMD3100: 7.11±0.70*#). Panel (d) As compared with group blank control, the SDF-1α expressions <i>in vitro</i> and <i>in vivo</i> was profoundly increased with atorvastatin therapy; however, the effects were reduced in group A+L-NAME. (<i>In vivo</i> study, sham operated: 5.53±0.73, blank control: 6.05±0.95, Ator: 10.88±1.33*#, A+L-NAME: 7.92±1.09*#, A+AMD3100: 9.86±1.21*#) and (in vitro study, sham operated: 3.84±0.47, blank control: 4.21±0.65, Ator: 8.12±0.77*#, A+L-NAME: 5.88±0.70*#, A+AMD3100: 7.76±0.73*#).</p