Relationship between the cumulative exposure to atherogenic index of plasma and ischemic stroke: A retrospective cohort study

Background: Atherogenic index of plasma (AIP) has been demonstrated as a surrogate marker for ischemic stroke, but there is limited evidence for the effect of long-term elevation of AIP on ischemic stroke. Therefore, we aimed to characterize the relationship between cumulative exposure to AIP and the risk of ischemic stroke. Methods: A total of 54,123 participants in the Kailuan Study who attended consecutive health examinations in 2006, 2008, and 2010 and had no history of ischemic stroke or cancer were included. The time-weighted cumulative AIP (cumAIP) was calculated as a weighted sum of the mean AIP values for each time interval and then normalized to the total duration of exposure (2006–2010). Participants were divided into four groups according to quartile of cumAIP: the Q1 group, ≤ −0.50; Q2 group, − 0.50 to − 0.12; Q3 group, − 0.12 to 0.28; and Q4 group, ≥ 0.28. Cox proportional hazard models were used to evaluate the relationship between cumAIP and ischemic stroke by calculating hazard ratios (HRs) and 95% confidence intervals (95% CIs). Results: After a median follow-up of 11.03 years, a total of 2,742 new ischemic stroke events occurred. The risk of ischemic stroke increased with increasing quartile of cumAIP. After adjustment for potential confounders, Cox regression models showed that participants in the Q2, Q3, and Q4 groups had significantly higher risks of ischemic stroke than those in the Q1 group. The HRs (95% CIs) for ischemic stroke in the Q2, Q3, and Q4 groups were 1.17 (1.03, 1.32), 1.33 (1.18, 1.50), and 1.45 (1.28, 1.64), respectively. The longer duration of high AIP exposure was significantly associated with increased ischemic stroke risk. Conclusions: High cumulative AIP is associated with a higher risk of ischemic stroke, which implies that the long-term monitoring and maintenance of an appropriate AIP may help prevent such events

Visit-to-visit variability in triglyceride-glucose index and diabetes:A 9-year prospective study in the Kailuan Study

Instruction/Aims: It is unknown whether variability in the triglyceride-glucose index (TyG-index) is associated with the risk of diabetes. Here, we sought to characterize the relationship between TyG-index variability and incident diabetes. Methods: We performed a prospective study of 48,013 participants in the Kailuan Study who did not have diabetes. The TyG-index was calculated as ln [triglyceride (TG, mg/dL) concentration × fasting blood glucose concentration (FBG, mg/dL)/2]. The TyG-index variability was assessed using the standard deviation (SD) of three TyG-index values that were calculated during 2006/07, 2008/09, and 2010/11. We used the Cox proportional hazard models to analyze the effect of TyG-index variability on incident diabetes. Results: A total of 4,055 participants were newly diagnosed with diabetes during the study period of 8.95 years (95% confidence interval (CI) 8.48–9.29 years). After adjustment for confounding factors, participants in the highest and second-highest quartiles had significantly higher risks of new-onset diabetes versus the lowest quartile, with hazard ratios (95% CIs) of 1.18 (1.08–1.29) and 1.13 (1.03–1.24), respectively (P trend< 0.05). These higher risks remained after further adjustment for the baseline TyG-index. Conclusions: A substantial fluctuation in TyG-index is associated with a higher risk of diabetes in the Chinese population, implying that it is important to maintain a normal and consistent TyG-index

Association of Age of Metabolic Syndrome Onset With Cardiovascular Diseases:The Kailuan Study

BACKGROUND: Metabolic syndrome (MetS) is associated with an increased risk of incident cardiovascular diseases (CVD), but the association between the new-onset MetS at different ages and the CVD risk remain unclear. METHODS: This was a prospective study comprising a total of 72,986 participants without MetS and CVD who participated in the Kailuan study baseline survey (July 2006 to October 2007). All participants received the biennial follow-up visit until December 31, 2019. In addition, 26,411 patients with new-onset MetS were identified from follow-up, and one control participant was randomly selected for each of them as a match for age ( ± 1 year) and sex. In the end, a total of 25,125 case-control pairs were involved. Moreover, the Cox proportional hazard model was established to calculate the hazard ratios (HR) for incident CVD across the onset age groups. RESULTS: According to the median follow-up for 8.47 years, 2,319 cases of incident CVD occurred. As MetS onset age increased, CVD hazards gradually decreased after adjusting for potential confounders. Compared with non-MetS controls, the HR and the 95% confidence interval (CI) for CVD were 1.84 (1.31–2.57) in the MetS onset age <45 years group, 1.67 (1.42–1.95) for the 45–54 years group, 1.36 (1.18–1.58) for the 55–64 years group, and 1.28 (1.10–1.50) for the ≥65 years group, respectively (p for interaction = 0.03). CONCLUSIONS: The relative risks of CVD differed across MetS onset age groups, and the associations was more intense in the MetS onset group at a younger age

Association of Cardiovascular Health Score Trajectory With Incident Myocardial Infarction in Hypertensive Patients

Background: The association between changes in cardiovascular health score (CHS) over time and myocardial infarction (MI) risk in hypertensive patients remains unclear. Method: This was a prospective study comprising 17 374 hypertensive patients from the Kailuan study cohort who underwent 3 surveys and were identified to be free of MI, stroke, or cancer from 2006 to 2010. CHS consisted of 7 cardiovascular health metrics (plasma glucose, total cholesterol, blood pressure, smoking, body mass index, physical activity, salt intake), ranging from 0 (worst) to 13 (best) in the study. CHS trajectories were developed during 2006 to 2010 to predict the MI risk from 2010 to 2020. Additionally, the Cox proportional hazard model was established to calculate the hazard ratio and 95% CI of incident MI in different trajectory groups. Result: This study identified the 5 CHS trajectories from 2006 to 2010: low-stable (n=1161; range, 4.7-4.5), moderate-decreasing (n=3928; decreased from 6.9 to 6.0), moderate-increasing (n=1014; increased from 5.6 to 7.8), high-stable I (n=7940; range, 8.1-8.2), and high-stable II (n=3331; range, 9.2-9.7). During the median follow-up of 10.04 years, 288 incident MI cases were identified. After adjusting for potential confounders, compared with low-stable group, the hazard ratio and 95% CI of MI were 0.24 (0.15-0.40) for high-stable II, 0.36 (0.24-0.54) for high-stable I, 0.46 (0.25-0.83) for moderate-increasing, and 0.61 (0.41-0.90) for moderate-decreasing, respectively. Conclusions: In hypertensive patients, high-stable CHS or improvement in CHS is associated with a lower risk of incident MI, when compared with low-stable CHS trajectory over time.</p

Triglyceride-glucose index trajectory and stroke incidence in patients with hypertension:a prospective cohort study

Background It has been suggested that the baseline triglyceride-glucose (TyG) index, a simple surrogate measure for insulin resistance, is significantly associated with the occurrence of stroke. Nevertheless, the impact of longitudinal patterns of TyG on the stroke risk in hypertensive patients is still unknown. Hence, this study aimed to investigate the association between TyG index trajectory and stroke risk among hypertensive patients. Methods This prospective study included 19,924 hypertensive patients from the Kailuan Study who underwent three waves survey and were free of myocardial infarction, cancer and stroke before or during 2010. The TyG index was calculated as ln [fasting triglyceride (mg/dL) x fasting plasma glucose (mg/dL)/2], and latent mixed modelling was used to identify the trajectory of TyG during the exposure period (2006-2010). Furthermore, the Cox proportional hazard models were applied to estimate the hazard ratio (HR) and 95% confidence interval (CI) for incident stroke of different trajectory groups. Results Five distinct TyG trajectory were identified during 2006-2010: low-stable (n = 2483; range, 8.03-8.06), moderate low-stable (n = 9666; range, 8.58-8.57), moderate high-stable (n = 5759; range, 9.16-9.09), elevated-stable (n = 1741; range, 9.79-9.75), and elevated-increasing (n = 275; range, 10.38-10.81). During the median follow-up of 9.97 years, 1,519 cases of incident stroke were identified, including 1,351 with ischemic stroke and 215 with hemorrhage stroke. After adjusting for confounding variables, the HR and 95% CI of stroke were 2.21 (1.49,3.28) for the elevated-increasing group, 1.43 (1.13,1.83) for the elevated-stable group, 1.35 (1.10,1.64) for the moderate high-stable group, 1.26 (1.06,1.52) for the moderate low-stable group, respectively, when compare with the low-stable group. Similar results were observed in ischemic stroke, but a significant association was not found between TyG trajectory and risk of hemorrhage stroke. Conclusion A long-term elevated TyG index in hypertensive patients is associated with an increased risk of stroke, especially ischemic stroke. This finding implies that regular monitoring of TyG index may assist in identifying individuals at a higher risk of stroke among patients with hypertension

Changes in Impaired Fasting Glucose and Borderline High Low-Density Lipoprotein-Cholesterol Status Alter the Risk of Cardiovascular Disease:A 9-Year Prospective Cohort Study

BackgroundWe aimed to characterize the relationships of the changes in impaired fasting glucose (IFG) and borderline high low-density lipoprotein-cholesterol (LDL-C) status with cardiovascular disease (CVD). MethodsA total of 36,537 participants who did not have previous CVD, diabetes mellitus, or high LDL-C (>= 4.1 mmol/L), nor were taking lipid-lowering drugs were recruited from the Kailuan study. The participants were allocated to six groups according to their baseline and follow-up fasting blood glucose (FBG) and LDL-C concentrations: (1) both were normal; (2) both normal at baseline, one abnormality subsequently; (3) both normal at baseline, both abnormal subsequently; (4) at least one abnormality that became normal; (5) at least one abnormality at baseline, a single abnormality subsequently; and (6) at least one abnormality, two abnormalities subsequently. The outcomes were CVD and subtypes of CVD (myocardial infarction and stroke). Multiple Cox regression models were used to calculate adjusted hazard ratio (HR) and confidence interval (95% CI). ResultsDuring a median follow-up period of 9.00 years, 1,753 participants experienced a CVD event. After adjustment for covariates, participants with IFG in combination with a borderline high LDL-C status at baseline and follow-up had higher risks of CVD (HR: 1.52; 95% CI: 1.04-2.23 and HR: 1.38, 95% CI: 1.13-1.70, respectively) compared with those with normal fasting blood glucose and LDL-C. Compared with participants that remained normal, those who changed from normality to having two abnormalities were at a higher risk of CVD (HR: 1.26; 95% CI: 0.98-1.61), as were those who changed from at least one abnormality to two abnormalities (HR: 1.48, 95% CI: 1.02-2.15). ConclusionChanges in IFG and borderline high LDL-C status alter the risk of CVD and its subtype, implying that it is important to focus on such individuals for the prevention and control of CVD

Supra-additive effect of chronic inflammation and atherogenic dyslipidemia on developing type 2 diabetes among young adults: A prospective cohort study

Background: Both elevated inflammation and atherogenic dyslipidemia are prominent in young-onset diabetes and are increasingly identified as biologically intertwined processes that contribute to diabetogenesis. We aimed to investigate the age-specific risks of type 2 diabetes (T2D) upon concomitant chronic inflammation and atherogenic dyslipidemia. Methods: Age-stratified Cox regression analysis of the risk of incident diabetes upon co-exposure to time-averaged cumulative high-sensitivity C-reactive protein (CumCRP) and atherogenic index of plasma (CumAIP) among 42,925 nondiabetic participants from a real-world, prospective cohort (Kailuan Study). Results: During a median 6.41 years of follow-up, 3987 T2D developed. Isolated CumAIP and CumCRP were significantly associated with incident T2D in the entire cohort and across all age subgroups. Both CumAIP and CumCRP were jointly associated with an increased risk of diabetes (P-interaction = 0.0126). Compared to CumAIP \u3c -0.0699 and CumCRP \u3c 1 mg/L, co-exposure to CumAIP ≥ − 0.0699 and CumCRP ≥ 3 mg/L had a significant hazard ratio (HR) [2.55 (2.23–2.92)] after adjusting for socio-demographic, life-style factors, family history of diabetes, blood pressure, renal function and medication use. The co-exposure-associated risks varied greatly by age distribution (P-interaction = 0.0193): \u3c 40 years, 6.26 (3.47–11.28); 40–49 years, 2.26 (1.77–2.89); 50–59 years, 2.51 (2.00–3.16); 60–69 years, 2.48 (1.86–3.30); ≥ 70 years, 2.10 (1.29–3.40). In young adults ( \u3c 45 years), both exposures had a significant supra-additive effect on diabetogenesis (relative excess risk due to interaction: 0.80, 95% CI 0.10–1.50). Conclusions: These findings highlight the need for age-specific combined assessment and management of chronic inflammation and dyslipidemia in primary prevention against T2D, particularly for young adults. The clinical benefit derived from dual-target intervention against dyslipidemia and inflammation will exceed the sum of each part alone in young adults

Using active learning selection approach for cross-project software defect prediction

Cross-project defect prediction (CPDP) technology can effectively ensure software quality, which plays an important role in software engineering. When encountering a newly developed project with insufficient training data, CPDP can be used to build defect predictors using other projects. However, CPDP does not take into account the prior knowledge of the target items and the class imbalance in the source item data. In this paper, we design an active learning selection algorithm for cross-project defect prediction to alleviate the above problems. First, we use clustering and active learning algorithms to filter and label some representative data from the target items and use these data as prior knowledge to guide the selection of source items. Then, the active learning algorithm is used to filter representative data from the source items. Finally, the balanced cross-item dataset is constructed using the active learning algorithm, and the defect prediction model is built. In this article, we selected 10 open-source projects by using common defect prediction models, active learning algorithms, and common evaluation metrics. The results show that the proposed algorithm can effectively filter the data, solve the class imbalance problem in cross-project data, and improve the defect prediction performance

Relationship between the cumulative exposure to atherogenic index of plasma and ischemic stroke:a retrospective cohort study

Background: Atherogenic index of plasma (AIP) has been demonstrated as a surrogate marker for ischemic stroke, but there is limited evidence for the effect of long-term elevation of AIP on ischemic stroke. Therefore, we aimed to characterize the relationship between cumulative exposure to AIP and the risk of ischemic stroke. Methods: A total of 54,123 participants in the Kailuan Study who attended consecutive health examinations in 2006, 2008, and 2010 and had no history of ischemic stroke or cancer were included. The time-weighted cumulative AIP (cumAIP) was calculated as a weighted sum of the mean AIP values for each time interval and then normalized to the total duration of exposure (2006–2010). Participants were divided into four groups according to quartile of cumAIP: the Q1 group, ≤−0.50; Q2 group, − 0.50 to − 0.12; Q3 group, − 0.12 to 0.28; and Q4 group, ≥ 0.28. Cox proportional hazard models were used to evaluate the relationship between cumAIP and ischemic stroke by calculating hazard ratios (HRs) and 95% confidence intervals (95% CIs). Results: After a median follow-up of 11.03 years, a total of 2,742 new ischemic stroke events occurred. The risk of ischemic stroke increased with increasing quartile of cumAIP. After adjustment for potential confounders, Cox regression models showed that participants in the Q2, Q3, and Q4 groups had significantly higher risks of ischemic stroke than those in the Q1 group. The HRs (95% CIs) for ischemic stroke in the Q2, Q3, and Q4 groups were 1.17 (1.03, 1.32), 1.33 (1.18, 1.50), and 1.45 (1.28, 1.64), respectively. The longer duration of high AIP exposure was significantly associated with increased ischemic stroke risk. Conclusions: High cumulative AIP is associated with a higher risk of ischemic stroke, which implies that the long-term monitoring and maintenance of an appropriate AIP may help prevent such events.</p

Impact of body mass index on long-term blood pressure variability: a cross-sectional study in a cohort of Chinese adults

Abstract Background Obesity and overweight are related to changes in blood pressure, but existing research has mainly focused on the impact of body mass index (BMI) on short-term blood pressure variability (BPV). The study aimed to examine the impact of BMI on long-term BPV. Methods Participants in the Kailuan study who attended all five annual physical examinations in 2006, 2008, 2010, 2012, and 2014 were selected as observation subjects. In total, 32,482 cases were included in the statistical analysis. According to the definition of obesity in China, BMI was divided into four groups: underweight (BMI < 18.5 kg/m2), normal weight (18.5 ≤ BMI < 24.0 kg/m2), overweight (24.0 ≤ BMI < 28.0 kg/m2), and obese (BMI ≥ 28.0 kg/m2). We used average real variability to evaluate long-term systolic BPV. The average real variability of systolic blood pressure (ARVSBP) was calculated as (|sbp2 − sbp1| + |sbp3 − sbp2 | + |sbp4 − sbp3| + |sbp5 − sbp4|)/4. Differences in ARVSBP by BMI group were analyzed using analysis of variance. Stepwise multivariate linear regression and multiple logistic regression analyses were used to assess the impact of BMI on ARVSBP. Results Participants’ average age was 46.6 ± 11.3 years, 24,502 were men, and 7980 were women. As BMI increases, the mean value of ARVSBP gradually increases. After adjusting for other confounding factors, stepwise multivariate linear regression analysis showed that ARVSBP increased by 0.077 for every one-unit increase in BMI. Multiple logistic regression analysis indicated that being obese or overweight, compared with being normal-weight, were risk factors for an increase in ARVSBP. The corresponding odds ratios of being obese or overweight were 1.23 (1.15–1.37) and 1.10 (1.04–1.15), respectively. Conclusions There was a positive correlation between BMI and ARVSBP, with ARVSBP increasing with a rise in BMI. BMI is a risk factor for an increase in ARVSBP. Trial registration Registration No.: CHiCTR-TNC1100 1489; Registration Date: June 01, 2006