Components of metabolic syndrome and their relation to the risk of incident cerebral infarction

Periodontitis and the incidence of metabolic syndrome: An 8-year longitudinal study of an adult Japanese cohort

AIM

To examine the effect of periodontitis on the development of metabolic syndrome (MetS) and MetS components.

MATERIALS AND METHODS

This study included 4761 participants aged 30-74 years who underwent health examinations at both baseline and 8-year follow-up. The Japanese MetS criteria were used for diagnosis. The Community Periodontal Index was used to assess periodontal status. The association between periodontal status and MetS incidence was examined by Poisson regression analysis.

RESULTS

Multivariate analysis revealed that individuals with a ≥6 mm periodontal pocket had a significantly higher relative risk (RR) for MetS onset, as compared to individuals without deep periodontal pockets (adjusted RR 1.30, 95% confidence interval [CI]: 1.01-1.67). Compared to individuals without a deep periodontal pocket, individuals with a ≥6 mm periodontal pocket had significantly higher RRs for developing two components of MetS; the RRs were 1.25 (95% CI: 1.01-1.56) for abdominal obesity and 1.39 (95% CI: 1.03-1.86) for hyperglycaemia.

CONCLUSIONS

Individuals with periodontitis had a significantly higher risk of MetS onset, possibly due to the influence of periodontitis on abdominal obesity and hyperglycaemia.

Does nutritional guidance reduce cardiovascular events in patients with type 2 diabetes mellitus? A retrospective cohort study using a nationwide claims database

AIM

To examine the effect of nutritional guidance (NG) provided by a registered dietitian under the direction of a physician on the development of subsequent cardiovascular events in patients with early-stage type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

This retrospective cohort study used the JMDC claims database to explore patients aged 18 years or older who first met the diagnostic criteria for T2DM at a health checkup between January 2011 and January 2019. The last day of the observation period was set for 28 February, 2021. Exposure was defined as receiving NG within 180 days of diagnosis of T2DM. The primary outcome was a composite endpoint of coronary artery disease (CAD) and cerebrovascular disease, and the secondary outcomes were each event, and time to event was compared. The propensity score weighting method was used for adjusting the distribution of confounding variables. Cox regression was conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

Overall, 31,378 patients met the eligibility criteria at an annual health checkup. 9.6% received NG (n = 3013). Patients who attended ≥ 1 NG after diagnosis had significantly reduced risk of cardiovascular composite (adjusted HR 0.75, 95% CI 0.58-0.97) and cerebrovascular disease (adjusted HR 0.65, 95% CI 0.47-0.90) during approximately 3.3 years of follow-up. In contrast, no difference was observed for CAD.

CONCLUSIONS

Receiving NG in early-stage diabetes may reduce the incidence of cardiovascular events, especially cerebrovascular events.

Impact of circuit rhythms disturbances on the clinical picture of the disease in patients with metabolic syndrome

The purpose of this study was to evaluatey the effect of dark deprivation of various durations on the course of the metabolic syndrome (MS).

Material and research methods. To achieve this aim, based on WHO criteria, we selected patients with MS and disturbed circadian rhythms – a total of 102 people (all men) from among people who constantly work at night, in whom a decrease in melatonin levels was recorded at a preliminary stage. In accordance with the tasks set, 102 patients (all men) of working age (young age according to WHO criteria) were divided into three groups of 33, 37 and 32 people, respectively, according to the criterion "duration of work at night and duration of history of MS": 0–5, 5–10 and over 10 years. In the first group, the mean age was 30.1 ± 1.4 years; in the second group, the mean age was 36.5 ± 1.5 years; in the third group, the mean age did not exceed 40.3 ± 0.9 years. As a control group, 45 patients with MS were examined, comparable with the main groups in terms of background pathology and age and different duration of MS history.

Results. It was found that the main symptom of MS - abdominal type of obesity (waist circumference more than 80 cm for men) was detected in 100% of patients of the main and control groups of observation. At the same time, it was found that the severity of abdominal obesity is combined with an increase in body mass index (BMI) and tends to increase in patients in the main groups, compared with patients from the control group. In the control group, 35.5% of the examined patients had grade 1-2 hypertension, and another 15.6% had elevated normal blood pressure.

Conclusions: In patients with MS, against the background of violations of regulatory mechanisms, the disease is more severe than in patients with a similar history of MS, but without violations of circadian rhythms. The severity of the course of MS against the background of a decrease in the level of melatonin increases with the duration of the violation of regulatory mechanisms. 

Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model

BACKGROUND

Development of a cerebral infarction (CI) risk prediction model by mining routine test big data with machine learning algorithms.

METHODS

Cohort 1 included 2017 CI patients and health checkers, and the optimal machine learning algorithms in Extreme gradient Boosting (XgBoost), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) were selected to mine all routine test data of the enrolled subjects for screening CI model features. Cohort 2 included patients with CI and Non-CI from 2018 to 2020 to develop an early warning model for CI and was analyzed in subgroups with a cutoff of 50 years. Cohort 3 included CI patients versus Non-CI patients in 2021, and a nomogram models was developed for comparison with the machine learning model.

RESULTS

The optimal algorithm XgBoost was used to develop a CI risk prediction model CI-Lab8 containing eight characteristics of fibrinogen, age, glucose, mean erythrocyte hemoglobin concentration, albumin, neutrophil absolute value, activated partial thromboplastin time, and triglycerides. The model had an AUC of 0.823 in cohort 2, significantly higher than the FIB (AUC = 0.737), which ranked first in feature importance. CI-Lab8 also had higher diagnostic accuracy in CI patients <50 years of age (AUC = 0.800), slightly lower than in CI patients ≥50 years of age (AUC = 0.856). Receiver operating characteristic curve, calibration curve, and decision curve analysis in cohort 3 showed CI-Lab8 to be superior to nomogram.

CONCLUSION

In this study, the CI risk prediction model developed by XgBoost algorithm outperformed the nomogram model and had higher diagnostic accuracy for CI patients in both <50 and ≥50 years old, which may assist clinical assessment for CI.