1.Study on association between insulin resistance and intermediate risk factors for non-communicable diseases
Khangai E ; Batzorig B ; Bayarbold D ; Enkhtur Ya ; Altaisaikhan Kh ; Oyunsuren E ; Oyuntugs B
Mongolian Journal of Health Sciences 2025;86(2):60-64
Background:
In Mongolia, the prevalence of non-communicable diseases and their intermediate risk factors has continuously
increased in recent years. From results of early detection and prevalence studies of non-communicable diseases in
Mongolia, studies linking intermediate risk factors to insulin resistance are scarce.
Aim:
To identify the prevalence of insulin resistance among the population and study its connection with intermediate
risk factors of non-communicable diseases.
Materials and Methods:
This study was approved by the MNUMS Ethics Committee on February 23, 2024 (2024/3-
02), and was conducted based on the data of participants who took part in the “Population-based Preventive and Early
Detection Screening of Infectious and Non-Infectious Diseases” organized by the Mongolian government from 2022 to
2023. Insulin resistance was identified using the triglyceride-glucose index (TyG), calculated by the formula Ln [fasting
triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]. “Ln” refers to the natural logarithm, based on Euler’s number, approximately
2.71828. TyG levels were classified into low risk (<8.5), medium risk (8.5-9.0), and high risk (>9.0). Defined
intermediate risk factors for non-communicable diseases according to stages of hypertension and diabetes.
Results:
The mean age of participants was 44.3±15.2 years, with 39.2% (n=49,270) male and 41.4% (n=49,749) residing
in urban areas. Among the participants, 59.1% had overweight or obesity, 23.6% had diabetes or impaired fasting glucose,
61.4% had hypertension, and 19.7% had elevated triglycerides. Analyzing by levels of insulin resistance risk, 62.8% of
the population had low risk, 22.5% medium risk, and 14.7% high risk. Comparing systolic blood pressure across levels
of insulin resistance risk showed that even without central obesity or diabetes, as the level of insulin resistance increased,
the level of systolic blood pressure also increased (low risk group: 117.0±11.7, medium risk group: 121.1±10.9, high risk
group: 123.5±16.2 mmHg). Regression analysis of the risk of hypertension by insulin resistance risk level showed that the
risk increased with higher levels of insulin resistance (medium risk group OR=1.35, p<0.0001; high risk group OR=1.63,
p<0.0001).
Conclusion
22.5% of the population is at medium risk and 14.7% at high risk of insulin resistance. The increase in hypertension
risk with higher insulin resistance levels is statistically significant, independent of central obesity and diabetes
stages.
2.Associations of secondary risk factors of non-communicable diseases
Khangai E ; Batzorig B ; Narantuya D ; Enkhtur Ya ; Oyuntugs B ; Bayarbold Dangaa ; Oyunsuren E
Diagnosis 2024;111(4):51-58
Background:
Obesity and metabolic disorders are significant contributors to hypertension and cardiovascular disease
(CVD). While body mass index (BMI) and waist circumference are known to be associated with systolic blood pressure (SBP), the interplay between adiposity, glucose levels, triglycerides, and SBP is
not fully understood. This study aims to investigate the relationships between BMI, waist circumference, glucose, triglycerides, and SBP in a large population-based cohort.
Methods:
A cross-sectional analysis was conducted on [insert total number] participants with complete data on BMI, waist circumference, blood pressure, glucose, and triglycerides. Descriptive statistics, ANOVA, Pearson correlations, mediation analysis, and multiple regression were used to explore the associations between variables. The moderation effect of glucose on the BMI-SBP relationship
was examined using an interaction term in the regression model.
Results:
The mean age of the study population was 44.3 ± 15.2 years. The mean BMI was 26.7 ± 4.9 kg/m², and 22.7% of participants were classified as obese. Central obesity, measured by waist
circumference, was prevalent in 55.9% of the population. BMI, waist circumference, glucose, and triglycerides were significantly associated with SBP (p < 0.0001). Mediation analysis showed that waist circumference partially mediated the effect of BMI on SBP. The interaction term for BMI and
glucose was significant (β = 0.32, p < 0.05), indicating that glucose levels moderated the relationship between BMI and SBP, with higher glucose levels amplifying the hypertensive effect of BMI.
Conclusion
This study highlights the complex interplay between BMI, waist circumference, glucose, triglycerides, and SBP. Waist circumference partially mediates the effect of BMI on SBP, and glucose levels moderate this relationship, amplifying the impact of obesity on blood pressure.
3.Assessment of secondary school indoor air quality
Suvd B ; Erdenetsetseg D ; Oyun-Erdene O ; Zul A ; Buuveidulam A ; Bilguun D ; Chinzorig B ; Suvd S ; Bayarbold D ; Burmaajav B
Mongolian Medical Sciences 2022;200(2):24-32
Introduction:
During this pandemic, overcrowding in classroom caused by a lack of educational facilities and poor indoor air quality are the main causes of respiratory diseases among children and adolescents. Therefore, it is essential to measure and assess the indoor air quality where children spend extended periods of time such as school.
Materials and methods:
This study covered four schools with old buildings and four schools with new buildings in Bayanzurkh, Sukhbaatar, Khan-Uul, Chingeltei district of Ulaanbaatar. We collected PM10 and PM2.5, carbon dioxide, air temperature, humidity, and microbiological count from chosen classrooms and compared to the MNS4585:2016 standard. SPSS-24 was used to do statistical analysis on the information gathered during the evaluation.
Results and Discussion:
The 24-hour average PM2.5 concentration was 64.3 (95% CI: 64.1-64.5) mcg/m3, which was 4.3 times higher than the WHO guideline value and 1.3 times higher than the MNS4585:2016 standard. The 24-hour average PM10 concentration was 85.3 (95 % CI: 85.1-85.6) mcg/m3, which is 1.9 times higher than WHO guideline value. In older school buildings, the 24-hour average PM2.5 concentration was 5.6 times higher than the WHO guideline value and 1.7 times higher than the MNS4585:2016; the average PM10 concentration was 2.8 times higher than the WHO guideline value and 1.3 times higher than the MNS4585:2016. The air temperature and carbon dioxide concentration in classroom was met the MNS4585: 2016. The average relative humidity of all schools is 24.2±6.5%, which is 14-16% lower than the MNS4585: 2016.
Conclusion
The indoor air quality of the school in new and old buildings was similar poor, therefore a variety of steps are needed to improve it.