Do dietary patterns explain high prevalence of cardiovascular risk factors among Pakistani urban adults? A cross-sectional study

Epidemiological trend and age-period-cohort effects on cardiovascular disease mortality and disability-adjusted life years attributable to dietary risks and high body mass index at the regional and country level across China and Pakistan

Background

Modifiable risk factors are major drivers of cardiovascular disease (CVD). We aimed to determine the epidemiological trend and age-period-cohort effects on CVD burden attributable to dietary risks and high body mass index (BMI) across China and Pakistan from 1990 to 2019.

Methods

Data on the all-ages and age-specific CVD burden, age-standardized CVD mortality and disability-adjusted life years (DALYs) rates were obtained from the Global Burden of Disease Study 2019. Joinpoint regression analysis was conducted to find temporal trends and age-period-cohort (APC) modeling was used to estimate age, period, and cohort effects on CVD burden.

Results

Between 1990 and 2019, the all-ages CVD burden attributable to dietary risks and high BMI increased by ~2-3-fold in China and by 3-5-fold in Pakistan. The diet-related CVD age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate significantly decreased in China but increased in Pakistan. Both countries showed a marked increasing trend of CVD ASMR and the age-standardized DALYs rate attributable to high BMI. Taiwan in China showed a remarkable reduction in CVD burden. However, in Pakistan, all regions observed a significantly increasing trend of CVD burden attributable to modifiable risk factors. A higher risk ratio of premature CVD mortality (<70 years) was observed among Chinese attributable to high BMI and among Pakistani attributable to dietary risks. In China, early birth cohorts showed a higher risk ratio and recent birth cohorts experienced a lower risk ratio of CVD burden compared with Pakistan.

Conclusion

In conclusion, dietary risks and high BMI caused a huge CVD burden across China and Pakistan.

An Exploratory Research to Assess Total Nutritional Knowledge of Students at the University of Lahore, Pakistan

Background: Good nutrition is imperative to achieve a better health status. The rate of obesity and related comorbidities is increasing all over the world. The main reasons behind this worse situation are the overconsumption of food groups or exceeding the recommended portion sizes. University life is a period when students are independent in making their food choices. The chances of adopting unhealthy eating behaviors are increased during this tenure. Nutritional knowledge is an important tool in assessing the nutritional status of an individual, group, and community. Objectives: The main objective of the current study was to assess the nutritional knowledge of university-going students. Methods: The pre-tested questionnaire was used to collect the data from the students of the University of Lahore, Pakistan. A total of 300 students participated in this study (150 from the Nutrition Department and 150 from the non-nutrition departments). Demographic data, anthropometric measurements, and total nutritional knowledge (TNK) scores were measured during the 4-month study duration. Additionally, gender-and department-based comparisons of various parameters were performed. The mean TNK of the Nutrition Department students was higher (34.89 ± 0.95/58) than non-nutrition departments (24.05 ± 1.00/58) (P = 0.01). Additionally, the mean TNK of female students was higher (28.53 ± 1.28/58) than male students (27.05 ± 1.27/58) (P = 0.03). Moreover, 61% and 63% of nutrition students were familiar with the relationship between high intake of salt and fats with chronic diseases, respectively. Results: A statistically significant relationship was not observed between gender (P = 0.78) and departments (P = 0.10) with body mass index (BMI). This finding shows that nutritional knowledge alone is not a predictor of the nutritional status (BMI) of an individual. Students, even from the Nutrition Department, failed to transform their knowledge about nutrition into healthy food choices and eating practices. Conclusions: It is necessary to initiate nutrition education programs emphasizing making healthy food choices. Moreover, nutritional knowledge related to dietary guidelines should be incorporated into daily life to remove the disease burden associated with obesity.

NDSP-10: The Cardiometabolic Risk Profile with Various Degrees of Dysglycemia in Younger and Older Adults: Findings from the Second National Diabetes Survey of Pakistan 2016-2017

Background: To assess and compare the cardiometabolic risk profile of the Pakistani population between younger (<45 years) and older adults (≥45 years). Methods: This is a substudy of a nationally representative community-based epidemiological second National Diabetes Survey of Pakistan (NDSP) 2016-2017. Out of 12,486 individuals approached, 10,834 participants agreed to be included (response rate 87%). Cardiovascular risk factors assessed were obesity, central obesity, hypertension, diabetes, hyperlipidemia, and tobacco use. Participants were stratified into young adults (<45 years) and older adults (≥45 years) and was subcategorized into four groups that is, diagnosed diabetes mellitus (DM), undiagnosed DM, prediabetes, and without diabetes. Results: Around 14.3% were prediabetes, 8.8% were undiagnosed DM, and 24% were diagnosed diabetes. Most participants in dysglycemic (prediabetes, undiagnosed DM, and diagnosed DM) groups were females and were from rural regions. Cardiovascular disease (CVD) risk factors were more noted in dysglycemic states, compared with without diabetes. Overall CVD risk profile was worse in males in both age categories, although the accumulation of four or five CVD risk factors at one point was more predominant in females in either age group. Conclusion: Overall, the preponderance of CVD risk factors, such as overweight, obesity, central obesity, tobacco use, ex-tobacco uses, and dyslipidemia, were substantially more prevalent in dysglycemia groups of young adults (<45 years) compared with older adults (≥45 years). Early and targeted intervention in young may prevent poor CVD outcomes as they age.

Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.