Air pollution and its impact on cardiovascular health - It's time to act fast!

Lipid Association of India 2023 update on cardiovascular risk assessment and lipid management in Indian patients: Consensus statement IV

OBJECTIVE

In 2016, the Lipid Association of India (LAI) developed a cardiovascular risk assessment algorithm and defined low-density lipoprotein cholesterol (LDL-C) goals for prevention of atherosclerotic cardiovascular disease (ASCVD) in Indians. The recent refinements in the role of various risk factors and subclinical atherosclerosis in prediction of ASCVD risk necessitated updating the risk algorithm and treatment goals.

METHODS

The LAI core committee held twenty-one meetings and webinars from June 2022 to July 2023 with experts across India and critically reviewed the latest evidence regarding the strategies for ASCVD risk prediction and the benefits and modalities for intensive lipid lowering. Based on the expert consensus and extensive review of published data, consensus statement IV was commissioned.

RESULTS

The young age of onset and a more aggressive nature of ASCVD in Indians necessitates emphasis on lifetime ASCVD risk instead of the conventional 10-year risk. It also demands early institution of aggressive preventive measures to protect the young population prior to development of ASCVD events. Wide availability and low cost of statins in India enable implementation of effective LDL-C-lowering therapy in individuals at high risk of ASCVD. Subjects with any evidence of subclinical atherosclerosis are likely to benefit the most from early aggressive interventions.

CONCLUSIONS

This document presents the updated risk stratification and treatment algorithm and describes the rationale for each modification. The intent of these updated recommendations is to modernize management of dyslipidemia in Indian patients with the goal of reducing the epidemic of ASCVD among Indians in Asia and worldwide.

Comorbidity Profiling in Rural and Urban Population of West Bengal, India: Report From a Community-Based Primary Healthcare System

Introduction The burden of non-communicable diseases (NCDs) is fast changing across the world, especially in the context of rapid urbanization, adoption of Western lifestyles, and an aging multi-morbid population. Over the last three decades, India has undergone a significant demographic and socioeconomic transition. For effective targeting of health system resources and services, it is essential to understand how the prevalence of NCDs varies among population groups across India. We set out to understand the distribution of NCDs and co-morbidities in urban and rural West Bengal. Methods As part of a service improvement project, data was collected from four urban and four rural community-based clinics across West Bengal, India. The reason for visiting the healthcare center was recorded as the primary diagnosis and co-morbidities were recorded per the Elixhauser comorbidity scoring criteria. Associations between all the demographic variables and NCDs were studied using the Poisson regression model and multivariate analysis. Demographic profile, co-morbidities, and Elixhauser comorbidity index were expressed as frequency (%), mean (standard deviation, SD), or median (interquartile range, IQR) as appropriate. Results We obtained data from 1244 patients of which 886 (71%) were from urban areas and 358 (29%) were from rural areas. Patients were mostly female (61%) and had a mean (SD) age of 53 (11) years. There was a positive correlation between living in an urban residence and age, body mass index (BMI), hypertension, cardiovascular disease (CVD), and respiratory disease. There was a positive correlation between CVD and age, male sex, living in an urban residence, and hypertension but did not correlate positively with diabetes. BMI positively correlated with living in an urban residence, hypertension, diabetes, and musculoskeletal disorders. We observed a significantly higher prevalence of musculoskeletal (p=0.002) and psychological diseases (p<0.001) in the rural population, while the prevalence of hypertension (p<0.001) and respiratory diseases among the participants living in urban areas was higher (p<0.001). There was no statistically significant difference in the prevalence of diabetes between urban and rural areas (p=0.38). In the multivariable analyses, we observed that increased age, being overweight, and living in urban areas were associated with hypertension (prevalence ratio (PR): 1.40, 1.30, and 1.30, respectively; all p-values <0.05). An interaction between sex and living area was associated with a lower prevalence of musculoskeletal diseases (PR: 0.34; 95%CI: 0.18-0.66), i.e., musculoskeletal diseases were less prevalent in males living in urban areas (p=0.002). Conclusion There is a rise in multimorbidity with changing demographic patterns and a narrowing of the urban-rural gap in disease distribution. More investment is required in risk factor prevention, screening, and treatment, with greater accessibility of healthcare resources for those in rural areas. Further work needs to be done to study the trends and distribution of NCDs in West Bengal to inform healthcare policy.