Hepatic steatosis is associated with cardiometabolic risk in a rural Indian population: A prospective cohort study.

BACKGROUND/OBJECTIVES: While adiposity and hepatic steatosis are linked to cardiovascular risk in developed countries, their prevalence and impact in low-income countries are poorly understood. We investigated the association of anthropomorphic variables and hepatic steatosis with cardiometabolic risk profiles and subclinical cardiovascular disease (CVD) in a large rural Indian cohort. METHODS: In 4691 individuals in the Birbhum Population Project in West Bengal, India, we performed liver ultrasonography, carotid ultrasound and biochemical and clinical profiling. We assessed the association of hepatic steatosis and anthropomorphic indices (BMI, waist circumference) with CVD risk factors (dysglycemia, dyslipidemia, hypertension) and subclinical CVD (by carotid intimal-medial thickness). RESULTS: Rural Indians exhibited a higher visceral adiposity index and pro-atherogenic dyslipidemia at a lower BMI than Americans. Individuals with any degree of hepatic steatosis by ultrasound had a greater probability of dysglycemia (adjusted odds ratio, OR=1.67, 95% CI 1.31-2.12, P<0.0001) and pro-atherogenic dyslipidemia (OR=1.33, 95% CI 1.07-1.63, P=0.009). We observed a positive association between liver fat, adiposity and carotid intimal-medial thickness (CIMT) in an unadjusted model (ß=0.02, P=0.0001); the former was extinguished after adjustment for cardiometabolic risk factors. CONCLUSIONS: In a large population of rural Indians, hepatic steatosis and waist circumference were associated with prevalent cardiometabolic risk and subclinical CVD at lower BMI relative to multi-ethnic Americans, though the association of the former with subclinical CVD was extinguished after adjustment. These results underscore the emerging relevance of hepatic steatosis and adiposity in the developing world, and suggest efforts to target these accessible phenotypes for cardiometabolic risk prevention.

Automatic detection of pectoral muscle using average gradient and shape based feature.

In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its' simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches.