Predictors of controlled attenuation parameter in metabolic dysfunction

BACKGROUND & AIMS

Hepatic fat content can be non-invasively estimated by controlled attenuation parameter (CAP) during transient elastography. The aim of this study was to examine the determinants and predictors of CAP values in individuals with metabolic dysfunction.

METHODS

We enrolled 1230 consecutive apparently healthy individuals (Liver-Bible-2022 cohort) with ≥3 metabolic dysfunction features. CAP was measured by Fibroscan. CAP determinants and predictors were identified using backward stepwise analysis and introduced in generalized linear models.

RESULTS

Participants were predominantly males (82.9%), mean age was 53.8 ± 6.4 years, 600 (48.8%) had steatosis (CAP ≥ 275 dB/m), and 27 had liver stiffness measurement (LSM) ≥ 8 kPa. CAP values correlated with LSM (p < 10-22). In multivariable analysis, fasting insulin and abdominal circumference (AC) were the main determinants of CAP (p < 10-6), together with body mass index (BMI; p < 10-4), age, diabetes, triglycerides, ferritin, and lower HDL and thyroid stimulating hormone (TSH; p < 0.05 for all). In a subset of 592 participants with thyroid hormone measurement, we found an association between higher free triiodothyronine levels, correlating with lower TSH, and CAP values, independent of TSH and of levothyroxine treatment (p = 0.0025). A clinical CAP score based on age, BMI, AC, HbA1c, ALT, and HDL predicted CAP ≥ 275 dB/m with moderate accuracy (AUROC = 0.73), which was better than that of the Fatty Liver Index and of ALT (AUROC = 0.70/0.61, respectively) and validated it in multiple cohorts.

CONCLUSION

Abdominal adiposity and insulin resistance severity were the main determinants of CAP in individuals with metabolic dysfunction and may improve steatotic liver disease risk stratification. CAP values were modulated by the hypophysis-thyroid axis.

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.