Background and Aims Little is known about how weight loss affects magnetic resonance imaging (MRI) of liver fat and volume or liver histology in patients with non-alcoholic steatohepatitis (NASH). We measured changes in liver fat and liver volume associated with weight loss using an advanced MRI method. Methods We analyzed data collected from a previous randomized controlled trial, in which 43 adult patients with biopsy-proven NASH underwent clinical evaluation, biochemical tests, and MRI and liver biopsy analyses at the start of the study and after 24 weeks. We compared data between patients who did and did not have at least a 5% decrease in body mass index (BMI) during the study period. Results Ten of 43 patients had at least a 5% decrease in BMI during the study period. These patients had a significant decrease in liver fat, based on MRI proton density fat fraction estimates (18.3% ±7.6 to 13.6% ±13.6, P=0.03)— a relative 25.5% reduction. They also had a significant decrease in liver volume (5.3%). However, no significant changes in levels of alanine aminotransferase or aspartate aminotransferase were observed with weight loss. Thirty-three patients without at least a 5% decrease in BMI had insignificant increases in estimated liver fat fraction and liver volume. Conclusions A reduction in BMI of at least 5% is associated with a significant decrease in liver fat and volume in patients with biopsy-proven NASH. These data should be considered in assessing effect size in studies of patients with non-alcoholic fatty liver disease or obesity that use MRI-estimated liver fat and volume as endpoints.

SUMMARY Background Ectopic fat deposition in the pancreas and its association with hepatic steatosis have not previously been examined in patients with biopsy-proven non-alcoholic fatty liver disease (NAFLD). Aim To quantify pancreatic fat using a novel magnetic resonance imaging (MRI) technique and determine whether it is associated with hepatic steatosis and/or fibrosis in patients with NAFLD. Methods This is a cross-sectional study including 43 adult patients with biopsy-proven NAFLD who underwent clinical evaluation, biochemical testing and MRI. The liver biopsy assessment was performed using the NASH-CRN histological scoring system, and liver and pancreas fat quantification was performed using a novel, validated MRI biomarker; the proton density fat fraction. Results The average MRI-determined pancreatic fat in patients with NAFLD was 8.5% and did not vary significantly between head, body, and tail of the pancreas. MRI-determined pancreatic fat content increased significantly with increasing histology-determined hepatic steatosis grade; 4.6% in grade 1; 7.7% in grade 2; 13.0% in grade 3 (P = 0.004) respectively. Pancreatic fat content was lower in patients with histology-determined liver fibrosis than in those without fibrosis (11.2% in stage 0 fibrosis vs. 5.8% in stage 1–2 fibrosis, and 6.9% in stage 3–4 fibrosis, P = 0.013). Pancreatic fat did not correlate with age, body mass index or diabetes status. Conclusions In patients with NAFLD, increased pancreatic fat is associated with hepatic steatosis. However, liver fibrosis is inversely associated with pancreatic fat content. Further studies are needed to determine underlying mechanisms to understand if pancreatic steatosis affects progression of NAFLD.

Drought is a slow-onset, creeping natural hazard and a recurrent phenomenon in the arid and semi-arid regions of Gujarat (India). In Asia, the standardized precipitation index (SPI) has gained wider acceptance in the detection and the estimation of the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI, in comparison with other indices, is that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. This index captures the accumulated deficit (SPI < 0) or surplus (SPI > 0) of precipitation over a specified period, and provides a normalized measure (i.e. spatially invariant Z score) of relative precipitation anomalies at multiple time scales. In the present study, monthly time series of rainfall data ) from 160 stations were used to derive SPI, particularly at 3-month time scales. This 3-month SPI was interpolated to depict spatial patterns of meteorological drought and its severity during typical drought and wet years. Correlation analysis was also done to evaluate usefulness of SPI to quantify effects of drought on food grain productivity. Further, time series of SPI were exploited to assess the drought risk in Gujarat.

In the present study, the Carnegie-Ames-Stanford Approach (CASA), a terrestrial biosphere model, has been used to investigate spatiotemporal pattern of net primary productivity (NPP) during 2003 over the Indian subcontinent. The model drivers at 2-min spatial resolution were derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer normalized difference vegetation index, weather inputs, and soil and land cover maps. The annual NPP was estimated to be 1.57 Pg C (at the rate of 544 g C m(-2)), of which 56% contributed by croplands (with 53% of geographic area of the country (GAC)), 18.5% by broadleaf deciduous forest (15% of GAC), 10% by broadleaf evergreen forest (5% of GAC), and 8% by mixed shrub and grassland (19% of GAC). There is very good agreement between the modeled NPP and ground-based cropland NPP estimates over the western India (R2=0.54; p=0.05). The comparison of CASA-based annual NPP estimates with the similar products from other operational algorithms such as C-fix and Moderate Resolution Imaging Spectroradiometer (MODIS) indicate that high agreement exists between the CASA and MODIS products over all land covers of the country, while agreement between CASA and C-Fix products is relatively low over the region dominated by agriculture and grassland, and the agreement is very low over the forest land. Sensitivity analysis suggest that the difference could be due to inclusion of variable light use efficiency (LUE) across different land cover types and environment stress scalars as downregulator of NPP in the present CASA model study. Sensitivity analysis further shows that the CASA model can overestimate the NPP by 50% of the national budget in absence of downregulators and underestimate the NPP by 27% of the national budget by the use of constant LUE (0.39 gC MJ(-1)) across different vegetation cover types.