SUMMARY Nonalcoholic fatty liver disease (NAFLD) is associated with increased cardiovascular and liver-related mortality. NAFLD is characterized by both triglyceride and free cholesterol (FC) accumulation without a corresponding increment in cholesterol esters. The aim of this study was to evaluate the expression of cholesterol metabolic genes in NAFLD and relate these to disease phenotype. NAFLD was associated with increased SREBP-2 maturation, HMG CoA reductase (HMGCR) expression and decreased phosphorylation of HMGCR. Cholesterol synthesis was increased as measured by the circulating desmosterol:cholesterol ratio. miR-34a, a microRNA increased in NAFLD, inhibited sirtuin-1 with downstream dephosphorylation of AMP kinase and HMGCR. Cholesterol ester hydrolase was increased while ACAT-2 remained unchanged. LDL receptor expression was significantly decreased and similar in NAFLD subjects on or off statins. HMGCR expression was correlated with FC, histologic severity of NAFLD and LDL-cholesterol. These data demonstrate dysregulated cholesterol metabolism in NAFLD which may contribute to disease severity and cardiovascular risks.

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IMPORTANCE Low-density lipoprotein cholesterol (LDL-C), a key cardiovascular disease marker, is often estimated by the Friedewald or Martin equation, but calculating LDL-C is less accurate in patients with a low LDL-C level or hypertriglyceridemia (triglyceride [TG] levels Ն400 mg/dL). OBJECTIVE To design a more accurate LDL-C equation for patients with a low LDL-C level and/or hypertriglyceridemia. DESIGN, SETTING, AND PARTICIPANTS Data on LDL-C levels and other lipid measures from 8656 patients seen at the National Institutes of Health Clinical Center between January 1, 1976, and June 2, 1999, were analyzed by the β-quantification reference method (18 715 LDL-C test results) and were randomly divided into equally sized training and validation data sets. Using TG and non-high-density lipoprotein cholesterol as independent variables, multiple least squares regression was used to develop an equation for very low-density lipoprotein cholesterol, which was then used in a second equation for LDL-C. Equations were tested against the internal validation data set and multiple external data sets of either β-quantification LDL-C results (n = 28 891) or direct LDL-C test results (n = 252 888). Statistical analysis was performed from August 7, 2018, to July 18, 2019. MAIN OUTCOMES AND MEASURES Concordance between calculated and measured LDL-C levels by β-quantification, as assessed by various measures of test accuracy (correlation coefficient [R 2 ], root mean square error [RMSE], mean absolute difference [MAD]), and percentage of patients misclassified at LDL-C treatment thresholds of 70, 100, and 190 mg/dL. RESULTSCompared with β-quantification, the new equation was more accurate than other LDL-C equations (slope, 0.964; RMSE = 15.2 mg/dL; R 2 = 0.9648; vs Friedewald equation: slope, 1.056; RMSE = 32 mg/dL; R 2 = 0.8808; vs Martin equation: slope, 0.945; RMSE = 25.7 mg/dL; R 2 = 0.9022), particularly for patients with hypertriglyceridemia (MAD = 24.9 mg/dL; vs Friedewald equation: MAD = 56.4 mg/dL; vs Martin equation: MAD = 44.8 mg/dL). The new equation calculates the LDL-C level in patients with TG levels up to 800 mg/dL as accurately as the Friedewald equation does for TG levels less than 400 mg/dL and was associated with 35% fewer misclassifications when patients with hypertriglyceridemia (TG levels, 400-800 mg/dL) were categorized into different LDL-C treatment groups. CONCLUSIONS AND RELEVANCEThe new equation can be readily implemented by clinical laboratories with no additional costs compared with the standard lipid panel. It will allow for more accurate calculation of LDL-C level in patients with low LDL-C levels and/or hypertriglyceridemia (TG levels, Յ800 mg/dL) and thus should improve the use of LDL-C level in cardiovascular disease risk management.

The multifunctional AMPK-activated protein kinase (AMPK) is an evolutionarily conserved energy sensor that plays an important role in cell proliferation, growth, and survival. It remains unclear whether AMPK functions as a tumor suppressor or a contextual oncogene. This is because although on one hand active AMPK inhibits mammalian target of rapamycin (mTOR) and lipogenesistwo crucial arms of cancer growth-AMPK also ensures viability by metabolic reprogramming in cancer cells. AMPK activation by two indirect AMPK agonists AICAR and metformin (now in over 50 clinical trials on cancer) has been correlated with reduced cancer cell proliferation and viability. Surprisingly, we found that compared with normal tissue, AMPK is constitutively activated in both human and mouse gliomas. Therefore, we questioned whether the antiproliferative actions of AICAR and metformin are AMPK independent. Both AMPK agonists inhibited proliferation, but through unique AMPK-independent mechanisms and both reduced tumor growth in vivo independent of AMPK. Importantly, A769662, a direct AMPK activator, had no effect on proliferation, uncoupling high AMPK activity from inhibition of proliferation. Metformin directly inhibited mTOR by enhancing PRAS40's association with RAPTOR, whereas AICAR blocked the cell cycle through proteasomal degradation of the G2M phosphatase cdc25c. Together, our results suggest that although AICAR and metformin are potent AMPK-independent antiproliferative agents, physiological AMPK activation in glioma may be a response mechanism to metabolic stress and anticancer agents.metabolism | glioma A MP-activated protein kinase (AMPK) is a molecular hub for cellular metabolic control (1-4). It is a heterotrimer of catalytic α, regulatory β, and γ subunits. The rising AMP:ATP ratio during energy stress leads to AMP-dependent phosphorylation of the catalytic α subunits. This activates AMPK which then phosphorylates numerous substrates to restore energy homeostasis. It phosphorylates acetyl CoA carboxylase (ACCα) to inhibit fatty acid (FA) synthesis (5) and TSC2 and RAPTOR (6, 7) to inhibit mammalian target of rapamycin (mTOR)C1. Because fatty acid synthesis and mTORC1 activity are essential for cell proliferation and growth (8), AMPK activation with two indirect AMPK agonists AICAR and metformin have been correlated with suppression of cell proliferation and growth (9-11).AICAR is metabolized to an AMP mimetic, ZMP that activates AMPK (12). Although AICAR does inhibit proliferation (11-15), it also causes AMPK-independent cellular and metabolic effects (12, 16) including inhibition of glucokinase, glycogen phosphorylase, and nucleotide biosynthesis (17, 18). Whether AICAR requires AMPK to suppress proliferation is questionable because although both AICAR and 2-deoxyglucose activated AMPK, only AICAR inhibited proliferation of trisomic mouse fibroblasts (11). Moreover, although AICAR strongly increases glucose uptake through AMPK activation in muscle cells, it reduced fluorodeoxyglucose-PET signals and inhibited glioma gro...