SUMMARY The adipocyte-derived hormone adiponectin promotes metabolic and cardiovascular health. Circulating adiponectin increases in lean states such as caloric restriction (CR), but the reasons for this paradox remain unclear. Unlike white adipose tissue (WAT), bone marrow adipose tissue (MAT) increases during CR, and both MAT and serum adiponectin increase in many other clinical conditions. Thus, we investigated if MAT contributes to circulating adiponectin. We find that adiponectin secretion is greater from MAT than from WAT. Notably, specific inhibition of MAT formation in mice results in decreased circulating adiponectin during CR, despite unaltered adiponectin expression in WAT. Inhibiting MAT formation also alters skeletal muscle adaptation to CR, suggesting that MAT exerts systemic effects. Finally, we reveal that both MAT and serum adiponectin increase during cancer therapy in humans. These observations identify MAT as an endocrine organ that contributes significantly to increased serum adiponectin during CR, and perhaps in other adverse states.

The mechanism of protein translocation across the endoplasmic reticulum membrane of eukaryotic cells and the plasma membrane of prokaryotic cells are thought to be evolutionarily related. Protein targeting to the eukaryotic translocation apparatus is mediated by the signal recognition particle (SRP), a cytosolic ribonucleoprotein, and the SRP receptor, an endoplasmic reticulum membrane protein. During targeting, the 54K SRP subunit (M(r) 54,000; SRP54), a GTP-binding protein, binds to signal sequences and then interacts with the alpha-subunit of the SRP receptor (SR alpha), another GTP-binding protein. Two proteins from Escherichia coli, Ffh and FTsY, structurally resemble SRP54 and SR alpha. Like SRP54, Ffh is a subunit of a cytosolic ribonucleoprotein that also contains the E. coli 4.5S RNA. Although there is genetic and biochemical evidence that the E. coli Ffh/4.5S ribonucleoprotein has an SRP-like function, there is no evidence for an SR alpha-like role for FtsY. Here we show that the Ffh/4.5S ribonucleoprotein binds tightly to FtsY in a GTP-dependent manner. This interaction results in the stimulation of GTP hydrolysis which can be inhibited by synthetic signal peptides. These properties mimic those of mammalian SRP and its receptor, suggesting that the E. coli Ffh/4.5S ribonucleoprotein and FtsY have functions in protein targeting that are similar to those of their mammalian counterparts.

The signal recognition particle (SRP) consists of one RNA and six protein subunits. The N-terminal domain of the 54K subunit contains a putative GTP-binding site, whereas the C-terminal domain binds signal sequences and SRP RNA. Binding of SRP to the signal sequence as it emerges from the ribosome creates a cytosolic targeting complex containing the nascent polypeptide chain, the translating ribosome, and SRP. This complex is directed to the endoplasmic reticulum membrane as a result of its interaction with the SRP receptor, a membrane protein composed of two subunits, SR alpha and SR beta, each of which also contains a GTP-binding domain. In the presence of GTP, SRP receptor binding to SRP causes the latter to dissociate from both the signal sequence and the ribosome. GTP is then hydrolysed so that SRP can be released from the SRP receptor and returned to the cytosol. Here we show that the 54K subunit (M(r) 54,000) of SRP (SRP54) is a GTP-binding protein stabilized in a nucleotide-free state by signal sequences, and that the SRP receptor both increases the affinity of SRP54 for GTP and activates its GTPase. We propose that nucleotide-mediated conformational changes in SRP54 regulate the release of signal sequences and the docking of ribosomes at the endoplasmic reticulum.

Background The Centers for Disease Control and Prevention (CDC) diabetes prevention program (DPP) has formed the foundation for Type 2 Diabetes Mellitus (T2DM) prevention efforts and lifestyle change modifications in multiple care settings. To our knowledge, no randomized controlled trial has verified the efficacy of a fully mobile version of CDC’s diabetes prevention program (DPP). Objective This study aimed to investigate the long-term weight loss and glycemic efficacy of a mobile-delivered DPP compared with a control group receiving usual medical care. Methods Adults with prediabetes (N=202) were recruited from a clinic and randomized to either a mobile-delivered, coach-guided DPP (Noom) or a control group that received regular medical care including a paper-based DPP curriculum and no formal intervention. The intervention group learned how to use the Noom program, how to interact with their coach, and the importance of maintaining motivation. They had access to an interactive coach-to-participant interface and group messaging, daily challenges for behavior change, DPP-based education articles, food logging, and automated feedback. Primary outcomes included changes in weight and hemoglobin A1c (HbA1c) levels at 6 and 12 months, respectively. Exploratory secondary outcomes included program engagement as a predictor of changes in weight and HbA1c levels. Results A total of 202 participants were recruited and randomized into the intervention (n=101) or control group (n=99). In the intention-to-treat (ITT) analyses, changes in the participants’ weight and BMI were significantly different at 6 months between the intervention and control groups, but there was no difference in HbA1c levels (mean difference 0.004%, SE 0.05; P=.94). Weight and BMI were lower in the intervention group by −2.64 kg (SE 0.71; P<.001) and −0.99 kg/m2 (SE 0.29; P=.001), respectively. These differences persisted at 12 months. However, in the analyses that did not involve ITT, program completers achieved a significant weight loss of 5.6% (SE 0.81; P<.001) at 6 months, maintaining 4.7% (SE 0.88; P<.001) of their weight loss at 12 months. The control group lost −0.15% at 6 months (SE 0.64; P=.85) and gained 0.33% (SE 0.70; P=.63) at 12 months. Those randomized to the intervention group who did not start the program had no meaningful weight or HbA1c level change, similar to the control group. At 1 year, the intervention group showed a 0.23% reduction in HbA1c levels; those who completed the intervention showed a 0.28% reduction. Those assigned to the control group had a 0.16% reduction in HbA1c levels. Conclusions This novel mobile-delivered DPP achieved significant weight loss reductions for up to 1 year compared with usual care. This type of intervention reduces the risk of overt diabetes without the added barriers of in-person interventions. Trial Registration ClinicalTrials.gov NCT03865342; https://clinicaltrials.gov/ct2/show/NCT03865342

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.