Owing to its high carrier mobility, conductivity, flexibility and optical transparency, graphene is a versatile material in micro- and macroelectronics. However, the low density of electrochemically active defects in graphene synthesized by chemical vapour deposition limits its application in biosensing. Here, we show that graphene doped with gold and combined with a gold mesh has improved electrochemical activity over bare graphene, sufficient to form a wearable patch for sweat-based diabetes monitoring and feedback therapy. The stretchable device features a serpentine bilayer of gold mesh and gold-doped graphene that forms an efficient electrochemical interface for the stable transfer of electrical signals. The patch consists of a heater, temperature, humidity, glucose and pH sensors and polymeric microneedles that can be thermally activated to deliver drugs transcutaneously. We show that the patch can be thermally actuated to deliver Metformin and reduce blood glucose levels in diabetic mice.

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P<5 × 10−8), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.

Lean body mass, consisting mostly of skeletal muscle, is important for healthy aging. We performed a genome-wide association study for whole body (20 cohorts of European ancestry with n = 38,292) and appendicular (arms and legs) lean body mass (n = 28,330) measured using dual energy X-ray absorptiometry or bioelectrical impedance analysis, adjusted for sex, age, height, and fat mass. Twenty-one single-nucleotide polymorphisms were significantly associated with lean body mass either genome wide (p < 5 × 10−8) or suggestively genome wide (p < 2.3 × 10−6). Replication in 63,475 (47,227 of European ancestry) individuals from 33 cohorts for whole body lean body mass and in 45,090 (42,360 of European ancestry) subjects from 25 cohorts for appendicular lean body mass was successful for five single-nucleotide polymorphisms in/near HSD17B11, VCAN, ADAMTSL3, IRS1, and FTO for total lean body mass and for three single-nucleotide polymorphisms in/near VCAN, ADAMTSL3, and IRS1 for appendicular lean body mass. Our findings provide new insight into the genetics of lean body mass.

Wearable bioelectronic technologies have made significant progresses in personalized health management through non-invasive monitoring of health indicators. However, current wearable systems cannot measure biochemical information and physiological signals simultaneously, which limits integrated data analysis and their widespread clinical applications. Here, an integrated multifunctional wearable health management system composed of a disposable sweat-based glucose sensing strip and a wearable smart band is reported. The integrated system with control software electrochemically analyzes sweat glucose levels and continuously monitors vital signs (i.e., heart rate, blood oxygen saturation level, and physical activity). Different sweat collecting sites and sweat generation methods are tested in short-and long-term studies with multiple human subjects by using the developed wearable system, leading to optimized protocols for health monitoring. By combining sweat glucose data and physiological monitoring data, pre-and post-exercise blood glucose levels and blood glucose changes resulting from physical activities are reliably estimated, providing key information for preventing hypoglycemic shock during intense exercise. The integrated wearable system offers a novel comprehensive personalized health management strategy through combined analysis of key metabolic and physiological health indicators.