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Individual genetic variation can affect the levels of protein in blood, but detailed data sets linking these two types of data are rare. Here, the authors carry out a genome-wide association study of levels of over a thousand different proteins, and describe many new SNP-protein interactions.

Post-stroke depression (PSD) is common but is not routinely assessed for in hospitalized patients. As a Comprehensive Stroke Center, we screen all stroke inpatients for depression, though the feasibility of early screening has not been established. We assessed the hypothesis that early depression screening in stroke patients is feasible. We also explored patient level factors associated with being screened for PSD and the presence of early PSD.The medical records of all patients admitted with ischemic stroke (IS) or intracerebral hemorrhage (ICH) between 01/02/13 and 15/04/13 were reviewed. A depression screen, modified from the Patient Health Questionnaire-9, was administered (maximum score 27, higher scores indicating worse depression). Patients were eligible if they did not have a medical condition precluding screening. Feasibility was defined as screening 75% of all eligible patients.Of 303 IS and ICH inpatients, 70% (211) were eligible for screening, and 75% (158) of all eligible patients were screened. More than one-third of all patients screened positive for depression (score > 4). Women (OR 2.06, 95% CI 1.06-4.01) and younger patients (OR 0.97, 95% CI 0.96-0.99) were more likely to screen positive. Screening positive was not associated with poor discharge/day 7 outcome (mRS > 3; OR 1.45, 95% CI 0.74-2.83).Screening stroke inpatients for depression is feasible and early depression after stroke is common. Women and younger patients are more likely to experience early PSD. Our results provide preliminary evidence supporting continued screening for depression in hospitalized stroke patients.

Nature Communications 8: Article number: 14357 (2017); Published 27 February 2017; Updated 11 April 2017 The original version of the Supplementary Information attached to this Article did not include Supplementary Note 1 The HTML has now been updated to include a corrected version of the Supplementary Information.

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.