Outcome of the Public Consultation on the Draft Opinion of the Scientific Panel on Dietetic Products, Nutrition, and Allergies (NDA) on Dietary Reference Values for Fats, Including Saturated Fatty Acids, Polyunsaturated Fatty Acids, Monounsaturated Fatty Acids, Trans Fatty Acids, and Cholesterol

On 2 July 2009, the EFSA Panel on Dietetic products, Nutrition and Allergies (NDA) endorsed a draft Opinion on Dietary Reference Values for fats to be released for public consultation. This Scientific Report summarises the comments received through the public consultation and outlines how these were taken into account in the final opinion. EFSA had received contributions from 40 interested parties (individuals, non-governmental organisations, industry organisations, academia and national assessment bodies). The main comments which were received during the public consultation related to: the availability of more recent data, the nomenclature used, the use of a non-European food composition data base, the impact of genetic factors in modulating the absorption, metabolism and health effects of different fatty acids, the definition of “nutritionally adequate diet”, the use of Dietary Reference Values in the labelling of foods, the translation of advice into food-based dietary guidelines, nutrient goals and recommendations, certain risk management issues, and to Dietary Reference Values of fats, individual fatty acids, and cholesterol. All the public comments received that related to the remit of EFSA were assessed and the Opinion on Dietary Reference Values for fats has been revised taking relevant comments into consideration.

Validation of Electronic Health Record Phenotyping of Bipolar Disorder Cases and Controls

Objective: The study was designed to validate use of elec-tronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. Method: EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype di- agnoses was calculated against diagnoses from direct semi- structured interviews of 190 patients by trained clinicians blind to EHR diagnosis. Results: The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR- classified control subject received a diagnosis of bipolar dis- order on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based clas- sifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. Conclusions: Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.