Deleterious Effects of Lard-enriched Diet on Tissues Fatty Acids Composition and Hypothalamic Insulin Actions

Altered tissue fatty acid (FA) composition may affect mechanisms involved in the control of energy homeostasis, including central insulin actions. In rats fed either standard chow or a lard-enriched chow (high in saturated/low in polyunsaturated FA, HS-LP) for eight weeks, we examined the FA composition of blood, hypothalamus, liver, and retroperitoneal, epididymal and mesenteric adipose tissues. Insulin-induced hypophagia and hypothalamic signaling were evaluated after intracerebroventricular insulin injection. HS-LP feeding increased saturated FA content in adipose tissues and serum while it decreased polyunsaturated FA content of adipose tissues, serum, and liver. Hypothalamic C20:5n-3 and C20:3n-6 contents increased while monounsaturated FA content decreased. HS-LP rats showed hyperglycemia, impaired insulin-induced hypophagia and hypothalamic insulin signaling. The results showed that, upon HS-LP feeding, peripheral tissues underwent potentially deleterious alterations in their FA composition, whist the hypothalamus was relatively preserved. However, hypothalamic insulin signaling and hypophagia were drastically impaired. These findings suggest that impairment of hypothalamic insulin actions by HS-LP feeding was not related to tissue FA composition.

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.