Hypertensive rats show increased renal excretion and decreased tissue concentrations of glycine betaine, a protective osmolyte with diuretic properties.

Hypertension leads to water-electrolyte disturbances and end-organ damage. Betaine is an osmolyte protecting cells against electrolyte imbalance and osmotic stress, particularly in the kidneys. This study aimed to evaluate tissue levels and hemodynamic and renal effects of betaine in normotensive and hypertensive rats. Betaine levels were assessed using high-performance liquid chromatography-mass spectrometry (HPLC-MS) in normotensive rats (Wistar-Kyoto, WKYs) and Spontaneously Hypertensive rats (SHRs), a model of genetic hypertension. Acute effects of IV betaine on blood pressure, heart rate, and minute diuresis were evaluated. Gene and protein expression of chosen kidney betaine transporters (SLC6a12 and SLC6a20) were assessed using real-time PCR and Western blot. Compared to normotensive rats, SHRs showed significantly lower concentration of betaine in blood serum, the lungs, liver, and renal medulla. These changes were associated with higher urinary excretion of betaine in SHRs (0.20 ± 0.04 vs. 0.09 ± 0.02 mg/ 24h/ 100g b.w., p = 0.036). In acute experiments, betaine increased diuresis without significantly affecting arterial blood pressure. The diuretic response was greater in SHRs than in WKYs. There were no significant differences in renal expression of betaine transporters between WKYs and SHRs. Increased renal excretion of betaine contributes to decreased concentration of the protective osmolyte in tissues of hypertensive rats. These findings pave the way for studies evaluating a causal relation between depleted betaine and hypertensive organ damage, including kidney injury.

Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: Case Validation Study From 2 Veterans Affairs Medical Centers.

BACKGROUND: Electronic medical records (EMRs) offer the promise of computationally identifying sarcoidosis cases. However, the accuracy of identifying these cases in the EMR is unknown. OBJECTIVE: The aim of this study is to determine the statistical performance of using the International Classification of Diseases (ICD) diagnostic codes to identify patients with sarcoidosis in the EMR. METHODS: We used the ICD diagnostic codes to identify sarcoidosis cases by searching the EMRs of the San Francisco and Palo Alto Veterans Affairs medical centers and randomly selecting 200 patients. To improve the diagnostic accuracy of the computational algorithm in cases where histopathological data are unavailable, we developed an index of suspicion to identify cases with a high index of suspicion for sarcoidosis (confirmed and probable) based on clinical and radiographic features alone using the American Thoracic Society practice guideline. Through medical record review, we determined the positive predictive value (PPV) of diagnosing sarcoidosis by two computational methods: using ICD codes alone and using ICD codes plus the high index of suspicion. RESULTS: Among the 200 patients, 158 (79%) had a high index of suspicion for sarcoidosis. Of these 158 patients, 142 (89.9%) had documentation of nonnecrotizing granuloma, confirming biopsy-proven sarcoidosis. The PPV of using ICD codes alone was 79% (95% CI 78.6%-80.5%) for identifying sarcoidosis cases and 71% (95% CI 64.7%-77.3%) for identifying histopathologically confirmed sarcoidosis in the EMRs. The inclusion of the generated high index of suspicion to identify confirmed sarcoidosis cases increased the PPV significantly to 100% (95% CI 96.5%-100%). Histopathology documentation alone was 90% sensitive compared with high index of suspicion. CONCLUSIONS: ICD codes are reasonable classifiers for identifying sarcoidosis cases within EMRs with a PPV of 79%. Using a computational algorithm to capture index of suspicion data elements could significantly improve the case-identification accuracy.