Recent reviews recommended the use of the aldosterone/renin ratio (ARR) to screen for primary hyperaldosteronism. However, widely different cutoff levels have been proposed, and test characteristics of ARR under different conditions of sampling are not known. We conducted a retrospective review among 45 subjects with carefully validated diagnoses of primary hyperaldosteronism and 17 subjects with essential hypertension to study the utility of ARR. Sixty-two patients with 75 sets of plasma renin activity (PRA), aldosterone, and ARR values from a postural study and 48 sets of values from a saline suppression test were analyzed. Ninety-four percent of these subjects underwent investigations because of hypokalemic hypertension.ARR yielded larger areas under the curve in the receiver-operating-characteristics curve than PRA or aldosterone under all conditions of testing. Our results confirmed the superiority of ARR to either aldosterone or PRA alone as a diagnostic test for primary hyperaldosteronism.ARR cutoff levels were significantly affected by the condition of testing. Depending on posture and time of day, it varied from 13.1-35.0 ng/dl per ng/ml.h in our study population. When using ARR for screening primary hyperaldosteronism, posture and time of sampling should be standardized both within and between centers to minimize variability in cutoff levels.

Bone mineral density (BMD) has been shown to be increased in postmenopausal females with postthyroidectomy hypoparathyroidism, but it is not known whether similar gains occur in patients with idiopathic hypoparathyroidism. In this study, we measured the BMD of lumbar spine and proximal femur in 14 patients, 8 with idiopathic hypoparathyroidism and 6 with postthyroidectomy hypoparathyroidism, using dual-energy x-ray absorptiometry. Their age ranged from 23-57 yr old, with a mean of 42.5 yr. The results showed that patients with hypoparathyroidism had a higher BMD than the normal age- and sex-matched population. This was particularly evident at the lumbar spine (L2-L4), with positive Z-score of 1.93 +/- 1.03, whereas Z-score at the femoral neck was 1.14 +/- 0.62 SD. Subgroup analysis showed that those with postthyroidectomy hypoparathyroidism had a mean lumbar spine BMD of 1.434 g/cm(2) and femoral neck BMD of 1.026 g/cm(2), compared with a mean BMD of 1.364 g/cm(2) and 1.022 g/cm(2) at spine and hip, respectively, for those with idiopathic hypoparathyroidism. Statistical analysis did not reveal any significant difference in the BMD, T-score, and Z-score of the bone, at these two sites, between the two groups. In conclusion, the state of chronic hypoparathyroidism is associated with increased BMD, especially at the lumbar spine. Those with idiopathic hypoparathyroidism have a similar degree of increase in BMD as those with postthyroidectomy hypoparathyroidism.

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Different postural control strategies are adopted by various subgroups of subjects with diabetes. Subjects with DPN demonstrated a significant shift from ankle to hip strategies for balance tests when vision was deprived. Implications for Rehabilitation The severity of diabetic peripheral neuropathy (DPN) is associated with the risk of fall. Different compensatory strategies in balance control have been adopted by different subgroups of people with diabetes. In order to minimize the risk of fall, specific balance training program should be offered to different subgroups of people with diabetes. The balance training should emphasize on optimizing the competence of their existing compensatory postural control strategies.

Background Existing predictive outcomes models for type 2 diabetes developed and validated in historical European populations may not be applicable for East Asian populations due to differences in the epidemiology and complications. Despite the continuum of risk across the spectrum of risk factor values, existing models are typically limited to diabetes alone and ignore the progression from prediabetes to diabetes. The objective of this study is to develop and externally validate a patient-level simulation model for prediabetes and type 2 diabetes in the East Asian population for predicting lifetime health outcomes. Methods and findings We developed a health outcomes model from a population-based cohort of individuals with prediabetes or type 2 diabetes: Hong Kong Clinical Management System (CMS, 97,628 participants) from 2006 to 2017. The Chinese Hong Kong Integrated Modeling and Evaluation (CHIME) simulation model comprises of 13 risk equations to predict mortality, micro- and macrovascular complications, and development of diabetes. Risk equations were derived using parametric proportional hazard models. External validation of the CHIME model was assessed in the China Health and Retirement Longitudinal Study (CHARLS, 4,567 participants) from 2011 to 2018 for mortality, ischemic heart disease, cerebrovascular disease, renal failure, cataract, and development of diabetes; and against 80 observed endpoints from 9 published trials using 100,000 simulated individuals per trial. The CHIME model was compared to United Kingdom Prospective Diabetes Study Outcomes Model 2 (UKPDS-OM2) and Risk Equations for Complications Of type 2 Diabetes (RECODe) by assessing model discrimination (C-statistics), calibration slope/intercept, root mean square percentage error (RMSPE), and R2. CHIME risk equations had C-statistics for discrimination from 0.636 to 0.813 internally and 0.702 to 0.770 externally for diabetes participants. Calibration slopes between deciles of expected and observed risk in CMS ranged from 0.680 to 1.333 for mortality, myocardial infarction, ischemic heart disease, retinopathy, neuropathy, ulcer of the skin, cataract, renal failure, and heart failure; 0.591 for peripheral vascular disease; 1.599 for cerebrovascular disease; and 2.247 for amputation; and in CHARLS outcomes from 0.709 to 1.035. CHIME had better discrimination and calibration than UKPDS-OM2 in CMS (C-statistics 0.548 to 0.772, slopes 0.130 to 3.846) and CHARLS (C-statistics 0.514 to 0.750, slopes −0.589 to 11.411); and small improvements in discrimination and better calibration than RECODe in CMS (C-statistics 0.615 to 0.793, slopes 0.138 to 1.514). Predictive error was smaller for CHIME in CMS (RSMPE 3.53% versus 10.82% for UKPDS-OM2 and 11.16% for RECODe) and CHARLS (RSMPE 4.49% versus 14.80% for UKPDS-OM2). Calibration performance of CHIME was generally better for trials with Asian participants (RMSPE 0.48% to 3.66%) than for non-Asian trials (RMPSE 0.81% to 8.50%). Main limitations include the limited number of outcomes recorded in the CHARLS cohort, and the generalizability of simulated cohorts derived from trial participants. Conclusions Our study shows that the CHIME model is a new validated tool for predicting progression of diabetes and its outcomes, particularly among Chinese and East Asian populations that has been lacking thus far. The CHIME model can be used by health service planners and policy makers to develop population-level strategies, for example, setting HbA1c and lipid targets, to optimize health outcomes.