Increased Prevalence of Albuminuria in HIV-Infected Adults with Diabetes

HIV and type 2 diabetes are known risk factors for albuminuria, but no previous reports have characterized albuminuria in HIV-infected patients with diabetes.We performed a cross-sectional study including 73 HIV-infected adults with type 2 diabetes, 82 HIV-infected non-diabetics, and 61 diabetic control subjects without HIV. Serum creatinine >1.5 mg/dL was exclusionary. Albuminuria was defined as urinary albumin/creatinine ratio >30 mg/g.The prevalence of albuminuria was significantly increased among HIV-infected diabetics (34% vs. 13% of HIV non-diabetic vs. 16% diabetic control, p = 0.005). HIV status and diabetes remained significant predictors of albuminuria after adjusting for age, race, BMI, and blood pressure. Albumin/creatinine ratio correlated significantly with HIV viral load (r = 0.28, p = 0.0005) and HIV-infected subjects with albuminuria had significantly greater cumulative exposure to abacavir (p = 0.01). In an adjusted multivariate regression analysis of HIV-infected subjects, the diagnosis of diabetes (p = 0.003), higher HIV viral load (p = 0.03) and cumulative exposure to abacavir (p = 0.0009) were significant independent predictors of albuminuria.HIV and diabetes appear to have additive effects on albuminuria which is also independently associated with increased exposure to abacavir and HIV viral load. Future research on the persistence, progression and management of albuminuria in this unique at-risk population is needed

Selecting Forecasting Methods

I examined six ways of selecting forecasting methods: Convenience, “what’s easy,” is inexpensive, but risky. Market popularity, “what others do,” sounds appealing but is unlikely to be of value because popularity and success may not be related and because it overlooks some methods. Structured judgment, “what experts advise,” which is to rate methods against prespecified criteria, is promising. Statistical criteria, “what should work,” are widely used and valuable, but risky if applied narrowly. Relative track records, “what has worked in this situation,” are expensive because they depend on conducting evaluation studies. Guidelines from prior research, “what works in this type of situation,” relies on published research and offers a low-cost, effective approach to selection. Using a systematic review of prior research, I developed a flow chart to guide forecasters in selecting among ten forecasting methods. Some key findings: Given enough data, quantitative methods are more accurate than judgmental methods. When large changes are expected, causal methods are more accurate than naive methods. Simple methods are preferable to complex methods; they are easier to understand, less expensive, and seldom less accurate. To select a judgmental method, determine whether there are large changes, frequent forecasts, conflicts among decision makers, and policy considerations. To select a quantitative method, consider the level of knowledge about relationships, the amount of change involved, the type of data, the need for policy analysis, and the extent of domain knowledge. When selection is difficult, combine forecasts from different methods

Early nutritional determinants of intrahepatocellular lipid deposition in preterm infants at term age

Background: We have previously shown that by term age, preterm infants have elevated intrahepatocellular lipid (IHCL) content and altered regional adiposity, both of which are risk factors for cardiometabolic illness in adult life. Preterm nutritional intake is a plausible determinant of these aberrant trajectories of development. Objective: We aimed to establish if macronutritional components of the preterm diet were determinants of IHCL deposition measured at term equivalent age, using 1H Magnetic Resonance spectroscopy (MRS). Methods: Prospective observational case–control study in a single UK neonatal unit. 1H MR spectra were acquired from 18 preterm infants (<32 weeks gestational age at birth) at term age and 31 healthy term infants, who acted as a control group. Neonatal nutritional information was collected from birth to 34+6 weeks postmenstrual age. Results: IHCL (median, interquartile range) was significantly higher in preterm-at-term infants compared with term-born infants: 0.735, 0–1.46 versus 0.138, 0–0.58; P=0.003. In preterm infants, IHCL was positively correlated with lipid intake in the first week of life (r=0.52, P=0.04). Conclusions: This study confirms our previous observation of elevated IHCL in preterm infants at term and suggests that early lipid intake may be a determinant. Future work is warranted to establish the clinical relevance and the role of nutritional intervention in attenuating or exacerbating this effect in preterm infants