Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

On-farm instant quality analysis

Near infrared reflectance (NIR) spectroscopy has been used to evaluate forage quality since the early 1980s. Until recently, these were relatively sensitive, large laboratory instruments that required finely-ground, dry forage samples for analysis. New technology has allowed the development of small, hand-held NIR units that can work with wet, chopped forage or silage

Alfalfa-Grass Mixtures -- 2017 Update

While almost 90 percent of alfalfa acreage in NY is sown with a perennial grass, alfalfa acreage in the rest of the USA may average more than 90 percent pure alfalfa. Interest appears to be growing in alfalfa-grass mixtures across the northern tier of states. Until recently, little research has been conducted on grass species selection or management of mixtures. The primary negative point with mixtures is not lower forage quality, but variable forage quality. The main cause of this variability is a variable alfalfa-grass ratio

Is day-to-day variation in bunkers worth correcting?

Progressive Dairy magazine is printed 20 times each year for forward-thinking U.S. dairy producers. The award-winning magazine's editors and contributors provide compelling features, helpful articles, insightful news analysis, and entertaining commentary about the people, practices and topics related to a dairy lifestyle.Everyone knows that feed from haylage and corn silage bunkers will vary in composition from day to day. What is not so clear is the magnitude of this variation, and whether it might be worth it, economically and environmentally, to rebalance dairy rations daily to correct the variation. Providing excess feed likely will mitigate the effects of day-to­day silage variability, but this increases feed costs and is less environmentally acceptable. Many farms rebalance dairy rations weekly. A few attempt daily rebalancing. A better understanding of day-to-day variability of bunkers within a week is the first step to assess the potential benefits of daily rebalancing of rations. The most practical component to focus on for daily rebalancing is dry matter (DM) concentration; however, DM can be difficult to measure accurately, particularly in mixed haylage.Progressive Dairy, Papillo

Alfalfa-grass mixes increase forage quality to support high-producing dairy cows

Seeding alfalfa with a grass produces a mixed forage with higher neutral detergent fiber digestibility (NDFD), important for high-producing dairy cows. Meadow fescue (MF) grass varieties originated in Europe and North Africa and over the past decade our lab has been investigating opportunities to seed alfalfa with meadow fescue grass varieties to improve the quality of alfalfa-grass mixtures for high-producing dairy cows. Given its hardy nature, persistency during the harsh winters in the Northeast is not typically an issue. Relative competitiveness and qualities of varieties, however, is largely unknown in the region

Harvest Timing of Standing Corn Using Near-Infrared Reflectance Spectroscopy

Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region

The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage Nutritive Value

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002

Okara as a protein supplement affects feed intake and milk composition of ewes and growth performance of lambs

Evaluating the feeding value of wet okara as a protein supplement for lactating ewes with twin lambs was the objective. A 4 × 4 Latin square replicated 2× (4 sheep, 4 treatments, 4 periods per square; 2 squares) was conducted to examine the influence of concentrate mix (okara or not) and type of forage (silage or hay) on ewe milk composition and growth of their lactating lambs. Treatment periods were 14 days (7 days adaptation and 7 days collection). Ewes (55 to 74.8 kg BW) were fed 1 of 4 diets: wheat middling and corn concentrate with mixed grass hay (TSH), okara and corn with mixed grass hay (OSH), soybean and wheat middlings with hay crop silage (TSS), and okara and corn with hay crop silage (OSS). Ewes fed hay diets had lower forage dry matter intakes than ewes fed silage. Intake of okara supplement was higher (P < 0.05) with OSH (3.64 kg/d) than with OSS (1.70 kg/d). There was no difference in supplement intake between TSH and TSS. There were no differences among diets for lamb daily gains or in ewe milk compositions among the diets. Okara is an effective source of protein for lactating ewes and their twin lambs

Practical Considerations for Using the NeoSpectra-Scanner Handheld Near-Infrared Reflectance Spectrometer to Predict the Nutritive Value of Undried Ensiled Forage

Prediction models of different types of forage were developed using a dataset of near-infrared reflectance spectra collected by three handheld NeoSpectra-Scanners and laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), Ash, and moisture content (MO) from a total of 555 undried ensiled corn, grass, and alfalfa samples. Data analyses and results of models developed in this study indicated that the scanning method significantly impacted the accuracy of the prediction of forage constituents, and using the NEO instrument with the sliding method improved calibration model performance (p p = 0.02), where the validation set with an independent instrument resulted in an RMSEP of 2.39 compared to 1.44 where the same instruments were used for both calibration and validation. Validation model performance for NDF, IVTD, NDFD, ADL, ADF, Ash, CP, and moisture content were 4.18, 3.86, 6.14, 1.10, 2.75, 1.42, 2.71, and 1.67 for alfalfa-grass silage samples and 3.22, 2.21, 4.55, 0.38, 2.07, 0.50, 0.51, and 1.62 for corn silage, respectively. Based on the results of this study, the handheld spectrometer would be useful for predicting moisture content in undried and unground alfalfa-grass (R2 = 0.97) and corn (R2 = 0.93) forage samples