Effect of Dietary Pulses in a Low Glycemic Index Diet on Renal Function in Participants with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background There is uncertainty in the effects of dietary pulses so far tested. Reducing the glycemic (GI) of the diet using Acarbose, the α‐glucosidase inhibitor, has been shown to be associated with a lower incidence of cardiovascular disease and hypertension in patients with impaired glucose tolerance. Similar dietary maneuvers to lower the glycemic index of the diet using plant protein (e.g. pulses) may be beneficial in people with diabetes. Objective To determine the effect of a low GI diet through increase pulse consumption, on renal function in study participants with type 2 diabetes mellitus. Methods We conducted a secondary analysis of a 12‐week randomized controlled trial in participants with type 2 diabetes mellitus. The intervention was a low GI diet with emphasis on pulses (LGI‐pulse diet, ~190 g/day) versus a high fiber control diet with emphasis on wheat products (HF‐wheat diet). Markers of renal function (urea, creatinine, albumin, albumin/creatinine ratio, blood urea nitrogen, estimated glomerular filtration rate, creatinine clearance, sodium, phosphorus, and potassium) were assessed in those who completed the study and provided 24h urine collections. Results We included 109 participants with type 2 diabetes mellitus who completed the study and provided 24 hr. urine collections, 52 in the LGI‐pulse diet, and 57 in the HF‐wheat control diet. Dietary protein intake (DPI) was not significantly different between diets. The change in urinary urea was positively correlated with the change of DPI (r=0.23, p=0.01) and animal protein (r=0.22, p=0.02), but not with plant protein, No significant changes within and between treatments were seen in markers of renal function. There was a lack of effect seen on markers of renal function despite a significant relation between dietary protein and urinary urea. Conclusions Increase in plant protein through increased dietary pulses consumption as part of a low GI diet did not affect renal function in participants with type 2 diabetes mellitus. Trial Registration NCT01063361. Support or Funding Information This work was supported by ABIP through the PURENet and the Saskatchewan Pulse Growers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".