Smoking Cessation Ameliorates Microalbuminuria With Reduction of Blood Pressure and Pulse Rate in Patients With Already Diagnosed Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Smoking cessation in newly diagnosed type 2 diabetes patients is reported to be associated with amelioration of metabolic parameters and blood pressure (BP), and the reduction of microalbuminuria. The aim of this study is to demonstrate changes in BP, pulse rate (PR), and microalbuminuria in already diagnosed diabetes patients who quit smoking. METHODS: We retrospectively evaluated diabetes outpatients who were habitual smokers, and who visited to our smoking cessation clinic. Patients were divided into two groups based on their smoking status at the termination of a 3-month smoking cessation program (smoking cessation group and smoking group), and analyzed systolic and diastolic BPs, PR, HbA1c, and body weight at the start date, and at 1, 3, 6, and 12 months thereafter. The urinary albumin-to-creatinine ratio was also measured at the start date and at 12 months. RESULTS: Thirty-five patients met our criteria. Mean diabetes duration was 12 years. Eighteen patients (52%) quit smoking. Success or failure of smoking cessation depended on nicotine dependence rather than good or bad glycemic control. Both BP and PR decreased significantly after 1 month or later in the smoking cessation group without worsening HbA1c, while both parameters did not show any changes in the smoking group. Microalbuminuria was also ameliorated significantly at 12 months compared with that at the start date in the smoking cessation group (95.8 ± 92.9 mg/gCr vs. 75.5 ± 96.3 mg/gCr, P = 0.0059), while it did not show a significant change in the smoking group. (61.9 ± 43.5 mg/gCr vs. 97.7 ± 90.4 mg/gCr, P = 0.1039). CONCLUSIONS: Smoking cessation might cause a reduction in chronic kidney disease progression through ameliorating microalbuminuria without metabolic adverse effects in patients already diagnosed with diabetes mellitus.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".