Application of prognostic nutrition index in prognostic analysis of acute complications of diabetes mellitus

Abstract

The invention provides an application of a prognostic nutritional index (PNI) in prognostic analysis of acute complications of diabetes mellitus, and provides a prognostic analysis system of acute complications of diabetes mellitus by verifying the prediction effect of the PNI in prognosis of patients with acute complications of diabetes mellitus. The ROC analysis result shows that the AUC of the PNI is 83.1%, the prediction effect on the prognosis of the acute complications of diabetes mellitus is superior to that of other inflammation and trophic factors, and under the AUC, the critical value of the PNI is 42.93, the corresponding sensitivity is 0.944, and the specificity is 0.568. It is indicated that PNI is significantly related to the total cause death risk of T2DM acute complication (DKA and HHS) patients, and PNI may become a simple, convenient and easy-to-popularize index for predicting prognosis of T2DM acute complication patients, and can be used as a medical decision reference.

Description

Technical Field

[0001] This invention belongs to the field of prognostic risk prediction technology for acute complications of diabetes, specifically involving the application of prognostic nutritional index in the prognostic prediction of acute complications of diabetes, and providing a prognostic analysis system for acute complications of diabetes. Background Technology

[0002] Diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar syndrome (HHS) are the two most serious acute complications of diabetes, and early prediction of mortality risk is crucial for disease prognosis. Because DKA and HHS progress rapidly, most patients are admitted through the emergency department, where limited testing can be performed in a short time, often resulting in missing data. Therefore, research on prognostic prediction for DKA and HHS patients is limited.

[0003] Current research focuses on the detection of certain gene loci and uses them as therapeutic targets for acute complications of diabetes. However, the gene testing procedures are complex and time-consuming, making them unsuitable for implementation in emergency departments and primary healthcare units. Previous studies have used the lightGBM model to predict the long-term prognosis of patients with hyperglycemic crisis, but the predictive model requires numerous demographic, clinical history, and laboratory parameters. For patients admitted to the hospital due to acute complications of T2DM (type 2 diabetes), the required time window for testing is insufficient. Therefore, this model is more suitable for predicting the long-term prognosis of patients with DKA and HHS.

[0004] Hyperglycemic crisis progresses rapidly and has a high mortality rate, making short-term prognosis prediction equally important. In contrast, hematological and nutritional indicators offer advantages such as lower cost, simplicity, speed, and easy accessibility, making them more suitable for assessing the short-term prognosis of emergency patients and assisting in treatment decisions at primary care hospitals. The development of diabetes is closely related to inflammation and nutritional levels; therefore, inflammation and nutrition-related indicators have the potential to become reliable prognostic tools for DKA and HHS. Clinicians can improve patient outcomes by monitoring and adjusting these indicators. Lymphocytes are a marker of long-term chronic inflammation, while albumin reflects the body's nutritional status and reserves. The Prognostic Nutrition Index (PNI), which combines these two indicators, has gained increasing attention in recent years as a comprehensive indicator of inflammation and nutrition and is currently used for prognostic prediction of cancer, chronic complications of diabetes, and coronary artery disease.

[0005] Lymphocyte count is an easily obtainable indicator in routine blood tests. It reflects the body's inflammatory and immune status; a low lymphocyte count indicates immunosuppression or a compromised immune system. Albumin is a routine indicator in liver function and blood biochemistry tests, reflecting liver synthetic function and nutritional status. In addition, albumin itself has free radical scavenging, antioxidant, and anti-inflammatory effects. In summary, a low PNI value may indicate poor nutritional status, immunosuppression, and a high level of inflammation, which are often unfavorable prognostic factors. Since diabetic patients often have long-term subclinical inflammation and a higher prevalence of anemia and malnutrition than healthy individuals, we hypothesize that the inflammation and nutritional indicator PNI can be used to predict the prognosis of patients with acute diabetic complications.

[0006] In existing studies, prognostic markers (PNIs) have been widely used to predict the risk and prognosis of type 2 diabetes mellitus (T2DM), chronic diabetic complications such as diabetic nephropathy, diabetic retinopathy, and diabetic cardiovascular complications. However, their prognostic value in acute diabetic complications remains unclear. Our analysis found that PNIs are an effective, simple, and cost-effective biomarker for predicting the prognosis of patients with acute diabetic complications. Clinicians can use this marker to better stratify risk and adjust treatment strategies, reduce the incidence of adverse outcomes, and improve patient outcomes. Summary of the Invention

[0007] This invention addresses the aforementioned technical problems by verifying the predictive role of the prognostic nutritional index (PNI) in the prognosis of patients with acute complications of diabetes, and thereby providing a prognostic analysis system for acute complications of diabetes.

[0008] This invention combines training and validation cohorts, using neutrophil-to-lymphocyte ratio (NLR), albumin / (neutrophil / lymphocyte) ratio (ANLR), (monocyte count + neutrophil count) / lymphocyte count (NMLR), and prognostic nutritional index (PNI) as candidate analytical indicators. First, continuous variables significantly associated with the prognosis of acute diabetic complications are identified. Then, whether these variables are independent influencing factors for all-cause mortality risk in acute diabetic complications is assessed. ROC curves are then used to evaluate and compare the predictive value of inflammation- and nutrition-related indicators PNI, NLR, NMLR, and ANLR for the prognosis of acute diabetic complications. PNI is found to be significantly associated with the all-cause mortality risk in patients with type 2 diabetes mellitus (T2DM) acute complications (DKA, HHS), suggesting that PNI may be a simple and easily applicable indicator for predicting the prognosis of patients with T2DM acute complications. Subsequently, mediation analysis is used to establish mediating factors between PNI and all-cause mortality risk. Finally, subgroup analysis is conducted to assess the reliability of PNI's prognostic impact in different populations, determining the reliability of PNI as an independent prognostic indicator.

[0009] Based on the above research, the technical solution to be protected by this invention is as follows:

[0010] In a first aspect, the present invention provides the application of the prognostic nutritional index (PNI) as a prognostic factor for acute complications of diabetes; and further provides the application of the PNI in constructing a prognostic analysis system for acute complications of diabetes.

[0011] Preferably, the PNI consists of two indicators: lymphocyte count and albumin level. The PNI calculation method is as follows:

[0012] Albumin + 5 × absolute lymphocyte count, where albumin is measured in g / L and absolute lymphocyte count is measured in 10-1. 9 / L.

[0013] Preferably, acute diabetic complications are selected from diabetic ketoacidosis (DKA) and / or hyperglycemic hyperosmolar syndrome (HHS).

[0014] ROC analysis showed that the area under the receiver operating characteristic (AUC) of PNI was 83.1%, indicating that PNI was better than other inflammatory and nutritional factors in predicting the prognosis of acute complications of diabetes. At this AUC, the cutoff value of PNI was 42.93, with a sensitivity of 0.944 and a specificity of 0.568.

[0015] In a second aspect, the present invention provides a prognostic analysis system for acute complications of diabetes, including an analysis module for analyzing the prognostic nutritional index (PNI) and determining low PNI scores as indicating poor prognosis.

[0016] Preferably, the critical value of PNI is 42.93. When it is lower than this value, it is judged as a poor prognosis.

[0017] In a further preferred embodiment, the analysis system of the present invention also includes an input display module, used to input the patient's basic information and the corresponding albumin content and absolute lymphocyte count, and to display the judgment results.

[0018] In a third aspect, the present invention provides a method for prognostic analysis of acute complications of diabetes, comprising the following steps: inputting the patient's basic information and corresponding albumin content and absolute lymphocyte count into the input display module; the calculation module calculates the PNI value using the following formula; and when the value is less than the critical value of 42.93, the patient is deemed to have a poor prognosis.

[0019] PNI = Albumin + 5 × Absolute Lymphocyte Count, where albumin is measured in g / L and the absolute lymphocyte count is measured in 10⁻⁶ cells / L. 9 / L.

[0020] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the pre-analysis method as described above.

[0021] The beneficial protections and effects of this invention are as follows:

[0022] This invention, through screening and validation, confirms the reliability of the Prognostic Nutrition Index (PNI) as a prognostic factor for acute complications of diabetes mellitus. ROC analysis results show that the area under the receiver operating characteristic (AUC) of PNI is 83.1%, indicating that its predictive effect on the prognosis of acute complications of diabetes mellitus is superior to other inflammatory and nutritional factors. At this AUC, the cutoff value for PNI is 42.93, corresponding to a sensitivity of 0.944 and a specificity of 0.568. This demonstrates that PNI is significantly associated with the all-cause mortality risk of patients with acute complications of type 2 diabetes mellitus (T2DM, DKA, HHS), and that PNI may become a simple and easily applicable indicator for predicting the prognosis of patients with acute complications of T2DM, serving as a reference for medical decision-making. Attached Figure Description

[0023] Figure 1 The ROC analysis comparison of training cohorts PNI, (-NLR), (-NMLR), HALP, and ANLR is shown.

[0024] Figure 2 The ROC analysis results for the validation queue PNI are shown.

[0025] Figure 3 The mediation analysis results are displayed.

[0026] Figure 4 The results of the subgroup analysis are shown. Detailed Implementation

[0027] The following embodiments and experimental examples further illustrate the present invention and should not be construed as limiting the invention. The embodiments do not include a detailed description of conventional methods, which are well known to those skilled in the art and described in numerous publications.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as are familiar to those skilled in the art. Furthermore, any methods and materials similar to or equivalent to those described herein may be applied to this invention, and the preferred embodiments and materials described in the specific implementation are for illustrative purposes only.

[0029] Unless otherwise specified, the experimental methods described in the following examples are generally performed under standard conditions or as recommended by the manufacturer.

[0030] I. Establishment of the Research Cohort

[0031] This invention employs a cross-sectional study design to collect data from patients with type 2 diabetes mellitus (T2DM) who were hospitalized and treated for acute complications of diabetes mellitus (DKA and HHS) at Tongji Hospital in Shanghai between 2002 and 2024.

[0032] According to the "Guidelines for the Prevention and Treatment of Diabetes in China (2024 Edition)," the diagnostic criteria for DKA and HHS are as follows:

[0033] Diagnostic criteria for DKA: (1) elevated blood glucose (≥11.1 mmol / L), or (2) history of diabetes mellitus with serum β-hydroxybutyrate ≥3.0 mmol / L or positive urine ketones (++ or above), (3) decreased blood pH (pH <7.3) and / or bicarbonate <18 mmol / L.

[0034] The diagnostic criteria for HHS must include all four of the following: (1) blood glucose ≥ 33.3 mmol / L; (2) effective plasma osmolality > 300 mOsm / (kg·H2O), or total plasma osmolality > 320 mOsm / (kg·H2O); (3) serum β-hydroxybutyrate concentration < 3.0 mmol / L, or urine ketones < ++; (4) arterial blood pH ≥ 7.3 or serum bicarbonate concentration ≥ 15 mmol / L.

[0035] Inclusion criteria were: type 2 diabetes, age ≥18 years, diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar syndrome (HHS). Exclusion criteria were: type 1 diabetes; specific types of diabetes; gestational diabetes; age <18 years; diabetes whose type could not be clearly defined; pregnancy and lactation; and missing important data. Ultimately, 810 patients were included in the cohort.

[0036] Cohort division: The 810 enrolled patients were randomly divided into a training cohort (n=648) and a validation cohort (n=162) at a ratio of 8:2, and relevant clinical data were collected and analyzed.

[0037] II. Data Collection

[0038] Demographic data, clinical history, and biochemical indicators of patients at initial admission were collected from the inpatient medical record system of Tongji Hospital, Shanghai. Key laboratory indicators included: serum albumin (ALB), neutrophil count, lymphocyte count, and monocyte count.

[0039] The following combined indicators were calculated: neutrophil-to-lymphocyte ratio (NLR), albumin / (neutrophil / lymphocyte) ratio (ANLR), (monocyte count + neutrophil count) / lymphocyte count (NMLR), and prognostic nutritional index (PNI). The calculation methods for each indicator are as follows:

[0040] (1) NLR calculation method: neutrophil count (10 9 / L) divided by lymphocyte count (10 9 / L).

[0041] (2) ANLR calculation method: albumin level (g / L) divided by neutrophil count (10 9 ) and lymphocyte count (10 9 ) of the commerce.

[0042] (3) NMLR calculation method: monocytes (10 9 / L) and neutrophil count (10 9 The sum of ( / L) divided by the lymphocyte count (10) 9 / L).

[0043] (4) PNI calculation method: albumin (g / L) + 5 × absolute lymphocyte count (10) 9 / L).

[0044] III. Statistical Analysis

[0045] All statistical assessments were performed using SPSS 25.0 software. First, independent samples t-tests (T-tests) and Mann-Whitney U tests were used to identify continuous variables significantly associated with the prognosis of acute diabetic complications. Chi-square tests were used for inter-group comparisons of categorical variables. For variables with p-values ​​< 0.05, binary logistic regression analysis was performed to assess whether the variable was an independent influencing factor for all-cause mortality risk associated with acute diabetic complications. Receiver operating characteristic (ROC) curves were used to assess and compare the predictive value of inflammation and nutrition-related indicators (PNI, NLR, NMLR, ANLR) for the prognosis of acute diabetic complications. Mediation analysis was then used to establish the mediating factor between PNI and all-cause mortality risk. Finally, subgroup analysis was performed to assess the reliability of the prognostic effect of PNI in different populations. A two-sided p-value < 0.05 was considered statistically significant. To assess the clinical value of PNI, validation was performed using an internal dataset (n=162), with validation metrics including AUC and OR.

[0046] IV. Experimental Results

[0047] The training cohort included 648 patients with acute complications of diabetes, of whom 21 died and 627 survived. Analysis revealed that the deceased group was older than the survivors (71.60±13.21 vs. 61.56±17.91, p=0.013), had higher blood glucose levels at admission (32.43±4.10 vs. 23.38±8.11, p<0.001), higher plasma osmolality at admission (363.42 (343.45, 375.13) vs. 304.45 (296.71, 313.73), p<0.001), lower PNI levels (33.48±7.52 vs. 44.21±8.31, p<0.001), and higher NLR levels (12.00 (7.88, 16.42)). The NMLR level was significantly higher (15.43±13.31 vs. 7.47±7.77, p<0.001). See Table 1 below for details.

[0048] Table 1. Comparison of baseline data between the death and survival groups in the training cohort.

[0049]

[0050] Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; TP, total protein; CRP, C-reactive protein; PCT, procalcitonin; LDL-c, low-density lipoprotein cholesterol; HDL-c, high-density lipoprotein cholesterol; TC, total cholesterol; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; NMLR, monocyte count + neutrophil count / lymphocyte count; HALP, (hemoglobin × albumin × lymphocytes) / platelets; ANLR, albumin / (neutrophils / lymphocytes).

[0051] Among inflammatory and nutritional factors such as PNI, NLR, NMLR, HALP, and ANLR, binary logistic regression analysis was used to further identify variables independently associated with prognosis. Model 1 was a coarse model without adjusting covariates, while Model 2 adjusted for covariates of age, sex, smoking, alcohol consumption, cardiovascular disease, and hypertension. The results after adjusting for covariates showed that each unit increase in PNI contributed 19.9% ​​to the decrease in all-cause mortality risk (OR=0.801, 95%CI 0.701-0.915, p<0.001) (Table 2). ROC analysis showed that the area under the receiver operating characteristic curve (AUC) for PNI was 83.1%, indicating that PNI was superior to other inflammatory and nutritional factors in predicting the prognosis of acute complications of diabetes. Figure 1Under this AUC, the critical value of PNI is 42.93, corresponding to a sensitivity of 0.944 and a specificity of 0.568.

[0052] Table 2. Binary logistic regression analysis of training cohorts PNI, NLR, NMLR, HALP, and ANLR.

[0053]

[0054] Abbreviations: OR, odds ratio; CI, confidence interval; Model 1: coarse model without covariate adjustment; Model 2: adjusted for age, sex, smoking, alcohol consumption, cardiovascular disease, and hypertension; Model 3: further adjusted for aspartate transferase, uric acid, glycated hemoglobin, and troponin.

[0055] The internal validation dataset included 162 patients, of whom 5 died and 158 survived. Compared with the training cohort, the validation cohort showed a similar distribution in age, PNI level, and proportion of deaths, indicating good comparability between the two datasets and the reliability of the validation dataset. In the internal validation dataset, PNI remained independently associated with prognosis, with each unit increase in PNI contributing 35.6% to the decrease in all-cause mortality risk (OR=0.644, 95%CI 0.432-0.962, p=0.032). ROC analysis showed an AUC of 89.2% for PNI, indicating that PNI maintained excellent predictive performance. Figure 2 ).

[0056] Furthermore, mediation analysis of the entire population showed that hemoglobin had a significant mediating effect between PNI and all-cause mortality risk (p < 0.05), with a mediating effect accounting for 23.18%. Figure 3 We also stratified the entire population based on clinical history and demographic data. Subgroup analysis showed that, in subgroups stratified by age, sex, smoking and alcohol consumption, cardiovascular and cerebrovascular diseases, hypertension, infection, hypoalbuminemia, and anemia, PNI did not show a significant protective effect against hypoalbuminemia (p=0.293) and anemia (p=0.482). However, the effect of PNI remained significant in patients without the above comorbidities, and no significant interaction effect was observed in any subgroup (interaction p-values ​​> 0.05), indicating the stability of PNI's prognostic predictive effect. Figure 4 ).

[0057] In conclusion, this study shows that PNI is significantly associated with the risk of all-cause mortality in patients with acute complications of type 2 diabetes mellitus (DKA, HHS). We believe that PNI may be a simple and easy-to-use indicator for predicting the prognosis of patients with acute complications of type 2 diabetes mellitus.

[0058] The undescribed parts of this invention are the same as or implemented using existing technology. The applicant declares that this invention is illustrated through the above embodiments, but the invention is not limited to the above detailed methods, i.e., it does not mean that the invention must rely on the above detailed methods to be implemented. Those skilled in the art should understand that any improvements to this invention, equivalent substitutions of raw materials for the product of this invention, additions of auxiliary components, and selection of specific methods all fall within the protection and disclosure scope of this invention.

Claims

1. Application of the prognostic nutritional index (PNI) as a prognostic factor for acute complications of diabetes.

2. Application of the Prognostic Nutrition Index (PNI) in constructing a prognostic analysis system for acute complications of diabetes.

3. The application according to claim 1 or 2, characterized in that, PNI consists of two indicators: lymphocyte count and albumin content.

4. The application according to claim 1 or 2, characterized in that, The PNI calculation method is as follows: Albumin + 5 × Absolute Lymphocyte Count, where albumin is measured in g / L and the absolute lymphocyte count is measured in 10^6 cells / L. 9 / L.

5. The application according to claim 1 or 2, characterized in that, Acute complications of diabetes are selected from diabetic ketoacidosis (DKA) and / or hyperglycemic hyperosmolar syndrome (HHS).

6. A prognostic analysis system for acute complications of diabetes, characterized in that, The system includes an analysis module that analyzes the Prognostic Nutrition Index (PNI) and identifies low PNI scores as indicating poor prognosis.

7. The prognostic analysis system for acute complications of diabetes according to claim 6, characterized in that, The analysis module performs individualized assessments of acute complications of diabetes based on the following algorithm: PNI = Albumin + 5 × Absolute Lymphocyte Count The albumin unit is g / L, and the absolute lymphocyte count unit is 102. 9 / L.

8. The prognostic analysis system for acute complications of diabetes according to claim 6, characterized in that, The critical value for PNI is 42.

93.

9. The prognostic analysis system for acute complications of diabetes according to claim 1, characterized in that, It also includes an input display module, which is used to input the patient's basic information as well as the corresponding albumin content and absolute lymphocyte count, and to display the judgment results.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the functions of the system as described in any one of claims 6 to 9.

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