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Коморбідний ендокринологічний пацієнт
Коморбідний ендокринологічний пацієнт

Международный эндокринологический журнал Том 20, №3, 2024

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Прогнозування ефективності реабілітації в пацієнтів із цукровим діабетом 2-го типу та діабетичною полінейропатією

Прогнозування ефективності реабілітації в пацієнтів із цукровим діабетом 2-го типу та діабетичною полінейропатією

Авторы: T.H. Bakaliuk (1), N.R. Makarchuk (1), H.O. Stelmakh (1), V.I. Pankiv (2), I.I. Kamyshna (1)
(1) - I. Horbachevsky Ternopil National Medical University, Ternopil, Ukraine
(2) - Ukrainian Scientific and Practical Centre of Endocrine Surgery, Transplantation of Endocrine Organs and Tissues of the Ministry of Health of Ukraine, Kyiv, Ukraine

Рубрики: Эндокринология

Разделы: Клинические исследования

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Резюме

Актуальність. Прогнозування ефективності реабілітації в пацієнтів із діабетичною полінейропатією (ДПН) при цукровому діабеті 2-го типу (ЦД2) має велике значення в сучасній клінічній практиці. Зважаючи на поширеність ЦД2 і його ускладнень, у тому числі ДПН, розробка прогностичних моделей дозволить персоналізувати лікувальні підходи, оптимізувати реабілітаційні програми та підвищити якість життя пацієнтів. Інтеграція новітніх методів аналізу даних та молекулярно-біологічних підходів у прогностичні моделі сприятиме розвитку інноваційних стратегій реабілітації та покращенню результатів лікування в цієї важливої категорії пацієнтів. Мета роботи: запропонувати багатофакторну регресійну математичну модель прогнозування ефективності реабілітації діабетичної полінейропатії. Матеріали та методи. Для побудови прогностичної моделі за допомогою багатофакторного регресійного аналізу обстежено 95 хворих на ЦД2. Для перевірки якості моделі використовували критерій Нейджелкерка (R2). Результати. Аналіз виявив значущі зв’язки між різними факторами та ефективністю реабілітації пацієнтів із діабетичною полінейропатією. Зокрема, збільшення віку пов’язане з передбачуваним зниженням ефективності реабілітації на 0,103. Крім того, збільшення тривалості цукрового діабету пов’язане з очікуваним зниженням ефективності реабілітації від 1,341 до 3,732 залежно від діапазону тривалості. Так само працевлаштування, тютюнокуріння, індекс маси тіла, рівень глікованого гемоглобіну (HbA1c), рухливість, здатність до самообслуговування, повсякденна діяльність, біль/дискомфорт, тривога/депресія, сенсорна чутливість, показники DN4 та профіль ліпідів були значущо пов’язані зі змінами в ефективності реабілітації. Регресійна модель продемонструвала високу ефективність та прийнятність із кореляційним коефіцієнтом (rxy) 0,997, що свідчить про сильний функціональний зв’язок. Крім того, модель була статистично значущою (p < 0,001). Ці результати підкреслюють важливість врахування багатьох факторів при прогнозуванні результатів реабілітації в пацієнтів із ДПН і підкреслюють потенційну корисність розробленої моделі в клінічній практиці. Висновки. Математична модель прогнозування ефективності реабілітації ДПН у хворих на ЦД2 демонструє високу прийнятність, якість та ефективність. Використання цієї моделі, що враховує 99,5 % факторів, дозволить підвищити точність та своєчасність реабілітації пацієнтів, покращити результати лікування, проводити регулярний моніторинг хворих із високим ризиком ускладнень, сприяти розробці інформаційних листів та адаптованих програм профілактики ДПН у хворих на ЦД2, а також створенню відповідних медичних калькуляторів та інформаційних систем.

Background. Predicting the effectiveness of rehabilitation in patients with diabetic polyneuropathy (DPN) and type 2 diabetes mellitus is of great importance in modern clinical practice. Given the prevalence of diabetes and its complications, including DPN, the development of predictive models will allow for personalized treatment approaches, optimization of rehabilitation programs, and improvement in the quality of life for patients. Integrating state-of-the-art data analysis methods and molecular-biological approaches into predictive models will contribute to the development of innovative rehabilitation strategies and improve treatment outcomes in this important patient population. The purpose of the study was to propose a multifactorial regression mathematical model for predicting the effectiveness of diabetic polyneuropathy rehabilitation. Materials and methods. Ninety-five patients with type 2 diabetes and DPN were examined to construct a predictive model of rehabilitation effectiveness using multiple regression analysis. The quality of the model was evaluated using the Nagelkerke criterion (R2). Results. The analysis revealed several significant associations between various factors and the effectiveness of rehabilitation in DPN patients. Specifically, an increase in age was associated with a predicted decrease in rehabilitation effectiveness by 0.103. Moreover, each increase in the duration of diabetes mellitus was associated with an expected decrease in rehabilitation effectiveness, ranging from 1.341 to 3.732 depending on the duration range. Similarly, changes in tobacco smoking, employment status, body mass index, glycated hemoglobin levels, mobility, self-care, usual activities, pain/discomfort, anxiety/depression, sensory sensitivities, DN4 scores, and lipid profile were all significantly associated with variations in rehabilitation effectiveness. The regression model demonstrated high explanatory power, with an observed correlation coefficient (rxy) of 0.997, indicating a strong functional relationship. Furthermore, the model was statistically significant (p < 0.001), sugges­ting that the identified predictors collectively explain 99.5 % of the observed variance in rehabilitation effectiveness. These findings underscore the importance of considering multiple factors when predicting rehabilitation outcomes in DPN patients and highlight the potential utility of the developed model in clinical practice. Conclusions. The proposed mathematical model for predicting the effectiveness of rehabilitation in type 2 diabetes patients with DPN demonstrates high acceptability, quality, and effectiveness. The application of this model, considering 99.5 % of DPN factors, will enhance the accuracy and timeliness of rehabilitation, improve treatment outcomes, facilitate regular monitoring of patients at high risk of complications, promote the development of informational leaflets and adapted programs for DPN prevention in type 2 diabetes patients, and facilitate the creation of relevant medical calculators and informational systems.


Ключевые слова

цукровий діабет; діабетична полінейропатія; реабілітація; фізична терапія; прогнозування

diabetes mellitus; diabetic polyneuropathy; rehabilitation; physical therapy; prognosis

doi: http://dx.doi.org/10.22141/2224-0721.20.3.2024.1383

Introduction

Diabetic polyneuropathy (DPN) is a common complication of type 2 diabetes mellitus (T2DM) that significantly impacts patients’ quality of life and functional abilities [1, 2]. Effective rehabilitation plays a crucial role in managing DPN and mitigating its adverse effects. However, predicting the effectiveness of rehabilitation (EOR) in DPN patients remains a challenge due to the multifactorial nature of the condition and individual variability in treatment response [3, 4].
In recent years, there has been growing interest in developing predictive models to anticipate rehabilitation outcomes [5, 6]. These models aim to identify the factors associated with rehabilitation effectiveness and provide healthcare professionals with valuable insights for personalized treatment planning and intervention strategies.
In this article, we focus on predicting the effectiveness of rehabilitation in patients with diabetic polyneuropathy and T2DM. We present a comprehensive analysis of various factors influencing rehabilitation outcomes, ranging from demographic and clinical variables to lifestyle factors and sensory sensitivities. By examining these factors collectively, we aim to develop a predictive model that can enhance the precision and timeliness of rehabilitation interventions in DPN patients. Through this study, we seek to contribute to the advancement of personalized medicine in diabetes care by providing clinicians with a valuable tool for optimizing rehabilitation strategies and improving patient outcomes.
Some studies [7] have proposed multifactorial regression models for the development of neurological complications of hypothyroidism.
Reducing the consequences of diabetes mellitus and decreasing the number of associated pathologies related to it is possible through the design of information-diagnostic systems for assessing and predicting the effectiveness of rehabilitation of DPN in patients with T2DM. The model includes diagnostic and prognostic evaluation. Therefore, a timely identification of existing factors affecting the effectiveness of DPN rehabilitation in patients with T2DM is a prospective task for their utilization in modifying real clinical practice.
The aim of the study was to propose a multifactorial regression mathematical model for predicting the effectiveness of diabetic polyneuropathy rehabilitation.

Materials and methods

To develop a mathematical model, 95 patients with T2DM were examined: 30 (31.58 %) with low DPN index, 34 (35.79 %) — with medium, and 31 (32.63 %) people with high DPN index. The data collected were used to construct a multifactorial regression model for predicting the development of diabetic foot ulcers.
The study was conducted after approval by the Ethics Committee in accordance with the fundamental principles of the WMA Declaration of Helsinki — Ethical Principles for Medical Research Involving Human Subjects (1964–2008) and international ethical and scientific standards for good clinical practice (1996). All patients provided informed consent to participate in the study. The diagnosis of T2DM was established in accordance with the Unified Clinical Protocol for Primary and Secondary (Specialized) Medical Care — Type 2 Diabetes Mellitus (Order of the Ministry of Health No. 1118 dated December 21, 2012).
The lipid profile of the blood was assessed based on the levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides (TG). Various methods were used: for TC — the Ilk method with Filicit-Diagnostics reagents, for HDL — reagents from ELITech diagnostics.
Sensitivity assessment was conducted using different methods: vibration sensitivity was measured with a 128 Hz tuning fork, temperature sensitivity was tested using the Tip-therm cylinder, and tactile sensitivity was assessed using a 5.07 caliper monofilament. Pain sensitivity was evaluated using a blunted needle.
The presence of neuropathic pain was determined with the DN4 questionnaire, which consisted of two sets of questions: the first is related to sensory symptoms, and the second set — to the clinical examination by a physician.
Quality of life assessment was performed using the EQ-5D-3L questionnaire, which included six items evaluating mobility, self-care, daily activities, pain, discomfort, and emotional instability [8–12].
Statistica 10.0 (StatSoft, Inc.) was used for the analysis. Quantitative variables following non-normal distribution were described using median (Me) and lower and upper quartiles (Q1-Q3). Categorical data were described with absolute and relative frequencies. Mann-Whitney U test was used to compare two groups on a quantitative variable whose distribution differed from the normal distribution. The direction and strength of the association between two quantitative indicators were estimated using Spearman’s correlation coefficient. The prognostic model characteri–zing the dependence of a quantitative variable on predictors was developed using ordinary least squares linear regression. Differences were considered statistically significant at p < 0.05.

Results

We performed a correlation analysis of the association bet–ween effectiveness of rehabilitation (score) and age (Table 1).
A weak positive association between age and effectiveness of rehabilitation (score) was estimated (Table 2). Observed dependence is described by a linear regression equation:
YAge = 0.299 × XEOR (score) + 45.312.
According to the Nagelkerke coefficient of determination R2 of the resulting model, 7.9 % of the observed variance of age were explained (Fig. 1).
Analysis of effectiveness of rehabilitation (score) was performed depending on age (Table 3).
In accordance with Table 3, when comparing the effectiveness of rehabilitation (score), statistically significant differences were revealed depending on age (p = 0.003) (applied method: the Kruskal-Wallis test).
We performed a correlation analysis of the association between effectiveness of rehabilitation (score) and duration of diabetes mellitus (Table 4).
A weak positive correlation between duration of T2DM and a decrease in the effectiveness of rehabilitation (score) was found.
Observed dependence is described by a linear regression equation:
YT2DM = 0.148 × XEOR (score) + 3.681.
According to the coefficient of determination R2 of the resulting model, 5.7 % of the observed variance of T2DM were explained (Fig. 2).
Analysis of effectiveness of rehabilitation (score) was performed depending on duration of T2DM (Table 5).
According to Table 5, when comparing the effectiveness of rehabilitation (score), statistically significant differences were revealed depending on duration of T2DM (p = 0.042) (applied method: the Kruskal-Wallis test).
Correlation analysis of the association between effectiveness of rehabilitation (score) and diabetic polyneuropathy was performed (Table 6).
Observed dependence of diabetic polyneuropathy on the effectiveness of rehabilitation (score) is described by a linear regression equation:
YDPN = 0.235 × XEOR (score) – 1.523.
According to the coefficient of determination R2 of the resulting model, 14.5 % of the observed variance of diabetic polyneuropathy were explained (Fig. 3).
We performed analysis of effectiveness of rehabilitation (score) depending on duration of diabetic polyneuropathy (Table 7).
According to Table 7, when comparing the effectiveness of rehabilitation (score), statistically significant differences were revealed depending on duration of diabetic polyneuro–pathy (p = 0.001) (applied method: the Kruskal-Wallis test).
Analysis of effectiveness of rehabilitation (score) was performed depending on gender (Table 8).
According to Table 8, when comparing the effectiveness of rehabilitation (score), statistically significant differences were revealed depending on gender (p = 0.011) (applied method: Welch’s t-test).
The dependence of effectiveness of rehabilitation (score) on quantitative variables was estimated using multiple linear regression (Table 9).
The observed association of effectiveness of rehabilitation (score) with age, duration of diabetes mellitus, duration of diabetic polyneuropathy (years), tobacco smoking, employment, body mass index, HbA1c, mobility, self-care, usual activities, pain/discomfort, anxiety/depression, vibration sensitivity, temperature sensitivity, pain sensitivity, tactile sensitivity, DN4 score, TC, LDL, HDL, TG level is presented by a linear regression equation:
YEOR (score) = 9.913 + 0.103Xage_1 + 1.341X3–5 + 2.221X6–10 + 3.372X11–15 + 3.732X16–20 + 0.897X4–6 + 1.660X7–9 + 2.920X10–20 + 4.748XQuit smoking + 9.817XYes + 0.841XNo + 1.040X31–40 + 0.751X7–8 + 1.784X9–10 + 2.617X11–13 + 1.862X14–20 + 0.958XSP + 0.922XSP + 1.185XSP + 0.996XMod. + 2.015XExtr. + 0.990XMod. + 2.015XExtr. + 1.152XNormal + 0.546XNormal + 1.047XNormal + 1.221XNormal + 0.836X3 + 1.712X4–5 + 2.942X6–7 + 4.321X8–10 +  1.515X5.3–6.1 + 2.694X> 6.2 + 1.081X3.4–4.0 + 2.038X4.1–4.8 + 3.262X≥ 4.9 + 0.954X< 1.03 + 0.623XTG level, where X3–5 — duration of T2DM (0 if 1–2, 1 if 3–5), X6–10 — duration of T2DM (0 if 1–2, 1 if 6–10), X11–15 — duration of T2DM (0 if 1–2, 1 if 11–15), X16–20 — duration of T2DM (0 if 1–2, 1 if 16–20), X4–6 — duration of DPN (0 if 2–3, 1 if 4–6), X7–9 — duration of DPN (0 if 2–3, 1 if 7–9), X10–20 — duration of DPN (0 if 2–3, 1 if 10–20), XQuit smoking — tobacco smoking (0 if no, 1 if quit smoking), XYes — tobacco smoking (0 if no, 1 if yes), XNo — employment (0 if yes, 1 if no), X31–40 — BMI (0 if 25–30, 1 if 31–40), X7–8 — HbA1c (0 if 4–6, 1 if 7–8), X9–10 — HbA1c (0 if 4–6, 1 if 9–10), X11–13 — HbA1c (0 if 4–6, 1 if 11–13), X14–20 — HbA1c (0 if 4–6, 1 if 14–20), XSP — mobility (0 if no problems, 1 if some problems), XSP — self-care (0 if no problems, 1 if some problems), XSP — usual activities (0 if no problems, 1 if some problems), XMod. — pain/discomfort (0 if no pain, 1 if mode–rate), XExtr. — pain/discomfort (0 if no pain, 1 if extreme), XMod. — anxiety/depression (0 if no, 1 if moderate), XExtr. — anxiety/depression (0 if no, 1 if extreme), XNormal — vibration sensitivity (0 if decreased, 1 if normal), XNormal — temperature sensitivity (0 if decreased, 1 if normal), XNormal — pain sensitivity (0 if decreased, 1 if normal), XNormal — tactile sensitivity (0 if decreased, 1 if normal), X3 — DN4 (0 if 0–2, 1 if 3), X4–5 — DN4 (0 if 0–2, 1 if 4–5), X6–7 — DN4 (0 if 0–2, 1 if 6–7), X8–10 — DN4 (0 if 0–2, 1 if 8–10), X5.3–6.1 — cholesterol (0 if < 5.2, 1 if 5.3–6.1), X> 6.2 — cholesterol (0 if < 5.2, 1 if > 6.2), X3.4–4.0 — LDL (0 if ≤ 3.3, 1 if 3.4–4.0), X4.1–4.8 — LDL (0 if ≤ 3.3, 1 if 4.1–4.8), X≥ 4.9 — LDL (0 if ≤ 3.3, 1 if ≥ 4.9), X< 1.03 — HDL (0 if ≥ 1.03, 1 if < 1.03).
With an increase in age by 1 year, a 0.103 decrease in effectiveness of rehabilitation (score) should be expec–ted. The change in duration of T2DM to 3–5 is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.341, to 6–10 — by 2.221, to 11–15 — by 3.372, to 16–20 — by 3.732. The change in duration of DPN to 4–6 is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.897, to 7–9 — by 1.660, to 10–20 — by 2.920. Quit smoking is associated with an expected decrease in effectiveness of rehabilitation (score) by 4.748, tobacco smoking is associated with an expected decrease in effectiveness of rehabilitation (score) by 9.817. The change of employment is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.841. The change in BMI to 31–40 is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.040. The change in HbA1c to 7–8 is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.751, to 9–10 — by 1.784, to 11–13 — by 2.617, to 14–20 — by 1.862. The change in mobility due to some problems is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.958. The change in self-care due to some problems is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.922. The change in usual activities due to some problems is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.185. The change in pain/discomfort to mode–rate is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.996, to extreme — by 2.015. The change in anxiety/depression to moderate is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.990, to extreme — by 2.015. The change in vibration sensitivity to normal is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.152. The change in temperature sensitivity to normal is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.546. The change in pain sensitivity to normal is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.047. The change in tactile sensitivity to normal is associated with an expec–ted decrease in effectiveness of rehabilitation (score) by 1.221. The change in DN4 score to 3 is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.836, to 4–5 — by 1.712, to 6–7 — by 2.942, to 8–10 — by 4.321. The change in cholesterol to 5.3–6.1 is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.515, to > 6.2 — by 2.694. The change in LDL to 3.4–4.0 is associated with an expected decrease in effectiveness of rehabilitation (score) by 1.081, to 4.1–4.8 — by 2.038, to ≥ 4.9 — by 3.262. The change in HDL by < 1.03 is associated with an expected decrease in effectiveness of rehabilitation (score) by 0.954. With a decrease in triglycerides level, a 0.623 of effectiveness of rehabilitation (score) should be expected.
The resulting regression model is characterized by the correlation coefficient rxy = 0.997, which corresponds to the functional relationship on the Chaddock scale. The model was statistically significant (p < 0.001). The resulting model explains 99.5 % of the observed variance of effectiveness of rehabilitation (score).

Discussion

Neuropathy is the most common complication of diabetes. 50 % of adults with diabetes will develop neuropathy in their lifetime. DPN is the major form of neuropathy (75 % of cases). Pharmacological treatments are recommended for pain management in DPN [13, 14].
Appropriate therapy for painful DPN is important because this pain contributes to a poor quality of life by cau–sing sleep disturbance, anxiety, and depression [15]. The basic principle for the management of painful DPN is to control hyperglycemia and other modifiable risk factors, but these may be insufficient for preventing or improving DPN [16].
The findings of our study underscore the complex interplay of various factors in predicting the efficacy of rehabilitation in patients with T2DM and DPN. Through a multifactorial regression analysis, we identified several key predictors associated with rehabilitation effectiveness, ranging from demographic and clinical characteristics to lifestyle factors and sensory sensitivities. One notable finding was the significant association between increasing age and reduced effectiveness of rehabilitation, as evidenced by the negative coefficient observed in our regression model. This highlights the importance of considering age-related factors when designing rehabilitation interventions for DPN patients, as older individuals may require tailored approaches to address their unique needs and limitations.
Furthermore, our analysis revealed a strong correlation between the duration of diabetes mellitus and rehabilitation outcomes. Specifically, longer duration was consistently associated with decreased effectiveness of rehabilitation, suggesting that early intervention and aggressive management of diabetes may lead to better outcomes in DPN patients. This emphasizes the critical role of timely diagnosis and comprehensive diabetes care in optimizing rehabilitation success.
Additionally, lifestyle factors such as tobacco smoking and employment status were found to significantly impact rehabilitation effectiveness. Patients who smoked or were unemployed demonstrated poorer outcomes compared to their non-smoking and employed counterparts, highligh–ting the importance of addressing modifiable risk factors in rehabilitation programs.
Our study identified several clinical parameters, including BMI and HbA1c levels, as important predictors of rehabilitation outcomes. Higher BMI and HbA1c were associated with reduced effectiveness of rehabilitation, emphasizing the need for comprehensive management of metabolic parameters in DPN patients to optimize treatment responses.
Sensory sensitivities, as assessed by measures such as vibration, temperature, and pain sensitivity, emerged as significant predictors of rehabilitation effectiveness. Patients with impaired sensory function exhibited poorer outcomes, suggesting the importance of incorporating sensory assessments into rehabilitation protocols to tailor interventions to individual patient needs.
Overall, our study highlights the multifaceted nature of predicting rehabilitation efficacy in T2DM and DPN patients. By identifying and considering the various factors influencing treatment outcomes, clinicians can deve–lop personalized rehabilitation strategies that address the unique needs and challenges of each patient, ultimately leading to improved clinical outcomes and enhanced quality of life.
The regression model demonstrated high explanatory power, with an observed correlation coefficient (rxy) of 0.997, indicating a strong functional relationship. Furthermore, the model was statistically significant (p < 0.001), suggesting that the identified predictors collectively explain 99.5 % of the observed variance in rehabilitation effectiveness. These findings underscore the importance of considering multiple factors when predicting rehabilitation outcomes in diabetic polyneuropathy patients and highlight the potential utility of the developed model in clinical practice.
The proposed mathematical model for predicting the effectiveness of rehabilitation in DPN patients achieves a coefficient of determination R2 of 0.995. This indicates that 99.5 % of the factors have been accounted for in the DPN rehabilitation prediction model, demonstrating its high quality.
Since the pathogenesis of DPN is multifactorial, its management is based on combination therapy, symptomatic, either pharmacological or non-pharmacological, treatments, and pathogenic treatment [17–19].
While pathogenic mechanisms stemming from hyperglycemia are likely to be significant contributors to DPN, future therapeutic strategies will require a more nuanced approach that considers a range of concurrent insults derived from the complex pathophysiology of diabetes beyond direct hyperglycemia [19–21].
Further prospective research could involve conducting ROC analysis to determine the sensitivity, specificity, and prognostic accuracy of the proposed mathematical model for predicting DPN rehabilitation effectiveness in T2DM patients.

Conclusions

For the first time, a mathematical model has been developed that includes predictors of rehabilitation effectiveness in DPN among type 2 diabetes patients, revealing that age, employment, tobacco smoking, duration of DPN, duration of diabetes mellitus, HbA1c levels, mobility, activity, pain, discomfort, vibration sensitivity, temperature sensitivity, pain sensitivity, tactile sensitivity, DN4 scores, total cholesterol, LDL cholesterol, and triglycerides play significant roles in the adverse prognosis of this condition.
The application of this prognostic model, considering 99.5 % of rehabilitation effectiveness factors, in real clinical practice will enable the early identification of patients with high and low rehabilitation potential.
 
Received 06.02.2024
Revised 19.04.2024
Accepted 30.04.2024

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