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Brain-derived neurotrophic factor (BDNF) levels can increase when patients are first diagnosed with type 2 diabetes mellitus and can change in response to glycemic control conditions throughout the course of the disease. However, the correlation between glycemic control and BDNF remains unclear. The objective of this study was to investigate whether glycemic control can predict the BDNF levels in patients with diabetic neuropathy, based on diabetic duration. Methods A cross-sectional study was conducted on 80 patients with diabetic neuropathy who were treated at a clinic in Central Java. We use glycated hemoglobin (HbA1c) levels as a parameter of glycemic control, which were measured according to the National Glycohemoglobin Standardization Program. BDNF serum levels were evaluated using the Enzyme-Linked Immunosorbent Assay (ELISA) method in the laboratory. Analysis was performed using the Spearman rank correlation and linear regression tests. Results The results of the study show that HbA1c levels in diabetic neuropathy patients with DM &lt;5 years and ≥5 years differed significantly (9.50% vs. 8.9%, p=0.049), while BDNF levels did not differ significantly (1136.98 pg/mL vs 948.07 pg/mL, p=0.172). There is a positive correlation between HbA1c and BDNF levels in patients with a diabetic duration of at least 5 years (r=0,423, p=0,007). There is no correlation between HbA1c and BDNF levels in patients with a diabetic duration of less than 5 years. The HbA1c level was a significant predictor of BDNF serum level in patients with DM duration of at least 5 years (B=120.317, 95% CI for B: 6.103 – 234.532, p=0.039). Conclusions The HbA1c levels significantly predict BDNF levels in patients with diabetic neuropathy who have had a diabetic duration of at least 5 years. \" } { \"@context\": \"http://schema.org\", \"@type\": \"BreadcrumbList\", \"itemListElement\": [ { \"@type\": \"ListItem\", \"position\": \"1\", \"item\": { \"@id\": \"https://f1000research.com/\", \"name\": \"Home\" } }, { \"@type\": \"ListItem\", \"position\": \"2\", \"item\": { \"@id\": \"https://f1000research.com/browse/articles\", \"name\": \"Browse\" } }, { \"@type\": \"ListItem\", \"position\": \"3\", \"item\": { \"@id\": \"https://f1000research.com/articles/14-1312/v1\", \"name\": \"Glycemic control predicts the Brain-derived neurotrophic factor levels...\" } } ] } Home Browse Glycemic control predicts the Brain-derived neurotrophic factor levels... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Suryani M, Ermi Tri Sulistiyowati MA and Sianturi M. Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.12688/f1000research.172834.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Brief Report Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] Maria Suryani https://orcid.org/0000-0002-8867-8339 1 , Maria Agustina Ermi Tri Sulistiyowati 1 , Medina Sianturi 1 Maria Suryani https://orcid.org/0000-0002-8867-8339 1 , Maria Agustina Ermi Tri Sulistiyowati 1 , Medina Sianturi 1 PUBLISHED 26 Nov 2025 Author details Author details 1 Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Central Java, Indonesia Maria Suryani Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Maria Agustina Ermi Tri Sulistiyowati Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Medina Sianturi Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Global Public Health gateway. Abstract Background Diabetic neuropathy is one of the complications of diabetes that occurs due to poor glycemic control. Brain-derived neurotrophic factor (BDNF) levels can increase when patients are first diagnosed with type 2 diabetes mellitus and can change in response to glycemic control conditions throughout the course of the disease. However, the correlation between glycemic control and BDNF remains unclear. The objective of this study was to investigate whether glycemic control can predict the BDNF levels in patients with diabetic neuropathy, based on diabetic duration. Methods A cross-sectional study was conducted on 80 patients with diabetic neuropathy who were treated at a clinic in Central Java. We use glycated hemoglobin (HbA1c) levels as a parameter of glycemic control, which were measured according to the National Glycohemoglobin Standardization Program. BDNF serum levels were evaluated using the Enzyme-Linked Immunosorbent Assay (ELISA) method in the laboratory. Analysis was performed using the Spearman rank correlation and linear regression tests. Results The results of the study show that HbA1c levels in diabetic neuropathy patients with DM <5 years and ≥5 years differed significantly (9.50% vs. 8.9%, p=0.049), while BDNF levels did not differ significantly (1136.98 pg/mL vs 948.07 pg/mL, p=0.172). There is a positive correlation between HbA1c and BDNF levels in patients with a diabetic duration of at least 5 years (r=0,423, p=0,007). There is no correlation between HbA1c and BDNF levels in patients with a diabetic duration of less than 5 years. The HbA1c level was a significant predictor of BDNF serum level in patients with DM duration of at least 5 years (B=120.317, 95% CI for B: 6.103 – 234.532, p=0.039). Conclusions The HbA1c levels significantly predict BDNF levels in patients with diabetic neuropathy who have had a diabetic duration of at least 5 years. READ ALL READ LESS Keywords BDNF; HbA1c; Diabetic duration; Diabetes; Diabetic neuropathy Corresponding Author(s) Maria Suryani ( [email protected] ) Close Corresponding author: Maria Suryani Competing interests: No competing interests were disclosed. Grant information: This study was funded by the Kemendiktisaintek, Indonesia grant 0419/C3/DT.05.00/2025 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Suryani M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Suryani M, Ermi Tri Sulistiyowati MA and Sianturi M. Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.12688/f1000research.172834.1 ) First published: 26 Nov 2025, 14 :1312 ( https://doi.org/10.12688/f1000research.172834.1 ) Latest published: 18 Apr 2026, 14 :1312 ( https://doi.org/10.12688/f1000research.172834.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction Approximately 50% of T2DM patients are found to have diabetic neuropathy complications. 1 An additional year of diabetic duration and a 1% increase in glycated hemoglobin (HbA1c) levels increase a person’s risk of developing diabetic neuropathy by one-fold. 2 Patients with diabetic neuropathy experience damage to large or small nerve fibers, or even both, which may cause a range of symptoms, including pain, numbness, muscle weakness, and autonomic dysfunction. 3 Patients with more severe diabetic neuropathy will experience a variety of signs and symptoms due to the extensive damage to these large and small nerves. 4 Prolonged hyperglycemia can cause inflammation and oxidative stress, resulting in damage to peripheral nerve fibers in patients with diabetic neuropathy. 5 , 6 The process of nerve damage involves the accumulation of oxidative and pro-inflammatory substances in the nerves. 6 The presence of inflammation and oxidative stress in T2DM patients affects the production of brain-derived neurotrophic factor (BDNF) levels, which ultimately changes BDNF levels. 7 BDNF levels may change throughout the course of T2DM, with an increase in BDNF may occur in prolonged hyperglycemia. 8 BDNF is a neurotrophin that plays an important role in the central nervous system and systemic or peripheral inflammatory conditions in T2DM with neuropathy. 9 , 10 BDNF functions in preventing nerve cell damage, maintaining nerve cell survival, and playing a role in nerve cell plasticity. 11 A decrease in BDNF levels can further worsen neuropathy. 12 According to a previous study, patients with diabetes have higher BDNF levels compared to healthy people. 11 Additionally, another study also found a positive correlation between insulin resistance and fasting blood sugar levels, with BDNF levels in patients with diabetes mellitus. 7 BDNF levels continued to increase with increasing pain severity in patients with diabetic neuropathy. 9 , 13 However, the correlation between glycemic control and BDNF levels is still unclear. The other study has shown that the HbA1c level is not correlated with BDNF levels. 14 In addition, the other previous study also found that BDNF levels decrease in patients with diabetes mellitus and diabetic neuropathy. 15 The duration of diabetes likely influences the relationship between glycemic control and serum BDNF levels. 16 This study aims to investigate whether glycemic control can predict BDNF levels in patients with diabetic neuropathy, based on diabetic duration. The diabetic duration will be divided into <5 years and ≥5 years. Methods Study design A cross-sectional study was conducted among patients with diabetic neuropathy in a primary health care facility in Central Java, Indonesia. Study population The study was conducted on 80 participants with diabetic neuropathy, who were treated at a primary health care facility in Central Java, Indonesia. The inclusion criteria were a minimum of 1 month of suffering from T2DM since diagnosis by a doctor, and having diabetic neuropathy. The exclusion criteria were having a stroke, an active ulcer, fracture in the foot. Data collection Data collection was done between July 2025 and August 2025. The duration of diabetes was measured by asking patients about the time between their diagnosis and the time of data collection in years. The diabetic duration was categorized into <5 years and ≥5 years. We use HbA1c as a parameter of glycemic control, while the BDNF serum level is used to measure the BDNF level. Venous blood samples were collected to measure BDNF and HbA1c levels. The blood samples were taken at the same time as the measurement of the duration of diabetes. HbA1c levels were determined by analyzing blood serum in a laboratory using an HPLC technique with the GNSP, following current institutional biosafety protocols, while BDNF serum levels were determined using Human BDNF enzyme-linked immunosorbent assay kit (CAT#RE2848H-96 wells 31.25-20000 pg/mL, Reedbiotech Ltd, Wuhan, China) according to the manufacturer’s instructions. We executed BDNF procedures at the GAKI laboratory of Diponegoro University, and HbA1c procedures at the PRODIA laboratory. We have also collected the patient characteristics such as age, body mass index (BMI), gender, working status, and diabetic neuropathy severity. The diabetic neuropathy symptom score (NSS) and diabetic neuropathy examination (DNE) were used as diabetic severity parameters. DNE score was defined as the accumulation score of the result measurement consists of eight items, including two tests of muscle strength, one of the reflex Achilles, and five tests of sensation. The min-max score is 0-16. The score was determined by doing a physical examination. The NSS score was defined as the accumulation score of the result measurement, consisting of sixteen items about the symptoms of neuropathy. The higher the score indicated the more severe the neuropathy. All collected data were evaluated for missing data during data entry. No missing data was found in this study. Data analysis The data were analyzed using SPSS 23.00. Categorical data were presented in the form of frequencies and percentages, while numerical data were presented in means and standard deviations. Kolmogorov-Smirnov was used to examine the distribution of numerical data. The Spearman Correlation test was used to examine the correlation between glycemic control and BDNF levels based on diabetic duration. Variables with p-values less than 0.25 in the Spearman analysis were then entered into a multivariate linear regression model. The linear regression analysis, by model, was used to examine the predictors of BDNF levels. Statistical significance was established using a threshold of p = 0.05. Ethical approval The study procedure was reviewed and approved by the external ethics committee of the University Widya Husada Semarang (31/EC/LPPM/UWHS/VII/2025). The study complied with the Helsinki Declaration. Written informed consent was obtained from participants of the study prior to the study. Results Table 1 shows that there are significant differences in age, employment status, and HbA1c values between patients who have had diabetes for less than 5 years and those who have had it for at least 5 years. The patients with a diabetic duration of at least 5 years were older and had lower HbA1c levels than patients with a diabetic duration of less than 5 years. The BDNF level of patients with a diabetic duration of at least 5 years was lower than that of patients with a diabetic duration of less than 5 years; however, the difference in BDNF level was not significant. Table 1. Characteristics of the participants. Characteristics Diabetic duration <5 years Diabetic duration ≥5 years P Age (year): mean (SD) 57.6 ± 9.6 62.63 ± 10.66 0.026 a BMI (Kg/m 2 ): mean ± SD 25.97 ± 5.49 27.48 ± 8.01 0.400 a Gender, number (%) Male 10 (62.5) 6 (37.5) 0.264 b Female 30 (46.9) 34 (53.1) Working status, number (%) Employee 19 (65.5) 10 (34.5) 0.036 b Unemployee 21 (41.2) 30 (58.8) Medication (%) Metformin or glimepiride 27 (69.2) 12 (30.8) 0.007 d Metformin and glimepiride 7 (26.9) 19 (73.1) Insulin and metformin/glimepiride 1 (20) 4 (80) Diabetic neuropathy duration (year), mean ± SD 1.90 ± 2.95 3.51 ± 4.25 0.024 a Diabetic neuropathy severity DNE score 10.18 ± 1.67 9.53 ± 2.58 0.313 a NSS score 7.97 ± 2.52 7.83 ± 2.71 0.689 a BDNF serum levels (pg/ml), mean ± SD 1136.98 ± 813.59 948.07 ± 69.98 0.172 a HbA1c levels (%), mean ± SD 9.55 ± 2.75 8.49 ± 1.87 0.049 c a Mann-Whitney U test, b Chi-square test, c Paired T test, d Kolmogorov-Smirnov test. Table 2 shows that there is a significant positive correlation between glycemic control and BDNF levels in patients who have had diabetes for at least 5 years (r = 0.423, p = 0.007). There is no significant correlation between glycemic control and BDNF levels in patients who have had diabetes for less than 5 years (r = 0.217, p = 0.178). Table 2. Correlation between glycemic control and BDNF levels in patients with different durations of T2DM. Diabetic duration <5 years Diabetic duration ≥5 years p r p r Glycemic control 0.178 0.217 0.007 0.423 Age 0.488 0.113 0.269 - 0.167 BMI 0.016 -0.37 0.591 -0.088 Gender 0.294 -0.170 0.195 -0.209 Working status 0.175 0.219 0.339 -0.155 Medication 0.095 0.267 0.748 0.052 Diabetic neuropathy duration 0.489 0.113 0.935 0.013 DNE score 0.401 0.136 0.018 0.372 NSS score 0.583 0.091 0.487 0.113 Table 3 shows that HbA1c level was not a predictor of BDNF in diabetic neuropathy patients with a duration of diabetes of less than 5 years, whereas in the group of patients with a duration of diabetes of at least 5 years, glycemic control was a significant predictor by 8.3% (B = 120.317, 95% CI for B: 6.103 – 234.532, p = 0.039). Table 3. Predictors of BDNF levels in multiple regression analysis, by model. Diabetic duration Model Variable B Coefficient p-value 95% CI <5 years 1 Adj. R 2 = 20.8% Glycemic control 45.210 .314 -44.593 – 135.013 BMI -32.867 .151 -78.266 – 12.532 Working status 453.731 .057 -15.228 – 922.690 Medication 230.489 .050 -.268 – 461.247 Final Adj. R 2 = 20.7% BMI -39.566 .073 -82.955 – 3.823 Working status 480.745 .043 15.040 – 946.449 Medication 231.568 .049 0.907 – 462.229 ≥5 years 1 Adj. R 2 = 9.8% Gender 105.483 0.730 -508.562 – 719.527 Glycemic control 97.163 0.103 -20.457 – 214.782 DNE score -70.387 0.116 -159.018 – 18.244 2 Adj. R 2 = 11.9% Glycemic control 96.590 0.100 -19.469 – 212.649 DNE score -66.377 0.120 -150.778 – 18.024 Final Adj. R 2 = 8.3% Glycemic control 120.317 0.039 6.103 – 234.532 Discussion This study investigated whether glycemic control can predict the BDNF levels in patients with diabetic neuropathy, based on diabetic duration. Patient characteristics showed that the majority of patients with diabetic neuropathy were elderly, female, and obese (>25 kg/m 2 ). The diabetic neuropathy can occur in T2DM patients aged less than 40, suffering from T2DM for less than 1 year, in all genders, whether obese or not. 17 However, diabetic neuropathy will increase in males, older adults, and longer diabetic duration. 17 The characteristics of patients with diabetic neuropathy with a duration of less than 5 years and at least 5 years did not differ significantly in terms of gender, BMI, diabetes medication, severity of diabetic neuropathy, duration of diabetic neuropathy, or BDNF levels. Although not significantly different, these various characteristics can influence BDNF levels in patients. The previous study has shown that women, obesity, diabetes medications such as metformin and glimepiride, and higher severity of diabetic neuropathy can alter BDNF levels. 18 The results showed that the characteristics of diabetic neuropathy patients with diabetes duration of less than 5 years and at least 5 years differed significantly in terms of age, occupation, and HbA1c levels. Diabetic neuropathy patients with a diabetes duration of at least 5 years had a higher mean age, were more unemployed, and had higher mean HbA1c levels compared to diabetic neuropathy patients with a diabetes duration of less than 5 years. The previous study has shown that age, activity, and HbA1c levels can influence a person’s BDNF levels. 18 – 20 This study did not show significant differences in BDNF serum levels in diabetic neuropathy patients with varying durations of diabetes. However, BDNF serum levels in diabetic neuropathy patients with longer durations of diabetes were lower than in patients with shorter durations. Bivariate test results showed that only HbA1c levels and DNE scores correlated with serum BDNF levels in patients with diabetes duration of at least 5 years. Multivariate analysis showed that HbA1c levels could predict BDNF serum levels. Previous studies have evaluated the correlation between HbA1c and BDNF, but these studies were conducted in diabetic patients without neuropathy. 7 , 15 Consistent with previous studies that showed higher HbA1c levels indicate higher BDNF levels. 7 This correlation was found in newly diagnosed diabetic patients and in patients who had diabetes for an average of 4 years. 7 In this study, patients with diabetic neuropathy who had suffered for at least 5 years had uncontrolled glycemic levels of 8.49%. When the HbA1c levels are high for a long time in patients, the complications of diabetic neuropathy can occur. 2 In this study, the patients had experienced diabetic neuropathy for 3.5 years, meaning that for the previous five years, they had experienced high HbA1c levels that triggered damage to their peripheral nerves. Inflammation and oxidative stress occur when blood sugar levels are uncontrolled, resulting in the accumulation of pro-inflammatory and oxidative substances in the peripheral nerves. 5 , 6 This triggers an increase in BDNF levels to improve glucose metabolism, overcome inflammation, and reduce oxidative stress. Furthermore, BDNF production functions to overcome peripheral nerve damage because BDNF plays a role in nerve cell plasticity and maintains nerve cell survival. 3 , 6 , 9 Conversely, no correlation was found between HbA1c and BDNF levels in patients with a duration of diabetes of less than 5 years. Although no correlation was found in this group, this study showed that HbA1c and BDNF levels were elevated. Elevated HbA1c and BDNF levels were also found in patients with diabetic neuropathy with a longer duration. The study shows that the worse a patient’s glycemic control, the higher their BDNF levels, and vice versa. This occurs because with higher hyperglycemia, more BDNF is produced to improve glucose metabolism. However, this study appears to indicate that the duration of hyperglycemia exposure in patients can influence the relationship between HbA1c and BDNF levels. In addition, the results of the DNE examination revealed peripheral nerve damage with a mean score of 9.53 out of a maximum of 16 in patients with diabetes for at least 5 years. The DNE examination revealed damage to large and small nerve fibers, which function in muscle movement, tendon reflexes, vibration sensation, pain sensation, touch sensation, and position sense. 21 Bivariate analysis results in patients with diabetes for at least 5 years showed a positive correlation between the severity of diabetic neuropathy and BDNF levels. This suggests that increased BDNF synthesis in response to nerve damage prevents further nerve damage that could worsen the patient’s neuropathy. The previous study has shown that patients with diabetic neuropathy who exhibit signs of hypersensitivity to pain have increased BDNF levels. 16 Increased pain levels in patients with neuropathy are a potential indicator of neuropathy severity. 16 This increase in pain generally occurs at night. This study found that patients also complained of leg pain, which worsened at night. The study also found that the bivariate test results indicate that BMI is correlated with BDNF levels, but the multivariate test results indicate that diabetes treatment and occupation are related to BDNF levels in patients with diabetic neuropathy with a duration of diabetes of less than 5 years. Obesity can worsen the patient’s insulin resistance. 22 , 23 Previous studies have shown that insulin resistance is associated with BDNF levels. 7 It appears that the presence of insulin resistance that occurs early due to obesity can explain the relationship between BMI and BDNF levels in patients. 7 , 11 Patients with T2DM will receive antidiabetic medicine to lower their blood sugar levels. 24 The patient’s HbA1c level indicates that the patient’s glycemic control is in the poor category and requires treatment. 24 The majority of patients receive antidiabetic medication, namely metformin and glimepiride. The previous study has shown that metformin and glimepiride increase BDNF levels, in addition to lowering blood sugar levels. 25 Furthermore, the study found that the majority of patients with diabetic neuropathy with a duration of less than 5 years were still active. Activity can affect BDNF levels. It appears that activity can stimulate BDNF production. This study has a limitation. There was a potential bias in determining diabetes duration because patients were newly diagnosed with T2DM, often after they experienced complications. The study was carried out with a small number of participants; thus, a study with a larger number of participants is needed in future studies. This study also only used HbA1c as a parameter of glycemic control. The study implies that HbA1c can be used as a predictor of BDNF levels, especially in patients who have diabetic neuropathy with a diabetic duration of at least 5 years. The health providers should monitor the HbA1c level regularly in patients with diabetic neuropathy. The future study can evaluate the other glycemic control parameters. Reporting guidelines This study was reported in accordance with the STROBE guidelines. Data availability statements Underlying data Zenodo: Dataset corresponding to the scientific paper “Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study” https://doi.org/10.5281/zenodo.17540079 26 The project contains the following underlying data: Data set diabetic neuropathy study.xlsx -raw data of participants’ characteristics, BDNF, and HbA1c. Data are available under the terms of the Creative Commons Zero “No right reserved” data waiver (CCO v1.0 Universal) Acknowledgements We thank all patients who have participated in this study. References 1. 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Davarpanah M, Shokri-Mashhadi N, Ziaei R, et al. : A systematic review and meta-analysis of association between brain-derived neurotrophic factor and type 2 diabetes and glycemic profile. Sci. Rep. 2021; 11 (1): 13773. PubMed Abstract | Publisher Full Text | Free Full Text 15. Moosaie F, Mohammadi S, Saghazadeh A, et al. : Brain-derived neurotrophic factor in diabetes mellitus: A systematic review and meta-analysis. PLoS One. 2023; 18 (2): e0268816. PubMed Abstract | Publisher Full Text | Free Full Text 16. Huang X, Xie Z, Wang C, et al. : Elevated peripheral brain-derived neurotrophic factor level associated with decreasing insulin secretion may forecast memory dysfunction in patients with long-term type 2 diabetes. Front. Physiol. 2022; 12 : 686838. PubMed Abstract | Publisher Full Text | Free Full Text 17. Pfannkuche A, Alhajjar A, Ming A, et al. : Prevalence and risk factors of diabetic peripheral neuropathy in a diabetics cohort: Register initiative “diabetes and nerves.”. Endocrine and Metabolic Science. 2020; 1 (1-2): 100053. Publisher Full Text 18. Sharma E, Behl T, Mehta V, et al. : Exploring the various aspects of brain-derived neurotropic factor (BDNF) in diabetes mellitus. CNS & neurological Disorders-Drug targets (Formerly current drug targets-CNS & neurological Disorders). 2021; 20 (1): 22–33. PubMed Abstract | Publisher Full Text 19. Boyuk B, Degirmencioglu S, Atalay H, et al. : Relationship between levels of brain-derived neurotrophic factor and metabolic parameters in patients with type 2 diabetes mellitus. J. Diabetes Res. 2014; 2014 (1): 978143. 20. Liu T, Canon MD, Shen L, et al. : The influence of the BDNF Val66Met polymorphism on the association of regular physical activity with cognition among individuals with diabetes. Biol. Res. Nurs. 2021; 23 (3): 318–330. PubMed Abstract | Publisher Full Text 21. Yang Z, Chen R, Zhang Y, et al. : Scoring systems to screen for diabetic peripheral neuropathy. Cochrane Database Syst. Rev. 2018; 2018 (7): CD010974. Publisher Full Text 22. Kesavadev J, Jawad F, Deeb A, et al. : Pathophysiology of type 2 diabetes. The Diabetes Textbook: Clinical Principles, Patient Management and Public Health Issues. Springer; 2023; 127–142. Publisher Full Text 23. Galicia-Garcia U, Benito-Vicente A, Jebari S, et al. : Pathophysiology of type 2 diabetes mellitus. Int. J. Mol. Sci. 2020; 21 (17): 6275. PubMed Abstract | Publisher Full Text | Free Full Text 24. Ceriello A, Colagiuri S: IDF global clinical practice recommendations for managing type 2 diabetes–2025. Diabetes Res. Clin. Pract. 2025; 222 : 112152. PubMed Abstract | Publisher Full Text 25. Anirudhan A, Ahmad SF, Bin ET, et al. : Comparative Efficacy of Metformin and Glimepiride in Modulating Pharmacological Network to Increase BDNF Levels and Benefit Type 2 Diabetes-Related Cognitive Impairment. Biomedicines. 2023; 11 (11): 2939. PubMed Abstract | Publisher Full Text | Free Full Text 26. Suryani M: Data set of study. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 26 Nov 2025 ADD YOUR COMMENT Comment Author details Author details 1 Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Central Java, Indonesia Maria Suryani Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Validation, Writing – Original Draft Preparation, Writing – Review & Editing Maria Agustina Ermi Tri Sulistiyowati Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Medina Sianturi Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This study was funded by the Kemendiktisaintek, Indonesia grant 0419/C3/DT.05.00/2025 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 18 Apr 2026, 14:1312 https://doi.org/10.12688/f1000research.172834.2 version 1 Published: 26 Nov 2025, 14:1312 https://doi.org/10.12688/f1000research.172834.1 Copyright © 2025 Suryani M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Suryani M, Ermi Tri Sulistiyowati MA and Sianturi M. Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.12688/f1000research.172834.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 26 Nov 2025 Views 0 Cite How to cite this report: Siegel E. Reviewer Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.190594.r439043 ) The direct URL for this report is: https://f1000research.com/articles/14-1312/v1#referee-response-439043 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 30 Dec 2025 Eric Siegel , University of Arkansas for Medical Sciences, Little Rock, AR, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.190594.r439043 The authors assert that the correlation between glycemic control and BDNF levels is still unclear. They also hypothesize that the duration of diabetes likely influences the relationship between glycemic control and serum BDNF levels, but they did not speculate how. ... Continue reading READ ALL The authors assert that the correlation between glycemic control and BDNF levels is still unclear. They also hypothesize that the duration of diabetes likely influences the relationship between glycemic control and serum BDNF levels, but they did not speculate how. Therefore, the authors seek to investigate whether glycemic control can predict BDNF levels in patients with diabetic neuropathy, based on diabetic duration. The Diabetes Mellitus durations (DMDs) are dichotomized as less than 5 years versus 5 years or longer, and patients are divided into two groups according to that dichotomization. The study is a cross-sectional study of eligible patients seen in a single health-care facility during a one-month time window. The shortness of the time window is a positive thing. HbA1c levels and BDNF levels were obtained from blood samples drawn during the time window. When they had their blood drawn, patients were asked how many years ago they were diagnosed with their diabetes. I suspect that the accuracy of the patients’ answers was good when the diabetes diagnosis was recent, but poor when the diabetes diagnosis was more than a few years back. This probably explains why the authors chose to divide patients into 2 groups based on a 5-year cutpoint for DMDs. I probably would have done the same. My concern is with how the authors did their analysis. It appears they did subgroup analyses of HbA1c versus BDNF level subgrouped by DMD group, using spearman correlation within each subgroup. This is the wrong approach. A much better approach would have been to perform regression analysis on all 80 patients using a regression model that has BDNF level as the dependent or “outcome” variable, and has DMD Group and HbA1c Level as the two independent or “predictor” variables. This regression model is an Analysis of Covariance (ANCOVA) model with DMD Group as the categorical predictor and HbA1c Level as the continuous predictor. One assumption of the ANCOVA model is that the relationship between BDNF and HbA1c is equal in both DMD groups. The authors will need to test the validity of that assumption. To do so, the authors will need to expand the ANCOVA model by adding to it the “DMD-x-HbA1c Interaction” as a third predictor variable. And then they will need to test the statistical significance of the DMD-x-HbA1c Interaction. If the interaction is statistically significant at p<0.05, then that result will provide strong support for the authors’ hypothesis that “the duration of diabetes likely influences the relationship between glycemic control and serum BDNF levels”. If the interaction is non-significant, say, with P>0.10, then that result will tend to falsify the authors’ hypothesis, and falsification of hypotheses are a good thing. If the interaction is “almost significant”, i.e., 0.05≤P≤0.10, then that is the worst-case scenario, but the authors still could spin it as weakly supportive of the authors’ hypothesis, but requiring additional studies with larger sample sizes to confirm. Table 2: I do not see the value of doing univariable spearman correlations of BDNF with Table 2’s factors subgrouped by DMD. I think it would have more value to remove the subgrouping by DMD and re-do the spearman correlations on all 80 patients considered as one group. Furthermore, I see strong risk of having a falsely significant result somewhere in Table 2. If a test’s significance level is P<0.05, then the test’s false-positive rate is alpha=0.05 by definition. With 18 tests in Table 2, each at alpha=0.05 per test, the expected number of false-positive results in Table 2 is 18*0.05=0.90 or almost 1. Which result in Table 2 is the false-positive result? There is no way to tell. The two best ways to avoid this quandary are (1) reduce the number of tests in Table 2, and/or (2) reduce each test’s significance level down to P<0.01. In Table 3, there is no value in building multivariable regression models subgrouped by DMD group. It would be much, much better to build multivariable regression models on all 80 patients in the study, and to include DMD Group as a predictor variable in those multivariable models. If the authors are worried that DMD group affects the size of the B coefficient for BDNF versus Glycemic control, then the statistically valid way to test for that possibility is to include the DMD-x-HbA1c interaction term in the multivariable models so that one can test its significance. Finally, the conclusions are framed in terms of predicting BDNF levels in patients with diabetic neuropathy. If the goal is to predict whether glycemic control in an individual patient can predict that patient’s BDNF levels, then spearman correlations, t-tests, and multivariable regressions are wholly inadequate to the task. To investigate patient-level prediction ability, one would need to explore ROC curves and more advanced methods. Other Concerns. The paper needs a STROBE flow diagram that shows (1) how many patients were initially evaluated, (2a) how many were excluded for having a stroke, (2b) how many were excluded for having an active ulcer, (2c) how many were excluded for a fracture in the foot, and (3) how many were included in the final analysis. If the STROBE flow diagram were also to show how many patients fell into the DMD <5-year group vs how many patients fell into the DMD ≥5-year group, that would be a plus. Table one indicates that the two DMD groups had exactly 40 patients per group, i.e., the two DMD groups were equal in size. If DMD was simply dichotomized at <5 vs ≥5 years, then we would expect the two DMD groups to be unequal in size because sample size would be a random variable. Did the authors do anything to control how many patients fell into each DMD group? Such as, impose a maximum on the number in one group? The authors say that Kolmogorov-Smirnov was used to examine the distribution of numerical data. Examine it for what? For goodness of adherence to the Normal (or other parametric) Distribution? Or for whether the two DMD groups had different distributions? The Kolmogorov-Smirnov test can be used to examine both. Please add some detail. The authors say that Kolmogorov-Smirnov was used to examine the distribution of numerical data. But in Table 1, the footnotes say it was used to compare Medication usages between DMD groups. Medication usage is categorical, not numeric. In Table 1, the footnotes say that the Paired T test was used to compare HbA1c levels between DMD groups. The Paired T test is the wrong test for this. In Table 2, the authors put the column of P-values first and the column of correlation coefficients second. This is backwards. This puts significance first and science second. The p-value by itself has no intrinsic meaning. All of its meaning comes from the correlation coefficient to which it is attached. The correlation coefficient is the thing that has intrinsic meaning. It is where the science lies. The size of the correlation coefficient is what we evaluate when we interpret the result scientifically. So please, next time, put science ahead of significance. Put the correlations first and the p-values second. Regarding Table 3, I see several different types of “Adj. R 2 ” results including two types with the word “Final” in them. That implies that the authors did some variable selection after they entered variables “with p-values less than 0.25 in the Spearman analysis” into the models, but the authors do not say what else they did after they entered those variables. More detail is needed. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Applied Biostatistics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Siegel E. Reviewer Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.190594.r439043 ) The direct URL for this report is: https://f1000research.com/articles/14-1312/v1#referee-response-439043 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 18 Apr 2026 Maria Suryani , Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia 18 Apr 2026 Author Response The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also ... Continue reading The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also to data analysis, research result tables, and discussion according to new result of the study. We have added STROBE diagram. The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also to data analysis, research result tables, and discussion according to new result of the study. We have added STROBE diagram. Competing Interests: We have no competing interest. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 18 Apr 2026 Maria Suryani , Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia 18 Apr 2026 Author Response The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also ... Continue reading The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also to data analysis, research result tables, and discussion according to new result of the study. We have added STROBE diagram. The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also to data analysis, research result tables, and discussion according to new result of the study. We have added STROBE diagram. Competing Interests: We have no competing interest. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Muntamah U. Reviewer Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.190594.r439051 ) The direct URL for this report is: https://f1000research.com/articles/14-1312/v1#referee-response-439051 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 27 Dec 2025 Ummu Muntamah , Universitas Ngudi Waluyo, Ungaran, Central Java, Indonesia Approved VIEWS 0 https://doi.org/10.5256/f1000research.190594.r439051 The manuscript is generally well written and clearly presented. However, the use of the term “predicts” may overstate the findings given the cross-sectional design. In addition, although relevant literature is cited, the manuscript would benefit from incorporating ... Continue reading READ ALL The manuscript is generally well written and clearly presented. However, the use of the term “predicts” may overstate the findings given the cross-sectional design. In addition, although relevant literature is cited, the manuscript would benefit from incorporating more recent studies and systematic reviews to strengthen the state of the art, particularly regarding the dual role of BDNF in diabetic neuropathy. Several studies have shown decreased BDNF in chronic diabetes. These contradictions have not been critically addressed (e.g., differences in disease stage, age, or comorbidities). The discussion would have been stronger if the authors had: distinguished the initial compensation phase versus long-term BDNF depletion, associated a duration of ≥5 years with the possibility of neurotrophic exhaustion. Need to be strengthened with literature from the last 5 years. Most of the references appear to be non-recent literature (many studies predate 2020). There are minimal references to: recent systematic reviews or meta-analyses, large studies related to BDNF as a biomarker for diabetic neuropathy, literature from the last 5 years on neurotrophins and diabetes complications. There is no discussion of recent literature on: the role of BDNF as a compensatory marker versus a marker of nerve damage, longitudinal dynamics of BDNF in chronic diabetes. This makes the state of the art section feel insufficiently robust. The design is appropriate for association analysis, but not ideal for predictive or mechanistic claims. Inclusion and exclusion criteria were clearly explained. All participants had diabetic neuropathy, so the population was relatively homogeneous. However: The sampling method (consecutive, convenience, random) was not explained.There was no sample size calculation, so statistical power cannot be determined. The study design is appropriate for exploring associations between glycemic control and BDNF levels in diabetic neuropathy. The laboratory methods and statistical analyses are generally sound. However, the cross-sectional nature of the study limits causal or predictive interpretations. The manuscript would benefit from clearer justification of the diabetes duration cut-off, reporting of regression assumptions, and consideration of biological confounders affecting BDNF levels. In general, the methods and analyses are reported clearly enough to understand what the researchers did, but not sufficiently to allow for exact replication by other researchers. Conceptual replication is still possible, but some important technical details are missing or not explicitly reported. The methods and statistical analyses are described clearly at a general level; however, several methodological details required for full replication are missing. Additional information on sampling procedures, blood collection protocols, ELISA assay handling, and regression diagnostics would strengthen the reproducibility of the study. The statistical analyses are generally appropriate and adequately performed. However, the interpretation occasionally overstates the findings, particularly through the use of predictive language in a cross-sectional design. Reporting of regression diagnostics and a more cautious interpretation of effect size would strengthen the statistical rigor of the manuscript. The manuscript does not provide access to the underlying individual-level data supporting the reported results. While summary statistics are presented, the absence of raw data, a data availability statement, and statistical code limits full reproducibility. The authors are encouraged to deposit anonymized source data in a public repository or clarify conditions for data access. the conclusions are generally supported by the results presented. However, some statements overstate the findings, particularly regarding predictive and mechanistic implications. Reframing the conclusions to emphasize associations rather than causality would improve alignment with the study design and results. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: non-communicable disease, innovation of health promotion I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Muntamah U. Reviewer Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.190594.r439051 ) The direct URL for this report is: https://f1000research.com/articles/14-1312/v1#referee-response-439051 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 18 Apr 2026 Maria Suryani , Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia 18 Apr 2026 Author Response Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical ... Continue reading Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical analysis. We have used the updated references according to our topic. Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical analysis. We have used the updated references according to our topic. Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 18 Apr 2026 Maria Suryani , Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia 18 Apr 2026 Author Response Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical ... Continue reading Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical analysis. We have used the updated references according to our topic. Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical analysis. We have used the updated references according to our topic. Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 26 Nov 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 18 Apr 26 read Version 1 26 Nov 25 read read Ummu Muntamah , Universitas Ngudi Waluyo, Ungaran, Indonesia Eric Siegel , University of Arkansas for Medical Sciences, Little Rock, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Siegel E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 Apr 2026 | for Version 2 Eric Siegel , University of Arkansas for Medical Sciences, Little Rock, AR, USA 0 Views copyright © 2026 Siegel E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article is much improved. Results from the ANCOVA regression analysis are provided in Table 3. However, Table 3 is not complete. It is missing things. Table 3 does contain the Mean Squares, F-statistics, p-values, and eta-squares. Those are good. Please keep them. Missing from Table 3 are the Sums of Squares and the Degrees of Freedom (DF) for each row in the table. Please add those things to Table 3. The SPSS output should have each row's Sum of Squares and DF. Each row's Sum of Squares provides the numerator in that row's calculation of eta-square. Also missing from Table 3 is the \"Total\" row that contains the Total Sum of Squares and the Total DF. Please add that row of Totals to Table 3. The SPSS output should have the Total Sum of Squares and Total DF. The Total Sum of Squares provides the denominator in the calculations of the eta-squares. Furthermore, the authors misinterpret the meaning of the eta-squares. The eta-squares do not measure accuracy. They measure how much of the data's variation (the Total Sum of Squares) is \"explained\" by the factor in the model. In that sense, they are exactly like R-squares and Partial R-squares from regression analysis. For example, the eta-square of 0.099 for corrected model does not mean 9.9% accuracy at predicting BDNF with HbA1c +DM Duration +Interaction together. It means that, together, HbA1c +DM Duration +Interaction \"explain\" 9.9% of the variability in BDNF. \"Explain\" is not the same as predict. Similarly, the eta-square of 0.083 for HbA1c does not mean that HbA1c can predict BDNF at 8.3%. It means that the HbA1c component of the corrected model \"explains\" 8.3% of the variability in BDNF. And that's when the corrected model has the other components as well as HbA1c. And again, \"explain\" is not the same as predict. In Summary, (1) Table 3 is missing important pieces of information, and (2) the eta-squares of Table 3 are wrongly interpreted as predictive instead of explanatory. Competing Interests No competing interests were disclosed. Reviewer Expertise Applied Biostatistics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 29 Apr 2026 Maria Suryani, Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia Dear Eric Siegel Thank you for your suggestion. The revised version of the manuscript incorporates the corrections suggested by the reviewer. Changes have been made to abstract section especially in result and conclusion. Changes have been made also to research result in table 3, and discussion according to new interpretation of result of the study in table 3. Thank you Best regards Maria Suryani (author) View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Siegel E. Peer Review Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.197388.r476298) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1312/v2#referee-response-476298 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Siegel E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 30 Dec 2025 | for Version 1 Eric Siegel , University of Arkansas for Medical Sciences, Little Rock, AR, USA 0 Views copyright © 2026 Siegel E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors assert that the correlation between glycemic control and BDNF levels is still unclear. They also hypothesize that the duration of diabetes likely influences the relationship between glycemic control and serum BDNF levels, but they did not speculate how. Therefore, the authors seek to investigate whether glycemic control can predict BDNF levels in patients with diabetic neuropathy, based on diabetic duration. The Diabetes Mellitus durations (DMDs) are dichotomized as less than 5 years versus 5 years or longer, and patients are divided into two groups according to that dichotomization. The study is a cross-sectional study of eligible patients seen in a single health-care facility during a one-month time window. The shortness of the time window is a positive thing. HbA1c levels and BDNF levels were obtained from blood samples drawn during the time window. When they had their blood drawn, patients were asked how many years ago they were diagnosed with their diabetes. I suspect that the accuracy of the patients’ answers was good when the diabetes diagnosis was recent, but poor when the diabetes diagnosis was more than a few years back. This probably explains why the authors chose to divide patients into 2 groups based on a 5-year cutpoint for DMDs. I probably would have done the same. My concern is with how the authors did their analysis. It appears they did subgroup analyses of HbA1c versus BDNF level subgrouped by DMD group, using spearman correlation within each subgroup. This is the wrong approach. A much better approach would have been to perform regression analysis on all 80 patients using a regression model that has BDNF level as the dependent or “outcome” variable, and has DMD Group and HbA1c Level as the two independent or “predictor” variables. This regression model is an Analysis of Covariance (ANCOVA) model with DMD Group as the categorical predictor and HbA1c Level as the continuous predictor. One assumption of the ANCOVA model is that the relationship between BDNF and HbA1c is equal in both DMD groups. The authors will need to test the validity of that assumption. To do so, the authors will need to expand the ANCOVA model by adding to it the “DMD-x-HbA1c Interaction” as a third predictor variable. And then they will need to test the statistical significance of the DMD-x-HbA1c Interaction. If the interaction is statistically significant at p<0.05, then that result will provide strong support for the authors’ hypothesis that “the duration of diabetes likely influences the relationship between glycemic control and serum BDNF levels”. If the interaction is non-significant, say, with P>0.10, then that result will tend to falsify the authors’ hypothesis, and falsification of hypotheses are a good thing. If the interaction is “almost significant”, i.e., 0.05≤P≤0.10, then that is the worst-case scenario, but the authors still could spin it as weakly supportive of the authors’ hypothesis, but requiring additional studies with larger sample sizes to confirm. Table 2: I do not see the value of doing univariable spearman correlations of BDNF with Table 2’s factors subgrouped by DMD. I think it would have more value to remove the subgrouping by DMD and re-do the spearman correlations on all 80 patients considered as one group. Furthermore, I see strong risk of having a falsely significant result somewhere in Table 2. If a test’s significance level is P<0.05, then the test’s false-positive rate is alpha=0.05 by definition. With 18 tests in Table 2, each at alpha=0.05 per test, the expected number of false-positive results in Table 2 is 18*0.05=0.90 or almost 1. Which result in Table 2 is the false-positive result? There is no way to tell. The two best ways to avoid this quandary are (1) reduce the number of tests in Table 2, and/or (2) reduce each test’s significance level down to P<0.01. In Table 3, there is no value in building multivariable regression models subgrouped by DMD group. It would be much, much better to build multivariable regression models on all 80 patients in the study, and to include DMD Group as a predictor variable in those multivariable models. If the authors are worried that DMD group affects the size of the B coefficient for BDNF versus Glycemic control, then the statistically valid way to test for that possibility is to include the DMD-x-HbA1c interaction term in the multivariable models so that one can test its significance. Finally, the conclusions are framed in terms of predicting BDNF levels in patients with diabetic neuropathy. If the goal is to predict whether glycemic control in an individual patient can predict that patient’s BDNF levels, then spearman correlations, t-tests, and multivariable regressions are wholly inadequate to the task. To investigate patient-level prediction ability, one would need to explore ROC curves and more advanced methods. Other Concerns. The paper needs a STROBE flow diagram that shows (1) how many patients were initially evaluated, (2a) how many were excluded for having a stroke, (2b) how many were excluded for having an active ulcer, (2c) how many were excluded for a fracture in the foot, and (3) how many were included in the final analysis. If the STROBE flow diagram were also to show how many patients fell into the DMD <5-year group vs how many patients fell into the DMD ≥5-year group, that would be a plus. Table one indicates that the two DMD groups had exactly 40 patients per group, i.e., the two DMD groups were equal in size. If DMD was simply dichotomized at <5 vs ≥5 years, then we would expect the two DMD groups to be unequal in size because sample size would be a random variable. Did the authors do anything to control how many patients fell into each DMD group? Such as, impose a maximum on the number in one group? The authors say that Kolmogorov-Smirnov was used to examine the distribution of numerical data. Examine it for what? For goodness of adherence to the Normal (or other parametric) Distribution? Or for whether the two DMD groups had different distributions? The Kolmogorov-Smirnov test can be used to examine both. Please add some detail. The authors say that Kolmogorov-Smirnov was used to examine the distribution of numerical data. But in Table 1, the footnotes say it was used to compare Medication usages between DMD groups. Medication usage is categorical, not numeric. In Table 1, the footnotes say that the Paired T test was used to compare HbA1c levels between DMD groups. The Paired T test is the wrong test for this. In Table 2, the authors put the column of P-values first and the column of correlation coefficients second. This is backwards. This puts significance first and science second. The p-value by itself has no intrinsic meaning. All of its meaning comes from the correlation coefficient to which it is attached. The correlation coefficient is the thing that has intrinsic meaning. It is where the science lies. The size of the correlation coefficient is what we evaluate when we interpret the result scientifically. So please, next time, put science ahead of significance. Put the correlations first and the p-values second. Regarding Table 3, I see several different types of “Adj. R 2 ” results including two types with the word “Final” in them. That implies that the authors did some variable selection after they entered variables “with p-values less than 0.25 in the Spearman analysis” into the models, but the authors do not say what else they did after they entered those variables. More detail is needed. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Applied Biostatistics I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 18 Apr 2026 Maria Suryani, Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia The revised version of the manuscript incorporates the corrections suggested by the reviewers. Changes have been made to abstract section especially in method and result. Changes have been made also to data analysis, research result tables, and discussion according to new result of the study. We have added STROBE diagram. View more View less Competing Interests We have no competing interest. reply Respond Report a concern Siegel E. Peer Review Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.190594.r439043) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1312/v1#referee-response-439043 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Muntamah U. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 Dec 2025 | for Version 1 Ummu Muntamah , Universitas Ngudi Waluyo, Ungaran, Central Java, Indonesia 0 Views copyright © 2025 Muntamah U. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript is generally well written and clearly presented. However, the use of the term “predicts” may overstate the findings given the cross-sectional design. In addition, although relevant literature is cited, the manuscript would benefit from incorporating more recent studies and systematic reviews to strengthen the state of the art, particularly regarding the dual role of BDNF in diabetic neuropathy. Several studies have shown decreased BDNF in chronic diabetes. These contradictions have not been critically addressed (e.g., differences in disease stage, age, or comorbidities). The discussion would have been stronger if the authors had: distinguished the initial compensation phase versus long-term BDNF depletion, associated a duration of ≥5 years with the possibility of neurotrophic exhaustion. Need to be strengthened with literature from the last 5 years. Most of the references appear to be non-recent literature (many studies predate 2020). There are minimal references to: recent systematic reviews or meta-analyses, large studies related to BDNF as a biomarker for diabetic neuropathy, literature from the last 5 years on neurotrophins and diabetes complications. There is no discussion of recent literature on: the role of BDNF as a compensatory marker versus a marker of nerve damage, longitudinal dynamics of BDNF in chronic diabetes. This makes the state of the art section feel insufficiently robust. The design is appropriate for association analysis, but not ideal for predictive or mechanistic claims. Inclusion and exclusion criteria were clearly explained. All participants had diabetic neuropathy, so the population was relatively homogeneous. However: The sampling method (consecutive, convenience, random) was not explained.There was no sample size calculation, so statistical power cannot be determined. The study design is appropriate for exploring associations between glycemic control and BDNF levels in diabetic neuropathy. The laboratory methods and statistical analyses are generally sound. However, the cross-sectional nature of the study limits causal or predictive interpretations. The manuscript would benefit from clearer justification of the diabetes duration cut-off, reporting of regression assumptions, and consideration of biological confounders affecting BDNF levels. In general, the methods and analyses are reported clearly enough to understand what the researchers did, but not sufficiently to allow for exact replication by other researchers. Conceptual replication is still possible, but some important technical details are missing or not explicitly reported. The methods and statistical analyses are described clearly at a general level; however, several methodological details required for full replication are missing. Additional information on sampling procedures, blood collection protocols, ELISA assay handling, and regression diagnostics would strengthen the reproducibility of the study. The statistical analyses are generally appropriate and adequately performed. However, the interpretation occasionally overstates the findings, particularly through the use of predictive language in a cross-sectional design. Reporting of regression diagnostics and a more cautious interpretation of effect size would strengthen the statistical rigor of the manuscript. The manuscript does not provide access to the underlying individual-level data supporting the reported results. While summary statistics are presented, the absence of raw data, a data availability statement, and statistical code limits full reproducibility. The authors are encouraged to deposit anonymized source data in a public repository or clarify conditions for data access. the conclusions are generally supported by the results presented. However, some statements overstate the findings, particularly regarding predictive and mechanistic implications. Reframing the conclusions to emphasize associations rather than causality would improve alignment with the study design and results. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise non-communicable disease, innovation of health promotion I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 18 Apr 2026 Maria Suryani, Nursing, Sekolah Tinggi Ilmu Kesehatan Elisabeth Semarang, Semarang, Indonesia Thank you for your suggestion to our article. The revised version of the manuscript incorporates the corrections suggested by the reviewer. We still use predictor term according to new statistical analysis. We have used the updated references according to our topic. View more View less Competing Interests None reply Respond Report a concern Muntamah U. Peer Review Report For: Glycemic control predicts the Brain-derived neurotrophic factor levels in diabetic neuropathy patients with a diabetic duration of at least 5 years: A cross-sectional study [version 1; peer review: 1 approved, 1 not approved] . F1000Research 2025, 14 :1312 ( https://doi.org/10.5256/f1000research.190594.r439051) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-1312/v1#referee-response-439051 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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