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. 2026 Jun 9;14:1312. Originally published 2025 Nov 26. [Version 4] doi: 10.12688/f1000research.172834.4

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

Maria Suryani 1,a, Maria Agustina Ermi Tri Sulistiyowati 1, Medina Sianturi 1
PMCID: PMC13596705  PMID: 42779986

Version Changes

Revised. Amendments from Version 3

This revised version of the manuscript incorporates corrections suggested by reviewers to the abstract, results, and discussion sections.

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 8 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 ANCOVA tests.

Results

Together, HbA1c levels, diabetic duration, and interactions between diabetic duration and HbA1c explained 9.9% of variability in BDNF levels (p=0.046). However, HbA1c levels explained 8.1% of the variability in BDNF levels (p = 0.011), with only minor contributions from diabetic duration and interaction them.

Conclusions

The HbA1c levels significantly explained the variability in BDNF levels in patients with diabetic neuropathy regardless of diabetic duration.

Keywords: BDNF; HbA1c; Diabetic duration; Diabetes; Diabetic neuropathy

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. Figure 1 shows that outlines the overarching process to be followed in the conduct of this study; from identifying relevant participant at title and abstract stage to data collected and analysis.

Figure 1. STROBE flow diagram.

Figure 1.

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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. Mann-Whitney U test, Chi square, and independent t-test were used to compare the characteristic of participant between patient who have diabetic duration <5 years and ≥5 years. Spearman rank test was used to examine the correlation between glycemic control and BDNF level. The regression analysis ANCOVA, by model was used to examine the predictors of BDNF levels (Glycemic control, diabetic duration and interaction between glycemic control and diabetic duration). 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, working status, medication, and HbA1c levels between patients who have had diabetes for less than 5 years and those who have had it for at least 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 b
 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
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a

Mann-Whitney U test.

b

Chi-square test.

c

Independent T test.

Table 2 shows that there is a significant correlation between glycemic control and BDNF (r = 0.312, p = 0.005).

Table 2. Correlation between HbA1c and BDNF level (n = 80).

BDNF
r p
HbA1c 0.312 0.005
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p: Spearman test.

Table 3 shows that, together, HbA1c level, diabetic duration, and the interactions between them explained ~10% of variability in BDNF levels (overall η 2 = 0.099; p = 0.046) when analyzed by GLM regression. However, HbA1c levels provided most of the regression model’s explanatory power (partial η 2 = 0.081; p = 0.011), with only minor contributions coming from diabetic duration (partial η 2 = 0.003; p = 0.617) and the duration *HbA1c interaction (partial η 2 = 0.004, p = 0.571).

Table 3. Predictors of BDNF levels in multiple regression analysis, by model.

Sum of Square df Mean square F p η 2
Corrected Model 4479133.84 3 1493044.614 2.790 0.046 0.099
HbA1c 3670941.455 1 3670941.455 6.860 0.011 0.081
Diabetic duration 135232.373 1 135232.373 0.253 0.617 0.003
Diabetic duration * HbA1c 173435.384 1 173435.384 0.324 0.571 0.004
Error 40670978.107 76 535144.449
Corrected Total 45150111.950 79
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R Square = 0.099, R Adj = 0.064.

Analyses were conducted by ANCOVA and were adjusted for other variables in the table.

Discussion

The study investigated whether glycemic control, as measured by HbA1c parameters, can predict BDNF levels in diabetic neuropathy patients based on the diabetic duration. Diabetic neuropathy will increase in patients with longer diabetic duration. 17 The study found that together, HbA1c levels, diabetic duration, and interactions between diabetic duration and HbA1c explained 9.9% of variability in BDNF levels. However, only HbA1c levels explained the variability in BDNF levels at 8.1%, with only minor contributions from diabetic duration and interaction them. It means that HbA1c level can explain BDNF level regardless of diabetic duration. In line with previous studies that found that HbA1c levels can predict BDNF levels. 18– 20

It seems that when hyperglycemia occurs in diabetic neuropathy patients, there will be damage to nerve cells due to the accumulation of oxidative and pro-inflammatory substances. 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, both large and small nerve fibers. 5, 6 Increasing BDNF synthesis in response to nerve damage prevents further nerve damage that could worsen the patient’s neuropathy. BDNF functions in preventing nerve cell damage, maintaining nerve cell survival, and playing a role in nerve cell plasticity. 3, 6, 9 The DNE examination revealed the condition of large and small nerve fibers, which function in muscle movement, tendon reflexes, vibration sensation, pain sensation, touch sensation, and position sense. 21 This study found that the results of DNE examination of patients showed the presence of nerve cell damage in all patients with various lengths of diabetic duration. This whole process demonstrates how HbA1c levels can explain the variability in BDNF levels in diabetic neuropathy patients.

The study divided the diabetic durations into less than 5 years and at least 5 years. There are significant differences in characteristics such as age, working status, medication, and HbA1c levels in patients with different diabetic durations. Patients with diabetes who were less than 5 years old had a lower mean age, were more unemployed, and were taking a single anti-diabetic medicine in the form of metformin or glimepiride compared to diabetic neuropathy patients with a diabetic duration of at least 5 years. It seems that differences in these ages, activity, and anti-diabetic medicine make up for differences in HbA1c levels. 22– 25 In this study, patients with diabetic neuropathy who had suffered for at least 5 years had uncontrolled glycemic levels of 8.49%, while those who had suffered for less than 5 years had glycemic control of 9.55%. This suggests that diabetic duration is related to HbA1c levels or that there is an interaction of diabetic duration with HbA1c.

The finding of a patient’s HbA1c levels associated with diabetic duration indirectly allows for the prediction of diabetic duration, and the interaction between diabetic duration and HbA1c can also predict BDNF levels. This study found that, together with diabetic duration, HbA1c levels, and the interaction between diabetic duration and HbA1c levels can significantly explain the variation of BDNF levels, even though only 9.9%. However, of all these predictors, only HbA1c can significantly explain the variability in BDNF levels. The study also showed that there are still other predictors not described in this study that may be able to explain the variability in BDNF levels.

This study has a limitation. There was a potential bias in determining diabetic duration because patients were newly diagnosed with T2DM, often after they experienced complications. Thus, we divided diabetic duration into two categories: less than 5 years and at least 5 years. 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.

The study implies that HbA1c can explain the variability in BDNF level, even though only 8.1%, especially in patients who have diabetic neuropathy regardless of diabetic duration. The health providers should monitor the HbA1c level regularly in patients with diabetic neuropathy. The future study can evaluate the other predictors of BDNF level with a large number of participants.

Acknowledgements

We thank all patients who have participated in this study.

Funding Statement

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.

[version 4; peer review: 2 approved]

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)

Reporting guidelines

This study was reported in accordance with the STROBE guidelines.

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F1000Res. 2026 Jun 12. doi: 10.5256/f1000research.203067.r492257

Reviewer response for version 4

Eric Siegel 1

The Results section of the Abstract contains one small, but important spelling error. The phrase "interaction them" should be corrected to read "interaction term".

Is the work clearly and accurately presented and does it cite the current literature?

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

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Applied Biostatistics

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.

F1000Res. 2026 May 28. doi: 10.5256/f1000research.200446.r488296

Reviewer response for version 3

Eric Siegel 1

Please make the following change to the Results part of the Abstract:

Old paragraph: 

Together, HbA1c levels, diabetic duration, and interactions between diabetic duration and HbA1c explained 9.9% of variability in BDNF levels and 6.4% after adjusted (p=0.046). However, only HbA1c levels explained 8.3% of the variability in BDNF levels (p = 0.011).

Please change to:

New paragraph:

Together, HbA1c levels, diabetic duration, and the interaction between them explained 9.9% of variability in BDNF levels (p=0.046). However, HbA1c levels  explained 8.3% of the variability in BDNF levels (p = 0.011), with only minor contributions from diabetic duration and the interaction term.  

In Table 3, please make the following two changes.

(1) Change the eta-square for HbA1c from 0.083 to 0.081. 

(2) Delete the value of the Mean Square for Corrected Total.

In the Results part that discusses Table 3, please change the paragraph as follows:

Old paragraph:

Table 3 shows that together HbA1c level, diabetic duration, and interactions between diabetic duration and HbA1c explained 9.9% of variability in BDNF levels and 6.4% after adjusted (p = 0.046). However, only HbA1c levels explained 8.3% of the variability in BDNF levels.

New paragraph:

Table 3 shows that, together, HbA1c level, diabetic duration, and the interaction between them explained ~10% of variability in BDNF levels (overall η 2 = 0.099; p = 0.046) when analyzed by GLM regression. However, HbA1c levels provided most of the regression model’s explanatory power (partial η 2 = 0.081; p = 0.011), with only minor contributions coming from Diabetic duration (partial η 2 = 0.003; p = 0.617) and the duration*HbA1c interaction (partial η 2 = 0.004; p = 0.571).

Is the work clearly and accurately presented and does it cite the current literature?

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

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

Reviewer Expertise:

Applied Biostatistics

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, however I have significant reservations, as outlined above.

F1000Res. 2026 Apr 21. doi: 10.5256/f1000research.197388.r476298

Reviewer response for version 2

Eric Siegel 1

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.

Is the work clearly and accurately presented and does it cite the current literature?

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

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

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.

F1000Res. 2026 Apr 27.
Maria Suryani 1

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)

F1000Res. 2025 Dec 30. doi: 10.5256/f1000research.190594.r439043

Reviewer response for version 1

Eric Siegel 1

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

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

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

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.

F1000Res. 2026 Mar 11.
Maria Suryani 1

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.

F1000Res. 2025 Dec 27. doi: 10.5256/f1000research.190594.r439051

Reviewer response for version 1

Ummu Muntamah 1

  • 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

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

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Partly

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.

F1000Res. 2026 Mar 11.
Maria Suryani 1

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.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Availability Statement

    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)

    Reporting guidelines

    This study was reported in accordance with the STROBE guidelines.

    Articles from F1000Research are provided here courtesy of F1000 Research Ltd

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