Does Capillary or Intravenous Collection of Dried Blood Spots Affect the Results of Amino Acid and Acylcarnitine Profile Studied with Tandem Mass Spectrometry?

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The article is a well-planned prospective study on an important practical question: whether it is possible to use venous blood for the analysis of dry blood spots by tandem mass spectrometry, if the standard is capillary blood. The work has a high practical significance for clinical laboratory diagnostics and management of patients with hereditary metabolic diseases. The study was conducted methodologically correctly, with a sufficient sample size (n=120) and the use of adequate statistical methods. The authors correctly point out the gap between theoretical standards (capillary blood for DBS) and real clinical practice, where venous blood is more commonly used and easier for staff to collect, especially in severe patients. The issue of the validity of using venous blood for these tests has been long overdue. There is indeed a lack of information in the literature and no official guidelines for interpreting DBS results obtained from venous blood. This study directly aims to fill this gap.

The article's conclusions are logical, well-considered, and fully consistent with the obtained results.

Typographical errors:  - Page 15, Discussion: "...in patients with PKU+HPIA..." — probably a typo, should be "PKU+HPA" (hyperphenylalaninemia), as elsewhere in the text.

The main focus of the revision should be on: It may be more clearly emphasized in the conclusions and abstract that the results are primarily applicable to the studied groups of diseases and require confirmation for patients with defects in fatty acid oxidation.

 

Author Response

First, we would like to thank you for evaluating our article. This revision note includes the changes we made to our article in line with the reviewers' suggestions, along with our answers to their questions. We have uploaded the "Revised Manuscript (clean version)" file, which contains a clean copy of the changes. Reviewers' questions and our answers are presented below:

Reviewer-1

1. Comments and Suggestions for Authors

The article is a well-planned prospective study on an important practical question: whether it is possible to use venous blood for the analysis of dry blood spots by tandem mass spectrometry, if the standard is capillary blood. The work has a high practical significance for clinical laboratory diagnostics and management of patients with hereditary metabolic diseases. The study was conducted methodologically correctly, with a sufficient sample size (n=120) and the use of adequate statistical methods. The authors correctly point out the gap between theoretical standards (capillary blood for DBS) and real clinical practice, where venous blood is more commonly used and easier for staff to collect, especially in severe patients. The issue of the validity of using venous blood for these tests has been long overdue. There is indeed a lack of information in the literature and no official guidelines for interpreting DBS results obtained from venous blood. This study directly aims to fill this gap.

The article's conclusions are logical, well-considered, and fully consistent with the obtained results.

 

Answer: Thank you for evaluating and summarizing our work, for pointing out our shortcomings, and for helping us improve the article.

 

2. Typographical errors:  - Page 15, Discussion: "...in patients with PKU+HPIA..." — probably a typo, should be "PKU+HPA" (hyperphenylalaninemia), as elsewhere in the text.

 

Answer: On page 15 of the manuscript, in the discussion section, the abbreviations PKU and HPA have been reviewed and corrected.

 

 

3. The main focus of the revision should be on: It may be more clearly emphasized in the conclusions and abstract that the results are primarily applicable to the studied groups of diseases and require confirmation for patients with defects in fatty acid oxidation.

 

Answer: "However, since the study group did not include any patients with fatty acid oxidation disorders, a separate confirmatory study is needed for this condition." This sentence has been added to the "abstract" section -"conclusion" subsection.

               “However, some acylcarnitine parameters used to diagnose fatty acid oxidation disorders showed lower agreement, and the mean values were statistically significantly different between capillary and venous samples in our study. Since no patients with fatty acid oxidation disorders were included in our study group, the use of venous rather than capillary samples for diagnosing these disorders cannot yet be considered reliable. Although our study provides information on amino acid and acylcarnitine profiles in specific aminoacidopathies and organic acidemias, further studies, including patients with fatty acid oxidation disorders, are needed, as this group was not represented in our cohort.” This paragraph was added to the conclusion part in the main text to emphasize that the use of venous acylcarnitine profiles rather than capillary is not yet reliable.

Reviewer 2 Report

Comments and Suggestions for Authors

Methodology

1. Please clarify if the use of EDTA tubes for intravenous samples affected the recovery or ionization of specific metabolites compared to direct capillary drops, as EDTA is known to interfere with certain mass spectrometry assays.

1. Address whether haematocrit levels were measured. Since haematocrit affects the spread of blood on filter paper (the "volumetric effect"), variations between capillary and venous blood could account for the significant differences in CO, C2, C16, and C18.1.
2. Provide data or specific citations regarding the "time intervals before laboratory analysis" mentioned in the discussion, as delays can significantly impact Glutamic acid and Arginine levels.

Statistical

• You report a Kappa coefficient of 0 or -0.01 for several parameters, including Arginine, C4, and C10.1. You must explicitly state that a Kappa of 0 indicates agreement no better than chance, likely due to a lack of pathological (out-of-range) samples in the study group.
• In Table 4, the direction of change (higher vs. lower) between venous and capillary samples is inconsistent across age groups for the same metabolites. A deeper analysis is required to determine if this is a physiological result of aging or a statistical artifact of the smaller subgroup sizes (n=30).
• Clarify that while Pearson correlation was high (r \approx 1.00) for parameters like C18, correlation does not inherently mean diagnostic agreement, especially for metabolites with narrow therapeutic ranges.

Clinical & Discussion

1. The study acknowledges the absence of patients with fatty acid oxidation defects. You must more strongly emphasize in the "Conclusion" that the findings may not yet apply to the diagnosis of conditions like VLCAD or MCAD, which rely heavily on the acylcarnitine species that showed significant differences (C16, C18:1).

1. For Argininemia, ASL deficiency, and IVA, n=1. The recommendation to use IV-DBS for these specific conditions is premature and should be framed as "preliminary observations" rather than a validated diagnostic alternative.
2. Reference Range Limitation: You utilized capillary reference ranges to evaluate venous samples. The discussion should explicitly state the clinical risk of "misclassification" if a venous sample falls near a capillary-derived cutoff point.

Formatting & Data Presentation

• Figure 1, In the pie chart for diagnostic distribution, ensure the percentages for smaller categories (e.g., Argininemia, Homocystinuria) are legible.
• Table 3, ensure that the "NA" (Not Applicable) labels for n=1 groups are consistently applied to prevent readers from assuming a lack of difference exists where a p-value simply could not be calculated.

Author Response

First, we would like to thank you for evaluating our article. This revision note includes the changes we made to our article in line with the reviewers' suggestions, along with our answers to their questions. We have uploaded the "Revised Manuscript (clean version)" file, which contains a clean copy of the changes. Reviewers' questions and our answers are presented below:

Methodology

1. Please clarify if the use of EDTA tubes for intravenous samples affected the recovery or ionization of specific metabolites compared to direct capillary drops, as EDTA is known to interfere with certain mass spectrometry assays.

Answer: Studies exist in the literature regarding the use of EDTA plasma in tandem mass spectrometry analyses of DBS samples. While some studies mention that EDTA may affect certain metabolites, others state that the use of EDTA plasma is "acceptable" and does not affect amino acid and acylcarnitine quantification. In light of this information, the following paragraph has been added to the "2.2 Sample Collection" subsection of the "Materials and Methods" section.

“In the literature, it has been mentioned that some endocannabinoids, fatty acid oxidation, and urea cycle submetabolites may exhibit EDTA-induced changes in a study using an EDTA-containing sample [16]. In another study investigating the direct effect of EDTA on newborn screening using tandem mass spectrometry with DBS, the authors showed that parameters such as biotinidase activity, 17-hydroxyprogesterone, TSH, and porphobilinogen synthase activity were affected, but they found that amino acid and acylcarnitine quantification were not affected by EDTA [17]. In addition, the American College of Medical Genetics and Genomics (ACMG) 2020 update also states that the use of EDTA-containing plasma samples in acylcarnitine analysis is "acceptable" [18].”

References 16, 17, and 18 have been added, and the reference order has been rearranged.

2. Address whether haematocrit levels were measured. Since haematocrit affects the spread of blood on filter paper (the "volumetric effect"), variations between capillary and venous blood could account for the significant differences in CO, C2, C16, and C18.1.

Answer: “Hematocrit (Hct) and hemoglobin (Hb) levels were also measured simultaneously in the patients.” This information has been added to the "Materials and Methods" section, under subsection "2.3. Sample Analysis," as the last sentence of the first paragraph.

                A comprehensive correlation and regression analysis was performed to evaluate the potential volumetric effect of hematocrit level on blood spread on filter paper. Htc's descriptive statistics are presented in the Table below:

Table. Descriptive Statistics for Htc and Hb.

Parameter	n	mean±SD	Median	Min.	Max.	Q1	Q3
Htc (%)	120	39.54±6.64	37.9	25	59.5	35.6	42.62
Hb (g/dL)	120	13.21±2.32	12.6	8.6	20.4	11.8	14.3

 

The relationship between Htc and Δ was investigated using Spearman correlation and simple linear regression by creating a variable Δ = Capillary − Venous difference for each metabolite. The results are summarized in the table below.

 

Table. Correlation and Regression Analysis Between Hematocrit and Venous-Capillary Difference (Δ).

Metabolite	n	Δ Mean±SD	ρ	p (Sp.)	β	R²	p (Reg.)
C0	120	2.1181±7.6226	-0.021	0.816	0.0003	0	0.997
C2	120	1.1932±4.061	0.047	0.606	0.0406	0.0044	0.474
C3	120	0.0767±0.5995	0.019	0.838	-0.0024	0.0007	0.774
C4	120	0.009±0.089	0.184	0.045	0.0026	0.0385	0.032	*
C5.1	120	0±0.0242	-0.069	0.454	-0.0004	0.015	0.183
C5	120	0.0001±0.3027	0.141	0.126	-0.002	0.0019	0.635
C5DC	120	-0.0151±0.1486	-0.077	0.401	-0.0018	0.0062	0.394
C5OH	120	-0.0068±0.0551	0.102	0.267	0.0007	0.0072	0.358
C6	120	0.0015±0.055	-0.211	0.021	-0.0015	0.0344	0.043	*
C8	120	-0.0052±0.0176	-0.001	0.989	0	0.0003	0.846
C8.1	120	-0.0052±0.017	-0.069	0.453	-0.0002	0.0047	0.459
C10	120	-0.0103±0.0497	-0.065	0.479	-0.0004	0.0028	0.565
C10.1	120	-0.0168±0.0545	0.018	0.849	0.0003	0.0016	0.664
C12	120	-0.0057±0.0318	-0.036	0.696	-0.0005	0.0088	0.307
C14	120	-0.0014±0.0442	-0.061	0.505	0.0007	0.0116	0.241
C14.1	120	-0.0068±0.0345	-0.166	0.070	-0.0004	0.0047	0.456
C14.2	120	-0.0013±0.0195	-0.131	0.155	-0.0002	0.0063	0.388
C16	120	0.0861±0.3737	0.064	0.487	0.0099	0.0305	0.056
C16.1	120	0.0069±0.0551	0.137	0.135	0.0018	0.0484	0.016
C18:2	120	0.0205±0.1877	0.126	0.170	0.0032	0.0131	0.214
C18	120	0.0309±0.18	0.066	0.472	0.0041	0.0225	0.102
C18:1	120	0.0892±0.4312	0.2	0.028	0.0134	0.0423	0.024	*
Arginine	120	8.0223±20.1679	-0.098	0.289	-0.241	0.0062	0.391
Aspartic acid	120	-28.6384±55.3767	0.108	0.242	0.3464	0.0017	0.654
Alanine	120	3.5949±62.9104	0.164	0.073	1.9697	0.0428	0.023
Citrulline	120	6.8466±11.1668	-0.076	0.410	-0.1584	0.0088	0.308
Glutamic acid	120	58.4209±52.417	-0.04	0.666	-0.0134	0	0.985
Glycine	120	23.6686±79.8046	0.1	0.277	0.5797	0.0023	0.602
Leu+Ileu	120	4.462±21.7883	-0.081	0.381	-0.2709	0.0068	0.372
Methionine	120	2.3892±11.738	-0.122	0.185	-0.1608	0.0082	0.325
Phe	120	5.1556±30.8968	-0.055	0.551	0.1598	0.0012	0.711
Tyrosine	120	10.1981±22.5553	0.006	0.950	0.1551	0.0021	0.622
Valine	120	2.8805±17.7668	-0.016	0.866	0.0448	0.0003	0.856
Phe/Tyr	120	-0.0182±0.5541	-0.138	0.134	-0.0051	0.0038	0.505
Leu/Phe	120	-0.6026±3.8853	-0.025	0.788	0.0132	0.0005	0.808
Met/Phe	120	-0.0885±0.4564	-0.032	0.730	-0.0002	0	0.977
ASA	120	0.0125±0.159	-0.039	0.672	-0.0019	0.0064	0.385

Δ: Capillary–Venous; ρ: Spearman correlation coefficient; β: regression coefficient; R²: coefficient of determination. *p<0.05. Yellow cells represent significant correlations.

Of the 37 metabolites/ratios examined, only 3 (8.1%) showed a statistically significant Spearman correlation between Htc and Δ: C4 (ρ=0.184; p=0.045), C6 (ρ=−0.211; p=0.021), and C18:1 (ρ=0.200; p=0.028). However, the effect size was very low in all of these correlations (R²<0.05); Htc explains at most 4.2% of the variance in the venous-capillary difference.

Specifically, when the parameters you mentioned were evaluated: no significant correlation was found between Htc and Δ for C0 (ρ=−0.021; p=0.816), C2 (ρ=0.047; p=0.606), and C16 (ρ=0.064; p=0.487). Although a weak positive correlation was observed in C18:1 (ρ=0.200; p=0.028; R²=0.042), the explained variance is negligible. In conclusion, our study showed that hematocrit levels did not systematically affect venous-capillary measurement differences. The observed significant differences are more likely due to the sampling method and biological variation rather than the volumetric effect of Htc.

“Patients' hematocrit (Hct) and hemoglobin (Hb) levels were also measured simultaneously. For Hct (%) the mean ± SD was 39.54 ± 6.64, median 37.9, min. 25, and max. 59.5; for Hb (g/dl), the mean ± SD was 13.21 ± 2.32, median 12.6, min. 8.6, and max. 20.4. The relationship between Htc and Δ was investigated using Spearman correlation and simple linear regression by creating a variable Δ = Capillary − Venous difference for each metabolite. Of the 37 parameters examined, only 3 (8.1%) showed a statistically significant Spearman correlation (ρ) between Htc and Δ: C4 (ρ=0.184; p=0.045), C6 (ρ=−0.211; p=0.021), and C18:1 (ρ=0.200; p=0.028). However, the effect size was very low in all of these correlations (R²<0.05); Htc explains at most 4.2% of the variance in the venous-capillary difference. Our study showed that hematocrit levels did not systematically affect differences in venous-capillary measurements.” This paragraph has been added to the "3. Results" section, under the subsection "3.2. Comparison of acylcarnitine and amino acid analyses obtained by venous and capillary methods in the study group," below Fig. 2.

3.Provide data or specific citations regarding the "time intervals before laboratory analysis" mentioned in the discussion, as delays can significantly impact Glutamic acid and Arginine levels.

Answer: “In our study, the analysis time interval varied between 4 and 21 days.” This information has been added to the discussion section at the end of the third paragraph. “In our study, blood samples were applied to DBS cards and allowed to dry completely at room temperature, horizontally, and without exposure to heat or light for at least 3 hours. After drying, the samples were placed in zip-lock bags and stored at +4°C until analysis.” This information has been added to the Materials and Methods section, “2.2. Sample Collection” subsection at the end of the second paragraph.

Statistical

4. You report a Kappa coefficient of 0 or -0.01 for several parameters, including Arginine, C4, and C10.1. You must explicitly state that a Kappa of 0 indicates agreement no better than chance, likely due to a lack of pathological (out-of-range) samples in the study group.

 

Answer: “The fact that the Kappa coefficient is close to zero for these parameters indicates that no better-than-chance fit could be demonstrated. This is due to the very low number of cases with out-of-reference values ​​for these metabolites in the study group.” We added this sentence as an additional explanation to the last paragraph under the subheading “3.3. Evaluation of acylcarnitine and amino acid profiles obtained via venous and capillary methods by the study group using Cohen's kappa analysis” within the “3. Results” section. With the addition, the final version of the paragraph is as follows:

  “Parameters with lower levels of agreement were Aspartic acid (κ = 0.224, 85% agreement; p = 0.184), Alanine (κ = 0.343, 78.3% agreement; p = 0.003), Glycine (κ = 0.335, 94.2% agreement; p = 0.169), and ASA (κ = 0.318, 96.7% agreement; p = 0.342). In addition, the kappa coefficient was calculated as 0 for VC4, VC5.1, VC8, VC10.1, VC14, VC14.2, and VC16.1, and as −0.01 for Arginine. The fact that the Kappa coefficient is close to zero for these parameters indicates that no better-than-chance fit could be demonstrated. This is due to the very low number of cases with out-of-reference values ​​for these metabolites in the study group. Although the percentage agreement between venous and capillary measurements was high for these parameters, the kappa values were not statistically significant due to class-distribution imbalances. Overall, a high level of agreement was observed between venous and capillary measurements for most parameters, and the homogeneity of distributions across the low/normal/high categories influenced the statistical significance of the kappa coefficients.”

5. In Table 4, the direction of change (higher vs. lower) between venous and capillary samples is inconsistent across age groups for the same metabolites. A deeper analysis is required to determine if this is a physiological result of aging or a statistical artifact of the smaller subgroup sizes (n=30).

Answer: Our study included 120 patients aged 0-18 years, who were further stratified into subgroups of 30 patients each, consisting of 15 girls and 15 boys in each age group. When each age subgroup and each gender subgroup were re-analyzed (e.g., 0-1 month old male n=15, 0-1 month old female n=15, all patients 0-1 month old n=30, etc.), a more understandable and simplified summary of the venous-capillary values ​​from 12 separate tables is presented in Table 4.

As you pointed out, we re-examined the directional discrepancy between age groups in Table 4 in detail. The median Δ direction and Wilcoxon test results for all metabolites for each age group (n=30 each) are summarized in the table below:

Table 5. Venous-Capillary Difference Direction and Consistency Analysis by Age Groups

Parameter	0–1 month (n=30)	1–24 month (n=30)	24–60 month (n=30)	60–216 month (n=30)	Consistency
C0	Cp>V*	Cp >V*	Cp >V*	Cp >V	consistent
C2	Cp >V*	Cp >V	Cp >V	V> Cp	inconsistent
C3	Cp >V	Cp >V	Cp >V	V> Cp	inconsistent
C4	Cp >V*	Cp >V	Cp >V	Cp >V	consistent
C5	Cp >V*	Cp >V*	Cp >V*	Cp >V	consistent
C8	Cp >V	V> Cp *	V> Cp	V> Cp*	inconsistent
C8.1	V> Cp*	V> Cp	Cp >V	V> Cp*	inconsistent
C10	V> Cp	V> Cp	V> Cp *	V> Cp*	consistent
C10.1	V> Cp	V> Cp	V> Cp	V> Cp*	consistent
C12	Cp >V	V> Cp	V> Cp	V> Cp*	inconsistent
C14	Cp >V	Cp >V	V> Cp *	V> Cp*	inconsistent
C16	Cp >V*	Cp >V	Cp >V	V> Cp	inconsistent
C18	Cp >V*	Cp >V	Cp >V	=	inconsistent
C18:1	Cp >V*	Cp >V	Cp >V	Cp >V	consistent
Arginine	Cp >V*	Cp >V*	Cp >V*	Cp >V*	consistent
Aspartic acid	V> Cp*	V> Cp *	V> Cp *	V> Cp*	consistent
Glutamic acid	Cp >V*	Cp >V*	Cp >V*	Cp >V*	consistent
Citrulline	Cp >V*	Cp >V*	Cp >V*	Cp >V*	consistent
Tyrosine	Cp >V*	Cp >V*	Cp >V	Cp >V*	consistent
Phenylalanine	Cp >V*	Cp >V*	Cp >V*	Cp >V*	consistent
Leu/Phe	V> Cp*	V> Cp*	V> Cp*	V> Cp *	consistent

Cp; capillary, V; venous, Cp>V: Capillary is higher; V>Cp: Venous is higher; *: Wilcoxon p<0.05. Parameters with directional inconsistency are shown in bold.

According to the analysis results, directional inconsistencies were detected in 8 out of 21 metabolites (38.1%) between age groups: C2, C3, C8, C8.1, C12, C14, C16, and C18. The key findings in evaluating this situation are as follows:

Firstly, the vast majority of metabolites with altered direction show statistically insignificant differences (p>0.05). That is, in the subgroups where directional changes were observed, these differences are within the limits of random fluctuation.

Secondly, the power analysis shows that with n=30, the power to detect small effect sizes (Cohen d=0.2) is only 12–15%. This means that the risk of type II error is high, and small true differences can randomly appear in either direction.

Most of the parameters showing consistent direction across all age groups were also statistically significant in each age group. Arginine, citrulline, glutamic acid, tyrosine, and phenylalanine were always found to be capillary > venous; aspartic acid and the Leu/Phe ratio were always found to be venous > capillary. This consistency reliably demonstrates that true biological differences exist in these metabolites independently of age.

Consequently, the explanation for the directional inconsistencies in Table 4 is that small subgroup sizes (n=30) lead to insufficient statistical power. In metabolites with large effect sizes, the direction is consistent and significant across all age groups; in those with small effect sizes, random fluctuation predominates.

Based on this information, the following explanation and Table 5 have been added below Table 4 in the subsection "3.2. Comparison of acylcarnitine and amino acid analyses obtained by venous and capillary methods in the study group" under section "3. Results".

“Consistency analysis was performed for each age subgroup (n=30) using the Wilcoxon test and the median Δ direction of all metabolites for parameters that were higher or lower in capillary versus venous samples, according to the age subgroups shown in Table 4. In the consistency analysis, directional inconsistency was detected between age subgroups in 8 parameters (C2, C3, C8, C8.1, C12, C14, C16, and C18). The consistency analysis of the parameters in age subgroups is presented in Table 5.”

Additionally, the following paragraph has been added as paragraph 7 to section "4. Discussion".

“In our study, when parameter analyses were examined in terms of consistency according to age subgroups, it was observed that most of the parameters showing consistent direction in all age groups were also statistically significant in every age group. Arg, Cit, Glu, Tyr, and Phe were always found to be capillary > venous; Asp and Leu/Phe ratio were always found to be venous > capillary. This consistency reliably demonstrates that true biological differences exist in these metabolites independently of age. Consequently, the explanation for the directional inconsistencies in our study is that small subgroup sizes (n=30) lead to insufficient statistical power. For metabolites with large effect sizes, the direction is consistent and significant across all age groups; for those with small effect sizes, random fluctuations dominate. In our study, inconsistencies were detected in the acylcarnitine parameters in age subgroup analyses; while capillary > venous results were mostly observed, venous > capillary results were particularly seen in the 60-216 month age range. In a study involving 163 healthy individuals examining plasma acylcarnitines, it was found that long-chain acylcarnitines increased with age in healthy subjects, and most odd-chain acylcarnitines decreased [26]. It has been suggested that mitochondrial dysfunction or mitochondrial reprogramming associated with aging may be the cause of this [26]. In a study investigating capillary-venous differences in 10 healthy male patients using lipidomics, researchers suggested that lipidomics, including acylcarnitines, exhibited similar results in capillary and venous sampling, and that some statistically significant differences observed in acylcarnitines might be due to random variations [12]. In the literature, the changes in acylcarnitine levels across age, disease, and mitochondrial responses to environmental factors remain poorly explained.”

6. Clarify that while Pearson correlation was high (r \approx 1.00) for parameters like C18, correlation does not inherently mean diagnostic agreement, especially for metabolites with narrow therapeutic ranges.

Answer: The Pearson correlation coefficient (r) assesses the strength of the linear relationship between two measurement methods; however, it cannot detect the presence of systematic bias. Even if one method systematically measures higher values ​​than the other, an r=1.00 value can be obtained. Therefore, Bland-Altman analysis was performed for selected metabolites to calculate the mean bias and 95% limits of agreement (LoA).

For example, although a very high correlation was observed for phenylalanine (Pearson r=0.966), the Bland-Altman analysis revealed a mean bias of 5.16±31.03 and quite wide limits of agreement of [−55.66; 65.97]. This wide LoA indicates that despite high correlation, significant measurement deviations can occur at the individual level, emphasizing the need for caution in clinical decision-making, especially with metabolites that have narrow reference ranges.

The following paragraph has been added as paragraph 9 to section "4. Discussion" before the “4.1. Suggestions for future research”subsection.

“In our study although a very high correlation was observed for some parameters such as C18, Phe, Tyr, the Bland-Altman analysis revealed a  bias and quite wide limits of agreement. This wide limits of agreement indicates that despite high correlation, significant measurement deviations can occur at the individual level, emphasizing the need for caution in clinical decision-making, especially with metabolites that have narrow reference ranges.”

Clinical & Discussion

7. The study acknowledges the absence of patients with fatty acid oxidation defects. You must more strongly emphasize in the "Conclusion" that the findings may not yet apply to the diagnosis of conditions like VLCAD or MCAD, which rely heavily on the acylcarnitine species that showed significant differences (C16, C18:1).

 

Answer: “However, some acylcarnitine parameters used to diagnose fatty acid oxidation disorders showed lower agreement, and the mean values were statistically significantly different between capillary and venous samples in our study. Since no patients with fatty acid oxidation disorders were included in our study group, the use of venous rather than capillary samples for diagnosing these disorders cannot yet be considered reliable. Although our study provides information on amino acid and acylcarnitine profiles in specific aminoacidopathies and organic acidemias, further studies, including patients with fatty acid oxidation disorders, are needed, as this group was not represented in our cohort.” This paragraph was added to the conclusion part in the main text to emphasize that the use of venous acylcarnitine profiles rather than capillary is not yet reliable.

8. For Argininemia, ASL deficiency, and IVA, n=1. The recommendation to use IV-DBS for these specific conditions is premature and should be framed as "preliminary observations" rather than a validated diagnostic alternative.

 

Answer: “The fact that our study included only one patient each from the argininemia, ASL deficiency, and IVA diagnostic groups means that our findings for these disease groups are at the "preliminary observations" level rather than diagnostic.” This sentence has been added to section “4.2. Limitations”.

 

9. Reference Range Limitation: You utilized capillary reference ranges to evaluate venous samples. The discussion should explicitly state the clinical risk of "misclassification" if a venous sample falls near a capillary-derived cutoff point.

Answer: “This situation carries the clinical risk of misclassification when using the capillary-derived cutoff point for venous samples. For this reason, as we emphasized in the suggestions for future research section, there is a need for studies that focus on reference ranges for dried blood samples obtained via intravenous sampling.” These sentences have been added to section “4.2. Limitations”.

Formatting & Data Presentation

10.Figure 1, In the pie chart for diagnostic distribution, ensure the percentages for smaller categories (e.g., Argininemia, Homocystinuria) are legible.

 

Answer: Figure 1 has been rearranged to make the percentage signs more legible.

 

 

11. Table 3, ensure that the "NA" (Not Applicable) labels for n=1 groups are consistently applied to prevent readers from assuming a lack of difference exists where a p-value simply could not be calculated.

 

Answer: “*N/A (Not Applicable): means that could not be evaluated as there was only one patient in the relevant group”. This explanation has been added to the explanations section below Table 3.

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank you for your careful and comprehensive revision of the manuscript. You have systematically and satisfactorily addressed the methodological, statistical, and clinical concerns raised in the previous review round. The addition of the haematocrit regression analysis, the Bland-Altman context for the correlation data, and the explicit clinical caveats regarding fatty acid oxidation defects and reference range misclassifications have greatly strengthened the manuscript.

I have only one minor formatting correction remaining before publication:

Figure 1 Legibility, While the data in the pie chart is valuable, the text labels for the smaller percentage categories (e.g., Argininemia, Homocystinuria, Tyrosinemia) appear to be too small or have a low resolution that causes blurring. Please increase the font size of the data labels or consider using a different chart format (such as a horizontal bar chart) to ensure all diagnostic categories are easily legible in the final typeset version.

The scientific and structural issues have been fully resolved. The manuscript should be accepted pending a quick formatting fix to the Figure 1 graphic to ensure readability.

Author Response

Thank you very much for your valuable feedback on improving this manuscript.

We have re-uploaded Figure 1 to improve readability.