Association of HIF1α, BNIP3, and BNIP3L with Hypoxia-Related Metabolic Stress in Metabolic Syndrome

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Comments to Authors 

            This study showed that hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

          Mitophagy is a selective process that targets the damaged, dysfunctional, or superfluous mitochondria for degradation through autophagy [1]. T-2 toxin-induced ROS accumulation activated HIF-1α in cardiomyocytes, which regulated BNIP3L-LC3 binding, mediated mitophagy, and ultimately caused myocardial injury [2].  

          Authors are kindly requested to emphasize the current concepts about these issues in the context of recent knowledge and the available literature. This articles should be quoted in the References list.

1.What is the main question addressed by the research?

to investigate the relationship between HIF1α, BNIP3, and BNIP3L in MetS and their potential as biomarkers.

2. What parts do you consider original or relevant for the field? What specific gap in the field does the paper address?

The paper is original and relevant. This paper showed that hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

3. What does it add to the subject area compared with other published material?

hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

4. What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

Nothing improvements regarding the methodology; none controls

5. Please describe how the conclusions are or are not consistent with the evidence and arguments presented. Please also indicate if all main questions posed were addressed and by which specific experiments.

The conclusions are consistent and showed that hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

6. Are the references appropriate? Yes
7. Please include any additional comments on the tables and figures and quality of the data. Good

References

1.      PPTC7 acts as an essential co-factor of the SCFFBXL4 ubiquitin ligase complex to restrict BNIP3/3L-dependent mitophagy. Cell Death Dis. 2025;16(1):145. Published 2025 Mar 1. doi:10.1038/s41419-025-07463-w.

2.      HIF-1α/BNIP3L-mediated mitophagy is involved in T-2 toxin-induced myocardial injury. Chem Biol Interact. 2026; 423: 111844. doi:10.1016/j.cbi.2025.111844.

Author Response

Comments to the Author

This study showed that hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

Mitophagy is a selective process that targets the damaged, dysfunctional, or superfluous mitochondria for degradation through autophagy [1]. T-2 toxin-induced ROS accumulation activated HIF-1α in cardiomyocytes, which regulated BNIP3L-LC3 binding, mediated mitophagy, and ultimately caused myocardial injury [2]. 

Authors are kindly requested to emphasize the current concepts about these issues in the context of recent knowledge and the available literature. These articles should be quoted in the References list.

 

Responses to the comments of Reviewer 1

We sincerely thank the reviewer for this constructive and insightful comment. In response, we have revised and expanded the Discussion section to better emphasize current concepts of hypoxia- and ROS-driven mitophagy in the context of recent literature. Specifically, we integrated mechanistic evidence describing ROS-induced HIF-1α stabilization and subsequent BNIP3/BNIP3L–LC3–mediated mitophagy, highlighting how sustained activation of this pathway may contribute to cellular injury rather than protection. In addition, recent studies demonstrating the tight regulatory control of BNIP3/BNIP3L-dependent mitophagy have been incorporated to support the concept of maladaptive or dysregulated mitophagy under chronic metabolic stress. The reviewer-suggested articles have been cited and discussed in appropriate context (Discussion section).

 

 

Comments 1. What is the main question addressed by the research?

to investigate the relationship between HIF1α, BNIP3, and BNIP3L in MetS and their potential as biomarkers.

Response: We thank the reviewer for this comment.

 

Comments 2. What parts do you consider original or relevant for the field? What specific gap in the field does the paper address?

The paper is original and relevant. This paper showed that hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

Response: We thank the reviewer for this comment.

 

Comments 3. What does it add to the subject area compared with other published material?

hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

Response: We thank the reviewer for this comment.

 

Comments 4. What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

Nothing improvements regarding the methodology; none controls.

Response: We thank the reviewer for this comment.

 

Comments 5. Please describe how the conclusions are or are not consistent with the evidence and arguments presented. Please also indicate if all main questions posed were addressed and by which specific experiments.

The conclusions are consistent and showed that hypoxia-induced upregulation of HIF1α, BNIP3, and BNIP3L may contribute to metabolic dysregulation via impaired mitophagy.

Response: We thank the reviewer for this comment.

 

Comments 6. Are the references appropriate? Yes

Response: We thank the reviewer for this comment.

 

Comments 7. Please include any additional comments on the tables and figures and quality of the data. Good

Response: We thank the reviewer for this comment.

Author Response File: Author Response.doc

Reviewer 2 Report

Comments and Suggestions for Authors

To the Editor and Authors,

I have reviewed the manuscript titled "Association of HIF1a Regulated BNIP3 and BNIP3L with Mitophagy and Metabolic Syndrome," which investigates the serum levels of HIF1a, BNIP3, and BNIP3L in patients with metabolic syndrome (MetS) compared to healthy controls¹¹. The study addresses a compelling intersection of hypoxia, mitochondrial quality control, and metabolic dysregulation, and the finding that these specific proteins are significantly elevated in the serum of MetS patients is novel and potentially clinically useful given the high AUC values reported². However, while the data is interesting, I have significant reservations regarding the interpretation of serum levels as a direct proxy for intracellular mitophagic activity, as well as the deterministic language used regarding causality in what is an observational study.

My primary scientific concern lies in the biological rationale for measuring BNIP3 and BNIP3L in serum to assess mitophagy³. As the authors correctly note in the introduction, BNIP3 and BNIP3L are mitochondrial outer membrane proteins involved in tagging mitochondria for autophagic degradation⁴⁴⁴⁴. Typically, these are intracellular markers. The manuscript needs to explicitly address the mechanism by which these proteins enter circulation. High serum levels could plausibly result from cellular necrosis, tissue damage, or exosomal secretion rather than functional mitophagy. Consequently, the conclusion that "upregulation... contribute[s] to metabolic dysregulation via impaired mitophagy" ⁵ is a substantial leap. Without tissue biopsies or mechanistic data, the authors cannot definitively claim that serum levels reflect intracellular mitophagic flux. I strongly recommend revising the discussion to address this limitation and explain what circulating levels of mitochondrial membrane proteins actually represent physiologically.

Furthermore, there is a concern regarding the distinction between correlation and causation. The title implies a regulatory mechanism ("HIF1a Regulated BNIP3") ⁶, and the abstract suggests these markers "contribute to" dysregulation⁷. Since this is a cross-sectional case-control study⁸, the data only supports an association. The heavy difference in BMI between the MetS group ($33.77 \pm 3.96$) and the control group ($23.51 \pm 2.72$) is a major confounding factor⁹. Since obesity itself causes adipose tissue hypoxia and subsequent HIF1a activation¹⁰, it is difficult to determine if the elevated markers are specific to the syndrome or simply a byproduct of increased adipose mass. I suggest the authors perform a multivariate regression analysis to control for BMI; this would clarify whether HIF1a and BNIP3 remain significant predictors of MetS independent of obesity.

Regarding the methodology, the sample size of 40 participants per group is relatively small¹¹. While the p-values are highly significant ($p=0.001$) ¹², the robustness of the ROC analysis—specifically for the sub-models like blood pressure or HDL—might be overstated given the limited sample numbers^13131313. Additionally, it would be beneficial for the authors to provide more details on the validation of the ELISA kits used (Bioassay Technology Laboratory and ELK Biotechnology)¹⁴, specifically regarding their sensitivity and specificity for detecting these specific proteins in human serum, as this is a non-standard application for these intracellular markers.

Finally, the presentation of the results could be improved to better visualize the data distribution. Figure 1 currently uses bar graphs with standard deviation¹⁵, but given the sample size of 40, dot plots or box-and-whisker plots would be more appropriate to reveal the spread of the data and any potential outliers. Additionally, Figure 3 presents a pathway model linking hypoxia to mitophagy and insulin resistance¹⁶; the caption should clearly label this as a "Hypothesized Mechanism" or "Proposed Model," as the current study did not experimentally test the downstream effects on insulin resistance or mitochondrial dysfunction, but rather inferred them from serum associations. With significant revisions to the interpretation of the data—specifically softening the claims of causality and addressing the physiological meaning of the serum biomarkers—this manuscript could make a valuable contribution to the field.

 

Author Response

Response to Reviewer 2 Comments

 

Comments to the Author

Comments 1. I have reviewed the manuscript titled "Association of HIF1a Regulated BNIP3 and BNIP3L with Mitophagy and Metabolic Syndrome," which investigates the serum levels of HIF1a, BNIP3, and BNIP3L in patients with metabolic syndrome (MetS) compared to healthy controls11. The study addresses a compelling intersection of hypoxia, mitochondrial quality control, and metabolic dysregulation, and the finding that these specific proteins are significantly elevated in the serum of MetS patients is novel and potentially clinically useful given the high AUC values reported2. However, while the data is interesting, I have significant reservations regarding the interpretation of serum levels as a direct proxy for intracellular mitophagic activity, as well as the deterministic language used regarding causality in what is an observational study.

Response: We thank the reviewer for this helpful suggestion. In response, we have revised the title and legend of Figure 3 to clearly indicate that it represents a hypothesized and conceptual model rather than an experimentally validated mechanism. The revised caption now explicitly states that the proposed links between hypoxia, mitophagy-related signaling, inflammation, and insulin resistance are inferred from serum associations and existing literature, as the present study did not directly assess mitophagic activity or mitochondrial function. We believe this clarification appropriately aligns the figure with the observational nature of our data.

 

Comments 2. My primary scientific concern lies in the biological rationale for measuring BNIP3 and BNIP3L in serum to assess mitophagy3. As the authors correctly note in the introduction, BNIP3 and BNIP3L are mitochondrial outer membrane proteins involved in tagging mitochondria for autophagic degradation4444. Typically, these are intracellular markers. The manuscript needs to explicitly address the mechanism by which these proteins enter circulation. High serum levels could plausibly result from cellular necrosis, tissue damage, or exosomal secretion rather than functional mitophagy. Consequently, the conclusion that "upregulation... contribute[s] to metabolic dysregulation via impaired mitophagy" 5 is a substantial leap. Without tissue biopsies or mechanistic data, the authors cannot definitively claim that serum levels reflect intracellular mitophagic flux. I strongly recommend revising the discussion to address this limitation and explain what circulating levels of mitochondrial membrane proteins actually represent physiologically.

Response: We thank the reviewer for highlighting the importance of distinguishing correlation from causation. In response, we have revised the title, abstract, and conclusions to consistently use association-based language and to explicitly acknowledge the observational nature of the study. All causal or deterministic expressions have been removed.

 

Comments 3. Furthermore, there is a concern regarding the distinction between correlation and causation. The title implies a regulatory mechanism ("HIF1a Regulated BNIP3") 6, and the abstract suggests these markers "contribute to" dysregulation7. Since this is a cross-sectional case-control study8, the data only supports an association. The heavy difference in BMI between the MetS group ($33.77 \pm 3.96$) and the control group ($23.51 \pm 2.72$) is a major confounding factor9. Since obesity itself causes adipose tissue hypoxia and subsequent HIF1a activation10, it is difficult to determine if the elevated markers are specific to the syndrome or simply a byproduct of increased adipose mass. I suggest the authors perform a multivariate regression analysis to control for BMI; this would clarify whether HIF1a and BNIP3 remain significant predictors of MetS independent of obesity.

Response: We thank the reviewer for highlighting the importance of distinguishing correlation from causation. In response, we have revised the title, abstract, and conclusions to consistently use association-based language and to explicitly acknowledge the observational nature of the study. All causal or deterministic expressions have been removed.

Regarding the reviewer’s concern about obesity-driven hypoxia as a confounding factor, we acknowledge that increased adiposity is a well-established inducer of adipose tissue hypoxia and subsequent HIF-1α activation. To directly address this issue, we performed an analysis of covariance (ANCOVA) with BMI included as a covariate and evaluated the group * BMI interaction for all investigated parameters. As now presented in Section 3.5 (BMI-Adjusted Group Comparisons) and Table 3, the group * BMI interaction was not statistically significant for HIF-1α, BNIP3, BNIP3L, or any other measured variable (all p > 0.05). Importantly, the differences between the metabolic syndrome and control groups remained evident after BMI adjustment.

These findings indicate that the observed elevations in HIF-1α, BNIP3, and BNIP3L cannot be explained solely by increased adipose mass and suggest that these alterations are at least partially independent of BMI. While obesity clearly contributes to hypoxia-related signaling, the persistence of group differences after BMI adjustment supports the notion that these markers are associated with metabolic syndrome beyond the effect of adiposity alone.

With respect to the suggestion to perform multivariate regression analysis, we acknowledge its value for identifying independent predictors. However, given the moderate sample size and the strong collinearity between metabolic syndrome status and BMI in our cohort, we considered ANCOVA with interaction testing to be the most statistically robust and parsimonious approach to address BMI as a confounder without overfitting the model. The absence of a significant group * BMI interaction provides direct evidence that BMI does not modify the relationship between group status and the investigated biomarkers.

We have clarified these points in the revised Results and Discussion sections and explicitly acknowledged the observational design and the absence of causal inference. We believe that these revisions substantially strengthen the methodological rigor of the study and directly address the reviewer’s concerns.

Table 3. Group * BMI interaction effects on study variables

Variable		Group		F	p	η2
		MetS	Control
		(Mean ± SD)	(Mean ± SD)
Group × BMI interaction effects	HIF1α	3.25 ± 2.17	1.25 ± 0.42	2.303	0.133	0.029
	BNIP3	1.23 ± 0.31	0.76 ± 0.17	0.048	0.828	0.001
	BNIP3L	0.41 ± 0.14	0.28 ± 0.09	0.011	0.917	0.000
	TG	213.6 ± 91.46	94.08 ± 30.09	3.163	0.079	0.040
	LDL	120.52 ± 26.12	95.07 ± 18.73	0.337	0.563	0.004
	HDL	37.81 ± 5.49	52.08 ± 11.89	2.544	0.115	0.032
	WC	108.1 ± 8.89	77.53 ± 13.23	0.576	0.450	0.008
	SBP	128.25 ± 10.83	114.75 ± 7.16	0.097	0.757	0.001
	DBP	82.5 ± 6.3	76.9 ± 12.85	0.397	0.531	0.005
	FG	137.1 ± 67.59	87.85 ± 8.37	0.694	0.408	0.009

MetS, metabolic syndrome; BMI, body mass index; HIF1α, hypoxia-inducible factor 1 alpha; BNIP3, Bcl-2/adenovirus E1B 19 kDa interacting protein 3; BNIP3L, BNIP3-like; TG, triglyceride; LDL, low-density lipoprotein; HDL, high-density lipoprotein; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FG, fasting glucose; SD, standard deviation; F, ANCOVA analysis test value; p value, statistical significance; η2, partial eta squared.

 

Comments 4. Regarding the methodology, the sample size of 40 participants per group is relatively small11. While the p-values are highly significant ($p=0.001$) 12, the robustness of the ROC analysis—specifically for the sub-models like blood pressure or HDL—might be overstated given the limited sample numbers13131313.

Response: We thank the reviewer. In the present study, 40 participants were included in each group. The relatively large effect sizes observed in the ANCOVA analyses (partial η² > 0.30) indicate that the statistical power was adequate despite the modest sample size. Moreover, the absence of a statistically significant group × BMI interaction suggests that the model did not exhibit instability attributable to insufficient sample size. Comparable sample sizes have also been reported in previous case–control studies with similar designs. Therefore, while the results should be interpreted with appropriate caution, we consider the sample size to be sufficient to support the primary analytical conclusions of the study.

The ROC analyses were performed to evaluate the discriminatory performance of the investigated variables and should be regarded as exploratory in nature. To control for potential confounding effects, BMI was included as a covariate and analysis of covariance (ANCOVA) was applied. In the ANCOVA models, the group * BMI interaction was not statistically significant. It is well recognized that ROC analyses are sensitive to small sample sizes; therefore, the ROC findings were interpreted as a preliminary assessment of discriminatory potential. In contrast, the BMI-adjusted ANCOVA results indicate that the observed biomarker differences cannot be solely attributed to increased adiposity and may reflect an association with metabolic syndrome independent of BMI.

 

Table. Results of ROC analysis of measured values ​​for patients

Variables	MetS-Control		Abdominal		Blood Pressure		FG		HDL		TG
	AUC (%95 CI)	Cut-off (Sens - Spec)	AUC (%95 CI)	Cut-off (Sens - Spec)	AUC (%95 CI)	Cut-off (Sens - Spec)	AUC (%95 CI)	Cut-off (Sens - Spec)	AUC (%95 CI)	Cut-off (Sens - Spec)	AUC (%95 CI)	Cut-off (Sens - Spec)
HIF1a	0,885 (0,816-0,954)	1,2 (0,98-0,68)	0,866 (0,789-0,942)	1 (0,98-0,77)	0,758 (0,651-0,865)	1,3 (0,95-0,55)	0,848 (0,764-0,932)	1,2 (0,97-0,53)	0,664 (0,546-0,783)	1 (0,91-0,81)	0,825 (0,737-0,913)	1,2 (0,97-0,73)
BNIP3	0,928 (0,873-0,982)	0,8 (0,98-0,63)	0,933 (0,88-0,986)	0,8 (0,98-0,62)	0,747 (0,638-0,857)	0,8 (0,95-0,75)	0,811 (0,714-0,908)	0,8 (0,97-0,69)	0,681 (0,56-0,801)	0,5 (0,98-0,97)	0,85 (0,765-0,936)	0,8 (0,97-0,68)
BNIP3L	0,77 (0,668-0,872)	0,2 (0,98-0,7)	0,754 (0,649-0,859)	0,2 (0,98-0,61)	0,631 (0,494-0,768)	0,2 (0,95-0,8)	0,72 (0,604-0,836)	0,2 (0,97-0,74)	0,612 (0,487-0,738)	0,2 (0,98-0,97)	0,699 (0,583-0,815)	0,2 (0,97-0,72)
BMI	0,994 (0,985-1)	27,9 (0,95-0,82)	0,987 (0,969-1)	29,5 (0,83-0,83)	0,845 (0,763-0,928)	29,2 (1-0,7)	0,944 (0,898-0,991)	28,9 (0,97-0,78)	0,755 (0,649-0,861)	20,3 (0,98-0,89)	0,933 (0,877-0,99)	27,7 (0,94-0,64)
WC	0,987 (0,97-1)	92,5 (1-0,93)	0,982 (0,961-1)	88,5 (0,98-0,84)	0,818 (0,727-0,909)	92,5 (0,95-0,6)	0,922 (0,866-0,978)	92,5 (0,97-0,74)	0,744 (0,634-0,854)	68,5 (0,93-0,81)	0,908 (0,837-0,98)	92,5 (0,97-0,8)
SBP	0,833 (0,747-0,919)	105  (0,9-0,83)	0,815 (0,724-0,906)	115 (0,93-0,59)	1 (1-1)	115 (1-0,68)	0,81 (0,71-0,911)	105 (1-0,94)	0,698 (0,582-0,813)	105 (0,98-0,94)	0,797 (0,7-0,893)	115 (0,97-0,6)
DBP	0,666 (0,547-0,785)	75 (0,98-0,83)	0,645 (0,525-0,766)	75 (0,88-0,85)	0,675 (0,517-0,834)	75 (0,85-0,87)	0,654 (0,528-0,781)	65 (1-0,94)	0,518 (0,391-0,646)	65 (0,98-0,94)	0,641 (0,517-0,764)	75 (0,91-0,82)
FG	0,878 (0,795-0,962)	76,5  (0,98-0,83)	0,863 (0,775-0,95)	75,5 (1-0,9)	0,806 (0,686-0,926)	76,5 (0,95-0,92)	1 (1-1)	75,5 (1-0,92)	0,73 (0,621-0,839)	75,5 (0,98-0,92)	0,835 (0,736-0,934)	76,5 (0,97-0,91)
TG	0,956 (0,914-0,999)	88,5 (0,98-0,58)	0,949 (0,904-0,994)	91,5 (0,98-0,51)	0,763 (0,655-0,87)	88,5 (0,55-0,68)	0,884 (0,809-0,958)	88,5 (0,97-0,61)	0,816 (0,722-0,91)	52,5 (0,98-0,97)	1 (1-1)	45 (1-0,98)
HbA1C	0,894 (0,826-0,961)	5,2 (0,98-0,68)	0,886 (0,817-0,956)	5,1 (1-0,8)	0,766 (0,65-0,882)	5,2 (0,95-0,78)	0,861 (0,778-0,944)	5,1 (1-0,84)	0,661 (0,541-0,781)	4,8 (0,98-0,94)	0,879 (0,804-0,953)	5,2 (0,97-0,71)
LDL	0,79 (0,692-0,889)	71,4 (0,98-0,93)	0,767 (0,663-0,871)	70,6 (0,98-0,95)	0,632 (0,496-0,768)	71,4 (0,95-0,95)	0,766 (0,664-0,867)	70,6 (1-0,94)	0,745 (0,639-0,852)	73,5 (0,98-0,81)	0,791 (0,692-0,891)	71,4 (0,97-0,93)
HDL	0,124 (0,043-0,205)	30,9 (0,93-1)	0,124 (0,043-0,206)	31,5 (0,93-0,95)	0,26 (0,151-0,37)	30,3 (0,95-0,97)	0,186 (0,092-0,279)	30,9 (0,92-0,98)	0,071 (0,02-0,121)	40,5 (0,86-0,89)	0,1 (0,03-0,171)	30,9 (0,91-0,98)

MetS, metabolic syndrome; HIF1α, hypoxia-inducible factor 1 alpha; BNIP3, Bcl-2/adenovirus E1B 19 kDa interacting protein 3; BNIP3L, BNIP3-like; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; FG, fasting glucose; HDL, high-density lipoprotein; TC, total cholesterol; TG, triglyceride; LDL, low-density lipoprotein; SD, standard deviation; p value, statistical significance; p < 0.05, there is a statistical difference between the groups (Bold values).Sens; sentivity, Sepc; specifity, AUC; Area under the curve, CI; confidence interval

 

Comments 5. Additionally, it would be beneficial for the authors to provide more details on the validation of the ELISA kits used (Bioassay Technology Laboratory and ELK Biotechnology)14, specifically regarding their sensitivity and specificity for detecting these specific proteins in human serum, as this is a non-standard application for these intracellular markers.

Response: We thank the reviewer for their valuable comment regarding the validation of ELISA kits and their applicability in the detection of HIF-1α, BNIP3, and BNIP3L in human serum. In response to this concern, we have expanded the Methods section as follows to provide additional technical details about test performance and validation. We believe this new paragraph may address the reviewer's concerns.

“Serum HIF-1α (Bioassay Technology Laboratory, Cat. No: E0422Hu, China), BNIP3 (ELK Biotechnology, Catalog No: ELK8447, China), and BNIP3L (ELK Biotechnology, Catalog No: ELK9461, China) levels were measured using commercially available Enzyme-Linked Immunosorbent Assay (ELISA) kits in accordance with the manufacturers’ protocols. All measurements were performed in duplicate using the same reagent lot to minimize inter-assay variability, and sample analysis was conducted in a blinded manner. The intra-assay coefficient of variation was maintained below 10%, as specified by the manufacturers. According to the manufacturers’ datasheets, the analytical measurement ranges of the ELISA kits were 0.05-15 ng/mL for HIF-1α and 0.16-10 ng/mL for both BNIP3 and BNIP3L, allowing reliable quantification of these proteins within the expected concentration ranges in human serum. Additionally, the reported analytical sensitivities were 0.01 ng/mL for HIF-1α, 0.095 ng/mL for BNIP3, and 0.094 ng/mL for BNIP3L.”

 

Comments 6. Finally, the presentation of the results could be improved to better visualize the data distribution. Figure 1 currently uses bar graphs with standard deviation15, but given the sample size of 40, dot plots or box-and-whisker plots would be more appropriate to reveal the spread of the data and any potential outliers.

Response: We thank the reviewer for this helpful suggestion. In accordance with the recommendation, Figure 1 has been revised by replacing bar graphs with dot plots to better illustrate data distribution and potential outliers. The updated figure is now presented below.

Figure 1. HIF1α (A), BNIP3 (B), and BNIP3L (C) analysis. Data expressed as mean ± S.D. ***p = 0.001 in comparison to control. HIF1α, hypoxia-inducible factor 1 alpha; BNIP3, Bcl-2/adenovirus E1B 19 kDa interacting protein 3; BNIP3L, BNIP3-like.

 

Comments 7. Additionally, Figure 3 presents a pathway model linking hypoxia to mitophagy and insulin resistance16; the caption should clearly label this as a "Hypothesized Mechanism" or "Proposed Model," as the current study did not experimentally test the downstream effects on insulin resistance or mitochondrial dysfunction but rather inferred them from serum associations. With significant revisions to the interpretation of the data—specifically softening the claims of causality and addressing the physiological meaning of the serum biomarkers—this manuscript could make a valuable contribution to the field.

Response: We thank the reviewer for this constructive suggestion regarding the interpretation and presentation of Figure 3. In response, we have revised the figure legend to explicitly indicate that the model represents a hypothesized and conceptual framework, rather than a validated mechanistic pathway:

Figure 3. Hypothesized model illustrating the potential involvement of HIF1α-related signaling in MetS. Under hypoxic or adipocyte stress conditions, increased HIF1α expression may be associated with elevated levels of the mitophagy-related proteins BNIP3 and BNIP3L. Although BNIP3 and BNIP3L are known regulators of mitochondrial quality control, the present study does not directly assess mitophagic activity or mitochondrial function. Therefore, the proposed links to mitochondrial stress, inflammation, and insulin resistance are inferred from observed serum associations and existing literature and should be interpreted as a conceptual framework rather than a validated mechanism. MetS, metabolic syndrome; HIF1α, hypoxia-inducible factor 1 alpha; BNIP3, Bcl-2/adenovirus E1B 19 kDa interacting protein 3; BNIP3L, BNIP3-like.

 

In addition, to avoid implying functional activation of mitophagy, the label in the figure has been revised from “↑ Mitophagy” to “↑ Mitophagy-related signaling.” These modifications were implemented to ensure consistency with the observational nature of the study and to clearly distinguish association from causation. We believe that these revisions substantially improve the clarity and scientific accuracy of the figure and directly address the reviewer’s concern.

Author Response File: Author Response.doc

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The revisions done and responses to the reviewer's comments are satisfactory.