Abstract
Background:
Chronic kidney disease (CKD) is a global health challenge, with diabetic kidney disease being a major cause in India. Mitochondrial dysfunction is central to kidney injury, and mitochondrial DNA copy number (mtDNA-CN) reflects mitochondrial health. This study compared mtDNA-CN in urine cell pellets from diabetic CKD (DCKD), non-DCKD (NDCKD), and healthy controls (HC) in a South Indian cohort.
Materials and Methods:
This prospective, cross-sectional study included 75 participants (25 DCKD, 25 NDCKD, 25 HC). Urine cell pellet mtDNA-CN was measured by qPCR. Statistical analysis used ANOVA/Kruskal–Wallis tests (P < 0.05 significant).
Results:
Participants averaged 55.08 ± 13.24 years old. DM patients showed significantly higher mtDNA-CN than non-DM patients (93.96 ± 14.01 vs. 49.04 ± 22.28; P = 0.0001). While no difference existed between CKD Stage III and IV, overall mtDNA-CN varied significantly across No CKD, Stage III, and Stage IV groups (P = 0.0001). A progressive increase in mtDNA-CN was observed from HC (31.84 ± 13.41) to NDCKD (66.24 ± 14.74) and then to DCKD (93.96 ± 14.01), with highly significant differences among these groups (P = 0.0001).
Conclusion:
Our findings indicate a progressive increase in urine cell pellet mtDNA-CN across healthy individuals, NDCKD, and DCKD. This pattern, particularly in DCKD, may reflect a compensatory mitochondrial response to metabolic stress, suggesting mtDNA-CN as a promising noninvasive biomarker for disease monitoring.
Keywords: Biomarker, chronic kidney disease, diabetic kidney disease, mitochondrial DNA copy number, South Indian cohort, urine cell pellets
Résumé
Contexte:
La maladie rénale chronique (MRC) représente un défi mondial majeur pour la santé publique, la néphropathie diabétique étant une cause importante en Inde.
Objectif:
Comparer le nombre de copies de l’ADN mitochondrial (mtDNA-CN) dans les culots cellulaires urinaires chez les patients atteints de MRC diabétique (DCKD), de MRC non diabétique (NDCKD) et chez des témoins sains (HC) dans une cohorte du sud de l’Inde.
Matériels et Méthodes:
Il s’agit d’une étude prospective et transversale incluant 75 participants (25 DCKD, 25 NDCKD et 25 HC). Le mtDNA-CN a été mesuré par PCR quantitative en temps réel à partir des culots cellulaires urinaires.
Résultats:
Une augmentation progressive du mtDNA-CN a été observée des témoins sains aux patients NDCKD, puis DCKD, avec des différences statistiquement significatives entre les groupes.
Conclusion:
L’augmentation du mtDNA-CN, particulièrement chez les patients atteints de DCKD, pourrait refléter une réponse mitochondriale compensatoire au stress métabolique, suggérant le mtDNA-CN comme biomarqueur non invasif potentiel pour la surveillance de la maladie.
Mots-clés: biomarqueur, maladie rénale chronique, néphropathie diabétique, nombre de copies d’ADN mitochondrial, cohorte du sud de l’Inde, culots cellulaires urinaires
INTRODUCTION
Chronic kidney disease (CKD) represents a major global health challenge, characterized by progressive loss of kidney function, significantly increasing morbidity and mortality worldwide. In India, the burden of CKD is substantial and continues to rise, with diabetes mellitus (DM) being the predominant cause, leading to diabetic kidney disease (DKD) and contributing significantly to end-stage renal disease. While hyperglycemia, hypertension, and dyslipidemia are well-established drivers of kidney damage in diabetes, the precise molecular mechanisms underlying the progression of DKD, especially in comparison to nondiabetic CKD (NDCKD), remain areas of active investigation within the Indian demographic. Understanding these distinct pathways could pave the way for more targeted therapeutic strategies relevant to this specific population.[1]
Mitochondria, often termed the “powerhouses of the cell,” are crucial for cellular energy production, metabolic regulation, and cellular homeostasis. Beyond their role in ATP synthesis, mitochondria are central to oxidative stress, inflammation, and apoptosis processes intimately linked to kidney injury and progression in both diabetic and nondiabetic contexts. Mitochondrial dysfunction, characterized by impaired oxidative phosphorylation, increased reactive oxygen species production, and altered mitochondrial dynamics, has emerged as a critical contributor to the pathogenesis of various chronic diseases, including CKD.[2]
Mitochondrial DNA copy number (mtDNA-CN), reflecting the abundance of mitochondria within a cell, is increasingly recognized as a dynamic biomarker of mitochondrial health and cellular stress.[3] Changes in mtDNA-CN can indicate either a compensatory increase in response to mitochondrial damage or a reduction due to degradation or insufficient biogenesis. Previous studies have implicated altered mtDNA-CN in various metabolic disorders and chronic diseases, including diabetes and general CKD.[4] However, comprehensive investigations into mtDNA-CN derived from urine cell pellets offering a more kidney-centric and noninvasive perspective and its associated systemic biomarkers have been limited, particularly within diverse Indian populations. Studies from the Indian subcontinent, especially concerning such detailed molecular insights into CKD differentiation, are still emerging.
This study addresses a significant gap by providing a unique investigation into mitochondrial dynamics within a coastal South Indian population. We hypothesize that the patterns of mitochondrial dysfunction, as reflected by mtDNA-CN and associated inflammatory, oxidative stress, and mitochondrial biogenesis markers, may differ distinctly between diabetic CKD (DCKD) and NDCKD due to varied underlying pathophysiological stressors. Therefore, this study aims to compare the mtDNA-CN in urine cell pellets among patients with DCKD, NDCKD, and healthy controls (HCs) from this specific geographical region. In addition, we will thoroughly investigate the association of mtDNA-CN with inflammatory, oxidative stress, and mitochondrial biogenesis markers in these groups, seeking to identify potential biomarkers for disease differentiation, progression monitoring, or targeted therapeutic avenues relevant to the regional context.
MATERIALS AND METHODS
Study design and ethical considerations
This was a prospective, cross-sectional, observational study conducted at Vinayaka Mission’s Medical College and Hospital, Karaikal, India, over an 18-month period, from January 2022 to June 2023. The study protocol, including all recruitment procedures, informed consent processes, and data collection methods, received approval from the Institutional Ethics Committee of Vinayaka Mission’s Medical College and Hospital, Karaikal. All procedures adhered strictly to the principles outlined in the Declaration of Helsinki (as revised in 2013) and Good Clinical Practice guidelines. Before participation, comprehensive verbal and written information regarding the study’s purpose, procedures, potential risks, and benefits was provided to all eligible individuals in their native language (e.g., Tamil or English). Written informed consent was obtained from each participant before any study-related procedures commenced. For participants who were illiterate, the informed consent process was witnessed by an impartial third party, and their thumbprint was obtained in addition to the witness’s signature, ensuring ethical standards were met for all participants.[5]
Study population and recruitment
A total of 75 participants were enrolled from both inpatient and outpatient departments of Vinayaka Mission’s Medical College and Hospital, Karaikal. Participants were divided into three equally sized groups, with 25 individuals in each: DCKD patients, NDCKD patients, and HCs. DCKD patients were defined as individuals diagnosed with Type 2 DM for at least 6 months and concurrent CKD Stages II, III, or IV. NDCKD patients included individuals with CKD Stages II, III, or IV but without a history of diabetes or prediabetes, as determined by fasting plasma glucose, 2-h postglucose, or HbA1c levels. HCs were age- and sex-matched individuals exhibiting normal kidney function (estimated Glomerular Filtration Rate [eGFR] >90 mL/min/1.73 m2) and no personal history of diabetes, kidney disease, hypertension, or other chronic systemic illnesses.[6]
Inclusion and exclusion criteria
All participants were required to be > 18 years of age and willing to provide informed consent and biological samples. The CKD diagnosis in both DCKD and NDCKD groups was confirmed based on serum creatinine levels and eGFR calculated using the CKD-EPI creatinine equation.[7] Specific CKD stages were defined as follows: Stage II (eGFR 60–89 mL/min/1.73 m2), Stage III (eGFR 30–59 mL/min/1.73 m2), and Stage IV (eGFR 15–29 mL/min/1.73 m2) according to kidney disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guidelines (KDIGO, 2013). The diagnosis of Type 2 DM in the DCKD group was established through medical records documenting the diagnosis for at least 6 months, current use of anti-diabetic medications, or confirmed diagnostic criteria as per American Diabetes Association (ADA) guidelines (fasting plasma glucose >126 mg/dL, 2-h plasma glucose >200 mg/dL during an oral glucose tolerance test, or HbA1c >6.5%) (ADA, 2024).
Exclusion criteria were rigorously applied to ensure homogeneity and minimize confounding factors. Participants with acute kidney injury, kidney diseases of specific etiologies, those undergoing dialysis, or with a history of kidney transplantation, were excluded. Additionally, individuals with a history of malignancy, active systemic infections (e.g., HIV, hepatitis B/C, tuberculosis), autoimmune diseases (apart from T2DM-related complications), severe cardiovascular disease (e.g., recent myocardial infarction, unstable angina, symptomatic heart failure NYHA Class III/IV), chronic liver disease, chronic inflammatory conditions, or those taking medications known to significantly influence mitochondrial function or DNA integrity were excluded. Participants with a history of alcohol or illicit drug abuse within the past six months, pregnant or lactating women, and those with cognitive impairment preventing informed consent were also excluded.[8]
Data collection and clinical parameters
We collected comprehensive demographic and clinical data, including age, sex, BMI, and disease duration. Current medications were recorded, and anthropometric measurements like height, weight, and blood pressure were taken using a standard automated sphygmomanometer (e.g., Omron HEM-7120). Fasting blood and first-morning urine samples were analyzed for standard renal function (serum creatinine, BUN, eGFR, UACR), glycemic control (fasting glucose, HbA1c), and lipid profiles, utilizing automated clinical chemistry analyzers (e.g., Cobas c 501, Roche Diagnostics). To explore novel parameters, we also quantified serum inflammatory markers (high-sensitivity C-reactive protein, interleukin-6, tumor necrosis factor-α) via ELISA (R and D Systems, Bio-Techne, Thermo Fisher Scientific kits), plasma oxidative stress markers (MDA, TAC) and red blood cell enzymatic antioxidant activity (SOD, Catalase) using commercial assay kits (Cayman Chemical, Abcam), and serum mitochondrial biogenesis markers (FGF21, GDF15) via ELISA (R and D Systems kits). All assays followed manufacturer protocols and relevant published methodologies.[,10,11]
Sample collection and processing
Fasting venous blood samples (approximately 10 mL) were collected from each participant. Blood was drawn into K2EDTA vacutainer tubes (BD Vacutainer, Franklin Lakes, NJ, USA) for DNA extraction and complete blood count, BD SST™ II Advance tubes (BD Vacutainer, Franklin Lakes, NJ, USA) for serum biochemical parameters and inflammatory/mitochondrial biogenesis markers, and BD Lithium Heparin tubes (BD Vacutainer, Franklin Lakes, NJ, USA) for oxidative stress markers. All blood samples were processed promptly, within one hour of collection, to minimize degradation. Serum and plasma were separated by centrifugation at 3000 rpm for 10 min at 4°C, after which aliquots were immediately stored at-80°C until further analysis. The buffy coat, an enriched source of leukocytes, was carefully collected from the EDTA tubes and stored at −80°C for genomic DNA (gDNA) extraction.[12]
First morning void urine samples (approximately 10 mL) were collected in sterile containers and transported to the laboratory on ice, with processing initiated within 2 h. Urine creatinine and albumin were measured promptly using automated clinical analyzers for UACR calculation. For the critical assessment of renal-specific mtDNA-CN, 5 mL of urine was centrifuged at 1000 g for 10 min at 4°C to pellet cellular components and debris. The supernatant was discarded, and the resulting urine cell pellet, which contains exfoliated renal cells and cellular fragments, was carefully aliquoted and stored at −80°C for subsequent mitochondrial DNA extraction. This approach was specifically chosen to focus on mitochondrial DNA reflecting kidney cellular health, as cellular mtDNA in urine is thought to be derived from renal tubular epithelial cells.[13,14]
Laboratory procedures
DNA extraction and quality control
gDNA was extracted from the buffy coat samples using the QIAmp DNA Blood Mini Kit (Qiagen, Hilden, Germany, Catalog No. 51104), following the manufacturer’s recommended protocol. This extracted gDNA served as the nuclear DNA (nDNA) reference for calculating mtDNA-CN. For the assessment of mitochondrial DNA (mtDNA) from urine, DNA was meticulously extracted from the stored urine cell pellets. Given the potentially low quantity of DNA in these samples, the QIAamp DNA Micro Kit (Qiagen, Hilden, Germany, Catalog No. 51104), which is optimized for small and precious samples, was utilized according to the manufacturer’s instructions.[15]
The quantity and purity of all extracted DNA samples were rigorously assessed using a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA concentration was determined from absorbance at 260 nm, and purity was evaluated by the A260/A280 and A260/A230 ratios. Only samples exhibiting A260/A280 ratios between 1.8 and 2.0 and A260/A230 ratios >1.8 were deemed acceptable for downstream quantitative real-time polymerase chain reaction (qPCR) analysis, ensuring minimal contamination. The integrity of the extracted DNA was further visually confirmed on a 1% agarose gel for a representative subset of samples.
Mitochondrial DNA copy number) measurement by quantitative real-time PCR (qPCR)
mtDNA-CN was precisely determined using quantitative real-time PCR (qPCR) based on the comparative C_t method (DeltaDeltaC_t). This method provides a relative ratio of a specific mitochondrial gene target to a single-copy nDNA gene, thereby reflecting the average mitochondrial DNA content per cell.[3] Two distinct gene targets were chosen for this analysis: the mitochondrial gene MT-ND1 (NADH dehydrogenase 1), a commonly used and highly stable mitochondrial target, and the single-copy nuclear gene B2M (Beta-2-microglobulin), serving as the internal reference for nDNA normalization.
Gene-specific primers and TaqMan® probes were custom-synthesized by Integrated DNA Technologies (IDT, Coralville, IA, USA) and validated for their high specificity and sensitivity. The sequences for the primers and probes used were as follows, based on commonly published and validated sequences:[16,17]
MT-ND1 Forward: 5’-GGCTATATACAACTACGCAAAGGC-3’
MT-ND1 Reverse: 5’-GGTAGATGTGGCGGGTTTTAGG-3’
MT-ND1 Probe: 5’-FAM-CCATCACCCACTCCATCAACCC-BHQ1-3’
B2M Forward: 5’-CCACTGAAAAAGATGAGTATGCCT-3’
B2M Reverse: 5’-CCAATCCAAATGCGGCATCTTCA-3’
B2M Probe: 5’-VIC-AGTTCACTACTCTCTC-BHQ1-3’.
Each qPCR reaction was performed in triplicate to ensure robust and reproducible results, in a total reaction volume of 20 µL. Each reaction mixture contained 1 × TaqMan Universal Master Mix II (Applied Biosystems, Foster City, CA, USA, Catalog No. 4440038), optimized concentrations of forward and reverse primers (200 nM each), 100 nM of the respective TaqMan probe, and 10 ng of template DNA. Nontemplate controls (NTCs) containing nuclease-free water instead of DNA template were included in each run to monitor for contamination. The qPCR reactions were conducted on a QuantStudio™ 5 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA). The thermocycling conditions were precisely set as follows: an initial denaturation step at 95°C for 10 min, followed by 40 cycles, each consisting of denaturation at 95°C for 15 s and a combined annealing and extension step at 60°C for 60 s. Standard curves were generated for both MT-ND1 and B2M targets using serial dilutions of a reference DNA sample (e.g. from a HC or a pooled sample) to confirm amplification efficiency and linearity, ensuring PCR efficiency between 90% and 110% with R2 > 0.98. The relative mtDNA-CN was calculated using the formula: 2DeltaCt, where DeltaCt = Ct_B2M − Ct_MT − ND1. A higher 2DeltaCt value indicates a higher relative mtDNA copy number.
Statistical analysis
All clinical and laboratory data were recorded and analyzed using SPSS software (IBM SPSS Statistics for Windows, Version 26.0; IBM, Armonk, NY, USA). Descriptive statistics summarized study population characteristics, using mean ± standard deviation, median (interquartile range), or frequencies (percentages) as appropriate after assessing normality with the Shapiro–Wilk test. Comparisons of mtDNA-CNs across the three groups were performed using one-way ANOVA with Tukey’s HSD post hoc tests for normally distributed data, or the Kruskal–Wallis H test followed by Dunn’s post hoc test with Bonferroni correction for nonnormally distributed data.[18] Categorical variables were compared using Chi-square or Fisher’s exact tests. We then explored relationships between mtDNA copy number and various clinical parameters using Pearson’s or Spearman’s correlation analyses. Multiple linear regression analysis identified independent predictors of mtDNA copy number, adjusting for confounders such as age, sex, BMI, eGFR, and HbA1c, after checking model assumptions. P < 0.05 indicated statistical significance.
RESULTS
Baseline characteristics of study participants
The study encompassed 75 cases, and the variables analyzed included age, systolic blood pressure (SBP), diastolic blood pressure (DBP), hemoglobin (HB), white blood cells (WBC), red blood cells (RBC), fasting glucose, postprandial glucose, HbA1C, weight, blood urea, creatinine, and eGFR. The sample size for each variable was 75. The minimum and maximum observed values, as well as the mean and standard deviation for each variable, are reported [Table 1].
Table 1.
Correlation between mitochondrial DNA copy number and clinical parameters
| Clinical parameter | Pearson correlation (r) | P | Statistically significant |
|---|---|---|---|
| eGFR | −0.123 | 0.292 | No |
| Creatinine | +0.032 | 0.783 | No |
| HbA1c | −0.069 | 0.554 | No |
| CRP | −0.054 | 0.646 | No |
| IL-6 | −0.049 | 0.677 | No |
| FGF21 | −0.125 | 0.283 | No |
| GDF15 | −0.113 | 0.331 | No |
eGFR=Estimated glomerular filtration rate, HbA1c=Glycosylated hemoglobin, CRP=C-reactive protein, IL-6=Interleukin-6, FGF21=Fibroblast growth factor 21, GDF15=Growth differentiation factor 15
The age of participants ranged from 20.00 to 84.00 years with a mean of 55.08 ± 13.24 years. SBP measurements varied from 90.00 to 190.00, with a mean of 141.87 ± 26.69. DBP measurements ranged from 60.00 to 110.00, with a mean of 85.60 ± 11.54. Hemoglobin levels were between 4.60 and 13.80, with a mean of 9.74 ± 1.89. WBC counts ranged from 2100.00 to 17770.00, with a mean of 8431.35 ± 2969.79. RBC counts ranged from 68.00 to 508.00, with a mean of 174.53 ± 111.84. Fasting glucose levels were between 68.00 and 380.00, with a mean of 135.96 ± 72.22. Post-prandial Glucose levels ranged from 100.00 to 420.00, with a mean of 175.08 ± 89.71. HbA1C values ranged from 4.50 to 11.00, with a mean of 6.26 ± 1.72. Weight measurements ranged from 46.00 to 92.00, with a mean of 65.94 ± 8.64. Blood Urea levels were between 16.00 and 148.00, with a mean of 59.47 ± 25.46. Creatinine levels ranged from 0.60 to 6.70, with a mean of 2.39 ± 1.35. eGFR values ranged from 16.00 to 127.00, with a mean of 44.70 ± 1.35.
Age and gender distribution
Out of the 75 participants, 50 (66.7%) were male and 25 (33.3%) were female. The male participants constituted the majority of the study sample. Four participants (5.3%) were aged <25 years, with 3 males (4.0%) and 1 female (1.3%). One participant (1.3%) was in the 26-35 years age group, consisting of 1 male (1.3%) and no females. Ten participants (13.3%) were between 36 and 45 years old, with 8 males (10.7%) and 2 females (2.7%). Twenty-two participants (29.3%) were in the 46–55 years age group, comprising 12 males (16.0%) and 10 females (13.3%). The largest age group was 56–65 years, with 25 participants (33.3%), consisting of 14 males (18.7%) and 11 females (14.7%). Thirteen participants (17.3%) were >65 years old, with 12 males (16.0%) and 1 female (1.3%).
Diabetes mellitus and chronic kidney disease (CKD) Distribution
Amongst the 75 participants, 25 (33.3%) cases had DM, and 50 (66.7%) cases had no DM. The distribution of CKD stages among the 75 participants showed that 25 (33.3%) had no CKD, 13 (17.3%) were in Stage III, and 37 (49.3%) were in Stage IV.
Among males, 17 (22.7%) cases had DM, and 33 (44.0%) cases had no DM, totaling 50 males (66.7%). Among females, 8 (10.7%) cases had DM and 17 (22.7%) cases had no DM, totaling 25 females (33.3%).
Regarding CKD distribution by gender, among males, 15 (20.0%) had no CKD, 10 (13.3%) were in stage III, and 25 (33.3%) were in Stage IV, totaling 50 male cases (66.7%). Among females, 10 (13.3%) had no CKD, 3 (4.0%) were in stage III, and 12 (16.0%) were in stage IV, totaling 25 female cases (33.3%).
The distribution of DM with CKD stages was also determined. Among females with DM, 0 (0.0%) had no CKD, 8 (10.7%) were in stage III, and 17 (22.7%) were in stage IV, totaling 25 cases (33.3%). Among males with DM, 25 (33.3%) had no CKD, 5 (6.7%) were in stage III, and 20 (26.7%) were in Stage IV, totaling 50 cases (66.7%). Overall, out of 75 cases, 25 (33.3%) had no CKD, 13 (17.3%) were in Stage III, and 37 (49.3%) were in stage IV.
Mitochondrial DNA copy number analysis
A comparison for DM with mtDNA-CN was conducted among the 75 participants [Table 2]. Among the 25 cases with DM, the mtDNA-CN had a minimum of 71.00 and a maximum of 120.00, with a mean ± standard deviation (SD) value of 93.96 ± 14.01. For the 50 cases without DM, the mtDNA-CN ranged from 10.00 to 100.00, with a mean ± SD value of 49.04 ± 22.28. The T value was 9.194 and the P = 0.0001, indicating statistical significance.
Table 2.
Mitochondrial DNA copy number (mitochondrial DNA copy number) analysis across study groups
| Comparison | Group | n | Minimum | Maximum | Mean±SD | Test statistic | P | Significance |
|---|---|---|---|---|---|---|---|---|
| Diabetes status (DM vs. Non-DM) | Diabetic (DCKD) | 25 | 71.00 | 120.00 | 93.96±14.01 | t=9.194 | <0.0001 | Yes |
| Nondiabetic (NDCKD + HC) | 50 | 10.00 | 100.00 | 49.04±22.28 | ||||
| CKD stage (stage III vs. IV) | Stage III | 13 | 45.00 | 109.00 | 80.31±19.64 | t=0.043 | 0.966 | No |
| Stage IV | 37 | 39.00 | 120.00 | 80.02±20.34 | ||||
| CKD progression (No CKD vs. stages) | No CKD | 25 | 10.00 | 52.00 | 31.84±13.42 | F=58.585 | <0.0001 | Yes |
| Stage III | 13 | 45.00 | 109.00 | 80.31±19.64 | ||||
| Stage IV | 37 | 39.00 | 120.00 | 80.02±20.34 | ||||
| Study group comparison | HC | 25 | 10.00 | 52.00 | 31.84±13.41 | F=122.339 | <0.0001 | Yes |
| NDCKD | 25 | 39.00 | 100.00 | 66.24±14.74 | ||||
| DCKD | 25 | 71.00 | 120.00 | 93.96±14.01 |
SD=Standard deviation, HC=Healthy controls, CKD=Chronic kidney disease, DCKD=Diabetic CKD, NDCKD=Nondiabetic CKD, DM=Diabetes mellitus
A comparison of mtDNA-CN across different stages of CKD (Stage III and Stage IV) was also performed. Among the 13 cases in stage III of CKD, the mtDNA-CN ranged from 45.00 to 109.00, with a mean ± SD value of 80.31 ± 19.64. For the 37 cases in stage IV of CKD, the mtDNA-CN ranged from 39.00 to 120.00, with a mean ± SD value of 80.02 ± 20.34. The T value was 0.043 and the P = 0.966, indicating that the test was statistically insignificant.
When comparing all CKD stages with mtDNA-CN, including cases with no CKD, the results showed that for the 25 cases with no CKD, the mtDNA-CN ranged from 10.00 to 52.00, with a mean ± SD value of 31.84 ± 13.42. The F value was 58.585 and the P = 0.0001, indicating statistical significance.
Finally, a comparison of mtDNA-CN was conducted across DCKD, NDCKD, and HCs. Among the 25 HCs (no CKD), the mtDNA-CN ranged from 10.00 to 52.00, with a mean ± SD value of 31.84 ± 13.41. For the 25 NDCKD cases, the mtDNA-CN ranged from 39.00 to 100.00, with a mean ± SD value of 66.24 ± 14.74. The 25 DCKD cases had mtDNA-CN s ranging from 71.00 to 120.00, with a mean ± SD value of 93.96 ± 14.01. The F value was 122.339 and the P = 0.0001, indicating that the differences across these groups were statistically significant.
Mitochondrial DNA copy number and disease association
A significant finding of this study relates to the association between mitochondrial DNA (mtDNA) copy number and disease states, specifically DM and CKD.
A comparison was conducted to evaluate the relationship between DM and mtDNA copy number. Among the 25 participants diagnosed with DM, the mtDNA copy number ranged from 71.00 to 120.00, with a mean of 93.96 ± 14.01. In contrast, the 50 participants without DM exhibited mtDNA copy numbers ranging from 10.00 to 100.00, with a significantly lower mean of 49.04 ± 22.28. The statistical analysis revealed a T value of 9.194 and a P = 0.0001, indicating a highly statistically significant difference in mtDNA copy number between individuals with and without DM.
The study also investigated the association between mtDNA copy number and the stage of CKD. When comparing only Stage III and Stage IV CKD patients, the 13 cases in Stage III had mtDNA copy numbers from 45.00–109.00 (mean: 80.31 ± 19.64). The 37 cases in Stage IV CKD showed mtDNA copy numbers ranging from 39.00–120.00 (mean: 80.02 ± 20.34). The statistical comparison between these two advanced CKD stages yielded a T value of 0.043 and P = 0.966, indicating no statistically significant difference in mtDNA copy number specifically between Stage III and Stage IV CKD.
However, when considering all CKD groups, including those with no CKD, a broader picture emerged. The 25 participants with no CKD had mtDNA copy numbers ranging from 10.00-52.00, with a mean of 31.84 ± 13.42. This significant difference across the “No CKD,” “Stage III CKD,” and “Stage IV CKD” groups was confirmed by an F value of 58.585 and a P = 0.0001, highlighting a statistically significant relationship between CKD status and mtDNA copy number.
Further, a direct comparison of mtDNA copy number was conducted among three distinct groups: DCKD patients, NDCKD patients, and HCs [Figure 1]. The 25 HCs exhibited mtDNA copy numbers from 10.00 to 52.00 (mean: 31.84 ± 13.41). The 25 NDCKD cases had mtDNA copy numbers between 39.00 and 100.00 (mean: 66.24 ± 14.74). The 25 DCKD cases displayed the highest mtDNA copy numbers, ranging from 71.00 to 120.00 (mean: 93.96 ± 14.01). The F value of 122.339 and a P = 0.0001 confirmed highly significant differences in mtDNA copy number across these three groups, suggesting a progressive increase in mtDNA copy number from healthy individuals to NDCKD, and further to DCKD patients.
Figure 1.
****denotes a highly statistically significant difference between the compared groups (P< 0.001), Red circle (●) Represents Healthy Controls (HC), Blue square (■) Represents Nondiabetic Chronic Kidney Disease (NDCKD), Black triangle (▲) Represents Diabetic Chronic Kidney Disease (DCKD), Quantification of mtDNA copy number in different experimental groups. Values were given as mean ± standard deviation. Results were significant at P < 0.001 by using GraphPad Software, San Diego, CA, USA
DISCUSSION
This study, involving 75 participants, reveals significant associations between mitochondrial DNA (mtDNA) copy number and both DM and CKD progression. These findings contribute to the ongoing dialogue on mitochondrial dysfunction’s role in chronic disease and raise compelling questions regarding the adaptive or pathological nature of mtDNA dynamics.
Our results showed that individuals with DM had significantly higher peripheral blood mtDNA copy numbers compared to those without DM (93.96 ± 14.01 vs. 49.04 ± 22.28; P = 0.0001). This stands in contrast to the majority of published studies that suggest a decrease in mtDNA copy number in patients with diabetes, particularly in those with diabetic complications. For instance, a meta-analysis by found that lower mtDNA copy number was associated with an increased risk of Type 2 Diabetes and Metabolic Syndrome. Similarly, Januszewski et al. reported lower mtDNA copy numbers in Type 1 Diabetes patients, especially those with DKD.[19,20]
Moreover, in Type 2 Diabetes, Al-Kafaji et al. observed a negative correlation between mtDNA copy number and severity of diabetic nephropathy, suggesting mtDNA depletion as the disease advances. These contradictory findings may stem from differences in cohort demographics, disease stages, or sample types. Notably, some studies have shown increased circulating mtDNA in early disease stages as part of a compensatory mitochondrial biogenesis response.[21]
In terms of CKD progression, our study found no significant difference in mtDNA copy number between Stage III and IV CKD patients, but a broader comparison including HCs revealed a significant overall trend (P = 0.0001). This suggests that mitochondrial changes are more prominent across the spectrum rather than between late stages. Several studies support this, including Fazzini et al., who found reduced mtDNA copy number in CKD patients and linked it to increased mortality and inflammation.[17] Similarly, He et al. in the CRIC study observed that lower mtDNA copy number predicted CKD progression, highlighting its prognostic value.[22]
The most striking finding of our study was a stepwise increase in mtDNA copy number across three groups: HCs (31.84 ± 13.41), NDCKD (66.24 ± 14.74), and DCKD (93.96 ± 14.01). While this seems to contradict the expected mitochondrial depletion in chronic disease, it might represent a complex compensatory response, as proposed by studies such as Petrica et al., who noted that both serum and urinary mtDNA levels reflect inflammatory status and renal injury even in early DKD.[23] Other studies, such as those by Huang et al., emphasize the dual nature of mtDNA – both as a marker and potential mediator of kidney injury.[24]
These increases in mtDNA could arise from mitochondrial biogenesis, oxidative stress-induced DNA release, or a reflection of heightened inflammation. Singh et al. further corroborated this by showing that bariatric surgery in obese T2D patients led to reduced urinary mtDNA levels, suggesting a reversible mitochondrial stress phenotype.[25]
In conclusion, our findings point to a nuanced and possibly stage-specific relationship between mtDNA copy number and chronic diseases such as DM and CKD. The increased mtDNA-CN in our cohort may reflect an early compensatory mechanism to metabolic stress, with implications for its use as a dynamic biomarker. Future longitudinal and mechanistic studies are needed to delineate whether these elevations represent protective adaptation, pathogenic signaling, or both.
CONCLUSION
This study highlights the intricate relationship between mitochondrial DNA (mtDNA) copy number, DM, and CKD. Our findings indicate a statistically significant increase in mtDNA copy number in individuals with DM compared to non-diabetic individuals, and a progressive elevation across HCs, NDCKD, and DCKD groups. While this observed increase in mtDNA copy number in our cohort contrasts with some literature reporting decreases in mtDNA-CN in chronic diseases, it may suggest a compensatory mechanism in response to metabolic stress and mitochondrial dysfunction in these conditions. The study underscores the complex and dynamic role of mtDNA in the pathogenesis and progression of both diabetes and kidney disease, warranting further investigation into the precise mechanisms and the potential of mtDNA copy number as a comprehensive biomarker or therapeutic target.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
We extend our sincere gratitude to all the participants who graciously volunteered their time and provided biological samples for this study. We also wish to thank the staff of Vinayaka Mission’s Medical College and Hospital, Karaikal, for their invaluable support in participant recruitment and data collection.
Funding Statement
Nil.
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