Parental clinical manifestation association with neonate KLRG1 expression and telomere Programming in a Pakistani Population

Parental clinical manifestation association with neonate KLRG1 expression and telomere Programming in a Pakistani Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Parental clinical manifestation association with neonate KLRG1 expression and telomere Programming in a Pakistani Population Sadia Farrukh, Saeeda Baig This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7009440/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2025 Read the published version in BMC Research Notes → Version 1 posted 16 You are reading this latest preprint version Abstract Objective This study investigates the association of parental clinical manifestations on newborn telomere biology and immune senescence markers, utilising 204 parent–newborn triads in Karachi, Pakistan. The demographic data collection was followed by quantification of telomere length (TL) using quantitative PCR (qPCR), while Sanger sequencing was performed to analyse variants in telomerase genes (TERC and TERT). Moreover, flow cytometry was used for the analysis of immune senescence markers (CD57 and KLRG1). Results The study revealed that CD57⁺KLRG1⁺ were significantly overexpressed in newborns from the diseased parent (p = 0.045), and particularly KLRG1 + expression was positively correlated with both maternal and paternal TLs (mother: r=0.395; P=0.003 , father: r=0.32; P=0.014). Parents with chronic or acute conditions (hypertension, COVID-19) exhibited shorter TLs (mother: 1.36 ± 1.02, 1.08 ± 0.81; father: 1.41 ± 0.91, 1.18 ± 0.98) compared to their newborns (1.48 +1.17, 2.2 ± 1.47). Furthermore, genotypic analysis revealed a predominance of the TERC C/C genotype among newborns of parents with diabetes [16 (79%)] (p > 0.05). In contrast, the TERT gene showed significant associations with diabetes [10 (50%)] and hypertension [9(56%)], with statistical significance (p = 0.00). The parental clinical manifestations may significantly influence newborn immune senescence, enabling the upregulation of KLRG1 markers and associations with telomere length. Telomere Telomere Length Telomerase TERC TERT Immune Markers Newborn CD57 KLRG1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Immunity ensures tissue homeostasis, but with age, its decline leads to immune senescence [ 1 ]. Telomere shortening driven by genetic and epigenetic factors is a key marker of this process and aging [ 2 – 3 ] and maintained by an enzyme called telomerase. The Telomerase RNA component (TERC) is widely expressed in cells, while telomerase reverse transcriptase (TERT), its protein component, has limited expression in somatic tissues but is active in germ cells, stem cells, and lymphocytes, making TERT a key regulator of telomerase activity. Like DNA polymerase, telomerase extends chromosome ends by adding nucleotide repeats [ 4 ]. Research has shown that telomere shortening or TERT mutations can be inherited and are linked to diseases like dyskeratosis congenita, aplastic anemia, and pulmonary fibrosis [ 5 ]. However, limited data exist on how environmental factors or diseases like diabetes, hypertension, and COVID-19 affect leukocyte telomere length (LTL) and its transmission to the next generation. Telomere shortening activates cell cycle inhibitors (p53, p21, p16) and increases senescent T cells by suppressing cyclins and Cdks [ 1 ]. KLRG1 and CD57 are key markers of immune senescence, with KLRG1 mainly expressed on NK and senescent T cells [ 6 , 7 ]. Whereas, KLRG1 regulates the Akt (ser473) pathway and T cell proliferation but fails to maintain telomere length due to telomerase downregulation [ 8 ]. Therefore, KLRG1 is a potential marker reflecting immune senescence and involved in infection-mediated cellular aging [ 9 – 10 ]. However, CD57⁺ expression in premature T cells is a marker of immune senescence and is associated with autoimmunity and immunodeficiency [ 11 ]. Its role varies across T cell subsets, and in later stages, CD57⁺ is expressed on both CD4⁺ and CD8⁺ T cells. These cells often lack the active form of CD45, leading to reduced expression of costimulatory molecules (CD27/CD28) and the chemokine receptor CCR7 [ 12 ]. In senescence cells, telomerase expression and activity are reduced, which ultimately promotes DNA damage by phosphorylation of the signalling pathway and causes telomere shortening, which initiates inflammaging [ 12 – 13 ]. The functional failure of exhausted T cells, like in Type 2 diabetes mellitus (T2DM), that fail to uptake glucose can be restored through immune checkpoint blockades, but energy imbalance may also be the root of their functional impairment, leading to immune senescence [ 14 – 16 ]. In our Previous research [ 17 ], it was found that immune senescence is more pronounced in parents than in newborns. Hence, it's crucial to investigate parental risk factors and their impact on newborns, particularly how parental clinical conditions influence immune senescence markers, telomere length (TL), and telomerase-related genes (TERC and TERT). Therefore, this study was designed to investigate the impact of parental clinical manifestations on telomere biology and immune senescence markers in newborns. Materials and Methods A total of 612 participants (204 mother-father-newborn triads) were enrolled after ethics approval from Ziauddin University, Karachi (Ref No. 3950721SFBC). Samples were collected from September 2021 to June 2022 using convenience sampling, with informed consent. Eligible participants included females aged 18–35 and males aged 18–45, excluding those with known cancers. Participants were categorized into chronic (diabetes, hypertension, anemia) and acute (COVID-19) disease groups. Blood samples (5 ml) from parents and umbilical cord blood from newborns were collected in EDTA tubes, stored at 4°C, and DNA was extracted using the Qiagen DNA Blood Mini Kit (catalog # 51306, Germany). DNA concentrations were measured using a spectrophotometer and stored at − 80°C for further analysis. Leukocyte telomere length quantification by qPCR Leukocyte telomere length (TL) was measured by qPCR using the multiplex method [ 17 ]. All qPCR tests used the reference DNA (n = 4)(pooled blood of healthy males and females) as a standard in all runs of qPCR. The standard curve was created using the 5 dilutions with a range of 150 − 1.85 ng of reference DNA. The experimental mother, father, and cord DNA were then measured using qPCR. The qPCR reaction procedure was done according to our published work [ 17 ]. Sanger sequencing for TERC and TERT gene polymorphism Following qPCR, further chronic and acute diseased (n = 53) and healthy (n = 10) participants were selected and subjected to Sanger sequencing for polymorphism detection. Variants of the TERC (rs10936599) and TERT (rs2736100) genes were chosen based on their association with telomere length and a minor allele frequency (MAF) > 5% in the Pakistani population, as identified from the 1000 Genomes database. Gene loci were amplified using specific primers (TERC: 617 bp; TERT: 544 bp) and thermal cycling conditions followed established protocols [ 17 ]. Flow Cytometry for Immune Senescence Detection A further flow cytometry technique was used for immune senescence detection. Participants were selected and grouped based on chronic and acute diseases (n = 53). The healthy (n = 10) group was considered as a control. Blood samples were collected in EDTA tubes within 24–48 hours and used to isolate Peripheral Blood Mononuclear Cells (PBMCs), which were cryopreserved. Cells were initially stored at − 20°C, then transferred to − 80°C for long-term preservation. For analysis, PBMCs were revived in culture media with FBS, suspended in PBS, and stained with monoclonal antibodies: FITC-CD57, PerCP-CD45, and PE-KLRG1 (Thermo Fisher). After a 30-minute incubation at 4°C in the dark, flow cytometry was performed using FACS Calibur (BD Biosciences). Gating was done using CD45, with FSC/SSC plots used to assess lymphocyte size and complexity. Data were visualized via dot plots and histograms using FACS DIVA software, distinguishing CD57⁺, KLRG1⁺, double-positive, and double-negative cells. Statistical Analysis The Statistical Package for Social Sciences (SPSS)(version 27) and GraphPad Prism Software (version 10.1.2) were used to analyze the data. The qualitative variables were calculated as frequencies and percentages, whereas the quantitative data were calculated as means and standard deviation (SD). The correlation between parents-newborn TL (T/S ratio) and immune markers was done by Pearson correlation. The mean difference between diseases, TL, TERC & TERT genes and immune senescence markers was done by ANOVA and chi-square test for results analysis. The statistical significance was defined as p < 0.05. Results A total of 612 participants were recruited for the study, the mean age of the mothers was 27 ± 3.12 years, while the mean age of the fathers was 34 ± 3.36 years. Parents aged less than 25 had longer TL (Mother (M): 1.54 ± 1.18, Father (F): 1.73 ± 1.14) compared to those above 35 years. In comparison, newborns (N) to younger parents had smaller TL (1.85 ± 1.40) (p = 0.04) than older parents (2.38 ± 1.62). Demographic analysis revealed significantly shorter telomere lengths (TL) in low socioeconomic status (SES) families across mothers (1.5 ± 1.14), fathers (1.41 ± 1.08), and newborns (1.95 ± 1.36) compared to high SES (p = 0.000). Newborns of graduate parents also showed significantly longer TL (2.35 ± 1.46; p = 0.007). Among white-collar jobs, newborns of teacher mothers (2.57 ± 1.27) and private-sector father employees (2.35 ± 1.20) had the longest TLs (p = 0.000). Furthermore, healthy parents showed longer TLs than those with chronic (M: 1.54 ± 1.37, F: 1.32 ± 1.10) or acute diseases (M: 0.98 ± 0.81, F: 1.18 ± 0.94), while their newborns had significantly longer TLs (2.32 ± 1.43, 2.2 ± 1.47; p = 0.048) (Table 1). In Fig. 1A-1B, TERC genotypes of telomerase enzyme were explored, and it was seen that in the disease group, heterozygous and homozygous genotypes CC, TC and TT were found. Overall effect of chronic diseases in newborns showed the CC genotype more (64%) compared to both parents having TC (M:43%, F:55%) (p > 0.05). Whereas, in acute diseases, only the genotype CC (60%) was seen in newborns and the TC in parents (M:80%, F:60%), but the results were not statistically significant (p > 0.05). Figure 1C, 1D highlight the overall impact of chronic and acute disease prevalence across different TERT genotypes in mothers, fathers, and newborns. TERT gene polymorphism also revealed three genotypes: CC, AC, and AA. Genotype AA was not found in mothers. Moreover, among chronic diseases, the genotype AC (M:54%, F:51%) was found in parents, whereas CC (53%) was in newborns (p = 0.01). Analysis of the TERC and TERT genes revealed a high frequency of the CC genotype in newborns of parents with chronic diseases, particularly diabetes [ TERC: 16 (79%)] (p > 0.05), TERT: 10(50%) (p < 0.05)]. Additionally, the AA genotype [3(15%)] was found in newborns but was absent in mothers. In acute diseases, statistical analysis revealed a significant association between the TERT genotype and disease susceptibility in newborns (p = 0.01), suggesting a potential genetic influence (Supplementary Tables 1, 2). Flow cytometry analysis was performed to assess immunosenescence in parents and their newborns. In cases of parent-newborn diseases, the mean expression of immune senescence markers (CD57⁺KLRG1⁺) on senescent T cells and natural killer cells was elevated (M:93.3%, F:74.2%, N:45.7%), while a decreased expression of markers was observed in healthy parent-newborn pairs (M:26.6%, F:42.5%, N:18.6%) (Fig. 2). Significant results were only seen in newborns with decreased expression compared to parents (p = 0.045)(Fig. 2I). In Table 2, the immunosenescence analysis revealed that healthy parents and their newborns had longer telomere lengths (M:1.89 ± 1.42, F: 1.66 ± 1.18, N: 2.32 ± 1.43) compared to diseased parents. Moreover, when further analysing the diseases, newborns of parents with chronic conditions, including diabetes and hypertension, had shorter telomere lengths (2.02 + 1.36, 1.48 ± 1.17) and increased expression of immune markers (3.1 ± 1.27, 3.5 ± 5.49) with the difference being statistically significant (p = 0.04) (Table 2). Correlation analysis showed a positive association between parental and newborn immune senescence markers, with maternal markers (r = 0.334; p = 0.013) correlating more strongly than paternal ones (r = 0.289; p = 0.033). A positive correlation was found between maternal and newborn KLRG1 (r = 0.524; p = 0.000), indicating greater maternal influence on newborn immune aging (Fig. 3, Supplementary Table 3) . Additionally, parental telomere length (T/S) was positively correlated with newborn KLRG1⁺ levels, while newborn telomere length negatively correlated with CD57 expression (r = − 0.269; p = 0.047) (Fig. 4, supplementary Table 4). Discussion Immune senescence Markers affected by Parental Diseases Clinical manifestations in parents upregulate immune senescence markers in utero were examined for the first time in the Pakistani population and have not been reported before in the literature. The telomere length (TL) of lymphocyte subsets (senescent T-cells, NK cells) between parents and neonates, particularly in different diseases, was also observed for the first time. It was discovered that parents with the diseases had newborns with upregulated immune markers (M:93.3%, F:74.2%, N:45.7%) with positive correlation (M: r = 0.33; P = 0.013; F: r = 0.289; P = 0.033), which was consistent with a study that highlighted adults with more expression of markers (r = 0.48,p = 0.002) than young children (4-8.5 months) [ 18 ]. Fathers with diabetes and hypertension showed elevated immune senescence markers and reduced telomere length compared to healthy fathers. Similar trends were observed in tuberculosis patients, though no effect was seen in their infants [ 19 ]. These upregulation of immunological senescence markers in fathers may provide important clues about the immune system's participation in the etiology of societal health inequities and their transmission to their infants. Mothers with diabetes and their newborns exhibited elevated levels of immune senescence markers (CD57⁺KLRG1⁺)(M: 6.63 ± 3.57, N: 3.5 ± 5.49). This finding is consistent with previous studies on patients with type 2 diabetes mellitus (T2DM), with systemic inflammation [ 16 ]. Maternal metabolic conditions like diabetes may affect fetal immune development and contribute to immune-related disease transmission. In contrast, COVID-19 mothers and newborns (M: 2.12 ± 1.27, N: 2.1 ± 2.82) showed downregulated immune markers, aligning with studies linking low NK cell levels to disease severity [ 20 , 21 ]. These changes may result from acute viral infections, leading to unregulated inflammation and activation of cytotoxic NK and CD8 + T cells. A positive correlation (M: r = 0.395; P = 0.003, F: r = 0.32; P = 0.014 ) between newborn KLRG1 and their parents' TL was seen in this study, which emphasizes the fact that expression of immunological senescence markers in newborns with TL alterations might be employed significantly as a marker of biological aging [ 18 , 22 , 23 ] or a reduction in the body's adaptive immunological response [ 24 ]. Telomere Length modification under the influence of parental diseases Looking deep down into telomere alterations, the parental diseases like diabetes nd hypertension showed a significant association not only with their own (parents') TL but also with the newborns. Shorter telomere length (TL) was observed in newborns to parents with a history of COVID-19, indicating a potential association between prior COVID-19 infection and telomere attrition. This was supported by a group of researchers from Spain, who found an association of shorter telomeres with increased severity of COVID-19 infection when measured among patients between the ages 29 and 85 years old [ 25 ]. Moreover, diabetic mothers had shorter telomeres (1.54 ± 1.37), consistent with a study showing that individuals with latent autoimmune diabetes of adulthood (LADA) had shorter telomeres compared to those with T2DM, particularly when compared to patients treated with metformin and insulin[ 26 ]. Oxidative stress is known to cause telomere shortening, contributing to cardiovascular diseases like hypertension [ 5 ]. In this study, hypertensive mothers had newborns with significantly shorter telomeres, suggesting an intergenerational effect. Conversely, longer telomeres are linked to increased cellular lifespan and may pose a germline risk for cancer development and progression [ 27 ]. TERT and TERC polymorphism and disease progression This study is the first to report TERC (rs10936599) and TERT (rs2736100) gene variations in parents and their newborns, revealing that the homozygous C/C genotype is prevalent in chronic diseases like diabetes and hypertension. In the TERC gene, the CC genotype was predominantly observed in newborns of parents with diabetes [16 (79%)] and COVID-19 [3 (60%)], though the association was not statistically significant (p > 0.05). Similar genotype patterns have been reported in previous studies on related diseases [ 5 , 28 ]. Moreover, newborns of parents with diabetes, hypertension, and COVID-19 showed significant associations with TERT gene variants (p = 0.00), supporting genetic inheritance patterns and aligning with previous studies linking maternal and newborn TL [ 29 – 30 ]. Similarly, a study found that TERT allele homozygotes had a lower prevalence of diabetes than heterozygotes (5.63% vs. 15.38%, p = 0.039) [ 31 ]. According to research on the genotype, the AC genotype was discovered to be a significant risk factor for "idiopathic pulmonary fibrosis" (IPF) in comparison to other lung diseases [ 29 , 32 , 33 ]. Telomere length, maintenance, and repair are influenced by genetic variations in telomerase genes, particularly SNPs [ 34 – 35 ]. A study found that the AC genotype was associated with telomere shortening and disease, while showing maternal genotypes, more commonly inherited by newborns, consistent with the role of perinatal genetic and lifestyle factors [ 36 ]. Notably, in this study, the CC genotype in newborns may indicate a lower disease risk compared to parents with AC or TC genotypes. The overarching effect of this study emphasized that parental health significantly influences newborn health and immune system development. Extensive literature exists on the impact of maternal risk factor modifications on newborn health [ 37 – 38 ]. However, this study highlights for the first time that modifying fathers' external factors like social status and environment may have a progressive effect on both telomeres and the immune senescence of newborns. Different disease exposure is included in this study, which strengthens the results and adds data to the literature. Limitations This study has several limitations. First, as a cross-sectional design, it involved a limited sample size, which may affect the strength of associations observed. Second, the absence of data on lifestyle, social determinants, and maternal nutrient deficiencies may have influenced immune senescence and telomere outcomes. Additionally, reliance on self-reported information introduces potential bias. The analysis was also restricted to a limited set of immune senescence markers and telomerase gene SNPs, which may not fully capture their role in disease development; moreover, including mRNA expression and telomerase protein level monitoring in both parents and newborns could better elucidate the dynamics of telomere biology. Conclusions It was found that parental diseases can significantly affect telomere biology, the TERT gene and up-regulation of the immune senescence markers, especially the KLRG1 in newborns. This is the first study to explore the associations between newborn TL, immune markers, and telomere maintenance genes (TERC and TERT) with parental telomere genetics, both globally and within a subset of Karachi, Pakistan. Declarations Availability of data and materials The sequence data generated and analyzed during the current study are publicly available on the NCBI database under the following accession numbers: TERC : OP046318–OP046360, TERT : OP081479–OP081528 Data supporting telomere length measurements and flow cytometry analyses are not publicly available to protect patient privacy, but they may be obtained from the corresponding author upon reasonable request. Acknowledgements: This article was based on the oral presentation at The 3rd International Online Conference on Cells, Basel, 2025, entitled-Cellular Signalling. The authors want to acknowledge Dr Rubina Hussain and Dr Rehan Imad for their endless support and intellectual contributions to the study. Deep appreciation for Ziauddin University and Hospital doctors, and Dow Research Institute of Bio-Technology and Bio-Sciences (DRIBBS) staff and colleagues in accomplishing the work. Funding This research was supported by a grant from the Higher Education Commission of Pakistan(HEC) National Research Program for Universities- NRPU by (Ref No. 20 15896/NRPU/R&D/HEC/2021 2021) and partially by the Ziauddin University (Ref no. Biochemistry.242.14/5/21) Author Contributions SF contributed to the study design, performed experiments, analyzed the data, and wrote the manuscript. SB gave the main concept of the study, supervised the whole research and revised the manuscript. The authors have read and agreed to the published version of the manuscript Ethics declarations The study was performed following the Declaration of Helsinki , and approval was obtained from the Ziauddin University Ethical Review Board (Ref No. 3950721SFBC). Consent for publication Informed consent for participation and publication was obtained from all the patients before the sample collection. Competing interests The author(s) declare(s) that they have no competing interests. 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Tables Table 1: Mean difference between T/S Ratio and environmental influence on parents and their newborns Variables Mother Father Newborn P-value n (%) TL (T/S Ratio) (Mean + SD) n (%) TL (T/S Ratio) (Mean + SD) TL (T/S Ratio) (Mean + SD) Age (yrs.) 35 5(3) 1.49 + 1.23 79(39) 1.38 + 1.01 2.38 + 1.62 Socioeconomic Status Low 102(50) 1.5 + 1.14 102(50) 1.41 + 1.08 1.95 + 1.36 0.000* High 102(50) 1.93 + 1.37 102(50) 1.70 + 1.12 2.05 + 2.21 Occupation Blue Collar Homemakers 164(80) 1.57 + 1.26 N/A N/A 2.04 + 1.48 0.000* Laborer N/A N/A 28(14) 1.42 + 1.2 2.07 + 1.32 Shopkeeper N/A N/A 7(3) 1.57 + 1.02 2.16 + 1.09 Doctor 8(4) 1.69 + 1.20 11(5) 1.64 + 1.23 2.19 + 1.35 White collar Business N/A N/A 43(21) 1.67 + 1.15 2.21 + 1.10 Private Job 19(9) 1.67 + 1.25 115(56) 1.37 + 1.20 2.35 + 1.20 Teacher 13(6) 1.62 + 1.24 N/A N/A 2.57 + 1.27 Education No education 25(12) 1.26 + 1.01 29(14) 1.85 + 1.45 1.49 + 1.32 0.007* Primary 27(13) 1.32 + 1.06 20(10) 1.51 + 0.99 1.76 + 1.31 secondary 32(16) 1.88 + 1.24 25(12) 1.78 + 0.95 2.07 + 1.02 undergraduate 37(18) 1.64 + 1.36 53(26) 1.64 + 1.08 2.22 + 1.77 Graduate 66(32) 1.94 + 1.44 57(28) 1.34 + 1.07 2.35 + 1.46 Postgraduate 17(8) 1.68 + 0.99 20(10) 1.50 + 1.15 2.30 + 1.40 Health Status Healthy 46(22) 1.89 + 1.42 121 (60) 1.66 + 1.18 2.34 + 1.20 0.048* Chronic Diseases 138(68) 1.54 + 1.37 68 (33) 1.32 + 1.1 2.32 + 1.43 Acute Diseases 20(10) 0.98 + 0.81 15(7) 1.18 + 0.94 2.2 + 1.47 *P value: significant N/A: Not Available Chronic Diseases: Diabetes, Hypertension, Anemia , Acute diseases: COVID-19 Table 2: Comparison of mother-newborn immune senescence markers in different diseases Parameters Mother (Mean + SD) Fathers (Mean + SD) Newborn (Mean + SD) Telomere Length (T/S ratio) CD57+ CD57+ KLRG1+ KLRG1+ Telomere Length (T/S ratio) CD57+ CD57+ KLRG1+ KLRG1+ Telomere Length (T/S ratio) CD57+ CD57+ KLRG1+ KLRG1+ Healthy n=10 1.89 + 1.42 2.5 + 2.58 4.16 + 2.97 3.25 + 4.26 1.66 + 1.18 2.36 + 1.85 4.23 + 4.29 3.61 + 4.19 2.32 + 1.43 2.20 + 0.87 2.45 + 4.34 1.74 + 4.23 Diabetes n=20 1.54 + 1.37 2.09 + 2.43 6.63 + 3.57 3.40 + 3.43 1.32 + 1.1 3.2 + 2.52 6.82 + 4.76 4.78 + 3.3 2.02 + 1.36 2.41 + 1.28 3.5 + 5.49 3.44 + 3.96 Hypertension n=16 1.36 + 1.02 5.0 + 5.9 5.4 + 4.94 3.31 + 3.8 1.41 + 0.91 2.57 + 0.39 5.83 + 3.23 4.07 + 6.95 1.48 + 1.17 3.4 + 0.56 3.1 + 1.27 1.2 + 9.61 Diabetes & Hypertension n=5 1.29 + 1.02 2.56 + 2.61 4.6 + 5.02 3.27 + 3.79 1.46 + 1.16 2.48 + 2.15 6.98 + 4.68 8.95 + 9.63 2.28 + 1.54 2.80 + 0.34 2.32 + 2.51 1.15 + 1.32 Anemia n=7 1.68 + 1.26 2.75 + 2.70 5.3 + 3.07 2.2 + 3.67 1.26 + 1.09 2.2 + 1.25 5.28 + 4.89 4.76 + 2.3 2.02 + 1.03 2.91 + 2.08 3.15 + 3.49 2.04 + 2.61 COVID-19 n=5 1.08 + 0.81 3.32 + 1.41 2.12 + 1.27 3.92 + 2.72 1.18 + 0.98 5.43 + 4.09 5.16 + 5.05 7.53 + 3.68 2.2 + 1.47 2.35 + 0.3 2.1 + 2.82 2.5 + 3.96 P value 0.89 0.91 0.02* 0.008* 0.11 0.87 0.60 0.14 0.67 0.21 0.04* 0.18 Additional Declarations No competing interests reported. Supplementary Files Figures.docx SupplementaryTable.docx Cite Share Download PDF Status: Published Journal Publication published 30 Oct, 2025 Read the published version in BMC Research Notes → Version 1 posted Editorial decision: Revision requested 04 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviews received at journal 28 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 24 Jul, 2025 Editor invited by journal 14 Jul, 2025 Submission checks completed at journal 11 Jul, 2025 First submitted to journal 11 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7009440","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":491458294,"identity":"dcfb1ec5-949d-4c49-bb12-8be35abdd7ca","order_by":0,"name":"Sadia Farrukh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACgwMMDHIgBjMzQwWDAYglQYQWY6iWM0RqYYBrYWwjQot8++GNB34wGMibzz5jbFw4z87Y4ADzwds8+Kw4k1ZwsIfBwHDOuRzj5Jnbks0MDrAlW+PVwpBjcICH4Q/jDB4e48O825htgFwzaXxa5PvfGBz8w2BgD9Eypx6ohf8bXi0MN3IMDvMwGCSCtCTzNhwGOoyHDa8WgxvPCg7LGBgkz+BhKzbmOXbcWPIwm7HlHLwOS9788U2Fge0MHubN0jw11YZ9x5sf3niDz2EQu5A5zASVj4JRMApGwSggBABkMUReuFTpKgAAAABJRU5ErkJggg==","orcid":"","institution":"The Aga Khan University","correspondingAuthor":true,"prefix":"","firstName":"Sadia","middleName":"","lastName":"Farrukh","suffix":""},{"id":491458298,"identity":"f7d4232e-d15a-4fd8-b0c8-d22405719a52","order_by":1,"name":"Saeeda Baig","email":"","orcid":"","institution":"Ziauddin University","correspondingAuthor":false,"prefix":"","firstName":"Saeeda","middleName":"","lastName":"Baig","suffix":""}],"badges":[],"createdAt":"2025-06-30 10:23:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7009440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7009440/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13104-025-07498-4","type":"published","date":"2025-10-30T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87831101,"identity":"953e3721-cd96-455e-9cf8-0c1f2227902d","added_by":"auto","created_at":"2025-07-29 12:27:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82335,"visible":true,"origin":"","legend":"\u003cp\u003eTERC and TERT genotype distribution among chronic and acute diseases in parents and newborns.\u003c/p\u003e\n\u003cp\u003eThe CC genotype was dominant in newborns with chronic (A) and acute diseases (B), whereas the TT genotype was seen only in chronic diseases. The TERT genotype CC was dominant in newborns with chronic (53%) and acute disease (60%) in newborns (C-D) with significant results (p=0.00).\u003c/p\u003e\n\u003cp\u003ens: non-significant, * significant\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/83aa80e621d20c8c4dc39957.png"},{"id":87831102,"identity":"bf35d883-9242-4de2-b40e-4b3e5100aa12","added_by":"auto","created_at":"2025-07-29 12:27:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154650,"visible":true,"origin":"","legend":"\u003cp\u003eImmune\u0026nbsp;senescence markers (CD57, KLRG1) in Parents and newborns.\u003c/p\u003e\n\u003cp\u003eHealthy\u0026nbsp;Mothers(M), Fathers(F) and Newborns(N) (A-C) had decreased expression of\u0026nbsp;CD57+KLRG1+ (M:26.6%, F:42.5%, N:18.6%) compared to diseased (D-F), which\u0026nbsp;showed upregulation of immune markers (M:93.3%, F:74.2%, N:45.7%). The\u0026nbsp;non-significant results were seen when the disease and healthy groups of mothers and fathers were explored (G-H). Significant expression of\u0026nbsp;CD57+KLRG1+ was seen in newborns (p=0.045)(I).\u003c/p\u003e\n\u003cp\u003eRed tiles: Diseased; Green tiles: Healthy; ns: non-significant;*: significant\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/9119e64911e034d27fe43887.png"},{"id":87831400,"identity":"4a7f8dbb-f114-4161-a899-2bd87948bcdd","added_by":"auto","created_at":"2025-07-29 12:35:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102820,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix among the immune senescence markers of\u0026nbsp;parents and newborns.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/8bba27e16fc800cdd9089d1e.png"},{"id":87830006,"identity":"b1e77c0d-9bac-4a95-9cb2-392bc4b4d789","added_by":"auto","created_at":"2025-07-29 12:19:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":381400,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix with scatter plot between TL and immune senescence markers of parents\u0026nbsp;and newborns.\u003c/p\u003e\n\u003cp\u003eIt was found that mothers' and fathers' TL (T/S) are positively correlated with\u0026nbsp;newborn immune senescence marker (KLRG1+)(M: r=0.395; P=0.003\u003cstrong\u003e), (\u003c/strong\u003eF: r=0.32; P=0.014), with significant results.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/2b04ec73084eb1f182f214cb.png"},{"id":95040449,"identity":"ddf9665b-f768-461b-bc53-4a0b008c325a","added_by":"auto","created_at":"2025-11-03 16:08:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1500887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/3b328dca-7186-4acc-ba0f-cb3ff7ce27b5.pdf"},{"id":87831105,"identity":"575b2542-2484-4329-b189-ad1350b81df8","added_by":"auto","created_at":"2025-07-29 12:27:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":177215,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/74c83efbf36e80a46df0ec67.docx"},{"id":87830007,"identity":"4951bc4c-c7e0-4ce8-93fe-671350bdb837","added_by":"auto","created_at":"2025-07-29 12:19:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28255,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7009440/v1/68038bb97b306b0ed7acfd25.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Parental clinical manifestation association with neonate KLRG1 expression and telomere Programming in a Pakistani Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmunity ensures tissue homeostasis, but with age, its decline leads to immune senescence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Telomere shortening driven by genetic and epigenetic factors is a key marker of this process and aging [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and maintained by an enzyme called telomerase. The Telomerase RNA component (TERC) is widely expressed in cells, while telomerase reverse transcriptase (TERT), its protein component, has limited expression in somatic tissues but is active in germ cells, stem cells, and lymphocytes, making TERT a key regulator of telomerase activity. Like DNA polymerase, telomerase extends chromosome ends by adding nucleotide repeats [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Research has shown that telomere shortening or TERT mutations can be inherited and are linked to diseases like dyskeratosis congenita, aplastic anemia, and pulmonary fibrosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, limited data exist on how environmental factors or diseases like diabetes, hypertension, and COVID-19 affect leukocyte telomere length (LTL) and its transmission to the next generation.\u003c/p\u003e\u003cp\u003eTelomere shortening activates cell cycle inhibitors (p53, p21, p16) and increases senescent T cells by suppressing cyclins and Cdks [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. KLRG1 and CD57 are key markers of immune senescence, with KLRG1 mainly expressed on NK and senescent T cells [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Whereas, KLRG1 regulates the Akt (ser473) pathway and T cell proliferation but fails to maintain telomere length due to telomerase downregulation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, KLRG1 is a potential marker reflecting immune senescence and involved in infection-mediated cellular aging [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, CD57⁺ expression in premature T cells is a marker of immune senescence and is associated with autoimmunity and immunodeficiency [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Its role varies across T cell subsets, and in later stages, CD57⁺ is expressed on both CD4⁺ and CD8⁺ T cells. These cells often lack the active form of CD45, leading to reduced expression of costimulatory molecules (CD27/CD28) and the chemokine receptor CCR7 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn senescence cells, telomerase expression and activity are reduced, which ultimately promotes DNA damage by phosphorylation of the signalling pathway and causes telomere shortening, which initiates inflammaging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The functional failure of exhausted T cells, like in Type 2 diabetes mellitus (T2DM), that fail to uptake glucose can be restored through immune checkpoint blockades, but energy imbalance may also be the root of their functional impairment, leading to immune senescence [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn our Previous research [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], it was found that immune senescence is more pronounced in parents than in newborns. Hence, it's crucial to investigate parental risk factors and their impact on newborns, particularly how parental clinical conditions influence immune senescence markers, telomere length (TL), and telomerase-related genes (TERC and TERT). Therefore, this study was designed to investigate the impact of parental clinical manifestations on telomere biology and immune senescence markers in newborns.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eA total of 612 participants (204 mother-father-newborn triads) were enrolled after ethics approval from Ziauddin University, Karachi (Ref No. 3950721SFBC). Samples were collected from September 2021 to June 2022 using convenience sampling, with informed consent. Eligible participants included females aged 18\u0026ndash;35 and males aged 18\u0026ndash;45, excluding those with known cancers. Participants were categorized into chronic (diabetes, hypertension, anemia) and acute (COVID-19) disease groups. Blood samples (5 ml) from parents and umbilical cord blood from newborns were collected in EDTA tubes, stored at 4\u0026deg;C, and DNA was extracted using the Qiagen DNA Blood Mini Kit (catalog # 51306, Germany). DNA concentrations were measured using a spectrophotometer and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for further analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLeukocyte telomere length quantification by qPCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLeukocyte telomere length (TL) was measured by qPCR using the multiplex method [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. All qPCR tests used the reference DNA (n\u0026thinsp;=\u0026thinsp;4)(pooled blood of healthy males and females) as a standard in all runs of qPCR. The standard curve was created using the 5 dilutions with a range of 150\u0026thinsp;\u0026minus;\u0026thinsp;1.85 ng of reference DNA. The experimental mother, father, and cord DNA were then measured using qPCR. The qPCR reaction procedure was done according to our published work [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSanger sequencing for TERC and TERT gene polymorphism\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFollowing qPCR, further chronic and acute diseased (n\u0026thinsp;=\u0026thinsp;53) and healthy (n\u0026thinsp;=\u0026thinsp;10) participants were selected and subjected to Sanger sequencing for polymorphism detection. Variants of the TERC (rs10936599) and TERT (rs2736100) genes were chosen based on their association with telomere length and a minor allele frequency (MAF)\u0026thinsp;\u0026gt;\u0026thinsp;5% in the Pakistani population, as identified from the 1000 Genomes database. Gene loci were amplified using specific primers (TERC: 617 bp; TERT: 544 bp) and thermal cycling conditions followed established protocols [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eFlow Cytometry for Immune Senescence Detection\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA further flow cytometry technique was used for immune senescence detection. Participants were selected and grouped based on chronic and acute diseases (n\u0026thinsp;=\u0026thinsp;53). The healthy (n\u0026thinsp;=\u0026thinsp;10) group was considered as a control. Blood samples were collected in EDTA tubes within 24\u0026ndash;48 hours and used to isolate Peripheral Blood Mononuclear Cells (PBMCs), which were cryopreserved. Cells were initially stored at \u0026minus;\u0026thinsp;20\u0026deg;C, then transferred to \u0026minus;\u0026thinsp;80\u0026deg;C for long-term preservation. For analysis, PBMCs were revived in culture media with FBS, suspended in PBS, and stained with monoclonal antibodies: FITC-CD57, PerCP-CD45, and PE-KLRG1 (Thermo Fisher). After a 30-minute incubation at 4\u0026deg;C in the dark, flow cytometry was performed using FACS Calibur (BD Biosciences). Gating was done using CD45, with FSC/SSC plots used to assess lymphocyte size and complexity. Data were visualized via dot plots and histograms using FACS DIVA software, distinguishing CD57⁺, KLRG1⁺, double-positive, and double-negative cells.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe Statistical Package for Social Sciences (SPSS)(version 27) and GraphPad Prism Software (version 10.1.2) were used to analyze the data. The qualitative variables were calculated as frequencies and percentages, whereas the quantitative data were calculated as means and standard deviation (SD). The correlation between parents-newborn TL (T/S ratio) and immune markers was done by Pearson correlation. The mean difference between diseases, TL, TERC \u0026amp; TERT genes and immune senescence markers was done by ANOVA and chi-square test for results analysis. The statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 612 participants were recruited for the study, the mean age of the mothers was 27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12 years, while the mean age of the fathers was 34\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36 years. Parents aged less than 25 had longer TL (Mother (M): 1.54\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.18, Father (F): 1.73\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.14) compared to those above 35 years. In comparison, newborns (N) to younger parents had smaller TL (1.85\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.40) (p\u0026thinsp;=\u0026thinsp;0.04) than older parents (2.38\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.62). Demographic analysis revealed significantly shorter telomere lengths (TL) in low socioeconomic status (SES) families across mothers (1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14), fathers (1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08), and newborns (1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36) compared to high SES (p\u0026thinsp;=\u0026thinsp;0.000). Newborns of graduate parents also showed significantly longer TL (2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46; p\u0026thinsp;=\u0026thinsp;0.007). Among white-collar jobs, newborns of teacher mothers (2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27) and private-sector father employees (2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20) had the longest TLs (p\u0026thinsp;=\u0026thinsp;0.000). Furthermore, healthy parents showed longer TLs than those with chronic (M: 1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37, F: 1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10) or acute diseases (M: 0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81, F: 1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94), while their newborns had significantly longer TLs (2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43, 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47; p\u0026thinsp;=\u0026thinsp;0.048) (Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;1A-1B, TERC genotypes of telomerase enzyme were explored, and it was seen that in the disease group, heterozygous and homozygous genotypes CC, TC and TT were found. Overall effect of chronic diseases in newborns showed the CC genotype more (64%) compared to both parents having TC (M:43%, F:55%) (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Whereas, in acute diseases, only the genotype CC (60%) was seen in newborns and the TC in parents (M:80%, F:60%), but the results were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eFigure 1C, 1D highlight the overall impact of chronic and acute disease prevalence across different TERT genotypes in mothers, fathers, and newborns. TERT gene polymorphism also revealed three genotypes: CC, AC, and AA. Genotype AA was not found in mothers. Moreover, among chronic diseases, the genotype AC (M:54%, F:51%) was found in parents, whereas CC (53%) was in newborns (p\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eAnalysis of the TERC and TERT genes revealed a high frequency of the CC genotype in newborns of parents with chronic diseases, particularly diabetes [ TERC: 16 (79%)] (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), TERT: 10(50%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)]. Additionally, the AA genotype [3(15%)] was found in newborns but was absent in mothers. In acute diseases, statistical analysis revealed a significant association between the TERT genotype and disease susceptibility in newborns (p\u0026thinsp;=\u0026thinsp;0.01), suggesting a potential genetic influence (Supplementary Tables\u0026nbsp;1, 2).\u003c/p\u003e\u003cp\u003eFlow cytometry analysis was performed to assess immunosenescence in parents and their newborns. In cases of parent-newborn diseases, the mean expression of immune senescence markers (CD57⁺KLRG1⁺) on senescent T cells and natural killer cells was elevated (M:93.3%, F:74.2%, N:45.7%), while a decreased expression of markers was observed in healthy parent-newborn pairs (M:26.6%, F:42.5%, N:18.6%) (Fig.\u0026nbsp;2). Significant results were only seen in newborns with decreased expression compared to parents (p\u0026thinsp;=\u0026thinsp;0.045)(Fig.\u0026nbsp;2I).\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;2, the immunosenescence analysis revealed that healthy parents and their newborns had longer telomere lengths (M:1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42, F: 1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18, N: 2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43) compared to diseased parents. Moreover, when further analysing the diseases, newborns of parents with chronic conditions, including diabetes and hypertension, had shorter telomere lengths (2.02\u0026thinsp;+\u0026thinsp;1.36, 1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17) and increased expression of immune markers (3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27, 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49) with the difference being statistically significant (p\u0026thinsp;=\u0026thinsp;0.04) (Table\u0026nbsp;2).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCorrelation analysis showed a positive association between parental and newborn immune senescence markers, with maternal markers (r\u0026thinsp;=\u0026thinsp;0.334; p\u0026thinsp;=\u0026thinsp;0.013) correlating more strongly than paternal ones (r\u0026thinsp;=\u0026thinsp;0.289; p\u0026thinsp;=\u0026thinsp;0.033). A positive correlation was found between maternal and newborn KLRG1 (r\u0026thinsp;=\u0026thinsp;0.524; p\u0026thinsp;=\u0026thinsp;0.000), indicating greater maternal influence on newborn immune aging \u003cb\u003e(Fig.\u0026nbsp;3, Supplementary Table\u0026nbsp;3)\u003c/b\u003e. Additionally, parental telomere length (T/S) was positively correlated with newborn KLRG1⁺ levels, while newborn telomere length negatively correlated with CD57 expression (r = \u0026minus;\u0026thinsp;0.269; p\u0026thinsp;=\u0026thinsp;0.047) \u003cb\u003e(Fig.\u0026nbsp;4, supplementary Table\u0026nbsp;4).\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eImmune senescence Markers affected by Parental Diseases\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eClinical manifestations in parents upregulate immune senescence markers in utero were examined for the first time in the Pakistani population and have not been reported before in the literature. The telomere length (TL) of lymphocyte subsets (senescent T-cells, NK cells) between parents and neonates, particularly in different diseases, was also observed for the first time. It was discovered that parents with the diseases had newborns with upregulated immune markers (M:93.3%, F:74.2%, N:45.7%) with positive correlation (M: r\u0026thinsp;=\u0026thinsp;0.33; P\u0026thinsp;=\u0026thinsp;0.013; F: r\u0026thinsp;=\u0026thinsp;0.289; P\u0026thinsp;=\u0026thinsp;0.033), which was consistent with a study that highlighted adults with more expression of markers (r\u0026thinsp;=\u0026thinsp;0.48,p\u0026thinsp;=\u0026thinsp;0.002) than young children (4-8.5 months) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFathers with diabetes and hypertension showed elevated immune senescence markers and reduced telomere length compared to healthy fathers. Similar trends were observed in tuberculosis patients, though no effect was seen in their infants [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These upregulation of immunological senescence markers in fathers may provide important clues about the immune system's participation in the etiology of societal health inequities and their transmission to their infants.\u003c/p\u003e\u003cp\u003eMothers with diabetes and their newborns exhibited elevated levels of immune senescence markers (CD57⁺KLRG1⁺)(M: 6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57, N: 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49). This finding is consistent with previous studies on patients with type 2 diabetes mellitus (T2DM), with systemic inflammation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Maternal metabolic conditions like diabetes may affect fetal immune development and contribute to immune-related disease transmission. In contrast, COVID-19 mothers and newborns (M: 2.12\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.27, N: 2.1\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.82) showed downregulated immune markers, aligning with studies linking low NK cell levels to disease severity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These changes may result from acute viral infections, leading to unregulated inflammation and activation of cytotoxic NK and CD8\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e\u003cp\u003eA positive correlation (M: r\u0026thinsp;=\u0026thinsp;0.395; P\u0026thinsp;=\u0026thinsp;0.003, F: r\u0026thinsp;=\u0026thinsp;0.32; P\u0026thinsp;=\u0026thinsp;0.014 ) between newborn KLRG1 and their parents' TL was seen in this study, which emphasizes the fact that expression of immunological senescence markers in newborns with TL alterations might be employed significantly as a marker of biological aging [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] or a reduction in the body's adaptive immunological response [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTelomere Length modification under the influence of parental diseases\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLooking deep down into telomere alterations, the parental diseases like diabetes nd hypertension showed a significant association not only with their own (parents') TL but also with the newborns. Shorter telomere length (TL) was observed in newborns to parents with a history of COVID-19, indicating a potential association between prior COVID-19 infection and telomere attrition. This was supported by a group of researchers from Spain, who found an association of shorter telomeres with increased severity of COVID-19 infection when measured among patients between the ages 29 and 85 years old [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, diabetic mothers had shorter telomeres (1.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37), consistent with a study showing that individuals with latent autoimmune diabetes of adulthood (LADA) had shorter telomeres compared to those with T2DM, particularly when compared to patients treated with metformin and insulin[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOxidative stress is known to cause telomere shortening, contributing to cardiovascular diseases like hypertension [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this study, hypertensive mothers had newborns with significantly shorter telomeres, suggesting an intergenerational effect. Conversely, longer telomeres are linked to increased cellular lifespan and may pose a germline risk for cancer development and progression [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eTERT and TERC polymorphism and disease progression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study is the first to report TERC (rs10936599) and TERT (rs2736100) gene variations in parents and their newborns, revealing that the homozygous C/C genotype is prevalent in chronic diseases like diabetes and hypertension. In the TERC gene, the CC genotype was predominantly observed in newborns of parents with diabetes [16 (79%)] and COVID-19 [3 (60%)], though the association was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similar genotype patterns have been reported in previous studies on related diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, newborns of parents with diabetes, hypertension, and COVID-19 showed significant associations with TERT gene variants (p\u0026thinsp;=\u0026thinsp;0.00), supporting genetic inheritance patterns and aligning with previous studies linking maternal and newborn TL [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, a study found that TERT allele homozygotes had a lower prevalence of diabetes than heterozygotes (5.63% vs. 15.38%, p\u0026thinsp;=\u0026thinsp;0.039) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. According to research on the genotype, the AC genotype was discovered to be a significant risk factor for \"idiopathic pulmonary fibrosis\" (IPF) in comparison to other lung diseases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTelomere length, maintenance, and repair are influenced by genetic variations in telomerase genes, particularly SNPs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A study found that the AC genotype was associated with telomere shortening and disease, while showing maternal genotypes, more commonly inherited by newborns, consistent with the role of perinatal genetic and lifestyle factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Notably, in this study, the CC genotype in newborns may indicate a lower disease risk compared to parents with AC or TC genotypes.\u003c/p\u003e\u003cp\u003eThe overarching effect of this study emphasized that parental health significantly influences newborn health and immune system development. Extensive literature exists on the impact of maternal risk factor modifications on newborn health [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, this study highlights for the first time that modifying fathers' external factors like social status and environment may have a progressive effect on both telomeres and the immune senescence of newborns. Different disease exposure is included in this study, which strengthens the results and adds data to the literature.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, as a cross-sectional design, it involved a limited sample size, which may affect the strength of associations observed. Second, the absence of data on lifestyle, social determinants, and maternal nutrient deficiencies may have influenced immune senescence and telomere outcomes. Additionally, reliance on self-reported information introduces potential bias. The analysis was also restricted to a limited set of immune senescence markers and telomerase gene SNPs, which may not fully capture their role in disease development; moreover, including mRNA expression and telomerase protein level monitoring in both parents and newborns could better elucidate the dynamics of telomere biology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIt was found that parental diseases can significantly affect telomere biology, the TERT gene and up-regulation of the immune senescence markers, especially the KLRG1 in newborns. This is the first study to explore the associations between newborn TL, immune markers, and telomere maintenance genes (TERC and TERT) with parental telomere genetics, both globally and within a subset of Karachi, Pakistan.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequence data generated and analyzed during the current study are publicly available on the NCBI database under the following accession numbers: \u003cstrong\u003eTERC\u003c/strong\u003e: OP046318–OP046360, \u003cstrong\u003eTERT\u003c/strong\u003e: OP081479–OP081528\u003c/p\u003e\n\u003cp\u003eData supporting telomere length measurements and flow cytometry analyses are not publicly available to protect patient privacy, but they may be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was based on the oral presentation at The 3rd International Online Conference on Cells, Basel, 2025, entitled-Cellular Signalling.\u0026nbsp;The authors want to acknowledge Dr Rubina Hussain and Dr Rehan Imad for their endless support and intellectual contributions to the study. Deep appreciation for Ziauddin University and Hospital doctors, and Dow Research Institute of Bio-Technology and Bio-Sciences (DRIBBS) staff and colleagues in accomplishing the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a grant from the Higher Education Commission of Pakistan(HEC) National Research Program for Universities- NRPU by (Ref No. 20 15896/NRPU/R\u0026amp;D/HEC/2021 2021) and partially by the Ziauddin University (Ref no. Biochemistry.242.14/5/21)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSF contributed to the study design, performed experiments, analyzed the data, and wrote the manuscript. SB gave the main concept of the study, supervised the whole research and revised the manuscript. The authors have read and agreed to the published version of the manuscript\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003eThe study was performed following\u0026nbsp;the\u0026nbsp;Declaration of Helsinki\u003c/a\u003e, and\u0026nbsp;approval was obtained from the Ziauddin University Ethical Review Board (Ref No. 3950721SFBC).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent for participation and publication was obtained from all the patients before the sample collection.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe author(s) declare(s) that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRodriguez IJ, Lalinde Ruiz N, Llano Le\u0026oacute;n M, Mart\u0026iacute;nez Enr\u0026iacute;quez L, Montilla Vel\u0026aacute;squez M del P, Ortiz Aguirre JP et al. 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Front Immunol. 2023;14.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Mean difference between T/S Ratio and environmental influence on parents and their newborns\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"102%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMother\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003eFather\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNewborn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTL (T/S Ratio)\u003c/p\u003e\n \u003cp\u003e(Mean\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTL (T/S Ratio)\u003c/p\u003e\n \u003cp\u003e(Mean\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTL (T/S Ratio)\u003c/p\u003e\n \u003cp\u003e(Mean\u003cu\u003e+\u003c/u\u003e SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eAge (yrs.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56(27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.54\u003cu\u003e+\u003c/u\u003e 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.73\u003cu\u003e+\u003c/u\u003e 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1.85\u003cu\u003e+\u003c/u\u003e 1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e25-35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e143(70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52\u003cu\u003e+\u003c/u\u003e 1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e114(60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.57\u003cu\u003e+\u003c/u\u003e 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.31\u003cu\u003e+\u003c/u\u003e 1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.49\u003cu\u003e+\u003c/u\u003e 1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79(39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.38\u003cu\u003e+\u003c/u\u003e 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.38\u003cu\u003e+\u003c/u\u003e 1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSocioeconomic Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5 \u003cu\u003e+\u003c/u\u003e 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41\u003cu\u003e+\u003c/u\u003e 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.95 \u003cu\u003e+\u003c/u\u003e 1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.93\u003cu\u003e+\u003c/u\u003e 1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102(50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.70 \u003cu\u003e+\u003c/u\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.05 \u003cu\u003e+\u003c/u\u003e 2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003eBlue Collar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHomemakers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.57\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.04\u003cu\u003e+\u003c/u\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLaborer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42 \u003cu\u003e+\u003c/u\u003e 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.07\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShopkeeper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.57 \u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.16\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDoctor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.69\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.64\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.19\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eWhite collar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBusiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43(21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.21\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrivate Job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.67\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115(56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.37\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.35\u0026nbsp;\u003cu\u003e+\u003c/u\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.62\u003cu\u003e+\u003c/u\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.57\u003cu\u003e+\u003c/u\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003eEducation \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.26\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.85\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1.49\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.32\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51\u003cu\u003e+\u0026nbsp;\u003c/u\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e1.76\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.88\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.78\u003cu\u003e+\u0026nbsp;\u003c/u\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.07\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eundergraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37(18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.64\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53(26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.64\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.22\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66(32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.94\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e57(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.34\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.35\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.68\u003cu\u003e+\u0026nbsp;\u003c/u\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.50\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.30\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eHealth Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46(22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.89 \u003cu\u003e+\u003c/u\u003e 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66\u003cu\u003e+\u003c/u\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e2.34 \u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.048*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic\u0026nbsp;Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e138(68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32\u003cu\u003e+\u003c/u\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.32\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute Diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.98\u003cu\u003e+\u003c/u\u003e 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003cu\u003e+\u003c/u\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.2\u0026nbsp;\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*P value: significant \u0026nbsp; N/A: Not Available\u003c/p\u003e\n\u003cp\u003eChronic\u0026nbsp;Diseases:\u0026nbsp;Diabetes, Hypertension, Anemia ,\u0026nbsp;Acute diseases:\u0026nbsp;COVID-19\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Comparison of mother-newborn immune senescence markers in different diseases\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"756\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMother (Mean \u003cu\u003e+\u0026nbsp;\u003c/u\u003eSD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eFathers (Mean \u003cu\u003e+\u0026nbsp;\u003c/u\u003eSD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eNewborn (Mean\u003cu\u003e+\u0026nbsp;\u003c/u\u003eSD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTelomere Length\u003c/p\u003e\n \u003cp\u003e(T/S ratio)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD57+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD57+\u003c/p\u003e\n \u003cp\u003eKLRG1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKLRG1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTelomere Length\u003c/p\u003e\n \u003cp\u003e(T/S ratio)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD57+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD57+\u003c/p\u003e\n \u003cp\u003eKLRG1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKLRG1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTelomere Length\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(T/S ratio)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD57+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCD57+\u003c/p\u003e\n \u003cp\u003eKLRG1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKLRG1+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003cp\u003en=10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.89 \u003cu\u003e+\u003c/u\u003e 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.16 \u003cu\u003e+\u003c/u\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.25 \u003cu\u003e+\u003c/u\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66\u003cu\u003e+\u003c/u\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.36\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.23\u003cu\u003e+\u003c/u\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.61\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.20 \u003cu\u003e+\u003c/u\u003e 0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.45\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.74\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003en=20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.54\u0026nbsp;\u003cu\u003e+\u003c/u\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.09\u003cu\u003e+\u003c/u\u003e 2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.63 \u003cu\u003e+\u003c/u\u003e3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.40 \u003cu\u003e+\u003c/u\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.32\u003cu\u003e+\u003c/u\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.2\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.82 \u003cu\u003e+\u003c/u\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.78\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.02\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.44 \u003cu\u003e+\u003c/u\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.36\u0026nbsp;\u003cu\u003e+\u003c/u\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0 \u003cu\u003e+\u003c/u\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4 \u003cu\u003e+\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.31 \u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41\u0026nbsp;\u003cu\u003e+\u003c/u\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.57\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.83 \u003cu\u003e+\u003c/u\u003e3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.07\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.48\u0026nbsp;\u003cu\u003e+\u003c/u\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.4 \u003cu\u003e+\u003c/u\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2 \u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes \u0026amp; Hypertension\u003c/p\u003e\n \u003cp\u003en=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29\u0026nbsp;\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.56\u003cu\u003e+\u0026nbsp;\u003c/u\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.6 \u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.27 \u003cu\u003e+\u003c/u\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.46\u003cu\u003e+\u003c/u\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.48 \u003cu\u003e+\u003c/u\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.98\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.95\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.28\u0026nbsp;\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.80 \u003cu\u003e+\u003c/u\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.15 \u003cu\u003e+\u003c/u\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnemia\u003c/p\u003e\n \u003cp\u003en=7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.68\u0026nbsp;\u003cu\u003e+\u003c/u\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.75\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.3 \u003cu\u003e+\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2 \u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26\u003cu\u003e+\u003c/u\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.28\u003cu\u003e+\u003cbr\u003e\u0026nbsp;\u003c/u\u003e4.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.76\u003cu\u003e+\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.02\u0026nbsp;\u003cu\u003e+\u0026nbsp;\u003c/u\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.91 \u003cu\u003e+\u003c/u\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.15\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.04 \u003cu\u003e+\u003c/u\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003cp\u003en=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08\u0026nbsp;\u003cu\u003e+\u0026nbsp;\u003c/u\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.32 \u003cu\u003e+\u003c/u\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.12\u003cu\u003e+\u003c/u\u003e 1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.92\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003cu\u003e+\u003c/u\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.43\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.16\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.53\u003cu\u003e+\u003c/u\u003e\u003c/p\u003e\n \u003cp\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2\u0026nbsp;\u003cu\u003e+\u003c/u\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.35 \u003cu\u003e+\u003c/u\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003cu\u003e+\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5 \u003cu\u003e+\u003c/u\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Telomere, Telomere Length, Telomerase, TERC, TERT, Immune Markers, Newborn, CD57, KLRG1","lastPublishedDoi":"10.21203/rs.3.rs-7009440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7009440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study investigates the association of parental clinical manifestations on newborn telomere biology and immune senescence markers, utilising 204 parent–newborn triads in Karachi, Pakistan. The demographic data collection was followed by quantification of telomere length (TL) using quantitative PCR (qPCR), while Sanger sequencing was performed to analyse variants in telomerase genes (TERC and TERT). Moreover, flow cytometry was used for the analysis of immune senescence markers (CD57 and KLRG1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that CD57⁺KLRG1⁺ were significantly overexpressed in newborns from the diseased parent (p = 0.045), and particularly KLRG1\u003csup\u003e+\u003c/sup\u003e expression was positively correlated with both maternal and paternal TLs (mother: r=0.395; P=0.003\u003cstrong\u003e, \u003c/strong\u003efather: r=0.32; P=0.014). Parents with chronic or acute conditions (hypertension, COVID-19) exhibited shorter TLs (mother: 1.36 ± 1.02, 1.08 ± 0.81; father: 1.41 ± 0.91, 1.18 ± 0.98) compared to their newborns (1.48 +1.17, 2.2 ± 1.47). Furthermore, genotypic analysis revealed a predominance of the TERC C/C genotype among newborns of parents with diabetes [16 (79%)] (p \u0026gt; 0.05). In contrast, the TERT gene showed significant associations with diabetes [10 (50%)] and hypertension [9(56%)], with statistical significance (p = 0.00). The parental clinical manifestations may significantly influence newborn immune senescence, enabling the upregulation of KLRG1 markers and associations with telomere length.\u003c/p\u003e","manuscriptTitle":"Parental clinical manifestation association with neonate KLRG1 expression and telomere Programming in a Pakistani Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 12:19:16","doi":"10.21203/rs.3.rs-7009440/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-04T09:34:09+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"311026388319105293365212945767025346349","date":"2025-07-30T02:34:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165354507953194973396135647796370067068","date":"2025-07-29T19:02:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T06:22:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T18:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265252926161095333090753522592599600687","date":"2025-07-28T04:51:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-27T10:09:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130726695931609818615657737172182209096","date":"2025-07-26T04:08:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64944206653762568148300643256253604777","date":"2025-07-25T07:24:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59016538204331813350939310756135735411","date":"2025-07-25T04:52:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224842950544563851094562236164950921579","date":"2025-07-25T01:34:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T18:59:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-24T09:54:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-14T17:49:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T05:57:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2025-07-11T05:54:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6839eb99-4c2b-4362-be71-389be826f494","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:04:32+00:00","versionOfRecord":{"articleIdentity":"rs-7009440","link":"https://doi.org/10.1186/s13104-025-07498-4","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2025-10-30 15:57:45","publishedOnDateReadable":"October 30th, 2025"},"versionCreatedAt":"2025-07-29 12:19:16","video":"","vorDoi":"10.1186/s13104-025-07498-4","vorDoiUrl":"https://doi.org/10.1186/s13104-025-07498-4","workflowStages":[]},"version":"v1","identity":"rs-7009440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7009440","identity":"rs-7009440","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}