Enlighten the Environmental Factors Associated with the Risk of Leukemia: Evidence from Case-Control Study

Abstract

Background

Leukemia stands as a complex disorder characterized by multifactorial influences, encompassing both polygenic and diverse environmental factors.

Aim

Consequently, this “case-control observational study” was conceived to elucidate the specific environmental variables contributing to an elevated risk of leukemia among individuals in northern India.

Method

Samples were collected for data curation using a convenient non-probability sampling method. Furthermore, blood samples were obtained for antigenic expression analysis, conducted with the assistance of a flow cytometer.

Result

A total of 272 individuals participated in the research study, comprising 111 patients and 161 controls. This diverse sample allowed for an in-depth exploration of the risk associations with various environmental factors. The findings revealed that lower levels of physical activity (odds ratio [OR]: 1.87), increased caffeine intake (OR: 2.06), reliance on a non-vegetarian diet (OR: 3.46), smoking (OR: 3.85), and alcohol consumption (OR: 4.81) demonstrated significant associations with leukemia. Also, antigenic expression using immunophenotyping revealed higher expression of different markers such as HLA-DR, CD45, CD19, CD, etc.

Conclusion

The present study showed that multiple factors such as low physical activity, caffeine intake, smoking, and alcohol consumption are critical risk attributes of leukemia susceptibility. These findings underscore the nuanced interplay between environmental elements and leukemia risk, shedding light on critical factors warranting attention in public health discourse.

Keywords

Leukemia North India risk factors smoking alcohol

Introduction

Leukemia is considered a prevalent and complex disorder that includes a heterogeneous group of hemopoietic cancers such as Acute Lymphocytic Leukemia (ALL), Acute Myelogenous Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), and Chronic Myelogenous Leukemia (CML).¹ Researchers have been exploring various factors to understand why some people are more prone to getting sick. There are two main areas of study—one looks at how our genes (the things we inherit from our family) affect our health, and the other looks at how the environment around us does. The first one is called genetic epidemiology, and it looks at how our genes can make us more or less likely to get sick. The second one is called environmental epidemiology, and it checks out how things like where we live or what we eat can impact our health.² With the implementation of a case-control observational approach in the north Indian population, the present research aimed to identify the environmental risk variables that are associated with an increased likelihood of developing leukemia.

Method

Ethical Permission & Consent to Participate

The present study design was rigorously reviewed and approved by the Institutional Ethical Committee (IEC) at the University of Jammu, as documented by reference number RA/19/3122. Before data and blood collection, each participant was thoroughly informed about the study’s purpose. Following this, written informed consent to participate was obtained from each participant, affirming their voluntary agreement to partake in the study.

Subject Enrollment

Patients were enrolled from the Department of Pediatrics at Shri Maharaja Gulab Singh Hospital (SMGSH), Shallamar, Jammu, India, using a convenient sampling strategy⁴ based on inclusion/exclusion criteria.² For the diagnosis of the suspected individuals (featured with leukemic symptoms), clinical studies including the total blood count/ total lymphocyte count (TLC), flow cytometry, and molecular analysis were used to confirm the diagnosis. Following the diagnosis, we found 111 individuals suffering from leukemia and to conduct a comparison, we recruited 161 healthy controls from the Out-Patient Departments (OPD).

When choosing the controls, the following criteria were considered which are: an individual must give their consent for participation, an individual must be from the same population, there should be no genetic diseases, and no genetic disorder from the family. The 3-4 ml of blood was drawn out from the confirmed patient in the 5 ml EDTA-coated vial only after the consent of subjects (guardian, where applicable).

Immunophenotyping Using a Flow Cytometer

In the current study, immunophenotyping using flow cytometry was utilized to determine the cell type and subtype of leukemia. Flow cytometry was performed using the BD FACS Canto II, and data were analyzed with FACS Diva software. A panel of monoclonal antibodies was used to detect specific surface antigens. The antibodies used included B-lymphoid lineage markers: CD10, CD19, CD20; T-lymphoid lineage markers: CD2, CD3, CD4, CD5, CD7, CD8; myeloid lineage markers: CD13, CD33, CD117; and lineage-nonspecific markers: HLA-DR, CD34, CD45. Each sample was processed to identify and quantify the presence of these markers. The expression of these antigens was used to classify the leukemia subtypes as follows: B-cell acute lymphoblastic leukemia (B-ALL), positive for CD10, CD19, CD20; T-cell acute lymphoblastic leukemia (T-ALL), positive for CD2, CD3, CD4, CD5, CD7, CD8; Acute Myeloid Leukemia (AML), positive for CD13, CD33, CD117; and Mixed Lineage Leukemia, characterized by the expression of both lymphoid and myeloid markers. Standard gating strategies were applied to separate populations of interest, and compensation was performed to correct for spectral overlap. The percentages of cells expressing each marker were recorded and analyzed to provide detailed immunophenotypic profiles for each leukemia subtype. The BD FACS Canto II and FACS Diva software packages were utilized throughout the acquiring and analyzing processes and all the process was contracted out to a facility in New Delhi, India called Dr. Lal-Path Labs.

Lifestyle Factors

The lifestyle factors considered in this study included physical activity, caffeine intake, diet, consanguinity, smoking, and alcohol consumption. Data on these factors were collected through structured questionnaires administered to all participants. This information was used to analyze potential lifestyle-related risk factors for leukemia.

Statistical Analysis

Descriptive data analyses were done using frequency distribution, mean, and standard deviation (where applicable). Concerning the inferential statistics, the differences between the variables were analyzed using different statistical tests such as a t-test for continuous variables and a chi-square analysis for discrete variables. Odds ratios (OR) with 95% confidence intervals (CI) were computed using a logistic regression model to determine the significance of the association between independent variables (risk variables) and leukemia susceptibility. A p value of <.05 was considered to be significant for the inferential statistics test. Different online free statistical tools were used depending upon the type of data and test required which include MedCalc’s OR calculator, Chi-Square Calculator - Up To 5×5, With Steps (socscistatistics.com), T-test calculator (graphpad.com).

Result

In the present analysis, a total of 272 individuals were enrolled which comprised 111 patients and 161 controls. The patients were grouped into pediatric and adult leukemic patients using the criteria of age such as ≥3 to ≤19 and ≥20 to ≤80, respectively. Detailed clinical features were collected (Figure 1A) which showed that the majority of patients have Hg (g/dl) in between 5 and 10 g/dl (n = 99), TLC (Total Lymphocyte count) in between 10,000 and 20,000 (n = 59), PC (Platelet Count) below 50,000 (n = 92) (Table 1). Also, patients showed increased fever (n = 95), anemia (n = 93), associated with paleness (n = 95), general weakness (n = 98), and frequent infection (n = 69) (Table 1). The detailed demographic characteristics of both groups were collected, and it was found that there was a greater number of male patients (n = 65) than there were female patients (n = 46) and that the majority (74.77%) of overall patients were from rural areas (n = 83) (Table 2). In terms of the body mass index, the majority of patients were underweight (n = 36), and this included both pediatric (n = 17) and adult (n = 19) patients (Table 2).

Figure 1.

(A) Pie Chart representing multiple clinical features of leukemia. (B). Forest plot representing the association value (OR/Odds Ratio) of different environmental factors with risk of leukemia.

Table 1.

Clinical features.

Variable	Type	Pediatric (n = 51)	Adult (n = 60)	Total (n = 111)
Blood group	A +	8	7	15
	AB +	10	5	15
	B +	18	28	46
	O –	1	0	1
	O +	14	20	34
Pulse rate	Normal	12	0	12
	Low	8	0	8
	High	31	0	31
Hemoglobin (g/dl)	<5	10	2	12
	5–10	41	58	99
	>10	0	0	0
TLC	4000–10000	5	8	13
	10000–20000	7	52	59
	20000–30000	10	0	10
	30000–40000	9	0	9
	40000–50000	11	0	11
	>50000	9	0	9
Platelet count	<50000	38	54	92
	50,000–100000	10	0	10
	100000–150000	3	0	3
	150000 and above	0	6	6
Fever	Yes	47	48	95
Fever	No	4	12	16
Anemic	Yes	48	45	93
Anemic	No	3	15	18
Paleness	Yes	47	48	95
Paleness	No	4	12	16
Frequent infection	Yes	25	44	69
Frequent infection	No	26	16	42
Abdomen pain	Yes	9	24	33
Abdomen pain	No	42	36	78
General weakness	Yes	46	52	98
General weakness	No	5	8	13
Organmegaly	Hepatomegaly	9	20	29
	Spleenomegaly	3	6	9
	Hepato-spleenomegaly	27	5	32
	None	12	29	41
Petechias	Yes	10	19	29
Petechias	No	41	41	82
Bruising	Yes	12	33	45
Bruising	No	39	27	66

Table 2.

Demographic Features.

Variable	Type	Pediatric (n = 51)	Controls (n = 61)	χ²	p value	Adult (n = 60)	Control (n = 100)	χ²	p value	Overall (n = 111)	Controls (n = 161)	χ²	p value
Religion	Hindu	27	48	8.32	=.003	31	61	3.90	=.142	58	109	10.86	=.0043
	Muslim	24	13			27	31			51	44
	Sikh	0	0			2	8			2	8
Gender	Male	30	37	0.03	=.84	35	63	0.34	=.55	65	100	0.34	=.55
Gender	Female	21	24	0.03	=.84	25	37	0.34	=.55	46	61	0.34	=.55
Dwelling	Rural	42	26	18.382	<.0001	41	31	21.11	<.00001	83	57	40.77	<.00001
Dwelling	Urban	9	35	18.382	<.0001	19	69	21.11	<.00001	28	104	40.77	<.00001
Education	Uneducated	5	1	13.27	=.0040	1	5	33.94	<.00001	6	6	32.20	<.00001
	Primary	10	4			32	12			42	16
	Middle	25	26			17	64			42	90
	Higher	11	30			10	19			21	49
Agegroup	0–5	22	10	NA	NA	0	0	NA	NA	22	10	NA	NA
	06–10	18	25			0	0			18	25
	11–15	11	26			0	0			11	26
	30–40	0	0			7	21			7	21
	41–50	0	0			12	38			12	38
	51–60	0	0			16	25			16	25
	>60	0	0			25	16			25	16
BMI	Normal	28	38	4.62	=.19	12	55	NA	NA	40	93	23.91	=.00002
	Obese	5	2			0	17			5	19
	OW	1	5			29	14			30	19
	UW	17	16			19	14			36	30

Legends
	Significant p value
	Not Significant p value
	Not Applicable

Immunophenotyping

Immunophenotyping was performed using a flow cytometer which enlightened the higher prevalence of ALL (96.08%) in comparison to other types (Table 3). In addition, basic antigenic expression was seen where it was observed that there was a high prevalence of CD45 (97.61%), HLA-DR (90.48%), and CD19 (80.95%) in B-ALL type (Table 4), and CD45 (100%), HLA-DR (85.71%), CD2, CD3, etc. in T-ALL (Table 4).

Table 3.

Cancer Type.

	Type	Pediatric	Adult	Overall Leukemia
Cancer Type	ALL	49	8	57
	AML	2	24	26
	CML	0	19	19
	CLL	0	9	9

Table 4.

Antigenic Expression.

Cell Type	Marker	Gene	Common Name	N	%
B-ALL	CD45	PTPRC	Protein Tyrosine Phosphatase Receptor Type C	41	97.62
	HLA-DR	HLA-DRB1	Human Leukocyte Antigen – DR	38	90.48
	CD34	CD34	Hematopoietic Progenitor Cell Antigen CD34	21	50.00
	CD10	MME	Membrane Metallo-endopeptidase	33	78.57
	CD19	CD19	CD19 Molecule	34	80.95
	CD20	MS4A1	Membrane Spanning 4-Domains a1	21	50.00
	CD22	CD22	CD22 Molecule	16	38.10
	CD2	CD2	CD2 Molecule	6	14.29
	CD3	CD3	CD3 Molecule	6	14.29
	CD8	CD8	CD8 Molecule	6	14.29
T-ALL	CD45	PTPRC	Protein Tyrosine Phosphatase Receptor Type C	7	100.00
	HLA-DR	HLA-DRB1	Human Leukocyte Antigen – DR	6	85.71
	CD34	CD34	Hematopoietic Progenitor Cell Antigen CD34	4	57.14
	CD2	CD2	CD2 Molecule	7	100.00
	CD3	CD3	CD3 Molecule	7	100.00
	CD4	CD4	CD4 Molecule	4	57.14
	CD5	CD5	CD5 Molecule	6	85.71
	CD7	CD7	CD7 Molecule	7	100.00
	CD8	CD8	CD8 Molecule	7	100.00
	CD10	MME	Membrane Metallo-endopeptidase	1	14.29
AML	CD45	PTPRC	Protein Tyrosine Phosphatase Receptor Type C	2	100.00
	HLA-DR	HLA-DRB1	Human Leukocyte Antigen – DR	2	100.00
	CD34	CD34	Hematopoietic Progenitor Cell Antigen CD34	1	50.00
	CD13	ANPEP	Alanyl Aminopeptidase, Membrane	2	100.00
	CD33	CD33	CD33 Molecule	2	100.00
	CD117	KIT	KIT Proto-Oncogene, Receptor Tyrosine Kinase	2	100.00

Statistical Association

Overall Leukemia Risk Factors

In our study exploration, distinct variations in the frequency of different environmental factors have been observed between the control and case groups (Figure 2A and B). In employing bivariate logistic regression, also recognized as OR, we identified a significant association between multiple factors and the risk of overall leukemia, as illustrated in Figure 1B. These factors include low physical activity (1.87 [1.11-3.13], p value = .017), increased caffeine intake (2.06 [1.2-3.4], p value = .005), consumption or dependence on a non-vegetarian diet (3.46 [2.08-5.74], p value < .0001), smoking (3.85 [1.96-7.58], p value = .0001), and alcohol consumption (3.05 [1.50-6.20], p value = .0020). However, consanguinity exhibited a non-significant association with the risk of overall leukemia (Table 5). Further categorization of the overall leukemic population into pediatric and adult leukemic groups still showed significant associations which are described below.

Table 5.

Independent Variable Association with the Risk of Leukemia.

Type	Variable	Type	Case	Controls	OR	CI	p value
Pediatric	Physical activeness	Yes	33	60	Reference
	Physical activeness	No	18	1	32.73	4.18–256.27	.0009**
	Caffeine intake	Yes	30	16	4.02	1.81–8.92	.0006**
	Caffeine intake	No	21	45	Reference
	Diet	Veg.	13	47	Reference
	Diet	No-Veg.	38	14	9.81	4.12–23.37	<.0001**
	Consanguinity	Yes	7	1	9.55	1.13–80.41	=.038*
	Consanguinity	No	44	60	Reference
Adult	Physical activeness	Yes	33	58	Reference
	Physical activeness	No	27	42	1.13	0.59–2.15	.71
	Caffeine intake	Yes	48	70	129.33	34.84–480.10	<.0001**
	Caffeine intake	No	12	30	Reference
	Diet	Veg.	26	58	Reference
	Diet	No-Veg.	34	42	1.81	0.95–3.45	.073
	Consanguinity	Yes	4	8	0.82	0.24–2.85	.756
	Consanguinity	No	56	92	Reference
	Smoking	Yes	36	28	3.86	1.96–7.59	=.0001**
	Smoking	No	24	72	Reference
	Alcohol	Yes	26	20	3.06	1.51–6.21	=.002*
	Alcohol	No	34	80	Reference
Overall	Physical activeness	Yes	66	118	0.53	0.31–0.89	=.017*
	Physical activeness	No	45	43	1.87	1.11–3.13	=.017*
	Caffeine intake	Yes	78	86	2.06	1.2–3.4	=.005*
	Caffeine intake	No	33	75	Reference
	Diet	Veg.	39	105	Reference
	Diet	No-Veg.	72	56	3.46	2.08–5.74	<.0001**
	Consanguinity	Yes	11	9	1.85	0.74–4.64	=.18
	Consanguinity	No	100	152	Reference
	Smoking	Yes	36	28	3.85	1.96–7.58	=.0001**
	Smoking	No	24	72	Reference
	Alcohol	Yes	26	20	3.05	1.50–6.20	=.0020*
	Alcohol	No	34	80	Reference

Note: *Significant Value, **Highly Significant Value, Veg: Vegetarian, Non-Veg.: Non-Vegetarian, OR: Odds ratio, CI: Confidence Interval.

Figure 2.

(A) Frequency distribution of multiple environmental factors in patient groups. (B) Frequency distribution of multiple environmental factors in control groups.

Analysis of Pediatric Leukemia Risk Factors

The analysis of pediatric leukemia cases identifies several key lifestyle risk factors. Physical inactivity is significantly associated with an increased risk of leukemia, with inactive children having an OR of 32.73 compared to their active counterparts (p = .0009) (Table 5). High caffeine intake is also correlated with a higher risk, indicated by an OR of 4.02 (p = .0006), and children following a non-vegetarian diet have a notably increased risk, with an OR of 9.81 (p < .0001). Additionally, consanguinity presents a significant risk, as children from consanguineous marriages exhibit an OR of 9.55 (p = .038) (Table 5). Furthermore, advanced parental age contributes significantly to leukemia risk (Table 6). Children of mothers aged 31-40 show an OR of 6.68 (p = .0002), while those with fathers aged 31-40 have an OR of 6.76 (p = .0002), and an even higher risk for fathers over 41, with an OR of 75.10 (p = .0032) (Table 6).

Table 6.

Parental age as a Risk Factor.

	Age	Pediatric Patients (n = 51) (%)	Pediatric Controls (n = 61) (%)	OR [95% CI]	p value
Maternal	<20	1 (1.96)	4 (6.55)	0.46 [0.048–4.27]	.49
	21–30	28 (54.90)	51 (83.61)	Reference
	31–40	22 (43.13)	6 (9.84)	6.68 [2.42–18.40]	.0002**
Paternal	<20	0 (0)	4 (6.55)	0.28 [0.01–5.60]	.4108
	21–30	19 (37.25)	50 (81.97)	Reference
	31–40	18 (35.29)	7 (11.48%)	6.76 [2.44–18.77]	.0002**
	>41	14 (27.45)	0 (0)	75.10 [4.27–1320–97]	.0032**

Note: *Significant Value, **Highly Significant Value, OR: Odds ratio, CI: Confidence Interval.

Analysis of Adult Leukemia Risk Factors

In examining the lifestyle factors associated with leukemia in adults, caffeine intake emerges as a major risk factor. Adults who consume caffeine regularly showed a significantly higher risk of developing leukemia compared to those who do not consume caffeine (p < .0001) (Table 5). Smoking and alcohol consumption are also significant contributors to increased leukemia risk in adults. Smokers have nearly four times the risk (OR = 3.86, p = .0001), and individuals who consume alcohol face a tripled risk (OR = 3.06, p = .002) compared to non-smokers and non-drinkers (Table 5). On the other hand, some factors do not show a significant impact on leukemia risk. For example, physical activity levels do not appear to significantly influence leukemia susceptibility (OR = 1.13, p = .71). Similarly, while a non-vegetarian diet shows a trend toward increased risk, it is not statistically significant in this analysis (OR = 1.81, p = .073) (Table 5). Consanguinity also does not significantly affect leukemia risk (OR = 0.82, p = .756) (Table 5).

Discussion

Leukemia is a complex disorder with a rising prevalence globally, increasing from 354.5 thousand in 1990 to 518.5 thousand in 2017.¹ Genetic epidemiology, including genome-wide association studies (GWAS), has identified multiple gene mutations that increase disease susceptibility (GWAS Catalog (ebi.ac.uk). Additionally, various environmental factors can influence this genetic susceptibility. In this research, we investigated environmental factors affecting leukemia risk in a sample from the Jammu division, representative of the northern Indian population.

In the present study, we have explored multiple factors such as smoking which was found a critical risk variable with the overall leukemia risk (3.85 [1.96-7.58], p value = .0001) and upon categorization, the risk was significantly associated with an adult population (Table 5). Interestingly, in the pediatric population parental smoking during conception significantly increases the risk of leukemia which was found in line with different studies^{3, 5–8} and this is due to presence of certain carcinogenic chemical such as benzene⁹ which have detrimental effect on the structure of DNA. When compared to non-smokers, male smokers have significantly greater levels of 8-oxo-2-deoxyguanosine (oxo8dG, an oxidant) in their sperm, which is significantly associated with an increase in the number of strand breaks in their DNA. This results in a higher level of DNA fragmentation.^{5, 10, 11} Other than smoking, alcohol was also seen as a significant risk variable for overall leukemia as well as for the adult population (Table 5), and it is important to note that alcohol consumption at elevated levels than moderate levels significantly accelerates the risk of disease.^{12, 13} We also investigated the likelihood of paternal alcohol dependence with respect to the pediatric population; however, we were unable to find any significant association between the risk of disease and paternal alcohol intake or alcohol dependence (Table 7) which was supported by multiple studies.¹⁴ In addition, multiple studies have shown that maternal alcohol consumption during conception is a risk factor for developing leukemia,^15–18 but we do not find any cases for the same factor.

Table 7.

Paternal Smoking and Alcohol Consumption.

Variable	Feature	Pediatric Patients (n = 51) (%)	Pediatric Controls (n = 61) (%)	OR [95% CI]	p value
Smoking	Non-smoker	13 (25.49)	44 (72.13)	Reference
	Before conception	2 (3.92)	15 (24.59)	0.45 [0.09–2.24]	.32
	During conception	36 (70.59)	2 (3.28)	60.92 [12.90–287.76]	<.0001**
Alcohol	No alcohol	19 (37.25)	33 (54.10)	Reference
Alcohol	During conception	32 (62.75)	28 (45.90)	1.98 [0.93–4.24]	.07

Note: *Significant Value, **Highly Significant Value, OR: Odds ratio, CI: Confidence Interval.

Other important environmental factors that were found to be significant risk for leukemia were eating habits which include type of food (vegetarian and non-vegetarian) and caffeine intake. Concerning dietary intake/ type of food, non-vegetarian food was found a significant risk for determining susceptibility to leukemia (Table 5) which was supported by multiple studies and highly recommended the vegetarian diet.^19–21 Other than the type of diet, caffeine/ tea consumption was found significantly associated with the risk but conflicting results have been shown by different studies conducted in the different populations such as Cerliani and group showed higher risk in contrast to Parodi and group which showed a protective effect.^{8, 20, 22} Multiple studies conducted on the Chinese population showed a protective/no role of tea consumption in the risk of leukemia and the possible reason might be that green tea does not contain caffeine.^{23, 24}

Confining with the pediatric population, multiple factors such as birth order, and parental age showed significant association with the disease risk. Concerning birth order, several studies have shown that increasing birth order significantly correlated with an increased risk of leukemia,^{25, 26} but multiple studies have observed contrasting results (Table 8).²⁷ Another factor, that is, parental age, both maternal as well paternal age showed a significant association with the risk of pediatric leukemia which was completely in line with the previously published studies^28–30 in contrast to Shu and group.²⁵

Table 8.

Birth Order.

Birth Order	Pediatric Patients (n = 51) (%)	Pediatric Controls (n = 61) (%)	OR [95% CI]	p value
1	21 (41.18)	30 (49.18)	Reference
2	16 (31.37)	26 (42.62)	0.87 [0.38–2.03]	.76
≥3	14 (27.45)	5 (8.20)	4.0 [1.25–12.80]	.019

Strengths & Limitations of the Present Research

The fact that our study is the first of its kind to be carried out in the population of Jammu division, which is representative of the north Indian population, is the key advantage that our research possesses. The sub-categorization of overall leukemia in the pediatric and adult population based on age criteria (≥3 to ≤19: pediatric and ≥20 to ≤80: adult) that was performed in this study is another significant and positive aspect of the current investigation. One of the major limitations of the study could be its relatively small sample size, which has a substantial impact on the bigger confidence interval with less precision, hence raising the possibility of error. Thus, increasing the size of the sample population will cause the confidence interval to become more precise and narrow.

Conclusion

Leukemia is a complex disorder that is caused due to multiple factors which are primarily grouped into genetic and environmental factors. The present study showed that multiple factors such as low physical activity, caffeine intake, smoking, and alcohol consumption are critical risk attributes of leukemia susceptibility. Concerning pediatric leukemia, increased maternal and paternal age, increased birth order, and paternal alcohol consumption are significantly associated with leukemia

Footnotes

Abbreviation

ALL: Acute Lymphocytic Leukemia; AML: Acute Myelogenous Leukemia; CLL: Chronic Lymphocytic Leukemia; CML: Chronic Myelogenous Leukemia; IEC: Institutional Ethical Committee; SMGSH: Shri Maharaja Gulab Singh Hospital; TBC: Total Blood Count; TLC: Total Lymphocyte Count; OPD: Out-Patient Department; OR: Odds Ratio; CI: Confidence Interval; PC: Platelet Count.

Acknowledgment

The authors gratefully acknowledged the patient and their family’s cooperation in giving their permission to participate in the study. Also, the authors are thankful to RGNF (Rajiv Gandhi National Fellowship) and RUSA 2.0, and Department of Zoology, and the Institute of Human Genetics, the University of Jammu which provided the lab space, is also gratefully acknowledged by the authors.

Author Contributions

Detail author’s contribution, according to the CRediT (Contributor Roles Taxonomy) System: PK & SD contributed to the study design, SKD& PM diagnosed the suspected patients, SD & AS drafted the manuscript, RB, AS & SD created tables and AS created the pictures/ graphs, JKR, RS helped in statistical analysis, PK, RKP edited the manuscript, Finally PK finalize the manuscript.

Data Availability Statement

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical Approval

The current study strategy underwent ethical scrutiny and received approval from the Institutional Ethical Committee (IEC) at the University of Jammu. This approval is documented with the reference number RA/19/3122.

Informed Consent

Data and blood collection were done after having informed written consent from each study participant.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Parvinder Kumar

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