Genetic alteration preceding any chronic diseases: using Human Genomic Project

The human genomic project in public health practice

Wissam S.A. Al-Janabi, Wayne State University/ School of Medicine.

Editor: Abdalla H. Sadoon


Recived: 05/17/2019

Accepted: 05/24/2019

Published: 05/29/2019

How to cite this article: Al-Janabi WSA. Genetic alteration preceding any chronic diseases: using Human Genomic Project. USAJMR 2019;1 doi: 10.35456/USAJMR001001002.

Conflicts of Interests: The authors decalre there are no competing interests

Acknowledgment: Special thanks to Abdala Sadoon for providing manuscript editing advice Also, thank you to Dr. Robert Ty Partridge for his constructive and supportive advice about the project in general.

Keywords: Public Health, Human genomic project, Structural Equation Model (SEM).

Significance for Public Health:

This article should promote the implementation of the human genomic project by family health practitioners. Utilizing the human genomic project on family health practitioner level will aid early detection of chronic conditions before even they manifest their effect on the patient, such chronic conditions like diabetes, hypertension, and most importantly cancer. Subsequently, clinicians and patients can take proactive steps to prevent or at least reduce the tiring and taxing sequelae of those chronic conditions. Further, this article will combine the effect of preventive medicine, public health and the human genomic project in one arena to fight those chronic conditions. In addition, this article enlightens the public health about the detrimental impacts of different kinds of stressors, like smoking, chronic pain or even distress and study their effects on molecular function and regulation. Furthermore, this article introduces the use of the Structural Equation Model (SEM) in the biomedical research field rather than limiting it to the psychological field.

Abstract: Since the human genomic project had been completed in 2003, scarce research studies have been done to evaluate the clinical relevance of this project to public health, specifically in the arena of prevention of chronic diseases. Utilizing the structural equation model, with a random sample from National Lung Screening Data. Using SAS software and Proc CALIS for the analysis to assess whether there is a genetic alteration/expression transpires prior to any chronic disease. And to encourage more research studies in this rookie field that merges both public health and prevention of chronic diseases with the human genome.

The results of all three proposed models boost the alternative theory, which indicates there is gene alteration/expression anterior to any chronic disease. Therefore, the results stimulate the use of the human genome project in the area of public health in a way that can curtail many dangerous chronic diseases before they hit.

Introduction: Although miserly studies have been conducted in this new field until now, all of them have shown the link between various kinds of stressors and molecular consequences, environmental stressors like smoking, other stressors, such as pain, and chronic health distress (1,2). These stressors are capable of influencing and activating autoimmune, immune and neuroendocrine systems, which over time will lead to maladaptive cellular and genetic alteration (1-3). One of the important findings that concatenate pain or chronic health distress with the genetic alteration/expression is leukocyte telomere length (TL) measurement (1,3-4).

Another persuasive example is the expression of opioid’s receptors rely on whether the patient has previously exposed to opioid or not (1,5,17). This type of research shades the light to an array of targets that can be utilized for prevention as well as treatment of many chronic diseases (1,5).

The three main players in developing chronic diseases are genes, environment plus the interaction between genes and environment (6).Thousands of citations on PubMed that show the link between “ cancer AND genes,” but scant studies that link “cancer AND exposure to various stressors,” nonetheless, these studies collectively elucidate the genetic factors as necessary determinants in acquiring diseases along with other stressors (6). Exploiting the new technologies to launch the human genomic projects in few stunning studies has enlightened the harmonious concatenation of some chronic diseases, such as coronary artery disease and gene variability/ expression (6). The cohort study of Monozygotic twins in Western European countries has established the link between chronic diseases, such as neurological diseases, lung diseases and autoimmune diseases along with environmental stressors, such as smoking and alcohol (6-8).

Many chronic diseases, such as cerebrovascular, cardio, kidney, endocrine/metabolic diseases have been linked to either lifestyle/environmental or together with a genetic predilection (8). Psycho-social stressors have also shown to play a role of mediators between the genetic alteration and chronic diseases (7-8,19-20).

Following the completion of the human genomic project in 2003, scholars are expecting to apply this enormous project in control and prevention of several chronic diseases (7,9-12). The implementation of the human genomic project in health promotion and disease prevention remains elusive for some scientists (7,13). Increasingly, general practitioners, academic, government, and other organizations have taken a leadership role in estimating the pertinent of this project with public health and further it to community-based interventions, in a new field dubbed public health genetics or genomics (7).Another factor that is considered as a complement to the human genomic project and has a tremendous effect on gene alteration is the family history. Family history is considered one of the most important and best genomic tools that can be exploited to pinpoint the myriad disease prevention and health promotion messages (7,14-16).

Sadly, studies have shown that family history tool is used only in less than 50% of primary care physicians (7,15).

Genetic tests should be assessed meticulously first, then integrated into public health promotion and disease prevention (7). This step became essential particularly with dramatic rages of chronic diseases nowadays; moreover,it is imperative to take proactive steps in the way that can curtail such increase in the tally of many chronic diseases (5,6).

Change in the gene expression rather than the gene code itself is called epigenetic (1,4). Both the extension and diversity of epigenetic studies have shown the effect of different types of stressors on gene expression (1,4). Emerging of the epigenetic field after the completion of the human genomic project along with family history tool should be used as a lethal weapon against the untethered increase in chronic diseases (5-7). Epigenetic has been implicated in aiding the born of many chronic diseases and has a major role in the development of chronic/persistent pain (9). Environmental factors play an imperative role in the sensitivity of pain, for instance, female smokers reported more chronic pain than non-smokers, and addicts on heroin often develop hyperalgesia compared to non-addict (9).

Collecting evidence-based database in the research of genomics and assurance of an adequate public health capacity in genomics will aid on the long run in paving the road for interpreting the genome discoveries into wise health prevention for innumerable chronic diseases (7). In the past decade, educational and planning resources have developed, like a web-based introduction to genomics at the University of Michigan (7).

The Control Disease Center (CDC) and many other alliances have inaugurated model approaches for obtaining and synthesizing available information for genetic tests (7,19-20). Interestingly, for the past few years, the CDC has promoted the integration of genomics across all public health functions, including training, and workforce development (7,19-20).


The sample for this model was chosen from a huge data that was collected for National Lung Screening Data. The original sample size was over 52,000 subjects, however, using the loop method to select 1,291 subjects randomly.

SAS software 9.4 was used along with PROC CALIS procedure for analysis. One of the pros for this PROC that it estimates and accounts for data normality by default. However, owing to the large sample size and binary variables in this sample, to procure more robust solution for non-normal data, it is recommended to use the Satorra-Bentler scaled chi-square test statistics, and associated sandwich-type standard error estimates. This method in psychometric literature is called robust Maximum Likelihood (ML). The number of observations that were excluded using this method range from 75-80 observations in each model analysis.

Demographic and Descriptive Features of the subjects:

Results: see attached diagrams & tables (for parameter estimates (non-standard and standard), variances and R square results) and tables for correlation matrices. See Figure 1, 2, and 3 for Model 1, 2, and 3 respectively. below references.

The first model and the second model sharing almost similar results except for the path from a variable of family history of any cancer instead of going to the latent exogenous variable, which is chronic health distress in model1, it goes to the latent endogenous variable, which is gene alteration (see diagram for model2). Both models seem to prove the alternative theory with chi-square value=514, P <0.0001. For model fit indices, see table below, both models have the same fit indices.

Although the chi-square of the model fit annuls the hypothesis that the model fits the data, bearing in mind, that chi-square statistic is very sensitive to sample size. (10) Chi-Square ratio was 1.5, which indicates good fit as long as < 2, normed fit index (NFI) and Tucker-Lewis Index (TLI) need a large sample size like this sample size, their values .54 & .24 respectively are not bad. The goodness of fit (GFI) and Adjusted Goodness of Fit (AGFI) were .96 & .94 indicate good fit. Root Mean Square Error of Approximation (RMSEA) with RMSEA 90% C.I with their values .055 with (0.049-0.061) respectively denote good fit. Collectively, both models have overall good fit indices. McDonald for centrality value, which is 0.897 shored up the good fit of the models. Comparative Fit Index (CFI) with the result of 0.65, indicating that the fit is OK. However, the Expected Cross Validation Index (ECVI) was only 0.33.

Model 3, after eliminating the family history of cancer, leaving sex, race and age variables to predict gene alteration variable directly. Keeping the same sample size and using satorra_Bentler scaled chi-square. See below; all model fit indices for model3:

Although chi-square result for the model proved the alternative theory, model fit indices, like CFI and NFI showed negative results, whereas other fit indices, such as NNFI, RMSEA along with 90% C.I RMSEA, GFI, and AGFI denoted its goodness of fit. Lagrange Multiplier statistics and Wald test statistics for model modification, are suggesting elimination or adding paths. However, their suggestions do not back up the primary theory and the research question. Hence, the first model is the best model amongst the three models that were proposed in this study because it harmonizes with the notion of the theory and holds overall good fit indices.

Discussion: SEM is a powerful tool that not only can be used in psychology, but also in the medical field as well as in the genetic arena (21-22). SEM technique can be used to assess more than one model under multivariate normality (21-22). The results of the three models which represent the alternative theory suggest that there is a form of gene alteration/ expression precedes any chronic diseases and rebut the null hypothesis of no genetic alteration/expression prior to chronic diseases. Exploiting this information along with using the human genomic project to compare genes of the patient at risk for chronic disease with a healthy one and take early steps to curtail this pathway. Most importantly, this study opens the gate for future studies that encourage the implementation of human genomic project in public health field and primary care practices in the way that can lead to primary prevention of several debilitating chronic diseases. Epigenetic studies have established that many stressors like smoking and pain can cause some kinds of gene alteration/expression, like shortening of the telomere, it is situated at the end of each strand of the DNA, which is responsible for halting any cell from going into senescence (3,7).

Limitations: One of the important limitations was the inability to perform the invariance measurements. Once the sample size is divided according to gender or race, both groups fail to reach optimization. Even, if the sample size was increased to three thousand instead of 1,291 subjects, the same problem returns.

Conclusion: Additional studies are necessary to ascertain the tangible relevance of gene alteration/expression and many chronic diseases. Prospective studies rather than retrospective are needed and should focus on exploring the gene alteration/expression as a mediator between different stressors and chronic diseases. Additional public health research is needed to establish the benefits of thousands of genetic variants and their interaction with the modifiable risk factors which ultimately contribute to developing chronic diseases. Success in this nascent field of genomics in public health promotion and chronic disease prevention will necessitate building appropriate capacity, in addition, preparing a versed and competent public health workforce, that is capable of evaluation and use of genomics information (7,20).


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Figure1: Model1

Figure2: Model2

Figure3: Model3