The Scientific Value of IHTI's 40-Year Longitudinal Database of Medical Biomarkers

IHTI's database was built in response to growing evidence that databases are not only increasingly being used for research in "hard" mathematics-based disciplines such as physics and engineering, but also in the more "soft" disciplines such as sociology, psychology as well as preventive medicine. Between the "hard" and "soft" disciplines lie such disciplines as biomedicine and healthcare which can also be subdivided into:

  1. fundamental biomedical research, related to the 'hard' science,
  2. clinical research combining both "hard" and "soft" data, and
  3. population-based prospective and retrospective research.

An example of the value of using databases to improve healthcare is the Framingham Heart Study. For 50 years, the Framingham study has been synonymous with the remarkable advances in the prevention of heart disease in the United States and as well as throughout the world. Framingham's database has:

  1. produced over 1,000 scientific papers,
  2. identified major risk factors associated with heart disease, stroke and other diseases,
  3. paved the way for researchers to undertake singular clinical trials based on Framingham findings,
  4. supported the revolution in preventive medicine, and
  5. forever changed the way the medical community and general public view the genesis of disease.

In the August 27, 2020 issue of the New England Journal of Medicine, Koff and Williams stress the need for a new research agenda for "Covid-19 and Immunity in an Aging Population." The authors highlight the need for longitudinal studies on aging populations similar to the Framingham studies as well as the Rotterdam studies. The authors point out that COVID-19 has highlighted the vulnerability of aging populations to emerging diseases and call for examinations of..."The impact of coexisting conditions and therapies on the effects of vaccines and infectious diseases. ...If we can delineate principles of effective immunity in the elderly, we might also be able to develop new strategies for broader disease prevention and control in older populations..." The authors also point out that the Rotterdam and Framingham data have provided some of the most important epidemiological studies in the annals of American medicine. While their contributions to heart research are legion, researchers continue utilizing their data to investigate stroke, dementia, osteoporosis, arthritis, diabetes, and eye disease.

Following the Framingham and Rotterdam examples, over the past 40 years IHTI has been collecting and storing data acquired from its clinical trials to create a database of "medical biomarkers." Encouraged by the contributions made by analyses of Framingham's database, over the past 40 years IHTI has been conducting clinical trials of non-pharmaceutical products and technologies that could potentially be integrated into medical treatment plans and future clinical trials. As of the date of this report, IHTI has amassed longitudinal data on over 50,000 medical biomarkers that could potentially be analyzed to meet some of the needs suggested by Koff and Williams. Toward this end IHTI has obtained written permission from its subjects to use their redacted data in future clinical trials.

While it would be presumptuous to suggest that IHTI's 40-Year Longitudinal Database of Medical Biomarkers will make similar contributions as the Framingham and Rotterdam's databases. While IHTI's database does contain data similar to those data in Framingham and Rotterdam, it also contains data not available in Framingham's or Rotterdam's databases. For example, IHTI's database contains over 1,000,000 medical biomarkers derived from DEXA Total Body scans, the "gold standard" for measurement of body composition (fat, lean, bone mineral density, and bone mineral content), fasting blood chemistry tests and self-reported Quality of Life Inventories with sub-scales measuring Depression, Anxiety, Eating Control, and Sleep Quality. Additionally, all tests in the database have corresponding measurements of age, gender, ethnicity, stadiometer-measured height and body weight as measured by an industrial-grade strain-gauge scale accurate within (+-) 1/10th pound. IHTI's database also contains a more diverse subject sample with data from virtually every state in the US as well as some data from subjects living outside of the US.

Another approach to estimating the scientific value of a database is to review the number of scientific publications that have been solely generated from inter- and intra-analyses of the variables within the database. With respect to that standard, the following 29 articles have been published exclusively from inter- and intra-analyses of existing data within the database:

  1. 2023: Harry G. Preuss MD, Gilbert R. Kaats PhD, Nate Mrvichin, Debasis Bagchi PhD. Impact Of Circulating Calcium Levels on Obesity, Insulin Resistance, and Other Aspects of the Metabolic Syndrome in Non-Diabetics: Another Medical Trade-Off. (Under preparation).
  2. 2022: Harry G. Preuss MD, Gilbert R. Kaats PhD, Nate Mrvichin, Okezie I. Aruoma PhD, DSc, Debasis Bagchi PhD, Rich Scheckenbach. Potential to Extend Productive Lifespan in Non-Diabetics by Maintaining Optimal Insulin Sensitivity: Ameliorating an Abated Version of the Metabolic Syndrome. (Under preparation).
  3. 2021: Preuss HG, Kaats GR, Mrvichin N, Aruoma OI, Bagchi D. Further Reflections on Combatting COVID-19 by Natural Means That Support Optimal Health. (Under preparation).
  4. 2021: Kaats GR, Mrvichin N, Nugent S, Stohs S, Preuss HG. Replication and extension of a previous study of the discordance between using a proposed Body Composition Change Index (BCCI) and the Body Mass Index (BMI) as outcome measures. (Under preparation).
  5. 2021: Preuss HG, Kaats GR, Mrvichin N, Bagchi D. Probing the Relationship Between Declining Renal Glomerular Filtration Over the Life Span and General Biological Aging: Does the Former Provide Means to Estimate the Latter?, Journal of the American College of Nutrition, DOI: 10.1080/07315724.2021.1977734
  6. 2021: Preuss HG, Kaats GR, Mrvichin N, Bagchi D, Scheckenbach R, Preuss JM. Assessing genders separately in non-diabetics regarding links between insulin resistance and fat mass with elements related to the metabolic syndrome. J Amer Coll Nutr. (Accepted for publication).
  7. 2021: Preuss HG, Kaats GR, Mrvichin N, Bagchi D, Scheckenbach R. Correlating circulating vitamin D3 with various aspects of the metabolic syndrome and non-alcoholic fatty liver disease in healthy volunteers free of clinical manifestations. J Amer Coll Nutr. 39:585-590.
  8. 2021: Preuss HG, Kaats GR, Mrvichin N, Bagchi D. Analyzing Blood Pressure Ascent During Aging In Non-Diabetics: Focusing on Links to Insulin Resistance and Body Fat Mass. J Am Coll Nutr. May-June 2021;40(4):317-326.
  9. 2020: Harry G. Preuss, Gilbert R. Kaats, Nate Mrvichin, Okezie I. Aruoma and Debasis Bagchi. Interplay Between Insulin Resistance and Body Fat Mass in Evolution of Perturbations Linked to the Metabolic Syndrome in Non-Diabetics: Emphasis on Inflammatory Factors. Journal of the American College of Nutrition. Received 05 Jun 2020, Accepted 02 Jul 2020, Published online: 06 Aug 2020.
  10. 2020: Preuss HG, Kaats GR, Mrvichin NA. Role of Insulin Resistance in BP Regulation During the Continuum of Risks Period in Non-Diabetics Volunteers. (Under preparation).
  11. 2020: Preuss HG, Kaats GR, Mrvichin NA, Bagchi D, Scheckenbach R. Correlating Circulating Vitamin D3 With Aspects of the Metabolic Syndrome and Non-Alcoholic Fatty Liver Disease in Healthy Female Volunteers. Under preparation.
  12. *2020: CHAPTER 12 Probing various pro and con health aspects of the glucose–insulin system in non-diabetics: focusing on insulin resistance and dietary implications. Harry G. Preuss, Nate Mrvichin, Gilbert R. Kaats, Jeffrey M. Preuss and Debasis Bagchi. 277-289.
  13. *2020: CHAPTER 13 Evaluating proposed surrogates to estimate insulin resistance in non-diabetics: emphasizing the ratio triglycerides/HDL-cholesterol versus fasting blood glucose. Nate Mrvichin, Gilbert R. Kaats, Debasis Bagchi and Harry G. Preuss. 291-304.
  14. *2020: CHAPTER 15 Assessing the triglyceride/HDL-cholesterol ratio as a surrogate for insulin resistance and its link to the metabolic syndrome in Hispanics and African-Americans. Harry G. Preuss, Nate Mrvichin, Debasis Bagchi and Gilbert R. Kaats. 325-345.
  15. *2020: CHAPTER 17 Linking fasting blood glucose quartiles of nondiabetic volunteers ages 21–84 years to metabolic syndrome components: focusing on the aging paradox. Harry G. Preuss, Nate Mrvichin, Debasis Bagchi and Gilbert R. Kaats. 361-374.

    16. *In: Dietary Sugar, Salt, and Fat in Human Health. Academic Press is an imprint of Elsevier. Editors Harry G. Preuss and Debasis Bagchi.

  16. 2019: Preuss HG, Kaats GR, Mrvichin N, Bagchi, Preuss JM. Circulating ALT Levels in Healthy Volunteers Over Life Span: Assessing Aging Paradox and Nutritional Implications. J Am Coll Nutr. 2019; 38(8): 661-669, https://doi.org/10.1080/07315724.2019.1580169.
  17. 2018: Preuss HG, Kaats GR, Mrvichin N, Bagchi D, Swaroop A. Risk factors for the metabolic syndrome and both age and fasting glucose levels in non-diabetic subjects. J Am Coll Nutr 37:302-307, 2018.
  18. 2018: Preuss HG, Kaats GR, Mrvichin N, Swaroop A, Bagchi D, Clouatre D, Preuss JM. Examining the relationship between nonalcoholic fatty liver disease and the metabolic syndrome in nondiabetic subjects. J Am Coll Nutr 37:457-465, 2018.
  19. 2018: Preuss HG, Mrvichin N, Kaats GR, Bagchi D. Reflecting on concepts relevant to contemplating the relationship between glucose/insulin perturbations and aging. J Amer Coll Nutr (Internet).
  20. 2018: Preuss HG, Mrvichin N, Kaats GR, Bagchi D. Examining the ratio triglyceride/HDL-cholesterol as a surrogate for insulin resistance in African-Americans and Hispanics. (In manuscript).
  21. 2018: Preuss HG, Kaats GR, Mrvichin N, Swaroop A, Bagchi D, Clouatre D, Preuss JM. Examining the relationship between nonalcoholic fatty liver disease and the metabolic syndrome in nondiabetic subjects. J Am Coll Nutr 2018 Apr 13:1-9. doi: 10.1080/07315724.2018.1443292. (Epub ahead of print) PMID:29652564.
  22. 2018: Preuss HG, Kaats GR, Mrvichin N, Bagchi D, Preuss JM. Cross-sectional examination of circulating ALT and AST levels in relatively healthy volunteers over their lifespan: Implications. (Submitted for publication).
  23. 2017: Preuss HG, Mrvichin N, Bagchi D, Preuss J, Perricone N, Kaats GR. General lack of correlations between individual age and signs of the metabolic syndrome in those with non-diabetic fasting glucose levels. J Am Coll Nutr 36:556-564, 2017.
  24. 2017: Nugent S, Kaats GR, Mrvichin N, Keith P, Preuss HG. Discordance Between Body Mass Index (BMI) and a Novel Body Composition Change Index (BCCI) as Outcome Measures in Weight Change Interventions. J Am Coll Nutr. 2017, (in press).
  25. 2017: Preuss HG, Clouatre D, Swaroop A, Bagchi M, Bagchi B, Kaats GR. Blood Pressure Regulation: Reviewing Evidence for Interplay Between Common Dietary Sugars and Table Salt. J Am Coll Nutr.2017, Vol 36, NO. 8, 677-684.
  26. 2017: Kaats G.R., Leckie R.B., Mrvichin N, Stohs S.J. Increased eating control and energy levels associated with consumption of a bitter orange (p-synephrine) extract chew—a randomized placebo-controlled study. Nutr. Diet. Suppl. 9, 29e-35e.
  27. 2017: Kaats GR, Preuss H, Mrvichin N. When Non-significant Analyses Mask Significant Results—The Value of Compliance Analyses of Dose Related Changes. The Food Science Journal.
  28. 2016: Preuss HG, Mrvichin N, Bagchi D, Preuss J, Perricone N, Kaats GR. Importance of fasting blood glucose in screening/tracking over-all health. The Original Internist 23:13-20, 2016.
  29. 2016: Preuss HG, Mrvichin N, Bagchi D, Preuss J, Perricone N, Kaats GR. Fasting circulating glucose levels in the non-diabetic range correlate appropriately with many components of the metabolic syndrome. The Original Internist 23:78-89, 2016.
  30. 2016: Preuss HG, Mrvichin N, Clouatre D, Preuss JM, Perricone NV, Kaats GR. General lack of correlations between age and signs of the metabolic syndrome in subjects with non-diabetic fasting glucose levels. In: Functional and Medical Foods for Chronic Diseases: Bioactive Compounds and Biomarkers. (eds) D Martirosyan, B Burnett, Dallas TX, pp 140-142.
  31. 2016: Kaats GR, Nugent S, Stohs S, Preuss HG. Using a body composition improvement index (BCI) to improve the assessment of nutritional interventions. Current Nutrition and Food Sciences. 12(3):156.