DeathClock App

How We Developed The Death Clock Application

We used previous work to develop our Biological Age Calculator titled “Death Clock App.” This previous work was drawn on as guidance to develop our calculator because the authors evaluated the categorical differences between various statistical methods for a biological age calculator (1,2,3,4,5). Within these previous works, authors used various anthropometrical, biochemical, and laboratory values from their participants as variables to predict biological age. Variables included height, weight, BMI, clinical blood markers, exercise frequency, smoking history, and participant demographics as predictor variables.

 

The method we used to predict Biological Age is the Multiple Linear Regression model because it has the greatest applicability with the variables chosen (7). Multiple linear regression is used when you want to model the relationship between a dependent variable and multiple independent variables. The goal of regression analysis is to determine how changes in the independent variables are related to changes in the dependent variable. Multiple linear regression can be used to make predictions about the dependent variable based on values of the independent variables. Regression analysis is a statistical method used to estimate the values of the regression coefficients by fitting a linear equation to the data. In this case, you would use multiple linear regression to estimate the values of a, b1, b2, ..., bn.

 

The resulting regression equation can then be used to predict the biological age of new individuals based on their biomarker values.

 

We used data gathered from the Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) data pool from 2015-2020. We then gathered the  variables we had chosen for our analysis from the various surveys provided. Information was then organized to remove participants that were lacking the data points we used in our analysis. Additionally, we then removed all data of participants under the age of 18 and over the age of 790. Furthermore, we then stratified each data set based on the following predictor variables—

a. Gender (male & female)

b. Age (18-44 & 46-79)

c. History of smoking (smoke & non-smoke)

 

This provided us with eight (8) data sets to perform our Multiple Linear Regression analysis.

When performing the analysis, we utilized the following variables to create the formula to calculate biological age—

 

            a. chronological age

            b. height (cm)

            c. weight (kg)

            d. ethnicity

            e. average daily caloric intake (kcals)

            f. exercise (minutes/day)

            g. sleep (hours/night)

            h. apolipoprotein B (mg/dL)

            i. 25-hydroxyvitamin D2 + D3 (nmol/L)

            j. Glycohemoglobin (HbA1c %)

            k. High-Sensitivity C-Reactive Protein (mg/L) 

The resulting formula uses each of the associated variables to a biomarker to predict biological age.

Citations for proof of work

  • Jia, L., Zhang, W., Jia, R., Zhang, H. & Chen, X. (2016). Construction Formula of Biological Age Using the Principal Component Analysis. BioMed Research International, 2016, 4697017. https://doi.org/10.1155/2016/4697017
  • Jylhävä, J., Pedersen, N. L. & Hägg, S. (2017). Biological Age Predictors. EBioMedicine, 21, 29–36. https://doi.org/10.1016/j.ebiom.2017.03.046
  • Jia, L., Zhang, W. & Chen, X. (2017). Common methods of biological age estimation. Clinical Interventions in Aging, 12, 759–772. https://doi.org/10.2147/cia.s134921
  • Wood, T., Kelly, C., Roberts, M. & Walsh, B. (2019). An interpretable machine learning model of biological age. F1000Research, 8, 17. https://doi.org/10.12688/f1000research.17555.1
  • Dubina, T. L., Mints, A. Ya. & Zhuk, E. V. (1984). Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age and assessment of biological age in cross-sectional and longitudinal studies. Experimental Gerontology, 19(2), 133–143. https://doi.org/10.1016/0531-5565(84)90016-0
  • Hollingsworth, J. W., Hashizume, A. & Jablon, S. (1965). Correlations between tests of aging in Hiroshima subjects--an attempt to define “physiologic age”. The Yale Journal of Biology and Medicine, 38(1), 11–26.

Data Collection

  1. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Questionnaire (or Examination Protocol, or Laboratory Protocol). Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2015-16,https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2015
  2. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Questionnaire (or Examination Protocol, or Laboratory Protocol). Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2017-18,https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017
  3. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Questionnaire (or Examination Protocol, or Laboratory Protocol). Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2019-2020,https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2019

Citations for Biological Significance of Data Points

  1. Olivieri, F., Marchegiani, F., Matacchione, G., Giuliani, A., Ramini, D., Fazioli, F., Sabbatinelli, J. & Bonafè, M. (2023). Sex/gender-related differences in inflammaging. Mechanisms of Ageing and Development, 211, 111792. https://doi.org/10.1016/j.mad.2023.111792
  2. Acciai, F. & Firebaugh, G. (2017). Why did life expectancy decline in the United States in 2015? A gender-specific analysis. Social Science & Medicine, 190, 174–180. https://doi.org/10.1016/j.socscimed.2017.08.004
  3. Stockman, J. A. (2013). Minimum amount of physical activity for reduced mortality and extended life expectancy: a prospective cohort study. Yearbook of Pediatrics, 2013, 233–235. https://doi.org/10.1016/j.yped.2011.12.019
  4. Reimers, A. K., Knapp, G. & Reimers, C.-D. (2018). Effects of Exercise on the Resting Heart Rate: A Systematic Review and Meta-Analysis of Interventional Studies. Journal of Clinical Medicine, 7(12), 503. https://doi.org/10.3390/jcm7120503
  5. Eppinga, R. N., Hagemeijer, Y., Burgess, S., Hinds, D. A., Stefansson, K., Gudbjartsson, D. F., Veldhuisen, D. J. van, Munroe, P. B., Verweij, N. & Harst, P. van der. (2016). Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality. Nature Genetics, 48(12), 1557–1563. https://doi.org/10.1038/ng.3708
  6. Sierra-Johnson, J., Fisher, R. M., Romero-Corral, A., Somers, V. K., Lopez-Jimenez, F., Öhrvik, J., Walldius, G., Hellenius, M.-L. & Hamsten, A. (2009). Concentration of apolipoprotein B is comparable with the apolipoprotein B/apolipoprotein A-I ratio and better than routine clinical lipid measurements in predicting coronary heart disease mortality: findings from a multi-ethnic US population. European Heart Journal, 30(6), 710–717. https://doi.org/10.1093/eurheartj/ehn347
  7. Amrein, K., Quraishi, S. A., Litonjua, A. A., Gibbons, F. K., Pieber, T. R., Camargo, C. A., Giovannucci, E. & Christopher, K. B. (2014). Evidence for a U-Shaped Relationship Between Prehospital Vitamin D Status and Mortality: A Cohort Study. The Journal of Clinical Endocrinology & Metabolism, 99(4), 1461–1469. https://doi.org/10.1210/jc.2013-3481
  8. Fan, X., Wang, J., Song, M., Giovannucci, E. L., Ma, H., Jin, G., Hu, Z., Shen, H. & Hang, D. (2020). Vitamin D Status and Risk of All-Cause and Cause-Specific Mortality in a Large Cohort: Results From the UK Biobank. The Journal of Clinical Endocrinology & Metabolism, 105(10), e3606–e3619. https://doi.org/10.1210/clinem/dgaa432
  9. Parrinello, C. M., Lutsey, P. L., Ballantyne, C. M., Folsom, A. R., Pankow, J. S. & Selvin, E. (2015). Six-year change in high-sensitivity C-reactive protein and risk of diabetes, cardiovascular disease, and mortality. American Heart Journal, 170(2), 380-389.e4. https://doi.org/10.1016/j.ahj.2015.04.017
  10. Stout, R. L., Fulks, M., Dolan, V. F., Magee, M. E. & Suarez, L. (2007). Relationship of hemoglobin A1c to mortality in nonsmoking insurance applicants. Journal of Insurance Medicine (New York, N.Y.), 39(3), 174–181.

 







10 Mar 2023