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Connect with CTV Regina. The number of females, males, smokers and non-smokers within each age group was comparable Supplementary Fig. The median age was 55 years. DNNs require large training datasets. To obtain a sufficiently large training sets we first selected samples with the same blood test date, that is, datasets consisting exclusively of blood-based biomarkers measured on the same day, so that our DNN could be trained consistently, relevantly, and accurately. Although deep learning models can automatically extract features from the data and usually outperform shallow machine learning at this task, it is a good practice to select a set of relevant features before training the network.
We optimized the feature spaces that were used to train the models for age prediction first excluding smoking status using a multifactorial adaptive statistical arbitrage model 13 for subsets of samples with various numbers of measured markers. We trained random forest RF models on distinct feature spaces and subsequently extracted FI values from each model. The accuracy of any predictor depends on the sample size and the feature space on which it is trained. This reconstruction successfully increased the number of available features from 14, 15, and 18 to 18, 20, and 23 features, respectively.
The blood marker with the largest contribution to the age-prediction model is glycated hemoglobin hemoglobin A1c , followed in descending order by blood urea, fasting serum glucose, and serum ferritin Supplementary Fig. Fasting glucose was among the most important features in our previous studies on deep learning-based hematological aging clocks 10 , Interestingly, the most important markers as selected by the arbitrage FI method demonstrate independent weak biweight mid-correlation, which shows the strength of a linear association between blood markers and age. The arbitrage FI method is more robust than the Pearson correlation coefficient, being a median-based measure that is less sensitive to outliers Supplementary Fig.
Input feature sets were chosen to contain the maximum number of available samples that displayed the features selected via RF-based arbitrage feature selection previous section. To predict individual age, we trained three DNNS on selected blood test input features of nonsmoking subjects. All three models achieved a relatively high correlation between predicted and actual chronological age. The deep neural network trained on 20 blood test input features achieved an MAE of 5. Samples from the tail ends of the distribution individuals younger than 35 years and those older than 75 years exhibited a higher error rate for age prediction.
Fasting glucose, sex, and red blood cell distribution width RDW were predicted to be the most important markers Fig. To investigate the effect of smoking on age prediction, we used neural networks trained on nonsmokers to calculate the age of the smokers and nonsmokers excluded from the training set. Model demonstrated R 2 of 0.
In the context of biological aging, this suggests that the contribution of tobacco smoking as an external factor of aging may eventually be masked by the intrinsically stochastic and physiologically deleterious nature of the aging process. Alternatively, the people most affected by smoking may have died at an earlier age and thus were be excluded from the old-age smoking group.
Deep learning-based hematological clocks demonstrated accelerated aging rates in smokers and revealed patient smoking status. A The prediction accuracy of the best-performing model trained on feature space extended with smoking status.
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The model, trained on 24 parameters, achieved an R 2 of 0. Smokers demonstrated a higher aging rate regardless of sex. However, these differences plateaued after 55 years of age. C The most important features in the classification of smoking status selected by the PFI method. HDL cholesterol, sex, and hemoglobin exhibited higher relative importance scores than other features used in model training. D The model trained on 23 parameters achieved an F1 score of 0. Confusion matrices. A Confusion matrices for the best-performing smoking status classifier, trained on 23 features, in number of samples left and percentage right.
Row values show predicted smoking status, and columns show actual smoking status. Most of the error smoking predictions occurred in individuals older than 55 years. B Confusion matrices for age prediction by age groups for the best model, trained on 24 parameters, in number of samples left and percentage right. Row values show actual chronological age group, and columns show predicted age group. To further evaluate the importance of smoking status in age prediction we included smoking status as an input feature along with blood test values and trained the new set of DNNs on the three extended sets of input features.
Smokers were included in the training set for this round. To robustly compare the performance of these models with models trained on nonsmokers, we used the same number of samples in the training sets. The best-performing deep neural network, which was trained on 24 blood test input features, performed better than the model trained on 23 input features without smoking status and achieved an R 2 of 0.
These results suggest that smoking status plays an important role in predicting age. The same analysis of sex as an input feature showed that predicted age increases slightly with a sex of 1 male Supplementary Fig. To explore whether the smoking status of patients could be assessed using only patient sex and their blood test values we trained three DNNs on the same input feature sets used in the prior models to classify smokers and nonsmokers. The best-performing smoking status classifier, which was trained on 23 blood test input features, achieved an accuracy of 0.
Curiously, most of the false-positive and false-negative smoking status predictions occurred in individuals older than 55 years Fig. This observation was consistent with the increased error rate that accompanied predictions of the ages of smokers and nonsmokers who were chronologically younger than 40 years. This trend was not observed in subjects older than 51 years and was therefore consistent with the observation made above. As shown in Fig. On average, female smokers were predicted to be twice as old as their chronological age as compared to non-smokers.
Male smokers, on average, were predicted to be one and a half times as old as their actual chronological age compared to nonsmokers. This phenomenon is not observed in smokers with a high blood glucose level. Bars indicate standard deviation. Our study, based exclusively on the analysis of routine blood test results, identifies complex nonlinear interactions between these test results, aging, and smoking status. Previous studies demonstrated that smoking exacerbates epigenetic aging 15 , 17 , but our study is the first to use blood test results to quantify this effect.
Although our hematological aging clocks are slightly less accurate in chronological age prediction than DNA-methylation-based predictors 18 , 19 , our method they are less expensive and more practical requiring only standard blood tests. Surprisingly, this effect disappears in the oldest subjects. At the same time, the study conducted by Levine and Crimmins showed similar results They showed that smokers from the 80 years old age group have no increase in mortality risk compared to smokers from other age groups.
This could suggest that susceptible elderly smokers may have died off as a consequence of their smoking habits. An alternative hypothesis is that tobacco smoking may stimulate the activation of repair processes; his phenomenon has been proposed as a potential mechanism of tobacco-smoking protection from Parkinsons disease Deep learning-based hematological aging clocks can serve as reasonably accurate predictors of age for relatively healthy individuals.
These clocks can also serve as accurate tools for evaluating the effect of lifestyle factors such as tobacco use on biological aging.
Furthermore, they can act as accurate classifiers of patient smoking status. Classifiers based on deep neural networks have the potential to support or even replace patient self-reporting and can thereby provide a better statistical assessment of the prevalence of tobacco smoking. The deep learning—based approach used in this study may be extended to analyze the combined effects of tobacco smoking and biochemically-defined diabetes mellitus and dyslipidemia as well as other potential morbidities.
Similarly, DNNs could be used to predict health trajectories and outcomes or to evaluate the extent to which various other environmental exposures, dietary factors, and genetic risks affect health and aging. CHC , the administrative dataset consisted of fully-anonymized records for , adult subjects. CHC approved protocol. Each record included smoking status, sex, age, and up to 66 blood biochemistry and hematology markers.
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Smokers and nonsmokers were matched for age distribution, sex, urban versus rural residence, and geographical latitude of residence. As per HREBA protocol, we did not have any information on either racial or ethnic origins, and analysis of any racial or ethnical effects was not permitted. Blood biochemistry datasets were first preprocessed and normalized as previously described 8.
We treated the age prediction as a regression task. The deep neural network was built by adjusting its hyperparameters e. To expand the feature space used to train our predictors, we applied regression and reconstructed missing values for part of the analyzed dataset. Marker values were reconstructed individually. Reconstruction of the missing values in this manner increased the size of each feature space from 14, 15, and 18 features to 18, 20, and 23 features, respectively.
We used multilayer feed-forward back propagation neural networks as deep models i. The Python 3. A grid search algorithm was used for multiple hyperparameters, optimizing for each feature space to achieve the greatest predictive accuracy. We minimized the MAE loss function using a back propagation algorithm. We trained the networks with five fold cross-validation to compensate for overfitting and to achieve more robust performance metrics. The optimized architectures of each DNN are presented in Supp. To predict smoking status, we trained three classifiers on three different feature spaces.
To do so, we again used simple feed-forward back propagation neural networks as deep models. Multiple hyperparameters were adjusted for each feature space to achieve the greatest predictive accuracy. We minimized binary cross-entropy loss function via the use of a back propagation algorithm. We trained each network with fivefold cross-validation to compensate for overfitting and to achieve more robust performance metrics. The Python scikit-learn library was used to train RF models The RF technique allows features to be ranked according to the decrease in accuracy averaged by each set of tree values i.
We trained RF models on distinct feature spaces using 80 decision-tree estimators, with some hyperparameters adjusted and others set to default.
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PFI is a wrapper method that we previously applied to determine the list of the most important blood test features for age prediction 12 , We applied the same technique for the age-prediction and smoking-status-prediction models discussed in the present study. If p -values were less than 0. The following metrics were used to evaluate the predictive accuracy of the age-prediction and smoking-status-prediction models:. R 2 shows the percentage of variance explained by the regression between predicted and actual age. MAE demonstrates average disagreement between the chronological age and the predicted age.
Aging ratio is the ratio of predicted age to observed chronological age. Precision shows specificity of a model and equals to a fraction of correctly predicted smoker samples to the all samples predicted as smokers;. Recall shows the sensitivity of a model and equals to a fraction of correctly predicted smoker samples compared to all smoker samples. Accuracy is a fraction of correctly predicted smoking status to the all values.
As per provisions of the strictly enforced Health Information Act of the Province of Alberta, Canada and decision of the Provincial Ethics Board, only aggregate result data may be presented in the manuscript, and the source fully anonymized administrative dataset containing individual blood test results constitutes private health information and will never be made public or deposited in any public database.
Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers
Requests for access to data have to be directed to Dr. Kovalchuk and will be handled in accordance with the Provincial Health Information Act. Zhavoronkov, A. Public Health 10 11 , — Xia, X. Molecular and Phenotypic Biomarkers of Aging. Jylhava, J. Biological Age Predictors. EBioMedicine 21 , 29—36 Ozerov, I.
Commun 7 , Aliper, A. Signaling pathway activation drift during aging: Hutchinson-Gilford Progeria Syndrome fibroblasts are comparable to normal middle-age and old-age cells. Aging 7 1 , 26—37 In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state. Aging 8 9 , — Thomas, I. Metformin; a review of its history and future: from lilac to longevity. Mamoshina, P.