Diabetes

Estimation of homeostatic dysregulation and frailty using biomarker variability: a principal component analysis of hemodialysis patients

In our previous survival study, we observed widespread positive correlations between LCVs in the baseline data which had been recorded during the year of 20024. The dataset analyzed in the present study was collected more than 12 years thereafter. Nevertheless, the correlation matrices derived from both datasets showed very similar patterns and levels (compare Table 5 in Ref. 4 and Supplementary Table S1), indicating their nearly constant correlation structure. Given that a high LCV level reflects dysfunction of the corresponding physiological regulatory system, the correlation coefficient between two different LCVs can represent the strength of the interaction (coupling) between two different physiological systems22. More specifically, the robust correlation (r > 0.5) between certain pairs of LCVs (Na-LCV/Cl-LCV, BUN-LCV/Cr-LCV, Alb-LCV/TP-LCV, and BUN-LCV/K-LCV, see Supplementary Table S1) indicates the proximity of the regulatory mechanisms of these paired parameters. Taking this idea one step further, the ubiquitous positive correlations among the LCVs imply an interconnected structure (i.e., network) of homeostatic regulation and suggest that its overall dysfunction can be estimated by PC1 of the LCVs22,23.

Changes in physiological variability in relation to aging and health can be viewed through two lenses that produce contradictory results. On the one hand, the “loss of complexity” paradigm suggests that high variability is a sign of a system that is able to adjust appropriately, and that loss of variability is a sign of loss of appropriate complexity (ref. 23 and others by Lipsitz and Goldberger). On the other hand, the “critical transitions” paradigm suggests that high variability is a marker of an impending critical transition in system state, usually undesirable in a health context24,25. Perhaps as a bridge between these two, Fossion et al. suggest that physiological variables can be divided into regulated variables (those that are kept stable, i.e., targets of homeostasis) and physiological responses (buffers that adjust in order to keep regulated variables stable)8. This is highly concordant with sub-cellular regulation as demonstrated by Nijhout et al.26. However, our results would seem to provide unmitigated support for the critical transition framework, with generalized increases in variability observed in frailty across all biomarkers, regardless of whether they might be thought to be regulated or responses. Why this is remains to be explored, though some publications have questioned whether observed changes in heart rate variability that motivated the “loss of complexity” framework are indeed reproducible27.

Glycemic variability (GV) has been assessed by repeated measurements of blood glucose, hemoglobin A1c, or GA levels with various sampling intervals. Despite the different definitions of GV, studies have generally reported associations between a high GV and adverse patient outcomes12,28,29. GV is an active topic of clinical medicine, and we were interested in the similarity between GV and other LCVs. In chronic HD treatment, the GA value has been recognized as a superior index of glycemic control30, and the dataset used in this study contained periodic GA values; therefore, both the GA-M and the GA-LCV were included in the analysis. In the diabetic patients, the GA-LCV was significantly correlated with 16 of the 19 LCVs and, like most of the other LCVs, exhibited negative correlations with Alb-M, Cr-M, and Na-M, all three of which are solid prognostic predictors (Fig. 2a and Supplementary Table S1).

While GA values were not available for non-diabetic subjects, we think that these correlations likely exist in the entire subjects for the following reasons: (a) GA-LCV was strongly correlated with GA-M in the diabetic patients (r = 0.54, Supplementary Table S1), (b) the diabetic patients had relatively high levels for the majority of LCVs (Table 1), and (c) the GA-M and GA-LCV levels of non-diabetic patients should be lower than those of diabetic patients. Accordingly, GA-LCV also seems to share common characteristics and significance with other LCVs. That is, the regulation of blood glucose is not independent from that of other blood components. Until now, a high GV has been discussed only in the context of diabetic complications. However, as a high GA-LCV level, along with a high GA-M level, is largely accompanied by wide fluctuations in other parameters, at least in HD patients, it could also be a manifestation of diverse (not necessarily diabetes-related) types of organ/tissue damages and accompanying physiological dysregulation. This interpretation is compatible with the presence of impaired glucose tolerance in patients with various chronic diseases31,32,33 as well as frail elderly34. Furthermore, it explains why dysglycemia in critically ill patients is associated with a high mortality and why strict glycemic control has a minimal effect on their prognosis35.

Most of the operational criteria for frailty are based on aggregate scores of survey items, which were selected empirically to capture physical, mental, and social well-being. Frailty assessments can thus be time-consuming and require the cooperation of the subjects, but they still entail some uncertainty because of the lack of an objective definition. Accordingly, biomarkers that complement frailty assessments have been sought and proposed36. Some of the LCVs examined in the present study are candidates for such biomarkers, since their values were often associated with aspects of frailty4. When comparing the levels of the 19 LCVs between frail and not-frail groups, the frail group actually showed elevated mean LCV values for almost all the parameters, though the difference was moderately significant for only 4 parameters (Table 3). In comparison, the PC1 score showed a more significant difference than each of the LCVs, indicating that the former is a reliable marker for estimating frailty. In line with this result, a multivariate logistic regression model containing only 4 predictors (PC1, age, HD duration, and diabetic status) reasonably discriminated the frailty status without the use of physical performance tests or questionnaires. While the PC1 score alone is a moderate predictor of frailty, the information it contains appears to be largely independent of age and other covariates in the subjects. Furthermore, the PC1 score was associated with many parameters in a manner that was consistent with their prognostic significance in HD patients (see the right side of Table 2). They include diabetes (or high GA-M), high GV, and low serum levels of Alb, Cr, K, Na, BUN, Hb, LDL-cholesterol, and so on. As deviated levels of these parameters are closely linked to frailty, sarcopenia, protein energy wasting, and mortality risk in HD patients36,37,38,39,40,41, our results indicate that the PC1 of LCVs is certainly a marker of adverse health conditions. It is currently unknown why blood levels of each of these parameters have a different (i.e., positive or negative) relationship with prognosis. This diversity probably reflects the uniqueness of each regulatory system, and we speculate that the common basis of various poor prognostic factors might be their close association with a high PC1 level, which denotes a diminished homeostatic capacity.

For HD patients, the PC1 score can be calculated from routine blood examinations and can also be used to objectively estimate their frailty status. We believe that the PCA-based frailty estimation is clinically applicable and will be a useful guide in selecting treatment options for patients with co-morbidities.

PCA has some favorable properties in exploring LCVs. Several biomarkers, including P, K, UA, and BUN, reportedly have a U-shaped relationship between their levels and mortality42,43,44. On the other hand, the extent of their variability appeared to have a monotonic effect on the mortality risk in previous studies4,45,46,47. Thus, we can simply apply LCVs to PCA without considering their normal or desirable values. Unlike ordinary multiple regression modelling, PCA is not impeded by multicollinearity, which was moderately but widely present among the LCVs. Moreover, since PCA is a non-supervised analysis, the resultant PC scores represent information inherent to the data and can be used as independent variables in different regression models for various definitions of frailty/sarcopenia as well as mortality. In PCA for variables with the same directional properties, as the number of variables increases, the result seems to be less affected by the number and selection of the variables48. Using this same dataset, we also examined a smaller PCA model based on 9 variables, namely LCVs of WBC, Hb, Plat, Alb, BUN, Cr, K, Ca, and P. Although the detailed results were omitted to avoid repetition, the PC1 score was strongly correlated with the score from the original PCA model based on 19 LCVs (r = 0.95) and provided nearly identical results in the logistic regression analyses for frailty. For example, the model corresponding to the original model #4 exhibited a P value of 0.008, an AUC of 0.861, and an accuracy of 0.844. This property helps to generate common PC1 scores from multiple datasets, each containing a different set of parameters.

The main limitation of this study is the relatively small sample size of patients who had completed the frailty assessment, which might have reduced the statistical power to detect differences between the frail and not-frail groups. We also excluded patients who were hospitalized at the time of the survey and those who could not respond to the questionnaire. Hence, the subjects who were included in the frailty analysis might have been less frail, compared with the overall subjects in the study. Although these situations may have weakened some of the results, we think that the presently reported conclusions are still very clear and reasonable.

Another limitation is the cross-sectional design. The regression model produced in this study demonstrated that HD patients with an older age, longer HD duration, and greater fluctuations in laboratory data were generally more frail. Considering the ageing/death-associated changes in Alb-LCV6, which is robustly correlated with PC1, this model seems to fit well with the expected time-dependent progression of frailty. However, a cross-sectional study addresses only the prevalence of frailty and can suffer from a selection bias and a survivorship bias. To investigate the relationship between PC1 and frailty more accurately, longitudinal data for both variables will need to be analyzed.

Finally, we should mention that this study was based solely on clinical data from HD patients. The reason for this is that the regular and frequent collection of multi-dimensional data is very difficult to achieve in other populations. Consequently, whether similar results are observable in other disease populations is currently unknown.

In summary, we applied a PCA to the levels of variability of 19 blood-based parameters to explore the physiological implications of variability. The 19 LCVs had similar characteristics and shared common information, which could be extracted as PC1. Compared with the original LCVs, the PC1 score was consistently correlated with frailty as well as various other negative health indicators in HD patients. We concluded that the PC1, which is a cumulative index of variability, is a measure of homeostatic dysregulation and can be used to estimate frailty.

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