Beyond Self-Reports: Changes in Biomarkers as Predictors of Mortality Dana A. Glei Noreen Goldman Germán Rodríguez Maxine Weinstein
Publication of Between Zeus and the Salmon: The Biodemography of Longevity (Wachter and Finch 1997) heightened interest in the collection of biological measures of chronic disease within demographic household surveys. Before the 1990s, the inclusion of such measures was generally limited to epidemiologic studies, which were often based on geographically limited samples and usually included only limited social data. Since then, a growing number of population-based surveys have collected biological measures alongside questionnaires that rely on self-reports of health and extensive household and individual information. As these “biosocial” surveys have proliferated, increasing levels of research funding are being devoted to their collection. Thus, it is important to weigh the benefits of biomarker collection against the costs: financial expense, logistical complications, increased respondent burden, ethical issues, and threats to privacy. There are many ways to quantify the value of the biomarkers being collected. Here, we focus on only one of the potential uses: their value in predicting mortality. We use mortality because it is a well-defined and salient health outcome with little measurement error, assuming that vital status is determined from virtually complete death registration records. Friedman and Kern (2014) argue that all-cause mortality is the single best measure of health, noting that it is used worldwide as a key indicator of public health. While biomarkers of individual diseases are of particular clinical interest, the biomarkers evaluated here relate to a broad range of diseases. Thus, we would obtain an incomplete—perhaps even biased—assessment of their predictive power if we used a specific disease or cause of death as the benchmark. In addition to strong validity, all-cause mortality avoids the possibility that studies of a single disease may miss some deaths that were attributed to a different cause, but that resulted indirectly from the disease of interest (Friedman and Kern 2014). POPULATION AND DEVELOPMENT REVIEW 40(2): 331–360 (JUNE 2014)
©2014 The Population Council, Inc.
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As biosocial surveys have evolved, some studies have begun longitudinal collection of biomarkers with the expectation that measurement at multiple survey waves will improve our understanding of the link between biomarkers and mortality (Crimmins and Vasunilashorn 2011). As yet, however, little data have been available to test this assumption. Prior studies have demonstrated that biomarkers are useful predictors of mortality, but few have assessed the prognostic value of change in biomarkers to determine whether they offer greater value than measurement of biomarker levels at a single wave. Collecting a wide range of biomarkers is costly, so it is also important to determine whether some biomarkers are more valuable than others or whether most of the power derives from markers representing a particular biological system. The most common approach, by far, for quantifying the predictive ability of a model is the C-statistic or Area Under the Receiver Operating Characteristic (ROC) Curve. Thus, we will assess the gain in predictive ability of a model after incorporating a set of biomarkers using ROC analysis. Prediction in the case where there are two possible outcome states (here, alive or dead) involves a tradeoff between the sensitivity of the prediction model and its specificity. Greater sensitivity means higher true positive rates—that is, those who died are more likely to be correctly classified by the model as dead; greater specificity means lower false positive rates—that is, those who survived are less likely to be wrongly classified as dead. Plotting true positive rates (sensitivity) against false positive rates (1 – specificity), each lying within the range 0 to 1, for varying levels of the cutoff threshold used to classify subjects into the two groups, yields what is termed the ROC curve for the model. A lower cutoff yields greater sensitivity but lower specificity. The model’s predictive ability is then quantified using the C-statistic or area under the ROC curve— denoted AUC, a summary measure of how well the model discriminates. AUC in the present case can be interpreted as the probability that those who actually died are assigned a higher predicted probability of death than those who survived (Pencina and D’Agostino 2004). An AUC of 0.5 would indicate that the model performs no better than chance alone; an AUC of 1.0 would represent perfect accuracy (100 percent sensitivity and specificity).1 In this analysis, we use longitudinal data from the 2000 and 2006 waves of the Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan, one of the first biosocial surveys to collect two rounds of biomarkers, to investigate the following four hypotheses motivated by the existing literature. First, we expect that biomarkers will have greater value for predicting mortality than self-reported measures of health when compared side by side. Second, we hypothesize that biomarkers will retain incremental prognostic value above and beyond self-reports. Third, we expect that measurement of change in biomarkers will yield better discrimination (i.e., ability to distinguish between those who died and those who did not) than a single measure-
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ment of biomarkers. Fourth, based on prior studies that have included a wide range of biomarkers (e.g., Crimmins and Vasunilashorn 2011; Newman et al. 2009; Sidorenkov, Nilssen, and Grjibovski 2012; Swindell et al. 2010), we anticipate that markers drawn from multiple biological systems will contribute prognostic value. A listing and brief description of the biomarkers used in the analysis are given in Appendix Table 1.
Predicting mortality Self-reported indicators of health status Health indicators obtained from self-reports are typically important mortality predictors. Indeed, many of the indicators incorporated into prognostic indexes designed for clinical use (Yourman et al. 2012) could be obtained via self-report, although their validity would need to be assessed. For the purposes of predicting longer-term mortality (over at least four years) in communitybased populations, two such indexes have demonstrated good (Schonberg et al. 2009) or very good (Lee et al. 2006b) discrimination, although none of the existing prognostic indexes has attained excellent discrimination (Yourman et al. 2012). Among the health indicators in these indexes are: mobility/ functional limitations, history of cancer, current diabetes, smoking status, self-assessed health status, and number of hospitalizations in the past year (Schonberg et al. 2009; Lee et al. 2006b; see also www.eprognosis.org). Other indicators include current chronic obstructive pulmonary disease (COPD), congestive heart failure, and body mass index, but self-reports may not reliably capture these variables. As suggested above, self-reported measures have a downside: potential inaccuracy. Misreporting may result from recall bias or from unwillingness to acknowledge health problems. Respondents may also be unaware of some underlying conditions that confer health risk. Then, too, there is a subjective component: for example, two people with similar levels of physical ability may differ in their perceived—and, consequently, reported—difficulty performing a specified task (Melzer et al. 2004). All of these factors can contribute to measurement error. One appeal of biological markers is that they may be less subject to such errors. Biomarkers Some studies that examine the relationship between biomarkers and mortality use individual biomarkers as predictors, whereas others develop a composite measure based on multiple markers. Studies also differ in the number and range of markers they use: some rely on a few closely related markers that reflect a common biological system, while others include a larger number
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of markers that represent multiple systems (e.g., cardiovascular, metabolic, immune/inflammatory, neuroendocrine, renal, hepatic). Given the extensive literature linking biomarkers and mortality, here we focus on the smaller set of studies that include markers drawn from multiple systems. We divide our review into two parts: studies that use individual markers and those that use multi-system summary scores.
Studies using individual markers representing multiple biological systems. Many studies have investigated the association between survival and a collection of individual biomarkers drawn from multiple biological systems (e.g., Crimmins, Kim, and Vasunilashorn 2010; Crimmins and Vasunilashorn 2011; Fried et al. 1998; Kistorp et al. 2005; Lan et al. 2007; Lee et al. 2006a; Newman et al. 2009; Sidorenkov, Nilssen, and Grjibovski 2012; Swindell et al. 2010; Turra et al. 2005). These studies demonstrate that cardiovascular, metabolic, inflammatory, neuroendocrine, and other markers that reflect disease are useful predictors of mortality. Here, we limit our review to studies that: 1) examined individual biomarkers drawn from multiple systems as predictors of all-cause mortality, where the markers include standard cardiovascular/metabolic markers (e.g., blood pressure, lipids, measures of glucose metabolism, and obesity) and at least one inflammatory marker (e.g., interleukin-6 (IL-6), C-reactive protein (CRP)); and 2) assessed the discriminatory ability of the set of biomarkers. We identified six such studies (see Appendix Table 2) based on five unique datasets in four countries: Costa Rica (Rosero-Bixby and Dow 2012), Germany (Haring et al. 2011), the Netherlands (Newson et al. 2010; Stork et al. 2006), and Taiwan (Goldman et al. 2006b; Goldman et al. 2009). Some of these studies also included neuroendocrine markers such as cortisol, dehydroepiandrosterone sulfate (DHEAS), epinephrine, and norepinephrine. The six studies also incorporated “other” biomarkers that cannot be easily classified into one of the above-mentioned groups. Half included a measure of renal function (e.g., creatinine clearance, urinary albumin). Two studies included serum albumin (Goldman et al. 2009; Newson et al. 2010), a nonspecific measure of underlying disease. Two studies (Goldman et al. 2006b; Goldman et al. 2009) incorporated insulin-like growth factor 1 (IGF-1), which affects muscle growth and is an indirect marker of inflammation. Haring et al. (2011) added a marker of liver function (glutamyltransferase). Two studies (Stork et al. 2006; Newson et al. 2010) incorporated carotid plaque burden, a marker of atherosclerosis. Rosero-Bixby and Dow (2012) included performance assessments (i.e., grip strength, gait speed, peak expiratory flow). Another study included a measure of bone density (Newson et al. 2010). As noted by Pencina et al. (2012), the gain in AUC (i.e., the difference in the AUC between the models with and without the indicator(s) in question) depends heavily on the strength of the baseline model (i.e., the model
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without the indicator being considered). All of the studies mentioned above controlled for sex, age, smoking, and some self-reported measures of baseline health status. Haring et al. (2011) controlled for the most extensive list of covariates, including multiple measures of socioeconomic status, health behaviors, disease history, self-assessed health status, perceived quality of life, and waist circumference. They had the strongest baseline model (AUC=0.88) among the six studies considered here. Most of these studies found that the addition of biomarkers led to a large gain in predictive ability.2 The Costa Rica study (Rosero-Bixby and Dow 2012) included 22 biomarkers, and the Taiwan studies (Goldman et al. 2009; Goldman et al. 2006b) included 13–16 biomarkers; these three studies found that the set of biomarkers accounted for a 0.06–0.08 increase in AUC. One of the Dutch studies included only three biomarkers (IL-6, CRP, and the number of carotid plaques), but those markers contributed 0.15 improvement in AUC (Stork et al. 2006). The other Dutch study (Newson et al. 2010) did not estimate the increase in AUC associated with the seven biomarkers (i.e., diastolic blood pressure, CRP, number of carotid plaques, and several other markers) compared with a model that included only the control variables (see Appendix Table 2). The incremental value attributable to biomarkers was much smaller in the Haring study than in the others, which may be explained by the limited set of selected biomarkers combined with an extensive set of control variables. Not surprisingly, the largest gain in AUC (0.15) was generated by the study with the weakest baseline model (AUC=0.62) (Stork et al. 2006).
Studies using multi-system biomarker summary scores. The allostatic load framework (McEwen and Stellar 1993) helped promote the use of composite measures of biological function across multiple systems (although not all multi-system scores are predicated on the allostatic load framework). A number of studies in the United States and Taiwan have found that multi-system measures can consistently predict all-cause mortality (Goldman et al. 2006a; Seeman et al. 2001; Seeman et al. 2004; Gruenewald et al. 2006; Karlamangla, Singer, and Seeman 2006; Borrell, Dallo, and Nguyen 2010; Crimmins, Kim, and Seeman 2009; Wang et al. 2006; Levine 2013)—see Appendix Table 2 for more details. Early formulations of such measures incorporated standard cardiovascular/metabolic markers as well as neuroendocrine markers. More recent versions typically include at least one inflammatory marker and often include some “other” marker(s) (e.g., serum albumin, creatinine clearance, homocysteine). In most cases the magnitude of the association between the multi-system score and mortality was substantial even among the studies that controlled for self-reported indicators of baseline health status (Goldman et al. 2006a; Seeman et al. 2004; Seeman et al. 2001; Wang et al. 2006). For example, Seeman et al. (2001) showed that the odds of dying within seven years were
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more than six times higher for someone with high-risk levels for at least seven of the ten biomarkers compared with someone with no high-risk markers. Wang et al. (2006) reported that the mortality rate was four times higher for individuals with high multi-system scores (i.e., top quintile) relative to those with low scores (i.e., bottom two quintiles), adjusted for age, sex, and conventional risk factors, although the multi-system score yielded only a small improvement in predictive ability (C-statistic increased from 0.80 to 0.82). However, standard clinical biomarkers were included as control variables rather than being incorporated into the multi-system score. None of the other studies quantified the predictive ability of the multi-system score. With one exception, prior studies investigating the link between a multisystem score and mortality have relied on biomarkers measured at only one survey wave. The exception, the study by Karlamangla, Singer, and Seeman (2006), examined changes in biomarkers over a 2.5-year period, finding that both starting levels and changes were associated with subsequent mortality. However, this study did not control for any self-reported health indicators, nor did it evaluate whether changes in biomarkers provided significantly better discriminatory ability than a one-time measurement. What this study adds In sum, existing prognostic indexes for predicting mortality among community-based populations of older adults have achieved C-statistic/AUC values ranging between 0.66 and 0.84 (Yourman et al. 2012). Our study extends earlier studies by quantifying the incremental improvement in discrimination associated with measurement of change in biomarkers compared with one-time measurement.
Methods Data Our data come from a cohort study in Taiwan (SEBAS), augmented by the 2003 wave of its parent study, the Taiwan Longitudinal Study of Aging (TLSA). Taiwan provides a good context for study because the level of life expectancy (e0 = 79.2 in 2010) is similar to that of other industrialized countries such as the US (e0 = 78.9 in 2010) (Human Mortality Database 2013), and the causeof-death structure is also similar to that of high-income Western countries. For example, nine out of the top ten causes of death in 2011 were the same in Taiwan (Department of Health, Taiwan 2012) and the US (Hoyert and Xu 2012). Many of the factors (e.g., age, sex, education, marital status, selfreported measures of health) that predict mortality in Taiwan are consistent with those observed in Western countries (Zimmer, Martin, and Lin 2005).
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The SEBAS cohort was based on a nationally representative sample of Taiwanese aged 54 and older in 2000, selected randomly using a multi-stage sampling design with oversampling of older persons (71+) and urban residents (Chang et al. 2012). In 2000, in-home interviews were completed with 1,497 respondents, 1,023 of whom also completed a physical examination. Exam participants did not differ significantly from nonparticipants in ways likely to introduce serious bias (Goldman et al. 2003). Six years later, a followup was conducted with those who completed the 2000 exam and survived to 2006: 757 completed the in-home interview and 639 participated in the physical examination. Details regarding response rates, sample attrition, and exam participation are provided elsewhere (Chang et al. 2012). The physical examination followed a similar protocol in both waves. Several weeks after the household interview, participants collected a 12-hour overnight urine sample, fasted overnight, and visited a nearby hospital the following morning for a physical examination that included collection of a blood specimen and measurements of blood pressure, height, weight, and waist and hip circumference. Compliance was high: in 2000, 96 percent fasted overnight and provided a urine specimen deemed suitable for analysis; the comparable figure was 88 percent in 2006. Union Clinical Laboratories (UCL) in Taipei analyzed the blood and urine specimens. In addition to the routine standardization and calibration tests, triplicate sets of specimens were provided by individuals outside the target sample (n=9 in 2000; n=10 in 2006): two sets were sent to UCL and the third set was sent to Quest Diagnostics in the US (San Juan Capistrano, CA). The results indicated high intra-lab reliability for duplicates sent to UCL (2000: ≥ 0.71; 2006: ≥ 0.85). With a few exceptions (glycoslyated hemoglobin, HbA1c; cortisol; urinary creatinine), inter-lab correlations between results from UCL and Quest Diagnostics exceeded 0.90 in both waves. Survival status as of January 1, 2012 was ascertained by linkage to the death certificate file maintained by the Taiwan Department of Health and to the household registration database maintained by the Ministry of the Interior. The analysis sample was based on the longitudinal cohort that completed the exam in both 2000 and 2006 (n=639, 104 of whom died by December 31, 2011). The mean length of mortality follow-up was 5.1 years (max, 5.3 years). Some (n=89) of the respondents had missing data for at least one covariate, typically a biomarker. To maximize the use of the data, we followed standard practices of multiple imputation (Schafer 1999; Rubin 1996) to handle missing data.3 Measures Biomarkers. We included 19 biomarkers that have been shown by prior studies to be associated with all-cause mortality. They comprised four groups of
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markers: 1) eight standard cardiovascular/metabolic risk factors—systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, glycoslyated hemoglobin, body mass index, and waist circumference; 2) four inflammatory markers—IL-6, CRP, soluble intercellular adhesion molecule 1 (sICAM-1), and soluble E-selectin; 3) four neuroendocrine markers—dehydroepiandrosterone sulfate (DHEAS), cortisol, epinephrine, and norepinephrine; and 4) three other markers that do not represent a common biological subsystem—creatinine clearance, serum albumin, and homocysteine. See Appendix Table 1 for more detailed information about each marker. We calculated an overall summary score based on these 19 markers and subscores for each of the four groups of markers.4 Each score was calculated by counting the number of markers for which the respondent exhibited a high-risk level. High risk was defined by established cutoffs for the standard cardiovascular/metabolic factors and CRP (see Table S1 in Supplementary Materials).5 For all other markers, which have no generally accepted clinical cutoffs, we defined high risk based on the weighted distribution of the 2000 sample: bottom quartile for DHEAS, creatinine clearance, and albumin; top quartile for other markers. Although other types of summary scores have been proposed (Juster, McEwen, and Lupien 2010; Seeman et al. 2010), we use a formulation similar to that employed by many other studies to represent a composite of biomarker values. Self-reported health indicators. On the basis of self-reports from 2006, we included six health indicators: 1) global self-assessed health (“Regarding your current state of health, do you feel it is excellent, good, average, not so good, or poor?”); 2) an index of mobility limitations; 3) whether the respondent was ever diagnosed with diabetes; 4) history of cancer; 5) number of hospitalizations in the past 12 months; and 6) smoking status (never, former, current). Mobility was based on self-reported difficulty performing eight physical tasks without assistance, each of which was coded on a four-point scale (0=no difficulty, 1=some difficulty, 2=great difficulty, 3=unable): stand for 15 minutes, squat, raise both hands overhead, grasp or turn objects with his or her fingers, lift or carry an object weighing 11–12kg, walk 200–300m, run 20–30m, and climb two or three flights of stairs. Based on the recommendations of Long and Pavalko (2004), we constructed the scale by summing the eight items (potential range 0–24), adding a constant (0.5), and taking the logarithm of the result, which allows for relative rather than absolute effects. Social and demographic characteristics. We controlled for key demographic and social characteristics that are known to be important predictors of mortality. Demographic variables comprised age, sex, urban residence, and ethnicity (Mainlander vs. Taiwanese). We also included educational attain-
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TABLE 1 Descriptive statistics for social and demographic characteristics, self-reported indicators of health status, biomarker summary scores, and survival status Analysis sample (N=639)
Social and demographic characteristics Age at the 2006 exam (60–97 years), mean (SD) Female, % Mainlander, % Urban resident, % Years of completed education (0–17), mean (SD) Social integration (–1.5 to 1.6), mean (SD) Perceived availability of social support (0.5–4.0), mean (SD) Self-reported health indicators Self-assessed health status (1–5, 5=excellent), mean (SD) Index of mobility limitations (–0.7 to 3.2), mean (SD) History of diabetes, % History of cancer, % Number of hospitalizations in the past 12 months (0–11), mean (SD) Smoking status in 2006 Never, % Former, % Current, %
72.0 44.4 12.9 42.3 5.3 0.1 3.1
(7.4)
(4.5) (0.5) (0.7)
3.0 (1.0) 0.7 (1.3) 19.9 4.8 0.3 (0.8) 59.1 22.2 18.7
Biomarker summary scores Overall biomarker risk in 2000 (0–13), mean (SD) Overall biomarker risk in 2006 (0–14), mean (SD) Change (2006 – 2000) in biomarker risk (–6 to +9), mean (SD) Declined by two or more points, % Declined by one point, % No change, % Increased by one point, % Increased by two points, % Increased by three or more points, %
4.5 (2.5) 5.1 (2.6) 0.6 (2.3) 17.3 15.2 18.1 15.9 13.5 20.0
Survival status Died between the 2006 exam and December 31, 2011, %
16.2
ment because of the well-established link between socioeconomic status and longevity both within and across countries. Finally, in light of the large literature documenting an association between social relationships—including marital status—and mortality risk (see meta-analysis by Holt-Lunstad, Smith, and Layton 2010), we adjusted for social integration and perceived social support.6 Table 1 presents values for all the variables in our analysis.
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Analytical strategy Our modeling strategy entails testing each of our four hypotheses in turn: first, by assessing the incremental contribution of self-reported health indicators versus biomarkers for predicting mortality relative to a baseline model that includes only the social and demographic control variables; second, by gauging the added value of biomarkers above and beyond self-reports; third, by determining whether changes in biomarkers yield more predictive power than one-time measurements; and finally, by exploring the prognostic value of biomarkers representing different biological systems. This was accomplished by fitting a sequence of models, starting with baseline controls and adding self-reports and several combinations of biomarkers and changes in biomarkers, as shown in Table 2. Descriptive statistics are weighted to account for oversampling and for differential response rates by age, sex, and other covariates. Using unweighted data, we estimate age-specific mortality using a Gompertz hazards model with time measured in terms of age. Initial tests (not shown) show no significant evidence that the age slope of mortality varies by sex. Nonetheless, there is evidence of non-proportional hazards (i.e., the effect varies by age) for a few covariates: perceived social support, current smoker, and the change (2000–06) in DHEAS. Therefore, we include interactions between these variables and age. To compare effect size across predictors, we standardize each of the continuous measures—to have a mean of zero and standard deviation (SD) of one—prior to fitting the hazards models. Thus, the coefficient represents the effect per SD.7 To compare the various models described in Table 2, we use measures of relative goodness of fit and discrimination. To measure relative goodness of fit, we use Akaike’s information criterion (AIC). The AIC compares the fit of one model relative to another (whether nested or not) based on the maximized log-likelihood, with a penalty for the number of parameters estimated (Akaike 1974; StataCorp 2011). To measure discrimination, we use the AUC (described earlier). We calculate the AUC by comparing the model-based predicted probability of dying by the end of follow-up (in supplementary materials, see endnote 5) with the observed binary outcome (death vs. survival). One drawback of the AUC measure is that it can be misleading if the ROC curves cross (i.e., the model with higher AUC does not have better sensitivity at all levels of specificity) (Hand 2010). In such cases, one must be cautious about concluding that the model with higher AUC is “better,” because this assessment depends on the tradeoff between specificity and sensitivity. In light of this potential problem, we inspect the ROC curves for all the comparisons presented here. Among nested models in which the change in AUC is significant, we find no appreciable crossover of the ROC curves; thus, we can conclude that the model with the higher AUC is better. However, in cases where the change in AUC is not significant and for
–112.4
0
9
242.9
0.745
*** p
Publication of Between Zeus and the Salmon: The Biodemography of Longevity (Wachter and Finch 1997) heightened interest in the collection of biological measures of chronic disease within demographic household surveys. Before the 1990s, the inclusion of such measures was generally limited to epidemiologic studies, which were often based on geographically limited samples and usually included only limited social data. Since then, a growing number of population-based surveys have collected biological measures alongside questionnaires that rely on self-reports of health and extensive household and individual information. As these “biosocial” surveys have proliferated, increasing levels of research funding are being devoted to their collection. Thus, it is important to weigh the benefits of biomarker collection against the costs: financial expense, logistical complications, increased respondent burden, ethical issues, and threats to privacy. There are many ways to quantify the value of the biomarkers being collected. Here, we focus on only one of the potential uses: their value in predicting mortality. We use mortality because it is a well-defined and salient health outcome with little measurement error, assuming that vital status is determined from virtually complete death registration records. Friedman and Kern (2014) argue that all-cause mortality is the single best measure of health, noting that it is used worldwide as a key indicator of public health. While biomarkers of individual diseases are of particular clinical interest, the biomarkers evaluated here relate to a broad range of diseases. Thus, we would obtain an incomplete—perhaps even biased—assessment of their predictive power if we used a specific disease or cause of death as the benchmark. In addition to strong validity, all-cause mortality avoids the possibility that studies of a single disease may miss some deaths that were attributed to a different cause, but that resulted indirectly from the disease of interest (Friedman and Kern 2014). POPULATION AND DEVELOPMENT REVIEW 40(2): 331–360 (JUNE 2014)
©2014 The Population Council, Inc.
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As biosocial surveys have evolved, some studies have begun longitudinal collection of biomarkers with the expectation that measurement at multiple survey waves will improve our understanding of the link between biomarkers and mortality (Crimmins and Vasunilashorn 2011). As yet, however, little data have been available to test this assumption. Prior studies have demonstrated that biomarkers are useful predictors of mortality, but few have assessed the prognostic value of change in biomarkers to determine whether they offer greater value than measurement of biomarker levels at a single wave. Collecting a wide range of biomarkers is costly, so it is also important to determine whether some biomarkers are more valuable than others or whether most of the power derives from markers representing a particular biological system. The most common approach, by far, for quantifying the predictive ability of a model is the C-statistic or Area Under the Receiver Operating Characteristic (ROC) Curve. Thus, we will assess the gain in predictive ability of a model after incorporating a set of biomarkers using ROC analysis. Prediction in the case where there are two possible outcome states (here, alive or dead) involves a tradeoff between the sensitivity of the prediction model and its specificity. Greater sensitivity means higher true positive rates—that is, those who died are more likely to be correctly classified by the model as dead; greater specificity means lower false positive rates—that is, those who survived are less likely to be wrongly classified as dead. Plotting true positive rates (sensitivity) against false positive rates (1 – specificity), each lying within the range 0 to 1, for varying levels of the cutoff threshold used to classify subjects into the two groups, yields what is termed the ROC curve for the model. A lower cutoff yields greater sensitivity but lower specificity. The model’s predictive ability is then quantified using the C-statistic or area under the ROC curve— denoted AUC, a summary measure of how well the model discriminates. AUC in the present case can be interpreted as the probability that those who actually died are assigned a higher predicted probability of death than those who survived (Pencina and D’Agostino 2004). An AUC of 0.5 would indicate that the model performs no better than chance alone; an AUC of 1.0 would represent perfect accuracy (100 percent sensitivity and specificity).1 In this analysis, we use longitudinal data from the 2000 and 2006 waves of the Social Environment and Biomarkers of Aging Study (SEBAS) in Taiwan, one of the first biosocial surveys to collect two rounds of biomarkers, to investigate the following four hypotheses motivated by the existing literature. First, we expect that biomarkers will have greater value for predicting mortality than self-reported measures of health when compared side by side. Second, we hypothesize that biomarkers will retain incremental prognostic value above and beyond self-reports. Third, we expect that measurement of change in biomarkers will yield better discrimination (i.e., ability to distinguish between those who died and those who did not) than a single measure-
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ment of biomarkers. Fourth, based on prior studies that have included a wide range of biomarkers (e.g., Crimmins and Vasunilashorn 2011; Newman et al. 2009; Sidorenkov, Nilssen, and Grjibovski 2012; Swindell et al. 2010), we anticipate that markers drawn from multiple biological systems will contribute prognostic value. A listing and brief description of the biomarkers used in the analysis are given in Appendix Table 1.
Predicting mortality Self-reported indicators of health status Health indicators obtained from self-reports are typically important mortality predictors. Indeed, many of the indicators incorporated into prognostic indexes designed for clinical use (Yourman et al. 2012) could be obtained via self-report, although their validity would need to be assessed. For the purposes of predicting longer-term mortality (over at least four years) in communitybased populations, two such indexes have demonstrated good (Schonberg et al. 2009) or very good (Lee et al. 2006b) discrimination, although none of the existing prognostic indexes has attained excellent discrimination (Yourman et al. 2012). Among the health indicators in these indexes are: mobility/ functional limitations, history of cancer, current diabetes, smoking status, self-assessed health status, and number of hospitalizations in the past year (Schonberg et al. 2009; Lee et al. 2006b; see also www.eprognosis.org). Other indicators include current chronic obstructive pulmonary disease (COPD), congestive heart failure, and body mass index, but self-reports may not reliably capture these variables. As suggested above, self-reported measures have a downside: potential inaccuracy. Misreporting may result from recall bias or from unwillingness to acknowledge health problems. Respondents may also be unaware of some underlying conditions that confer health risk. Then, too, there is a subjective component: for example, two people with similar levels of physical ability may differ in their perceived—and, consequently, reported—difficulty performing a specified task (Melzer et al. 2004). All of these factors can contribute to measurement error. One appeal of biological markers is that they may be less subject to such errors. Biomarkers Some studies that examine the relationship between biomarkers and mortality use individual biomarkers as predictors, whereas others develop a composite measure based on multiple markers. Studies also differ in the number and range of markers they use: some rely on a few closely related markers that reflect a common biological system, while others include a larger number
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of markers that represent multiple systems (e.g., cardiovascular, metabolic, immune/inflammatory, neuroendocrine, renal, hepatic). Given the extensive literature linking biomarkers and mortality, here we focus on the smaller set of studies that include markers drawn from multiple systems. We divide our review into two parts: studies that use individual markers and those that use multi-system summary scores.
Studies using individual markers representing multiple biological systems. Many studies have investigated the association between survival and a collection of individual biomarkers drawn from multiple biological systems (e.g., Crimmins, Kim, and Vasunilashorn 2010; Crimmins and Vasunilashorn 2011; Fried et al. 1998; Kistorp et al. 2005; Lan et al. 2007; Lee et al. 2006a; Newman et al. 2009; Sidorenkov, Nilssen, and Grjibovski 2012; Swindell et al. 2010; Turra et al. 2005). These studies demonstrate that cardiovascular, metabolic, inflammatory, neuroendocrine, and other markers that reflect disease are useful predictors of mortality. Here, we limit our review to studies that: 1) examined individual biomarkers drawn from multiple systems as predictors of all-cause mortality, where the markers include standard cardiovascular/metabolic markers (e.g., blood pressure, lipids, measures of glucose metabolism, and obesity) and at least one inflammatory marker (e.g., interleukin-6 (IL-6), C-reactive protein (CRP)); and 2) assessed the discriminatory ability of the set of biomarkers. We identified six such studies (see Appendix Table 2) based on five unique datasets in four countries: Costa Rica (Rosero-Bixby and Dow 2012), Germany (Haring et al. 2011), the Netherlands (Newson et al. 2010; Stork et al. 2006), and Taiwan (Goldman et al. 2006b; Goldman et al. 2009). Some of these studies also included neuroendocrine markers such as cortisol, dehydroepiandrosterone sulfate (DHEAS), epinephrine, and norepinephrine. The six studies also incorporated “other” biomarkers that cannot be easily classified into one of the above-mentioned groups. Half included a measure of renal function (e.g., creatinine clearance, urinary albumin). Two studies included serum albumin (Goldman et al. 2009; Newson et al. 2010), a nonspecific measure of underlying disease. Two studies (Goldman et al. 2006b; Goldman et al. 2009) incorporated insulin-like growth factor 1 (IGF-1), which affects muscle growth and is an indirect marker of inflammation. Haring et al. (2011) added a marker of liver function (glutamyltransferase). Two studies (Stork et al. 2006; Newson et al. 2010) incorporated carotid plaque burden, a marker of atherosclerosis. Rosero-Bixby and Dow (2012) included performance assessments (i.e., grip strength, gait speed, peak expiratory flow). Another study included a measure of bone density (Newson et al. 2010). As noted by Pencina et al. (2012), the gain in AUC (i.e., the difference in the AUC between the models with and without the indicator(s) in question) depends heavily on the strength of the baseline model (i.e., the model
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without the indicator being considered). All of the studies mentioned above controlled for sex, age, smoking, and some self-reported measures of baseline health status. Haring et al. (2011) controlled for the most extensive list of covariates, including multiple measures of socioeconomic status, health behaviors, disease history, self-assessed health status, perceived quality of life, and waist circumference. They had the strongest baseline model (AUC=0.88) among the six studies considered here. Most of these studies found that the addition of biomarkers led to a large gain in predictive ability.2 The Costa Rica study (Rosero-Bixby and Dow 2012) included 22 biomarkers, and the Taiwan studies (Goldman et al. 2009; Goldman et al. 2006b) included 13–16 biomarkers; these three studies found that the set of biomarkers accounted for a 0.06–0.08 increase in AUC. One of the Dutch studies included only three biomarkers (IL-6, CRP, and the number of carotid plaques), but those markers contributed 0.15 improvement in AUC (Stork et al. 2006). The other Dutch study (Newson et al. 2010) did not estimate the increase in AUC associated with the seven biomarkers (i.e., diastolic blood pressure, CRP, number of carotid plaques, and several other markers) compared with a model that included only the control variables (see Appendix Table 2). The incremental value attributable to biomarkers was much smaller in the Haring study than in the others, which may be explained by the limited set of selected biomarkers combined with an extensive set of control variables. Not surprisingly, the largest gain in AUC (0.15) was generated by the study with the weakest baseline model (AUC=0.62) (Stork et al. 2006).
Studies using multi-system biomarker summary scores. The allostatic load framework (McEwen and Stellar 1993) helped promote the use of composite measures of biological function across multiple systems (although not all multi-system scores are predicated on the allostatic load framework). A number of studies in the United States and Taiwan have found that multi-system measures can consistently predict all-cause mortality (Goldman et al. 2006a; Seeman et al. 2001; Seeman et al. 2004; Gruenewald et al. 2006; Karlamangla, Singer, and Seeman 2006; Borrell, Dallo, and Nguyen 2010; Crimmins, Kim, and Seeman 2009; Wang et al. 2006; Levine 2013)—see Appendix Table 2 for more details. Early formulations of such measures incorporated standard cardiovascular/metabolic markers as well as neuroendocrine markers. More recent versions typically include at least one inflammatory marker and often include some “other” marker(s) (e.g., serum albumin, creatinine clearance, homocysteine). In most cases the magnitude of the association between the multi-system score and mortality was substantial even among the studies that controlled for self-reported indicators of baseline health status (Goldman et al. 2006a; Seeman et al. 2004; Seeman et al. 2001; Wang et al. 2006). For example, Seeman et al. (2001) showed that the odds of dying within seven years were
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more than six times higher for someone with high-risk levels for at least seven of the ten biomarkers compared with someone with no high-risk markers. Wang et al. (2006) reported that the mortality rate was four times higher for individuals with high multi-system scores (i.e., top quintile) relative to those with low scores (i.e., bottom two quintiles), adjusted for age, sex, and conventional risk factors, although the multi-system score yielded only a small improvement in predictive ability (C-statistic increased from 0.80 to 0.82). However, standard clinical biomarkers were included as control variables rather than being incorporated into the multi-system score. None of the other studies quantified the predictive ability of the multi-system score. With one exception, prior studies investigating the link between a multisystem score and mortality have relied on biomarkers measured at only one survey wave. The exception, the study by Karlamangla, Singer, and Seeman (2006), examined changes in biomarkers over a 2.5-year period, finding that both starting levels and changes were associated with subsequent mortality. However, this study did not control for any self-reported health indicators, nor did it evaluate whether changes in biomarkers provided significantly better discriminatory ability than a one-time measurement. What this study adds In sum, existing prognostic indexes for predicting mortality among community-based populations of older adults have achieved C-statistic/AUC values ranging between 0.66 and 0.84 (Yourman et al. 2012). Our study extends earlier studies by quantifying the incremental improvement in discrimination associated with measurement of change in biomarkers compared with one-time measurement.
Methods Data Our data come from a cohort study in Taiwan (SEBAS), augmented by the 2003 wave of its parent study, the Taiwan Longitudinal Study of Aging (TLSA). Taiwan provides a good context for study because the level of life expectancy (e0 = 79.2 in 2010) is similar to that of other industrialized countries such as the US (e0 = 78.9 in 2010) (Human Mortality Database 2013), and the causeof-death structure is also similar to that of high-income Western countries. For example, nine out of the top ten causes of death in 2011 were the same in Taiwan (Department of Health, Taiwan 2012) and the US (Hoyert and Xu 2012). Many of the factors (e.g., age, sex, education, marital status, selfreported measures of health) that predict mortality in Taiwan are consistent with those observed in Western countries (Zimmer, Martin, and Lin 2005).
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The SEBAS cohort was based on a nationally representative sample of Taiwanese aged 54 and older in 2000, selected randomly using a multi-stage sampling design with oversampling of older persons (71+) and urban residents (Chang et al. 2012). In 2000, in-home interviews were completed with 1,497 respondents, 1,023 of whom also completed a physical examination. Exam participants did not differ significantly from nonparticipants in ways likely to introduce serious bias (Goldman et al. 2003). Six years later, a followup was conducted with those who completed the 2000 exam and survived to 2006: 757 completed the in-home interview and 639 participated in the physical examination. Details regarding response rates, sample attrition, and exam participation are provided elsewhere (Chang et al. 2012). The physical examination followed a similar protocol in both waves. Several weeks after the household interview, participants collected a 12-hour overnight urine sample, fasted overnight, and visited a nearby hospital the following morning for a physical examination that included collection of a blood specimen and measurements of blood pressure, height, weight, and waist and hip circumference. Compliance was high: in 2000, 96 percent fasted overnight and provided a urine specimen deemed suitable for analysis; the comparable figure was 88 percent in 2006. Union Clinical Laboratories (UCL) in Taipei analyzed the blood and urine specimens. In addition to the routine standardization and calibration tests, triplicate sets of specimens were provided by individuals outside the target sample (n=9 in 2000; n=10 in 2006): two sets were sent to UCL and the third set was sent to Quest Diagnostics in the US (San Juan Capistrano, CA). The results indicated high intra-lab reliability for duplicates sent to UCL (2000: ≥ 0.71; 2006: ≥ 0.85). With a few exceptions (glycoslyated hemoglobin, HbA1c; cortisol; urinary creatinine), inter-lab correlations between results from UCL and Quest Diagnostics exceeded 0.90 in both waves. Survival status as of January 1, 2012 was ascertained by linkage to the death certificate file maintained by the Taiwan Department of Health and to the household registration database maintained by the Ministry of the Interior. The analysis sample was based on the longitudinal cohort that completed the exam in both 2000 and 2006 (n=639, 104 of whom died by December 31, 2011). The mean length of mortality follow-up was 5.1 years (max, 5.3 years). Some (n=89) of the respondents had missing data for at least one covariate, typically a biomarker. To maximize the use of the data, we followed standard practices of multiple imputation (Schafer 1999; Rubin 1996) to handle missing data.3 Measures Biomarkers. We included 19 biomarkers that have been shown by prior studies to be associated with all-cause mortality. They comprised four groups of
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markers: 1) eight standard cardiovascular/metabolic risk factors—systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, glycoslyated hemoglobin, body mass index, and waist circumference; 2) four inflammatory markers—IL-6, CRP, soluble intercellular adhesion molecule 1 (sICAM-1), and soluble E-selectin; 3) four neuroendocrine markers—dehydroepiandrosterone sulfate (DHEAS), cortisol, epinephrine, and norepinephrine; and 4) three other markers that do not represent a common biological subsystem—creatinine clearance, serum albumin, and homocysteine. See Appendix Table 1 for more detailed information about each marker. We calculated an overall summary score based on these 19 markers and subscores for each of the four groups of markers.4 Each score was calculated by counting the number of markers for which the respondent exhibited a high-risk level. High risk was defined by established cutoffs for the standard cardiovascular/metabolic factors and CRP (see Table S1 in Supplementary Materials).5 For all other markers, which have no generally accepted clinical cutoffs, we defined high risk based on the weighted distribution of the 2000 sample: bottom quartile for DHEAS, creatinine clearance, and albumin; top quartile for other markers. Although other types of summary scores have been proposed (Juster, McEwen, and Lupien 2010; Seeman et al. 2010), we use a formulation similar to that employed by many other studies to represent a composite of biomarker values. Self-reported health indicators. On the basis of self-reports from 2006, we included six health indicators: 1) global self-assessed health (“Regarding your current state of health, do you feel it is excellent, good, average, not so good, or poor?”); 2) an index of mobility limitations; 3) whether the respondent was ever diagnosed with diabetes; 4) history of cancer; 5) number of hospitalizations in the past 12 months; and 6) smoking status (never, former, current). Mobility was based on self-reported difficulty performing eight physical tasks without assistance, each of which was coded on a four-point scale (0=no difficulty, 1=some difficulty, 2=great difficulty, 3=unable): stand for 15 minutes, squat, raise both hands overhead, grasp or turn objects with his or her fingers, lift or carry an object weighing 11–12kg, walk 200–300m, run 20–30m, and climb two or three flights of stairs. Based on the recommendations of Long and Pavalko (2004), we constructed the scale by summing the eight items (potential range 0–24), adding a constant (0.5), and taking the logarithm of the result, which allows for relative rather than absolute effects. Social and demographic characteristics. We controlled for key demographic and social characteristics that are known to be important predictors of mortality. Demographic variables comprised age, sex, urban residence, and ethnicity (Mainlander vs. Taiwanese). We also included educational attain-
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TABLE 1 Descriptive statistics for social and demographic characteristics, self-reported indicators of health status, biomarker summary scores, and survival status Analysis sample (N=639)
Social and demographic characteristics Age at the 2006 exam (60–97 years), mean (SD) Female, % Mainlander, % Urban resident, % Years of completed education (0–17), mean (SD) Social integration (–1.5 to 1.6), mean (SD) Perceived availability of social support (0.5–4.0), mean (SD) Self-reported health indicators Self-assessed health status (1–5, 5=excellent), mean (SD) Index of mobility limitations (–0.7 to 3.2), mean (SD) History of diabetes, % History of cancer, % Number of hospitalizations in the past 12 months (0–11), mean (SD) Smoking status in 2006 Never, % Former, % Current, %
72.0 44.4 12.9 42.3 5.3 0.1 3.1
(7.4)
(4.5) (0.5) (0.7)
3.0 (1.0) 0.7 (1.3) 19.9 4.8 0.3 (0.8) 59.1 22.2 18.7
Biomarker summary scores Overall biomarker risk in 2000 (0–13), mean (SD) Overall biomarker risk in 2006 (0–14), mean (SD) Change (2006 – 2000) in biomarker risk (–6 to +9), mean (SD) Declined by two or more points, % Declined by one point, % No change, % Increased by one point, % Increased by two points, % Increased by three or more points, %
4.5 (2.5) 5.1 (2.6) 0.6 (2.3) 17.3 15.2 18.1 15.9 13.5 20.0
Survival status Died between the 2006 exam and December 31, 2011, %
16.2
ment because of the well-established link between socioeconomic status and longevity both within and across countries. Finally, in light of the large literature documenting an association between social relationships—including marital status—and mortality risk (see meta-analysis by Holt-Lunstad, Smith, and Layton 2010), we adjusted for social integration and perceived social support.6 Table 1 presents values for all the variables in our analysis.
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Analytical strategy Our modeling strategy entails testing each of our four hypotheses in turn: first, by assessing the incremental contribution of self-reported health indicators versus biomarkers for predicting mortality relative to a baseline model that includes only the social and demographic control variables; second, by gauging the added value of biomarkers above and beyond self-reports; third, by determining whether changes in biomarkers yield more predictive power than one-time measurements; and finally, by exploring the prognostic value of biomarkers representing different biological systems. This was accomplished by fitting a sequence of models, starting with baseline controls and adding self-reports and several combinations of biomarkers and changes in biomarkers, as shown in Table 2. Descriptive statistics are weighted to account for oversampling and for differential response rates by age, sex, and other covariates. Using unweighted data, we estimate age-specific mortality using a Gompertz hazards model with time measured in terms of age. Initial tests (not shown) show no significant evidence that the age slope of mortality varies by sex. Nonetheless, there is evidence of non-proportional hazards (i.e., the effect varies by age) for a few covariates: perceived social support, current smoker, and the change (2000–06) in DHEAS. Therefore, we include interactions between these variables and age. To compare effect size across predictors, we standardize each of the continuous measures—to have a mean of zero and standard deviation (SD) of one—prior to fitting the hazards models. Thus, the coefficient represents the effect per SD.7 To compare the various models described in Table 2, we use measures of relative goodness of fit and discrimination. To measure relative goodness of fit, we use Akaike’s information criterion (AIC). The AIC compares the fit of one model relative to another (whether nested or not) based on the maximized log-likelihood, with a penalty for the number of parameters estimated (Akaike 1974; StataCorp 2011). To measure discrimination, we use the AUC (described earlier). We calculate the AUC by comparing the model-based predicted probability of dying by the end of follow-up (in supplementary materials, see endnote 5) with the observed binary outcome (death vs. survival). One drawback of the AUC measure is that it can be misleading if the ROC curves cross (i.e., the model with higher AUC does not have better sensitivity at all levels of specificity) (Hand 2010). In such cases, one must be cautious about concluding that the model with higher AUC is “better,” because this assessment depends on the tradeoff between specificity and sensitivity. In light of this potential problem, we inspect the ROC curves for all the comparisons presented here. Among nested models in which the change in AUC is significant, we find no appreciable crossover of the ROC curves; thus, we can conclude that the model with the higher AUC is better. However, in cases where the change in AUC is not significant and for
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