As of August 2016, a total of 182 subjects who were 55 years or older participated in the study. Among the subjects, 109 had mild cognitive impairment (MCI), and 73 had clinically diagnosed AD dementia (ADD). They underwent comprehensive KBASE baseline assessment including clinical examination, neuropsychological assessments, neuroimaging, including magnetic resonance imaging (MRI), PiB-PET, and blood tests. Individuals with MCI (age 55–90 [inclusive]) met the following criteria: (a) memory complaint reported by themselves or reported by an informant or a clinician; (b) presence of objective memory impairment; (c) intact functional activities; and (d) non-demented. All MCI subjects had a global CDR score of 0.5. With respect to criterion (b), MCI individuals scored at least 1.0 standard deviation (SD) below the respective age, education, and gender-specific mean on at least one of the four memory tests that were part of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery (namely, Word List Memory, Word List Recall, Word List Recognition, and Constructional Recall test) (Lee et al., 2004). Inclusion criteria for patients with ADD (age 55–90 [inclusive]) were: (a) meet both criteria of the Diagnostic and Statistical Manual, 4th Edition for dementia (Americal Psychiatric Association, 2000) and the criteria for probable ADD set according to the National Institute of Aging and Alzheimer's Association (NIA-AA) guidelines (McKhann et al., 2011), and (b) have a global CDR score of 0.5 or 1. The exclusion criteria were: (a) presence of any psychiatric or neurological disorders that could affect mental function, (b) severe communication problems that would create difficulty in a assessment or brain scan, (c) contraindications for MRI scanning, (d) absence of a reliable informant, or (e) illiteracy.

All participants underwent standardized clinical assessment by trained psychiatrists, based on the KBASE protocol, which corresponded with the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-K) (Lee et al., 2002). In addition, participants underwent KBASE neuropsychological assessment by trained neuropsychologists while incorporating the CERAD neuropsychological battery (Lee et al., 2004), which contained the Mini-Mental State Examination (MMSE). Due to the influence of gender, age, and education on MMSE score (Piccinin et al., 2013), we used the z-score of MMSE instead (Lee et al., 2004). Body mass index (BMI) was calculated for all participants from their heights and weights. Through a weekly clinical review panel meeting (chaired by DL) with a clinical review panel composed of several geriatric psychiatrists, clinical neuropsychologists, and psychometrists, individuals were allocated to either MCI group or ADD group. Ineligible individuals were excluded.

Participants underwent three-dimensional PiB-PET imaging and T1-weighted MR using a Biograph mMR scanner (Siemens, Washington DC, USA). Forty minutes after intravenous injection of 555 MBq of ¹¹C-PiB, a 30-min emission scan was obtained. The data were reconstructed into a 256 × 256 image matrix using iterative methods (6 iterations with 21 subsets) and were corrected for uniformity, ultrashort echo time (UTE)-based attenuation, and decay reduction. Sagittal T1-weighted images (repetition time = 1,670 ms; echo time = 1.89 ms; field of view, 250 mm; 256 × 256 matrix with 1.0 mm slice thickness) were acquired. Images were pre-processed using Statistical Parametric Mapping 8 (SPM8) implemented in Smith and Barton (2014) (MathWorks, Natick, MA). PiB-PET data were co-registered to the individual T1 images and transformation parameters to a standard Montreal Neurological Institute (MNI) template were computed. Individual Brain Atlases using Statistical Parametric Mapping Software (IBASPM) software was used for the inverse transformation parameters, which transformed the coordinates from the automatic anatomic labeling (AAL) 116 atlas (Tzourio-Mazoyer et al., 2002) into an individual space for each subject (resampling voxel size = 1 × 0.98 × 0.98 mm). Only the gray matter (GM) was used for extraction by applying GM mask to each individual in order to exclude white matter and CSF space.

Based on previous studies (Jack et al., 2008; Reiman et al., 2009; Choe et al., 2014; Park et al., 2017), the cerebral regional mean ¹¹C-PiB uptake values were calculated using the individual AAL116 atlas from the T1-coregistered PiB-PET images and the cerebellar gray matter ¹¹C-PiB uptake values were used for the quantitative normalization. To determine the regions of interest (ROIs), the AAL algorithm and a region-combining method (Reiman et al., 2009) were applied and brain regions were divided into the frontal, lateral parietal, posterior cingulate-precuneus (PC-PRC), and lateral temporal regions, where prominent ¹¹C-PiB retention has been reported (Klunk et al., 2004). The standardized uptake value ratio (SUVR) of each ROI was obtained by dividing the mean value for all voxels within the ROI by the mean cerebellar gray matter uptake value. Subjects were classified as PiB positive (PiB+) if the SUVR value exceeded 1.4 in at least one of the four ROIs (i.e., frontal, lateral temporal, lateral parietal, and PC-PRC) or as PiB negative (PiB-) if the SUVR values of all four ROIs were equal to or less than 1.4 (Reiman et al., 2009; Choe et al., 2014). Being classified as PiB− would mean that subjects have negative amyloid deposition, while being classified as PiB+ would mean that subjects have positive amyloid deposition.

Blood samples were taken via venipuncture in the morning (around 9 am) after overnight fasting and were collected into serum separator tubes (Becton, Dickinson and Co., Franklin Lakes, NJ, USA). The tubes were stored at room temperature (RT) for 30 min and then were centrifuged at 1,300 g for 10 min at RT. Separated serum was used for assays. Serum phosphorus, calcium, and iron levels were measured at Seoul Clinical Laboratories, using an enzymatic colorimetric method (ADVIA 1800 Autoanalyzer; Siemens, Washington DC, USA). The levels of zinc and copper were determined by inductively coupled plasma-mass spectroscopy (ICP-MS) using a Varian 820-MS ICP mass spectrometer (Bruker, Victoria, Australia). Additionally, apolipoprotein E (APOE) genotyping was done by using the genomic DNA, as previously described (Wenham et al., 1991).

To investigate the relationship between serum phosphorus and global cerebral Aβ deposition, partial correlation analysis was used, with correction for the influence by the different covariates (CV; age and sex). Variables were logarithmically transformed to adjust skewness of the distribution and to normalize variance. To compare the serum phosphorus level between PiB− and PiB+ with statistical control for the effects of CV (age and sex), analysis of covariance (ANCOVA) was performed, and p-values were obtained by pairwise comparison. For the independent association between serum phosphorus level and global cerebral Aβ deposition, multiple regression analyses were conducted after controlling for age, sex, ApoE ε4 carrier status, calcium, MMSE z-score, and others. To identify the relationship between serum phosphorus level and risk of accumulation of global cerebral Aβ, relative risk (95% confidence interval) tests were conducted. In addition, we carried out logistic regression analysis followed by receiver operating characteristic (ROC) curve analysis to show the discrimination power of ApoE ε4 carrier status and phosphorus on PiB positivity. The predicted probabilities were used to calculate the discrimination power for the prediction of PiB positivity. Comparison of ROC curves was performed according to a previous report, DeLong et al. (1988). For the demographic data of subjects (Table 1), independent t-tests were performed to compare values (age, education, MMSE z-score, and global amyloid deposition). Chi-squared tests were carried out to evaluate the intergroup differences of categorical variables (sex, CDR score, and ApoE ε4 carrier status). All statistical analyses were performed using Medcalc 17.2 (Medcalc Software, Ostend, Belgium). P values were stated in each figure or table to indicate statistical significance as appropriate.

A total of 182 subjects (109 MCI subjects and 73 ADD subjects) were included in this study. Table 1 shows the demographic and clinical characteristics of the participants (Table 1). Amongst the MCI subjects, 50.5% were PiB+ (n = 55), whereas 78.1% of ADD patients were PiB+ (n = 57). No differences were observed in age (p = 0.256) and sex (p = 0.830) between MCI and ADD groups (Table 1).

The partial correlation analyses revealed a significant correlation between the serum level of phosphorus and the deposition of cerebral amyloid in both MCI (r = −0.23, df = 105, p = 0.018) and ADD (r = −0.26, df = 69, p = 0.031) groups (Figure 1A). A similar correlation was observed when the two groups were combined (r = −0.24, df = 178, p = 0.001; Figure 1B, left). In the combined (MC1 plus ADD) group, PiB+ subjects had a lower serum level of phosphorus as compared with PiB− subjects (p = 0.009; Figure 1B, right). To identify the relationship between the level of phosphorus and the deposition risk of cerebral amyloid, we conducted a relative risk (RR) analysis. We categorized subgroups into quartiles (quartile 1, ≤3.4 mg/dL; quartile 2, >3.4 and ≤3.7 mg/dL; quartile 3, >3.7 and ≤4.0 mg/dL; quartile 4, >4.0 mg/dL), as described in previous reports (Dhingra et al., 2007, 2010; Ye et al., 2016). The subjects with low levels of phosphorus (quartile 1, ≤3.4 mg/dL) showed higher risk of PiB positivity (RR = 1.50, p = 0.028; 95% CI, 1.04–2.14; Table 2). Chi-squared test for trend also showed that the proportion of PiB+:PiB− significantly decreased, as the quartile number increased (first quartile to fourth quartile) (p = 0.020, χ² = 5.447). Furthermore, the multiple regression analyses were conducted in three models (set 1, MCI only; set 2, ADD only; set 3, both MCI and ADD; Table 3). Age, sex, and ApoE ε4 carrier status were included as covariates. Serum phosphorus showed a significant negative correlation with cerebral Aβ deposition in all three models (set 1, β = −0.193 and p = 0.037; set 2, β = −0.260 and p = 0.034; set 3, β = −0.226 and p = 0.003; Table 3). In addition, serum phosphorus level was correlated with cerebral Aβ deposition even after controlling for serum calcium levels and/or MMSE score with age, sex, and ApoE ε4 types (age, sex, calcium, and ApoE as covariates, β = −0.066 and p < 0.001; age, sex, MMSE score and ApoE as covariates, β = −0.066 and p < 0.001; age, sex, calcium, MMSE score and ApoE as covariates, β = −0.067 and p < 0.001; Table 4).

We performed logistic regression and ROC curve analysis, using independent variables (ApoE ε4 carrier status and serum phosphorus, Figure 2). Each of the ROC curves had a significant p-value (p < 0.0001, Figure 2A). AUC value was significantly enhanced by combining the two variables (AUC, 0.757–0.800; Figure 2A, gray and black line; Figure 2B, p = 0.0343, comparison of ROC curves analysis). These results suggest that the serum level of phosphorus may be used as a blood-based biomarker for PiB positivity or AD in conjunction with ApoE ε4 genotype for cognitively impaired population.

Partial correlation plots showed that the serum levels of other ions such as calcium, iron, zinc, and copper levels were not significantly correlated with either cerebral Aβ deposition (SUVR) (Figures 3B–E, left graphs; p > 0.05) or serum phosphorus levels (Figures 3B–E, right graphs; p > 0.05) after controlling for covariates, such as age and sex. Body mass index (BMI) showed no significant association with cerebral Aβ deposition either in cognitively impaired subjects (Figure 3A). In addition, multiple regression analyses showed that serum phosphorus was significantly associated with cerebral Aβ deposition even after controlling for covariates, including other ions and BMI (age, sex, BMI, calcium, iron, zinc, and copper as covariates, β = −0.072 and p < 0.01; Table 5). These results indicate that blood phosphorus level is associated with cerebral Aβ deposition, while other ion levels, including calcium, iron, zinc, and copper, are not correlated with brain Aβ deposition in cognitively impaired subjects.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.