Lower Serum Calcium as a Potentially Associated Factor for Conversion of Mild Cognitive Impairment to Early Alzheimer’s Disease in the Japanese Alzheimer’s Disease Neuroimaging Initiative

Abstract

Background:

Effect of serum calcium level to the incidence of mild cognitive impairment (MCI) conversion to early Alzheimer’s disease (AD) remains uncertain.

Objective:

To investigate association between baseline serum calcium and the MCI conversion in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) study cohort.

Methods:

In this sub-analysis of J-ADNI study, we reviewed data from MCI participants at baseline regarding their conversion to early AD during the 3 years of observation period and assessed the associated factors including serum calcium level. In addition, we compared our results from the J-ADNI study with the corresponding results from the North American (NA)-ADNI.

Results:

Of 234 eligible MCI participants from the J-ADNI cohort, 121 (51.7%) converted to AD during the first 36 months of observation. Using univariate analysis, being female, having shorter years of education, and lower serum calcium level were correlated with increased risk of MCI-to-AD conversion exclusively in J-ADNI cohort. The lower corrected serum calcium level remained as one of conversion-associated factors in the J-ADNI cohort even after adjustment for multiple confounding variables, although this was not observed in the NA-ADNI cohort.

Conclusion:

Our findings suggest that lower serum calcium may be associated with an increased risk of MCI conversion to AD in Japanese cohorts. The reason for this correlation remains unclear and further external validation using other Asian cohorts is needed. It would be interesting for future AD studies to obtain serum calcium levels and other related factors, such as vitamin D levels, culture-specific dietary or medication information.

Keywords

Alzheimer’s disease calcium conversion Japanese Alzheimer’s Disease Neuroimaging Initiative mild cognitive impairment

INTRODUCTION

Patients with mild cognitive impairment (MCI) [1] can convert to Alzheimer’s disease (AD) at an annual rate as high as 16.5%, as reported for the first year in the North American Alzheimer’s Disease Neuroimaging Initiative (NA-ADNI) study [2, 3]. Several risk factors were reported to contribute to MCI patients conversion to AD, including AD biomarkers such as increased total tau (t-tau) levels and decreased amyloid-β_1–42 (Aβ₄₂) levels in cerebrospinal fluid (CSF) [4 –7], structural abnormalities in brain magnetic resonance imaging (MRI), especially in the medial temporal lobe [8 –10], and neuropsychological changes involving multiple cognitive domains [11, 12], all of these being informative in predicting MCI conversion [13 –16].

However, there has been insufficient evidence regarding the effect of serum calcium level to the incidence of MCI conversion to AD [17] so far, in contrast to the lower blood vitamin D level frequently reported as a factor associated with the increased risk of dementia or the faster cognitive decline [18]. The potential association of amyloid accumulation with the dysfunction in neuronal calcium homeostasis is considered in in vitro studies: it has been suspected that serum calcium abnormality may enhance dysregulation in neuronal calcium homeostasis [18] leading to the amyloid accumulation [19, 20]. However, in earlier cohort studies, unlike the established biomarkers as raised above such as APOE ɛ4 allele(s), amyloid-positron emission tomography (PET), CSF Aβ₄₂, or brain atrophy, the reported effect of serum calcium level to the AD development/progression has been inconsistent; it is reported that the patients with higher serum calcium (not always apparent hypercalcemia) showed higher incidence of AD or the faster AD progression in longitudinal Western cohort studies [21, 22], while AD patients showed lower serum calcium (not always apparent hypocalcemia) than control patients in cross-sectional Asian or Western cohort studies [23 –25]. To address this inconsistency, here we investigated risk factors associated with MCI conversion in the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) cohort, which has recently completed its entire project [26, 27], and compared our results with those from the NA-ADNI, especially focusing on the serum calcium level and its related factors. Our study may help understanding the potential role of serum calcium level in the AD pathology.

METHODS

Sample datasets

This study is a sub-analysis of the J-ADNI study results. We obtained the J-ADNI dataset from the National Bioscience Database Center (NBDC), with approval from its data access committee (https://humandbs.biosciencedbc.jp/en/hum0043-v1). The J-ADNI was launched in 2007 as a public-private partnership, led by Principal Investigator Takeshi Iwatsubo, MD. The primary goal of J-ADNI was to test whether clinical and neuropsychological assessment, together with biological markers including, but not limited to, serial MRI and PET, could be combined to measure the progression of MCI and mild AD in the Japanese population. As such, the investigators within the J-ADNI contributed to the design and implementation of the J-ADNI and/or provided data, but they did not participate in analyses within, or in writing of, this report. A complete list of J-ADNI investigators can be found at: https://humandbs.biosciencedbc.jp/en/hum0043-j-adni-authors.

The inclusion/exclusion criteria used in the J-ADNI largely correspond to those for the NA-ADNI [2, 26] and they are described at: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000001668. General inclusion criteria for MCI participants were as follows: participants themselves or participants’ family must have presented with complaints of memory disturbances, participants’ age must be within the range of 60 to 84 years at baseline, they must be native Japanese speakers, their total Mini-Mental State Examination (MMSE) score must fall in the range of 24 to 30 (inclusive), and their Clinical Dementia Rating (CDR) and memory box of CDR should be 0.5 and 0.5 or greater, respectively. Moreover, patients who had medical history of central nervous system disorders or trauma, who could not receive brain MRI examination, who had an acute brain-related event at the time of screening, who had recent history of depression or alcohol abuse, or who had etiology of treatable dementia (such as vitamin B₁₂ deficiency, folate deficiency, thyroid abnormality, or syphilis) were excluded from the study. The follow-up period was 3 years (36 months) for MCI.

In addition, we obtained the NA-ADNI datasets from ADNI-1, ADNI-GO, and ADNI-2 from the Laboratory of Neuro Imaging (LONI), with their approval. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI was to test whether clinical and neuropsychological assessments and biomarkers, such as serial MRI and PET, could be combined to measure the progression of MCI and early AD. Eligible participants whose diagnosis at baseline (column ‘Dx_bl’ in the ‘ADNIMERGE’ file within the dataset) corresponded to late MCI were included in the analysis. The data used in the preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu), under permission (downloaded on July 2018).

Assessments and clinical variables

We used the incidence of MCI conversion to AD as the primary measure in this study. In cases where participants presented conversion and subsequent reversion during the observation period, the participants were considered as converted. The clinical and laboratory data used for analysis included the participants’ sex, age, body weight, body height, family history of dementia, marital status, smoking habits, neuropsychological functional tests’ results, Geriatric Depression Scale (GDS) scores, fasting blood sugar level, total cholesterol (T-cho) level, uric acid (UA) level, electrolytes’ level (sodium, potassium, calcium), phosphate level, C-reactive protein (CRP) level, serum albumin (Alb) level, serum creatinine level, estimated glomerular filtration rate (eGFR) [28], creatinine clearance (calculated from Cockroft-Gault equation) [29], plasma cell counts (white blood cells, WBC; blood hemoglobin level, Hb; blood platelet level, Plt), CSF Aβ₄₂ level, CSF t-tau, and phosphorylated tau (p-tau) levels, Fazekas periventricular hyperintensity (PVH) score, deep and subcortical white matter hyperintensity (DWMH) scores from the screening MRI scan (scored by a trained radiologist blind to clinical information), APOE ɛ4 allele status, amyloid positivity (defined as either amyloid-PET positive or CSF Aβ₄₂ less than 333 pg/mL) [26], and total intracranial and hippocampal volumes of brain. From the several neuropsychological tests used in the J-ADNI cohort, we chose the Clinical Dementia Rating Sum of Boxes (CDR-SOB), Alzheimer’s Disease Assessment Scale – cognitive subscale 13 (ADAS-cog13), MMSE, and Functional Activities Questionnaire (FAQ) to assess the longitudinal cognitive and global function of participants. The progression speed of these scores is calculated by taking the difference between the baseline score and the last visit score and dividing by the number of months in the maximum follow-up period. Serum calcium level was corrected in cases of hypoalbuminemia (serum Alb less than 4 g/dL), where corrected serum calcium level (mg/dL) = raw calcium level (mg/dL) + (4 – serum albumin level (g/dL)) [30]. Serum calcium was measured by arsenazo III method using Serotec Ca-AL kit (Serotec, Sapporo, Japan). Amyloid positivity was defined by visual inspection of amyloid-PET images, using Pittsburgh Compound B as a ligand, performed by trained radiologists who were blinded to any clinical information [31]. The ethnicity of participants in the J-ADNI cohort is almost entirely eastern-Asian, although ethnicity was not always recorded.

Corresponding data from the NA-ADNI datasets were acquired as follows: CSF biomarkers were obtained from the ‘UPENNBIOMK9_04_19_17.csv’ file on the database. In addition, AV45-using amyloid-PET scan results we obtained using the feature ‘SUMMARYSUVR_WHOLECEREBNORM_1.11CUTOFF’ in the ‘UCBERKELEYAV45_12_08_15.csv’ file on the database. As the renal function measure in NA-ADNI, we used the creatinine (CCr) levels alone.

Statistical analyses

All statistical analyses were performed using R version 3.3.3 (https://www.r-project.org). For categorical data, we used Fisher exact test and summarized the data as median and interquartile range (IQR). For continuous data, we used Wilcoxon rank sum test and summarized the data as frequency or %. A p-value of <0.05 were considered statistically significant.

In the conversion-free period survival analysis, we used log-rank test by regarding the conversion from MCI to AD as the event occurred. The censoring of observation was determined when the participant converted during the observation period, dropped off or finished the entire 36 months of observation. In adjusting multivariate factors, we used a binomial logistic regression model for the incidence of conversion.

Ethics

The study protocol was approved by the University of Tokyo ethics committee (11628).

RESULTS

Identifying risk factors for conversion to AD in the J-ADNI cohort

Out of 234 eligible MCI participants, 121 (51.7%) converted to AD during the 3 years of observational period (mean annual conversion rate: 21.54%). Among these 121 converters, 4 participants showed reversion to MCI during the observation. In the NA-ADNI results, 238 (42.3%) out of 563 eligible MCI participants converted to AD within the first 3 years of observation (mean annual conversion rate: 16.7%), thus showing a significantly lower conversion-rate compared to J-ADNI (p = 0.016, Fisher exact test).

Table 1 summarizes the univariate analysis results comparing the basic characteristics of AD-converted participants with those of non-converted participants in the J-ADNI cohort. Several factors were significantly associated with increased conversion risk, including: having APOE

Table 1

Basic clinical features of MCI participants from J-ADNI

	conversion (+) (n = 121) median/frequency	conversion (+) IQR/%	conversion (–) (n = 113) median/frequency	conversion (–) IQR/%	p
Age (y/o)	74	(69–77)	73	(68–77)	0.475
sex: female	(70/121)	57.85	(48/113)	42.48	0.026
smoking history	(40/121)	33.06	(42/113)	37.17	0.584
education (y)	12	(11–16)	12	(12–16)	0.025
BMI (kg/m²)	21.81	(20.12–23.83)	22.55	(20.4–24.2)	0.158
APOEɛ4 status (allele ≥1)	(82/120)	68.33	(39/112)	34.82	<0.001
PET (PIB) positive	(23/26)	88.46	(17/36)	47.22	0.001
CSF Aβ₄₂ < 333 (pg/mL)	(32/43)	74.42	(22/45)	48.89	0.017
Amyloid (+)	(44/54)	81.48	(31/59)	52.54	0.001
baseline MMSE	26	(25–27)	27	(25–28)	0.001
baseline ADAS-cog13	23	(19–25.7)	16.7	(13.3–21.3)	<0.001
baseline FAQ	3	(1–7)	2	(0–4)	<0.001
baseline CDR-SOB	1.5	(1–2.5)	1	(0.5–2)	<0.001
baseline NPIQ	1	(0–2)	1	(0–2)	0.527
baseline GDS	2	(1–3)	2	(1–3)	0.955
Total hippocampal volume (mL)	5.337	(4.870–5.870)	6.274	(5.651–7.106)	<0.001
intracranial volume (mL)	1430	(1343–1541)	1512	(1379–1648)	0.024
taking donepezil	(78/121)	64.46	(51/113)	45.13	0.004
Fazekas PVH (0–3)	1	(1-2)	1	(1-2)	0.703
DWMH (0–3)	1	(1-2)	1	(1-2)	0.459

The ‘Amyloid’ denotes that the participant satisfied the CSF Aβ₄₂ < 333 and/or PIB-PET positive.

ɛ4 allele(s) (Odds Ratio [OR] = 4.01), being amyloid-positive (as defined by amyloid-PET or CSF Aβ_42 ; OR = 3.92), being female (OR = 1.85), having a smaller number of education years, lower MMSE scores, higher ADAS-cog13, FAQ scores, and CDR-SOB scores at baseline, smaller hippocampal volume, or taking donepezil at baseline (OR = 2.20). Meanwhile, there were no significant associations between conversion and age or history of smoking.

In the corresponding basic characteristic analysis in the NA-ADNI results, a statistically significant association with AD conversion was seen for the APOE ɛ4 status (OR = 2.63), amyloid-PET positivity (OR = 10.6), medication status of donepezil (OR = 2.46), smaller hippocampal volume, and baseline cognitive and neuropsychiatric functional scales (MMSE, ADAS-cog13, FAQ, and CDR-SB) (Supplementary Table 1).

Medical history for the J-ADNI cohort is summarized in Table 2. There was a negative association between conversion risk and medical history of musculoskeletal (OR = 0.43) or dermatological disorders (OR = 0.20). These results are only partially reproduced in NA-ADNI participants, where only those with neurological medical history were less likely to convert to AD (OR = 0.65) (Supplementary Table 2).

Table 2

Medical history of MCI participants from J-ADNI

History disorders of:	conversion (+) (n = 121) frequency	conversion (+) %	conversion (–) (n = 113) frequency	conversion (–) %	p
neurological	(1/121)	0.83	(6/113)	5.31	0.059
cardiovascular	(48/121)	39.67	(59/113)	52.21	0.066
malignancy	(10/121)	8.26	(12/113)	10.62	0.655
endocrinological	(31/121)	25.62	(41/113)	36.28	0.09
psychiatric	(3/121)	2.48	(5/113)	4.42	0.487
head	(29/121)	23.97	(41/113)	36.28	0.046
respiratory	(12/121)	9.92	(13/113)	11.5	0.833
dermatological	(3/121)	2.48	(13/113)	11.5	0.008
musculoskeletal	(19/121)	15.7	(34/113)	30.09	0.012
gastroenterological	(28/121)	23.14	(36/113)	31.86	0.145
hepatological	(9/121)	7.44	(7/113)	6.19	0.799
hematological	(1/121)	0.83	(2/113)	1.77	0.611
renal	(19/121)	15.7	(22/113)	19.47	0.494
allergic	(9/121)	7.44	(19/113)	16.81	0.042
surgical	(30/121)	24.79	(34/113)	30.09	0.382
hypertension	(39/121)	32.23	(51/113)	45.13	0.045
dyslipidemia	(7/121)	5.79	(7/113)	6.19	1.000
diabetes mellitus	(7/121)	5.79	(11/113)	9.73	0.328

A detailed medication history at baseline was available for the J-ADNI cohort, as shown in Table 3. Patients taking donepezil or not taking antihypertensive drugs were more likely to convert to AD (OR = 2.20 and OR = 0.47, respectively). In addition, there was a significant tendency in the difference of conversion incidence between those taking and not taking ‘Yokukansan’ (OR = 3.45, p = 0.052), one of the Kampo (or Chinese herbal medicine) drugs which is sometimes prescribed for neuropsychiatric symptoms in Japan [32]. There was no significant difference in the prevalence of conversion in those taking vitamin D supplements or peroral calcium supplements. Similarly, in the NA-ADNI results, those taking donepezil (OR = 2.46) or memantine (OR = 2.82) were more likely to develop conversion (Supplementary Table 3) (note that the medication items obtained from NA-ADNI does not correspond to that of J-ADNI).

Table 3

Medication history of MCI participants from J-ADNI

	conversion (+) frequency	conversion (+) %	conversion (–) frequency	conversion (–) %	p
anti-hypertension	(38/121)	31.4	(56/113)	49.56	0.005
anti-diabetes	(7/121)	5.79	(12/113)	10.62	0.232
anti-dyslipidemia	(32/121)	26.45	(37/113)	32.74	0.317
anti-hyperuremic acid	(2/121)	1.65	(7/113)	6.19	0.093
NSAIDs	(4/121)	3.31	(5/113)	4.42	0.742
histamine-blocker	(15/121)	12.4	(17/113)	15.04	0.574
drugs for osteoporosis	(5/121)	4.13	(10/113)	8.85	0.184
calcium supplements	(2/121)	1.65	(2/113)	1.77	1.000
vitamin D	(4/121)	3.31	(4/113)	3.54	1.000
’ Yokukansan ’	(11/121)	9.09	(3/113)	2.65	0.052
drugs for urination	(6/121)	4.96	(10/113)	8.85	0.303

NSAIDS, non-steroidal anti-inflammatory drugs.

Hematological results for the J-ADNI are summarized in Table 4. MCI converters in the J-ADNI cohort showed lower corrected serum calcium, higher serum T-cho, lower serum UA, higher total CSF t-tau and CSF p-tau, and lower CSF Aβ₄₂ (CSF results not shown; [26]). There was no difference in the baseline renal function or in its change in the 3 years of observation. In contrast, MCI converters in the NA-ADNI cohort showed no significant difference in any of the corresponding serum laboratory data (Supplementary Table 4). CSF biomarkers all had robust significant differences as in the J-ADNI.

Table 4

Laboratory features of MCI participants from J-ADNI

	conversion (+) median	conversion (+) IQR	conversion (–) median	conversion (–) IQR	p
Alb (g/dL)	4.3	(4.1–4.4)	4.3	(4.1–4.5)	0.064
Cre (mg/dL)	0.67	(0.58–0.77)	0.73	(0.57–0.82)	0.302
CCr (mL/min)	78.29	(66.16–94.87)	79.63	(68.12–98.35)	0.640
eGFR (mL/min/1.73 m²)	73.68	(63.58–82.69)	73.73	(65.30–83.86)	0.387
Change in eGFR (baseline – at 3 y)	5.06	(–2.16–10.39)	5.82	(2.46–9.59)	0.321
corrected Ca (mg/dL)	9.1	(9–9.3)	9.2	(9.1–9.4)	0.010
iP (mg/dL)	3.3	(3–3.8)	3.5	(3.1–3.8)	0.478
Tcho (mg/dL)	215	(193–231)	202	(177–227)	0.029
UA (mg/dL)	4.7	(3.9–5.7)	4.9	(4.4–6.1)	0.024
WBC (^*10³/mm³)	5500	(4900–6400)	5500	(4600–6200)	0.226
Hb (g/dL)	13.4	(12.5–14.3)	13.5	(12.8–14.3)	0.329
Plt (^*10⁴/mm³)	22	(18.7–25.2)	21.8	(18.3–25.9)	0.893
CRP (mg/dL)	0.05	(0.03–0.11)	0.06	(0.04–0.11)	0.225

Note that CSF biomarkers were measured in only a part of entire participants. Alb, albumin; Cre, creatinine; CCr, creatinine clearance; Ca, calcium; iP, phosphate; Tcho, total cholesterol; UA, uremic acid; WBC, white blood cells; Hb, hemoglobin; Plt, platelet; CRP, C-reactive protein.

Regarding serum calcium, where normal levels are in the rage of 8.7–10.2 mg/dL, patients with hypocalcemia (corrected calcium <8.7 mg/dL) amounted to 4.1% (5/121) of the total number of converters and 0.9% (1/113) of the total number of non-converters, without a statistically significant difference (p = 0.214). Patients with hypercalcemia (corrected calcium >10.2 mg/dL) were 0% (0/121) of the total number of converters and 0% (0/113) in non-converters in J-ADNI. When the MCI participants in the J-ADNI cohort were split into two groups according to their level of corrected serum calcium—higher or lower than its median value (cut-off: 9.2 mg/dL), there was a significant difference in the overall conversion rate: 58.7% (64/109) in the subgroup with lower serum calcium level (corrected serum calcium <9.2 mg/dL) against 45.6% (57/125) in the subgroup with higher serum calcium level (corrected serum calcium ≥9.2 mg/dL; p = 0.049). These two subgroups also showed a significant difference in the conversion-free period survival analysis (log-rank test, p = 0.011) (Fig. 1). However, we could not replicate these results with the NA-ADNI data: differences in the incidence of conversion to AD were non-significant between the higher calcium subgroup (conversion rate of 45.1% (130/288), corrected serum calcium ≥9.9 mg/dL) and the lower calcium subgroup (conversion rate of 38.3% (95/248), corrected serum calcium <9.9 mg/dL; p = 0.115). Moreover, conversion-free period survival analysis showed opposite results compared to the J-ADNI, with the higher calcium subgroup showing significantly longer periods until conversion compared to the lower calcium subgroup (log-rank, p = 0.031) (Fig. 1).

Fig.1

Kaplan-Meier conversion-free survival curves in accordance with higher/lower serum calcium level in each of J-ADNI (A) or NA-ADNI (B). In J-ADNI (A), MCI participants with lower serum calcium levels (corrected calcium <9.2 mg/dL) showed significantly shorter conversion-free period than those with higher serum calcium levels (corrected calcium ≥9.2 mg/dL; log-rank test, p = 0.011). Conversely, in NA-ADNI (B), MCI subjects with higher serum calcium levels showed significantly shorter conversion-free period than those with lower calcium levels (log-rank test, p = 0.031).

Multivariate adjustment of serum calcium effect on MCI conversion

Next, we performed multivariate analysis for MCI conversion, focusing mainly on the effect of serum calcium. Conversion was set as the objective variable and the effect of serum calcium was assessed while adjusting for confounding factors including: age, sex, education period, APOE ɛ4 status, blood laboratory data at baseline (serum phosphate level, serum UA, t-cho, eGFR at baseline, total neuropsychological scores at baseline (ADAS-cog13 and FAQ), medication history (donepezil, ‘Yokukansan’, calcium supplements or vitamin D supplements), past medical history (cardiovascular, neurological, musculoskeletal, or head disorders), and Fazekas PVH score and the total brain volume at baseline (intracranial volume and total hippocampal volume), assessed with MRI. The variables above were chosen by considering each variable’s medical or statistical significance in relation to the MCI conversion. It should be noted that we have not included the amyloid-positivity results into the multivariate analysis. Our rationale for this choice was based on a concern about the possibility of statistical instability due to the relatively small size of sample size of the amyloid data (n = 50 among converter and n = 47 among non-converters). A stepwise, backward and forward selection procedure (function ‘stepAIC’ in R) was used, and the derived final model included serum calcium level as shown in Table 5 (model A). Serum calcium level remained in the derived predictive model: adjusted OR = 1.132 per 0.1 mg/dL decrease (95% CI: 1.016–1.266). When we adjusted for the changes in renal function during the 3 years of observation (total n = 149), serum calcium level was still a significant factor: adjusted OR = 1.156 per 0.1 mg/dL decrease (95% CI: 1.015–1.329). Interestingly, the change in eGFR during the 3 years of observation was also significantly associated with conversion, with the magnitude of the decrease in eGFR being negatively correlated with the risk of conversion (adjusted OR = 0.939 per-1 mL/min/1.73 m² decrease from baseline eGFR; 95% CI: 0.889–0.987) (Supplementary Table 5, model B). The same result was obtained in an alternative model using CCr instead of eGFR (data not shown), in which body weight is accounted for in the calculation. There was no evidence for multicollinearity for any of the variables, with their variance inflation factor being less than 5. The logistic regression analysis was also performed on data from the NA-ADNI cohort, with results sharing similar features, including the finding of baseline cognitive scores and hippocampal volume as significant predictors. However, the findings regarding serum calcium level could not be reproduced (results not shown). Among factors shown in Table 5 (model A), higher baseline ADAS-cog13 had the best predictive performance for MCI conversion both in J-ADNI or NA-ADNI cohort (area under curve (AUC) = 0.746 in J-ADNI and AUC = 0.756 in NA-ADNI).

Table 5

Multivariate adjustment for the effect of serum calcium level onto the MCI conversion risk

	adjusted OR	lower 95%	upper 95%	p
(Intercept)	>1000	1.820	>1000	0.040
Baseline ADAS-cog13 (per-1 increase)	1.098	1.028	1.177	0.006^**
Baseline FAQ (per-1 increase)	1.150	1.038	1.292	0.012^*
History of neurological disorders (yes/no)	0.157	0.006	1.813	0.185
Baseline serum T-cho (per-1 mg/dL increase)	1.008	0.998	1.020	0.137
APOEɛ4 status (per-1 allele increase)	2.166	1.198	4.006	0.012^*
Baseline corrected serum calcium (per-0.1 mg/dL decrease)	1.132	1.016	1.266	0.026^*
Medication history of donepezil (yes/no)	1.685	0.836	3.416	0.145
Atrophy in total hippocampal volume at baseline (per-1 mL decrease)	1.923	1.303	2.927	0.001^**

(Model A). When delta-eGFR is not included into adjustment (total n = 202): Lower serum calcium level was associated with higher MCI conversion risk, even with adjustment for multivariate confounding factors. Note that amyloid positivity data was not included in the multivariate adjustment because of the small sample size.

Since the above multivariate analysis did not included amyloid-positivity results among the covariates adjusted for, we assessed association between the serum calcium level and this factor. Results showed no significant difference in the serum calcium level between those with and without amyloid positivity (p = 1.000), amyloid-PET positivity (p = 0.562), higher/lower CSF Aβ₄₂ level (p = 0.522), or APOE ɛ4 status (p = 0.534). Furthermore, there was no association between the serum calcium level and the raw level of CSF Aβ₄₂, t-tau or p-tau (Spearman’s rank correlation: p = 0.987, p = 0.738 and p = 0.605, respectively). In addition, we checked for potential confounders of the calcium level by screened for factors having significant association with the serum calcium level; such factors were age (p = 0.002), serum phosphate (p < 0.001, Spearman’s rank correlation), Alb (p < 0.001), T-cho level (p < 0.001), and medication history of ‘Yokukansan’ (p = 0.002), all of which are corrected for in the above multivariate adjustment. In addition, participants with lower serum calcium level was slightly but significantly had higher Fazekas PVH than those with median or higher serum calcium level (p = 0.005, Wilcoxon rank sum test). We summarized basic clinical features between subgroups with lower and higher serum calcium level in the Supplementary Table 6.

DISCUSSION

The J-ADNI is a large-scale prospective observational study with a comprehensive dataset of an Asian cohort [26], launched in 2008 and led by Takeshi Iwatsubo. The primary goal of the J-ADNI study was the same as that of the NA-ADNI [2, 3], which is to investigate whether biological markers, such as serial MRI and PET scan, and clinical and neuropsychological assessments could be combined to measure the progression of MCI and early AD. The J-ADNI study was recently completed and yielded comprehensive datasets.

In this sub-analysis of the J-ADNI study, we searched for MCI conversion risk factors and compared the results obtained from the J-ADNI data with those obtained with the NA-ADNI data, especially focusing on the effect of serum calcium level. A few differences aside, the results obtained from the J-ADNI and NA-ADNI shared many similarities, such as their annual MCI conversion rate and the association of APOE ɛ4 and amyloid-PET positivity with conversion. The main differences between the J-ADNI and NA-ADNI cohorts regarded the association of sex [33, 34] and serum calcium with MCI conversion risk. While the association between sex and MCI conversion was investigated, in detail, in our previous report [27], lower serum calcium was identified here as a novel, significant factor associated even after adjustment with multiple variables in the J-ADNI.

Although our results suggested that lower serum calcium may be associated with increased risk of conversion, earlier studies with Finland and Dutch cohorts reported an association of higher serum calcium with worsening of cognitive decline and prevalence of dementia, including AD [21, 22]. In addition, the NA-ADNI cohort showed that higher serum calcium level may actually be associated with the faster MCI conversion occurrence, as shown in the log-rank test. The putative mechanism linking hypercalcemia to the incidence of dementia may include systemic dehydration and neuronal abnormality in calcium homeostasis. Systemic dehydration is induced by hypercalcemia because it requires the kidney to excrete the excess of calcium in the urine. As for the neuronal dysfunction in calcium homeostasis, it is suspected that serum calcium abnormality may enhance dysregulation in neuronal calcium homeostasis [18] leading to the amyloid accumulation [19, 20]. Thus, there are several lines of evidence supporting the hypothesis that higher blood calcium level can contribute to a higher incidence of dementia or to faster cognitive decline.

Meanwhile, the mechanism through which the lower serum calcium may have caused higher incidence of conversion remains unclear, although the association between lower serum calcium and AD had been suggested in some earlier cross-sectional studies: lower serum calcium level was observed with a significantly higher frequency in AD subjects than in subjects with vascular dementia [23, 24], age-matched normal subjects [24], or control subjects [25]. Moreover, the serum calcium level was reported to be negatively correlated with the degree of AD [35]. In addition, it is reported by a recent report that genetically increased serum calcium level was associated with the decreased risk of AD [36] in a Mendelian randomization study using serum calcium genome wide association study data. Combined with these earlier study results, it is possible to infer that the relationship between serum calcium level and incidence of MCI conversion or cognitive decline follows a U-shaped curve, with both too high or too low serum calcium level being deleterious. Plus, the MCI participants from our cohort may have been more sensitive to lower serum calcium level than to higher serum calcium level.

In our univariate analysis, we found an association between lower baseline serum calcium levels and a subsequent large decrease in eGFR. This association was reported previously in chronic kidney disease (CKD) patients from Western countries [37] and in CKD-free subjects from Japan [38]. Despite such association between the baseline lower serum calcium levels and the subsequent decrease in eGFR, these two factors showed the opposite effect on the MCI conversion in our multivariate result. Although the underlying mechanisms behind these patterns are unclear, this observation suggests that the association between lower serum calcium and MCI conversion risk is not a mere reflection of the comorbid temporal change in eGFR.

Another possible explanation for lower serum calcium levels being associated with increased risk of conversion is the culture-dependent differences which may have been overlooked by the study protocol. For example, factors associated with lifestyle, including dietary behavior and gut microbiota [39], may differ between the United States and Japan and thus influence the effect of serum calcium levels on MCI conversion. This inference is supported by, e.g., the higher prevalence of taking ‘Yokukansan’ medication in converters than in non-converters, although this medication was not a significant factor in the multivariate analysis. This Kampo (or Chinese herbal medicine) drug is sometimes prescribed for neuropsychiatric symptoms [32] and is quite specific to the J-ADNI cohort, not being observed in the NA-ADNI cohort. The higher prevalence of taking ‘Yokukansan’ medication in converters reflects the need for dementia treatment, just as the higher prevalence of taking donepezil in converters in J-ADNI and NA-ADNI cohorts. While the ‘Yokukansan’ drug contains glycyrrhizin, which can cause hypokalemia via pseudohyperaldosteronism [40], lower serum calcium would not be a side-effect of taking ‘Yokukansan’ as this drug seems to have almost no effect on serum calcium level, as reported by a prospective randomized cross-over study [41] in which none of the 108 enrolled participants developed hypocalcemia.

One potentially critical confounding factor regarding the serum calcium level is the blood vitamin D level at baseline, which was not measured in either the J-ADNI or in the NA-ADNI studies, even though several previous studies implied an association between blood vitamin D levels and cognitive decline. In an in vitro study, vitamin D was reported to reverse the age-related inflammatory process in hippocampus [42]. In addition, vitamin D was reported to enhance the phagocytic clearance of amyloid plaques [43] and to reduce amyloid-induced apoptosis of neurons [44]. In a Mendelian randomization study, results suggested that genetically increased serum 25-hydroxyvitamin D was associated with a decreased risk of AD [45]. In clinical cohort studies, the association between the low vitamin D level and the incidence of dementia or faster cognitive decline is reported in a systematic review [46]. However, it remains some controversies on the significance of lower vitamin D level onto the risk of dementia, because oral vitamin D supplementation had no significant effect on the incidence of cognitive decline [18 , 47], and the reverse causation is possible in the association between lower vitamin D and cognitive decline [48, 49]. It is uncertain from our results if the lower serum calcium level as an associated factor for MCI conversion is independent of the potentially-latent lower vitamin D level or not.

Our study has some limitations, including possible confounding factors that were not considered, such as time-dependent changes in the serum calcium level, blood vitamin D level at baseline, and changes in dietary behavior. In addition, the small sample size of the CSF data and the amyloid-PET data prevented us from adjusting for these factors in the multivariate analysis. This restriction further suggests that the results from the current study should be applied for relatively-limited situations such as where amyloid-PET or CSF biomarker data are not available. The statistical stability of the J-ADNI analyses is another matter of concern, as this study as a smaller sample size than the NA-ADNI.

Despite these limitations, lower serum calcium may genuinely be a risk factor for MCI conversion, at least in the J-ADNI cohort, although its mechanism remains unclear. Since our findings could not be replicated with NA-ADNI data, external validation in other cohorts, especially Asia cohorts, are necessary.

At last, we want to emphasize that although it was statistically feasible to compare results from the J-ADNI and NA-ADNI cohorts, the interpretation of this comparisons must be made with great care, as hidden culture-specific factors may influence the results. Therefore, factors such as dietary behavior and the use of specific medications, as well as the potential confounding effect of vitamin D levels, must be evaluated in the future AD studies.

Footnotes

ACKNOWLEDGMENTS

This research was supported by AMED under Grant Number 16dk0207020h, 16dk0207028h and Japan Society for the Promotion of Science grant 17K09794.

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

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